From 153584d1f9e3c8d32b93b5fa25e738004f42785b Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Thu, 16 Jun 2022 17:07:02 +0100 Subject: [PATCH 001/177] add cbmr.py file --- nimare/meta/cbmr.py | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 nimare/meta/cbmr.py diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py new file mode 100644 index 000000000..8e38643d2 --- /dev/null +++ b/nimare/meta/cbmr.py @@ -0,0 +1,7 @@ + +from msilib.schema import Class + +from nimare.base import Estimator + +Class CBMREstimator(Estimator): + pass From 59a3742487c5588942f82793342bf984e35806ec Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 18 Jun 2022 12:22:30 +0100 Subject: [PATCH 002/177] create a design matrix function for cbmr --- nimare/meta/cbmr.py | 34 ++++++++++++++++++++++--- nimare/tests/conftest.py | 23 +++++++++++++++++ nimare/utils.py | 54 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 107 insertions(+), 4 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 8e38643d2..5a249acae 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,7 +1,33 @@ +from numpy import spacing +from nimare.base import Estimator +from nimare.utils import get_template, get_masker, B_spline_bases +import nibabel as nib -from msilib.schema import Class -from nimare.base import Estimator +class CBMREstimator(Estimator): + def __init__(self, model="Poisson", penalty=None, spline_knots_spacing=5, mask=None, **kwargs): + super().__init__(**kwargs) + if mask is not None: + mask = get_masker(mask) + self.masker = mask + + self.model = model + self.penalty = penalty + self.spline_knots_spacing = spline_knots_spacing + + def _preprocess_input(self, dataset): + masker = self.masker or dataset.masker + + mask_img = masker.mask_img or masker.labels_img + if isinstance(mask_img, str): + mask_img = nib.load(mask_img) + masker_voxels = mask_img._dataobj + design_matrix = B_spline_bases( + masker_voxels=masker_voxels, spacing=self.spline_knots_spacing + ) + + return design_matrix + + def _fit(self, dataset): -Class CBMREstimator(Estimator): - pass + pass diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index bc1f5c2e2..8e29f9560 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -57,6 +57,29 @@ def testdata_cbma(): return dset +@pytest.fixture(scope="session") +def testdata_cbmr(): + """Generate coordinate-based dataset for tests.""" + dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset = nimare.dataset.Dataset(dset_file) + + # Only retain one peak in each study in coordinates + # Otherwise centers of mass will be obscured in kernel tests by overlapping + # kernels + dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) + + n_rows = dset.annotations.shape[0] + dset.annotations["group_id"] = ["group_1"] * n_rows # group_id + dset.annotations[ + "sample_sizes" + ] = dset.metadata.sample_sizes # sample sizes as study-level covariates + dset.annotations["study_level_covariates"] = np.random.rand( + n_rows, 1 + ) # random study-level covariates + + return dset + + @pytest.fixture(scope="session") def testdata_cbma_full(): """Generate more complete coordinate-based dataset for tests. diff --git a/nimare/utils.py b/nimare/utils.py index 08a463d59..546f74163 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -19,6 +19,8 @@ from nimare import references from nimare.due import due +import patsy + LGR = logging.getLogger(__name__) @@ -1034,3 +1036,55 @@ def unique_rows(ar): _, unique_row_indices = np.unique(ar_row_view, return_index=True) ar_out = ar[unique_row_indices] return ar_out + + +def coef_spline_bases(axis_coords, spacing, margin): + ## create B-spline basis for x/y/z coordinate + wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) + knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) + design_matrix = patsy.dmatrix( + "bs(x, knots=knots, degree=3,include_intercept=False)", + data={"x": wider_axis_coords}, + return_type="matrix", + ) + design_array = np.array(design_matrix)[:, 1:] # remove the first column (every element is 1) + coef_spline = design_array[margin : -margin + 1, :] + # remove the basis with no/weakly support from the square + supported_basis = np.sum(coef_spline, axis=0) != 0 + coef_spline = coef_spline[:, supported_basis] + + return coef_spline + + +def B_spline_bases(masker_voxels, spacing, margin=10): + dim_mask = masker_voxels.shape + n_brain_voxel = np.sum(masker_voxels) + # remove the blank space around the brain mask + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + + x_spline = coef_spline_bases(xx, spacing, margin) + y_spline = coef_spline_bases(yy, spacing, margin) + z_spline = coef_spline_bases(zz, spacing, margin) + + # create spatial design matrix by tensor product of spline bases in 3 dimesion + X = np.kron(np.kron(x_spline, y_spline), z_spline) # Row sums of X are all 1=> There is no need to re-normalise X + # remove the voxels outside brain mask + axis_dim = [x_spline.shape[0], y_spline.shape[0], z_spline.shape[0]] + brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) + for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] + + # remove tensor product basis that have no support in the brain + x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] + support_basis = np.empty(shape=(0,), dtype=np.int) + for bx in range(x_df): + for by in range(y_df): + for bz in range(z_df): + basis_index = bz + z_df*by + z_df*y_df*bx + basis_coef = X[:, basis_index] + if np.max(basis_coef) >= 0.1: + support_basis = np.append(support_basis, basis_index) + X = X[brain_voxels_index, support_basis] + + return X From 3371a9843b91e43dd5228ef52802d58c39ddd7a2 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 18 Jun 2022 12:23:26 +0100 Subject: [PATCH 003/177] add test file for cbmr --- nimare/tests/test_meta_cbmr.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 nimare/tests/test_meta_cbmr.py diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py new file mode 100644 index 000000000..83324d995 --- /dev/null +++ b/nimare/tests/test_meta_cbmr.py @@ -0,0 +1,8 @@ +from nimare.meta.cbmr import CBMREstimator + + +def test_CBMREstimator(testdata_cbmr): + + cbmr = CBMREstimator() + X = cbmr._preprocess_input(testdata_cbmr) + cbmr.fit(testdata_cbmr) From 8d2a151d45c7194227f91faeeab3f109e04f640b Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 15 Jul 2022 15:31:05 +0100 Subject: [PATCH 004/177] modify pre-process and training function in cbmr --- nimare/meta/cbmr.py | 128 ++++++++++++++++++++++++++++++--- nimare/tests/test_meta_cbmr.py | 8 ++- nimare/utils.py | 22 ++++-- 3 files changed, 140 insertions(+), 18 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 5a249acae..c1e8ea1ff 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,19 +1,25 @@ +from attr import has from numpy import spacing from nimare.base import Estimator from nimare.utils import get_template, get_masker, B_spline_bases import nibabel as nib - +import numpy as np +from nimare.utils import mm2vox, vox2idx +import torch class CBMREstimator(Estimator): - def __init__(self, model="Poisson", penalty=None, spline_knots_spacing=5, mask=None, **kwargs): + _required_inputs = {"coordinates": ("coordinates", None)} + + def __init__(self, groups=False, moderators=None, moderators_center=True, moderators_scale=True, mask=None, **kwargs): super().__init__(**kwargs) if mask is not None: mask = get_masker(mask) self.masker = mask - self.model = model - self.penalty = penalty - self.spline_knots_spacing = spline_knots_spacing + self.groups = groups + self.moderators = moderators + self.moderators_center = moderators_center # either boolean or a list of strings + self.moderators_scale = moderators_scale def _preprocess_input(self, dataset): masker = self.masker or dataset.masker @@ -21,13 +27,113 @@ def _preprocess_input(self, dataset): mask_img = masker.mask_img or masker.labels_img if isinstance(mask_img, str): mask_img = nib.load(mask_img) - masker_voxels = mask_img._dataobj - design_matrix = B_spline_bases( - masker_voxels=masker_voxels, spacing=self.spline_knots_spacing - ) + + ma_values = self._collect_inputs(dataset, drop_invalid=True) + self.inputs_['mask_img'] = mask_img + + for name, (type_, _) in self._required_inputs.items(): + if type_ == "coordinates": + if hasattr(self, "groups"): + ## to do: raise an error if group column doesn't exist in dataset.annotations + group_names = dataset.annotations['group_id'].unique() + gb = dataset.annotations.groupby('group_id') + multiple_groups = [gb.get_group(x)['study_id'] for x in gb.groups] + if hasattr(self, "moderators"): + moderators_array = np.stack([dataset.annotations[moderator_name] for moderator_name in self.moderators], axis=1) + moderators_array = moderators_array.astype(np.float64) + if isinstance(self.moderators_center, bool): + ## to do: if moderators_center & moderators_array is a list of moderators names, only operate on the chosen moderators + if self.moderators_center: + moderators_array -= np.mean(moderators_array, axis=0) + if self.moderators_scale: + moderators_array /= np.var(moderators_array, axis=0) + # Calculate IJK matrix indices for target mask + # Mask space is assumed to be the same as the Dataset's space + # These indices are used directly by any KernelTransformer + xyz = dataset.coordinates[['x', 'y', 'z']].values + ijk = mm2vox(xyz, mask_img.affine) + if hasattr(self, "moderators"): + study_id = dataset.coordinates['study_id'] + study_index = [np.where(study_id.unique()==i)[0].item() for i in study_id] + self.inputs_["coordinates"]["study_index"] = study_index + self.inputs_["coordinates"][["i", "j", "k"]] = ijk + foci_idx = vox2idx(ijk, mask_img._dataobj) + self.inputs_["coordinates"]['foci_idx'] = foci_idx + # Y & y & y_t + n_study = np.shape(study_id.unique())[0] + masker_voxels = np.sum(mask_img._dataobj).astype(int) + Y = np.zeros((n_study, masker_voxels)) + + y = np.sum(Y, axis=0) + y_t = np.sum(Y, axis=1) - return design_matrix - def _fit(self, dataset): + + def _fit(self, dataset, spline_spacing): + masker_voxels = self.inputs_['mask_img']._dataobj + X = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) pass + + + def _optimizer(self, model, y, Z, y_t, penalty, lr, tol, iter): + # optimization + optimizer = torch.optim.LBFGS(model.parameters(), lr) + prev_loss = torch.tensor(float('inf')) + loss_diff = torch.tensor(float('inf')) + step = 0 + count = 0 + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=0.995) + while torch.abs(loss_diff) > tol: + if step <= iter: + scheduler.step() + def closure(): + optimizer.zero_grad() + loss = model(self.X, y, Z, y_t) + loss.backward() + return loss + loss = optimizer.step(closure) + # reset L_BFGS if NAN appears + if torch.any(torch.isnan(model.beta_linear.weight)): + print("Reset lbfgs optimiser ......") + count += 1 + if count > 10: + break + model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) + if self.covariates == True: + model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) + if self.model == 'NB': + model.theta = torch.nn.Parameter(last_state['theta']) + if self.model == 'Clustered_NB': + model.alpha = torch.nn.Parameter(last_state['alpha']) + loss_diff = torch.tensor(float('inf')) + optimizer = torch.optim.LBFGS(model.parameters(), lr) + continue + else: + last_state = copy.deepcopy(model.state_dict()) + print("step {0}: loss {1}".format(step, loss)) + loss_diff = loss - prev_loss + prev_loss = loss + step = step + 1 + else: + print('it did not converge \n') + print('The difference of loss in the current and previous iteration is', loss_diff) + exit() + return + + def train(self, model, penalty, covariates, iter=1500, lr=0.01, tol=1e-4): + self.model = model + self.penalty = penalty + self.covariates = covariates + # model & optimization process + for i in range(100): + model = self.model_structure(model=self.model, penalty=self.penalty, covariates=self.covariates) + optimization = self._optimizer(model=model, y=self.y, Z=self.Z, y_t=self.y_t, penalty=self.penalty, lr=lr, tol=tol, iter=iter) + # beta + beta = model.beta_linear.weight + beta = beta.detach().cpu().numpy().T + print(np.all(np.isnan(beta))) + if np.all(np.isnan(beta)): + print('restart the optimisation!') + continue + else: diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 83324d995..19809b85b 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -3,6 +3,8 @@ def test_CBMREstimator(testdata_cbmr): - cbmr = CBMREstimator() - X = cbmr._preprocess_input(testdata_cbmr) - cbmr.fit(testdata_cbmr) + cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age']) + prep = cbmr._preprocess_input(testdata_cbmr) + fit = cbmr._fit(dataset=testdata_cbmr, spline_spacing=5) + + diff --git a/nimare/utils.py b/nimare/utils.py index 546f74163..8d33b6913 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1074,17 +1074,31 @@ def B_spline_bases(masker_voxels, spacing, margin=10): axis_dim = [x_spline.shape[0], y_spline.shape[0], z_spline.shape[0]] brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] - + X = X[brain_voxels_index, :] # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] - support_basis = np.empty(shape=(0,), dtype=np.int) + support_basis = [] for bx in range(x_df): for by in range(y_df): for bz in range(z_df): basis_index = bz + z_df*by + z_df*y_df*bx basis_coef = X[:, basis_index] if np.max(basis_coef) >= 0.1: - support_basis = np.append(support_basis, basis_index) - X = X[brain_voxels_index, support_basis] + support_basis.append(basis_index) + X = X[:, support_basis] return X + +def vox2idx(ijk, masker_voxels): + dim_mask = masker_voxels.shape + n_brain_voxel = np.sum(masker_voxels).astype(int) + n_foci = ijk.shape[0] + + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + x_dim, y_dim, z_dim = xx.shape[0], yy.shape[0], zz.shape[0] + foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci)] + foci_index = np.array(foci_index) + + return foci_index From d014234c19f87f3b759b81dc9c3b423337fd9332 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 15 Jul 2022 15:32:51 +0100 Subject: [PATCH 005/177] modify the dataset.anotations in cbmr --- nimare/tests/conftest.py | 30 ++++++++++++++++++------------ 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 8e29f9560..8823a1527 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -1,9 +1,9 @@ """Generate fixtures for tests.""" import os from shutil import copyfile - import nibabel as nib import numpy as np +import pandas as pd import pytest from nilearn.image import resample_img @@ -56,7 +56,6 @@ def testdata_cbma(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) return dset - @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" @@ -67,19 +66,14 @@ def testdata_cbmr(): # Otherwise centers of mass will be obscured in kernel tests by overlapping # kernels dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) - + # set up group_id & moderators n_rows = dset.annotations.shape[0] - dset.annotations["group_id"] = ["group_1"] * n_rows # group_id - dset.annotations[ - "sample_sizes" - ] = dset.metadata.sample_sizes # sample sizes as study-level covariates - dset.annotations["study_level_covariates"] = np.random.rand( - n_rows, 1 - ) # random study-level covariates - + dset.annotations['group_id'] = ["group_1" if i%2==0 else 'group_2' for i in range(n_rows)] + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["avg_age"] = np.arange(n_rows) + return dset - @pytest.fixture(scope="session") def testdata_cbma_full(): """Generate more complete coordinate-based dataset for tests. @@ -90,6 +84,18 @@ def testdata_cbma_full(): dset = nimare.dataset.Dataset(dset_file) return dset +# @pytest.fixture(scope="session") +# def testdata_cbmr_full(): +# """Generate coordinate-based dataset for tests.""" +# dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") +# dset = nimare.dataset.Dataset(dset_file) +# # generate group_id & moderator +# n_rows = dset.annotations.shape[0] +# dset.annotations["sample_sizes"] = dset.metadata.sample_sizes # sample sizes as study-level covariates +# groups = pd.DataFrame({'study_id':dset.annotations['study_id'], 'group_id': ["group_1" if i%2==0 else 'group_2' for i in range(n_rows)]}) +# moderators = pd.DataFrame({'study_id': dset.annotations['study_id'], 'moderator': np.random.rand(n_rows)}) # random study-level covariates +# return dset, groups, moderators + @pytest.fixture(scope="session") def testdata_laird(): From f741eeb5c78b482af24db137c6ba4e9d3fb0e4aa Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 15 Jul 2022 17:49:27 +0100 Subject: [PATCH 006/177] add documentation in utils functions --- nimare/meta/cbmr.py | 120 ++++++++++++++++++++++---------------------- nimare/utils.py | 46 ++++++++++++++++- setup.cfg | 1 + 3 files changed, 105 insertions(+), 62 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index c1e8ea1ff..3674c46bc 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -76,64 +76,64 @@ def _fit(self, dataset, spline_spacing): pass - def _optimizer(self, model, y, Z, y_t, penalty, lr, tol, iter): - # optimization - optimizer = torch.optim.LBFGS(model.parameters(), lr) - prev_loss = torch.tensor(float('inf')) - loss_diff = torch.tensor(float('inf')) - step = 0 - count = 0 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=0.995) - while torch.abs(loss_diff) > tol: - if step <= iter: - scheduler.step() - def closure(): - optimizer.zero_grad() - loss = model(self.X, y, Z, y_t) - loss.backward() - return loss - loss = optimizer.step(closure) - # reset L_BFGS if NAN appears - if torch.any(torch.isnan(model.beta_linear.weight)): - print("Reset lbfgs optimiser ......") - count += 1 - if count > 10: - break - model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) - if self.covariates == True: - model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) - if self.model == 'NB': - model.theta = torch.nn.Parameter(last_state['theta']) - if self.model == 'Clustered_NB': - model.alpha = torch.nn.Parameter(last_state['alpha']) - loss_diff = torch.tensor(float('inf')) - optimizer = torch.optim.LBFGS(model.parameters(), lr) - continue - else: - last_state = copy.deepcopy(model.state_dict()) - print("step {0}: loss {1}".format(step, loss)) - loss_diff = loss - prev_loss - prev_loss = loss - step = step + 1 - else: - print('it did not converge \n') - print('The difference of loss in the current and previous iteration is', loss_diff) - exit() - return + # def _optimizer(self, model, y, Z, y_t, penalty, lr, tol, iter): + # # optimization + # optimizer = torch.optim.LBFGS(model.parameters(), lr) + # prev_loss = torch.tensor(float('inf')) + # loss_diff = torch.tensor(float('inf')) + # step = 0 + # count = 0 + # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=0.995) + # while torch.abs(loss_diff) > tol: + # if step <= iter: + # scheduler.step() + # def closure(): + # optimizer.zero_grad() + # loss = model(self.X, y, Z, y_t) + # loss.backward() + # return loss + # loss = optimizer.step(closure) + # # reset L_BFGS if NAN appears + # if torch.any(torch.isnan(model.beta_linear.weight)): + # print("Reset lbfgs optimiser ......") + # count += 1 + # if count > 10: + # break + # model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) + # if self.covariates == True: + # model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) + # if self.model == 'NB': + # model.theta = torch.nn.Parameter(last_state['theta']) + # if self.model == 'Clustered_NB': + # model.alpha = torch.nn.Parameter(last_state['alpha']) + # loss_diff = torch.tensor(float('inf')) + # optimizer = torch.optim.LBFGS(model.parameters(), lr) + # continue + # else: + # last_state = copy.deepcopy(model.state_dict()) + # print("step {0}: loss {1}".format(step, loss)) + # loss_diff = loss - prev_loss + # prev_loss = loss + # step = step + 1 + # else: + # print('it did not converge \n') + # print('The difference of loss in the current and previous iteration is', loss_diff) + # exit() + # return - def train(self, model, penalty, covariates, iter=1500, lr=0.01, tol=1e-4): - self.model = model - self.penalty = penalty - self.covariates = covariates - # model & optimization process - for i in range(100): - model = self.model_structure(model=self.model, penalty=self.penalty, covariates=self.covariates) - optimization = self._optimizer(model=model, y=self.y, Z=self.Z, y_t=self.y_t, penalty=self.penalty, lr=lr, tol=tol, iter=iter) - # beta - beta = model.beta_linear.weight - beta = beta.detach().cpu().numpy().T - print(np.all(np.isnan(beta))) - if np.all(np.isnan(beta)): - print('restart the optimisation!') - continue - else: + # def train(self, model, penalty, covariates, iter=1500, lr=0.01, tol=1e-4): + # self.model = model + # self.penalty = penalty + # self.covariates = covariates + # # model & optimization process + # for i in range(100): + # model = self.model_structure(model=self.model, penalty=self.penalty, covariates=self.covariates) + # optimization = self._optimizer(model=model, y=self.y, Z=self.Z, y_t=self.y_t, penalty=self.penalty, lr=lr, tol=tol, iter=iter) + # # beta + # beta = model.beta_linear.weight + # beta = beta.detach().cpu().numpy().T + # print(np.all(np.isnan(beta))) + # if np.all(np.isnan(beta)): + # print('restart the optimisation!') + # continue + # else: diff --git a/nimare/utils.py b/nimare/utils.py index 8d33b6913..984dea58e 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1039,6 +1039,19 @@ def unique_rows(ar): def coef_spline_bases(axis_coords, spacing, margin): + """ + Coefficient of cubic B-spline bases in any x/y/z direction + + Parameters + ---------- + axis_coords : value range in x/y/z direction + spacing: (equally spaced) knots spacing in x/y/z direction, + margin: extend the region where B-splines are constructed (min-margin, max_margin) + to avoid weakly-supported B-spline on the edge + Returns + ------- + coef_spline : 2-D ndarray (n_points x n_spline_bases) + """ ## create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) @@ -1057,6 +1070,22 @@ def coef_spline_bases(axis_coords, spacing, margin): def B_spline_bases(masker_voxels, spacing, margin=10): + """ Cubic B-spline bases for spatial intensity + + The whole coefficient matrix is constructed by taking tensor product of + all B-spline bases coefficient matrix in three direction. + + Parameters + ---------- + masker_voxels : matrix with element either 0 or 1, indicating if it's within brain mask, + spacing: (equally spaced) knots spacing in x/y/z direction, + margin: extend the region where B-splines are constructed (min-margin, max_margin) + to avoid weakly-supported B-spline on the edge + Returns + ------- + X : 2-D ndarray (n_voxel x n_spline_bases) + only keeps with within-brain voxels + """ dim_mask = masker_voxels.shape n_brain_voxel = np.sum(masker_voxels) # remove the blank space around the brain mask @@ -1078,22 +1107,35 @@ def B_spline_bases(masker_voxels, spacing, margin=10): # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] support_basis = [] + # find and remove weakly supported B-spline bases for bx in range(x_df): for by in range(y_df): for bz in range(z_df): basis_index = bz + z_df*by + z_df*y_df*bx basis_coef = X[:, basis_index] - if np.max(basis_coef) >= 0.1: + if np.max(basis_coef) >= 0.1: support_basis.append(basis_index) X = X[:, support_basis] return X def vox2idx(ijk, masker_voxels): + """ + Convert coordinates in voxel space to integer index (between 0 and n-voxel) + + Parameters + ---------- + ijk: (x,y,z) coordinates in voxel space + masker_voxels : matrix with element either 0 or 1, indicating if it's within brain mask, + spacing: (equally spaced) knots spacing in x/y/z direction + Returns + ------- + foci_index : 1-D ndarray (n_voxel, ) + """ dim_mask = masker_voxels.shape n_brain_voxel = np.sum(masker_voxels).astype(int) n_foci = ijk.shape[0] - + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] diff --git a/setup.cfg b/setup.cfg index 6a4932af7..7da488b1c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -49,6 +49,7 @@ install_requires = numba # used by sparse numpy pandas + patsy pymare~=0.0.4rc2 # nimare.meta.ibma and stats requests # nimare.extract scikit-learn # nimare.annotate and nimare.decode From e3267589cfd81ecf6727ffe7465688dd7a88978e Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 16 Jul 2022 19:10:08 +0100 Subject: [PATCH 007/177] update model structure --- nimare/meta/cbmr.py | 101 +++++++++++++++++++++++++++++---- nimare/tests/test_meta_cbmr.py | 4 +- nimare/utils.py | 9 ++- 3 files changed, 96 insertions(+), 18 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 3674c46bc..36d3a9ccb 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -45,33 +45,55 @@ def _preprocess_input(self, dataset): ## to do: if moderators_center & moderators_array is a list of moderators names, only operate on the chosen moderators if self.moderators_center: moderators_array -= np.mean(moderators_array, axis=0) + if isinstance(self.moderators_scale, bool): if self.moderators_scale: moderators_array /= np.var(moderators_array, axis=0) + self.inputs_["moderators_array"] = moderators_array # Calculate IJK matrix indices for target mask # Mask space is assumed to be the same as the Dataset's space # These indices are used directly by any KernelTransformer xyz = dataset.coordinates[['x', 'y', 'z']].values ijk = mm2vox(xyz, mask_img.affine) - if hasattr(self, "moderators"): - study_id = dataset.coordinates['study_id'] - study_index = [np.where(study_id.unique()==i)[0].item() for i in study_id] - self.inputs_["coordinates"]["study_index"] = study_index + + study_id = dataset.coordinates['study_id'] + study_index = [np.where(study_id.unique()==i)[0].item() for i in study_id] + self.inputs_["coordinates"]["study_index"] = study_index self.inputs_["coordinates"][["i", "j", "k"]] = ijk foci_idx = vox2idx(ijk, mask_img._dataobj) self.inputs_["coordinates"]['foci_idx'] = foci_idx - # Y & y & y_t + n_study = np.shape(study_id.unique())[0] masker_voxels = np.sum(mask_img._dataobj).astype(int) - Y = np.zeros((n_study, masker_voxels)) - - y = np.sum(Y, axis=0) - y_t = np.sum(Y, axis=1) - + n_foci_per_voxel = np.zeros((masker_voxels, 1)) + n_foci_per_voxel[foci_idx, :] += 1 + self.inputs_['n_foci_per_voxel'] = n_foci_per_voxel + n_foci_per_study = np.zeros((n_study, 1)) + n_foci_per_study[study_index, :] += 1 + self.inputs_['n_foci_per_study'] = n_foci_per_study + def _model_structure(self, model, penalty, device): + # beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect + beta_dim = 2627 + if hasattr(self, "moderators"): + gamma_dim = self.inputs_["moderators_array"].shape[1] + study_level_covariates = True + else: + gamma_dim = None + study_level_covariates = False + if model == 'Poisson': + cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, study_level_covariates=study_level_covariates, penalty=penalty) + if 'cuda' in device: + cbmr_model = cbmr_model.cuda() + + return cbmr_model - def _fit(self, dataset, spline_spacing): + def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu'): masker_voxels = self.inputs_['mask_img']._dataobj - X = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) + # Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) + # self.inputs_['Coef_spline_bases'] = Coef_spline_bases + + model = self._model_structure(model, penalty, device) + pass @@ -137,3 +159,58 @@ def _fit(self, dataset, spline_spacing): # print('restart the optimisation!') # continue # else: + + +class GLMPoisson(torch.nn.Module): + def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, penalty='No'): + super().__init__() + self.study_level_covariates = study_level_covariates + # initialization for beta + self.beta_linear = torch.nn.Linear(beta_dim, 1, bias=False)#.double() + torch.nn.init.uniform_(self.beta_linear.weight, a=-0.01, b=0.01) + # gamma + if self.study_level_covariates: + self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def forward(self, X, y, Z=None, y_t=None): + # mu^X = exp(X * beta) + log_mu_X = self.beta_linear(X) + mu_X = torch.exp(log_mu_X) + # n_study + n_study = y_t.shape[0] + if self.covariates == True: + # mu^Z = exp(Z * gamma) + log_mu_Z = self.gamma_linear(Z) + mu_Z = torch.exp(log_mu_Z) + else: + log_mu_Z = torch.zeros(n_study, 1, device='cuda') + mu_Z = torch.ones(n_study, 1, device='cuda') + # Under the assumption that Y_ij is either 0 or 1 + # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] + log_l = torch.sum(torch.mul(y, log_mu_X)) + torch.sum(torch.mul(y_t, log_mu_Z)) - torch.sum(mu_X) * torch.sum(mu_Z) + if self.penalty == 'No': + l = log_l + elif self.penalty == 'Firth': + I = self.Fisher_information(X, mu_X, Z, mu_Z) + eig_vals = torch.linalg.eig(I)[0].real + log_det_I = torch.sum(torch.log(eig_vals)) + l = log_l + 1/2 * log_det_I + # start_time = time.time() + # beta = self.beta_linear.weight.T + # gamma = self.gamma_linear.weight.T + # params = (beta, gamma) + # # l = GLMPoisson._log_likelihood(beta, gamma, X, y, Z, y_t) + # nll = lambda beta, gamma: -GLMPoisson._log_likelihood(beta, gamma, X, y, Z, y_t) + # h = torch.autograd.functional.hessian(nll, params, create_graph=False) + # n_params = len(h) + # # approximate hessian matrix by its diagonal matrix + # h_beta = h[0][0].view(self.beta_dim, -1) + # h_gamma = h[1][1].view(self.gamma_dim, -1) + # h_diagonal_beta, h_diagonal_gamma = torch.diagonal(h_beta, 0), torch.diagonal(h_gamma, 0) + # # # Firth-type penalty + # log_det_I = torch.sum(torch.log(h_diagonal_beta)) + torch.sum(torch.log(h_diagonal_gamma)) + # l = log_l + 1/2 * log_det_I + # print(log_det_I) + + return -l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 19809b85b..1471a1cb5 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -5,6 +5,4 @@ def test_CBMREstimator(testdata_cbmr): cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age']) prep = cbmr._preprocess_input(testdata_cbmr) - fit = cbmr._fit(dataset=testdata_cbmr, spline_spacing=5) - - + fit = cbmr._fit(dataset=testdata_cbmr, model='Poisson', penalty=False) diff --git a/nimare/utils.py b/nimare/utils.py index 984dea58e..d0f57932c 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1020,7 +1020,7 @@ def unique_rows(ar): ... [1, 0, 1]], np.uint8) >>> unique_rows(ar) array([[0, 1, 0], - [1, 0, 1]], dtype=uint8) + [1, 0, 1]], dtype=uint8) Copyright (C) 2019, the scikit-image team All rights reserved. @@ -1140,7 +1140,10 @@ def vox2idx(ijk, masker_voxels): yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] x_dim, y_dim, z_dim = xx.shape[0], yy.shape[0], zz.shape[0] + brain_voxels_index = [(z - np.min(zz))+ z_dim * (y - np.min(yy))+ y_dim * z_dim * (x - np.min(xx)) + for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci)] - foci_index = np.array(foci_index) + foci_brain_index = [brain_voxels_index.index(j) for j in foci_index] + foci_brain_index = np.array(foci_brain_index) - return foci_index + return foci_brain_index From 85a6d1122b6f4f13efda6b8d50577be02a357a8e Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 16 Jul 2022 23:23:39 +0100 Subject: [PATCH 008/177] update optimizer function --- nimare/meta/cbmr.py | 190 ++++++++++++++++++++------------------------ 1 file changed, 84 insertions(+), 106 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 36d3a9ccb..835eed29a 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -72,8 +72,7 @@ def _preprocess_input(self, dataset): self.inputs_['n_foci_per_study'] = n_foci_per_study def _model_structure(self, model, penalty, device): - # beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect - beta_dim = 2627 + beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect if hasattr(self, "moderators"): gamma_dim = self.inputs_["moderators_array"].shape[1] study_level_covariates = True @@ -88,77 +87,79 @@ def _model_structure(self, model, penalty, device): return cbmr_model def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu'): + self.model = model masker_voxels = self.inputs_['mask_img']._dataobj - # Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) - # self.inputs_['Coef_spline_bases'] = Coef_spline_bases + Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) + self.inputs_['Coef_spline_bases'] = Coef_spline_bases - model = self._model_structure(model, penalty, device) + cbmr_model = self._model_structure(model, penalty, device) + optimisation = self._optimizer(cbmr_model, penalty, lr, tol, n_iter, device) + return - pass - + def _update(self, model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, gamma=0.99): + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay + scheduler.step() + def closure(): + optimizer.zero_grad() + loss = model(penalty, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) + loss.backward() + return loss + loss = optimizer.step(closure) - # def _optimizer(self, model, y, Z, y_t, penalty, lr, tol, iter): - # # optimization - # optimizer = torch.optim.LBFGS(model.parameters(), lr) - # prev_loss = torch.tensor(float('inf')) - # loss_diff = torch.tensor(float('inf')) - # step = 0 - # count = 0 - # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=0.995) - # while torch.abs(loss_diff) > tol: - # if step <= iter: - # scheduler.step() - # def closure(): - # optimizer.zero_grad() - # loss = model(self.X, y, Z, y_t) - # loss.backward() - # return loss - # loss = optimizer.step(closure) - # # reset L_BFGS if NAN appears - # if torch.any(torch.isnan(model.beta_linear.weight)): - # print("Reset lbfgs optimiser ......") - # count += 1 - # if count > 10: - # break - # model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) - # if self.covariates == True: - # model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) - # if self.model == 'NB': - # model.theta = torch.nn.Parameter(last_state['theta']) - # if self.model == 'Clustered_NB': - # model.alpha = torch.nn.Parameter(last_state['alpha']) - # loss_diff = torch.tensor(float('inf')) - # optimizer = torch.optim.LBFGS(model.parameters(), lr) - # continue - # else: - # last_state = copy.deepcopy(model.state_dict()) - # print("step {0}: loss {1}".format(step, loss)) - # loss_diff = loss - prev_loss - # prev_loss = loss - # step = step + 1 - # else: - # print('it did not converge \n') - # print('The difference of loss in the current and previous iteration is', loss_diff) - # exit() - # return - # def train(self, model, penalty, covariates, iter=1500, lr=0.01, tol=1e-4): - # self.model = model - # self.penalty = penalty - # self.covariates = covariates - # # model & optimization process - # for i in range(100): - # model = self.model_structure(model=self.model, penalty=self.penalty, covariates=self.covariates) - # optimization = self._optimizer(model=model, y=self.y, Z=self.Z, y_t=self.y_t, penalty=self.penalty, lr=lr, tol=tol, iter=iter) - # # beta - # beta = model.beta_linear.weight - # beta = beta.detach().cpu().numpy().T - # print(np.all(np.isnan(beta))) - # if np.all(np.isnan(beta)): - # print('restart the optimisation!') - # continue - # else: + + pass + + def _optimizer(self, model, penalty, lr, tol, n_iter, device): + optimizer = torch.optim.LBFGS(model.parameters(), lr) + # load dataset info to torch.tensor + Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) + if hasattr(self, "moderators"): + moderators_array = torch.tensor(self.inputs_['moderators_array'], dtype=torch.float64, device=device) + n_foci_per_voxel = torch.tensor(self.inputs_['n_foci_per_voxel'], dtype=torch.float64, device=device) + n_foci_per_study = torch.tensor(self.inputs_['n_foci_per_study'], dtype=torch.float64, device=device) + for i in range(n_iter): + self._update(model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) + + + # while torch.abs(loss_diff) > tol: + # if step <= n_iter: + # scheduler.step() + # def closure(): + # optimizer.zero_grad() + # loss = model(self.X, y, Z, y_t) + # loss.backward() + # return loss + # loss = optimizer.step(closure) + # # reset L_BFGS if NAN appears + # if torch.any(torch.isnan(model.beta_linear.weight)): + # print("Reset lbfgs optimiser ......") + # count += 1 + # if count > 10: + # print('optimisation failed') + # break + # model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) + # if self.covariates == True: + # model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) + # if self.model == 'NB': + # model.theta = torch.nn.Parameter(last_state['theta']) + # if self.model == 'Clustered_NB': + # model.alpha = torch.nn.Parameter(last_state['alpha']) + # loss_diff = torch.tensor(float('inf')) + # optimizer = torch.optim.LBFGS(model.parameters(), lr) + # continue + # else: + # last_state = copy.deepcopy(model.state_dict()) + # print("step {0}: loss {1}".format(step, loss)) + # loss_diff = loss - prev_loss + # prev_loss = loss + # step = step + 1 + # else: + # print('it did not converge \n') + # print('The difference of loss in the current and previous iteration is', loss_diff) + # exit() + # return class GLMPoisson(torch.nn.Module): @@ -166,51 +167,28 @@ def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, super().__init__() self.study_level_covariates = study_level_covariates # initialization for beta - self.beta_linear = torch.nn.Linear(beta_dim, 1, bias=False)#.double() + self.beta_linear = torch.nn.Linear(beta_dim, 1, bias=False).double() torch.nn.init.uniform_(self.beta_linear.weight, a=-0.01, b=0.01) # gamma if self.study_level_covariates: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def forward(self, X, y, Z=None, y_t=None): - # mu^X = exp(X * beta) - log_mu_X = self.beta_linear(X) + def forward(self, penalty, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study): + # spatial effect: mu^X = exp(X * beta) + log_mu_X = self.beta_linear(Coef_spline_bases) mu_X = torch.exp(log_mu_X) - # n_study - n_study = y_t.shape[0] - if self.covariates == True: - # mu^Z = exp(Z * gamma) - log_mu_Z = self.gamma_linear(Z) - mu_Z = torch.exp(log_mu_Z) - else: - log_mu_Z = torch.zeros(n_study, 1, device='cuda') - mu_Z = torch.ones(n_study, 1, device='cuda') - # Under the assumption that Y_ij is either 0 or 1 - # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - log_l = torch.sum(torch.mul(y, log_mu_X)) + torch.sum(torch.mul(y_t, log_mu_Z)) - torch.sum(mu_X) * torch.sum(mu_Z) - if self.penalty == 'No': - l = log_l - elif self.penalty == 'Firth': - I = self.Fisher_information(X, mu_X, Z, mu_Z) - eig_vals = torch.linalg.eig(I)[0].real - log_det_I = torch.sum(torch.log(eig_vals)) - l = log_l + 1/2 * log_det_I - # start_time = time.time() - # beta = self.beta_linear.weight.T - # gamma = self.gamma_linear.weight.T - # params = (beta, gamma) - # # l = GLMPoisson._log_likelihood(beta, gamma, X, y, Z, y_t) - # nll = lambda beta, gamma: -GLMPoisson._log_likelihood(beta, gamma, X, y, Z, y_t) - # h = torch.autograd.functional.hessian(nll, params, create_graph=False) - # n_params = len(h) - # # approximate hessian matrix by its diagonal matrix - # h_beta = h[0][0].view(self.beta_dim, -1) - # h_gamma = h[1][1].view(self.gamma_dim, -1) - # h_diagonal_beta, h_diagonal_gamma = torch.diagonal(h_beta, 0), torch.diagonal(h_gamma, 0) - # # # Firth-type penalty - # log_det_I = torch.sum(torch.log(h_diagonal_beta)) + torch.sum(torch.log(h_diagonal_gamma)) - # l = log_l + 1/2 * log_det_I - # print(log_det_I) + # if self.covariates == True: + # # mu^Z = exp(Z * gamma) + # log_mu_Z = self.gamma_linear(Z) + # mu_Z = torch.exp(log_mu_Z) + # else: + # log_mu_Z = torch.zeros(n_study, 1, device='cuda') + # mu_Z = torch.ones(n_study, 1, device='cuda') + # # Under the assumption that Y_ij is either 0 or 1 + # # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] + # log_l = torch.sum(torch.mul(y, log_mu_X)) + torch.sum(torch.mul(y_t, log_mu_Z)) - torch.sum(mu_X) * torch.sum(mu_Z) + # if self.penalty == 'No': + # l = log_l return -l From fe80124595c060512041b11c4ae52935e9853e97 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sun, 17 Jul 2022 15:51:35 +0100 Subject: [PATCH 009/177] [skip ci][wip] update loss function --- nimare/meta/cbmr.py | 89 +++++++++++++-------------------------------- 1 file changed, 26 insertions(+), 63 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 835eed29a..e3eb0ee72 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -39,7 +39,10 @@ def _preprocess_input(self, dataset): gb = dataset.annotations.groupby('group_id') multiple_groups = [gb.get_group(x)['study_id'] for x in gb.groups] if hasattr(self, "moderators"): - moderators_array = np.stack([dataset.annotations[moderator_name] for moderator_name in self.moderators], axis=1) + study_id_moderators = dataset.annotations.set_index('study_id').index + study_id_coordinates = dataset.coordinates.set_index('study_id').index + moderators_with_coordinates = dataset.annotations[study_id_moderators.isin(study_id_coordinates)] # moderators dataframe where foci exist in selected studies + moderators_array = np.stack([moderators_with_coordinates[moderator_name] for moderator_name in self.moderators], axis=1) moderators_array = moderators_array.astype(np.float64) if isinstance(self.moderators_center, bool): ## to do: if moderators_center & moderators_array is a list of moderators names, only operate on the chosen moderators @@ -97,7 +100,7 @@ def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter return - def _update(self, model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, gamma=0.99): + def _update(self, model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss, gamma=0.99): scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay scheduler.step() def closure(): @@ -106,10 +109,8 @@ def closure(): loss.backward() return loss loss = optimizer.step(closure) - - - - pass + + return loss def _optimizer(self, model, penalty, lr, tol, n_iter, device): optimizer = torch.optim.LBFGS(model.parameters(), lr) @@ -119,48 +120,15 @@ def _optimizer(self, model, penalty, lr, tol, n_iter, device): moderators_array = torch.tensor(self.inputs_['moderators_array'], dtype=torch.float64, device=device) n_foci_per_voxel = torch.tensor(self.inputs_['n_foci_per_voxel'], dtype=torch.float64, device=device) n_foci_per_study = torch.tensor(self.inputs_['n_foci_per_study'], dtype=torch.float64, device=device) + prev_loss = torch.tensor(float('inf')) # initialization loss difference for i in range(n_iter): - self._update(model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) - - - # while torch.abs(loss_diff) > tol: - # if step <= n_iter: - # scheduler.step() - # def closure(): - # optimizer.zero_grad() - # loss = model(self.X, y, Z, y_t) - # loss.backward() - # return loss - # loss = optimizer.step(closure) - # # reset L_BFGS if NAN appears - # if torch.any(torch.isnan(model.beta_linear.weight)): - # print("Reset lbfgs optimiser ......") - # count += 1 - # if count > 10: - # print('optimisation failed') - # break - # model.beta_linear.weight = torch.nn.Parameter(last_state['beta_linear.weight']) - # if self.covariates == True: - # model.gamma_linear.weight = torch.nn.Parameter(last_state['gamma_linear.weight']) - # if self.model == 'NB': - # model.theta = torch.nn.Parameter(last_state['theta']) - # if self.model == 'Clustered_NB': - # model.alpha = torch.nn.Parameter(last_state['alpha']) - # loss_diff = torch.tensor(float('inf')) - # optimizer = torch.optim.LBFGS(model.parameters(), lr) - # continue - # else: - # last_state = copy.deepcopy(model.state_dict()) - # print("step {0}: loss {1}".format(step, loss)) - # loss_diff = loss - prev_loss - # prev_loss = loss - # step = step + 1 - # else: - # print('it did not converge \n') - # print('The difference of loss in the current and previous iteration is', loss_diff) - # exit() - # return - + loss = self._update(model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss) + loss_diff = loss - prev_loss + if torch.abs(loss_diff) < tol: + break + prev_loss = loss + + return class GLMPoisson(torch.nn.Module): def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, penalty='No'): @@ -176,19 +144,14 @@ def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, def forward(self, penalty, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study): # spatial effect: mu^X = exp(X * beta) - log_mu_X = self.beta_linear(Coef_spline_bases) - mu_X = torch.exp(log_mu_X) - # if self.covariates == True: - # # mu^Z = exp(Z * gamma) - # log_mu_Z = self.gamma_linear(Z) - # mu_Z = torch.exp(log_mu_Z) - # else: - # log_mu_Z = torch.zeros(n_study, 1, device='cuda') - # mu_Z = torch.ones(n_study, 1, device='cuda') - # # Under the assumption that Y_ij is either 0 or 1 - # # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - # log_l = torch.sum(torch.mul(y, log_mu_X)) + torch.sum(torch.mul(y_t, log_mu_Z)) - torch.sum(mu_X) * torch.sum(mu_Z) - # if self.penalty == 'No': - # l = log_l - - return -l + log_mu_spatial = self.beta_linear(Coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + if torch.is_tensor(moderators_array): + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(moderators_array) + mu_moderators = torch.exp(log_mu_moderators) + # Under the assumption that Y_ij is either 0 or 1 + # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] + log_l = torch.sum(torch.mul(n_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(n_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) + + return -log_l From 3edaa743421f07c169f5c4e80b623a4ad4b45747 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 22 Jul 2022 15:46:14 +0100 Subject: [PATCH 010/177] update _fit function in CBMR --- nimare/meta/cbmr.py | 40 ++++++++++++++++++++++++---------- nimare/tests/test_meta_cbmr.py | 4 ++-- nimare/utils.py | 18 +++++++++++++++ 3 files changed, 48 insertions(+), 14 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e3eb0ee72..304386ff8 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -4,7 +4,7 @@ from nimare.utils import get_template, get_masker, B_spline_bases import nibabel as nib import numpy as np -from nimare.utils import mm2vox, vox2idx +from nimare.utils import mm2vox, vox2idx, intensity2voxel import torch class CBMREstimator(Estimator): @@ -89,17 +89,6 @@ def _model_structure(self, model, penalty, device): return cbmr_model - def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu'): - self.model = model - masker_voxels = self.inputs_['mask_img']._dataobj - Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) - self.inputs_['Coef_spline_bases'] = Coef_spline_bases - - cbmr_model = self._model_structure(model, penalty, device) - optimisation = self._optimizer(cbmr_model, penalty, lr, tol, n_iter, device) - - return - def _update(self, model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss, gamma=0.99): scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay scheduler.step() @@ -130,6 +119,33 @@ def _optimizer(self, model, penalty, lr, tol, n_iter, device): return + def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu'): + self.model = model + masker_voxels = self.inputs_['mask_img']._dataobj + Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) + self.inputs_['Coef_spline_bases'] = Coef_spline_bases + + cbmr_model = self._model_structure(model, penalty, device) + optimisation = self._optimizer(cbmr_model, penalty, lr, tol, n_iter, device) + + # beta: regression coef of spatial effect + self._beta = cbmr_model.beta_linear.weight + self._beta = self._beta.detach().numpy().T + + studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, self._beta)) + studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) + + if hasattr(self, "moderators"): + self._gamma = cbmr_model.gamma_linear.weight + self._gamma = self._gamma.detach().numpy().T + + moderator_results = np.exp(np.matmul(self.inputs_["moderators_array"], self._gamma)) + + + return + + + class GLMPoisson(torch.nn.Module): def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, penalty='No'): super().__init__() diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 1471a1cb5..7f81b6948 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -2,7 +2,7 @@ def test_CBMREstimator(testdata_cbmr): - + """Unit test for CBMR estimator.""" cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age']) prep = cbmr._preprocess_input(testdata_cbmr) - fit = cbmr._fit(dataset=testdata_cbmr, model='Poisson', penalty=False) + fit = cbmr._fit(dataset=testdata_cbmr, model='Poisson', penalty=False, tol=1e8) diff --git a/nimare/utils.py b/nimare/utils.py index d0f57932c..e5c41f2ea 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1147,3 +1147,21 @@ def vox2idx(ijk, masker_voxels): foci_brain_index = np.array(foci_brain_index) return foci_brain_index + +def intensity2voxel(intensity, masker_voxels): + masker_dim = masker_voxels.shape + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + + # correspondence between xyz coordinates and spatial intensity + brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) + brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) + + intensity_array = np.zeros(masker_dim) + for i in range(brain_voxel_intensity.shape[0]): + coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] + coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) + intensity_array[coord_x, coord_y, coord_z] = coord_intensity + + return intensity_array From cadffa6a1185f374d7e391212a828fad0169b623 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Wed, 27 Jul 2022 19:34:49 +0100 Subject: [PATCH 011/177] [wip][skip ci] allow other data types as pre-process inputs --- nimare/meta/cbmr.py | 30 +++++++++++++++++++++++++----- nimare/tests/test_meta_cbmr.py | 2 +- 2 files changed, 26 insertions(+), 6 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 304386ff8..fa00b09a7 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,3 +1,4 @@ +import string from attr import has from numpy import spacing from nimare.base import Estimator @@ -34,23 +35,41 @@ def _preprocess_input(self, dataset): for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": if hasattr(self, "groups"): - ## to do: raise an error if group column doesn't exist in dataset.annotations - group_names = dataset.annotations['group_id'].unique() - gb = dataset.annotations.groupby('group_id') - multiple_groups = [gb.get_group(x)['study_id'] for x in gb.groups] + if 'group_id' not in dataset.annotations.columns: + print('123') + raise ValueError("group_id must exist in the dataset in group-wise CBMR") + else: + group_names = dataset.annotations['group_id'].unique() + gb = dataset.annotations.groupby('group_id') + multiple_groups = [gb.get_group(x)['study_id'] for x in gb.groups] if hasattr(self, "moderators"): study_id_moderators = dataset.annotations.set_index('study_id').index study_id_coordinates = dataset.coordinates.set_index('study_id').index moderators_with_coordinates = dataset.annotations[study_id_moderators.isin(study_id_coordinates)] # moderators dataframe where foci exist in selected studies moderators_array = np.stack([moderators_with_coordinates[moderator_name] for moderator_name in self.moderators], axis=1) moderators_array = moderators_array.astype(np.float64) + # standardize mean if isinstance(self.moderators_center, bool): - ## to do: if moderators_center & moderators_array is a list of moderators names, only operate on the chosen moderators if self.moderators_center: moderators_array -= np.mean(moderators_array, axis=0) + elif isinstance(self.moderators_center, str): + index_moderators_center = self.moderators.index(self.moderators_center) + moderators_array[:,index_moderators_center] -= np.mean(moderators_array[:, index_moderators_center], axis=0) + elif isinstance(self.moderators_center, list): + index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] + for i in index_moderators_center: + moderators_array[:,i] -= np.mean(moderators_array[:, i], axis=0) + # standardize var if isinstance(self.moderators_scale, bool): if self.moderators_scale: moderators_array /= np.var(moderators_array, axis=0) + elif isinstance(self.moderators_scale, str): + index_moderators_scale = self.moderators.index(self.moderators_scale) + moderators_array[:,index_moderators_scale] /= np.var(moderators_array[:, index_moderators_scale], axis=0) + elif isinstance(self.moderators_scale, list): + index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] + for i in index_moderators_scale: + moderators_array[:,i] /= np.var(moderators_array[:, i], axis=0) self.inputs_["moderators_array"] = moderators_array # Calculate IJK matrix indices for target mask # Mask space is assumed to be the same as the Dataset's space @@ -136,6 +155,7 @@ def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) if hasattr(self, "moderators"): + self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.detach().numpy().T diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 7f81b6948..415668db7 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -3,6 +3,6 @@ def test_CBMREstimator(testdata_cbmr): """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age']) + cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age'], moderators_center=['sample_sizes', 'avg_age'], moderators_scale=['sample_sizes', 'avg_age']) prep = cbmr._preprocess_input(testdata_cbmr) fit = cbmr._fit(dataset=testdata_cbmr, model='Poisson', penalty=False, tol=1e8) From c632d292f01e5aa1ed896beef5857f62daef82f3 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Thu, 28 Jul 2022 12:27:11 +0100 Subject: [PATCH 012/177] use a sparse array instead of numpy --- nimare/utils.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/nimare/utils.py b/nimare/utils.py index e5c41f2ea..c8face88e 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -20,6 +20,7 @@ from nimare.due import due import patsy +import sparse LGR = logging.getLogger(__name__) @@ -1096,14 +1097,20 @@ def B_spline_bases(masker_voxels, spacing, margin=10): x_spline = coef_spline_bases(xx, spacing, margin) y_spline = coef_spline_bases(yy, spacing, margin) z_spline = coef_spline_bases(zz, spacing, margin) + x_spline_coords = x_spline.nonzero() + y_spline_coords = y_spline.nonzero() + z_spline_coords = z_spline.nonzero() + x_spline_sparse = sparse.COO(x_spline_coords, x_spline[x_spline_coords]) + y_spline_sparse = sparse.COO(y_spline_coords, y_spline[y_spline_coords]) + z_spline_sparse = sparse.COO(z_spline_coords, z_spline[z_spline_coords]) # create spatial design matrix by tensor product of spline bases in 3 dimesion - X = np.kron(np.kron(x_spline, y_spline), z_spline) # Row sums of X are all 1=> There is no need to re-normalise X + X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # Row sums of X are all 1=> There is no need to re-normalise X # remove the voxels outside brain mask - axis_dim = [x_spline.shape[0], y_spline.shape[0], z_spline.shape[0]] + axis_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] - X = X[brain_voxels_index, :] + X = X[brain_voxels_index, :].todense() # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] support_basis = [] From a22048db2c50e9930f8458031f9f3f05f22e3d5f Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 29 Jul 2022 15:06:51 +0100 Subject: [PATCH 013/177] [skip ci][wip] allow for multiple-group cbmr --- nimare/meta/cbmr.py | 196 ++++++++++++++++++++------------- nimare/tests/test_meta_cbmr.py | 14 ++- 2 files changed, 131 insertions(+), 79 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index fa00b09a7..bdfe165e7 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -7,21 +7,35 @@ import numpy as np from nimare.utils import mm2vox, vox2idx, intensity2voxel import torch +import logging +LGR = logging.getLogger(__name__) class CBMREstimator(Estimator): _required_inputs = {"coordinates": ("coordinates", None)} - def __init__(self, groups=False, moderators=None, moderators_center=True, moderators_scale=True, mask=None, **kwargs): + def __init__(self, multiple_groups=False, moderators=None, moderators_center=True, moderators_scale=True, mask=None, + spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu', **kwargs): super().__init__(**kwargs) if mask is not None: mask = get_masker(mask) self.masker = mask - self.groups = groups + self.multiple_groups = multiple_groups self.moderators = moderators self.moderators_center = moderators_center # either boolean or a list of strings self.moderators_scale = moderators_scale + self.spline_spacing = spline_spacing + self.model = model + self.penalty = penalty + self.n_iter = n_iter + self.lr = lr + self.tol = tol + self.device = device + + # Initialize optimisation parameters + self.iter = 0 + def _preprocess_input(self, dataset): masker = self.masker or dataset.masker @@ -34,120 +48,145 @@ def _preprocess_input(self, dataset): for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": - if hasattr(self, "groups"): - if 'group_id' not in dataset.annotations.columns: - print('123') + study_id_annotations = dataset.annotations.set_index('study_id').index + study_id_coordinates = dataset.coordinates.set_index('study_id').index + # remove study_id without any coordinates + valid_study_bool = study_id_annotations.isin(study_id_coordinates) + dataset_annotations = dataset.annotations[valid_study_bool] + all_group_study_id = dict() + if self.multiple_groups: + if 'group_id' not in dataset_annotations.columns: raise ValueError("group_id must exist in the dataset in group-wise CBMR") else: - group_names = dataset.annotations['group_id'].unique() - gb = dataset.annotations.groupby('group_id') - multiple_groups = [gb.get_group(x)['study_id'] for x in gb.groups] + group_names = list(dataset_annotations['group_id'].unique()) + if len(group_names) == 1: + raise ValueError('Only a single group exists in the dataset') + for group_name in group_names: + group_study_id_bool = dataset_annotations['group_id'] == group_name + group_study_id = dataset_annotations.loc[group_study_id_bool]['study_id'] + all_group_study_id[group_name] = group_study_id.unique().tolist() + else: + all_group_study_id['single_group'] = dataset_annotations['study_id'].unique().tolist() + self.inputs_['all_group_study_id'] = all_group_study_id + # collect studywise moderators if specficed if hasattr(self, "moderators"): - study_id_moderators = dataset.annotations.set_index('study_id').index - study_id_coordinates = dataset.coordinates.set_index('study_id').index - moderators_with_coordinates = dataset.annotations[study_id_moderators.isin(study_id_coordinates)] # moderators dataframe where foci exist in selected studies - moderators_array = np.stack([moderators_with_coordinates[moderator_name] for moderator_name in self.moderators], axis=1) - moderators_array = moderators_array.astype(np.float64) - # standardize mean - if isinstance(self.moderators_center, bool): - if self.moderators_center: - moderators_array -= np.mean(moderators_array, axis=0) - elif isinstance(self.moderators_center, str): - index_moderators_center = self.moderators.index(self.moderators_center) - moderators_array[:,index_moderators_center] -= np.mean(moderators_array[:, index_moderators_center], axis=0) - elif isinstance(self.moderators_center, list): - index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] - for i in index_moderators_center: - moderators_array[:,i] -= np.mean(moderators_array[:, i], axis=0) - # standardize var - if isinstance(self.moderators_scale, bool): - if self.moderators_scale: - moderators_array /= np.var(moderators_array, axis=0) - elif isinstance(self.moderators_scale, str): - index_moderators_scale = self.moderators.index(self.moderators_scale) - moderators_array[:,index_moderators_scale] /= np.var(moderators_array[:, index_moderators_scale], axis=0) - elif isinstance(self.moderators_scale, list): - index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] - for i in index_moderators_scale: - moderators_array[:,i] /= np.var(moderators_array[:, i], axis=0) - self.inputs_["moderators_array"] = moderators_array + all_group_moderators = dict() + for group_name in all_group_study_id.keys(): + df_group = dataset_annotations.loc[dataset_annotations['study_id'].isin(all_group_study_id[group_name])] + group_moderators = np.stack([df_group[moderator_name] for moderator_name in self.moderators], axis=1) + group_moderators = group_moderators.astype(np.float64) + # standardize mean + if isinstance(self.moderators_center, bool): + if self.moderators_center: + group_moderators -= np.mean(group_moderators, axis=0) + elif isinstance(self.moderators_center, str): + index_moderators_center = self.moderators.index(self.moderators_center) + group_moderators[:,index_moderators_center] -= np.mean(group_moderators[:, index_moderators_center], axis=0) + elif isinstance(self.moderators_center, list): + index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] + for i in index_moderators_center: + group_moderators[:,i] -= np.mean(group_moderators[:, i], axis=0) + # standardize var + if isinstance(self.moderators_scale, bool): + if self.moderators_scale: + group_moderators /= np.std(group_moderators, axis=0) + elif isinstance(self.moderators_scale, str): + index_moderators_scale = self.moderators.index(self.moderators_scale) + group_moderators[:,index_moderators_scale] /= np.std(group_moderators[:, index_moderators_scale], axis=0) + elif isinstance(self.moderators_scale, list): + index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] + for i in index_moderators_scale: + group_moderators[:,i] /= np.std(group_moderators[:, i], axis=0) + all_group_moderators[group_name] = group_moderators + self.inputs_["all_group_moderators"] = all_group_moderators # Calculate IJK matrix indices for target mask # Mask space is assumed to be the same as the Dataset's space # These indices are used directly by any KernelTransformer - xyz = dataset.coordinates[['x', 'y', 'z']].values - ijk = mm2vox(xyz, mask_img.affine) + all_foci_per_voxel, all_foci_per_study = dict(), dict() + for group_name in all_group_study_id.keys(): + group_study_id = all_group_study_id[group_name] + group_coordinates = dataset.coordinates.loc[dataset.coordinates['study_id'].isin(group_study_id)] + + group_xyz = group_coordinates[['x', 'y', 'z']].values + group_ijk = mm2vox(group_xyz, mask_img.affine) + group_foci_idx = vox2idx(group_ijk, mask_img._dataobj) + + n_group_study = len(group_study_id) + masker_voxels = np.sum(mask_img._dataobj).astype(int) + group_foci_per_voxel = np.zeros((masker_voxels, 1)) + group_foci_per_voxel[group_foci_idx, :] += 1 + group_foci_per_study = np.array([(group_coordinates['study_id']==i).sum() for i in group_study_id]) + group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) + + all_foci_per_voxel[group_name] = group_foci_per_voxel + all_foci_per_study[group_name] = group_foci_per_study - study_id = dataset.coordinates['study_id'] - study_index = [np.where(study_id.unique()==i)[0].item() for i in study_id] - self.inputs_["coordinates"]["study_index"] = study_index - self.inputs_["coordinates"][["i", "j", "k"]] = ijk - foci_idx = vox2idx(ijk, mask_img._dataobj) - self.inputs_["coordinates"]['foci_idx'] = foci_idx - - n_study = np.shape(study_id.unique())[0] - masker_voxels = np.sum(mask_img._dataobj).astype(int) - n_foci_per_voxel = np.zeros((masker_voxels, 1)) - n_foci_per_voxel[foci_idx, :] += 1 - self.inputs_['n_foci_per_voxel'] = n_foci_per_voxel - n_foci_per_study = np.zeros((n_study, 1)) - n_foci_per_study[study_index, :] += 1 - self.inputs_['n_foci_per_study'] = n_foci_per_study + self.inputs_['all_foci_per_voxel'] = all_foci_per_voxel + self.inputs_['all_foci_per_study'] = all_foci_per_study + def _model_structure(self, model, penalty, device): beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect if hasattr(self, "moderators"): - gamma_dim = self.inputs_["moderators_array"].shape[1] - study_level_covariates = True + gamma_dim = list(self.inputs_["all_group_moderators"].values())[0].shape[1] + study_level_moderators = True else: gamma_dim = None - study_level_covariates = False + study_level_moderators = False + self.n_groups = len(self.inputs_["all_group_study_id"]) if model == 'Poisson': - cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, study_level_covariates=study_level_covariates, penalty=penalty) + cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, n_groups=self.n_groups, study_level_moderators=study_level_moderators, penalty=penalty) if 'cuda' in device: cbmr_model = cbmr_model.cuda() return cbmr_model - def _update(self, model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss, gamma=0.99): + def _update(self, model, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss, gamma=0.99): scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay scheduler.step() + + self.iter += 1 def closure(): optimizer.zero_grad() - loss = model(penalty, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) + loss = model(Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) loss.backward() return loss loss = optimizer.step(closure) return loss - def _optimizer(self, model, penalty, lr, tol, n_iter, device): + def _optimizer(self, model, lr, tol, n_iter, device): optimizer = torch.optim.LBFGS(model.parameters(), lr) # load dataset info to torch.tensor Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) if hasattr(self, "moderators"): - moderators_array = torch.tensor(self.inputs_['moderators_array'], dtype=torch.float64, device=device) + for group_name in self.inputs_['all_group_study_id'].keys(): + moderators_array = torch.tensor(self.inputs_['all_group_moderators'][group_name], dtype=torch.float64, device=device) n_foci_per_voxel = torch.tensor(self.inputs_['n_foci_per_voxel'], dtype=torch.float64, device=device) n_foci_per_study = torch.tensor(self.inputs_['n_foci_per_study'], dtype=torch.float64, device=device) - prev_loss = torch.tensor(float('inf')) # initialization loss difference + + if self.iter == 0: + prev_loss = torch.tensor(float('inf')) # initialization loss difference + for i in range(n_iter): - loss = self._update(model, penalty, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss) + loss = self._update(model, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss) loss_diff = loss - prev_loss + LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") if torch.abs(loss_diff) < tol: break prev_loss = loss return - def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu'): - self.model = model + def _fit(self, dataset): masker_voxels = self.inputs_['mask_img']._dataobj - Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=spline_spacing) + Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) self.inputs_['Coef_spline_bases'] = Coef_spline_bases - cbmr_model = self._model_structure(model, penalty, device) - optimisation = self._optimizer(cbmr_model, penalty, lr, tol, n_iter, device) + cbmr_model = self._model_structure(self.model, self.penalty, self.device) + optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) - # beta: regression coef of spatial effect + # beta: regression coef of spatial effec self._beta = cbmr_model.beta_linear.weight self._beta = self._beta.detach().numpy().T @@ -155,7 +194,6 @@ def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) if hasattr(self, "moderators"): - self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.detach().numpy().T @@ -167,18 +205,24 @@ def _fit(self, dataset, spline_spacing=5, model='Poisson', penalty=False, n_iter class GLMPoisson(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, study_level_covariates=False, penalty='No'): + def __init__(self, beta_dim=None, gamma_dim=None, n_groups=None, study_level_moderators=False, penalty='No'): super().__init__() - self.study_level_covariates = study_level_covariates + self.n_groups = n_groups + self.study_level_moderators = study_level_moderators # initialization for beta - self.beta_linear = torch.nn.Linear(beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.beta_linear.weight, a=-0.01, b=0.01) + beta_linear_weights = list() + for i in range(self.n_groups): + beta_linear_i = torch.nn.Linear(beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_i.weight, a=-0.01, b=0.01) + beta_linear_weights.append(beta_linear_i.weight) + beta_linear_weights = torch.stack(beta_linear_weights) + self.beta_linear_weights = torch.nn.Parameter(beta_linear_weights, requires_grad=True) # gamma - if self.study_level_covariates: + if self.study_level_moderators: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def forward(self, penalty, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study): + def forward(self, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study): # spatial effect: mu^X = exp(X * beta) log_mu_spatial = self.beta_linear(Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 415668db7..eb3a54e1e 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,8 +1,16 @@ from nimare.meta.cbmr import CBMREstimator +import logging -def test_CBMREstimator(testdata_cbmr): +# logging.getLogger().setLevel(logging.DEBUG) + +def test_CBMREstimator(testdata_cbmr, caplog): """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(moderators=['sample_sizes', 'avg_age'], moderators_center=['sample_sizes', 'avg_age'], moderators_scale=['sample_sizes', 'avg_age']) + cbmr = CBMREstimator(multiple_groups=True, moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, tol=1e8) prep = cbmr._preprocess_input(testdata_cbmr) - fit = cbmr._fit(dataset=testdata_cbmr, model='Poisson', penalty=False, tol=1e8) + with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbmr"): + cbmr.fit(dataset=testdata_cbmr) + print('1234') + # with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): + # meta.fit(testdata_cbma) + # assert "Loading pre-generated MA maps" not in caplog.text From ab450fa024b6ba96b6535cad57e01877953d69e9 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sun, 31 Jul 2022 16:38:17 +0100 Subject: [PATCH 014/177] [skip ci][wip] fix conflict to merge --- .codecov.yml | 2 - docs/api.rst | 3 +- docs/cli.rst | 4 +- docs/conf.py | 1 + docs/links.rst | 2 - examples/02_meta-analyses/10_peaks2maps.py | 41 -- .../misc-notebooks/save_nidm_to_dset.ipynb | 4 +- nimare/annotate/cogat.py | 4 - nimare/annotate/gclda.py | 7 - nimare/annotate/lda.py | 7 - nimare/base.py | 4 +- nimare/cli.py | 40 -- nimare/correct.py | 22 +- nimare/decode/continuous.py | 4 - nimare/decode/discrete.py | 8 - nimare/decode/encode.py | 3 - nimare/diagnostics.py | 109 +++- nimare/due.py | 65 --- nimare/extract/__init__.py | 2 - nimare/extract/extract.py | 78 --- nimare/generate.py | 11 +- nimare/meta/cbma/ale.py | 130 +++-- nimare/meta/cbma/base.py | 83 ++- nimare/meta/cbma/mkda.py | 209 +++++--- nimare/meta/cbmr.py | 3 +- nimare/meta/ibma.py | 41 +- nimare/meta/kernel.py | 137 ++--- nimare/meta/utils.py | 502 ++++++------------ nimare/references.py | 198 ------- nimare/results.py | 75 ++- nimare/tests/conftest.py | 3 +- nimare/tests/test_diagnostics.py | 25 +- nimare/tests/test_meta_ale.py | 22 +- nimare/tests/test_meta_cbmr.py | 4 +- nimare/tests/test_meta_kernel.py | 2 +- nimare/tests/utils.py | 9 +- nimare/transforms.py | 25 +- nimare/utils.py | 178 ++++++- nimare/workflows/__init__.py | 2 - nimare/workflows/conperm.py | 2 +- nimare/workflows/peaks2maps.py | 62 --- pyproject.toml | 1 - setup.cfg | 16 +- setup_BACKUP_7408.cfg | 129 +++++ setup_BASE_7408.cfg | 134 +++++ setup_LOCAL_7408.cfg | 135 +++++ setup_REMOTE_7408.cfg | 124 +++++ 47 files changed, 1424 insertions(+), 1248 deletions(-) delete mode 100644 examples/02_meta-analyses/10_peaks2maps.py delete mode 100644 nimare/due.py delete mode 100644 nimare/references.py delete mode 100644 nimare/workflows/peaks2maps.py create mode 100644 setup_BACKUP_7408.cfg create mode 100644 setup_BASE_7408.cfg create mode 100644 setup_LOCAL_7408.cfg create mode 100644 setup_REMOTE_7408.cfg diff --git a/.codecov.yml b/.codecov.yml index ea9b6c7e5..1e82480d9 100644 --- a/.codecov.yml +++ b/.codecov.yml @@ -16,6 +16,4 @@ coverage: ignore: - 'nimare/tests/' - - 'nimare/due.py' - 'nimare/_version.py' - - 'nimare/references.py' diff --git a/docs/api.rst b/docs/api.rst index ac54477b4..a9120444d 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -94,6 +94,7 @@ For more information about the components of coordinate-based meta-analysis in N :toctree: generated/ :template: class.rst + diagnostics.FocusFilter diagnostics.Jackknife diagnostics.FocusCounter @@ -222,7 +223,6 @@ For more information about fetching data from the internet, see :ref:`fetching t extract.download_nidm_pain extract.download_cognitive_atlas extract.download_abstracts - extract.download_peaks2maps_model extract.utils.get_data_dirs @@ -310,7 +310,6 @@ For more information about fetching data from the internet, see :ref:`fetching t workflows.ale_sleuth_workflow workflows.conperm_workflow workflows.macm_workflow - workflows.peaks2maps_workflow workflows.scale_workflow diff --git a/docs/cli.rst b/docs/cli.rst index c57f64e80..2ea0c3b49 100644 --- a/docs/cli.rst +++ b/docs/cli.rst @@ -2,8 +2,8 @@ Command Line Interface ======================== NiMARE provides several workflows as command-line interfaces, including ALE -meta-analysis, meta-analytic coactivation modeling (MACM) analysis, peaks2maps -image reconstruction, and contrast map meta-analysis. +meta-analysis, meta-analytic coactivation modeling (MACM) analysis, +and contrast map meta-analysis. Each workflow should generate a boilerplate paragraph with details about the workflow and citations that can be used in a manuscript. diff --git a/docs/conf.py b/docs/conf.py index b278fe173..f755d1c8d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -109,6 +109,7 @@ # ----------------------------------------------------------------------------- napoleon_google_docstring = False napoleon_numpy_docstring = True +napoleon_custom_sections = ["License"] napoleon_include_init_with_doc = True napoleon_include_private_with_doc = False napoleon_include_special_with_doc = False diff --git a/docs/links.rst b/docs/links.rst index f5eda984e..ebc720893 100644 --- a/docs/links.rst +++ b/docs/links.rst @@ -66,8 +66,6 @@ .. _OpenNeuro: https://openneuro.org -.. _peaks2maps: https://doi.org/10.7490/f1000research.1116395.1 - .. _PyMARE: https://pymare.readthedocs.io/en/latest/ .. _scikit-learn: https://scikit-learn.org/stable/developers/index.html diff --git a/examples/02_meta-analyses/10_peaks2maps.py b/examples/02_meta-analyses/10_peaks2maps.py deleted file mode 100644 index 916bebc43..000000000 --- a/examples/02_meta-analyses/10_peaks2maps.py +++ /dev/null @@ -1,41 +0,0 @@ -""" - -.. _metas_peaks2maps: - -================================ -Generate MA maps with peaks2maps -================================ - -.. warning:: - peaks2maps has been deprecated within NiMARE and will be removed in version 0.0.13. -""" -############################################################################### -# Start with the necessary imports -# ----------------------------------------------------------------------------- -import os - -from nilearn.plotting import plot_glass_brain - -from nimare.dataset import Dataset -from nimare.meta.kernel import Peaks2MapsKernel -from nimare.utils import get_resource_path - -############################################################################### -# Load Dataset -# ----------------------------------------------------------------------------- -dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") -dset = Dataset(dset_file) - -############################################################################### -# Run peaks2maps -# ----------------------------------------------------------------------------- -k = Peaks2MapsKernel() -imgs = k.transform(dset, return_type="image") - -############################################################################### -# Plot modeled activation maps -# ----------------------------------------------------------------------------- -for img in imgs: - display = plot_glass_brain( - img, display_mode="lyrz", plot_abs=False, colorbar=True, vmax=1, threshold=0 - ) diff --git a/examples/misc-notebooks/save_nidm_to_dset.ipynb b/examples/misc-notebooks/save_nidm_to_dset.ipynb index db729d93e..94e6ea552 100755 --- a/examples/misc-notebooks/save_nidm_to_dset.ipynb +++ b/examples/misc-notebooks/save_nidm_to_dset.ipynb @@ -181,7 +181,7 @@ " binarized[binarized>0] = 1\n", " binarized[binarized<0] = 0\n", " binarized = binarized.astype(int)\n", - " labeled = ndimage.measurements.label(binarized, np.ones((3, 3, 3)))[0]\n", + " labeled = ndimage.label(binarized, np.ones((3, 3, 3)))[0]\n", " clust_ids = sorted(list(np.unique(labeled)[1:]))\n", " ijk = np.hstack([np.where(data * (labeled == c) == np.max(data * (labeled == c))) for c in clust_ids])\n", " ijk = ijk.T\n", @@ -259,7 +259,7 @@ " binarized[binarized>0] = 1\n", " binarized[binarized<0] = 0\n", " binarized = binarized.astype(int)\n", - " labeled = ndimage.measurements.label(binarized, np.ones((3, 3, 3)))[0]\n", + " labeled = ndimage.label(binarized, np.ones((3, 3, 3)))[0]\n", " clust_ids = sorted(list(np.unique(labeled)[1:]))\n", " \n", " peak_vals = np.array([np.max(data * (labeled == c)) for c in clust_ids])\n", diff --git a/nimare/annotate/cogat.py b/nimare/annotate/cogat.py index a6264598a..1f26d6dd5 100755 --- a/nimare/annotate/cogat.py +++ b/nimare/annotate/cogat.py @@ -5,16 +5,13 @@ import numpy as np import pandas as pd -from nimare import references from nimare.annotate import utils -from nimare.due import due from nimare.extract import download_cognitive_atlas from nimare.utils import _uk_to_us LGR = logging.getLogger(__name__) -@due.dcite(references.COGNITIVE_ATLAS, description="Introduces the Cognitive Atlas.") class CogAtLemmatizer(object): """Replace synonyms and abbreviations with Cognitive Atlas identifiers in text. @@ -94,7 +91,6 @@ def transform(self, text, convert_uk=True): return text -@due.dcite(references.COGNITIVE_ATLAS, description="Introduces the Cognitive Atlas.") def extract_cogat(text_df, id_df=None, text_column="abstract"): """Extract Cognitive Atlas terms and count instances using regular expressions. diff --git a/nimare/annotate/gclda.py b/nimare/annotate/gclda.py index 658870421..4668f8b6b 100755 --- a/nimare/annotate/gclda.py +++ b/nimare/annotate/gclda.py @@ -8,15 +8,12 @@ from nilearn._utils import load_niimg from scipy.stats import multivariate_normal -from nimare import references from nimare.base import NiMAREBase -from nimare.due import due from nimare.utils import get_template LGR = logging.getLogger(__name__) -@due.dcite(references.GCLDAMODEL) class GCLDAModel(NiMAREBase): """Generate a generalized correspondence latent Dirichlet allocation (GCLDA) topic model. @@ -717,10 +714,6 @@ def _update_regions(self): self.topics["regions_mu"][i_topic, j_region, ...] = mu self.topics["regions_sigma"][i_topic, j_region, ...] = sigma - @due.dcite( - references.LOG_LIKELIHOOD, - description="Describes method for computing log-likelihood used in model.", - ) def compute_log_likelihood(self, model=None, update_vectors=True): """Compute log-likelihood of a model object given current model. diff --git a/nimare/annotate/lda.py b/nimare/annotate/lda.py index 513f5eee6..f113cc7cf 100644 --- a/nimare/annotate/lda.py +++ b/nimare/annotate/lda.py @@ -2,17 +2,10 @@ import pandas as pd from sklearn.decomposition import LatentDirichletAllocation -from nimare import references from nimare.annotate.text import generate_counts from nimare.base import NiMAREBase -from nimare.due import due -@due.dcite(references.LDA, description="Introduces LDA.") -@due.dcite( - references.LDAMODEL, - description="First use of LDA for automated annotation of neuroimaging literature.", -) class LDAModel(NiMAREBase): """Generate a latent Dirichlet allocation (LDA) topic model. diff --git a/nimare/base.py b/nimare/base.py index b1ed307bb..088d3ac14 100644 --- a/nimare/base.py +++ b/nimare/base.py @@ -328,11 +328,11 @@ def fit(self, dataset, drop_invalid=True): """ self._collect_inputs(dataset, drop_invalid=drop_invalid) self._preprocess_input(dataset) - maps = self._fit(dataset) + maps, tables = self._fit(dataset) if hasattr(self, "masker") and self.masker is not None: masker = self.masker else: masker = dataset.masker - return MetaResult(self, masker, maps) + return MetaResult(self, mask=masker, maps=maps, tables=tables) diff --git a/nimare/cli.py b/nimare/cli.py index cc89ffd5d..bb4fc563b 100644 --- a/nimare/cli.py +++ b/nimare/cli.py @@ -6,7 +6,6 @@ from nimare.workflows.ale import ale_sleuth_workflow from nimare.workflows.conperm import conperm_workflow from nimare.workflows.macm import macm_workflow -from nimare.workflows.peaks2maps import peaks2maps_workflow from nimare.workflows.scale import scale_workflow @@ -139,45 +138,6 @@ def _get_parser(): default=10000, ) - # Contrast permutation applied to Peaks2Maps-reconstructed maps - peaks2maps_parser = subparsers.add_parser( - "peaks2maps", - help=( - "Method for performing coordinate-based meta-analysis that " - "uses a pretrained deep neural network to reconstruct " - "unthresholded maps from peak coordinates. The reconstructed " - "maps are evaluated for statistical significance using a " - "permutation-based approach with Family Wise Error multiple " - "comparison correction. " - "WARNING: " - "The peaks2maps workflow is deprecated and will be removed in NiMARE version 0.0.13." - ), - ) - peaks2maps_parser.set_defaults(func=peaks2maps_workflow) - peaks2maps_parser.add_argument( - "sleuth_file", - type=lambda x: _is_valid_file(parser, x), - help=("Sleuth text file to analyze."), - ) - peaks2maps_parser.add_argument( - "--output_dir", - dest="output_dir", - metavar="PATH", - type=str, - help=("Output directory."), - default=".", - ) - peaks2maps_parser.add_argument( - "--prefix", dest="prefix", type=str, help=("Common prefix for output maps."), default="" - ) - peaks2maps_parser.add_argument( - "--n_iters", - dest="n_iters", - type=int, - help=("Number of iterations for permutation testing."), - default=10000, - ) - # MACM macm_parser = subparsers.add_parser( "macm", diff --git a/nimare/correct.py b/nimare/correct.py index 24b703ace..14485cd4a 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -153,7 +153,7 @@ def transform(self, result): Returns ------- result : :obj:`~nimare.results.MetaResult` - MetaResult with new corrected maps added. + MetaResult with new corrected maps and tables added. """ correction_method = f"correct_{self._correction_method}_{self.method}" @@ -172,15 +172,18 @@ def transform(self, result): "Using correction method implemented in Estimator: " f"{est.__class__.__module__}.{est.__class__.__name__}.{correction_method}." ) - corr_maps = getattr(est, correction_method)(result, **self.parameters) + corr_maps, corr_tables = getattr(est, correction_method)(result, **self.parameters) else: self._collect_inputs(result) - corr_maps = self._transform(result, method=correction_method) + corr_maps, corr_tables = self._transform(result, method=correction_method) # Update corrected map names and add them to maps dict corr_maps = {(k + self._name_suffix): v for k, v in corr_maps.items()} result.maps.update(corr_maps) + corr_tables = {(k + self._name_suffix): v for k, v in corr_tables.items()} + result.tables.update(corr_tables) + # Update the estimator as well, in order to retain updated null distributions result.estimator = est @@ -208,6 +211,9 @@ def _transform(self, result, method): The map names must _not_ include the ``_name_suffix``:, as that will be added in ``transform()`` (i.e., return "p" not "p_corr-FDR_q-0.05_method-indep"). + corr_tables : :obj:`dict` + An empty dictionary meant to contain any tables (pandas DataFrames) produced by the + correction procedure. """ p = result.maps["p"] @@ -217,7 +223,7 @@ def _transform(self, result, method): p_no_nans = p[nonnan_mask] # Call the correction method - p_corr_no_nans = getattr(self, method)(p_no_nans) + p_corr_no_nans, tables = getattr(self, method)(p_no_nans) # Unmask the corrected p values based on the NaN mask p_corr[nonnan_mask] = p_corr_no_nans @@ -225,7 +231,7 @@ def _transform(self, result, method): # Create a dictionary of the corrected results corr_maps = {"p": p_corr} self._generate_secondary_maps(result, corr_maps) - return corr_maps + return corr_maps, tables class FWECorrector(Corrector): @@ -289,7 +295,7 @@ def correct_fwe_bonferroni(self, p): -------- nimare.stats.bonferroni """ - return bonferroni(p) + return bonferroni(p), {} class FDRCorrector(Corrector): @@ -357,7 +363,7 @@ def correct_fdr_indep(self, p): -------- pymare.stats.fdr """ - return fdr(p, q=self.alpha, method="bh") + return fdr(p, q=self.alpha, method="bh"), {} def correct_fdr_negcorr(self, p): """Perform Benjamini-Yekutieli FDR correction. @@ -397,4 +403,4 @@ def correct_fdr_negcorr(self, p): -------- pymare.stats.fdr """ - return fdr(p, q=self.alpha, method="by") + return fdr(p, q=self.alpha, method="by"), {} diff --git a/nimare/decode/continuous.py b/nimare/decode/continuous.py index 11fa7861e..afd161e38 100755 --- a/nimare/decode/continuous.py +++ b/nimare/decode/continuous.py @@ -8,10 +8,8 @@ from nilearn.masking import apply_mask from tqdm.auto import tqdm -from nimare import references from nimare.decode.base import Decoder from nimare.decode.utils import weight_priors -from nimare.due import due from nimare.meta.cbma.base import CBMAEstimator from nimare.meta.cbma.mkda import MKDAChi2 from nimare.stats import pearson @@ -20,7 +18,6 @@ LGR = logging.getLogger(__name__) -@due.dcite(references.GCLDA_DECODING, description="Describes decoding methods using GC-LDA.") def gclda_decode_map(model, image, topic_priors=None, prior_weight=1): r"""Perform image-to-text decoding for continuous inputs using method from Rubin et al. (2017). @@ -110,7 +107,6 @@ def gclda_decode_map(model, image, topic_priors=None, prior_weight=1): return decoded_df, topic_weights -@due.dcite(references.NEUROSYNTH, description="Introduces Neurosynth.") class CorrelationDecoder(Decoder): """Decode an unthresholded image by correlating the image with meta-analytic maps. diff --git a/nimare/decode/discrete.py b/nimare/decode/discrete.py index cd0606d3a..ffb5ee86f 100755 --- a/nimare/decode/discrete.py +++ b/nimare/decode/discrete.py @@ -6,17 +6,14 @@ from scipy import special from scipy.stats import binom -from nimare import references from nimare.decode.base import Decoder from nimare.decode.utils import weight_priors -from nimare.due import due from nimare.meta.kernel import KernelTransformer, MKDAKernel from nimare.stats import one_way, pearson, two_way from nimare.transforms import p_to_z from nimare.utils import _check_type, get_masker -@due.dcite(references.GCLDA_DECODING, description="Citation for GCLDA decoding.") def gclda_decode_roi(model, roi, topic_priors=None, prior_weight=1.0): r"""Perform image-to-text decoding for discrete inputs using method from Rubin et al. (2017). @@ -113,7 +110,6 @@ def gclda_decode_roi(model, roi, topic_priors=None, prior_weight=1.0): return decoded_df, topic_weights -@due.dcite(references.BRAINMAP_DECODING, description="Citation for BrainMap-style decoding.") class BrainMapDecoder(Decoder): """Perform image-to-text decoding for discrete inputs according to the BrainMap method. @@ -212,7 +208,6 @@ def transform(self, ids, ids2=None): return results -@due.dcite(references.BRAINMAP_DECODING, description="Citation for BrainMap-style decoding.") def brainmap_decode( coordinates, annotations, @@ -388,7 +383,6 @@ def brainmap_decode( return out_df -@due.dcite(references.NEUROSYNTH, description="Introduces Neurosynth.") class NeurosynthDecoder(Decoder): """Perform discrete functional decoding according to Neurosynth's meta-analytic method. @@ -499,7 +493,6 @@ def transform(self, ids, ids2=None): return results -@due.dcite(references.NEUROSYNTH, description="Introduces Neurosynth.") def neurosynth_decode( coordinates, annotations, @@ -672,7 +665,6 @@ def neurosynth_decode( return out_df -@due.dcite(references.NEUROSYNTH, description="Introduces Neurosynth.") class ROIAssociationDecoder(Decoder): """Perform discrete functional decoding according to Neurosynth's ROI association method. diff --git a/nimare/decode/encode.py b/nimare/decode/encode.py index 8355e6215..b4f335009 100755 --- a/nimare/decode/encode.py +++ b/nimare/decode/encode.py @@ -3,12 +3,9 @@ from nilearn.masking import unmask from sklearn.feature_extraction.text import CountVectorizer -from nimare import references from nimare.decode.utils import weight_priors -from nimare.due import due -@due.dcite(references.GCLDA_DECODING, description="Citation for GCLDA encoding.") def gclda_encode(model, text, out_file=None, topic_priors=None, prior_weight=1.0): r"""Perform text-to-image encoding according to the method described in Rubin et al. (2017). diff --git a/nimare/diagnostics.py b/nimare/diagnostics.py index ed2250ff5..21bb8233e 100644 --- a/nimare/diagnostics.py +++ b/nimare/diagnostics.py @@ -13,7 +13,14 @@ from tqdm.auto import tqdm from nimare.base import NiMAREBase -from nimare.utils import _check_ncores, mm2vox, tqdm_joblib, vox2mm +from nimare.utils import ( + _check_ncores, + _get_cluster_coms, + get_masker, + mm2vox, + tqdm_joblib, + vox2mm, +) LGR = logging.getLogger(__name__) @@ -77,8 +84,6 @@ def transform(self, result): cluster in the thresholded map. There is one row for each experiment, as well as one more row at the top of the table (below the header), which has the center of mass of each cluster. - The centers of mass are not guaranteed to fall within the actual clusters, but can - serve as a useful heuristic for identifying them. There is one column for each cluster, with column names being integers indicating the cluster's associated value in the ``labeled_cluster_img`` output. labeled_cluster_img : :obj:`nibabel.nifti1.Nifti1Image` @@ -135,7 +140,7 @@ def transform(self, result): # Let's label the clusters in the thresholded map so we can use it as a NiftiLabelsMasker # This won't work when the Estimator's masker isn't a NiftiMasker... :( conn = ndimage.generate_binary_structure(3, 2) - labeled_cluster_arr, n_clusters = ndimage.measurements.label(thresh_arr, conn) + labeled_cluster_arr, n_clusters = ndimage.label(thresh_arr, conn) labeled_cluster_img = nib.Nifti1Image( labeled_cluster_arr, affine=target_img.affine, @@ -147,19 +152,11 @@ def transform(self, result): contribution_table = pd.DataFrame(index=rows) return contribution_table, labeled_cluster_img - # Identify center of mass for each cluster - # This COM may fall outside the cluster, but it is a useful heuristic for identifying them - cluster_ids = list(range(1, n_clusters + 1)) - cluster_coms = ndimage.center_of_mass( - labeled_cluster_arr, - labeled_cluster_arr, - cluster_ids, - ) - cluster_coms = np.array(cluster_coms) + cluster_coms = _get_cluster_coms(labeled_cluster_arr) cluster_coms = vox2mm(cluster_coms, target_img.affine) cluster_com_strs = [] - for i_peak in range(len(cluster_ids)): + for i_peak in range(cluster_coms.shape[0]): x, y, z = cluster_coms[i_peak, :].astype(int) xyz_str = f"({x}, {y}, {z})" cluster_com_strs.append(xyz_str) @@ -169,7 +166,7 @@ def transform(self, result): cluster_masker.fit(labeled_cluster_img) # Create empty contribution table - contribution_table = pd.DataFrame(index=rows, columns=cluster_ids) + contribution_table = pd.DataFrame(index=rows, columns=list(range(1, n_clusters + 1))) contribution_table.index.name = "Cluster ID" contribution_table.loc["Center of Mass"] = cluster_com_strs @@ -295,8 +292,6 @@ def transform(self, result): cluster in the thresholded map. There is one row for each experiment, as well as one more row at the top of the table (below the header), which has the center of mass of each cluster. - The centers of mass are not guaranteed to fall within the actual clusters, but can - serve as a useful heuristic for identifying them. There is one column for each cluster, with column names being integers indicating the cluster's associated value in the ``labeled_cluster_img`` output. labeled_cluster_img : :obj:`nibabel.nifti1.Nifti1Image` @@ -341,7 +336,7 @@ def transform(self, result): # Let's label the clusters in the thresholded map so we can use it as a NiftiLabelsMasker # This won't work when the Estimator's masker isn't a NiftiMasker... :( conn = ndimage.generate_binary_structure(3, 2) - labeled_cluster_arr, n_clusters = ndimage.measurements.label(thresh_arr, conn) + labeled_cluster_arr, n_clusters = ndimage.label(thresh_arr, conn) labeled_cluster_img = nib.Nifti1Image( labeled_cluster_arr, affine=target_img.affine, @@ -353,23 +348,17 @@ def transform(self, result): contribution_table = pd.DataFrame(index=rows) return contribution_table, labeled_cluster_img - # Identify center of mass for each cluster - # This COM may fall outside the cluster, but it is a useful heuristic for identifying them - cluster_ids = list(range(1, n_clusters + 1)) - cluster_coms = ndimage.center_of_mass( - labeled_cluster_arr, labeled_cluster_arr, cluster_ids - ) - cluster_coms = np.array(cluster_coms) + cluster_coms = _get_cluster_coms(labeled_cluster_arr) cluster_coms = vox2mm(cluster_coms, target_img.affine) cluster_com_strs = [] - for i_peak in range(len(cluster_ids)): + for i_peak in range(cluster_coms.shape[0]): x, y, z = cluster_coms[i_peak, :].astype(int) xyz_str = f"({x}, {y}, {z})" cluster_com_strs.append(xyz_str) # Create empty contribution table - contribution_table = pd.DataFrame(index=rows, columns=cluster_ids) + contribution_table = pd.DataFrame(index=rows, columns=list(range(1, n_clusters + 1))) contribution_table.index.name = "Cluster ID" contribution_table.loc["Center of Mass"] = cluster_com_strs @@ -407,3 +396,69 @@ def _transform(self, expid, coordinates_df, labeled_cluster_map, affine): focus_counts.append(n_included_voxels) return expid, focus_counts + + +class FocusFilter(NiMAREBase): + """Remove coordinates outside of the Dataset's mask from the Dataset. + + .. versionadded:: 0.0.13 + + Parameters + ---------- + mask : :obj:`str`, :class:`~nibabel.nifti1.Nifti1Image`, \ + :class:`~nilearn.maskers.NiftiMasker` or similar, or None, optional + Mask(er) to use. If None, uses the masker of the Dataset provided in ``transform``. + + Notes + ----- + This filter removes any coordinates outside of the brain mask. + It does not remove studies without coordinates in the brain mask, since a Dataset does not + need to have coordinates for all studies (e.g., some may only have images). + """ + + def __init__(self, mask=None): + if mask is not None: + mask = get_masker(mask) + + self.masker = mask + + def transform(self, dataset): + """Apply the filter to a Dataset. + + Parameters + ---------- + dataset : :obj:`~nimare.dataset.Dataset` + The Dataset to filter. + + Returns + ------- + dataset : :obj:`~nimare.dataset.Dataset` + The filtered Dataset. + """ + masker = self.masker or dataset.masker + + # Get matrix indices for in-brain voxels in the mask + mask_ijk = np.vstack(np.where(masker.mask_img.get_fdata())).T + + # Get matrix indices for Dataset coordinates + dset_xyz = dataset.coordinates[["x", "y", "z"]].values + + # mm2vox automatically rounds the coordinates + dset_ijk = mm2vox(dset_xyz, masker.mask_img.affine) + + keep_idx = [] + for i, coord in enumerate(dset_ijk): + # Check if each coordinate in Dataset is within the mask + # If it is, log that coordinate in keep_idx + if len(np.where((mask_ijk == coord).all(axis=1))[0]): + keep_idx.append(i) + + LGR.info( + f"{dset_ijk.shape[0] - len(keep_idx)}/{dset_ijk.shape[0]} coordinates fall outside of " + "the mask. Removing them." + ) + + # Only retain coordinates inside the brain mask + dataset.coordinates = dataset.coordinates.iloc[keep_idx] + + return dataset diff --git a/nimare/due.py b/nimare/due.py deleted file mode 100644 index 743142a8c..000000000 --- a/nimare/due.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Stub file for a guaranteed safe import of duecredit constructs: if duecredit -is not available. -To use it, place it into your project codebase to be imported, e.g. copy as - cp stub.py /path/tomodule/module/due.py -Note that it might be better to avoid naming it duecredit.py to avoid shadowing -installed duecredit. -Then use in your code as - from nimare.due import due, Doi, BibTeX -See https://github.com/duecredit/duecredit/blob/master/README.md for examples. -Origin: Originally a part of the duecredit -Copyright: 2015-2016 DueCredit developers -License: BSD-2 -""" - -__version__ = "0.0.5" - - -class InactiveDueCreditCollector(object): - """Just a stub at the Collector which would not do anything""" - - def _donothing(self, *args, **kwargs): - """Perform no good and no bad""" - pass - - def dcite(self, *args, **kwargs): - """If I could cite I would""" - - def nondecorating_decorator(func): - return func - - return nondecorating_decorator - - cite = load = add = _donothing - - def __repr__(self): - return self.__class__.__name__ + "()" - - -def _donothing_func(*args, **kwargs): - """Perform no good and no bad""" - pass - - -try: - from duecredit import BibTeX, Doi, Url, due - - if "due" in locals() and not hasattr(due, "cite"): - raise RuntimeError("Imported due lacks .cite. DueCredit is now disabled") -except Exception as e: - if type(e).__name__ != "ImportError": - import logging - - logging.getLogger("duecredit").error("Failed to import duecredit due to %s" % str(e)) - # Initiate due stub - due = InactiveDueCreditCollector() - BibTeX = Doi = Url = _donothing_func - -# Emacs mode definitions -# Local Variables: -# mode: python -# py-indent-offset: 4 -# tab-width: 4 -# indent-tabs-mode: nil -# End: diff --git a/nimare/extract/__init__.py b/nimare/extract/__init__.py index 71afe7526..3a832bf13 100644 --- a/nimare/extract/__init__.py +++ b/nimare/extract/__init__.py @@ -4,7 +4,6 @@ download_abstracts, download_cognitive_atlas, download_nidm_pain, - download_peaks2maps_model, fetch_neuroquery, fetch_neurosynth, ) @@ -13,7 +12,6 @@ "download_nidm_pain", "download_cognitive_atlas", "download_abstracts", - "download_peaks2maps_model", "fetch_neuroquery", "fetch_neurosynth", "utils", diff --git a/nimare/extract/extract.py b/nimare/extract/extract.py index 315e27bf6..b3f190365 100644 --- a/nimare/extract/extract.py +++ b/nimare/extract/extract.py @@ -5,18 +5,13 @@ import os import os.path as op import shutil -import tarfile import time import zipfile from glob import glob -from io import BytesIO -from lzma import LZMAFile from urllib.request import urlopen import numpy as np import pandas as pd -import requests -from tqdm.auto import tqdm from nimare.dataset import Dataset from nimare.extract.utils import ( @@ -468,76 +463,3 @@ def download_abstracts(dataset, email): dataset.texts, df, left_on="study_id", right_on="study_id", how="left" ) return dataset - - -def download_peaks2maps_model(data_dir=None, overwrite=False): - """Download the trained Peaks2Maps model from OHBM 2018. - - .. deprecated:: 0.0.11 - `download_peaks2maps_model` will be removed in NiMARE 0.0.13. - - .. versionadded:: 0.0.2 - - Parameters - ---------- - data_dir : :obj:`pathlib.Path` or :obj:`str` or None, optional - Where to put the trained model. - If None, then download to the automatic NiMARE data directory. - Default is None. - overwrite : bool, optional - Whether to overwrite an existing model or not. Default is False. - - Returns - ------- - data_dir : str - Path to folder containing model. - """ - url = "https://zenodo.org/record/1257721/files/ohbm2018_model.tar.xz?download=1" - - temp_dataset_name = "peaks2maps_model_ohbm2018__temp" - data_dir = _get_dataset_dir("", data_dir=data_dir) - temp_data_dir = _get_dataset_dir(temp_dataset_name, data_dir=data_dir) - - dataset_name = "peaks2maps_model_ohbm2018" - if dataset_name not in data_dir: # allow data_dir to include model folder - data_dir = temp_data_dir.replace(temp_dataset_name, dataset_name) - - desc_file = op.join(data_dir, "description.txt") - if op.isfile(desc_file) and overwrite is False: - shutil.rmtree(temp_data_dir) - return data_dir - - LGR.info("Downloading the model (this is a one-off operation)...") - # Streaming, so we can iterate over the response. - r = requests.get(url, stream=True) - f = BytesIO() - - # Total size in bytes. - total_size = int(r.headers.get("content-length", 0)) - block_size = 1024 * 1024 - wrote = 0 - for data in tqdm( - r.iter_content(block_size), - total=np.ceil(total_size // block_size), - unit="MB", - unit_scale=True, - ): - wrote = wrote + len(data) - f.write(data) - if total_size != 0 and wrote != total_size: - raise Exception("Download interrupted") - - f.seek(0) - LGR.info(f"Uncompressing the model to {temp_data_dir}...") - tf_file = tarfile.TarFile(fileobj=LZMAFile(f), mode="r") - tf_file.extractall(temp_data_dir) - - os.rename(op.join(temp_data_dir, "ohbm2018_model"), data_dir) - shutil.rmtree(temp_data_dir) - - with open(desc_file, "w") as fo: - fo.write("The trained Peaks2Maps model from OHBM 2018.") - - LGR.debug(f"Dataset moved to {data_dir}") - - return data_dir diff --git a/nimare/generate.py b/nimare/generate.py index 07508e78f..e952ae0e4 100644 --- a/nimare/generate.py +++ b/nimare/generate.py @@ -2,6 +2,7 @@ from itertools import zip_longest import numpy as np +import sparse from nimare.dataset import Dataset from nimare.io import convert_neurovault_to_dataset @@ -266,7 +267,9 @@ def _create_foci(foci, foci_percentage, fwhm, n_studies, n_noise_foci, rng, spac # create a probability map for each peak kernel = get_ale_kernel(template_img, fwhm)[1] foci_prob_maps = { - tuple(peak): compute_ale_ma(template_data.shape, np.atleast_2d(peak), kernel) + tuple(peak): compute_ale_ma(template_img, np.atleast_2d(peak), kernel=kernel).reshape( + template_data.shape + ) for peak in ground_truth_foci_ijks if peak.size } @@ -274,12 +277,12 @@ def _create_foci(foci, foci_percentage, fwhm, n_studies, n_noise_foci, rng, spac # get study specific instances of each foci signal_studies = int(round(foci_percentage * n_studies)) signal_ijks = { - peak: np.argwhere(prob_map)[ + peak: sparse.argwhere(prob_map)[ rng.choice( - np.argwhere(prob_map).shape[0], + sparse.argwhere(prob_map).shape[0], size=signal_studies, replace=True, - p=prob_map[np.nonzero(prob_map)] / sum(prob_map[np.nonzero(prob_map)]), + p=(prob_map[prob_map.nonzero()] / sum(prob_map[prob_map.nonzero()])).todense(), ) ] for peak, prob_map in foci_prob_maps.items() diff --git a/nimare/meta/cbma/ale.py b/nimare/meta/cbma/ale.py index 634ece4bf..80e5c97df 100755 --- a/nimare/meta/cbma/ale.py +++ b/nimare/meta/cbma/ale.py @@ -3,11 +3,10 @@ import numpy as np import pandas as pd +import sparse from joblib import Parallel, delayed from tqdm.auto import tqdm -from nimare import references -from nimare.due import due from nimare.meta.cbma.base import CBMAEstimator, PairwiseCBMAEstimator from nimare.meta.kernel import ALEKernel from nimare.stats import null_to_p, nullhist_to_p @@ -17,22 +16,13 @@ LGR = logging.getLogger(__name__) -@due.dcite(references.ALE1, description="Introduces ALE.") -@due.dcite( - references.ALE2, - description="Modifies ALE algorithm to eliminate within-experiment " - "effects and generate MA maps based on subject group " - "instead of experiment.", -) -@due.dcite( - references.ALE3, - description="Modifies ALE algorithm to allow FWE correction and to " - "more quickly and accurately generate the null " - "distribution for significance testing.", -) class ALE(CBMAEstimator): """Activation likelihood estimation. + .. versionchanged:: 0.0.12 + + - Use a 4D sparse array for modeled activation maps. + Parameters ---------- kernel_transformer : :obj:`~nimare.meta.kernel.KernelTransformer`, optional @@ -151,6 +141,18 @@ def __init__( def _compute_summarystat_est(self, ma_values): stat_values = 1.0 - np.prod(1.0 - ma_values, axis=0) + + # np.array type is used by _determine_histogram_bins to calculate max_poss_ale + if isinstance(stat_values, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + stat_values = stat_values.todense().reshape(-1) # Indexing a .reshape(-1) is faster + stat_values = stat_values[mask_data.reshape(-1)] + + # This is used by _compute_null_approximate + self.__n_mask_voxels = stat_values.shape[0] + return stat_values def _determine_histogram_bins(self, ma_maps): @@ -158,17 +160,14 @@ def _determine_histogram_bins(self, ma_maps): Parameters ---------- - ma_maps + ma_maps : :obj:`sparse._coo.core.COO` + MA maps. Notes ----- This method adds one entry to the null_distributions_ dict attribute: "histogram_bins". """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: + if not isinstance(ma_maps, sparse._coo.core.COO): raise ValueError(f"Unsupported data type '{type(ma_maps)}'") # Determine bins for null distribution histogram @@ -176,7 +175,9 @@ def _determine_histogram_bins(self, ma_maps): # Assuming values of 0, .001, .002, etc., bins are -.0005-.0005, .0005-.0015, etc. INV_STEP_SIZE = 100000 step_size = 1 / INV_STEP_SIZE - max_ma_values = np.max(ma_values, axis=1) + # Need to convert to dense because np.ceil is too slow with sparse + max_ma_values = ma_maps.max(axis=[1, 2, 3]).todense() + # round up based on resolution max_ma_values = np.ceil(max_ma_values * INV_STEP_SIZE) / INV_STEP_SIZE max_poss_ale = self._compute_summarystat(max_ma_values) @@ -189,7 +190,7 @@ def _compute_null_approximate(self, ma_maps): Parameters ---------- - ma_maps : list of imgs or numpy.ndarray + ma_maps : :obj:`sparse._coo.core.COO` MA maps. Notes @@ -199,19 +200,11 @@ def _compute_null_approximate(self, ma_maps): - "histogram_bins" - "histweights_corr-none_method-approximate" """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: + if not isinstance(ma_maps, sparse._coo.core.COO): raise ValueError(f"Unsupported data type '{type(ma_maps)}'") assert "histogram_bins" in self.null_distributions_.keys() - def just_histogram(*args, **kwargs): - """Collect the first output (weights) from numpy histogram.""" - return np.histogram(*args, **kwargs)[0].astype(float) - # Derive bin edges from histogram bin centers for numpy histogram function bin_centers = self.null_distributions_["histogram_bins"] step_size = bin_centers[1] - bin_centers[0] @@ -219,7 +212,22 @@ def just_histogram(*args, **kwargs): bin_edges = bin_centers - (step_size / 2) bin_edges = np.append(bin_centers, bin_centers[-1] + step_size) - ma_hists = np.apply_along_axis(just_histogram, 1, ma_values, bins=bin_edges, density=False) + n_exp = ma_maps.shape[0] + n_bins = bin_centers.shape[0] + ma_hists = np.zeros((n_exp, n_bins)) + data = ma_maps.data + coords = ma_maps.coords + for exp_idx in range(n_exp): + # The first column of coords is the fourth dimension of the dense array + study_ma_values = data[coords[0, :] == exp_idx] + + n_nonzero_voxels = study_ma_values.shape[0] + n_zero_voxels = self.__n_mask_voxels - n_nonzero_voxels + + ma_hists[exp_idx, :] = np.histogram(study_ma_values, bins=bin_edges, density=False)[ + 0 + ].astype(float) + ma_hists[exp_idx, 0] += n_zero_voxels # Normalize MA histograms to get probabilities ma_hists /= ma_hists.sum(1)[:, None] @@ -258,6 +266,7 @@ class ALESubtraction(PairwiseCBMAEstimator): - Use memmapped array for null distribution and remove ``memory_limit`` parameter. - Support parallelization and add progress bar. - Add ALE-difference (stat) and -log10(p) (logp) maps to results. + - Use a 4D sparse array for modeled activation maps. .. versionchanged:: 0.0.8 @@ -352,16 +361,17 @@ def _fit(self, dataset1, dataset2): coords_key="coordinates2", ) - n_grp1, n_voxels = ma_maps1.shape - # Get ALE values for the two groups and difference scores grp1_ale_values = self._compute_summarystat_est(ma_maps1) grp2_ale_values = self._compute_summarystat_est(ma_maps2) diff_ale_values = grp1_ale_values - grp2_ale_values del grp1_ale_values, grp2_ale_values + n_grp1 = ma_maps1.shape[0] + n_voxels = diff_ale_values.shape[0] + # Combine the MA maps into a single array to draw from for null distribution - ma_arr = np.vstack((ma_maps1, ma_maps2)) + ma_arr = sparse.concatenate((ma_maps1, ma_maps2)) del ma_maps1, ma_maps2 @@ -408,16 +418,24 @@ def _fit(self, dataset1, dataset2): z_arr = p_to_z(p_values, tail="two") * diff_signs logp_arr = -np.log10(p_values) - images = { + maps = { "stat_desc-group1MinusGroup2": diff_ale_values, "p_desc-group1MinusGroup2": p_values, "z_desc-group1MinusGroup2": z_arr, "logp_desc-group1MinusGroup2": logp_arr, } - return images + return maps, {} def _compute_summarystat_est(self, ma_values): stat_values = 1.0 - np.prod(1.0 - ma_values, axis=0) + + if isinstance(stat_values, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + stat_values = stat_values.todense().reshape(-1) # Indexing a .reshape(-1) is faster + stat_values = stat_values[mask_data.reshape(-1)] + return stat_values def _run_permutation(self, i_iter, n_grp1, ma_arr, iter_diff_values): @@ -440,8 +458,8 @@ def _run_permutation(self, i_iter, n_grp1, ma_arr, iter_diff_values): gen = np.random.default_rng(seed=i_iter) id_idx = np.arange(ma_arr.shape[0]) gen.shuffle(id_idx) - iter_grp1_ale_values = 1.0 - np.prod(1.0 - ma_arr[id_idx[:n_grp1], :], axis=0) - iter_grp2_ale_values = 1.0 - np.prod(1.0 - ma_arr[id_idx[n_grp1:], :], axis=0) + iter_grp1_ale_values = self._compute_summarystat_est(ma_arr[id_idx[:n_grp1], :]) + iter_grp2_ale_values = self._compute_summarystat_est(ma_arr[id_idx[n_grp1:], :]) iter_diff_values[i_iter, :] = iter_grp1_ale_values - iter_grp2_ale_values def _alediff_to_p_voxel(self, i_voxel, stat_value, voxel_null): @@ -467,10 +485,6 @@ def correct_fwe_montecarlo(self): ) -@due.dcite( - references.SCALE, - description=("Introduces the specific co-activation likelihood estimation (SCALE) algorithm."), -) class SCALE(CBMAEstimator): r"""Specific coactivation likelihood estimation. @@ -480,6 +494,7 @@ class SCALE(CBMAEstimator): - Remove unused parameters ``voxel_thresh`` and ``memory_limit``. - Use memmapped array for null distribution. + - Use a 4D sparse array for modeled activation maps. .. versionchanged:: 0.0.10 @@ -584,7 +599,8 @@ def _fit(self, dataset): ) # Determine bins for null distribution histogram - max_ma_values = np.max(ma_values, axis=1) + max_ma_values = ma_values.max(axis=[1, 2, 3]).todense() + max_poss_ale = self._compute_summarystat_est(max_ma_values) self.null_distributions_["histogram_bins"] = np.round( np.arange(0, max_poss_ale + 0.001, 0.0001), 4 @@ -592,6 +608,8 @@ def _fit(self, dataset): stat_values = self._compute_summarystat_est(ma_values) + del ma_values + iter_df = self.inputs_["coordinates"].copy() rand_idx = np.random.choice(self.xyz.shape[0], size=(iter_df.shape[0], self.n_iters)) rand_xyz = self.xyz[rand_idx, :] @@ -622,12 +640,12 @@ def _fit(self, dataset): logp_values = -np.log10(p_values) logp_values[np.isinf(logp_values)] = -np.log10(np.finfo(float).eps) - # Write out unthresholded value images - images = {"stat": stat_values, "logp": logp_values, "z": z_values} - return images + # Write out unthresholded value maps + maps = {"stat": stat_values, "logp": logp_values, "z": z_values} + return maps, {} def _compute_summarystat_est(self, data): - """Generate ALE-value array and null distribution from list of contrasts. + """Generate ALE-value array and null distribution from a list of contrasts. For ALEs on the original dataset, computes the null distribution. For permutation ALEs and all SCALEs, just computes ALE values. @@ -635,16 +653,22 @@ def _compute_summarystat_est(self, data): """ if isinstance(data, pd.DataFrame): ma_values = self.kernel_transformer.transform( - data, masker=self.masker, return_type="array" + data, masker=self.masker, return_type="sparse" ) - elif isinstance(data, list): - ma_values = self.masker.transform(data) - elif isinstance(data, np.ndarray): + elif isinstance(data, (np.ndarray, sparse._coo.core.COO)): ma_values = data else: raise ValueError(f"Unsupported data type '{type(data)}'") stat_values = 1.0 - np.prod(1.0 - ma_values, axis=0) + + if isinstance(stat_values, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + stat_values = stat_values.todense().reshape(-1) # Indexing a .reshape(-1) is faster + stat_values = stat_values[mask_data.reshape(-1)] + return stat_values def _scale_to_p(self, stat_values, scale_values): diff --git a/nimare/meta/cbma/base.py b/nimare/meta/cbma/base.py index ea0c1cab3..e1d1cff4a 100644 --- a/nimare/meta/cbma/base.py +++ b/nimare/meta/cbma/base.py @@ -6,7 +6,9 @@ import nibabel as nib import numpy as np import pandas as pd +import sparse from joblib import Parallel, delayed +from nilearn.input_data import NiftiMasker from scipy import ndimage from tqdm.auto import tqdm @@ -37,6 +39,7 @@ class CBMAEstimator(Estimator): * Remove *low_memory* option * CBMA-specific elements of ``MetaEstimator`` excised and moved into ``CBMAEstimator``. * Generic kwargs and args converted to named kwargs. All remaining kwargs are for kernels. + * Use a 4D sparse array for modeled activation maps. .. versionchanged:: 0.0.8 @@ -151,6 +154,13 @@ def _fit(self, dataset): """ self.dataset = dataset self.masker = self.masker or dataset.masker + + if not isinstance(self.masker, NiftiMasker): + raise ValueError( + f"A {type(self.masker)} mask has been detected. " + "Only NiftiMaskers are allowed for this Estimator." + ) + self.null_distributions_ = {} ma_values = self._collect_ma_maps( @@ -178,7 +188,7 @@ def _fit(self, dataset): p_values, z_values = self._summarystat_to_p(stat_values, null_method=self.null_method) images = {"stat": stat_values, "p": p_values, "z": z_values} - return images + return images, {} def _compute_weights(self, ma_values): """Perform optional weight computation routine. @@ -189,7 +199,7 @@ def _compute_weights(self, ma_values): """ return None - def _collect_ma_maps(self, coords_key="coordinates", maps_key="ma_maps", return_type="array"): + def _collect_ma_maps(self, coords_key="coordinates", maps_key="ma_maps"): """Collect modeled activation maps from Estimator inputs. Parameters @@ -206,12 +216,32 @@ def _collect_ma_maps(self, coords_key="coordinates", maps_key="ma_maps", return_ Returns ------- - ma_maps : :obj:`numpy.ndarray` - 2D numpy array of shape (n_studies, n_voxels) with MA values. + ma_maps : :obj:`sparse._coo.core.COO` + Return a 4D sparse array of shape + (n_studies, mask.shape) with MA maps. """ if maps_key in self.inputs_.keys(): LGR.debug(f"Loading pre-generated MA maps ({maps_key}).") - ma_maps = self.masker.transform(self.inputs_[maps_key]) + all_exp = [] + all_coords = [] + all_data = [] + for i_exp, img in enumerate(self.inputs_[maps_key]): + img_data = nib.load(img).get_fdata() + nonzero_idx = np.where(img_data != 0) + + all_exp.append(np.full(nonzero_idx[0].shape[0], i_exp)) + all_coords.append(np.vstack(nonzero_idx)) + all_data.append(img_data[nonzero_idx]) + + n_studies = len(self.inputs_[maps_key]) + shape = img_data.shape + kernel_shape = (n_studies,) + shape + + exp = np.hstack(all_exp) + coords = np.vstack((exp.flatten(), np.hstack(all_coords))) + data = np.hstack(all_data).flatten() + + ma_maps = sparse.COO(coords, data, shape=kernel_shape) else: LGR.debug(f"Generating MA maps from coordinates ({coords_key}).") @@ -219,7 +249,7 @@ def _collect_ma_maps(self, coords_key="coordinates", maps_key="ma_maps", return_ ma_maps = self.kernel_transformer.transform( self.inputs_[coords_key], masker=self.masker, - return_type=return_type, + return_type="sparse", ) return ma_maps @@ -234,12 +264,13 @@ def _compute_summarystat(self, data): Parameters ---------- - data : array, pandas.DataFrame, or list of img_like + data : array, sparse._coo.core.COO, pandas.DataFrame, or list of img_like Data from which to estimate summary statistics. The data can be: (1) a 1d contrast-len or 2d contrast-by-voxel array of MA values, - (2) a DataFrame containing coordinates to produce MA values, - or (3) a list of imgs containing MA values. + (2) a 4d sparse array of MA maps, + (3) a DataFrame containing coordinates to produce MA values, + or (4) a list of imgs containing MA values. Returns ------- @@ -248,13 +279,13 @@ def _compute_summarystat(self, data): """ if isinstance(data, pd.DataFrame): ma_values = self.kernel_transformer.transform( - data, masker=self.masker, return_type="array" + data, masker=self.masker, return_type="sparse" ) elif isinstance(data, list): ma_values = self.masker.transform(data) - elif isinstance(data, np.ndarray): + elif isinstance(data, (np.ndarray, sparse._coo.core.COO)): ma_values = data - elif not isinstance(data, np.ndarray): + else: raise ValueError(f"Unsupported data type '{type(data)}'") # Apply weights before returning @@ -410,6 +441,14 @@ def _compute_null_reduced_montecarlo(self, ma_maps, n_iters=10000): -------- This method is only retained for testing and algorithm development. """ + if isinstance(ma_maps, sparse._coo.core.COO): + masker = self.dataset.masker if not self.masker else self.masker + mask = masker.mask_img + mask_data = mask.get_fdata().astype(bool) + + ma_maps = ma_maps.todense() + ma_maps = ma_maps[:, mask_data] + n_studies, n_voxels = ma_maps.shape null_ijk = np.random.choice(np.arange(n_voxels), (n_iters, n_studies)) iter_ma_values = ma_maps[np.arange(n_studies), tuple(null_ijk)].T @@ -440,7 +479,7 @@ def _compute_null_montecarlo_permutation(self, iter_xyz, iter_df): iter_df[["x", "y", "z"]] = iter_xyz iter_ma_maps = self.kernel_transformer.transform( - iter_df, masker=self.masker, return_type="array" + iter_df, masker=self.masker, return_type="sparse" ) iter_ss_map = self._compute_summarystat(iter_ma_maps) @@ -546,7 +585,7 @@ def _correct_fwe_montecarlo_permutation( iter_df[["x", "y", "z"]] = iter_xyz iter_ma_maps = self.kernel_transformer.transform( - iter_df, masker=self.masker, return_type="array" + iter_df, masker=self.masker, return_type="sparse" ) iter_ss_map = self._compute_summarystat(iter_ma_maps) @@ -712,7 +751,7 @@ def correct_fwe_montecarlo( # cluster-label thresh_stat_values = self.masker.inverse_transform(stat_values).get_fdata() thresh_stat_values[thresh_stat_values <= ss_thresh] = 0 - labeled_matrix, _ = ndimage.measurements.label(thresh_stat_values, conn) + labeled_matrix, _ = ndimage.label(thresh_stat_values, conn) cluster_labels, idx, cluster_sizes = np.unique( labeled_matrix, @@ -776,14 +815,14 @@ def correct_fwe_montecarlo( if vfwe_only: # Return unthresholded value images - images = { + maps = { "logp_level-voxel": logp_vfwe_values, "z_level-voxel": z_vfwe_values, } else: # Return unthresholded value images - images = { + maps = { "logp_level-voxel": logp_vfwe_values, "z_level-voxel": z_vfwe_values, "logp_desc-size_level-cluster": logp_csfwe_values, @@ -792,12 +831,16 @@ def correct_fwe_montecarlo( "z_desc-mass_level-cluster": z_cmfwe_values, } - return images + return maps, {} class PairwiseCBMAEstimator(CBMAEstimator): """Base class for pairwise coordinate-based meta-analysis methods. + .. versionchanged:: 0.0.12 + + - Use a 4D sparse array for modeled activation maps. + .. versionchanged:: 0.0.8 * [REF] Use saved MA maps, when available. @@ -865,11 +908,11 @@ def fit(self, dataset1, dataset2, drop_invalid=True): self.inputs_["coordinates2"] = self.inputs_.pop("coordinates") # Now run the Estimator-specific _fit() method. - maps = self._fit(dataset1, dataset2) + maps, tables = self._fit(dataset1, dataset2) if hasattr(self, "masker") and self.masker is not None: masker = self.masker else: masker = dataset1.masker - return MetaResult(self, masker, maps) + return MetaResult(self, mask=masker, maps=maps, tables=tables) diff --git a/nimare/meta/cbma/mkda.py b/nimare/meta/cbma/mkda.py index 2193fbe47..392c79500 100644 --- a/nimare/meta/cbma/mkda.py +++ b/nimare/meta/cbma/mkda.py @@ -1,5 +1,4 @@ """CBMA methods from the multilevel kernel density analysis (MKDA) family.""" -import gc import logging import nibabel as nib @@ -11,8 +10,6 @@ from scipy.stats import chi2 from tqdm.auto import tqdm -from nimare import references -from nimare.due import due from nimare.meta.cbma.base import CBMAEstimator, PairwiseCBMAEstimator from nimare.meta.kernel import KDAKernel, MKDAKernel from nimare.meta.utils import _calculate_cluster_measures @@ -23,12 +20,15 @@ LGR = logging.getLogger(__name__) -@due.dcite(references.MKDA, description="Introduces MKDA.") class MKDADensity(CBMAEstimator): r"""Multilevel kernel density analysis- Density analysis. The MKDA density method was originally introduced in :footcite:t:`wager2007meta`. + .. versionchanged:: 0.0.12 + + - Use a 4D sparse array for modeled activation maps. + Parameters ---------- kernel_transformer : :obj:`~nimare.meta.kernel.KernelTransformer`, optional @@ -79,6 +79,7 @@ class MKDADensity(CBMAEstimator): If ``null_method == "approximate"``: + - ``histogram_means``: Array of mean value per experiment. - ``histogram_bins``: Array of bin centers for the null distribution histogram, ranging from zero to the maximum possible summary statistic value for the Dataset. - ``histweights_corr-none_method-approximate``: Array of weights for the null @@ -166,66 +167,94 @@ def _compute_weights(self, ma_values): return weight_vec def _compute_summarystat_est(self, ma_values): - return ma_values.T.dot(self.weight_vec_).ravel() + ma_values = ma_values.reshape((ma_values.shape[0], -1)) + stat_values = ma_values.T.dot(self.weight_vec_) + + if isinstance(ma_values, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + stat_values = stat_values[mask_data.reshape(-1)].ravel() + # This is used by _compute_null_approximate + self.__n_mask_voxels = stat_values.shape[0] + else: + # np.array type is used by _compute_null_reduced_montecarlo + stat_values = stat_values.ravel() + + return stat_values def _determine_histogram_bins(self, ma_maps): """Determine histogram bins for null distribution methods. Parameters ---------- - ma_maps + ma_maps : :obj:`sparse._coo.core.COO` + MA maps. + The ma_maps can be a 4d sparse array of MA maps, Notes ----- - This method adds one entry to the null_distributions_ dict attribute: "histogram_bins". + This method adds two entries to the null_distributions_ dict attribute: "histogram_bins", + and "histogram_means" only if ``null_method == "approximate"``. """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: + if not isinstance(ma_maps, sparse._coo.core.COO): raise ValueError(f"Unsupported data type '{type(ma_maps)}'") - prop_active = ma_values.mean(1) + n_exp = ma_maps.shape[0] + prop_active = np.zeros(n_exp) + data = ma_maps.data + coords = ma_maps.coords + for exp_idx in range(n_exp): + # The first column of coords is the fourth dimension of the dense array + study_ma_values = data[coords[0, :] == exp_idx] + + n_nonzero_voxels = study_ma_values.shape[0] + n_zero_voxels = self.__n_mask_voxels - n_nonzero_voxels + + prop_active[exp_idx] = np.mean(np.hstack([study_ma_values, np.zeros(n_zero_voxels)])) + self.null_distributions_["histogram_bins"] = np.arange(len(prop_active) + 1, step=1) + if self.null_method.startswith("approximate"): + # To speed things up in _compute_null_approximate, we save the means too, + self.null_distributions_["histogram_means"] = prop_active + def _compute_null_approximate(self, ma_maps): """Compute uncorrected null distribution using approximate solution. Parameters ---------- - ma_maps : list of imgs or numpy.ndarray - MA maps. + ma_maps + Modeled activation maps. Unused for this estimator. Notes ----- - This method adds two entries to the null_distributions_ dict attribute: - "histogram_bins" and "histogram_weights". + This method adds one entry to the null_distributions_ dict attribute: "histogram_weights". """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: - raise ValueError(f"Unsupported data type '{type(ma_maps)}'") + assert "histogram_means" in self.null_distributions_.keys() # MKDA maps are binary, so we only have k + 1 bins in the final # histogram, where k is the number of studies. We can analytically # compute the null distribution by convolution. - prop_active = ma_values.mean(1) + # prop_active contains the mean value per experiment + prop_active = self.null_distributions_["histogram_means"] + ss_hist = 1.0 for exp_prop in prop_active: ss_hist = np.convolve(ss_hist, [1 - exp_prop, exp_prop]) - self.null_distributions_["histogram_bins"] = np.arange(len(prop_active) + 1, step=1) + self.null_distributions_["histweights_corr-none_method-approximate"] = ss_hist -@due.dcite(references.MKDA, description="Introduces MKDA.") class MKDAChi2(PairwiseCBMAEstimator): r"""Multilevel kernel density analysis- Chi-square analysis. The MKDA chi-square method was originally introduced in :footcite:t:`wager2007meta`. + .. versionchanged:: 0.0.12 + + - Use a 4D sparse array for modeled activation maps. + .. versionchanged:: 0.0.8 * [REF] Use saved MA maps, when available. @@ -317,28 +346,24 @@ def _fit(self, dataset1, dataset2): ma_maps1 = self._collect_ma_maps( maps_key="ma_maps1", coords_key="coordinates1", - return_type="sparse", ) n_selected = ma_maps1.shape[0] n_selected_active_voxels = ma_maps1.sum(axis=0) if isinstance(n_selected_active_voxels, sparse._coo.core.COO): - masker = dataset1.masker if not self.masker else self.masker - mask = masker.mask_img - mask_data = mask.get_fdata().astype(bool) + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) # Indexing the sparse array is slow, perform masking in the dense array n_selected_active_voxels = n_selected_active_voxels.todense().reshape(-1) n_selected_active_voxels = n_selected_active_voxels[mask_data.reshape(-1)] del ma_maps1 - gc.collect() # Generate MA maps and calculate count variables for second dataset ma_maps2 = self._collect_ma_maps( maps_key="ma_maps2", coords_key="coordinates2", - return_type="sparse", ) n_unselected = ma_maps2.shape[0] n_unselected_active_voxels = ma_maps2.sum(axis=0) @@ -347,7 +372,6 @@ def _fit(self, dataset1, dataset2): n_unselected_active_voxels = n_unselected_active_voxels[mask_data.reshape(-1)] del ma_maps2 - gc.collect() n_mappables = n_selected + n_unselected @@ -408,7 +432,7 @@ def _fit(self, dataset1, dataset2): del pFgA_sign, pAgU - images = { + maps = { "prob_desc-A": pA, "prob_desc-AgF": pAgF, "prob_desc-FgA": pFgA, @@ -421,7 +445,7 @@ def _fit(self, dataset1, dataset2): "p_desc-consistency": pAgF_p_vals, "p_desc-specificity": pFgA_p_vals, } - return images + return maps, {} def _run_fwe_permutation(self, iter_xyz1, iter_xyz2, iter_df1, iter_df2, conn, voxel_thresh): """Run a single permutation of the Monte Carlo FWE correction procedure. @@ -465,18 +489,31 @@ def _run_fwe_permutation(self, iter_xyz1, iter_xyz2, iter_df1, iter_df2, conn, v # Generate MA maps and calculate count variables for first dataset temp_ma_maps1 = self.kernel_transformer.transform( - iter_df1, self.masker, return_type="array" + iter_df1, self.masker, return_type="sparse" ) n_selected = temp_ma_maps1.shape[0] - n_selected_active_voxels = np.sum(temp_ma_maps1, axis=0) + n_selected_active_voxels = temp_ma_maps1.sum(axis=0) + + if isinstance(n_selected_active_voxels, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + # Indexing the sparse array is slow, perform masking in the dense array + n_selected_active_voxels = n_selected_active_voxels.todense().reshape(-1) + n_selected_active_voxels = n_selected_active_voxels[mask_data.reshape(-1)] + del temp_ma_maps1 # Generate MA maps and calculate count variables for second dataset temp_ma_maps2 = self.kernel_transformer.transform( - iter_df2, self.masker, return_type="array" + iter_df2, self.masker, return_type="sparse" ) n_unselected = temp_ma_maps2.shape[0] - n_unselected_active_voxels = np.sum(temp_ma_maps2, axis=0) + n_unselected_active_voxels = temp_ma_maps2.sum(axis=0) + if isinstance(n_unselected_active_voxels, sparse._coo.core.COO): + n_unselected_active_voxels = n_unselected_active_voxels.todense().reshape(-1) + n_unselected_active_voxels = n_unselected_active_voxels[mask_data.reshape(-1)] + del temp_ma_maps2 # Currently unused conditional probabilities @@ -565,9 +602,9 @@ def _apply_correction(self, stat_values, voxel_thresh, vfwe_null, csfwe_null, cm # Label positive and negative clusters separately labeled_matrix = np.empty(stat_map_thresh.shape, int) - labeled_matrix, _ = ndimage.measurements.label(stat_map_thresh > 0, conn) + labeled_matrix, _ = ndimage.label(stat_map_thresh > 0, conn) n_positive_clusters = np.max(labeled_matrix) - temp_labeled_matrix, _ = ndimage.measurements.label(stat_map_thresh < 0, conn) + temp_labeled_matrix, _ = ndimage.label(stat_map_thresh < 0, conn) temp_labeled_matrix[temp_labeled_matrix > 0] += n_positive_clusters labeled_matrix = labeled_matrix + temp_labeled_matrix del temp_labeled_matrix @@ -627,8 +664,8 @@ def correct_fwe_montecarlo(self, result, voxel_thresh=0.001, n_iters=5000, n_cor Returns ------- - images : :obj:`dict` - Dictionary of 1D arrays corresponding to masked images generated by + maps : :obj:`dict` + Dictionary of 1D arrays corresponding to masked maps generated by the correction procedure. The following arrays are generated by this method: @@ -824,7 +861,7 @@ def correct_fwe_montecarlo(self, result, voxel_thresh=0.001, n_iters=5000, n_cor pFgA_logp_csfwe_vals = -np.log10(pFgA_p_csfwe_vals) pFgA_logp_csfwe_vals[np.isinf(pFgA_logp_csfwe_vals)] = -np.log10(eps) - images = { + maps = { # Consistency analysis "p_desc-consistency_level-voxel": pAgF_p_vfwe_vals, "z_desc-consistency_level-voxel": pAgF_z_vfwe_vals, @@ -846,7 +883,7 @@ def correct_fwe_montecarlo(self, result, voxel_thresh=0.001, n_iters=5000, n_cor "z_desc-specificitySize_level-cluster": pFgA_z_csfwe_vals, "logp_desc-specificitySize_level-cluster": pFgA_logp_csfwe_vals, } - return images + return maps, {} def correct_fdr_indep(self, result, alpha=0.05): """Perform FDR correction using the Benjamini-Hochberg method. @@ -866,8 +903,8 @@ def correct_fdr_indep(self, result, alpha=0.05): Returns ------- - images : :obj:`dict` - Dictionary of 1D arrays corresponding to masked images generated by + maps : :obj:`dict` + Dictionary of 1D arrays corresponding to masked maps generated by the correction procedure. The following arrays are generated by this method: 'z_desc-consistency_level-voxel' and 'z_desc-specificity_level-voxel'. @@ -894,18 +931,20 @@ def correct_fdr_indep(self, result, alpha=0.05): pFgA_p_FDR = fdr(pFgA_p_vals, q=alpha, method="bh") pFgA_z_FDR = p_to_z(pFgA_p_FDR, tail="two") * pFgA_sign - images = { + maps = { "z_desc-consistency_level-voxel": pAgF_z_FDR, "z_desc-specificity_level-voxel": pFgA_z_FDR, } - return images + return maps, {} -@due.dcite(references.KDA1, description="Introduces the KDA algorithm.") -@due.dcite(references.KDA2, description="Also introduces the KDA algorithm.") class KDA(CBMAEstimator): r"""Kernel density analysis. + .. versionchanged:: 0.0.12 + + - Use a 4D sparse array for modeled activation maps. + Parameters ---------- kernel_transformer : :obj:`~nimare.meta.kernel.KernelTransformer`, optional @@ -1034,12 +1073,11 @@ def _compute_summarystat_est(self, ma_values): Parameters ---------- - data : array, pandas.DataFrame, or list of img_like - Data from which to estimate ALE scores. - The data can be: + ma_maps : :obj:`numpy.ndarray` or :obj:`sparse._coo.core.COO` + MA maps. + The ma_maps can be: (1) a 1d contrast-len or 2d contrast-by-voxel array of MA values, - (2) a DataFrame containing coordinates to produce MA values, - or (3) a list of imgs containing MA values. + or (2) a 4d sparse array of MA maps, Returns ------- @@ -1047,7 +1085,21 @@ def _compute_summarystat_est(self, ma_values): OF values. One value per voxel. """ # OF is just a sum of MA values. - stat_values = np.sum(ma_values, axis=0) + if isinstance(ma_values, sparse._coo.core.COO): + # NOTE: This may not work correctly with a non-NiftiMasker. + mask_data = self.masker.mask_img.get_fdata().astype(bool) + + stat_values = ma_values.sum(axis=0) + + stat_values = stat_values.todense().reshape(-1) + stat_values = stat_values[mask_data.reshape(-1)] + + # This is used by _compute_null_approximate + self.__n_mask_voxels = stat_values.shape[0] + else: + # np.array type is used by _determine_histogram_bins to calculate max_poss_value + stat_values = np.sum(ma_values, axis=0) + return stat_values def _determine_histogram_bins(self, ma_maps): @@ -1055,18 +1107,14 @@ def _determine_histogram_bins(self, ma_maps): Parameters ---------- - ma_maps - Modeled activation maps. Unused for this estimator. + ma_maps : :obj:`sparse._coo.core.COO` + MA maps. Notes ----- This method adds one entry to the null_distributions_ dict attribute: "histogram_bins". """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: + if not isinstance(ma_maps, sparse._coo.core.COO): raise ValueError(f"Unsupported data type '{type(ma_maps)}'") # assumes that groupby results in same order as MA maps @@ -1091,7 +1139,9 @@ def _determine_histogram_bins(self, ma_maps): N_BINS = 100000 # The maximum possible MA value is the max value from each MA map, # unlike the case with a summation-based kernel. - max_ma_values = np.max(ma_values, axis=1) + # Need to convert to dense because np.ceil is too slow with sparse + max_ma_values = ma_maps.max(axis=[1, 2, 3]).todense() + # round up based on resolution # hardcoding 1000 here because figuring out what to round to was difficult. max_ma_values = np.ceil(max_ma_values * 1000) / 1000 @@ -1111,7 +1161,7 @@ def _compute_null_approximate(self, ma_maps): Parameters ---------- - ma_maps : list of imgs or numpy.ndarray + ma_maps : :obj:`sparse._coo.core.COO` MA maps. Notes @@ -1119,17 +1169,9 @@ def _compute_null_approximate(self, ma_maps): This method adds two entries to the null_distributions_ dict attribute: "histogram_bins" and "histogram_weights". """ - if isinstance(ma_maps, list): - ma_values = self.masker.transform(ma_maps) - elif isinstance(ma_maps, np.ndarray): - ma_values = ma_maps - else: + if not isinstance(ma_maps, sparse._coo.core.COO): raise ValueError(f"Unsupported data type '{type(ma_maps)}'") - def just_histogram(*args, **kwargs): - """Collect the first output (weights) from numpy histogram.""" - return np.histogram(*args, **kwargs)[0].astype(float) - # Derive bin edges from histogram bin centers for numpy histogram function bin_centers = self.null_distributions_["histogram_bins"] step_size = bin_centers[1] - bin_centers[0] @@ -1137,7 +1179,22 @@ def just_histogram(*args, **kwargs): bin_edges = bin_centers - (step_size / 2) bin_edges = np.append(bin_centers, bin_centers[-1] + step_size) - ma_hists = np.apply_along_axis(just_histogram, 1, ma_values, bins=bin_edges, density=False) + n_exp = ma_maps.shape[0] + n_bins = bin_centers.shape[0] + ma_hists = np.zeros((n_exp, n_bins)) + data = ma_maps.data + coords = ma_maps.coords + for exp_idx in range(n_exp): + # The first column of coords is the fourth dimension of the dense array + study_ma_values = data[coords[0, :] == exp_idx] + + n_nonzero_voxels = study_ma_values.shape[0] + n_zero_voxels = self.__n_mask_voxels - n_nonzero_voxels + + ma_hists[exp_idx, :] = np.histogram(study_ma_values, bins=bin_edges, density=False)[ + 0 + ].astype(float) + ma_hists[exp_idx, 0] += n_zero_voxels # Normalize MA histograms to get probabilities ma_hists /= ma_hists.sum(1)[:, None] diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index bdfe165e7..407c05584 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -5,6 +5,7 @@ from nimare.utils import get_template, get_masker, B_spline_bases import nibabel as nib import numpy as np +import pandas as pd from nimare.utils import mm2vox, vox2idx, intensity2voxel import torch import logging @@ -161,7 +162,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) if hasattr(self, "moderators"): for group_name in self.inputs_['all_group_study_id'].keys(): - moderators_array = torch.tensor(self.inputs_['all_group_moderators'][group_name], dtype=torch.float64, device=device) + all_moderators_array = torch.tensor(self.inputs_['all_group_moderators'][group_name], dtype=torch.float64, device=device) n_foci_per_voxel = torch.tensor(self.inputs_['n_foci_per_voxel'], dtype=torch.float64, device=device) n_foci_per_study = torch.tensor(self.inputs_['n_foci_per_study'], dtype=torch.float64, device=device) diff --git a/nimare/meta/ibma.py b/nimare/meta/ibma.py index 3538c7f58..8a7d9eccd 100755 --- a/nimare/meta/ibma.py +++ b/nimare/meta/ibma.py @@ -5,6 +5,7 @@ import nibabel as nib import numpy as np +import pandas as pd import pymare from nilearn._utils.niimg_conversions import _check_same_fov from nilearn.image import concat_imgs, resample_to_img @@ -178,11 +179,11 @@ def _fit(self, dataset): est = pymare.estimators.FisherCombinationTest() est.fit_dataset(pymare_dset) est_summary = est.summary() - results = { + maps = { "z": _boolean_unmask(est_summary.z.squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(est_summary.p.squeeze(), self.inputs_["aggressive_mask"]), } - return results + return maps, {} class Stouffers(IBMAEstimator): @@ -261,11 +262,11 @@ def _fit(self, dataset): est.fit_dataset(pymare_dset) est_summary = est.summary() - results = { + maps = { "z": _boolean_unmask(est_summary.z.squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(est_summary.p.squeeze(), self.inputs_["aggressive_mask"]), } - return results + return maps, {} class WeightedLeastSquares(IBMAEstimator): @@ -349,13 +350,17 @@ def _fit(self, dataset): fe_stats = est_summary.get_fe_stats() # tau2 is an float, not a map, so it can't go in the results dictionary - results = { + maps = { "z": _boolean_unmask(fe_stats["z"].squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(fe_stats["p"].squeeze(), self.inputs_["aggressive_mask"]), "est": _boolean_unmask(fe_stats["est"].squeeze(), self.inputs_["aggressive_mask"]), "se": _boolean_unmask(fe_stats["se"].squeeze(), self.inputs_["aggressive_mask"]), } - return results + + tables = { + "level-estimator": pd.DataFrame(columns=["tau2"], data=[self.tau2]), + } + return maps, tables class DerSimonianLaird(IBMAEstimator): @@ -426,14 +431,14 @@ def _fit(self, dataset): est_summary = est.summary() fe_stats = est_summary.get_fe_stats() - results = { + maps = { "z": _boolean_unmask(fe_stats["z"].squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(fe_stats["p"].squeeze(), self.inputs_["aggressive_mask"]), "est": _boolean_unmask(fe_stats["est"].squeeze(), self.inputs_["aggressive_mask"]), "se": _boolean_unmask(fe_stats["se"].squeeze(), self.inputs_["aggressive_mask"]), "tau2": _boolean_unmask(est_summary.tau2.squeeze(), self.inputs_["aggressive_mask"]), } - return results + return maps, {} class Hedges(IBMAEstimator): @@ -503,14 +508,14 @@ def _fit(self, dataset): est.fit_dataset(pymare_dset) est_summary = est.summary() fe_stats = est_summary.get_fe_stats() - results = { + maps = { "z": _boolean_unmask(fe_stats["z"].squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(fe_stats["p"].squeeze(), self.inputs_["aggressive_mask"]), "est": _boolean_unmask(fe_stats["est"].squeeze(), self.inputs_["aggressive_mask"]), "se": _boolean_unmask(fe_stats["se"].squeeze(), self.inputs_["aggressive_mask"]), "tau2": _boolean_unmask(est_summary.tau2.squeeze(), self.inputs_["aggressive_mask"]), } - return results + return maps, {} class SampleSizeBasedLikelihood(IBMAEstimator): @@ -595,7 +600,7 @@ def _fit(self, dataset): est.fit_dataset(pymare_dset) est_summary = est.summary() fe_stats = est_summary.get_fe_stats() - results = { + maps = { "z": _boolean_unmask(fe_stats["z"].squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(fe_stats["p"].squeeze(), self.inputs_["aggressive_mask"]), "est": _boolean_unmask(fe_stats["est"].squeeze(), self.inputs_["aggressive_mask"]), @@ -606,7 +611,7 @@ def _fit(self, dataset): self.inputs_["aggressive_mask"], ), } - return results + return maps, {} class VarianceBasedLikelihood(IBMAEstimator): @@ -701,14 +706,14 @@ def _fit(self, dataset): est.fit_dataset(pymare_dset) est_summary = est.summary() fe_stats = est_summary.get_fe_stats() - results = { + maps = { "z": _boolean_unmask(fe_stats["z"].squeeze(), self.inputs_["aggressive_mask"]), "p": _boolean_unmask(fe_stats["p"].squeeze(), self.inputs_["aggressive_mask"]), "est": _boolean_unmask(fe_stats["est"].squeeze(), self.inputs_["aggressive_mask"]), "se": _boolean_unmask(fe_stats["se"].squeeze(), self.inputs_["aggressive_mask"]), "tau2": _boolean_unmask(est_summary.tau2.squeeze(), self.inputs_["aggressive_mask"]), } - return results + return maps, {} class PermutedOLS(IBMAEstimator): @@ -790,11 +795,11 @@ def _fit(self, dataset): # Convert t to z, preserving signs dof = self.parameters_["tested_vars"].shape[0] - self.parameters_["tested_vars"].shape[1] z_map = t_to_z(t_map, dof) - images = { + maps = { "t": _boolean_unmask(t_map.squeeze(), self.inputs_["aggressive_mask"]), "z": _boolean_unmask(z_map.squeeze(), self.inputs_["aggressive_mask"]), } - return images + return maps, {} def correct_fwe_montecarlo(self, result, n_iters=10000, n_cores=1): """Perform FWE correction using the max-value permutation method. @@ -859,10 +864,10 @@ def correct_fwe_montecarlo(self, result, n_iters=10000, n_cores=1): sign = np.sign(t_map) sign[sign == 0] = 1 z_map = p_to_z(p_map, tail="two") * sign - images = { + maps = { "logp_level-voxel": _boolean_unmask( log_p_map.squeeze(), self.inputs_["aggressive_mask"] ), "z_level-voxel": _boolean_unmask(z_map.squeeze(), self.inputs_["aggressive_mask"]), } - return images + return maps, {} diff --git a/nimare/meta/kernel.py b/nimare/meta/kernel.py index 9c5e9f541..3dbbafe7f 100644 --- a/nimare/meta/kernel.py +++ b/nimare/meta/kernel.py @@ -8,25 +8,16 @@ import logging import os -import warnings from hashlib import md5 import nibabel as nib import numpy as np import pandas as pd import sparse -from nilearn import image -from nimare import references from nimare.base import NiMAREBase -from nimare.due import due -from nimare.meta.utils import ( - compute_ale_ma, - compute_kda_ma, - compute_p2m_ma, - get_ale_kernel, -) -from nimare.utils import _add_metadata_to_dataframe, _safe_transform, mm2vox, vox2mm +from nimare.meta.utils import compute_ale_ma, compute_kda_ma, get_ale_kernel +from nimare.utils import _add_metadata_to_dataframe, _safe_transform, mm2vox LGR = logging.getLogger(__name__) @@ -206,9 +197,9 @@ def transform(self, dataset, masker=None, return_type="image"): # Loop over exp ids since sparse._coo.core.COO is not iterable for i_exp, id_ in enumerate(transformed_maps[1]): if isinstance(transformed_maps[0][i_exp], sparse._coo.core.COO): + # This step is slow, but it is here just in case user want a + # return_type = "array", "image", or "dataset" kernel_data = transformed_maps[0][i_exp].todense() - else: - kernel_data = transformed_maps[0][i_exp] if return_type == "array": img = kernel_data[mask_data] @@ -266,14 +257,6 @@ def _transform(self, mask, coordinates): pass -@due.dcite( - references.ALE2, - description=( - "Modifies ALE algorithm to eliminate within-experiment " - "effects and generate MA maps based on subject group " - "instead of experiment." - ), -) class ALEKernel(KernelTransformer): """Generate ALE modeled activation images from coordinates and sample size. @@ -309,40 +292,34 @@ def __init__(self, fwhm=None, sample_size=None): self.sample_size = sample_size def _transform(self, mask, coordinates): - kernels = {} # retain kernels in dictionary to speed things up - exp_ids = coordinates["id"].unique() - - transformed = [] - for i_exp, id_ in enumerate(exp_ids): - data = coordinates.loc[coordinates["id"] == id_] - - ijk = np.vstack((data.i.values, data.j.values, data.k.values)).T.astype(int) - if self.sample_size is not None: - sample_size = self.sample_size - elif self.fwhm is None: - # Extract from input - sample_size = data.sample_size.astype(float).values[0] - - if self.fwhm is not None: - assert np.isfinite(self.fwhm), "FWHM must be finite number" - if self.fwhm not in kernels.keys(): - _, kern = get_ale_kernel(mask, fwhm=self.fwhm) - kernels[self.fwhm] = kern - else: - kern = kernels[self.fwhm] - - else: - assert np.isfinite(sample_size), "Sample size must be finite number" - if sample_size not in kernels.keys(): - _, kern = get_ale_kernel(mask, sample_size=sample_size) - kernels[sample_size] = kern - else: - kern = kernels[sample_size] - - kernel_data = compute_ale_ma(mask.shape, ijk, kern) - - transformed.append(kernel_data) + ijks = coordinates[["i", "j", "k"]].values + exp_idx = coordinates["id"].values + + use_dict = True + kernel = None + if self.sample_size is not None: + sample_sizes = self.sample_size + use_dict = False + elif self.fwhm is None: + sample_sizes = coordinates["sample_size"].values + else: + sample_sizes = None + + if self.fwhm is not None: + assert np.isfinite(self.fwhm), "FWHM must be finite number" + _, kernel = get_ale_kernel(mask, fwhm=self.fwhm) + use_dict = False + + transformed = compute_ale_ma( + mask, + ijks, + kernel=kernel, + exp_idx=exp_idx, + sample_sizes=sample_sizes, + use_dict=use_dict, + ) + exp_ids = np.unique(exp_idx) return transformed, exp_ids @@ -351,7 +328,7 @@ class KDAKernel(KernelTransformer): .. versionchanged:: 0.0.12 - * Remove low-memory option for kernel transformers. + * Remove low-memory option in favor of sparse arrays for kernel transformers. Parameters ---------- @@ -368,14 +345,11 @@ def __init__(self, r=10, value=1): self.value = value def _transform(self, mask, coordinates): - dims = mask.shape - vox_dims = mask.header.get_zooms() ijks = coordinates[["i", "j", "k"]].values exp_idx = coordinates["id"].values transformed = compute_kda_ma( - dims, - vox_dims, + mask, ijks, self.r, self.value, @@ -402,48 +376,3 @@ class MKDAKernel(KDAKernel): """ _sum_overlap = False - - -class Peaks2MapsKernel(KernelTransformer): - """Generate peaks2maps modeled activation images from coordinates. - - .. deprecated:: 0.0.11 - `Peaks2MapsKernel` will be removed in NiMARE 0.0.13. - - Parameters - ---------- - model_dir : :obj:`str`, optional - Path to model directory. Default is "auto". - - Warnings - -------- - Peaks2MapsKernel is not intended for serious research. - We strongly recommend against using it for any meaningful analyses. - """ - - def __init__(self, model_dir="auto"): - warnings.warn( - "Peaks2MapsKernel is deprecated, and will be removed in NiMARE version 0.0.13.", - DeprecationWarning, - ) - - # Use private attribute to hide value from get_params. - # get_params will find model_dir=None, which is *very important* when a path is provided. - self._model_dir = model_dir - - def _transform(self, mask, coordinates): - transformed = [] - coordinates_list = [] - ids = [] - for id_, data in coordinates.groupby("id"): - ijk = np.vstack((data.i.values, data.j.values, data.k.values)).T.astype(int) - xyz = vox2mm(ijk, mask.affine) - coordinates_list.append(xyz) - ids.append(id_) - - imgs = compute_p2m_ma(coordinates_list, skip_out_of_bounds=True, model_dir=self._model_dir) - resampled_imgs = [] - for img in imgs: - resampled_imgs.append(image.resample_to_img(img, mask).get_fdata()) - transformed = list(zip(resampled_imgs, ids)) - return transformed diff --git a/nimare/meta/utils.py b/nimare/meta/utils.py index f54138c95..9d75ddd7f 100755 --- a/nimare/meta/utils.py +++ b/nimare/meta/utils.py @@ -1,284 +1,18 @@ """Utilities for coordinate-based meta-analysis estimators.""" import logging -import os +import warnings -import nibabel as nib import numpy as np -import numpy.linalg as npl import sparse from scipy import ndimage -from nimare import references -from nimare.due import due -from nimare.extract import download_peaks2maps_model from nimare.utils import unique_rows -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" LGR = logging.getLogger(__name__) -def model_fn(features, labels, mode, params): - """Run model function used internally by peaks2maps. - - .. deprecated:: 0.0.11 - `model_fn` will be removed in NiMARE 0.0.13. - - .. versionadded:: 0.0.4 - - """ - import tensorflow as tf - from tensorflow.python.estimator.export.export_output import PredictOutput - - ngf = 64 - layers = [] - - training_flag = mode == tf.estimator.ModeKeys.TRAIN - - input_images_placeholder = tf.expand_dims(features, -1) - - conv_args = { - "strides": 2, - "kernel_size": 4, - "padding": "valid", - "activation": tf.nn.leaky_relu, - "kernel_initializer": tf.random_normal_initializer(0, 0.02), - "name": "conv", - "use_bias": False, - } - - deconv_args = conv_args.copy() - deconv_args["padding"] = "same" - deconv_args["name"] = "deconv" - - batchnorm_args = { - "scale": True, - "gamma_initializer": tf.random_normal_initializer(1.0, 0.02), - "center": True, - "beta_initializer": tf.zeros_initializer(), - "name": "batchnorm", - "training": training_flag, - } - - def pad_and_conv(input, out_channels, conv_args): - padded_input = tf.pad(input, [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]], mode="CONSTANT") - convolved = tf.compat.v1.layers.conv3d(padded_input, out_channels, **conv_args) - return convolved - - # encoder_1: [batch, 256, 256, in_channels] => [batch, 128, 128, ngf] - with tf.compat.v1.variable_scope("encoder_1"): - this_args = conv_args.copy() - output = pad_and_conv(input_images_placeholder, ngf, this_args) - layers.append(output) - - layer_specs = [ - (ngf * 2, 0.2), - # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4] - (ngf * 2, 0.2), - # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4] - (ngf * 4, 0.2), - # encoder_4: [batch, 32, 32, ngf * 4] => [batch, 16, 16, ngf * 8] - (ngf * 8, 0.2), - # encoder_5: [batch, 16, 16, ngf * 8] => [batch, 8, 8, ngf * 8] - # ngf * 8, - # # encoder_6: [batch, 8, 8, ngf * 8] => [batch, 4, 4, ngf * 8] - ] - - for out_channels, dropout in layer_specs: - with tf.compat.v1.variable_scope("encoder_%d" % (len(layers) + 1)): - # [batch, in_height, in_width, in_channels] => [batch, in_height/2, in_width/2, - # out_channels] - convolved = pad_and_conv(layers[-1], out_channels, conv_args) - output = tf.compat.v1.layers.batch_normalization(convolved, **batchnorm_args) - if dropout > 0.0: - output = tf.compat.v1.layers.dropout(output, rate=dropout, training=training_flag) - layers.append(output) - - layer_specs = [ - # (ngf * 8, 0.5), - # # decoder_6: [batch, 4, 4, ngf * 8 * 2] => [batch, 8, 8, ngf * 8 * 2] - (ngf * 8, 0.2), - # decoder_5: [batch, 8, 8, ngf * 8 * 2] => [batch, 16, 16, ngf * 8 * 2] - (ngf * 4, 0.2), - # decoder_4: [batch, 16, 16, ngf * 8 * 2] => [batch, 32, 32, ngf * 4 * 2] - (ngf * 2, 0.2), - # decoder_3: [batch, 32, 32, ngf * 4 * 2] => [batch, 64, 64, ngf * 2 * 2] - (ngf * 2, 0.2), - # decoder_2: [batch, 64, 64, ngf * 2 * 2] => [batch, 128, 128, ngf * 2] - ] - - num_encoder_layers = len(layers) - for decoder_layer, (out_channels, dropout) in enumerate(layer_specs): - skip_layer = num_encoder_layers - decoder_layer - 1 - with tf.compat.v1.variable_scope("decoder_%d" % (skip_layer + 1)): - if decoder_layer == 0: - # first decoder layer doesn't have skip connections - # since it is directly connected to the skip_layer - input = layers[-1] - else: - input = tf.concat([layers[-1], layers[skip_layer]], axis=4) - - output = tf.compat.v1.layers.conv3d_transpose(input, out_channels, **deconv_args) - output = tf.compat.v1.layers.batch_normalization(output, **batchnorm_args) - - if dropout > 0.0: - output = tf.compat.v1.layers.dropout(output, rate=dropout, training=training_flag) - - layers.append(output) - - # decoder_1: [batch, 128, 128, ngf * 2] => [batch, 256, 256, generator_outputs_channels] - with tf.compat.v1.variable_scope("decoder_1"): - input = tf.concat([layers[-1], layers[0]], axis=4) - this_args = deconv_args.copy() - this_args["activation"] = None - output = tf.compat.v1.layers.conv3d_transpose(input, 1, **this_args) - layers.append(output) - - predictions = tf.squeeze(layers[-1], -1) - - if mode == tf.estimator.ModeKeys.PREDICT: - temp = tf.compat.v1.saved_model.signature_constants - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=predictions, - export_outputs={temp.DEFAULT_SERVING_SIGNATURE_DEF_KEY: PredictOutput(predictions)}, - ) - else: - labels, filenames = labels - loss = tf.losses.mean_squared_error(labels, predictions) - - # Add a scalar summary for the snapshot loss. - # Create the gradient descent optimizer with the given learning rate. - optimizer = tf.train.AdamOptimizer(params.learning_rate) - # Use the optimizer to apply the gradients that minimize the loss - # (and also increment the global step counter) as a single training step. - extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(extra_update_ops): - train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) - - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=predictions, - loss=loss, - train_op=train_op, - export_outputs={"output": predictions}, - ) - - -def _get_resize_arg(target_shape): - """Get resizing arguments, as used by peaks2maps. - - .. deprecated:: 0.0.11 - `_get_resize_arg` will be removed in NiMARE 0.0.13. - - .. versionadded:: 0.0.1 - - """ - mni_shape_mm = np.array([148.0, 184.0, 156.0]) - target_resolution_mm = np.ceil(mni_shape_mm / np.array(target_shape)).astype(np.int32) - target_affine = np.array( - [ - [4.0, 0.0, 0.0, -75.0], - [0.0, 4.0, 0.0, -105.0], - [0.0, 0.0, 4.0, -70.0], - [0.0, 0.0, 0.0, 1.0], - ] - ) - target_affine[0, 0] = target_resolution_mm[0] - target_affine[1, 1] = target_resolution_mm[1] - target_affine[2, 2] = target_resolution_mm[2] - return target_affine, list(target_shape) - - -def _get_generator(contrasts_coordinates, target_shape, affine, skip_out_of_bounds=False): - """Get generator, as used by peaks2maps.""" - - def generator(): - for contrast in contrasts_coordinates: - encoded_coords = np.zeros(list(target_shape)) - for real_pt in contrast: - vox_pt = np.rint(nib.affines.apply_affine(npl.inv(affine), real_pt)).astype(int) - if skip_out_of_bounds and (vox_pt[0] >= 32 or vox_pt[1] >= 32 or vox_pt[2] >= 32): - continue - encoded_coords[vox_pt[0], vox_pt[1], vox_pt[2]] = 1 - yield (encoded_coords, encoded_coords) - - return generator - - -@due.dcite( - references.PEAKS2MAPS, - description="Transforms coordinates of peaks to unthresholded maps using a deep " - "convolutional neural net.", -) -def compute_p2m_ma( - contrasts_coordinates, skip_out_of_bounds=True, tf_verbosity_level=None, model_dir="auto" -): - """Generate modeled activation (MA) maps using deep ConvNet model peaks2maps. - - .. deprecated:: 0.0.11 - `compute_p2m_ma` will be removed in NiMARE 0.0.13. - - Parameters - ---------- - contrasts_coordinates : list of lists that are len == 3 - List of contrasts and their coordinates - skip_out_of_bounds : bool, optional - Remove coordinates outside of the bounding box of the peaks2maps model - tf_verbosity_level : int - Tensorflow verbosity logging level - model_dir : str, optional - Location of peaks2maps model. Default is "auto". - - Returns - ------- - ma_values : array-like - 1d array of modeled activation values. - """ - try: - import tensorflow as tf - except ImportError as e: - if "No module named 'tensorflow'" in str(e): - raise Exception( - "tensorflow not installed - see https://www.tensorflow.org/install/ " - "for instructions" - ) - else: - raise - - if tf_verbosity_level is None: - tf_verbosity_level = tf.compat.v1.logging.FATAL - target_shape = (32, 32, 32) - affine, _ = _get_resize_arg(target_shape) - tf.compat.v1.logging.set_verbosity(tf_verbosity_level) - - def generate_input_fn(): - dataset = tf.compat.v1.data.Dataset.from_generator( - _get_generator( - contrasts_coordinates, target_shape, affine, skip_out_of_bounds=skip_out_of_bounds - ), - (tf.float32, tf.float32), - (tf.TensorShape(target_shape), tf.TensorShape(target_shape)), - ) - dataset = dataset.batch(1) - iterator = dataset.make_one_shot_iterator() - return iterator.get_next() - - # download_peaks2maps_model expects None for "auto" - model_dir = None if model_dir == "auto" else model_dir - model_dir = download_peaks2maps_model(data_dir=model_dir) - model = tf.estimator.Estimator(model_fn, model_dir=model_dir) - - results = model.predict(generate_input_fn) - results = [result for result in results] - assert len(results) == len(contrasts_coordinates), "returned %d" % len(results) - - niis = [nib.Nifti1Image(np.squeeze(result), affine) for result in results] - return niis - - def compute_kda_ma( - shape, - vox_dims, + mask, ijks, r, value=1.0, @@ -291,6 +25,8 @@ def compute_kda_ma( * Remove low-memory option in favor of sparse arrays. * Return 4D sparse array. + * `shape` and `vox_dims` parameters have been removed. That information is now extracted + from the new parameter `mask`. .. versionadded:: 0.0.4 @@ -298,10 +34,9 @@ def compute_kda_ma( Parameters ---------- - shape : :obj:`tuple` - Shape of brain image + buffer. Typically (91, 109, 91). - vox_dims : array_like - Size (in mm) of each dimension of a voxel. + mask : img_like + Mask to extract the MA maps shape (typically (91, 109, 91)) and voxel dimension. + The mask is applied the data coordinated before creating the kernel_data. ijks : array-like Indices of foci. Each row is a coordinate, with the three columns corresponding to index in each of three dimensions. @@ -325,6 +60,11 @@ def compute_kda_ma( is returned, where the first dimension has size equal to the number of unique experiments, and the remaining 3 dimensions are equal to `shape`. """ + shape = mask.shape + vox_dims = mask.header.get_zooms() + + mask_data = mask.get_fdata().astype(bool) + if exp_idx is None: exp_idx = np.ones(len(ijks)) @@ -358,46 +98,62 @@ def _convolve_sphere(kernel, peaks): # Convolve spheres sphere_coords = np.zeros((kernel.shape[1] * len(peaks), 3), dtype=int) chunk_idx = np.arange(0, (kernel.shape[1]), dtype=int) - for i, peak in enumerate(peaks): + for peak in peaks: sphere_coords[chunk_idx, :] = kernel.T + peak chunk_idx = chunk_idx + kernel.shape[1] return sphere_coords - temp_idx = 0 all_coords = [] + all_exp = [] + all_data = [] # Loop over experiments - for i, exp in enumerate(exp_idx_uniq): + for i_exp, _ in enumerate(exp_idx_uniq): # Index peaks by experiment - curr_exp_idx = exp_idx == i + curr_exp_idx = exp_idx == i_exp peaks = ijks[curr_exp_idx] all_spheres = _convolve_sphere(kernel, peaks) - if not sum_overlap: + if sum_overlap: + # if sum_overlap, counts=list + all_spheres, counts = unique_rows(all_spheres, return_counts=True) + counts = counts * value + else: + # if not sum_overlap, counts=value all_spheres = unique_rows(all_spheres) + counts = value # Mask coordinates beyond space idx = np.all( np.concatenate([all_spheres >= 0, np.less(all_spheres, shape)], axis=1), axis=1 ) + all_spheres = all_spheres[idx, :] + if sum_overlap: + counts = counts[idx] + + ma_values = np.zeros(shape) + ma_values[tuple(all_spheres.T)] = counts + # Set voxel outside the mask to zero. + ma_values[~mask_data] = 0 - n_brain_voxels = all_spheres.shape[0] - all_coords.append(np.vstack([np.full((1, n_brain_voxels), i), all_spheres.T])) - temp_idx += n_brain_voxels + nonzero_idx = np.where(ma_values > 0) - # Usually coords.shape[1] < n_coords, since n_brain_voxels < n_voxels sometimes - coords = np.hstack(all_coords) - coords = coords[:, :temp_idx] + all_exp.append(np.full(nonzero_idx[0].shape[0], i_exp)) + all_coords.append(np.vstack(nonzero_idx)) + all_data.append(ma_values[nonzero_idx]) - data = np.full(coords.shape[1], value) + exp = np.hstack(all_exp) + coords = np.vstack((exp.flatten(), np.hstack(all_coords))) + + data = np.hstack(all_data).flatten() kernel_data = sparse.COO(coords, data, shape=kernel_shape) return kernel_data -def compute_ale_ma(shape, ijk, kernel): +def compute_ale_ma(mask, ijks, kernel=None, exp_idx=None, sample_sizes=None, use_dict=False): """Generate ALE modeled activation (MA) maps. Replaces the values around each focus in ijk with the contrast-specific @@ -405,60 +161,140 @@ def compute_ale_ma(shape, ijk, kernel): accounts for foci which are near to one another and may have overlapping kernels. + .. versionchanged:: 0.0.12 + + * This function now returns a 4D sparse array. + * `shape` parameter has been removed. That information is now extracted + from the new parameter `mask`. + * Replace `ijk` with `ijks`. + * New parameters: `exp_idx`, `sample_sizes`, and `use_dict`. + Parameters ---------- - shape : tuple - Shape of brain image + buffer. Typically (91, 109, 91) + (30, 30, 30). - ijk : array-like + mask : img_like + Mask to extract the MA maps shape (typically (91, 109, 91)) and voxel dimension. + The mask is applied to the coordinates before creating the kernel_data. + ijks : array-like Indices of foci. Each row is a coordinate, with the three columns corresponding to index in each of three dimensions. - kernel : array-like + kernel : array-like, or None, optional 3D array of smoothing kernel. Typically of shape (30, 30, 30). + exp_idx : array_like + Optional indices of experiments. If passed, must be of same length as + ijks. Each unique value identifies all coordinates in ijk that come from + the same experiment. If None passed, it is assumed that all coordinates + come from the same experiment. + sample_sizes : array_like, :obj:`int` or None, optional + Array of smaple sizes or sample size, used to derive FWHM for Gaussian kernel. + use_dict : :obj:`bool`, optional + If True, empty kernels dictionary is used to retain the kernel for each element of + sample_sizes. If False and sample_sizes is int, the ale kernel is calculated for + sample_sizes. If False and sample_sizes is None, the unique kernels is used. Returns ------- - ma_values : array-like - 1d array of modeled activation values. + kernel_data : :obj:`sparse._coo.core.COO` + 4D sparse array. If `exp_idx` is none, a 3d array in the same + shape as the `shape` argument is returned. If `exp_idx` is passed, a 4d array + is returned, where the first dimension has size equal to the number of + unique experiments, and the remaining 3 dimensions are equal to `shape`. """ - ma_values = np.zeros(shape) - mid = int(np.floor(kernel.shape[0] / 2.0)) - mid1 = mid + 1 - for j_peak in range(ijk.shape[0]): - i, j, k = ijk[j_peak, :] - xl = max(i - mid, 0) - xh = min(i + mid1, ma_values.shape[0]) - yl = max(j - mid, 0) - yh = min(j + mid1, ma_values.shape[1]) - zl = max(k - mid, 0) - zh = min(k + mid1, ma_values.shape[2]) - xlk = mid - (i - xl) - xhk = mid - (i - xh) - ylk = mid - (j - yl) - yhk = mid - (j - yh) - zlk = mid - (k - zl) - zhk = mid - (k - zh) - - if ( - (xl >= 0) - & (xh >= 0) - & (yl >= 0) - & (yh >= 0) - & (zl >= 0) - & (zh >= 0) - & (xlk >= 0) - & (xhk >= 0) - & (ylk >= 0) - & (yhk >= 0) - & (zlk >= 0) - & (zhk >= 0) - ): - ma_values[xl:xh, yl:yh, zl:zh] = np.maximum( - ma_values[xl:xh, yl:yh, zl:zh], kernel[xlk:xhk, ylk:yhk, zlk:zhk] - ) - return ma_values - - -@due.dcite(references.ALE_KERNEL, description="Introduces sample size-dependent kernels to ALE.") + if use_dict: + if kernel is not None: + warnings.warn("The kernel provided will be replace by an empty dictionary.") + kernels = {} # retain kernels in dictionary to speed things up + if not isinstance(sample_sizes, np.ndarray): + raise ValueError("To use a kernel dictionary sample_sizes must be a list.") + elif sample_sizes is not None: + if not isinstance(sample_sizes, int): + raise ValueError("If use_dict is False, sample_sizes provided must be integer.") + else: + if kernel is None: + raise ValueError("3D array of smoothing kernel must be provided.") + + if exp_idx is None: + exp_idx = np.ones(len(ijks)) + + shape = mask.shape + mask_data = mask.get_fdata().astype(bool) + + exp_idx_uniq, exp_idx = np.unique(exp_idx, return_inverse=True) + n_studies = len(exp_idx_uniq) + + kernel_shape = (n_studies,) + shape + all_exp = [] + all_coords = [] + all_data = [] + for i_exp, _ in enumerate(exp_idx_uniq): + + # Index peaks by experiment + curr_exp_idx = exp_idx == i_exp + ijk = ijks[curr_exp_idx] + + if use_dict: + # Get sample_size from input + sample_size = sample_sizes[curr_exp_idx][0] + if sample_size not in kernels.keys(): + _, kernel = get_ale_kernel(mask, sample_size=sample_size) + kernels[sample_size] = kernel + else: + kernel = kernels[sample_size] + elif sample_sizes is not None: + _, kernel = get_ale_kernel(mask, sample_size=sample_sizes) + + mid = int(np.floor(kernel.shape[0] / 2.0)) + mid1 = mid + 1 + ma_values = np.zeros(shape) + for j_peak in range(ijk.shape[0]): + i, j, k = ijk[j_peak, :] + xl = max(i - mid, 0) + xh = min(i + mid1, ma_values.shape[0]) + yl = max(j - mid, 0) + yh = min(j + mid1, ma_values.shape[1]) + zl = max(k - mid, 0) + zh = min(k + mid1, ma_values.shape[2]) + xlk = mid - (i - xl) + xhk = mid - (i - xh) + ylk = mid - (j - yl) + yhk = mid - (j - yh) + zlk = mid - (k - zl) + zhk = mid - (k - zh) + + if ( + (xl >= 0) + & (xh >= 0) + & (yl >= 0) + & (yh >= 0) + & (zl >= 0) + & (zh >= 0) + & (xlk >= 0) + & (xhk >= 0) + & (ylk >= 0) + & (yhk >= 0) + & (zlk >= 0) + & (zhk >= 0) + ): + + ma_values[xl:xh, yl:yh, zl:zh] = np.maximum( + ma_values[xl:xh, yl:yh, zl:zh], kernel[xlk:xhk, ylk:yhk, zlk:zhk] + ) + # Set voxel outside the mask to zero. + ma_values[~mask_data] = 0 + nonzero_idx = np.where(ma_values > 0) + + all_exp.append(np.full(nonzero_idx[0].shape[0], i_exp)) + all_coords.append(np.vstack(nonzero_idx)) + all_data.append(ma_values[nonzero_idx]) + + exp = np.hstack(all_exp) + coords = np.vstack((exp.flatten(), np.hstack(all_coords))) + data = np.hstack(all_data).flatten() + + kernel_data = sparse.COO(coords, data, shape=kernel_shape) + + return kernel_data + + def get_ale_kernel(img, sample_size=None, fwhm=None): """Estimate 3D Gaussian and sigma (in voxels) for ALE kernel given sample size or fwhm.""" if sample_size is not None and fwhm is not None: @@ -483,7 +319,7 @@ def get_ale_kernel(img, sample_size=None, fwhm=None): data = np.zeros((31, 31, 31)) mid = int(np.floor(data.shape[0] / 2.0)) data[mid, mid, mid] = 1.0 - kernel = ndimage.filters.gaussian_filter(data, sigma_vox, mode="constant") + kernel = ndimage.gaussian_filter(data, sigma_vox, mode="constant") # Crop kernel to drop surrounding zeros mn = np.min(np.where(kernel > np.spacing(1))[0]) @@ -529,12 +365,12 @@ def _calculate_cluster_measures(arr3d, threshold, conn, tail="upper"): arr3d[np.abs(arr3d) <= threshold] = 0 labeled_arr3d = np.empty(arr3d.shape, int) - labeled_arr3d, _ = ndimage.measurements.label(arr3d > 0, conn) + labeled_arr3d, _ = ndimage.label(arr3d > 0, conn) if tail == "two": # Label positive and negative clusters separately n_positive_clusters = np.max(labeled_arr3d) - temp_labeled_arr3d, _ = ndimage.measurements.label(arr3d < 0, conn) + temp_labeled_arr3d, _ = ndimage.label(arr3d < 0, conn) temp_labeled_arr3d[temp_labeled_arr3d > 0] += n_positive_clusters labeled_arr3d = labeled_arr3d + temp_labeled_arr3d del temp_labeled_arr3d diff --git a/nimare/references.py b/nimare/references.py deleted file mode 100644 index a9d9f28a9..000000000 --- a/nimare/references.py +++ /dev/null @@ -1,198 +0,0 @@ -"""References to be imported and injected at relevant places throughout the library.""" -from nimare.due import BibTeX, Doi - -TEXT2BRAIN = Doi("https://doi.org/10.1007/978-3-030-00931-1_67") - -WORD2BRAIN = Doi("10.1101/299024") - -BOLTZMANNMODEL = BibTeX( - """ - @article{DBLP:journals/corr/MontiLLAM16, - author = {Ricardo Pio Monti and Romy Lorenz and Robert Leech and - Christoforos Anagnostopoulos and Giovanni Montana}, - title = {Text-mining the NeuroSynth corpus using Deep Boltzmann - Machines}, - journal = {CoRR}, - volume = {abs/1605.00223}, - year = {2016}, - url = {http://arxiv.org/abs/1605.00223}, - archivePrefix = {arXiv}, - eprint = {1605.00223}, - timestamp = {Wed, 07 Jun 2017 14:42:40 +0200}, - biburl = {https://dblp.org/rec/bib/journals/corr/MontiLLAM16}, - bibsource = {dblp computer science bibliography, https://dblp.org}} - """ -) - -GCLDAMODEL = Doi("10.1371/journal.pcbi.1005649") - -LDA = BibTeX( - """ - @article{blei2003latent, - title={Latent dirichlet allocation}, - author={Blei, David M and Ng, Andrew Y and Jordan, Michael I}, - journal={Journal of machine Learning research}, - volume={3}, - number={Jan}, - pages={993--1022}, - year={2003}} - """ -) - -MALLET = BibTeX( - """ - @article{mallettoolbox, - title={MALLET: A Machine Learning for Language Toolkit.}, - author={McCallum, Andrew K}, - year={2002}} - """ -) - -LDAMODEL = Doi("10.1371/journal.pcbi.1002707") - -COGNITIVE_ATLAS = Doi("10.3389/fninf.2011.00017") - -COGNITIVE_PARADIGM_ONTOLOGY = Doi("10.1007/s12021-011-9126-x") - -ATHENA = Doi("10.3389/fnins.2019.00494") - -LOG_LIKELIHOOD = Doi("10.1145/1577069.1755845") - -GCLDA_DECODING = Doi("10.1371/journal.pcbi.1005649") - -NEUROSYNTH = Doi("10.1038/nmeth.1635") - -BRAINMAP_DECODING = Doi("10.1007/s00429-013-0698-0") - -ALE1 = BibTeX( - """ - @article{turkeltaub2002meta, - title={Meta-analysis of the functional neuroanatomy of single-word - reading: method and validation}, - author={Turkeltaub, Peter E and Eden, Guinevere F and Jones, - Karen M and Zeffiro, Thomas A}, - journal={Neuroimage}, - volume={16}, - number={3}, - pages={765--780}, - year={2002}, - publisher={Elsevier} - } - """ -) - -ALE2 = Doi("10.1002/hbm.21186") - -ALE3 = Doi("10.1016/j.neuroimage.2011.09.017") - -ALE_KERNEL = Doi("10.1002/hbm.20718") - -SCALE = Doi("10.1016/j.neuroimage.2014.06.007") - -MKDA = Doi("10.1093/scan/nsm015") - -KDA1 = Doi("10.1016/S1053-8119(03)00078-8") - -KDA2 = Doi("10.1016/j.neuroimage.2004.03.052") - -BHICP = Doi("10.1198/jasa.2011.ap09735") - -HPGRF = BibTeX( - """ - @article{kang2014bayesian, - title={A Bayesian hierarchical spatial point process model for - multi-type neuroimaging meta-analysis}, - author={Kang, Jian and Nichols, Thomas E and Wager, Tor D and - Johnson, Timothy D}, - journal={The annals of applied statistics}, - volume={8}, - number={3}, - pages={1800}, - year={2014}, - publisher={NIH Public Access} - } - """ -) - -SBLFR = Doi("10.1111/biom.12713") - -SBR = Doi("10.1214/11-AOAS523") - -PEAKS2MAPS = Doi("10.7490/f1000research.1116395.1") - -FISHERS = BibTeX( - """ - @article{fisher1932statistical, - title={Statistical methods for research workers, Edinburgh: - Oliver and Boyd, 1925}, - author={Fisher, RA}, - journal={Google Scholar}, - year={1932} - } - """ -) - -STOUFFERS = BibTeX( - """ - @article{stouffer1949american, - title={The American soldier: Adjustment during army life. (Studies - in social psychology in World War II), Vol. 1}, - author={Stouffer, Samuel A and Suchman, Edward A and DeVinney, - Leland C and Star, Shirley A and Williams Jr, Robin M}, - year={1949}, - publisher={Princeton Univ. Press} - } - """ -) - -WEIGHTED_STOUFFERS = BibTeX( - """ - @article{zaykin2011optimally, - title={Optimally weighted Z-test is a powerful method for - combining probabilities in meta-analysis}, - author={Zaykin, Dmitri V}, - journal={Journal of evolutionary biology}, - volume={24}, - number={8}, - pages={1836--1841}, - year={2011}, - publisher={Wiley Online Library} - } - """ -) - -CBP = Doi("10.1002/hbm.22138") - -MAMP = Doi("10.1016/j.neuroimage.2015.08.027") - -MAPBOT = Doi("10.1016/j.neuroimage.2017.06.032") - -T2Z_TRANSFORM = BibTeX( - """ - @article{hughett2007accurate, - title={Accurate Computation of the F-to-z and t-to-z Transforms - for Large Arguments}, - author={Hughett, Paul and others}, - journal={Journal of Statistical Software}, - volume={23}, - number={1}, - pages={1--5}, - year={2007}, - publisher={Foundation for Open Access Statistics} - } - """ -) - -T2Z_IMPLEMENTATION = Doi("10.5281/zenodo.32508") - -LANCASTER_TRANSFORM = Doi("10.1002/hbm.20345") - -LANCASTER_TRANSFORM_VALIDATION = Doi("10.1016/j.neuroimage.2010.02.048") - -META_CLUSTER = Doi("10.1016/j.neuroimage.2015.06.044") - -META_CLUSTER2 = Doi("10.1162/netn_a_00050") - -META_ICA = Doi("10.1162/jocn_a_00077") - -META_ICA2 = Doi("10.1162/jocn_a_00077") diff --git a/nimare/results.py b/nimare/results.py index 5c4c2a92d..25cb034ff 100644 --- a/nimare/results.py +++ b/nimare/results.py @@ -3,6 +3,8 @@ import logging import os +import numpy as np +import pandas as pd from nibabel.funcs import squeeze_image from nimare.utils import get_masker @@ -19,8 +21,10 @@ class MetaResult(object): The Estimator used to generate the maps in the MetaResult. mask : Niimg-like or `nilearn.input_data.base_masker.BaseMasker` Mask for converting maps between arrays and images. - maps : :obj:`dict` or None, optional - Maps to store in the object. Default is None. + maps : None or :obj:`dict` of :obj:`numpy.ndarray`, optional + Maps to store in the object. The maps must be provided as 1D numpy arrays. Default is None. + tables : None or :obj:`dict` of :obj:`pandas.DataFrame`, optional + Pandas DataFrames to store in the object. Default is None. Attributes ---------- @@ -29,13 +33,32 @@ class MetaResult(object): masker : :class:`~nilearn.input_data.NiftiMasker` or similar Masker object. maps : :obj:`dict` - Keys are map names and values are arrays. + Keys are map names and values are 1D arrays. + tables : :obj:`dict` + Keys are table levels and values are pandas DataFrames. """ - def __init__(self, estimator, mask, maps=None): + def __init__(self, estimator, mask, maps=None, tables=None): self.estimator = copy.deepcopy(estimator) self.masker = get_masker(mask) - self.maps = maps or {} + + maps = maps or {} + tables = tables or {} + + for map_name, map_ in maps.items(): + if not isinstance(map_, np.ndarray): + raise ValueError(f"Maps must be numpy arrays. '{map_name}' is a {type(map_)}") + + if map_.ndim != 1: + LGR.warning(f"Map '{map_name}' should be 1D, not {map_.ndim}D. Squeezing.") + map_ = np.squeeze(map_) + + for table_name, table in tables.items(): + if not isinstance(table, pd.DataFrame): + raise ValueError(f"Tables must be DataFrames. '{table_name}' is a {type(table)}") + + self.maps = maps + self.tables = tables def get_map(self, name, return_type="image"): """Get stored map as image or array. @@ -94,7 +117,47 @@ def save_maps(self, output_dir=".", prefix="", prefix_sep="_", names=None): outpath = os.path.join(output_dir, filename) img.to_filename(outpath) + def save_tables(self, output_dir=".", prefix="", prefix_sep="_", names=None): + """Save result tables to TSV files. + + Parameters + ---------- + output_dir : :obj:`str`, optional + Output directory in which to save results. If the directory doesn't + exist, it will be created. Default is current directory. + prefix : :obj:`str`, optional + Prefix to prepend to output file names. + Default is None. + prefix_sep : :obj:`str`, optional + Separator to add between prefix and default file names. + Default is _. + names : None or :obj:`list` of :obj:`str`, optional + Names of specific tables to write out. If None, save all tables. + Default is None. + """ + if prefix == "": + prefix_sep = "" + + if not prefix.endswith(prefix_sep): + prefix = prefix + prefix_sep + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + names = names or list(self.tables.keys()) + tables = {k: self.tables[k] for k in names} + + for tabletype, table in tables.items(): + filename = prefix + tables + ".tsv" + outpath = os.path.join(output_dir, filename) + table.to_csv(outpath, sep="\t", index=False) + def copy(self): """Return copy of result object.""" - new = MetaResult(self.estimator, self.masker, copy.deepcopy(self.maps)) + new = MetaResult( + self.estimator, + mask=self.masker, + maps=copy.deepcopy(self.maps), + tables=copy.deepcopy(self.tables), + ) return new diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 8823a1527..5612613fe 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -68,7 +68,8 @@ def testdata_cbmr(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) # set up group_id & moderators n_rows = dset.annotations.shape[0] - dset.annotations['group_id'] = ["group_1" if i%2==0 else 'group_2' for i in range(n_rows)] + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'dementia' for i in range(n_rows)] + dset.annotations['treatment'] = [False if i%2==0 else True for i in range(n_rows)] dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) diff --git a/nimare/tests/test_diagnostics.py b/nimare/tests/test_diagnostics.py index 3502cf0d9..4b33fd252 100644 --- a/nimare/tests/test_diagnostics.py +++ b/nimare/tests/test_diagnostics.py @@ -4,7 +4,7 @@ import pytest from nilearn.input_data import NiftiLabelsMasker -from nimare.diagnostics import FocusCounter, Jackknife +from nimare import diagnostics from nimare.meta import cbma, ibma from nimare.tests.utils import get_test_data_path @@ -42,7 +42,7 @@ def test_jackknife_smoke( else: res = meta.fit(testdata) - jackknife = Jackknife(target_image=target_image, voxel_thresh=1.65) + jackknife = diagnostics.Jackknife(target_image=target_image, voxel_thresh=1.65) if n_samples == "twosample": with pytest.raises(AttributeError): @@ -64,13 +64,13 @@ def test_jackknife_with_custom_masker_smoke(testdata_ibma): meta = ibma.SampleSizeBasedLikelihood(mask=masker) res = meta.fit(testdata_ibma) - jackknife = Jackknife(target_image="z", voxel_thresh=0.5) + jackknife = diagnostics.Jackknife(target_image="z", voxel_thresh=0.5) cluster_table, labeled_img = jackknife.transform(res) assert cluster_table.shape[0] == len(meta.inputs_["id"]) + 1 # A Jackknife with a target_image that isn't present in the MetaResult raises a ValueError. with pytest.raises(ValueError): - jackknife = Jackknife(target_image="doggy", voxel_thresh=0.5) + jackknife = diagnostics.Jackknife(target_image="doggy", voxel_thresh=0.5) jackknife.transform(res) @@ -99,7 +99,7 @@ def test_focuscounter_smoke( else: res = meta.fit(testdata) - counter = FocusCounter(target_image=target_image, voxel_thresh=1.65) + counter = diagnostics.FocusCounter(target_image=target_image, voxel_thresh=1.65) if n_samples == "twosample": with pytest.raises(AttributeError): @@ -107,3 +107,18 @@ def test_focuscounter_smoke( else: cluster_table, labeled_img = counter.transform(res) assert cluster_table.shape[0] == len(meta.inputs_["id"]) + 1 + + +def test_focusfilter(testdata_laird): + """Ensure that the FocusFilter removes out-of-mask coordinates. + + The Laird dataset contains 16 foci outside of the MNI brain mask, which the filter should + remove. + """ + n_coordinates_all = testdata_laird.coordinates.shape[0] + ffilter = diagnostics.FocusFilter() + filtered_dset = ffilter.transform(testdata_laird) + n_coordinates_filtered = filtered_dset.coordinates.shape[0] + assert n_coordinates_all == 1117 + assert n_coordinates_filtered == 1101 + assert n_coordinates_filtered <= n_coordinates_all diff --git a/nimare/tests/test_meta_ale.py b/nimare/tests/test_meta_ale.py index 535d95398..97018818d 100644 --- a/nimare/tests/test_meta_ale.py +++ b/nimare/tests/test_meta_ale.py @@ -6,10 +6,12 @@ import nibabel as nib import numpy as np import pytest +from nilearn.input_data import NiftiLabelsMasker import nimare from nimare.correct import FDRCorrector, FWECorrector from nimare.meta import ale +from nimare.tests.utils import get_test_data_path from nimare.utils import vox2mm @@ -45,13 +47,6 @@ def test_ALE_ma_map_reuse(testdata_cbma, tmp_path_factory, caplog): meta.fit(dset) assert "Loading pre-generated MA maps" in caplog.text - # If there is a memory limit along with pre-generated images, then we should still see the - # logger message. - meta = ale.ALE(kernel__sample_size=20) - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - meta.fit(dset) - assert "Loading pre-generated MA maps" in caplog.text - def test_ALESubtraction_ma_map_reuse(testdata_cbma, tmp_path_factory, caplog): """Test that MA maps are re-used when appropriate.""" @@ -307,3 +302,16 @@ def test_SCALE_smoke(testdata_cbma, tmp_path_factory): meta.save(out_file) assert os.path.isfile(out_file) + + +def test_ALE_non_nifti_masker(testdata_cbma): + """Unit test for ALE with non-NiftiMasker. + + CBMA estimators don't work with non-NiftiMasker (e.g., a NiftiLabelsMasker). + """ + atlas = os.path.join(get_test_data_path(), "test_pain_dataset", "atlas.nii.gz") + masker = NiftiLabelsMasker(atlas) + meta = ale.ALE(mask=masker, n_iters=10) + + with pytest.raises(ValueError): + meta.fit(testdata_cbma) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index eb3a54e1e..1877db10c 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,10 +1,8 @@ from nimare.meta.cbmr import CBMREstimator - import logging -# logging.getLogger().setLevel(logging.DEBUG) - def test_CBMREstimator(testdata_cbmr, caplog): + logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" cbmr = CBMREstimator(multiple_groups=True, moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, tol=1e8) prep = cbmr._preprocess_input(testdata_cbmr) diff --git a/nimare/tests/test_meta_kernel.py b/nimare/tests/test_meta_kernel.py index 553459e0b..9fe416612 100644 --- a/nimare/tests/test_meta_kernel.py +++ b/nimare/tests/test_meta_kernel.py @@ -2,7 +2,7 @@ import nibabel as nib import numpy as np import pytest -from scipy.ndimage.measurements import center_of_mass +from scipy.ndimage import center_of_mass from nimare.meta import kernel from nimare.utils import get_masker, get_template, mm2vox diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 174fc464e..b54b09a15 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -41,18 +41,13 @@ def _create_signal_mask(ground_truth_foci_ijks, mask): Binary image representing regions not expected to be significant within the brain. """ - dims = mask.shape - vox_dims = mask.header.get_zooms() - # area where I'm reasonably certain there are significant results - sig_prob_map = compute_kda_ma( - dims, vox_dims, ground_truth_foci_ijks, r=2, value=1, sum_overlap=False - ) + sig_prob_map = compute_kda_ma(mask, ground_truth_foci_ijks, r=2, value=1, sum_overlap=False) sig_prob_map = sig_prob_map[0].todense() # area where I'm reasonably certain there are not significant results nonsig_prob_map = compute_kda_ma( - dims, vox_dims, ground_truth_foci_ijks, r=14, value=1, sum_overlap=False + mask, ground_truth_foci_ijks, r=14, value=1, sum_overlap=False ) nonsig_prob_map = nonsig_prob_map[0].todense() diff --git a/nimare/transforms.py b/nimare/transforms.py index 952f6909d..793a7ffa4 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -11,9 +11,7 @@ from nilearn.reporting import get_clusters_table from scipy import stats -from nimare import references from nimare.base import NiMAREBase -from nimare.due import due from nimare.utils import _dict_to_coordinates, _dict_to_df, _listify, get_masker LGR = logging.getLogger(__name__) @@ -695,8 +693,6 @@ def p_to_z(p, tail="two"): return z -@due.dcite(references.T2Z_TRANSFORM, description="Introduces T-to-Z transform.") -@due.dcite(references.T2Z_IMPLEMENTATION, description="Python implementation of T-to-Z transform.") def t_to_z(t_values, dof): """Convert t-statistics to z-statistics. @@ -717,6 +713,27 @@ def t_to_z(t_values, dof): z_values : array_like Z-statistics + License + ------- + The MIT License (MIT) + Copyright (c) 2015 Vanessa Sochat + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software + and associated documentation files (the "Software"), to deal in the Software without + restriction, including without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the + Software is furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or + substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + References ---------- .. footbibliography:: diff --git a/nimare/utils.py b/nimare/utils.py index c8face88e..ce55a05cf 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -15,9 +15,7 @@ import numpy as np import pandas as pd from nilearn.input_data import NiftiMasker - -from nimare import references -from nimare.due import due +from scipy import ndimage import patsy import sparse @@ -209,16 +207,6 @@ def mm2vox(xyz, affine): return ijk -@due.dcite( - references.LANCASTER_TRANSFORM, - description="Introduces the Lancaster MNI-to-Talairach transform, " - "as well as its inverse, the Talairach-to-MNI " - "transform.", -) -@due.dcite( - references.LANCASTER_TRANSFORM_VALIDATION, - description="Validates the Lancaster MNI-to-Talairach and Talairach-to-MNI transforms.", -) def tal2mni(coords): """Convert coordinates from Talairach space to MNI space. @@ -287,16 +275,6 @@ def tal2mni(coords): return out_coords -@due.dcite( - references.LANCASTER_TRANSFORM, - description="Introduces the Lancaster MNI-to-Talairach transform, " - "as well as its inverse, the Talairach-to-MNI " - "transform.", -) -@due.dcite( - references.LANCASTER_TRANSFORM_VALIDATION, - description="Validates the Lancaster MNI-to-Talairach and Talairach-to-MNI transforms.", -) def mni2tal(coords): """Convert coordinates from MNI space Talairach space. @@ -986,7 +964,7 @@ def __call__(self, *args, **kwargs): tqdm_object.close() -def unique_rows(ar): +def unique_rows(ar, return_counts=False): """Remove repeated rows from a 2D array. In particular, if given an array of coordinates of shape @@ -996,11 +974,16 @@ def unique_rows(ar): ---------- ar : 2-D ndarray The input array. + return_counts : :obj:`bool`, optional + If True, also return the number of times each unique item appears in ar. Returns ------- ar_out : 2-D ndarray A copy of the input array with repeated rows removed. + unique_counts : :obj:`np.ndarray`, optional + The number of times each of the unique values comes up in the original array. + Only provided if return_counts is True. Raises ------ @@ -1023,6 +1006,8 @@ def unique_rows(ar): array([[0, 1, 0], [1, 0, 1]], dtype=uint8) + License + ------- Copyright (C) 2019, the scikit-image team All rights reserved. """ @@ -1034,6 +1019,7 @@ def unique_rows(ar): # see each row as a single item, we create a view of each row as a # byte string of length itemsize times number of columns in `ar` ar_row_view = ar.view("|S%d" % (ar.itemsize * ar.shape[1])) +<<<<<<< HEAD _, unique_row_indices = np.unique(ar_row_view, return_index=True) ar_out = ar[unique_row_indices] return ar_out @@ -1172,3 +1158,147 @@ def intensity2voxel(intensity, masker_voxels): intensity_array[coord_x, coord_y, coord_z] = coord_intensity return intensity_array +======= + if return_counts: + _, unique_row_indices, counts = np.unique( + ar_row_view, return_index=True, return_counts=True + ) + + return ar[unique_row_indices], counts + else: + _, unique_row_indices = np.unique(ar_row_view, return_index=True) + + return ar[unique_row_indices] + + +def _cluster_nearest_neighbor(ijk, labels_index, labeled): + """Find the nearest neighbor for given points in the corresponding cluster. + + Parameters + ---------- + ijk : :obj:`numpy.ndarray` + (n_pts, 3) array of query points. + labels_index : :obj:`numpy.ndarray` + (n_pts,) array of corresponding cluster indices. + labeled : :obj:`numpy.ndarray` + 3D array with voxels labeled according to cluster index. + + Returns + ------- + nbrs : :obj:`numpy.ndarray` + (n_pts, 3) nearest neighbor points. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + labels = labeled[labeled > 0] + clusters_ijk = np.array(labeled.nonzero()).T + nbrs = np.zeros_like(ijk) + for ii, (lab, point) in enumerate(zip(labels_index, ijk)): + lab_ijk = clusters_ijk[labels == lab] + dist = np.linalg.norm(lab_ijk - point, axis=1) + nbrs[ii] = lab_ijk[np.argmin(dist)] + + return nbrs + + +def _get_cluster_coms(labeled_cluster_arr): + """Get the center of mass of each cluster in a labeled array. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + cluster_ids = np.unique(labeled_cluster_arr)[1:] + n_clusters = cluster_ids.size + + # Identify center of mass for each cluster + # This COM may fall outside the cluster, but it is a useful heuristic for identifying them + cluster_ids = np.arange(1, n_clusters + 1, dtype=int) + cluster_coms = ndimage.center_of_mass(labeled_cluster_arr, labeled_cluster_arr, cluster_ids) + cluster_coms = np.array(cluster_coms).astype(int) + + # NOTE: The following comes from Nilearn + # Determine if all subpeaks are within the cluster + # They may not be if the cluster is binary and has a shape where the COM is + # outside the cluster, like a donut. + coms_outside_clusters = ( + labeled_cluster_arr[cluster_coms[:, 0], cluster_coms[:, 1], cluster_coms[:, 2]] + != cluster_ids + ) + if np.any(coms_outside_clusters): + LGR.warning( + "Attention: At least one of the centers of mass falls outside of the cluster body. " + "Identifying the nearest in-cluster voxel." + ) + + # Replace centers of mass with their nearest neighbor points in the + # corresponding clusters. Note this is also equivalent to computing the + # centers of mass constrained to points within the cluster. + cluster_coms[coms_outside_clusters, :] = _cluster_nearest_neighbor( + cluster_coms[coms_outside_clusters, :], + cluster_ids[coms_outside_clusters], + labeled_cluster_arr, + ) + + return cluster_coms +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 diff --git a/nimare/workflows/__init__.py b/nimare/workflows/__init__.py index bcea02795..3ce5446cd 100644 --- a/nimare/workflows/__init__.py +++ b/nimare/workflows/__init__.py @@ -3,13 +3,11 @@ from .ale import ale_sleuth_workflow from .conperm import conperm_workflow from .macm import macm_workflow -from .peaks2maps import peaks2maps_workflow from .scale import scale_workflow __all__ = [ "ale_sleuth_workflow", "conperm_workflow", "macm_workflow", - "peaks2maps_workflow", "scale_workflow", ] diff --git a/nimare/workflows/conperm.py b/nimare/workflows/conperm.py index 6a9c19ea9..254edd742 100644 --- a/nimare/workflows/conperm.py +++ b/nimare/workflows/conperm.py @@ -69,7 +69,7 @@ def conperm_workflow(contrast_images, mask_image=None, output_dir=None, prefix=" ) res = {"logp": log_p_map, "t": t_map} # The t_test function will stand in for the Estimator in the results object - res = MetaResult(permuted_ols, mask_image, maps=res) + res = MetaResult(permuted_ols, mask=mask_image, maps=res, tables={}) boilerplate = boilerplate.format( n_studies=n_studies, diff --git a/nimare/workflows/peaks2maps.py b/nimare/workflows/peaks2maps.py deleted file mode 100644 index 2a1e8cf82..000000000 --- a/nimare/workflows/peaks2maps.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Perform meta-analysis on images constructed from coordinates using the Peaks2Maps kernel.""" -import logging -import os -import pathlib - -import numpy as np -from nilearn.image import resample_to_img -from nilearn.masking import apply_mask -from nilearn.mass_univariate import permuted_ols - -from nimare.base import MetaResult -from nimare.io import convert_sleuth_to_dataset -from nimare.meta.kernel import Peaks2MapsKernel - -LGR = logging.getLogger(__name__) - - -def peaks2maps_workflow(sleuth_file, output_dir=None, prefix=None, n_iters=10000): - """Run the peaks2maps workflow. - - .. deprecated:: 0.0.11 - `peaks2maps_workflow` will be removed in NiMARE 0.0.13. - - """ - LGR.info("Loading coordinates...") - dset = convert_sleuth_to_dataset(sleuth_file) - - LGR.info("Reconstructing unthresholded maps...") - k = Peaks2MapsKernel(resample_to_mask=False) - imgs = k.transform(dset, return_type="image") - - mask_img = resample_to_img(dset.mask, imgs[0], interpolation="nearest") - z_data = apply_mask(imgs, mask_img) - - LGR.info("Estimating the null distribution...") - log_p_map, t_map, _ = permuted_ols( - np.ones((z_data.shape[0], 1)), - z_data, - confounding_vars=None, - model_intercept=False, # modeled by tested_vars - n_perm=n_iters, - two_sided_test=True, - random_state=42, - n_jobs=1, - verbose=0, - ) - res = {"logp": log_p_map, "t": t_map} - - res = MetaResult(permuted_ols, maps=res, mask=mask_img) - - if output_dir is None: - output_dir = os.path.dirname(os.path.abspath(sleuth_file)) - else: - pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True) - - if prefix is None: - base = os.path.basename(sleuth_file) - prefix, _ = os.path.splitext(base) - prefix += "_" - - LGR.info("Saving output maps...") - res.save_maps(output_dir=output_dir, prefix=prefix) diff --git a/pyproject.toml b/pyproject.toml index 456cb6d10..f90b323dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,6 @@ exclude = ''' )/ | versioneer.py | nimare/_version.py - | nimare/due.py ) ''' diff --git a/setup.cfg b/setup.cfg index 7da488b1c..1933f95bf 100644 --- a/setup.cfg +++ b/setup.cfg @@ -48,8 +48,12 @@ install_requires = nilearn>=0.7.1 numba # used by sparse numpy +<<<<<<< HEAD pandas patsy +======= + pandas>=1.1.0 +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 pymare~=0.0.4rc2 # nimare.meta.ibma and stats requests # nimare.extract scikit-learn # nimare.annotate and nimare.decode @@ -61,12 +65,6 @@ packages = find: include_package_data = False [options.extras_require] -peaks2maps-cpu = - tensorflow>=2.0.0 - appdirs -peaks2maps-gpu = - tensorflow-gpu>=2.0.0 - appdirs doc = m2r matplotlib @@ -89,8 +87,6 @@ tests = flake8-isort pytest pytest-cov -duecredit = - duecredit minimum = indexed_gzip==1.4 nibabel==3.0 @@ -101,8 +97,6 @@ minimum = scikit-learn==0.22 scipy==1.5 # 1.6 drops Python 3.6 support all = - %(duecredit)s - %(peaks2maps-cpu)s %(doc)s %(tests)s @@ -128,7 +122,7 @@ parentdir_prefix = [flake8] max-line-length = 99 -exclude = *build/,_version.py,due.py +exclude = *build/,_version.py putty-ignore = */__init__.py : +F401 ignore = E203,E402,E722,W503 diff --git a/setup_BACKUP_7408.cfg b/setup_BACKUP_7408.cfg new file mode 100644 index 000000000..1933f95bf --- /dev/null +++ b/setup_BACKUP_7408.cfg @@ -0,0 +1,129 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy +<<<<<<< HEAD + pandas + patsy +======= + pandas>=1.1.0 +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_BASE_7408.cfg b/setup_BASE_7408.cfg new file mode 100644 index 000000000..6a4932af7 --- /dev/null +++ b/setup_BASE_7408.cfg @@ -0,0 +1,134 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +peaks2maps-cpu = + tensorflow>=2.0.0 + appdirs +peaks2maps-gpu = + tensorflow-gpu>=2.0.0 + appdirs +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +duecredit = + duecredit +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(duecredit)s + %(peaks2maps-cpu)s + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py,due.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_LOCAL_7408.cfg b/setup_LOCAL_7408.cfg new file mode 100644 index 000000000..7da488b1c --- /dev/null +++ b/setup_LOCAL_7408.cfg @@ -0,0 +1,135 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas + patsy + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +peaks2maps-cpu = + tensorflow>=2.0.0 + appdirs +peaks2maps-gpu = + tensorflow-gpu>=2.0.0 + appdirs +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +duecredit = + duecredit +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(duecredit)s + %(peaks2maps-cpu)s + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py,due.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_REMOTE_7408.cfg b/setup_REMOTE_7408.cfg new file mode 100644 index 000000000..59d103597 --- /dev/null +++ b/setup_REMOTE_7408.cfg @@ -0,0 +1,124 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas>=1.1.0 + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy From b3ee6f1a13104a58c505d1b34996c75b80674de2 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Mon, 1 Aug 2022 17:53:30 +0100 Subject: [PATCH 015/177] [skip CI][wip] modify settings in pre_process --- nimare/meta/cbmr.py | 101 +++++++++++++++++++-------------- nimare/tests/conftest.py | 6 +- nimare/tests/test_meta_cbmr.py | 2 +- 3 files changed, 62 insertions(+), 47 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 407c05584..efb8f6ff7 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -6,6 +6,7 @@ import nibabel as nib import numpy as np import pandas as pd +import scipy from nimare.utils import mm2vox, vox2idx, intensity2voxel import torch import logging @@ -14,14 +15,14 @@ class CBMREstimator(Estimator): _required_inputs = {"coordinates": ("coordinates", None)} - def __init__(self, multiple_groups=False, moderators=None, moderators_center=True, moderators_scale=True, mask=None, + def __init__(self, group_names=None, moderators=None, moderators_center=True, moderators_scale=True, mask=None, spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu', **kwargs): super().__init__(**kwargs) if mask is not None: mask = get_masker(mask) self.masker = mask - self.multiple_groups = multiple_groups + self.group_names = group_names self.moderators = moderators self.moderators_center = moderators_center # either boolean or a list of strings self.moderators_scale = moderators_scale @@ -55,63 +56,49 @@ def _preprocess_input(self, dataset): valid_study_bool = study_id_annotations.isin(study_id_coordinates) dataset_annotations = dataset.annotations[valid_study_bool] all_group_study_id = dict() - if self.multiple_groups: - if 'group_id' not in dataset_annotations.columns: - raise ValueError("group_id must exist in the dataset in group-wise CBMR") + if isinstance(self.group_names, type(None)): + all_group_study_id[self.group_names] = dataset_annotations['study_id'].unique().tolist() + elif isinstance(self.group_names, str): + if self.group_names not in dataset_annotations.columns: + raise ValueError("group_names: {} does not exist in the dataset".format(self.group_names)) else: - group_names = list(dataset_annotations['group_id'].unique()) - if len(group_names) == 1: - raise ValueError('Only a single group exists in the dataset') - for group_name in group_names: - group_study_id_bool = dataset_annotations['group_id'] == group_name + uniq_groups = list(dataset_annotations[self.group_names].unique()) + for group in uniq_groups: + group_study_id_bool = dataset_annotations[self.group_names] == group group_study_id = dataset_annotations.loc[group_study_id_bool]['study_id'] - all_group_study_id[group_name] = group_study_id.unique().tolist() - else: - all_group_study_id['single_group'] = dataset_annotations['study_id'].unique().tolist() + all_group_study_id[group] = group_study_id.unique().tolist() + elif isinstance(self.group_names, list): + not_exist_group_names = [group for group in self.group_names if group not in dataset_annotations.columns] + if len(not_exist_group_names) > 0: + raise ValueError("group_names: {} does not exist in the dataset".format(not_exist_group_names)) + uniq_group_splits = dataset_annotations[self.group_names].drop_duplicates().values.tolist() + for group in uniq_group_splits: + group_study_id_bool = (dataset_annotations[self.group_names] == group).all(axis=1) + group_study_id = dataset_annotations.loc[group_study_id_bool]['study_id'] + all_group_study_id['_'.join(group)] = group_study_id.unique().tolist() self.inputs_['all_group_study_id'] = all_group_study_id # collect studywise moderators if specficed if hasattr(self, "moderators"): all_group_moderators = dict() - for group_name in all_group_study_id.keys(): - df_group = dataset_annotations.loc[dataset_annotations['study_id'].isin(all_group_study_id[group_name])] + for group in all_group_study_id.keys(): + df_group = dataset_annotations.loc[dataset_annotations['study_id'].isin(all_group_study_id[group])] group_moderators = np.stack([df_group[moderator_name] for moderator_name in self.moderators], axis=1) group_moderators = group_moderators.astype(np.float64) - # standardize mean - if isinstance(self.moderators_center, bool): - if self.moderators_center: - group_moderators -= np.mean(group_moderators, axis=0) - elif isinstance(self.moderators_center, str): - index_moderators_center = self.moderators.index(self.moderators_center) - group_moderators[:,index_moderators_center] -= np.mean(group_moderators[:, index_moderators_center], axis=0) - elif isinstance(self.moderators_center, list): - index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] - for i in index_moderators_center: - group_moderators[:,i] -= np.mean(group_moderators[:, i], axis=0) - # standardize var - if isinstance(self.moderators_scale, bool): - if self.moderators_scale: - group_moderators /= np.std(group_moderators, axis=0) - elif isinstance(self.moderators_scale, str): - index_moderators_scale = self.moderators.index(self.moderators_scale) - group_moderators[:,index_moderators_scale] /= np.std(group_moderators[:, index_moderators_scale], axis=0) - elif isinstance(self.moderators_scale, list): - index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] - for i in index_moderators_scale: - group_moderators[:,i] /= np.std(group_moderators[:, i], axis=0) - all_group_moderators[group_name] = group_moderators + group_moderators = self._standardize_moderators(group_moderators) + all_group_moderators[group] = group_moderators self.inputs_["all_group_moderators"] = all_group_moderators # Calculate IJK matrix indices for target mask # Mask space is assumed to be the same as the Dataset's space # These indices are used directly by any KernelTransformer all_foci_per_voxel, all_foci_per_study = dict(), dict() - for group_name in all_group_study_id.keys(): - group_study_id = all_group_study_id[group_name] + for group in all_group_study_id.keys(): + group_study_id = all_group_study_id[group] group_coordinates = dataset.coordinates.loc[dataset.coordinates['study_id'].isin(group_study_id)] - + # group-wise foci coordinates group_xyz = group_coordinates[['x', 'y', 'z']].values group_ijk = mm2vox(group_xyz, mask_img.affine) group_foci_idx = vox2idx(group_ijk, mask_img._dataobj) - + # number of foci per voxel/study n_group_study = len(group_study_id) masker_voxels = np.sum(mask_img._dataobj).astype(int) group_foci_per_voxel = np.zeros((masker_voxels, 1)) @@ -119,12 +106,38 @@ def _preprocess_input(self, dataset): group_foci_per_study = np.array([(group_coordinates['study_id']==i).sum() for i in group_study_id]) group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) - all_foci_per_voxel[group_name] = group_foci_per_voxel - all_foci_per_study[group_name] = group_foci_per_study + all_foci_per_voxel[group] = group_foci_per_voxel + all_foci_per_study[group] = group_foci_per_study self.inputs_['all_foci_per_voxel'] = all_foci_per_voxel self.inputs_['all_foci_per_study'] = all_foci_per_study + def _standardize_moderators(self, moderators_array): + # standardize mean + if isinstance(self.moderators_center, bool): + if self.moderators_center: + moderators_array -= np.mean(moderators_array, axis=0) + elif isinstance(self.moderators_center, str): + index_moderators_center = self.moderators.index(self.moderators_center) + moderators_array[:,index_moderators_center] -= np.mean(moderators_array[:, index_moderators_center], axis=0) + elif isinstance(self.moderators_center, list): + index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] + for i in index_moderators_center: + moderators_array[:,i] -= np.mean(moderators_array[:, i], axis=0) + + # standardize var + if isinstance(self.moderators_scale, bool): + if self.moderators_scale: + moderators_array /= np.std(moderators_array, axis=0) + elif isinstance(self.moderators_scale, str): + index_moderators_scale = self.moderators.index(self.moderators_scale) + moderators_array[:,index_moderators_scale] /= np.std(moderators_array[:, index_moderators_scale], axis=0) + elif isinstance(self.moderators_scale, list): + index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] + for i in index_moderators_scale: + moderators_array[:,i] /= np.std(moderators_array[:, i], axis=0) + + return moderators_array def _model_structure(self, model, penalty, device): beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 5612613fe..3b75cd3de 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -11,6 +11,7 @@ from nimare.tests.utils import get_test_data_path from nimare.utils import get_resource_path +import random # Only enable the following once in a while for a check for SettingWithCopyWarnings # pd.options.mode.chained_assignment = "raise" @@ -68,8 +69,9 @@ def testdata_cbmr(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) # set up group_id & moderators n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'dementia' for i in range(n_rows)] - dset.annotations['treatment'] = [False if i%2==0 else True for i in range(n_rows)] + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] + dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] + dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 1877db10c..574c727f0 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -4,7 +4,7 @@ def test_CBMREstimator(testdata_cbmr, caplog): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(multiple_groups=True, moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, tol=1e8) + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, tol=1e8) prep = cbmr._preprocess_input(testdata_cbmr) with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbmr"): cbmr.fit(dataset=testdata_cbmr) From 0262c662f084f9954c067bd70a1c54df5900b00c Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Wed, 3 Aug 2022 12:14:30 +0100 Subject: [PATCH 016/177] [skip ci][wip] implemented group-wise CBMR and fix problems --- nimare/meta/cbmr.py | 95 ++++++++++++++++++++-------------- nimare/tests/conftest.py | 30 ++++++----- nimare/tests/test_meta_cbmr.py | 9 ++-- nimare/utils.py | 3 +- pyproject.toml | 2 + 5 files changed, 83 insertions(+), 56 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index efb8f6ff7..24dae73f1 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -147,22 +147,23 @@ def _model_structure(self, model, penalty, device): else: gamma_dim = None study_level_moderators = False - self.n_groups = len(self.inputs_["all_group_study_id"]) + self.groups = list(self.inputs_['all_group_study_id'].keys()) if model == 'Poisson': - cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, n_groups=self.n_groups, study_level_moderators=study_level_moderators, penalty=penalty) + cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) if 'cuda' in device: cbmr_model = cbmr_model.cuda() return cbmr_model - def _update(self, model, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss, gamma=0.99): + def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay scheduler.step() self.iter += 1 + scheduler.step() def closure(): optimizer.zero_grad() - loss = model(Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study) + loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) loss.backward() return loss loss = optimizer.step(closure) @@ -174,16 +175,23 @@ def _optimizer(self, model, lr, tol, n_iter, device): # load dataset info to torch.tensor Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) if hasattr(self, "moderators"): - for group_name in self.inputs_['all_group_study_id'].keys(): - all_moderators_array = torch.tensor(self.inputs_['all_group_moderators'][group_name], dtype=torch.float64, device=device) - n_foci_per_voxel = torch.tensor(self.inputs_['n_foci_per_voxel'], dtype=torch.float64, device=device) - n_foci_per_study = torch.tensor(self.inputs_['n_foci_per_study'], dtype=torch.float64, device=device) - + all_group_moderators_tensor = dict() + for group in self.inputs_['all_group_study_id'].keys(): + group_moderators_tensor = torch.tensor(self.inputs_['all_group_moderators'][group], dtype=torch.float64, device=device) + all_group_moderators_tensor[group] = group_moderators_tensor + else: + all_group_moderators_tensor = None + all_foci_per_voxel_tensor, all_foci_per_study_tensor = dict(), dict() + for group in self.inputs_['all_group_study_id'].keys(): + group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=device) + group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=device) + all_foci_per_voxel_tensor[group] = group_foci_per_voxel + all_foci_per_study_tensor[group] = group_foci_per_study + if self.iter == 0: prev_loss = torch.tensor(float('inf')) # initialization loss difference - for i in range(n_iter): - loss = self._update(model, optimizer, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study, prev_loss) + loss = self._update(model, optimizer, Coef_spline_bases, all_group_moderators_tensor, all_foci_per_voxel_tensor, all_foci_per_study_tensor, prev_loss) loss_diff = loss - prev_loss LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") if torch.abs(loss_diff) < tol: @@ -200,18 +208,20 @@ def _fit(self, dataset): cbmr_model = self._model_structure(self.model, self.penalty, self.device) optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) - # beta: regression coef of spatial effec - self._beta = cbmr_model.beta_linear.weight - self._beta = self._beta.detach().numpy().T + # beta: regression coef of spatial effect + for group in self.inputs_['all_group_study_id'].keys(): + group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight + group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().T - studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, self._beta)) - studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) + studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) + studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) if hasattr(self, "moderators"): self._gamma = cbmr_model.gamma_linear.weight - self._gamma = self._gamma.detach().numpy().T - - moderator_results = np.exp(np.matmul(self.inputs_["moderators_array"], self._gamma)) + self._gamma = self._gamma.cpu().detach().numpy().T + for group in self.inputs_['all_group_study_id'].keys(): + group_moderators = self.inputs_["all_group_moderators"][group] + moderator_effect = np.exp(np.matmul(group_moderators, self._gamma)) return @@ -219,33 +229,42 @@ def _fit(self, dataset): class GLMPoisson(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, n_groups=None, study_level_moderators=False, penalty='No'): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): super().__init__() - self.n_groups = n_groups + self.groups = groups self.study_level_moderators = study_level_moderators # initialization for beta - beta_linear_weights = list() - for i in range(self.n_groups): - beta_linear_i = torch.nn.Linear(beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_i.weight, a=-0.01, b=0.01) - beta_linear_weights.append(beta_linear_i.weight) - beta_linear_weights = torch.stack(beta_linear_weights) - self.beta_linear_weights = torch.nn.Parameter(beta_linear_weights, requires_grad=True) + all_beta_linears = dict() + for group in groups: + beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) # gamma if self.study_level_moderators: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def forward(self, Coef_spline_bases, moderators_array, n_foci_per_voxel, n_foci_per_study): + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 # spatial effect: mu^X = exp(X * beta) - log_mu_spatial = self.beta_linear(Coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) - if torch.is_tensor(moderators_array): - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(moderators_array) + for group in all_foci_per_voxel.keys(): + log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) - # Under the assumption that Y_ij is either 0 or 1 - # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - log_l = torch.sum(torch.mul(n_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(n_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) - + group_foci_per_voxel = all_foci_per_voxel[group] + group_foci_per_study = all_foci_per_study[group] + # Under the assumption that Y_ij is either 0 or 1 + # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] + group_log_l = torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) + log_l += group_log_l + return -log_l diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 3b75cd3de..31dcff69d 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -67,7 +67,7 @@ def testdata_cbmr(): # Otherwise centers of mass will be obscured in kernel tests by overlapping # kernels dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) - # set up group_id & moderators + # set up group columns & moderators n_rows = dset.annotations.shape[0] dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] @@ -87,17 +87,23 @@ def testdata_cbma_full(): dset = nimare.dataset.Dataset(dset_file) return dset -# @pytest.fixture(scope="session") -# def testdata_cbmr_full(): -# """Generate coordinate-based dataset for tests.""" -# dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") -# dset = nimare.dataset.Dataset(dset_file) -# # generate group_id & moderator -# n_rows = dset.annotations.shape[0] -# dset.annotations["sample_sizes"] = dset.metadata.sample_sizes # sample sizes as study-level covariates -# groups = pd.DataFrame({'study_id':dset.annotations['study_id'], 'group_id': ["group_1" if i%2==0 else 'group_2' for i in range(n_rows)]}) -# moderators = pd.DataFrame({'study_id': dset.annotations['study_id'], 'moderator': np.random.rand(n_rows)}) # random study-level covariates -# return dset, groups, moderators +@pytest.fixture(scope="session") +def testdata_cbmr_full(): + """Generate more complete coordinate-based dataset for tests. + + Same as above, except returns all coords, not just one per study. + """ + dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset = nimare.dataset.Dataset(dset_file) + # set up group columns & moderators + n_rows = dset.annotations.shape[0] + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] + # dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] + # dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["avg_age"] = np.arange(n_rows) + + return dset @pytest.fixture(scope="session") diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 574c727f0..926adec2c 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,13 +1,12 @@ from nimare.meta.cbmr import CBMREstimator import logging -def test_CBMREstimator(testdata_cbmr, caplog): +def test_CBMREstimator(testdata_cbmr_full, caplog): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, tol=1e8) - prep = cbmr._preprocess_input(testdata_cbmr) - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbmr"): - cbmr.fit(dataset=testdata_cbmr) + cbmr = CBMREstimator(group_names=['diagnosis'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e-2) + prep = cbmr._preprocess_input(testdata_cbmr_full) + cbmr.fit(dataset=testdata_cbmr_full) print('1234') # with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): # meta.fit(testdata_cbma) diff --git a/nimare/utils.py b/nimare/utils.py index 6d999a5f7..3eb5fd04b 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1272,7 +1272,8 @@ def vox2idx(ijk, masker_voxels): x_dim, y_dim, z_dim = xx.shape[0], yy.shape[0], zz.shape[0] brain_voxels_index = [(z - np.min(zz))+ z_dim * (y - np.min(yy))+ y_dim * z_dim * (x - np.min(xx)) for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] - foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci)] + foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci) + if masker_voxels[ijk[i, 0], ijk[i, 1], ijk[i, 2]]==1] foci_brain_index = [brain_voxels_index.index(j) for j in foci_index] foci_brain_index = np.array(foci_brain_index) diff --git a/pyproject.toml b/pyproject.toml index f90b323dd..e06bc216a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,8 @@ markers = [ "performance_estimators: mark tests that measure estimator performance", "performance_correctors: mark tests that measure corrector performance", ] +log_cli = true +log_cli_level = "DEBUG" [tool.isort] profile = "black" From 7fd47bcfb79c062ae1c9c4140a263007f26584c9 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 5 Aug 2022 17:31:05 +0100 Subject: [PATCH 017/177] [skip ci][wip] add results as inputs to MetaResults --- nimare/dataset.py | 1 + nimare/meta/cbmr.py | 39 ++++++++-- nimare/tests/test_meta_cbmr.py | 2 +- nimare/utils.py | 3 +- setup.cfg | 10 +-- setup_BACKUP_7408.cfg | 129 ------------------------------- setup_BASE_7408.cfg | 134 -------------------------------- setup_LOCAL_7408.cfg | 135 --------------------------------- setup_REMOTE_7408.cfg | 124 ------------------------------ 9 files changed, 35 insertions(+), 542 deletions(-) delete mode 100644 setup_BACKUP_7408.cfg delete mode 100644 setup_BASE_7408.cfg delete mode 100644 setup_LOCAL_7408.cfg delete mode 100644 setup_REMOTE_7408.cfg diff --git a/nimare/dataset.py b/nimare/dataset.py index 262a02893..824221134 100755 --- a/nimare/dataset.py +++ b/nimare/dataset.py @@ -127,6 +127,7 @@ def __repr__(self): experiments in the Dataset represented as well. """ # Get default parameter values for the object + signature = inspect.signature(self.__init__) defaults = { k: v.default diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 24dae73f1..a5387ae58 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -8,6 +8,7 @@ import pandas as pd import scipy from nimare.utils import mm2vox, vox2idx, intensity2voxel +from nimare.diagnostics import FocusFilter import torch import logging @@ -50,9 +51,12 @@ def _preprocess_input(self, dataset): for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": + # remove dataset coordinates outside of mask + focus_filter = FocusFilter(mask=masker) + dataset = focus_filter.transform(dataset) + # remove study_id without any coordinates study_id_annotations = dataset.annotations.set_index('study_id').index study_id_coordinates = dataset.coordinates.set_index('study_id').index - # remove study_id without any coordinates valid_study_bool = study_id_annotations.isin(study_id_coordinates) dataset_annotations = dataset.annotations[valid_study_bool] all_group_study_id = dict() @@ -203,28 +207,47 @@ def _optimizer(self, model, lr, tol, n_iter, device): def _fit(self, dataset): masker_voxels = self.inputs_['mask_img']._dataobj Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) + P = Coef_spline_bases.shape[1] self.inputs_['Coef_spline_bases'] = Coef_spline_bases cbmr_model = self._model_structure(self.model, self.penalty, self.device) optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) + spatial_regression_coef, spatial_intensity_values = dict(), dict() # beta: regression coef of spatial effect for group in self.inputs_['all_group_study_id'].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().T - - studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) + group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) + spatial_regression_coef[group] = group_beta_linear_weight + studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) + # studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) + spatial_intensity_values[group] = studywise_spatial_intensity + spatial_regression_coef = pd.DataFrame.from_dict(spatial_regression_coef, orient='columns') + # study-level moderators + moderators_effect_values = dict() if hasattr(self, "moderators"): self._gamma = cbmr_model.gamma_linear.weight - self._gamma = self._gamma.cpu().detach().numpy().T + self._gamma = self._gamma.cpu().detach().numpy().flatten() + # moderators_regression_coef['all_groups'] = self._gamma for group in self.inputs_['all_group_study_id'].keys(): group_moderators = self.inputs_["all_group_moderators"][group] - moderator_effect = np.exp(np.matmul(group_moderators, self._gamma)) + moderators_effect = np.exp(np.matmul(group_moderators, self._gamma)) + moderators_effect_values[group] = moderators_effect + moderators_regression_coef = pd.DataFrame(self._gamma) + + maps = { + "group-specific_StudywiseIntensity": spatial_intensity_values, + 'group-specific_moderators_effect': moderators_effect_values, + } + tables = { + 'spatial_regression_coef': spatial_regression_coef, + 'moderators_regression_coef': moderators_regression_coef, + } - return + + return maps, tables diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 926adec2c..df9b770d9 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -4,7 +4,7 @@ def test_CBMREstimator(testdata_cbmr_full, caplog): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(group_names=['diagnosis'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e-2) + cbmr = CBMREstimator(group_names=['diagnosis'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e8) prep = cbmr._preprocess_input(testdata_cbmr_full) cbmr.fit(dataset=testdata_cbmr_full) print('1234') diff --git a/nimare/utils.py b/nimare/utils.py index 3eb5fd04b..6d999a5f7 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1272,8 +1272,7 @@ def vox2idx(ijk, masker_voxels): x_dim, y_dim, z_dim = xx.shape[0], yy.shape[0], zz.shape[0] brain_voxels_index = [(z - np.min(zz))+ z_dim * (y - np.min(yy))+ y_dim * z_dim * (x - np.min(xx)) for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] - foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci) - if masker_voxels[ijk[i, 0], ijk[i, 1], ijk[i, 2]]==1] + foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci)] foci_brain_index = [brain_voxels_index.index(j) for j in foci_index] foci_brain_index = np.array(foci_brain_index) diff --git a/setup.cfg b/setup.cfg index 59c90c5b5..fef9d24cc 100644 --- a/setup.cfg +++ b/setup.cfg @@ -48,16 +48,8 @@ install_requires = nilearn>=0.7.1 numba # used by sparse numpy -<<<<<<< HEAD -<<<<<<< HEAD - pandas - patsy -======= - pandas>=1.1.0 ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 -======= pandas>=1.1.0 ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 + patsy pymare~=0.0.4rc2 # nimare.meta.ibma and stats requests # nimare.extract scikit-learn # nimare.annotate and nimare.decode diff --git a/setup_BACKUP_7408.cfg b/setup_BACKUP_7408.cfg deleted file mode 100644 index 1933f95bf..000000000 --- a/setup_BACKUP_7408.cfg +++ /dev/null @@ -1,129 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy -<<<<<<< HEAD - pandas - patsy -======= - pandas>=1.1.0 ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_BASE_7408.cfg b/setup_BASE_7408.cfg deleted file mode 100644 index 6a4932af7..000000000 --- a/setup_BASE_7408.cfg +++ /dev/null @@ -1,134 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -peaks2maps-cpu = - tensorflow>=2.0.0 - appdirs -peaks2maps-gpu = - tensorflow-gpu>=2.0.0 - appdirs -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -duecredit = - duecredit -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(duecredit)s - %(peaks2maps-cpu)s - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py,due.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_LOCAL_7408.cfg b/setup_LOCAL_7408.cfg deleted file mode 100644 index 7da488b1c..000000000 --- a/setup_LOCAL_7408.cfg +++ /dev/null @@ -1,135 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas - patsy - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -peaks2maps-cpu = - tensorflow>=2.0.0 - appdirs -peaks2maps-gpu = - tensorflow-gpu>=2.0.0 - appdirs -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -duecredit = - duecredit -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(duecredit)s - %(peaks2maps-cpu)s - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py,due.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_REMOTE_7408.cfg b/setup_REMOTE_7408.cfg deleted file mode 100644 index 59d103597..000000000 --- a/setup_REMOTE_7408.cfg +++ /dev/null @@ -1,124 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas>=1.1.0 - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy From 48d4b576f93a808987f3b92a9c9a400e59344e44 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 6 Aug 2022 17:56:22 +0100 Subject: [PATCH 018/177] [skip ci][wip] modify standardization of group moderators --- nimare/meta/cbmr.py | 64 ++++++++-------------------------- nimare/tests/conftest.py | 4 +-- nimare/tests/test_meta_cbmr.py | 10 +++--- nimare/utils.py | 31 ++++++++-------- 4 files changed, 36 insertions(+), 73 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a5387ae58..a5fc86126 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox, vox2idx, intensity2voxel +from nimare.utils import mm2vox, vox2idx from nimare.diagnostics import FocusFilter import torch import logging @@ -16,8 +16,8 @@ class CBMREstimator(Estimator): _required_inputs = {"coordinates": ("coordinates", None)} - def __init__(self, group_names=None, moderators=None, moderators_center=True, moderators_scale=True, mask=None, - spline_spacing=5, model='Poisson', penalty=False, n_iter=1000, lr=1e-2, tol=1e-2, device='cpu', **kwargs): + def __init__(self, group_names=None, moderators=None, mask=None, spline_spacing=5, model='Poisson', penalty=False, + n_iter=1000, lr=1e-2, tol=1e-2, device='cpu', **kwargs): super().__init__(**kwargs) if mask is not None: mask = get_masker(mask) @@ -25,8 +25,6 @@ def __init__(self, group_names=None, moderators=None, moderators_center=True, mo self.group_names = group_names self.moderators = moderators - self.moderators_center = moderators_center # either boolean or a list of strings - self.moderators_scale = moderators_scale self.spline_spacing = spline_spacing self.model = model @@ -45,8 +43,6 @@ def _preprocess_input(self, dataset): mask_img = masker.mask_img or masker.labels_img if isinstance(mask_img, str): mask_img = nib.load(mask_img) - - ma_values = self._collect_inputs(dataset, drop_invalid=True) self.inputs_['mask_img'] = mask_img for name, (type_, _) in self._required_inputs.items(): @@ -54,41 +50,36 @@ def _preprocess_input(self, dataset): # remove dataset coordinates outside of mask focus_filter = FocusFilter(mask=masker) dataset = focus_filter.transform(dataset) - # remove study_id without any coordinates - study_id_annotations = dataset.annotations.set_index('study_id').index - study_id_coordinates = dataset.coordinates.set_index('study_id').index - valid_study_bool = study_id_annotations.isin(study_id_coordinates) - dataset_annotations = dataset.annotations[valid_study_bool] + valid_dset_annotations = dataset.annotations[dataset.annotations['id'].isin(self.inputs_['id'])] all_group_study_id = dict() if isinstance(self.group_names, type(None)): - all_group_study_id[self.group_names] = dataset_annotations['study_id'].unique().tolist() + all_group_study_id[self.group_names] = valid_dset_annotations['study_id'].unique().tolist() elif isinstance(self.group_names, str): - if self.group_names not in dataset_annotations.columns: + if self.group_names not in valid_dset_annotations.columns: raise ValueError("group_names: {} does not exist in the dataset".format(self.group_names)) else: - uniq_groups = list(dataset_annotations[self.group_names].unique()) + uniq_groups = list(valid_dset_annotations[self.group_names].unique()) for group in uniq_groups: - group_study_id_bool = dataset_annotations[self.group_names] == group - group_study_id = dataset_annotations.loc[group_study_id_bool]['study_id'] + group_study_id_bool = valid_dset_annotations[self.group_names] == group + group_study_id = valid_dset_annotations.loc[group_study_id_bool]['study_id'] all_group_study_id[group] = group_study_id.unique().tolist() elif isinstance(self.group_names, list): - not_exist_group_names = [group for group in self.group_names if group not in dataset_annotations.columns] + not_exist_group_names = [group for group in self.group_names if group not in dataset.annotations.columns] if len(not_exist_group_names) > 0: raise ValueError("group_names: {} does not exist in the dataset".format(not_exist_group_names)) - uniq_group_splits = dataset_annotations[self.group_names].drop_duplicates().values.tolist() + uniq_group_splits = valid_dset_annotations[self.group_names].drop_duplicates().values.tolist() for group in uniq_group_splits: - group_study_id_bool = (dataset_annotations[self.group_names] == group).all(axis=1) - group_study_id = dataset_annotations.loc[group_study_id_bool]['study_id'] + group_study_id_bool = (valid_dset_annotations[self.group_names] == group).all(axis=1) + group_study_id = valid_dset_annotations.loc[group_study_id_bool]['study_id'] all_group_study_id['_'.join(group)] = group_study_id.unique().tolist() self.inputs_['all_group_study_id'] = all_group_study_id # collect studywise moderators if specficed if hasattr(self, "moderators"): all_group_moderators = dict() for group in all_group_study_id.keys(): - df_group = dataset_annotations.loc[dataset_annotations['study_id'].isin(all_group_study_id[group])] + df_group = valid_dset_annotations.loc[valid_dset_annotations['study_id'].isin(all_group_study_id[group])] group_moderators = np.stack([df_group[moderator_name] for moderator_name in self.moderators], axis=1) group_moderators = group_moderators.astype(np.float64) - group_moderators = self._standardize_moderators(group_moderators) all_group_moderators[group] = group_moderators self.inputs_["all_group_moderators"] = all_group_moderators # Calculate IJK matrix indices for target mask @@ -116,33 +107,6 @@ def _preprocess_input(self, dataset): self.inputs_['all_foci_per_voxel'] = all_foci_per_voxel self.inputs_['all_foci_per_study'] = all_foci_per_study - def _standardize_moderators(self, moderators_array): - # standardize mean - if isinstance(self.moderators_center, bool): - if self.moderators_center: - moderators_array -= np.mean(moderators_array, axis=0) - elif isinstance(self.moderators_center, str): - index_moderators_center = self.moderators.index(self.moderators_center) - moderators_array[:,index_moderators_center] -= np.mean(moderators_array[:, index_moderators_center], axis=0) - elif isinstance(self.moderators_center, list): - index_moderators_center = [self.moderators.index(moderator_name) for moderator_name in self.moderators_center] - for i in index_moderators_center: - moderators_array[:,i] -= np.mean(moderators_array[:, i], axis=0) - - # standardize var - if isinstance(self.moderators_scale, bool): - if self.moderators_scale: - moderators_array /= np.std(moderators_array, axis=0) - elif isinstance(self.moderators_scale, str): - index_moderators_scale = self.moderators.index(self.moderators_scale) - moderators_array[:,index_moderators_scale] /= np.std(moderators_array[:, index_moderators_scale], axis=0) - elif isinstance(self.moderators_scale, list): - index_moderators_scale = [self.moderators.index(moderator_name) for moderator_name in self.moderators_scale] - for i in index_moderators_scale: - moderators_array[:,i] /= np.std(moderators_array[:, i], axis=0) - - return moderators_array - def _model_structure(self, model, penalty, device): beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect if hasattr(self, "moderators"): diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 31dcff69d..31d9eeaaa 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -98,8 +98,8 @@ def testdata_cbmr_full(): # set up group columns & moderators n_rows = dset.annotations.shape[0] dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] - # dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] - # dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] + dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index df9b770d9..51353f834 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,12 +1,14 @@ from nimare.meta.cbmr import CBMREstimator +from nimare.utils import standardize_field import logging -def test_CBMREstimator(testdata_cbmr_full, caplog): +def test_CBMREstimator(testdata_cbmr_full): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - cbmr = CBMREstimator(group_names=['diagnosis'], moderators=['sample_sizes', 'avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e8) - prep = cbmr._preprocess_input(testdata_cbmr_full) - cbmr.fit(dataset=testdata_cbmr_full) + dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e8) + # prep = cbmr._preprocess_input(dset) + cbmr.fit(dataset=dset) print('1234') # with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): # meta.fit(testdata_cbma) diff --git a/nimare/utils.py b/nimare/utils.py index 6d999a5f7..4e6d56289 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1278,20 +1278,17 @@ def vox2idx(ijk, masker_voxels): return foci_brain_index -def intensity2voxel(intensity, masker_voxels): - masker_dim = masker_voxels.shape - xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] - yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] - zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] - - # correspondence between xyz coordinates and spatial intensity - brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) - brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) - - intensity_array = np.zeros(masker_dim) - for i in range(brain_voxel_intensity.shape[0]): - coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] - coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) - intensity_array[coord_x, coord_y, coord_z] = coord_intensity - - return intensity_array \ No newline at end of file +def standardize_field(dataset, metadata): + # if isinstance(metadata, str): + # moderators = dataset.annotations[metadata] + # elif isinstance(metadata, list): + moderators = dataset.annotations[metadata] + standardize_moderators = moderators - np.mean(moderators, axis=0) + standardize_moderators /= np.std(standardize_moderators, axis=0) + if isinstance(metadata, str): + column_name = 'standardized_' + metadata + elif isinstance(metadata, list): + column_name = ['standardized_' + moderator for moderator in metadata] + dataset.annotations[column_name] = standardize_moderators + + return dataset From 5e6107f2bec33456994446bdff7c943cba33574c Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sun, 7 Aug 2022 14:05:26 +0100 Subject: [PATCH 019/177] [skip ci][wip] implement NB regression --- nimare/meta/cbmr.py | 113 ++++++++++++++++++++++++++------- nimare/tests/test_meta_cbmr.py | 7 +- 2 files changed, 92 insertions(+), 28 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a5fc86126..32afaa16a 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -118,6 +118,8 @@ def _model_structure(self, model, penalty, device): self.groups = list(self.inputs_['all_group_study_id'].keys()) if model == 'Poisson': cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) + elif model == 'NB': + cbmr_model = GLMNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) if 'cuda' in device: cbmr_model = cbmr_model.cuda() @@ -176,40 +178,34 @@ def _fit(self, dataset): cbmr_model = self._model_structure(self.model, self.penalty, self.device) optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) - - spatial_regression_coef, spatial_intensity_values = dict(), dict() + + maps, tables = dict(), dict() + spatial_regression_coef, overdispersion_param = dict(), dict() # beta: regression coef of spatial effect for group in self.inputs_['all_group_study_id'].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) spatial_regression_coef[group] = group_beta_linear_weight - studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - # studywise_spatial_intensity = intensity2voxel(studywise_spatial_intensity, self.inputs_['mask_img']._dataobj) - spatial_intensity_values[group] = studywise_spatial_intensity - spatial_regression_coef = pd.DataFrame.from_dict(spatial_regression_coef, orient='columns') + maps[group+'_group_StudywiseIntensity'] = studywise_spatial_intensity + # overdispersion parameter: alpha + if self.model == 'NB': + alpha = cbmr_model.all_alpha_sqrt[group]**2 + alpha = alpha.cpu().detach().numpy() + overdispersion_param[group] = alpha + tables['spatial_regression_coef'] = pd.DataFrame.from_dict(spatial_regression_coef, orient='index') + # study-level moderators - moderators_effect_values = dict() if hasattr(self, "moderators"): self._gamma = cbmr_model.gamma_linear.weight - self._gamma = self._gamma.cpu().detach().numpy().flatten() - # moderators_regression_coef['all_groups'] = self._gamma + self._gamma = self._gamma.cpu().detach().numpy() for group in self.inputs_['all_group_study_id'].keys(): group_moderators = self.inputs_["all_group_moderators"][group] - moderators_effect = np.exp(np.matmul(group_moderators, self._gamma)) - moderators_effect_values[group] = moderators_effect - moderators_regression_coef = pd.DataFrame(self._gamma) - - maps = { - "group-specific_StudywiseIntensity": spatial_intensity_values, - 'group-specific_moderators_effect': moderators_effect_values, - } - - tables = { - 'spatial_regression_coef': spatial_regression_coef, - 'moderators_regression_coef': moderators_regression_coef, - } - + moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) + maps[group+'_group_ModeratorsEffect'] = moderators_effect.flatten() + tables['moderators_regression_coef'] = pd.DataFrame(self._gamma, columns=self.moderators) + if self.model == 'NB': + tables['over_dispersion_param'] = pd.DataFrame.from_dict(overdispersion_param, orient='index') return maps, tables @@ -255,3 +251,74 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc log_l += group_log_l return -log_l + +class GLMNB(torch.nn.Module): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): + super().__init__() + self.groups = groups + self.study_level_moderators = study_level_moderators + # initialization for beta + all_beta_linears, all_alpha_sqrt = dict(), dict() + for group in groups: + beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + # initialization for alpha + alpha_init_group = torch.tensor(1e-2).double() + all_alpha_sqrt[group] = torch.nn.Parameter(torch.sqrt(alpha_init_group), requires_grad=True) + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + self.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + # gamma + if self.study_level_moderators: + self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def _three_term(y, r): + max_foci = np.int(torch.max(y).item()) + sum_three_term = 0 + for k in range(max_foci): + foci_index = (y == k+1).nonzero()[:,0] + r_j = r[foci_index] + n_voxel = list(foci_index.shape)[0] + y_j = torch.tensor([k+1]*n_voxel).double() + y_j = y_j.reshape((n_voxel, 1)) + # y=0 => sum_three_term = 0 + sum_three_term += torch.sum(torch.lgamma(y_j+r_j) - torch.lgamma(y_j+1) - torch.lgamma(r_j)) + + return sum_three_term + + + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 + # spatial effect: mu^X = exp(X * beta) + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group]**2 + v = 1 / alpha + log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), + # Therefore, we use moment matching approach, + # create a new NB approximation to the mixture of NB distributions: + # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha + numerator = mu_spatial**2 * torch.sum(mu_moderators**2) + denominator = mu_spatial**2 * torch.sum(mu_moderators)**2 + estimated_sum_alpha = alpha * numerator / denominator + ## moment matching NB distribution + p = numerator / (v*mu_spatial*torch.sum(mu_moderators) + numerator) + r = v * denominator / numerator + + group_foci_per_voxel = all_foci_per_voxel[group] + # group_foci_per_study = all_foci_per_study[group] + group_log_l = GLMNB._three_term(group_foci_per_voxel,r) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + log_l += group_log_l + + return -log_l \ No newline at end of file diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 51353f834..3927d067d 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -6,10 +6,7 @@ def test_CBMREstimator(testdata_cbmr_full): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='Poisson', penalty=False, lr=0.1, tol=1e8) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='NB', penalty=False, lr=0.1, tol=1) # prep = cbmr._preprocess_input(dset) cbmr.fit(dataset=dset) - print('1234') - # with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - # meta.fit(testdata_cbma) - # assert "Loading pre-generated MA maps" not in caplog.text + print('123') \ No newline at end of file From c6865587af94f6eb78c7a4b0748634f0be89438b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 25 Aug 2022 18:41:15 +0100 Subject: [PATCH 020/177] [skip ci][wip]remove vox2idx function and simplify the code --- nimare/meta/cbmr.py | 12 +++++++----- nimare/tests/test_meta_cbmr.py | 2 +- nimare/utils.py | 29 ----------------------------- 3 files changed, 8 insertions(+), 35 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 32afaa16a..f23ec2458 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox, vox2idx +from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter import torch import logging @@ -92,12 +92,14 @@ def _preprocess_input(self, dataset): # group-wise foci coordinates group_xyz = group_coordinates[['x', 'y', 'z']].values group_ijk = mm2vox(group_xyz, mask_img.affine) - group_foci_idx = vox2idx(group_ijk, mask_img._dataobj) + group_foci_per_voxel = np.zeros(mask_img.shape, dtype=int) + for ijk in group_ijk: + group_foci_per_voxel[ijk[0], ijk[1], ijk[2]] += 1 + # will not work with maskers that aren't NiftiMaskers + group_foci_per_voxel = nib.Nifti1Image(group_foci_per_voxel, mask_img.affine, mask_img.header) + group_foci_per_voxel = masker.transform(group_foci_per_voxel).transpose() # number of foci per voxel/study n_group_study = len(group_study_id) - masker_voxels = np.sum(mask_img._dataobj).astype(int) - group_foci_per_voxel = np.zeros((masker_voxels, 1)) - group_foci_per_voxel[group_foci_idx, :] += 1 group_foci_per_study = np.array([(group_coordinates['study_id']==i).sum() for i in group_study_id]) group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 3927d067d..99741b576 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -9,4 +9,4 @@ def test_CBMREstimator(testdata_cbmr_full): cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='NB', penalty=False, lr=0.1, tol=1) # prep = cbmr._preprocess_input(dset) cbmr.fit(dataset=dset) - print('123') \ No newline at end of file + \ No newline at end of file diff --git a/nimare/utils.py b/nimare/utils.py index 4e6d56289..ce6124dd8 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1249,35 +1249,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): return X -def vox2idx(ijk, masker_voxels): - """ - Convert coordinates in voxel space to integer index (between 0 and n-voxel) - - Parameters - ---------- - ijk: (x,y,z) coordinates in voxel space - masker_voxels : matrix with element either 0 or 1, indicating if it's within brain mask, - spacing: (equally spaced) knots spacing in x/y/z direction - Returns - ------- - foci_index : 1-D ndarray (n_voxel, ) - """ - dim_mask = masker_voxels.shape - n_brain_voxel = np.sum(masker_voxels).astype(int) - n_foci = ijk.shape[0] - - xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] - yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] - zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] - x_dim, y_dim, z_dim = xx.shape[0], yy.shape[0], zz.shape[0] - brain_voxels_index = [(z - np.min(zz))+ z_dim * (y - np.min(yy))+ y_dim * z_dim * (x - np.min(xx)) - for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] - foci_index = [ijk[i, 2] - np.min(zz)+ z_dim * (ijk[i, 1] - np.min(yy))+ y_dim * z_dim * (ijk[i, 0] - np.min(xx)) for i in range(n_foci)] - foci_brain_index = [brain_voxels_index.index(j) for j in foci_index] - foci_brain_index = np.array(foci_brain_index) - - return foci_brain_index - def standardize_field(dataset, metadata): # if isinstance(metadata, str): # moderators = dataset.annotations[metadata] From 171d5a65c846c39f32f8968bf424985919f944c7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 26 Aug 2022 22:23:45 +0100 Subject: [PATCH 021/177] [skip ci][wip]develp CNB model --- nimare/meta/cbmr.py | 67 ++++++++++++++++++++++++++++++++++ nimare/tests/test_meta_cbmr.py | 2 +- 2 files changed, 68 insertions(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f23ec2458..e9d27a375 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -11,6 +11,7 @@ from nimare.diagnostics import FocusFilter import torch import logging +import copy LGR = logging.getLogger(__name__) class CBMREstimator(Estimator): @@ -122,6 +123,8 @@ def _model_structure(self, model, penalty, device): cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) elif model == 'NB': cbmr_model = GLMNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) + elif model == 'clustered_NB': + cbmr_model = GLMCNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) if 'cuda' in device: cbmr_model = cbmr_model.cuda() @@ -139,7 +142,23 @@ def closure(): loss.backward() return loss loss = optimizer.step(closure) + # reset the L-BFGS params if NaN appears in coefficient of regression + if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): + all_beta_linears, all_alpha_sqrt = dict(), dict() + for group in self.inputs_['all_group_study_id'].keys(): + beta_dim = model.all_beta_linears[group].weight.shape[1] + beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() + beta_linear_group.weight = torch.nn.Parameter(self.last_state['all_beta_linears.'+group+'.weight']) + group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) + all_beta_linears[group] = beta_linear_group + all_alpha_sqrt[group] = group_alpha_sqrt + model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + LGR.debug(f"Reset L-BFGS optimizer......") + else: + self.last_state = copy.deepcopy(model.state_dict()) # need to change the variable name? + return loss def _optimizer(self, model, lr, tol, n_iter, device): @@ -323,4 +342,52 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_log_l = GLMNB._three_term(group_foci_per_voxel,r) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) log_l += group_log_l + return -log_l + +class GLMCNB(torch.nn.Module): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): + super().__init__() + self.groups = groups + self.study_level_moderators = study_level_moderators + # initialization for beta + all_beta_linears, all_alpha = dict(), dict() + for group in groups: + beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + # initialization for alpha + alpha_init_group = torch.tensor(1e-2).double() + all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + self.all_alpha = torch.nn.ParameterDict(all_alpha) + # gamma + if self.study_level_moderators: + self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha[group] + v = 1 / alpha + log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + + group_foci_per_voxel = all_foci_per_voxel[group] + group_foci_per_study = all_foci_per_study[group] + group_n_study, group_n_voxel = mu_moderators.shape[0], mu_spatial.shape[0] + + group_log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_moderators + v)) \ + + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) + log_l += group_log_l + return -log_l \ No newline at end of file diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 99741b576..fe587b07e 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -6,7 +6,7 @@ def test_CBMREstimator(testdata_cbmr_full): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='NB', penalty=False, lr=0.1, tol=1) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='clustered_NB', penalty=False, lr=0.1, tol=1) # prep = cbmr._preprocess_input(dset) cbmr.fit(dataset=dset) \ No newline at end of file From b786a3d8171d2d20ea38047499c688802e7aa211 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 19 Sep 2022 20:26:23 +0100 Subject: [PATCH 022/177] adjustment to Firth penalty --- nimare/meta/cbmr.py | 162 +++++++++++++++++++++++++++++---- nimare/tests/test_meta_cbmr.py | 3 +- 2 files changed, 144 insertions(+), 21 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e9d27a375..f36ec0541 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -12,6 +12,7 @@ import torch import logging import copy +from functorch import hessian LGR = logging.getLogger(__name__) class CBMREstimator(Estimator): @@ -34,6 +35,9 @@ def __init__(self, group_names=None, moderators=None, mask=None, spline_spacing= self.lr = lr self.tol = tol self.device = device + if self.device == 'cuda' and not torch.cuda.is_available(): + LGR.debug(f"cuda not found, use device 'cpu'") + self.device = 'cpu' # Initialize optimisation parameters self.iter = 0 @@ -120,21 +124,19 @@ def _model_structure(self, model, penalty, device): study_level_moderators = False self.groups = list(self.inputs_['all_group_study_id'].keys()) if model == 'Poisson': - cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) + cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) elif model == 'NB': - cbmr_model = GLMNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) + cbmr_model = GLMNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) elif model == 'clustered_NB': - cbmr_model = GLMCNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty) + cbmr_model = GLMCNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) if 'cuda' in device: cbmr_model = cbmr_model.cuda() return cbmr_model - def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay - scheduler.step() - + def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): self.iter += 1 + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay scheduler.step() def closure(): optimizer.zero_grad() @@ -144,17 +146,26 @@ def closure(): loss = optimizer.step(closure) # reset the L-BFGS params if NaN appears in coefficient of regression if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): - all_beta_linears, all_alpha_sqrt = dict(), dict() + all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() for group in self.inputs_['all_group_study_id'].keys(): beta_dim = model.all_beta_linears[group].weight.shape[1] beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() beta_linear_group.weight = torch.nn.Parameter(self.last_state['all_beta_linears.'+group+'.weight']) - group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) - all_beta_linears[group] = beta_linear_group - all_alpha_sqrt[group] = group_alpha_sqrt + + if self.model == 'NB': + group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) + all_alpha_sqrt[group] = group_alpha_sqrt + elif self.model == 'clustered_NB': + group_alpha = torch.nn.Parameter(self.last_state['all_alpha.'+group]) + all_alpha[group] = group_alpha + model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + if self.model == 'NB': + model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + elif self.model == 'clustered_NB': + model.all_alpha = torch.nn.ParameterDict(all_alpha) + LGR.debug(f"Reset L-BFGS optimizer......") else: self.last_state = copy.deepcopy(model.state_dict()) # need to change the variable name? @@ -181,6 +192,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): if self.iter == 0: prev_loss = torch.tensor(float('inf')) # initialization loss difference + for i in range(n_iter): loss = self._update(model, optimizer, Coef_spline_bases, all_group_moderators_tensor, all_foci_per_voxel_tensor, all_foci_per_study_tensor, prev_loss) loss_diff = loss - prev_loss @@ -233,10 +245,14 @@ def _fit(self, dataset): class GLMPoisson(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty=False, device='cpu'): super().__init__() + self.beta_dim = beta_dim + self.gamma_dim = gamma_dim self.groups = groups self.study_level_moderators = study_level_moderators + self.penalty = penalty + self.device = device # initialization for beta all_beta_linears = dict() for group in groups: @@ -249,6 +265,16 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + def _log_likelihood(self, beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + log_mu_spatial = torch.matmul(Coef_spline_bases, beta) + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = torch.matmul(moderators, gamma) + mu_moderators = torch.exp(log_mu_moderators) + log_l = torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) \ + - torch.sum(mu_spatial) * torch.sum(mu_moderators) + + return log_l + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -271,13 +297,33 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_log_l = torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) log_l += group_log_l + if self.penalty == True: + # Firth-type penalty + for group in all_foci_per_voxel.keys(): + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + gamma = self.gamma_linear.weight.T + group_foci_per_voxel = all_foci_per_voxel[group] + group_foci_per_study = all_foci_per_study[group] + group_moderators = all_moderators[group] + nll = lambda beta: -self._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + params = (beta) + F = torch.autograd.functional.hessian(nll, params, create_graph=True) # vectorize=True, outer_jacobian_strategy='forward-mode' + F = F.reshape((beta_dim, beta_dim)) + eig_vals = torch.real(torch.linalg.eigvals(F)) #torch.eig(F, eigenvectors=False)[0][:,0] + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty return -log_l class GLMNB(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No', device='cpu'): super().__init__() self.groups = groups self.study_level_moderators = study_level_moderators + self.penalty = penalty + self.device = device # initialization for beta all_beta_linears, all_alpha_sqrt = dict(), dict() for group in groups: @@ -294,20 +340,36 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def _three_term(y, r): - max_foci = np.int(torch.max(y).item()) + def _three_term(y, r, device): + max_foci = torch.max(y).to(dtype=torch.int64, device=device) sum_three_term = 0 for k in range(max_foci): foci_index = (y == k+1).nonzero()[:,0] r_j = r[foci_index] n_voxel = list(foci_index.shape)[0] - y_j = torch.tensor([k+1]*n_voxel).double() + y_j = torch.tensor([k+1]*n_voxel, device=device).double() y_j = y_j.reshape((n_voxel, 1)) # y=0 => sum_three_term = 0 sum_three_term += torch.sum(torch.lgamma(y_j+r_j) - torch.lgamma(y_j+1) - torch.lgamma(r_j)) return sum_three_term + def _log_likelihood(self, alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study): + v = 1 / alpha + log_mu_spatial = Coef_spline_bases @ beta + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = group_moderators @ gamma + mu_moderators = torch.exp(log_mu_moderators) + numerator = mu_spatial**2 * torch.sum(mu_moderators**2) + denominator = mu_spatial**2 * torch.sum(mu_moderators)**2 + estimated_sum_alpha = alpha * numerator / denominator + + p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) + r = v * denominator / numerator + + log_l = GLMNB._three_term(group_foci_per_voxel,r, device=self.device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + + return log_l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): @@ -339,16 +401,38 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_foci_per_voxel = all_foci_per_voxel[group] # group_foci_per_study = all_foci_per_study[group] - group_log_l = GLMNB._three_term(group_foci_per_voxel,r) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + group_log_l = GLMNB._three_term(group_foci_per_voxel,r, device=self.device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) log_l += group_log_l + if self.penalty == True: + # Firth-type penalty + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group]**2 + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + gamma = self.gamma_linear.weight.detach().T + group_foci_per_voxel = all_foci_per_voxel[group] + group_foci_per_study = all_foci_per_study[group] + group_moderators = all_moderators[group] + # a = -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + nll = lambda beta: -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + params = (beta) + F = torch.autograd.functional.hessian(nll, params, create_graph=True) + F = F.reshape((beta_dim, beta_dim)) + eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty + return -log_l class GLMCNB(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No'): + def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty=True, device='cpu'): super().__init__() self.groups = groups self.study_level_moderators = study_level_moderators + self.penalty = penalty # initialization for beta all_beta_linears, all_alpha = dict(), dict() for group in groups: @@ -365,6 +449,22 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + def _log_likelihood(self, alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study): + v = 1 / alpha + log_mu_spatial = Coef_spline_bases @ beta + mu_spatial = torch.exp(log_mu_spatial) + log_mu_moderators = group_moderators @ gamma + mu_moderators = torch.exp(log_mu_moderators) + mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators + + group_n_study, group_n_voxel = mu_moderators.shape[0], mu_spatial.shape[0] + + log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ + + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) + + return log_l + + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -386,8 +486,30 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_foci_per_study = all_foci_per_study[group] group_n_study, group_n_voxel = mu_moderators.shape[0], mu_spatial.shape[0] - group_log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_moderators + v)) \ + mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators + group_log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) log_l += group_log_l + + if self.penalty == True: + # Firth-type penalty + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha[group] + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + gamma = self.gamma_linear.weight.T + group_foci_per_voxel = all_foci_per_voxel[group] + group_foci_per_study = all_foci_per_study[group] + group_moderators = all_moderators[group] + nll = lambda beta: -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + params = (beta) + F = torch.autograd.functional.hessian(nll, params, create_graph=True) # vectorize=True, outer_jacobian_strategy='forward-mode' + # F = hessian(nll)(beta) + F = F.reshape((beta_dim, beta_dim)) + eig_vals = torch.real(torch.linalg.eigvals(F)) + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty return -log_l \ No newline at end of file diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index fe587b07e..32e87a9e3 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -6,7 +6,8 @@ def test_CBMREstimator(testdata_cbmr_full): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], model='clustered_NB', penalty=False, lr=0.1, tol=1) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=15, model='clustered_NB', penalty=True, lr=1e-2, tol=1e-2, device='cuda') # prep = cbmr._preprocess_input(dset) cbmr.fit(dataset=dset) + \ No newline at end of file From e0510154ceff4f7ff10bd512f2bda12b5f5aaf71 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 24 Sep 2022 16:27:42 +0100 Subject: [PATCH 023/177] [skip CI][wip] implement index2voxel function --- nimare/meta/cbmr.py | 5 +++-- nimare/tests/test_meta_cbmr.py | 3 ++- nimare/transforms.py | 2 +- nimare/utils.py | 18 +++++++++++++++--- 4 files changed, 21 insertions(+), 7 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f36ec0541..8a1029740 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox +from nimare.utils import mm2vox, index2vox from nimare.diagnostics import FocusFilter import torch import logging @@ -220,6 +220,7 @@ def _fit(self, dataset): group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) spatial_regression_coef[group] = group_beta_linear_weight studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) + studywise_spatial_intensity = index2vox(studywise_spatial_intensity, masker_voxels) maps[group+'_group_StudywiseIntensity'] = studywise_spatial_intensity # overdispersion parameter: alpha if self.model == 'NB': @@ -308,7 +309,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_moderators = all_moderators[group] nll = lambda beta: -self._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) params = (beta) - F = torch.autograd.functional.hessian(nll, params, create_graph=True) # vectorize=True, outer_jacobian_strategy='forward-mode' + F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') F = F.reshape((beta_dim, beta_dim)) eig_vals = torch.real(torch.linalg.eigvals(F)) #torch.eig(F, eigenvectors=False)[0][:,0] del F diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 32e87a9e3..6eaa03780 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -6,8 +6,9 @@ def test_CBMREstimator(testdata_cbmr_full): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=15, model='clustered_NB', penalty=True, lr=1e-2, tol=1e-2, device='cuda') + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') # prep = cbmr._preprocess_input(dset) cbmr.fit(dataset=dset) + \ No newline at end of file diff --git a/nimare/transforms.py b/nimare/transforms.py index 793a7ffa4..3aa6a279e 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -651,7 +651,7 @@ def z_to_p(z, tail="two"): if tail == "two": p = stats.norm.sf(abs(z)) * 2 elif tail == "one": - p = stats.norm.sf(abs(z)) + p = stats.norm.sf(z) else: raise ValueError('Argument "tail" must be one of ["one", "two"]') diff --git a/nimare/utils.py b/nimare/utils.py index ce6124dd8..416782c27 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1250,9 +1250,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): return X def standardize_field(dataset, metadata): - # if isinstance(metadata, str): - # moderators = dataset.annotations[metadata] - # elif isinstance(metadata, list): moderators = dataset.annotations[metadata] standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) @@ -1263,3 +1260,18 @@ def standardize_field(dataset, metadata): dataset.annotations[column_name] = standardize_moderators return dataset + + +def index2vox(vals, masker_voxels): + print('23') + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] + spline_voxel_index = np.arange(np.prod(image_dim)) + for i in spline_voxel_index: + print('13') + + + + return From c38aa31344bf44843a7a7e048c66f69022292c10 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 24 Sep 2022 23:03:56 +0100 Subject: [PATCH 024/177] [skip CI][wip] add implementation for SE of regression coefficient --- nimare/meta/cbmr.py | 40 ++++++++++++++++++++++++++++------ nimare/tests/conftest.py | 2 +- nimare/tests/test_meta_cbmr.py | 6 +++-- nimare/utils.py | 18 +++++++-------- 4 files changed, 47 insertions(+), 19 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 8a1029740..bc61487fa 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox, index2vox +from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter import torch import logging @@ -220,8 +220,8 @@ def _fit(self, dataset): group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) spatial_regression_coef[group] = group_beta_linear_weight studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - studywise_spatial_intensity = index2vox(studywise_spatial_intensity, masker_voxels) - maps[group+'_group_StudywiseIntensity'] = studywise_spatial_intensity + # studywise_spatial_intensity = index2vox(studywise_spatial_intensity, masker_voxels) + maps[group+'_Studywise_Spatial_Intensity'] = studywise_spatial_intensity # overdispersion parameter: alpha if self.model == 'NB': alpha = cbmr_model.all_alpha_sqrt[group]**2 @@ -236,10 +236,36 @@ def _fit(self, dataset): for group in self.inputs_['all_group_study_id'].keys(): group_moderators = self.inputs_["all_group_moderators"][group] moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) - maps[group+'_group_ModeratorsEffect'] = moderators_effect.flatten() + maps[group+'_ModeratorsEffect'] = moderators_effect.flatten() tables['moderators_regression_coef'] = pd.DataFrame(self._gamma, columns=self.moderators) + else: + self._gamma = None if self.model == 'NB': tables['over_dispersion_param'] = pd.DataFrame.from_dict(overdispersion_param, orient='index') + + # standard error + Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + for group in self.inputs_['all_group_study_id'].keys(): + group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) + group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) + group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight + if hasattr(self, "moderators"): + gamma = cbmr_model.gamma_linear.weight + group_moderators = self.inputs_["all_group_moderators"][group] + group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) + else: + group_moderators = None + nll = lambda beta, gamma: -GLMPoisson._log_likelihood(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + params = (group_beta_linear_weight, gamma) + F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + + spatial_dim = group_beta_linear_weight.shape[1] + F_spatial_coef = F[0][0].reshape((spatial_dim, spatial_dim)) + Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) + if hasattr(self, "moderators"): + moderators_dim = gamma.shape[1] + F_moderators_coef = F[1][1].reshape((moderators_dim, moderators_dim)) + Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) return maps, tables @@ -266,10 +292,10 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def _log_likelihood(self, beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - log_mu_spatial = torch.matmul(Coef_spline_bases, beta) + def _log_likelihood(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = torch.matmul(moderators, gamma) + log_mu_moderators = torch.matmul(moderators, gamma.T) mu_moderators = torch.exp(log_mu_moderators) log_l = torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) \ - torch.sum(mu_spatial) * torch.sum(mu_moderators) diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 31d9eeaaa..bb22b4aa6 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -60,7 +60,7 @@ def testdata_cbma(): @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" - dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset_file = os.path.join(get_test_data_path(), "neurosynth.json") dset = nimare.dataset.Dataset(dset_file) # Only retain one peak in each study in coordinates diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 6eaa03780..afad68196 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -7,8 +7,10 @@ def test_CBMREstimator(testdata_cbmr_full): """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') - # prep = cbmr._preprocess_input(dset) - cbmr.fit(dataset=dset) + cbmr_res = cbmr.fit(dataset=dset) + # p_map = cbmr_res.get_map('p') + # p_vals = p_map.dataobj + \ No newline at end of file diff --git a/nimare/utils.py b/nimare/utils.py index 416782c27..d7faafef2 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1262,15 +1262,15 @@ def standardize_field(dataset, metadata): return dataset -def index2vox(vals, masker_voxels): - print('23') - xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] - yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] - zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] - image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] - spline_voxel_index = np.arange(np.prod(image_dim)) - for i in spline_voxel_index: - print('13') +# def index2vox(vals, masker_voxels): +# print('23') +# xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] +# yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] +# zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] +# image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] +# spline_voxel_index = np.arange(np.prod(image_dim)) +# for i in spline_voxel_index: +# print('13') From 27a8c8e0cf37a820ef604b825510c356b18f88c5 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 29 Sep 2022 22:51:27 +0100 Subject: [PATCH 025/177] [skip CI][WIP] implementing CBMRInference --- nimare/meta/cbmr.py | 97 ++++++++++++++++++++++++++-------- nimare/tests/conftest.py | 32 ++++++++--- nimare/tests/test_meta_cbmr.py | 20 +++++-- 3 files changed, 117 insertions(+), 32 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index bc61487fa..66b7212ad 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,3 +1,4 @@ +from importlib.util import set_loader import string from attr import has from numpy import spacing @@ -9,6 +10,8 @@ import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter +from nimare.transforms import z_to_p +from nimare import transforms import torch import logging import copy @@ -58,7 +61,7 @@ def _preprocess_input(self, dataset): valid_dset_annotations = dataset.annotations[dataset.annotations['id'].isin(self.inputs_['id'])] all_group_study_id = dict() if isinstance(self.group_names, type(None)): - all_group_study_id[self.group_names] = valid_dset_annotations['study_id'].unique().tolist() + all_group_study_id[str(self.group_names)] = valid_dset_annotations['study_id'].unique().tolist() elif isinstance(self.group_names, str): if self.group_names not in valid_dset_annotations.columns: raise ValueError("group_names: {} does not exist in the dataset".format(self.group_names)) @@ -213,22 +216,22 @@ def _fit(self, dataset): optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() - spatial_regression_coef, overdispersion_param = dict(), dict() + Spatial_Regression_Coef = dict() # beta: regression coef of spatial effect for group in self.inputs_['all_group_study_id'].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) - spatial_regression_coef[group] = group_beta_linear_weight - studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - # studywise_spatial_intensity = index2vox(studywise_spatial_intensity, masker_voxels) - maps[group+'_Studywise_Spatial_Intensity'] = studywise_spatial_intensity + Spatial_Regression_Coef[group] = group_beta_linear_weight + group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) + maps[group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity + group_foci_per_voxel = self.inputs_['all_foci_per_voxel'][group].reshape((-1)) + maps[group+'_Foci_Per_Voxel'] = group_foci_per_voxel # overdispersion parameter: alpha - if self.model == 'NB': - alpha = cbmr_model.all_alpha_sqrt[group]**2 - alpha = alpha.cpu().detach().numpy() - overdispersion_param[group] = alpha - tables['spatial_regression_coef'] = pd.DataFrame.from_dict(spatial_regression_coef, orient='index') - + # if self.model == 'NB': + # alpha = cbmr_model.all_alpha_sqrt[group]**2 + # alpha = alpha.cpu().detach().numpy() + # overdispersion_param[group] = alpha + tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') # study-level moderators if hasattr(self, "moderators"): self._gamma = cbmr_model.gamma_linear.weight @@ -237,13 +240,14 @@ def _fit(self, dataset): group_moderators = self.inputs_["all_group_moderators"][group] moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) maps[group+'_ModeratorsEffect'] = moderators_effect.flatten() - tables['moderators_regression_coef'] = pd.DataFrame(self._gamma, columns=self.moderators) + tables['Moderators_Regression_Coef'] = pd.DataFrame(self._gamma, columns=self.moderators) else: self._gamma = None - if self.model == 'NB': - tables['over_dispersion_param'] = pd.DataFrame.from_dict(overdispersion_param, orient='index') + # if self.model == 'NB': + # tables['over_dispersion_param'] = pd.DataFrame.from_dict(overdispersion_param, orient='index') # standard error + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) for group in self.inputs_['all_group_study_id'].keys(): group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) @@ -255,21 +259,72 @@ def _fit(self, dataset): group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) else: group_moderators = None - nll = lambda beta, gamma: -GLMPoisson._log_likelihood(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + nll = lambda beta, gamma: -GLMPoisson._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) params = (group_beta_linear_weight, gamma) F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') - + # Inference on regression coefficient of spatial effect spatial_dim = group_beta_linear_weight.shape[1] F_spatial_coef = F[0][0].reshape((spatial_dim, spatial_dim)) Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) - if hasattr(self, "moderators"): - moderators_dim = gamma.shape[1] - F_moderators_coef = F[1][1].reshape((moderators_dim, moderators_dim)) - Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + Var_spatial_coef = np.diag(Cov_spatial_coef) + SE_spatial_coef = np.sqrt(Var_spatial_coef) + spatial_regression_coef_se[group] = SE_spatial_coef + + Var_log_spatial_intensity = np.einsum('ij,ji->i', self.inputs_['Coef_spline_bases'], Cov_spatial_coef @ self.inputs_['Coef_spline_bases'].T) + SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) + log_spatial_intensity_se[group] = SE_log_spatial_intensity + + group_studywise_spatial_intensity = maps[group+'_Studywise_Spatial_Intensity'] + SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity + spatial_intensity_se[group] = SE_spatial_intensity + + tables['Spatial_Regression_Coef_SE'] = pd.DataFrame.from_dict(spatial_regression_coef_se, orient='index') + tables['Log_Spatial_Intensity_SE'] = pd.DataFrame.from_dict(log_spatial_intensity_se, orient='index') + tables['Spatial_Intensity_SE'] = pd.DataFrame.from_dict(spatial_intensity_se, orient='index') + + # Inference on regression coefficient of moderators + if hasattr(self, "moderators"): + gamma = gamma.cpu().detach().numpy() + moderators_dim = gamma.shape[1] + F_moderators_coef = F[1][1].reshape((moderators_dim, moderators_dim)) + Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) + SE_moderators = np.sqrt(Var_moderators) + tables['Moderators_Regression_SE'] = pd.DataFrame(SE_moderators, columns=self.moderators) return maps, tables +class CBMRInference(object): + + def __init__(self, CBMRResults, spatial_homogeneity=True, t_con_group=None, t_con_moderator=None): + self.maps, self.tables = CBMRResults.maps, CBMRResults.tables + self.group_names = self.tables['Spatial_Regression_Coef'].index.values.tolist() + self.n_groups = len(self.group_names) + self.spatial_homogeneity = spatial_homogeneity + self.t_con_group = t_con_group + self.t_con_moderator = t_con_moderator + def _contrast(self): + Spatial_Intensity_SE = self.tables['Spatial_Intensity_SE'] + if self.spatial_homogeneity: # GLH 1 group + for group in self.group_names: + # mu_0 under null hypothesis + group_foci_per_voxel, group_moderators_effect = self.maps[group+'_Foci_Per_Voxel'], self.maps[group+'_ModeratorsEffect'] + n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] + null_spatial_intensity = np.sum(group_foci_per_voxel) / (n_voxels * n_study) + SE_spatial_intensity = Spatial_Intensity_SE.loc[Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) + group_Z_stat = (self.maps[group+'_Studywise_Spatial_Intensity'] - null_spatial_intensity) / SE_spatial_intensity + self.maps[group+'_z'] = group_Z_stat + group_p_vals = z_to_p(group_Z_stat, tail='one') + self.maps[group+'_p'] = group_p_vals + + if not isinstance(self.t_con_group, type(None)): + self.t_con_group = np.array(self.t_con_group).reshape((-1, self.n_groups)) + + # Wald_statistics_moderators = gamma / np.sqrt(Var_moderators) + # p_moderators = transforms.z_to_p(z=Wald_statistics_moderators, tail='two') + + return class GLMPoisson(torch.nn.Module): def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty=False, device='cpu'): diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index bb22b4aa6..dcbe8aea1 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -57,10 +57,20 @@ def testdata_cbma(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) return dset +@pytest.fixture(scope="session") +def testdata_cbma_full(): + """Generate more complete coordinate-based dataset for tests. + + Same as above, except returns all coords, not just one per study. + """ + dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset = nimare.dataset.Dataset(dset_file) + return dset + @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" - dset_file = os.path.join(get_test_data_path(), "neurosynth.json") + dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") dset = nimare.dataset.Dataset(dset_file) # Only retain one peak in each study in coordinates @@ -78,34 +88,42 @@ def testdata_cbmr(): return dset @pytest.fixture(scope="session") -def testdata_cbma_full(): +def testdata_cbmr_full(): """Generate more complete coordinate-based dataset for tests. Same as above, except returns all coords, not just one per study. """ - dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset_file = os.path.join(get_test_data_path(), "neurosynth_dset.json") dset = nimare.dataset.Dataset(dset_file) + # set up group columns & moderators + n_rows = dset.annotations.shape[0] + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] + dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] + dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["avg_age"] = np.arange(n_rows) + return dset @pytest.fixture(scope="session") -def testdata_cbmr_full(): +def testdata_cbmr_laird(): """Generate more complete coordinate-based dataset for tests. Same as above, except returns all coords, not just one per study. """ - dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset_file = os.path.join(get_test_data_path(), "neurosynth_laird_studies.json") dset = nimare.dataset.Dataset(dset_file) # set up group columns & moderators n_rows = dset.annotations.shape[0] dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + if 'year' in dset.metadata.columns: + dset.annotations["publication_year"] = [dset.metadata['year'][i] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) return dset - @pytest.fixture(scope="session") def testdata_laird(): """Load data from dataset into global variables.""" diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index afad68196..a59031921 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,16 +1,28 @@ -from nimare.meta.cbmr import CBMREstimator +from nimare.meta.cbmr import CBMREstimator, CBMRInference from nimare.utils import standardize_field import logging -def test_CBMREstimator(testdata_cbmr_full): +def test_CBMREstimator(testdata_cbmr_laird): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_full, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') + dset = standardize_field(dataset=testdata_cbmr_laird, metadata=["publication_year", 'avg_age']) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') cbmr_res = cbmr.fit(dataset=dset) # p_map = cbmr_res.get_map('p') # p_vals = p_map.dataobj +def test_CBMRInference(testdata_cbmr_laird): + logging.getLogger().setLevel(logging.DEBUG) + """Unit test for CBMR estimator.""" + dset = standardize_field(dataset=testdata_cbmr_laird, metadata=["publication_year", 'avg_age']) + cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') + cbmr_res = cbmr.fit(dataset=dset) + inference = CBMRInference(CBMRResults=cbmr_res, spatial_homogeneity=True, t_con_group=[1, 0]) + a = inference._contrast() + + + + \ No newline at end of file From c0da20dacb4ac6b2d7cceb8f6436799092228040 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 30 Sep 2022 15:49:33 +0100 Subject: [PATCH 026/177] [skip CI][wip] implement spatial homogeneity test --- nimare/meta/cbmr.py | 37 ++++++++++++++++++++-------------- nimare/tests/test_meta_cbmr.py | 4 ++-- 2 files changed, 24 insertions(+), 17 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 66b7212ad..761f7c1e7 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -305,22 +305,29 @@ def __init__(self, CBMRResults, spatial_homogeneity=True, t_con_group=None, t_co self.t_con_moderator = t_con_moderator def _contrast(self): - Spatial_Intensity_SE = self.tables['Spatial_Intensity_SE'] - if self.spatial_homogeneity: # GLH 1 group - for group in self.group_names: - # mu_0 under null hypothesis - group_foci_per_voxel, group_moderators_effect = self.maps[group+'_Foci_Per_Voxel'], self.maps[group+'_ModeratorsEffect'] - n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] - null_spatial_intensity = np.sum(group_foci_per_voxel) / (n_voxels * n_study) - SE_spatial_intensity = Spatial_Intensity_SE.loc[Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) - group_Z_stat = (self.maps[group+'_Studywise_Spatial_Intensity'] - null_spatial_intensity) / SE_spatial_intensity - self.maps[group+'_z'] = group_Z_stat - group_p_vals = z_to_p(group_Z_stat, tail='one') - self.maps[group+'_p'] = group_p_vals - if not isinstance(self.t_con_group, type(None)): - self.t_con_group = np.array(self.t_con_group).reshape((-1, self.n_groups)) - + self.t_con_group = np.array(self.t_con_group) + if self.t_con_group.shape[1] != self.n_groups: + raise ValueError("The shape of contrast matrix doesn't match with groups") + if np.any(np.sum(self.t_con_group, axis=1)==0): + raise ValueError("Conflict happens between the input contrast matrix and spatial homogeneity test") + self.t_con_group = self.t_con_group / np.sum(self.t_con_group, axis=1) + + Spatial_Intensity_SE = self.tables['Spatial_Intensity_SE'] + if self.spatial_homogeneity: # GLH 1 group + for group in self.group_names: + # mu_0 under null hypothesis + group_foci_per_voxel, group_moderators_effect = self.maps[group+'_Foci_Per_Voxel'], self.maps[group+'_ModeratorsEffect'] + n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] + null_spatial_intensity = np.sum(group_foci_per_voxel) / (n_voxels * n_study) + SE_spatial_intensity = Spatial_Intensity_SE.loc[Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) + group_Z_stat = (self.maps[group+'_Studywise_Spatial_Intensity'] - null_spatial_intensity) / SE_spatial_intensity + self.maps[group+'_z'] = group_Z_stat + group_p_vals = z_to_p(group_Z_stat, tail='one') + self.maps[group+'_p'] = group_p_vals + else: # GLH multiple groups + print('123') + # Wald_statistics_moderators = gamma / np.sqrt(Var_moderators) # p_moderators = transforms.z_to_p(z=Wald_statistics_moderators, tail='two') diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index a59031921..58149ba11 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,9 +16,9 @@ def test_CBMRInference(testdata_cbmr_laird): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_laird, metadata=["publication_year", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference(CBMRResults=cbmr_res, spatial_homogeneity=True, t_con_group=[1, 0]) + inference = CBMRInference(CBMRResults=cbmr_res, spatial_homogeneity=True, t_con_group=[[1, 0, 0, 0], [0, 0, 0, 1]]) a = inference._contrast() From 8be35d8b2383d346306bf8fcf55ee66ce6498b3e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 7 Oct 2022 15:59:11 +0100 Subject: [PATCH 027/177] [skip ci][wip] implement CBMRInference group-wise comparison --- nimare/meta/cbmr.py | 152 +++++++++++++++++++++++---------- nimare/tests/conftest.py | 19 ++++- nimare/tests/test_meta_cbmr.py | 22 +++-- 3 files changed, 136 insertions(+), 57 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 761f7c1e7..e6d17e662 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -216,36 +216,34 @@ def _fit(self, dataset): optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() - Spatial_Regression_Coef = dict() + Spatial_Regression_Coef, overdispersion_param = dict(), dict() # beta: regression coef of spatial effect for group in self.inputs_['all_group_study_id'].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) Spatial_Regression_Coef[group] = group_beta_linear_weight group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - maps[group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity - group_foci_per_voxel = self.inputs_['all_foci_per_voxel'][group].reshape((-1)) - maps[group+'_Foci_Per_Voxel'] = group_foci_per_voxel + maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity # overdispersion parameter: alpha - # if self.model == 'NB': - # alpha = cbmr_model.all_alpha_sqrt[group]**2 - # alpha = alpha.cpu().detach().numpy() - # overdispersion_param[group] = alpha + if self.model == 'NB': + alpha = cbmr_model.all_alpha_sqrt[group]**2 + alpha = alpha.cpu().detach().numpy() + overdispersion_param[group] = alpha tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') + if self.model == 'NB': + tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) # study-level moderators if hasattr(self, "moderators"): + self.moderators_effect = dict() self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.cpu().detach().numpy() for group in self.inputs_['all_group_study_id'].keys(): group_moderators = self.inputs_["all_group_moderators"][group] - moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) - maps[group+'_ModeratorsEffect'] = moderators_effect.flatten() + group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) + self.moderators_effect[group] = group_moderators_effect tables['Moderators_Regression_Coef'] = pd.DataFrame(self._gamma, columns=self.moderators) else: self._gamma = None - # if self.model == 'NB': - # tables['over_dispersion_param'] = pd.DataFrame.from_dict(overdispersion_param, orient='index') - # standard error spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) @@ -274,7 +272,7 @@ def _fit(self, dataset): SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) log_spatial_intensity_se[group] = SE_log_spatial_intensity - group_studywise_spatial_intensity = maps[group+'_Studywise_Spatial_Intensity'] + group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'] SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity spatial_intensity_se[group] = SE_spatial_intensity @@ -295,38 +293,104 @@ def _fit(self, dataset): return maps, tables class CBMRInference(object): - - def __init__(self, CBMRResults, spatial_homogeneity=True, t_con_group=None, t_con_moderator=None): - self.maps, self.tables = CBMRResults.maps, CBMRResults.tables - self.group_names = self.tables['Spatial_Regression_Coef'].index.values.tolist() + def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device='cpu'): + self.device = device + self.CBMRResults = CBMRResults + self.group_names = self.CBMRResults.tables['Spatial_Regression_Coef'].index.values.tolist() self.n_groups = len(self.group_names) - self.spatial_homogeneity = spatial_homogeneity - self.t_con_group = t_con_group - self.t_con_moderator = t_con_moderator - + # Conduct group-wise spatial homogeneity test by default + self.t_con_group = np.eye(self.n_groups) if not t_con_group else np.array(t_con_group) + if self.t_con_group.shape[1] != self.n_groups: + raise ValueError("The shape of group-wise intensity contrast matrix doesn't match with groups") + con_group_zero_row = np.where(np.sum(np.abs(self.t_con_group), axis=1)==0)[0] + if len(con_group_zero_row) > 0: # remove zero rows in contrast matrix + self.t_con_group = np.delete(self.t_con_group, con_group_zero_row, axis=0) + n_contrasts_group = self.t_con_group.shape[0] + self.t_con_group = self.t_con_group / np.sum(np.abs(self.t_con_group), axis=1).reshape((n_contrasts_group, -1)) + + if hasattr(self.CBMRResults.estimator, "moderators"): + self.n_moderators = len(CBMRResults.estimator.moderators) + self.t_con_moderator = np.eye(self.n_moderators) if not t_con_moderator else np.array(t_con_moderator) + # test the existence of effect of moderators + if self.t_con_moderator.shape[1] != self.n_moderators: + raise ValueError("The shape of moderators contrast matrix doesn't match with moderators") + con_moderator_zero_row = np.where(np.sum(np.abs(self.t_con_moderator), axis=1)==0)[0] + if len(con_moderator_zero_row) > 0: # remove zero rows in contrast matrix + self.t_con_moderator = np.delete(self.t_con_moderator, con_moderator_zero_row, axis=0) + n_contrasts_moderator = self.t_con_moderator.shape[0] + self.t_con_moderator = self.t_con_moderator / np.sum(np.abs(self.t_con_moderator), axis=1).reshape((n_contrasts_moderator, -1)) + + if self.device == 'cuda' and not torch.cuda.is_available(): + LGR.debug(f"cuda not found, use device 'cpu'") + self.device = 'cpu' + + def _log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): + n_groups = len(all_spatial_coef) + all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] + all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + if moderator_coef is not None: + all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + l = 0 + for i in range(n_groups): + l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + return l + + def _Fisher_info(self): + Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] + involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] + involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in self.GLH_involved_groups_index], dtype=torch.float64, device=self.device) + n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape + if not isinstance(self.CBMRResults.estimator, type(None)): + involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] + involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + moderator_coef_dim = involved_moderator_coef.shape[0] + a = CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + params = (involved_spatial_coef, involved_moderator_coef) + n_params = len(params) + nll = lambda all_beta, gamma: -CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + h = torch.autograd.functional.hessian(nll, params, create_graph=False) + h_spatial_coef, h_moderator_coef = list(), list() + for i in range(n_params): + h_spatial_coef_i = h[0][i].view(n_involved_groups*spatial_coef_dim, -1) + h_moderator_coef_i = h[1][i].view(moderator_coef_dim, -1) + h_spatial_coef.append(h_spatial_coef_i) + h_moderator_coef.append(h_moderator_coef_i) + h_spatial_coef = torch.cat(h_spatial_coef, dim=1) + h_moderator_coef = torch.cat(h_moderator_coef, dim=1) + h = torch.cat([h_spatial_coef, h_moderator_coef], dim=0) + + return h.detach().cpu().numpy() + + def _contrast(self): - if not isinstance(self.t_con_group, type(None)): - self.t_con_group = np.array(self.t_con_group) - if self.t_con_group.shape[1] != self.n_groups: - raise ValueError("The shape of contrast matrix doesn't match with groups") - if np.any(np.sum(self.t_con_group, axis=1)==0): - raise ValueError("Conflict happens between the input contrast matrix and spatial homogeneity test") - self.t_con_group = self.t_con_group / np.sum(self.t_con_group, axis=1) - - Spatial_Intensity_SE = self.tables['Spatial_Intensity_SE'] - if self.spatial_homogeneity: # GLH 1 group - for group in self.group_names: - # mu_0 under null hypothesis - group_foci_per_voxel, group_moderators_effect = self.maps[group+'_Foci_Per_Voxel'], self.maps[group+'_ModeratorsEffect'] - n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] - null_spatial_intensity = np.sum(group_foci_per_voxel) / (n_voxels * n_study) - SE_spatial_intensity = Spatial_Intensity_SE.loc[Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) - group_Z_stat = (self.maps[group+'_Studywise_Spatial_Intensity'] - null_spatial_intensity) / SE_spatial_intensity - self.maps[group+'_z'] = group_Z_stat - group_p_vals = z_to_p(group_Z_stat, tail='one') - self.maps[group+'_p'] = group_p_vals - else: # GLH multiple groups - print('123') + self.GLH_involved_groups_index = np.where(np.any(self.t_con_group!=0, axis=0))[0].tolist() + self.GLH_involved_groups = [self.group_names[i] for i in self.GLH_involved_groups_index] + Log_Spatial_Intensity_SE = self.CBMRResults.tables['Log_Spatial_Intensity_SE'] + if np.all(np.count_nonzero(self.t_con_group, axis=1)==1): # GLH 1 group + for group in self.GLH_involved_groups: + # mu_0 under null hypothesis + group_foci_per_voxel = self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group] + group_moderators_effect = self.CBMRResults.estimator.moderators_effect[group] + n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] + null_log_spatial_intensity = np.log(np.sum(group_foci_per_voxel) / (n_voxels * n_study)) + SE_log_spatial_intensity = Log_Spatial_Intensity_SE.loc[Log_Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) + group_Z_stat = (np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) - null_log_spatial_intensity) / SE_log_spatial_intensity + self.CBMRResults.maps['Group_'+group+'_z'] = group_Z_stat + group_p_vals = z_to_p(group_Z_stat, tail='one') + self.CBMRResults.maps['Group_'+group+'_p'] = group_p_vals + else: # GLH multiple groups + simp_t_con_group = self.t_con_group[:,~np.all(self.t_con_group == 0, axis = 0)] # contrast matrix of involved groups only + all_log_intensity_per_voxel = list() + for group in self.GLH_involved_groups: + group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) + all_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + all_log_intensity_per_voxel = np.stack(all_log_intensity_per_voxel, axis=0) + Contrast_log_intensity = np.matmul(simp_t_con_group, all_log_intensity_per_voxel) + # Correlation of involved group-wise spatial coef + I = self._Fisher_info() + # Wald_statistics_moderators = gamma / np.sqrt(Var_moderators) # p_moderators = transforms.z_to_p(z=Wald_statistics_moderators, tail='two') diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index dcbe8aea1..575b3210a 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -10,7 +10,7 @@ import nimare from nimare.tests.utils import get_test_data_path from nimare.utils import get_resource_path - +from nimare.generate import create_coordinate_dataset import random # Only enable the following once in a while for a check for SettingWithCopyWarnings # pd.options.mode.chained_assignment = "raise" @@ -124,6 +124,23 @@ def testdata_cbmr_laird(): return dset +@pytest.fixture(scope="session") +def testdata_cbmr_simulated(): + """Simulate coordinate-based dataset for tests. + """ + # simulate + ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000) + # set up group columns: diagnosis & drug_status + n_rows = dset.annotations.shape[0] + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] + dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] + dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + # set up moderators: sample sizes & avg_age + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["avg_age"] = np.arange(n_rows) + + return dset + @pytest.fixture(scope="session") def testdata_laird(): """Load data from dataset into global variables.""" diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 58149ba11..423ed873c 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -2,23 +2,21 @@ from nimare.utils import standardize_field import logging -def test_CBMREstimator(testdata_cbmr_laird): - logging.getLogger().setLevel(logging.DEBUG) - """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_laird, metadata=["publication_year", 'avg_age']) - cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') - cbmr_res = cbmr.fit(dataset=dset) - # p_map = cbmr_res.get_map('p') - # p_vals = p_map.dataobj +# def test_CBMREstimator(testdata_cbmr_simulated): +# logging.getLogger().setLevel(logging.DEBUG) +# """Unit test for CBMR estimator.""" +# dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) +# cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e4, device='cuda') +# cbmr_res = cbmr.fit(dataset=dset) -def test_CBMRInference(testdata_cbmr_laird): +def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_laird, metadata=["publication_year", 'avg_age']) - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_publication_year', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e5, device='cuda') + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference(CBMRResults=cbmr_res, spatial_homogeneity=True, t_con_group=[[1, 0, 0, 0], [0, 0, 0, 1]]) + inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1, 0, 0, -1], [0, -1, 0, 1]], device='cuda') a = inference._contrast() From 8e7338012b14b8551b73fbc418f28c0194e37e3f Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 28 Oct 2022 17:26:29 +0100 Subject: [PATCH 028/177] formalize GLH contrast variable --- nimare/meta/cbmr.py | 317 +++++++++++++++++++++++---------- nimare/results.py | 1 + nimare/tests/test_meta_cbmr.py | 4 +- 3 files changed, 222 insertions(+), 100 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e6d17e662..88cb83cb9 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -82,7 +82,7 @@ def _preprocess_input(self, dataset): all_group_study_id['_'.join(group)] = group_study_id.unique().tolist() self.inputs_['all_group_study_id'] = all_group_study_id # collect studywise moderators if specficed - if hasattr(self, "moderators"): + if self.moderators: all_group_moderators = dict() for group in all_group_study_id.keys(): df_group = valid_dset_annotations.loc[valid_dset_annotations['study_id'].isin(all_group_study_id[group])] @@ -119,7 +119,7 @@ def _preprocess_input(self, dataset): def _model_structure(self, model, penalty, device): beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect - if hasattr(self, "moderators"): + if self.moderators: gamma_dim = list(self.inputs_["all_group_moderators"].values())[0].shape[1] study_level_moderators = True else: @@ -179,7 +179,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): optimizer = torch.optim.LBFGS(model.parameters(), lr) # load dataset info to torch.tensor Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) - if hasattr(self, "moderators"): + if self.moderators: all_group_moderators_tensor = dict() for group in self.inputs_['all_group_study_id'].keys(): group_moderators_tensor = torch.tensor(self.inputs_['all_group_moderators'][group], dtype=torch.float64, device=device) @@ -233,7 +233,7 @@ def _fit(self, dataset): if self.model == 'NB': tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) # study-level moderators - if hasattr(self, "moderators"): + if self.moderators: self.moderators_effect = dict() self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.cpu().detach().numpy() @@ -251,13 +251,13 @@ def _fit(self, dataset): group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - if hasattr(self, "moderators"): + if self.moderators: gamma = cbmr_model.gamma_linear.weight group_moderators = self.inputs_["all_group_moderators"][group] group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) else: group_moderators = None - nll = lambda beta, gamma: -GLMPoisson._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) + nll = lambda beta, gamma: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) params = (group_beta_linear_weight, gamma) F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') # Inference on regression coefficient of spatial effect @@ -281,7 +281,7 @@ def _fit(self, dataset): tables['Spatial_Intensity_SE'] = pd.DataFrame.from_dict(spatial_intensity_se, orient='index') # Inference on regression coefficient of moderators - if hasattr(self, "moderators"): + if self.moderators: gamma = gamma.cpu().detach().numpy() moderators_dim = gamma.shape[1] F_moderators_coef = F[1][1].reshape((moderators_dim, moderators_dim)) @@ -296,105 +296,201 @@ class CBMRInference(object): def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device='cpu'): self.device = device self.CBMRResults = CBMRResults + self.t_con_group = t_con_group + self.t_con_moderator = t_con_moderator self.group_names = self.CBMRResults.tables['Spatial_Regression_Coef'].index.values.tolist() self.n_groups = len(self.group_names) - # Conduct group-wise spatial homogeneity test by default - self.t_con_group = np.eye(self.n_groups) if not t_con_group else np.array(t_con_group) - if self.t_con_group.shape[1] != self.n_groups: - raise ValueError("The shape of group-wise intensity contrast matrix doesn't match with groups") - con_group_zero_row = np.where(np.sum(np.abs(self.t_con_group), axis=1)==0)[0] - if len(con_group_zero_row) > 0: # remove zero rows in contrast matrix - self.t_con_group = np.delete(self.t_con_group, con_group_zero_row, axis=0) - n_contrasts_group = self.t_con_group.shape[0] - self.t_con_group = self.t_con_group / np.sum(np.abs(self.t_con_group), axis=1).reshape((n_contrasts_group, -1)) - - if hasattr(self.CBMRResults.estimator, "moderators"): - self.n_moderators = len(CBMRResults.estimator.moderators) - self.t_con_moderator = np.eye(self.n_moderators) if not t_con_moderator else np.array(t_con_moderator) - # test the existence of effect of moderators - if self.t_con_moderator.shape[1] != self.n_moderators: - raise ValueError("The shape of moderators contrast matrix doesn't match with moderators") - con_moderator_zero_row = np.where(np.sum(np.abs(self.t_con_moderator), axis=1)==0)[0] - if len(con_moderator_zero_row) > 0: # remove zero rows in contrast matrix - self.t_con_moderator = np.delete(self.t_con_moderator, con_moderator_zero_row, axis=0) - n_contrasts_moderator = self.t_con_moderator.shape[0] - self.t_con_moderator = self.t_con_moderator / np.sum(np.abs(self.t_con_moderator), axis=1).reshape((n_contrasts_moderator, -1)) + if self.t_con_group is not False: + # Conduct group-wise spatial homogeneity test by default + self.t_con_group = [np.eye(self.n_groups)] if not self.t_con_group else [np.array(con_group) for con_group in self.t_con_group] + self.t_con_group = [con_group.reshape((1,-1)) if len(con_group.shape)==1 else con_group for con_group in self.t_con_group] # 2D contrast matrix/vector + if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): + wrong_con_group_idx = np.where([con_group.shape[1] != self.n_groups for con_group in self.t_con_group])[0].tolist() + raise ValueError("The shape of {}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups".format(str(wrong_con_group_idx))) + con_group_zero_row = [np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] for con_group in self.t_con_group] + if np.any([len(zero_row)>0 for zero_row in con_group_zero_row]): # remove zero rows in contrast matrix + self.t_con_group = [np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) for i in range(len(self.t_con_group))] + if np.any([con_group.shape[0]== 0 for con_group in self.t_con_group]): + raise ValueError('One or more of contrast vectors(s) in group-wise intensity contrast matrix are all zeros') + n_contrasts_group = [con_group.shape[0] for con_group in self.t_con_group] + self._Name_of_con_group() + # standardization + self.t_con_group = [con_group / np.sum(np.abs(con_group), axis=1).reshape((-1,1)) for con_group in self.t_con_group] + + if self.t_con_moderator is not False: + if hasattr(self.CBMRResults.estimator, "moderators"): + self.moderator_names = self.CBMRResults.estimator.moderators + self.n_moderators = len(self.moderator_names) + self.t_con_moderator = [np.eye(self.n_moderators)] if not self.t_con_moderator else [np.array(con_moderator) for con_moderator in self.t_con_moderator] + self.t_con_moderator = [con_moderator.reshape((1,-1)) if len(con_moderator.shape)==1 else con_moderator for con_moderator in self.t_con_moderator] + # test the existence of effect of moderators + if np.any([con_moderator.shape[1] != self.n_moderators for con_moderator in self.t_con_moderator]): + wrong_con_moderator_idx = np.where([con_moderator.shape[1] != self.n_moderators for con_moderator in self.t_con_moderator])[0].tolist() + raise ValueError("The shape of {}th contrast vector(s) in moderators contrast matrix doesn't match with moderators".format(str(wrong_con_moderator_idx))) + con_moderator_zero_row = [np.where(np.sum(np.abs(con_modereator), axis=1)==0)[0] for con_modereator in self.t_con_moderator] + if np.any([len(zero_row)>0 for zero_row in con_moderator_zero_row]): # remove zero rows in contrast matrix + self.t_con_moderator = [np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) for i in range(len(self.t_con_moderator))] + if np.any([con_moderator.shape[0]== 0 for con_moderator in self.t_con_moderator]): + raise ValueError('One or more of contrast vectors(s) in modereators contrast matrix are all zeros') + n_contrasts_moderator = [con_moderator.shape[0] for con_moderator in self.t_con_moderator] + self._Name_of_con_moderator() + self.t_con_moderator = [con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1,1)) for con_moderator in self.t_con_moderator] if self.device == 'cuda' and not torch.cuda.is_available(): LGR.debug(f"cuda not found, use device 'cpu'") self.device = 'cpu' - def _log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): - n_groups = len(all_spatial_coef) - all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] - all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] - if moderator_coef is not None: - all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] - l = 0 - for i in range(n_groups): - l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) - return l - - def _Fisher_info(self): - Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) - involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] - involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] - involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in self.GLH_involved_groups_index], dtype=torch.float64, device=self.device) - n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape - if not isinstance(self.CBMRResults.estimator, type(None)): - involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in self.GLH_involved_groups] - involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) - moderator_coef_dim = involved_moderator_coef.shape[0] - a = CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - params = (involved_spatial_coef, involved_moderator_coef) - n_params = len(params) - nll = lambda all_beta, gamma: -CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - h = torch.autograd.functional.hessian(nll, params, create_graph=False) - h_spatial_coef, h_moderator_coef = list(), list() - for i in range(n_params): - h_spatial_coef_i = h[0][i].view(n_involved_groups*spatial_coef_dim, -1) - h_moderator_coef_i = h[1][i].view(moderator_coef_dim, -1) - h_spatial_coef.append(h_spatial_coef_i) - h_moderator_coef.append(h_moderator_coef_i) - h_spatial_coef = torch.cat(h_spatial_coef, dim=1) - h_moderator_coef = torch.cat(h_moderator_coef, dim=1) - h = torch.cat([h_spatial_coef, h_moderator_coef], dim=0) + def _Name_of_con_group(self): + self.t_con_group_name = list() + for con_group in self.t_con_group: + con_group_name = list() + for num, idx in enumerate(con_group): + if np.sum(idx) != 0: # homogeneity test + nonzero_con_group_info = str() + nonzero_group_index = np.where(idx!=0)[0].tolist() + nonzero_group_name = [self.group_names[i] for i in nonzero_group_index] + nonzero_con = [int(idx[i]) for i in nonzero_group_index] + for i in range(len(nonzero_group_index)): + nonzero_con_group_info += str(abs(nonzero_con[i])) + 'x' + str(nonzero_group_name[i]) + con_group_name.append('homo_test_' + nonzero_con_group_info) + else: # group-comparison test + pos_group_idx, neg_group_idx = np.where(idx>0)[0].tolist(), np.where(idx<0)[0].tolist() + pos_group_name, neg_group_name = [self.group_names[i] for i in pos_group_idx], [self.group_names[i] for i in neg_group_idx] + pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [int(idx[i]) for i in neg_group_idx] + pos_con_group_info, neg_con_group_info = str(), str() + for i in range(len(pos_group_idx)): + pos_con_group_info += str(pos_group_con[i]) + 'x' + str(pos_group_name[i]) + for i in range(len(neg_group_idx)): + neg_con_group_info += str(abs(neg_group_con[i])) + 'x' + str(neg_group_name[i]) + con_group_name.append(pos_con_group_info + 'VS' + neg_con_group_info) + self.t_con_group_name.append(con_group_name) + return + + def _Name_of_con_moderator(self): + self.t_con_moderator_name = list() + for con_moderator in self.t_con_moderator: + con_moderator_name = list() + for num, idx in enumerate(con_moderator): + if np.sum(idx) != 0: # homogeneity test + nonzero_con_moderator_info = str() + nonzero_moderator_index = np.where(idx!=0)[0].tolist() + nonzero_moderator_name = [self.moderator_names[i] for i in nonzero_moderator_index] + nonzero_con = [int(idx[i]) for i in nonzero_moderator_index] + for i in range(len(nonzero_moderator_index)): + nonzero_con_moderator_info += str(abs(nonzero_con[i])) + 'x' + str(nonzero_moderator_name[i]) + con_moderator_name.append('Effect_of_' + nonzero_con_moderator_info) + else: # group-comparison test + pos_moderator_idx, neg_moderator_idx = np.where(idx>0)[0].tolist(), np.where(idx<0)[0].tolist() + pos_moderator_name, neg_moderator_name = [self.group_names[i] for i in pos_moderator_idx], [self.group_names[i] for i in neg_moderator_idx] + pos_moderator_con, neg_moderator_con = [int(idx[i]) for i in pos_moderator_idx], [int(idx[i]) for i in neg_moderator_idx] + pos_con_moderator_info, neg_con_moderator_info = str(), str() + for i in range(len(pos_moderator_idx)): + pos_con_moderator_info += str(pos_moderator_con[i]) + 'x' + str(pos_moderator_name[i]) + for i in range(len(neg_moderator_idx)): + neg_con_moderator_info += str(abs(neg_moderator_con[i])) + 'x' + str(neg_moderator_name[i]) + con_moderator_name.append(pos_con_moderator_info + 'VS' + neg_con_moderator_info) + self.t_con_moderator_name.append(con_moderator_name) + return + + # def _log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): + # n_groups = len(all_spatial_coef) + # all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] + # all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + # if moderator_coef is not None: + # all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] + # all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + # l = 0 + # for i in range(n_groups): + # l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + # return l + + def _Fisher_info(self, GLH_involved_index, inference): + if inference == 'group': + Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + GLH_involved = [self.group_names[i] for i in GLH_involved_index] + involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device) + n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape + if hasattr(self.CBMRResults.estimator, "moderators"): + involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + moderator_coef_dim = involved_moderator_coef.shape[0] + # a = CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + params = (involved_spatial_coef) + n_params = len(params) + nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + h = torch.autograd.functional.hessian(nll, params, create_graph=False) + h = h.view(n_involved_groups*spatial_coef_dim, -1) return h.detach().cpu().numpy() def _contrast(self): - self.GLH_involved_groups_index = np.where(np.any(self.t_con_group!=0, axis=0))[0].tolist() - self.GLH_involved_groups = [self.group_names[i] for i in self.GLH_involved_groups_index] Log_Spatial_Intensity_SE = self.CBMRResults.tables['Log_Spatial_Intensity_SE'] - if np.all(np.count_nonzero(self.t_con_group, axis=1)==1): # GLH 1 group - for group in self.GLH_involved_groups: - # mu_0 under null hypothesis - group_foci_per_voxel = self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group] - group_moderators_effect = self.CBMRResults.estimator.moderators_effect[group] - n_voxels, n_study = group_foci_per_voxel.shape[0], group_moderators_effect.shape[0] - null_log_spatial_intensity = np.log(np.sum(group_foci_per_voxel) / (n_voxels * n_study)) - SE_log_spatial_intensity = Log_Spatial_Intensity_SE.loc[Log_Spatial_Intensity_SE.index == group].to_numpy().reshape((-1)) - group_Z_stat = (np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) - null_log_spatial_intensity) / SE_log_spatial_intensity - self.CBMRResults.maps['Group_'+group+'_z'] = group_Z_stat - group_p_vals = z_to_p(group_Z_stat, tail='one') - self.CBMRResults.maps['Group_'+group+'_p'] = group_p_vals - else: # GLH multiple groups - simp_t_con_group = self.t_con_group[:,~np.all(self.t_con_group == 0, axis = 0)] # contrast matrix of involved groups only - all_log_intensity_per_voxel = list() - for group in self.GLH_involved_groups: - group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) - all_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - all_log_intensity_per_voxel = np.stack(all_log_intensity_per_voxel, axis=0) - Contrast_log_intensity = np.matmul(simp_t_con_group, all_log_intensity_per_voxel) - # Correlation of involved group-wise spatial coef - I = self._Fisher_info() - + if self.t_con_group is not False: + con_group_count = 0 + for con_group in self.t_con_group: + con_group_involved_index = np.where(np.any(con_group!=0, axis=0))[0].tolist() + con_group_involved = [self.group_names[i] for i in con_group_involved_index] + n_con_group_involved = len(con_group_involved) + simp_con_group = con_group[:,~np.all(con_group == 0, axis = 0)] # contrast matrix of involved groups only + if np.all(np.count_nonzero(con_group, axis=1)==1): # GLH: homogeneity test + involved_log_intensity_per_voxel = list() + for group in con_group_involved: + group_foci_per_voxel = self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group] + group_foci_per_study = self.CBMRResults.estimator.inputs_['all_foci_per_study'][group] + n_voxels, n_study = group_foci_per_voxel.shape[0], group_foci_per_study.shape[0] + group_null_log_spatial_intensity = np.log(np.sum(group_foci_per_voxel) / (n_voxels * n_study)) + group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) + group_log_intensity_per_voxel = group_log_intensity_per_voxel - group_null_log_spatial_intensity + involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack(involved_log_intensity_per_voxel, axis=0) + else: # GLH: group-comparison + involved_log_intensity_per_voxel = list() + for group in con_group_involved: + group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) + involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack(involved_log_intensity_per_voxel, axis=0) + Contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) + m, n_brain_voxel = Contrast_log_intensity.shape + # Correlation of involved group-wise spatial coef + F = self._Fisher_info(con_group_involved_index, inference='group') + Cov = np.linalg.inv(F) + spatial_coef_dim = self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy().shape[1] + Cov_log_intensity = list() + for k in range(n_con_group_involved): + for s in range(n_con_group_involved): + Cov_beta_ks = Cov[k*spatial_coef_dim: (k+1)*spatial_coef_dim, s*spatial_coef_dim: (s+1)*spatial_coef_dim] + Cov_group_log_intensity = np.empty(shape=(0, )) + for j in range(n_brain_voxel): + x_j = self.CBMRResults.estimator.inputs_['Coef_spline_bases'][j, :].reshape((1, spatial_coef_dim)) + Cov_group_log_intensity_j = x_j @ Cov_beta_ks @ x_j.T + Cov_group_log_intensity = np.concatenate((Cov_group_log_intensity, Cov_group_log_intensity_j.reshape(1,)), axis=0) + Cov_log_intensity.append(Cov_group_log_intensity) + Cov_log_intensity = np.stack(Cov_log_intensity, axis=0) # (m^2, n_voxels) + # GLH on log_intensity (eta) + chi_sq_statistics = list() + for j in range(n_brain_voxel): + Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) + V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) + CV_jC = simp_con_group @ V_j @ simp_con_group.T + CV_jC_inv = np.linalg.inv(CV_jC) + chi_sq_statistics_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + chi_sq_statistics.append(chi_sq_statistics_j) + chi_sq_statistics = np.array(chi_sq_statistics).reshape(n_brain_voxel, 1) + p_vals = 1 - scipy.stats.chi2.cdf(chi_sq_statistics, df=m) + + con_group_name = self.t_con_group_name[con_group_count] + if len(con_group_name) == 1: + self.CBMRResults.maps[con_group_name[0] +'_chi_sq'] = chi_sq_statistics + self.CBMRResults.maps[con_group_name[0] +'_p'] = p_vals + else: + self.CBMRResults.maps['GLH_' + str(con_group_count) +'_chi_sq'] = chi_sq_statistics + self.CBMRResults.maps['GLH_' + str(con_group_count) +'_p'] = p_vals + self.CBMRResults.metadata['GLH_' + str(con_group_count)] = con_group_name + con_group_count += 1 - # Wald_statistics_moderators = gamma / np.sqrt(Var_moderators) - # p_moderators = transforms.z_to_p(z=Wald_statistics_moderators, tail='two') - return class GLMPoisson(torch.nn.Module): @@ -418,7 +514,7 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def _log_likelihood(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + def _log_likelihood_single_group(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) log_mu_moderators = torch.matmul(moderators, gamma.T) @@ -428,6 +524,18 @@ def _log_likelihood(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, return log_l + def _log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): + n_groups = len(all_spatial_coef) + all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] + all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + if moderator_coef is not None: + all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + l = 0 + for i in range(n_groups): + l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + return l + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -441,24 +549,37 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc for group in all_foci_per_voxel.keys(): log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) group_foci_per_voxel = all_foci_per_voxel[group] group_foci_per_study = all_foci_per_study[group] + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) # Under the assumption that Y_ij is either 0 or 1 # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] group_log_l = torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) log_l += group_log_l - if self.penalty == True: + if self.penalty: # Firth-type penalty for group in all_foci_per_voxel.keys(): beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] - gamma = self.gamma_linear.weight.T group_foci_per_voxel = all_foci_per_voxel[group] group_foci_per_study = all_foci_per_study[group] - group_moderators = all_moderators[group] + if self.study_level_moderators: + gamma = self.gamma_linear.weight.T + group_moderators = all_moderators[group] + gamma, group_moderators = [gamma], [group_moderators] + else: + gamma, group_moderators = None, None + + all_spatial_coef = torch.stack([beta]) + all_foci_per_voxel, all_foci_per_study = torch.stack([group_foci_per_voxel]), torch.stack([group_foci_per_study]) + # a = -GLMPoisson._log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, gamma, group_moderators) nll = lambda beta: -self._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) params = (beta) F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') diff --git a/nimare/results.py b/nimare/results.py index 25cb034ff..3d5ce0e0a 100644 --- a/nimare/results.py +++ b/nimare/results.py @@ -59,6 +59,7 @@ def __init__(self, estimator, mask, maps=None, tables=None): self.maps = maps self.tables = tables + self.metadata = {} def get_map(self, name, return_type="image"): """Get stored map as image or array. diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 423ed873c..3e366c562 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -14,9 +14,9 @@ def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1, 0, 0, -1], [0, -1, 0, 1]], device='cuda') + inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]], t_con_moderator=[[[1,0],[0,1]], [1, -1]], device='cuda') # [[2, 0, 0, -2], [0, -2, 1, 1]] a = inference._contrast() From e6c1b9289f6019dca548a44bf16f0d8b383d831e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 30 Oct 2022 20:13:44 +0000 Subject: [PATCH 029/177] [skip ci][wip] implemented Cov in GLH for all three models --- nimare/meta/cbmr.py | 274 +++++++++++++++++++++++---------- nimare/tests/test_meta_cbmr.py | 7 +- 2 files changed, 201 insertions(+), 80 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 88cb83cb9..acc95c56f 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -13,9 +13,9 @@ from nimare.transforms import z_to_p from nimare import transforms import torch +import functorch import logging import copy -from functorch import hessian LGR = logging.getLogger(__name__) class CBMREstimator(Estimator): @@ -211,7 +211,7 @@ def _fit(self, dataset): Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) P = Coef_spline_bases.shape[1] self.inputs_['Coef_spline_bases'] = Coef_spline_bases - + cbmr_model = self._model_structure(self.model, self.penalty, self.device) optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) @@ -229,8 +229,12 @@ def _fit(self, dataset): alpha = cbmr_model.all_alpha_sqrt[group]**2 alpha = alpha.cpu().detach().numpy() overdispersion_param[group] = alpha + elif self.model == 'clustered_NB': + alpha = cbmr_model.all_alpha[group] + alpha = alpha.cpu().detach().numpy() + overdispersion_param[group] = alpha tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') - if self.model == 'NB': + if self.model == 'NB' or self.model == 'clustered_NB': tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) # study-level moderators if self.moderators: @@ -256,13 +260,20 @@ def _fit(self, dataset): group_moderators = self.inputs_["all_group_moderators"][group] group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) else: - group_moderators = None - nll = lambda beta, gamma: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) - params = (group_beta_linear_weight, gamma) - F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + gamma, group_moderators = None, None + if 'Overdispersion_Coef' in tables.keys(): + alpha = torch.tensor(tables['Overdispersion_Coef'].to_dict()['alpha'][group], dtype=torch.float64, device=self.device) + # a = -GLMCNB._log_likelihood_single_group(alpha, group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + if self.model == 'Poisson': + nll = lambda beta: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + elif self.model == 'NB': + nll = lambda beta: -GLMNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + elif self.model == 'clustered_NB': + nll = lambda beta: -GLMCNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + F = functorch.hessian(nll)(group_beta_linear_weight) # Inference on regression coefficient of spatial effect spatial_dim = group_beta_linear_weight.shape[1] - F_spatial_coef = F[0][0].reshape((spatial_dim, spatial_dim)) + F_spatial_coef = F.reshape((spatial_dim, spatial_dim)) Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) Var_spatial_coef = np.diag(Cov_spatial_coef) SE_spatial_coef = np.sqrt(Var_spatial_coef) @@ -282,9 +293,11 @@ def _fit(self, dataset): # Inference on regression coefficient of moderators if self.moderators: - gamma = gamma.cpu().detach().numpy() moderators_dim = gamma.shape[1] - F_moderators_coef = F[1][1].reshape((moderators_dim, moderators_dim)) + nll = lambda gamma: -GLMPoisson._log_likelihood_single_group(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + params = (gamma) + F_moderators_coef = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + F_moderators_coef = F_moderators_coef.reshape((moderators_dim, moderators_dim)) Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) SE_moderators = np.sqrt(Var_moderators) @@ -318,7 +331,7 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=' self.t_con_group = [con_group / np.sum(np.abs(con_group), axis=1).reshape((-1,1)) for con_group in self.t_con_group] if self.t_con_moderator is not False: - if hasattr(self.CBMRResults.estimator, "moderators"): + if self.CBMRResults.estimator.moderators: self.moderator_names = self.CBMRResults.estimator.moderators self.n_moderators = len(self.moderator_names) self.t_con_moderator = [np.eye(self.n_moderators)] if not self.t_con_moderator else [np.array(con_moderator) for con_moderator in self.t_con_moderator] @@ -335,7 +348,8 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=' n_contrasts_moderator = [con_moderator.shape[0] for con_moderator in self.t_con_moderator] self._Name_of_con_moderator() self.t_con_moderator = [con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1,1)) for con_moderator in self.t_con_moderator] - + else: + self.t_con_moderator = False if self.device == 'cuda' and not torch.cuda.is_available(): LGR.debug(f"cuda not found, use device 'cpu'") self.device = 'cpu' @@ -381,7 +395,7 @@ def _Name_of_con_moderator(self): con_moderator_name.append('Effect_of_' + nonzero_con_moderator_info) else: # group-comparison test pos_moderator_idx, neg_moderator_idx = np.where(idx>0)[0].tolist(), np.where(idx<0)[0].tolist() - pos_moderator_name, neg_moderator_name = [self.group_names[i] for i in pos_moderator_idx], [self.group_names[i] for i in neg_moderator_idx] + pos_moderator_name, neg_moderator_name = [self.moderator_names[i] for i in pos_moderator_idx], [self.moderator_names[i] for i in neg_moderator_idx] pos_moderator_con, neg_moderator_con = [int(idx[i]) for i in pos_moderator_idx], [int(idx[i]) for i in neg_moderator_idx] pos_con_moderator_info, neg_con_moderator_info = str(), str() for i in range(len(pos_moderator_idx)): @@ -391,40 +405,56 @@ def _Name_of_con_moderator(self): con_moderator_name.append(pos_con_moderator_info + 'VS' + neg_con_moderator_info) self.t_con_moderator_name.append(con_moderator_name) return - - # def _log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): - # n_groups = len(all_spatial_coef) - # all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] - # all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] - # if moderator_coef is not None: - # all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] - # all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] - # l = 0 - # for i in range(n_groups): - # l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) - # return l - - def _Fisher_info(self, GLH_involved_index, inference): - if inference == 'group': - Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) - GLH_involved = [self.group_names[i] for i in GLH_involved_index] - involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device) - n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape - if hasattr(self.CBMRResults.estimator, "moderators"): - involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) - moderator_coef_dim = involved_moderator_coef.shape[0] - # a = CBMRInference._log_likelihood(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - params = (involved_spatial_coef) - n_params = len(params) + + def _Fisher_info_spatial_coef(self, GLH_involved_index): + Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + GLH_involved = [self.group_names[i] for i in GLH_involved_index] + involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + if 'Overdispersion_Coef' in self.CBMRResults.tables.keys(): + involved_overdispersion_coef = torch.tensor([self.CBMRResults.tables['Overdispersion_Coef'].to_numpy()[i, :] for i in GLH_involved_index], dtype=torch.float64, device=self.device) + involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device) + n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape + if self.CBMRResults.estimator.moderators: + involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] + involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + else: + involved_group_moderators, involved_moderator_coef = None, None + # a = GLMPoisson._log_likelihood_mult_group(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators, self.device) + if self.CBMRResults.estimator.model == 'Poisson': nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - h = torch.autograd.functional.hessian(nll, params, create_graph=False) - h = h.view(n_involved_groups*spatial_coef_dim, -1) + elif self.CBMRResults.estimator.model == 'NB': + nll = lambda all_spatial_coef: -GLMNB._log_likelihood_mult_group(involved_overdispersion_coef, all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + elif self.CBMRResults.estimator.model == 'clustered_NB': + nll = lambda all_spatial_coef: -GLMCNB._log_likelihood_mult_group(involved_overdispersion_coef, all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + h = functorch.hessian(nll)(involved_spatial_coef) + h = h.view(n_involved_groups*spatial_coef_dim, -1) return h.detach().cpu().numpy() + def _Fisher_info_moderator_coef(self): + Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + all_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in self.group_names] + all_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in self.group_names] + all_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in range(self.n_groups)], dtype=torch.float64, device=self.device) + + all_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + moderator_coef_dim, _ = all_moderator_coef.shape + all_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in self.group_names] + + if 'Overdispersion_Coef' in self.CBMRResults.tables.keys(): + all_overdispersion_coef = torch.tensor(self.CBMRResults.tables['Overdispersion_Coef'].to_numpy(), dtype=torch.float64, device=self.device) + + if self.CBMRResults.estimator.model == 'Poisson': + nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) + elif self.CBMRResults.estimator.model == 'NB': + nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) + elif self.CBMRResults.estimator.model == 'clustered_NB': + nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) + h = functorch.hessian(nll)(all_moderator_coef) + h = h.view(moderator_coef_dim, moderator_coef_dim) + + return h.detach().cpu().numpy() def _contrast(self): Log_Spatial_Intensity_SE = self.CBMRResults.tables['Log_Spatial_Intensity_SE'] @@ -455,8 +485,8 @@ def _contrast(self): Contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) m, n_brain_voxel = Contrast_log_intensity.shape # Correlation of involved group-wise spatial coef - F = self._Fisher_info(con_group_involved_index, inference='group') - Cov = np.linalg.inv(F) + F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) + Cov_spatial_coef = np.linalg.inv(F_spatial_coef) spatial_coef_dim = self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy().shape[1] Cov_log_intensity = list() for k in range(n_con_group_involved): @@ -470,27 +500,48 @@ def _contrast(self): Cov_log_intensity.append(Cov_group_log_intensity) Cov_log_intensity = np.stack(Cov_log_intensity, axis=0) # (m^2, n_voxels) # GLH on log_intensity (eta) - chi_sq_statistics = list() + chi_sq_spatial = list() for j in range(n_brain_voxel): Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) CV_jC = simp_con_group @ V_j @ simp_con_group.T CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_statistics_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j - chi_sq_statistics.append(chi_sq_statistics_j) - chi_sq_statistics = np.array(chi_sq_statistics).reshape(n_brain_voxel, 1) - p_vals = 1 - scipy.stats.chi2.cdf(chi_sq_statistics, df=m) + chi_sq_spatial_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + chi_sq_spatial.append(chi_sq_spatial_j) + chi_sq_spatial = np.array(chi_sq_spatial).reshape(n_brain_voxel, 1) + p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) con_group_name = self.t_con_group_name[con_group_count] if len(con_group_name) == 1: - self.CBMRResults.maps[con_group_name[0] +'_chi_sq'] = chi_sq_statistics - self.CBMRResults.maps[con_group_name[0] +'_p'] = p_vals + self.CBMRResults.maps[con_group_name[0] +'_chi_sq'] = chi_sq_spatial + self.CBMRResults.maps[con_group_name[0] +'_p'] = p_vals_spatial else: - self.CBMRResults.maps['GLH_' + str(con_group_count) +'_chi_sq'] = chi_sq_statistics - self.CBMRResults.maps['GLH_' + str(con_group_count) +'_p'] = p_vals - self.CBMRResults.metadata['GLH_' + str(con_group_count)] = con_group_name + self.CBMRResults.maps['spatial_coef_GLH_' + str(con_group_count) +'_chi_sq'] = chi_sq_spatial + self.CBMRResults.maps['spatial_coef_GLH_' + str(con_group_count) +'_p'] = p_vals_spatial + self.CBMRResults.metadata['spatial_coef_GLH_' + str(con_group_count)] = con_group_name con_group_count += 1 - + + if self.t_con_moderator is not False: + con_moderator_count = 0 + for con_moderator in self.t_con_moderator: + m_con_moderator, _ = con_moderator.shape + moderator_coef = self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T + Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) + F_moderator_coef = self._Fisher_info_moderator_coef() + Cov_moderator_coef = np.linalg.inv(F_moderator_coef) + chi_sq_moderator = Contrast_moderator_coef.T @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) @ Contrast_moderator_coef + p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) + + con_moderator_name = self.t_con_moderator_name[con_moderator_count] + if len(con_moderator_name) == 1: + self.CBMRResults.tables[con_moderator_name[0] +'_chi_sq'] = chi_sq_moderator + self.CBMRResults.tables[con_moderator_name[0] +'_p'] = p_vals_moderator + else: + self.CBMRResults.tables['moderator_coef_GLH_' + str(con_moderator_count) +'_chi_sq'] = chi_sq_moderator + self.CBMRResults.tables['moderator_coef_GLH_' + str(con_moderator_count) +'_p'] = p_vals_moderator + self.CBMRResults.metadata['moderator_coef_GLH_' + str(con_moderator_count)] = con_moderator_name + con_moderator_count += 1 + return class GLMPoisson(torch.nn.Module): @@ -514,23 +565,31 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def _log_likelihood_single_group(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + def _log_likelihood_single_group(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device='cpu'): log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = torch.matmul(moderators, gamma.T) - mu_moderators = torch.exp(log_mu_moderators) + if gamma is not None: + log_mu_moderators = torch.matmul(moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = foci_per_study.shape + log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) log_l = torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) \ - torch.sum(mu_spatial) * torch.sum(mu_moderators) return log_l - def _log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None): + def _log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): n_groups = len(all_spatial_coef) all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] if moderator_coef is not None: all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + else: + all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] l = 0 for i in range(n_groups): l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) @@ -628,12 +687,17 @@ def _three_term(y, r, device): return sum_three_term - def _log_likelihood(self, alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study): + def _log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device='cpu'): v = 1 / alpha - log_mu_spatial = Coef_spline_bases @ beta + log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = group_moderators @ gamma - mu_moderators = torch.exp(log_mu_moderators) + if gamma is not None: + log_mu_moderators = torch.matmul(group_moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) numerator = mu_spatial**2 * torch.sum(mu_moderators**2) denominator = mu_spatial**2 * torch.sum(mu_moderators)**2 estimated_sum_alpha = alpha * numerator / denominator @@ -641,10 +705,35 @@ def _log_likelihood(self, alpha, beta, gamma, Coef_spline_bases, group_moderator p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator - log_l = GLMNB._three_term(group_foci_per_voxel,r, device=self.device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + log_l = GLMNB._three_term(group_foci_per_voxel,r, device=device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) return log_l + def _log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): + all_v = 1 / all_overdispersion_coef + n_groups = len(all_foci_per_voxel) + all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] + all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + if moderator_coef is not None: + all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + else: + all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + + all_numerator = [all_spatial_intensity[i]**2 * torch.sum(all_moderator_effect[i]**2) for i in range(n_groups)] + all_denominator = [all_spatial_intensity[i]**2 * torch.sum(all_moderator_effect[i])**2 for i in range(n_groups)] + all_estimated_sum_alpha = [all_overdispersion_coef[i,:] * all_numerator[i] / all_denominator[i] for i in range(n_groups)] + + p = [all_numerator[i] / (all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) + all_denominator[i]) for i in range(n_groups)] + r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] + + l = 0 + for i in range(n_groups): + l += GLMNB._three_term(all_foci_per_voxel[i],r[i], device=device) + torch.sum(r[i]*torch.log(1-p[i]) + all_foci_per_voxel[i]*torch.log(p[i])) + + return l + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -660,8 +749,13 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = all_foci_per_study[group].shape + log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), # Therefore, we use moment matching approach, # create a new NB approximation to the mixture of NB distributions: @@ -707,6 +801,7 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.groups = groups self.study_level_moderators = study_level_moderators self.penalty = penalty + self.device = device # initialization for beta all_beta_linears, all_alpha = dict(), dict() for group in groups: @@ -723,21 +818,41 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - def _log_likelihood(self, alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study): + def _log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device='cpu'): v = 1 / alpha - log_mu_spatial = Coef_spline_bases @ beta + log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = group_moderators @ gamma - mu_moderators = torch.exp(log_mu_moderators) + if gamma is not None: + log_mu_moderators = torch.matmul(group_moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - - group_n_study, group_n_voxel = mu_moderators.shape[0], mu_spatial.shape[0] + group_n_study, _ = group_foci_per_study.shape log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) return log_l + def _log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): + n_groups = len(all_foci_per_voxel) + all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] + all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + if moderator_coef is not None: + all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + else: + all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] + all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + + all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] + l = 0 + for i in range(n_groups): + l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + return l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): @@ -753,13 +868,16 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) - group_foci_per_voxel = all_foci_per_voxel[group] group_foci_per_study = all_foci_per_study[group] - group_n_study, group_n_voxel = mu_moderators.shape[0], mu_spatial.shape[0] - + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + mu_moderators = torch.exp(log_mu_moderators) + group_n_study, _ = group_foci_per_study.shape mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators group_log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 3e366c562..b809c3d73 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -14,13 +14,16 @@ def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='NB', penalty=False, lr=1e-6, tol=1e6, device='cuda') cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]], t_con_moderator=[[[1,0],[0,1]], [1, -1]], device='cuda') # [[2, 0, 0, -2], [0, -2, 1, 1]] a = inference._contrast() + + # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] + # [[[1,0],[0,1]], [1, -1]] - \ No newline at end of file + \ No newline at end of file From 988c5b422b431c0f39bddb662c4e57bb724aa81b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 6 Nov 2022 23:12:18 +0000 Subject: [PATCH 030/177] [skip CI][wip] add a demonstration for CBMREstimator & CBMRInference --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 294 +++++++++++++++++++ nimare/meta/cbmr.py | 21 +- nimare/tests/test_meta_cbmr.py | 4 +- nimare/utils.py | 29 +- 4 files changed, 322 insertions(+), 26 deletions(-) create mode 100644 examples/02_meta-analyses/10_plot_cbmr.ipynb diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb new file mode 100644 index 000000000..8f5575937 --- /dev/null +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Coordinate-based meta-regression algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A tour of CBMR algorithms in NiMARE.\n", + "\n", + "This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "source": [ + "import nimare\n", + "import os \n", + "from nimare.dataset import Dataset\n", + "from nimare.utils import get_resource_path, standardize_field,index2vox\n", + "from nimare.meta.cbmr import CBMREstimator\n", + "from nilearn.plotting import plot_stat_map\n", + "from nimare.generate import create_coordinate_dataset\n", + "import nibabel as nib \n", + "import numpy as np\n", + "\n", + "import logging\n", + "import sys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# data simulation \n", + "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", + "# set up group columns: diagnosis & drug_status \n", + "n_rows = dset.annotations.shape[0]\n", + "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", + "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", + "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", + "# set up `study-level moderators`: sample sizes & avg_age\n", + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", 'avg_age']) # standardisation\n", + "# load mask image from dataset\n", + "mask_img = dset.masker.mask_img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group-wise spatial intensity estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", + " niimg = new_img_like(niimg, data, niimg.affine)\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", + " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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d84wzSojzbgNE54plof09ZzH4Oz83btyIxX/7G9auXYtjhg0DYOW9lGlxNu7GGGOMMcaUImVFuoMsZp+a8Iu7McYY08yhMk31l95i2rdvD6DQ8wmdQlDdTrIFD32aF6NWh9tVxWcZk1R9lj30h67HsDzqfz0psqrmlVQ2KvhxqP96+r7XvPk71X/avjsIoKkLtnE3xhhjjGlCXnzpJZvJlDjpVKrov4Zgxd0YY4xppkyZMgUAcNhhhwGI7K9p601bd6q+VOKpbjfE64r6Qle1m2VhnlT9k9RyemlpG/OCy3owD/WhzjTVFl7LxDLXxz2wrg/gd9q60787veMwL5aV1+riiy+uc96m5eAXd2OMMcYYYxpAqiyFVLr2gW5DBsOAX9yNMcaYZgv9sFOtTlKzqRLT2wpRJbomrzJJduBJLyrcTjt7zYufVKjj8iS0F6fyzvpx39r8zyd5wokjtOsPy510blg29etOpZ3bea2MqQm/uBtjjDHGGNMA0mUppItQ3G3jbowxxpg8fv/73wMAunTpAiBS2hmVlHbXVIVp060231SHVfWmnTmV7TCNYuH+VLfXrl0LoNAunWzevDmvDuE21oPRVzUN+q+vj+16WEYgUsp5DgnVfl0foPXUc7/XXnvllZnX7owzzqhXWU3zxl5ljDHGGGNMk3L77bejW7duaNOmDQYNGoTnn3++xv0feOABHHLIIWjTpg2OPPJIPP7443m/P/TQQzjuuOOw5557IpVKYfHixQVp3HnnnTjmmGPQrl07pFKp3OCxXpSlkSriD2UNe/W24m6MMcY0M9q1aweg0G+7elXhdvXUQnWYCva6desARPbdTIc+y8M0VL1XuJ1l01mAJHt67sdZgHCb1kv3rau3HM44qEoOAJ988kleHlTOqZhT3ed25q3XhPB8MQ/u15KYPXs2xo8fj2nTpmHQoEGYPHkyRowYgSVLlsTa/i9YsABnnXUWJk2ahJNOOgkzZ87EyJEj8fLLL+OII44AkFk7MHToUJxxxhm48MILY/PduHEjjj/+eBx//PH40Y9+tF3ruK3wi7sxxhhjjGkybrnlFlx44YU477zzAADTpk3DY489hunTp+OKK64o2P/WW2/F8ccfj8suuwwAMHHiRMybNw9TpkzBtGnTAADf/va3AQDLli1LzPeSSy4BADzzzDMNrkMqnUKqrAivMrCNuzHGGGMCqPbyk95iqExT9dX91Pc64XYq2PxOJT4uTVW1VUnn/rQNp5kCFWhVpqlEh3kmqdhUylkPtT/XMqmnGh5HFT3Mk8o489A01TsO0+bshJ5LKveq4LcUtmzZgpdeeilP8U6n0xg+fDgWLlwYe8zChQsxfvz4vG0jRozAnDlztmdRayRdlkK6iBf3dANf3FtW6zDGGGOMMTsMq1evRmVlJTp16pS3vVOnTqioqIg9pqKiok77NyesuDcBf/zjHwEAu+22G4DCFeeqfKxZswZA3VaYc1X6HnvsEZum5skoet/4xjfqXB9jSolZs2YBKLRhVb/NSVEf2ZfGjBmz/QtrTB247bbbcv8feOCBACJVl2o2v7MdM2Iq1WBVzWmfTU8q/CSh55cklV5/VyWezymWMUnJZt6hr3mmmaSk81nHPBRVx5N+D+up9vT0rMNzxXOnqj1t4xlBlXmy7Lw23D+8nj/4wQ9iy2d2HFLpNFJFzJakpJ/UFSvuxhhjjDGmSejYsSPKysqwYsWKvO0rVqxA586dY4/p3LlznfZvTlhxN8YYY5oBoZKts6y0y6YdtSro3I8ePKgwU12mr3FVpsM81e+6RitNmsWi4ty1a1cAkScbbldvM6ENuKrWVL2pXqsNvPqp15k0blcln55igCjSK1GbflXaV61aBSCaUeAMN5V6VfCT1gg0V8rLy9GvXz/Mnz8fI0eOBJC5rvPnz8fFF18ce8zgwYMxf/783OJSAJg3bx4GDx7cCCWOp7Fs3P3ivh2huQo7PKck99tvPwCFNwi9ARFO8T399NMAgGOPPTYxT+5z0EEH5aVNdJqUNwaWccGCBQCiqTzeaBwIwpQav/vd7wBEAVr0pUE/iZrM6O9k6tSpuf/14f/d7363QWU3xpiWxPjx4zFmzBj0798fAwcOxOTJk7Fhw4acl5nRo0eja9eumDRpEgBg3LhxGDZsGG6++WaceOKJmDVrFl588UXceeeduTTXrFmD5cuX46OPPgIALFmyBEBGracyX1FRgYqKCrz77rsAgNdeew277bYb9t9//0TzqqbGpjLGGGOMMabJGDVqFG666SZcffXV6NOnDxYvXoy5c+fmFqAuX74cH3/8cW7/IUOGYObMmbjzzjvRu3dvPPjgg5gzZ07OhzsAPPLII+jbty9OPPFEAMCZZ56Jvn375txFAhm3k3379s35ef/KV76Cvn374pFHHqlzHVJlqaL/GkKqOklOMvVm/vz5AKIpOqpxVPI4nchPnQ7T6UZOZfL4N954A0CkigORmn/YYYcBiBbkhOGogWjqjuiUHj95PH/n1OVXv/rVxHob01Tcd999APIXztEkQBV09q+k6W1dfKczYjWFTFcVP8nVnvYvlmHs2LE1V9SYGpgyZUru/0MPPRRA5AZR7+UbN24EgJzySHMNvihpQCaSZGoS/q99hNv5fNEZKvZRzgir+c6nn34KIFrcSVMTIHLywMW1HTp0yEubz0DOZLNsOgPH+0LSDFy4Xeue9BpFEx/aYvOeRM8nvDb6rsBr8+abb+bSSjIZMU3P+vXr0b59e/z3kf2wSw3PB7KhshJff+0lrFu3rl7BtmwqY4wxxhhjTAPIqOlFeJVBvAejYvGL+zbi0Ucfzf2vi3s40ucIX90+UhHQ7xzFUyGgUsJFQmFACF04RAWeKgpH8qpk8Lu6/uJ3KiBUNcJ6nnTSSbWcFWO2D7/97W8BRAoe2ynt2YFC1VvDsCcp7kRnp3RmLFyLojNXqvLrTFYYsj0sC92/qaIXzsIxDdvRG0Vni4DCGV+qvuqOWGd6tS3zOO7PZ0tN7iCT1G2dfSbsB+xb7M/sL3p8uE33UbeWhGVh/XQ2TM9XnJtIHquzejwnOuPAevI4nnsq68wjabbdmBC/uBtjjDHGGNMA7FWmRKBNIW3LgeRwzqpyqz0gR9tq/6rE2dgm2d2qysgyceSvear6T0WA+7MuYd1te2e2F1TWqaZpsCRVBUN1LCnAUlKfqE1pS+qvYV5qD69pqDu7JHdv6j4vVP9ZPvY/luOiiy6KTcu0HMIQ8I8//jgA4N+GDNlm6b/40ku5tqs28eE2omq3zvwm2cITtXmvSXHnPjymTZs2sWnq/mrLn9SHqa4DhTbrunaF7iIZKErdWnI7n686A8d0w+tpdnxSqRRS6dpfylNVDXtxt1cZY4wxxhhjSgAr7kVy9913A4gUBVWiN2zYkNuX9uUcXVMRo1qtNnXqZUZRu3S1nw23qaofKuQ15cEy8XfWj3WgChHWk3X/zW9+k5cX1QL6XzWmWKiwq22rKlJJNrNxqJKutq2qlmtaqqapYl8Tug+P1XtAUr1qykPt6kOPIoBnwlo6XBO1Lenfrx8A4K8LFuTaKj29ANH6Lu0rCrfzWaHez4iq3+wPofqdFNwpKa0ktT/Jmww/w3pqMCs+L6mk8xg+L9WDnK67UeV+e1w7s/1Jl6WRLmJxarq6YZq5FXdjjDHGGGNKACvuCUyfPh0AcMABBwAA+vbtC6DQH+0777wDAHmBAWhbx5XjHHXTzo2qvdq7qt0rR/UcvWv46FAh0N/ULy7t+NRnrebNkT/LzHToNzesJ/3/9uzZMy9N5kF/9u+//z4A4Dvf+Q6MieOee+4BELV5nWVSxY39r7YoqMWgfprVGw2pKcKqqvRazqT+pvupX2vt13HHJpX/1ltvBRCpelbgWxZhnI9tzaZNmwpmZ4Go31JBT1onwucSf+czU9u9eqUha9asyf2/zz775O2TNCPGfqOe1JLKyrJw/7Ce/I33Kz4vqcozEnnHjh3z6ss81RsWP3nNtue1M9uPYoMrpapt426MMcYYY0yzx4q7QOXvwAMPBBCtDleljKoW92M0UwD46KOPAABdunQBENm9cXSu/m+T/MyqXS8J/UfXtC1Mg4pGUiRHfqrtHpUE1in0GsC6qz0j02IkO9aT53bMmDGxZTUtj7vuugtA1N6oRGm7TFLTVKErJrqhpqXrQ7Qdqy2s2r7GkeQ9Rte1JKVRk2epJPt4ojMG/G4vNC2LCy64AACwOXtP35Zs2LAhVtnW9qxtkWtX6JWFv7P/85mhMUx0/UmouKtP+KSoxKtWrQIQxT/hdj6n+YxMUt7D5zHVdz4fOaPN9wU+R9977z0AUTRXPj9ZBh6v9veO0VCaWHE3xhhjjDHG5LDinuUPf/gDAGDfffcFEI2gOYrXiGgccXOkTDs7IFKnae9GGzqqCurBhaiP2yS72Zr8uKtdn3rSUFt3tbljGakusA7cn+pEWH71mqOR9pgnzy3P9amnnlpQD9O8uffeewFEypsq7EkeIlQFq4ttu/YjtSNP8i6RpJKT0Ld6khcY3Z7kZYMU46mGJJ0T9TOvtr0s9x133JF3/Pe+972i8zYtm3Q6Hdvn1GsSlee1a9cCKHz+sC1SgedzR5V3bev0XhOS5FWmoqICQKTS63OLz3K1T+csdlyf1ecnFXVup2c51oPvBEuXLgVQGB09afbMlBb2KmOMMcYYY4zJ0eIV97lz5wIAunbtmrddI4nyO0fhtFunrVoYfW2PPfYAEKkMVJ41gqra4qkPdvWcobbvoTqnq/RV0WCaauuuKr9GieN21imsJ4/luVBFUmcauB8/ee6PP/54mOaD2tTOmj079796jdHopaqOq8cUflc/yOopIg5t82yvqvYr6ns5TmlM2iepPFqfJH/vWv+aqCmya1yaqvJRgQ/LMnbs2FrzNTsmU6dOBQCcd+652zzt3XffPddn+WwACteHfPDBBwAK+wGfhfSewuNWr14NIDm2ifo9D7cR5s1nM9NkeVkWloH3JCrvLBM9yjH9sJ7Mg2kmRU4m++23X14eLJPei/jM5LVz/ysxirRxRwNt3Fv8i7sxxhjTIqnOvmCmPPluTENJp1JIp2t/KU/XwSQyjhb34v7AAw8AiEbP9EWepJjpdn5XzzChVxeuLOeoO7SFjctD1TdVv1U1p5IfqnDcxnIlKepJCp8qIsyzXbt2eXUK66n2/0meNHiM+sul+k9/77RBPP3002GaD2eOGhW7fdqvfpX7P8kbRZKCpd6R2MZqshXV39SGVdV8VfWT1qbElV89LensmiroSYp6nAeZpH2T7lVJ5y7JU0+YvpW/0oXPtu1BeXl5zn6b9txA1KeotKsCT8WZzxWd9WLbpF0611TpOhMq2OE2XS/DNJJm2ridCruuEaFdOtdmhfUktIvXvqT14vOXM/t81jFPqv+dOnUqyMMYxcNsY4wxpiVQXZX3d9vtd+CZP/052maMqTepsnTRfw2hxSjutKfmiJZRTTV6WlKktqSoirT5ppcMIBr5cxRN1AZVlTO1U+d39RvN0XyomqtfaFUA+TvT1CinqrqpjWGc3Szrrl46tF46C6AzC5z9oFpj2/fSpK7+oi/6j/8AANwxdWqBWpykgusaDm2voa/l2jw1qMqnyjrRe0Qc2n/Y99mmdeZLI6rqrJzmHdYlyfe7KotE+6P+Xts6AwCYNm1aXh72M71jwZnk0LsZo3bWRHl5eY3rQpLo3Llz7DOBM781xTgAoucln8O0+VYYsZt58Tiq6WEafM7wGIX9QCOaJ+3HOrBOXJsFRLPFnNXgTILen3TtTVK01m7dugGIVH0e/5e//CWXJ6OWe0batJgXd2OMMaa50//LffM3hEq6qOr/ceEFdUr7zbfeKhCkjDEZ0mUppItYnJquso17jTz99NMAIiVCFXO1kVXFXVU5ospaOMpPUqmTFD1F7eepxqmNLSPBAZG6wpE8y6V5J6GqI8ugymCorjCPJHt5VfL0nKvKqPb0vHbHHntsjWU3TUtDIzN+L2s/Pe1XvyqYMVIbd7apJHvucA1G6HkiJClSsfaRpIjAcXbqSb7ek7zFaH2SPEzF+X9PUjM1mqzOOKgNu96P9JzG1Zlp33nnnQCsvDc106dPBwD06tWrUfJr27Zt7hlD23Cqz0CkTuuMGVGbb97zk2aB6BmGefC4sJ9zX+7DY7Q/a1/StWRJ/SNOcacnGlXIuZ0zA+oBjueOqj/LoDFQ4t4R+A7Da/6d73ynYB/TMmj2L+7GGGNMcyentKutejG26/YqY0yDSRXpDjJlxb2QOXPm5P6n7RhHvBwhq3cVVYVVcSdJClo4fcjRtnpToZIc570hzJvKAX/nqJ2fVKpDpUNnDmh/rja2tfmqZhmpVur+YT1VJdR9dfW+fqqax/Roe8hodOH1HDlyZGz5TeOTqLQnvSjU8nKQTqdrVZPVpp3E2bgnzZIl9YUkby3aD9lu48qqEYhVxebvGrWVM1xJ8RfCsmr/US9Vtc0SMm9d10PCe17SmgKm8ausZyDeZ6wCNi70rlKMn/9tQatWrXLPN7aF0Fa8tjgG2p6o2ivax7SNhvFEiKr8SdGK1YtM3ExTXB3CevIYfdbzHkHlPemeo7MEWhb2Td4XgGhWP/SoY1omzfLF3RhjjGlRVGUGeykOnGtanM0XbCvtxmwzivUYk6qyVxljTFPCF4XsZ6oqf+1C7uUg6TPLdy+8EAAwNevBxJQm4UxMmxhl1DQcznQceuihAOJjC2wPWrdunfOgQjV4U3C9a1vHpLPNnFFSv+c6a6Qe1cJ01aNa0poN7sc8tUyKlimsJxV/jYquM9yEZaMi/+mnnwIoVM9ZVtrThzMLzJ/nnW3gP7LeuUzLoVm9uP/6178GAPTv37/gN3YEdix1UaidXaesa3PBxsUmQOQaih2fv/FTpzX1JqXT7eyw/K7uIsNt3IfTeuz4rK8ujtOpTZaRaXN6TusSHpt0bnRBq57bpJs1rxXzZuhpILrGF2Zf8kzzo1WrVokmatpWOKUc9/BNcnGqwZqSAhSRJLeS4X5Ji0w5lR7n1jGE/S1pwWhcedTURfMkSS5uddo+6XyE++iUvt4n7777bpx15pmxdTTbh0F9j8z8szVrVlWVvR9XxbgYzQ6Wq8ta532vzYztzbfeyrntNcbEky5DkV5lGpZPs3pxN8Y0Irkp+ewU/RdZ29fKL/J+T6WzMQNaZe24W2X9oXuavlkx+tvfLthG9d3KuzGmuZNKp5BKF7E4tYh9aqJZvbgfdNBBAPKVMCrOSb5nkxaq1RTeHCh0IRcGZ9HAF7oAJQmqVuvXrwcQKfcayplhlkPFndsYhpoLcKi+sf50v1Wbe0imE7rAAvLrmRSOXt1gqqqf5MqPx2kgmHCKktfYNF/KysoKApToQrEktTgOzjbxk/cEHsP+lbQAU10hkrgAaCy3uoxMcvdIdOGruqgL99e+q7MO/OTsm5ZbZ/aS6pdU17i06hPEx9Sf0L1x6ous29PKrOLOATSV92CxeHXr7GLH1K55v9W2tDU02VCzldD0JOl5qe2YbZjPRt7z2WZ1ASk/6bDglVdeyaXdt2/Gm47ObrEsdEjB/s++xv3VxCYpYFlYT8488xlNeJ44463uIFkGfld3mDwf6mYyrA/LEQbbMi2LZvXibozZ/uRsmLNKe3pL1oPCprXZ71nvC3wpaJ0ZNFa1zXgsKlgYJ8r7d847L/f/bVOmbNOym21PnNKeI9tGtqzJRLos36NzYxTJGGManXQ6jXQRi1PTlV6cirvuugsAcOSRGVu/ONdpoToNFKpNur8GZOKnHhenolPdVgVPVTZV36gsq1quwRy4X6hycRsXvbD8HMEzD25X95dJMw5UEOLqoOdAbdd1AZKqiiTJxV9c2TgDwGt+/vnnwzRfqCaz7WibSgruFaJ9mPBYba86Y6Su6ViWMC/2eVWzVXEj/F2Dq5EkVTxEy6N9W4NZJQV3SQpAE56LJBd7SUq82b7Q3jydTueU9vTnmQFz6vPMjG31xqyrxeB6p3fZHQBQWZadOW0duRusidatWxf0j7jAZUmBlMhee+0FILqPsx/zGcc+l+TOmO0wnHnlNu3P+sl2T5fHLAvV8TVr1tRYh7CeWneeG3ULqWVLCmioAR1rms1gWl5z0HJpFi/uxhhjjDHGNBVFB2AqYp+aaBYv7rTHVmUJiEbyVKRVHa7NdpOjWyoESSHXayIpGIWqWBxda/AVjupVhQhtv3ffffe8fXisutuKC+gSV7Yke/zwuKSgEqyX2vkl2SHrtUhKL/yf19w0AVx0Si8WWROZ1JoPAQBbV32Yt3vZXl0BAOmsSUwlPVpkF63WtEiVbV9nzLRtsH3HqWLsT2pfqkqz5sHZKu3rzDP03qIqPWcKaLPLNs4ysEzsw6ria+CZmhR35qFqXpI3Hc2Dx8XdC9XuPVQKzxw1qmB/AHneTKgCl63/OFPfvz4IAGj79YvjjzU1cs899wAADjzwQAw8tAcAIPX5Z9nPjNJeueojAEDV2lWZg1q1zh3fap9umX2zZmu12bYvfO45dOvWLe/5wHbEdVXhfZ222WxTtAWnuk3oMYzPCLYzfUawvW2SYG8vvvhi7n9NW4MzqfrN73ym89nJz1WrVuWVLa4MrDvVe6LPUZ6HDz/M3BNV1U8KBKn3E6Dw3LLfs02MGTMGpmXQLF7cjTHGGGOMaSqKDsBUxD41UdIv7tOnTwcQ2bbH+UrmKDnJV3OSvbUqfdy/GK8saruuaep2pp3kLUJX4MeFgea+amurilltfqKTbGtrmllQJU+94qiNcNK6gqRrFObNenbtmlFx2QYcan37o4tSU19k11p8llGfPv/H6wCATf/8AABQ1iZzrah/tdp198xxVVm1TwI3xSnvF2TXMPzqzjszaUt/VkU7bFuqJHO2SfuVeq5hmlTutF/G2cyr/bj2L6apdrjq4Ua9T5BQ3Ve7ePWlrsq72r5ze5J3jTiK8hoTE4ArtSWjBlet+icAYP2SdwAAf7v1GADAUU8+U3u6JgdV4bzrwf6zJRvnY32mP25dnZnlSLWOPIGVtd8z/5haaN26dUF70vYWtlW2KarDVMPZ9/hsUBtxtk3Cfs5nSFKcgzAt7YN8FqoCr2vK2Df5bFcFn2vOwjIm3Xd4TjRWBGORUMVXSwA+22t6r1B1nvVkmzAth5J+cTfGGGOMMaapSaXTSBUhchSzT02U9It7jx4ZGz/1pR4qt+qNQu3g+bvaYTMt2ujV5tc9VK6TfE4nwd85clblmaPxlStXxqYfbmM96ONVoygyj9rKVJtP2/A3taVVBZ32jFRddP2Aeg5QVSVUOriNabENmO1DGL4+p6hW5gdcqvw00y4/r8i4/Nu4MuPZqE2HTN9pk7WFhyjsqSJ9SAPJtqMkzmOKqnhJYdZV9ePvSSp5nN05lbPaIqiyT6i9PcvNdFi/uDgUTEujOqtHC/W8U9tMYJw/97gIqefWZksb3r+ybaWaZdwlcy/b8+C9AQBvnncyAODQux+pOU0DIH8dRnU669mnLLumgcp6dnt1ZfY6lwUzKdnfchFUa4mcGnqToVrM9qM+zIFItebn6tWrAURtlnblSfEM2A90xokeVGgjHufffO+9987LS9PQGAk6083nK5+3rAPvA5wtCOvOfXhu+N6g9x72RdaDeemzjsezD7K+YZ5afl2bY5o/Jf3ibowxxhhjTFOTLivSj3tLtnGnGs4RN9XkUDHiKFU9LyT5T9btOrol6pkiVACSorHqiF/VBo7SO3funFcPVdSoKIRRTHVVOhU6niNV1WryQx9XzySFBChU5/Xc6TlnudUeWH3bUjEJ1UbWg0oE62e2LZvFi0MeOeU9O5u1NeurPHszotK+c+eMPW26XeazulVGyarJi0wSF15wAQDgF5MnZ9KUNqOzN+Fv2j61Xaq9ua5vqc3zFFC4fkNnodhOQ7/MYRrsE/ydCh6hChhXHvXbrjMDOquo/U77tNoEA4V9uGiyajBtq3c5+PBMfbL3ODRwyrilsX79epw96vTMl+xaguqqbICznbL3+j2z5zbbL/P8uGfXmeT8uCf0xUXPP5+7/+pMlc4ShbPQ/P/vf/87gMjrCpXpJNU7yaMY82Z8EvaLsB1ym0YfTUpT273Ogq1btw4AsHz5cgBAly5dCuqZ5JlJZ7eS1nVpNFd+Zx4V2dlLliUsp86AhDMBpokpcnEqGvji7rumMcYYY4wxJUBJKu7Tpk0DAAwaNAhAoQIVKkYcfVOlpr01FXjCNKh8Jflu1pFznBKtUQVV3daRvirVSZ4puNqdI+xQXWQa3Ed9OSflnaQmJikf4WyGKpm6j9orqtKuain3ozqpygmQrPqwTVx00UWx9THbHtrYpnfbHQDQZv/umc/s72UdMjan6Y4ZD0CVbTLXtTrrx726Hsr7pZdcAgCYcvvtACKbWfVsFG5TTw2chUuK5aAKtvpgj/MCpTN1SX1Yo0/qJxVK9UoRKvU6E6f9iso6y6T1V5tYlonphOq+rimJs2suIFx7k51lqd41o7i36paNRVGVPbfZtrD1nxmFttV+h9eefgtk6tSpAPJnH7cnW7duLYiay0+dCQ3bF5+vbEPq51xnXePiLwBRG+Vzuqa4KdrHktZQEVXJNV4Ky8y8WaewjFp37qtp66wePdTsv//+AKJzybgkVNGZZ9hX165dC6DwWc4ysI2MHTu24ByZxiGVLtIdZEtenGqMMca0KLiwlAHMyrMuRbMmbOmOmZfC1ju3Kzi0epfM4kgOmOpjtmaMaVpK8sVdlQCOsNUuFEhWB6jAq4cGompwnPob5h2S5Kdc/bCqCsfRtSoEH330UV7ZeVzoMYYqAdV42gTSPo+oP9wke/wkNT2sb5Ldv/qb12iRhOeY+/NTvQGEsyPq2SDOp71pGA899BC+dsIJyTvwxaF1tk/s3gkA0HrXrLqWfaGobp3pczmlvVXWZrZVed5+9Xl5oE0o24zarQOFbVzXUCShNu/qmUrbXgj7pKraqlqqhyX1LqF9Jiwz+4OWI0lZTLLxVf/2cYTl+8bIkYn7heTNpGRfEKuybSCVfcks8GaStbn+PBvtc6fd831pt3TYzhvLZ3dlZWWBpzX13qbtLdzWoUMHAIVrwTSycNJ6r9rWgdXkPaq2tWQkqQxMm15qqJKH/Z15Mg32U6ah0Vr5POZ9i8fTywy/07adx4XRWlku3pf0eZtUT9N42B2kMcYYYwAAZ515Zv4GdemY1TuqOCDeKWNGlgqCLeVMl1pH5lDGmNKiJF/cORr95JNMhLiOHTsCiLe/VBtSKln8pFKdFCG0mMihiu6rtuxJnlxYRrXjpoqukd5o8wZEMwo8lqNy2rwzzyS1UcuUFN21mFE981Zf1UlpJ5WF1zmcSVFftmwDNUV9NHWD6lABfFEo4xR95sWgih4qJAJqbjo+u3+1KuwNmKanL/HfzZoFIGoXcdFM2c40kmptniw0sqjGRAj7gvp+ZxpU0miLnxQRVT3YqKoZ3lM0yiLrmeSfXb8TvTeyzOF9lOWozftUIpx9yV76ar5dahvgDF+6JB9J243f/OY3ABrfe1bcfZ5tWNdPhfdxKsrsD2ybbLO6hkzbrK47Ybtnutyf34HCaLLa7/W7rjNhmdgX9V7CvGh3Hqah/Vtn2ljeVasyM0m9evXKO4627RpJVb3EAdE51HpqpFi2mQuynrhM45EqSyNVxOx/XmyFeuC7pDHGGFOq5AbK2UFWmgGZsqZpwUt4btDdADM1Y0zTUpIv7jrip8rF7XEeGGqzgU6y165NlYvz467bWC6NdEg4ktbV7czrkEMOyTuOo/p+/foV1FM9aSSp/aoyEJ2ZUJUyrGdShNhiZy9qU/HUHjisu5arNrtlUzt//OMfAUQ2nYnkbNiz15E26yRJUd8OLwk1RQ1MirWg/Y+/c2aH7U3tVFVlC72vMPbCvvvuCwDo1Clj96/2qEllZJ6c7Vi2bBkA4IMPPigos8Zm0PU4OlPAvkJVUO1yebwq90CRXmRqIncPaBW7vcDW3S+SeVBNbuz7WzqdLsiTZaEdtiq9QFTON998EwDQvXv3vH1rin8Sble7eqZLv+aMDA5EKrV6sFFFOimeQ9Laj6VLlwIAjjzySABR/wGifsF7Jfs/lXWWVyOZE94fmBfroMfFrSljv1VPNnxme71X05Eq0o97Ub7ea8BvO8YYY0ypI4Of3KAo7j0uYYA094knGs3lpDGmfpTkiztH/ly5zlFqnO20juyTvKgkfU+ywVPVLi5PVZw5IqZd9htvvAEAWLJkCQBg8ODBAIDDDjsMQDQKV1UibkSt21Q9o/LHPBcuXAgAOPjgg/PypM2d1iuuTnoutAx1XR+Q5O8+PLfMQ330Onpcw6ENZ2VlJe6YOhVt27bFeeeeW7jjDqSOqi1o2NbU04yq8/QawTavirRGXtV4A3EqqKrz6rEl6f5DeE+jIsdYFf/85z9z+7z66qsACn1m877I8rEs3I8KPKNYqo/2OF/Z1dXV+PY558SWNdqpKv+zJmpT2rOfjNzbJiaKa0uC16qx1vDstttuubbLF3jaeOtsJ/tLOPNEDjjgAAD50b3DNGrzaqYxCHT2+sADD8zty21qu64+45PWWum55f6sA/tDXD05S8d68VxRDecnZ8nYR3UtgM5sqT/4MC2dedeZDw+8mo50Ol3U+05d1kzGUZIv7sYYY0xL4ulnnslbnP/1k06q+YAdYGBtTEvCpjIxTJkyBUBkc6b+W9UXcvh/bR5MkkjyEKMKdNwqfLXTU5t8Rk9bsWIFAOCpp54CALz00ksAgGOOOQZAZDerKnqcuqieImgj+8wzzwAotBFkGTRCXVxEWP2udVfFLskXPEmKXJmUTlgvwjZA7zlsIxdffDFMcTz++OMAInvNuPPeGMyaPbsgAilfVFSBU7vuuFkoVdppA9u3b18AhbNLSW1efydx+2nbrW2mj9Rmh8t7ABDZDb/33nsAgBdeeAEA8PHHHwOI1HoqhOqHXu1pdcaS5+3ss86KLWsBorinKrcW/hbVNP+rXy5j+cMf/gAg8pimfv+3F3vttVfuOUN1mMo67bc5e8vZobBfsL+y3Gx7LD/bVtwMWfg7VW+dwaKaHHoaU4VZPTPpPUVnilW55oyVquJhPjwH7DOc8VUvbhqngX7b+TtjtLAM7Jv8rOl66z1DfeSzDZ166qmJaeyI3H777fj5z3+OiooK9O7dG7fddhsGDhyYuP8DDzyACRMmYNmyZejZsyduuOEGfO1rX8v9Xl1djWuuuQa//vWvsXbtWvzbv/0bpk6dip49ewLIvBsde+yxsWk///zzGDBgwLat4DbAd01jjDGmxHjs8ccx94knMPeJJzDvyScblNbqTz7B2++8s41KZkz9mD17NsaPH49rrrkGL7/8Mnr37o0RI0Zg5cqVsfsvWLAAZ511Fs4//3y88sorGDlyJEaOHInXX389t8+NN96IX/7yl5g2bRoWLVqEXXbZBSNGjMiZDg4ZMgQff/xx3t8FF1yA7t27o3///nUqPxX3Yv4aQkkp7mpzpyqWRuIEopG9Kl1J6m8SSd5l4kbESf6j47w2AMg1DtqucjX77NmzAUSje/qA/dKXvgQg35ct1VKm8fbbbwMoVNdoG8g0CMvExpxkrx5uT1IV9Zja/NfrdrVbjrMt5DnlsTwXtu+rO2wjvPZhm77n3ntz1/ycb32rXun/9r77cv+r/amqRUlejJI8NVGpivOjzH2ptA8ZMiRvX1XeVB1TtU/LEuaVFM1U+wbLrd6bVIGsaaaQCuB+++0HIJo1e+WVVwAAf//73wFE6p/aADNtjdSs9si1okr71qytfFVl4T4FCyatvNeEeiPSNRP1iS9SE61atcqp5ETty3lvZdnY5kM7bO2n2q55DNue9mN9XmsZdM1WuK/2Gd3O+xzz0Fk89cqieYZ26Cw3Z+10PRrPlcZtYFlWr16ddz6o2LPMquiH50jjTCT5wG9bgutDbrnlFlx44YU477zzAADTpk3DY489hunTp+OKK64o2P/WW2/F8ccfj8suuwwAMHHiRMybNw9TpkzBtGnTUF1djcmTJ+Oqq67CKaecAgC499570alTJ8yZMwdnnnkmysvLc16BgMx5ffjhh/GDH/wgcYa0qfHd0hhjjClx5j35JJ559tncXzG8u3Qp1mbNJ41pSrZs2YKXXnoJw4cPz21Lp9MYPnx4zpmGsnDhwrz9AWDEiBG5/d977z1UVFTk7dO+fXsMGjQoMc1HHnkEn3zySW7wUBdSqTRS6SL+GihUlJTiboxpOu6eMQNApPpRuVIbaVV4TTODKnpVdnbki8xMTWrr53nf8w5pnZ01o4JVVtyjx95ljGkZrF69GpWVlbk1OqRTp0546623Yo+pqKiI3Z+xBvhZ0z7KXXfdhREjRuStK9rRKKkXd51mTgpdHE751rYotbaFkYpO4YXThIqaxKg7Op3i4qJbLjLj1ByPoxkM7bdGjBiRS+uJJ57Iy1MX8XHqjnloGZLKqPuFddIXtaRzWVvQjdquRXg9dXFw0qJFUzxc6KVBvGpbSKkmJkSnxzmNHB6jU/9JAVqImtbogrG4xZ9sCzSR0eln/UyCZV27di2A+KBEeu/RwYwuOtP7BstNszCa89AdYNy+eq5ockdzuHnz5uWVn/Vn2knu8Dzgano0mBZNKmjOpi544+57f/rznwEUthOmzcWntZl/sq+xbet9P+w/bDssbxi0CIj6K/sB+5I+V5MG/3HPiqT2qvXWxepq+kNYBt4X486L1p3nRkUNDYSoAdd4HXl8McEJWQ+eO+bBc64uk03xfPDBB3jiiSfw+9//vl7H26uMMcaYRmfUGWfE/6A27VllPb0l84KT3pQxuaDyDgDVFCeqMgOQKk4RZ6PteohgjAEynojKyspyXvbIihUr8mzQQzp37lzj/vxcsWJFXvTaFStWoE+fPgXp3X333dhzzz1x8skn16sOfnGPIWkUztEq1apwpJm0MFLVblXyqK5R4aBywE/moQp3uE2VLOZBpYN56GKTbt26AQBee+21vLT5yTLGLVzRBWYsA9NUd1taJlVTSZyrTQ0SwTJQqeCnBohR5YYkKZ9xykHcAkHAinux0AUkULggWQMMqUpE2Be4X1KbCRdoMS/CYzRtbVMsg7pw07YU9vMjjjgCQPELllXN48wXF3vSswHLECp1DObEhwMX+jFvBmBhOdn3dbaDi8z5yWBtYTh3uuEjem6Y1xnZF/A/Z5VXLnrndWHZVMU1TY/e87n4nn2OQbWoumoQLaAwgJjew5MC+6lzBXUzSOLUb5ZLHTKo8s57gi5WVdeMRPt33CJ0nQHUZ4TOKOrCUcKFotxfZ62B5KBOunhYrQJ0u16bpBnlMG1u48JY9nedGSi1/lxeXo5+/fph/vz5GDlyJIDMeZg/f36ie+fBgwdj/vz5uOSSS3Lb5s2blwtm2b17d3Tu3Bnz58/PvaivX78eixYtwtixY/PSqq6uxt13343Ro0fv8LMVfssxxhhjjDFNyvjx4zFmzBj0798fAwcOxOTJk7Fhw4bcQtHRo0eja9eumDRpEgBg3LhxGDZsGG6++WaceOKJmDVrFl588UXceeedADKDmUsuuQTXX389evbsie7du2PChAno0qVLbnBAnnrqKbz33nu44IIL6l3+dFka6SLU9GL2qYmSfHHnaJQjZnXjFKfcJtmsc1+qaVTC1DaVgYs4ytXgFGGeSa6sdHSudnLcj0EaNHCTjt5DxUDdN2oZNPCDqik68k8KHBPWgaoDVUOeO6qEVAioTK5ZswZAdO6oStZ2bUK07syDyo0pjlDhTrIzVSVXbVuTFLikwFzhPurOU23dk4Kk8Di1/Y4LGsXFRUn9T/sM86K3gXfffTcvTyVsc1TpGPCMyjuDfPC+wXarivynn36alybPHc8L+xQQ3YuovGsgKVXchg0bBiByH/n0008DiO4J7I9hUJuaSHFRatYkJr0xU/bKFcszZf9sbbRveXYWtEsmaFR160z9q6uysxXpfHeRJoMq7jrDy2vGfsAZmnBGS9NIWiOW5MZX3YbyPqFrJuLWwui9m88GojPcqv7rmhZNt6bgg0lrV7RP8ZzpfjUFVSTsp3w/0PVYer2IPsv1/qczFaFqznsH+23STEpta3Z2ZEaNGoVVq1bh6quvRkVFBfr06YO5c+fmFpcuX74879oPGTIEM2fOxFVXXYUrr7wSPXv2xJw5c3KzrQBw+eWXY8OGDfjud7+LtWvXYujQoZg7d26Bm+m77roLQ4YMwSGHHNI4lW0AJfnibowxxhhjmhcXX3xxomkMI8CHnH766Tj99NMT00ulUrjuuutw3XXX1ZjvzJkz61TO2LzSKaSKiK2QSjfMP3xJvbjrSFpH41SlQiWMI2CqUjriZchhDaBAdVjVRSprVDo05HFYLo7odATMPKiaMG8NOc/faTfIEbeqLUCkplHZ4Dmg/RvT5Iie26maxI3wgWg0zzKGdanpHACFYZypFFBdpDrUpUsXAIXXRpX78BxovYr1ENLSoW176BlF7cV1dkXVoKRgSRogJE4BUuWcaJ6qzDOtHj165P1O9ZnphkHJagsipjaxfCi8k40gqTajVNHY9kKbVy03+x8DoR1wwAEAorbOc832zL5E1Zt9Q+1zw3PCEPTsXwy4pJ52uD/XuXzzm98EADz88MN5eahynwhnUrKLU6vWZmYrv/hnpq6bV6zO7dpmz4xtfnqX7L1q58w9ILfQ1cSiKjLbNdsg77VsJ2w/NdlEJ93bNU+dWWM7U9WcZWK7C9PkJ/sSXe8xfLx6U1HFnWUvRk1OUtaTPO+wX6hXlhdeeAFAtKCRs2XqtQWIzgmf2YTP5q5du+aVRd9Zkmb71LtUOKup67e4D689+zHbhp+FjU9jLU71/KQxxhhjjDElQEkp7jl/54H9UkinrGeHmliTVXupqFPN5miVtu60QVUfr7ramIpHOLqlwqE+XZMUTSpkHDlzZE+7LipqVMwOOuggAPk27vThTLtcepBgGhzpMw/1tJG0Ol69toSzHOohhPVU7xYs//LlGRtYeuDgeeK1oCLPvHltQttbXg9VT9Vm2sTDa6PXDii0aU+ahVEvMuoRJsmDQpiHpqXb1SfxYYcdlved7Zzw+of9MMmrgtrsM81//OMfAApVMXp04XoW7d8hWg+e5/feey8v7/333z8vD/WyQTUtzouGnncqbHrfYLm1TNw+atQoAMCDDz4IIJoJK5qsal79eXZ2YF2mHBtXFqbTelPWJ7Yq7UXatre0QEy857HNUdnl/ZuqMO+ROtsJJM84sX1TMdfZLfXexvuzzg7xGRKn7LK9qnckqtqMNaDPNvUipZ5h4rzn8Fzx+ar3Hx7L59OyZcsARM8SPitZRp6XJM9VQLS+hOeE55/nijNrOjvJMjAPHsfvSbFMwmN5/vl8ZRvguVbvbqbxsOJujDHGGGOMyVFSintSVM+6sEd2pMxPeoH45z//CSBSq1Rp5+ido1yOwjnajfOMouqBpqlRJKk4cz+O5jXAgKYTt43fqWRovdQ+WdUZ9aMd59eUNoI8J6qwa72pFLz//vsACu3yqQQm+b8P99UIlWpnbeLhuQ3tNVXd0nZJ1Pe/2rTH+foP0w/3SfJoocoUfe9SeXzllVcARG1PYzeE9WJb4bFJMwH0164xDqgoqrLOeod9jn1X/VVTcacSt2TJkry82T8J+zyvhUZ7BQpnDPQ6cN0Ood2tnnPmdeqppwIA7r//fgDAH+fMAQB8Q9yl5aDnn9aZc5/eLZN+efae2nZDNJtTvlvW3r5t9ryUtcr/NLGoXbraL6uHEd57w/bPdqueW/R+TNhveU+lYsvjub/6jg/v11xzwnLwmMMPPxxA1CcZBZxKM2fQGPhGbcd1RvX555/P/Ua7eY2irTMLjzzyCIDCWQyu7WAZeRyfUzzXYSwFnenlPnwf0PgvOiuhdulJ3mlCG3fmwXsdrw/bhK6HqSmqu9k+pFLp4hanNtCLlhV3Y4wxxhhjSoCSkj2+853vAIjsHbcFx2R9HN+fdQVEdUp9UOtoNy5So6K+atXejajiybzoC/rggw8GUBhtMfRDqhEYeQzT0HIn+U5nGdWvdhysO9PUiHSq9PDcckU+VRmqElQpVPkJlU0qE2obyO9sIyaeuHZbm5/zJI8pOjPC66Q28GF757XVNFkuKkxcs8G06Huc11/bZZytPCMPU5FLqg+9yaiNLOups020b+U6GCDqi3oOmSbbKfvwG2+8ASBSSqmcsu8kKXBAoT9qjbLIY+jR40tf+lJeGdXWmdft6KOPBgC8/PLLiCWrElWns4+N8ky/rd49c48pP6QfAKBsr4rokKwf9/QeGZvsytYZBb6ailORytMdU6di/PjxRe3bHAjbFlC43oTKLq8dr234TEjyKpIUgVxhHjpLx+9xnsY4S8VP5sH2S9tv3q/ZR5k2lXg+v/RZye+bgncAVdo1RgnTZB78vXfv3gAi9VvXjmhfDmcBNG6EeqriudMZOE2THnmS1PGaZvL1+pC4tmAah1RZGdJivZC0X0Ow4m6MMcYYY0wJUFKKO7n3t78FAIz+9rfzf6iLb2BRer519tkAgIXPPQegUGWjfZuqjqqWh/+ruqlREZPUbFVC6EXmzTffzEsn3E/Vax6jacZFuQMK7eNUCY07jtu0PDxXtOvVPNS2ncdRReG5j1OE+BvtePXcmppR++gQqkYaEVVtWbUtsc3x2qgHiPA68jd+Mk8qz1/+8pcBRG2DUUyTvAbFeXYhPOapp54CEClrPIZejpLSVD/utN/l76HPeNY9KdKj2hdzzQk9XFDFV4Wd9sShD+ck/9tab/YnerShZ56kSJm8Z7z44osAgN/cdRfatWuHMzS4SVZxp4171S7ZGBm0ec8q8CGV5VkFtpwRU4t79Nz6y18W1Lc5M2HCBADA17/+dQDJzwp97sQ9S5KO0f6rsRL4O/sglWb286To20Dhmii2a1WemQYjXPLZxjUg9JpD1Zh58D4/cODAgvrqTB9noZkmy3DooYcCiO45Gr9AI4GzTmE9dT0Qv/Nc8Vj16sb91Sd7Tc88RZ/J6jtfZwPYpiZOnFhr2qZh2KuMMcYYY4wxJkdJKu45lY0Ku3ymKpNXU1fTmwEVH1HeBx91FADgzbfeAhDZnGqEN4781WY33Eft35JUbapwSTbG/NRV/VTSgGgUzn3Uvk19xxO1pVXVNcnDSNy5UH/1tNvl71Qy1IaY6dDuUZWi0IaPvqZVza1JeTURNSk6VN7CqKrhMRqJUNUwoop7nD91XmMqcrRDp1323/72NwDJEVXVrptqeGgbrB4f2HbY5tnvdCZMvc7wd67BSPIPH3esbtd1L5ydYl+m6q1eq8KYDTqzoWlrnqrmE41GyesansPPPvsM991/PzZt2oQLL7ggv7JU3qmmt8rGqYi7/1LNLcvO5tRi237rL3+JdDqd+2spJMVM0OcP+56q5OG50uudZPOuKrA+l7R/62xQOCPC5w9tt3msRu7WNWOchaVP9b/+9a8AgGHZNWg6uxeeJ+av/ZdpaB66Fksjq6qvda7JCn3lM3/a8qsqr/FG9Dg9p7X14bB+3Id56zuIrn2p6X5lti2NpbiX5Iu7McYYY4wxOwqpdJHuIBsoRpTkizujDqIq60Whkp9ZH95bM8pYamtWhQ1OUnV5ZhRdpbaWogCp0k61jUqHqlRxqB9zHQkTKnrMU0ffHM1TOVu0aFHeceGxgwYNAhCNstVWP8kuXZUBlpkqeZxSq3aW/K7niqiiy3NHJZT1435UG6mmApGSc8ABBwCIzpH6ujfx1GQTqyq2tg2djVHFVr2daByD8Bh6GBo8eDAAYMGCBQCieApU1qj+akRRxl9Qe9bQ7pz2phqdVKMGE5aX7ZeRFNUen4p96C9d4ySw36mdPOH6j9WrV+dtpyqoilzY1zUP/sZj2I94jjWtJAU7zk6ftrqtWrXCtF/9Cu3atcPZZ52VfyDvnVk1vbom+/ValPY7pk5FZWVlXhmSlOLmSNIzQteR8PzExdcgSXbwSR7R1Had91p+6jMvab1UiNrPq4ca9WzE/s12R9t3eqNhn+SzASi0VWe/ZB7sB8yDeSZ5x2I92W/omY2fITobyYiwRGcK9Ti9P+izv6Z1XmwTrJfev/R+bJoPJfnibowxxhhjzI6CTWVqgLbTOaX9i6xv583ZKKabMwpt5b8ySi59CQNAqt3eAIDqVEbJo32mKkEc8VOdU/sxHQnHqYpqe6eKR22qXJLiSeWQtncAsO++++btoyN6zUNXoHO0rmXUlfpxtvxqZ859qXhSYVcViWlTZa2oyPh/1sixXbt2zR3DbVoutglTM3r9w21ErxPbaZI3E91fVaMwfV6noUOHAgD+93//F0DURqiOsT2rhyL+TtWbirV6dQjLzcioLD+VOabF7ezrbFtsa/Q+o/UJZ3k4a0TlneXX+AkaAVMVSabDmQONiRDmu0niWRxyyCEACn2AJ3mRYZ4a0ZjnC4j6F1XNnXfeGXMefhitWrXCSSeeiFjqERXwV3femeg5qBhPG82Fm266CUA0A6XtRu9/hOco9Aeu9/ikmQtVw/W4uBkmID66J4/R9SDsa+wPSXbX6s+cz4YPP/ww7/fwnsL2ynOS5GVJUb/tPMd89utanjBdjUpLODOgNu7MK2m2S98R1G8/UNiPNS4My6/1ZZsyzYeSfHE3xhhjjDFmRyGVThWnuKdrNzOriZJ+cadNe2pLRq1L/SujQG/559sAgMpPMgpuqk3kQ7a8x+GZbTtlfdK2jo8u9uW+fQEAi55/HkCkFFJ9oMoQZzPMEa+OiFVpV5VbV+AnRXIbMmQIAODBBx/M5cltqgRQoVHVpdgyqa/f0KZSlQ09N1RJVa1X21ymQ7t1qo1x6wioZFABVF/xpmbOOOMMAMCdd96Z26bXUe1OtR0neaFg29H0aHMNRNE5H3/8cQDRtaZarLMubFO059T2SPVc7dGBwjUWLPfKlSsBRGsnWA+mRdWMebCdql/nEO5DZZA2uBqJmXlrX+E5Zx4aJ4JKfPi/3nteeuklAJEtbo8ePQBENsqh/T8Q9Z1nn30WQBTNlesFgKifceaD16Wqqgr/M3duriyff/45zjv33ILzUhtTbr8997+qjMVG+GyOaORNztCw7fG6kLj4DLzPqteyJOWWzwyd+VC7dP7OT6rrYdpJCjO387nEmTZNi/eMcH1TXHpx2/idbZbnknmwnnEeaoDoHLO+cXFTeJ51fYl6YVP1W2dKiO7P+0N4r4mbLQ3rp5Fsw35smhcl/eJujDHGGGNMU2OvMjWQGyFXZpXorI175bqs6vZxxi5u/bKM7Vt5u0hxL+uQUZ/SHTOeaVJZ3+9Jmo6OmJNG2OGoXX1NJ60U11Xf3K5KAO12aYNLFS8sG7fR5lePUY8YWg+1iVeVXFXVEFUfqCKqesD9+J3qIm3YqSKpx4RQKaSKYl+1DSNUftQOW31Hq+9xjS+gszxsK7S1psoOAP/93/8NIJrBojrMY9WLE/sC1XP6eaaazLKyLYV9gmkk2fhSiezXrx+AqG1RvSe0/SbF+MymEq3RgXXWST3vdOvWLW87/btzJiKsMz91FoJ50/aXkSPpiYfnhWVSz1GhjTyvk7YR3l/CNvPb++5DOp3ORaGuicm33pp3fPi/ei1pSV5lCNdV9OrVC0Ch2s1zpJ66wvsz9+EMEp8FSVG02fe0H+saF+bJNhAq0UyD/VXXZen9mmlx9odtj57j2DY5G6R250ChFxVGCOa9g+eSeey99955ZWCaWk/Wi+c2tGfXfqxp6DOe5yVpvQnR9QThc41p61ocKu4668J6m+ZHSb64G2OMMcYYs6OQSpchlU52ER7u1xBK8sU9aZW4UlVZlfeZ+ZL1Oc6RLtUcRl9NxdvL6Qibo2/1DBMeoyN6ljvJ0wvVEqoMalMceswA8r1KqNLOkbzayiXZsKvtO8usSnbczALTTPKSQ3WEZaGnAOahtre0b6SyENrVJ6n4xbYJkyG0k9T1GoraUmvbCG1cgUjRiluLwd/or5weUuiFRW1a2XaoejFPthluV1tgINmml6pe//79AUTt9+WXX85Lg2X82te+BiBqh1S6Qt/qVLffykZc5m9J/Ujbq/ZTKvVU00K1T5VTHktVkzNXrA+38zrxHsHttO3nOQy9hOj9gcfq/Y+fW7duxe8feCBXJu6v9Y+7NurNhLRExd0YY5IoyRd3Y4wxprlCEymaTnEwxcEaB4YcjCUFEwKigSgHwSqsqDmkujFm3uo8gYTBkJiGOkVgHkyDA27CgSoHyyrqHHTQQQCiAXI4mKPJG83ueAzz5sCUghHFA5aBQlGSSSvPbTh45uBYgzrpddLBqJ5rNafltVJXr0DhwldeT11MzHKyDZlGJF2W+StmvwZQ2i/u2Qh91a0yN4my9pkO2nqfTOfvkHXLk2od+JndbffsMVkVt4GLBIwxxhhjTAsnnS7unbIlLk5Vk5HtCaeAqSBwOpkjYi544e9A4eibU/McCXNUnTQqJ7pwTRcohQt0qFiouy2mQaVDF5npyJ/qA8vOIE9xobhZHi5go/qgriN5TBjEBShUi7idZWcZwgVXVEnUPKMx20RzIDSVUeVGA3poH9BFW7y+bOc0kfn973+ft3+4j7orZZ5sA2qKwfZNl6G6qJrHs38CkcmZLtLr3bs3gKjNPJ91+cr2e9RRRwEoNLFR16mhCRdNffjJRbRUCHUxJ9F+SbMimvHQfWToUpPl0iA3DKTEhXw8t3RRyX5KVZO/62LjuDrzXLJNsG8mLTrk9dOgVao4xpneqeLZEkO2//SnPwUQtQde2yQXp3EuM9WUUc0g1QxKr5UGNFKzNe4XPvv0+vKTbTVp8aaawGm9eN+gWh7e/zVAkirQmqY++/R+p2WPq6c+q3U2Iyn4VVIwRpZNyxAX8DDJEQOfo3y/YBsyzY+SfHE3xhhjjDFmRyFVVoZUjAASt19DKMkXd6rcNJHBTtmAPRlRAuU9MmpQ2Z4Zm7dwWiK9e9YVVKvMCLmai1ETwnSrIsYRMEfftKt7/fXXc8dwBN83G8SJapsuQAsVO6DQRZYuYOPoPG5hV1L4eQ0ioy7k+ElVi4sDqT6yjMuWLcs7HgCOOOKIvLzUjaMG7tF60v0eVVZ1JUZVJbT34/+quDsQU90455xzcv/fc889AAoVN6JhynVhMPvAl7/8ZQDA//zP/wCIFG4uQAWi9sWgQGwDVPGSVD22TyqPVODpqpHu46gqA9HiTLYV2gvTXSLdpbEvDxgwIK++qvySuAWn7C9Uuw4++OC8c/P2228XnIsQtTvmeYoL8MZtvI+w//BcsB9xwXqnTp0AROc8yY1k3CLQcAEuEM1o6IyH2lzr7IQqjHEzeEyT55f1a4mKO2E7p522umjVz/B88jyybRJVbDXwkroQZjvRoGjMK1Siw0XKYRo8Ru8tuh/z4EyvukbWWdmwfLS153fOErHdq5MIPR8soz5/WYZw5lefxSx3ktLO+5m62tVrofeR8HomXXNNi23GNF9K8sXdGGOMMcaYHQYvTk0mFzCkVXbRKZWxsqz9XOusvVnb3QuOrcz+llPrE5R2onafqr5xREw1D4jUMip7qnjoKDwpIIba4Onv+j3cpnbm6g5S81QVUWcJVCEN61GbMqnbmSdtbakYUJ3U9QOhKqEuMrmPwzvXH23jqrSpnSrPPQNnMeDJ008/DSAKGkNVLLTLZRAgqsAanlzVMubFAGMaAExtYMO2Qnvzd999F+MvvbTmkxDwWnb2TG199TyF6iFt0anyU8UcOnQoAGDw4MEAotkIDQ6lfTl0awnkq4TqVUKvC7/TtpcqpdZH68F+FyrcrLOeA703qYqpnkhYprhAQVovlicp7ZYE1yf07NkTQOG6KF1jEMLrznaiNtJsYzr7wU/ObrFtJtnXh+58eb1ZrqSAf3HPrjBvPjPZjhiQSNfGhGmzPpzpS5qFJrp2jJ9sm+F6GSC//+uaKrVx1/04G6Aquc5uMB11dxvuo2tTtN+wzZjmS0m+uBtjjDHGGLPDkE4Xqbi3QK8yOdWX7iCpmme/I6vEV5fvoofmFPbqslZ535PQETTVH46g40b1VBWovNOfqirnHHWr2s2RP+tJbyxJZampvFQCWRYdrasXCI7eWQfaDFMJCNU45s+RPsupqgrPDe0WqdRyNkDVV3riiPOYwPw1zHM4E2DqBu3dZ82aBaDQ04GuzejRowcAoHv37gCA+fPnA4h8LatiyusLRGoQP5km92HboOLE3/mdfYNKVufOnfPy5O9HZ5Xu+nCkrN144803ARQqd+G6ioULFwIotOlmOdk3WF6uGdH7h94DNLw8ECmB7MM628Q0OAtB9ZL7UcXTdTuq5MfVRz2V8Fi11dVZGm1DJJy1ULtgnoMbb7wRLZVrrrkGQDSbpesR9LqEwbN0PQKv+yeffJKXFlH7a6LPqyRvNEChrTrbj3oQ02BuLD/v67yfs81yDQv7HOsARKo19+ExvGfw2ZfkxU37GmcadNYg7P9q467nhujaj6RzzjUMPG+8duH++rxVLzr8zjZjmi8l+eJujDHGGGPMjkIqnUaqCDW9mH1qoiRf3E8/7bT8DeIZpkCBj6MWbzIkKby7RjaL8/pAhYMKgI7s1Q82f6evatrqcfRNOz9V6sNtVKSp7FHpo9r9zjvvACiMbEfVQm0Uqb7FrYJX9Yzqiq6wJ6wf7eW5H+2XGdmO6XL/0M5PfQqr329Tf84880wAwOzZswFE14FtgXa2VKSeeeYZAJGPcV4LVaNCpYrKOq/Xl770JQCRhxd+sg9QWeP1Vn/HbEtse/379atX3WvisEMPBQC8+dZbeWVasGBBbh/1hc4+zn6n/ZGKItfBaMTFJP/OQKF6zU+1R1fvE6FdMFAYzTLJ3j4sD1FFnZ/qA1vXpJC4Mqnf8CR/1S0RzlBxXZB6+1EbaSDqj9yXbVFtuXm91aZbZ2L0ucPvoSqs/SC0fwciRV2PZf/l9oqKith02N/j0Oeuqvfq8UZnFNk3mZfOhoX1TDoXJCkGBPPiOWWZeG14f9RrFx6raz+Ytm3bWw4l+eJujDHGGGPMDkOqSK8yqRboVaZWilTTi4GKkEZdS/KXHkJlUu11OXLmCJl+V1Uxo0pH9YHKPcv04x//OJfXokWL8vbhJ9P4+9//npcH60OVgbbFapuY5H85/I2oUqaRNkNb5/A7bRBZZtrzqo9fIFJPNO+4qI+mfowaNSp2+5NPPgkA+Nvf/gYgagvq0YXXgm0onJ3imgkqzbruQWen1BMK+wrbFvPonVXua6U63sMEgFrvF2yPjEwarr1QtVjXa3C2bMKECXlpMjLmaTqLKIR23hqbQWc41Me6qvjqC1w9S8VF4SQ648g2oDMGvNclebIh4XamwTbgWbSIV199FUDUTzQSqc52htDbCvsnP/UeqrM7up+2E+YZrr/g9WQatN1mW2W/ZZnUvznz5HGMe/Duu+8CiF/vpfbxzIPPF/VowzyZBp/TrA+f15xZU09rQOE6E1XYk86lxk/Ra8LzojbvQOFMAdOuqqrC4KOOwqCBA3G17dublkZyB9nwN1tjjDHGGGPMdqd5Ku7bgJdfeQVAZH+to11V2EJUuVL1ifZrtFekskQl4Oyzz85Lj8pB7969E8s7aNCgGuvDNCdNmhRbBvVDq+pdnPcItaHVyK+EeVFJo8LB7VRVeDyVj7goearq8lP96pptz/DhwwEAt9xyC4DC2RmdjVJlF4iuH9sd1XuidrZsA2xTbAvcr1abdirs+kniVPYE5Z3eZh555JG8sgCFdb/qqqtqLleW2pR2cvnll+f+v+mmmzLFzPZJnn+Wh+eMaLwItSuuybZd7WnV53fSOhaiUVB1XUycz3hu+9nPflZQnpYKZ1x++9vfAojWP+mapNDWOil2B6+7XjvuRzVf17iwnbDvxUW/1XbC/s57vs4OaRRxjRTLGeNiouhSjdfYJUxT7eg5e8tnH8uontbiIgszLZ4Lnb3Qc8k0knzh67sCP8PryeugM1KczQNatgemHQEvTjXGGGOMMXn07dMn7/sDDz6Ib3/7201TGNPolOSL+3NZe26ORqmK98j6ld4WMM2kCG8ccXPUG6cqEo1spook7X7HjRu3LYpeIz/60Y8ARMqN+p9Vv8A6oxDWUxU/3U54LqmiUNlQLztJUfNCZUij+qmaYrY/vF7qjUTXcKhHCaCwXdEnPG3eeQy/U3FTO9WBAwbEF06U9dTWTP9MVX6R/zt3LwvaTTa2Q3VZVuVKUN5PPPFEAPl+3Gn3ToVte/LDH/4QAPDzn/8cQHKEVPVWpedQ/bjrzFn4m+7DT97/1N4+yfZX0w3RGQFTyGuvvQYgmoXVcxWeV70WvO56/dlvdVZZZ7l4zXnv5SwnvwNR32ceOsvKezuvNZ99/L569eq8/VgffqeqHodGUGWafEZwLQ7zZL105lAjyrJOYT25L7cl+VZX23Y+05LOPa8V04lbGxLXP9guTBPTSDbuJfnibowxxhizo7BbIFS0z77Qd9lnn8T9/54d7BfDPp07Y5+siGFMSb64qz0YR6AfZ72vhHZwn332GQ4/7LCi016b9XCi9m7MIynv0LZT7fiIjqr5u9qkNgbMUxU1tX9VG7xQcadioQoOVQVuV8VH7RvVtp15MJ1QueU2ehBQ+02z/VEll/2NbUqjnIa24KrIsS1Qeed6B/prVnW/VjWWSvsXGeX7/of+G8uWLcPVF2Y85aQqsxGCGXV5p8imvoqRljUOhCjvfbJrTf6RjYgMRFFjGeGyMbjssssAAFOnTgVQ6GlH19aoH3eNxEhClY/XOum+p9GgVZ1Vrxk62xjOlDHtq6++uvbKt1Bow3zvvfcCiKKFsq+FXkh0PZZ6heGnzpbErdsCCiPr8lqH6xb0nq+zz+qlje2HSjoVd85m7b333nll4kxcHCwX82bUcKI28CyL9gtdR6UzFeExzLM+z5/WrVsXeF3S81bs7JNt23cQ0ukiFXfbuBtjjDHGNDq7Zxe51pVe2YB2DeH3DzyA0aNHNzgdU1qU5Is7bdaoitMPOEetoWeKVCqF5f/8Z26kTnWQI9t9990XQDSCVlVCbTrV24ruBxRGVVVbUlXvm8KmU8ug0fE0ypzaGob/q8KuXgtU1Sfqg5gqA9OjQhIqIrSZ5DVn+WiXaBoPqk287pwF4Xf+rp5igEjl47Vmn1G/z7y+VPP7JHlWqsq200Bpf//99wFk2mHVP98EAFRvyfye3iVrm9qxay4JjbhcXVPkZeRH9GX/P/LII2s8ZnswduxYAMB1110HIDrfjGjLT12LoDNe/AxnD3lf0Ci46s1EVXteN/ZTfjI9HnfJJZfUo8bmhRdeABA9v3QmCyicFUmagdFrmuR1Rp8VOosS/q/tgXC7Pjd1vRejaPOe0qtXLwA1z06zPEuXLs2rLz1YqZeruGd3XFnjZiJ0JrqpeOGFF/zivgORKitDqoiYMsXsUxMl+eJujDHGGNMSWbBwIf74xz82dTFME1GSL+5vvplRz/r37w8gUoio6oS+UjlC52hb/aOqfZsq7KpM62hdfVgDkTqlo3FVPvg9KVLl9oR5PvroowAK1Rb91FXx4W+qXKhKpyvjea547hkNkLMhTJfHhWsWeI3VLpNt4hvf+EaRZ8DUF72uSb6M2VZCZZrHcjZF+xk/1aNQYmTcrE37jTf/AkC+vW1oL7p5ScZffOWWrKeMrO1seXl0r0hlbdyrW7fNSzvJu8wRhx8OILPITGcZmoIk2/DJkycDiNRMzpSpaq7nHii0UU5C1XrOgHHNAq8L86Z3K1M/brvtNgDA9ddfDwA4+uijAUQzkkDUt+j9hdeGM9XqFYr3bT4zk/qcKu+6pgyIrrPa0WtkV1WuOTvE9sPIyoz3QC9T9BADRHbxjDDOZwLXyTBNtmuWQb3JaDRglpl1Cs8Hz1FTKe4LFizItQGzA5FOF2e/bht3Y4wxxpjGZ82nn6KsrCznSca0YOwOMpkrr7wSAPC73/0OQKQkqaINRKNsKmE64k/yX66fur+u1A/VRv7PEbzalKoS0pSwDDyHLKMq8OpJAKhdDdVzqOsHqIwwbV2hH3c91T8uvQ+wTZjGg+1bowKq0h6u4aBSpW2f11PTIAdnbVyVn0zKRNeMW0cR+lTe8PG6vOPK22XV9S2R3/UU22mu3cZ711Bat26d6y87Qp9W1I78mmuuAVAYOZKfcbEatA8TXYvAGbFPPvkEQBTl1WwfGKGX0YwPPPDA3G/sU+xz6kud23W9FtFnonoh4kxbeH9mG6Lyz32poCfFEmB/Zx5U1vmd7Ym272G0UPWVrlFXmbau32JZWFZ+ZywG3t/oMz88P7puRyMCb2+KjcxsmicN0+uNMcYYY8x2546pU9FGxEez45BKlxX91xBKUnEnH3/8MYDI16v6BwcKPbxodEe1rYvzgAEUv0oeiJQ+KgEcwasy0Nij9DhYBpZJPUzwfKgyAhR62klC/QJT4aCnH/VYo1FQw/OkMx5sA2b7Q1tpXg9eR/VKQaVdvc2Ex/Bas32p4hbazdbEf/3oCgCR8q6q4eV9M/3684qsYlyWXcPSLqPKpXaOIjFWZ2+m1WzPtG2vxda9srIyVx96vNqRufbaa4ve9xe/yKwd0D558cUXb9MyGWOaP7fffjt+/vOfo6KiAr1798Ztt92GgQMHJu7/wAMPYMKECVi2bBl69uyJG264AV/72tdyv1dXV+Oaa67Br3/9a6xduxb/9m//hqlTp6Jn4Grz5JNPxuLFi7Fy5Up06NABw4cPxw033IAuXboAAJYtW4bu3bsX5L1w4UIcddRR27D2246SfnE3xhhjWjrjx48HAEyZMiW3jS4Uk0xkdAGpmiFqIEEdoO++++4F5aAgxjRpykhCV6NAofClroD3yUYeZZ4cGIfmdzTPYXm4KJVpqCjANFRQYr1p7kXzUZqHhma2zCtcXL9y1Sq0bt0aHWLOS0P5zV134eKLL85d51Jk9uzZGD9+PKZNm4ZBgwZh8uTJGDFiBJYsWZITX0MWLFiAs846C5MmTcJJJ52EmTNnYuTIkXj55ZdxxBFHAMgEnvrlL3+Je+65B927d8eECRMwYsQIvPHGG7l2ceyxx+LKK6/EPvvsgw8//BA//OEPcdppp2HBggV5+T355JM4POtwAIhMrupEqsjFqQkiULH4xd0YU/JcdXnmgTbp5lvztpd3OxQA0GrPbOhxeo1qn70p7xZ5p6guz/qIVv/tDbzJGmNMS+eWW27BhRdeiPPOOw8AMG3aNDz22GOYPn06rrjiioL9b731Vhx//PG5CNETJ07EvHnzMGXKFEybNg3V1dWYPHkyrrrqKpxyyikAMhGFO3XqhDlz5uDMM88EAFx66aW5NA844ABcccUVGDlyJL744ou8mcQ999wzty5iR6ekX9w5+pw/fz6AaEQdmsdwhM/pfX5XN1Q8hq4JOVpTMxBO4XOxjIZsBiL1QN0+qrLx7W9/u65V3uawDE888QSAwtDy6j4zNHvQgDscoXJfVWpoMsSFRTyX3I8L+zR0e6iMaLCqUlYgSg11H8e2wdDinHrk9aQpVOhSkGoYr6MuFNMgXMWazJBwMXlS6PZtTVlZWa5v877QXAgfembHJzRheuqpp/J+o9KuLkuTnpG8D/OT2zWIVvjs42/cl6Zw6j6R/Zr3fN4Hdt1117wyqkkdlVkqrgDw+uuvAyg0w9N6Mi/WU11Fa4BEwnTCevJeyHqG96mVq1bltofncr9ssKz6UOqmaVu2bMFLL72U5wY2nU5j+PDhWLhwYewxCxcuLHi+jxgxAnPmzAEAvPfee6ioqMDw4cNzv7dv3x6DBg3CwoULcy/uIWvWrMH999+PIUOGFJj/nXzyydi8eTN69eqFyy+/HCeffHKd61ms/XpDbdwtJRljjDHGmO3C6tWrUVlZmfPQQzp16pTzv69UVFTUuD8/i0nz//2//4dddtkFe+65J5YvX46HH34499uuu+6Km2++GQ888AAee+wxDB06FCNHjsQjjzxSv8o2AiWtuJO///3vAKJw42HAF6JhmtUWjyoiVWGOvjVAE0fQVBOZLo8DItWAeWgYaB67I8EysROwzDyXrGfo7k4Vc9abCoaqLzxHugCR14RKiR4Xwt94zb/61a/Wo7amPmh4cl5PLhCmMqWBfLjwO/yN11rbQJJr0UXPPw8AGKQLmbJmLNXp/NmAH33//Mz2LZn2m94jfyF4detMv65uFaly1a2y28pa5aVdG72yC6GWvf9+zj7WmKbmgw8+AAAcdNBBAKL+qgqzOmzgPZ/700aefZXKNhXrEKbF/kxbcKahjht4H1BXk9yP93veF/hCFi4CZzmZl7pwZprq/lJt/DX4oir04fOI/+tCfOZN95esV1VVFd5+551cnnyvqK6uRqcY+25iDzLbhssuuwznn38+3n//fVx77bUYPXo0Hn30UaRSKXTs2DFP2R8wYAA++ugj/PznP6+76p5OF+nH3TbuxhhjjDFmB6Rjx44oKyvLRVImK1asSLQr79y5c43783PFihW5Rcz83qdPn4L8O3bsiF69euHQQw/Ffvvth+eeew6DBw+OzXvQoEGYN29enerYmDSLF/f//M//BABMnz4dQGYBAlF7XI6iOTJWd4e6slxt7hSOvENbeM2Do24qFXG2V00Ny/TQQw8BiM6L2p+HrhlZ96RzQzVCQ0arXbPaCfKcx9m4v//++wCia24aj+9973sAonDren05a0Nbd7WJB6JrymutM2FEg8LUGlo8q7j/vx9mlJOqrAvHVOusYlWVb7eaU9PLoltgNbel66a4k2XLlpW8LappPrz88ssAonVbOmOWtJZI3RSrEs1+z89wlozqN9OkwqyBD3X9lyrYVP/5LGAdmP7q1atzaXXs2DFvH6a9atWqvLzVO0xt7odZJq7lCs+L3q/Uywyfm0w76Vx//vnneH/5cnz66afo07s3mivl5eXo168f5s+fj5EjRwLInIP58+cn3jMHDx6M+fPn5wWQmzdvXu5lu3v37ujcuTPmz5+fe1Ffv349Fi1ahLFjxyaWJTz3SSxevDhvMFA06SK9ylhxN8YYY4wxOyrjx4/HmDFj0L9/fwwcOBCTJ0/Ghg0bcl5mRo8eja5du2LSpEkAgHHjxmHYsGG4+eabceKJJ2LWrFl48cUXceeddwLIDI4uueQSXH/99ejZs2fOHWSXLl1yg4NFixbhhRdewNChQ9GhQwcsXboUEyZMwIEHHpgbANxzzz0oLy9H3759AWTEy+nTp+M3v/lNneuYKitDKsHcU/drCM3qxf073/kOgChoCBD5YuUIWFfWqx9ZjvT5yVE2bb+pBPKT6dbk/YJpfPjhh/WsWePBMjIgQZJXnfA3PScc0VKBpYqSZFNINYJqCu0YqbKGvoDt5WLHgddTZ514PeOCk7EtcB+1bWcbYp/h9iTb9wKolmcV92r5XkCcql5P94/vvvtuvY4zZnvAgGn85IsJFWTep6nAsz/rfVxt4tXDWPhMULt4Xd/E5656XlN1W2fEeS+hChquE+M2ps3ycR/1EsN7j95TWEadCaa9ejizrP7mVVFn/Vlubmd9db3A559/jkXPP49XXnkFAPJU5ubCqFGjsGrVKlx99dWoqKhAnz59MHfu3Ny71fLly/NmZ4cMGYKZM2fiqquuwpVXXomePXtizpw5eR6FLr/8cmzYsAHf/e53sXbtWgwdOhRz587NXaudd94ZDz30EK655hps2LAB++yzD44//nhcddVVeesXJ06ciPfffx+tWrXCIYccgtmzZ+O0005rpDNTd5rVi7sxxhhjjNnxuPjiixNNY5555pmCbaeffjpOP/30xPRSqRSuu+46XHfddbG/H3nkkQVuUZUxY8ZgzJgxNe5TNOmyIhenWnEvIFRlf/azTCh0juo4EuNoi+oCR8RUBNX3OLfzeH7qfkCkIqpfWLXz2xHRVf66Wj5uX54LPYc8J3qOOOvB/VXVp+rChSlxwRlM0/GDH/wAQGTrThWJCle3bt3ytuv1BQq9S6idKdsfj+V+L770EgCgf79+NRdSVfNGCKJk+3azI0L19ne/+x0AYL/99sv7ncqyRhqlIs0+SDWU9tz8PbQVpkLO/h3GVAnT4vOXzwL2b+ahHsv4HKLNe/gs5TadrVM/7Ro5lnmp2q8e5xifhOmH5VfFXWcOWS/Wh3nw/qaxTZqj0m62Pc3yxd0YY4wxxphGw4r7toFq7T333AMgGm2rhxNVFagwcztHxjxObfhCBYAjflUdLrjggm1Ys+0Dy0h1hmoFz0tYT27juWC91Re+eiWozRaa362079hQeSfXX389gMjLDNtK6IFBfUezn/Gah36Pw9/VG8O8J58EEK3JOHdbTXXWg1/deSfGjRvXZPkbUwwvvPACgGQPKHxO6TNQ789UmfksDW3c2X95rD4L+Z2KtCrWvHfwk2mrbXw4i6frYGg3TvWfirzGGeF9iWVK8gCjqn+YBvPUGUT9znObpMDz2px11lkwpjaa/Yu7McYYY4wx25NUOo1UEa4ei9mnJlrMizsXHzzxxBMACiO0cdSt6rCq5hwpUymg2hxGFCXcVpO/0B0VlpnnRe0Iw21UHWjjrD5uk/zkqqrK7dtsoYhpVK666ioAwI033ggA+PKXvwwg33sD2wavudqlcruuIVm5ciWAyH8zVTWqYb/M2tszr+9nfc43BlbbTSlwyy23AAB++tOfAgCOPvrovN/ZdzTuiK53otKua5yAqP9ynROP1TgqnJVt3749gEjB5vOU9wld66KzAeE27sN6UDlnmnqvYawW9T2vyjvrG6r8zJ/nSOvLvJI82LB+9CLDa2NMMbSYF3djjDHGGGO2C6kibdxTtnGvE2+//TYA4LDDDgOQHC1Ot6svWyrvNSkAPPbcc8/dtpVoBFjmBx98EEB8PanKq8977sNzRAUjLdND3I+fvDYjRozYhjUxjc3ll18OALlAGvvuu2/ut7322gtANFtDqFBR/frHP/4BIFK02P/4Sah0sa0x/XvuvRdf+cpX8tJMp9M4uFevhlUuoE0JeIkyRrnyyisBAHfddRcA4PDDDwcQqcVUg6mOq+07t1PJ5icQPTfp+5yfGimVar16qtF4K3qc2qWH2zRttVFn2WhXTsWd9VMPc+rxKnx+af34LGQe6kFOZ5X5rOO1MKYutLgXd2OMMcYYY7YpqVRxrodjXCTXKZvqOAfdLQh6m9GV9mqfTl+uXKlOVEUOjz3ppJO2fYGbiEcffRRAoVIKRCoDoUr6ySefAIjs/Hgs91+7di0A27S3JBgog20ijF4HJEckVM8XVNi5roJtjnb1ANCjRw8Ahe1TPT6sWrUKAPDaa6/l/U6ljYq91THTHJk5cyaAKP4C+yDbva7fUttxRicHImWZSrR6YyPsr7SP79ChQ17aOuOt8VRoGw5EEWE1Kroq5XyW857BNPWZzvsC02E9Qxt3RvNWxZ3wWcc0eL9atmwZAODss8+GaT6sX78e7du3x6eLn0a73QrfkQr2/9dn6NDnWKxbty5vxqpYtn9UEmOMMcYYY0yDafGKe135+c9/DiBSBFUJBJp39LPJkyfn/qcdH5sQbQcvu+yyRi+XKU2owLMtUb2jCsa2RftVtUtVpeu4447L/U/FTddSEPZdeqxZvHgxAMcPMC2TqVOnAgB6ZdeBaCwT9lH9Hnoa08ihSXEY1Eacx1GpVhWc/Z0qOfsqAPTp0wdApG6rfTnVfc4cUFFXG31dm6aRz0NvadzGcrGe+p1p0KZ97NixMM0PKu5r/vZs0Yr7Hr2HWXE3xhhjjDGmOePFqXWkpavJzXk2wTQdVOTUl7SqYBpZlVBlC73OqDcJHpsUadFKu2nJUA2eMGECgMjzGteKqCcY9p9QiWY/VTtz7ddcU8bfud6Jn9xf4znw91Dl57a99947rz5U5/UYXa/G7epVhnVRrzpAZIvPY1g+lptesd544w0AwMSJE2FaAKl0kYtTG6aZW3E3xhhjjDGmBLDiboxpMtSOlN4XVMHidvXjzOM6deoEIF8VU49PqqwxD3qVMcZE6vD48eMBAB07dgRQGA2UfTFcZ6IxPegthsdq3AVupwKv9uVMj59cjxLOrHEb151p9HNGZ1UvM1yTxbTolYb3FHqfYd6h7bx6w2K5abP/wgsvAHBE1BZHKlWcq8cGuoO04m6MMcYYY0wJsMO9uH/44Yc444wzsPvuu6Ndu3Y45ZRTcvZixph8Sr2/TJgwARMmTMDWrVuxdetWbNy4ERs3bsQXX3yBL774Ivd906ZN2LRpE6qqqlBVVYU2bdqgTZs26NixY95fOp3O/ZWVleX9hb+l02msX78e69evx9q1a3N2sMYYY0y9SKeL/2sAO5SpzGeffYZjj804pb/yyivRunVr/OIXv8CwYcOwePHi3KISY4z7izFm+0Ezj+9973sAgGHDhgEADjjggLz9aPYCROYzGsiQC0FphlJRUQEgOcgRTU84oF6xYgUA4Jxzzkks76xZswBEZnM0v1FzPA0O1aVLl7w8uVidJkDcHi6I5zby/vvvAwCeffZZAMAdd9yRWE5jGsoO9eJ+xx134J133sHzzz+PAQMGAABOOOEEHHHEEbj55pvx05/+tIlLaMyOQ3PqL/ToMmnSJACF/tn5oOQLAaM80uOF7g9ED2Y+cNXmffny5Xl5G2OMMfWlOpVGdREeY4rZpybqFIDp6aefxv/5P/8HDz30EL7xjW/k/TZz5kx861vfwoIFCzB48OB6FWbgwIEAgOeffz5v+4gRI7B06VK8++679UrXmKZg06ZNuXDcr7zySm5x05o1a3D44Yeje/fu+POf/1wQDrxYmmN/4Yu7vmQX++IezjKoUsZjuUiNQVxqUvGMMfnQXeSXvvQlAMgLILPPPvsAiBZ8sq9Riefrhi4253aq4atXrwYQLQytSx+97777AESLSbm4VlV93ndZVt3O+wfL+vHHH+fyYDlfffVVAHb32NJhAKZP3ny+6ABMex46sHECMB1zzDHYb7/9cP/99xf8dv/99+PAAw/E4MGD8fnnn2P16tVF/ZGqqiq8+uqr6N+/f0HaAwcOxNKlS3OrwI0pBdq2bYt77rkH7777Lv7rv/4rt/373/8+1q1bhxkzZqCsrMz9xRhjjDFFUSdTmVQqhXPOOQe33HIL1q1bl3OztGrVKvzv//5v7uXkd7/7Hc4777yi0uRIe82aNfj8889zI/YQbvvoo49w8MEH16XIxjQpgwYNwuWXX44bbrgB3/jGN7BixQrMmjULkydPzoUWd3+J+NGPfpT3/frrrwdQqMCzjhqgJQzMwm3qWpIDmlBBM8YUh6rL1113Xe7/ESNGAIj6oSrrGvxM7c+5H/voueeeW+fyUZ2fMWMGgMglJfNi2XhP4f1By8h7LVX/RYsW5fK4+uqrAQCnn356nctnmjGNFICpzjbuo0ePxqRJk/Dggw/i/PPPBwDMnj0bW7duzXWYESNGYN68eXVKl51D/aMC0cOZ+xhTSvz4xz/Go48+ijFjxuCzzz7DsGHD8J//+Z+5391fjDHGGFMMdX5xP+SQQzBgwADcf//9uRf3+++/H0cddRQOOuggABk1LE4JrAnao9W0yCwMgGBMqVBeXo7p06djwIABaNOmDe6+++6c+gO4v9TEVVddlfedC2533TVjR0hVjOcz9HBBFY/KGpW2N998EwBw2WWXba9iG9NioPoMABdddBEA4IgjjgCA3Kwi7Xhp807Yf2kGSFe29GTTEKjW08ML18PQ5j0lQXA0iNLbb78NAHj99dcBANOmTWtwmUwzZ0dV3IGM6j5u3Dh88MEH+Pzzz/Hcc89hypQpud83bdqEdevWFZVW586dAQB77LEHdtppp9jpa26j2yZjSo0nnngCQOal+p133kH37t1zv7m/GGOMMaYY6uRVhqxevRpdunTBT37yE2zatAnXX389Pvroo9xIdsaMGXW22QWAAQMGIJVKFXjJOO6447B06VIsXbq0rkU1psl59dVXMWDAAHzrW9/C4sWLsXr1arz22mu5NSLuL8Vz4403AgCOP/54AIVh10PTISruNB364IMPAGRcZhpjGo+xY8cCiPoi1W7231tvvbXRyjJu3DgAhbbsnKmcOnVqo5XFNA/oVWb126+g3W671b7/v/6Fjr361turTL0U944dO+KEE07Afffdh82bN+P444/PvbQD9bPZBYDTTjsNV1xxBV588cWct4wlS5bgqaeewg9/+MP6FNWYJuWLL77Aueeeiy5duuDWW2/Fe++9hwEDBuDSSy/F9OnTAbi/GGOMMaY46qW4A8Af/vAHnHbaaQAyi1PPOOOMBhfmX//6F/r27Yt//etf+OEPf4jWrVvjlltuQWVlJRYvXoy99tqrwXkY05hcc801mDhxIubPn49jjz0WAPCTn/wEV111FR577DF87Wtfq3faLbG/UJk77rjjAEQLcHkbC21o6S1i48aNACJ/95dcckmjlNUYY0zzJ6e4v/O34hX3nr0bx497yNe//nV06NAB7du3x8knn1zfZPLYbbfd8Mwzz+ArX/kKrr/+ekyYMAG9e/fGs88+2yxfQkzz5uWXX8ZPf/pTXHzxxbmXdiATqXPAgAG48MILcyG964P7izHGGNOyqLfivnXrVnTp0gVf//rXcdddd23rchljTCJvvPEGgEKvOqEfd9q409afM4TGGGPMtiKnuL/7avGK+0FfalwbdwCYM2cOVq1ahdGjR9c3CWOMMcYYY0qfHdUd5KJFi/Dqq69i4sSJ6Nu3L4YNG9agAhhjTF057LDDAACXX3553vZwApEeK2655ZbGK5gxxhizHanza//UqVMxduxY7L333rj33nu3R5mMMcYYY4wpGapT6aL/GkK9bdyNMcYYY4xpydDGfdU/3ijaxn2vHoc1vo27McYYY4wxBhnb9fT2t3Fv2NHGGGOMMcaYRsGKuzHGGGOMMQ2hkbzKWHE3xhhjjDGmBLDibowxxhhjTEOw4m6MMca0TKqqqjBt2jT06dMHu+66Kzp16oQTTjgBCxYsaOqiGWOaEL+4G2OMMTsYl112GcaOHYsjjzwSt9xyC/7v//2/ePvttzFs2DA8//zzTV08Y4xCxb2YvwZgUxljjDFmB2Lr1q2YOnUqTjvtNPz2t7/NbT/99NPRo0cP3H///Rg4cGATltAYo1SnUkUFV6pOpRqUjxV3Y4wxpgaWLVuGVCqV+Let+eKLL7Bp0yZ06tQpb/vee++NdDqNtm3bbvM8jTGlgRV3Y4wxpgb22muvPOUbyLxcX3rppSgvLwcAbNy4ERs3bqw1rbKyMnTo0KHGfdq2bYtBgwZhxowZGDx4MI4++misXbsWEydORIcOHfDd7363/pUxxmwfGmlxql/cjTHGmBrYZZddcM455+Rt+/73v4/PPvsM8+bNAwDceOONuPbaa2tN64ADDsCyZctq3e++++7DqFGj8vLt0aMH/vrXv6JHjx51q4AxptngF3djjDGmDtx777244447cPPNN+PYY48FAIwePRpDhw6t9dhizVx22203HH744Rg8eDC++tWvoqKiAj/72c8wcuRI/PnPf0bHjh0bVAdjzDYmlcr8FbNfQ7Kprq6ublAKxhhjTAth8eLFGDJkCEaOHImZM2c2KK1169Zh06ZNue/l5eXYY489sHXrVvTt2xfHHHMMbrvtttzv77zzDg4//HBceumluOGGGxqUtzFm27B+/Xq0b98eKz9cjnbt2hW1/95d98e6deuK2l/x4lRjjDGmCD799FOceuqp6NWrF37zm9/k/fbZZ5+hoqKi1r9Vq1bljhk3bhz22Wef3N83v/lNAMCf/vQnvP766zj55JPz8ujZsycOPfRQ/PWvf93+lTWmBXH77bejW7duaNOmDQYNGlQ/l6t2B2mMMcbsGFRVVeFb3/oW1q5diyeffBI777xz3u833XRTnW3cL7/88jwbdi5aXbFiBQCgsrKy4PgvvvgCW7durW81jDHC7NmzMX78eEybNg2DBg3C5MmTMWLECCxZsgR77713UxevAL+4G2OMMbVw7bXX4oknnsD//M//oHv37gW/18fG/bDDDsNhhx1WsE+vXr0AALNmzcLxxx+f2/7yyy9jyZIl9ipjzDbklltuwYUXXojzzjsPADBt2jQ89thjmD59Oq644oqi06lOpYv0427F3RhjjNluvPbaa5g4cSK+8pWvYOXKlbjvvvvyfj/nnHPQo0ePbebtpV+/fvj3f/933HPPPVi/fj2OO+44fPzxx7jtttvQtm1bXHLJJdskH2NaOlu2bMFLL72EH/3oR7lt6XQaw4cPx8KFC5uwZMn4xd0YY4ypgU8++QTV1dV49tln8eyzzxb8rq4itwUPP/wwbrrpJsyaNQtz585FeXk5jj76aEycOBEHH3zwNs/PmJbI6tWrUVlZWRDsrFOnTnjrrbfqlNb6f31WlP36+n99Vqd0Fb+4G2OMMTVwzDHHoLEdsLVt2xYTJkzAhAkTGjVfY0zdKC8vR+fOndEza+JWDJ07d84Fb6srfnE3xhhjjDEtjo4dO6KsrCy3IJysWLECnTt3LiqNNm3a4L333sOWLVuKzre8vBxt2rSpU1mJX9yNMcYYY0yLo7y8HP369cP8+fMxcuRIABkPUvPnz8fFF19cdDpt2rSp94t4XfGLuzHGGGOMaZGMHz8eY8aMQf/+/TFw4EBMnjwZGzZsyHmZ2dHwi7sxxhhjjGmRjBo1CqtWrcLVV1+NiooK9OnTB3Pnzi1YsLqjkKpu7BU3xhhjjDHGmDrTMC/wxhhjjDHGmEbBL+7GGGOMMcaUAH5xN8YYY4wxpgTwi7sxxhhjjDElgF/cjTHGGGOMKQH84m6MMcYYY0wJ4Bd3Y4wxxhhjSgC/uBtjjDHGGFMC+MXdGGOMMcaYEsAv7sYYY4wxxpQAfnE3xhhjjDGmBPCLuzHGGGOMMSWAX9yNMcYYY4wpAfzibowxxhhjTAngF3djjDHGGGNKAL+4G2OMMcYYUwL4xd0YY4wxxpgS4P8DT5X/oiMmkCYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", + " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", + " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from nimare.meta.cbmr import CBMRInference\n", + "# Group-wise spatial homogeneity test\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", + " t_con_moderator=None, device='cuda')\n", + "inference._contrast()\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"homo_test_1xschizophrenia_No_chi_sq\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=5\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Group comparison test between two groups\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,-1,0,0]],\n", + " t_con_moderator=None, device='cuda')\n", + "inference._contrast()\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"1xschizophrenia_NoVS1xdepression_Yes_chi_sq\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) for study-level moderators" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.94563486]]\n" + ] + } + ], + "source": [ + "# Test for existence of effect of study-level moderators\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,0]], device='cuda')\n", + "inference._contrast()\n", + "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", + "print(sample_size_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.99838466]]\n" + ] + } + ], + "source": [ + "# Test for existence of effect of study-level moderators\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,-1]], device='cuda')\n", + "inference._contrast()\n", + "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", + "print(effect_diff_p)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.8 ('torch': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "vscode": { + "interpreter": { + "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index acc95c56f..81ea2b3fb 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -223,7 +223,7 @@ def _fit(self, dataset): group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) Spatial_Regression_Coef[group] = group_beta_linear_weight group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity + maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity#.reshape((1,-1)) # overdispersion parameter: alpha if self.model == 'NB': alpha = cbmr_model.all_alpha_sqrt[group]**2 @@ -283,7 +283,7 @@ def _fit(self, dataset): SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) log_spatial_intensity_se[group] = SE_log_spatial_intensity - group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'] + group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'].reshape((-1)) SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity spatial_intensity_se[group] = SE_spatial_intensity @@ -488,27 +488,25 @@ def _contrast(self): F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) Cov_spatial_coef = np.linalg.inv(F_spatial_coef) spatial_coef_dim = self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy().shape[1] - Cov_log_intensity = list() + Cov_log_intensity = np.empty(shape=(0,n_brain_voxel)) for k in range(n_con_group_involved): for s in range(n_con_group_involved): - Cov_beta_ks = Cov[k*spatial_coef_dim: (k+1)*spatial_coef_dim, s*spatial_coef_dim: (s+1)*spatial_coef_dim] - Cov_group_log_intensity = np.empty(shape=(0, )) + Cov_beta_ks = Cov_spatial_coef[k*spatial_coef_dim: (k+1)*spatial_coef_dim, s*spatial_coef_dim: (s+1)*spatial_coef_dim] + Cov_group_log_intensity = np.empty(shape=(1, 0)) for j in range(n_brain_voxel): x_j = self.CBMRResults.estimator.inputs_['Coef_spline_bases'][j, :].reshape((1, spatial_coef_dim)) Cov_group_log_intensity_j = x_j @ Cov_beta_ks @ x_j.T - Cov_group_log_intensity = np.concatenate((Cov_group_log_intensity, Cov_group_log_intensity_j.reshape(1,)), axis=0) - Cov_log_intensity.append(Cov_group_log_intensity) - Cov_log_intensity = np.stack(Cov_log_intensity, axis=0) # (m^2, n_voxels) + Cov_group_log_intensity = np.concatenate((Cov_group_log_intensity, Cov_group_log_intensity_j), axis=1) + Cov_log_intensity = np.concatenate((Cov_log_intensity, Cov_group_log_intensity), axis=0) # (m^2, n_voxels) # GLH on log_intensity (eta) - chi_sq_spatial = list() + chi_sq_spatial = np.empty(shape=(0, )) for j in range(n_brain_voxel): Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) CV_jC = simp_con_group @ V_j @ simp_con_group.T CV_jC_inv = np.linalg.inv(CV_jC) chi_sq_spatial_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j - chi_sq_spatial.append(chi_sq_spatial_j) - chi_sq_spatial = np.array(chi_sq_spatial).reshape(n_brain_voxel, 1) + chi_sq_spatial = np.concatenate((chi_sq_spatial, chi_sq_spatial_j.reshape(1,)), axis=0) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) con_group_name = self.t_con_group_name[con_group_count] @@ -530,6 +528,7 @@ def _contrast(self): F_moderator_coef = self._Fisher_info_moderator_coef() Cov_moderator_coef = np.linalg.inv(F_moderator_coef) chi_sq_moderator = Contrast_moderator_coef.T @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) @ Contrast_moderator_coef + chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) con_moderator_name = self.t_con_moderator_name[con_moderator_count] diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index b809c3d73..8ae6e9289 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -14,9 +14,9 @@ def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='NB', penalty=False, lr=1e-6, tol=1e6, device='cuda') + cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]], t_con_moderator=[[[1,0],[0,1]], [1, -1]], device='cuda') # [[2, 0, 0, -2], [0, -2, 1, 1]] + inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False, t_con_moderator=[[1,0]], device='cuda') a = inference._contrast() # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] diff --git a/nimare/utils.py b/nimare/utils.py index d7faafef2..9a3d60918 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1262,16 +1262,19 @@ def standardize_field(dataset, metadata): return dataset -# def index2vox(vals, masker_voxels): -# print('23') -# xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] -# yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] -# zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] -# image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] -# spline_voxel_index = np.arange(np.prod(image_dim)) -# for i in spline_voxel_index: -# print('13') - - - - return +def index2vox(vals, masker_voxels): + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] + voxel_array = np.zeros(shape=masker_voxels.shape) + index_count = 0 + for i in range(image_dim[0]): + for j in range(image_dim[1]): + for k in range(image_dim[2]): + x,y,z = xx[i], yy[j], zz[k] + if masker_voxels[x,y,z] == 1: + voxel_array[x,y,z] = vals[index_count] + index_count += 1 + + return voxel_array From f4cd61ebbdfcadb93b93d1cf15021f68a77cfb7a Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 20 Nov 2022 19:39:15 +0000 Subject: [PATCH 031/177] [skip CI][wip] modify example files for demonstrating CBMR --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 45 ++++++-------------- nimare/meta/cbmr.py | 10 +++-- 2 files changed, 19 insertions(+), 36 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 8f5575937..88431495d 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -89,34 +89,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", - " niimg = new_img_like(niimg, data, niimg.affine)\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", - " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -222,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -244,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -263,6 +237,13 @@ "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", "print(effect_diff_p)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 81ea2b3fb..68d876c4e 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -100,7 +100,7 @@ def _preprocess_input(self, dataset): # group-wise foci coordinates group_xyz = group_coordinates[['x', 'y', 'z']].values group_ijk = mm2vox(group_xyz, mask_img.affine) - group_foci_per_voxel = np.zeros(mask_img.shape, dtype=int) + group_foci_per_voxel = np.zeros(mask_img.shape, dtype=np.int32) for ijk in group_ijk: group_foci_per_voxel[ijk[0], ijk[1], ijk[2]] += 1 # will not work with maskers that aren't NiftiMaskers @@ -140,13 +140,13 @@ def _model_structure(self, model, penalty, device): def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): self.iter += 1 scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay - scheduler.step() def closure(): optimizer.zero_grad() loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) loss.backward() return loss loss = optimizer.step(closure) + scheduler.step() # reset the L-BFGS params if NaN appears in coefficient of regression if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() @@ -413,7 +413,8 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] if 'Overdispersion_Coef' in self.CBMRResults.tables.keys(): involved_overdispersion_coef = torch.tensor([self.CBMRResults.tables['Overdispersion_Coef'].to_numpy()[i, :] for i in GLH_involved_index], dtype=torch.float64, device=self.device) - involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device) + involved_spatial_coef = np.stack([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index]) + involved_spatial_coef = torch.tensor(involved_spatial_coef, dtype=torch.float64, device=self.device) n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape if self.CBMRResults.estimator.moderators: involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] @@ -436,7 +437,8 @@ def _Fisher_info_moderator_coef(self): Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) all_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in self.group_names] all_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in self.group_names] - all_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in range(self.n_groups)], dtype=torch.float64, device=self.device) + all_spatial_coef = np.stack([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in range(self.n_groups)]) + all_spatial_coef = torch.tensor(all_spatial_coef, dtype=torch.float64, device=self.device) all_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) moderator_coef_dim, _ = all_moderator_coef.shape From 024797d64e952e5e88701911998fac40d1eecc12 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 2 Dec 2022 18:04:53 +0000 Subject: [PATCH 032/177] add documentation to functions. --- nimare/meta/cbmr.py | 1609 +++++++++++++++++++++++++------- nimare/tests/test_meta_cbmr.py | 48 +- 2 files changed, 1300 insertions(+), 357 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 68d876c4e..3421d8737 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -18,18 +18,104 @@ import copy LGR = logging.getLogger(__name__) + + class CBMREstimator(Estimator): + """Coordinate-based meta-regression with a spatial model. + + Parameters + ---------- + group_names : :obj:`~str` or obj:`~list` or obj:`~None`, optional + CBMR allows dataset to be categorized into mutiple groups, according to group names. + Default is one-group CBMR. + moderators : :obj:`~str` or obj:`~list` or obj:`~None`, optional + CBMR can accommodate study-level moderators (e.g. sample size, year of publication). + Default is CBMR without study-level moderators. + model : {"Poisson", "NB", "clustered NB"}, optional + Stochastic models in CBMR. The available options are + + ======================= ================================================================= + "Poisson" (default) This is the most efficient and widely used method, but slightly + less accurate, because Poisson model is an approximation for + low-rate Binomial data, but cannot account over-dispersion in + foci counts and may underestimate the standard error. + + "NB" This method is much slower and less stable, but slightly more + accurate. Negative Binomial (NB) model asserts foci counts follow + a NB distribution, and allows for anticipated excess variance + relative to Poisson (there's parameter alpha shared by all studies + and all voxels to index excess variance). + + "clustered NB" This method is also an efficient but less accurate approach. + Clustered NB model is "random effect" Poisson model, which asserts + that the random effects are latent characteristics of each study, + and represent a shared effect over the entire brain for a given + study. + ======================= ================================================================= + penalty: :obj:`~bool`, optional + Currently, the only available option is Firth-type penalty, which penalizes likelihood function + by Jeffrey's invariant prior and guarantees convergent estimates. + spline_spacing: :obj:`~int`, optional + Spatial structure of foci counts is parameterized by coefficient of cubic B-spline bases in CBMR. + Spatial smoothness in CBMR is determined by spline spacing, which is shared across x,y,z dimension. + Default is 10 (20mm). + n_iters: :obj:`int`, optional + Number of iterations limit in optimisation of log-likelihood function. + Default is 10000. + lr: :obj:`float`, optional + Learning rate in optimization of log-likelihood function. + Default is 1e-2 for Poisson and clustered NB model, and 1e-3 for NB model. + tol: :obj:`float`, optional + Stopping criteria w.r.t difference of log-likelihood function in two consecutive iterations. + Default is 1e-2 + device: :obj:`string`, optional + Device type ('cpu' or 'cuda') represents the device on which operations will be allocated + Default is 'cpu' + **kwargs + Keyword arguments. Arguments for the Estimator can be assigned here, + Another optional argument is ``mask``. + + Attributes + ---------- + masker : :class:`~nilearn.input_data.NiftiMasker` or similar + Masker object. + inputs_ : :obj:`dict` + Inputs to the Estimator. For CBMR estimators, there is only multiple keys: coordinates, + mask_img (Niftiimage of brain mask), id (study id), all_group_study_id (study id categorized + by groups), all_group_moderators (study-level moderators categorized by groups if exist), + Coef_spline_bases (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), + all_foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), + all_foci_per_study (study-wise sum of foci count across space, categorized by groups). + + + Notes + ----- + Available correction methods: :meth:`~nimare.meta.cbmr.CBMRInference`. + """ + _required_inputs = {"coordinates": ("coordinates", None)} - def __init__(self, group_names=None, moderators=None, mask=None, spline_spacing=5, model='Poisson', penalty=False, - n_iter=1000, lr=1e-2, tol=1e-2, device='cpu', **kwargs): + def __init__( + self, + group_names=None, + moderators=None, + mask=None, + spline_spacing=5, + model="Poisson", + penalty=False, + n_iter=1000, + lr=1e-2, + tol=1e-2, + device="cpu", + **kwargs, + ): super().__init__(**kwargs) if mask is not None: mask = get_masker(mask) self.masker = mask self.group_names = group_names - self.moderators = moderators + self.moderators = moderators self.spline_spacing = spline_spacing self.model = model @@ -38,55 +124,115 @@ def __init__(self, group_names=None, moderators=None, mask=None, spline_spacing= self.lr = lr self.tol = tol self.device = device - if self.device == 'cuda' and not torch.cuda.is_available(): + if self.device == "cuda" and not torch.cuda.is_available(): LGR.debug(f"cuda not found, use device 'cpu'") - self.device = 'cpu' + self.device = "cpu" # Initialize optimisation parameters self.iter = 0 def _preprocess_input(self, dataset): + """Mask required input images using either the Dataset's mask or the Estimator's. + + Also, categorize study id, voxelwise sum of foci counts across studies, study-wise sum of foci counts + across space into multiple groups. And summarize study-level moderators into multiple groups (if exist). + + Parameters + ---------- + dataset : :obj:`~nimare.dataset.Dataset` + In this method, the Dataset is used to (1) select the appropriate mask image, + (2) categorize it into multiple groups according to group type in annotations, + (3) summarize group-wise study id, foci per voxel, foci per study, moderators (if exist), + (4) extract sample size metadata and use it as one of study-level moderators. + + Attributes + ---------- + inputs_ : :obj:`dict` + Specifically, (1) a “mask_img” key will be added (Niftiimage of brain mask), + (2) an 'id' key will be added (id of all studies in the dataset), + (3) an 'all_group_study_id' key will be added (study id categorized by groups), + (4) a 'Coef_spline_bases' key will be added (spatial matrix of coefficient of cubic + B-spline bases in x,y,z dimension), + (5) an 'all_foci_per_voxel' key will be added (voxelwise sum of foci count across + studies, categorized by groups), + (6) an 'all_foci_per_study' key will be added (study-wise sum of foci count across + space, categorized by groups), + (7) an 'all_group_moderators' key may be added if study-level moderators are considered' + """ masker = self.masker or dataset.masker mask_img = masker.mask_img or masker.labels_img if isinstance(mask_img, str): mask_img = nib.load(mask_img) - self.inputs_['mask_img'] = mask_img + self.inputs_["mask_img"] = mask_img for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": # remove dataset coordinates outside of mask focus_filter = FocusFilter(mask=masker) dataset = focus_filter.transform(dataset) - valid_dset_annotations = dataset.annotations[dataset.annotations['id'].isin(self.inputs_['id'])] + valid_dset_annotations = dataset.annotations[ + dataset.annotations["id"].isin(self.inputs_["id"]) + ] all_group_study_id = dict() if isinstance(self.group_names, type(None)): - all_group_study_id[str(self.group_names)] = valid_dset_annotations['study_id'].unique().tolist() + all_group_study_id[str(self.group_names)] = ( + valid_dset_annotations["study_id"].unique().tolist() + ) elif isinstance(self.group_names, str): - if self.group_names not in valid_dset_annotations.columns: - raise ValueError("group_names: {} does not exist in the dataset".format(self.group_names)) + if self.group_names not in valid_dset_annotations.columns: + raise ValueError( + "group_names: {} does not exist in the dataset".format( + self.group_names + ) + ) else: uniq_groups = list(valid_dset_annotations[self.group_names].unique()) for group in uniq_groups: group_study_id_bool = valid_dset_annotations[self.group_names] == group - group_study_id = valid_dset_annotations.loc[group_study_id_bool]['study_id'] + group_study_id = valid_dset_annotations.loc[group_study_id_bool][ + "study_id" + ] all_group_study_id[group] = group_study_id.unique().tolist() elif isinstance(self.group_names, list): - not_exist_group_names = [group for group in self.group_names if group not in dataset.annotations.columns] + not_exist_group_names = [ + group + for group in self.group_names + if group not in dataset.annotations.columns + ] if len(not_exist_group_names) > 0: - raise ValueError("group_names: {} does not exist in the dataset".format(not_exist_group_names)) - uniq_group_splits = valid_dset_annotations[self.group_names].drop_duplicates().values.tolist() + raise ValueError( + "group_names: {} does not exist in the dataset".format( + not_exist_group_names + ) + ) + uniq_group_splits = ( + valid_dset_annotations[self.group_names].drop_duplicates().values.tolist() + ) for group in uniq_group_splits: - group_study_id_bool = (valid_dset_annotations[self.group_names] == group).all(axis=1) - group_study_id = valid_dset_annotations.loc[group_study_id_bool]['study_id'] - all_group_study_id['_'.join(group)] = group_study_id.unique().tolist() - self.inputs_['all_group_study_id'] = all_group_study_id + group_study_id_bool = ( + valid_dset_annotations[self.group_names] == group + ).all(axis=1) + group_study_id = valid_dset_annotations.loc[group_study_id_bool][ + "study_id" + ] + all_group_study_id["_".join(group)] = group_study_id.unique().tolist() + self.inputs_["all_group_study_id"] = all_group_study_id # collect studywise moderators if specficed if self.moderators: + if isinstance(self.moderators, str): + self.moderators = [ + self.moderators + ] # convert moderators to a single-element list if it's a string all_group_moderators = dict() for group in all_group_study_id.keys(): - df_group = valid_dset_annotations.loc[valid_dset_annotations['study_id'].isin(all_group_study_id[group])] - group_moderators = np.stack([df_group[moderator_name] for moderator_name in self.moderators], axis=1) + df_group = valid_dset_annotations.loc[ + valid_dset_annotations["study_id"].isin(all_group_study_id[group]) + ] + group_moderators = np.stack( + [df_group[moderator_name] for moderator_name in self.moderators], + axis=1, + ) group_moderators = group_moderators.astype(np.float64) all_group_moderators[group] = group_moderators self.inputs_["all_group_moderators"] = all_group_moderators @@ -96,180 +242,359 @@ def _preprocess_input(self, dataset): all_foci_per_voxel, all_foci_per_study = dict(), dict() for group in all_group_study_id.keys(): group_study_id = all_group_study_id[group] - group_coordinates = dataset.coordinates.loc[dataset.coordinates['study_id'].isin(group_study_id)] + group_coordinates = dataset.coordinates.loc[ + dataset.coordinates["study_id"].isin(group_study_id) + ] # group-wise foci coordinates - group_xyz = group_coordinates[['x', 'y', 'z']].values + group_xyz = group_coordinates[["x", "y", "z"]].values group_ijk = mm2vox(group_xyz, mask_img.affine) group_foci_per_voxel = np.zeros(mask_img.shape, dtype=np.int32) for ijk in group_ijk: group_foci_per_voxel[ijk[0], ijk[1], ijk[2]] += 1 # will not work with maskers that aren't NiftiMaskers - group_foci_per_voxel = nib.Nifti1Image(group_foci_per_voxel, mask_img.affine, mask_img.header) + group_foci_per_voxel = nib.Nifti1Image( + group_foci_per_voxel, mask_img.affine, mask_img.header + ) group_foci_per_voxel = masker.transform(group_foci_per_voxel).transpose() # number of foci per voxel/study n_group_study = len(group_study_id) - group_foci_per_study = np.array([(group_coordinates['study_id']==i).sum() for i in group_study_id]) + group_foci_per_study = np.array( + [(group_coordinates["study_id"] == i).sum() for i in group_study_id] + ) group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) all_foci_per_voxel[group] = group_foci_per_voxel all_foci_per_study[group] = group_foci_per_study - - self.inputs_['all_foci_per_voxel'] = all_foci_per_voxel - self.inputs_['all_foci_per_study'] = all_foci_per_study + + self.inputs_["all_foci_per_voxel"] = all_foci_per_voxel + self.inputs_["all_foci_per_study"] = all_foci_per_study def _model_structure(self, model, penalty, device): - beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect + """Specify stochastic models for CBMR with or without Firth-type penalty. + + For stochastic models, there're three options: Poisson, NB, clustered NB models. + For penalty term, we only consider Firth-type penalty currently. + + Parameters + ---------- + model : :obj:`str` + Name of stochastic model in CBMR: Poisson, NB or clustered NB models. + penalty : :obj:`bool` + Whether to penalize log-likelihood function with Firth-type penalty. + device : :obj:`str` + Device type ('cpu' or 'cuda') represents the device on which operations will be allocated + """ + beta_dim = self.inputs_["Coef_spline_bases"].shape[1] # regression coef of spatial effect if self.moderators: gamma_dim = list(self.inputs_["all_group_moderators"].values())[0].shape[1] study_level_moderators = True else: gamma_dim = None study_level_moderators = False - self.groups = list(self.inputs_['all_group_study_id'].keys()) - if model == 'Poisson': - cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) - elif model == 'NB': - cbmr_model = GLMNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) - elif model == 'clustered_NB': - cbmr_model = GLMCNB(beta_dim=beta_dim, gamma_dim=gamma_dim, groups=self.groups, study_level_moderators=study_level_moderators, penalty=penalty, device=device) - if 'cuda' in device: + self.groups = list(self.inputs_["all_group_study_id"].keys()) + if model == "Poisson": + cbmr_model = GLMPoisson( + beta_dim=beta_dim, + gamma_dim=gamma_dim, + groups=self.groups, + study_level_moderators=study_level_moderators, + penalty=penalty, + device=device, + ) + elif model == "NB": + cbmr_model = GLMNB( + beta_dim=beta_dim, + gamma_dim=gamma_dim, + groups=self.groups, + study_level_moderators=study_level_moderators, + penalty=penalty, + device=device, + ) + elif model == "clustered_NB": + cbmr_model = GLMCNB( + beta_dim=beta_dim, + gamma_dim=gamma_dim, + groups=self.groups, + study_level_moderators=study_level_moderators, + penalty=penalty, + device=device, + ) + if "cuda" in device: cbmr_model = cbmr_model.cuda() - + return cbmr_model - def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): + def _update( + self, + model, + optimizer, + Coef_spline_bases, + all_moderators, + all_foci_per_voxel, + all_foci_per_study, + prev_loss, + gamma=0.999, + ): + """One iteration in optimization with L-BFGS. + + Adjust learning rate based on the number of iteration (with learning rate decay parameter `gamma`, default value is 0.999). + Reset L-BFGS optimizer if NaN occurs. + """ self.iter += 1 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=gamma + ) # learning rate decay + def closure(): optimizer.zero_grad() loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) loss.backward() return loss + loss = optimizer.step(closure) scheduler.step() # reset the L-BFGS params if NaN appears in coefficient of regression - if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): + if any( + [ + torch.any(torch.isnan(model.all_beta_linears[group].weight)) + for group in self.inputs_["all_group_study_id"].keys() + ] + ): + if self.iter == 1: # NaN occurs in the first iteration + raise ValueError( + "The current learing rate {} gives rise to NaN values, adjust it to a smaller value.".format( + str(self.lr) + ) + ) all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() - for group in self.inputs_['all_group_study_id'].keys(): + for group in self.inputs_["all_group_study_id"].keys(): beta_dim = model.all_beta_linears[group].weight.shape[1] beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - beta_linear_group.weight = torch.nn.Parameter(self.last_state['all_beta_linears.'+group+'.weight']) + beta_linear_group.weight = torch.nn.Parameter( + self.last_state["all_beta_linears." + group + ".weight"] + ) all_beta_linears[group] = beta_linear_group - - if self.model == 'NB': - group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) + + if self.model == "NB": + group_alpha_sqrt = torch.nn.Parameter( + self.last_state["all_alpha_sqrt." + group] + ) all_alpha_sqrt[group] = group_alpha_sqrt - elif self.model == 'clustered_NB': - group_alpha = torch.nn.Parameter(self.last_state['all_alpha.'+group]) + elif self.model == "clustered_NB": + group_alpha = torch.nn.Parameter(self.last_state["all_alpha." + group]) all_alpha[group] = group_alpha - + model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - if self.model == 'NB': + if self.model == "NB": model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - elif self.model == 'clustered_NB': + elif self.model == "clustered_NB": model.all_alpha = torch.nn.ParameterDict(all_alpha) LGR.debug(f"Reset L-BFGS optimizer......") - else: - self.last_state = copy.deepcopy(model.state_dict()) # need to change the variable name? + else: + self.last_state = copy.deepcopy( + model.state_dict() + ) # need to change the variable name? return loss - def _optimizer(self, model, lr, tol, n_iter, device): + def _optimizer(self, model, lr, tol, n_iter, device): + """Optimize regression coefficient of CBMR via L-BFGS algorithm. + + Optimization terminates if the absolute value of difference of log-likelihood in two consecutive iterations is below `tol` + + Parameters + ---------- + model : :obj:`~nimare.dataset.Dataset` + Stochastic model used in CBMR. + lr : :obj:`~float` + Learning rate of L-BFGS. + tol : :obj:`~float` + Stopping criteria of L-BFGS. + n_iter : :obj:`~int` + Maximum iterations limit of L-BFGS. + device : :obj:`~str` + Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. + """ optimizer = torch.optim.LBFGS(model.parameters(), lr) # load dataset info to torch.tensor - Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) + Coef_spline_bases = torch.tensor( + self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=device + ) if self.moderators: all_group_moderators_tensor = dict() - for group in self.inputs_['all_group_study_id'].keys(): - group_moderators_tensor = torch.tensor(self.inputs_['all_group_moderators'][group], dtype=torch.float64, device=device) + for group in self.inputs_["all_group_study_id"].keys(): + group_moderators_tensor = torch.tensor( + self.inputs_["all_group_moderators"][group], dtype=torch.float64, device=device + ) all_group_moderators_tensor[group] = group_moderators_tensor else: all_group_moderators_tensor = None all_foci_per_voxel_tensor, all_foci_per_study_tensor = dict(), dict() - for group in self.inputs_['all_group_study_id'].keys(): - group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=device) - group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=device) + for group in self.inputs_["all_group_study_id"].keys(): + group_foci_per_voxel = torch.tensor( + self.inputs_["all_foci_per_voxel"][group], dtype=torch.float64, device=device + ) + group_foci_per_study = torch.tensor( + self.inputs_["all_foci_per_study"][group], dtype=torch.float64, device=device + ) all_foci_per_voxel_tensor[group] = group_foci_per_voxel all_foci_per_study_tensor[group] = group_foci_per_study if self.iter == 0: - prev_loss = torch.tensor(float('inf')) # initialization loss difference + prev_loss = torch.tensor(float("inf")) # initialization loss difference for i in range(n_iter): - loss = self._update(model, optimizer, Coef_spline_bases, all_group_moderators_tensor, all_foci_per_voxel_tensor, all_foci_per_study_tensor, prev_loss) + loss = self._update( + model, + optimizer, + Coef_spline_bases, + all_group_moderators_tensor, + all_foci_per_voxel_tensor, + all_foci_per_study_tensor, + prev_loss, + ) loss_diff = loss - prev_loss LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") if torch.abs(loss_diff) < tol: break prev_loss = loss - + return def _fit(self, dataset): - masker_voxels = self.inputs_['mask_img']._dataobj - Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) + """Perform coordinate-based meta-regression (CBMR) on dataset. + + Estimate group-wise spatial regression coefficients and its standard error via inverse Fisher Information matrix, + estimate standard error of group-wise log intensity, group-wise intensity via delta method. For NB or clustered model, + estimate regression coefficient of overdispersion. Similarly, estimate regression coefficient of study-level moderators + (if exist), as well as its standard error via Fisher Information matrix. Save these outcomes in `tables`. + Also, estimate group-wise spatial intensity (per study) and save the results in `maps`. + + Parameters + ---------- + dataset : :obj:`~nimare.dataset.Dataset` + Dataset to analyze. + """ + masker_voxels = self.inputs_["mask_img"]._dataobj + Coef_spline_bases = B_spline_bases( + masker_voxels=masker_voxels, spacing=self.spline_spacing + ) P = Coef_spline_bases.shape[1] - self.inputs_['Coef_spline_bases'] = Coef_spline_bases - + self.inputs_["Coef_spline_bases"] = Coef_spline_bases + cbmr_model = self._model_structure(self.model, self.penalty, self.device) optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) - + maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() # beta: regression coef of spatial effect - for group in self.inputs_['all_group_study_id'].keys(): + for group in self.inputs_["all_group_study_id"].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) + group_beta_linear_weight = ( + group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) + ) Spatial_Regression_Coef[group] = group_beta_linear_weight - group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity#.reshape((1,-1)) + group_studywise_spatial_intensity = np.exp( + np.matmul(Coef_spline_bases, group_beta_linear_weight) + ) + maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" + ] = group_studywise_spatial_intensity # .reshape((1,-1)) # overdispersion parameter: alpha - if self.model == 'NB': - alpha = cbmr_model.all_alpha_sqrt[group]**2 + if self.model == "NB": + alpha = cbmr_model.all_alpha_sqrt[group] ** 2 alpha = alpha.cpu().detach().numpy() overdispersion_param[group] = alpha - elif self.model == 'clustered_NB': + elif self.model == "clustered_NB": alpha = cbmr_model.all_alpha[group] alpha = alpha.cpu().detach().numpy() overdispersion_param[group] = alpha - tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') - if self.model == 'NB' or self.model == 'clustered_NB': - tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) + tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( + Spatial_Regression_Coef, orient="index" + ) + if self.model == "NB" or self.model == "clustered_NB": + tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( + overdispersion_param, orient="index", columns=["alpha"] + ) # study-level moderators if self.moderators: self.moderators_effect = dict() self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.cpu().detach().numpy() - for group in self.inputs_['all_group_study_id'].keys(): + for group in self.inputs_["all_group_study_id"].keys(): group_moderators = self.inputs_["all_group_moderators"][group] group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) self.moderators_effect[group] = group_moderators_effect - tables['Moderators_Regression_Coef'] = pd.DataFrame(self._gamma, columns=self.moderators) + tables["Moderators_Regression_Coef"] = pd.DataFrame( + self._gamma, columns=self.moderators + ) else: self._gamma = None # standard error - spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() - Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) - for group in self.inputs_['all_group_study_id'].keys(): - group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) - group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( + dict(), + dict(), + dict(), + ) + Coef_spline_bases = torch.tensor( + self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=self.device + ) + for group in self.inputs_["all_group_study_id"].keys(): + group_foci_per_voxel = torch.tensor( + self.inputs_["all_foci_per_voxel"][group], dtype=torch.float64, device=self.device + ) + group_foci_per_study = torch.tensor( + self.inputs_["all_foci_per_study"][group], dtype=torch.float64, device=self.device + ) group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight if self.moderators: gamma = cbmr_model.gamma_linear.weight group_moderators = self.inputs_["all_group_moderators"][group] - group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) + group_moderators = torch.tensor( + group_moderators, dtype=torch.float64, device=self.device + ) else: gamma, group_moderators = None, None - if 'Overdispersion_Coef' in tables.keys(): - alpha = torch.tensor(tables['Overdispersion_Coef'].to_dict()['alpha'][group], dtype=torch.float64, device=self.device) + if "Overdispersion_Coef" in tables.keys(): + alpha = torch.tensor( + tables["Overdispersion_Coef"].to_dict()["alpha"][group], + dtype=torch.float64, + device=self.device, + ) # a = -GLMCNB._log_likelihood_single_group(alpha, group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - if self.model == 'Poisson': - nll = lambda beta: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - elif self.model == 'NB': - nll = lambda beta: -GLMNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - elif self.model == 'clustered_NB': - nll = lambda beta: -GLMCNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + if self.model == "Poisson": + nll = lambda beta: -GLMPoisson._log_likelihood_single_group( + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + self.device, + ) + elif self.model == "NB": + nll = lambda beta: -GLMNB._log_likelihood_single_group( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + self.device, + ) + elif self.model == "clustered_NB": + nll = lambda beta: -GLMCNB._log_likelihood_single_group( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + self.device, + ) F = functorch.hessian(nll)(group_beta_linear_weight) # Inference on regression coefficient of spatial effect spatial_dim = group_beta_linear_weight.shape[1] @@ -278,275 +603,660 @@ def _fit(self, dataset): Var_spatial_coef = np.diag(Cov_spatial_coef) SE_spatial_coef = np.sqrt(Var_spatial_coef) spatial_regression_coef_se[group] = SE_spatial_coef - - Var_log_spatial_intensity = np.einsum('ij,ji->i', self.inputs_['Coef_spline_bases'], Cov_spatial_coef @ self.inputs_['Coef_spline_bases'].T) + + Var_log_spatial_intensity = np.einsum( + "ij,ji->i", + self.inputs_["Coef_spline_bases"], + Cov_spatial_coef @ self.inputs_["Coef_spline_bases"].T, + ) SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) log_spatial_intensity_se[group] = SE_log_spatial_intensity - - group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'].reshape((-1)) + + group_studywise_spatial_intensity = maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" + ].reshape((-1)) SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity spatial_intensity_se[group] = SE_spatial_intensity - tables['Spatial_Regression_Coef_SE'] = pd.DataFrame.from_dict(spatial_regression_coef_se, orient='index') - tables['Log_Spatial_Intensity_SE'] = pd.DataFrame.from_dict(log_spatial_intensity_se, orient='index') - tables['Spatial_Intensity_SE'] = pd.DataFrame.from_dict(spatial_intensity_se, orient='index') + tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( + spatial_regression_coef_se, orient="index" + ) + tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + log_spatial_intensity_se, orient="index" + ) + tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + spatial_intensity_se, orient="index" + ) # Inference on regression coefficient of moderators if self.moderators: moderators_dim = gamma.shape[1] - nll = lambda gamma: -GLMPoisson._log_likelihood_single_group(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - params = (gamma) - F_moderators_coef = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + nll = lambda gamma: -GLMPoisson._log_likelihood_single_group( + group_beta_linear_weight, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + self.device, + ) + params = gamma + F_moderators_coef = torch.autograd.functional.hessian( + nll, + params, + create_graph=False, + vectorize=True, + outer_jacobian_strategy="forward-mode", + ) F_moderators_coef = F_moderators_coef.reshape((moderators_dim, moderators_dim)) Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) SE_moderators = np.sqrt(Var_moderators) - tables['Moderators_Regression_SE'] = pd.DataFrame(SE_moderators, columns=self.moderators) + tables["Moderators_Regression_SE"] = pd.DataFrame( + SE_moderators, columns=self.moderators + ) return maps, tables + class CBMRInference(object): - def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device='cpu'): + """Statistical inference on outcomes (intensity estimation and study-level + moderator regressors) of CBMR. + + Parameters + ---------- + CBMRResults : :obj:`~nimare.results.MetaResult` + Results of optimized regression coefficients of CBMR, as well as their + standard error in `tables`. Results of estimated spatial intensity function + (per study) in `maps`. + t_con_group : :obj:`~bool` or obj:`~list` or obj:`~None`, optional + Contrast matrix for homogeneity test or group comparison on estimated spatial + intensity function. + For boolean inputs, no statistical inference will be conducted for spatial intensity + if `t_con_group` is False, and spatial homogeneity test for groupwise intensity + function will be conducted if `t_con_group` is True. + For list inputs, generialized linear hypothesis (GLH) testing will be conducted for + each element independently. We also allow any element of `t_con_group` in list type, + which represents GLH is conducted for all contrasts in this element simultaneously. + Default is homogeneity test on group-wise estimated intensity function. + t_con_moderators : :obj:`~bool` or obj:`~list` or obj:`~None`, optional + Contrast matrix for testing the existence of one or more study-level moderator effects. + For boolean inputs, no statistical inference will be conducted for study-level moderators + if `t_con_moderators` is False, and statistical inference on the effect of each study-level + moderators will be conducted if `t_con_group` is True. + For list inputs, generialized linear hypothesis (GLH) testing will be conducted for + each element independently. We also allow any element of `t_con_moderators` in list type, + which represents GLH is conducted for all contrasts in this element simultaneously. + Default is statistical inference on the effect of each study-level moderators + device: :obj:`string`, optional + Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. + Default is 'cpu'. + """ + + def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device="cpu"): self.device = device self.CBMRResults = CBMRResults self.t_con_group = t_con_group self.t_con_moderator = t_con_moderator - self.group_names = self.CBMRResults.tables['Spatial_Regression_Coef'].index.values.tolist() + self.group_names = self.CBMRResults.tables["Spatial_Regression_Coef"].index.values.tolist() self.n_groups = len(self.group_names) if self.t_con_group is not False: # Conduct group-wise spatial homogeneity test by default - self.t_con_group = [np.eye(self.n_groups)] if not self.t_con_group else [np.array(con_group) for con_group in self.t_con_group] - self.t_con_group = [con_group.reshape((1,-1)) if len(con_group.shape)==1 else con_group for con_group in self.t_con_group] # 2D contrast matrix/vector + self.t_con_group = ( + [np.eye(self.n_groups)] + if not self.t_con_group + else [np.array(con_group) for con_group in self.t_con_group] + ) + self.t_con_group = [ + con_group.reshape((1, -1)) if len(con_group.shape) == 1 else con_group + for con_group in self.t_con_group + ] # 2D contrast matrix/vector if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): - wrong_con_group_idx = np.where([con_group.shape[1] != self.n_groups for con_group in self.t_con_group])[0].tolist() - raise ValueError("The shape of {}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups".format(str(wrong_con_group_idx))) - con_group_zero_row = [np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] for con_group in self.t_con_group] - if np.any([len(zero_row)>0 for zero_row in con_group_zero_row]): # remove zero rows in contrast matrix - self.t_con_group = [np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) for i in range(len(self.t_con_group))] - if np.any([con_group.shape[0]== 0 for con_group in self.t_con_group]): - raise ValueError('One or more of contrast vectors(s) in group-wise intensity contrast matrix are all zeros') + wrong_con_group_idx = np.where( + [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] + )[0].tolist() + raise ValueError( + "The shape of {}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups".format( + str(wrong_con_group_idx) + ) + ) + con_group_zero_row = [ + np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] + for con_group in self.t_con_group + ] + if np.any( + [len(zero_row) > 0 for zero_row in con_group_zero_row] + ): # remove zero rows in contrast matrix + self.t_con_group = [ + np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) + for i in range(len(self.t_con_group)) + ] + if np.any([con_group.shape[0] == 0 for con_group in self.t_con_group]): + raise ValueError( + "One or more of contrast vectors(s) in group-wise intensity contrast matrix are all zeros" + ) n_contrasts_group = [con_group.shape[0] for con_group in self.t_con_group] self._Name_of_con_group() # standardization - self.t_con_group = [con_group / np.sum(np.abs(con_group), axis=1).reshape((-1,1)) for con_group in self.t_con_group] - + self.t_con_group = [ + con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) + for con_group in self.t_con_group + ] + if self.t_con_moderator is not False: if self.CBMRResults.estimator.moderators: self.moderator_names = self.CBMRResults.estimator.moderators self.n_moderators = len(self.moderator_names) - self.t_con_moderator = [np.eye(self.n_moderators)] if not self.t_con_moderator else [np.array(con_moderator) for con_moderator in self.t_con_moderator] - self.t_con_moderator = [con_moderator.reshape((1,-1)) if len(con_moderator.shape)==1 else con_moderator for con_moderator in self.t_con_moderator] + self.t_con_moderator = ( + [np.eye(self.n_moderators)] + if not self.t_con_moderator + else [np.array(con_moderator) for con_moderator in self.t_con_moderator] + ) + self.t_con_moderator = [ + con_moderator.reshape((1, -1)) + if len(con_moderator.shape) == 1 + else con_moderator + for con_moderator in self.t_con_moderator + ] # test the existence of effect of moderators - if np.any([con_moderator.shape[1] != self.n_moderators for con_moderator in self.t_con_moderator]): - wrong_con_moderator_idx = np.where([con_moderator.shape[1] != self.n_moderators for con_moderator in self.t_con_moderator])[0].tolist() - raise ValueError("The shape of {}th contrast vector(s) in moderators contrast matrix doesn't match with moderators".format(str(wrong_con_moderator_idx))) - con_moderator_zero_row = [np.where(np.sum(np.abs(con_modereator), axis=1)==0)[0] for con_modereator in self.t_con_moderator] - if np.any([len(zero_row)>0 for zero_row in con_moderator_zero_row]): # remove zero rows in contrast matrix - self.t_con_moderator = [np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) for i in range(len(self.t_con_moderator))] - if np.any([con_moderator.shape[0]== 0 for con_moderator in self.t_con_moderator]): - raise ValueError('One or more of contrast vectors(s) in modereators contrast matrix are all zeros') - n_contrasts_moderator = [con_moderator.shape[0] for con_moderator in self.t_con_moderator] + if np.any( + [ + con_moderator.shape[1] != self.n_moderators + for con_moderator in self.t_con_moderator + ] + ): + wrong_con_moderator_idx = np.where( + [ + con_moderator.shape[1] != self.n_moderators + for con_moderator in self.t_con_moderator + ] + )[0].tolist() + raise ValueError( + "The shape of {}th contrast vector(s) in moderators contrast matrix doesn't match with moderators".format( + str(wrong_con_moderator_idx) + ) + ) + con_moderator_zero_row = [ + np.where(np.sum(np.abs(con_modereator), axis=1) == 0)[0] + for con_modereator in self.t_con_moderator + ] + if np.any( + [len(zero_row) > 0 for zero_row in con_moderator_zero_row] + ): # remove zero rows in contrast matrix + self.t_con_moderator = [ + np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) + for i in range(len(self.t_con_moderator)) + ] + if np.any( + [con_moderator.shape[0] == 0 for con_moderator in self.t_con_moderator] + ): + raise ValueError( + "One or more of contrast vectors(s) in modereators contrast matrix are all zeros" + ) + n_contrasts_moderator = [ + con_moderator.shape[0] for con_moderator in self.t_con_moderator + ] self._Name_of_con_moderator() - self.t_con_moderator = [con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1,1)) for con_moderator in self.t_con_moderator] + self.t_con_moderator = [ + con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) + for con_moderator in self.t_con_moderator + ] else: self.t_con_moderator = False - if self.device == 'cuda' and not torch.cuda.is_available(): + if self.device == "cuda" and not torch.cuda.is_available(): LGR.debug(f"cuda not found, use device 'cpu'") - self.device = 'cpu' + self.device = "cpu" def _Name_of_con_group(self): + """Define the name of GLH contrasts on spatial intensity estimation. + + And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_group` + exists). + """ self.t_con_group_name = list() for con_group in self.t_con_group: con_group_name = list() - for num, idx in enumerate(con_group): - if np.sum(idx) != 0: # homogeneity test + for num, idx in enumerate(con_group): + if np.sum(idx) != 0: # homogeneity test nonzero_con_group_info = str() - nonzero_group_index = np.where(idx!=0)[0].tolist() + nonzero_group_index = np.where(idx != 0)[0].tolist() nonzero_group_name = [self.group_names[i] for i in nonzero_group_index] nonzero_con = [int(idx[i]) for i in nonzero_group_index] for i in range(len(nonzero_group_index)): - nonzero_con_group_info += str(abs(nonzero_con[i])) + 'x' + str(nonzero_group_name[i]) - con_group_name.append('homo_test_' + nonzero_con_group_info) - else: # group-comparison test - pos_group_idx, neg_group_idx = np.where(idx>0)[0].tolist(), np.where(idx<0)[0].tolist() - pos_group_name, neg_group_name = [self.group_names[i] for i in pos_group_idx], [self.group_names[i] for i in neg_group_idx] - pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [int(idx[i]) for i in neg_group_idx] + nonzero_con_group_info += ( + str(abs(nonzero_con[i])) + "x" + str(nonzero_group_name[i]) + ) + con_group_name.append("homo_test_" + nonzero_con_group_info) + else: # group-comparison test + pos_group_idx, neg_group_idx = ( + np.where(idx > 0)[0].tolist(), + np.where(idx < 0)[0].tolist(), + ) + pos_group_name, neg_group_name = [ + self.group_names[i] for i in pos_group_idx + ], [self.group_names[i] for i in neg_group_idx] + pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [ + int(idx[i]) for i in neg_group_idx + ] pos_con_group_info, neg_con_group_info = str(), str() for i in range(len(pos_group_idx)): - pos_con_group_info += str(pos_group_con[i]) + 'x' + str(pos_group_name[i]) + pos_con_group_info += str(pos_group_con[i]) + "x" + str(pos_group_name[i]) for i in range(len(neg_group_idx)): - neg_con_group_info += str(abs(neg_group_con[i])) + 'x' + str(neg_group_name[i]) - con_group_name.append(pos_con_group_info + 'VS' + neg_con_group_info) + neg_con_group_info += ( + str(abs(neg_group_con[i])) + "x" + str(neg_group_name[i]) + ) + con_group_name.append(pos_con_group_info + "VS" + neg_con_group_info) self.t_con_group_name.append(con_group_name) return - + def _Name_of_con_moderator(self): + """Define the name of GLH contrasts on regressors of study-level moderators. + + And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_moderators` + exists). + """ self.t_con_moderator_name = list() for con_moderator in self.t_con_moderator: con_moderator_name = list() - for num, idx in enumerate(con_moderator): - if np.sum(idx) != 0: # homogeneity test + for num, idx in enumerate(con_moderator): + if np.sum(idx) != 0: # homogeneity test nonzero_con_moderator_info = str() - nonzero_moderator_index = np.where(idx!=0)[0].tolist() - nonzero_moderator_name = [self.moderator_names[i] for i in nonzero_moderator_index] + nonzero_moderator_index = np.where(idx != 0)[0].tolist() + nonzero_moderator_name = [ + self.moderator_names[i] for i in nonzero_moderator_index + ] nonzero_con = [int(idx[i]) for i in nonzero_moderator_index] for i in range(len(nonzero_moderator_index)): - nonzero_con_moderator_info += str(abs(nonzero_con[i])) + 'x' + str(nonzero_moderator_name[i]) - con_moderator_name.append('Effect_of_' + nonzero_con_moderator_info) - else: # group-comparison test - pos_moderator_idx, neg_moderator_idx = np.where(idx>0)[0].tolist(), np.where(idx<0)[0].tolist() - pos_moderator_name, neg_moderator_name = [self.moderator_names[i] for i in pos_moderator_idx], [self.moderator_names[i] for i in neg_moderator_idx] - pos_moderator_con, neg_moderator_con = [int(idx[i]) for i in pos_moderator_idx], [int(idx[i]) for i in neg_moderator_idx] + nonzero_con_moderator_info += ( + str(abs(nonzero_con[i])) + "x" + str(nonzero_moderator_name[i]) + ) + con_moderator_name.append("Effect_of_" + nonzero_con_moderator_info) + else: # group-comparison test + pos_moderator_idx, neg_moderator_idx = ( + np.where(idx > 0)[0].tolist(), + np.where(idx < 0)[0].tolist(), + ) + pos_moderator_name, neg_moderator_name = [ + self.moderator_names[i] for i in pos_moderator_idx + ], [self.moderator_names[i] for i in neg_moderator_idx] + pos_moderator_con, neg_moderator_con = [ + int(idx[i]) for i in pos_moderator_idx + ], [int(idx[i]) for i in neg_moderator_idx] pos_con_moderator_info, neg_con_moderator_info = str(), str() for i in range(len(pos_moderator_idx)): - pos_con_moderator_info += str(pos_moderator_con[i]) + 'x' + str(pos_moderator_name[i]) + pos_con_moderator_info += ( + str(pos_moderator_con[i]) + "x" + str(pos_moderator_name[i]) + ) for i in range(len(neg_moderator_idx)): - neg_con_moderator_info += str(abs(neg_moderator_con[i])) + 'x' + str(neg_moderator_name[i]) - con_moderator_name.append(pos_con_moderator_info + 'VS' + neg_con_moderator_info) + neg_con_moderator_info += ( + str(abs(neg_moderator_con[i])) + "x" + str(neg_moderator_name[i]) + ) + con_moderator_name.append( + pos_con_moderator_info + "VS" + neg_con_moderator_info + ) self.t_con_moderator_name.append(con_moderator_name) return def _Fisher_info_spatial_coef(self, GLH_involved_index): - Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + Coef_spline_bases = torch.tensor( + self.CBMRResults.estimator.inputs_["Coef_spline_bases"], + dtype=torch.float64, + device=self.device, + ) GLH_involved = [self.group_names[i] for i in GLH_involved_index] - involved_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - involved_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - if 'Overdispersion_Coef' in self.CBMRResults.tables.keys(): - involved_overdispersion_coef = torch.tensor([self.CBMRResults.tables['Overdispersion_Coef'].to_numpy()[i, :] for i in GLH_involved_index], dtype=torch.float64, device=self.device) - involved_spatial_coef = np.stack([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index]) - involved_spatial_coef = torch.tensor(involved_spatial_coef, dtype=torch.float64, device=self.device) + involved_group_foci_per_voxel = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_foci_per_voxel"][group], + dtype=torch.float64, + device=self.device, + ) + for group in GLH_involved + ] + involved_group_foci_per_study = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_foci_per_study"][group], + dtype=torch.float64, + device=self.device, + ) + for group in GLH_involved + ] + if "Overdispersion_Coef" in self.CBMRResults.tables.keys(): + involved_overdispersion_coef = torch.tensor( + [ + self.CBMRResults.tables["Overdispersion_Coef"].to_numpy()[i, :] + for i in GLH_involved_index + ], + dtype=torch.float64, + device=self.device, + ) + involved_spatial_coef = np.stack( + [ + self.CBMRResults.tables["Spatial_Regression_Coef"] + .to_numpy()[i, :] + .reshape((-1, 1)) + for i in GLH_involved_index + ] + ) + involved_spatial_coef = torch.tensor( + involved_spatial_coef, dtype=torch.float64, device=self.device + ) n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape if self.CBMRResults.estimator.moderators: - involved_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in GLH_involved] - involved_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + involved_group_moderators = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_group_moderators"][group], + dtype=torch.float64, + device=self.device, + ) + for group in GLH_involved + ] + involved_moderator_coef = torch.tensor( + self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T, + dtype=torch.float64, + device=self.device, + ) else: involved_group_moderators, involved_moderator_coef = None, None # a = GLMPoisson._log_likelihood_mult_group(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators, self.device) - if self.CBMRResults.estimator.model == 'Poisson': - nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - elif self.CBMRResults.estimator.model == 'NB': - nll = lambda all_spatial_coef: -GLMNB._log_likelihood_mult_group(involved_overdispersion_coef, all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) - elif self.CBMRResults.estimator.model == 'clustered_NB': - nll = lambda all_spatial_coef: -GLMCNB._log_likelihood_mult_group(involved_overdispersion_coef, all_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators) + if self.CBMRResults.estimator.model == "Poisson": + nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group( + all_spatial_coef, + Coef_spline_bases, + involved_group_foci_per_voxel, + involved_group_foci_per_study, + involved_moderator_coef, + involved_group_moderators, + ) + elif self.CBMRResults.estimator.model == "NB": + nll = lambda all_spatial_coef: -GLMNB._log_likelihood_mult_group( + involved_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + involved_group_foci_per_voxel, + involved_group_foci_per_study, + involved_moderator_coef, + involved_group_moderators, + ) + elif self.CBMRResults.estimator.model == "clustered_NB": + nll = lambda all_spatial_coef: -GLMCNB._log_likelihood_mult_group( + involved_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + involved_group_foci_per_voxel, + involved_group_foci_per_study, + involved_moderator_coef, + involved_group_moderators, + ) h = functorch.hessian(nll)(involved_spatial_coef) - h = h.view(n_involved_groups*spatial_coef_dim, -1) + h = h.view(n_involved_groups * spatial_coef_dim, -1) return h.detach().cpu().numpy() def _Fisher_info_moderator_coef(self): - Coef_spline_bases = torch.tensor(self.CBMRResults.estimator.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) - all_group_foci_per_voxel = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) for group in self.group_names] - all_group_foci_per_study = [torch.tensor(self.CBMRResults.estimator.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) for group in self.group_names] - all_spatial_coef = np.stack([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in range(self.n_groups)]) + Coef_spline_bases = torch.tensor( + self.CBMRResults.estimator.inputs_["Coef_spline_bases"], + dtype=torch.float64, + device=self.device, + ) + all_group_foci_per_voxel = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_foci_per_voxel"][group], + dtype=torch.float64, + device=self.device, + ) + for group in self.group_names + ] + all_group_foci_per_study = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_foci_per_study"][group], + dtype=torch.float64, + device=self.device, + ) + for group in self.group_names + ] + all_spatial_coef = np.stack( + [ + self.CBMRResults.tables["Spatial_Regression_Coef"] + .to_numpy()[i, :] + .reshape((-1, 1)) + for i in range(self.n_groups) + ] + ) all_spatial_coef = torch.tensor(all_spatial_coef, dtype=torch.float64, device=self.device) - - all_moderator_coef = torch.tensor(self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T, dtype=torch.float64, device=self.device) + + all_moderator_coef = torch.tensor( + self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T, + dtype=torch.float64, + device=self.device, + ) moderator_coef_dim, _ = all_moderator_coef.shape - all_group_moderators = [torch.tensor(self.CBMRResults.estimator.inputs_['all_group_moderators'][group], dtype=torch.float64, device=self.device) for group in self.group_names] - - if 'Overdispersion_Coef' in self.CBMRResults.tables.keys(): - all_overdispersion_coef = torch.tensor(self.CBMRResults.tables['Overdispersion_Coef'].to_numpy(), dtype=torch.float64, device=self.device) - - if self.CBMRResults.estimator.model == 'Poisson': - nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) - elif self.CBMRResults.estimator.model == 'NB': - nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) - elif self.CBMRResults.estimator.model == 'clustered_NB': - nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, all_group_moderators) + all_group_moderators = [ + torch.tensor( + self.CBMRResults.estimator.inputs_["all_group_moderators"][group], + dtype=torch.float64, + device=self.device, + ) + for group in self.group_names + ] + + if "Overdispersion_Coef" in self.CBMRResults.tables.keys(): + all_overdispersion_coef = torch.tensor( + self.CBMRResults.tables["Overdispersion_Coef"].to_numpy(), + dtype=torch.float64, + device=self.device, + ) + + if self.CBMRResults.estimator.model == "Poisson": + nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group( + all_spatial_coef, + Coef_spline_bases, + all_group_foci_per_voxel, + all_group_foci_per_study, + all_moderator_coef, + all_group_moderators, + ) + elif self.CBMRResults.estimator.model == "NB": + nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + all_group_foci_per_voxel, + all_group_foci_per_study, + all_moderator_coef, + all_group_moderators, + ) + elif self.CBMRResults.estimator.model == "clustered_NB": + nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + all_group_foci_per_voxel, + all_group_foci_per_study, + all_moderator_coef, + all_group_moderators, + ) h = functorch.hessian(nll)(all_moderator_coef) h = h.view(moderator_coef_dim, moderator_coef_dim) - + return h.detach().cpu().numpy() def _contrast(self): - Log_Spatial_Intensity_SE = self.CBMRResults.tables['Log_Spatial_Intensity_SE'] + """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. + + Estimate group-wise spatial regression coefficients and its standard error via inverse Fisher Information matrix, + estimate standard error of group-wise log intensity, group-wise intensity via delta method. For NB or clustered model, + estimate regression coefficient of overdispersion. Similarly, estimate regression coefficient of study-level moderators + (if exist), as well as its standard error via Fisher Information matrix. Save these outcomes in `tables`. + Also, estimate group-wise spatial intensity (per study) and save the results in `maps`. + + Parameters + ---------- + dataset : :obj:`~nimare.dataset.Dataset` + Dataset to analyze. + """ + Log_Spatial_Intensity_SE = self.CBMRResults.tables["Log_Spatial_Intensity_SE"] if self.t_con_group is not False: con_group_count = 0 - for con_group in self.t_con_group: - con_group_involved_index = np.where(np.any(con_group!=0, axis=0))[0].tolist() + for con_group in self.t_con_group: + con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() con_group_involved = [self.group_names[i] for i in con_group_involved_index] n_con_group_involved = len(con_group_involved) - simp_con_group = con_group[:,~np.all(con_group == 0, axis = 0)] # contrast matrix of involved groups only - if np.all(np.count_nonzero(con_group, axis=1)==1): # GLH: homogeneity test + simp_con_group = con_group[ + :, ~np.all(con_group == 0, axis=0) + ] # contrast matrix of involved groups only + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test involved_log_intensity_per_voxel = list() for group in con_group_involved: - group_foci_per_voxel = self.CBMRResults.estimator.inputs_['all_foci_per_voxel'][group] - group_foci_per_study = self.CBMRResults.estimator.inputs_['all_foci_per_study'][group] - n_voxels, n_study = group_foci_per_voxel.shape[0], group_foci_per_study.shape[0] - group_null_log_spatial_intensity = np.log(np.sum(group_foci_per_voxel) / (n_voxels * n_study)) - group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) - group_log_intensity_per_voxel = group_log_intensity_per_voxel - group_null_log_spatial_intensity + group_foci_per_voxel = self.CBMRResults.estimator.inputs_[ + "all_foci_per_voxel" + ][group] + group_foci_per_study = self.CBMRResults.estimator.inputs_[ + "all_foci_per_study" + ][group] + n_voxels, n_study = ( + group_foci_per_voxel.shape[0], + group_foci_per_study.shape[0], + ) + group_null_log_spatial_intensity = np.log( + np.sum(group_foci_per_voxel) / (n_voxels * n_study) + ) + group_log_intensity_per_voxel = np.log( + self.CBMRResults.maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" + ] + ) + group_log_intensity_per_voxel = ( + group_log_intensity_per_voxel - group_null_log_spatial_intensity + ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack(involved_log_intensity_per_voxel, axis=0) - else: # GLH: group-comparison + involved_log_intensity_per_voxel = np.stack( + involved_log_intensity_per_voxel, axis=0 + ) + else: # GLH: group-comparison involved_log_intensity_per_voxel = list() for group in con_group_involved: - group_log_intensity_per_voxel = np.log(self.CBMRResults.maps['Group_'+group+'_Studywise_Spatial_Intensity']) + group_log_intensity_per_voxel = np.log( + self.CBMRResults.maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" + ] + ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack(involved_log_intensity_per_voxel, axis=0) - Contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack( + involved_log_intensity_per_voxel, axis=0 + ) + Contrast_log_intensity = np.matmul( + simp_con_group, involved_log_intensity_per_voxel + ) m, n_brain_voxel = Contrast_log_intensity.shape # Correlation of involved group-wise spatial coef F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) Cov_spatial_coef = np.linalg.inv(F_spatial_coef) - spatial_coef_dim = self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy().shape[1] - Cov_log_intensity = np.empty(shape=(0,n_brain_voxel)) + spatial_coef_dim = ( + self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] + ) + Cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) for k in range(n_con_group_involved): for s in range(n_con_group_involved): - Cov_beta_ks = Cov_spatial_coef[k*spatial_coef_dim: (k+1)*spatial_coef_dim, s*spatial_coef_dim: (s+1)*spatial_coef_dim] + Cov_beta_ks = Cov_spatial_coef[ + k * spatial_coef_dim : (k + 1) * spatial_coef_dim, + s * spatial_coef_dim : (s + 1) * spatial_coef_dim, + ] Cov_group_log_intensity = np.empty(shape=(1, 0)) for j in range(n_brain_voxel): - x_j = self.CBMRResults.estimator.inputs_['Coef_spline_bases'][j, :].reshape((1, spatial_coef_dim)) + x_j = self.CBMRResults.estimator.inputs_["Coef_spline_bases"][ + j, : + ].reshape((1, spatial_coef_dim)) Cov_group_log_intensity_j = x_j @ Cov_beta_ks @ x_j.T - Cov_group_log_intensity = np.concatenate((Cov_group_log_intensity, Cov_group_log_intensity_j), axis=1) - Cov_log_intensity = np.concatenate((Cov_log_intensity, Cov_group_log_intensity), axis=0) # (m^2, n_voxels) + Cov_group_log_intensity = np.concatenate( + (Cov_group_log_intensity, Cov_group_log_intensity_j), axis=1 + ) + Cov_log_intensity = np.concatenate( + (Cov_log_intensity, Cov_group_log_intensity), axis=0 + ) # (m^2, n_voxels) # GLH on log_intensity (eta) - chi_sq_spatial = np.empty(shape=(0, )) + chi_sq_spatial = np.empty(shape=(0,)) for j in range(n_brain_voxel): Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) - V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) + V_j = Cov_log_intensity[:, j].reshape( + (n_con_group_involved, n_con_group_involved) + ) CV_jC = simp_con_group @ V_j @ simp_con_group.T CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_spatial_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j - chi_sq_spatial = np.concatenate((chi_sq_spatial, chi_sq_spatial_j.reshape(1,)), axis=0) + chi_sq_spatial_j = ( + Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + ) + chi_sq_spatial = np.concatenate( + ( + chi_sq_spatial, + chi_sq_spatial_j.reshape( + 1, + ), + ), + axis=0, + ) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) con_group_name = self.t_con_group_name[con_group_count] if len(con_group_name) == 1: - self.CBMRResults.maps[con_group_name[0] +'_chi_sq'] = chi_sq_spatial - self.CBMRResults.maps[con_group_name[0] +'_p'] = p_vals_spatial + self.CBMRResults.maps[con_group_name[0] + "_chi_sq"] = chi_sq_spatial + self.CBMRResults.maps[con_group_name[0] + "_p"] = p_vals_spatial else: - self.CBMRResults.maps['spatial_coef_GLH_' + str(con_group_count) +'_chi_sq'] = chi_sq_spatial - self.CBMRResults.maps['spatial_coef_GLH_' + str(con_group_count) +'_p'] = p_vals_spatial - self.CBMRResults.metadata['spatial_coef_GLH_' + str(con_group_count)] = con_group_name + self.CBMRResults.maps[ + "spatial_coef_GLH_" + str(con_group_count) + "_chi_sq" + ] = chi_sq_spatial + self.CBMRResults.maps[ + "spatial_coef_GLH_" + str(con_group_count) + "_p" + ] = p_vals_spatial + self.CBMRResults.metadata[ + "spatial_coef_GLH_" + str(con_group_count) + ] = con_group_name con_group_count += 1 - - if self.t_con_moderator is not False: + + if self.t_con_moderator is not False: con_moderator_count = 0 - for con_moderator in self.t_con_moderator: + for con_moderator in self.t_con_moderator: m_con_moderator, _ = con_moderator.shape - moderator_coef = self.CBMRResults.tables['Moderators_Regression_Coef'].to_numpy().T - Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) + moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T + Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) F_moderator_coef = self._Fisher_info_moderator_coef() Cov_moderator_coef = np.linalg.inv(F_moderator_coef) - chi_sq_moderator = Contrast_moderator_coef.T @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) @ Contrast_moderator_coef + chi_sq_moderator = ( + Contrast_moderator_coef.T + @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) + @ Contrast_moderator_coef + ) chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) - + con_moderator_name = self.t_con_moderator_name[con_moderator_count] if len(con_moderator_name) == 1: - self.CBMRResults.tables[con_moderator_name[0] +'_chi_sq'] = chi_sq_moderator - self.CBMRResults.tables[con_moderator_name[0] +'_p'] = p_vals_moderator + self.CBMRResults.tables[con_moderator_name[0] + "_chi_sq"] = chi_sq_moderator + self.CBMRResults.tables[con_moderator_name[0] + "_p"] = p_vals_moderator else: - self.CBMRResults.tables['moderator_coef_GLH_' + str(con_moderator_count) +'_chi_sq'] = chi_sq_moderator - self.CBMRResults.tables['moderator_coef_GLH_' + str(con_moderator_count) +'_p'] = p_vals_moderator - self.CBMRResults.metadata['moderator_coef_GLH_' + str(con_moderator_count)] = con_moderator_name + self.CBMRResults.tables[ + "moderator_coef_GLH_" + str(con_moderator_count) + "_chi_sq" + ] = chi_sq_moderator + self.CBMRResults.tables[ + "moderator_coef_GLH_" + str(con_moderator_count) + "_p" + ] = p_vals_moderator + self.CBMRResults.metadata[ + "moderator_coef_GLH_" + str(con_moderator_count) + ] = con_moderator_name con_moderator_count += 1 - + return + class GLMPoisson(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty=False, device='cpu'): + def __init__( + self, + beta_dim=None, + gamma_dim=None, + groups=None, + study_level_moderators=False, + penalty=False, + device="cpu", + ): super().__init__() self.beta_dim = beta_dim self.gamma_dim = gamma_dim @@ -561,12 +1271,14 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) all_beta_linears[group] = beta_linear_group self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - # gamma + # gamma if self.study_level_moderators: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - - def _log_likelihood_single_group(beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device='cpu'): + + def _log_likelihood_single_group( + beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" + ): log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) if gamma is not None: @@ -574,28 +1286,62 @@ def _log_likelihood_single_group(beta, gamma, Coef_spline_bases, moderators, foc mu_moderators = torch.exp(log_mu_moderators) else: n_study, _ = foci_per_study.shape - log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) - log_l = torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) \ - - torch.sum(mu_spatial) * torch.sum(mu_moderators) + log_l = ( + torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) + - torch.sum(mu_spatial) * torch.sum(mu_moderators) + ) return log_l - def _log_likelihood_mult_group(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): + def _log_likelihood_mult_group( + all_spatial_coef, + Coef_spline_bases, + all_foci_per_voxel, + all_foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): n_groups = len(all_spatial_coef) - all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] - all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + all_log_spatial_intensity = [ + torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] if moderator_coef is not None: - all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] else: - all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study in all_foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] l = 0 for i in range(n_groups): - l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + l += ( + torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) + - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + ) return l - + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -606,7 +1352,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc all_log_mu_moderators[group] = log_mu_moderators log_l = 0 # spatial effect: mu^X = exp(X * beta) - for group in all_foci_per_voxel.keys(): + for group in all_foci_per_voxel.keys(): log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) group_foci_per_voxel = all_foci_per_voxel[group] @@ -616,43 +1362,75 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc mu_moderators = torch.exp(log_mu_moderators) else: n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) mu_moderators = torch.exp(log_mu_moderators) # Under the assumption that Y_ij is either 0 or 1 # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - group_log_l = torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) + group_log_l = ( + torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) + - torch.sum(mu_spatial) * torch.sum(mu_moderators) + ) log_l += group_log_l - + if self.penalty: - # Firth-type penalty - for group in all_foci_per_voxel.keys(): + # Firth-type penalty + for group in all_foci_per_voxel.keys(): beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_study = all_foci_per_study[group] if self.study_level_moderators: gamma = self.gamma_linear.weight.T group_moderators = all_moderators[group] gamma, group_moderators = [gamma], [group_moderators] - else: + else: gamma, group_moderators = None, None - + all_spatial_coef = torch.stack([beta]) - all_foci_per_voxel, all_foci_per_study = torch.stack([group_foci_per_voxel]), torch.stack([group_foci_per_study]) + all_foci_per_voxel, all_foci_per_study = torch.stack( + [group_foci_per_voxel] + ), torch.stack([group_foci_per_study]) # a = -GLMPoisson._log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, gamma, group_moderators) - nll = lambda beta: -self._log_likelihood(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) - params = (beta) - F = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + nll = lambda beta: -self._log_likelihood( + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta + F = torch.autograd.functional.hessian( + nll, + params, + create_graph=False, + vectorize=True, + outer_jacobian_strategy="forward-mode", + ) F = F.reshape((beta_dim, beta_dim)) - eig_vals = torch.real(torch.linalg.eigvals(F)) #torch.eig(F, eigenvectors=False)[0][:,0] + eig_vals = torch.real( + torch.linalg.eigvals(F) + ) # torch.eig(F, eigenvectors=False)[0][:,0] del F group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) del eig_vals log_l += group_firth_penalty return -log_l + class GLMNB(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty='No', device='cpu'): + def __init__( + self, + beta_dim=None, + gamma_dim=None, + groups=None, + study_level_moderators=False, + penalty="No", + device="cpu", + ): super().__init__() self.groups = groups self.study_level_moderators = study_level_moderators @@ -666,75 +1444,136 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder all_beta_linears[group] = beta_linear_group # initialization for alpha alpha_init_group = torch.tensor(1e-2).double() - all_alpha_sqrt[group] = torch.nn.Parameter(torch.sqrt(alpha_init_group), requires_grad=True) + all_alpha_sqrt[group] = torch.nn.Parameter( + torch.sqrt(alpha_init_group), requires_grad=True + ) self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) self.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - # gamma + # gamma if self.study_level_moderators: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - + def _three_term(y, r, device): max_foci = torch.max(y).to(dtype=torch.int64, device=device) sum_three_term = 0 for k in range(max_foci): - foci_index = (y == k+1).nonzero()[:,0] + foci_index = (y == k + 1).nonzero()[:, 0] r_j = r[foci_index] n_voxel = list(foci_index.shape)[0] - y_j = torch.tensor([k+1]*n_voxel, device=device).double() + y_j = torch.tensor([k + 1] * n_voxel, device=device).double() y_j = y_j.reshape((n_voxel, 1)) # y=0 => sum_three_term = 0 - sum_three_term += torch.sum(torch.lgamma(y_j+r_j) - torch.lgamma(y_j+1) - torch.lgamma(r_j)) - + sum_three_term += torch.sum( + torch.lgamma(y_j + r_j) - torch.lgamma(y_j + 1) - torch.lgamma(r_j) + ) + return sum_three_term - - def _log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device='cpu'): + + def _log_likelihood_single_group( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + device="cpu", + ): v = 1 / alpha log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: + if gamma is not None: log_mu_moderators = torch.matmul(group_moderators, gamma.T) mu_moderators = torch.exp(log_mu_moderators) else: n_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators)**2 + denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 estimated_sum_alpha = alpha * numerator / denominator p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator - log_l = GLMNB._three_term(group_foci_per_voxel,r, device=device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + log_l = GLMNB._three_term(group_foci_per_voxel, r, device=device) + torch.sum( + r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) + ) return log_l - - def _log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): + + def _log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + all_foci_per_voxel, + all_foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): all_v = 1 / all_overdispersion_coef n_groups = len(all_foci_per_voxel) - all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] - all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + all_log_spatial_intensity = [ + torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] if moderator_coef is not None: - all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] else: - all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] - - all_numerator = [all_spatial_intensity[i]**2 * torch.sum(all_moderator_effect[i]**2) for i in range(n_groups)] - all_denominator = [all_spatial_intensity[i]**2 * torch.sum(all_moderator_effect[i])**2 for i in range(n_groups)] - all_estimated_sum_alpha = [all_overdispersion_coef[i,:] * all_numerator[i] / all_denominator[i] for i in range(n_groups)] - - p = [all_numerator[i] / (all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) + all_denominator[i]) for i in range(n_groups)] + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study in all_foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + + all_numerator = [ + all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i] ** 2) + for i in range(n_groups) + ] + all_denominator = [ + all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i]) ** 2 + for i in range(n_groups) + ] + all_estimated_sum_alpha = [ + all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] + for i in range(n_groups) + ] + + p = [ + all_numerator[i] + / ( + all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) + + all_denominator[i] + ) + for i in range(n_groups) + ] r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] - + l = 0 for i in range(n_groups): - l += GLMNB._three_term(all_foci_per_voxel[i],r[i], device=device) + torch.sum(r[i]*torch.log(1-p[i]) + all_foci_per_voxel[i]*torch.log(p[i])) - + l += GLMNB._three_term(all_foci_per_voxel[i], r[i], device=device) + torch.sum( + r[i] * torch.log(1 - p[i]) + all_foci_per_voxel[i] * torch.log(p[i]) + ) + return l - + def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() @@ -745,8 +1584,8 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc all_log_mu_moderators[group] = log_mu_moderators log_l = 0 # spatial effect: mu^X = exp(X * beta) - for group in all_foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group]**2 + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group] ** 2 v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) @@ -755,28 +1594,32 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc mu_moderators = torch.exp(log_mu_moderators) else: n_group_study, _ = all_foci_per_study[group].shape - log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) mu_moderators = torch.exp(log_mu_moderators) # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), # Therefore, we use moment matching approach, - # create a new NB approximation to the mixture of NB distributions: + # create a new NB approximation to the mixture of NB distributions: # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators)**2 + denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 estimated_sum_alpha = alpha * numerator / denominator ## moment matching NB distribution - p = numerator / (v*mu_spatial*torch.sum(mu_moderators) + numerator) + p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator group_foci_per_voxel = all_foci_per_voxel[group] # group_foci_per_study = all_foci_per_study[group] - group_log_l = GLMNB._three_term(group_foci_per_voxel,r, device=self.device) + torch.sum(r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + group_log_l = GLMNB._three_term( + group_foci_per_voxel, r, device=self.device + ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) log_l += group_log_l - + if self.penalty == True: - # Firth-type penalty - for group in all_foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group]**2 + # Firth-type penalty + for group in all_foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group] ** 2 beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] gamma = self.gamma_linear.weight.detach().T @@ -784,8 +1627,16 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_foci_per_study = all_foci_per_study[group] group_moderators = all_moderators[group] # a = -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) - nll = lambda beta: -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) - params = (beta) + nll = lambda beta: -self._log_likelihood( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta F = torch.autograd.functional.hessian(nll, params, create_graph=True) F = F.reshape((beta_dim, beta_dim)) eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) @@ -793,11 +1644,20 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) del eig_vals log_l += group_firth_penalty - + return -log_l + class GLMCNB(torch.nn.Module): - def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moderators=False, penalty=True, device='cpu'): + def __init__( + self, + beta_dim=None, + gamma_dim=None, + groups=None, + study_level_moderators=False, + penalty=True, + device="cpu", + ): super().__init__() self.groups = groups self.study_level_moderators = study_level_moderators @@ -811,48 +1671,97 @@ def __init__(self, beta_dim=None, gamma_dim=None, groups=None, study_level_moder all_beta_linears[group] = beta_linear_group # initialization for alpha alpha_init_group = torch.tensor(1e-2).double() - all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) + all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) self.all_alpha = torch.nn.ParameterDict(all_alpha) - # gamma + # gamma if self.study_level_moderators: self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - - def _log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device='cpu'): + + def _log_likelihood_single_group( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + device="cpu", + ): v = 1 / alpha log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) if gamma is not None: log_mu_moderators = torch.matmul(group_moderators, gamma.T) mu_moderators = torch.exp(log_mu_moderators) - else: + else: n_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0]*n_study, dtype=torch.float64, device=device).reshape((-1,1)) + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators group_n_study, _ = group_foci_per_study.shape - log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ - + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) + log_l = ( + group_n_study * v * torch.log(v) + - group_n_study * torch.lgamma(v) + + torch.sum(torch.lgamma(group_foci_per_study + v)) + - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(group_foci_per_voxel * log_mu_spatial) + + torch.sum(group_foci_per_study * log_mu_moderators) + ) return log_l - def _log_likelihood_mult_group(all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, moderator_coef=None, all_moderators=None, device='cpu'): + def _log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + Coef_spline_bases, + all_foci_per_voxel, + all_foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): n_groups = len(all_foci_per_voxel) - all_log_spatial_intensity = [torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups)] - all_spatial_intensity = [torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity] + all_log_spatial_intensity = [ + torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] if moderator_coef is not None: - all_log_moderator_effect = [torch.matmul(moderator, moderator_coef) for moderator in all_moderators] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] else: - all_log_moderator_effect = [torch.tensor([0]*foci_per_study.shape[0], dtype=torch.float64, device=device).reshape((-1,1)) for foci_per_study in all_foci_per_study] - all_moderator_effect = [torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect] - - all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study in all_foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + + all_mu_sum_per_study = [ + torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups) + ] l = 0 for i in range(n_groups): - l += torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + l += ( + torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) + - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + ) return l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): @@ -864,7 +1773,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc log_mu_moderators = self.gamma_linear(group_moderators) all_log_mu_moderators[group] = log_mu_moderators log_l = 0 - for group in all_foci_per_voxel.keys(): + for group in all_foci_per_voxel.keys(): alpha = self.all_alpha[group] v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) @@ -876,17 +1785,25 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc mu_moderators = torch.exp(log_mu_moderators) else: n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0]*n_group_study, device=self.device).reshape((-1,1)) + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) mu_moderators = torch.exp(log_mu_moderators) group_n_study, _ = group_foci_per_study.shape mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - group_log_l = group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) + torch.sum(torch.lgamma(group_foci_per_study + v)) - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) \ - + torch.sum(group_foci_per_voxel * log_mu_spatial) + torch.sum(group_foci_per_study * log_mu_moderators) + group_log_l = ( + group_n_study * v * torch.log(v) + - group_n_study * torch.lgamma(v) + + torch.sum(torch.lgamma(group_foci_per_study + v)) + - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(group_foci_per_voxel * log_mu_spatial) + + torch.sum(group_foci_per_study * log_mu_moderators) + ) log_l += group_log_l - + if self.penalty == True: - # Firth-type penalty - for group in all_foci_per_voxel.keys(): + # Firth-type penalty + for group in all_foci_per_voxel.keys(): alpha = self.all_alpha[group] beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] @@ -894,9 +1811,19 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_foci_per_voxel = all_foci_per_voxel[group] group_foci_per_study = all_foci_per_study[group] group_moderators = all_moderators[group] - nll = lambda beta: -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) - params = (beta) - F = torch.autograd.functional.hessian(nll, params, create_graph=True) # vectorize=True, outer_jacobian_strategy='forward-mode' + nll = lambda beta: -self._log_likelihood( + alpha, + beta, + gamma, + Coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta + F = torch.autograd.functional.hessian( + nll, params, create_graph=True + ) # vectorize=True, outer_jacobian_strategy='forward-mode' # F = hessian(nll)(beta) F = F.reshape((beta_dim, beta_dim)) eig_vals = torch.real(torch.linalg.eigvals(F)) @@ -905,4 +1832,4 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc del eig_vals log_l += group_firth_penalty - return -log_l \ No newline at end of file + return -log_l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 8ae6e9289..e15ac1594 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -2,28 +2,44 @@ from nimare.utils import standardize_field import logging -# def test_CBMREstimator(testdata_cbmr_simulated): -# logging.getLogger().setLevel(logging.DEBUG) -# """Unit test for CBMR estimator.""" -# dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) -# cbmr = CBMREstimator(group_names='diagnosis', moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=5, model='Poisson', penalty=False, lr=1e-2, tol=1e4, device='cuda') -# cbmr_res = cbmr.fit(dataset=dset) + +def test_CBMREstimator(testdata_cbmr_simulated): + logging.getLogger().setLevel(logging.DEBUG) + """Unit test for CBMR estimator.""" + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) + cbmr = CBMREstimator( + group_names="diagnosis", + moderators=["standardized_sample_sizes", "standardized_avg_age"], + spline_spacing=5, + model="Poisson", + penalty=False, + lr=1e-2, + tol=1e4, + device="cuda", + ) + cbmr_res = cbmr.fit(dataset=dset) def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", 'avg_age']) - cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1e6, device='cuda') + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) + cbmr = CBMREstimator( + group_names=["diagnosis", "drug_status"], + moderators=["standardized_sample_sizes", "standardized_avg_age"], + spline_spacing=10, + model="NB", + penalty=False, + lr=1e-4, + tol=1e-1, + device="cuda", + ) cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False, t_con_moderator=[[1,0]], device='cuda') + inference = CBMRInference( + CBMRResults=cbmr_res, t_con_group=False, t_con_moderator=[[1, 0]], device="cuda" + ) a = inference._contrast() - + # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] # [[[1,0],[0,1]], [1, -1]] - - - - - - \ No newline at end of file + # ['standardized_sample_sizes', 'standardized_avg_age'] From 7c1b8ad49baf56a1d4d01a4d50ef5a9834b4d9d5 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 9 Dec 2022 20:54:12 +0000 Subject: [PATCH 033/177] solve some issues suggested by flake8 --- nimare/meta/cbmr.py | 165 ++++++++++++++++----------------- nimare/tests/test_meta_cbmr.py | 6 +- 2 files changed, 84 insertions(+), 87 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 3421d8737..ee89cb885 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,17 +1,11 @@ -from importlib.util import set_loader -import string -from attr import has -from numpy import spacing from nimare.base import Estimator -from nimare.utils import get_template, get_masker, B_spline_bases +from nimare.utils import get_masker, B_spline_bases import nibabel as nib import numpy as np import pandas as pd import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter -from nimare.transforms import z_to_p -from nimare import transforms import torch import functorch import logging @@ -56,8 +50,9 @@ class CBMREstimator(Estimator): Currently, the only available option is Firth-type penalty, which penalizes likelihood function by Jeffrey's invariant prior and guarantees convergent estimates. spline_spacing: :obj:`~int`, optional - Spatial structure of foci counts is parameterized by coefficient of cubic B-spline bases in CBMR. - Spatial smoothness in CBMR is determined by spline spacing, which is shared across x,y,z dimension. + Spatial structure of foci counts is parameterized by coefficient of cubic B-spline bases + in CBMR. Spatial smoothness in CBMR is determined by spline spacing, which is shared across + x,y,z dimension. Default is 10 (20mm). n_iters: :obj:`int`, optional Number of iterations limit in optimisation of log-likelihood function. @@ -66,7 +61,8 @@ class CBMREstimator(Estimator): Learning rate in optimization of log-likelihood function. Default is 1e-2 for Poisson and clustered NB model, and 1e-3 for NB model. tol: :obj:`float`, optional - Stopping criteria w.r.t difference of log-likelihood function in two consecutive iterations. + Stopping criteria w.r.t difference of log-likelihood function in two consecutive + iterations. Default is 1e-2 device: :obj:`string`, optional Device type ('cpu' or 'cuda') represents the device on which operations will be allocated @@ -80,10 +76,14 @@ class CBMREstimator(Estimator): masker : :class:`~nilearn.input_data.NiftiMasker` or similar Masker object. inputs_ : :obj:`dict` - Inputs to the Estimator. For CBMR estimators, there is only multiple keys: coordinates, - mask_img (Niftiimage of brain mask), id (study id), all_group_study_id (study id categorized - by groups), all_group_moderators (study-level moderators categorized by groups if exist), - Coef_spline_bases (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), + Inputs to the Estimator. For CBMR estimators, there is only multiple keys: + coordinates, + mask_img (Niftiimage of brain mask), + id (study id), + all_group_study_id (study id categorized by groups), + all_group_moderators (study-level moderators categorized by groups if exist), + Coef_spline_bases (spatial matrix of coefficient of cubic B-spline + bases in x,y,z dimension), all_foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), all_foci_per_study (study-wise sum of foci count across space, categorized by groups). @@ -125,7 +125,7 @@ def __init__( self.tol = tol self.device = device if self.device == "cuda" and not torch.cuda.is_available(): - LGR.debug(f"cuda not found, use device 'cpu'") + LGR.debug("cuda not found, use device cpu") self.device = "cpu" # Initialize optimisation parameters @@ -134,15 +134,17 @@ def __init__( def _preprocess_input(self, dataset): """Mask required input images using either the Dataset's mask or the Estimator's. - Also, categorize study id, voxelwise sum of foci counts across studies, study-wise sum of foci counts - across space into multiple groups. And summarize study-level moderators into multiple groups (if exist). + Also, categorize study id, voxelwise sum of foci counts across studies, study-wise sum of + foci counts across space into multiple groups. And summarize study-level moderators into + multiple groups (if exist). Parameters ---------- dataset : :obj:`~nimare.dataset.Dataset` In this method, the Dataset is used to (1) select the appropriate mask image, (2) categorize it into multiple groups according to group type in annotations, - (3) summarize group-wise study id, foci per voxel, foci per study, moderators (if exist), + (3) summarize group-wise study id, foci per voxel, foci per study, moderators + (if exist), (4) extract sample size metadata and use it as one of study-level moderators. Attributes @@ -157,7 +159,7 @@ def _preprocess_input(self, dataset): studies, categorized by groups), (6) an 'all_foci_per_study' key will be added (study-wise sum of foci count across space, categorized by groups), - (7) an 'all_group_moderators' key may be added if study-level moderators are considered' + (7) an 'all_group_moderators' key may be added if study-level moderators exists """ masker = self.masker or dataset.masker @@ -182,9 +184,7 @@ def _preprocess_input(self, dataset): elif isinstance(self.group_names, str): if self.group_names not in valid_dset_annotations.columns: raise ValueError( - "group_names: {} does not exist in the dataset".format( - self.group_names - ) + f"group_names: {self.group_names} does not exist in the dataset" ) else: uniq_groups = list(valid_dset_annotations[self.group_names].unique()) @@ -202,9 +202,7 @@ def _preprocess_input(self, dataset): ] if len(not_exist_group_names) > 0: raise ValueError( - "group_names: {} does not exist in the dataset".format( - not_exist_group_names - ) + f"group_names: {not_exist_group_names} does not exist in the dataset" ) uniq_group_splits = ( valid_dset_annotations[self.group_names].drop_duplicates().values.tolist() @@ -282,7 +280,8 @@ def _model_structure(self, model, penalty, device): penalty : :obj:`bool` Whether to penalize log-likelihood function with Firth-type penalty. device : :obj:`str` - Device type ('cpu' or 'cuda') represents the device on which operations will be allocated + Device type ('cpu' or 'cuda') represents the device on which operations will + be allocated """ beta_dim = self.inputs_["Coef_spline_bases"].shape[1] # regression coef of spatial effect if self.moderators: @@ -337,8 +336,8 @@ def _update( ): """One iteration in optimization with L-BFGS. - Adjust learning rate based on the number of iteration (with learning rate decay parameter `gamma`, default value is 0.999). - Reset L-BFGS optimizer if NaN occurs. + Adjust learning rate based on the number of iteration (with learning rate decay parameter + `gamma`, default value is 0.999).Reset L-BFGS optimizer if NaN occurs. """ self.iter += 1 scheduler = torch.optim.lr_scheduler.ExponentialLR( @@ -362,9 +361,8 @@ def closure(): ): if self.iter == 1: # NaN occurs in the first iteration raise ValueError( - "The current learing rate {} gives rise to NaN values, adjust it to a smaller value.".format( - str(self.lr) - ) + """The current learing rate {str(self.lr)} gives rise to NaN values, adjust + to a smaller value.""" ) all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() for group in self.inputs_["all_group_study_id"].keys(): @@ -390,7 +388,7 @@ def closure(): elif self.model == "clustered_NB": model.all_alpha = torch.nn.ParameterDict(all_alpha) - LGR.debug(f"Reset L-BFGS optimizer......") + LGR.debug("Reset L-BFGS optimizer......") else: self.last_state = copy.deepcopy( model.state_dict() @@ -401,7 +399,8 @@ def closure(): def _optimizer(self, model, lr, tol, n_iter, device): """Optimize regression coefficient of CBMR via L-BFGS algorithm. - Optimization terminates if the absolute value of difference of log-likelihood in two consecutive iterations is below `tol` + Optimization terminates if the absolute value of difference of log-likelihood in + two consecutive iterations is below `tol` Parameters ---------- @@ -414,7 +413,8 @@ def _optimizer(self, model, lr, tol, n_iter, device): n_iter : :obj:`~int` Maximum iterations limit of L-BFGS. device : :obj:`~str` - Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. + Device type ('cpu' or 'cuda') represents the device on + which operations will be allocated. """ optimizer = torch.optim.LBFGS(model.parameters(), lr) # load dataset info to torch.tensor @@ -465,10 +465,12 @@ def _optimizer(self, model, lr, tol, n_iter, device): def _fit(self, dataset): """Perform coordinate-based meta-regression (CBMR) on dataset. - Estimate group-wise spatial regression coefficients and its standard error via inverse Fisher Information matrix, - estimate standard error of group-wise log intensity, group-wise intensity via delta method. For NB or clustered model, - estimate regression coefficient of overdispersion. Similarly, estimate regression coefficient of study-level moderators - (if exist), as well as its standard error via Fisher Information matrix. Save these outcomes in `tables`. + (1)Estimate group-wise spatial regression coefficients and its standard error via inverse + Fisher Information matrix; + (2)estimate standard error of group-wise log intensity, group-wise intensity via delta + method. For NB or clustered model, estimate regression coefficient of overdispersion. + Similarly, estimate regression coefficient of study-level moderators (if exist), as well + as its standard error via Fisher Information matrix. Save these outcomes in `tables`. Also, estimate group-wise spatial intensity (per study) and save the results in `maps`. Parameters @@ -484,7 +486,7 @@ def _fit(self, dataset): self.inputs_["Coef_spline_bases"] = Coef_spline_bases cbmr_model = self._model_structure(self.model, self.penalty, self.device) - optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) + self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() @@ -562,7 +564,6 @@ def _fit(self, dataset): dtype=torch.float64, device=self.device, ) - # a = -GLMCNB._log_likelihood_single_group(alpha, group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) if self.model == "Poisson": nll = lambda beta: -GLMPoisson._log_likelihood_single_group( beta, @@ -716,9 +717,8 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] )[0].tolist() raise ValueError( - "The shape of {}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups".format( - str(wrong_con_group_idx) - ) + f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise + intensity contrast matrix doesn't match with groups""" ) con_group_zero_row = [ np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] @@ -733,9 +733,9 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" ] if np.any([con_group.shape[0] == 0 for con_group in self.t_con_group]): raise ValueError( - "One or more of contrast vectors(s) in group-wise intensity contrast matrix are all zeros" + """One or more of contrast vectors(s) in group-wise intensity + contrast matrix are all zeros""" ) - n_contrasts_group = [con_group.shape[0] for con_group in self.t_con_group] self._Name_of_con_group() # standardization self.t_con_group = [ @@ -772,9 +772,8 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" ] )[0].tolist() raise ValueError( - "The shape of {}th contrast vector(s) in moderators contrast matrix doesn't match with moderators".format( - str(wrong_con_moderator_idx) - ) + f"""The shape of {str(wrong_con_moderator_idx)}th contrast vector(s) in + moderators contrast matrix doesn't match with moderators""" ) con_moderator_zero_row = [ np.where(np.sum(np.abs(con_modereator), axis=1) == 0)[0] @@ -791,11 +790,9 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" [con_moderator.shape[0] == 0 for con_moderator in self.t_con_moderator] ): raise ValueError( - "One or more of contrast vectors(s) in modereators contrast matrix are all zeros" + """One or more of contrast vectors(s) in modereators contrast matrix + are all zeros""" ) - n_contrasts_moderator = [ - con_moderator.shape[0] for con_moderator in self.t_con_moderator - ] self._Name_of_con_moderator() self.t_con_moderator = [ con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) @@ -804,7 +801,7 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" else: self.t_con_moderator = False if self.device == "cuda" and not torch.cuda.is_available(): - LGR.debug(f"cuda not found, use device 'cpu'") + LGR.debug("cuda not found, use device 'cpu'") self.device = "cpu" def _Name_of_con_group(self): @@ -957,7 +954,6 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): ) else: involved_group_moderators, involved_moderator_coef = None, None - # a = GLMPoisson._log_likelihood_mult_group(involved_spatial_coef, Coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, involved_group_moderators, self.device) if self.CBMRResults.estimator.model == "Poisson": nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group( all_spatial_coef, @@ -1083,18 +1079,20 @@ def _Fisher_info_moderator_coef(self): def _contrast(self): """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. - Estimate group-wise spatial regression coefficients and its standard error via inverse Fisher Information matrix, - estimate standard error of group-wise log intensity, group-wise intensity via delta method. For NB or clustered model, - estimate regression coefficient of overdispersion. Similarly, estimate regression coefficient of study-level moderators - (if exist), as well as its standard error via Fisher Information matrix. Save these outcomes in `tables`. - Also, estimate group-wise spatial intensity (per study) and save the results in `maps`. + Estimate group-wise spatial regression coefficients and its standard error via inverse + Fisher Information matrix, estimate standard error of group-wise log intensity, + group-wise intensity via delta method. For NB or clustered model, estimate regression + coefficient of overdispersion. Similarly, estimate regression coefficient of study-level + moderators (if exist), as well as its standard error via Fisher Information matrix. + Save these outcomes in `tables`. Also, estimate group-wise spatial intensity (per study) + and save the results in `maps`. Parameters ---------- dataset : :obj:`~nimare.dataset.Dataset` Dataset to analyze. """ - Log_Spatial_Intensity_SE = self.CBMRResults.tables["Log_Spatial_Intensity_SE"] + # Log_Spatial_Intensity_SE = self.CBMRResults.tables["Log_Spatial_Intensity_SE"] if self.t_con_group is not False: con_group_count = 0 for con_group in self.t_con_group: @@ -1333,14 +1331,14 @@ def _log_likelihood_mult_group( torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect ] - l = 0 + log_l = 0 for i in range(n_groups): - l += ( + log_l += ( torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) ) - return l + return log_l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): @@ -1389,11 +1387,10 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc else: gamma, group_moderators = None, None - all_spatial_coef = torch.stack([beta]) + # all_spatial_coef = torch.stack([beta]) all_foci_per_voxel, all_foci_per_study = torch.stack( [group_foci_per_voxel] ), torch.stack([group_foci_per_study]) - # a = -GLMPoisson._log_likelihood(all_spatial_coef, Coef_spline_bases, all_foci_per_voxel, all_foci_per_study, gamma, group_moderators) nll = lambda beta: -self._log_likelihood( beta, gamma, @@ -1494,7 +1491,7 @@ def _log_likelihood_single_group( mu_moderators = torch.exp(log_mu_moderators) numerator = mu_spatial**2 * torch.sum(mu_moderators**2) denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 - estimated_sum_alpha = alpha * numerator / denominator + # estimated_sum_alpha = alpha * numerator / denominator p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator @@ -1551,10 +1548,10 @@ def _log_likelihood_mult_group( all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i]) ** 2 for i in range(n_groups) ] - all_estimated_sum_alpha = [ - all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] - for i in range(n_groups) - ] + # all_estimated_sum_alpha = [ + # all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] + # for i in range(n_groups) + # ] p = [ all_numerator[i] @@ -1566,13 +1563,13 @@ def _log_likelihood_mult_group( ] r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] - l = 0 + log_l = 0 for i in range(n_groups): - l += GLMNB._three_term(all_foci_per_voxel[i], r[i], device=device) + torch.sum( + log_l += GLMNB._three_term(all_foci_per_voxel[i], r[i], device=device) + torch.sum( r[i] * torch.log(1 - p[i]) + all_foci_per_voxel[i] * torch.log(p[i]) ) - return l + return log_l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): @@ -1604,8 +1601,8 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha numerator = mu_spatial**2 * torch.sum(mu_moderators**2) denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 - estimated_sum_alpha = alpha * numerator / denominator - ## moment matching NB distribution + # estimated_sum_alpha = alpha * numerator / denominator + # moment matching NB distribution p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator @@ -1616,7 +1613,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) log_l += group_log_l - if self.penalty == True: + if self.penalty: # Firth-type penalty for group in all_foci_per_voxel.keys(): alpha = self.all_alpha_sqrt[group] ** 2 @@ -1626,7 +1623,6 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc group_foci_per_voxel = all_foci_per_voxel[group] group_foci_per_study = all_foci_per_study[group] group_moderators = all_moderators[group] - # a = -self._log_likelihood(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study) nll = lambda beta: -self._log_likelihood( alpha, beta, @@ -1752,17 +1748,17 @@ def _log_likelihood_mult_group( for log_moderator_effect in all_log_moderator_effect ] - all_mu_sum_per_study = [ - torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups) - ] - l = 0 + # all_mu_sum_per_study = [ + # torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups) + # ] + log_l = 0 for i in range(n_groups): - l += ( + log_l += ( torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) ) - return l + return log_l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): if isinstance(all_moderators, dict): @@ -1783,6 +1779,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc if self.study_level_moderators: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) + else: n_group_study, _ = group_foci_per_study.shape log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( @@ -1801,7 +1798,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc ) log_l += group_log_l - if self.penalty == True: + if self.penalty: # Firth-type penalty for group in all_foci_per_voxel.keys(): alpha = self.all_alpha[group] diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index e15ac1594..c48db08d8 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -8,7 +8,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) cbmr = CBMREstimator( - group_names="diagnosis", + group_names="diagnosiss", moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=5, model="Poisson", @@ -17,7 +17,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): tol=1e4, device="cuda", ) - cbmr_res = cbmr.fit(dataset=dset) + cbmr.fit(dataset=dset) def test_CBMRInference(testdata_cbmr_simulated): @@ -38,7 +38,7 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, t_con_group=False, t_con_moderator=[[1, 0]], device="cuda" ) - a = inference._contrast() + inference._contrast() # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] # [[[1,0],[0,1]], [1, -1]] From 9882c51f4e08fec0b4d8e9ae5de5fce5cd75dc4a Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Wed, 21 Dec 2022 15:30:01 +0000 Subject: [PATCH 034/177] [skip CI][WIP] fix a bug in log-likelihood function of CNB model --- nimare/meta/cbmr.py | 33 ++++++++++----------- nimare/tests/test_meta_cbmr.py | 39 ++++++++++++------------- nimare/utils.py | 52 +++++++++++++++++++++------------- 3 files changed, 68 insertions(+), 56 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index ee89cb885..b338e762c 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1130,7 +1130,7 @@ def _contrast(self): involved_log_intensity_per_voxel = np.stack( involved_log_intensity_per_voxel, axis=0 ) - else: # GLH: group-comparison + else: # GLH: group comparison involved_log_intensity_per_voxel = list() for group in con_group_involved: group_log_intensity_per_voxel = np.log( @@ -1159,15 +1159,8 @@ def _contrast(self): k * spatial_coef_dim : (k + 1) * spatial_coef_dim, s * spatial_coef_dim : (s + 1) * spatial_coef_dim, ] - Cov_group_log_intensity = np.empty(shape=(1, 0)) - for j in range(n_brain_voxel): - x_j = self.CBMRResults.estimator.inputs_["Coef_spline_bases"][ - j, : - ].reshape((1, spatial_coef_dim)) - Cov_group_log_intensity_j = x_j @ Cov_beta_ks @ x_j.T - Cov_group_log_intensity = np.concatenate( - (Cov_group_log_intensity, Cov_group_log_intensity_j), axis=1 - ) + X = self.CBMRResults.estimator.inputs_["Coef_spline_bases"] + Cov_group_log_intensity = (X.dot(Cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) Cov_log_intensity = np.concatenate( (Cov_log_intensity, Cov_group_log_intensity), axis=0 ) # (m^2, n_voxels) @@ -1722,6 +1715,8 @@ def _log_likelihood_mult_group( device="cpu", ): n_groups = len(all_foci_per_voxel) + all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] + # estimated intensity and log estimated intensity all_log_spatial_intensity = [ torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) ] @@ -1747,17 +1742,20 @@ def _log_likelihood_mult_group( torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect ] + all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] + all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in all_foci_per_study] - # all_mu_sum_per_study = [ - # torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups) - # ] log_l = 0 for i in range(n_groups): log_l += ( - torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) - - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) - ) + all_group_n_study[i] * all_v[i] * torch.log(all_v[i]) + - all_group_n_study[i] * torch.lgamma(all_v[i]) + + torch.sum(torch.lgamma(all_foci_per_study[i] + all_v[i])) + - torch.sum((all_foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) + + torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) + ) + return log_l def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): @@ -1779,7 +1777,6 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc if self.study_level_moderators: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) - else: n_group_study, _ = group_foci_per_study.shape log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index c48db08d8..dbd5f0ee5 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -3,21 +3,22 @@ import logging -def test_CBMREstimator(testdata_cbmr_simulated): - logging.getLogger().setLevel(logging.DEBUG) - """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) - cbmr = CBMREstimator( - group_names="diagnosiss", - moderators=["standardized_sample_sizes", "standardized_avg_age"], - spline_spacing=5, - model="Poisson", - penalty=False, - lr=1e-2, - tol=1e4, - device="cuda", - ) - cbmr.fit(dataset=dset) +# def test_CBMREstimator(testdata_cbmr_simulated): +# logging.getLogger().setLevel(logging.DEBUG) +# """Unit test for CBMR estimator.""" +# dset = standardize_field(dataset=testdata_cbmr_simulated, +# metadata=["sample_sizes", "avg_age"]) +# cbmr = CBMREstimator( +# group_names="diagnosis", +# moderators=["standardized_sample_sizes", "standardized_avg_age"], +# spline_spacing=5, +# model="Poisson", +# penalty=False, +# lr=1e-1, +# tol=1e4, +# device="cuda", +# ) +# cbmr.fit(dataset=dset) def test_CBMRInference(testdata_cbmr_simulated): @@ -28,15 +29,15 @@ def test_CBMRInference(testdata_cbmr_simulated): group_names=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model="NB", + model="clustered_NB", penalty=False, - lr=1e-4, - tol=1e-1, + lr=1e-3, + tol=1e2, device="cuda", ) cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference( - CBMRResults=cbmr_res, t_con_group=False, t_con_moderator=[[1, 0]], device="cuda" + CBMRResults=cbmr_res, t_con_group=[[1, 1, 1, 1]], t_con_moderator=[[1, 0]], device="cuda" ) inference._contrast() diff --git a/nimare/utils.py b/nimare/utils.py index 9a3d60918..d592762a4 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1162,6 +1162,7 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms + def coef_spline_bases(axis_coords, spacing, margin): """ Coefficient of cubic B-spline bases in any x/y/z direction @@ -1169,14 +1170,14 @@ def coef_spline_bases(axis_coords, spacing, margin): Parameters ---------- axis_coords : value range in x/y/z direction - spacing: (equally spaced) knots spacing in x/y/z direction, + spacing: (equally spaced) knots spacing in x/y/z direction, margin: extend the region where B-splines are constructed (min-margin, max_margin) - to avoid weakly-supported B-spline on the edge + to avoid weakly-supported B-spline on the edge Returns ------- coef_spline : 2-D ndarray (n_points x n_spline_bases) """ - ## create B-spline basis for x/y/z coordinate + # create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) design_matrix = patsy.dmatrix( @@ -1194,24 +1195,24 @@ def coef_spline_bases(axis_coords, spacing, margin): def B_spline_bases(masker_voxels, spacing, margin=10): - """ Cubic B-spline bases for spatial intensity + """Cubic B-spline bases for spatial intensity The whole coefficient matrix is constructed by taking tensor product of - all B-spline bases coefficient matrix in three direction. + all B-spline bases coefficient matrix in three direction. Parameters ---------- masker_voxels : matrix with element either 0 or 1, indicating if it's within brain mask, - spacing: (equally spaced) knots spacing in x/y/z direction, + spacing: (equally spaced) knots spacing in x/y/z direction, margin: extend the region where B-splines are constructed (min-margin, max_margin) - to avoid weakly-supported B-spline on the edge + to avoid weakly-supported B-spline on the edge Returns ------- X : 2-D ndarray (n_voxel x n_spline_bases) only keeps with within-brain voxels """ - dim_mask = masker_voxels.shape - n_brain_voxel = np.sum(masker_voxels) + # dim_mask = masker_voxels.shape + # n_brain_voxel = np.sum(masker_voxels) # remove the blank space around the brain mask xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] @@ -1228,11 +1229,19 @@ def B_spline_bases(masker_voxels, spacing, margin=10): z_spline_sparse = sparse.COO(z_spline_coords, z_spline[z_spline_coords]) # create spatial design matrix by tensor product of spline bases in 3 dimesion - X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # Row sums of X are all 1=> There is no need to re-normalise X + # Row sums of X are all 1=> There is no need to re-normalise X + X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # remove the voxels outside brain mask axis_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] - brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) - for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] + brain_voxels_index = [ + (z - np.min(zz)) + + axis_dim[2] * (y - np.min(yy)) + + axis_dim[1] * axis_dim[2] * (x - np.min(xx)) + for x in xx + for y in yy + for z in zz + if masker_voxels[x, y, z] == 1 + ] X = X[brain_voxels_index, :].todense() # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] @@ -1241,22 +1250,23 @@ def B_spline_bases(masker_voxels, spacing, margin=10): for bx in range(x_df): for by in range(y_df): for bz in range(z_df): - basis_index = bz + z_df*by + z_df*y_df*bx + basis_index = bz + z_df * by + z_df * y_df * bx basis_coef = X[:, basis_index] - if np.max(basis_coef) >= 0.1: + if np.max(basis_coef) >= 0.1: support_basis.append(basis_index) X = X[:, support_basis] return X + def standardize_field(dataset, metadata): moderators = dataset.annotations[metadata] standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) if isinstance(metadata, str): - column_name = 'standardized_' + metadata + column_name = "standardized_" + metadata elif isinstance(metadata, list): - column_name = ['standardized_' + moderator for moderator in metadata] + column_name = ["standardized_" + moderator for moderator in metadata] dataset.annotations[column_name] = standardize_moderators return dataset @@ -1272,9 +1282,13 @@ def index2vox(vals, masker_voxels): for i in range(image_dim[0]): for j in range(image_dim[1]): for k in range(image_dim[2]): - x,y,z = xx[i], yy[j], zz[k] - if masker_voxels[x,y,z] == 1: - voxel_array[x,y,z] = vals[index_count] + x, y, z = xx[i], yy[j], zz[k] + if masker_voxels[x, y, z] == 1: + voxel_array[x, y, z] = vals[index_count] index_count += 1 return voxel_array + +def contrast_matrix_generator(): + + return From f70e6acde52c847893a0af822482af0823322191 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 10 Jan 2023 14:11:08 +0000 Subject: [PATCH 035/177] [skip CI][WIP] Update code according to comments --- nimare/meta/cbmr.py | 269 ++++++++++++++++----------------- nimare/tests/test_meta_cbmr.py | 10 +- nimare/tests/utils.py | 13 ++ nimare/utils.py | 13 -- 4 files changed, 152 insertions(+), 153 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index b338e762c..e82573288 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -19,7 +19,7 @@ class CBMREstimator(Estimator): Parameters ---------- - group_names : :obj:`~str` or obj:`~list` or obj:`~None`, optional + group_categories : :obj:`~str` or obj:`~list` or obj:`~None`, optional CBMR allows dataset to be categorized into mutiple groups, according to group names. Default is one-group CBMR. moderators : :obj:`~str` or obj:`~list` or obj:`~None`, optional @@ -53,7 +53,7 @@ class CBMREstimator(Estimator): Spatial structure of foci counts is parameterized by coefficient of cubic B-spline bases in CBMR. Spatial smoothness in CBMR is determined by spline spacing, which is shared across x,y,z dimension. - Default is 10 (20mm). + Default is 10 (20mm with 2mm brain atlas template). n_iters: :obj:`int`, optional Number of iterations limit in optimisation of log-likelihood function. Default is 10000. @@ -80,12 +80,12 @@ class CBMREstimator(Estimator): coordinates, mask_img (Niftiimage of brain mask), id (study id), - all_group_study_id (study id categorized by groups), + studies_by_groups (study id categorized by groups), all_group_moderators (study-level moderators categorized by groups if exist), Coef_spline_bases (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), - all_foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), - all_foci_per_study (study-wise sum of foci count across space, categorized by groups). + foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), + foci_per_study (study-wise sum of foci count across space, categorized by groups). Notes @@ -97,10 +97,10 @@ class CBMREstimator(Estimator): def __init__( self, - group_names=None, + group_categories=None, moderators=None, mask=None, - spline_spacing=5, + spline_spacing=10, model="Poisson", penalty=False, n_iter=1000, @@ -114,7 +114,7 @@ def __init__( mask = get_masker(mask) self.masker = mask - self.group_names = group_names + self.group_categories = group_categories self.moderators = moderators self.spline_spacing = spline_spacing @@ -152,14 +152,14 @@ def _preprocess_input(self, dataset): inputs_ : :obj:`dict` Specifically, (1) a “mask_img” key will be added (Niftiimage of brain mask), (2) an 'id' key will be added (id of all studies in the dataset), - (3) an 'all_group_study_id' key will be added (study id categorized by groups), + (3) an 'studies_by_group' key will be added (study id categorized by groups), (4) a 'Coef_spline_bases' key will be added (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), - (5) an 'all_foci_per_voxel' key will be added (voxelwise sum of foci count across + (5) an 'foci_per_voxel' key will be added (voxelwise sum of foci count across studies, categorized by groups), - (6) an 'all_foci_per_study' key will be added (study-wise sum of foci count across + (6) an 'foci_per_study' key will be added (study-wise sum of foci count across space, categorized by groups), - (7) an 'all_group_moderators' key may be added if study-level moderators exists + (7) an 'moderators_by_group' key may be added if study-level moderators exists """ masker = self.masker or dataset.masker @@ -176,74 +176,70 @@ def _preprocess_input(self, dataset): valid_dset_annotations = dataset.annotations[ dataset.annotations["id"].isin(self.inputs_["id"]) ] - all_group_study_id = dict() - if isinstance(self.group_names, type(None)): - all_group_study_id[str(self.group_names)] = ( + studies_by_group = dict() + if self.group_categories is None: + studies_by_group["default"] = ( valid_dset_annotations["study_id"].unique().tolist() ) - elif isinstance(self.group_names, str): - if self.group_names not in valid_dset_annotations.columns: + unique_groups = ["default"] + elif isinstance(self.group_categories, str): + if self.group_categories not in valid_dset_annotations.columns: raise ValueError( - f"group_names: {self.group_names} does not exist in the dataset" + f"group_names: {self.group_categories} does not exist in the dataset" ) else: - uniq_groups = list(valid_dset_annotations[self.group_names].unique()) - for group in uniq_groups: - group_study_id_bool = valid_dset_annotations[self.group_names] == group + unique_groups = list(valid_dset_annotations[self.group_categories].unique()) + for group in unique_groups: + group_study_id_bool = valid_dset_annotations[self.group_categories] == group group_study_id = valid_dset_annotations.loc[group_study_id_bool][ "study_id" ] - all_group_study_id[group] = group_study_id.unique().tolist() - elif isinstance(self.group_names, list): - not_exist_group_names = [ - group - for group in self.group_names - if group not in dataset.annotations.columns - ] - if len(not_exist_group_names) > 0: + studies_by_group[group] = group_study_id.unique().tolist() + elif isinstance(self.group_categories, list): + missing_categories = set(self.group_categories) - set(dataset.annotations.columns) + if missing_categories: raise ValueError( - f"group_names: {not_exist_group_names} does not exist in the dataset" + f"Category_names: {missing_categories} do/does not exist in the dataset." ) - uniq_group_splits = ( - valid_dset_annotations[self.group_names].drop_duplicates().values.tolist() + unique_groups = ( + valid_dset_annotations[self.group_categories].drop_duplicates().values.tolist() ) - for group in uniq_group_splits: + for group in unique_groups: group_study_id_bool = ( - valid_dset_annotations[self.group_names] == group + valid_dset_annotations[self.group_categories] == group ).all(axis=1) group_study_id = valid_dset_annotations.loc[group_study_id_bool][ "study_id" ] - all_group_study_id["_".join(group)] = group_study_id.unique().tolist() - self.inputs_["all_group_study_id"] = all_group_study_id + studies_by_group["_".join(group)] = group_study_id.unique().tolist() + self.inputs_["studies_by_group"] = studies_by_group # collect studywise moderators if specficed if self.moderators: if isinstance(self.moderators, str): self.moderators = [ self.moderators ] # convert moderators to a single-element list if it's a string - all_group_moderators = dict() - for group in all_group_study_id.keys(): + moderators_by_group = dict() + for group in studies_by_group.keys(): df_group = valid_dset_annotations.loc[ - valid_dset_annotations["study_id"].isin(all_group_study_id[group]) + valid_dset_annotations["study_id"].isin(studies_by_group[group]) ] group_moderators = np.stack( [df_group[moderator_name] for moderator_name in self.moderators], axis=1, ) - group_moderators = group_moderators.astype(np.float64) - all_group_moderators[group] = group_moderators - self.inputs_["all_group_moderators"] = all_group_moderators - # Calculate IJK matrix indices for target mask - # Mask space is assumed to be the same as the Dataset's space - # These indices are used directly by any KernelTransformer - all_foci_per_voxel, all_foci_per_study = dict(), dict() - for group in all_group_study_id.keys(): - group_study_id = all_group_study_id[group] + moderators_by_group[group] = group_moderators + self.inputs_["moderators_by_group"] = moderators_by_group + + foci_per_voxel, foci_per_study = dict(), dict() + for group in studies_by_group.keys(): + group_study_id = studies_by_group[group] group_coordinates = dataset.coordinates.loc[ dataset.coordinates["study_id"].isin(group_study_id) ] - # group-wise foci coordinates + # Group-wise foci coordinates + # Calculate IJK matrix indices for target mask + # Mask space is assumed to be the same as the Dataset's space group_xyz = group_coordinates[["x", "y", "z"]].values group_ijk = mm2vox(group_xyz, mask_img.affine) group_foci_per_voxel = np.zeros(mask_img.shape, dtype=np.int32) @@ -261,11 +257,11 @@ def _preprocess_input(self, dataset): ) group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) - all_foci_per_voxel[group] = group_foci_per_voxel - all_foci_per_study[group] = group_foci_per_study + foci_per_voxel[group] = group_foci_per_voxel + foci_per_study[group] = group_foci_per_study - self.inputs_["all_foci_per_voxel"] = all_foci_per_voxel - self.inputs_["all_foci_per_study"] = all_foci_per_study + self.inputs_["foci_per_voxel"] = foci_per_voxel + self.inputs_["foci_per_study"] = foci_per_study def _model_structure(self, model, penalty, device): """Specify stochastic models for CBMR with or without Firth-type penalty. @@ -285,13 +281,16 @@ def _model_structure(self, model, penalty, device): """ beta_dim = self.inputs_["Coef_spline_bases"].shape[1] # regression coef of spatial effect if self.moderators: - gamma_dim = list(self.inputs_["all_group_moderators"].values())[0].shape[1] + gamma_dim = list(self.inputs_["moderators_by_group"].values())[0].shape[1] study_level_moderators = True else: gamma_dim = None study_level_moderators = False - self.groups = list(self.inputs_["all_group_study_id"].keys()) - if model == "Poisson": + self.groups = list(self.inputs_["studies_by_group"].keys()) + model = model.lower() + if model not in ["poisson", "nb", "clustered_nb"]: + raise ValueError("The input model is not supported, we only allow poisson, nb or clustered_nb model.") + if model == "poisson": cbmr_model = GLMPoisson( beta_dim=beta_dim, gamma_dim=gamma_dim, @@ -300,7 +299,7 @@ def _model_structure(self, model, penalty, device): penalty=penalty, device=device, ) - elif model == "NB": + elif model == "nb": cbmr_model = GLMNB( beta_dim=beta_dim, gamma_dim=gamma_dim, @@ -309,7 +308,7 @@ def _model_structure(self, model, penalty, device): penalty=penalty, device=device, ) - elif model == "clustered_NB": + elif model == "clustered_nb": cbmr_model = GLMCNB( beta_dim=beta_dim, gamma_dim=gamma_dim, @@ -329,8 +328,8 @@ def _update( optimizer, Coef_spline_bases, all_moderators, - all_foci_per_voxel, - all_foci_per_study, + foci_per_voxel, + foci_per_study, prev_loss, gamma=0.999, ): @@ -346,7 +345,7 @@ def _update( def closure(): optimizer.zero_grad() - loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) + loss = model(Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study) loss.backward() return loss @@ -356,7 +355,7 @@ def closure(): if any( [ torch.any(torch.isnan(model.all_beta_linears[group].weight)) - for group in self.inputs_["all_group_study_id"].keys() + for group in self.inputs_["studies_by_group"].keys() ] ): if self.iter == 1: # NaN occurs in the first iteration @@ -365,7 +364,7 @@ def closure(): to a smaller value.""" ) all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() - for group in self.inputs_["all_group_study_id"].keys(): + for group in self.inputs_["studies_by_group"].keys(): beta_dim = model.all_beta_linears[group].weight.shape[1] beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() beta_linear_group.weight = torch.nn.Parameter( @@ -422,24 +421,24 @@ def _optimizer(self, model, lr, tol, n_iter, device): self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=device ) if self.moderators: - all_group_moderators_tensor = dict() - for group in self.inputs_["all_group_study_id"].keys(): - group_moderators_tensor = torch.tensor( - self.inputs_["all_group_moderators"][group], dtype=torch.float64, device=device + moderators_by_group_tensor = dict() + for group in self.inputs_["studies_by_group"].keys(): + moderators_tensor = torch.tensor( + self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=device ) - all_group_moderators_tensor[group] = group_moderators_tensor + moderators_by_group_tensor[group] = moderators_tensor else: - all_group_moderators_tensor = None - all_foci_per_voxel_tensor, all_foci_per_study_tensor = dict(), dict() - for group in self.inputs_["all_group_study_id"].keys(): - group_foci_per_voxel = torch.tensor( - self.inputs_["all_foci_per_voxel"][group], dtype=torch.float64, device=device + moderators_by_group_tensor = None + foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() + for group in self.inputs_["studies_by_group"].keys(): + group_foci_per_voxel_tensor = torch.tensor( + self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=device ) - group_foci_per_study = torch.tensor( - self.inputs_["all_foci_per_study"][group], dtype=torch.float64, device=device + group_foci_per_study_tensor = torch.tensor( + self.inputs_["foci_per_study"][group], dtype=torch.float64, device=device ) - all_foci_per_voxel_tensor[group] = group_foci_per_voxel - all_foci_per_study_tensor[group] = group_foci_per_study + foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor + foci_per_study_tensor[group] = group_foci_per_study_tensor if self.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference @@ -449,9 +448,9 @@ def _optimizer(self, model, lr, tol, n_iter, device): model, optimizer, Coef_spline_bases, - all_group_moderators_tensor, - all_foci_per_voxel_tensor, - all_foci_per_study_tensor, + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, prev_loss, ) loss_diff = loss - prev_loss @@ -491,7 +490,7 @@ def _fit(self, dataset): maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() # beta: regression coef of spatial effect - for group in self.inputs_["all_group_study_id"].keys(): + for group in self.inputs_["studies_by_group"].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight group_beta_linear_weight = ( group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) @@ -524,7 +523,7 @@ def _fit(self, dataset): self.moderators_effect = dict() self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.cpu().detach().numpy() - for group in self.inputs_["all_group_study_id"].keys(): + for group in self.inputs_["studies_by_groups"].keys(): group_moderators = self.inputs_["all_group_moderators"][group] group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) self.moderators_effect[group] = group_moderators_effect @@ -542,12 +541,12 @@ def _fit(self, dataset): Coef_spline_bases = torch.tensor( self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=self.device ) - for group in self.inputs_["all_group_study_id"].keys(): + for group in self.inputs_["studies_by_groups"].keys(): group_foci_per_voxel = torch.tensor( - self.inputs_["all_foci_per_voxel"][group], dtype=torch.float64, device=self.device + self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device ) group_foci_per_study = torch.tensor( - self.inputs_["all_foci_per_study"][group], dtype=torch.float64, device=self.device + self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device ) group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight if self.moderators: @@ -903,7 +902,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): GLH_involved = [self.group_names[i] for i in GLH_involved_index] involved_group_foci_per_voxel = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_foci_per_voxel"][group], + self.CBMRResults.estimator.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device, ) @@ -911,7 +910,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): ] involved_group_foci_per_study = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_foci_per_study"][group], + self.CBMRResults.estimator.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device, ) @@ -996,7 +995,7 @@ def _Fisher_info_moderator_coef(self): ) all_group_foci_per_voxel = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_foci_per_voxel"][group], + self.CBMRResults.estimator.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device, ) @@ -1004,7 +1003,7 @@ def _Fisher_info_moderator_coef(self): ] all_group_foci_per_study = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_foci_per_study"][group], + self.CBMRResults.estimator.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device, ) @@ -1106,10 +1105,10 @@ def _contrast(self): involved_log_intensity_per_voxel = list() for group in con_group_involved: group_foci_per_voxel = self.CBMRResults.estimator.inputs_[ - "all_foci_per_voxel" + "foci_per_voxel" ][group] group_foci_per_study = self.CBMRResults.estimator.inputs_[ - "all_foci_per_study" + "foci_per_study" ][group] n_voxels, n_study = ( group_foci_per_voxel.shape[0], @@ -1292,8 +1291,8 @@ def _log_likelihood_single_group( def _log_likelihood_mult_group( all_spatial_coef, Coef_spline_bases, - all_foci_per_voxel, - all_foci_per_study, + foci_per_voxel, + foci_per_study, moderator_coef=None, all_moderators=None, device="cpu", @@ -1316,9 +1315,9 @@ def _log_likelihood_mult_group( else: all_log_moderator_effect = [ torch.tensor( - [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + [0] * foci_per_study_i.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) - for foci_per_study in all_foci_per_study + for foci_per_study_i in foci_per_study ] all_moderator_effect = [ torch.exp(log_moderator_effect) @@ -1327,13 +1326,13 @@ def _log_likelihood_mult_group( log_l = 0 for i in range(n_groups): log_l += ( - torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) ) return log_l - def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1343,11 +1342,11 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc all_log_mu_moderators[group] = log_mu_moderators log_l = 0 # spatial effect: mu^X = exp(X * beta) - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] if self.study_level_moderators: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) @@ -1368,11 +1367,11 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc if self.penalty: # Firth-type penalty - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] - group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] if self.study_level_moderators: gamma = self.gamma_linear.weight.T group_moderators = all_moderators[group] @@ -1381,7 +1380,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc gamma, group_moderators = None, None # all_spatial_coef = torch.stack([beta]) - all_foci_per_voxel, all_foci_per_study = torch.stack( + foci_per_voxel, foci_per_study = torch.stack( [group_foci_per_voxel] ), torch.stack([group_foci_per_study]) nll = lambda beta: -self._log_likelihood( @@ -1499,14 +1498,14 @@ def _log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, - all_foci_per_voxel, - all_foci_per_study, + foci_per_voxel, + foci_per_study, moderator_coef=None, all_moderators=None, device="cpu", ): all_v = 1 / all_overdispersion_coef - n_groups = len(all_foci_per_voxel) + n_groups = len(foci_per_voxel) all_log_spatial_intensity = [ torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) ] @@ -1526,7 +1525,7 @@ def _log_likelihood_mult_group( torch.tensor( [0] * foci_per_study.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) - for foci_per_study in all_foci_per_study + for foci_per_study in foci_per_study ] all_moderator_effect = [ torch.exp(log_moderator_effect) @@ -1558,13 +1557,13 @@ def _log_likelihood_mult_group( log_l = 0 for i in range(n_groups): - log_l += GLMNB._three_term(all_foci_per_voxel[i], r[i], device=device) + torch.sum( - r[i] * torch.log(1 - p[i]) + all_foci_per_voxel[i] * torch.log(p[i]) + log_l += GLMNB._three_term(foci_per_voxel[i], r[i], device=device) + torch.sum( + r[i] * torch.log(1 - p[i]) + foci_per_voxel[i] * torch.log(p[i]) ) return log_l - def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1574,7 +1573,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc all_log_mu_moderators[group] = log_mu_moderators log_l = 0 # spatial effect: mu^X = exp(X * beta) - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): alpha = self.all_alpha_sqrt[group] ** 2 v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) @@ -1583,7 +1582,7 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) else: - n_group_study, _ = all_foci_per_study[group].shape + n_group_study, _ = foci_per_study[group].shape log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( (-1, 1) ) @@ -1599,8 +1598,8 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator - group_foci_per_voxel = all_foci_per_voxel[group] - # group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + # group_foci_per_study = foci_per_study[group] group_log_l = GLMNB._three_term( group_foci_per_voxel, r, device=self.device ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) @@ -1608,13 +1607,13 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc if self.penalty: # Firth-type penalty - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): alpha = self.all_alpha_sqrt[group] ** 2 beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] gamma = self.gamma_linear.weight.detach().T - group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] group_moderators = all_moderators[group] nll = lambda beta: -self._log_likelihood( alpha, @@ -1708,13 +1707,13 @@ def _log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, Coef_spline_bases, - all_foci_per_voxel, - all_foci_per_study, + foci_per_voxel, + foci_per_study, moderator_coef=None, all_moderators=None, device="cpu", ): - n_groups = len(all_foci_per_voxel) + n_groups = len(foci_per_voxel) all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] # estimated intensity and log estimated intensity all_log_spatial_intensity = [ @@ -1736,29 +1735,29 @@ def _log_likelihood_mult_group( torch.tensor( [0] * foci_per_study.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) - for foci_per_study in all_foci_per_study + for foci_per_study in foci_per_study ] all_moderator_effect = [ torch.exp(log_moderator_effect) for log_moderator_effect in all_log_moderator_effect ] all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] - all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in all_foci_per_study] + all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] log_l = 0 for i in range(n_groups): log_l += ( all_group_n_study[i] * all_v[i] * torch.log(all_v[i]) - all_group_n_study[i] * torch.lgamma(all_v[i]) - + torch.sum(torch.lgamma(all_foci_per_study[i] + all_v[i])) - - torch.sum((all_foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) - + torch.sum(all_foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(all_foci_per_study[i] * all_log_moderator_effect[i]) + + torch.sum(torch.lgamma(foci_per_study[i] + all_v[i])) + - torch.sum((foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) + + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) ) return log_l - def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study): + def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1767,13 +1766,13 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc log_mu_moderators = self.gamma_linear(group_moderators) all_log_mu_moderators[group] = log_mu_moderators log_l = 0 - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): alpha = self.all_alpha[group] v = 1 / alpha log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] if self.study_level_moderators: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) @@ -1797,13 +1796,13 @@ def forward(self, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foc if self.penalty: # Firth-type penalty - for group in all_foci_per_voxel.keys(): + for group in foci_per_voxel.keys(): alpha = self.all_alpha[group] beta = self.all_beta_linears[group].weight.T beta_dim = beta.shape[0] gamma = self.gamma_linear.weight.T - group_foci_per_voxel = all_foci_per_voxel[group] - group_foci_per_study = all_foci_per_study[group] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] group_moderators = all_moderators[group] nll = lambda beta: -self._log_likelihood( alpha, diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index dbd5f0ee5..656f3f572 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,5 +1,5 @@ from nimare.meta.cbmr import CBMREstimator, CBMRInference -from nimare.utils import standardize_field +from nimare.tests.utils import standardize_field import logging @@ -26,13 +26,13 @@ def test_CBMRInference(testdata_cbmr_simulated): """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) cbmr = CBMREstimator( - group_names=["diagnosis", "drug_status"], + group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model="clustered_NB", + model="Poisson", penalty=False, - lr=1e-3, - tol=1e2, + lr=1e-1, + tol=1e6, device="cuda", ) cbmr_res = cbmr.fit(dataset=dset) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 940965b79..c8d8d532f 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -120,3 +120,16 @@ def _transform_res(meta, meta_res, corr): if isinstance(corr_expectation, type(pytest.raises(ValueError))): pytest.xfail("this meta-analysis & corrector combo fails") return cres + + +def standardize_field(dataset, metadata): + moderators = dataset.annotations[metadata] + standardize_moderators = moderators - np.mean(moderators, axis=0) + standardize_moderators /= np.std(standardize_moderators, axis=0) + if isinstance(metadata, str): + column_name = "standardized_" + metadata + elif isinstance(metadata, list): + column_name = ["standardized_" + moderator for moderator in metadata] + dataset.annotations[column_name] = standardize_moderators + + return dataset diff --git a/nimare/utils.py b/nimare/utils.py index d592762a4..9c9c23f78 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1259,19 +1259,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): return X -def standardize_field(dataset, metadata): - moderators = dataset.annotations[metadata] - standardize_moderators = moderators - np.mean(moderators, axis=0) - standardize_moderators /= np.std(standardize_moderators, axis=0) - if isinstance(metadata, str): - column_name = "standardized_" + metadata - elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in metadata] - dataset.annotations[column_name] = standardize_moderators - - return dataset - - def index2vox(vals, masker_voxels): xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] From d103ceb14d4f3614d434911afc8cf644ddecc00e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 10 Jan 2023 16:50:57 +0000 Subject: [PATCH 036/177] [skip CI][WIP] solve conflicts in code --- nimare/meta/cbmr.py | 115 ++++++++++++++++++++++---------------------- 1 file changed, 57 insertions(+), 58 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e82573288..d24ee1c68 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -82,7 +82,7 @@ class CBMREstimator(Estimator): id (study id), studies_by_groups (study id categorized by groups), all_group_moderators (study-level moderators categorized by groups if exist), - Coef_spline_bases (spatial matrix of coefficient of cubic B-spline + coef_spline_bases (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), foci_per_study (study-wise sum of foci count across space, categorized by groups). @@ -153,7 +153,7 @@ def _preprocess_input(self, dataset): Specifically, (1) a “mask_img” key will be added (Niftiimage of brain mask), (2) an 'id' key will be added (id of all studies in the dataset), (3) an 'studies_by_group' key will be added (study id categorized by groups), - (4) a 'Coef_spline_bases' key will be added (spatial matrix of coefficient of cubic + (4) a 'coef_spline_bases' key will be added (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), (5) an 'foci_per_voxel' key will be added (voxelwise sum of foci count across studies, categorized by groups), @@ -279,7 +279,7 @@ def _model_structure(self, model, penalty, device): Device type ('cpu' or 'cuda') represents the device on which operations will be allocated """ - beta_dim = self.inputs_["Coef_spline_bases"].shape[1] # regression coef of spatial effect + beta_dim = self.inputs_["coef_spline_bases"].shape[1] # regression coef of spatial effect if self.moderators: gamma_dim = list(self.inputs_["moderators_by_group"].values())[0].shape[1] study_level_moderators = True @@ -326,7 +326,7 @@ def _update( self, model, optimizer, - Coef_spline_bases, + coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study, @@ -345,7 +345,7 @@ def _update( def closure(): optimizer.zero_grad() - loss = model(Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study) + loss = model(coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study) loss.backward() return loss @@ -417,8 +417,8 @@ def _optimizer(self, model, lr, tol, n_iter, device): """ optimizer = torch.optim.LBFGS(model.parameters(), lr) # load dataset info to torch.tensor - Coef_spline_bases = torch.tensor( - self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=device + coef_spline_bases = torch.tensor( + self.inputs_["coef_spline_bases"], dtype=torch.float64, device=device ) if self.moderators: moderators_by_group_tensor = dict() @@ -447,7 +447,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): loss = self._update( model, optimizer, - Coef_spline_bases, + coef_spline_bases, moderators_by_group_tensor, foci_per_voxel_tensor, foci_per_study_tensor, @@ -478,11 +478,11 @@ def _fit(self, dataset): Dataset to analyze. """ masker_voxels = self.inputs_["mask_img"]._dataobj - Coef_spline_bases = B_spline_bases( + coef_spline_bases = B_spline_bases( masker_voxels=masker_voxels, spacing=self.spline_spacing ) - P = Coef_spline_bases.shape[1] - self.inputs_["Coef_spline_bases"] = Coef_spline_bases + P = coef_spline_bases.shape[1] + self.inputs_["coef_spline_bases"] = coef_spline_bases cbmr_model = self._model_structure(self.model, self.penalty, self.device) self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) @@ -497,7 +497,7 @@ def _fit(self, dataset): ) Spatial_Regression_Coef[group] = group_beta_linear_weight group_studywise_spatial_intensity = np.exp( - np.matmul(Coef_spline_bases, group_beta_linear_weight) + np.matmul(coef_spline_bases, group_beta_linear_weight) ) maps[ "Group_" + group + "_Studywise_Spatial_Intensity" @@ -523,8 +523,8 @@ def _fit(self, dataset): self.moderators_effect = dict() self._gamma = cbmr_model.gamma_linear.weight self._gamma = self._gamma.cpu().detach().numpy() - for group in self.inputs_["studies_by_groups"].keys(): - group_moderators = self.inputs_["all_group_moderators"][group] + for group in self.inputs_["studies_by_group"].keys(): + group_moderators = self.inputs_["moderators_by_group"][group] group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) self.moderators_effect[group] = group_moderators_effect tables["Moderators_Regression_Coef"] = pd.DataFrame( @@ -538,10 +538,10 @@ def _fit(self, dataset): dict(), dict(), ) - Coef_spline_bases = torch.tensor( - self.inputs_["Coef_spline_bases"], dtype=torch.float64, device=self.device + coef_spline_bases = torch.tensor( + self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device ) - for group in self.inputs_["studies_by_groups"].keys(): + for group in self.inputs_["studies_by_group"].keys(): group_foci_per_voxel = torch.tensor( self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device ) @@ -551,7 +551,7 @@ def _fit(self, dataset): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight if self.moderators: gamma = cbmr_model.gamma_linear.weight - group_moderators = self.inputs_["all_group_moderators"][group] + group_moderators = self.inputs_["moderators_by_group"][group] group_moderators = torch.tensor( group_moderators, dtype=torch.float64, device=self.device ) @@ -567,7 +567,7 @@ def _fit(self, dataset): nll = lambda beta: -GLMPoisson._log_likelihood_single_group( beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, @@ -578,7 +578,7 @@ def _fit(self, dataset): alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, @@ -589,7 +589,7 @@ def _fit(self, dataset): alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, @@ -606,8 +606,8 @@ def _fit(self, dataset): Var_log_spatial_intensity = np.einsum( "ij,ji->i", - self.inputs_["Coef_spline_bases"], - Cov_spatial_coef @ self.inputs_["Coef_spline_bases"].T, + self.inputs_["coef_spline_bases"], + Cov_spatial_coef @ self.inputs_["coef_spline_bases"].T, ) SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) log_spatial_intensity_se[group] = SE_log_spatial_intensity @@ -634,16 +634,15 @@ def _fit(self, dataset): nll = lambda gamma: -GLMPoisson._log_likelihood_single_group( group_beta_linear_weight, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device, ) - params = gamma F_moderators_coef = torch.autograd.functional.hessian( nll, - params, + gamma, create_graph=False, vectorize=True, outer_jacobian_strategy="forward-mode", @@ -894,8 +893,8 @@ def _Name_of_con_moderator(self): return def _Fisher_info_spatial_coef(self, GLH_involved_index): - Coef_spline_bases = torch.tensor( - self.CBMRResults.estimator.inputs_["Coef_spline_bases"], + coef_spline_bases = torch.tensor( + self.CBMRResults.estimator.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device, ) @@ -956,7 +955,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): if self.CBMRResults.estimator.model == "Poisson": nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group( all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, @@ -966,7 +965,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): nll = lambda all_spatial_coef: -GLMNB._log_likelihood_mult_group( involved_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, @@ -976,7 +975,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): nll = lambda all_spatial_coef: -GLMCNB._log_likelihood_mult_group( involved_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, involved_moderator_coef, @@ -988,8 +987,8 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): return h.detach().cpu().numpy() def _Fisher_info_moderator_coef(self): - Coef_spline_bases = torch.tensor( - self.CBMRResults.estimator.inputs_["Coef_spline_bases"], + coef_spline_bases = torch.tensor( + self.CBMRResults.estimator.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device, ) @@ -1044,7 +1043,7 @@ def _Fisher_info_moderator_coef(self): if self.CBMRResults.estimator.model == "Poisson": nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group( all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, @@ -1054,7 +1053,7 @@ def _Fisher_info_moderator_coef(self): nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, @@ -1064,7 +1063,7 @@ def _Fisher_info_moderator_coef(self): nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, all_moderator_coef, @@ -1158,7 +1157,7 @@ def _contrast(self): k * spatial_coef_dim : (k + 1) * spatial_coef_dim, s * spatial_coef_dim : (s + 1) * spatial_coef_dim, ] - X = self.CBMRResults.estimator.inputs_["Coef_spline_bases"] + X = self.CBMRResults.estimator.inputs_["coef_spline_bases"] Cov_group_log_intensity = (X.dot(Cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) Cov_log_intensity = np.concatenate( (Cov_log_intensity, Cov_group_log_intensity), axis=0 @@ -1267,9 +1266,9 @@ def __init__( torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) def _log_likelihood_single_group( - beta, gamma, Coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" + beta, gamma, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" ): - log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) if gamma is not None: log_mu_moderators = torch.matmul(moderators, gamma.T) @@ -1290,7 +1289,7 @@ def _log_likelihood_single_group( def _log_likelihood_mult_group( all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, @@ -1299,7 +1298,7 @@ def _log_likelihood_mult_group( ): n_groups = len(all_spatial_coef) all_log_spatial_intensity = [ - torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) ] all_spatial_intensity = [ torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity @@ -1332,7 +1331,7 @@ def _log_likelihood_mult_group( ) return log_l - def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1343,7 +1342,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st log_l = 0 # spatial effect: mu^X = exp(X * beta) for group in foci_per_voxel.keys(): - log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] @@ -1386,7 +1385,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st nll = lambda beta: -self._log_likelihood( beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, @@ -1463,14 +1462,14 @@ def _log_likelihood_single_group( alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device="cpu", ): v = 1 / alpha - log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) if gamma is not None: log_mu_moderators = torch.matmul(group_moderators, gamma.T) @@ -1497,7 +1496,7 @@ def _log_likelihood_single_group( def _log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, @@ -1507,7 +1506,7 @@ def _log_likelihood_mult_group( all_v = 1 / all_overdispersion_coef n_groups = len(foci_per_voxel) all_log_spatial_intensity = [ - torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) ] all_spatial_intensity = [ torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity @@ -1563,7 +1562,7 @@ def _log_likelihood_mult_group( return log_l - def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1576,7 +1575,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st for group in foci_per_voxel.keys(): alpha = self.all_alpha_sqrt[group] ** 2 v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) if self.study_level_moderators: log_mu_moderators = all_log_mu_moderators[group] @@ -1619,7 +1618,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, @@ -1671,14 +1670,14 @@ def _log_likelihood_single_group( alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device="cpu", ): v = 1 / alpha - log_mu_spatial = torch.matmul(Coef_spline_bases, beta.T) + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) mu_spatial = torch.exp(log_mu_spatial) if gamma is not None: log_mu_moderators = torch.matmul(group_moderators, gamma.T) @@ -1706,7 +1705,7 @@ def _log_likelihood_single_group( def _log_likelihood_mult_group( all_overdispersion_coef, all_spatial_coef, - Coef_spline_bases, + coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, @@ -1717,7 +1716,7 @@ def _log_likelihood_mult_group( all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] # estimated intensity and log estimated intensity all_log_spatial_intensity = [ - torch.matmul(Coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) ] all_spatial_intensity = [ torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity @@ -1757,7 +1756,7 @@ def _log_likelihood_mult_group( return log_l - def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): if isinstance(all_moderators, dict): all_log_mu_moderators = dict() for group in all_moderators.keys(): @@ -1769,7 +1768,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st for group in foci_per_voxel.keys(): alpha = self.all_alpha[group] v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](Coef_spline_bases) + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] @@ -1808,7 +1807,7 @@ def forward(self, Coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st alpha, beta, gamma, - Coef_spline_bases, + coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, From 7de6b783d070c2ae006414b91e3e9773d0b88c8d Mon Sep 17 00:00:00 2001 From: James Kent Date: Tue, 10 Jan 2023 14:33:25 -0600 Subject: [PATCH 037/177] restructure code --- nimare/meta/cbmr.py | 742 ++------------------------------- nimare/meta/models.py | 590 ++++++++++++++++++++++++++ nimare/tests/test_meta_cbmr.py | 5 +- 3 files changed, 636 insertions(+), 701 deletions(-) create mode 100644 nimare/meta/models.py diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index d24ee1c68..eaaf90831 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -6,6 +6,7 @@ import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter +from nimare.meta import models import torch import functorch import logging @@ -101,7 +102,7 @@ def __init__( moderators=None, mask=None, spline_spacing=10, - model="Poisson", + model=models.Poisson, penalty=False, n_iter=1000, lr=1e-2, @@ -213,6 +214,7 @@ def _preprocess_input(self, dataset): ] studies_by_group["_".join(group)] = group_study_id.unique().tolist() self.inputs_["studies_by_group"] = studies_by_group + self.groups = self.inputs_["studies_by_group"].keys() # collect studywise moderators if specficed if self.moderators: if isinstance(self.moderators, str): @@ -263,65 +265,6 @@ def _preprocess_input(self, dataset): self.inputs_["foci_per_voxel"] = foci_per_voxel self.inputs_["foci_per_study"] = foci_per_study - def _model_structure(self, model, penalty, device): - """Specify stochastic models for CBMR with or without Firth-type penalty. - - For stochastic models, there're three options: Poisson, NB, clustered NB models. - For penalty term, we only consider Firth-type penalty currently. - - Parameters - ---------- - model : :obj:`str` - Name of stochastic model in CBMR: Poisson, NB or clustered NB models. - penalty : :obj:`bool` - Whether to penalize log-likelihood function with Firth-type penalty. - device : :obj:`str` - Device type ('cpu' or 'cuda') represents the device on which operations will - be allocated - """ - beta_dim = self.inputs_["coef_spline_bases"].shape[1] # regression coef of spatial effect - if self.moderators: - gamma_dim = list(self.inputs_["moderators_by_group"].values())[0].shape[1] - study_level_moderators = True - else: - gamma_dim = None - study_level_moderators = False - self.groups = list(self.inputs_["studies_by_group"].keys()) - model = model.lower() - if model not in ["poisson", "nb", "clustered_nb"]: - raise ValueError("The input model is not supported, we only allow poisson, nb or clustered_nb model.") - if model == "poisson": - cbmr_model = GLMPoisson( - beta_dim=beta_dim, - gamma_dim=gamma_dim, - groups=self.groups, - study_level_moderators=study_level_moderators, - penalty=penalty, - device=device, - ) - elif model == "nb": - cbmr_model = GLMNB( - beta_dim=beta_dim, - gamma_dim=gamma_dim, - groups=self.groups, - study_level_moderators=study_level_moderators, - penalty=penalty, - device=device, - ) - elif model == "clustered_nb": - cbmr_model = GLMCNB( - beta_dim=beta_dim, - gamma_dim=gamma_dim, - groups=self.groups, - study_level_moderators=study_level_moderators, - penalty=penalty, - device=device, - ) - if "cuda" in device: - cbmr_model = cbmr_model.cuda() - - return cbmr_model - def _update( self, model, @@ -484,7 +427,15 @@ def _fit(self, dataset): P = coef_spline_bases.shape[1] self.inputs_["coef_spline_bases"] = coef_spline_bases - cbmr_model = self._model_structure(self.model, self.penalty, self.device) + cbmr_model = self.model( + beta_dim=self.inputs_["coef_spline_bases"].shape[1], + gamma_dim=len(self.moderators) if self.moderators else None, + groups=self.groups, + study_level_moderators=bool(self.moderators), + penalty=self.penalty, + device=self.device, + ) + self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() @@ -503,18 +454,19 @@ def _fit(self, dataset): "Group_" + group + "_Studywise_Spatial_Intensity" ] = group_studywise_spatial_intensity # .reshape((1,-1)) # overdispersion parameter: alpha - if self.model == "NB": + if isinstance(cbmr_model, models.NegativeBinomial): alpha = cbmr_model.all_alpha_sqrt[group] ** 2 alpha = alpha.cpu().detach().numpy() overdispersion_param[group] = alpha - elif self.model == "clustered_NB": + elif isinstance(cbmr_model, models.ClusteredNegativeBinomial): alpha = cbmr_model.all_alpha[group] alpha = alpha.cpu().detach().numpy() overdispersion_param[group] = alpha + tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( Spatial_Regression_Coef, orient="index" ) - if self.model == "NB" or self.model == "clustered_NB": + if isinstance(cbmr_model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( overdispersion_param, orient="index", columns=["alpha"] ) @@ -557,45 +509,30 @@ def _fit(self, dataset): ) else: gamma, group_moderators = None, None + + ll_single_group_kwargs = { + "gamma": gamma, + "coef_spline_bases": coef_spline_bases, + "moderators": group_moderators, + "foci_per_voxel": group_foci_per_voxel, + "foci_per_study": group_foci_per_study, + "device": self.device, + } + if "Overdispersion_Coef" in tables.keys(): - alpha = torch.tensor( + ll_single_group_kwargs['alpha'] = torch.tensor( tables["Overdispersion_Coef"].to_dict()["alpha"][group], dtype=torch.float64, device=self.device, ) - if self.model == "Poisson": - nll = lambda beta: -GLMPoisson._log_likelihood_single_group( - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - self.device, - ) - elif self.model == "NB": - nll = lambda beta: -GLMNB._log_likelihood_single_group( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - self.device, - ) - elif self.model == "clustered_NB": - nll = lambda beta: -GLMCNB._log_likelihood_single_group( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - self.device, + + # create a negative log-likelihood function + def nll_beta(beta): + return -self.model._log_likelihood_single_group( + beta=beta, **ll_single_group_kwargs, ) - F = functorch.hessian(nll)(group_beta_linear_weight) + + F = functorch.hessian(nll_beta)(group_beta_linear_weight) # Inference on regression coefficient of spatial effect spatial_dim = group_beta_linear_weight.shape[1] F_spatial_coef = F.reshape((spatial_dim, spatial_dim)) @@ -631,17 +568,17 @@ def _fit(self, dataset): # Inference on regression coefficient of moderators if self.moderators: moderators_dim = gamma.shape[1] - nll = lambda gamma: -GLMPoisson._log_likelihood_single_group( - group_beta_linear_weight, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - self.device, - ) + # modify ll_single_group_kwargs so that beta is fixed and gamma can vary + del ll_single_group_kwargs["gamma"] + ll_single_group_kwargs["beta"] = group_beta_linear_weight + + def nll_gamma(gamma): + return -self.model._log_likelihood_single_group( + gamma=gamma, **ll_single_group_kwargs, + ) + F_moderators_coef = torch.autograd.functional.hessian( - nll, + nll_gamma, gamma, create_graph=False, vectorize=True, @@ -1234,594 +1171,3 @@ def _contrast(self): con_moderator_count += 1 return - - -class GLMPoisson(torch.nn.Module): - def __init__( - self, - beta_dim=None, - gamma_dim=None, - groups=None, - study_level_moderators=False, - penalty=False, - device="cpu", - ): - super().__init__() - self.beta_dim = beta_dim - self.gamma_dim = gamma_dim - self.groups = groups - self.study_level_moderators = study_level_moderators - self.penalty = penalty - self.device = device - # initialization for beta - all_beta_linears = dict() - for group in groups: - beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - - def _log_likelihood_single_group( - beta, gamma, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" - ): - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) - mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(moderators, gamma.T) - mu_moderators = torch.exp(log_mu_moderators) - else: - n_study, _ = foci_per_study.shape - log_mu_moderators = torch.tensor( - [0] * n_study, dtype=torch.float64, device=device - ).reshape((-1, 1)) - mu_moderators = torch.exp(log_mu_moderators) - log_l = ( - torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) - + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) - - torch.sum(mu_spatial) * torch.sum(mu_moderators) - ) - - return log_l - - def _log_likelihood_mult_group( - all_spatial_coef, - coef_spline_bases, - foci_per_voxel, - foci_per_study, - moderator_coef=None, - all_moderators=None, - device="cpu", - ): - n_groups = len(all_spatial_coef) - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) - ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity - ] - if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - else: - all_log_moderator_effect = [ - torch.tensor( - [0] * foci_per_study_i.shape[0], dtype=torch.float64, device=device - ).reshape((-1, 1)) - for foci_per_study_i in foci_per_study - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - log_l = 0 - for i in range(n_groups): - log_l += ( - torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) - - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) - ) - return log_l - - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators - log_l = 0 - # spatial effect: mu^X = exp(X * beta) - for group in foci_per_voxel.keys(): - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) - else: - n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - mu_moderators = torch.exp(log_mu_moderators) - # Under the assumption that Y_ij is either 0 or 1 - # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - group_log_l = ( - torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) - + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - - torch.sum(mu_spatial) * torch.sum(mu_moderators) - ) - log_l += group_log_l - - if self.penalty: - # Firth-type penalty - for group in foci_per_voxel.keys(): - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: - gamma = self.gamma_linear.weight.T - group_moderators = all_moderators[group] - gamma, group_moderators = [gamma], [group_moderators] - else: - gamma, group_moderators = None, None - - # all_spatial_coef = torch.stack([beta]) - foci_per_voxel, foci_per_study = torch.stack( - [group_foci_per_voxel] - ), torch.stack([group_foci_per_study]) - nll = lambda beta: -self._log_likelihood( - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - params = beta - F = torch.autograd.functional.hessian( - nll, - params, - create_graph=False, - vectorize=True, - outer_jacobian_strategy="forward-mode", - ) - F = F.reshape((beta_dim, beta_dim)) - eig_vals = torch.real( - torch.linalg.eigvals(F) - ) # torch.eig(F, eigenvectors=False)[0][:,0] - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals - log_l += group_firth_penalty - return -log_l - - -class GLMNB(torch.nn.Module): - def __init__( - self, - beta_dim=None, - gamma_dim=None, - groups=None, - study_level_moderators=False, - penalty="No", - device="cpu", - ): - super().__init__() - self.groups = groups - self.study_level_moderators = study_level_moderators - self.penalty = penalty - self.device = device - # initialization for beta - all_beta_linears, all_alpha_sqrt = dict(), dict() - for group in groups: - beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group - # initialization for alpha - alpha_init_group = torch.tensor(1e-2).double() - all_alpha_sqrt[group] = torch.nn.Parameter( - torch.sqrt(alpha_init_group), requires_grad=True - ) - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - self.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - - def _three_term(y, r, device): - max_foci = torch.max(y).to(dtype=torch.int64, device=device) - sum_three_term = 0 - for k in range(max_foci): - foci_index = (y == k + 1).nonzero()[:, 0] - r_j = r[foci_index] - n_voxel = list(foci_index.shape)[0] - y_j = torch.tensor([k + 1] * n_voxel, device=device).double() - y_j = y_j.reshape((n_voxel, 1)) - # y=0 => sum_three_term = 0 - sum_three_term += torch.sum( - torch.lgamma(y_j + r_j) - torch.lgamma(y_j + 1) - torch.lgamma(r_j) - ) - - return sum_three_term - - def _log_likelihood_single_group( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - device="cpu", - ): - v = 1 / alpha - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) - mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(group_moderators, gamma.T) - mu_moderators = torch.exp(log_mu_moderators) - else: - n_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor( - [0] * n_study, dtype=torch.float64, device=device - ).reshape((-1, 1)) - mu_moderators = torch.exp(log_mu_moderators) - numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 - # estimated_sum_alpha = alpha * numerator / denominator - - p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) - r = v * denominator / numerator - - log_l = GLMNB._three_term(group_foci_per_voxel, r, device=device) + torch.sum( - r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) - ) - - return log_l - - def _log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, - coef_spline_bases, - foci_per_voxel, - foci_per_study, - moderator_coef=None, - all_moderators=None, - device="cpu", - ): - all_v = 1 / all_overdispersion_coef - n_groups = len(foci_per_voxel) - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) - ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity - ] - if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - else: - all_log_moderator_effect = [ - torch.tensor( - [0] * foci_per_study.shape[0], dtype=torch.float64, device=device - ).reshape((-1, 1)) - for foci_per_study in foci_per_study - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - - all_numerator = [ - all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i] ** 2) - for i in range(n_groups) - ] - all_denominator = [ - all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i]) ** 2 - for i in range(n_groups) - ] - # all_estimated_sum_alpha = [ - # all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] - # for i in range(n_groups) - # ] - - p = [ - all_numerator[i] - / ( - all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) - + all_denominator[i] - ) - for i in range(n_groups) - ] - r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] - - log_l = 0 - for i in range(n_groups): - log_l += GLMNB._three_term(foci_per_voxel[i], r[i], device=device) + torch.sum( - r[i] * torch.log(1 - p[i]) + foci_per_voxel[i] * torch.log(p[i]) - ) - - return log_l - - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators - log_l = 0 - # spatial effect: mu^X = exp(X * beta) - for group in foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group] ** 2 - v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) - if self.study_level_moderators: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) - else: - n_group_study, _ = foci_per_study[group].shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - mu_moderators = torch.exp(log_mu_moderators) - # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), - # Therefore, we use moment matching approach, - # create a new NB approximation to the mixture of NB distributions: - # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha - numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 - # estimated_sum_alpha = alpha * numerator / denominator - # moment matching NB distribution - p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) - r = v * denominator / numerator - - group_foci_per_voxel = foci_per_voxel[group] - # group_foci_per_study = foci_per_study[group] - group_log_l = GLMNB._three_term( - group_foci_per_voxel, r, device=self.device - ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) - log_l += group_log_l - - if self.penalty: - # Firth-type penalty - for group in foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group] ** 2 - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] - gamma = self.gamma_linear.weight.detach().T - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - group_moderators = all_moderators[group] - nll = lambda beta: -self._log_likelihood( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - params = beta - F = torch.autograd.functional.hessian(nll, params, create_graph=True) - F = F.reshape((beta_dim, beta_dim)) - eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals - log_l += group_firth_penalty - - return -log_l - - -class GLMCNB(torch.nn.Module): - def __init__( - self, - beta_dim=None, - gamma_dim=None, - groups=None, - study_level_moderators=False, - penalty=True, - device="cpu", - ): - super().__init__() - self.groups = groups - self.study_level_moderators = study_level_moderators - self.penalty = penalty - self.device = device - # initialization for beta - all_beta_linears, all_alpha = dict(), dict() - for group in groups: - beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group - # initialization for alpha - alpha_init_group = torch.tensor(1e-2).double() - all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - self.all_alpha = torch.nn.ParameterDict(all_alpha) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) - - def _log_likelihood_single_group( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - device="cpu", - ): - v = 1 / alpha - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) - mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(group_moderators, gamma.T) - mu_moderators = torch.exp(log_mu_moderators) - else: - n_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor( - [0] * n_study, dtype=torch.float64, device=device - ).reshape((-1, 1)) - mu_moderators = torch.exp(log_mu_moderators) - mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - group_n_study, _ = group_foci_per_study.shape - - log_l = ( - group_n_study * v * torch.log(v) - - group_n_study * torch.lgamma(v) - + torch.sum(torch.lgamma(group_foci_per_study + v)) - - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) - + torch.sum(group_foci_per_voxel * log_mu_spatial) - + torch.sum(group_foci_per_study * log_mu_moderators) - ) - - return log_l - - def _log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, - coef_spline_bases, - foci_per_voxel, - foci_per_study, - moderator_coef=None, - all_moderators=None, - device="cpu", - ): - n_groups = len(foci_per_voxel) - all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] - # estimated intensity and log estimated intensity - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) - ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity - ] - if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - else: - all_log_moderator_effect = [ - torch.tensor( - [0] * foci_per_study.shape[0], dtype=torch.float64, device=device - ).reshape((-1, 1)) - for foci_per_study in foci_per_study - ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect - ] - all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] - all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] - - log_l = 0 - for i in range(n_groups): - log_l += ( - all_group_n_study[i] * all_v[i] * torch.log(all_v[i]) - - all_group_n_study[i] * torch.lgamma(all_v[i]) - + torch.sum(torch.lgamma(foci_per_study[i] + all_v[i])) - - torch.sum((foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) - + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) - ) - - return log_l - - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators - log_l = 0 - for group in foci_per_voxel.keys(): - alpha = self.all_alpha[group] - v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) - else: - n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - mu_moderators = torch.exp(log_mu_moderators) - group_n_study, _ = group_foci_per_study.shape - mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - group_log_l = ( - group_n_study * v * torch.log(v) - - group_n_study * torch.lgamma(v) - + torch.sum(torch.lgamma(group_foci_per_study + v)) - - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) - + torch.sum(group_foci_per_voxel * log_mu_spatial) - + torch.sum(group_foci_per_study * log_mu_moderators) - ) - log_l += group_log_l - - if self.penalty: - # Firth-type penalty - for group in foci_per_voxel.keys(): - alpha = self.all_alpha[group] - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] - gamma = self.gamma_linear.weight.T - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - group_moderators = all_moderators[group] - nll = lambda beta: -self._log_likelihood( - alpha, - beta, - gamma, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - params = beta - F = torch.autograd.functional.hessian( - nll, params, create_graph=True - ) # vectorize=True, outer_jacobian_strategy='forward-mode' - # F = hessian(nll)(beta) - F = F.reshape((beta_dim, beta_dim)) - eig_vals = torch.real(torch.linalg.eigvals(F)) - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals - log_l += group_firth_penalty - - return -log_l diff --git a/nimare/meta/models.py b/nimare/meta/models.py new file mode 100644 index 000000000..b0d2be381 --- /dev/null +++ b/nimare/meta/models.py @@ -0,0 +1,590 @@ + +import abc +import torch + + +class GeneralLinearModel(torch.nn.Module): + def __init__( + self, + beta_dim=None, + gamma_dim=None, + groups=None, + study_level_moderators=False, + penalty=False, + device="cpu", + ): + super().__init__() + self.beta_dim = beta_dim + self.gamma_dim = gamma_dim + self.groups = groups + self.study_level_moderators = study_level_moderators + self.penalty = penalty + self.device = device + + @abc.abstractmethod + def _log_likelihood_single_group(self, **kwargs): + """Document this.""" + return + + @abc.abstractmethod + def _log_likelihood_mult_group(self, **kwargs): + """Document this.""" + return + + @abc.abstractmethod + def forward(self, **kwargs): + """Document this.""" + return + + +class Poisson(GeneralLinearModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # initialization for beta + all_beta_linears = dict() + for group in self.groups: + beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + # gamma + if self.study_level_moderators: + self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def _log_likelihood_single_group( + beta, gamma, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" + ): + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + mu_spatial = torch.exp(log_mu_spatial) + if gamma is not None: + log_mu_moderators = torch.matmul(moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = foci_per_study.shape + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) + mu_moderators = torch.exp(log_mu_moderators) + log_l = ( + torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) + - torch.sum(mu_spatial) * torch.sum(mu_moderators) + ) + + return log_l + + def _log_likelihood_mult_group( + all_spatial_coef, + coef_spline_bases, + foci_per_voxel, + foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): + n_groups = len(all_spatial_coef) + all_log_spatial_intensity = [ + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] + if moderator_coef is not None: + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + else: + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study_i.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study_i in foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + log_l = 0 + for i in range(n_groups): + log_l += ( + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) + - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + ) + return log_l + + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 + # spatial effect: mu^X = exp(X * beta) + for group in foci_per_voxel.keys(): + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) + mu_moderators = torch.exp(log_mu_moderators) + # Under the assumption that Y_ij is either 0 or 1 + # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] + group_log_l = ( + torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) + - torch.sum(mu_spatial) * torch.sum(mu_moderators) + ) + log_l += group_log_l + + if self.penalty: + # Firth-type penalty + for group in foci_per_voxel.keys(): + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] + if self.study_level_moderators: + gamma = self.gamma_linear.weight.T + group_moderators = all_moderators[group] + gamma, group_moderators = [gamma], [group_moderators] + else: + gamma, group_moderators = None, None + + # all_spatial_coef = torch.stack([beta]) + foci_per_voxel, foci_per_study = torch.stack( + [group_foci_per_voxel] + ), torch.stack([group_foci_per_study]) + nll = lambda beta: -self._log_likelihood( + beta, + gamma, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta + F = torch.autograd.functional.hessian( + nll, + params, + create_graph=False, + vectorize=True, + outer_jacobian_strategy="forward-mode", + ) + F = F.reshape((beta_dim, beta_dim)) + eig_vals = torch.real( + torch.linalg.eigvals(F) + ) # torch.eig(F, eigenvectors=False)[0][:,0] + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty + return -log_l + + +class NegativeBinomial(GeneralLinearModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # initialization for beta + all_beta_linears, all_alpha_sqrt = dict(), dict() + for group in self.groups: + beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + # initialization for alpha + alpha_init_group = torch.tensor(1e-2).double() + all_alpha_sqrt[group] = torch.nn.Parameter( + torch.sqrt(alpha_init_group), requires_grad=True + ) + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + self.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + # gamma + if self.study_level_moderators: + self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def _three_term(y, r, device): + max_foci = torch.max(y).to(dtype=torch.int64, device=device) + sum_three_term = 0 + for k in range(max_foci): + foci_index = (y == k + 1).nonzero()[:, 0] + r_j = r[foci_index] + n_voxel = list(foci_index.shape)[0] + y_j = torch.tensor([k + 1] * n_voxel, device=device).double() + y_j = y_j.reshape((n_voxel, 1)) + # y=0 => sum_three_term = 0 + sum_three_term += torch.sum( + torch.lgamma(y_j + r_j) - torch.lgamma(y_j + 1) - torch.lgamma(r_j) + ) + + return sum_three_term + + def _log_likelihood_single_group( + alpha, + beta, + gamma, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + device="cpu", + ): + v = 1 / alpha + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + mu_spatial = torch.exp(log_mu_spatial) + if gamma is not None: + log_mu_moderators = torch.matmul(group_moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) + mu_moderators = torch.exp(log_mu_moderators) + numerator = mu_spatial**2 * torch.sum(mu_moderators**2) + denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 + # estimated_sum_alpha = alpha * numerator / denominator + + p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) + r = v * denominator / numerator + + log_l = NegativeBinomial._three_term(group_foci_per_voxel, r, device=device) + torch.sum( + r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) + ) + + return log_l + + def _log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + coef_spline_bases, + foci_per_voxel, + foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): + all_v = 1 / all_overdispersion_coef + n_groups = len(foci_per_voxel) + all_log_spatial_intensity = [ + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] + if moderator_coef is not None: + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + else: + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study in foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + + all_numerator = [ + all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i] ** 2) + for i in range(n_groups) + ] + all_denominator = [ + all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i]) ** 2 + for i in range(n_groups) + ] + # all_estimated_sum_alpha = [ + # all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] + # for i in range(n_groups) + # ] + + p = [ + all_numerator[i] + / ( + all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) + + all_denominator[i] + ) + for i in range(n_groups) + ] + r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] + + log_l = 0 + for i in range(n_groups): + log_l += NegativeBinomial._three_term(foci_per_voxel[i], r[i], device=device) + torch.sum( + r[i] * torch.log(1 - p[i]) + foci_per_voxel[i] * torch.log(p[i]) + ) + + return log_l + + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 + # spatial effect: mu^X = exp(X * beta) + for group in foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group] ** 2 + v = 1 / alpha + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = foci_per_study[group].shape + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) + mu_moderators = torch.exp(log_mu_moderators) + # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), + # Therefore, we use moment matching approach, + # create a new NB approximation to the mixture of NB distributions: + # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha + numerator = mu_spatial**2 * torch.sum(mu_moderators**2) + denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 + # estimated_sum_alpha = alpha * numerator / denominator + # moment matching NB distribution + p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) + r = v * denominator / numerator + + group_foci_per_voxel = foci_per_voxel[group] + # group_foci_per_study = foci_per_study[group] + group_log_l = NegativeBinomial._three_term( + group_foci_per_voxel, r, device=self.device + ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) + log_l += group_log_l + + if self.penalty: + # Firth-type penalty + for group in foci_per_voxel.keys(): + alpha = self.all_alpha_sqrt[group] ** 2 + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + gamma = self.gamma_linear.weight.detach().T + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] + group_moderators = all_moderators[group] + nll = lambda beta: -self._log_likelihood( + alpha, + beta, + gamma, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta + F = torch.autograd.functional.hessian(nll, params, create_graph=True) + F = F.reshape((beta_dim, beta_dim)) + eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty + + return -log_l + + +class ClusteredNegativeBinomial(GeneralLinearModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # initialization for beta + all_beta_linears, all_alpha = dict(), dict() + for group in self.groups: + beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() + torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) + all_beta_linears[group] = beta_linear_group + # initialization for alpha + alpha_init_group = torch.tensor(1e-2).double() + all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) + self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + self.all_alpha = torch.nn.ParameterDict(all_alpha) + # gamma + if self.study_level_moderators: + self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + + def _log_likelihood_single_group( + alpha, + beta, + gamma, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + device="cpu", + ): + v = 1 / alpha + log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + mu_spatial = torch.exp(log_mu_spatial) + if gamma is not None: + log_mu_moderators = torch.matmul(group_moderators, gamma.T) + mu_moderators = torch.exp(log_mu_moderators) + else: + n_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor( + [0] * n_study, dtype=torch.float64, device=device + ).reshape((-1, 1)) + mu_moderators = torch.exp(log_mu_moderators) + mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators + group_n_study, _ = group_foci_per_study.shape + + log_l = ( + group_n_study * v * torch.log(v) + - group_n_study * torch.lgamma(v) + + torch.sum(torch.lgamma(group_foci_per_study + v)) + - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(group_foci_per_voxel * log_mu_spatial) + + torch.sum(group_foci_per_study * log_mu_moderators) + ) + + return log_l + + def _log_likelihood_mult_group( + all_overdispersion_coef, + all_spatial_coef, + coef_spline_bases, + foci_per_voxel, + foci_per_study, + moderator_coef=None, + all_moderators=None, + device="cpu", + ): + n_groups = len(foci_per_voxel) + all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] + # estimated intensity and log estimated intensity + all_log_spatial_intensity = [ + torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + ] + all_spatial_intensity = [ + torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + ] + if moderator_coef is not None: + all_log_moderator_effect = [ + torch.matmul(moderator, moderator_coef) for moderator in all_moderators + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + else: + all_log_moderator_effect = [ + torch.tensor( + [0] * foci_per_study.shape[0], dtype=torch.float64, device=device + ).reshape((-1, 1)) + for foci_per_study in foci_per_study + ] + all_moderator_effect = [ + torch.exp(log_moderator_effect) + for log_moderator_effect in all_log_moderator_effect + ] + all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] + all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] + + log_l = 0 + for i in range(n_groups): + log_l += ( + all_group_n_study[i] * all_v[i] * torch.log(all_v[i]) + - all_group_n_study[i] * torch.lgamma(all_v[i]) + + torch.sum(torch.lgamma(foci_per_study[i] + all_v[i])) + - torch.sum((foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) + + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) + ) + + return log_l + + def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): + if isinstance(all_moderators, dict): + all_log_mu_moderators = dict() + for group in all_moderators.keys(): + group_moderators = all_moderators[group] + # mu^Z = exp(Z * gamma) + log_mu_moderators = self.gamma_linear(group_moderators) + all_log_mu_moderators[group] = log_mu_moderators + log_l = 0 + for group in foci_per_voxel.keys(): + alpha = self.all_alpha[group] + v = 1 / alpha + log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + mu_spatial = torch.exp(log_mu_spatial) + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] + if self.study_level_moderators: + log_mu_moderators = all_log_mu_moderators[group] + mu_moderators = torch.exp(log_mu_moderators) + else: + n_group_study, _ = group_foci_per_study.shape + log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + (-1, 1) + ) + mu_moderators = torch.exp(log_mu_moderators) + group_n_study, _ = group_foci_per_study.shape + mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators + group_log_l = ( + group_n_study * v * torch.log(v) + - group_n_study * torch.lgamma(v) + + torch.sum(torch.lgamma(group_foci_per_study + v)) + - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(group_foci_per_voxel * log_mu_spatial) + + torch.sum(group_foci_per_study * log_mu_moderators) + ) + log_l += group_log_l + + if self.penalty: + # Firth-type penalty + for group in foci_per_voxel.keys(): + alpha = self.all_alpha[group] + beta = self.all_beta_linears[group].weight.T + beta_dim = beta.shape[0] + gamma = self.gamma_linear.weight.T + group_foci_per_voxel = foci_per_voxel[group] + group_foci_per_study = foci_per_study[group] + group_moderators = all_moderators[group] + nll = lambda beta: -self._log_likelihood( + alpha, + beta, + gamma, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) + params = beta + F = torch.autograd.functional.hessian( + nll, params, create_graph=True + ) # vectorize=True, outer_jacobian_strategy='forward-mode' + # F = hessian(nll)(beta) + F = F.reshape((beta_dim, beta_dim)) + eig_vals = torch.real(torch.linalg.eigvals(F)) + del F + group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) + del eig_vals + log_l += group_firth_penalty + + return -log_l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 656f3f572..0b0e85a76 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -28,12 +28,11 @@ def test_CBMRInference(testdata_cbmr_simulated): cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], - spline_spacing=10, - model="Poisson", + spline_spacing=20, penalty=False, lr=1e-1, tol=1e6, - device="cuda", + device="cpu", ) cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference( From 1253adba9605f946f921f8410d2cd700ac8deb3b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Wed, 11 Jan 2023 04:18:29 +0000 Subject: [PATCH 038/177] [skip CI][WIP] replace variable name and remove study_level_moderators --- nimare/meta/cbmr.py | 24 ++-- nimare/meta/models.py | 237 ++++++++++++++++----------------- nimare/tests/test_meta_cbmr.py | 6 +- 3 files changed, 128 insertions(+), 139 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index eaaf90831..a430316cf 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -168,7 +168,13 @@ def _preprocess_input(self, dataset): if isinstance(mask_img, str): mask_img = nib.load(mask_img) self.inputs_["mask_img"] = mask_img - + + # generate spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension + coef_spline_bases = B_spline_bases( + masker_voxels=mask_img._dataobj, spacing=self.spline_spacing + ) + self.inputs_["coef_spline_bases"] = coef_spline_bases + for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": # remove dataset coordinates outside of mask @@ -420,18 +426,10 @@ def _fit(self, dataset): dataset : :obj:`~nimare.dataset.Dataset` Dataset to analyze. """ - masker_voxels = self.inputs_["mask_img"]._dataobj - coef_spline_bases = B_spline_bases( - masker_voxels=masker_voxels, spacing=self.spline_spacing - ) - P = coef_spline_bases.shape[1] - self.inputs_["coef_spline_bases"] = coef_spline_bases - cbmr_model = self.model( - beta_dim=self.inputs_["coef_spline_bases"].shape[1], - gamma_dim=len(self.moderators) if self.moderators else None, + spatial_coef_dim=self.inputs_["coef_spline_bases"].shape[1], + moderators_coef_dim=len(self.moderators) if self.moderators else None, groups=self.groups, - study_level_moderators=bool(self.moderators), penalty=self.penalty, device=self.device, ) @@ -444,11 +442,11 @@ def _fit(self, dataset): for group in self.inputs_["studies_by_group"].keys(): group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight group_beta_linear_weight = ( - group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) + group_beta_linear_weight.cpu().detach().numpy().flatten() ) Spatial_Regression_Coef[group] = group_beta_linear_weight group_studywise_spatial_intensity = np.exp( - np.matmul(coef_spline_bases, group_beta_linear_weight) + np.matmul(self.inputs_["coef_spline_bases"], group_beta_linear_weight) ) maps[ "Group_" + group + "_Studywise_Spatial_Intensity" diff --git a/nimare/meta/models.py b/nimare/meta/models.py index b0d2be381..f4272d3e4 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -6,18 +6,16 @@ class GeneralLinearModel(torch.nn.Module): def __init__( self, - beta_dim=None, - gamma_dim=None, + spatial_coef_dim=None, + moderators_coef_dim=None, groups=None, - study_level_moderators=False, penalty=False, device="cpu", ): super().__init__() - self.beta_dim = beta_dim - self.gamma_dim = gamma_dim + self.spatial_coef_dim = spatial_coef_dim + self.moderators_coef_dim = moderators_coef_dim self.groups = groups - self.study_level_moderators = study_level_moderators self.penalty = penalty self.device = device @@ -40,38 +38,43 @@ def forward(self, **kwargs): class Poisson(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) - # initialization for beta - all_beta_linears = dict() + # initialization for spatial regression coefficients + all_spatial_coef_linears = dict() for group in self.groups: - beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) + all_spatial_coef_linears[group] = spatial_coef_linear_group + self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) + # initialization for regression coefficients of moderators + if self.moderators_coef_dim: + self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) def _log_likelihood_single_group( - beta, gamma, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, device="cpu" + group_spatial_coef, + moderators_coef, + coef_spline_bases, + moderators, + foci_per_voxel, + foci_per_study, + device="cpu" ): - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(moderators, gamma.T) - mu_moderators = torch.exp(log_mu_moderators) - else: + if moderators_coef is None: n_study, _ = foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) + else: + log_mu_moderators = torch.matmul(moderators, moderators_coef.T) + mu_moderators = torch.exp(log_mu_moderators) log_l = ( torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) ) - return log_l def _log_likelihood_mult_group( @@ -123,17 +126,16 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st all_log_mu_moderators = dict() for group in all_moderators.keys(): group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) + log_mu_moderators = self.moderators_linear(group_moderators) all_log_mu_moderators[group] = log_mu_moderators log_l = 0 - # spatial effect: mu^X = exp(X * beta) + # spatial effect for group in foci_per_voxel.keys(): - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: + if self.moderators_coef_dim: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) else: @@ -154,38 +156,31 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st if self.penalty: # Firth-type penalty for group in foci_per_voxel.keys(): - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] + group_spatial_coef = self.all_spatial_coef_linears[group].weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: - gamma = self.gamma_linear.weight.T + if self.moderators_coef_dim: + moderators_coef = self.moderators_linear.weight group_moderators = all_moderators[group] - gamma, group_moderators = [gamma], [group_moderators] else: - gamma, group_moderators = None, None - - # all_spatial_coef = torch.stack([beta]) - foci_per_voxel, foci_per_study = torch.stack( - [group_foci_per_voxel] - ), torch.stack([group_foci_per_study]) - nll = lambda beta: -self._log_likelihood( - beta, - gamma, + moderators_coef, group_moderators = None, None + + nll = lambda group_spatial_coef: -Poisson._log_likelihood_single_group( + group_spatial_coef, + moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, ) - params = beta F = torch.autograd.functional.hessian( nll, - params, + group_spatial_coef, create_graph=False, vectorize=True, outer_jacobian_strategy="forward-mode", ) - F = F.reshape((beta_dim, beta_dim)) + F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) eig_vals = torch.real( torch.linalg.eigvals(F) ) # torch.eig(F, eigenvectors=False)[0][:,0] @@ -199,23 +194,22 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st class NegativeBinomial(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) - # initialization for beta - all_beta_linears, all_alpha_sqrt = dict(), dict() + # initialization for group-wise spatial coefficient of regression + all_spatial_coef_linears, all_overdispersion_sqrt = dict(), dict() for group in self.groups: - beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group + spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) + all_spatial_coef_linears[group] = spatial_coef_linear_group # initialization for alpha - alpha_init_group = torch.tensor(1e-2).double() - all_alpha_sqrt[group] = torch.nn.Parameter( - torch.sqrt(alpha_init_group), requires_grad=True + overdispersion_init_group = torch.tensor(1e-2).double() + all_overdispersion_sqrt[group] = torch.nn.Parameter( + torch.sqrt(overdispersion_init_group), requires_grad=True ) - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - self.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) + self.all_overdispersion_sqrt = torch.nn.ParameterDict(all_overdispersion_sqrt) + if self.moderators_coef_dim: + self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) def _three_term(y, r, device): max_foci = torch.max(y).to(dtype=torch.int64, device=device) @@ -234,20 +228,20 @@ def _three_term(y, r, device): return sum_three_term def _log_likelihood_single_group( - alpha, - beta, - gamma, + group_overdispersion, + group_spatial_coef, + moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device="cpu", - ): - v = 1 / alpha - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + ): + v = 1 / group_overdispersion + log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(group_moderators, gamma.T) + if moderators_coef is not None: + log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: n_study, _ = group_foci_per_study.shape @@ -342,17 +336,16 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st all_log_mu_moderators = dict() for group in all_moderators.keys(): group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) + log_mu_moderators = self.moderators_linear(group_moderators) all_log_mu_moderators[group] = log_mu_moderators log_l = 0 - # spatial effect: mu^X = exp(X * beta) + # spatial effect for group in foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group] ** 2 - v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + overdispersion = self.all_overdispersion_sqrt[group] ** 2 + v = 1 / overdispersion + log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) - if self.study_level_moderators: + if self.moderators_coef_dim: log_mu_moderators = all_log_mu_moderators[group] mu_moderators = torch.exp(log_mu_moderators) else: @@ -382,25 +375,24 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st if self.penalty: # Firth-type penalty for group in foci_per_voxel.keys(): - alpha = self.all_alpha_sqrt[group] ** 2 - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] - gamma = self.gamma_linear.weight.detach().T + group_overdispersion = self.all_overdispersion_sqrt[group] ** 2 + group_spatial_coef = self.all_spatial_coef_linears[group].weight + moderators_coef = self.moderators_linear.weight.detach() group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] group_moderators = all_moderators[group] - nll = lambda beta: -self._log_likelihood( - alpha, - beta, - gamma, + + nll = lambda group_spatial_coef: -NegativeBinomial._log_likelihood_single_group( + group_overdispersion, + group_spatial_coef, + moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, ) - params = beta - F = torch.autograd.functional.hessian(nll, params, create_graph=True) - F = F.reshape((beta_dim, beta_dim)) + F = torch.autograd.functional.hessian(nll, group_spatial_coef, create_graph=True) + F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) del F group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) @@ -413,37 +405,37 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st class ClusteredNegativeBinomial(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) - # initialization for beta - all_beta_linears, all_alpha = dict(), dict() + # initialization for spatial regression coefficient + all_spatial_coef_linears, all_overdispersion = dict(), dict() for group in self.groups: - beta_linear_group = torch.nn.Linear(self.beta_dim, 1, bias=False).double() - torch.nn.init.uniform_(beta_linear_group.weight, a=-0.01, b=0.01) - all_beta_linears[group] = beta_linear_group - # initialization for alpha - alpha_init_group = torch.tensor(1e-2).double() - all_alpha[group] = torch.nn.Parameter(alpha_init_group, requires_grad=True) - self.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - self.all_alpha = torch.nn.ParameterDict(all_alpha) - # gamma - if self.study_level_moderators: - self.gamma_linear = torch.nn.Linear(self.gamma_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.gamma_linear.weight, a=-0.01, b=0.01) + spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) + all_spatial_coef_linears[group] = spatial_coef_linear_group + # initialization for overdispersion parameter + overdispersion_init_group = torch.tensor(1e-2).double() + all_overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) + self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) + self.all_overdispersion = torch.nn.ParameterDict(all_overdispersion) + # regression coefficient for moderators + if self.moderators_coef_dim: + self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) def _log_likelihood_single_group( - alpha, - beta, - gamma, + group_overdispersion, + group_spatial_coef, + moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device="cpu", ): - v = 1 / alpha - log_mu_spatial = torch.matmul(coef_spline_bases, beta.T) + v = 1 / group_overdispersion + log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) - if gamma is not None: - log_mu_moderators = torch.matmul(group_moderators, gamma.T) + if moderators_coef is not None: + log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: n_study, _ = group_foci_per_study.shape @@ -524,14 +516,13 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st all_log_mu_moderators = dict() for group in all_moderators.keys(): group_moderators = all_moderators[group] - # mu^Z = exp(Z * gamma) - log_mu_moderators = self.gamma_linear(group_moderators) + log_mu_moderators = self.moderators_linear(group_moderators) all_log_mu_moderators[group] = log_mu_moderators log_l = 0 for group in foci_per_voxel.keys(): - alpha = self.all_alpha[group] - v = 1 / alpha - log_mu_spatial = self.all_beta_linears[group](coef_spline_bases) + group_overdispersion = self.all_overdispersion[group] + v = 1 / group_overdispersion + log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) mu_spatial = torch.exp(log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] @@ -559,28 +550,26 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st if self.penalty: # Firth-type penalty for group in foci_per_voxel.keys(): - alpha = self.all_alpha[group] - beta = self.all_beta_linears[group].weight.T - beta_dim = beta.shape[0] - gamma = self.gamma_linear.weight.T + group_overdispersion = self.all_overdispersion[group] + group_spatial_coef = self.all_spatial_coef_linears[group].weight + moderators_coef = self.moderators_linear.weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] group_moderators = all_moderators[group] - nll = lambda beta: -self._log_likelihood( - alpha, - beta, - gamma, + + nll = lambda group_spatial_coef: -ClusteredNegativeBinomial._log_likelihood_single_group( + group_overdispersion, + group_spatial_coef, + moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, ) - params = beta F = torch.autograd.functional.hessian( - nll, params, create_graph=True - ) # vectorize=True, outer_jacobian_strategy='forward-mode' - # F = hessian(nll)(beta) - F = F.reshape((beta_dim, beta_dim)) + nll, group_spatial_coef, create_graph=True + ) + F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) eig_vals = torch.real(torch.linalg.eigvals(F)) del F group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 0b0e85a76..a71b64f48 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,5 +1,6 @@ from nimare.meta.cbmr import CBMREstimator, CBMRInference from nimare.tests.utils import standardize_field +from nimare.meta import models import logging @@ -28,8 +29,9 @@ def test_CBMRInference(testdata_cbmr_simulated): cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], - spline_spacing=20, - penalty=False, + spline_spacing=10, + model=models.Poisson, + penalty=True, lr=1e-1, tol=1e6, device="cpu", From e00a62159ad7fe731a5f1b4904d7fb1456c83ee2 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 12 Jan 2023 16:15:28 +0000 Subject: [PATCH 039/177] [skip CI][WIP] changed variables names to be more intuitive. --- nimare/meta/cbmr.py | 181 +++++++++-------- nimare/meta/models.py | 357 ++++++++++++++++----------------- nimare/tests/test_meta_cbmr.py | 109 ++++++++-- 3 files changed, 354 insertions(+), 293 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a430316cf..6f3d230e9 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -38,8 +38,8 @@ class CBMREstimator(Estimator): "NB" This method is much slower and less stable, but slightly more accurate. Negative Binomial (NB) model asserts foci counts follow a NB distribution, and allows for anticipated excess variance - relative to Poisson (there's parameter alpha shared by all studies - and all voxels to index excess variance). + relative to Poisson (there's an overdispersion parameter shared + by all studies and all voxels to index excess variance). "clustered NB" This method is also an efficient but less accurate approach. Clustered NB model is "random effect" Poisson model, which asserts @@ -192,7 +192,7 @@ def _preprocess_input(self, dataset): elif isinstance(self.group_categories, str): if self.group_categories not in valid_dset_annotations.columns: raise ValueError( - f"group_names: {self.group_categories} does not exist in the dataset" + f"Category_names: {self.group_categories} does not exist in the dataset" ) else: unique_groups = list(valid_dset_annotations[self.group_categories].unique()) @@ -228,7 +228,7 @@ def _preprocess_input(self, dataset): self.moderators ] # convert moderators to a single-element list if it's a string moderators_by_group = dict() - for group in studies_by_group.keys(): + for group in self.groups: df_group = valid_dset_annotations.loc[ valid_dset_annotations["study_id"].isin(studies_by_group[group]) ] @@ -240,7 +240,7 @@ def _preprocess_input(self, dataset): self.inputs_["moderators_by_group"] = moderators_by_group foci_per_voxel, foci_per_study = dict(), dict() - for group in studies_by_group.keys(): + for group in self.groups: group_study_id = studies_by_group[group] group_coordinates = dataset.coordinates.loc[ dataset.coordinates["study_id"].isin(group_study_id) @@ -276,7 +276,7 @@ def _update( model, optimizer, coef_spline_bases, - all_moderators, + moderators, foci_per_voxel, foci_per_study, prev_loss, @@ -294,7 +294,7 @@ def _update( def closure(): optimizer.zero_grad() - loss = model(coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study) + loss = model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) loss.backward() return loss @@ -303,8 +303,8 @@ def closure(): # reset the L-BFGS params if NaN appears in coefficient of regression if any( [ - torch.any(torch.isnan(model.all_beta_linears[group].weight)) - for group in self.inputs_["studies_by_group"].keys() + torch.any(torch.isnan(model.spatial_coef_linears[group].weight)) + for group in self.groups ] ): if self.iter == 1: # NaN occurs in the first iteration @@ -312,35 +312,35 @@ def closure(): """The current learing rate {str(self.lr)} gives rise to NaN values, adjust to a smaller value.""" ) - all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() - for group in self.inputs_["studies_by_group"].keys(): - beta_dim = model.all_beta_linears[group].weight.shape[1] - beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - beta_linear_group.weight = torch.nn.Parameter( - self.last_state["all_beta_linears." + group + ".weight"] + spatial_coef_linears, overdispersion_sqrt, overdispersion = dict(), dict(), dict() + for group in self.groups: + + group_spatial_linear = torch.nn.Linear(model.spatial_coef_dim, 1, bias=False).double() + group_spatial_linear.weight = torch.nn.Parameter( + self.last_state["spatial_coef_linears." + group + ".weight"] ) - all_beta_linears[group] = beta_linear_group + spatial_coef_linears[group] = group_spatial_linear - if self.model == "NB": - group_alpha_sqrt = torch.nn.Parameter( - self.last_state["all_alpha_sqrt." + group] + if isinstance(model, models.NegativeBinomial): + group_overdispersion_sqrt = torch.nn.Parameter( + self.last_state["overdispersion_sqrt." + group] ) - all_alpha_sqrt[group] = group_alpha_sqrt - elif self.model == "clustered_NB": - group_alpha = torch.nn.Parameter(self.last_state["all_alpha." + group]) - all_alpha[group] = group_alpha + overdispersion_sqrt[group] = group_overdispersion_sqrt + elif isinstance(model, models.ClusteredNegativeBinomial): + group_overdispersion = torch.nn.Parameter(self.last_state["overdispersion." + group]) + overdispersion[group] = group_overdispersion - model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - if self.model == "NB": - model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - elif self.model == "clustered_NB": - model.all_alpha = torch.nn.ParameterDict(all_alpha) + model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + if isinstance(model, models.NegativeBinomial): + model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) + elif isinstance(model, models.ClusteredNegativeBinomial): + model.overdispersion = torch.nn.ParameterDict(overdispersion) LGR.debug("Reset L-BFGS optimizer......") else: self.last_state = copy.deepcopy( model.state_dict() - ) # need to change the variable name? + ) return loss @@ -371,7 +371,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): ) if self.moderators: moderators_by_group_tensor = dict() - for group in self.inputs_["studies_by_group"].keys(): + for group in self.groups: moderators_tensor = torch.tensor( self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=device ) @@ -379,7 +379,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): else: moderators_by_group_tensor = None foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() - for group in self.inputs_["studies_by_group"].keys(): + for group in self.groups: group_foci_per_voxel_tensor = torch.tensor( self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=device ) @@ -433,55 +433,55 @@ def _fit(self, dataset): penalty=self.penalty, device=self.device, ) - + self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() - # beta: regression coef of spatial effect - for group in self.inputs_["studies_by_group"].keys(): - group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - group_beta_linear_weight = ( - group_beta_linear_weight.cpu().detach().numpy().flatten() + # regression coef of spatial effect + for group in self.groups: + group_spatial_coef_linear_weight = cbmr_model.spatial_coef_linears[group].weight + group_spatial_coef_linear_weight = ( + group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() ) - Spatial_Regression_Coef[group] = group_beta_linear_weight + Spatial_Regression_Coef[group] = group_spatial_coef_linear_weight group_studywise_spatial_intensity = np.exp( - np.matmul(self.inputs_["coef_spline_bases"], group_beta_linear_weight) + np.matmul(self.inputs_["coef_spline_bases"], group_spatial_coef_linear_weight) ) maps[ "Group_" + group + "_Studywise_Spatial_Intensity" ] = group_studywise_spatial_intensity # .reshape((1,-1)) - # overdispersion parameter: alpha + # overdispersion parameter if isinstance(cbmr_model, models.NegativeBinomial): - alpha = cbmr_model.all_alpha_sqrt[group] ** 2 - alpha = alpha.cpu().detach().numpy() - overdispersion_param[group] = alpha + group_overdispersion = cbmr_model.overdispersion_sqrt[group] ** 2 + group_overdispersion = group_overdispersion.cpu().detach().numpy() + overdispersion_param[group] = group_overdispersion elif isinstance(cbmr_model, models.ClusteredNegativeBinomial): - alpha = cbmr_model.all_alpha[group] - alpha = alpha.cpu().detach().numpy() - overdispersion_param[group] = alpha + group_overdispersion = cbmr_model.overdispersion[group] + group_overdispersion = group_overdispersion.cpu().detach().numpy() + overdispersion_param[group] = group_overdispersion tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( Spatial_Regression_Coef, orient="index" ) if isinstance(cbmr_model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( - overdispersion_param, orient="index", columns=["alpha"] + overdispersion_param, orient="index", columns=["overdispersion"] ) # study-level moderators if self.moderators: self.moderators_effect = dict() - self._gamma = cbmr_model.gamma_linear.weight - self._gamma = self._gamma.cpu().detach().numpy() - for group in self.inputs_["studies_by_group"].keys(): + self._moderators_coef = cbmr_model.moderators_linear.weight + self._moderators_coef = self._moderators_coef.cpu().detach().numpy() + for group in self.groups: group_moderators = self.inputs_["moderators_by_group"][group] - group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) + group_moderators_effect = np.exp(np.matmul(group_moderators, self._moderators_coef.T)) self.moderators_effect[group] = group_moderators_effect tables["Moderators_Regression_Coef"] = pd.DataFrame( - self._gamma, columns=self.moderators + self._moderators_coef, columns=self.moderators ) else: - self._gamma = None + self._moderators_coef = None # standard error spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( dict(), @@ -491,25 +491,25 @@ def _fit(self, dataset): coef_spline_bases = torch.tensor( self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device ) - for group in self.inputs_["studies_by_group"].keys(): + for group in self.groups: group_foci_per_voxel = torch.tensor( self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device ) group_foci_per_study = torch.tensor( self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device ) - group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight + group_spatial_coef = torch.tensor(cbmr_model.spatial_coef_linears[group].weight, + dtype=torch.float64, device=self.device) if self.moderators: - gamma = cbmr_model.gamma_linear.weight - group_moderators = self.inputs_["moderators_by_group"][group] group_moderators = torch.tensor( - group_moderators, dtype=torch.float64, device=self.device + self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device ) + moderators_coef = torch.tensor(self._moderators_coef, dtype=torch.float64, device=self.device) else: - gamma, group_moderators = None, None + group_moderators, moderators_coef = None, None ll_single_group_kwargs = { - "gamma": gamma, + "moderators_coef": moderators_coef, "coef_spline_bases": coef_spline_bases, "moderators": group_moderators, "foci_per_voxel": group_foci_per_voxel, @@ -518,22 +518,22 @@ def _fit(self, dataset): } if "Overdispersion_Coef" in tables.keys(): - ll_single_group_kwargs['alpha'] = torch.tensor( - tables["Overdispersion_Coef"].to_dict()["alpha"][group], + ll_single_group_kwargs['overdispersion'] = torch.tensor( + tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], dtype=torch.float64, device=self.device, ) # create a negative log-likelihood function - def nll_beta(beta): + def nll_spatial_coef(group_spatial_coef): return -self.model._log_likelihood_single_group( - beta=beta, **ll_single_group_kwargs, + group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, ) - F = functorch.hessian(nll_beta)(group_beta_linear_weight) + F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) # Inference on regression coefficient of spatial effect - spatial_dim = group_beta_linear_weight.shape[1] - F_spatial_coef = F.reshape((spatial_dim, spatial_dim)) + + F_spatial_coef = F_spatial_coef.reshape((cbmr_model.spatial_coef_dim, cbmr_model.spatial_coef_dim)) Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) Var_spatial_coef = np.diag(Cov_spatial_coef) SE_spatial_coef = np.sqrt(Var_spatial_coef) @@ -549,7 +549,7 @@ def nll_beta(beta): group_studywise_spatial_intensity = maps[ "Group_" + group + "_Studywise_Spatial_Intensity" - ].reshape((-1)) + ] SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity spatial_intensity_se[group] = SE_spatial_intensity @@ -565,26 +565,25 @@ def nll_beta(beta): # Inference on regression coefficient of moderators if self.moderators: - moderators_dim = gamma.shape[1] # modify ll_single_group_kwargs so that beta is fixed and gamma can vary - del ll_single_group_kwargs["gamma"] - ll_single_group_kwargs["beta"] = group_beta_linear_weight + del ll_single_group_kwargs["moderators_coef"] + ll_single_group_kwargs["group_spatial_coef"] = group_spatial_coef - def nll_gamma(gamma): + def nll_moderators_coef(moderators_coef): return -self.model._log_likelihood_single_group( - gamma=gamma, **ll_single_group_kwargs, + moderators_coef=moderators_coef, **ll_single_group_kwargs, ) F_moderators_coef = torch.autograd.functional.hessian( - nll_gamma, - gamma, + nll_moderators_coef, + moderators_coef, create_graph=False, vectorize=True, outer_jacobian_strategy="forward-mode", ) - F_moderators_coef = F_moderators_coef.reshape((moderators_dim, moderators_dim)) + F_moderators_coef = F_moderators_coef.reshape((cbmr_model.moderators_coef_dim, cbmr_model.moderators_coef_dim)) Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) - Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) + Var_moderators = np.diag(Cov_moderators_coef).reshape((1, cbmr_model.moderators_coef_dim)) SE_moderators = np.sqrt(Var_moderators) tables["Moderators_Regression_SE"] = pd.DataFrame( SE_moderators, columns=self.moderators @@ -888,8 +887,8 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): else: involved_group_moderators, involved_moderator_coef = None, None if self.CBMRResults.estimator.model == "Poisson": - nll = lambda all_spatial_coef: -GLMPoisson._log_likelihood_mult_group( - all_spatial_coef, + nll = lambda spatial_coef: -GLMPoisson._log_likelihood_mult_group( + spatial_coef, coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, @@ -897,9 +896,9 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): involved_group_moderators, ) elif self.CBMRResults.estimator.model == "NB": - nll = lambda all_spatial_coef: -GLMNB._log_likelihood_mult_group( + nll = lambda spatial_coef: -GLMNB._log_likelihood_mult_group( involved_overdispersion_coef, - all_spatial_coef, + spatial_coef, coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, @@ -907,9 +906,9 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): involved_group_moderators, ) elif self.CBMRResults.estimator.model == "clustered_NB": - nll = lambda all_spatial_coef: -GLMCNB._log_likelihood_mult_group( + nll = lambda spatial_coef: -GLMCNB._log_likelihood_mult_group( involved_overdispersion_coef, - all_spatial_coef, + spatial_coef, coef_spline_bases, involved_group_foci_per_voxel, involved_group_foci_per_study, @@ -943,7 +942,7 @@ def _Fisher_info_moderator_coef(self): ) for group in self.group_names ] - all_spatial_coef = np.stack( + spatial_coef = np.stack( [ self.CBMRResults.tables["Spatial_Regression_Coef"] .to_numpy()[i, :] @@ -951,7 +950,7 @@ def _Fisher_info_moderator_coef(self): for i in range(self.n_groups) ] ) - all_spatial_coef = torch.tensor(all_spatial_coef, dtype=torch.float64, device=self.device) + spatial_coef = torch.tensor(spatial_coef, dtype=torch.float64, device=self.device) all_moderator_coef = torch.tensor( self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T, @@ -969,7 +968,7 @@ def _Fisher_info_moderator_coef(self): ] if "Overdispersion_Coef" in self.CBMRResults.tables.keys(): - all_overdispersion_coef = torch.tensor( + overdispersion_coef = torch.tensor( self.CBMRResults.tables["Overdispersion_Coef"].to_numpy(), dtype=torch.float64, device=self.device, @@ -977,7 +976,7 @@ def _Fisher_info_moderator_coef(self): if self.CBMRResults.estimator.model == "Poisson": nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group( - all_spatial_coef, + spatial_coef, coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, @@ -986,8 +985,8 @@ def _Fisher_info_moderator_coef(self): ) elif self.CBMRResults.estimator.model == "NB": nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, + overdispersion_coef, + spatial_coef, coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, @@ -996,8 +995,8 @@ def _Fisher_info_moderator_coef(self): ) elif self.CBMRResults.estimator.model == "clustered_NB": nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, + overdispersion_coef, + spatial_coef, coef_spline_bases, all_group_foci_per_voxel, all_group_foci_per_study, diff --git a/nimare/meta/models.py b/nimare/meta/models.py index f4272d3e4..22865cc1e 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -39,12 +39,12 @@ class Poisson(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) # initialization for spatial regression coefficients - all_spatial_coef_linears = dict() + spatial_coef_linears = dict() for group in self.groups: spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) - all_spatial_coef_linears[group] = spatial_coef_linear_group - self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) + spatial_coef_linears[group] = spatial_coef_linear_group + self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) # initialization for regression coefficients of moderators if self.moderators_coef_dim: self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() @@ -78,90 +78,90 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( - all_spatial_coef, + spatial_coef, coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, - all_moderators=None, + moderators=None, device="cpu", ): - n_groups = len(all_spatial_coef) - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + n_groups = len(spatial_coef) + log_spatial_intensity = [ + torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + spatial_intensity = [ + torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators + log_moderator_effect = [ + torch.matmul(group_moderator, moderator_coef) for group_moderator in moderators ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] else: - all_log_moderator_effect = [ + log_moderator_effect = [ torch.tensor( [0] * foci_per_study_i.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) for foci_per_study_i in foci_per_study ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] log_l = 0 for i in range(n_groups): log_l += ( - torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) - - torch.sum(all_spatial_intensity[i]) * torch.sum(all_moderator_effect[i]) + torch.sum(foci_per_voxel[i] * log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * log_moderator_effect[i]) + - torch.sum(spatial_intensity[i]) * torch.sum(moderator_effect[i]) ) return log_l - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - log_mu_moderators = self.moderators_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators + def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + if isinstance(moderators, dict): + log_mu_moderators = dict() + for group in self.groups: + group_moderators = moderators[group] + group_log_mu_moderators = self.moderators_linear(group_moderators) + log_mu_moderators[group] = group_log_mu_moderators log_l = 0 # spatial effect - for group in foci_per_voxel.keys(): - log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) + for group in self.groups: + group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) + group_mu_spatial = torch.exp(group_log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] if self.moderators_coef_dim: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) + group_log_mu_moderators = log_mu_moderators[group] + group_mu_moderators = torch.exp(group_log_mu_moderators) else: n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( (-1, 1) ) - mu_moderators = torch.exp(log_mu_moderators) + group_mu_moderators = torch.exp(group_log_mu_moderators) # Under the assumption that Y_ij is either 0 or 1 # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] group_log_l = ( - torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) - + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - - torch.sum(mu_spatial) * torch.sum(mu_moderators) + torch.sum(torch.mul(group_foci_per_voxel, group_log_mu_spatial)) + + torch.sum(torch.mul(group_foci_per_study, group_log_mu_moderators)) + - torch.sum(group_mu_spatial) * torch.sum(group_mu_moderators) ) log_l += group_log_l if self.penalty: # Firth-type penalty - for group in foci_per_voxel.keys(): - group_spatial_coef = self.all_spatial_coef_linears[group].weight + for group in self.groups: + group_spatial_coef = self.spatial_coef_linears[group].weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] if self.moderators_coef_dim: moderators_coef = self.moderators_linear.weight - group_moderators = all_moderators[group] + group_moderators = moderators[group] else: moderators_coef, group_moderators = None, None @@ -173,20 +173,20 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st group_foci_per_voxel, group_foci_per_study, ) - F = torch.autograd.functional.hessian( + group_F = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=False, vectorize=True, outer_jacobian_strategy="forward-mode", ) - F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - eig_vals = torch.real( - torch.linalg.eigvals(F) - ) # torch.eig(F, eigenvectors=False)[0][:,0] - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals + group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real( + torch.linalg.eigvals(group_F) + ) + del group_F + group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) + del group_eig_vals log_l += group_firth_penalty return -log_l @@ -195,18 +195,18 @@ class NegativeBinomial(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) # initialization for group-wise spatial coefficient of regression - all_spatial_coef_linears, all_overdispersion_sqrt = dict(), dict() + spatial_coef_linears, overdispersion_sqrt = dict(), dict() for group in self.groups: spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) - all_spatial_coef_linears[group] = spatial_coef_linear_group + spatial_coef_linears[group] = spatial_coef_linear_group # initialization for alpha overdispersion_init_group = torch.tensor(1e-2).double() - all_overdispersion_sqrt[group] = torch.nn.Parameter( + overdispersion_sqrt[group] = torch.nn.Parameter( torch.sqrt(overdispersion_init_group), requires_grad=True ) - self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) - self.all_overdispersion_sqrt = torch.nn.ParameterDict(all_overdispersion_sqrt) + self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + self.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) if self.moderators_coef_dim: self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) @@ -263,65 +263,60 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, + overdispersion_coef, + spatial_coef, coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, - all_moderators=None, + moderators=None, device="cpu", ): - all_v = 1 / all_overdispersion_coef + v = 1 / overdispersion_coef n_groups = len(foci_per_voxel) - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + log_spatial_intensity = [ + torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + spatial_intensity = [ + torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators + log_moderator_effect = [ + torch.matmul(group_moderator, moderator_coef) for group_moderator in moderators ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] else: - all_log_moderator_effect = [ + log_moderator_effect = [ torch.tensor( [0] * foci_per_study.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) for foci_per_study in foci_per_study ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] - all_numerator = [ - all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i] ** 2) + numerators = [ + spatial_intensity[i] ** 2 * torch.sum(moderator_effect[i] ** 2) for i in range(n_groups) ] - all_denominator = [ - all_spatial_intensity[i] ** 2 * torch.sum(all_moderator_effect[i]) ** 2 + denominators = [ + spatial_intensity[i] ** 2 * torch.sum(moderator_effect[i]) ** 2 for i in range(n_groups) ] - # all_estimated_sum_alpha = [ - # all_overdispersion_coef[i, :] * all_numerator[i] / all_denominator[i] - # for i in range(n_groups) - # ] - p = [ - all_numerator[i] + numerators[i] / ( - all_v[i] * all_spatial_intensity[i] * torch.sum(all_moderator_effect[i]) - + all_denominator[i] + v[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]) + + denominators[i] ) for i in range(n_groups) ] - r = [all_v[i] * all_denominator[i] / all_numerator[i] for i in range(n_groups)] + r = [v[i] * denominators[i] / numerators[i] for i in range(n_groups)] log_l = 0 for i in range(n_groups): @@ -331,39 +326,39 @@ def _log_likelihood_mult_group( return log_l - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - log_mu_moderators = self.moderators_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators + def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + if isinstance(moderators, dict): + log_mu_moderators = dict() + for group in self.groups: + group_moderators = moderators[group] + group_log_mu_moderators = self.moderators_linear(group_moderators) + log_mu_moderators[group] = group_log_mu_moderators log_l = 0 # spatial effect - for group in foci_per_voxel.keys(): - overdispersion = self.all_overdispersion_sqrt[group] ** 2 - v = 1 / overdispersion - log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) + for group in self.groups: + group_overdispersion = self.overdispersion_sqrt[group] ** 2 + group_v = 1 / group_overdispersion + group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) + group_mu_spatial = torch.exp(group_log_mu_spatial) if self.moderators_coef_dim: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) + group_log_mu_moderators = log_mu_moderators[group] + group_mu_moderators = torch.exp(group_log_mu_moderators) else: n_group_study, _ = foci_per_study[group].shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( (-1, 1) ) - mu_moderators = torch.exp(log_mu_moderators) + group_mu_moderators = torch.exp(group_log_mu_moderators) # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), # Therefore, we use moment matching approach, # create a new NB approximation to the mixture of NB distributions: # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha - numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 + group_numerator = group_mu_spatial**2 * torch.sum(group_mu_moderators**2) + group_denominator = group_mu_spatial**2 * torch.sum(group_mu_moderators) ** 2 # estimated_sum_alpha = alpha * numerator / denominator # moment matching NB distribution - p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) - r = v * denominator / numerator + p = group_numerator / (group_v * group_mu_spatial * torch.sum(group_mu_moderators) + group_numerator) + r = group_v * group_denominator / group_numerator group_foci_per_voxel = foci_per_voxel[group] # group_foci_per_study = foci_per_study[group] @@ -374,13 +369,13 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st if self.penalty: # Firth-type penalty - for group in foci_per_voxel.keys(): - group_overdispersion = self.all_overdispersion_sqrt[group] ** 2 - group_spatial_coef = self.all_spatial_coef_linears[group].weight + for group in self.groups: + group_overdispersion = self.overdispersion_sqrt[group] ** 2 + group_spatial_coef = self.spatial_coef_linears[group].weight moderators_coef = self.moderators_linear.weight.detach() group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - group_moderators = all_moderators[group] + group_moderators = moderators[group] nll = lambda group_spatial_coef: -NegativeBinomial._log_likelihood_single_group( group_overdispersion, @@ -391,12 +386,12 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st group_foci_per_voxel, group_foci_per_study, ) - F = torch.autograd.functional.hessian(nll, group_spatial_coef, create_graph=True) - F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - eig_vals = eig_vals = torch.real(torch.linalg.eigvals(F)) - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals + group_F = torch.autograd.functional.hessian(nll, group_spatial_coef, create_graph=True) + group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) + del group_F + group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) + del group_eig_vals log_l += group_firth_penalty return -log_l @@ -406,16 +401,16 @@ class ClusteredNegativeBinomial(GeneralLinearModel): def __init__(self, **kwargs): super().__init__(**kwargs) # initialization for spatial regression coefficient - all_spatial_coef_linears, all_overdispersion = dict(), dict() + spatial_coef_linears, overdispersion = dict(), dict() for group in self.groups: - spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) - all_spatial_coef_linears[group] = spatial_coef_linear_group + group_spatial_coef_linear = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + torch.nn.init.uniform_(group_spatial_coef_linear.weight, a=-0.01, b=0.01) + spatial_coef_linears[group] = group_spatial_coef_linear # initialization for overdispersion parameter overdispersion_init_group = torch.tensor(1e-2).double() - all_overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) - self.all_spatial_coef_linears = torch.nn.ModuleDict(all_spatial_coef_linears) - self.all_overdispersion = torch.nn.ParameterDict(all_overdispersion) + overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) + self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + self.overdispersion = torch.nn.ParameterDict(overdispersion) # regression coefficient for moderators if self.moderators_coef_dim: self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() @@ -458,104 +453,104 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( - all_overdispersion_coef, - all_spatial_coef, + overdispersion_coef, + spatial_coef, coef_spline_bases, foci_per_voxel, foci_per_study, moderator_coef=None, - all_moderators=None, + moderators=None, device="cpu", ): n_groups = len(foci_per_voxel) - all_v = [1 / overdispersion_coef for overdispersion_coef in all_overdispersion_coef] + v = [1 / group_overdispersion_coef for group_overdispersion_coef in overdispersion_coef] # estimated intensity and log estimated intensity - all_log_spatial_intensity = [ - torch.matmul(coef_spline_bases, all_spatial_coef[i, :, :]) for i in range(n_groups) + log_spatial_intensity = [ + torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] - all_spatial_intensity = [ - torch.exp(log_spatial_intensity) for log_spatial_intensity in all_log_spatial_intensity + spatial_intensity = [ + torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: - all_log_moderator_effect = [ - torch.matmul(moderator, moderator_coef) for moderator in all_moderators + log_moderator_effect = [ + torch.matmul(group_moderator, moderator_coef) for group_moderator in moderators ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] else: - all_log_moderator_effect = [ + log_moderator_effect = [ torch.tensor( [0] * foci_per_study.shape[0], dtype=torch.float64, device=device ).reshape((-1, 1)) for foci_per_study in foci_per_study ] - all_moderator_effect = [ - torch.exp(log_moderator_effect) - for log_moderator_effect in all_log_moderator_effect + moderator_effect = [ + torch.exp(group_log_moderator_effect) + for group_log_moderator_effect in log_moderator_effect ] - all_mu_sum_per_study = [torch.sum(all_spatial_intensity[i]) * all_moderator_effect[i] for i in range(n_groups)] - all_group_n_study = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] + mu_sum_per_study = [torch.sum(spatial_intensity[i]) * moderator_effect[i] for i in range(n_groups)] + n_study_list = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] log_l = 0 for i in range(n_groups): log_l += ( - all_group_n_study[i] * all_v[i] * torch.log(all_v[i]) - - all_group_n_study[i] * torch.lgamma(all_v[i]) - + torch.sum(torch.lgamma(foci_per_study[i] + all_v[i])) - - torch.sum((foci_per_study[i] + all_v[i]) * torch.log(all_mu_sum_per_study[i] + all_v[i])) - + torch.sum(foci_per_voxel[i] * all_log_spatial_intensity[i]) - + torch.sum(foci_per_study[i] * all_log_moderator_effect[i]) + n_study_list[i] * v[i] * torch.log(v[i]) + - n_study_list[i] * torch.lgamma(v[i]) + + torch.sum(torch.lgamma(foci_per_study[i] + v[i])) + - torch.sum((foci_per_study[i] + v[i]) * torch.log(mu_sum_per_study[i] + v[i])) + + torch.sum(foci_per_voxel[i] * log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * log_moderator_effect[i]) ) return log_l - def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_study): - if isinstance(all_moderators, dict): - all_log_mu_moderators = dict() - for group in all_moderators.keys(): - group_moderators = all_moderators[group] - log_mu_moderators = self.moderators_linear(group_moderators) - all_log_mu_moderators[group] = log_mu_moderators + def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + if isinstance(moderators, dict): + log_mu_moderators = dict() + for group in self.groups: + group_moderators = moderators[group] + group_log_mu_moderators = self.moderators_linear(group_moderators) + log_mu_moderators[group] = group_log_mu_moderators log_l = 0 - for group in foci_per_voxel.keys(): - group_overdispersion = self.all_overdispersion[group] - v = 1 / group_overdispersion - log_mu_spatial = self.all_spatial_coef_linears[group](coef_spline_bases) - mu_spatial = torch.exp(log_mu_spatial) + for group in self.groups: + group_overdispersion = self.overdispersion[group] + group_v = 1 / group_overdispersion + group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) + group_mu_spatial = torch.exp(group_log_mu_spatial) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - if self.study_level_moderators: - log_mu_moderators = all_log_mu_moderators[group] - mu_moderators = torch.exp(log_mu_moderators) + if self.moderators_coef_dim: + group_log_mu_moderators = log_mu_moderators[group] + group_mu_moderators = torch.exp(group_log_mu_moderators) else: n_group_study, _ = group_foci_per_study.shape - log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( + group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( (-1, 1) ) - mu_moderators = torch.exp(log_mu_moderators) + group_mu_moderators = torch.exp(group_log_mu_moderators) group_n_study, _ = group_foci_per_study.shape - mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators + group_mu_sum_per_study = torch.sum(group_mu_spatial) * group_mu_moderators group_log_l = ( - group_n_study * v * torch.log(v) - - group_n_study * torch.lgamma(v) - + torch.sum(torch.lgamma(group_foci_per_study + v)) - - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) - + torch.sum(group_foci_per_voxel * log_mu_spatial) - + torch.sum(group_foci_per_study * log_mu_moderators) + group_n_study * group_v * torch.log(group_v) + - group_n_study * torch.lgamma(group_v) + + torch.sum(torch.lgamma(group_foci_per_study + group_v)) + - torch.sum((group_foci_per_study + group_v) * torch.log(group_mu_sum_per_study + group_v)) + + torch.sum(group_foci_per_voxel * group_log_mu_spatial) + + torch.sum(group_foci_per_study * group_log_mu_moderators) ) log_l += group_log_l if self.penalty: # Firth-type penalty - for group in foci_per_voxel.keys(): - group_overdispersion = self.all_overdispersion[group] - group_spatial_coef = self.all_spatial_coef_linears[group].weight + for group in self.groups: + group_overdispersion = self.overdispersion[group] + group_spatial_coef = self.spatial_coef_linears[group].weight moderators_coef = self.moderators_linear.weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - group_moderators = all_moderators[group] + group_moderators = moderators[group] nll = lambda group_spatial_coef: -ClusteredNegativeBinomial._log_likelihood_single_group( group_overdispersion, @@ -566,14 +561,14 @@ def forward(self, coef_spline_bases, all_moderators, foci_per_voxel, foci_per_st group_foci_per_voxel, group_foci_per_study, ) - F = torch.autograd.functional.hessian( + group_F = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=True ) - F = F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - eig_vals = torch.real(torch.linalg.eigvals(F)) - del F - group_firth_penalty = 0.5 * torch.sum(torch.log(eig_vals)) - del eig_vals + group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) + del group_F + group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) + del group_eig_vals log_l += group_firth_penalty return -log_l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index a71b64f48..e50bd7f06 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -2,24 +2,24 @@ from nimare.tests.utils import standardize_field from nimare.meta import models import logging +import torch +import numpy as np - -# def test_CBMREstimator(testdata_cbmr_simulated): -# logging.getLogger().setLevel(logging.DEBUG) -# """Unit test for CBMR estimator.""" -# dset = standardize_field(dataset=testdata_cbmr_simulated, -# metadata=["sample_sizes", "avg_age"]) -# cbmr = CBMREstimator( -# group_names="diagnosis", -# moderators=["standardized_sample_sizes", "standardized_avg_age"], -# spline_spacing=5, -# model="Poisson", -# penalty=False, -# lr=1e-1, -# tol=1e4, -# device="cuda", -# ) -# cbmr.fit(dataset=dset) +def test_CBMREstimator(testdata_cbmr_simulated): + logging.getLogger().setLevel(logging.DEBUG) + """Unit test for CBMR estimator.""" + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) + cbmr = CBMREstimator( + group_categories=["diagnosis", "drug_status"], + moderators=["standardized_sample_sizes", "standardized_avg_age"], + spline_spacing=10, + model=models.Poisson, + penalty=False, + lr=1e-1, + tol=1e6, + device="cpu", + ) + cbmr.fit(dataset=dset) def test_CBMRInference(testdata_cbmr_simulated): @@ -30,7 +30,7 @@ def test_CBMRInference(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model=models.Poisson, + model=models.ClusteredNegativeBinomial, penalty=True, lr=1e-1, tol=1e6, @@ -42,6 +42,73 @@ def test_CBMRInference(testdata_cbmr_simulated): ) inference._contrast() - # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] - # [[[1,0],[0,1]], [1, -1]] - # ['standardized_sample_sizes', 'standardized_avg_age'] +# [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] +# [[[1,0],[0,1]], [1, -1]] + +def test_CBMREstimator_update(testdata_cbmr_simulated): + cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) + + cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) + cbmr._preprocess_input(testdata_cbmr_simulated) + cbmr_model = cbmr.model( + spatial_coef_dim=cbmr.inputs_["coef_spline_bases"].shape[1], + moderators_coef_dim=len(cbmr.moderators) if cbmr.moderators else None, + groups=cbmr.groups, + penalty=cbmr.penalty, + device=cbmr.device, + ) + + optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + # load dataset info to torch.tensor + coef_spline_bases = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) + if cbmr.moderators: + moderators_by_group_tensor = dict() + for group in cbmr_model.groups: + moderators_tensor = torch.tensor( + cbmr_model.inputs_["moderators_by_group"][group], dtype=torch.float64, device=cbmr.device + ) + moderators_by_group_tensor[group] = moderators_tensor + else: + moderators_by_group_tensor = None + foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() + for group in cbmr_model.groups: + group_foci_per_voxel_tensor = torch.tensor( + cbmr.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=cbmr.device + ) + group_foci_per_study_tensor = torch.tensor( + cbmr.inputs_["foci_per_study"][group], dtype=torch.float64, device=cbmr.device + ) + foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor + foci_per_study_tensor[group] = group_foci_per_study_tensor + optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + if cbmr.iter == 0: + prev_loss = torch.tensor(float("inf")) # initialization loss difference + + loss = cbmr._update( + cbmr_model, + optimizer, + torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) + + # deliberately set the first spatial coefficient to nan + nan_coef = torch.tensor(cbmr_model.spatial_coef_linears['default'].weight) + nan_coef[:, 0] = float('nan') + cbmr_model.spatial_coef_linears['default'].weight = torch.nn.Parameter(nan_coef) + + loss = cbmr._update( + cbmr_model, + optimizer, + torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) + + + + From bd88e32d7e93a26a28e1c1db17a9b4962ee243e7 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 12 Jan 2023 11:56:11 -0600 Subject: [PATCH 040/177] reorganize model classes to be partially initialized --- nimare/meta/cbmr.py | 50 ++++++++++---------- nimare/meta/models.py | 103 +++++++++++++++++++++++++----------------- 2 files changed, 88 insertions(+), 65 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 6f3d230e9..f7f921227 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -119,7 +119,7 @@ def __init__( self.moderators = moderators self.spline_spacing = spline_spacing - self.model = model + self.model = model(penalty=penalty, device=device) self.penalty = penalty self.n_iter = n_iter self.lr = lr @@ -426,21 +426,25 @@ def _fit(self, dataset): dataset : :obj:`~nimare.dataset.Dataset` Dataset to analyze. """ - cbmr_model = self.model( - spatial_coef_dim=self.inputs_["coef_spline_bases"].shape[1], - moderators_coef_dim=len(self.moderators) if self.moderators else None, - groups=self.groups, - penalty=self.penalty, - device=self.device, - ) - - self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) + init_weight_kwargs = { + 'groups': self.groups, + 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], + 'moderators_coef_dim': len(self.moderators) if self.moderators else None, + } + if isinstance(self.model, models.NegativeBinomial): + init_weight_kwargs['square_root'] = True + if isinstance(self.model, models.ClusteredNegativeBinomial): + init_weight_kwargs['square_root'] = False + + self.model.init_weights(**init_weight_kwargs) + + self._optimizer(self.model, self.lr, self.tol, self.n_iter, self.device) maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() # regression coef of spatial effect for group in self.groups: - group_spatial_coef_linear_weight = cbmr_model.spatial_coef_linears[group].weight + group_spatial_coef_linear_weight = self.model.spatial_coef_linears[group].weight group_spatial_coef_linear_weight = ( group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() ) @@ -452,26 +456,26 @@ def _fit(self, dataset): "Group_" + group + "_Studywise_Spatial_Intensity" ] = group_studywise_spatial_intensity # .reshape((1,-1)) # overdispersion parameter - if isinstance(cbmr_model, models.NegativeBinomial): - group_overdispersion = cbmr_model.overdispersion_sqrt[group] ** 2 + if isinstance(self.model, models.NegativeBinomial): + group_overdispersion = self.model.overdispersion_sqrt[group] ** 2 group_overdispersion = group_overdispersion.cpu().detach().numpy() overdispersion_param[group] = group_overdispersion - elif isinstance(cbmr_model, models.ClusteredNegativeBinomial): - group_overdispersion = cbmr_model.overdispersion[group] + elif isinstance(self.model, models.ClusteredNegativeBinomial): + group_overdispersion = self.model.overdispersion[group] group_overdispersion = group_overdispersion.cpu().detach().numpy() overdispersion_param[group] = group_overdispersion tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( Spatial_Regression_Coef, orient="index" ) - if isinstance(cbmr_model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): + if isinstance(self.model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( overdispersion_param, orient="index", columns=["overdispersion"] ) # study-level moderators if self.moderators: self.moderators_effect = dict() - self._moderators_coef = cbmr_model.moderators_linear.weight + self._moderators_coef = self.model.moderators_linear.weight self._moderators_coef = self._moderators_coef.cpu().detach().numpy() for group in self.groups: group_moderators = self.inputs_["moderators_by_group"][group] @@ -498,7 +502,7 @@ def _fit(self, dataset): group_foci_per_study = torch.tensor( self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device ) - group_spatial_coef = torch.tensor(cbmr_model.spatial_coef_linears[group].weight, + group_spatial_coef = torch.tensor(self.model.spatial_coef_linears[group].weight, dtype=torch.float64, device=self.device) if self.moderators: group_moderators = torch.tensor( @@ -507,7 +511,7 @@ def _fit(self, dataset): moderators_coef = torch.tensor(self._moderators_coef, dtype=torch.float64, device=self.device) else: group_moderators, moderators_coef = None, None - + ll_single_group_kwargs = { "moderators_coef": moderators_coef, "coef_spline_bases": coef_spline_bases, @@ -532,8 +536,8 @@ def nll_spatial_coef(group_spatial_coef): F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) # Inference on regression coefficient of spatial effect - - F_spatial_coef = F_spatial_coef.reshape((cbmr_model.spatial_coef_dim, cbmr_model.spatial_coef_dim)) + + F_spatial_coef = F_spatial_coef.reshape((self.model.spatial_coef_dim, self.model.spatial_coef_dim)) Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) Var_spatial_coef = np.diag(Cov_spatial_coef) SE_spatial_coef = np.sqrt(Var_spatial_coef) @@ -581,9 +585,9 @@ def nll_moderators_coef(moderators_coef): vectorize=True, outer_jacobian_strategy="forward-mode", ) - F_moderators_coef = F_moderators_coef.reshape((cbmr_model.moderators_coef_dim, cbmr_model.moderators_coef_dim)) + F_moderators_coef = F_moderators_coef.reshape((self.model.moderators_coef_dim, self.model.moderators_coef_dim)) Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) - Var_moderators = np.diag(Cov_moderators_coef).reshape((1, cbmr_model.moderators_coef_dim)) + Var_moderators = np.diag(Cov_moderators_coef).reshape((1, self.model.moderators_coef_dim)) SE_moderators = np.sqrt(Var_moderators) tables["Moderators_Regression_SE"] = pd.DataFrame( SE_moderators, columns=self.moderators diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 22865cc1e..0be3d8659 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -18,6 +18,13 @@ def __init__( self.groups = groups self.penalty = penalty self.device = device + + # initialization for spatial regression coefficients + if self.spatial_coef_dim and self.groups: + self.init_spatial_weights() + # initialization for regression coefficients of moderators + if self.moderators_coef_dim: + self.init_moderator_weights() @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): @@ -34,21 +41,62 @@ def forward(self, **kwargs): """Document this.""" return - -class Poisson(GeneralLinearModel): - def __init__(self, **kwargs): - super().__init__(**kwargs) + def init_spatial_weights(self): + """Document this.""" # initialization for spatial regression coefficients spatial_coef_linears = dict() for group in self.groups: - spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + spatial_coef_linear_group = torch.nn.Linear( + self.spatial_coef_dim, 1, bias=False + ).double() torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) spatial_coef_linears[group] = spatial_coef_linear_group self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - # initialization for regression coefficients of moderators - if self.moderators_coef_dim: - self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) + + def init_moderator_weights(self): + """Document this.""" + self.moderators_linear = torch.nn.Linear( + self.moderators_coef_dim, 1, bias=False + ).double() + torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) + + def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): + """Document this.""" + self.groups = groups + self.spatial_coef_dim = spatial_coef_dim + self.moderators_coef_dim = moderators_coef_dim + self.init_spatial_weights() + self.init_moderator_weights() + + +class OverdispersionModel(GeneralLinearModel): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + square_root = kwargs.get("square_root", False) + if self.groups: + self.init_overdispersion_weights(square_root=square_root) + + def init_overdispersion_weights(self, square_root=False): + """Document this.""" + overdispersion = dict() + for group in self.groups: + # initialization for alpha + overdispersion_init_group = torch.tensor(1e-2).double() + if square_root: + overdispersion_init_group = torch.sqrt(overdispersion_init_group) + overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) + self.overdispersion = torch.nn.ParameterDict(overdispersion) + + def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim, square_root=False): + """Document this.""" + super().init_weights(groups, spatial_coef_dim, moderators_coef_dim) + self.init_overdispersion_weights(square_root=square_root) + + +class Poisson(GeneralLinearModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) def _log_likelihood_single_group( group_spatial_coef, @@ -191,25 +239,10 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) return -log_l -class NegativeBinomial(GeneralLinearModel): +class NegativeBinomial(OverdispersionModel): def __init__(self, **kwargs): + kwargs['square_root'] = True super().__init__(**kwargs) - # initialization for group-wise spatial coefficient of regression - spatial_coef_linears, overdispersion_sqrt = dict(), dict() - for group in self.groups: - spatial_coef_linear_group = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(spatial_coef_linear_group.weight, a=-0.01, b=0.01) - spatial_coef_linears[group] = spatial_coef_linear_group - # initialization for alpha - overdispersion_init_group = torch.tensor(1e-2).double() - overdispersion_sqrt[group] = torch.nn.Parameter( - torch.sqrt(overdispersion_init_group), requires_grad=True - ) - self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - self.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) - if self.moderators_coef_dim: - self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) def _three_term(y, r, device): max_foci = torch.max(y).to(dtype=torch.int64, device=device) @@ -397,24 +430,10 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) return -log_l -class ClusteredNegativeBinomial(GeneralLinearModel): +class ClusteredNegativeBinomial(OverdispersionModel): def __init__(self, **kwargs): + kwargs['square_root'] = False super().__init__(**kwargs) - # initialization for spatial regression coefficient - spatial_coef_linears, overdispersion = dict(), dict() - for group in self.groups: - group_spatial_coef_linear = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(group_spatial_coef_linear.weight, a=-0.01, b=0.01) - spatial_coef_linears[group] = group_spatial_coef_linear - # initialization for overdispersion parameter - overdispersion_init_group = torch.tensor(1e-2).double() - overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) - self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - self.overdispersion = torch.nn.ParameterDict(overdispersion) - # regression coefficient for moderators - if self.moderators_coef_dim: - self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() - torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) def _log_likelihood_single_group( group_overdispersion, From f64ad48b4fe9552b9d4629d2cd0ef5dc39393a74 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 12 Jan 2023 23:19:37 +0000 Subject: [PATCH 041/177] [skip CI][WIP] set some params as attribute of CBMREstimator Class. --- nimare/meta/cbmr.py | 20 ++-- nimare/meta/models.py | 170 +++++++++++++-------------------- nimare/tests/test_meta_cbmr.py | 10 +- 3 files changed, 84 insertions(+), 116 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f7f921227..3c42b6b65 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -344,7 +344,7 @@ def closure(): return loss - def _optimizer(self, model, lr, tol, n_iter, device): + def _optimizer(self, model): """Optimize regression coefficient of CBMR via L-BFGS algorithm. Optimization terminates if the absolute value of difference of log-likelihood in @@ -364,16 +364,16 @@ def _optimizer(self, model, lr, tol, n_iter, device): Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. """ - optimizer = torch.optim.LBFGS(model.parameters(), lr) + optimizer = torch.optim.LBFGS(model.parameters(), self.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( - self.inputs_["coef_spline_bases"], dtype=torch.float64, device=device + self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device ) if self.moderators: moderators_by_group_tensor = dict() for group in self.groups: moderators_tensor = torch.tensor( - self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=device + self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device ) moderators_by_group_tensor[group] = moderators_tensor else: @@ -381,10 +381,10 @@ def _optimizer(self, model, lr, tol, n_iter, device): foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() for group in self.groups: group_foci_per_voxel_tensor = torch.tensor( - self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=device + self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device ) group_foci_per_study_tensor = torch.tensor( - self.inputs_["foci_per_study"][group], dtype=torch.float64, device=device + self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device ) foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor foci_per_study_tensor[group] = group_foci_per_study_tensor @@ -392,7 +392,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): if self.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - for i in range(n_iter): + for i in range(self.n_iter): loss = self._update( model, optimizer, @@ -404,7 +404,7 @@ def _optimizer(self, model, lr, tol, n_iter, device): ) loss_diff = loss - prev_loss LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") - if torch.abs(loss_diff) < tol: + if torch.abs(loss_diff) < self.tol: break prev_loss = loss @@ -438,7 +438,7 @@ def _fit(self, dataset): self.model.init_weights(**init_weight_kwargs) - self._optimizer(self.model, self.lr, self.tol, self.n_iter, self.device) + self._optimizer(self.model) maps, tables = dict(), dict() Spatial_Regression_Coef, overdispersion_param = dict(), dict() @@ -527,7 +527,7 @@ def _fit(self, dataset): dtype=torch.float64, device=self.device, ) - + # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self.model._log_likelihood_single_group( diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 0be3d8659..11eee3855 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -66,14 +66,14 @@ def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): self.spatial_coef_dim = spatial_coef_dim self.moderators_coef_dim = moderators_coef_dim self.init_spatial_weights() - self.init_moderator_weights() + if moderators_coef_dim: + self.init_moderator_weights() class OverdispersionModel(GeneralLinearModel): - def __init__(self, **kwargs): + square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) - square_root = kwargs.get("square_root", False) if self.groups: self.init_overdispersion_weights(square_root=square_root) @@ -99,12 +99,13 @@ def __init__(self, **kwargs): super().__init__(**kwargs) def _log_likelihood_single_group( + self, group_spatial_coef, - moderators_coef, - coef_spline_bases, - moderators, - foci_per_voxel, - foci_per_study, + moderators_coef, + coef_spline_bases, + moderators, + foci_per_voxel, + foci_per_study, device="cpu" ): log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) @@ -126,6 +127,7 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( + self, spatial_coef, coef_spline_bases, foci_per_voxel, @@ -170,35 +172,23 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - if isinstance(moderators, dict): - log_mu_moderators = dict() - for group in self.groups: - group_moderators = moderators[group] - group_log_mu_moderators = self.moderators_linear(group_moderators) - log_mu_moderators[group] = group_log_mu_moderators log_l = 0 - # spatial effect for group in self.groups: - group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) - group_mu_spatial = torch.exp(group_log_mu_spatial) + group_spatial_coef = self.spatial_coef_linears[group].weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - if self.moderators_coef_dim: - group_log_mu_moderators = log_mu_moderators[group] - group_mu_moderators = torch.exp(group_log_mu_moderators) + if isinstance(moderators, dict): + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] else: - n_group_study, _ = group_foci_per_study.shape - group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - group_mu_moderators = torch.exp(group_log_mu_moderators) - # Under the assumption that Y_ij is either 0 or 1 - # l = [Y_g]^T * log(mu^X) + [Y^t]^T * log(mu^Z) - [1^T mu_g^X]*[1^T mu_g^Z] - group_log_l = ( - torch.sum(torch.mul(group_foci_per_voxel, group_log_mu_spatial)) - + torch.sum(torch.mul(group_foci_per_study, group_log_mu_moderators)) - - torch.sum(group_mu_spatial) * torch.sum(group_mu_moderators) - ) + moderators_coef, group_moderators = None, None + group_log_l = self._log_likelihood_single_group( + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study) log_l += group_log_l if self.penalty: @@ -213,7 +203,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) else: moderators_coef, group_moderators = None, None - nll = lambda group_spatial_coef: -Poisson._log_likelihood_single_group( + nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_spatial_coef, moderators_coef, coef_spline_bases, @@ -261,6 +251,7 @@ def _three_term(y, r, device): return sum_three_term def _log_likelihood_single_group( + self, group_overdispersion, group_spatial_coef, moderators_coef, @@ -296,6 +287,7 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( + self, overdispersion_coef, spatial_coef, coef_spline_bases, @@ -360,57 +352,41 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - if isinstance(moderators, dict): - log_mu_moderators = dict() - for group in self.groups: - group_moderators = moderators[group] - group_log_mu_moderators = self.moderators_linear(group_moderators) - log_mu_moderators[group] = group_log_mu_moderators log_l = 0 - # spatial effect for group in self.groups: - group_overdispersion = self.overdispersion_sqrt[group] ** 2 - group_v = 1 / group_overdispersion - group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) - group_mu_spatial = torch.exp(group_log_mu_spatial) - if self.moderators_coef_dim: - group_log_mu_moderators = log_mu_moderators[group] - group_mu_moderators = torch.exp(group_log_mu_moderators) - else: - n_group_study, _ = foci_per_study[group].shape - group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - group_mu_moderators = torch.exp(group_log_mu_moderators) - # Now the sum of NB variates are no long NB distributed (since mu_ij != mu_i'j), - # Therefore, we use moment matching approach, - # create a new NB approximation to the mixture of NB distributions: - # alpha' = sum_i mu_{ij}^2 / (sum_i mu_{ij})^2 * alpha - group_numerator = group_mu_spatial**2 * torch.sum(group_mu_moderators**2) - group_denominator = group_mu_spatial**2 * torch.sum(group_mu_moderators) ** 2 - # estimated_sum_alpha = alpha * numerator / denominator - # moment matching NB distribution - p = group_numerator / (group_v * group_mu_spatial * torch.sum(group_mu_moderators) + group_numerator) - r = group_v * group_denominator / group_numerator - + group_overdispersion = self.overdispersion[group] ** 2 + group_spatial_coef = self.spatial_coef_linears[group].weight group_foci_per_voxel = foci_per_voxel[group] - # group_foci_per_study = foci_per_study[group] - group_log_l = NegativeBinomial._three_term( - group_foci_per_voxel, r, device=self.device - ) + torch.sum(r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p)) + group_foci_per_study = foci_per_study[group] + if isinstance(moderators, dict): + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] + else: + moderators_coef, group_moderators = None, None + group_log_l = self._log_likelihood_single_group( + group_overdispersion, + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study) log_l += group_log_l if self.penalty: # Firth-type penalty for group in self.groups: - group_overdispersion = self.overdispersion_sqrt[group] ** 2 + group_overdispersion = self.overdispersion[group] ** 2 group_spatial_coef = self.spatial_coef_linears[group].weight - moderators_coef = self.moderators_linear.weight.detach() + if self.moderators_coef_dim: + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] + else: + moderators_coef, group_moderators = None, None group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - group_moderators = moderators[group] - nll = lambda group_spatial_coef: -NegativeBinomial._log_likelihood_single_group( + nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_overdispersion, group_spatial_coef, moderators_coef, @@ -436,6 +412,7 @@ def __init__(self, **kwargs): super().__init__(**kwargs) def _log_likelihood_single_group( + self, group_overdispersion, group_spatial_coef, moderators_coef, @@ -472,6 +449,7 @@ def _log_likelihood_single_group( return log_l def _log_likelihood_mult_group( + self, overdispersion_coef, spatial_coef, coef_spline_bases, @@ -526,39 +504,25 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - if isinstance(moderators, dict): - log_mu_moderators = dict() - for group in self.groups: - group_moderators = moderators[group] - group_log_mu_moderators = self.moderators_linear(group_moderators) - log_mu_moderators[group] = group_log_mu_moderators log_l = 0 for group in self.groups: group_overdispersion = self.overdispersion[group] - group_v = 1 / group_overdispersion - group_log_mu_spatial = self.spatial_coef_linears[group](coef_spline_bases) - group_mu_spatial = torch.exp(group_log_mu_spatial) + group_spatial_coef = self.spatial_coef_linears[group].weight group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - if self.moderators_coef_dim: - group_log_mu_moderators = log_mu_moderators[group] - group_mu_moderators = torch.exp(group_log_mu_moderators) + if isinstance(moderators, dict): + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] else: - n_group_study, _ = group_foci_per_study.shape - group_log_mu_moderators = torch.tensor([0] * n_group_study, device=self.device).reshape( - (-1, 1) - ) - group_mu_moderators = torch.exp(group_log_mu_moderators) - group_n_study, _ = group_foci_per_study.shape - group_mu_sum_per_study = torch.sum(group_mu_spatial) * group_mu_moderators - group_log_l = ( - group_n_study * group_v * torch.log(group_v) - - group_n_study * torch.lgamma(group_v) - + torch.sum(torch.lgamma(group_foci_per_study + group_v)) - - torch.sum((group_foci_per_study + group_v) * torch.log(group_mu_sum_per_study + group_v)) - + torch.sum(group_foci_per_voxel * group_log_mu_spatial) - + torch.sum(group_foci_per_study * group_log_mu_moderators) - ) + moderators_coef, group_moderators = None, None + group_log_l = self._log_likelihood_single_group( + group_overdispersion, + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study) log_l += group_log_l if self.penalty: @@ -566,12 +530,16 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) for group in self.groups: group_overdispersion = self.overdispersion[group] group_spatial_coef = self.spatial_coef_linears[group].weight - moderators_coef = self.moderators_linear.weight + if self.moderators_coef_dim: + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] + else: + moderators_coef, group_moderators = None, None group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] group_moderators = moderators[group] - nll = lambda group_spatial_coef: -ClusteredNegativeBinomial._log_likelihood_single_group( + nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_overdispersion, group_spatial_coef, moderators_coef, diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index e50bd7f06..08cb2444d 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -13,14 +13,14 @@ def test_CBMREstimator(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model=models.Poisson, - penalty=False, - lr=1e-1, + model=models.ClusteredNegativeBinomial, + penalty=True, + lr=1e-4, tol=1e6, - device="cpu", + device="cpu" ) cbmr.fit(dataset=dset) - +# ["standardized_sample_sizes", "standardized_avg_age"], def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) From c5dfec643efa4f1f0a8b70f78f07d3f5802d8d08 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 13 Jan 2023 21:10:59 +0000 Subject: [PATCH 042/177] restruct inference code to models --- nimare/meta/cbmr.py | 392 +++++++++++++++++---------------- nimare/meta/models.py | 151 ++++++++++++- nimare/tests/test_meta_cbmr.py | 8 +- 3 files changed, 353 insertions(+), 198 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 3c42b6b65..f24b04917 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -21,27 +21,27 @@ class CBMREstimator(Estimator): Parameters ---------- group_categories : :obj:`~str` or obj:`~list` or obj:`~None`, optional - CBMR allows dataset to be categorized into mutiple groups, according to group names. + CBMR allows dataset to be categorized into mutiple groups, according to group categories. Default is one-group CBMR. moderators : :obj:`~str` or obj:`~list` or obj:`~None`, optional CBMR can accommodate study-level moderators (e.g. sample size, year of publication). Default is CBMR without study-level moderators. - model : {"Poisson", "NB", "clustered NB"}, optional + model : : :obj:`~nimare.meta.models.GeneralLinearModel`, optional Stochastic models in CBMR. The available options are - ======================= ================================================================= - "Poisson" (default) This is the most efficient and widely used method, but slightly + ======================= ================================================================== + Poisson (default) This is the most efficient and widely used method, but slightly less accurate, because Poisson model is an approximation for low-rate Binomial data, but cannot account over-dispersion in foci counts and may underestimate the standard error. - "NB" This method is much slower and less stable, but slightly more + NegativeBinomial This method might be slower and less stable, but slightly more accurate. Negative Binomial (NB) model asserts foci counts follow a NB distribution, and allows for anticipated excess variance - relative to Poisson (there's an overdispersion parameter shared - by all studies and all voxels to index excess variance). + relative to Poisson (there's an group-wise overdispersion parameter + shared by all studies and all voxels to index excess variance). - "clustered NB" This method is also an efficient but less accurate approach. + ClusteredNegativeBinomial This method is also an efficient but less accurate approach. Clustered NB model is "random effect" Poisson model, which asserts that the random effects are latent characteristics of each study, and represent a shared effect over the entire brain for a given @@ -61,6 +61,9 @@ class CBMREstimator(Estimator): lr: :obj:`float`, optional Learning rate in optimization of log-likelihood function. Default is 1e-2 for Poisson and clustered NB model, and 1e-3 for NB model. + lr_decay: :obj:`float`, optional + Multiplicative factor of learning rate decay. + Default is 0.999. tol: :obj:`float`, optional Stopping criteria w.r.t difference of log-likelihood function in two consecutive iterations. @@ -88,7 +91,6 @@ class CBMREstimator(Estimator): foci_per_voxel (voxelwise sum of foci count across studies, categorized by groups), foci_per_study (study-wise sum of foci count across space, categorized by groups). - Notes ----- Available correction methods: :meth:`~nimare.meta.cbmr.CBMRInference`. @@ -106,6 +108,7 @@ def __init__( penalty=False, n_iter=1000, lr=1e-2, + lr_decay=0.999, tol=1e-2, device="cpu", **kwargs, @@ -123,6 +126,7 @@ def __init__( self.penalty = penalty self.n_iter = n_iter self.lr = lr + self.lr_decay = lr_decay self.tol = tol self.device = device if self.device == "cuda" and not torch.cuda.is_available(): @@ -143,9 +147,8 @@ def _preprocess_input(self, dataset): ---------- dataset : :obj:`~nimare.dataset.Dataset` In this method, the Dataset is used to (1) select the appropriate mask image, - (2) categorize it into multiple groups according to group type in annotations, - (3) summarize group-wise study id, foci per voxel, foci per study, moderators - (if exist), + (2) categorize studies into multiple groups according to group categories in annotations, + (3) summarize group-wise study id, moderators (if exist), foci per voxel, foci per study, (4) extract sample size metadata and use it as one of study-level moderators. Attributes @@ -153,14 +156,15 @@ def _preprocess_input(self, dataset): inputs_ : :obj:`dict` Specifically, (1) a “mask_img” key will be added (Niftiimage of brain mask), (2) an 'id' key will be added (id of all studies in the dataset), - (3) an 'studies_by_group' key will be added (study id categorized by groups), - (4) a 'coef_spline_bases' key will be added (spatial matrix of coefficient of cubic + (3) a 'coef_spline_bases' key will be added (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), - (5) an 'foci_per_voxel' key will be added (voxelwise sum of foci count across + (4) an 'studies_by_group' key will be added (study id categorized by groups), + (5) an 'moderators_by_group' key will be added (study-level moderators categorized + by groups) if study-level moderators are considered, + (6) an 'foci_per_voxel' key will be added (voxelwise sum of foci count across studies, categorized by groups), - (6) an 'foci_per_study' key will be added (study-wise sum of foci count across - space, categorized by groups), - (7) an 'moderators_by_group' key may be added if study-level moderators exists + (7) an 'foci_per_study' key will be added (study-wise sum of foci count across + space, categorized by groups). """ masker = self.masker or dataset.masker @@ -220,7 +224,7 @@ def _preprocess_input(self, dataset): ] studies_by_group["_".join(group)] = group_study_id.unique().tolist() self.inputs_["studies_by_group"] = studies_by_group - self.groups = self.inputs_["studies_by_group"].keys() + self.groups = list(self.inputs_["studies_by_group"].keys()) # collect studywise moderators if specficed if self.moderators: if isinstance(self.moderators, str): @@ -273,28 +277,27 @@ def _preprocess_input(self, dataset): def _update( self, - model, optimizer, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, prev_loss, - gamma=0.999, ): """One iteration in optimization with L-BFGS. Adjust learning rate based on the number of iteration (with learning rate decay parameter - `gamma`, default value is 0.999).Reset L-BFGS optimizer if NaN occurs. + `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous + iteration) if NaN occurs. """ self.iter += 1 scheduler = torch.optim.lr_scheduler.ExponentialLR( - optimizer, gamma=gamma + optimizer, gamma=self.lr_decay ) # learning rate decay def closure(): optimizer.zero_grad() - loss = model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) + loss = self.model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) loss.backward() return loss @@ -303,7 +306,7 @@ def closure(): # reset the L-BFGS params if NaN appears in coefficient of regression if any( [ - torch.any(torch.isnan(model.spatial_coef_linears[group].weight)) + torch.any(torch.isnan(self.model.spatial_coef_linears[group].weight)) for group in self.groups ] ): @@ -315,36 +318,36 @@ def closure(): spatial_coef_linears, overdispersion_sqrt, overdispersion = dict(), dict(), dict() for group in self.groups: - group_spatial_linear = torch.nn.Linear(model.spatial_coef_dim, 1, bias=False).double() + group_spatial_linear = torch.nn.Linear(self.model.spatial_coef_dim, 1, bias=False).double() group_spatial_linear.weight = torch.nn.Parameter( self.last_state["spatial_coef_linears." + group + ".weight"] ) spatial_coef_linears[group] = group_spatial_linear - if isinstance(model, models.NegativeBinomial): + if isinstance(self.model, models.NegativeBinomial): group_overdispersion_sqrt = torch.nn.Parameter( self.last_state["overdispersion_sqrt." + group] ) overdispersion_sqrt[group] = group_overdispersion_sqrt - elif isinstance(model, models.ClusteredNegativeBinomial): + elif isinstance(self.model, models.ClusteredNegativeBinomial): group_overdispersion = torch.nn.Parameter(self.last_state["overdispersion." + group]) overdispersion[group] = group_overdispersion - model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - if isinstance(model, models.NegativeBinomial): - model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) - elif isinstance(model, models.ClusteredNegativeBinomial): - model.overdispersion = torch.nn.ParameterDict(overdispersion) + self.model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + if isinstance(self.model, models.NegativeBinomial): + self.model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) + elif isinstance(self.model, models.ClusteredNegativeBinomial): + self.model.overdispersion = torch.nn.ParameterDict(overdispersion) LGR.debug("Reset L-BFGS optimizer......") else: self.last_state = copy.deepcopy( - model.state_dict() + self.model.state_dict() ) return loss - def _optimizer(self, model): + def _optimizer(self): """Optimize regression coefficient of CBMR via L-BFGS algorithm. Optimization terminates if the absolute value of difference of log-likelihood in @@ -364,7 +367,7 @@ def _optimizer(self, model): Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. """ - optimizer = torch.optim.LBFGS(model.parameters(), self.lr) + optimizer = torch.optim.LBFGS(self.model.parameters(), self.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device @@ -394,7 +397,6 @@ def _optimizer(self, model): for i in range(self.n_iter): loss = self._update( - model, optimizer, coef_spline_bases, moderators_by_group_tensor, @@ -413,13 +415,14 @@ def _optimizer(self, model): def _fit(self, dataset): """Perform coordinate-based meta-regression (CBMR) on dataset. - (1)Estimate group-wise spatial regression coefficients and its standard error via inverse + (1) Estimate group-wise spatial regression coefficients and its standard error via + inverse of Fisher Information matrix; Similarly, estimate regression coefficient of + study-level moderators (if exist), as well as its standard error via inverse of Fisher Information matrix; - (2)estimate standard error of group-wise log intensity, group-wise intensity via delta - method. For NB or clustered model, estimate regression coefficient of overdispersion. - Similarly, estimate regression coefficient of study-level moderators (if exist), as well - as its standard error via Fisher Information matrix. Save these outcomes in `tables`. - Also, estimate group-wise spatial intensity (per study) and save the results in `maps`. + (2) Estimate standard error of group-wise log intensity, group-wise intensity via delta + method; + (3) For NegativeBinomial or ClusteredNegativeBinomial model, estimate regression + coefficient of overdispersion.s Parameters ---------- @@ -432,166 +435,169 @@ def _fit(self, dataset): 'moderators_coef_dim': len(self.moderators) if self.moderators else None, } if isinstance(self.model, models.NegativeBinomial): - init_weight_kwargs['square_root'] = True + init_weight_kwargs["square_root"] = True if isinstance(self.model, models.ClusteredNegativeBinomial): - init_weight_kwargs['square_root'] = False + init_weight_kwargs["square_root"] = False self.model.init_weights(**init_weight_kwargs) - self._optimizer(self.model) + self._optimizer() maps, tables = dict(), dict() - Spatial_Regression_Coef, overdispersion_param = dict(), dict() - # regression coef of spatial effect - for group in self.groups: - group_spatial_coef_linear_weight = self.model.spatial_coef_linears[group].weight - group_spatial_coef_linear_weight = ( - group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() - ) - Spatial_Regression_Coef[group] = group_spatial_coef_linear_weight - group_studywise_spatial_intensity = np.exp( - np.matmul(self.inputs_["coef_spline_bases"], group_spatial_coef_linear_weight) - ) - maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] = group_studywise_spatial_intensity # .reshape((1,-1)) + moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None + maps, tables = self.model.inference_outcome(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + + # Spatial_Regression_Coef, overdispersion_param = dict(), dict() + # # regression coef of spatial effect + # for group in self.groups: + # group_spatial_coef_linear_weight = self.model.spatial_coef_linears[group].weight + # group_spatial_coef_linear_weight = ( + # group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() + # ) + # Spatial_Regression_Coef[group] = group_spatial_coef_linear_weight + # group_studywise_spatial_intensity = np.exp( + # np.matmul(self.inputs_["coef_spline_bases"], group_spatial_coef_linear_weight) + # ) + # maps[ + # "Group_" + group + "_Studywise_Spatial_Intensity" + # ] = group_studywise_spatial_intensity # .reshape((1,-1)) # overdispersion parameter - if isinstance(self.model, models.NegativeBinomial): - group_overdispersion = self.model.overdispersion_sqrt[group] ** 2 - group_overdispersion = group_overdispersion.cpu().detach().numpy() - overdispersion_param[group] = group_overdispersion - elif isinstance(self.model, models.ClusteredNegativeBinomial): - group_overdispersion = self.model.overdispersion[group] - group_overdispersion = group_overdispersion.cpu().detach().numpy() - overdispersion_param[group] = group_overdispersion - - tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( - Spatial_Regression_Coef, orient="index" - ) - if isinstance(self.model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): - tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( - overdispersion_param, orient="index", columns=["overdispersion"] - ) + # if isinstance(self.model, models.NegativeBinomial): + # group_overdispersion = self.model.overdispersion_sqrt[group] ** 2 + # group_overdispersion = group_overdispersion.cpu().detach().numpy() + # overdispersion_param[group] = group_overdispersion + # elif isinstance(self.model, models.ClusteredNegativeBinomial): + # group_overdispersion = self.model.overdispersion[group] + # group_overdispersion = group_overdispersion.cpu().detach().numpy() + # overdispersion_param[group] = group_overdispersion + + # # tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( + # # Spatial_Regression_Coef, orient="index" + # # ) + # if isinstance(self.model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): + # tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( + # overdispersion_param, orient="index", columns=["overdispersion"] # study-level moderators - if self.moderators: - self.moderators_effect = dict() - self._moderators_coef = self.model.moderators_linear.weight - self._moderators_coef = self._moderators_coef.cpu().detach().numpy() - for group in self.groups: - group_moderators = self.inputs_["moderators_by_group"][group] - group_moderators_effect = np.exp(np.matmul(group_moderators, self._moderators_coef.T)) - self.moderators_effect[group] = group_moderators_effect - tables["Moderators_Regression_Coef"] = pd.DataFrame( - self._moderators_coef, columns=self.moderators - ) - else: - self._moderators_coef = None + # if self.moderators: + # self.moderators_effect = dict() + # self._moderators_coef = self.model.moderators_linear.weight + # self._moderators_coef = self._moderators_coef.cpu().detach().numpy() + # for group in self.groups: + # group_moderators = self.inputs_["moderators_by_group"][group] + # group_moderators_effect = np.exp(np.matmul(group_moderators, self._moderators_coef.T)) + # self.moderators_effect[group] = group_moderators_effect + # tables["Moderators_Regression_Coef"] = pd.DataFrame( + # self._moderators_coef, columns=self.moderators + # ) + # else: + # self._moderators_coef = None # standard error - spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( - dict(), - dict(), - dict(), - ) - coef_spline_bases = torch.tensor( - self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device - ) - for group in self.groups: - group_foci_per_voxel = torch.tensor( - self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device - ) - group_foci_per_study = torch.tensor( - self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device - ) - group_spatial_coef = torch.tensor(self.model.spatial_coef_linears[group].weight, - dtype=torch.float64, device=self.device) - if self.moderators: - group_moderators = torch.tensor( - self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device - ) - moderators_coef = torch.tensor(self._moderators_coef, dtype=torch.float64, device=self.device) - else: - group_moderators, moderators_coef = None, None - - ll_single_group_kwargs = { - "moderators_coef": moderators_coef, - "coef_spline_bases": coef_spline_bases, - "moderators": group_moderators, - "foci_per_voxel": group_foci_per_voxel, - "foci_per_study": group_foci_per_study, - "device": self.device, - } - - if "Overdispersion_Coef" in tables.keys(): - ll_single_group_kwargs['overdispersion'] = torch.tensor( - tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], - dtype=torch.float64, - device=self.device, - ) + # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( + # dict(), + # dict(), + # dict(), + # ) + # coef_spline_bases = torch.tensor( + # self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device + # ) + # for group in self.groups: + # group_foci_per_voxel = torch.tensor( + # self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device + # ) + # group_foci_per_study = torch.tensor( + # self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device + # ) + # group_spatial_coef = torch.tensor(self.model.spatial_coef_linears[group].weight, + # dtype=torch.float64, device=self.device) + # if self.moderators: + # group_moderators = torch.tensor( + # self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device + # ) + # moderators_coef = torch.tensor(self._moderators_coef, dtype=torch.float64, device=self.device) + # else: + # group_moderators, moderators_coef = None, None + + # ll_single_group_kwargs = { + # "moderators_coef": moderators_coef, + # "coef_spline_bases": coef_spline_bases, + # "moderators": group_moderators, + # "foci_per_voxel": group_foci_per_voxel, + # "foci_per_study": group_foci_per_study, + # "device": self.device, + # } + + # if "Overdispersion_Coef" in tables.keys(): + # ll_single_group_kwargs['overdispersion'] = torch.tensor( + # tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], + # dtype=torch.float64, + # device=self.device, + # ) - # create a negative log-likelihood function - def nll_spatial_coef(group_spatial_coef): - return -self.model._log_likelihood_single_group( - group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, - ) - - F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) - # Inference on regression coefficient of spatial effect - - F_spatial_coef = F_spatial_coef.reshape((self.model.spatial_coef_dim, self.model.spatial_coef_dim)) - Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) - Var_spatial_coef = np.diag(Cov_spatial_coef) - SE_spatial_coef = np.sqrt(Var_spatial_coef) - spatial_regression_coef_se[group] = SE_spatial_coef - - Var_log_spatial_intensity = np.einsum( - "ij,ji->i", - self.inputs_["coef_spline_bases"], - Cov_spatial_coef @ self.inputs_["coef_spline_bases"].T, - ) - SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) - log_spatial_intensity_se[group] = SE_log_spatial_intensity - - group_studywise_spatial_intensity = maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] - SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity - spatial_intensity_se[group] = SE_spatial_intensity - - tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( - spatial_regression_coef_se, orient="index" - ) - tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - log_spatial_intensity_se, orient="index" - ) - tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - spatial_intensity_se, orient="index" - ) - - # Inference on regression coefficient of moderators - if self.moderators: - # modify ll_single_group_kwargs so that beta is fixed and gamma can vary - del ll_single_group_kwargs["moderators_coef"] - ll_single_group_kwargs["group_spatial_coef"] = group_spatial_coef - - def nll_moderators_coef(moderators_coef): - return -self.model._log_likelihood_single_group( - moderators_coef=moderators_coef, **ll_single_group_kwargs, - ) - - F_moderators_coef = torch.autograd.functional.hessian( - nll_moderators_coef, - moderators_coef, - create_graph=False, - vectorize=True, - outer_jacobian_strategy="forward-mode", - ) - F_moderators_coef = F_moderators_coef.reshape((self.model.moderators_coef_dim, self.model.moderators_coef_dim)) - Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) - Var_moderators = np.diag(Cov_moderators_coef).reshape((1, self.model.moderators_coef_dim)) - SE_moderators = np.sqrt(Var_moderators) - tables["Moderators_Regression_SE"] = pd.DataFrame( - SE_moderators, columns=self.moderators - ) + # # create a negative log-likelihood function + # def nll_spatial_coef(group_spatial_coef): + # return -self.model._log_likelihood_single_group( + # group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, + # ) + + # F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) + # # Inference on regression coefficient of spatial effect + + # F_spatial_coef = F_spatial_coef.reshape((self.model.spatial_coef_dim, self.model.spatial_coef_dim)) + # Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) + # Var_spatial_coef = np.diag(Cov_spatial_coef) + # SE_spatial_coef = np.sqrt(Var_spatial_coef) + # spatial_regression_coef_se[group] = SE_spatial_coef + + # Var_log_spatial_intensity = np.einsum( + # "ij,ji->i", + # self.inputs_["coef_spline_bases"], + # Cov_spatial_coef @ self.inputs_["coef_spline_bases"].T, + # ) + # SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) + # log_spatial_intensity_se[group] = SE_log_spatial_intensity + + # group_studywise_spatial_intensity = maps[ + # "Group_" + group + "_Studywise_Spatial_Intensity" + # ] + # SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity + # spatial_intensity_se[group] = SE_spatial_intensity + + # tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( + # spatial_regression_coef_se, orient="index" + # ) + # tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + # log_spatial_intensity_se, orient="index" + # ) + # tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + # spatial_intensity_se, orient="index" + # ) + + # # Inference on regression coefficient of moderators + # if self.moderators: + # # modify ll_single_group_kwargs so that spatial_coef is fixed + # # and moderators_coef can vary + # del ll_single_group_kwargs["moderators_coef"] + # ll_single_group_kwargs["group_spatial_coef"] = group_spatial_coef + + # def nll_moderators_coef(moderators_coef): + # return -self.model._log_likelihood_single_group( + # moderators_coef=moderators_coef, **ll_single_group_kwargs, + # ) + + # F_moderators_coef = torch.autograd.functional.hessian( + # nll_moderators_coef, + # moderators_coef, + # create_graph=False, + # vectorize=True, + # outer_jacobian_strategy="forward-mode", + # ) + # F_moderators_coef = F_moderators_coef.reshape((self.model.moderators_coef_dim, self.model.moderators_coef_dim)) + # Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + # Var_moderators = np.diag(Cov_moderators_coef).reshape((1, self.model.moderators_coef_dim)) + # SE_moderators = np.sqrt(Var_moderators) + # tables["Moderators_Regression_SE"] = pd.DataFrame( + # SE_moderators, columns=self.moderators + # ) return maps, tables diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 11eee3855..b518faf87 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1,7 +1,9 @@ import abc import torch - +import numpy as np +import pandas as pd +import functorch class GeneralLinearModel(torch.nn.Module): def __init__( @@ -68,8 +70,143 @@ def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): self.init_spatial_weights() if moderators_coef_dim: self.init_moderator_weights() + + def extract_optimized_params(self, coef_spline_bases, moderators_by_group): + """Document this.""" + spatial_regression_coef, spatial_intensity_estimation = dict(), dict() + for group in self.groups: + # Extract optimized spatial regression coefficients from the model + group_spatial_coef_linear_weight = self.spatial_coef_linears[group].weight + group_spatial_coef_linear_weight = group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() + spatial_regression_coef[group] = group_spatial_coef_linear_weight + # Estimate group-specific spatial intensity + group_spatial_intensity_estimation = np.exp(np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)) + spatial_intensity_estimation["Group_" + group + "_Studywise_Spatial_Intensity"] = group_spatial_intensity_estimation + + # Extract optimized regression coefficient of study-level moderators from the model + if self.moderators_coef_dim: + moderators_effect = dict() + moderators_coef = self.moderators_linear.weight + moderators_coef = moderators_coef.cpu().detach().numpy() + for group in self.groups: + group_moderators = moderators_by_group[group] + group_moderators_effect = np.exp(np.matmul(group_moderators, moderators_coef.T)) + moderators_effect[group] = group_moderators_effect.flatten() + else: + moderators_coef, moderators_effect = None, None + + return spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect + + def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Document this.""" + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() + for group in self.groups: + group_foci_per_voxel = torch.tensor( + foci_per_voxel[group], dtype=torch.float64, device=self.device) + group_foci_per_study = torch.tensor( + foci_per_study[group], dtype=torch.float64, device=self.device + ) + group_spatial_coef = torch.tensor(self.spatial_coef_linears[group].weight, + dtype=torch.float64, device=self.device) + + if self.moderators_coef_dim: + group_moderators = torch.tensor( + moderators_by_group[group], dtype=torch.float64, device=self.device + ) + moderators_coef = torch.tensor(self.moderators_linear.weight, dtype=torch.float64, device=self.device) + else: + group_moderators, moderators_coef = None, None + + ll_single_group_kwargs = { + "moderators_coef": moderators_coef if self.moderators_coef_dim else None, + "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "moderators": group_moderators if self.moderators_coef_dim else None, + "foci_per_voxel": group_foci_per_voxel, + "foci_per_study": group_foci_per_study, + "device": self.device, + } + + # create a negative log-likelihood function + def nll_spatial_coef(group_spatial_coef): + return -self._log_likelihood_single_group( + group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, + ) + + F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) + F_spatial_coef = F_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) + var_spatial_coef = np.diag(cov_spatial_coef) + se_spatial_coef = np.sqrt(var_spatial_coef) + spatial_regression_coef_se[group] = se_spatial_coef + + var_log_spatial_intensity = np.einsum( + "ij,ji->i", + coef_spline_bases, + cov_spatial_coef @ coef_spline_bases.T, + ) + se_log_spatial_intensity = np.sqrt(var_log_spatial_intensity) + log_spatial_intensity_se[group] = se_log_spatial_intensity + group_studywise_spatial_intensity = np.exp( + np.matmul(coef_spline_bases, group_spatial_coef.detach().cpu().numpy().T) + ).flatten() + se_spatial_intensity = group_studywise_spatial_intensity * se_log_spatial_intensity + spatial_intensity_se[group] = se_spatial_intensity + # Inference on regression coefficient of moderators + if self.moderators_coef_dim: + # modify ll_single_group_kwargs so that spatial_coef is fixed + # and moderators_coef can vary + del ll_single_group_kwargs["moderators_coef"] + ll_single_group_kwargs["group_spatial_coef"] = group_spatial_coef + + def nll_moderators_coef(moderators_coef): + return -self._log_likelihood_single_group( + moderators_coef=moderators_coef, **ll_single_group_kwargs, + ) + + F_moderators_coef = torch.autograd.functional.hessian( + nll_moderators_coef, + moderators_coef, + create_graph=False, + vectorize=True, + outer_jacobian_strategy="forward-mode", + ) + F_moderators_coef = F_moderators_coef.reshape((self.moderators_coef_dim, self.moderators_coef_dim)) + cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + var_moderators = np.diag(cov_moderators_coef).reshape((1, self.moderators_coef_dim)) + se_moderators = np.sqrt(var_moderators) + else: + se_moderators = None + return spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators + + def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Document this.""" + tables = dict() + # Extract optimized regression coefficients from model + spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect = self.extract_optimized_params(coef_spline_bases, moderators_by_group) + tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(spatial_regression_coef, orient="index") + maps = spatial_intensity_estimation + if self.moderators_coef_dim: + tables["Moderators_Regression_Coef"] = pd.DataFrame(moderators_coef) + tables["Moderators_Effect"] = pd.DataFrame.from_dict(moderators_effect, orient="index") + + # Estimate standard error of regression coefficient and (Log-)spatial intensity + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( + spatial_regression_coef_se, orient="index" + ) + tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + log_spatial_intensity_se, orient="index" + ) + tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + spatial_intensity_se, orient="index" + ) + if self.moderators_coef_dim: + tables["Moderators_Regression_SE"] = pd.DataFrame(se_moderators) + return maps, tables + + class OverdispersionModel(GeneralLinearModel): def __init__(self, **kwargs): square_root = kwargs.pop("square_root", False) @@ -93,6 +230,18 @@ def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim, square_roo super().init_weights(groups, spatial_coef_dim, moderators_coef_dim) self.init_overdispersion_weights(square_root=square_root) + def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Document this.""" + maps, tables = super(GeneralLinearModel, self).inference_outcome(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + overdispersion_param = dict() + for group in self.groups: + group_overdispersion = self.overdispersion[group] + group_overdispersion = group_overdispersion.cpu().detach().numpy() + overdispersion_param[group] = group_overdispersion + tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( + overdispersion_param, orient="index", columns=["overdispersion"]) + + return maps, tables class Poisson(GeneralLinearModel): def __init__(self, **kwargs): diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 08cb2444d..9a6e56919 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -13,10 +13,10 @@ def test_CBMREstimator(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model=models.ClusteredNegativeBinomial, - penalty=True, - lr=1e-4, - tol=1e6, + model=models.NegativeBinomial, + penalty=False, + lr=1e-6, + tol=1e8, device="cpu" ) cbmr.fit(dataset=dset) From 7a655507e29414d3191996279732827f8919e13d Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 13 Jan 2023 21:29:31 +0000 Subject: [PATCH 043/177] add some code for overdispersion model class. --- nimare/meta/models.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index b518faf87..36805ab15 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -126,6 +126,12 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci "device": self.device, } + # if "Overdispersion_Coef" in tables.keys(): + # ll_single_group_kwargs['overdispersion'] = torch.tensor( + # tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], + # dtype=torch.float64, + # device=self.device, + # ) # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( @@ -232,7 +238,7 @@ def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim, square_roo def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" - maps, tables = super(GeneralLinearModel, self).inference_outcome(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + maps, tables = super().inference_outcome(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) overdispersion_param = dict() for group in self.groups: group_overdispersion = self.overdispersion[group] From 6b51276dbe021c9940f3a932aa90ef5e0bc319e3 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 13 Jan 2023 17:51:43 -0600 Subject: [PATCH 044/177] change model to use optimizer --- nimare/meta/cbmr.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f24b04917..c74f45324 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -122,6 +122,7 @@ def __init__( self.moderators = moderators self.spline_spacing = spline_spacing + # self.model = model(penalty=penalty, device=device, lr=lr, lr_decay=lr_decay, tol=tol, n_iter=n_iter) self.model = model(penalty=penalty, device=device) self.penalty = penalty self.n_iter = n_iter From 320a712833eecec5b654e9d95c0d7527cc6b8b4e Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 13 Jan 2023 17:54:51 -0600 Subject: [PATCH 045/177] change model names --- nimare/meta/models.py | 89 +++++++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 41 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 36805ab15..f35616947 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -5,7 +5,8 @@ import pandas as pd import functorch -class GeneralLinearModel(torch.nn.Module): + +class GeneralLinearModelEstimator(torch.nn.Module): def __init__( self, spatial_coef_dim=None, @@ -97,6 +98,7 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): return spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect + def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() @@ -125,13 +127,14 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci "foci_per_study": group_foci_per_study, "device": self.device, } - - # if "Overdispersion_Coef" in tables.keys(): - # ll_single_group_kwargs['overdispersion'] = torch.tensor( - # tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], - # dtype=torch.float64, - # device=self.device, - # ) + + if getattr(self, 'overdispersion'): + ll_single_group_kwargs['overdispersion'] = torch.tensor( + self.overdispersion[group], + dtype=torch.float64, + device=self.device, + ) + # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( @@ -184,6 +187,7 @@ def nll_moderators_coef(moderators_coef): se_moderators = np.sqrt(var_moderators) else: se_moderators = None + return spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): @@ -213,7 +217,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox return maps, tables -class OverdispersionModel(GeneralLinearModel): +class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) @@ -245,11 +249,12 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox group_overdispersion = group_overdispersion.cpu().detach().numpy() overdispersion_param[group] = group_overdispersion tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( - overdispersion_param, orient="index", columns=["overdispersion"]) + overdispersion_param, orient="index", columns=["overdispersion"]) return maps, tables -class Poisson(GeneralLinearModel): + +class PoissonEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): super().__init__(**kwargs) @@ -384,7 +389,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) return -log_l -class NegativeBinomial(OverdispersionModel): +class NegativeBinomialEstimator(OverdispersionModelEstimator): def __init__(self, **kwargs): kwargs['square_root'] = True super().__init__(**kwargs) @@ -407,23 +412,23 @@ def _three_term(y, r, device): def _log_likelihood_single_group( self, - group_overdispersion, + overdispersion, group_spatial_coef, moderators_coef, coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, + moderators, + foci_per_voxel, + foci_per_study, device="cpu", ): - v = 1 / group_overdispersion + v = 1 / overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is not None: - log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) + log_mu_moderators = torch.matmul(moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: - n_study, _ = group_foci_per_study.shape + n_study, _ = foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) @@ -435,8 +440,8 @@ def _log_likelihood_single_group( p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator - log_l = NegativeBinomial._three_term(group_foci_per_voxel, r, device=device) + torch.sum( - r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) + log_l = NegativeBinomial._three_term(foci_per_voxel, r, device=device) + torch.sum( + r * torch.log(1 - p) + foci_per_voxel * torch.log(p) ) return log_l @@ -519,13 +524,15 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) else: moderators_coef, group_moderators = None, None group_log_l = self._log_likelihood_single_group( - group_overdispersion, - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study) + group_overdispersion, + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study + ) + log_l += group_log_l if self.penalty: @@ -561,44 +568,44 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) return -log_l -class ClusteredNegativeBinomial(OverdispersionModel): +class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): def __init__(self, **kwargs): kwargs['square_root'] = False super().__init__(**kwargs) def _log_likelihood_single_group( self, - group_overdispersion, + overdispersion, group_spatial_coef, moderators_coef, coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, + moderators, + foci_per_voxel, + foci_per_study, device="cpu", ): - v = 1 / group_overdispersion + v = 1 / overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is not None: - log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) + log_mu_moderators = torch.matmul(moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: - n_study, _ = group_foci_per_study.shape + n_study, _ = foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - group_n_study, _ = group_foci_per_study.shape + group_n_study, _ = foci_per_study.shape log_l = ( group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) - + torch.sum(torch.lgamma(group_foci_per_study + v)) - - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) - + torch.sum(group_foci_per_voxel * log_mu_spatial) - + torch.sum(group_foci_per_study * log_mu_moderators) + + torch.sum(torch.lgamma(foci_per_study + v)) + - torch.sum((foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(foci_per_voxel * log_mu_spatial) + + torch.sum(foci_per_study * log_mu_moderators) ) return log_l From ea0ad276511da0f7245199a8c952dd005e2ee10e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 15 Jan 2023 04:57:08 +0000 Subject: [PATCH 046/177] refactor the optimizer functions into the model class --- nimare/meta/cbmr.py | 426 +++++++++++---------------------- nimare/meta/models.py | 175 +++++++++++--- nimare/tests/test_meta_cbmr.py | 40 ++-- 3 files changed, 299 insertions(+), 342 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f24b04917..e40d6f82e 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -122,7 +122,7 @@ def __init__( self.moderators = moderators self.spline_spacing = spline_spacing - self.model = model(penalty=penalty, device=device) + self.model = model(penalty=penalty, lr=lr, lr_decay=lr_decay, n_iter=n_iter, tol=tol, device=device) self.penalty = penalty self.n_iter = n_iter self.lr = lr @@ -275,142 +275,142 @@ def _preprocess_input(self, dataset): self.inputs_["foci_per_voxel"] = foci_per_voxel self.inputs_["foci_per_study"] = foci_per_study - def _update( - self, - optimizer, - coef_spline_bases, - moderators, - foci_per_voxel, - foci_per_study, - prev_loss, - ): - """One iteration in optimization with L-BFGS. - - Adjust learning rate based on the number of iteration (with learning rate decay parameter - `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous - iteration) if NaN occurs. - """ - self.iter += 1 - scheduler = torch.optim.lr_scheduler.ExponentialLR( - optimizer, gamma=self.lr_decay - ) # learning rate decay - - def closure(): - optimizer.zero_grad() - loss = self.model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) - loss.backward() - return loss - - loss = optimizer.step(closure) - scheduler.step() - # reset the L-BFGS params if NaN appears in coefficient of regression - if any( - [ - torch.any(torch.isnan(self.model.spatial_coef_linears[group].weight)) - for group in self.groups - ] - ): - if self.iter == 1: # NaN occurs in the first iteration - raise ValueError( - """The current learing rate {str(self.lr)} gives rise to NaN values, adjust - to a smaller value.""" - ) - spatial_coef_linears, overdispersion_sqrt, overdispersion = dict(), dict(), dict() - for group in self.groups: + # def _update( + # self, + # optimizer, + # coef_spline_bases, + # moderators, + # foci_per_voxel, + # foci_per_study, + # prev_loss, + # ): + # """One iteration in optimization with L-BFGS. + + # Adjust learning rate based on the number of iteration (with learning rate decay parameter + # `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous + # iteration) if NaN occurs. + # """ + # self.iter += 1 + # scheduler = torch.optim.lr_scheduler.ExponentialLR( + # optimizer, gamma=self.lr_decay + # ) # learning rate decay + + # def closure(): + # optimizer.zero_grad() + # loss = self.model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) + # loss.backward() + # return loss + + # loss = optimizer.step(closure) + # scheduler.step() + # # reset the L-BFGS params if NaN appears in coefficient of regression + # if any( + # [ + # torch.any(torch.isnan(self.model.spatial_coef_linears[group].weight)) + # for group in self.groups + # ] + # ): + # if self.iter == 1: # NaN occurs in the first iteration + # raise ValueError( + # """The current learing rate {str(self.lr)} gives rise to NaN values, adjust + # to a smaller value.""" + # ) + # spatial_coef_linears, overdispersion_sqrt, overdispersion = dict(), dict(), dict() + # for group in self.groups: - group_spatial_linear = torch.nn.Linear(self.model.spatial_coef_dim, 1, bias=False).double() - group_spatial_linear.weight = torch.nn.Parameter( - self.last_state["spatial_coef_linears." + group + ".weight"] - ) - spatial_coef_linears[group] = group_spatial_linear - - if isinstance(self.model, models.NegativeBinomial): - group_overdispersion_sqrt = torch.nn.Parameter( - self.last_state["overdispersion_sqrt." + group] - ) - overdispersion_sqrt[group] = group_overdispersion_sqrt - elif isinstance(self.model, models.ClusteredNegativeBinomial): - group_overdispersion = torch.nn.Parameter(self.last_state["overdispersion." + group]) - overdispersion[group] = group_overdispersion - - self.model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - if isinstance(self.model, models.NegativeBinomial): - self.model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) - elif isinstance(self.model, models.ClusteredNegativeBinomial): - self.model.overdispersion = torch.nn.ParameterDict(overdispersion) - - LGR.debug("Reset L-BFGS optimizer......") - else: - self.last_state = copy.deepcopy( - self.model.state_dict() - ) - - return loss - - def _optimizer(self): - """Optimize regression coefficient of CBMR via L-BFGS algorithm. - - Optimization terminates if the absolute value of difference of log-likelihood in - two consecutive iterations is below `tol` - - Parameters - ---------- - model : :obj:`~nimare.dataset.Dataset` - Stochastic model used in CBMR. - lr : :obj:`~float` - Learning rate of L-BFGS. - tol : :obj:`~float` - Stopping criteria of L-BFGS. - n_iter : :obj:`~int` - Maximum iterations limit of L-BFGS. - device : :obj:`~str` - Device type ('cpu' or 'cuda') represents the device on - which operations will be allocated. - """ - optimizer = torch.optim.LBFGS(self.model.parameters(), self.lr) - # load dataset info to torch.tensor - coef_spline_bases = torch.tensor( - self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device - ) - if self.moderators: - moderators_by_group_tensor = dict() - for group in self.groups: - moderators_tensor = torch.tensor( - self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device - ) - moderators_by_group_tensor[group] = moderators_tensor - else: - moderators_by_group_tensor = None - foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() - for group in self.groups: - group_foci_per_voxel_tensor = torch.tensor( - self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device - ) - group_foci_per_study_tensor = torch.tensor( - self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device - ) - foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor - foci_per_study_tensor[group] = group_foci_per_study_tensor - - if self.iter == 0: - prev_loss = torch.tensor(float("inf")) # initialization loss difference - - for i in range(self.n_iter): - loss = self._update( - optimizer, - coef_spline_bases, - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss, - ) - loss_diff = loss - prev_loss - LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") - if torch.abs(loss_diff) < self.tol: - break - prev_loss = loss + # group_spatial_linear = torch.nn.Linear(self.model.spatial_coef_dim, 1, bias=False).double() + # group_spatial_linear.weight = torch.nn.Parameter( + # self.last_state["spatial_coef_linears." + group + ".weight"] + # ) + # spatial_coef_linears[group] = group_spatial_linear + + # if isinstance(self.model, models.NegativeBinomial): + # group_overdispersion_sqrt = torch.nn.Parameter( + # self.last_state["overdispersion_sqrt." + group] + # ) + # overdispersion_sqrt[group] = group_overdispersion_sqrt + # elif isinstance(self.model, models.ClusteredNegativeBinomial): + # group_overdispersion = torch.nn.Parameter(self.last_state["overdispersion." + group]) + # overdispersion[group] = group_overdispersion + + # self.model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + # if isinstance(self.model, models.NegativeBinomial): + # self.model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) + # elif isinstance(self.model, models.ClusteredNegativeBinomial): + # self.model.overdispersion = torch.nn.ParameterDict(overdispersion) + + # LGR.debug("Reset L-BFGS optimizer......") + # else: + # self.last_state = copy.deepcopy( + # self.model.state_dict() + # ) + + # return loss + + # def _optimizer(self): + # """Optimize regression coefficient of CBMR via L-BFGS algorithm. + + # Optimization terminates if the absolute value of difference of log-likelihood in + # two consecutive iterations is below `tol` + + # Parameters + # ---------- + # model : :obj:`~nimare.dataset.Dataset` + # Stochastic model used in CBMR. + # lr : :obj:`~float` + # Learning rate of L-BFGS. + # tol : :obj:`~float` + # Stopping criteria of L-BFGS. + # n_iter : :obj:`~int` + # Maximum iterations limit of L-BFGS. + # device : :obj:`~str` + # Device type ('cpu' or 'cuda') represents the device on + # which operations will be allocated. + # """ + # optimizer = torch.optim.LBFGS(self.model.parameters(), self.lr) + # # load dataset info to torch.tensor + # coef_spline_bases = torch.tensor( + # self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device + # ) + # if self.moderators: + # moderators_by_group_tensor = dict() + # for group in self.groups: + # moderators_tensor = torch.tensor( + # self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device + # ) + # moderators_by_group_tensor[group] = moderators_tensor + # else: + # moderators_by_group_tensor = None + # foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() + # for group in self.groups: + # group_foci_per_voxel_tensor = torch.tensor( + # self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device + # ) + # group_foci_per_study_tensor = torch.tensor( + # self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device + # ) + # foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor + # foci_per_study_tensor[group] = group_foci_per_study_tensor + + # if self.iter == 0: + # prev_loss = torch.tensor(float("inf")) # initialization loss difference + + # for i in range(self.n_iter): + # loss = self._update( + # optimizer, + # coef_spline_bases, + # moderators_by_group_tensor, + # foci_per_voxel_tensor, + # foci_per_study_tensor, + # prev_loss, + # ) + # loss_diff = loss - prev_loss + # LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") + # if torch.abs(loss_diff) < self.tol: + # break + # prev_loss = loss - return + # return def _fit(self, dataset): """Perform coordinate-based meta-regression (CBMR) on dataset. @@ -441,163 +441,11 @@ def _fit(self, dataset): self.model.init_weights(**init_weight_kwargs) - self._optimizer() + moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None + self.model._optimizer(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) maps, tables = dict(), dict() - moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None maps, tables = self.model.inference_outcome(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) - - # Spatial_Regression_Coef, overdispersion_param = dict(), dict() - # # regression coef of spatial effect - # for group in self.groups: - # group_spatial_coef_linear_weight = self.model.spatial_coef_linears[group].weight - # group_spatial_coef_linear_weight = ( - # group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() - # ) - # Spatial_Regression_Coef[group] = group_spatial_coef_linear_weight - # group_studywise_spatial_intensity = np.exp( - # np.matmul(self.inputs_["coef_spline_bases"], group_spatial_coef_linear_weight) - # ) - # maps[ - # "Group_" + group + "_Studywise_Spatial_Intensity" - # ] = group_studywise_spatial_intensity # .reshape((1,-1)) - # overdispersion parameter - # if isinstance(self.model, models.NegativeBinomial): - # group_overdispersion = self.model.overdispersion_sqrt[group] ** 2 - # group_overdispersion = group_overdispersion.cpu().detach().numpy() - # overdispersion_param[group] = group_overdispersion - # elif isinstance(self.model, models.ClusteredNegativeBinomial): - # group_overdispersion = self.model.overdispersion[group] - # group_overdispersion = group_overdispersion.cpu().detach().numpy() - # overdispersion_param[group] = group_overdispersion - - # # tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( - # # Spatial_Regression_Coef, orient="index" - # # ) - # if isinstance(self.model, (models.NegativeBinomial, models.ClusteredNegativeBinomial)): - # tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( - # overdispersion_param, orient="index", columns=["overdispersion"] - # study-level moderators - # if self.moderators: - # self.moderators_effect = dict() - # self._moderators_coef = self.model.moderators_linear.weight - # self._moderators_coef = self._moderators_coef.cpu().detach().numpy() - # for group in self.groups: - # group_moderators = self.inputs_["moderators_by_group"][group] - # group_moderators_effect = np.exp(np.matmul(group_moderators, self._moderators_coef.T)) - # self.moderators_effect[group] = group_moderators_effect - # tables["Moderators_Regression_Coef"] = pd.DataFrame( - # self._moderators_coef, columns=self.moderators - # ) - # else: - # self._moderators_coef = None - # standard error - # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( - # dict(), - # dict(), - # dict(), - # ) - # coef_spline_bases = torch.tensor( - # self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device - # ) - # for group in self.groups: - # group_foci_per_voxel = torch.tensor( - # self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device - # ) - # group_foci_per_study = torch.tensor( - # self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device - # ) - # group_spatial_coef = torch.tensor(self.model.spatial_coef_linears[group].weight, - # dtype=torch.float64, device=self.device) - # if self.moderators: - # group_moderators = torch.tensor( - # self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device - # ) - # moderators_coef = torch.tensor(self._moderators_coef, dtype=torch.float64, device=self.device) - # else: - # group_moderators, moderators_coef = None, None - - # ll_single_group_kwargs = { - # "moderators_coef": moderators_coef, - # "coef_spline_bases": coef_spline_bases, - # "moderators": group_moderators, - # "foci_per_voxel": group_foci_per_voxel, - # "foci_per_study": group_foci_per_study, - # "device": self.device, - # } - - # if "Overdispersion_Coef" in tables.keys(): - # ll_single_group_kwargs['overdispersion'] = torch.tensor( - # tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], - # dtype=torch.float64, - # device=self.device, - # ) - - # # create a negative log-likelihood function - # def nll_spatial_coef(group_spatial_coef): - # return -self.model._log_likelihood_single_group( - # group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, - # ) - - # F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) - # # Inference on regression coefficient of spatial effect - - # F_spatial_coef = F_spatial_coef.reshape((self.model.spatial_coef_dim, self.model.spatial_coef_dim)) - # Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) - # Var_spatial_coef = np.diag(Cov_spatial_coef) - # SE_spatial_coef = np.sqrt(Var_spatial_coef) - # spatial_regression_coef_se[group] = SE_spatial_coef - - # Var_log_spatial_intensity = np.einsum( - # "ij,ji->i", - # self.inputs_["coef_spline_bases"], - # Cov_spatial_coef @ self.inputs_["coef_spline_bases"].T, - # ) - # SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) - # log_spatial_intensity_se[group] = SE_log_spatial_intensity - - # group_studywise_spatial_intensity = maps[ - # "Group_" + group + "_Studywise_Spatial_Intensity" - # ] - # SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity - # spatial_intensity_se[group] = SE_spatial_intensity - - # tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( - # spatial_regression_coef_se, orient="index" - # ) - # tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - # log_spatial_intensity_se, orient="index" - # ) - # tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - # spatial_intensity_se, orient="index" - # ) - - # # Inference on regression coefficient of moderators - # if self.moderators: - # # modify ll_single_group_kwargs so that spatial_coef is fixed - # # and moderators_coef can vary - # del ll_single_group_kwargs["moderators_coef"] - # ll_single_group_kwargs["group_spatial_coef"] = group_spatial_coef - - # def nll_moderators_coef(moderators_coef): - # return -self.model._log_likelihood_single_group( - # moderators_coef=moderators_coef, **ll_single_group_kwargs, - # ) - - # F_moderators_coef = torch.autograd.functional.hessian( - # nll_moderators_coef, - # moderators_coef, - # create_graph=False, - # vectorize=True, - # outer_jacobian_strategy="forward-mode", - # ) - # F_moderators_coef = F_moderators_coef.reshape((self.model.moderators_coef_dim, self.model.moderators_coef_dim)) - # Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) - # Var_moderators = np.diag(Cov_moderators_coef).reshape((1, self.model.moderators_coef_dim)) - # SE_moderators = np.sqrt(Var_moderators) - # tables["Moderators_Regression_SE"] = pd.DataFrame( - # SE_moderators, columns=self.moderators - # ) return maps, tables diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 36805ab15..744a96dde 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -4,7 +4,10 @@ import numpy as np import pandas as pd import functorch +import logging +import copy +LGR = logging.getLogger(__name__) class GeneralLinearModel(torch.nn.Module): def __init__( self, @@ -12,6 +15,10 @@ def __init__( moderators_coef_dim=None, groups=None, penalty=False, + lr = 0.1, + lr_decay=0.999, + n_iter=1000, + tol=1e-2, device="cpu", ): super().__init__() @@ -19,6 +26,10 @@ def __init__( self.moderators_coef_dim = moderators_coef_dim self.groups = groups self.penalty = penalty + self.lr = lr + self.lr_decay = lr_decay + self.n_iter = n_iter + self.tol = tol self.device = device # initialization for spatial regression coefficients @@ -27,7 +38,9 @@ def __init__( # initialization for regression coefficients of moderators if self.moderators_coef_dim: self.init_moderator_weights() - + # initialization for iteration set up + self.iter = 0 + @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): """Document this.""" @@ -71,6 +84,116 @@ def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): if moderators_coef_dim: self.init_moderator_weights() + def _update( + self, + optimizer, + coef_spline_bases, + moderators, + foci_per_voxel, + foci_per_study, + prev_loss, + ): + """One iteration in optimization with L-BFGS. + + Adjust learning rate based on the number of iteration (with learning rate decay parameter + `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous + iteration) if NaN occurs. + """ + self.iter += 1 + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=self.lr_decay + ) # learning rate decay + + def closure(): + optimizer.zero_grad() + loss = self(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) + loss.backward() + return loss + + loss = optimizer.step(closure) + scheduler.step() + # reset the L-BFGS params if NaN appears in coefficient of regression + if any( + [ + torch.any(torch.isnan(self.spatial_coef_linears[group].weight)) + for group in self.groups + ] + ): + if self.iter == 1: # NaN occurs in the first iteration + raise ValueError( + """The current learing rate {str(self.lr)} gives rise to NaN values, adjust + to a smaller value.""" + ) + spatial_coef_linears, overdispersion = dict(), dict() + for group in self.groups: + group_spatial_linear = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + group_spatial_linear.weight = torch.nn.Parameter( + self.last_state["spatial_coef_linears." + group + ".weight"] + ) + spatial_coef_linears[group] = group_spatial_linear + + if hasattr(self, "overdispersion"): + group_overdispersion = torch.nn.Parameter( + self.last_state["overdispersion." + group] + ) + overdispersion[group] = group_overdispersion + self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) + if hasattr(self, "overdispersion"): + self.overdispersion = torch.nn.ParameterDict(overdispersion) + LGR.debug("Reset L-BFGS optimizer......") + else: + self.last_state = copy.deepcopy( + self.state_dict() + ) + + return loss + + def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + optimizer = torch.optim.LBFGS(self.parameters(), self.lr) + # load dataset info to torch.tensor + coef_spline_bases = torch.tensor( + coef_spline_bases, dtype=torch.float64, device=self.device + ) + if moderators_by_group: + moderators_by_group_tensor = dict() + for group in self.groups: + moderators_tensor = torch.tensor( + moderators_by_group[group], dtype=torch.float64, device=self.device + ) + moderators_by_group_tensor[group] = moderators_tensor + else: + moderators_by_group_tensor = None + foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() + for group in self.groups: + group_foci_per_voxel_tensor = torch.tensor( + foci_per_voxel[group], dtype=torch.float64, device=self.device + ) + group_foci_per_study_tensor = torch.tensor( + foci_per_study[group], dtype=torch.float64, device=self.device + ) + foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor + foci_per_study_tensor[group] = group_foci_per_study_tensor + + if self.iter == 0: + prev_loss = torch.tensor(float("inf")) # initialization loss difference + + for i in range(self.n_iter): + loss = self._update( + optimizer, + coef_spline_bases, + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) + loss_diff = loss - prev_loss + LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") + if torch.abs(loss_diff) < self.tol: + break + prev_loss = loss + + return + def extract_optimized_params(self, coef_spline_bases, moderators_by_group): """Document this.""" spatial_regression_coef, spatial_intensity_estimation = dict(), dict() @@ -120,18 +243,14 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci ll_single_group_kwargs = { "moderators_coef": moderators_coef if self.moderators_coef_dim else None, "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), - "moderators": group_moderators if self.moderators_coef_dim else None, - "foci_per_voxel": group_foci_per_voxel, - "foci_per_study": group_foci_per_study, + "group_moderators": group_moderators if self.moderators_coef_dim else None, + "group_foci_per_voxel": group_foci_per_voxel, + "group_foci_per_study": group_foci_per_study, "device": self.device, } - - # if "Overdispersion_Coef" in tables.keys(): - # ll_single_group_kwargs['overdispersion'] = torch.tensor( - # tables["Overdispersion_Coef"].to_dict()["overdispersion"][group], - # dtype=torch.float64, - # device=self.device, - # ) + + if hasattr(self, "overdispersion"): + ll_single_group_kwargs['group_overdispersion'] = self.overdispersion[group] # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( @@ -170,14 +289,7 @@ def nll_moderators_coef(moderators_coef): return -self._log_likelihood_single_group( moderators_coef=moderators_coef, **ll_single_group_kwargs, ) - - F_moderators_coef = torch.autograd.functional.hessian( - nll_moderators_coef, - moderators_coef, - create_graph=False, - vectorize=True, - outer_jacobian_strategy="forward-mode", - ) + F_moderators_coef = functorch.hessian(nll_moderators_coef)(moderators_coef) F_moderators_coef = F_moderators_coef.reshape((self.moderators_coef_dim, self.moderators_coef_dim)) cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) var_moderators = np.diag(cov_moderators_coef).reshape((1, self.moderators_coef_dim)) @@ -211,8 +323,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox if self.moderators_coef_dim: tables["Moderators_Regression_SE"] = pd.DataFrame(se_moderators) return maps, tables - - + class OverdispersionModel(GeneralLinearModel): def __init__(self, **kwargs): square_root = kwargs.pop("square_root", False) @@ -258,25 +369,25 @@ def _log_likelihood_single_group( group_spatial_coef, moderators_coef, coef_spline_bases, - moderators, - foci_per_voxel, - foci_per_study, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, device="cpu" ): log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is None: - n_study, _ = foci_per_study.shape + n_study, _ = group_foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) else: - log_mu_moderators = torch.matmul(moderators, moderators_coef.T) + log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) log_l = ( - torch.sum(torch.mul(foci_per_voxel, log_mu_spatial)) - + torch.sum(torch.mul(foci_per_study, log_mu_moderators)) + torch.sum(torch.mul(group_foci_per_voxel, log_mu_spatial)) + + torch.sum(torch.mul(group_foci_per_study, log_mu_moderators)) - torch.sum(mu_spatial) * torch.sum(mu_moderators) ) return log_l @@ -389,14 +500,14 @@ def __init__(self, **kwargs): kwargs['square_root'] = True super().__init__(**kwargs) - def _three_term(y, r, device): - max_foci = torch.max(y).to(dtype=torch.int64, device=device) + def _three_term(self, y, r): + max_foci = torch.max(y).to(dtype=torch.int64, device=self.device) sum_three_term = 0 for k in range(max_foci): foci_index = (y == k + 1).nonzero()[:, 0] r_j = r[foci_index] n_voxel = list(foci_index.shape)[0] - y_j = torch.tensor([k + 1] * n_voxel, device=device).double() + y_j = torch.tensor([k + 1] * n_voxel, device=self.device).double() y_j = y_j.reshape((n_voxel, 1)) # y=0 => sum_three_term = 0 sum_three_term += torch.sum( @@ -435,7 +546,7 @@ def _log_likelihood_single_group( p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) r = v * denominator / numerator - log_l = NegativeBinomial._three_term(group_foci_per_voxel, r, device=device) + torch.sum( + log_l = self._three_term(group_foci_per_voxel, r) + torch.sum( r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) ) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 9a6e56919..3d4bb642d 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -13,14 +13,13 @@ def test_CBMREstimator(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age"], spline_spacing=10, - model=models.NegativeBinomial, + model=models.ClusteredNegativeBinomial, penalty=False, lr=1e-6, tol=1e8, device="cpu" ) cbmr.fit(dataset=dset) -# ["standardized_sample_sizes", "standardized_avg_age"], def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) @@ -46,19 +45,22 @@ def test_CBMRInference(testdata_cbmr_simulated): # [[[1,0],[0,1]], [1, -1]] def test_CBMREstimator_update(testdata_cbmr_simulated): - cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) + cbmr = CBMREstimator(model=models.Poisson, lr=1e-4) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) cbmr._preprocess_input(testdata_cbmr_simulated) - cbmr_model = cbmr.model( - spatial_coef_dim=cbmr.inputs_["coef_spline_bases"].shape[1], - moderators_coef_dim=len(cbmr.moderators) if cbmr.moderators else None, - groups=cbmr.groups, - penalty=cbmr.penalty, - device=cbmr.device, - ) + init_weight_kwargs = { + 'groups': cbmr.groups, + 'spatial_coef_dim': cbmr.inputs_["coef_spline_bases"].shape[1], + 'moderators_coef_dim': len(cbmr.moderators) if cbmr.moderators else None, + } + if isinstance(cbmr.model, models.NegativeBinomial): + init_weight_kwargs["square_root"] = True + if isinstance(cbmr.model, models.ClusteredNegativeBinomial): + init_weight_kwargs["square_root"] = False + cbmr.model.init_weights(**init_weight_kwargs) - optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + optimizer = torch.optim.LBFGS(cbmr.model.parameters(), cbmr.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) if cbmr.moderators: @@ -71,7 +73,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): else: moderators_by_group_tensor = None foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() - for group in cbmr_model.groups: + for group in cbmr.model.groups: group_foci_per_voxel_tensor = torch.tensor( cbmr.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=cbmr.device ) @@ -80,12 +82,11 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): ) foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor foci_per_study_tensor[group] = group_foci_per_study_tensor - optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + if cbmr.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - loss = cbmr._update( - cbmr_model, + loss = cbmr.model._update( optimizer, torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), moderators_by_group_tensor, @@ -95,12 +96,11 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): ) # deliberately set the first spatial coefficient to nan - nan_coef = torch.tensor(cbmr_model.spatial_coef_linears['default'].weight) + nan_coef = torch.tensor(cbmr.model.spatial_coef_linears['default'].weight) nan_coef[:, 0] = float('nan') - cbmr_model.spatial_coef_linears['default'].weight = torch.nn.Parameter(nan_coef) + cbmr.model.spatial_coef_linears['default'].weight = torch.nn.Parameter(nan_coef) - loss = cbmr._update( - cbmr_model, + loss = cbmr.model._update( optimizer, torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), moderators_by_group_tensor, @@ -110,5 +110,3 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): ) - - From b55433dd14f4786467ad291467768cbbe75e4547 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 16 Jan 2023 10:50:11 -0600 Subject: [PATCH 047/177] create a fit method for models --- nimare/meta/models.py | 55 ++++++++++++++++++++++++++++++++++++------- 1 file changed, 46 insertions(+), 9 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 744a96dde..b73db42ff 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -8,7 +8,7 @@ import copy LGR = logging.getLogger(__name__) -class GeneralLinearModel(torch.nn.Module): +class GeneralLinearModelEstimator(torch.nn.Module): def __init__( self, spatial_coef_dim=None, @@ -40,6 +40,26 @@ def __init__( self.init_moderator_weights() # initialization for iteration set up self.iter = 0 + + # after fitting, the following attributes will be created + self.spatial_regression_coef = None + self.spatial_intensity_estimation = None + self.moderators_coef = None + self.moderators_effect = None + self.spatial_regression_coef_se = None + self.log_spatial_intensity_se = None + self.spatial_intensity_se = None + self.se_moderators = None + self.params = ( + self.spatial_regression_coef, + self.spatial_intensity_estimation, + self.moderators_coef, + self.moderators_effect, + self.spatial_regression_coef_se, + self.log_spatial_intensity_se, + self.spatial_intensity_se, + self.se_moderators, + ) @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): @@ -193,7 +213,15 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc prev_loss = loss return - + + def fit(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Fit the model.""" + self._optimizer(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + self.extract_optimized_params(coef_spline_bases, moderators_by_group) + self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + + return + def extract_optimized_params(self, coef_spline_bases, moderators_by_group): """Document this.""" spatial_regression_coef, spatial_intensity_estimation = dict(), dict() @@ -217,8 +245,11 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): moderators_effect[group] = group_moderators_effect.flatten() else: moderators_coef, moderators_effect = None, None - - return spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect + + self.spatial_regression_coef = spatial_regression_coef + self.spatial_intensity_estimation = spatial_intensity_estimation + self.moderators_coef = moderators_coef + self.moderators_effect = moderators_effect def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" @@ -296,10 +327,16 @@ def nll_moderators_coef(moderators_coef): se_moderators = np.sqrt(var_moderators) else: se_moderators = None - return spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators - - def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + + self.spatial_regression_coef_se = spatial_regression_coef_se + self.log_spatial_intensity_se = log_spatial_intensity_se + self.spatial_intensity_se = spatial_intensity_se + self.se_moderators = se_moderators + + def summary(self): """Document this.""" + if not all(self.params): + raise ValueError("Run fit first") tables = dict() # Extract optimized regression coefficients from model spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect = self.extract_optimized_params(coef_spline_bases, moderators_by_group) @@ -324,7 +361,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox tables["Moderators_Regression_SE"] = pd.DataFrame(se_moderators) return maps, tables -class OverdispersionModel(GeneralLinearModel): +class OverdispersionModel(GeneralLinearModelEstimator): def __init__(self, **kwargs): square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) @@ -360,7 +397,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox return maps, tables -class Poisson(GeneralLinearModel): +class Poisson(GeneralLinearModelEstimator): def __init__(self, **kwargs): super().__init__(**kwargs) From a62f26ced5209203df3113b8d85f79c0a9749875 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 16 Jan 2023 22:09:43 +0000 Subject: [PATCH 048/177] add summary to model fit --- nimare/meta/cbmr.py | 149 ++------------------------------- nimare/meta/models.py | 85 +++++++++---------- nimare/tests/test_meta_cbmr.py | 4 +- 3 files changed, 48 insertions(+), 190 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e40d6f82e..8c712c130 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -104,7 +104,7 @@ def __init__( moderators=None, mask=None, spline_spacing=10, - model=models.Poisson, + model=models.PoissonEstimator, penalty=False, n_iter=1000, lr=1e-2, @@ -275,143 +275,6 @@ def _preprocess_input(self, dataset): self.inputs_["foci_per_voxel"] = foci_per_voxel self.inputs_["foci_per_study"] = foci_per_study - # def _update( - # self, - # optimizer, - # coef_spline_bases, - # moderators, - # foci_per_voxel, - # foci_per_study, - # prev_loss, - # ): - # """One iteration in optimization with L-BFGS. - - # Adjust learning rate based on the number of iteration (with learning rate decay parameter - # `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous - # iteration) if NaN occurs. - # """ - # self.iter += 1 - # scheduler = torch.optim.lr_scheduler.ExponentialLR( - # optimizer, gamma=self.lr_decay - # ) # learning rate decay - - # def closure(): - # optimizer.zero_grad() - # loss = self.model(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) - # loss.backward() - # return loss - - # loss = optimizer.step(closure) - # scheduler.step() - # # reset the L-BFGS params if NaN appears in coefficient of regression - # if any( - # [ - # torch.any(torch.isnan(self.model.spatial_coef_linears[group].weight)) - # for group in self.groups - # ] - # ): - # if self.iter == 1: # NaN occurs in the first iteration - # raise ValueError( - # """The current learing rate {str(self.lr)} gives rise to NaN values, adjust - # to a smaller value.""" - # ) - # spatial_coef_linears, overdispersion_sqrt, overdispersion = dict(), dict(), dict() - # for group in self.groups: - - # group_spatial_linear = torch.nn.Linear(self.model.spatial_coef_dim, 1, bias=False).double() - # group_spatial_linear.weight = torch.nn.Parameter( - # self.last_state["spatial_coef_linears." + group + ".weight"] - # ) - # spatial_coef_linears[group] = group_spatial_linear - - # if isinstance(self.model, models.NegativeBinomial): - # group_overdispersion_sqrt = torch.nn.Parameter( - # self.last_state["overdispersion_sqrt." + group] - # ) - # overdispersion_sqrt[group] = group_overdispersion_sqrt - # elif isinstance(self.model, models.ClusteredNegativeBinomial): - # group_overdispersion = torch.nn.Parameter(self.last_state["overdispersion." + group]) - # overdispersion[group] = group_overdispersion - - # self.model.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - # if isinstance(self.model, models.NegativeBinomial): - # self.model.overdispersion_sqrt = torch.nn.ParameterDict(overdispersion_sqrt) - # elif isinstance(self.model, models.ClusteredNegativeBinomial): - # self.model.overdispersion = torch.nn.ParameterDict(overdispersion) - - # LGR.debug("Reset L-BFGS optimizer......") - # else: - # self.last_state = copy.deepcopy( - # self.model.state_dict() - # ) - - # return loss - - # def _optimizer(self): - # """Optimize regression coefficient of CBMR via L-BFGS algorithm. - - # Optimization terminates if the absolute value of difference of log-likelihood in - # two consecutive iterations is below `tol` - - # Parameters - # ---------- - # model : :obj:`~nimare.dataset.Dataset` - # Stochastic model used in CBMR. - # lr : :obj:`~float` - # Learning rate of L-BFGS. - # tol : :obj:`~float` - # Stopping criteria of L-BFGS. - # n_iter : :obj:`~int` - # Maximum iterations limit of L-BFGS. - # device : :obj:`~str` - # Device type ('cpu' or 'cuda') represents the device on - # which operations will be allocated. - # """ - # optimizer = torch.optim.LBFGS(self.model.parameters(), self.lr) - # # load dataset info to torch.tensor - # coef_spline_bases = torch.tensor( - # self.inputs_["coef_spline_bases"], dtype=torch.float64, device=self.device - # ) - # if self.moderators: - # moderators_by_group_tensor = dict() - # for group in self.groups: - # moderators_tensor = torch.tensor( - # self.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device - # ) - # moderators_by_group_tensor[group] = moderators_tensor - # else: - # moderators_by_group_tensor = None - # foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() - # for group in self.groups: - # group_foci_per_voxel_tensor = torch.tensor( - # self.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=self.device - # ) - # group_foci_per_study_tensor = torch.tensor( - # self.inputs_["foci_per_study"][group], dtype=torch.float64, device=self.device - # ) - # foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor - # foci_per_study_tensor[group] = group_foci_per_study_tensor - - # if self.iter == 0: - # prev_loss = torch.tensor(float("inf")) # initialization loss difference - - # for i in range(self.n_iter): - # loss = self._update( - # optimizer, - # coef_spline_bases, - # moderators_by_group_tensor, - # foci_per_voxel_tensor, - # foci_per_study_tensor, - # prev_loss, - # ) - # loss_diff = loss - prev_loss - # LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") - # if torch.abs(loss_diff) < self.tol: - # break - # prev_loss = loss - - # return - def _fit(self, dataset): """Perform coordinate-based meta-regression (CBMR) on dataset. @@ -434,18 +297,18 @@ def _fit(self, dataset): 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], 'moderators_coef_dim': len(self.moderators) if self.moderators else None, } - if isinstance(self.model, models.NegativeBinomial): + if isinstance(self.model, models.NegativeBinomialEstimator): init_weight_kwargs["square_root"] = True - if isinstance(self.model, models.ClusteredNegativeBinomial): + if isinstance(self.model, models.ClusteredNegativeBinomialEstimator): init_weight_kwargs["square_root"] = False self.model.init_weights(**init_weight_kwargs) moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None - self.model._optimizer(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + self.model.fit(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + - maps, tables = dict(), dict() - maps, tables = self.model.inference_outcome(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + maps, tables = self.model.summary() return maps, tables diff --git a/nimare/meta/models.py b/nimare/meta/models.py index cb99cb211..66703db89 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -50,16 +50,6 @@ def __init__( self.log_spatial_intensity_se = None self.spatial_intensity_se = None self.se_moderators = None - self.params = ( - self.spatial_regression_coef, - self.spatial_intensity_estimation, - self.moderators_coef, - self.moderators_effect, - self.spatial_regression_coef_se, - self.log_spatial_intensity_se, - self.spatial_intensity_se, - self.se_moderators, - ) @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): @@ -288,7 +278,6 @@ def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, ) - F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) F_spatial_coef = F_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) @@ -336,33 +325,39 @@ def nll_moderators_coef(moderators_coef): def summary(self): """Document this.""" - if not all(self.params): + params = ( + self.spatial_regression_coef, + self.spatial_intensity_estimation, + self.spatial_regression_coef_se, + self.log_spatial_intensity_se, + self.spatial_intensity_se, + ) + if any([param is None for param in params]): raise ValueError("Run fit first") tables = dict() - # Extract optimized regression coefficients from model - spatial_regression_coef, spatial_intensity_estimation, moderators_coef, moderators_effect = self.extract_optimized_params(coef_spline_bases, moderators_by_group) - tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(spatial_regression_coef, orient="index") - maps = spatial_intensity_estimation + # Extract optimized regression coefficients from model and store them in 'tables' + tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(self.spatial_regression_coef, orient="index") + maps = self.spatial_intensity_estimation if self.moderators_coef_dim: - tables["Moderators_Regression_Coef"] = pd.DataFrame(moderators_coef) - tables["Moderators_Effect"] = pd.DataFrame.from_dict(moderators_effect, orient="index") + tables["Moderators_Regression_Coef"] = pd.DataFrame(self.moderators_coef) + tables["Moderators_Effect"] = pd.DataFrame.from_dict(self.moderators_effect, orient="index") - # Estimate standard error of regression coefficient and (Log-)spatial intensity - spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + # Estimate standard error of regression coefficient and (Log-)spatial intensity and store them in 'tables' + # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( - spatial_regression_coef_se, orient="index" + self.spatial_regression_coef_se, orient="index" ) tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - log_spatial_intensity_se, orient="index" + self.log_spatial_intensity_se, orient="index" ) tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( - spatial_intensity_se, orient="index" + self.spatial_intensity_se, orient="index" ) if self.moderators_coef_dim: - tables["Moderators_Regression_SE"] = pd.DataFrame(se_moderators) + tables["Moderators_Regression_SE"] = pd.DataFrame(self.se_moderators) return maps, tables -class OverdispersionModel(GeneralLinearModelEstimator): +class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) @@ -398,7 +393,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox return maps, tables -class Poisson(GeneralLinearModelEstimator): +class PoissonEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): super().__init__(**kwargs) @@ -556,23 +551,23 @@ def _three_term(self, y, r): def _log_likelihood_single_group( self, - overdispersion, + group_overdispersion, group_spatial_coef, moderators_coef, coef_spline_bases, - moderators, - foci_per_voxel, - foci_per_study, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, device="cpu", ): - v = 1 / overdispersion + v = 1 / group_overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is not None: - log_mu_moderators = torch.matmul(moderators, moderators_coef.T) + log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: - n_study, _ = foci_per_study.shape + n_study, _ = group_foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) @@ -719,37 +714,37 @@ def __init__(self, **kwargs): def _log_likelihood_single_group( self, - overdispersion, + group_overdispersion, group_spatial_coef, moderators_coef, coef_spline_bases, - moderators, - foci_per_voxel, - foci_per_study, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, device="cpu", ): - v = 1 / overdispersion + v = 1 / group_overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is not None: - log_mu_moderators = torch.matmul(moderators, moderators_coef.T) + log_mu_moderators = torch.matmul(group_moderators, moderators_coef.T) mu_moderators = torch.exp(log_mu_moderators) else: - n_study, _ = foci_per_study.shape + n_study, _ = group_foci_per_study.shape log_mu_moderators = torch.tensor( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) mu_sum_per_study = torch.sum(mu_spatial) * mu_moderators - group_n_study, _ = foci_per_study.shape + group_n_study, _ = group_foci_per_study.shape log_l = ( group_n_study * v * torch.log(v) - group_n_study * torch.lgamma(v) - + torch.sum(torch.lgamma(foci_per_study + v)) - - torch.sum((foci_per_study + v) * torch.log(mu_sum_per_study + v)) - + torch.sum(foci_per_voxel * log_mu_spatial) - + torch.sum(foci_per_study * log_mu_moderators) + + torch.sum(torch.lgamma(group_foci_per_study + v)) + - torch.sum((group_foci_per_study + v) * torch.log(mu_sum_per_study + v)) + + torch.sum(group_foci_per_voxel * log_mu_spatial) + + torch.sum(group_foci_per_study * log_mu_moderators) ) return log_l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 9a6e56919..e97aa469f 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -11,9 +11,9 @@ def test_CBMREstimator(testdata_cbmr_simulated): dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=["standardized_sample_sizes", "standardized_avg_age"], + moderators=None, spline_spacing=10, - model=models.NegativeBinomial, + model=models.ClusteredNegativeBinomialEstimator, penalty=False, lr=1e-6, tol=1e8, From 2ec109f3f4ec32989adbb6db3010c39e2cafcda7 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 16 Jan 2023 18:06:43 -0600 Subject: [PATCH 049/177] function name suggestions --- nimare/meta/cbmr.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 8c712c130..473ffb3dd 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -297,15 +297,16 @@ def _fit(self, dataset): 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], 'moderators_coef_dim': len(self.moderators) if self.moderators else None, } - if isinstance(self.model, models.NegativeBinomialEstimator): - init_weight_kwargs["square_root"] = True - if isinstance(self.model, models.ClusteredNegativeBinomialEstimator): - init_weight_kwargs["square_root"] = False self.model.init_weights(**init_weight_kwargs) moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None - self.model.fit(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + self.model.fit( + self.inputs_["coef_spline_bases"], + moderators_by_group, + self.inputs_["foci_per_voxel"], + self.inputs_["foci_per_study"] + ) maps, tables = self.model.summary() @@ -373,6 +374,7 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups""" ) + # remove zero rows in contrast matrix (function: remove_empty_rows) con_group_zero_row = [ np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] for con_group in self.t_con_group @@ -390,7 +392,7 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" contrast matrix are all zeros""" ) self._Name_of_con_group() - # standardization + # standardization (function: standardize_contrast) self.t_con_group = [ con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) for con_group in self.t_con_group From 2f9ad20d2c0e3cef16b506a159b8648a2f1145ce Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 16 Jan 2023 18:07:13 -0600 Subject: [PATCH 050/177] make square_root an attribute --- nimare/meta/models.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 66703db89..694f0ca77 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -359,26 +359,26 @@ def summary(self): class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): - square_root = kwargs.pop("square_root", False) + self.square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) if self.groups: - self.init_overdispersion_weights(square_root=square_root) + self.init_overdispersion_weights() - def init_overdispersion_weights(self, square_root=False): + def init_overdispersion_weights(self): """Document this.""" overdispersion = dict() for group in self.groups: # initialization for alpha overdispersion_init_group = torch.tensor(1e-2).double() - if square_root: + if self.square_root: overdispersion_init_group = torch.sqrt(overdispersion_init_group) overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) self.overdispersion = torch.nn.ParameterDict(overdispersion) - def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim, square_root=False): + def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): """Document this.""" super().init_weights(groups, spatial_coef_dim, moderators_coef_dim) - self.init_overdispersion_weights(square_root=square_root) + self.init_overdispersion_weights() def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" From 6e3f42501f7c1e9c5c69c709877ec69cc36c1679 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 19 Jan 2023 22:48:53 +0000 Subject: [PATCH 051/177] allow categorical variables in CBMR --- nimare/meta/cbmr.py | 23 +++++++++++------------ nimare/tests/conftest.py | 2 ++ nimare/tests/test_meta_cbmr.py | 6 +++--- nimare/tests/utils.py | 17 +++++++++++++++-- nimare/utils.py | 12 +++++++++--- 5 files changed, 40 insertions(+), 20 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 473ffb3dd..e7e0efbcd 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,5 +1,5 @@ from nimare.base import Estimator -from nimare.utils import get_masker, B_spline_bases +from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators import nibabel as nib import numpy as np import pandas as pd @@ -12,6 +12,7 @@ import logging import copy + LGR = logging.getLogger(__name__) @@ -227,6 +228,7 @@ def _preprocess_input(self, dataset): self.groups = list(self.inputs_["studies_by_group"].keys()) # collect studywise moderators if specficed if self.moderators: + valid_dset_annotations, self.moderators = dummy_encoding_moderators(valid_dset_annotations, self.moderators) if isinstance(self.moderators, str): self.moderators = [ self.moderators @@ -297,17 +299,15 @@ def _fit(self, dataset): 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], 'moderators_coef_dim': len(self.moderators) if self.moderators else None, } + if isinstance(self.model, models.NegativeBinomialEstimator): + init_weight_kwargs["square_root"] = True + if isinstance(self.model, models.ClusteredNegativeBinomialEstimator): + init_weight_kwargs["square_root"] = False self.model.init_weights(**init_weight_kwargs) moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None - self.model.fit( - self.inputs_["coef_spline_bases"], - moderators_by_group, - self.inputs_["foci_per_voxel"], - self.inputs_["foci_per_study"] - ) - + self.model.fit(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) maps, tables = self.model.summary() @@ -347,8 +347,8 @@ class CBMRInference(object): Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. Default is 'cpu'. """ - - def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device="cpu"): + + def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device="cpu"): self.device = device self.CBMRResults = CBMRResults self.t_con_group = t_con_group @@ -374,7 +374,6 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups""" ) - # remove zero rows in contrast matrix (function: remove_empty_rows) con_group_zero_row = [ np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] for con_group in self.t_con_group @@ -392,7 +391,7 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" contrast matrix are all zeros""" ) self._Name_of_con_group() - # standardization (function: standardize_contrast) + # standardization self.t_con_group = [ con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) for con_group in self.t_con_group diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 575b3210a..800d5a854 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -138,6 +138,8 @@ def testdata_cbmr_simulated(): # set up moderators: sample sizes & avg_age dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) + dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)] + dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column return dset diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index e97aa469f..c064d8baa 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -8,12 +8,12 @@ def test_CBMREstimator(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=None, + moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, - model=models.ClusteredNegativeBinomialEstimator, + model=models.NegativeBinomialEstimator, penalty=False, lr=1e-6, tol=1e8, diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index c8d8d532f..3096585f4 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -5,6 +5,7 @@ import nibabel as nib import numpy as np import pytest +import warnings from nimare.meta.utils import compute_kda_ma @@ -123,13 +124,25 @@ def _transform_res(meta, meta_res, corr): def standardize_field(dataset, metadata): - moderators = dataset.annotations[metadata] + # moderators = dataset.annotations[metadata] + categorical_metadata, numerical_metadata = [], [] + for metadata_name in metadata: + if np.array_equal(dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(str)): + categorical_metadata.append(metadata_name) + elif np.array_equal(dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(float)): + numerical_metadata.append(metadata_name) + if len(categorical_metadata) > 0: + warnings.warn(f"Categorical metadata {categorical_metadata} can't be standardized.") + if len(numerical_metadata) == 0: + raise ValueError("No numerical metadata found.") + + moderators = dataset.annotations[numerical_metadata] standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) if isinstance(metadata, str): column_name = "standardized_" + metadata elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in metadata] + column_name = ["standardized_" + moderator for moderator in numerical_metadata] dataset.annotations[column_name] = standardize_moderators return dataset diff --git a/nimare/utils.py b/nimare/utils.py index 9c9c23f78..7e87ccafa 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1276,6 +1276,12 @@ def index2vox(vals, masker_voxels): return voxel_array -def contrast_matrix_generator(): - - return +def dummy_encoding_moderators(dataset_annotations, moderators): + for moderator in moderators: + if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): + moderators.remove(moderator) # remove moderators that are dummy encoded + categories_unique = dataset_annotations[moderator].unique().tolist() + for category in categories_unique: + dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) + moderators.append(category) # add dummy encoded moderators + return dataset_annotations, moderators From dac6287dee394d924a2abd2e8aa9bcefb6e695c7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 19 Jan 2023 23:13:23 +0000 Subject: [PATCH 052/177] fix a bug --- nimare/meta/cbmr.py | 5 ----- nimare/tests/test_meta_cbmr.py | 2 +- nimare/tests/utils.py | 5 +++-- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e7e0efbcd..bcc20f3d0 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -299,11 +299,6 @@ def _fit(self, dataset): 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], 'moderators_coef_dim': len(self.moderators) if self.moderators else None, } - if isinstance(self.model, models.NegativeBinomialEstimator): - init_weight_kwargs["square_root"] = True - if isinstance(self.model, models.ClusteredNegativeBinomialEstimator): - init_weight_kwargs["square_root"] = False - self.model.init_weights(**init_weight_kwargs) moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index c064d8baa..2aab23979 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -13,7 +13,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, - model=models.NegativeBinomialEstimator, + model=models.PoissonEstimator, penalty=False, lr=1e-6, tol=1e8, diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 3096585f4..f2724faad 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -5,7 +5,7 @@ import nibabel as nib import numpy as np import pytest -import warnings +import logging from nimare.meta.utils import compute_kda_ma @@ -13,6 +13,7 @@ # duplicated in test_estimator_performance ALPHA = 0.05 +LGR = logging.getLogger(__name__) def get_test_data_path(): """Return the path to test datasets, terminated with separator. @@ -132,7 +133,7 @@ def standardize_field(dataset, metadata): elif np.array_equal(dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(float)): numerical_metadata.append(metadata_name) if len(categorical_metadata) > 0: - warnings.warn(f"Categorical metadata {categorical_metadata} can't be standardized.") + LGR.warning(f"Categorical metadata {categorical_metadata} can't be standardized.") if len(numerical_metadata) == 0: raise ValueError("No numerical metadata found.") From e1c801fab7736d2644a9153e30f9ac2707d675bb Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 20 Jan 2023 22:15:32 +0000 Subject: [PATCH 053/177] new changes on inference class --- nimare/meta/cbmr.py | 474 +++++++++++++++++---------------- nimare/meta/models.py | 1 - nimare/tests/test_meta_cbmr.py | 10 +- 3 files changed, 250 insertions(+), 235 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index bcc20f3d0..a709339b2 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -351,108 +351,118 @@ def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device=" self.group_names = self.CBMRResults.tables["Spatial_Regression_Coef"].index.values.tolist() self.n_groups = len(self.group_names) if self.t_con_group is not False: - # Conduct group-wise spatial homogeneity test by default - self.t_con_group = ( - [np.eye(self.n_groups)] - if not self.t_con_group - else [np.array(con_group) for con_group in self.t_con_group] - ) - self.t_con_group = [ - con_group.reshape((1, -1)) if len(con_group.shape) == 1 else con_group - for con_group in self.t_con_group - ] # 2D contrast matrix/vector - if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): - wrong_con_group_idx = np.where( - [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] - )[0].tolist() - raise ValueError( - f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise - intensity contrast matrix doesn't match with groups""" - ) - con_group_zero_row = [ - np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] - for con_group in self.t_con_group - ] - if np.any( - [len(zero_row) > 0 for zero_row in con_group_zero_row] - ): # remove zero rows in contrast matrix - self.t_con_group = [ - np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) - for i in range(len(self.t_con_group)) - ] - if np.any([con_group.shape[0] == 0 for con_group in self.t_con_group]): - raise ValueError( - """One or more of contrast vectors(s) in group-wise intensity - contrast matrix are all zeros""" - ) - self._Name_of_con_group() - # standardization - self.t_con_group = [ - con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) - for con_group in self.t_con_group - ] + self._preprocess_t_con_group() if self.t_con_moderator is not False: if self.CBMRResults.estimator.moderators: - self.moderator_names = self.CBMRResults.estimator.moderators - self.n_moderators = len(self.moderator_names) - self.t_con_moderator = ( - [np.eye(self.n_moderators)] - if not self.t_con_moderator - else [np.array(con_moderator) for con_moderator in self.t_con_moderator] - ) - self.t_con_moderator = [ - con_moderator.reshape((1, -1)) - if len(con_moderator.shape) == 1 - else con_moderator - for con_moderator in self.t_con_moderator - ] - # test the existence of effect of moderators - if np.any( - [ - con_moderator.shape[1] != self.n_moderators - for con_moderator in self.t_con_moderator - ] - ): - wrong_con_moderator_idx = np.where( - [ - con_moderator.shape[1] != self.n_moderators - for con_moderator in self.t_con_moderator - ] - )[0].tolist() - raise ValueError( - f"""The shape of {str(wrong_con_moderator_idx)}th contrast vector(s) in - moderators contrast matrix doesn't match with moderators""" - ) - con_moderator_zero_row = [ - np.where(np.sum(np.abs(con_modereator), axis=1) == 0)[0] - for con_modereator in self.t_con_moderator - ] - if np.any( - [len(zero_row) > 0 for zero_row in con_moderator_zero_row] - ): # remove zero rows in contrast matrix - self.t_con_moderator = [ - np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) - for i in range(len(self.t_con_moderator)) - ] - if np.any( - [con_moderator.shape[0] == 0 for con_moderator in self.t_con_moderator] - ): - raise ValueError( - """One or more of contrast vectors(s) in modereators contrast matrix - are all zeros""" - ) - self._Name_of_con_moderator() - self.t_con_moderator = [ - con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) - for con_moderator in self.t_con_moderator - ] + self._preprocess_t_con_moderator() else: self.t_con_moderator = False + # device check if self.device == "cuda" and not torch.cuda.is_available(): LGR.debug("cuda not found, use device 'cpu'") self.device = "cpu" + def _preprocess_t_con_group(self): + # Conduct group-wise spatial homogeneity test by default + self.t_con_group = ( + [np.eye(self.n_groups)] + if not self.t_con_group + else [np.array(con_group) for con_group in self.t_con_group] + ) + # make sure contrast matrix/vector is 2D + self.t_con_group = [ + con_group.reshape((1, -1)) if len(con_group.shape) == 1 else con_group + for con_group in self.t_con_group + ] + if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): + wrong_con_group_idx = np.where( + [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] + )[0].tolist() + raise ValueError( + f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise + intensity contrast matrix doesn't match with groups""" + ) + # remove zero rows in contrast matrix + con_group_zero_row = [ + np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] + for con_group in self.t_con_group + ] + if np.any( + [len(zero_row) > 0 for zero_row in con_group_zero_row] + ): + # remove zero rows in contrast matrix + self.t_con_group = [ + np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) + for i in range(len(self.t_con_group)) + ] + if np.any([con_group.shape[0] == 0 for con_group in self.t_con_group]): + raise ValueError( + """One or more of contrast vectors(s) in group-wise intensity + contrast matrix are all zeros""" + ) + self._Name_of_con_group() + # standardization + self.t_con_group = [ + con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) + for con_group in self.t_con_group + ] + + def _preprocess_t_con_moderator(self): + self.moderator_names = self.CBMRResults.estimator.moderators + self.n_moderators = len(self.moderator_names) + self.t_con_moderator = ( + [np.eye(self.n_moderators)] + if not self.t_con_moderator + else [np.array(con_moderator) for con_moderator in self.t_con_moderator] + ) + self.t_con_moderator = [ + con_moderator.reshape((1, -1)) + if len(con_moderator.shape) == 1 + else con_moderator + for con_moderator in self.t_con_moderator + ] + # test the existence of effect of moderators + if np.any( + [ + con_moderator.shape[1] != self.n_moderators + for con_moderator in self.t_con_moderator + ] + ): + wrong_con_moderator_idx = np.where( + [ + con_moderator.shape[1] != self.n_moderators + for con_moderator in self.t_con_moderator + ] + )[0].tolist() + raise ValueError( + f"""The shape of {str(wrong_con_moderator_idx)}th contrast vector(s) in + moderators contrast matrix doesn't match with moderators""" + ) + con_moderator_zero_row = [ + np.where(np.sum(np.abs(con_modereator), axis=1) == 0)[0] + for con_modereator in self.t_con_moderator + ] + if np.any( + [len(zero_row) > 0 for zero_row in con_moderator_zero_row] + ): # remove zero rows in contrast matrix + self.t_con_moderator = [ + np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) + for i in range(len(self.t_con_moderator)) + ] + if np.any( + [con_moderator.shape[0] == 0 for con_moderator in self.t_con_moderator] + ): + raise ValueError( + """One or more of contrast vectors(s) in modereators contrast matrix + are all zeros""" + ) + self._Name_of_con_moderator() + self.t_con_moderator = [ + con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) + for con_moderator in self.t_con_moderator + ] + def _Name_of_con_group(self): """Define the name of GLH contrasts on spatial intensity estimation. @@ -590,7 +600,7 @@ def _Fisher_info_spatial_coef(self, GLH_involved_index): if self.CBMRResults.estimator.moderators: involved_group_moderators = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_group_moderators"][group], + self.CBMRResults.estimator.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device, ) @@ -677,7 +687,7 @@ def _Fisher_info_moderator_coef(self): moderator_coef_dim, _ = all_moderator_coef.shape all_group_moderators = [ torch.tensor( - self.CBMRResults.estimator.inputs_["all_group_moderators"][group], + self.CBMRResults.estimator.inputs_["moderators_by_group"][group], dtype=torch.float64, device=self.device, ) @@ -743,145 +753,151 @@ def _contrast(self): """ # Log_Spatial_Intensity_SE = self.CBMRResults.tables["Log_Spatial_Intensity_SE"] if self.t_con_group is not False: - con_group_count = 0 - for con_group in self.t_con_group: - con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() - con_group_involved = [self.group_names[i] for i in con_group_involved_index] - n_con_group_involved = len(con_group_involved) - simp_con_group = con_group[ - :, ~np.all(con_group == 0, axis=0) - ] # contrast matrix of involved groups only - if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test - involved_log_intensity_per_voxel = list() - for group in con_group_involved: - group_foci_per_voxel = self.CBMRResults.estimator.inputs_[ - "foci_per_voxel" - ][group] - group_foci_per_study = self.CBMRResults.estimator.inputs_[ - "foci_per_study" - ][group] - n_voxels, n_study = ( - group_foci_per_voxel.shape[0], - group_foci_per_study.shape[0], - ) - group_null_log_spatial_intensity = np.log( - np.sum(group_foci_per_voxel) / (n_voxels * n_study) - ) - group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] - ) - group_log_intensity_per_voxel = ( - group_log_intensity_per_voxel - group_null_log_spatial_intensity - ) - involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack( - involved_log_intensity_per_voxel, axis=0 + self.GLH_con_group() + + if self.t_con_moderator is not False: + self.GLH_con_moderator() + return + + def GLH_con_group(self): + con_group_count = 0 + for con_group in self.t_con_group: + con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() + con_group_involved = [self.group_names[i] for i in con_group_involved_index] + n_con_group_involved = len(con_group_involved) + simp_con_group = con_group[ + :, ~np.all(con_group == 0, axis=0) + ] # contrast matrix of involved groups only + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + involved_log_intensity_per_voxel = list() + for group in con_group_involved: + group_foci_per_voxel = self.CBMRResults.estimator.inputs_[ + "foci_per_voxel" + ][group] + group_foci_per_study = self.CBMRResults.estimator.inputs_[ + "foci_per_study" + ][group] + n_voxels, n_study = ( + group_foci_per_voxel.shape[0], + group_foci_per_study.shape[0], ) - else: # GLH: group comparison - involved_log_intensity_per_voxel = list() - for group in con_group_involved: - group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] - ) - involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack( - involved_log_intensity_per_voxel, axis=0 + group_null_log_spatial_intensity = np.log( + np.sum(group_foci_per_voxel) / (n_voxels * n_study) ) - Contrast_log_intensity = np.matmul( - simp_con_group, involved_log_intensity_per_voxel - ) - m, n_brain_voxel = Contrast_log_intensity.shape - # Correlation of involved group-wise spatial coef - F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) - Cov_spatial_coef = np.linalg.inv(F_spatial_coef) - spatial_coef_dim = ( - self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] - ) - Cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) - for k in range(n_con_group_involved): - for s in range(n_con_group_involved): - Cov_beta_ks = Cov_spatial_coef[ - k * spatial_coef_dim : (k + 1) * spatial_coef_dim, - s * spatial_coef_dim : (s + 1) * spatial_coef_dim, + group_log_intensity_per_voxel = np.log( + self.CBMRResults.maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" ] - X = self.CBMRResults.estimator.inputs_["coef_spline_bases"] - Cov_group_log_intensity = (X.dot(Cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) - Cov_log_intensity = np.concatenate( - (Cov_log_intensity, Cov_group_log_intensity), axis=0 - ) # (m^2, n_voxels) - # GLH on log_intensity (eta) - chi_sq_spatial = np.empty(shape=(0,)) - for j in range(n_brain_voxel): - Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) - V_j = Cov_log_intensity[:, j].reshape( - (n_con_group_involved, n_con_group_involved) ) - CV_jC = simp_con_group @ V_j @ simp_con_group.T - CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_spatial_j = ( - Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + group_log_intensity_per_voxel = ( + group_log_intensity_per_voxel - group_null_log_spatial_intensity ) - chi_sq_spatial = np.concatenate( - ( - chi_sq_spatial, - chi_sq_spatial_j.reshape( - 1, - ), - ), - axis=0, + involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack( + involved_log_intensity_per_voxel, axis=0 + ) + else: # GLH: group comparison + involved_log_intensity_per_voxel = list() + for group in con_group_involved: + group_log_intensity_per_voxel = np.log( + self.CBMRResults.maps[ + "Group_" + group + "_Studywise_Spatial_Intensity" + ] ) - p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) - - con_group_name = self.t_con_group_name[con_group_count] - if len(con_group_name) == 1: - self.CBMRResults.maps[con_group_name[0] + "_chi_sq"] = chi_sq_spatial - self.CBMRResults.maps[con_group_name[0] + "_p"] = p_vals_spatial - else: - self.CBMRResults.maps[ - "spatial_coef_GLH_" + str(con_group_count) + "_chi_sq" - ] = chi_sq_spatial - self.CBMRResults.maps[ - "spatial_coef_GLH_" + str(con_group_count) + "_p" - ] = p_vals_spatial - self.CBMRResults.metadata[ - "spatial_coef_GLH_" + str(con_group_count) - ] = con_group_name - con_group_count += 1 - - if self.t_con_moderator is not False: - con_moderator_count = 0 - for con_moderator in self.t_con_moderator: - m_con_moderator, _ = con_moderator.shape - moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T - Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) - F_moderator_coef = self._Fisher_info_moderator_coef() - Cov_moderator_coef = np.linalg.inv(F_moderator_coef) - chi_sq_moderator = ( - Contrast_moderator_coef.T - @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) - @ Contrast_moderator_coef + involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack( + involved_log_intensity_per_voxel, axis=0 + ) + Contrast_log_intensity = np.matmul( + simp_con_group, involved_log_intensity_per_voxel + ) + m, n_brain_voxel = Contrast_log_intensity.shape + # Correlation of involved group-wise spatial coef + + F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) + Cov_spatial_coef = np.linalg.inv(F_spatial_coef) + spatial_coef_dim = ( + self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] + ) + Cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) + for k in range(n_con_group_involved): + for s in range(n_con_group_involved): + Cov_beta_ks = Cov_spatial_coef[ + k * spatial_coef_dim : (k + 1) * spatial_coef_dim, + s * spatial_coef_dim : (s + 1) * spatial_coef_dim, + ] + X = self.CBMRResults.estimator.inputs_["coef_spline_bases"] + Cov_group_log_intensity = (X.dot(Cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + Cov_log_intensity = np.concatenate( + (Cov_log_intensity, Cov_group_log_intensity), axis=0 + ) # (m^2, n_voxels) + # GLH on log_intensity (eta) + chi_sq_spatial = np.empty(shape=(0,)) + for j in range(n_brain_voxel): + Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) + V_j = Cov_log_intensity[:, j].reshape( + (n_con_group_involved, n_con_group_involved) ) - chi_sq_moderator = chi_sq_moderator.item() - p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) - - con_moderator_name = self.t_con_moderator_name[con_moderator_count] - if len(con_moderator_name) == 1: - self.CBMRResults.tables[con_moderator_name[0] + "_chi_sq"] = chi_sq_moderator - self.CBMRResults.tables[con_moderator_name[0] + "_p"] = p_vals_moderator - else: - self.CBMRResults.tables[ - "moderator_coef_GLH_" + str(con_moderator_count) + "_chi_sq" - ] = chi_sq_moderator - self.CBMRResults.tables[ - "moderator_coef_GLH_" + str(con_moderator_count) + "_p" - ] = p_vals_moderator - self.CBMRResults.metadata[ - "moderator_coef_GLH_" + str(con_moderator_count) - ] = con_moderator_name - con_moderator_count += 1 + CV_jC = simp_con_group @ V_j @ simp_con_group.T + CV_jC_inv = np.linalg.inv(CV_jC) + chi_sq_spatial_j = ( + Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + ) + chi_sq_spatial = np.concatenate( + ( + chi_sq_spatial, + chi_sq_spatial_j.reshape( + 1, + ), + ), + axis=0, + ) + p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) - return + con_group_name = self.t_con_group_name[con_group_count] + if len(con_group_name) == 1: + self.CBMRResults.maps[con_group_name[0] + "_chi_sq"] = chi_sq_spatial + self.CBMRResults.maps[con_group_name[0] + "_p"] = p_vals_spatial + else: + self.CBMRResults.maps[ + "spatial_coef_GLH_" + str(con_group_count) + "_chi_sq" + ] = chi_sq_spatial + self.CBMRResults.maps[ + "spatial_coef_GLH_" + str(con_group_count) + "_p" + ] = p_vals_spatial + self.CBMRResults.metadata[ + "spatial_coef_GLH_" + str(con_group_count) + ] = con_group_name + con_group_count += 1 + + def GLH_con_moderator(self): + con_moderator_count = 0 + for con_moderator in self.t_con_moderator: + m_con_moderator, _ = con_moderator.shape + moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T + Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) + F_moderator_coef = self._Fisher_info_moderator_coef() + Cov_moderator_coef = np.linalg.inv(F_moderator_coef) + chi_sq_moderator = ( + Contrast_moderator_coef.T + @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) + @ Contrast_moderator_coef + ) + chi_sq_moderator = chi_sq_moderator.item() + p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) + + con_moderator_name = self.t_con_moderator_name[con_moderator_count] + if len(con_moderator_name) == 1: + self.CBMRResults.tables[con_moderator_name[0] + "_chi_sq"] = chi_sq_moderator + self.CBMRResults.tables[con_moderator_name[0] + "_p"] = p_vals_moderator + else: + self.CBMRResults.tables[ + "moderator_coef_GLH_" + str(con_moderator_count) + "_chi_sq" + ] = chi_sq_moderator + self.CBMRResults.tables[ + "moderator_coef_GLH_" + str(con_moderator_count) + "_p" + ] = p_vals_moderator + self.CBMRResults.metadata[ + "moderator_coef_GLH_" + str(con_moderator_count) + ] = con_moderator_name + con_moderator_count += 1 \ No newline at end of file diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 694f0ca77..0cf84c58a 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -241,7 +241,6 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): self.moderators_coef = moderators_coef self.moderators_effect = moderators_effect - def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 2aab23979..5a50915d5 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -25,20 +25,20 @@ def test_CBMREstimator(testdata_cbmr_simulated): def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age"]) + dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=["standardized_sample_sizes", "standardized_avg_age"], + moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, - model=models.ClusteredNegativeBinomial, - penalty=True, + model=models.PoissonEstimator, + penalty=False, lr=1e-1, tol=1e6, device="cpu", ) cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference( - CBMRResults=cbmr_res, t_con_group=[[1, 1, 1, 1]], t_con_moderator=[[1, 0]], device="cuda" + CBMRResults=cbmr_res, t_con_group=[[1, 0, 0, 0]], t_con_moderator=[[1, 0, 0, 0]], device="cuda" ) inference._contrast() From 404ff61c54964e3bc45a360f89cabb7a23d704c0 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 22 Jan 2023 20:21:53 +0000 Subject: [PATCH 054/177] solve conflict --- nimare/meta/cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a709339b2..7e2f1e28f 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -813,7 +813,7 @@ def GLH_con_group(self): ) m, n_brain_voxel = Contrast_log_intensity.shape # Correlation of involved group-wise spatial coef - + self.CBMRResults.estimator.model.summary() F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) Cov_spatial_coef = np.linalg.inv(F_spatial_coef) spatial_coef_dim = ( From e5809518827c5fde8d8d8f1c3c0f7cb327afb9ce Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 24 Jan 2023 03:36:45 +0000 Subject: [PATCH 055/177] restruct code in CBMRInference --- nimare/meta/cbmr.py | 338 +++++++++++---------------------- nimare/meta/models.py | 81 +++++++- nimare/tests/test_meta_cbmr.py | 10 +- 3 files changed, 193 insertions(+), 236 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 7e2f1e28f..863bfb3cb 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -11,6 +11,7 @@ import functorch import logging import copy +import re LGR = logging.getLogger(__name__) @@ -343,25 +344,103 @@ class CBMRInference(object): Default is 'cpu'. """ - def __init__(self, CBMRResults, t_con_group=None, t_con_moderator=None, device="cpu"): + def __init__(self, CBMRResults, device="cpu"): self.device = device self.CBMRResults = CBMRResults + self.groups = self.CBMRResults.estimator.groups + self.n_groups = len(self.groups) + + # visialize group/moderator names and their indices in contrast array + self.group_reference_dict, self.moderator_reference_dict = dict(), dict() + LGR.info("Group Reference in contrast array") + for i in range(self.n_groups): + self.group_reference_dict[self.groups[i]] = i + LGR.info(f"{self.groups[i]} = index_{i}") + if self.CBMRResults.estimator.moderators: + n_moderators = len(self.CBMRResults.estimator.moderators) + LGR.info("Moderator Reference in contrast array") + for j in range(n_moderators): + self.moderator_reference_dict[self.CBMRResults.estimator.moderators[j]] = j + LGR.info(f"{self.CBMRResults.estimator.moderators[j]} = index_{j}") + + # device check + if self.device == "cuda" and not torch.cuda.is_available(): + LGR.debug("cuda not found, use device 'cpu'") + self.device = "cpu" + + def create_contrast(self, contrast_name, type="group"): + if isinstance(contrast_name, str): + contrast_name = [contrast_name] + contrast_matrix = list() + if type == "group": # contrast matrix for spatial intensity + for contrast in contrast_name: + contrast_vector = np.zeros(self.n_groups) + if contrast.startswith("homo_test_"): # homogeneity test + contrast_groups = contrast.split("homo_test_",1)[1] + if contrast_groups not in self.groups: + raise ValueError(f"{contrast_groups} is not a valid group name.") + contrast_vector[self.group_reference_dict[contrast_groups]] = 1 + elif "VS" in contrast: # group comparison + contrast_groups = contrast.split("VS") + if not set(contrast_groups).issubset(set(self.groups)): + not_valid_groups = set(contrast_groups).difference(set(self.groups)) + raise ValueError(f"{not_valid_groups} is not a valid group name.") + contrast_vector[self.group_reference_dict[contrast_groups[0]]] = 1 + contrast_vector[self.group_reference_dict[contrast_groups[1]]] = -1 + contrast_matrix.append(contrast_vector) + + elif type == "moderator": # contrast matrix for moderator effect + n_moderators = len(self.CBMRResults.estimator.moderators) + for contrast in contrast_name: + contrast_vector = np.zeros(n_moderators) + if contrast.startswith("moderator_"): # moderator effect + contrast_moderators = contrast.split("moderator_",1)[1] + if contrast_moderators not in self.CBMRResults.estimator.moderators: + raise ValueError(f"{contrast_moderators} is not a valid moderator name.") + contrast_vector[self.moderator_reference_dict[contrast_moderators]] = 1 + elif "VS" in contrast: + contrast_moderators = contrast.split("VS") + if not set(contrast_moderators).issubset(set(self.CBMRResults.estimator.moderators)): + not_valid_moderators = set(contrast_moderators).difference(set(self.CBMRResults.estimator.moderators)) + raise ValueError(f"{not_valid_moderators} is not a valid moderator name.") + contrast_vector[self.moderator_reference_dict[contrast_moderators[0]]] = 1 + contrast_vector[self.moderator_reference_dict[contrast_moderators[1]]] = -1 + else: + raise ValueError(f"{contrast} is not a valid contrast type.") + contrast_matrix.append(contrast_vector) + + return contrast_matrix + + def compute_contrast(self, t_con_group=None, t_con_moderator=None): + """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. + + Estimate group-wise spatial regression coefficients and its standard error via inverse + Fisher Information matrix, estimate standard error of group-wise log intensity, + group-wise intensity via delta method. For NB or clustered model, estimate regression + coefficient of overdispersion. Similarly, estimate regression coefficient of study-level + moderators (if exist), as well as its standard error via Fisher Information matrix. + Save these outcomes in `tables`. Also, estimate group-wise spatial intensity (per study) + and save the results in `maps`. + + Parameters + ---------- + dataset : :obj:`~nimare.dataset.Dataset` + Dataset to analyze. + """ + self.t_con_group = t_con_group self.t_con_moderator = t_con_moderator - self.group_names = self.CBMRResults.tables["Spatial_Regression_Coef"].index.values.tolist() - self.n_groups = len(self.group_names) + if self.t_con_group is not False: + # preprocess and standardize group contrast self._preprocess_t_con_group() - + # GLH test for group contrast + self._GLH_con_group() if self.t_con_moderator is not False: - if self.CBMRResults.estimator.moderators: - self._preprocess_t_con_moderator() - else: - self.t_con_moderator = False - # device check - if self.device == "cuda" and not torch.cuda.is_available(): - LGR.debug("cuda not found, use device 'cpu'") - self.device = "cpu" + # preprocess and standardize moderator contrast + self._preprocess_t_con_moderator() + # GLH test for moderator contrast + self._GLH_con_moderator() def _preprocess_t_con_group(self): # Conduct group-wise spatial homogeneity test by default @@ -476,7 +555,7 @@ def _Name_of_con_group(self): if np.sum(idx) != 0: # homogeneity test nonzero_con_group_info = str() nonzero_group_index = np.where(idx != 0)[0].tolist() - nonzero_group_name = [self.group_names[i] for i in nonzero_group_index] + nonzero_group_name = [self.groups[i] for i in nonzero_group_index] nonzero_con = [int(idx[i]) for i in nonzero_group_index] for i in range(len(nonzero_group_index)): nonzero_con_group_info += ( @@ -489,8 +568,8 @@ def _Name_of_con_group(self): np.where(idx < 0)[0].tolist(), ) pos_group_name, neg_group_name = [ - self.group_names[i] for i in pos_group_idx - ], [self.group_names[i] for i in neg_group_idx] + self.groups[i] for i in pos_group_idx + ], [self.groups[i] for i in neg_group_idx] pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [ int(idx[i]) for i in neg_group_idx ] @@ -526,7 +605,7 @@ def _Name_of_con_moderator(self): nonzero_con_moderator_info += ( str(abs(nonzero_con[i])) + "x" + str(nonzero_moderator_name[i]) ) - con_moderator_name.append("Effect_of_" + nonzero_con_moderator_info) + con_moderator_name.append("ModeratorEffect_of_" + nonzero_con_moderator_info) else: # group-comparison test pos_moderator_idx, neg_moderator_idx = ( np.where(idx > 0)[0].tolist(), @@ -552,218 +631,12 @@ def _Name_of_con_moderator(self): ) self.t_con_moderator_name.append(con_moderator_name) return - - def _Fisher_info_spatial_coef(self, GLH_involved_index): - coef_spline_bases = torch.tensor( - self.CBMRResults.estimator.inputs_["coef_spline_bases"], - dtype=torch.float64, - device=self.device, - ) - GLH_involved = [self.group_names[i] for i in GLH_involved_index] - involved_group_foci_per_voxel = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["foci_per_voxel"][group], - dtype=torch.float64, - device=self.device, - ) - for group in GLH_involved - ] - involved_group_foci_per_study = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["foci_per_study"][group], - dtype=torch.float64, - device=self.device, - ) - for group in GLH_involved - ] - if "Overdispersion_Coef" in self.CBMRResults.tables.keys(): - involved_overdispersion_coef = torch.tensor( - [ - self.CBMRResults.tables["Overdispersion_Coef"].to_numpy()[i, :] - for i in GLH_involved_index - ], - dtype=torch.float64, - device=self.device, - ) - involved_spatial_coef = np.stack( - [ - self.CBMRResults.tables["Spatial_Regression_Coef"] - .to_numpy()[i, :] - .reshape((-1, 1)) - for i in GLH_involved_index - ] - ) - involved_spatial_coef = torch.tensor( - involved_spatial_coef, dtype=torch.float64, device=self.device - ) - n_involved_groups, spatial_coef_dim, _ = involved_spatial_coef.shape - if self.CBMRResults.estimator.moderators: - involved_group_moderators = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["moderators_by_group"][group], - dtype=torch.float64, - device=self.device, - ) - for group in GLH_involved - ] - involved_moderator_coef = torch.tensor( - self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T, - dtype=torch.float64, - device=self.device, - ) - else: - involved_group_moderators, involved_moderator_coef = None, None - if self.CBMRResults.estimator.model == "Poisson": - nll = lambda spatial_coef: -GLMPoisson._log_likelihood_mult_group( - spatial_coef, - coef_spline_bases, - involved_group_foci_per_voxel, - involved_group_foci_per_study, - involved_moderator_coef, - involved_group_moderators, - ) - elif self.CBMRResults.estimator.model == "NB": - nll = lambda spatial_coef: -GLMNB._log_likelihood_mult_group( - involved_overdispersion_coef, - spatial_coef, - coef_spline_bases, - involved_group_foci_per_voxel, - involved_group_foci_per_study, - involved_moderator_coef, - involved_group_moderators, - ) - elif self.CBMRResults.estimator.model == "clustered_NB": - nll = lambda spatial_coef: -GLMCNB._log_likelihood_mult_group( - involved_overdispersion_coef, - spatial_coef, - coef_spline_bases, - involved_group_foci_per_voxel, - involved_group_foci_per_study, - involved_moderator_coef, - involved_group_moderators, - ) - h = functorch.hessian(nll)(involved_spatial_coef) - h = h.view(n_involved_groups * spatial_coef_dim, -1) - - return h.detach().cpu().numpy() - - def _Fisher_info_moderator_coef(self): - coef_spline_bases = torch.tensor( - self.CBMRResults.estimator.inputs_["coef_spline_bases"], - dtype=torch.float64, - device=self.device, - ) - all_group_foci_per_voxel = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["foci_per_voxel"][group], - dtype=torch.float64, - device=self.device, - ) - for group in self.group_names - ] - all_group_foci_per_study = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["foci_per_study"][group], - dtype=torch.float64, - device=self.device, - ) - for group in self.group_names - ] - spatial_coef = np.stack( - [ - self.CBMRResults.tables["Spatial_Regression_Coef"] - .to_numpy()[i, :] - .reshape((-1, 1)) - for i in range(self.n_groups) - ] - ) - spatial_coef = torch.tensor(spatial_coef, dtype=torch.float64, device=self.device) - - all_moderator_coef = torch.tensor( - self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T, - dtype=torch.float64, - device=self.device, - ) - moderator_coef_dim, _ = all_moderator_coef.shape - all_group_moderators = [ - torch.tensor( - self.CBMRResults.estimator.inputs_["moderators_by_group"][group], - dtype=torch.float64, - device=self.device, - ) - for group in self.group_names - ] - - if "Overdispersion_Coef" in self.CBMRResults.tables.keys(): - overdispersion_coef = torch.tensor( - self.CBMRResults.tables["Overdispersion_Coef"].to_numpy(), - dtype=torch.float64, - device=self.device, - ) - - if self.CBMRResults.estimator.model == "Poisson": - nll = lambda all_moderator_coef: -GLMPoisson._log_likelihood_mult_group( - spatial_coef, - coef_spline_bases, - all_group_foci_per_voxel, - all_group_foci_per_study, - all_moderator_coef, - all_group_moderators, - ) - elif self.CBMRResults.estimator.model == "NB": - nll = lambda all_moderator_coef: -GLMNB._log_likelihood_mult_group( - overdispersion_coef, - spatial_coef, - coef_spline_bases, - all_group_foci_per_voxel, - all_group_foci_per_study, - all_moderator_coef, - all_group_moderators, - ) - elif self.CBMRResults.estimator.model == "clustered_NB": - nll = lambda all_moderator_coef: -GLMCNB._log_likelihood_mult_group( - overdispersion_coef, - spatial_coef, - coef_spline_bases, - all_group_foci_per_voxel, - all_group_foci_per_study, - all_moderator_coef, - all_group_moderators, - ) - h = functorch.hessian(nll)(all_moderator_coef) - h = h.view(moderator_coef_dim, moderator_coef_dim) - - return h.detach().cpu().numpy() - - def _contrast(self): - """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. - - Estimate group-wise spatial regression coefficients and its standard error via inverse - Fisher Information matrix, estimate standard error of group-wise log intensity, - group-wise intensity via delta method. For NB or clustered model, estimate regression - coefficient of overdispersion. Similarly, estimate regression coefficient of study-level - moderators (if exist), as well as its standard error via Fisher Information matrix. - Save these outcomes in `tables`. Also, estimate group-wise spatial intensity (per study) - and save the results in `maps`. - - Parameters - ---------- - dataset : :obj:`~nimare.dataset.Dataset` - Dataset to analyze. - """ - # Log_Spatial_Intensity_SE = self.CBMRResults.tables["Log_Spatial_Intensity_SE"] - if self.t_con_group is not False: - self.GLH_con_group() - - if self.t_con_moderator is not False: - self.GLH_con_moderator() - return - def GLH_con_group(self): + def _GLH_con_group(self): con_group_count = 0 for con_group in self.t_con_group: con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() - con_group_involved = [self.group_names[i] for i in con_group_involved_index] + con_group_involved = [self.groups[i] for i in con_group_involved_index] n_con_group_involved = len(con_group_involved) simp_con_group = con_group[ :, ~np.all(con_group == 0, axis=0) @@ -813,8 +686,9 @@ def GLH_con_group(self): ) m, n_brain_voxel = Contrast_log_intensity.shape # Correlation of involved group-wise spatial coef - self.CBMRResults.estimator.model.summary() - F_spatial_coef = self._Fisher_info_spatial_coef(con_group_involved_index) + moderators_by_group = self.CBMRResults.estimator.inputs_["moderators_by_group"] if self.CBMRResults.estimator.moderators else None + F_spatial_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_spatial(con_group_involved, self.CBMRResults.estimator.inputs_["coef_spline_bases"], + moderators_by_group, self.CBMRResults.estimator.inputs_["foci_per_voxel"], self.CBMRResults.estimator.inputs_["foci_per_study"]) Cov_spatial_coef = np.linalg.inv(F_spatial_coef) spatial_coef_dim = ( self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] @@ -870,13 +744,17 @@ def GLH_con_group(self): ] = con_group_name con_group_count += 1 - def GLH_con_moderator(self): + def _GLH_con_moderator(self): con_moderator_count = 0 for con_moderator in self.t_con_moderator: m_con_moderator, _ = con_moderator.shape moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) - F_moderator_coef = self._Fisher_info_moderator_coef() + + moderators_by_group = self.CBMRResults.estimator.inputs_["moderators_by_group"] if self.CBMRResults.estimator.moderators else None + F_moderator_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_moderator(self.CBMRResults.estimator.inputs_["coef_spline_bases"], + moderators_by_group, self.CBMRResults.estimator.inputs_["foci_per_voxel"], self.CBMRResults.estimator.inputs_["foci_per_study"]) + Cov_moderator_coef = np.linalg.inv(F_moderator_coef) chi_sq_moderator = ( Contrast_moderator_coef.T @@ -900,4 +778,6 @@ def GLH_con_moderator(self): self.CBMRResults.metadata[ "moderator_coef_GLH_" + str(con_moderator_count) ] = con_moderator_name - con_moderator_count += 1 \ No newline at end of file + con_moderator_count += 1 + + return \ No newline at end of file diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 0cf84c58a..767886688 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -356,6 +356,79 @@ def summary(self): tables["Moderators_Regression_SE"] = pd.DataFrame(self.se_moderators) return maps, tables + def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Document this.""" + n_involved_groups = len(involved_groups) + involved_foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in involved_groups] + involved_foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in involved_groups] + spatial_coef = [torch.tensor(self.spatial_coef_linears[group].weight.T, dtype=torch.float64, device=self.device) for group in involved_groups] + spatial_coef = torch.stack(spatial_coef, dim=0) + if self.moderators_coef_dim: + involved_moderators_by_group = [torch.tensor( + moderators_by_group[group], dtype=torch.float64, device=self.device + ) for group in involved_groups] + moderators_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) + else: + involved_moderators_by_group, moderators_coef = None, None + + ll_mult_group_kwargs = { + "moderator_coef": moderators_coef, + "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "foci_per_voxel": involved_foci_per_voxel, + "foci_per_study": involved_foci_per_study, + "moderators": involved_moderators_by_group, + "device": self.device + } + + if hasattr(self, "overdispersion"): + ll_mult_group_kwargs['overdispersion_coef'] = [self.overdispersion[group] for group in involved_groups] + # create a negative log-likelihood function + def nll_spatial_coef(spatial_coef): + return -self._log_likelihood_mult_group( + spatial_coef=spatial_coef, **ll_mult_group_kwargs, + ) + + h = functorch.hessian(nll_spatial_coef)(spatial_coef) + h = h.view(n_involved_groups * self.spatial_coef_dim, -1) + + return h.detach().cpu().numpy() + + def FisherInfo_MultipleGroup_moderator(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """Document this.""" + foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in self.groups] + foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in self.groups] + spatial_coef = [torch.tensor(self.spatial_coef_linears[group].weight.T, dtype=torch.float64, device=self.device) for group in self.groups] + spatial_coef = torch.stack(spatial_coef, dim=0) + + if self.moderators_coef_dim: + moderators_by_group = [torch.tensor( + moderators_by_group[group], dtype=torch.float64, device=self.device + ) for group in self.groups] + moderator_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) + else: + moderators_by_group, moderator_coef = None, None + + ll_mult_group_kwargs = { + "spatial_coef": spatial_coef, + "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "foci_per_voxel": foci_per_voxel, + "foci_per_study": foci_per_study, + "moderators": moderators_by_group, + "device": self.device + } + if hasattr(self, "overdispersion"): + ll_mult_group_kwargs['overdispersion_coef'] = [self.overdispersion[group] for group in self.groups] + # create a negative log-likelihood function w.r.t moderator coefficients + def nll_moderator_coef(moderator_coef): + return -self._log_likelihood_mult_group( + moderator_coef=moderator_coef, **ll_mult_group_kwargs, + ) + + h = functorch.hessian(nll_moderator_coef)(moderator_coef) + h = h.view(self.moderators_coef_dim, self.moderators_coef_dim) + + return h.detach().cpu().numpy() + class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) @@ -427,11 +500,11 @@ def _log_likelihood_single_group( def _log_likelihood_mult_group( self, spatial_coef, + moderator_coef, coef_spline_bases, foci_per_voxel, foci_per_study, - moderator_coef=None, - moderators=None, + moderators, device="cpu", ): n_groups = len(spatial_coef) @@ -595,7 +668,7 @@ def _log_likelihood_mult_group( moderators=None, device="cpu", ): - v = 1 / overdispersion_coef + v = [1 / overdispersion_params for overdispersion_params in overdispersion_coef] n_groups = len(foci_per_voxel) log_spatial_intensity = [ torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) @@ -643,7 +716,7 @@ def _log_likelihood_mult_group( log_l = 0 for i in range(n_groups): - log_l += NegativeBinomial._three_term(foci_per_voxel[i], r[i], device=device) + torch.sum( + log_l += self._three_term(foci_per_voxel[i], r[i]) + torch.sum( r[i] * torch.log(1 - p[i]) + foci_per_voxel[i] * torch.log(p[i]) ) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 5a50915d5..29721665f 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -33,14 +33,18 @@ def test_CBMRInference(testdata_cbmr_simulated): model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e6, + tol=1e4, device="cpu", ) cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference( - CBMRResults=cbmr_res, t_con_group=[[1, 0, 0, 0]], t_con_moderator=[[1, 0, 0, 0]], device="cuda" + CBMRResults=cbmr_res, device="cuda" ) - inference._contrast() + t_con_group = inference.create_contrast(["homo_test_schizophrenia_Yes", "schizophrenia_YesVSschizophrenia_No"], type='group') + t_con_moderator = inference.create_contrast(["moderator_standardized_sample_sizes", "standardized_sample_sizesVSstandardized_avg_age"], type='moderator') + contrast_result = inference.compute_contrast(t_con_group=t_con_group, t_con_moderator=t_con_moderator) + # inference.summary() + # [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] # [[[1,0],[0,1]], [1, -1]] From 5b19e4d3e95808d0fdc3c084eda0a85fba755e7a Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 26 Jan 2023 20:51:11 +0000 Subject: [PATCH 056/177] add documentation foor create_contrast function --- nimare/meta/cbmr.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 863bfb3cb..1cc6fc917 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -369,6 +369,26 @@ def __init__(self, CBMRResults, device="cpu"): self.device = "cpu" def create_contrast(self, contrast_name, type="group"): + """Create contrast matrix for generalized hypothesis testing (GLH). + + (1) if `type` is "group", create contrast matrix for GLH on spatial intensity; + if `contrast_name` begins with 'homo_test_', followed by a valid group name, + create a contrast matrix for homogeneity test on estimated group spatial intensity; + if `contrast_name` comes in the form of "group1VSgroup2", with valid group names + "group1" and "group2", create a contrast matrix for group comparison on estimated + group spatial intensity; + (2) if `type` is "moderator", create contrast matrix for GLH on study-level moderators; + if `contrast_name` begins with 'moderator_', followed by a valid moderator name, + we create a contrast matrix for testing if the effect of this moderator exists; + if `contrast_name` comes in the form of "moderator1VSmoderator2", with valid moderator names + "modeator1" and "moderator2", we create a contrast matrix for testing if the effect of + these two moderators are different. + + Parameters + ---------- + contrast_name : :obj:`~string` + Name of contrast in GLH. + """ if isinstance(contrast_name, str): contrast_name = [contrast_name] contrast_matrix = list() From ea73c728cec4cb1e6a93c081fbc696466b113c99 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 26 Jan 2023 22:31:36 +0000 Subject: [PATCH 057/177] add new steps: remove duplicate rows in contrast matrix --- nimare/meta/cbmr.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 1cc6fc917..1b0194b50 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -373,7 +373,7 @@ def create_contrast(self, contrast_name, type="group"): (1) if `type` is "group", create contrast matrix for GLH on spatial intensity; if `contrast_name` begins with 'homo_test_', followed by a valid group name, - create a contrast matrix for homogeneity test on estimated group spatial intensity; + create a contrast matrix for one-group homogeneity test on spatial intensity; if `contrast_name` comes in the form of "group1VSgroup2", with valid group names "group1" and "group2", create a contrast matrix for group comparison on estimated group spatial intensity; @@ -444,8 +444,12 @@ def compute_contrast(self, t_con_group=None, t_con_moderator=None): Parameters ---------- - dataset : :obj:`~nimare.dataset.Dataset` - Dataset to analyze. + t_con_group : :obj:`~list`, optional + Contrast matrix for GLH on group-wise spatial intensity estimation. + Default is None (group-wise homogeneity test for all groups). + t_con_moderator : :obj:`~list`, optional + Contrast matrix for GLH on moderator effects. + Default is None (tests if moderator effects exist for all moderators). """ self.t_con_group = t_con_group @@ -474,6 +478,7 @@ def _preprocess_t_con_group(self): con_group.reshape((1, -1)) if len(con_group.shape) == 1 else con_group for con_group in self.t_con_group ] + # raise error if dimension of contrast matrix/vector doesn't match with number of groups if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): wrong_con_group_idx = np.where( [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] @@ -482,7 +487,7 @@ def _preprocess_t_con_group(self): f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise intensity contrast matrix doesn't match with groups""" ) - # remove zero rows in contrast matrix + # remove zero rows in contrast matrix (if exist) con_group_zero_row = [ np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] for con_group in self.t_con_group @@ -490,7 +495,6 @@ def _preprocess_t_con_group(self): if np.any( [len(zero_row) > 0 for zero_row in con_group_zero_row] ): - # remove zero rows in contrast matrix self.t_con_group = [ np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) for i in range(len(self.t_con_group)) @@ -500,13 +504,17 @@ def _preprocess_t_con_group(self): """One or more of contrast vectors(s) in group-wise intensity contrast matrix are all zeros""" ) + # name of GLH contrasts and save to `tables` later self._Name_of_con_group() - # standardization + # standardization (row sum 1) self.t_con_group = [ con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) for con_group in self.t_con_group ] - + # remove duplicate rows in contrast matrix (after standardization) + uniq_con_group_idx = np.unique(self.t_con_group, axis=0, return_index=True)[1].tolist() + self.t_con_group = [self.t_con_group[i] for i in uniq_con_group_idx[::-1]] + def _preprocess_t_con_moderator(self): self.moderator_names = self.CBMRResults.estimator.moderators self.n_moderators = len(self.moderator_names) @@ -561,6 +569,10 @@ def _preprocess_t_con_moderator(self): con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) for con_moderator in self.t_con_moderator ] + # remove duplicate rows in contrast matrix (after standardization) + uniq_con_moderator_idx = np.unique(self.t_con_moderator, axis=0, return_index=True)[1].tolist() + self.t_con_moderator = [self.t_con_moderator[i] for i in uniq_con_moderator_idx[::-1]] + return def _Name_of_con_group(self): """Define the name of GLH contrasts on spatial intensity estimation. From 9c2e7aa09998b248ef751dacf6f6c82bed808492 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 26 Jan 2023 23:22:54 +0000 Subject: [PATCH 058/177] modify documentation and comments --- nimare/meta/cbmr.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 1b0194b50..a51fa4cc2 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -670,9 +670,10 @@ def _GLH_con_group(self): con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() con_group_involved = [self.groups[i] for i in con_group_involved_index] n_con_group_involved = len(con_group_involved) + # Simplify contrast matrix by removing irrelevant columns simp_con_group = con_group[ :, ~np.all(con_group == 0, axis=0) - ] # contrast matrix of involved groups only + ] if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test involved_log_intensity_per_voxel = list() for group in con_group_involved: @@ -811,5 +812,3 @@ def _GLH_con_moderator(self): "moderator_coef_GLH_" + str(con_moderator_count) ] = con_moderator_name con_moderator_count += 1 - - return \ No newline at end of file From 02bc3fa3bbf4e87d63fe835e136cabd8ab2b6d76 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 27 Jan 2023 21:12:57 +0000 Subject: [PATCH 059/177] change function name to snake case --- nimare/meta/cbmr.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a51fa4cc2..491245409 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -505,7 +505,7 @@ def _preprocess_t_con_group(self): contrast matrix are all zeros""" ) # name of GLH contrasts and save to `tables` later - self._Name_of_con_group() + self._name_of_con_group() # standardization (row sum 1) self.t_con_group = [ con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) @@ -564,7 +564,7 @@ def _preprocess_t_con_moderator(self): """One or more of contrast vectors(s) in modereators contrast matrix are all zeros""" ) - self._Name_of_con_moderator() + self._name_of_con_moderator() self.t_con_moderator = [ con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) for con_moderator in self.t_con_moderator @@ -574,7 +574,7 @@ def _preprocess_t_con_moderator(self): self.t_con_moderator = [self.t_con_moderator[i] for i in uniq_con_moderator_idx[::-1]] return - def _Name_of_con_group(self): + def _name_of_con_group(self): """Define the name of GLH contrasts on spatial intensity estimation. And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_group` @@ -616,7 +616,7 @@ def _Name_of_con_group(self): self.t_con_group_name.append(con_group_name) return - def _Name_of_con_moderator(self): + def _name_of_con_moderator(self): """Define the name of GLH contrasts on regressors of study-level moderators. And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_moderators` From 050d47028e3072923a2268f768489d28a495839b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 28 Jan 2023 05:01:21 +0000 Subject: [PATCH 060/177] restruct code and remove repetition --- nimare/meta/cbmr.py | 358 +++++++++++++++++---------------- nimare/tests/test_meta_cbmr.py | 2 +- 2 files changed, 186 insertions(+), 174 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 491245409..c1af04015 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -124,7 +124,9 @@ def __init__( self.moderators = moderators self.spline_spacing = spline_spacing - self.model = model(penalty=penalty, lr=lr, lr_decay=lr_decay, n_iter=n_iter, tol=tol, device=device) + self.model = model( + penalty=penalty, lr=lr, lr_decay=lr_decay, n_iter=n_iter, tol=tol, device=device + ) self.penalty = penalty self.n_iter = n_iter self.lr = lr @@ -161,7 +163,7 @@ def _preprocess_input(self, dataset): (3) a 'coef_spline_bases' key will be added (spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension), (4) an 'studies_by_group' key will be added (study id categorized by groups), - (5) an 'moderators_by_group' key will be added (study-level moderators categorized + (5) an 'moderators_by_group' key will be added (study-level moderators categorized by groups) if study-level moderators are considered, (6) an 'foci_per_voxel' key will be added (voxelwise sum of foci count across studies, categorized by groups), @@ -174,13 +176,13 @@ def _preprocess_input(self, dataset): if isinstance(mask_img, str): mask_img = nib.load(mask_img) self.inputs_["mask_img"] = mask_img - + # generate spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension coef_spline_bases = B_spline_bases( masker_voxels=mask_img._dataobj, spacing=self.spline_spacing ) self.inputs_["coef_spline_bases"] = coef_spline_bases - + for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": # remove dataset coordinates outside of mask @@ -201,21 +203,29 @@ def _preprocess_input(self, dataset): f"Category_names: {self.group_categories} does not exist in the dataset" ) else: - unique_groups = list(valid_dset_annotations[self.group_categories].unique()) + unique_groups = list( + valid_dset_annotations[self.group_categories].unique() + ) for group in unique_groups: - group_study_id_bool = valid_dset_annotations[self.group_categories] == group + group_study_id_bool = ( + valid_dset_annotations[self.group_categories] == group + ) group_study_id = valid_dset_annotations.loc[group_study_id_bool][ "study_id" ] studies_by_group[group] = group_study_id.unique().tolist() elif isinstance(self.group_categories, list): - missing_categories = set(self.group_categories) - set(dataset.annotations.columns) + missing_categories = set(self.group_categories) - set( + dataset.annotations.columns + ) if missing_categories: raise ValueError( f"Category_names: {missing_categories} do/does not exist in the dataset." ) unique_groups = ( - valid_dset_annotations[self.group_categories].drop_duplicates().values.tolist() + valid_dset_annotations[self.group_categories] + .drop_duplicates() + .values.tolist() ) for group in unique_groups: group_study_id_bool = ( @@ -229,7 +239,9 @@ def _preprocess_input(self, dataset): self.groups = list(self.inputs_["studies_by_group"].keys()) # collect studywise moderators if specficed if self.moderators: - valid_dset_annotations, self.moderators = dummy_encoding_moderators(valid_dset_annotations, self.moderators) + valid_dset_annotations, self.moderators = dummy_encoding_moderators( + valid_dset_annotations, self.moderators + ) if isinstance(self.moderators, str): self.moderators = [ self.moderators @@ -245,7 +257,7 @@ def _preprocess_input(self, dataset): ) moderators_by_group[group] = group_moderators self.inputs_["moderators_by_group"] = moderators_by_group - + foci_per_voxel, foci_per_study = dict(), dict() for group in self.groups: group_study_id = studies_by_group[group] @@ -281,13 +293,13 @@ def _preprocess_input(self, dataset): def _fit(self, dataset): """Perform coordinate-based meta-regression (CBMR) on dataset. - (1) Estimate group-wise spatial regression coefficients and its standard error via - inverse of Fisher Information matrix; Similarly, estimate regression coefficient of + (1) Estimate group-wise spatial regression coefficients and its standard error via + inverse of Fisher Information matrix; Similarly, estimate regression coefficient of study-level moderators (if exist), as well as its standard error via inverse of Fisher Information matrix; (2) Estimate standard error of group-wise log intensity, group-wise intensity via delta method; - (3) For NegativeBinomial or ClusteredNegativeBinomial model, estimate regression + (3) For NegativeBinomial or ClusteredNegativeBinomial model, estimate regression coefficient of overdispersion.s Parameters @@ -296,14 +308,19 @@ def _fit(self, dataset): Dataset to analyze. """ init_weight_kwargs = { - 'groups': self.groups, - 'spatial_coef_dim': self.inputs_["coef_spline_bases"].shape[1], - 'moderators_coef_dim': len(self.moderators) if self.moderators else None, + "groups": self.groups, + "spatial_coef_dim": self.inputs_["coef_spline_bases"].shape[1], + "moderators_coef_dim": len(self.moderators) if self.moderators else None, } self.model.init_weights(**init_weight_kwargs) moderators_by_group = self.inputs_["moderators_by_group"] if self.moderators else None - self.model.fit(self.inputs_["coef_spline_bases"], moderators_by_group, self.inputs_["foci_per_voxel"], self.inputs_["foci_per_study"]) + self.model.fit( + self.inputs_["coef_spline_bases"], + moderators_by_group, + self.inputs_["foci_per_voxel"], + self.inputs_["foci_per_study"], + ) maps, tables = self.model.summary() @@ -343,13 +360,13 @@ class CBMRInference(object): Device type ('cpu' or 'cuda') represents the device on which operations will be allocated. Default is 'cpu'. """ - - def __init__(self, CBMRResults, device="cpu"): + + def __init__(self, CBMRResults, device="cpu"): self.device = device self.CBMRResults = CBMRResults self.groups = self.CBMRResults.estimator.groups self.n_groups = len(self.groups) - + # visialize group/moderator names and their indices in contrast array self.group_reference_dict, self.moderator_reference_dict = dict(), dict() LGR.info("Group Reference in contrast array") @@ -362,7 +379,7 @@ def __init__(self, CBMRResults, device="cpu"): for j in range(n_moderators): self.moderator_reference_dict[self.CBMRResults.estimator.moderators[j]] = j LGR.info(f"{self.CBMRResults.estimator.moderators[j]} = index_{j}") - + # device check if self.device == "cuda" and not torch.cuda.is_available(): LGR.debug("cuda not found, use device 'cpu'") @@ -372,10 +389,10 @@ def create_contrast(self, contrast_name, type="group"): """Create contrast matrix for generalized hypothesis testing (GLH). (1) if `type` is "group", create contrast matrix for GLH on spatial intensity; - if `contrast_name` begins with 'homo_test_', followed by a valid group name, + if `contrast_name` begins with 'homo_test_', followed by a valid group name, create a contrast matrix for one-group homogeneity test on spatial intensity; - if `contrast_name` comes in the form of "group1VSgroup2", with valid group names - "group1" and "group2", create a contrast matrix for group comparison on estimated + if `contrast_name` comes in the form of "group1VSgroup2", with valid group names + "group1" and "group2", create a contrast matrix for group comparison on estimated group spatial intensity; (2) if `type` is "moderator", create contrast matrix for GLH on study-level moderators; if `contrast_name` begins with 'moderator_', followed by a valid moderator name, @@ -392,46 +409,50 @@ def create_contrast(self, contrast_name, type="group"): if isinstance(contrast_name, str): contrast_name = [contrast_name] contrast_matrix = list() - if type == "group": # contrast matrix for spatial intensity + if type == "group": # contrast matrix for spatial intensity for contrast in contrast_name: contrast_vector = np.zeros(self.n_groups) - if contrast.startswith("homo_test_"): # homogeneity test - contrast_groups = contrast.split("homo_test_",1)[1] - if contrast_groups not in self.groups: - raise ValueError(f"{contrast_groups} is not a valid group name.") - contrast_vector[self.group_reference_dict[contrast_groups]] = 1 - elif "VS" in contrast: # group comparison - contrast_groups = contrast.split("VS") + if contrast in self.groups: # homogeneity test + contrast_vector[self.group_reference_dict[contrast]] = 1 + elif "-" in contrast: # group comparison + contrast_groups = contrast.split("-") if not set(contrast_groups).issubset(set(self.groups)): not_valid_groups = set(contrast_groups).difference(set(self.groups)) raise ValueError(f"{not_valid_groups} is not a valid group name.") contrast_vector[self.group_reference_dict[contrast_groups[0]]] = 1 contrast_vector[self.group_reference_dict[contrast_groups[1]]] = -1 + else: + raise ValueError( + f"{contrast} is not a valid contrast name.") contrast_matrix.append(contrast_vector) - - elif type == "moderator": # contrast matrix for moderator effect + + elif type == "moderator": # contrast matrix for moderator effect n_moderators = len(self.CBMRResults.estimator.moderators) for contrast in contrast_name: contrast_vector = np.zeros(n_moderators) - if contrast.startswith("moderator_"): # moderator effect - contrast_moderators = contrast.split("moderator_",1)[1] + if contrast.startswith("moderator_"): # moderator effect + contrast_moderators = contrast.split("moderator_", 1)[1] if contrast_moderators not in self.CBMRResults.estimator.moderators: raise ValueError(f"{contrast_moderators} is not a valid moderator name.") contrast_vector[self.moderator_reference_dict[contrast_moderators]] = 1 elif "VS" in contrast: contrast_moderators = contrast.split("VS") - if not set(contrast_moderators).issubset(set(self.CBMRResults.estimator.moderators)): - not_valid_moderators = set(contrast_moderators).difference(set(self.CBMRResults.estimator.moderators)) + if not set(contrast_moderators).issubset( + set(self.CBMRResults.estimator.moderators) + ): + not_valid_moderators = set(contrast_moderators).difference( + set(self.CBMRResults.estimator.moderators) + ) raise ValueError(f"{not_valid_moderators} is not a valid moderator name.") contrast_vector[self.moderator_reference_dict[contrast_moderators[0]]] = 1 contrast_vector[self.moderator_reference_dict[contrast_moderators[1]]] = -1 else: raise ValueError(f"{contrast} is not a valid contrast type.") contrast_matrix.append(contrast_vector) - + return contrast_matrix - - def compute_contrast(self, t_con_group=None, t_con_moderator=None): + + def compute_contrast(self, t_con_group=None, t_con_moderator=None): """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. Estimate group-wise spatial regression coefficients and its standard error via inverse @@ -451,128 +472,112 @@ def compute_contrast(self, t_con_group=None, t_con_moderator=None): Contrast matrix for GLH on moderator effects. Default is None (tests if moderator effects exist for all moderators). """ - + self.t_con_group = t_con_group self.t_con_moderator = t_con_moderator - + if self.t_con_group is not False: # preprocess and standardize group contrast - self._preprocess_t_con_group() + self.t_con_group_name, self.t_con_group = self._preprocess_t_con_regressor(attr_list=["t_con_group", "groups", "n_groups"], type='groups') # GLH test for group contrast - self._GLH_con_group() + # self._glh_con_group() if self.t_con_moderator is not False: + self.moderators = self.CBMRResults.estimator.moderators + self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast - self._preprocess_t_con_moderator() + self.t_con_moderator_name, self.t_con_moderator = self._preprocess_t_con_regressor(attr_list=["t_con_moderator", "moderators", "n_moderators"], type='moderators') # GLH test for moderator contrast - self._GLH_con_moderator() + # self._glh_con_moderator() - def _preprocess_t_con_group(self): + def _preprocess_t_con_regressor(self, attr_list, type): + # regressor can be either groups or moderators + t_con_regressor, regressors, n_regressors = [getattr(self, attr) for attr in attr_list] # Conduct group-wise spatial homogeneity test by default - self.t_con_group = ( - [np.eye(self.n_groups)] - if not self.t_con_group - else [np.array(con_group) for con_group in self.t_con_group] - ) + t_con_regressor = [np.eye(n_regressors)] if not self.t_con_group else [np.array(con_regressor) for con_regressor in t_con_regressor] # make sure contrast matrix/vector is 2D - self.t_con_group = [ - con_group.reshape((1, -1)) if len(con_group.shape) == 1 else con_group - for con_group in self.t_con_group - ] + t_con_regressor = [ + con_regressor.reshape((1, -1)) if len(con_regressor.shape) == 1 else con_regressor + for con_regressor in t_con_regressor + ] # raise error if dimension of contrast matrix/vector doesn't match with number of groups - if np.any([con_group.shape[1] != self.n_groups for con_group in self.t_con_group]): - wrong_con_group_idx = np.where( - [con_group.shape[1] != self.n_groups for con_group in self.t_con_group] + if np.any([con_regressor.shape[1] != n_regressors for con_regressor in t_con_regressor]): + wrong_con_regressor_idx = np.where( + [con_regressor.shape[1] != n_regressors for con_regressor in t_con_regressor] )[0].tolist() raise ValueError( - f"""The shape of {str(wrong_con_group_idx)}th contrast vector(s) in group-wise - intensity contrast matrix doesn't match with groups""" + f"""The shape of {str(wrong_con_regressor_idx)}th contrast vector(s) in contrast matrix doesn't match with {type}.""" ) # remove zero rows in contrast matrix (if exist) - con_group_zero_row = [ - np.where(np.sum(np.abs(con_group), axis=1) == 0)[0] - for con_group in self.t_con_group + con_regressor_zero_row = [ + np.where(np.sum(np.abs(con_regressor), axis=1) == 0)[0] for con_regressor in t_con_regressor ] - if np.any( - [len(zero_row) > 0 for zero_row in con_group_zero_row] - ): - self.t_con_group = [ - np.delete(self.t_con_group[i], con_group_zero_row[i], axis=0) - for i in range(len(self.t_con_group)) + if np.any([len(zero_row) > 0 for zero_row in con_regressor_zero_row]): + t_con_regressor = [ + np.delete(t_con_regressor[i], con_regressor_zero_row[i], axis=0) + for i in range(len(t_con_regressor)) ] - if np.any([con_group.shape[0] == 0 for con_group in self.t_con_group]): + if np.any([con_regressor.shape[0] == 0 for con_regressor in t_con_regressor]): raise ValueError( - """One or more of contrast vectors(s) in group-wise intensity - contrast matrix are all zeros""" + """One or more of contrast vector(s) in {type} contrast matrix are all zeros.""" ) # name of GLH contrasts and save to `tables` later - self._name_of_con_group() + t_con_regressor_name = self._name_of_con_regressor(t_con_regressor=t_con_regressor, regressors=regressors, type=type) # standardization (row sum 1) - self.t_con_group = [ - con_group / np.sum(np.abs(con_group), axis=1).reshape((-1, 1)) - for con_group in self.t_con_group + t_con_regressor = [ + con_regressor / np.sum(np.abs(con_regressor), axis=1).reshape((-1, 1)) + for con_regressor in t_con_regressor ] # remove duplicate rows in contrast matrix (after standardization) - uniq_con_group_idx = np.unique(self.t_con_group, axis=0, return_index=True)[1].tolist() - self.t_con_group = [self.t_con_group[i] for i in uniq_con_group_idx[::-1]] + uniq_con_regressor_idx = np.unique(t_con_regressor, axis=0, return_index=True)[1].tolist() + t_con_regressor = [t_con_regressor[i] for i in uniq_con_regressor_idx[::-1]] - def _preprocess_t_con_moderator(self): - self.moderator_names = self.CBMRResults.estimator.moderators - self.n_moderators = len(self.moderator_names) - self.t_con_moderator = ( - [np.eye(self.n_moderators)] - if not self.t_con_moderator - else [np.array(con_moderator) for con_moderator in self.t_con_moderator] - ) - self.t_con_moderator = [ - con_moderator.reshape((1, -1)) - if len(con_moderator.shape) == 1 - else con_moderator - for con_moderator in self.t_con_moderator - ] - # test the existence of effect of moderators - if np.any( - [ - con_moderator.shape[1] != self.n_moderators - for con_moderator in self.t_con_moderator - ] - ): - wrong_con_moderator_idx = np.where( - [ - con_moderator.shape[1] != self.n_moderators - for con_moderator in self.t_con_moderator - ] - )[0].tolist() - raise ValueError( - f"""The shape of {str(wrong_con_moderator_idx)}th contrast vector(s) in - moderators contrast matrix doesn't match with moderators""" - ) - con_moderator_zero_row = [ - np.where(np.sum(np.abs(con_modereator), axis=1) == 0)[0] - for con_modereator in self.t_con_moderator - ] - if np.any( - [len(zero_row) > 0 for zero_row in con_moderator_zero_row] - ): # remove zero rows in contrast matrix - self.t_con_moderator = [ - np.delete(self.t_con_moderator[i], con_moderator_zero_row[i], axis=0) - for i in range(len(self.t_con_moderator)) - ] - if np.any( - [con_moderator.shape[0] == 0 for con_moderator in self.t_con_moderator] - ): - raise ValueError( - """One or more of contrast vectors(s) in modereators contrast matrix - are all zeros""" - ) - self._name_of_con_moderator() - self.t_con_moderator = [ - con_moderator / np.sum(np.abs(con_moderator), axis=1).reshape((-1, 1)) - for con_moderator in self.t_con_moderator - ] - # remove duplicate rows in contrast matrix (after standardization) - uniq_con_moderator_idx = np.unique(self.t_con_moderator, axis=0, return_index=True)[1].tolist() - self.t_con_moderator = [self.t_con_moderator[i] for i in uniq_con_moderator_idx[::-1]] - return + return t_con_regressor, t_con_regressor_name + + def _name_of_con_regressor(self, t_con_regressor, regressors, type): + """Define the name of GLH contrasts on spatial intensity estimation. + + And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_group` + exists). + """ + t_con_regressor_name = list() + for con_regressor in t_con_regressor: + con_regressor_name = list() + for num, idx in enumerate(con_regressor): + if np.sum(idx) != 0: # homogeneity test + nonzero_con_regressor_info = str() + nonzero_regressor_index = np.where(idx != 0)[0].tolist() + nonzero_regressor_name = [regressors[i] for i in nonzero_regressor_index] + nonzero_con = [int(idx[i]) for i in nonzero_regressor_index] + for i in range(len(nonzero_regressor_index)): + nonzero_con_regressor_info += ( + str(abs(nonzero_con[i])) + "x" + str(nonzero_regressor_name[i]) + ) + if type == 'groups': + con_regressor_name.append("homo_test_" + nonzero_con_regressor_info) + elif type == 'moderators': + con_regressor_name.append("ModeratorEffect_of_" + nonzero_con_info) + else: # group-comparison test + pos_regressor_idx, neg_regressor_idx = ( + np.where(idx > 0)[0].tolist(), + np.where(idx < 0)[0].tolist(), + ) + pos_regressor_name, neg_regressor_name = [regressors[i] for i in pos_regressor_idx], [ + regressors[i] for i in neg_regressor_idx + ] + pos_group_con, neg_group_con = [int(idx[i]) for i in pos_regressor_idx], [ + int(idx[i]) for i in neg_regressor_idx + ] + pos_con_regressor_info, neg_con_regressor_info = str(), str() + for i in range(len(pos_regressor_idx)): + pos_con_regressor_info += str(pos_group_con[i]) + "x" + str(pos_regressor_name[i]) + for i in range(len(neg_regressor_idx)): + neg_con_regressor_info += ( + str(abs(neg_group_con[i])) + "x" + str(neg_regressor_name[i]) + ) + con_regressor_name.append(pos_con_regressor_info + "VS" + neg_con_regressor_info) + t_con_regressor_name.append(con_regressor_name) + + return t_con_regressor_name def _name_of_con_group(self): """Define the name of GLH contrasts on spatial intensity estimation. @@ -599,9 +604,9 @@ def _name_of_con_group(self): np.where(idx > 0)[0].tolist(), np.where(idx < 0)[0].tolist(), ) - pos_group_name, neg_group_name = [ - self.groups[i] for i in pos_group_idx - ], [self.groups[i] for i in neg_group_idx] + pos_group_name, neg_group_name = [self.groups[i] for i in pos_group_idx], [ + self.groups[i] for i in neg_group_idx + ] pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [ int(idx[i]) for i in neg_group_idx ] @@ -663,26 +668,24 @@ def _name_of_con_moderator(self): ) self.t_con_moderator_name.append(con_moderator_name) return - - def _GLH_con_group(self): + + def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_group: con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() con_group_involved = [self.groups[i] for i in con_group_involved_index] n_con_group_involved = len(con_group_involved) # Simplify contrast matrix by removing irrelevant columns - simp_con_group = con_group[ - :, ~np.all(con_group == 0, axis=0) - ] + simp_con_group = con_group[:, ~np.all(con_group == 0, axis=0)] if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test involved_log_intensity_per_voxel = list() for group in con_group_involved: - group_foci_per_voxel = self.CBMRResults.estimator.inputs_[ - "foci_per_voxel" - ][group] - group_foci_per_study = self.CBMRResults.estimator.inputs_[ - "foci_per_study" - ][group] + group_foci_per_voxel = self.CBMRResults.estimator.inputs_["foci_per_voxel"][ + group + ] + group_foci_per_study = self.CBMRResults.estimator.inputs_["foci_per_study"][ + group + ] n_voxels, n_study = ( group_foci_per_voxel.shape[0], group_foci_per_study.shape[0], @@ -691,9 +694,7 @@ def _GLH_con_group(self): np.sum(group_foci_per_voxel) / (n_voxels * n_study) ) group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] + self.CBMRResults.maps["Group_" + group + "_Studywise_Spatial_Intensity"] ) group_log_intensity_per_voxel = ( group_log_intensity_per_voxel - group_null_log_spatial_intensity @@ -706,22 +707,27 @@ def _GLH_con_group(self): involved_log_intensity_per_voxel = list() for group in con_group_involved: group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[ - "Group_" + group + "_Studywise_Spatial_Intensity" - ] + self.CBMRResults.maps["Group_" + group + "_Studywise_Spatial_Intensity"] ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) involved_log_intensity_per_voxel = np.stack( involved_log_intensity_per_voxel, axis=0 ) - Contrast_log_intensity = np.matmul( - simp_con_group, involved_log_intensity_per_voxel - ) + Contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) m, n_brain_voxel = Contrast_log_intensity.shape # Correlation of involved group-wise spatial coef - moderators_by_group = self.CBMRResults.estimator.inputs_["moderators_by_group"] if self.CBMRResults.estimator.moderators else None - F_spatial_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_spatial(con_group_involved, self.CBMRResults.estimator.inputs_["coef_spline_bases"], - moderators_by_group, self.CBMRResults.estimator.inputs_["foci_per_voxel"], self.CBMRResults.estimator.inputs_["foci_per_study"]) + moderators_by_group = ( + self.CBMRResults.estimator.inputs_["moderators_by_group"] + if self.CBMRResults.estimator.moderators + else None + ) + F_spatial_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_spatial( + con_group_involved, + self.CBMRResults.estimator.inputs_["coef_spline_bases"], + moderators_by_group, + self.CBMRResults.estimator.inputs_["foci_per_voxel"], + self.CBMRResults.estimator.inputs_["foci_per_study"], + ) Cov_spatial_coef = np.linalg.inv(F_spatial_coef) spatial_coef_dim = ( self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] @@ -742,9 +748,7 @@ def _GLH_con_group(self): chi_sq_spatial = np.empty(shape=(0,)) for j in range(n_brain_voxel): Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) - V_j = Cov_log_intensity[:, j].reshape( - (n_con_group_involved, n_con_group_involved) - ) + V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) CV_jC = simp_con_group @ V_j @ simp_con_group.T CV_jC_inv = np.linalg.inv(CV_jC) chi_sq_spatial_j = ( @@ -777,17 +781,25 @@ def _GLH_con_group(self): ] = con_group_name con_group_count += 1 - def _GLH_con_moderator(self): + def _glh_con_moderator(self): con_moderator_count = 0 for con_moderator in self.t_con_moderator: m_con_moderator, _ = con_moderator.shape moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) - - moderators_by_group = self.CBMRResults.estimator.inputs_["moderators_by_group"] if self.CBMRResults.estimator.moderators else None - F_moderator_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_moderator(self.CBMRResults.estimator.inputs_["coef_spline_bases"], - moderators_by_group, self.CBMRResults.estimator.inputs_["foci_per_voxel"], self.CBMRResults.estimator.inputs_["foci_per_study"]) - + + moderators_by_group = ( + self.CBMRResults.estimator.inputs_["moderators_by_group"] + if self.CBMRResults.estimator.moderators + else None + ) + F_moderator_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_moderator( + self.CBMRResults.estimator.inputs_["coef_spline_bases"], + moderators_by_group, + self.CBMRResults.estimator.inputs_["foci_per_voxel"], + self.CBMRResults.estimator.inputs_["foci_per_study"], + ) + Cov_moderator_coef = np.linalg.inv(F_moderator_coef) chi_sq_moderator = ( Contrast_moderator_coef.T diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 29721665f..6adf20234 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -40,7 +40,7 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) - t_con_group = inference.create_contrast(["homo_test_schizophrenia_Yes", "schizophrenia_YesVSschizophrenia_No"], type='group') + t_con_group = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type='group') t_con_moderator = inference.create_contrast(["moderator_standardized_sample_sizes", "standardized_sample_sizesVSstandardized_avg_age"], type='moderator') contrast_result = inference.compute_contrast(t_con_group=t_con_group, t_con_moderator=t_con_moderator) # inference.summary() From a761b07f714c16bea9f596ec718c7c580b4fb019 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 29 Jan 2023 23:16:39 +0000 Subject: [PATCH 061/177] reconstruct code, remove repeated code --- nimare/meta/cbmr.py | 102 ++------------------------------- nimare/tests/test_meta_cbmr.py | 10 ++-- 2 files changed, 11 insertions(+), 101 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index c1af04015..b07c60a63 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -478,16 +478,16 @@ def compute_contrast(self, t_con_group=None, t_con_moderator=None): if self.t_con_group is not False: # preprocess and standardize group contrast - self.t_con_group_name, self.t_con_group = self._preprocess_t_con_regressor(attr_list=["t_con_group", "groups", "n_groups"], type='groups') + self.t_con_group, self.t_con_group_name = self._preprocess_t_con_regressor(attr_list=["t_con_group", "groups", "n_groups"], type='groups') # GLH test for group contrast - # self._glh_con_group() + self._glh_con_group() if self.t_con_moderator is not False: self.moderators = self.CBMRResults.estimator.moderators self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast - self.t_con_moderator_name, self.t_con_moderator = self._preprocess_t_con_regressor(attr_list=["t_con_moderator", "moderators", "n_moderators"], type='moderators') + self.t_con_moderator, self.t_con_moderator_name = self._preprocess_t_con_regressor(attr_list=["t_con_moderator", "moderators", "n_moderators"], type='moderators') # GLH test for moderator contrast - # self._glh_con_moderator() + self._glh_con_moderator() def _preprocess_t_con_regressor(self, attr_list, type): # regressor can be either groups or moderators @@ -555,7 +555,7 @@ def _name_of_con_regressor(self, t_con_regressor, regressors, type): if type == 'groups': con_regressor_name.append("homo_test_" + nonzero_con_regressor_info) elif type == 'moderators': - con_regressor_name.append("ModeratorEffect_of_" + nonzero_con_info) + con_regressor_name.append("ModeratorEffect_of_" + nonzero_con_regressor_info) else: # group-comparison test pos_regressor_idx, neg_regressor_idx = ( np.where(idx > 0)[0].tolist(), @@ -574,100 +574,10 @@ def _name_of_con_regressor(self, t_con_regressor, regressors, type): neg_con_regressor_info += ( str(abs(neg_group_con[i])) + "x" + str(neg_regressor_name[i]) ) - con_regressor_name.append(pos_con_regressor_info + "VS" + neg_con_regressor_info) + con_regressor_name.append(pos_con_regressor_info + " - " + neg_con_regressor_info) t_con_regressor_name.append(con_regressor_name) return t_con_regressor_name - - def _name_of_con_group(self): - """Define the name of GLH contrasts on spatial intensity estimation. - - And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_group` - exists). - """ - self.t_con_group_name = list() - for con_group in self.t_con_group: - con_group_name = list() - for num, idx in enumerate(con_group): - if np.sum(idx) != 0: # homogeneity test - nonzero_con_group_info = str() - nonzero_group_index = np.where(idx != 0)[0].tolist() - nonzero_group_name = [self.groups[i] for i in nonzero_group_index] - nonzero_con = [int(idx[i]) for i in nonzero_group_index] - for i in range(len(nonzero_group_index)): - nonzero_con_group_info += ( - str(abs(nonzero_con[i])) + "x" + str(nonzero_group_name[i]) - ) - con_group_name.append("homo_test_" + nonzero_con_group_info) - else: # group-comparison test - pos_group_idx, neg_group_idx = ( - np.where(idx > 0)[0].tolist(), - np.where(idx < 0)[0].tolist(), - ) - pos_group_name, neg_group_name = [self.groups[i] for i in pos_group_idx], [ - self.groups[i] for i in neg_group_idx - ] - pos_group_con, neg_group_con = [int(idx[i]) for i in pos_group_idx], [ - int(idx[i]) for i in neg_group_idx - ] - pos_con_group_info, neg_con_group_info = str(), str() - for i in range(len(pos_group_idx)): - pos_con_group_info += str(pos_group_con[i]) + "x" + str(pos_group_name[i]) - for i in range(len(neg_group_idx)): - neg_con_group_info += ( - str(abs(neg_group_con[i])) + "x" + str(neg_group_name[i]) - ) - con_group_name.append(pos_con_group_info + "VS" + neg_con_group_info) - self.t_con_group_name.append(con_group_name) - return - - def _name_of_con_moderator(self): - """Define the name of GLH contrasts on regressors of study-level moderators. - - And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_moderators` - exists). - """ - self.t_con_moderator_name = list() - for con_moderator in self.t_con_moderator: - con_moderator_name = list() - for num, idx in enumerate(con_moderator): - if np.sum(idx) != 0: # homogeneity test - nonzero_con_moderator_info = str() - nonzero_moderator_index = np.where(idx != 0)[0].tolist() - nonzero_moderator_name = [ - self.moderator_names[i] for i in nonzero_moderator_index - ] - nonzero_con = [int(idx[i]) for i in nonzero_moderator_index] - for i in range(len(nonzero_moderator_index)): - nonzero_con_moderator_info += ( - str(abs(nonzero_con[i])) + "x" + str(nonzero_moderator_name[i]) - ) - con_moderator_name.append("ModeratorEffect_of_" + nonzero_con_moderator_info) - else: # group-comparison test - pos_moderator_idx, neg_moderator_idx = ( - np.where(idx > 0)[0].tolist(), - np.where(idx < 0)[0].tolist(), - ) - pos_moderator_name, neg_moderator_name = [ - self.moderator_names[i] for i in pos_moderator_idx - ], [self.moderator_names[i] for i in neg_moderator_idx] - pos_moderator_con, neg_moderator_con = [ - int(idx[i]) for i in pos_moderator_idx - ], [int(idx[i]) for i in neg_moderator_idx] - pos_con_moderator_info, neg_con_moderator_info = str(), str() - for i in range(len(pos_moderator_idx)): - pos_con_moderator_info += ( - str(pos_moderator_con[i]) + "x" + str(pos_moderator_name[i]) - ) - for i in range(len(neg_moderator_idx)): - neg_con_moderator_info += ( - str(abs(neg_moderator_con[i])) + "x" + str(neg_moderator_name[i]) - ) - con_moderator_name.append( - pos_con_moderator_info + "VS" + neg_con_moderator_info - ) - self.t_con_moderator_name.append(con_moderator_name) - return def _glh_con_group(self): con_group_count = 0 diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 6adf20234..ffc70ac88 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,8 +15,8 @@ def test_CBMREstimator(testdata_cbmr_simulated): spline_spacing=10, model=models.PoissonEstimator, penalty=False, - lr=1e-6, - tol=1e8, + lr=1e-1, + tol=1e4, device="cpu" ) cbmr.fit(dataset=dset) @@ -30,10 +30,10 @@ def test_CBMRInference(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, - model=models.PoissonEstimator, + model=models.ClusteredNegativeBinomialEstimator, penalty=False, - lr=1e-1, - tol=1e4, + lr=1e-8, + tol=1e6, device="cpu", ) cbmr_res = cbmr.fit(dataset=dset) From 06629b88228c2e2f31ac887d844b5849d821d580 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Wed, 1 Feb 2023 01:42:25 +0000 Subject: [PATCH 062/177] correct testing cases of z_to_p function --- nimare/tests/test_meta_cbmr.py | 4 ++-- nimare/tests/test_transforms.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index ffc70ac88..fd8bddb52 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -32,8 +32,8 @@ def test_CBMRInference(testdata_cbmr_simulated): spline_spacing=10, model=models.ClusteredNegativeBinomialEstimator, penalty=False, - lr=1e-8, - tol=1e6, + lr=1e-6, + tol=1e8, device="cpu", ) cbmr_res = cbmr.fit(dataset=dset) diff --git a/nimare/tests/test_transforms.py b/nimare/tests/test_transforms.py index 7f3928837..a574f9828 100644 --- a/nimare/tests/test_transforms.py +++ b/nimare/tests/test_transforms.py @@ -256,10 +256,11 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma): (-1.959963, "one", 0.975), (-1.959963, "two", 0.05), ([0.0, 1.959963, -1.959963], "two", [1.0, 0.05, 0.05]), + ([0.0, 1.959963, -1.959963], "one", [1.0, 0.025, 0.975]), ], ) def test_z_to_p(z, tail, expected_p): """Test z to p conversion.""" p = transforms.z_to_p(z, tail) - + assert np.all(np.isclose(p, expected_p)) From 17bd65e2c436f167e4da64aba755998b6bf696d4 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Wed, 1 Feb 2023 21:35:15 +0000 Subject: [PATCH 063/177] add regular expression code to CBMRInference --- nimare/meta/cbmr.py | 94 +++++++++++++++++++--------------- nimare/tests/test_meta_cbmr.py | 7 +-- 2 files changed, 58 insertions(+), 43 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index b07c60a63..f7b937efa 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -366,25 +366,47 @@ def __init__(self, CBMRResults, device="cpu"): self.CBMRResults = CBMRResults self.groups = self.CBMRResults.estimator.groups self.n_groups = len(self.groups) - + self.moderators = self.CBMRResults.estimator.moderators # visialize group/moderator names and their indices in contrast array self.group_reference_dict, self.moderator_reference_dict = dict(), dict() LGR.info("Group Reference in contrast array") for i in range(self.n_groups): self.group_reference_dict[self.groups[i]] = i LGR.info(f"{self.groups[i]} = index_{i}") - if self.CBMRResults.estimator.moderators: - n_moderators = len(self.CBMRResults.estimator.moderators) + if self.moderators: + self.n_moderators = len(self.moderators) LGR.info("Moderator Reference in contrast array") - for j in range(n_moderators): - self.moderator_reference_dict[self.CBMRResults.estimator.moderators[j]] = j - LGR.info(f"{self.CBMRResults.estimator.moderators[j]} = index_{j}") + for j in range(self.n_moderators): + self.moderator_reference_dict[self.moderators[j]] = j + LGR.info(f"{self.moderators[j]} = index_{j}") # device check if self.device == "cuda" and not torch.cuda.is_available(): LGR.debug("cuda not found, use device 'cpu'") self.device = "cpu" + def create_regular_expressions(self): + """ + Create regular expressions for parsing contrast names. + creates the following attributes: + self.groups_regular_expression: regular expression for parsing group names + self.moderators_regular_expression: regular expression for parsing moderator names + + usage: + >>> self.groups_regular_expression.match("group1 - group2").groupdict() + """ + + operator = '(\\ ?(?P[+-]?)\\ ??)' + for attr in ['groups', 'moderators']: + groups = getattr(self, attr) + first_group, second_group = [ + f"(?P<{order}>{'|'.join([re.escape(g) for g in groups])})" + for order in ["first", "second"] + ] + reg_expr = re.compile(first_group + "(" + operator + second_group + "?)") + + setattr(self, "{}_regular_expression".format(attr), reg_expr) + def create_contrast(self, contrast_name, type="group"): """Create contrast matrix for generalized hypothesis testing (GLH). @@ -406,48 +428,41 @@ def create_contrast(self, contrast_name, type="group"): contrast_name : :obj:`~string` Name of contrast in GLH. """ + self.create_regular_expressions() + if isinstance(contrast_name, str): contrast_name = [contrast_name] contrast_matrix = list() if type == "group": # contrast matrix for spatial intensity for contrast in contrast_name: contrast_vector = np.zeros(self.n_groups) - if contrast in self.groups: # homogeneity test + contrast_match = self.groups_regular_expression.match(contrast) + # check validity of contrast name + if contrast_match is None: + raise ValueError(f"{contrast} is not a valid contrast.") + groups_contrast = contrast_match.groupdict() + # create contrast matrix + if all(groups_contrast.values()): # group comparison + contrast_vector[self.group_reference_dict[groups_contrast["first"]]] = 1 + contrast_vector[self.group_reference_dict[groups_contrast["second"]]] = int(contrast_match["operator"] + "1") + else: # homogeneity test contrast_vector[self.group_reference_dict[contrast]] = 1 - elif "-" in contrast: # group comparison - contrast_groups = contrast.split("-") - if not set(contrast_groups).issubset(set(self.groups)): - not_valid_groups = set(contrast_groups).difference(set(self.groups)) - raise ValueError(f"{not_valid_groups} is not a valid group name.") - contrast_vector[self.group_reference_dict[contrast_groups[0]]] = 1 - contrast_vector[self.group_reference_dict[contrast_groups[1]]] = -1 - else: - raise ValueError( - f"{contrast} is not a valid contrast name.") contrast_matrix.append(contrast_vector) elif type == "moderator": # contrast matrix for moderator effect - n_moderators = len(self.CBMRResults.estimator.moderators) for contrast in contrast_name: - contrast_vector = np.zeros(n_moderators) - if contrast.startswith("moderator_"): # moderator effect - contrast_moderators = contrast.split("moderator_", 1)[1] - if contrast_moderators not in self.CBMRResults.estimator.moderators: - raise ValueError(f"{contrast_moderators} is not a valid moderator name.") - contrast_vector[self.moderator_reference_dict[contrast_moderators]] = 1 - elif "VS" in contrast: - contrast_moderators = contrast.split("VS") - if not set(contrast_moderators).issubset( - set(self.CBMRResults.estimator.moderators) - ): - not_valid_moderators = set(contrast_moderators).difference( - set(self.CBMRResults.estimator.moderators) - ) - raise ValueError(f"{not_valid_moderators} is not a valid moderator name.") - contrast_vector[self.moderator_reference_dict[contrast_moderators[0]]] = 1 - contrast_vector[self.moderator_reference_dict[contrast_moderators[1]]] = -1 - else: - raise ValueError(f"{contrast} is not a valid contrast type.") + contrast_vector = np.zeros(self.n_moderators) + contrast_match = self.moderators_regular_expression.match(contrast) + if contrast_match is None: + raise ValueError(f"{contrast} is not a valid contrast.") + moderators_contrast = contrast_match.groupdict() + if all(moderators_contrast.values()): # moderator comparison + moderator_groups = list(map(moderators_contrast.get, ["first", "second"])) + contrast_vector[self.moderator_reference_dict[moderators_contrast["first"]]] = 1 + contrast_vector[self.moderator_reference_dict[moderators_contrast["second"]]] = int(moderators_contrast["operator"] + "1") + else: # moderator effect + contrast_vector[self.moderator_reference_dict[contrast]] = 1 + contrast_matrix.append(contrast_vector) return contrast_matrix @@ -482,7 +497,6 @@ def compute_contrast(self, t_con_group=None, t_con_moderator=None): # GLH test for group contrast self._glh_con_group() if self.t_con_moderator is not False: - self.moderators = self.CBMRResults.estimator.moderators self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast self.t_con_moderator, self.t_con_moderator_name = self._preprocess_t_con_regressor(attr_list=["t_con_moderator", "moderators", "n_moderators"], type='moderators') @@ -628,7 +642,7 @@ def _glh_con_group(self): # Correlation of involved group-wise spatial coef moderators_by_group = ( self.CBMRResults.estimator.inputs_["moderators_by_group"] - if self.CBMRResults.estimator.moderators + if self.moderators else None ) F_spatial_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_spatial( @@ -700,7 +714,7 @@ def _glh_con_moderator(self): moderators_by_group = ( self.CBMRResults.estimator.inputs_["moderators_by_group"] - if self.CBMRResults.estimator.moderators + if self.moderators else None ) F_moderator_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_moderator( diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index fd8bddb52..eb761b8ba 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -32,16 +32,17 @@ def test_CBMRInference(testdata_cbmr_simulated): spline_spacing=10, model=models.ClusteredNegativeBinomialEstimator, penalty=False, - lr=1e-6, - tol=1e8, + lr=1e-1, + tol=1e4, device="cpu", ) + # ["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], cbmr_res = cbmr.fit(dataset=dset) inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) t_con_group = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type='group') - t_con_moderator = inference.create_contrast(["moderator_standardized_sample_sizes", "standardized_sample_sizesVSstandardized_avg_age"], type='moderator') + t_con_moderator = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type='moderator') contrast_result = inference.compute_contrast(t_con_group=t_con_group, t_con_moderator=t_con_moderator) # inference.summary() From 3eb64326f62509421417993eb58a246a6fa17bbe Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:21:52 +0000 Subject: [PATCH 064/177] [skip CI][WIP] update example file based on reconstructed code --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 234 +++++++++++++------ nimare/meta/cbmr.py | 195 ++++++---------- nimare/meta/models.py | 17 +- nimare/tests/test_meta_cbmr.py | 19 +- nimare/tests/utils.py | 4 +- 5 files changed, 257 insertions(+), 212 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 88431495d..48fefc57c 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -34,15 +34,15 @@ "import nimare\n", "import os \n", "from nimare.dataset import Dataset\n", - "from nimare.utils import get_resource_path, standardize_field,index2vox\n", - "from nimare.meta.cbmr import CBMREstimator\n", + "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", + "from nimare.tests.utils import standardize_field\n", + "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", + "from nimare.meta import models\n", "from nilearn.plotting import plot_stat_map\n", "from nimare.generate import create_coordinate_dataset\n", "import nibabel as nib \n", "import numpy as np\n", - "\n", - "import logging\n", - "import sys" + "import scipy\n" ] }, { @@ -58,26 +58,27 @@ "metadata": {}, "outputs": [], "source": [ - "# data simulation \n", + "# data simulation\n", "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", "# set up group columns: diagnosis & drug_status \n", "n_rows = dset.annotations.shape[0]\n", "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", - "# set up `study-level moderators`: sample sizes & avg_age\n", + "# set up moderators: sample sizes & avg_age\n", "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", 'avg_age']) # standardisation\n", - "# load mask image from dataset\n", - "mask_img = dset.masker.mask_img" + "# categorical moderator: schizophrenia_subtype\n", + "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", + "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Group-wise spatial intensity estimation" + "## Estimate group-specific spatial intensity functions" ] }, { @@ -89,29 +90,53 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", - " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", + "cbmr = CBMREstimator(\n", + " group_categories=[\"diagnosis\", \"drug_status\"],\n", + " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\"],\n", + " spline_spacing=10,\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1e-1,\n", + " tol=1,\n", + " device=\"cpu\",\n", + " )\n", "cbmr_res = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", + " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -121,21 +146,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", - " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -144,7 +175,7 @@ }, { "data": { - "image/png": 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yrgtk+bsuqA3PrdBNJMujMw8WiRo2yTxt3JN57FPp8bU62pgCZfTo0Rg+fDgmTZqUflAbY4wxxmzPbDeK+9SpU/H000/nbD/nnHPS9mLGbE0uvPBCHHXUUSguLsYZZ5xR39kxxpg644knngCQUYmpDhPaZVOhbteuHYDKXTHSxpv7UGmmas3vVNqpXC9btiwrTSruVMF5vNrAAxmXixrESd1CMo2ePXtGnpsBp9SWn2mFdvUK9+GxLIe6muR14bW3V7OGRd7uIGsnuG8/HfcpU6ZEbj/ppJPccTfbhB/96Efo168fbr75Zpx++umVPpiNMcYYY+qbRHk4dDXGGGNMg+Xll18GkFGaVaGm7Tq9qdAund+pGlemvFcFux0M0DRv3jwAwOrVqwFklHWKKVTqaWe/aNGi9Lm6d+8OIDNzQKWc5aES36ZNGwBA//79I8tTm3JoeZYvX571PW4Ggdf+wAMPrHEeTP2zevVqtG3bFvd22BU7JKsWANeVlWLCyo9QUlKSrpfVwTbuxhhjjDHGFADbjamMMcYYY7YNXENGW3Uq1LTD5ifVbSrV9KYSp7SHXmWI7kP1Wyf46SOeaVMtpxqu5otqMw9kPLVoXA6mqeVjmkxD/b9rmlFGCVHebYDMtWJeaH/PWQz+zk/OIPDeHHbYYTlpmcKh0dm4G2OMMcYYU4gU5ekOMp99KsMdd2OMMaaBQ2Wa6i+9xbRt2xZArucTOoWguh1nCx76NM9HrQ63q4rPPMap+sx76A9dj2F+1P96XGRVTSsub1Two1D/9fR9r2nzd6r/tH23f3dTHdxxN8YYY4ypR4butx82rF+PFhUDKlN4JBOJvIIr1TYAkzvuxhhjTANl8uTJAIBBgwYByNhf09abtu5UfanEU92ujdcV9YWuajfzwjSp+sep5fTS0jKic8tyMA31oc5zqi285ol5rol7YF0fwO+0dad/d9q2My3mFUjdr7POOqvaaZvGgzvuxhhjjDH1wJ577FHfWTBbiURRAolk1QPd2gyGAXfcjTHGmAYL/bBTrY5Ts6kS09sKUSW6Mq8ycXbgcR0VbqedvabFTyrUUWkS2otTeWf5uG9V/ufjPOFEEdr1h/mOuzbMm/p1D5V2wvtlTBzuuBtjjDGmwTBwwIDI7Qs++2ybpdm8YqCA8qBT3yRlbtOycycgkUTXLl3w7/ffr/Q83z/iCGyoMK2xvXthkSxKIJmH4m4bd2OMMcZk8ec//xkA0K1bNwAZpZ1RSWl3TVWYNt1q8011WFVv2plT2Q7PkS/cn+r2119/DSDXLp1s2LAhqwzhNpaD0VejKC0trZHtephHIKOU8xoCQKeOHfM+j157Y6qDa40xxhhjCpZvjxqFofvth6H7DsHQfYcAZVtSf6Wbsv769+mF9m12RPu2bdCsSRGaNSlCk2QCTYqKqvxr3qxZ+q/1jjui9Y47olPHjujUYWd06rBzOo3EpnWpv41rUn+bNyCxeUNKiS8vq7ow3K+8DBvWrcWGdWu3/QWsAS+++CKOOOIIdOvWDYlEAn/9618r3f+kk05CIpHI+Rs8ePA2zedf/vIX7LbbbmjRogX23HNPPPXUU1m/X3nlldhtt93QqlUrtG/fHmPGjMFrr71Ws8SKkkjk8Yei2nW93XE3xhhjGhht2rRBmzZt0LRpUzRt2hQ77LADdthhh/T2du3aoV27dunvLVu2zPLWUlpaitLSUqxevRqrV6/GqlWrsGrVKqxYsQIrVqzAsmXLsGzZMqxbty79t2XLFmzZsgXl5eWxPtwBpDttzNvGjRuxceNGrF+/HuvXr09/Zx74x+2bN29O/4XqeyGg175QWbt2Lfbee2/ceeedee1/++23Y8mSJem///znP9hpp51w1FFH1TgPM2fORO/evWN/nzVrFo499liceuqpeOeddzB+/HiMHz8e7733XnqfgQMHYvLkyZg7dy5efvll9O7dG4ceeihWrFhR43xta2wqY4wxxpiC4rhjj818KU2ZriS2VHyWbs7ajmSqq1Ne1DTrOypMVcoTFRpmQrRM/Q5kVPOKT6aZ3Jhyr5lcX5L1e1nzVCCrslapoEy9e/UCAHzw4YdVlnF7Zty4cRg3blze+7dt2za9CBkA/vrXv+Krr77CySefnN5WVlaGG2+8Eb///e+xdOlSDBw4EJdddhmOPPLIGuXx9ttvx2GHHYYLL7wQADBx4kQ8++yzmDx5Mu666y4AwHHHHZd1zK233op77rkH7777Lr7zne9UK71EMoFEUR5eZWAbd2OMMcYE0H6an/QWQ+8x9NOu+6nvdcLttHnn95KSkvQ+ek71sqKeabg/PcHQxp126PydUKEO0yxUunXrlr6WjZF77rkHY8aMQa+KgQwAXH/99Zg+fTruuusuDBgwAC+++CJOOOEEdOzYEQcddFC105g9ezbOP//8rG1jx46NNevZtGkTfv/736Nt27bYe++9q51esiiBZB4d96Q77sYYY4xpVJRtSf+b3JiyA6fandiYWqBatvrL1PYdUh3+8h3apT6bpgYv5RXKeyKtwFco88mKBayVKO5U9ZNrV6XSWvgBAGD9Z6nPss2p/DXvlfJwk9jj2xVpt6hmQRseixcvxt///nc88MAD6W0bN27Eddddh+eeew4jR44EAPTt2xcvv/wyfve739Wo47506VJ07tw5a1vnzp2xdOnSrG1PPPEEfvKTn2DdunXo2rUrnn32WXTo0KEGJasb3HGvBx577DEAQOvWqSk0XVmvyseXX6YePkcffXTeadCjwE477RR5Tk2TUfR++MMfVrs8xhQSDz74IICMosg2oH6b46I+si1NmDBh22fWmGpwxx13pP/v168fgIxCTjWb31mPGTF1551TphyqmtPunZ5U1Pd46PklTqXX31WJ53uKeWRbVMWdaauv+ULk888/R1lZGTpUXPfKuOOOO3D22WfXQa7qhnvvvRft2rXD+PHj09vmzZuHdevW4ZBDDsnad9OmTRgyZEj6O+srgPS6h3DbCSeckDaDyZeDDz4Yc+bMwcqVK/GHP/wBRx99NF577bVq+9RPJJNI5OEpKFHJ+o98cMfdGGOMMQXB/iNGAAASmzLuKZPrvkr98+UiAMCmL+YDADZ8sRAA0LyiA9Z0l9Rgpqh96nt5kwrlvagiGBVt4JtUuLhMRnSRKpT+5IaU2FX6WWqh46LH/g8AsGDGJ1m79zvscwDALv1TnU/auucQ5XEmHy80BUZ5eTmmTp2Kn/70p1muRNesWQMAePLJJ9G9e/esY2jeBQBz5sxJ///aa6/hf/7nfzBz5sz0tnDBb5cuXbBs2bKscy1btgxdunTJ2taqVSv0798f/fv3x/77748BAwbgnnvuwcUXX1zjcm5L3HE3xjRafhgoPnE88Kc/bfuMGLMVCJVsnWWl73B6YVEFnftRZaSSzk5Txwo/5aqih2mq33WNVho3i0VlnR02+mLnduZd0y5kVq1aldVxrYy4aK+FyAsvvIB58+bh1FNPzdo+aNAgNG/eHAsXLqzULKZ///7p/7/44gs0adIka1vIyJEjMWPGDJx77rnpbc8++2zaFCeOsrKyGnkrso17A4DmKu3atQOQmeLp0aMHgMzDS8M86/QgpxGff/55AKlpnTi4DyuyTl3qNCkfCMzjrFmzAGRGv5y6rI6ZjjHbA3+q6HAzQIt2GipzVxfC45UpU6ak/1czmp/97Gc1yLExZmuQYDCpGH/Z5TSLK8r2EJO2X69Q1aOeEPwtUeGxpqwkZeP+9YKVAICFX6SU+GYVETT7bN6ip6icAlHZ16xZg3nz5qW/L1iwAHPmzMFOO+2Enj174uKLL8aiRYtw3333ZR13zz33YMSIEdhjjz2ytrdu3RoXXHABzjvvPJSVleHAAw9ESUkJXnnlFbRp06ZGponnnHMODjroINxyyy04/PDD8eCDD+LNN9/E73//ewApl5bXXnstvv/976Nr165YuXIl7rzzTixatKhWbiq3Ne64G2MaLX+cPj2nA3/iT38KAJhWXFwPOTLGmO2fN998M0tEpPeWCRMmoLi4GEuWLMHChQuzjikpKcEjjzyC22+/PfKcEydORMeOHXH99dfj008/Rbt27bDvvvvikksuqVEeDzjgADzwwAO49NJLcckll2DAgAH461//mh40FBUV4cMPP8S9996LlStXYuedd8awYcPw0ksv1SgwVKLI7iALlhkzZgAA+vTpAyCjxnHBjU4P6jSYTjdyKpO2W3/84x8BZFRxIKPmDxo0CEBG+dOpOFXz2WlhiGkuWqXK3759+6wyVdevqTF1wfTp0wFkL5zjVKcuOmX70lkn7cDzOJ0RqyxkOvf9f//v/2WlEbc4XE0GzjzzzMoLakye8FmvdY2zrjQ/oW9tNaGJq+dxdTfcFvdd34HaBlu0aJG1ne0lctYrWARILzHJHVNlSbZLmfXQf0tyx3apvFfYtpc1rQg0Rdt2epEpoleZbC8zWWWq8DRT3qRF1jl33r0bAGDAypTtfVHT1DnbDayYYW9WkWbF8evXr482gQk92WzH6vvo0aMrnbUsjhA+2rZti3Xr1uXuXEEikcA555yDc845J+88fPbZZ5Xuc9RRR8Wq5y1atMCjjz6aV1rbE+64G2NMwN333FPfWTDGGFNgpBT3PLzKoHYDMnfctxJPPPFE+n9d3EPlg+qBun2kKq7fOZqlwsEFO1wkFAah0IVDVOC5sIgje12IxO/q+ovfqc7QdWVYzu9973tVXBVjtg2cdeJMEetpqMyx/bAusx3FqX2qHvHcRNWxcPZKZ65U5dcZLQ28wrzQnZ+qmeEsHM9hO3qjqKtGIHfGl8q6uiPWmV6tyzyO+/PdUpk7SO6r60t4Tk2T7YBti+2Z7aW8vByvzJqFkpISfHdsxm0go5NSKU82S73/ijrtktpeoayXNm+V9T2tqFNFZxuPi6QKAGUVZaqQ84u6pgIIdTpgKACgTe+uqUMrOnA77DE0K488Z+vWrbPVZ6YVquwV26Luq2m8uONujDHGGGNMLbBXmQJh8uTJADK25UB8OGdVubmfKh5qQ6hE2R5WZY+oeaKaommq+k9FgPuzLGHZzzrrrMi0jaktVNapuGmwJFUFQ3dxcQGW4tqEKpNxbu+iFMo4D1F6Dn6PCwmvaXH/UP1n/tj+mI8zzjgj8lym8RCGd3/qqacAZJRxneVhECPWWbYD1i/O8HJmV2eKed4wMmXobzvcl+jMr763tD0wz9w/q00H9uflzVLvJUYlLae6zeiqqqAnqbCLoh6lsCvl2fbvpa1T5W+257cAAM1331hx7oo2XOG3vawij3P+9S+UlZWhVatW0fbeEXkI76vZfkkkEkgk81icWla7jnsetdQYY4wxxhhT31hxz5Np06YByCgKqkSvXbs2vS/ty7lqn4oY1WpdMc/f1d6PqF262s+G21TVDxXyytJgnvg7y8cyUOkIy8my33333Vlp0Rby5JNPjkzLmDiosKttq6qFcTazUaiSznrLOq9quZ5LPcGoYl8Zug+P1WdAXLkqS0Pt6qnAE8+ENW6omKvirnWQdYzPbT7j1csMt+sM8qpVq9Jpcn2XthWF25lGXCAincEK8/rwI48AALp27YpvHXBA9nFREU9D8lHW46iwQU+r9cmUJ5vSCi8zaRt12s1XeLyhQp9IJLLW3Tw3YwaaNGmC0ZUEHTKFQbIoiWQei1OT5bXTzK24G2OMMcYYUwBYcY9h6tSpAIBevVIrxocMGQIgM+KnyvXJJ58AAJYsWZI+lr5ouWqfSgVtCKmAqL2rKiBUu9X3bZQXDP2Nx1BlobcYHqO+rPmpqgvP88033+SUs0uXLgCAAQMGZJ2TadD3++effw4AOOWUU2BMFPfeey+ATJ3XWSZV3Nj+VC3PNxpqiPprV280JM4ntf4flc+49qb7cXtcm486Ni7/DHJCddMKfOOCcT50HRPRusm2x7a2cmUqEiijZ/O9pp6OQjtttlsq6HHrRPhe4u88t9Z79UpDvvzyy/T/Xbt2xSuzZqXfr7vskvIkw5lvVf85E8F2pLb+RGccFi9enFXOEcOHp46jsk6VXxR3fr79zjvpNNUb1ubNm/H3p59GaWkpvnf44TCFSd4BmMpt426MMcYYY0yDx4q7QOWvX79+ADJR5VQp4+ic+73//vvpc3Bk3q1bKpIaR/RUNNT/rSp7VBfUrpdo9NO4beE5NCKd2tjyk3lU5Z1lCr0GsOyqaPBcO++8c1Y5eW0nTJgQmVfT+LinItgR6xuVKK2XamceF404VM3UTjwuAqquD9F6rEqleqmJIs57jK5riTtHZZ6l4uzjic4Y8Lu90DQuTjvtNADA73//ewAZFVzbjkZO/eqrrwBk3lv0GqO27lFrPbQ+a13k2pVly5Zl/c60+c7QGCa6/iRU3NUnfFxk1xUrVgDIeMnhdr6n+Y7UdqWz10BqluH5mTPT6n2LFi0w9tBDEcVfHn44HYGc70/mgdeSed20aRPu++MfHaOhQLHibowxxhhjjEljxb2CRypWqNM+jjbhHMVzpMzRPO36OFLu2rVr+lxUp7nankoHVQX14ELUx22c3WxlftzVC4V60lBbd7W5Yx6pLrAM3J/qRJh/9ZqjkfaYJq8tr/WPf/zjnHKYhs19990HIKO8qcKuyrSq5KQ6tu3ajtSOXNuTKnWqkpPQt3qcFxjdHudlg+TjqYbEXRP1M8/y8rnCfP/2t7/NOv7nP/953mmbwoH3XaNk8x22aNEiABmPMD179szaj/WMCryq5SHqsYYzuLST1/cP6yLPyfeOKu9a15nXEH1mkKVLlwLIqPT63uJ14LuuXbt2ADKz2FFtVt+fVNSj6NmzZ7pPMH/+fAC50dHjZs9MYVFXXmXccTfGGGOMqQWvv/FGTofeHXKzLWj0Hfenn34aANC9e/es7RpJlN85Cqf6QFs1KgkAsNNOOwHIqAxUntX/rdriqQ929Zyhtu+hOqer9FXR4DnV1l1Vfh6vK+pZprCcPJbXQhVJnWngfvzktT/ssMNgGi7FxcXp/9VrjEYvVXVcPaZo9Ea2IVUTo9A6z/qqar/C7Wozns8+cfnR8sT5e9fyV0ZlkV2jzqmdCirwYV7OPPPMKtM12ydTpkzJ+h73XunQoQMAoEePHgBy64fWPVWk+W4ActeHfPHFFwBy2wHfhfR8w+PoySYuton6fw+3EabNdzPPyfwyL8wDn0lU3pknepTj+cNyMg2eMy5yMuG1ZRrMkz6L+M7kvXP7KzDytHFHLW3cG33H3RhjjDENj549eqBnRac55N25cyP332vPPbO+f1UhvhmTD8lEAslk1Z3yZDVMIqNodB33v/zlLwAyo2f6Io9TzHQ7v6tnmNCrC1fpc9Qd2sJGpaHqm6rfqppTyQ+VEG5jvuIU9TiFTxURptmmTZusMoXlVPv/OE8aPEZ9+1L9p7932iAeddRRMIUPlXYqWEC8TXqcN4o4BUu9I7GOVTY1rb+pDauq+arqx61Nicq/elrS2TUtf5yiHjXlHrdv3LMq7trFeeoJz2/lr3Dhu43Qjlz9mHO2WX2w6/on1nH+Tjt0+ksHMm2KSrsq8FSc+V7RWS+mSbt0rqnSdSY9gs44t+l6maVLl0Z22nkM0+LzKapdLV68OMcvfAjt4rUtabl4bXmt+a5jmlT/6cHHmMpodB13Y4wxxjQ8vnXAAen/dxHz15BBu++e1/kGDxqU/r9txWBjacUAyBglUZREIo/FqYkyL07NC9pTc0TLqKYcdaste1VeLHgcbb7pJQPIjPw5iiZqg6ojfLVT53f1G83RfKiaq19oVQD5O8+pUU5VdVMbwyi7WZZdvXRouXQWQGcWOPtBtca274UNfbNTXQvrYpwirmpxnAqudrdaX0Nfy1UtDFOVT5V1os+IKLT9sO2zTuvMl0at1Fk5TTssS5zvd1XaibZH/b2qdQYAcNddd2WlYT/T2xecSQ69m9F2nfeXz+sPPvgAQO7Mkn6yvuvzm3U76p3Amd/KYhwAmfcl38O0+VYYsZtp8Tgq8uE5whgj25ImTZqkZ4s5q8FnnT6fdO2NXlvu37t3bwAZVZ/Hv/zyy+l0GbXcM9Km0XTcjTHGGGOM2RYkixJI5rE4NVlmG/dKef755wFklAhVzNVGVhV3VeWIKmtUBoB4lTpO0VPUfp5qnNrYMhIckFFXOJJnvjTtOFR1ZB5UGQzVFaYRZy+vSp5ec1UZ1Z6e9+7ggw+uNO9m++Duu+8GkFHFVA0H4pVltjOdMVIbd54zzp47XIMRep4IiYtUrG0kLiJwlJ16nK/3OG8xWp44D1NR/t/j1EyNiKkzDmrDrs8jvaZRZea5GY3Tynv9MnXqVADAwIEDY/fhPePzmso73xUaUVW9llFd1uNoG87fgYzirjNmRG2++cyPmwWiZximwePCdq75rAvoiUYVcm7nM1CvJa8dZwlYHo2BEtVHYB+G9/yUU07ZauUxhUWD77gbY4wxxhizLUnk6Q4yYcU9l7/+9a/p/2k7xhEvR8jqXUVVYVXcSZyCFtqzc7St3lSoJEd5bwjTpnLA3zlq5ydVy1Bh0JkDqiNqY1uVr2rmkWql7h+WU1VC3VcjN+qnqnk839q1awFkotGF93P8+PGR+Tf1x7333gsge50HkDuLE25Tj0m6/kHR+qvKdpSNe9wsWVxbiPPWou1QZwdCNAKxqtjqoUNnuOLiL4R51WuoXqqqmiVU7yBxfrDD/7WN8xy/+93vAGSeM1YB6xZ6V+H9oZILZOogP7mPvl/0faTqMesHz60zanxeA1XHMdD6FHqcitovLrpxGE+EVDWrvLVYu3Ztuuz6ruczgtcu7pmjswR6L3R9AZCZ1e8R4ynHNB4aZMfdGGOMMWZrMPe999JmPcbEYa8yxhhjjMmBMx27V7g15IxTqLjrLBTVa0au/s9//gMgow7rrLPORvOTHlSoBvP48Ni4dUyq7nNGSf2e66yRelQLz6se1bYFyWQyq5xU/DUqus5wE+aN9+Krr74CkKues7y8R+HMAtPndWcd+O///u+aF8wUJA2q4/6HP/wBADB06NCc39gQ2LDUxZU+rHTKuioXbOEDkw82fZjyU6fk9SGl0+1ssPyu7iLDbdyH05ds+CyvLo7TqU3mkefm9FzUi6Eq8wZd0KrXNu5hzXvFtBl6Gsjc49NPPz0yTVP3sL4rUeZmVblFiwsapNv5qQvrQuJcnGqwprgARVoOJdwvbpEpp9Kj3DqGsL3FLRiNyo+aumiaJM7FrU7bx12PcJ848wo+s6ZNmwYAOPnkkyPLacy2ZM6//oX169dj5P77b5XzffDhh1W6lTWGJIuQp1eZ2qXToDruxhhjjDHG1DWJZAKJZB6LU/PYpzIaVMe9f//+ALKVMCrOGgyJxC1Uqyy8OZDrQi4MzkLXjEQXoMRB1YohqalkaihnhlkOFXduYxhqLvqh+sby006vqoU8PA/VbxKWMy4cvbrBVFU/zpUfj9NAMOEUJe+xqX8YaIn1U9tQWD9J3AyXqtyqxOtitzi1OArONvGTzwRdIBu3AFNdIZKoAGjMty70i3P3SHTha2UzENp2ddaBn5x903zrzF5c+eLKGnUufrIcVt63LereWJ+1QMYRA98BfJ+oC0ZdGE3U0QFRsxWau4TbFK3HrMN8NzIt1lm+v7Qd0WHBO++8kz73kCFDsspZlbvlfJj73ns5M8VhOTnzrLONvFac8VZ3kLzW/K73gtdD3UwCudc7DLZlGhcNquNujDHGGGNMXZNMJpHMY3FqstSLU9PK35577gkg2nWaqn+qNun+GpCJn3pclIpOdVsVPFXZVH2jsqxquQZz4H6hssBtXPTC/HMEzzR0oVGcLS23U0GIKoNeA1V/dAGSqookzsVfVN44A8B7fuqpp8LUD6xzqsDp/Y+qM6wLOrMV55aV+2udigvuFaJtmPBYza/OGKlrOs07kGnzqmar4kb4u7rDJHGqeIjmR9u2BrOKC+6i6n6YlgbS0dkxfS7Y5r1u2GmnnQDktp/w3rEesG6yvWo71eBh+q7kebR9RAUuiwukRDp27Agg8xxnO+Y7jnmIc2fMehjOvHKbtufqsHjJEnz55ZeR+Y4KglZWVoYeu+yC7t26AeW5s329e/YAEql78/Irr6TzFhfQUAM6VjabwXOxDpjGR4PouBtjjDHGGFNf5B2AKY99KqNBdNxpj63KEpAZyVNtUHW4KttNjm6pEMSFXK+MuGAUqmJxdK3BVzjSVxUitP1u165d1j48Vt1tRQV0icpbnD1+lBKhnyyX2vnF2SHrvYg7X/g/77mpexjunsSpxbTnjLp/aj+uirqqXKoCat1g/VZVDMi0J7UvVaVZ0+BslbZ1phl6b1GVnnbnGvyGeWCe2IZVxdfAM5Up7kxD1bw4bzqaRtwahXAfEqfW6v567c3WgcHO+vXrByBzT2kTHarNumZI2ww/3333XQAZBbdz585Zx2v75vm4riqsA8wH7zttwam0E3oM4ztC6w1hecJ3HQC8+eab6f/13C1btsQHH34IANh9t91QGcuWL0fr1q3Rrl279Dt0xYoV6bwNqnC3mQUV9rItWd8TpRXfuaYrmcr7gd/6FgDgoT//Oa3q671Q15v6PAFyry3bPevEhAkTKi2raTg0iI67McYYY4wx9UXeAZjy2KcyCrrjPnXqVAAZ2/YoX8kcqcf5ao6zt1alj/vn45VFbXv1nLo9KjQ8kOunmQpgVBho7qu2tqqYVeUnOs62trKZBVXy1CuO2gjHrSuIu0dh2ixn9+7dAWTqgEOtb3uKi4sBZNtdArl1Q8N2h7/rbJK2T7XDVbtt3V8V7bBuqZLMNLVdqX02z8nZK22XUTbzaj+u7YvnVDtc9XCj3idIqO6rXbzalavyrtdQbZl57sq8ylQ1sxjnA57fHSxm60BVWOtXZfdO67m2Ib5XGC9Dbca1Pml9C+sq6xTVYarhbHt8N6h9PNMizCPfIXFxDsJzaRvcYYcd8PEnn2R5xemxyy4AgM8XLsxp73y3d+zYETvwGFXXESjrFdsSW1LHJyq+lxdVeF5rUuGtp2nq+h5z9NEAgKnTpuVYAvAeVNavUHWe5WSdMI2Hgu64G2OMMcYYU98kkkkk8jCfzmefyijojnvfvn0B5PpSD1UftZ1V+z7+rnbYPBdt9Kry6x4q13E+p+Pg7xw5q2rF0fjy5csjzx9uYzno41WjKDKNqvKkap7mNfxNbWlVQac9I1UXXT+gNpiqqoRqDLfxXKwDZtsxffp0ABlvR3GoEqcqGpB7T1lHWE9VPdPZHKK201EeUzT9uDDrqvrx9ziVPMrunMpZVRFUWT61t2e+eR6WLyoOBc+lUZ2ZTz4D1PNOVTOBUf7c4yKkxinrcX7qeU4r77VD12GwLqh3FiATT0RnvtR+mrbtWje13tAenftFRUymas3PlStXZuWLa8Xi6omujyHMI23Eo/ybd+rUKSstPUdZWRk+X7gwJ2o4r8fy5csxcMCAip2pplesCatQ1QEgsTlV7sSmija/NuXJrbys4hnUql3qFC3bpj4rvMuUN02my8J3ub7reK/YXkJPNzqDHecxzzR8CrrjbowxxhhjTH2TLMrTj3tjtnGnGs4RN9XkUDHiKFU9L8T5T9btOrolcf6Lw99U1VY7UFUbqHB06dIlqxyqqFFRCKOY6qp0KnS8RqqqVeaHPqqccQoJkKvO67XTa64KkM5m8JOKSag2shxUIlg+s+2gXWpVnpjU3jaqjVEd0rrAY+OimMatuYiz4w5/0/qp9VLtzXV9S1Wep8Iyx81CsZ7GrQ/gdeDvVDcJZ92i8qN+23VmQGcVtd1pm9ZImUBuG46LIlvVTB7Tomein/3sZ5Xub7JhW+SzUb2dRamvfJ/Q7pyzOvxOdMYlLh6HzhKFs9D8/9///jcAoG3blOpMBV7ffXGRkPW9w/gkbBfhjBu3afTRuHNqvadP9tSXCqV9c+odm9iUOndyfUnmQq1JvXtKVy1N5WXR/NT2ZMXMXY+Ual/UtU+qDBU27+VNMu9rXnveR/UKtHRp6twlJZl0ta/BcrNOmO2APBenopYd99rHBjbGGGOMMcZscwpScb/rrrsAACNGjACQq/KEihFH31SpaW9NBZ6oJ4w4382qUEcp0RpVUNVtVR1URYzzTMEV+hxhh+oiz8F91JdzXNpVqad6fKi0qZKp+6i9oirtqpZyP6qTqpwA8aoP68QZZ5wRWR5Tfeixhyoe74fed1WRSZSnizif0hrZV4nzlELFMcoWXn0iE87Cxc0gqIKtPtijvEDp7EJcG9bok/pJhVLXAITXWGfitF3prIaWX1VZ5onnCdV9XVPCa6f3tiq1trLniKmaKVOmAMjMPvI+8L2m66SAzLuOz1PGvuD7Y5cKlXnhwoUAMuuitN5ofdOZ0LB+MU3WIdZnojNtUfEXgEwd5Xu6srgp2sbi1lARnRGMe35tK9asWYOePXsCyFxL3huq6LyOYVv9+uuvAeS+y3ntWEfOPPPMbZl9UwmJZJ7uIBvz4lRjjDHGmJrQvVu31D+6GLXCRKZoTSoY05Yv5qWPKV2xCACw7oslAIBlb30EIGO33P3bqUFViwrTmUSzlLiAFqkB1AnHHw8AePGll7ZyaUxjoSA77qoEcIStdqFAvDpApUI9NBBV9qLU3zDtkDg/5eqHVVUojq5VIVi8eHFW3nlc6EGAygbVFNoEduODqQL1hxtnmxqnpofljbP7V3/zGi2S8Bpzf36qN4BwdkQ9G0T5tDe149FHHwWQUfXiVGSi7VE9L4X3XT208N6qpxf1b66KvNaZqEidWsd1DUUcmgf1TKV1L4RtUlVtVS3Vw5J6l9A2E+aZ1yzOA4+mGWfjq/7to4jLX1SU6pA4hVTvE2fKAM+WVQbrORV11g/WSdqthxFGWWe4HqhHjx4AMh5aGCGU9tX8Tnt09bSm3tuiZse4rX379gBy14JpZOGq/P/HrQOrzHtUVWvJSLhGrD5o2rRp+l6wDLRtZ7sK7yfLzOeSvm/jymnqDruDNMYYY4zZynSucB2Zo7RvTJmoUGnf/PE7AICv/zU3fey6pasAAN98kRoQffbSfwAAzXaoMN/rnnLD2alraqDUpGvKZXGiIphT5e4gjKmaguy4czS6alWqAdFfbZRfWbUhpVLBTyrVcRFC84kcqsSpTFV5cmEe1Y6bKjpH31TcaPMGZGYUeCxH5bR5Z5pxaqPmKS66az6jeqatvqrjzh2XF97ncCZFfdmyDthmduvB2RqqoqHNM5BR4lQ9U88vUco0j1F/4Dpzwt9VuVaf60yL9SIqmql6ponzNhE3A6azcyRsC+r7nedQW/y4iKjqwUZVzfCZolEWdZ2A+mfX70SfjXotw3zExXNQv9OqyOtaG23zOgtnsrn77rsB5MYTifPJHuWDn+8N1jXaU/P9wXfExx9/DCDX2wxhHa7snvJYtgfmh3VW15BpndU1ESwnz8v9wzxqNFlt9/q9Kk9qdUXTpk3T90IjqaqXOCB3jVFUpFggU2dOO+20bZp/k0uiKIlEHrP/iaLa9VcKsuNujDHGGFMjKtTvOKV9y2cfAAC+evtfAIAvP/w8c2hpRTCyDalBZ5uuqUFEpz1TA6CdB1cEhuycUtzLm1cEnUrYiZ/ZOhRkx11H/FS5uD3KA0NVNtBx9tpVqXJRftx1m6qMqg5zJM18q1K22267ZR1HdW6//fbLKad60ohT+6l8qIqoMxOqUobljIsQm+/sRVXKh9oDh2XXfFVlt2yq5rHHHgOQiZCq9TDOI5HOrKini6i2oZ6FVBUjVdlQVxY1MC7Wgp6Tv3Nmh/WN51bvMmpnD2R8ZdNTR+fOnQHk2qPG5ZFpcrbjs88+AwB88cUXOXnW2Ay6HkdnCthWqJjqDIneg3AmQWcxtQ3r2h9VDLWdKmFakydPBgCcddZZkfs2Rqgm6ztEPR2pF58Q/sZ7w3vGOqpeZeKihDMvtMNWpTc85oMPUh3fPn36ZO1bWfyTcLva1fO89GvOvIblUg82qkjn+96pKzp37pzTblhOjbkQ/s86wH6PriXzeq/6I5GnH/e8fL1Xgns7xhhjjGnwdK0YYGds21OdXwZYKvtqOQCgtOIz2SzVRdppt17pczRtlRr4lIvZ6I79+wEAmvXfK/V765QCX9q8IkhgMnWuV2bNSruDNKYmFGTHnSN/rlznKDXKdlpH9nG2lnHf42zw4iIHhseo4swRMe2y33//fQDARx+l3EmNHDkSADBo0CAAmVG4KgRRI2rdpuoZlT+mOXv2bADArrvumpUmbe60XFFl0muheaju+oA4f/fhtVUbZ346elztoe9g9Q+uqnBVbSAuKmL4m3qoUK8lqqhrG1CFPsoWXD2YqDrfqWKBGuu8KtIaeVXjDUTN8qg6rx5bqoowymca1VDGqvjPf/6T3ufdd98FkOszWz2OMC/cjwo8vYaoj/YoTzAsh9qiq+94tYVX709KlOpprxi58F7xXnIGRNeI6HoFIHcmhseyntNOPPT9DmTuDZV07qeznTyProEBgF69Uh1d9dySr1cz9SWvs9f9+vXLKafGSIiLzlzftGvXLmctgM5sqa95IFdhV1We9yucATF1SzKZzKu/U501k1EUZMfdGGOMMaYmJCoUdyrv6e1NKgSAtqnBfJvdU4OTZKvW6X2SrdpU/EM/7RUDl7YpIaC0ZYXDiyYVLjubVvxuG/cGj01lIqAN5J577gkg13+rqnbh/1V5MIkjzkOMqopRapGqIWqTz+myZcuWAQD++c9/AgDeeustAMDo0aMBZOxmVUWPUhdVeaGN7MyZMwHk2ggyDxqhLioirH7XsqtiF+cLnsRFrow7T1guwjpAzwi2k60+Tz31FICMbXtc1E+iyroqQEqoTKsirap2VTbRhPvFRUcN92G+aAM7ZMgQALmzS3F1Xn8nUftp3a1qpo/EtRmmwWcAkLEbXrBgAQDgjTfeAAAsWZIKCEO1nkqozlqoPa3OWEb5wic626IzCnG2y3Hfw+0s+x133AEAOPvss9FYeeSRRwBkPKap3/84wlkwzrTo2irGBeGzn/VFIwZTiaeyTj/vnL3l7FB4D6kcM9+se8y/tlstj6rk+rygmhx6GlOFWT0eaVTj+qZz587p8vBe8DqpP/7K8qzPDPXyxTr04x//eKvmn7z44ou46aab8NZbb2HJkiV47LHHMH78+Nj9lyxZgl/96ld48803MW/ePPzyl7/EpEmTtkneQmbOnInzzz8f//73v9GjRw9ceumlOOmkk9K/T5kyBVOmTEmvKxo8eDAuv/xyjBs3bpvnraYUVMfdGGOMMaZGlMtgu8LuvLxpajCSbJMazDdtURHtlGY4zTLuJ8ubpfYt57FFFWZiTZtnbU8r7BWfH3/ySTrgUkNg7dq12HvvvXHKKafgRz/6UZX7b9y4ER07dsSll16K2267bavk4bPPPkOfPn1iBdgFCxbg8MMPxxlnnIH7778fM2bMwGmnnYauXbti7NixAFKiyA033IABAwagvLwc9957L37wgx/gnXfeweDBg6uVHyvuEajNnapYGokTyIzsVemqShFS4rzLRI2I4/xHR3ltAIChQ4cCyNiuzp8/HwDw0EMPAcgoHAMHDgQA7LVXavFL6MuWainPQZ+8qq7RNpDnIMwT7WDjlLZwe5yqqMdU5b8+zkd0lPcOot4VeC1s31d91M9znIcljTPA/TSSJ+9XlH202p/GeV6qynuTel+gKhiq/tyXSvsBBxyQta+qx+obW9U+zUuYVlw0U20bzLd6b1IFsrKZQl5/RsKkcvrOO6lgMf/+978BZGah1AaY59ZIzVGRJPVZpc80VVJV/dPrQiorn2My5Hoj0jUTceuHwlloXcPAe0G7eUZUpTrOT6L25Xy2Mm88X9i+tZ1qveYxGgtC66I+c7TtMQ/hvlqndHsYhbQ+6NChQzovK1euBJC5HlyjxTxr5Gggd+ZMYz/osya8RtuCcePGVUuV7t27N26//XYAwNSpU2P3u/vuu3HLLbdgwYIF6N27N375y1/i5z//eY3yeNddd6FPnz645ZZbAAC77747Xn75Zdx2223pjvsRRxyRdcy1116LKVOm4NVXX612x72uKKiOuzHGGGNMjahQv9M+1YsqBjrNU8JPqdijl/P3JplBbY6iLude/c03sQv4TeXcf//9uPzyyzF58mQMGTIE77zzDk4//XS0atUKEyZMqPb5Zs+ejTFjxmRtGzt2LM4999zI/UtLS/GXv/wFa9euTTsLqQ6JRBKJPEyyErVc7+COuzHGGGOMqVeuuOIK3HLLLWnTmz59+uD999/H7373uxp13JcuXZpew0E6d+6M1atXY/369elZiblz52LkyJHYsGEDdtxxRzz22GNpT3vbIwXVcddpZjXf4FRvOOVb1aLUqhZGKjqFV1nIbp0e1sV7OsXFRbdcZMaFPjyOZjDvvfceAKSnegDgmWeeyUpTA1dwmpNpaB7i8qj7hWXi/xoQS4+pKuhGVfcivJ+6OFjdhjkQU/XhQi8N4lXVQko1MSE6PR5OUce5S4wL0ELUFIPHsV5HLf5kXaCJjE4/62cczCtDxKvrNiD32aMLPnXRmT43mG+aGdGch2YNUfvqtaLJHc3hnn322az8s/w8t5r1aHnDNHSxoC4m5v1QN61MQ+9zZSaGTL8xLzTXYFo0qaA5m7rgrey5R3Msvd/qBjTu3cf9WAf0uR+2H9475pd1jbC9sh2wLel7NS6gVNS7Is4EU9tHVptL27ZXKOtNxEwsKdczUEiXLluW42JSTXniAiGqa111vZtPkCiWg9eOafCaq8vkQmHt2rWYP38+Tj31VJx++unp7Vu2bMkKrjV48GB8/nkqkq2aygLAqFGj8Pe//71aae+6666YM2cOSkpK8PDDD2PChAl44YUXqt15t427McYYY4xp8HCd3h/+8Id0/AoSrq176qmn0gPaRYsWYfTo0ZgzZ07699C2v0uXLmmvfWTZsmVo06ZN1n7NmjVD//79AaQi0r/xxhu4/fbb8bvf/a5aZXDHPYK4UThHq1SrwpFm3MJIHSmrkkd1jQoHlQN+qqIUVqw4JYtp0M0W09DFJr179waQmr4Jz62LA6MWrugCM+aB51R3W5onVVNJlKtNDRLBPFDB5acGiFHlhsQpn1HKQdQCQcCKe77QBSSQuyBZAwypSkTYFrhfXJ3h+cK0SJxbQa1TzIO6ntS6FLbzPfbYA0D+C5ZVzePMFxd7Ll++PCsP4YOfwZzoZpUL/Zh269ats/LJtq+zHXx58ZPB2kLFiW74iF4bpnX00UcDAF566SUAmUXvvC/Mm6q44X1kfnUmQWdAdEG+Pou1DkXdL93WmO2D9ZlPRZFtjq4eqbqqeg7kulrVZ3hcYD+9l+pmkESp33EuKFV55zNBF6tqUCGidSNqEbrOBuk7orS0FB98+CGaNm2K/gzgFGNr/PnChenzsfxsV82bN48N6qSLh9UqQLfrvYmbUQ7PzW10Z8t86YxJobWfzp07o1u3bvj0009x/PHHx+7H4F5A5t6w062MHDky610HpGYiq7JfLysrS9fN7RH3cowxxhhjTN6sWbMG8+bNS39fsGAB5syZg5122gk9e/bExRdfjEWLFuG+++5L70NlfM2aNVixYgXmzJmDZs2apU1SrrrqKvzyl79E27Ztcdhhh2Hjxo1488038dVXX+H888+vdh7POOMMTJ48GRdddBFOOeUU/POf/8Sf//xnPPnkk+l9Lr74YowbNw49e/bEN998gwceeAAzZ85Mmx9Xh2RREsk81PR89qmMguy4czTKUTs/ddQaEmezzn2pplEJU9tUBi7iKFeDU4Rpxrmy0tG52ghyP/p61cBNOnoPlUx136h54Dnj3NPpyD8ucExYBqoOVA157agSUiGgMkn3Y7x2VCWrujchWnZ1dWbyI1S44+xMVclV29Y4BS4uMFe4j7qDVBtoVdE03LrafkfZTjNoUVz70zbDtGbPng0A6ZdS3DqWsM7RnRsDnlF5HzBgAIDMc4P1VhX5r776KuucahvONgVknkVU3jWQlCpuBx10EICM+8jnn38eQOaZwPbIdhzWDeaH+aaSrmsSdKYrLihbnJvM8BhSlYvehowq7jrDy3vGdsAZmnBGS88Rt0Yszo2vug3lc0LXTESthdF7yXcD0RluvddxQZPyCT4Yt3YlbFPvf/BBui7HuSqtbO0L2wX7B7oWRO8X0Xe5Pv90piJUzdkG2W7jZlKqWrOztXjzzTdx8MEHp7+zYz1hwgQUFxdjyZIlWLhwYdYxDH4HpAJNPvDAA+jVq1c6+NFpp52GHXbYATfddBMuvPBCtGrVCnvuuWesF5iq6NOnD5588kmcd955uP3227HLLrvg7rvvzlofuHz5cpx44olYsmQJ2rZti7322gvPPPMMDjnkkBqlWRcUZMfdGGOMMcbUD6NHj650cF1cXJyzLZ/B+HHHHYfjjjsurzz07t27ynOOHj06HeMiinvuuSevtPIhkUzk5w4yWXlk8KooqI67jqR1NE5VKlTCOAKmKqUjXoYc5siZ26kOq7pIZY1KB/PC7WG+4kb0TIOqCdPWkPP8nXaDHHGr2gJk1DQqG7wGtH9TLxDcTtUkaoQPZEbzzGNYlsquAZAbxplKAdVFqkPdunUDkHtvVLkPr4GWK18PIY0d2vuFnlHUXlxnV1QNiguWpAFCohQgVc6JpqnKPM/Vt2/frN+pPvO8oXeBqoKIqU3szJkzAQCffPJJVl74O1U01r3Qdl7zzfbHQGi0y2Rd57VmfWZbourNthHa5xJeE4agZ/tiwCX1tMP9uc6FrtYef/zxrDT4jAzvF49leXgNogLEhPnUYF5MI06BjNrWmNuyqsis17z+fNbyOrP+VGYTHfds1zR1Zo31TFVz5on1LjwnP9mWli5dCgAYNmxYVl7YDrTzxbznoybHKetxnndYv9QryxtvvAEgtaARyMyWFRUVoXtFu80hWG8y+9VX0+/m7t27Z+VF+yxxs326RiSc1dRZLe7De882xrrRmNtPfVFXi1Nrd7QxxhhjjDGmTigoxT0qhDqQGWFSfQv9RtMGnSoZR7BU1Klmc7RKW3faoGrYYPVwQsUjSqVS38ZxiiYVMo6cqW4xcADLQ8WMK6hDG3f6cKZdLj1I8Bwc6TMN9bQRtzpevbaEsxzqIYTlVO8WzD/t3eiBg9eJ94KKPNPmvaEKCWTuh6qnajNtolFFNERt2uNmYdSLjHqEifOgEKah59Lt6pOYi5f4PVwUBWTuf9gOo7wqdBB78JBPP/0UQK4qRo8ufJZo+w7RcvA6L1iwICtPPXv2zEpDvWxQTYvyoqHXnc8/fW4w35onbj/mmGMAAA8//DCAzExY6LVGPXNUFbtB64zaHatddXi/dH1DY27LfOaxznE2ls9vqsJ8RupsJxA/48TrTMVc36vqvY3PZ50d4jskStllfVHvSFS1GWtA323qRUrrX5T3HF4rvl/1+cNj+X6iPTXfJXxXrlq1CufV0Jaa5+e14syazk4yD7z2vG/8HhfLJDyW15/vV9YBXmv17mbqDivuxhhjjDHGmDQFpbjraJxqFkeztMFTlRzIVYLUFvw///kPgIxapefg6F2Ve452ozyjaH71nOphgYoz96PCoQEEosqn2/idSoaWS+2TVZ3h770qlMHK+LJCqVOFXctNpYBRz9Qun0pgnP/7cF/1K6121iYaXttwHYiqW1ovifr+V5v2KF//4fnDfeI8Wqgytc8++wDIKI9cZET7VPUXHpaLdYXHah1XLrrwQgDAzbfcAiCjKKqyznKHbY5tV/1V8xlFJe6jjz4CkCk/2yfRKJdRtuTq5UfvA9ftENrd6jVnWj/+8Y8BAPfff39OGTQqo9aRqOiZYVpah+Ki7Ib7Rtn1NzbULl3tl9XDCJ+9Yf1nvVXPLfo8Jrw3vKfqZYj7q+/48D5x1pv54DGDBw8GkGmTjAJOpZkzaN///vcBZNRjvR4sw+uvv57+jXbzGkVbZxb+9re/Acidxdh5551x9FFHobY0b9483R/Q+C/aPtQuPc47TWjjznvONsP7wzqh7aayqO5m25BIJPNbnBoTPyBfrLgbY4wxxhhTABSU4n7KKacAAP7xj38AyPVhS0IlTFdicySs3h/Uk4v6IdbRblTkP0V91aq9G1HFk2nRF/Suu+4KIDfaItXGcBtH2zyG59B8x/lOZx6j7Hfj2KnCJpZ5UB/r6kWHXgp47alKqCcK5iW8n1Qm1DaQ31lHTDRR9bYqP+dxHlNUEeV9Uhv4sL6r/2+eUyN0cs0Gz0Xf47z/Wn+jbK4ZeXjYsGHYpcLLQz5c8KtfAQBumzQJQO66FtqAcx0MkGmLeg1ZHtZTtuH3338fQEYppXLKthOnwAG5Pt41yiKPoUePvfbaKyuPauvM+zZq1CgAwNtvv51Oi/lTf9M8Ru+DzmowTdYZXYsQ1o24NRW33norANQoAEuhEtYtIPfaUNnlfeB1Dt8JcV5F4iKQK0xDZ+n4PcrTGGep+Mk0WH9p+83nNdsoz00lnu8vfVfye7iOTZV2jS3AczIN/r733nvjgCoiaFaHjRs35twfnYHT5wM98sSp45XN5Ov9IVF1wdQNiaIiJKuY2eV+tcGKuzHGGGOMMQVAQSnuhKvCqU5xFEs77hBVitQelKNw2ltz9KoqG+2v9bgo7wjqu1WPqUr1ViWEXmQ++OCDrPOE+6l6zWP0nFF+k4Fc+7iaeHTgMbxWtOvVNNS2ncdRReG1j8oDf6Mdr15bUzlqHx1CxUojoqotq9Yl1jneG/UAEd5H/sZPpklld9999wWQqRuMYhrnNaiymSEe889//hMn/vSnsfvFwTyyndJ+l2mGPuNZ9rhIj2pfzGcVn2VU8VVhpz1xOHMY53+bqCpJjzb0zBMXKZPPjDfffDPnN32maV3Q+0l0Bk/rX1TE6bi0GwOXXXYZAOCII44AEP+u0PdO1Lsk7hhtvxorgb+zDXJGm+08Lvo2kOvBhfValWeeY4899gCQebdxDQi95lA1Zhp8zg8fPjynvDrTxxlgnpN52H333QFknjlbi82bN6evFa+denXTtSGksneeotYA6jtfo0izTk2cOLG6RTLVpK68yhRkx90YY4wxpr6Y/eqrVQpixmwLCrLjrr5c+Uk/xOqjPPwtTgXnyJ6jVI7OqeprhDe1jQ8brtqQciQcp2pThYuzMeanruqnkhaWi/uofZteK6K2tKq6fhqkEf4e5p9pq7962u3yWlEBUU8VPA/tHlUpCm34eB9Vza2OTX5jpjJFh8pbGFU1PEZ9c6saRlRxj/IOwntMRY6eIWiX/a9//QtAfERVtZGmGh7aBvO3MN5BPtxw441Z31lPuQajMm8ncd5U9JnAa8PZKbZlqt7qtSosg85s6Lk1TVXziUaj5H0NryEVRPVuojb9cd6C9Fmnz98opVh/i5uZbIjExUzQ94++r6Kup97vuJkLVYH1vaTtW2eDwncC3z+03eaxGrlb14xxFpY+1V955RUAwEEHHZRVFr6Xw+sUFyuA59A0+L26zwVl8+bN6b4G24Wuy9LnQJziXlUbBnJnV5i29kF07Utj9s5U11hxN8YYY4ypJ5YuW5Z2phAGuXJn2ESRSObpDrKWYkRBdtwZdZD2YxxZsmHRTziQUbRoz6bqvCpFHIWr0k61jUqHqlRRqB9zHQkTKnpMU0ffHM1TOXvttdeyjguPHTFiBIB4W33mSdVuVQaYZ6rkUUqt2lmqf31V/VXR5bXTiI3cj2oj1VQgo+T06tULQOYaqa97E01lNrGqYmvd0NkYVWzV24nGMQiPoYehkRVeHWbNmgUgE0+ByhrVX50Z++KLLwDk2rOGdudUiytrow/9+c/p/5lfpsloj2q/zVmq0F+6xklgu2PZ9Xpz/cfKlSuztlMVVEUubOuaBn/jMWxHvMZ6rjgFO8pOn7a6PAfvC+uAznTps0DrQpzKH26LWyfQGIh7R+g6El4jfb6HxNnBx3lE09kSPmv5qfcsH/MQtZ9XDzXq2Yjtm/WOtu/0RsM2yXcDkHm371wRFZntkmmwHagnpHzWRTVp0iTtmY2fITobyYiwRGcK9Th9Pui7v7J1XqwTLBfbmEZz92x0w6MgO+7GGGOMMbVh7nvvpQfqanKjA15jqsKmMpVA22mORtnANKopkFFiqXBRLePoVD3RcBTO32lHpgqSjoSjVEV9EKjiUZUqF6d4Ul3nFB4A7LLLLln76Ihe09AV6HGKmK7Uj7LlVztz7kvFkwq7qkg8N1XWpUuXAsiNHNs98MHNbZov1glTOXr/w21E7xPraZw3k7iomVE2yrxPBx54IIBMTAbWEapjrM/qoYi/sx1TsWYewpgOzPeSJUtww403oqioCBdecEFO2VgutnXWLda1hQsXRpYnnOXhrBGfJ8y/xk/QCJiqSPI8nDnQmAhhuqEvawDYbbfdAOT6AI/z1sI0NaLxkiVL0vuyffHZqna1SlxEZlV5o1TbqtYHNAZuvvlmAJkZKK03+vwjvEahP3B9xsfNXKgarsdFzTAB0dE9eYyuB2G7ZXuIMzVRf+Z8NyxatCjr97D+sb7GRfGN85HOPOisENV+XcsTnlej0hLODKiNO9OKazfaR4iKaaDtWOPCMP9aXtYp03AoyI67McYYY4wx2wuJZCI/xT1ZOy9EBd1xV88UtHsLR8a0S+O+VOQ+/vhjABmFXT2/qH9iKoVUH6gyRNllcsSrI2JV2lXl1hX4qghwxH3AAQcAAB5++OF0mtymSgAVGlVd8s1TjwrlsFLKY2xQE9kVeParr2alpX7tabdOtTHKDpZKBhVA9RVvKufoo48GAPz+979Pb1OFSu1OtR7HeaFg3dHzsX0CmeicTz31FIDMvaZarLMubG+051Tf07RrVXt0IHeNRWlpaZbHGFUveS6qZkyD9ZRKXJgG4T5UBvks0kjMy5cvzyqXrhdgGhongkp8+L8+e9566y0AmWde3759AWRslEP7fyDTdl544QUAmWiuXC8AZNoZZz54X9R+VtValkvrhCqtYV1RlTHfCJ8NEY28yRkaXk/1QR4Vn4HPWfVaFqfc8l7qGhe1S+fv/KS6Hp47TmHmdrY9zrTpufjMCNc3RZ0vahu/s87yWjINljPKQw2QucYsb1TcFF5nXV+iXpRU/Y4zv9H91TIgLJfOfLJ8Gsk2bMemYVHQHXdjjDHGGGPqG3uVqQRVFzjKp21nuNKeCjv3pVJBu2nax1Ep05Xn/E7iRtjhqL0qn8X6u9rNqxLAMtC+lCpeOJrnNtr86jHqEUPLoTbxu9CuPEpN57aKz0TpluztFUp7eVFF9UqmPlUZoLrIe8F7ox4TQqWQKop91daOUPlRO2z1Ha2+xzW+gM7ysC6xPVJlB4D/+7//A5CZwaI6zGPVixPbAtVz+nmmmsy8si6FbYLniLPxZdveb7/9AGTqFtV7EnqpCstXmc9squIaHZj76QwXt/fu3TtrO/27cyYiLDM/dRaCafPZxsiR9MTD68I8UZk795xzEMUTTz6ZU0f0uaqzhZontQXWGb/wf7V/b0xeZQjXVQwcOBBArtrNa6SeukKFlvtwBonvgrigQeopiPvpGhemyToQKtE8B9urrsvS5zXPxdkfPjPoOY51k7NBancO5HpRYYRgPjt4LZlGp06dsvLAc2o5WS5e27AOazvWc+g7ntclbr0J0fUE4XuN59a1OHyvar+I5TYNj4LsuBtjjDHGGLO9kEgWIZGMdz8c7lcbCrLjzlE6R6AcpfJ76GGEKi5HzVTTqOLyXFy9vuuuuwLIjUynI2yOvtUzTHiMjujV44J6eqFaQpVBbYpDjxlhuYFcpZ0jee7TpcLbBRVALZfavudQltmeoLJeobQntmzM3qdCYUd5hV/6pqm0Dj3kEADA5DvvBJC5Lmp7S/tGKgvhDIraAFaZbxNJaCep6zUUtaXmsayXoY0rkFG0otZi8Df6K6eHFHphUZtWtkO2X6bJOsPtagsMxNv0UtUbOnQogEy7e/vtt7POwTx+97vfBZCph1S6Qj/QVLc//PDDrN/0WhGtr2rTynZKNS1U+1Q55bFUNfnMY3m4nfeJzwhuTyvtMpPGmbPvHX44AODvTz+dPlaff/zU9qnrc5Rwu3ozIY1RcTfGmDgKsuNujDHGNFRoIkXTKQ6uOVjjwJCDMX6PMhnkQJSDYBWMNEiQuvBk2moORcJgSBrIUNPgOTjgJhyocrCsok7//v0BZAbI4WCOJm80u+MxTJuLzinOUTxgHigUxZm08tqGg2cOjtW0Vu+TDkb1Wqs5Le+VunoFche+8n7qYmLmk3XI1CHJotRfPvvVAnfcGwn/qbBv7dunDwDg09o06jJR5mjrXvHZ+HxAGFN4/PzMM1P/VMyUJThjpn75m6Q6I+MOOwwA8JdgBsUYY0wFyWTqL5/9akFBdtw5XcvRLlUHjuY5lQxkRsC6cENdPPEYjqS5P6eAqSBwSp4jYi544e9A7uibU/McCXNUHTcqJ7pwTRcohQt0qFiouy2eQ91Q6QIcXpfQdd+2gtda1SJuZ97VpRyQUUnUPEPNiEzlhKYyqtxoQA9tA7poi3WH9ZwmMn/+85+z9g/3UXelTJN1QE0xWL/pMlQXVfN4tk8gY3Kmi/T23ntvAJk68/rrrwPIPE/2339/ALnmHeo6NTThoqkPP7mIlgqhLuYkLAfPRbMimvHQfWTYLpkvDXLDQEpcyMdry4X3NCGkqllTNm/enG6bcYsOef80aJUqjlGL+lXxbIwh26+77joAmfrAexvl4hSIdpmpblp1YauaSeq90oBGarbG/cJ3n95ffrKuxi3eVBM4LRefG1TLw+e/BkhSBVrPqSq3Pu8071Hl1He1zmbEBb+KC8bIvGkeogKUxTli4HuU/QvWIdPwKMiOuzHGGGOMMdsLiaIiJIryWJyaxz6VUZAdd6rctF3j6DvKfRhVNI6IqRRR2aMLOLW544hZFTGmwdE37eree++99LEcwQ8ZMgRARm3TBWihYgfkusjSBWzq/jIcjceFn9cgMqRXhbutzytcRlHV4uJAfu/UIXWNs4IpUW0or/isWIxanv6sqJR0B1lx7KuvvZZVTl573gt1Jcb7Gtr78X9V3B2IqXqccMIJ6f/vvfdeALmKG9Ew5bowmG1g3333BQD8/e9/B5BRuLkAFcjULwYF0vYXp+qxPlJ5pAJPV410HxcuTOfiTNYV2gvTXSLdpbEtDxs2LKu8qvySqAWnfM5Q7eIid14bBnwLr0WI2h3zOkUFeOM2PkfYfngt2I64YL1zxcJ0XvO4IDCxJnBlFdehoh0fd+yxAIAnn3oq1uZaZydUYVR3rmG+NBheY1TcCes533XqolU/w+vJ66gujVWx1cBL6kKYdVODojGtUInWRcrqhlifLbof01ixYgWAXNfIOisb5o+29vzOGTfWe3USodeDedT3L/MQzvzqu5j5jlPa+TxTV7t6L/Q5Et7PuHuu52KdMQ2Xguy4G2OMMcYYs93gxanxcCTNUTlVtqgwwdxXA75QIaK9JxWxOHWN6O8cEVPNAzJqGZU9VTx0FB4XEENt8PT3KBdrGvhEA718UOGuTtNUFZGzBJ0q1IpQcafyllbaKxavZTKRzPqc/drrkXnktadiwHuj6wdCVUJdZHIfh3euOVrHVWlTO1VeewbOYsCT559/HkAmaAxVsdAul0GAqAJreHJVy5gWA4yFbTvMG21gw7pCe/N58+ZlHcu2Tzv0sWPHAshV/9TWV69TqB7SFp0qP1XMAw88EAAwcuRIAJnZCA0OpW05dGsZ5i0ss85MqXtO2vZSpdTy5MDylGa7g4yjvLw8dnZQPZEwT1GBgrRczGfcuRsTXJ8wYMAAALnronSNQQjvO+uJ2kizjunsBz85u8W6GWdfH7rz5f1mvuIC/sW5B2XafGeyHjEgka6NCc/N8nCmL24WmujaMX6ybobrZYDs9q9rqtTGXffjbICq5Dq7wfOou9twH5Zd16nx3KwzpuFSkB13Y4wxxhhjthuSyTwV90boVYbqHEfGtOWkRxiOZoHcUTW9UlDxo9cHqoe0QaVdnI6gqf5wBB01qqeqQOWd/lRVOWc+Ve1mXllOlisuLyG6D5VA5kVH6+oFgqN3loGh1qkEhHb5e+6xR076ALC04hryvvDacIaE15rnUvWV9yTKYwLzqWGeea1M9aG9+4MPPggg19OBzmT17dsXANCnwrXojBkzAGR8LatiyvsLZNQgfvKc3Id1g4oTf+d3tg0qWV26dMlKM1zLwVk21nUeM3fuXAAZlZ7EKdHqjYKE6ypmz54NINemm2myDTO/n332WdY59ZqrB6lQ4aMSyHLpbBPPwfJRveR+VPEe/9vfAAA/OOJ7ADIzaYmi7OtQHqO8JxKJ9PNGbXV1liZqNhTInrVQu2Beg9/85jeR6TcGrrjiCgCZ2SzWB137wOsdvvtYT9R3OoMQ6vtD7a+Jvq/ivNEAubbqbA/qQUyDuTH/fK7zec46yzUsbHMsA5BRrbkPj+Ezg+++OC9u2tY406CzBmH7Vxt3vTZE137EXXOuYeB1470L9+f/2k60TrDOmIZLQXbcjTHGGGOM2V5IJJNI5KGm57NPZRRkx51qOEe5VBJo4xYqALoKnQoy7au5ApujVdrgkrjw7hrZLMrrA/NFBUBH9uoHW2cFaKvH0Tft/FSpD7dRkaayR6WPavcnn3ySdT2Yb14ntVFUbzyhsvb2O+9kXQMqFbrCnrB8vH/cj/bLjGyntsihnZ/6FFa/36bm/OQnPwEAPPTQQwAy94F1gXa2vM8zZ84EkPExznuhalSoVFFZ5/3aa6+9AGQ8vPCTbYDKGu+3+jtmXdK1HOE2tZtn2kyD5VPf8Koo8jzM06xZs9Jpsa6r5wq2O22PVBS5DkYjLsb5dwZy1Wt+qics9T4R2gWH5YGuWUEMFfs9849/pNPk85Cf6gNb16SQqDyp3/A4f9WNEc5Q8b2lXsLURhrItEfuy7qottysB2rTrWtc9L3D76EqrO0gtH8HMoq6Hsu2yu18T+t52N6j0Peuqvfq8UZnFNk2mZbOhoXljLsWJC4GBNPiNWWeeG/4fNR7Fx6raz94btu2Nx4KsuNujDHGGGPMdkMiT68yiUboVUa9XlApoIIb2oOqOsVjaPfGEe6nn36a9Z0jYipCauca5y89hMqk2usyTxwhU/VXxYwqHdUHKobM05VXXplO67UKH+nch588x7///e+sNFgeqgy0LVbbxDj/y+FvRJUyjbQZ2jqH33kvmGfeP/XyAWTUE01b1X1Tc4455pjI7c899xwA4F//+heATF1Qjy68F6xD4ewU7c6pNOu6B52dUk8obCusW6q0h/WT9Yd1mu2Nqh0/46J6xq0pYWTScO2FqsW6XoOzZZdddlnWORkZ88gjj0RlhHbeGptBZzh05kBVfPUF/sw//gEgc9/GfOc7WWk/+9xzsTbrvN46Y8D7UZUnm3A7z6EzIwZ49913AWTaiUYi5ad6MAMyM9Fsn/zUZ6jO7uh+6oGMaYbvW95PnoO226yrbLfMk/o3Z5o8jmvO6Bkqar2X2sczDb5f1KMN0+Q5+J5mefi+5syaeloDcteZ6LMi7lpqDBa9J7wuavMO5M4U8Nxs16wjph6pI3eQtTO0McYYY4wxxtQJBam4E7V71dE6kGvPx32o+NEzhkZkpI0Z0dGuKmwhulJe1Seem/aKVJaoBBx33HFZ56NysPfee0dchRQjRoyI/S085/XXXx+ZB/VDq+pdlPcItaHVyK+EaVFJ47XmdqoqPJ7KR1SUPFV11WOI2XaMGTMGAHDrrbcCyJ2d0dkoVXaBzP1jvaN6T9TOlnWAdYp1gfuprWxoa0pVkmsoqO5r/AC2P5ZH2zafIZzVomeLsF5q2S+99FLkQ1VKO7nooovS/998880AMm2S15/50WeXxotQu2JVVJ948sn0sWpPqz6/49axEI2Cql5lQlWd+3LbDTfcAJOCMy5//OMfAWTWP+mapLD+6zVXu2q9d7puQde4sJ6w7UVFv9V6wvbOZ77ODmkUcY0UyxnjfKLoUo3XWTieU+3oOXvLdx/zqJ7W1JY8PBevhc5e6LXkOeJ84WtfgZ/h/eR90BkpzuY1Zu9L2wtenGqMMcaYeqdtRaeW/39c4eTAGFP3FGTHnaNdjlJpNxvlVUZVHB1FUyFilEUddcdFeGMeeL4oVZFoZDNVJJn/c845p9Jybw0uvvhiABnlRv3Pql9gnVEIy6mKn24nVDypovAaq5eduKh5oTKkUf1UTTHbHt4v9V6iazjUowSQW6/oE54zYDyG36m4qZ2qKlxRfsKpPHONCNOmF5w4zw/qQYrbGf2UhH7caffOY7YlF1xwAQDgpptuAhAfIVVnDPQaqtcdnTkLf9N9+Mnnn9rbx9n+6nlDdEbA5MIYBJyF1WsVXle9F7zvev/ZZnRWWWe5Qtq0aZOe5WwTdOrZDpmGzrLy2a7vbn5n7A/ux/LwO1X1KDSCKs/JdwTX4jBNlktnDjWiLMsUlpP7clucb3XtR/CdFnftea94nqi1IXpu1gmzHWAbd2OMMcbUF926ds1S20mXzp2x75Ah9ZAjY0xBKu5qD6YRGkM7OPVQwpGurszm6Jt2b3HqQ1zaoW2n2vERHVXzd7VJrQuYpipqcddJZw2AXP/XakPI7ar4qH2j2rYzDZ4nVG65jR4E1H7TbHtUyWV7Y53SKKehYqeKHOsClXeNXKzqvtqy8zvrQaiKffjhhwByo+xSYYvzE876R2VO19KQMC1GjWWEy7rgwgsvBABMmTIFQLynnTg/7hqJkYQqH+913HNPo0GrPbSuP9LZxnCmjOe+/PLLqy58I4V2zPfddx+ATLRQtrXQC4mux1KvMPzU2ZKodVtxNG/ePGuWS5/52mbUSxvrD5V0Ku6czerUqVNWnjgTFwXbKdNm1HCiNvDMi7YLXUelMxXhMUwz7v2j15Sf+q6Lu27hjArvE3/jTKJt27cjksk8FXfbuBtjjDFmKzF82LDUP+UxnfiEJ+uNqS8KsuNOmzUqXvQDzlFr6JlClWSqg+qLVvfn72rTqd5WdD8gN6qq2pKqel8fNp2aB42Op1Hm1NYw/F8VdvVaoKo+UR/EVBJ4PiokoSJCm0nec+aPdomm7qDaxPtOZZvf+bt6igEy6hHvNduM+n3m/aWaH+evn+soaGsOAJ9//nnWMbqGgmj0Q/X8oGqaeowAMu1/zz33jMzftuTMM88EAFx99dUAMtebtvz81LUIOuPFz3D2UH3aq+2tKuyE943tlJ8aH+Pcc8+tQYnNG2+8ASCzNktnsoDcWZG4GRi9p9VZL1RaWpql0GscA4Xb9b2p670YRZvPlIEDBwKofHaa9Xf+/PkAMuVVL1JxeYjLa9RMhM5E6zNC+xd6Dl13okq8zjQCmXvMfVkHTjzxxMj8m7onUVSERB4xZfLZpzI8bDbGGGOMMaYAKEjF/YMPPgAADB06FEBm1EpVJ1TMOELnaFv9o6p9myrsqkzraF1H1EBuBEaiyge/x0Wq3JYwzSeeeAJArtqin7oqPvxNlQtV6XRlPK8Vrz2jAXI2hOflceGaBd5jVSpYJ374wx/meQVMTdH7GufLmHWFfsTDYzmbou1MbdjVHpfH0xaeyhwjlIb2tmovSq8SOsPD76q0q0LJuqZRmMNroeeoS+JswydNmgQgo2aqv3q2wyhf+HHrABRV6zkDxvvEa8a06d3K1Iw77rgDAHDNNdcAAEaNGgUgMyMJZOot13nx3nCmWj008bldnTVX5eXlkWvKeJ/Vjl5nu1S55uwQ6w9jLzDeA71MsS0DGbt42nyznXKdDM/Jes08qDcZjQbMPLNM4Wwfr1GcbTv35Zo5jdbKa87tLC/boq4TCtOaNWsWgEwdMNsRyWR+9uu2cTfGGGPMVoe27LR1t227MfHUkTvIguy4X3LJJQCAP/3pTwAySpIq2kCu3aqO+OP8l8fZrsVFFA3VRv6vvqVVwdseon0yD7yGzKMq8Lx+4QyFqqGKXkNdP0BlhOfWFfpR91O9/dD7AOuEqTtYvzUqoCrt4RoOKlVa93k/9RyESiI9Rbz66qsAcmeEovxYM/1BgwYByNQv1kPOGKjPZZ0N4O866wZk2sv20KYVtSO/4oorAORGjuRnVKwGbcNE1yJwRmzVqlUAMlFezbaBEXoZzbhfv37p31hf2ebUlzq363qt6tCqVaus5zPrENsr6xIV5bhYAuoliso6v7M+cYaN0ULDcrJuatRVnlvXbzEvzCu/c+0Kn2/0VhdeH123o+9NjZLOT/UWo5GEmSZnD8I0abufb1Rm03ApyI67McYYY7YNS5ctQ7NmzbBThZmJlXZjqiaRLEIiDzU9n30qo6A77rRrpa9X9Q8O5Hp40eiOalsX5QEDyH+VPBAfgVGVAR1t1wdqr6seJng9VBkBcj3txKF+galw0CeveqxRrwbhddIZD9YBs+2hrTTvB++jeqWg0q7eZsJjeK9Zv1RxC+1mw+1Uvw455BAAwOuvv56VZpRqyHNTiVP1WOuvtktV7km4doPlocer7Zmrrroq731vu+02ALlt8qyzztqqeTLGFBZXXnllzrNk1113TcfPUIqLi3HyySdnbWvevPk2jzZ955134qabbsLSpUux995744477sDw4cMBpGbsr7jiCvzjH//AwoUL0bFjR4wfPx4TJ05Mz0hujxR0x90YY4xp7Jx//vkAgMmTJ6e30YVinImMLiBVk7CysjKsXLUq/bs6dtiyZUvaDSuhIMZjaMpIwsWWQK7wpa6Au3btCiAzSObAOBxE0zyHeeGiVJ5DRQGeQwUllpvmXjQfpXloaGbLtOKcWOi5WT4NQKWuOdW96scff5w+B+/x9sTgwYPx3HPPpb+rIwqlTZs2+Oijj9LfqxL9qqK4uBjFxcWYOXNm5O8PPfQQzj//fNx1110YMWIEJk2ahLFjx+Kjjz5Cp06dsHjxYixevBg333wzBg0ahM8//xxnnHEGFi9ejIcffrj6GUrkuTi1ljNYnv8yxhhjjDHVokmTJujSpUv6L/T2E0UikcjanzOoZOPGjbjgggvQvXt3tGrVCiNGjIjtlOfDrbfeitNPPx0nn3wyBg0ahLvuugs77LADpk6dCgDYY4898Mgjj+CII45Av3798F//9V+49tpr8X//93/1El8nXwpacecIdMaMGQAyo97QPIYjfKoFGjaYI2QeQ9eEHMXriJBT+FwsoyGbAeQoFNxHlY2f/vSn1S3yVod5eOaZZwDkhpZXlSU0e9CAOzRF4L4atIVTYlxYxGvJ/biwT0O3h+qFNqbtUYVoqOjCK9YNLhjt1q0bgMz9pClU6FKQahjvoy4U0yBcrCNsp/yddWT//fcHALzyyitZeQIy9YaqXZw6pqYxGihNyx9ljsNtfC40FM4777z6zoKpBqEJ0z//+c+s36i0q8vSuHekqsDcrkG0wncff+O+NIVT94ls13zm8zlAN4jqTILnoVnsHnvskU7zvffeA5BrhqflZFosp7qKjmv3PE9YTj4LWE417dMAS/pOi3Mfq4G0tneTtE8++QTdunVDixYtMHLkSFx//fXo2bNn7P5r1qxBr169UFZWhn333RfXXXcdBg8enP79rLPOwvvvv48HH3wQ3bp1w2OPPYbDDjsMc+fOxYABA6qVt02bNuGtt97Kcj2bTCYxZswYzJ49O/a4kpIStGnTpsrZgyjqysbdirsxxhhjjMmbESNGoLi4GE8//TSmTJmCBQsWYNSoUVniXsiuu+6KqVOn4vHHH8f06dNRVlaGAw44AF988QUAYOHChZg2bRr+8pe/YNSoUejXrx8uuOACHHjggZg2bVq187dy5UqUlpbmqPqdO3dOe+2JOmbixIn42c9+Vu306pKCVtzJv//9bwCZcONhwBeiip3a4lGNoyrM0bcGaOIImmoizxsusKBqoCGKmQaP3Z5gnljJmWdeS5YzdHenijnLTQVD1RdeI12AyHtCpUSPC+FvvOff+c53alBaUxM0PDnvJxcIUz3SQD5c+B3+xnutdaAql3RUy6hcMU+comXAn3Df3XbbLbIcmqe4YCq6qJyECzZZDtrHGlPfsEPUv39/AJn2qgqzOmzgM5/700aedZzKdlSgJp6LbYa24DyHOm7gc4BtUIOdqetWdrjCReDMJ9PSdqyuGamkqo2/Bl9UhT58H/F/XYjPtOn+kuVSm3d1P80ycD/eu+2ZcePGpf/fa6+9MGLECPTq1Qt//vOfceqpp+bsP3LkSIwcOTL9/YADDsDuu++O3/3ud5g4cSLmzp2L0tLS9NoMsnHjxvRs/sKFC9NufYHU9d28eXO67wGkXEPXxD306tWrcfjhh2PQoEG48sorq308gIoATPn4cXcAJmOMMcYYU0+0a9cOAwcOxLx58/Lav2nTphgyZEh6/zVr1qCoqAhvvfVWjoDDjnm3bt0wZ86c9PZHH30UjzzyCO6///70Ni4q7tChA4qKirLEHCAl7tDHP/nmm29w2GGHoXXr1njsscdyvGhtbzSIjvsvf/lLAEgvOOjVq1f6N7XH5SiaI111d6gry9XmTuHIO1TjNA2OuqlU/OQnP6l2Gbc1zNOjjz4KIHNd1P48tAdm2eOuDdUIDRmtds1qJ8hrHmXj/vnnnwPI3HNTd/z85z8HkAm1rfeXsza0dVebeCBzT+Ns14nak6u3Bl2jEmWPSJtUqvGqeqlqz7rN/eLcRZJwNo7BUbZ3m1TTeHj77bcBZNZt6YxZ3FoiXfOhSjTbfZQLVirHPCdVbQ18qOu/VMGm+s93AcvA869cuTJ9LrZv7sNzr1ixIitt9Q5Tlfth5ommH+F10eeVepnhM4PnjrvWGgSK5ea9O/HEE1EorFmzBvPnz8977V5paSnmzp2L7373uwCAIUOGoLS0FMuXL8eoUaMij2nSpEl6BglIrXlo2bJl1jbSrFkz7LfffpgxYwbGjx8PIHXdZ8yYkfWcXr16NcaOHYvmzZvjb3/7W2x/Ji+SeXqVseJujDHGGGPqigsuuABHHHEEevXqhcWLF+OKK65AUVERjj32WACpQUf37t1x/fXXAwCuvvpq7L///ujfvz++/vpr3HTTTfj8889x2mmnAUi5Lz3++ONx4okn4pZbbsGQIUOwYsUKzJgxA3vttRcOP/zwaufx/PPPx4QJEzB06FAMHz4ckyZNwtq1a9P+5FevXo1DDz0U69atw/Tp07F69eq0mVPHjh2rHU04UVSERB7H5LNPZTSojvspp5wCIBM0BMhMm3AErCvr1Y8sR7z85Cibtt9U9vjJ8+qq8hCeY9GiRTUsWd3BPPbp0wdAvFed8De9JlQTqMBSRYmzKaQaQTWFdoxUU0NfwPZysf3A+6mzTuqLOHz4sS6oP2PuwzrENsPtqryrpybdH8i0WfVkEae8q0clom0gSt3Pd3rYmLqCAdP4OWTIEAAZBZntgAo827M+x9UmXj2Mhe8EtYvX9U1872q7VXVbZ8T5LKGHqHCdGLfx3Mwf99H2zGePdsqYR50JZkcuVGLV37wq6iw/883tLK+uF2Bac+fOBZC5Z9szX3zxBY499lisWrUKHTt2xIEHHohXX3017Tls4cKFWbOtX331FU4//XQsXboU7du3x3777YdZs2Zl2axPmzYN11xzDX71q19h0aJF6NChA/bff39873vfq1EejznmGKxYsQKXX345li5din322QdPP/10+t3w9ttv47XXXgOAHNV+wYIF6N27d43S3dY0qI67McYYY4zZtjz44IOV/q7+12+77bYsUTWKpk2b4qqrrso7uvNJJ52Ek046qdJ9zjrrrFgTxtGjR8c6JagRyaI8F6dacc8hVGVvuOEGABn1jaNmjpCpLnBETEVQfY9zO4/np+4H5HqhUE8a2zO6yl9Xy0fty2uh11BXyvM7Zz24vyqaVF24qOTXv/517Qpltipnn302gIytO1UkKlxUKbg9ykZcbdXVzpT1j8dqpEHWS65FiQqWQQWFaakNryrn/F09QeiMEuv7J598kj7Wtu1me+Xcc88FAPzpT38CAPTo0SPrd6q9GmmUijTbINse7bn5e+hthQo5204YUyU8F9+/fBdo+1aPZWx7tHkP36XcprN16qddI8cyLVX71eMcPZqEzwv1Ya8qPvdluVgepsFnjMY24b0ypjIaZMfdGGOMMcaYOsOK+9aBau29994LIDPaVg8nqipQYeZ2jox5nNrwhQqAeqfgCJ6LMLZnmEeqM1QreF3CcnIbrwXLrb7w1StBVbbQ/G6lffuGyju55pprAGS8zLCuhB4Y1Hc025lGNVU/zuqNgeo+12SwHYZ2q1zfwvannh7U1l3zorNMPI6qWai4G7O988YbbwCI94DCdqL1X5/PVJn5Lg1t3OOiEsfNdqlizWcHP3lutY0PZ/F0HQztxqn+U5HXOCN8LmlsCLVXV9U/PAfT1BlE/c5rG6fA895wYacxldHgO+7GGGOMMcZsSxLJJBJ5uHrMZ5/KaDQd9wkTJgAAnnnmGQC5Edo46lZ1WFVzjpSpFFBtDiOKEm6LigC6vcM887qoHWG4jaoDVVD1cRvnJ1dVVW7nvTKFxaWXXgoA+M1vfgMA2HfffQFkq+Bx/tdVgdc1JMuXLweQ8d9MVY1qmHrACNFIqfzOc7BNU6FTTze6NuXVV18FAJxzzjlRl8GY7ZJbb70VAHDdddcBQI6vbNZ3jTui652otOsaJyDTfrnOicdqHBXOyrZt2xZApt3yfco2qGtdombDdOaA5aByznPqs4brY9T3vCrvLG+o8jN9XiMtL9OK82DD8r3zzjsAMvfGmHxoNB13Y4wxxhhjtgmJPG3cE7ZxrxYff/wxAKR9h8ZFi9Pt6suWKl1lCgCPrcpd0fYI8/zwww8DiC4nVXn1ea9+szVCJeF+/OS9GTt27FYsialrLrroIgBIB97YZZdd0r/Rxy9nawgVKqpfn376KYCMosX2p4o6lS7WNZ4fyF0zoZ4eqBQyhDY9Tw0YMCDreEZgfPPNNwHY84MpbC655BIAwD333AMAGDx4MICMWsz2QXVcbd+5nUo2P4HMe5O+z/mpkVKp1qunGo23osepXXq4Tc+tNurMG+3KqbizfOphTj1ehe8vLR/fhUxDZ+l0VpnvOt4LY6pDo+u4G2OMMcYYs1VJJIBEHvbrES6Sq5VM+Vb1Pl940NuMrrRX+3T6cqUdLFEVOTy2ptG+tkeeeOIJALlKKZDrnYMq6apVqwBk7Px4LPf/+uuvAdimvTFx9dVXA8jUCX6SuIiE6vmCCjvXVbDO0a4eAPr27Qsgt36qxwcq6oxayN+ptHEWwOqYaYg88MADADLxF9gGWe91/ZbajtN7E5BRlqlEqzc2wvbKWa/27dtnnVtnvDWeCm3DgUxEWI2Krko53+V8ZvCc+k7XGTmWM7RxZzRvVdwJ33U8B59Xn332GQDguOOOg2k4rF69Gm3btsVXc55Hm9a5faSc/b9Zg/b7HIySkpKsGat8qd3SVmOMMcYYY0yd0OgV9+py0003AcgogqoEAg3bBnbSpEnp/2nHxypE28ELL7ywzvNlChMq8KxLVO+ogrFu0X5V7VJV6Tr00EPT/1Nx07UUhG2XHmto6+74AaYxMmXKFADAwIEDAeTGMmEb1e+hpzGNHBoXh0FtxHkclWpVwdneqZKzrQLAPvvsAyCjbqt9OdV9zhxQUVcbfV2bppHPQ29p3MZ8sZz6neegTfuZZ54J0/Cg4v7lv17IW3Hfae+DrLgbY4wxxhjTkPHi1GrS2NXkhjybYOoPKnLqS1pVMI2sSqiyhV5n1JsEj42LtGil3TRmqAZfdtllADKe17hWRD3BsP2ESjTbqdqZa7vmmjL+zvVO/OT+Gs+Bv4cqP7d16tQpqzxU5/UYXa/G7epVhmVRrzpAxhafxzB/zDe9Yr3//vsAgIkTJ8I0AhLJPBen1k4zt+JujDHGGGNMAWDF3RhTb6gdKb0vqILF7erHmcfRB3uoiqnHJ1XWmAa9yhhjMurw+eefDwDo0KEDgNxooGyL4ToTjelBbzE8VuMucDsVeLUv5/n4yfUo4cwat3HdmUY/Z3RW9TLDNVk8F73S8JlC7zNMO7SdV29YzDdt9t944w0Ajoja6Egk8nP1WEt3kFbcjTHGGGOMKQC2u477okWLcPTRR6Ndu3Zo06YNfvCDH6TtxYwx2RR6e7nssstw2WWXYcuWLdiyZQvWrVuHdevWYfPmzdi8eXP6+/r167F+/XqUlZWhrKwMLVq0QIsWLdChQ4esv2Qymf4rKirK+gt/SyaTWL16NVavXo2vv/46bQdrjDHG1IhkMv+/WrBdmcqsWbMGBx+cckp/ySWXoGnTprjttttw0EEHYc6cOelFJcYYtxdjzLaDZh4///nPAQAHHXQQAKBXr15Z+9HsBciYz2ggQy4EpRnK0qVLAcQHOaLpCQfUy5YtAwCccMIJsfl98MEHAWTM5mh+o+Z4GhyqW7duWWlysTpNgLg9XBDPbeTzzz8HALzwwgsAgN/+9rex+TSmtmxXHfff/va3+OSTT/D6669j2LBhAIBx48Zhjz32wC233ILrrruunnNozPZDQ2ov9Ohy/fXXA8j1z84XJTsEjPJIjxe6P5B5MfOFqzbvCxcuzErbGGOMqSnliSTK8/AYk88+lVGtAEzPP/88/uu//guPPvoofvjDH2b99sADD+D444/HrFmzMHLkyBplZvjw4QCA119/PWv72LFjMX/+fMybN69G5zWmPli/fn06HPc777yTXtz05ZdfYvDgwejTpw9eeumlnHDg+dIQ2ws77trJzrfjHs4yqFLGY7lIjUFcKlPxjDHZ0F3kXnvtBQBZAWS6du0KILPgk22NSjy7G7rYnNuphq9cuRJAZmFoddro9OnTAWQWk3Jxrar6fO4yr7qdzw/mdcmSJek0mM93330XgN09NnYYgGnVB6/nHYBp592H100AptGjR6NHjx64//77c367//770a9fP4wcORIbN27EypUr8/ojZWVlePfddzF06NCccw8fPhzz589PrwI3phBo2bIl7r33XsybNw//+7//m97+i1/8AiUlJSguLkZRUZHbizHGGGPyolqmMolEAieccAJuvfVWlJSUpN0srVixAv/4xz/SnZM//elPOPnkk/M6J0faX375JTZu3JgesYdw2+LFi7HrrrtWJ8vG1CsjRozARRddhBtvvBE//OEPsWzZMjz44IOYNGlSOrS420uGiy++OOv7NddcAyBXgWcZNUBLGJiF29S1JAc0oYJmjMkPVZevvvrq9P9jx44FkGmHqqxr8DO1P+d+bKMnnXRStfNHdb64uBhAxiUl02Le+Ezh80HzyGctVf/XXnstncbll18OADjqqKOqnT/TgKmjAEzVtnE/8cQTcf311+Phhx/GqaeeCgB46KGHsGXLlnSDGTt2LJ599tlqnZeNQ/2jApmXM/cxppC48sor8cQTT2DChAlYs2YNDjroIPzyl79M/+72Yowxxph8qHbHfbfddsOwYcNw//33pzvu999/P/bff3/0798fQEoNi1ICK4P2aJUtMgsDIBhTKDRr1gxTp07FsGHD0KJFC0ybNi2t/gBuL5Vx6aWXZn3ngtsdd0zZEVIV4/UMPVxQxaOyRqXtgw8+AABceOGF2yrbxjQaqD4DwBlnnAEA2GOPPQAgPatIO17avBO2X5oB0pUtPdnUBqr19PDC9TC0eU9IEBwNovTxxx8DAN577z0AwF133VXrPJkGzvaquAMp1f2cc87BF198gY0bN+LVV1/F5MmT07+vX78eJSUleZ2rS5cuAICddtoJzZs3j5y+5ja6bTKm0HjmmWcApDrVn3zyCfr06ZP+ze3FGGOMMflQLa8yZOXKlejWrRuuvfZarF+/Htdccw0WL16cHskWFxdX22YXAIYNG4ZEIpHjJePQQw/F/PnzMX/+/Opm1Zh6591338WwYcNw/PHHY86cOVi5ciXmzp2bXiPi9pI/v/nNbwAAhx12GIDcsOuh6RAVd5oOffHFFwBSLjONMXXHmWeeCSDTFql2s/3efvvtdZaXc845B0CuLTtnKqdMmVJneTENA3qVWfnxO2jTunXV+3/zDToMHFJjrzI1Utw7dOiAcePGYfr06diwYQMOO+ywdKcdqJnNLgAceeSR+PWvf40333wz7S3jo48+wj//+U9ccMEFNcmqMfXK5s2bcdJJJ6Fbt264/fbbsWDBAgwbNgznnXcepk6dCsDtxRhjjDH5USPFHQAeeeQRHHnkkQBSi1OPPvroWmfmm2++wZAhQ/DNN9/gggsuQNOmTXHrrbeitLQUc+bMQceOHWudhjF1yRVXXIGJEydixowZOPjggwEA1157LS699FI8+eST+O53v1vjczfG9kJl7tBDDwWQWYDLx1hoQ0tvEevWrQOQ8Xd/7rnn1klejTHGNHzSivsn/8pfcR+wd934cQ854ogj0L59e7Rt2xbf//73a3qaLFq3bo2ZM2fi29/+Nq655hpcdtll2HvvvfHCCy80yE6Iadi8/fbbuO6663DWWWelO+1AKlLnsGHDcPrpp6dDetcEtxdjjDGmcVFjxX3Lli3o1q0bjjjiCNxzzz1bO1/GGBPL+++/DyDXq07ox5027rT15wyhMcYYs7VIK+7z3s1fce+/V93auAPAX//6V6xYsQInnnhiTU9hjDHGGGNM4bO9uoN87bXX8O6772LixIkYMmQIDjrooFplwBhjqsugQYMAABdddFHW9nACkR4rbr311rrLmDHGGLMNqXa3f8qUKTjzzDPRqVMn3HfffdsiT8YYY4wxxhQM5Ylk3n+1ocY27sYYY4wxxjRmaOO+4tP387Zx79h3UN3buBtjjDHGGGOQsl1Pbnsb99odbYwxxhhjjKkTrLgbY4wxxhhTG+rIq4wVd2OMMcYYYwoAK+7GGGOMMcbUBivuxhhjTOOkrKwMd911F/bZZx/suOOO6Ny5M8aNG4dZs2bVd9aMMfWIO+7GGGPMdsaFF16IM888E3vuuSduvfVW/OpXv8LHH3+Mgw46CK+//np9Z88Yo1Bxz+evFthUxhhjjNmO2LJlC6ZMmYIjjzwSf/zjH9PbjzrqKPTt2xf3338/hg8fXo85NMYo5YlEXsGVyhOJWqVjxd0YY4yphM8++wyJRCL2b2uzefNmrF+/Hp07d87a3qlTJySTSbRs2XKrp2mMKQysuBtjjDGV0LFjxyzlG0h1rs877zw0a9YMALBu3TqsW7euynMVFRWhffv2le7TsmVLjBgxAsXFxRg5ciRGjRqFr7/+GhMnTkT79u3xs5/9rOaFMcZsG+pocao77sYYY0wltGrVCieccELWtl/84hdYs2YNnn32WQDAb37zG1x11VVVnqtXr1747LPPqtxv+vTpOOaYY7LS7du3L1555RX07du3egUwxjQY3HE3xhhjqsF9992H3/72t7jllltw8MEHAwBOPPFEHHjggVUem6+ZS+vWrTF48GCMHDkS3/nOd7B06VLccMMNGD9+PF566SV06NChVmUwxmxlEonUXz771SaZ8vLy8lqdwRhjjGkkzJkzBwcccADGjx+PBx54oFbnKikpwfr169PfmzVrhp122glbtmzBkCFDMHr0aNxxxx3p3z/55BMMHjwY5513Hm688cZapW2M2TqsXr0abdu2xfJFC9GmTZu89u/UvSdKSkry2l/x4lRjjDEmD7766iv8+Mc/xsCBA3H33Xdn/bZmzRosXbq0yr8VK1akjznnnHPQtWvX9N+PfvQjAMCLL76I9957D9///vez0hgwYAB23313vPLKK9u+sMY0Iu6880707t0bLVq0wIgRI2rmctXuII0xxpjtg7KyMhx//PH4+uuv8dxzz2GHHXbI+v3mm2+uto37RRddlGXDzkWry5YtAwCUlpbmHL9582Zs2bKlpsUwxggPPfQQzj//fNx1110YMWIEJk2ahLFjx+Kjjz5Cp06d6jt7ObjjbowxxlTBVVddhWeeeQZ///vf0adPn5zfa2LjPmjQIAwaNChnn4EDBwIAHnzwQRx22GHp7W+//TY++ugje5UxZity66234vTTT8fJJ58MALjrrrvw5JNPYurUqfj1r3+d93nKE8k8/bhbcTfGGGO2GXPnzsXEiRPx7W9/G8uXL8f06dOzfj/hhBPQt2/frebtZb/99sMhhxyCe++9F6tXr8ahhx6KJUuW4I477kDLli1x7rnnbpV0jGnsbNq0CW+99RYuvvji9LZkMokxY8Zg9uzZ9ZizeNxxN8YYYyph1apVKC8vxwsvvIAXXngh53d1Fbk1ePzxx3HzzTfjwQcfxNNPP41mzZph1KhRmDhxInbdddetnp4xjZGVK1eitLQ0J9hZ586d8eGHH1brXKu/WZOX/frqb9ZU67yKO+7GGGNMJYwePRp17YCtZcuWuOyyy3DZZZfVabrGmOrRrFkzdOnSBQMqTNzyoUuXLungbdXFHXdjjDHGGNPo6NChA4qKitILwsmyZcvQpUuXvM7RokULLFiwAJs2bco73WbNmqFFixbVyitxx90YY4wxxjQ6mjVrhv322w8zZszA+PHjAaQ8SM2YMQNnnXVW3udp0aJFjTvi1cUdd2OMMcYY0yg5//zzMWHCBAwdOhTDhw/HpEmTsHbt2rSXme0Nd9yNMcYYY0yj5JhjjsGKFStw+eWXY+nSpdhnn33w9NNP5yxY3V5IlNf1ihtjjDHGGGNMtamdF3hjjDHGGGNMneCOuzHGGGOMMQWAO+7GGGOMMcYUAO64G2OMMcYYUwC4426MMcYYY0wB4I67McYYY4wxBYA77sYYY4wxxhQA7rgbY4wxxhhTALjjbowxxhhjTAHgjrsxxhhjjDEFgDvuxhhjjDHGFADuuBtjjDHGGFMAuONujDHGGGNMAeCOuzHGGGOMMQWAO+7GGGOMMcYUAO64G2OMMcYYUwC4426MMcYYY0wB8P8BRqOUWBsfw+kAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -154,17 +185,19 @@ } ], "source": [ - "from nimare.meta.cbmr import CBMRInference\n", - "# Group-wise spatial homogeneity test\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", - " t_con_moderator=None, device='cuda')\n", - "inference._contrast()\n", + "# homoogeneity test for each group\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + " \n", "plot_stat_map(\n", - " cbmr_res.get_map(\"homo_test_1xschizophrenia_No_chi_sq\"),\n", + " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " threshold=5\n", + " threshold=30,\n", ")" ] }, @@ -172,18 +205,90 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IZjIlBVdqagAmv7gbY4wxbZSbb74ZADBq1CgAsf01bb1p607Vl0o81e2meF1RX+iqdrMszJOqf5paTi8t3D+E9WAe6kOdaaotvJaJZd4Y98C6PoDfaetO/+60bWdeLCuv1XnnndfovE37wS/uxhhjjDHGNIFMRQaZbMMD3aYMhgG/uBtjjDFtFvphp1qdpmZTJaa3FaJKdH1eZdLswNNeVLiddvaaFz+pUCflSWgvTuWd9eO+DfmfT/OEk0Ro1x+WO+3csGzq151KO7fzWhlTH35xN8YYY4wxpglkKzLIlqC428bdGGOMMQX87ne/AwAMHDgQQKy0Myop7a6pCtOmW22+qQ6r6k07cyrbYRqlwv2pbi9fvhxAsV06Wbt2bUEdwm2sB6Ovahr0X78xtuthGYFYKec5JFT7dX2A1lPP/VZbbVVQZl67k046aaPKato2jpxqjDHGGGPKnnnz5uG0007DlltuiS5dumDXXXfFSy+9lLr/GWecgUwmU/S38847Nz7ziiwyJfyhommv3lbcjTHGmDZGz549ART7bVevKtyunlqoDlPBrqqqAhDbdzMd+iwP01D1XuF2lk1nAdLs6bkfZwHCbVov3bex3nI446AqOQAsWbKkIA8q51TMqe5zO/PWa0J4vpgH9zONY9myZfj85z+Pgw8+GH/961+x1VZb4d1330WfPn1Sj/nFL36Ba6+9Nv+9uroau+++O0488cTmKPJG4Rd3Y4wxxhhT1lx33XUYPHgw7rzzzvy2YcOG1XtMr1698oujAeAPf/gDli1bhvHjxzc6/0w2g0xFCV5lYBt3Y4wxxgRQ7eUnvcVQmabqq/up73XC7VSw+Z1KfFKaqmqrks79aRtOG3cq0KpMU4kO80xTsamUsx5qf65lUk81PI4qepgnlXHmoWmqdxymzdkJPZdU7lXBN43jj3/8Iw477DCceOKJePrppzFo0CB885vfxNlnn11yGnfccQcOOeQQDBkypNH5ZysyyJbw4p5t4ou7W4cxxhhjjClr3n//fUyaNAnbb789HnvsMUyYMAHf+ta3MHXq1JKO/+STT/DXv/4VZ5111mYuadOw4t4CPPTQQwCAHj16AAAO2GtXAMA/X34DQLHysXTpUgCNW2HOVelbbLFFYpq6yp1R9I499thG18eYcuK+++4DUGzDqn6b06I+si+NGzdu8xfWmEZw00035f8fMWIEgFjVpZrN72zHjJhKNVhVc9pn05MKP0no+SVNpdffVYnnc4plTFOymXfoa55ppinpfNYxD0XV8bTfw3qqPT096/Bc8dypak/beEZQZZ4sO68N9w+v5/nnn59YPhNTW1uLvffeG1dffTUAYM8998SMGTNwyy23lHS/njp1Knr37o1jjjlmo/LPZLPIlDBbkpF+0lisuBtjjDHGmLJmwIABGDVqVMG2nXbaCXPnzm3w2Lq6OkyePBn//d//XeDitDVixb0ZqP7w3wCAug7RKP3IsbtH3ytyjaOJoy9jjDEmVLJ1lpV22bSjVgWd+zF6JxVmqsv0Na7KdJin+l3XaKVps1hUnAcNGgQg9mTD7eptJrQBV9WaqjfVa7WBVz/1OpPG7ark01MMgILFjOGxmjaV80WLFgGIZxQ4w80XRFXw09YImPr5/Oc/j5kzZxZsmzVrVkn26k8//TTee+89fO1rX9vo/JvLxt0v7psRmqsct88OJe3PKUud8uMU35NPPgkAOPjgg1PT4D7bbbcdgOKpS50m5Y2BN6XnnnsOQDyVxxuNA0GYcuPee+8FEAdo0ZcG/SRqMqO/k0mTJuX/14f/17/+9SaV3Riz+ei8YWXB97Udu7dQScym5Nvf/jbGjh2Lq6++GieddBKmT5+OW2+9Fbfeemt+n0svvRTz5s3DXXfdVXDsHXfcgX322Qe77LJLcxe70fjFfROzYcH7+f+P23en5J3qags/jTHGGGPMRjN69Gg89NBDuPTSS3H55Zdj2LBhuPHGG3Hqqafm9/n000+LTGeqqqrwwAMP4Be/+EWT8s9U2B1ku4PuqnS6kVOZnPL7zW9+AyBWxYFYrad9F5U/tdVKc1vFKT1dyMPABU888QQA4Itf/OJG1MyYzcu0adMAFC6co0mAKujsX2nT22mKuy52S4L7/r//9/8K8khbHK7T9RMmTKi/osaUCO/12tY460rzE5p9qAlNWjtPa7vhtrTv7Fs6Q8XvnTt3LtjO/sJZs/pgGjSV4QJWPgMLzE4yhcv7ampqiu4DWofQPEfrnjZ7x3Opbh557rXMajpkGs+RRx6JI488MvX3KVOmFG3r1asXVq9evRlLtWnxi/smpmP/4fmX3AN22x4AkKmtLtinLuM1wcYYY4wxbYVIcS/BqwyaZm3hF/dNxCOPPJL/n4t7GgtH3VQIOHqnwsEFO1wkFAaE0IVDVOC56IUjeV2IxO/q+ovfqc7QdWVYz/pGtcZsTjjrxJkittNQmVOlTMOwpynuhGkTXSgWqmI6c6Wqvc5ohSHbw7LQ/ZsqeuEsHNOwHb1R1FUjELchtkmqv7ynsz3pTK+2ZR7H/flsqc8dJPfV9SVMU/NkP2DfYn9mf0maFdOZBF1UqsGMamtrsTrbOV+/bDYL1NUVBYdiHkluInmszurxnOhsBevJ43juqfIyj7TZdmNC/OJujDHGGGNME7BXmTJh/dL5AGL7dCAesc9asKLgN46qt92ia5gE5i6NbNXTbBKVJNvDhuwR1XaOI3+O8Lkf1X6qFFQEuH9Yz5tvvhkAcN555yXmbUxTobJONU2DJakqGKpjaQGW0vpEQ0obf09SKNXGlcdqGurOLs3dm7rPC9V/lo/9j+U455xzEtMy7YeLLroo//9f/vIXALEKrLM8tAFXhZrtizO8nNnldrZdptu/f/98mmluDYnO/OpzS/sDy8z961PcuQ+Pob28pqn7c5ZZf9c+THUdABYsWFCwTdeucN0Az7G6teR2Pl/12jDd8Hqa1k8mk0EmW8Li1Nqmvbjb2NoYY4wxxpgywIp7idx5550AYkXh+CMi7yrZNcsBAAfsuA0AoLZLHJjh9fcil0NUxKhWf7w82a5N7duI2qWr/Wy4TW3XQ4W8vjyojPB3KgFUCKhCrFq1Kn8MVcDbb7+9IC+qBePHj0/My5g0qLCrbasqUmk2s0mokl5g24pitVzTUjVNFfv60H14rHrNSKtXWh5nn3piwffb7v59XoEnnglr31AxV8Vd2yDbGO/bvMerlxlu1xnkJUuW5PPk+i7tKwq3M4+0SJWqfmtZw23ad9LSSlP70zzg8DOspwaz4vOSSjqP4Tnjc1XX1+h5YB147Ux5ka3IIlvC4tRsXdM0cyvuxhhjjDHGlAFW3FOYPHkyAOC0ow8HAJx69BEAgLc/imzbZrz/MQDg3XffBQAcd+hBAICpv38Yw4YNAxCvHOeom3ZuVEDU3lUVEI7q1fdtkhcM/Y3HUGWhHR+PUV/W/FTVhekwRPOnn36az3PrrbcGAGy//fYFaTIPusX88MMPAQBnnnkmjEli6tSpAOI2r7NMqrhRNW8oCmopsI1rGmqfW1+EVVXptZxp/U334/a0Ps99MrW5/HKuZr9+ygkAgF/f/ft8ORlMhKqeFfj2BeN86Domom2TfY99bfHixQDi6NlqM66zs0Dcb6mgp60T4XOJvzNtbffqlYYsXbo0//+AAQPy++w7anguoyyAGiyviV9x2LfUk1pY1u510UxfpnotugGoy3aI3pIqKrHl4MiW/+WZH+TLyXPG5yVVeUYi79u3b0F9mad6w+Inr1kYo8WUDyUHYKqzjbsxxhhjjDFtHivuApW/0758KACgYkWkMGdqohHxqL69AQB1XaLRN5WAe//0GIBITf/kk08AAAMHDgQQ271xdK7+b5P8zALFdr0kKapaWqQ1XWGfFsmRn2q7RyWBdQq9BowYMQJAsT0j09pyyy0L6slzO27cuMSymvbHHXfcASBub1SitF2qnbnOLCXNQqVFN9S0dH2ItmNVKtX2NYk07zG6rkXT+AZt12siVfCWe/9QlHZ4v2Bwt3AuQGcM+N1eaNoXZ511FgDg1ltvBVAcQZRtTyOnLlu2DED83KLXGLV1T1rrkRZ1mG2Ra1folYW/M28+MzSGia4/CRX3mpoaHLT3btGX6lwch2w8+8vyLVq0CEDsJYfb+Zzu3Llz3JHqcjN863OzCR1ir057bT+4II+HHn86P6PNc8nn6AcffAAgjkDO5yfLwHOp9veO0VCeWHE3xhhjjDHG5LHinuOBBx4AAGyzTeQdJpMbudetXA4AqFkVrfLOdIrUiGxlpGDvMTCyr1u2bEA+LarTtHej0kFVQT24EPVxm2Y3W58fd/VCoZ401NZdbe5YRir1rAP3pzoRll+95mikPebJc8tzffzxxxfVw7Rt7rrrLgCx8qYKe5qHCLVxbYxtu/YjtSNP8y6RppKT0Ld6mhcY3Z7mZSNPTkHProuUuAknR9GJf/37vxTsNvn3f0R1dTW+/pVjog2ZWF1XP/Nq28ty/+pXvypI85vf/Gb9ZTNlCa+72nbTNn3evHkAYo8w2267bcF+bP9U4FUtD1GPNVSeaSevzx+2RabJ544q79r/GxOdfP78KNYKVXp9bvE8LFmyBCMHbFFyuiSbzeYVdZaXkctZD74TzJ49G0BxdPSk6Kym/GgurzJ+cTfGGGNM2fLUS6+jY8eO+PzuOwEAPlmxtiiwUinM+nQpevfuja26RY4laCJbAAfJJbiDNWZz0O5f3B999FEAwKBBgwp/oALYIRdVsVOksKM6txp+Tc7Or3OkMh+0x0gAwAszP8IWW0SjdqoMVJ7V/63a76kPdvWcobbvobqnq/RV0WCaauuuKr9GieN21im8GfJYKjGqSOpMA/fjJ8/94YcfDtN2mTJlSv5/9Rqj0UtVHVePLxq9kX1I1cQktM2zvarar6jv5SSlMW2ftPJofYryztnYojqqF/223zrtdwW73XrfHwrSzGQyiZFdgXSbfkIFPizLhAkTEstvWj+TJk0q+J72XKHnk8GDI9ttbR/a3ukxhX2WzwageH3Ixx9H3te0H/BZSO8pPI6ebNJim6jf83BbSG1tbd6OnGmyvCwLy8B7EpX3lStXYqsdhhWlmUbv3r1TIycTnlvmwTLpvYjPTF47978yo0QbdzTRxr3dv7gbY4wxpvz517/fxpAhQ5qczqszP8ilU5EfyFCIA+IX7zQHEqZ9ks1kkM02/FKebeJsTbt7cf/9738PIFYJ6Iu8SDHr3BMAUNMhtxKfChhXm9PbRUXuFHaIlOyOHTvmV5Zz1B3awhbkkRKxUdVvVc15AwmVEG7jqvU0RT1N4VNFhHn27BmdB9Yp9F5DlSDNL72qqerbl+o//b3TBvHEEwujQpryhEp76JM4zSY9zRtFmoKl3pHYxuqzFdXfeIwq0Zq22u0mRR/W8qunJZ1d0/rnv9MbRsdOBb9nAlv5NHt+vYepjT5R22VdHxOmb+WvfOGzjdCOnFE52Q4426w+2HX9E9s4f6f9Nu25gbhPUWlXBZ6KM58rOuvFPGmXzjVVus6ECna4TdfLMI20mTZu5/1JPdnQLp1rs8J6Eq4B076k9eK55bnms455Uv2nBx9j6qPdvbgbY4wxpv3xuZFDAQCZDTk3j3W1QO8tUNcxenF+9b2PWqhkpi2QqcgiU8Li1EytF6eWBO2pOaJlVFONnsYR8weLqgp+j23uIiWMysKqlZGt+7JlH+f348ifo2iiniVUOVM7dX5Xv9EsS6iaq19oVQD5O9PUKKequqmNYZLdLO3d1UuH1ktnAXRmgbMfPKe2fS9v6Jud6lrYFtMUcVWL01RwtbvV9hpOXTfkqUFVPlXWid4jktD+w77PNq0zXxq1kt9vvef+gvTO/MpxAIDb7v59Pq80//GqLBLtj/p7Q+sMAOCWW24pyMN+plsXnElmFFEgtl3n9eX9+u233wZQPLOkn2zvev9m2056JnDmt74YB0DsVYrPYZqeKIzYzbx4HNX0MA2Wk8copUZV1hlw1olrs4B4tpizGrzX6f1J194kRWsFgKFDhwKIVX0e/+yzz+bzZNRyz0ibdvPibowxxph2TC6wWWZ9tCCVbp+xNnrZ/9yQyPyntlM0GPj3ux82cwFNOZOtyCBbwuLUbK1t3OvlySefBBArEertQW1k1SZPVTmiylo4yk9TqdMUPUUjq1KNU/WfkeCAWF3hSJ7l0rzTUNWRZVBlMFRXmEeavbwqeXrO1fZf7el57Q4++OB6y25aB7fffjuAWBVTNRwovuaE/UxnjNTGnWmm2XOHazBCzxMhaZGKtY+kRQROslNP8/WufUPTSpuF4+9TfveHorLV1NTkvcyQ2+7+fVFETJ1xUBt2vR/pOU2qM9NmNE4r7y3L5MmTAQAjR45M3YfXjPdrKu98VmhEVfVaRnVZj6NtOH8HYnVaZ8yI2nzznp82C0TPMMyDx4X9XMvJY7Q/p3mNqo9sNpuouNMTjSrk3M57oJ5LnjvOErA+GgMl6R2B7zC85meeeWaj62PaBm3+xd0YY4wxpoj1uUCLOber2VwANNREn7sNixbH2vbdlEKmRHeQGSvuxfzhD3/I/0/bMY54OUJW7yqqCqviTtIUtNCenaNt9aZCJTnJe0OYN5UD/s5ROz+pWoZKh84cUB1RG9uGfFWzjFQrdf+wnqoS6r66el8/Vc1jeqtWResGGI0uvJ7HHHNMYvlNyzF16lQAcbsnOosTblOPSbr+QdH2q8p2ko172ixZWl9Is4HVfqizAyEagVhVbPXQoTNcafEXwrJms1lkanN556b+v37KCQCA2+97sMFZQvUOospkeM9LW1PANH79618DiO8zVgGbF3pX4fWhkgvEbZCf3EefL/o8UvWY7YNp64wa79dAw3EMtD2FHqeS9kuLbpwUXElV/pKjFZdANpstqCfT1mc97xE8d2n3HJ0l0Guh6wuAeFY/9Khj2idt8sXdGGOMMaaAXNTT/GeOurU5LzO5AIvZusIBxu7D4kW/HyxZBWOSsFcZY4wxJeHw6+0LznTstNNOAOIZp1Bx11koKtG01f7oo8j8g+qwzjrrbDQ/6UGFajCPD49NW8ek6j5nlNTvuc4aqUe1MF31qJa2ZiMp/sLGsGbNmrzir1HRdYabsGy8FsuWLQNQrJ6zrLxG4cwCzzPPO9vAN77xjSbXyZQXberF/bbbbgMA7L333kW/sSOwY6mLK+3sOmXdkAu28IbJG5veTPmpU/J6k9LpdnZYfld3keE27sNpPXZ81lcXx+nUJsvItDk9l/RgaMi8QRe06rlNu1nzWjHvMBIer/HZZ5+dmKdpftjelSRzs4bcoqUFDdLt/NSFdSFpLk41WFNagCKthxLul7bIlFPpaiKksL+lLRgtpTxAdD7UZEn7vJY57dwm7ZtmXsF71p133gkAGD9+fGoZjWkp6iqivl+Xzd0buH1d9IytWxM9N2mKVtEj96yui/vEqG0iRxBW3o2SrUCJXmWalk+benE3xpj2wK33PojKykqcccKXAQDTHnykZD/VxhhjNj2ZbAaZbAmLU0vYpz7a1Iv7dtttB6BQCaPirMGQSNpCtYam19SFXBicha4ZiS5ASYOqFUNSU8nUUM4Msxwq7tzGMNRc9EP1jfWn+62GFuwwndAFFlBYz7Rw9OoGU1X9NFd+PE4DwYRTsbzGpuVhoCW2T+1DYfskaTNcqnKrEq+L3dLU4iQ428RP3hN0gWzaAkx1hUiSAqCx3LrQL83dI9GFr/XNQCT13crKyqJzwtk3LbfO7KXVL62uSWnxk/Ww8r55UffGeq8FYkcMfAbweaIuGHVhNFFHB0TNVmjuEm5TtB2zDfPZyLzYZvn80n5EhwWvvvpqPu0999yzoJ767OZ56NSpE+YsXI4NGzZgZL/uLDByBYs+VkfP3gy9zayLPit6xXWk/fuwvlsC6IE35y7I/8ZzxRlvdQfJc83vei14PtTNZFgfnu8w2JZpX7SpF3djjGlPTLn/j4keNowxxjQv2WwW2RIWp2ZrvDg1r/ztuuuuAIpdpwHF6p+qTbq/BmTipx6XpKJT3VYFT1U2Vd+oLKtarsEcuF+ornAbF72w/BzBMw9daJRmS8vtfClIqoOeA1V/dAGSqookzcVfUtk4A8Br/rWvfQ2mZWCbUwVOr39Sm2FbUHUszS0r99c2lRbcK0T7MOGxWl6dMVLXdFp2IO7zqmar4kb4u7rDJGmqeIiWR/u2BrNKC+6i6n6YV0Mu9vS+YJv35mGLLbYAUNx/wmvHdsC2yf6q/VSDh+mzkulo/0gKXJYWSIlstdVWAOL7OPsxn3EsQ5o7Y7bDcOaV27Q/6yfPVa9evVDXMTcz1jE3E8/7Tc7GHdXLo3xXRQp83eo4wGLFVuz/cXBDnht1C6llSwtoqAEd65vNYFpsA6b90SZe3I0xxhhjjGkpSg7AVMI+9dEmXtxpj63KEhCP5Kk2qDrckO0mR7dUCNJCrtdHWjAKVbE4utbgK1Q0VIUIbb979+5dsA+PVXdbSQFdksqWZo8fHpcWVIL1Uju/NDtkvRZp6YX/85qb5ofh7kmaWkx7zqTrp/bjqqirsqsqoLYNtu9Q/VMbdrUvVaVZ8+BslfZ15hl6b1GVnrbuGvyGZWCZ2IdVxdfAM/Up7sxD1bw0bzqaR9oahXAfkqbW6v567s2mgcHORowYASC+prSJDmctdc2Q9hl+vv766wBiBbd///4Fx2v/ZnpcVxW2AZaD151eyKi0E3oM4zNC2w1hfcJnHQC89NJL+f81bTUdU/V7w4YN+Oyz6Jk+sHP0XM92i2zNsSBykVm9oip3dPRZESjudTmPM5VdomN3HLgNAOCTldVF54rnYd68eQCApUuXAii+Fup6U+8nQPG5Zb9nmxg3bhxM+6BNvLgbY4wxxhjTUpQcgKmEfeqjrF/cJ0+eDCC2bU/ylcyRepqv5jR7a1X6uH8pXlnUtlfT1O1JoeGB4pDkVACTwkBzX7W1HbPLyII0Z3+6pOD3NOVdbWvrm1lQJU+94qiNcNq6grRrFObNeg4aNAhA3AYcan3zM2XKFACFdpdAcdvQsN3h7zqbpP1T7XDVblv3V0U7bFuqJDNP7Vdqn800qdxpv0yymVf7ce1fTFPtcNXDjXqfIKG6r3bxaleuyrueQ7VlZtr1eZVpaGYxzQc8vztYzKaBM6vavuq7dtrOtQ/xucJ4GQ3ZZWt7C9sq2xTVYarh7Ht8Nqh9PPMiLCOfIWlxDsK0tA927doVOwzuX5Du3MUrCs7D7DXR/iN6ReXM9ugdfeZs2zesivpGJhv3v2zO00wdz3kQZZXnRGNF8NxyhkEtAXgN6nuvUHWe9WSbMO2Hsn5xN8YYY4wxpqXJZLPIlGA+Xco+9VHWL+7Dhw8HUOxLPVR91HZW7fv4u9phMy3a6DXk1z1UrtN8TqfB3zlyVtWKo/GFCxcmph9uYz3o4zVDJVLyaKhMquZpWcPf1JZWFXTaM1J10fUDaoOpqkqoxnAb02IbMJuPadOmAYi9HaWhSpyqaEDxNWUboQKl6pnO5hC1nU7ymKL5p4VZV9WPv6ep5El251TOGoqgyvqpvT3LzXRYv6Q4FExLozqrRwv1vNPQTGCSP/e0CKlpynqan3qmaeW9aeg6DLYF9c4CxPFEdOZL7adp265tU9sN1WLulxQxmTPS/Fy8eHFBubhWLK2d6PoYwjLSRjzJv3m/fv3yee24TfQ/qqM+nKmJzs+QLaL8Zy+sKjgfNd0jxb3joBGF5flseXR8h/heUtEn2reuc84XfEV0fletWpw/13rv4fVhPfgs12cdj2d/YX2B4hnsNI95pu1T1i/uxhhjjDHGtDTZihL9uLdnG3eq4RxxU00OFSOOUtXzQpr/ZN2uo1uS5r84/E1VbbUDVbWBo/Stt966oB6qqFG9CKOY6qp0jRRL5X3LLtEIv2pDuh/6pHqmKSRAsTqv507PuSpAOpvBT6ouYV2oUFCJYBswmw/apTbkiUntbZP6GNUhbQs8Ni2KadqaizQ77vA3bZ/aLtXeXNe3NOR5Kqxz2iwU22na+gCeB/5OdZNw1i2pPOq3XWcGdFZR+5326aSATtqH06LINjSTx7zomejrX/96vfubQtgXeW9Ub2dJ6iufJ7Q756wOvxOdcUmLx6GzROEsNP9/8803AUQ+04FYgddnX1okZH3uMD4J+0U448ZtBdFHc3bnmQ25aKrro33qquM+EJblw2VrUFtbi+E9I6W+Q85zTG335VAq+kT71HaO+vSbH35a5Mddz6U+Z8NoruF3nr/58+cDAKqqqvLH6LsG6802YVoBJS5ORRNf3Jt2tDHGGGOMMaZZKEvF/ZZbbgEA7LPPPgCKVZ5QMaJSRZWa9tZU4Il6wkjz3awj5yQlWqMKqscLVR1URUzzTMEV+hxhh+oi0+A+ebUkk1MA6wo/e+UOXbaufn/tWodQaVMlU/fZfcTgaMea3Mr+TLT/9HfmFJSR9aD6QHVS/WgD6aoP28Q555yTWB/TeOixhyoer4ded1WRSZKnizSf0hrZV0nzlELFMckWXn0iE87Cpc0gqIKtPtiTvEDp7EJaH9bok/pJhVLXAITnWGfidAZLZzW0/qrKskxMJ1T3dU0Jz51e24bU2vruI6ZhJk2aBCCefeR14HNN10kB8bOO91PGvuDzY5ttIv/jc+fOBRCvi9J2o+1NZ0LD9sU82YbYnonOtCXFXwDiNsrndH1xU7SPlRJbpXPnzinxUuqfgU6ioqIiX06dbdT7FtcJbbvttgDic8lrQxWd5zHsq8uXLwdQ/B7Bc8c2MmHChEbXwWwaMtkS3UG258WpxhhjjDEAsPv2kdtF1OTM3HImL5ncIlWa0Azo3g1AR3y0QgaxnSKTlWy33gCAimyxe0YuSq3rwAFu/Yv2jdnUlOWLuyoBHGGrXSiQrg5QqVAPDUSVvST1N8w7JM1PufphVRWKo2tVHT/55JOCsvO40IMAlQ2qKbQJ7DV0YFH5gFh579MpSmtFdXK9VE0P65tm959X7HIr+bNrI/UgUx2Vd9+hkbrwzvKagv35qR4F1q1bh6265ZRaehDomlvZn8tz1NBjE+tpGs+DDz4IIFb10lRkov1RPS+FfUs9tFCpUk8v6t9cFXltM0mROrW/6BqKNLQM6plKPd+EsE+qqq2qpXpYUu8SGmE1LDPPWZoHHs0zLdqz+rdPIq18SVGqQ9LUT71OnCkDPFtWH2znVNTZPtgmabceRhhlm+F6oMGDoxlQejZZtGgRgNi+mt9pj66e1tR7W9LsGLf16dMHQPFaMI0s3JD//7R1YPV5j2oMlZWVqXk1Jg31tqTRWvk85rlmmXkt+J227TwuvJ6sM+9L+rzd2PKbTYfdQRpjjDHGbCwMjrQhNzil8FbHl9zkgEd12dzC9o7RAKOuIjBL7RSZ5/37/Xl2jmBahLJ8cedodMmSKAoo/dUm+ZVVG1IqFfykUp0WIbSUyKFKmsqUFN0xqaxqx00VnaNvKm60eQPiGQUey1H5olWRMt2va/2XumeHqIwrqgs9TahyVsqoPq8IVEcr4jPro6nEmkXRzEG2S3Tj22Hr7aJ6ZAtvfr0QqwyZDWujJdSrc+URv/RFPiz69NctppFQHaKKFNo8A7GapOqZen5JUqZ5jCpUGg2Yv6tyrT7XmRf7flI0U/VMk+bBIm0GTGfnSNgX1Pe7epmgLX5aRFT1YKOqZnhP0SiLuk5A/bPrd6L3Rj2XYTnS4jmo32lV5Hm907zO6EynKeT2228HUBxPJM0ne5IPfj432NZoT83nB9dFzZo1C0CxtxnCNlzfNeWx7A8sD9usriHTNqtrIlhPpsv9wzJqNNnGkMlk8mXS/l3Ksdq/9X7F8nI2Y+TIKJI5j+O10Eiq6iUOKF5jlBQpFojbzFlnndWo+pimk6nIIlNP9Nt4v6at8SnLF3djjDHGmJDZny5BTU0NdhjQp/AH2qrnlPT8p1JXKEzVdYgG2rWV3fLbajtRaFrexNIas3GU5Yu7jvipcnF7kgcGVZuUNHvthlS5JD/uuk1VRrUL5Ui6cHV7nNeOO+5YcBxH9XvttVdRPdWTRr4sLL8qZ/KdyvuqukLlU+sd/p/mOQPVOdUlp7SvnvFS9HtNlEfPL+SU3L47FZShYsWn+f9rly6IjslNdeZzz92I86Nb3pgH7QizcTz00EMAYs8H2g7VFpTbNQqoerpI6hvqA1rVcdKQDXV9UQPTYi1omvyda2TYD9VOVVX0cCaCvrLpqaN//2jmR+1R08rIPDnbMWfOHADAxx9/XFRmqq+qtmrkVF2/Q1VQZ0j0GoQzCTqLqX1e1/6oYqj3DyXM6+abbwYAnHfeeYn7tkeoJuszRD0dqRefEP7Ga8NrxjaqXmXSooSzLLTDVqU3PObtt98GAAwbNqxg3/rin4Tb1a6e6dKvOcsa1ks92JRCbW1to7zRhGSz2fy9kv2fyjrLq5HMCc+99hs9LmlNGduAerJhW2joXcdsPjIl+nEvydd7PZTli7sxxhhjTCI518N1FHg6RYOL2o7RQPrjFeuTF6lTcc8p8rU52/a6ynhw8sGCZYlmucY0F2X54s6RP1euswOqEhhuU9s5VYDSvqfZ4KVFDgyP0RE8R8S0zX/rrbcAADNnzgQA7LfffgCAUaNGAYhH4apKJI2odVuxelbaCI8KfHfkFO6OUb2Wry+29ddzkTbSr1sfKX8r5kRK+qp5iwEAXYZEakxmq6i+PbM537cfzswfu/rdSLmpWZuz+8+NVLMdc/5/KzsUfF//3usAgG4nX1pSfU0MfQerf3BVhRvqA2lREcPf1EOFei1RRV37gCr0Sbbg6sFE1fl+/aIIiLQzVUVaI69qvIGkB7+q8+qxpaEIo7ynUZFjrIqPPvoov8/rr0dtXH1mq8cRloX7UYGn1xD10Z6kPLIeaouuvuPVFp6zF2lrYpKUYXvFKIbXiteSL4y6RkTXKwDFMzE8lu2cduKh73cgvjZU0rmfxgdgOroGBgCGDIncMobRvcM0dDYvbe2Drr9gnUaMGFFUT42RUB+ZTKZe71D1EdaX7Zz14rmiGs5PzpLxXOtaAB0IqD/4MC2dedeZD42abpqPbDZb0szNxqzHCCnLF3djjDHGtB0G9OrS8E7ojbfmfNLgXlTakVPKazvkBpYdcy+169ckHVZ0fF2HYq8ygBdUm2RsKpMAbSB33XVXAMX+W1W1C/9PU7jSPLzo8URt8OrztqJqiNrkM3raggWRHfc//vEPAMDLL78MADjooIMAxHazqqInqYuqvNBGdqtuhTZ2uggnjVzgVfTpyLxqsGxDsqcZVezqOua8X/SKFM0eg3N2fSx3h8LrV1UTlbl3j95x/rkGXrMhd7PMiaYVOTv5Op6L3HduN6Xzl7/8BUBs254W9ZOosq4KkBIq06pIq6rdkE004X5p0VHDfVgu2sDuueeeAGKlnSRFQU76nSTtV9QHGpjpI3rOtSy8BwCx3fAHH3wAAHjxxRcBAJ9+Gs1oUa2nMqizFmpPqzOWSb7wic626IxCmu1y2vdwO+t+0003AQDOP/98tFceeOABALHHNPX7n0aoHnOmRddWMS4Inz9sLxoxmOowlXXab3PdBmeHwmtI5ZjlZttj+bXfbozy2LVr14KYLaowN3TvYJnTlOuG6Ny5c1GcCXp+o4ceXQvCfkS/7fyd14JlUH/89ZVJ7xnq5Ytt6Pjjjy+pXm2Ra6+9FpdeeikuuOAC3HjjjYn7TJkyBePHjy/Y1qlTp0Z7GWpOyurF3RhjjDFtD0Y5Td+h9Jf8DxdVFbmWjV5o1zQo1gEAKgq9z8xbtsomKGXGiy++iF//+tfYbbfdGty3Z8+eeZNloLTBXxJW3BNQmztVsTjiDG3QOLLXC9GQIqSkeZdJGhGn+Y9O8toAAHvvvTeA2HZ19uzZAIDf/va3AGKFgz5g2RBDX7ZUS5kGffJy5H7ZxRfkKioqYYnKewjV98/qklVF8llFZNfXq0+k9nfdKSp3p36RV4KKPpGNcc9MpMZU1eUU+MGj8ml07xypPV2rluSKK3bVeTdfueuQEKLa1I/6eU7zsKRxBrifRvJk/0uyj9aHaZrnpYa8N6n3hSQ/ytyXSvvYsWML9lX1WNUxnR3QsoR5pUUz1b7BcmtcCVUg65sp5PlnJEwqp6+++ioA4M033wQQq39qA8y0NVKz2iOH9SF6T1MlVdU/PS+kvvo11ua4LaLeiHTNRFp8kXAWWtcw8FrQbp4RVamO85OofTmfvywb0wv7t/ZTbdc8RmNBbNiwAehZGOMgjUwmky8Dv4d5pD2jeZ9jmdSOPuqLye8BtbW1BXboLDdn7XQ9Gs+Vxm1gWRYvjtZ58XxQsWeZVdEHimfONPaD3mvCc9TeWLlyJU499VTcdtttuPLKKxvcP5PJ5D0DlQNNe+03xhhjjGkqtdXRX8366I/f+VdXu1FCUykM7dsTA3rEL9l1mSzqMtlI5W+E0m9aB+eeey7+67/+C4ccckhJ+69cuRJDhgzB4MGDcfTRR+fFj8aSyWSRyZbw18Q2VVaKu9l4rvj5LwEAEy+cEG1orPIe/p6bPuye8wJTVVP/tFJt15z3n8HRjAGV9ky3nMcEyXt5RaSQ1tbWAv12wapVq7DNoJ2jY2plYdBmupEbY4wxpry477778Morr+TX/jTEDjvsgMmTJ2O33XZDVVUVrr/+eowdOxZvvvlmwdqi1kRZvbjrNLO6f+JUbzjl29Ci1LSFd2mLQtRsoL6Q3To9rIv3dIqLi265yIxTczyOZjAzZswAABx22GH5tB577LGCPDVwxea0z9PFtyT/vQSTwiQ0rLrZPHChlwbxamghpZqYEJ0e5zRyeIxO/acFaCFqisHj2K6TFn+y7dNERqef9TMNlpUh4pN8OOu9Rxd86qIzvW+w3DQzojkPzRqS9tVzRZM7usp7/PHHC8rP+jPtNHd4odmg9kG95moyw2vPT+ah17k+E0Pm354DMmkwLZpU0JxNXfAm+iTPQXMNvd7qBjTt2cf92AaYjrZxIL52LC/bGmF/ZT9gX8pkMshsyC0GpBhDVTJnb15H3+x1dQX9PM3MVfuHLlZX05+CPHOfc5etjsqWyRScF607z432Aw2EqK511fWuLk5PgvXguWMePOf8rs472gMfffQRLrjgAjz++OOJbkqT2G+//fKuuIHoebHTTjvh17/+Na644opG5W8bd7NJ+Z+vj4v+4U2xKbbutbRDLq3xrczm1ib0yHmV6RIp6nVyk6zvBX/h6ihPPrg0KiTh4Ob0008vqWzGGGOMKX9efvllLFy4EJ/73Ofy22pqavDMM8/g5ptvxrp16xr089+xY0fsueeeeO+99xqdv1/cE0gL68zRKkdY4Ugzye0YUKx2q5JHdY0viFQO+KmKUtgY0pQs5kE3W8xDF5sMHToUAPDGG28UpK2LA5MWrqg63RwLvVTJoILLT16X7foVq4chacpnknKQtEAQqF95MjF0AQnEbVwXTPJcqkpE2Be4n7Y1TS/Mi6S5FdQ2xTKoCzdVAcN+vssuuwAofbZJZ+E480V7x4ULFxaUIVTqGMyJoc250I95MwALy8m+r7MdXGTOTwZrC8O50w0f0XPDvE466SQAwD//+U8A8aJ3XheWTVXc8DqqoqiLiHX2RWcOdPZG713h9dJt7XmRqt7z6YiAfY6uHqm6qnoOFLtaVXfJaYH99Fqqm0GSNGud5oJSlXfeE8LFqtn1qwr2iRX23P0jGz//kxah62yQPiN0RlEXjkaVyeWZ89u+fv2KxFlrDU5HdPGwWgXodr02OgsYClO6EJwLY9nfdcakPfafL37xi/n3JjJ+/HjsuOOO+N73vldScK6amhq88cYb+NKXvrS5itlk/JZjjDHGGGPKmh49euQFG9KtWzdsueWW+e2nn346Bg0ahGuuuQYAcPnll2PffffFdttth+XLl+OnP/0pPvzwQ5x11lmNzj9bkUW2BDW9lH3qoyxf3Dka5YiZnzpqDUmzWee+VNOohKltalVVFYB4lKvBKcI81Q4+zZ5e7eS4H4M0qO24jt5DxUBHklqGIhOZ3GdmY0xmqEpk6OKrUEWkSkiFgMok3Y/x3FGV7JONyto7wZR96drC8mjd1dWZKY1Q4Q7tTIHidqduTBtS4NICc4X7qDtItYFWFU3Dravtd5LtNBcWpfU/7TNbdcupw7n9+42I3C3++c9/RhJhm6M7NwY8o/K+/fbbA4jvG2y3qsgvW7asIE21DWefAuJ7EZV3DSSlituBBx4IIHYf+eSTTwKI7zPsj1T/w7bB8rDcVNJ1TYLOdKUFZUtzkxkeQ0ryt91GUcVdZ3h5zdgPOEMTzmhpGmlrxNJcJavbUN4ndM1E0loYvZZ8NhCd4a6rq0PdqhWF+3TKrUmR501tbW29wQfT1q5on+I5q62txVbdItepyCntsz9dUu/aF/YLvh/oWhC9XkSf5Xr/05mKUDVnH2S/TZtJaWjNTntn7ty5Be1n2bJlOPvsszF//nz06dMHe+21F5577jmMGjWqnlRalrJ8cTfGGGOMMaY+nnrqqXq///znP8fPf/7zTZJXJpuJI8M3sF9TKKsXdx1Jq4pOVSpUwjgCpiqlI16GHNYAClSHVV2kskalQ0Meh+WiOpWmJFE1Yd4acp6/026QI25VW4BYTaOywXNA+7e8wk53ilTWdXTeCMWdn1t3j+o5d26hYqJhnKkUUF2kOlQ7cCAAoG/nXLqBy8ctO6SUp2OkMgzo1rtg865D+jdc/nYMbdtDzyhqL65eJlQNSguWpAFCkhQgVc6J5qnKPNMaPnx4we9Un5luGJRMFfVwxqpPx5yaW1sNcAlFTZTGVTfdVlAW5k0V7aJzzgQA3P1QrMRrudn/GAhtyJAhAICBubbOc80+zb5M1Zt9Q+1zw3PCEPTsXwy4pJ52uD/Xzhx33HEAgIcffrggD94jw+vFY1kfnoOkADFhOTWYF/NIUyCTtrVn5VBVZLZrnn8+b3ie2X7qs4lOW4OieerMGtuZquYsE9tdmCY/3333XQDA/PlR0L3Ro0cXlKWyshI7DI7u2XVzXgEQB9Wr65Cb0cs9Z95buCKxTaQp62med9i+Qq8sb835JO8+kIF4OFumXlvCc8JnNuGzedCgQQVl0XcWPfdpa0TCWU2d1eI+vPbsY2wb7bn/tBTNtTjVkQWMMcYYY4wpA8pKcU8KoQ7EI0yqb6HfaNqgU3nmCJaKOtVsjlZp604bVA0brB5O8qpxgkqlPl3TFE0qZBw5c2Tfv3//gvpQMdtuu+0AFNq404czXRjRg8T5Z3w1yqs6Z/tYnfM+UCsrzhvjDpKKO+2Ra6I0x4yKyvV/b8wEEJ9zln/u3LkAYg8cPE+8Fsgp8n07JYwnU2z0TeNQRTREbdqT7CyBYi8y6hEmzYNCmIemFW4/+9QT4/1T7JzrcvvfOu13AOK2FPbDJK8K/bp2AFAXRWZEYUCva349DUCxKkaPLt/46rG5zAvtUkO0fjzPH3zwQUGZtt1224I81MsG1TT1OBWmwfx5/9P7BsutZeL2k08+GQBw//33A4hnwkKvNeqZQ9VYTVvbjNodq111eL10fUN7tnHnzAufX1R2+YygKsznVzjjS9JmnHieqZjrc1W9t/H+rLNDfG4lKbtsL+odiao2Yw2sWbMGOw6KnrXZzlFdMx1znqI65mZ1OkXb1y1elug9h+eKz1e9/7AsfA7PmTMHQPxs57OSZeR5SfNcBcR9hOeE55/nijNrOjvJMjAPHsfvqbFQgmN5/vl8ZRvguVbvbqb5sOJujDHGGGOMyVNWirv6dNYgPLTBU5UcKFaC1Bb8o48+AhCrVZoGR++q3HO0m+S1RsuraaqHBdqAcz+O5hcsWFBwXFL9dFv+O5VpKu3VUX0zNaIWNqRgB8GW6ipydne1udkHCju5NPbbdQcAsfLOslAp+PDDDwEU2+VTCfxoeVS2BQsWYK+dImUmr7xaaW8SbHOhvaaqn9ouiUb3U5t29W6g6Yf7JHm0+PpXI9trVAcegvR659rhjZPvBVDsLzysF9tVt27dMLB3zgfzhpwXiFyUxlDR//6ZkdJ/7ZQHAADfG39CQZ51tYUzfeNPjhT4yfc9mO+76q+a9ygqcTNnziyoP2fRiEa5TLIlVy8/eh24bofQ7lbPOfM6/vjjAQB33313UR3UvlfbSFL0zDAvbUNpUXbDfZPs+tsbapeu9svqYYTPpbD9U51W3+J6Pya8Nrym6mWI+6vv+PA6cdab5eAxO++8M4C4TzJQXvfu3fNrmuq65gLz5Z4vdR2jOr4zf0U+PdZh+vTp+TxpN89zpvchqtl//OMfARTPYnBtB8vI4/ic4rkOYylo5HTuw/cBjf+i/UPt0tO804Q27syDfYbXh21C+019Ud3N5iGTyZa2OLXE4JVpWHE3xhhjjDGmDCgrxf3MMyNvDn/7298AFPuwJaH6rCuxORJW7w/qyUX9EOtoNynyn6K+atXejajiybzoC3qHHSIFW6MtUm0Mt3G0zWPy3mRyHjOyOcWxdk3O/k1t3bMSWYz+mjsH0ScZwa5jLv8KccAuyvtzr78DID63XJHPc09VQj1RdOjQAf9+98OC80XlknVXW8FDDz0UJp2kdtuQn/M0jymqiLLfqQ18eP3U/3ddXV3edjyzLurHbKvRDjmlKqdQXDv1oYJ0SJLNNSPojR49Gtl1n+XyWJXLY31h+gGXnnJY9E+ur+RnmGiDnSsL1fptttkm3x71HFIxYztlH37rrbcAxEoplVP2/TQFDij2R61RFnkMPXrstttuAIrt5nkOed0OOOAAAMArr7ySz4vlU3/TPEavg9pVM0+2GV2LELaNtDUVN9xwAwDgoosuQnuBa6yInhsqu7wOPM/hMyHNq0haBHKFeegsHb+zjYb50O6dn8yD7Ze237xfh1Eu63K27OxvsxevQnV1dUGfYnsJ17Gp0q6xBajus/z8fffddwcQv0fo2hHty+F7hsaN0KizPHc6A6dp0iNPmjpe30y+Xh+S1BZM85CpqEC2hOismRL2qQ8r7sYYY4wxxpQBZaW4E64KpzrFUSzt00NUKVJ7UI7CaW/N0at6j6F9mx6X5B1BfbfqMWl+6LXMhF5k3n777YJ0wv1UveYxcaK5MuWi0xUp7jmlnSv6Mx1yUSQrcypdZewfu7ZrtKL+g8XRiL9Dh0LbuiFb5bxZ5NLeb/edAADPvRapjGrbznpQReG5T1KE+BvtePXcmvpR++gQqkYaEVVtWVWhZ5vjtWEfU9va8LcNGzbg3PGn5jbm1OWc0p7ZEPuYZ7u9+p6/FaUV5pFEdXU1Tj4sUpGzqyOPKXWrlgMAatcneNXhbFOu7TNyY74o/F1mmNauXZuvu/ZpvQdQIeW9ivcyeqlQhZ32xOHMYZr/baKqJD3aMBJgWqRM3jNeeumlot/0nqZtgddVy8ZyaxtKim6ZVq725I964sSJAICjjjoKQPqzQp87Sc+StGO0/2qsBP7O+zOVZvZzPT5sm+rBhe1alWemoeHpQ6qqqvKqMfPgfX7MmDFF++tMH2ehua6EZdhpp+h5xNkkjTzM41h/1imsp/YDfue54rHs5xo5WC0B6nvmKWoNoL7zdTaAbeqKK65oMG3TNOxVxhhjjDFtm2wHINsBdR27oq5jV/x88r14ZfYnDR9nTDulLBV3tUHjJ/0Q074uhL+lqeAc2XOUytE5VX0qYGm28aFapDakHAnrqm9V4dJsjPmpq/qppIX14j55+zaJcqo27HmFPaesZ3K+dJH7XtspUiVqu8Qr6ucsXJ54LqgafLQkUuJ5nfR39VTBdGj3qEpRaMPH66hqbn3Kq4mpT9Gh8hZGVQ2PUd/cqoYRVdyTvIOkzTZFP8bpXTMtik6qaqHaSNObRWgbHNqN5u3S2f4ZnTFY48H/r/r9cwXFmXjG0ellFdK8qeg9geeGM3nsy1S91WtVGLNBZzY0bc1T1XyikW15XcNzSAVRvZuoTX+atyC91+n9N6kd6G/1tpU2RlrMBH3+6PMq6Xzq9U6buVAVWJ9L2r91NiicZeHzh7bbPJblUi8sOrPNbf/6178AAAceeGBBXfhcDs9TWqwApsE8ONPL7yyDRlZVX+tckxX6ymf+fNdQVZ7nTu8DaYp7Q304rB/3Yd76DqJrX9qzd6bmprkU97J8cTfGGGNM+fP8m+/lBRkGQzKmHMlkS3QH2UQxoixf3Bl1kPZjHFlyREz/q0CsaNGeTdV5VYo4ClelnR5PqHSoSpWE+m/XkTCh8sw8dfTN0TyVsxdeeKHguPDYffbZB0Awys7klJqKnA1u10hF6NClW8H2ug65elXmlIKO0Wh+9oKcbfDyJUX1VTtL9a+vq9pV0WVaGrGR+1FtpJoKxErOkCFDCs6R+ro3ydRnE6sqtqrHagOviq16O9E4BuExvXv3zq+ByIjnmLrAx+0lp0dqd12HXN/IeTL62e2Rz3G1Z6VKBuTUYvqEznmsYB6ZzusL8wby9vQTT/+vwvLQpr2ig5Qzqtcnn3xSFP+Bqh3rrueb3pEWL15csJ2qoCpyYV/XPPgbj2E/ohcnTStNwU6y06etLtOg4sk2oDNdei/QtpCm8ofb0tYJtAfSnhG6joTnKEmxJml28Gke0XS2hPdafuo1U0U/CbWfVw816tmIzwy2O67rojcaRjflswEotlXn+ifmwX6gnpDSvGNpdGB6d+NniM5GMiIs0ZlCPU7vD/rsr2+dF9sE68U+ptHcPRvd9ijLF3djjDHGGGNaCzaVqQf6POZolCNjjWoKxEosFS56WuDoVD3RcBTO32lHpgqSjoSTVEW1vVPFoyFVLk3xpLpO2zsg8iUd7pM/lja9OaWylmpmTj2k0j5nSWTLmnMQg2w2GrXrSv2wnvQeM+vjhQXnhvvSPpYKu6pITJtRa+fPnw+gOHLsoEGD8sdwm5aLbcLUj7bNcBvR66RrE3S/tKiZSTbKvE77779/7ENdfakHNu6Q9koFneoaFWv16sBy33n/n/Dpp5/my/+9c8YV5JmkuAcVLMj7yl9NBQD877e+UbDbggUL8rNGvJ+wP2rcB42AqYok0+HMAdt7qJrxnhb6sgaAHXfcEUCxD/A0by3MUyMa83wBcf/ivVXtahX1JZ9mf5yk2ja0PqA9cP311wMAnnsuWmeh7Ubvf4TnKFzXoV5G0mYuVA3X49jONCJvUnRPHkPVl2lS9WZ/SLO7Vn/mfDbMmzev4Pew/bG9pkXxTfORrn7beY6p9utanjBdjUpLODOgNu7MK63f6DtCUkwD7ccaF4bl1/qyTZm2Q1m+uBtjjDHGGNNayGQzpSnu2YbNzOqjrF/cOTqlTSft3sKRMe3SuC8VuVmzZgGIFXb1/KL+iakUUn2gypBkl8kRr46IVWlXu09dgZ8WyW3s2LEAgPvvvz+fJ7epErBwZfTZv1tONegYKQH0/FJbuzKxTFTTs+tz57KaI//afMRIrIpmMXboH6lxb3y0pODccJZD1Xq1zeV5od061cYkO1gqGVQAmYcpjZNOOgkAcOutt+a3qUKldqfajtO8ULDtaHrsn0AcnfMvf/kLvnZSzn6dCjvvRuHxnBnKKe3P5mIBsCxU3mn7yjIAxWssampqcPUvJ+e/q3rJtKia/ceYPQqO/98LJkRlybXjy376i/xvbMtUBnkv0kjMCxdGs1N6H+E5p9qncSKoxIf/673n5ZdfBhDf84YPHw4gtlEO7f+BuO88/fTTAOJorlwvAMT9jGuF2CbUflbVWtZL20SaPXH4W1r7ak9o5E3O0PB88rqQpPgMvM+q17I05ZbXUte4qF06f+cn1fUw7TSFmdvZ9zgbq2nxnhGub0pKL2kbv7PN8lwyD9YzyUMNEJ9j1jcpbgrPs64vUS9Kqn7rTAnR/dUyIKyXznyyfhrJNuzHpm1R1i/uxhhjjDHGtDT2KlMPqi5wlE/bznClPRV27kulgnbTtI+jUqYrz/mdpI2ww1F7Qz6L9Xe1m1clgHWgfSlVvHA0z220+dVjFq+NRukchWs9mPfwrjl1fH4Uba52bcKonVFWc77es90jpXLXYdE5nTlvUUH5VAngd6qLvBa8NuoxIVQKqaLYV23TCJUftcNW39Hqe1zjC+gsD9sS+yNVdgD405/+BCCaweL6iwxynl9oz94huKnltk1/+30Asc0r1WSWlW0p7BNU79JsfNm399prLwBx26J6/4/prwEAnnjiiYLjSvGZTVVcowPrrJN63hk6dGjBdvp35/qPsM781AiYzJv3NkaO/PjjjwvOC8vEe4JeNyCeadQ2ovdVnS3UMqktsM5Chv+r/Xt78ipD5s6dCwAYOXIkgGK1m+dIPXWFCi334QwSnwXaFol6CuJ+usaFebINhEo00+Bsl67L0vs10+LsD9sePcexbXI2SO3OgWIvKowQzOcozyXz6NevX0EZmKbWk/XiuQ3bsPZjTUOf8TwvaetNiK4nCJ9rTJvXkX2Oiru+F7Hepu1Rli/uxhhjjDHGtBYy2Yo4yF8D+zWFsnxx5yidI1COUvk99DBCFZejZqppVHGZFpW8HXbYAUBxZDodYXP0rZ5hwmN0RK8eF9SbDNUSqgxqUxx6zAjrDRQr7RzJq62c2tWzDNtW5uz4Pn032m9+NFqvq867mYnrl/MBX7FVzttLz0jB+Pfs6BzqrAXPJcvCc83zora3tG+kshDOoKSp+GmeA0wyoZ2krtdQ1Jaax7JdhjauQKxoJa3F4G+DBw/G9BmzsGDBAhz1xUiRz4h/dAB48c2oPbL/Mk+2GW5XW+AovWSbXqp6e++9N4C4T7zyyisFadCn+pe+9CUAcTuk0hX6Vqe6/c477xT8pueKaHvVfkqlnmpaqPapcspjqWrynsf6cDuvE+8R3E7bfvXRDhSr3jxW73/81P6p63OUcLt6MyHtUXE3xpg0yvLF3RhjjGmr0ESKplMcTHGwxoEhB2NpwYSAeCBKAUUFIw0SpC48mbeaQ5EwGJIGMtQ8mAYH3IQDVQ6WVdTZbrvtAMQD5HAwR6GIi7J5DPPmwJSCEcUDloFCUZpJK89tOHjm4FhFKr1OOhjVc63mtLxW6uoVKF74yuupi4lZTrYh04xkK/KmxA3u1wT84t7GGb51oX/z2Z8uSdwvUx09AKqrot9rcp8bVuX8uXcOVLgOOdvnrpH/55rKrrlf4hu4MaXypyf+mfd7zrUmROMsGGOMMa2SbLbAOqHe/ZpAWb64c7qWo12qDhzNcyoZiEfAunBDXTzxGI6kuT+ngKkgcDqZI2IueOHvQPHom1PzHAlzVJ02Kie6cE0XKIULdKhYqLstRReZbUpXa6ynmjLpwmCea1WLuJ1lV5dyQKySqHmGmhGZ+glNZVS50YAe2gd00RavL68/TWR+97vfFewf7sO+wDSZJ9uAmmKwfdNlqC6q5vHsn0BscqaL9HbffXcAcZuZPn06gPh+su+++wIoNu9Qd66hCRcHHvycPXs2gFgh1MWchPVgWjQrohkP3UeGLjVZLg1yw0BKXMjHc8vBEE0IqWryd11snFRnnku2CfbNtEWHvH4ahEsVx6RF/ap4tseQ7VdffTWAuD3w2ia5OAWS7+PqplUXtqoZlF4rDWjEvNnvuV/4rNHry0+21bTFm2oCp/XifYNqeXj/1wBJqkBrmvrs0/udlj2pnvqs1tmMtOBXacEYWTYtQ1KAsjRHDHyO8v2Cbci0Pcryxd0YY4wxxpjWQqaiApkEASRpv6ZQli/uVLlpu8bRd5L7MKpoHBFTKaKyRxdwanPHEbMqYsyDo2/a1c2YMSN/LEfwe+65J4BYbdMFaKFiBxS7yNIFbOr+MhyN64h+zx2GAQCy6yOlLLM+yntI70j1/mDpmoI06jrkFuN2zblk7BYpPJWdckpCZaz0dOgXLUqt69o7dyztGQtVFQ3co/Xkuee1UFdivK6hvR//V8XdgZgax2mnnZb/f+rUqQCKFTeiYcp1YTD7wOc+9zkAwF//+lcAscK9YMGCfFoMqMKgQNr/0lQ9qq5UHqnAf/jhhwBi93HhwnQuzmRbob0w3SXOnTsX/33sf2HsbjtGB9TllLic/eE7cyMXjKpmJi045X2GahcXufPcMOBbeC5C1O6Y5ykpwBu38T7C/sNzwX7EBes0N+I5T3MjmbQINFyAC8QzGjrjoTbXOjuhCqO6cw3T1GB47VFxJ2znfNapi1b9DM8nz6O6NFbFVgMvsT2pMs90tP+HSrQuUmYaPEbvLbof81i0KHIprK6RdVY2LB9t7fmds0Rs9+okQs8Hy6jPX5YhnPnVZzHLnaa0836mrnb1Wuh9JLyeaddc02KbMW2XsnxxN8YYY4wxptXgxanpcCTNUTlVtqQwwdyX+1ABo0JEe08qYmnqGtHfOSKmmgfEahmVPVU8dBSeFhBDbfD09yQXa3k1vibn9nF1VIaaBZF7x4o+kY3gsC0jW+E3P4rOYU2/SB3tMCBS6jM5l49163KzGZ1ixT3bK7I3rOkU7fPx0mhGQc+VBrLQMvLcUzHgtdH1A6EqoS4yuY/DO288addN7VXVppqBsxjw5MknnwQQB42hKhba5TIIEFVgDU+uahnzYoCxsG+HZaMNbNhWaG/+3nvvFRzLvj979mygNlDfcv9n6gpnilQJJqF6SFt0qvxUMffff38AwH777Qcgno3Q4FDal0O3lmFZwjrrzJS656RtL1VKtV3WeqgLx7DOeg703qQqpnoiYZmSAgVpvVietLTbE1yfsP322wMoXhelawxCeN3ZTtRGmm1MZz/4ydktts00+/rQnS+vN8vFNqUB/9LcgzJvPjPZjhiQSNfGhGmzPpzp0/oounaMn2yb4XoZoLD/65oqtXHX/TgboCq5zm4wHXV3G+6ja1O03+gCf9P2KMsXd2OMMcYYY1oN2WyJins79CpDdY4jY9py0mtJUgARjqbplYKKH70+UD2kDSrt4nQETfWHI+ikUT1VBSrv9KeqyjnLqTazLCvryXqllSUkvw/Dx+dUxLrVubDIuf2yOfv0OXPmAIhnHI79j0gZzGZzPntr1hemB6CuY85rT0VU/hUrFhSUU1UVnhvOkPBc0x5S1VdekySPCVRcNMwzz5VpPLR3v++++wAUezrQmazhw4cDAIYNi2ZnnnjiCQCxr2VVTEOXjlSD+Mk0uQ/bBhUn/s7v7BtUsrbeeuuCPEObbM6ysX/xmDfeeANATqXPJNxAc7buuwyPFPs3P5hXcF5IuK7i//7v/wAU23QzT/YNlpf9Tu8feg/Q8PJArASyXjrbxDQ4C0H1kvtRxdN1O6rkJ9VHPZXwWLXV1VmapNnQMN3wf/X89ZOf/ATtlR/96EcA4tksXY+g1yV89ul6BA1CqM8Ptb8m+rxK80YDFNuqs/2oBzEN5sby877O+znbLNewsM+xDkCsWnMfHsN7Bp/DaV7ctK9xpkFnDcL+rzbuem6Irv1IO+dcw8DzxmsX7s//tZ9om2CbMW2XsnxxN8YYY4wxprWQyWaRKUFNL2Wf+ijLF3eq4RzlUkmgjVuoAOgq9PnzI08RtK/mCmyOVmmDS9LCu2tksySvDywXFQAd2asfbJ0VoK0eR9+081OlPtyWt0+lksgw8rU5RSynvGfWRwoe/U3zPE156FEAwPijD4mOq86l1yFWYai4v/TO+wXngOqKrrAnrB+vH/ej/TIj26ktcmjnpz6F1e+32Xi+8pWvAAB++9vfAoivA/sO7WypSD311FMAYh/jvBaqRoVKFZV1Xq/ddtsNQOThJfxkH6Cyxuut/o7Zltj2QkVXFWX2Q+Y9ZMgQ/Omp/8ur/PTE9Pp7cwvOi9qhs0zPPfdcfh/1hc4+zlkmKu1U3qkoch2MRlxM8+8MFKvX/FR7dPU+obEdNJplmr19WB6iijo/1Qe2rkkhSWVSv+Fp/qrbI5yh4nNLvf2ojTQQ90fuy7aotty83mrTrTMx+tzh91AV1n4Q2r8DsaKux7Kvcjuf05oO+3sS+txV9V493uiMIvsm89LZsLCeaeeCpMWAYF48pywTrw3vj3rtwmN17QfTtm17+6EsX9yNMcYYY4xpNWRK9CqTaYdeZdTrBZUCKrihPaiqUzyGdm8c4b7//vsF3zkipiKkUddUgUqyN6cyqfa6LBNHyFT9VTGjSkf1gYohy/TjH/84n9cLL7xQsE8dR/w5W/VMh1zEuuqccrc+Gsl/a/wpAIBH//USgPgc/uvtSHVUv7sh+psqZRppU8PX8zuvBcvO66c+foFYPdG8k6I+mo3j5JNPTtz+97//HQDw73//G0CsXKtHF14Lqknh7BTtzqk067oHnZ1STyjsK2xbqrSHMy9sP2zT7G9U7fjJPGa8XzjblramhJFJw7UXqhbreg3Olk2cOLEgTUbGPOGEE1AfoZ03y8FzpDMc6mNdVXz1Ba7RHuuLpqwzjjzfOmPA65HmyYaE25mGzowY4PXXXwcQ9xONRKqznSGciWb/5KfeQ3V2R/fTdsI8w+ctryfToO022yr7LcsU+jc//dgvRYnkZon/742Z+TVn9AyVtN5L7eOZB58v6tGGeTINPqdZHz6vObPG81DfOhO9V6SdS43BoteE50Vt3oHimQKmzX7NNmJakGZyB9k0QxtjjDHGGGNMs1CWijtRu1d+crQKFNvzcR8qfvSMoREZaWNGdLSrCluIKleqPjFt2itSWaIScMoppxSkR+Vg9913TzgLEfvss0/yD32iYyuGRlFcr7nmGgDAxSP3jsqYG/mxTKoIqIeY0O5UbWg18ith2lTSeK65naoKj6fykRQlT1Vd9RhiNh+HHBKte7jhhhsAFHuO0NkoVXaB+Pqx7VO9J2pnyzbANsW2wP3UVja0NaUqyTUUVPc1fgD7H+ujfZveZX415R4AsWeLsF1q3X/wgx+gFBpS2sl3v/vd/P/XX389gLhP8vyzPHrv0ngRaldcn2272tOqz++0dSxEo6CqV5kkn/Hcdu211xaVp73CGZff/OY3AOL1T+o3PWz/es7VrlqvHfdjv9E1Lmwn7HtJ0W+1nbC/856vs0NpUcSZFmedS4miSzVeZ+H4TFc7es7e8j2CZWSZNaJsWE+mxXOhsxd6LplGmi98fVfgZ3g9eR10Roqzee3Z+1JrwYtTjTHGGNMm2WfUCAAjkFkbmYdkVkYD67rOPeo97j/2/RwA4B/Pv7JZy2dMa6UsX9w52uUolXazSV5lVMXRUTQVIkZZ1FF3WoQ3loHpJamKRCObqSLJ8l9wwQX11ntTcOmllwIA1i+O7HmffjFSPNW3rdrFkrCeqvjpdkLFkzMhPMfqZSctal6oDGlUP1VTzOZHI4qyTegaDvUoARS3K/qE5wwYj+F3Km5qp6oKV5KfcCrP8+fPxwVf+28A/xnHNaiI2strs+bk92eZdh0aRfCt+CyaCaqbG/WRbx0dRUGt6R7Zxv/9pbfyx9LunQrb5uQ73/kOAOCnP/0pgPQIqeqtSs+het3RmbPwN92Hn7z/qb19mu2vphuiMwKmGMYg4CysnqvwvOq14HXX688+o7PKOsvFa857L2c5+R2I+yHz0FlW3tvrW0dBVq9ena8Pj6Oqrv8DxRFUmQefEVyLw+ct66UzhxpRlnUK68l9uS3Nt7q+R/CZlnbuea2YTtLaEE2bbcK0AprJxr0sX9yNMcYYU36M7NcdQHdkV0eD88zKaBFo3nFCh06Jxx2wx07RPyW89BvTlinLF3e1B9MIjaEdnHoo4UhXV2Zz9E27tzT1IS3v0LZT7fiIjqr5u9qkNgd/ejryQkPFgGVJO086awDE50wVHKoK3K6Kj9o3qm0782A6oXLLbfQgoPabZvOjSi77G9uxRjkNbcFVkWNboPKukYtV3Vdbdn5nOwhVsXfeeQdArq3klPbMupW5fIvVZcKp+w3vvAgAWPXeu1H9cr7lK3eO1pMcMnoXAMB7i1bmo8YywmVzcPHFFwMAJk2aBCDd006aH3eNxEhClY/XOu2+p9GgVZ3V9Uc62xjOlDHtH/7whw1Xvp1CO+a77roLQBwtlH0t9EKi67HUKww/dbYkad0WUBxZl9c6nOXSe772L22D9bF06VL069cvtUyclSMsF/NeuHBhwe9qA8+yaJl0HZXOVITHMM+054+eU37qsy7tvIUzKrxO/I3e5mzb3orIZktU3G3jbowxxphWzMCenQF0RmbNcgBAZkMuAOHayJwmwxeeDoXBjGjTnlkTuQ2u69wTxrRnyvLFnTZrVNfoB5yj1tAzhSrJVAfVF63urzbf6nlBR8ihaqVRVdWWVNX7lrDp1DJodDyNMqe2huH/qrCr1wK1kyXqg5hKAtOjQhIqIrSZ5DVn+WiXaJoPqk287pwF4Xf+rp5igFg94rVmn1G/z7y+VPPT/PVzHQVtzQHgww8/LDomwwjCiD732mlE/rd35kYKVv7FoUPyuol8GnVxpEb2/1133TXxmM3JhAkTAACXX345gPh8M6ItP3Utgs548TOcPeR9QaPgqjcTVe153dhP+anxMS688MKNqLF58cVoNohrs3QmCyieFUmbgdFrmuZ1Rp8VOosS/l9f/I9SWbp0KT777LP8i7uiM9UsD6OBs77qRYplS3p2h6jdelhPnYlWxV3fLzQNXXeiSrzONALxNea+bAOnn356YvlN85OpqECmhJgypexTH2X54m6MMcaY8oFBATMdGKwrMqnLbJH7nrNtf/ifr+Cjjz7KH5fZEL2wcsBcm7Lg2Zj2Qlm+uL/99tsAgL33jnyRc9RKVSf0lcpROUfb6h9V7dtUYVdlWkfrOqIGYnVKR+GqfPB7WqTKzQnzfOSRRwAUqy36qaviw9/SPNLo7AThueK5ZzRAzoYwXR4XrlngNValgm3i2GOPLfEMmI1Fr2uaL2O2FfoRD4/lbIr2M7VhV3tcHk9beD7gGaE0tLctsBfN2bQzbgFfAmIFPr5v1FZG9ek4eGRUv9w+2e69ozS4eC4Tz8LpLENLkGYbfuONNwKIvWmov3r2wyRf+ElrAJJQtZ4zYLxOvC7Mm96tzMZx0003AQCuvPJKAMABBxwAIJ6RBOK+xXVevDacqVYPTbxvNzS7pSpz0poyXudCO/riqK5J9OnTJ99+GHshCea7ePFiALHNN58JXCfDGSe2a5ZNvcloNGCeL9YpPB88R2m27dyXa+Y0WivPObezvuyLuk4ozOu5554DELcB04rIZkuzX7eNuzHGGGNaM3MWLkeXLl3Qv0c0MM50LDR1eXnmBwWDfJKhC9fcoPt3j/wt79rRmFaF3UGm8/3vfx8AcO+99wKIlSRVtIF4lE0lLLTRBopHwKom6Ig6LaJoqDbyf/UtrTaGrSHaJ8vAc8gyqgKvngSAYjVU0XOo6weojDBtXaGfdD3V2w+jWLJNmOaD7VujAqrSHq7hoFKlbZ/XU9MgVBLpKeL5558HUDwjlOTHumANSU4lR6ZYKWMZ3p8ftante0R9osOAKLpypjLXPjvkVPVs3EfYX1pDn1bUjvxHP/oRgOLIkfxMitWgfZjoWgTOiC1ZErn4Y5RXs3lghF5GMx4xIl6zwfbMPqe+1Lld12sRfSaqFyLOtIX3Z7Yh9lfuS0V53bp16N+jf2JdKisr83kwfkPS+q+PPvooHy00rCfbJtfJMA3eS3T9Ftsuy8rvjMXA+xu91YXnR9ft6HNTo6TzU73FaCRh5snZgzBP2u6XGpXZtF3K8sXdGGOMMeXHzI8WFJlPqkOHRDbBgldjNieZbEXsHamB/ZpCWb+4066Vvl7VPzhQfEPQ6I5UE2gHl+QBA2h45XlSdE+OrjmCV2VAR9stAcvAMqmHCZ4P9dEOFHvaSUN98FLhoB9e9Vijnn7C86QzHmwDZvNDW2leD15H9UpBpV29zYTH8Fqzfalf9tBuNtxO9es///M/AQDTp08vyDNp9qeysjJW2vMqYrKv6rB+tZ16R2nmlPc62snT20zgC571ocer1sxll11W8r4///nPART3yfPOO2+TlskYY5rKNddcgwcffBDvvPMOunTpgrFjx+K6667DDjvskHrMbbfdhrvuugszZswAAOy11164+uqrMWbMmOYqdqMp6xd3Y4wxpr1z0UUXAQBuvvnm/LaRI6PF1WkmMrqAVM0QNZCgDtDpgjWEghjTpCkjCRdbAsXCl7oCHjBgAOpoC18Xm1aGJjQ0z2F5uCiVaagowMG1CkqsN829aD5K89DQzJZ5pTmx0LRZP6bFvNQ1p7pXnTVrVj4NXmOTztNPP41zzz0Xo0ePRnV1Nb7//e/j0EMPxVtvvZUqyj711FP46le/irFjx6Jz58647rrrcOihh+LNN9/MO84omUyJi1MzTZs98ou7MabNc/X/uwVHHXUUALXXXlUQmTfknU+XAwBqa6MXl1HbFtrmvvmhZ3uMMaa18OijjxZ8nzJlCvr164eXX34ZX/jCFxKPufvuuwu+33777XjggQfwxBNPtFof+WX94s4R6BNPPAEgHvWG5jEc4XP6W8MG8yHOYzjC4ihezUA4hc/FV8yTo3sgHl2r20dVNv77v/+7sVXe5LAMjz32GIDi0PLqPjM0e9CAO1wUxH1VqaHJEBcW8VxyPy7s09DtoXqhi5WsQjQfuvCKbYMLRgcOHAggvp40hQpdClIN43XUhWIahIttRIO+sI3su+++AIB//etfBWUC4nYzYMCAgnKrOqYmaxooLY3QLIf/877QVvj2t7/d0kUwjSA0YfrHP/5R8BuVdnVZmvaMVBWY2zWIVvjs42/clyqnuk9kv+Y9n/cBDqJDZxLPvPpWvl9169YNI0eOxC677JLPkyYOaoan9WRerKe6ik7r90wnrCfvhaynmvZpgCV9pqW5j9VAWjZJaxo6e1IKq1evxoYNGxp1DGkuG3ev9jDGGGOMMW2G2tpaXHjhhfj85z9fMNBriO9973sYOHAgDjnkkM1YuqZR1oo7efPNNwHE4caTfMGqYqe2eFTjqApz9K0BmjiCpprIdHkcEKsGGqKYefDY1gTLxMV/LDPPJesZurtTxZz1poKh6gvPkS5A5DWhUqLHhfA3XvMvfvGLG1FbszGw/fL68npygTDVIw3kw4Xf4W+81toG0lyLEqplVK5YJvp1ZsCfcN8dd9wxsR5aprRgKizbGx/MKzg+XLDJelDhMaal+fjjjwEA2223HYC4v6rCrA4beM/n/rSRZxunsk3FOoRpsc/QFpxpqOMG3gfU1ST3Y5/kfYFuEsNF4Cwn89J+rK4ZqWarjb8GX1SFPnwe8X9diM+86f6S9VKbd3W1yTpwP147s/Gce+65mDFjBp599tmSj7n22mtx33334amnntq4YHrZbIl+3G3jbowxxhhjDM477zw88sgjeOaZZ7DNNtuUdMz111+Pa6+9Fn//+9+x2267beYSNo028eL+rW99CwAwefJkAMCQIUPyv6k9LkfRHOmqu0NdWa42dwpH3qEtvObBUTeViq985SuNruPmhmV68MEHAcTnRe3PQ3tg1j3t3FCN0JDRatesdoI850k27h9++CGA+Jqb5uOb3/wmgDjUtl5fztrQ1l1t4oH4mqbZrhMNCqPeGnSNivqFBmKbVKrxqnqpap93AyneNNLcnYazcQyOYptU01p45ZVXAMTrtnTGLG0tka75UCWa/T7JBSuVY6ZJVVsDH+r6L1Wwqf7zWcA6MP3Fixfn02L/5j5Me9GiRQV5q3eYhtwPs0xcyxWeF71fqZcZ3jOYdtq51iBQrDevXWtdHNlaqaurw/nnn4+HHnoITz31FIYNG1bScT/5yU9w1VVX4bHHHsPee++98QXIluhVxoq7McYYY4xpz5x77rm455578PDDD6NHjx5506pevXrlB2qnn346Bg0ahGuuuQYAcN111+GHP/wh7rnnHgwdOjR/TPfu3VM9jqWRqahApgFzT+7XFNrUi/uZZ54JIA4aAsSriTkC1pX16keWI15+cpRN228qe/xkurqqPIRpzJs3byNr1nywjByppnnVCX/Tc0I1gQosVZQ0m0KqEVRT2HGopoa+gO3lovXA66mzTuqLOFTk2BbUnzH3YRtin+F2Vd7VU5PuD8R9Vj1ZpCnv6lGJaB9IUvffe++9om3GtCQMmMbPPffcE0CsILMfUIFnf9b7uNrEq4ex8JmgdvG6vonPXe23qm7rjDjvJfQQFa4T4zamzfJxH+3PvPfoehqWUWeCaa8eziyrv3lV1Fl/XQ/D+up6Aeb1xhtvAIivmWkckyZNAgAcdNBBBdvvvPNOnHHGGQCAuXPnFswCT5o0CevXr8cJJ5xQcMyPfvQj/PjHP96cxd1o2tSLuzHGGGOMaX+kORgIeeqppwq+z5kzZ9MVIFtR4uJUK+5FhKrstddeCyBW3zhq5giZ6gJHxFQE1fc4t/N4fup+QKwiql9YtfNrjegqf10tn7Qvz4WeQ10pz++c9eD+qmhSdaGHkEsuuaRplTKblPPPPx9AbOtOFYkK19ChQwu2J9mIq6262pmy/fFYjTTIdsm1KEk+1+lNg3mpDa8q5/xdPUHojBLb+7vvvps/1rbtprVy4YUXAgDuvfdeAMDgwYMLfqfaq5FGqUizD7Lv0Z6bv4feVqiQs++EMVXCtPj85bNA+7d6LGPfo+lC+CzlNp2tUz/tGjmWeanarx7nGJ8kvF+oD3tV8bkv68X6MA/eYzS2Ca+VMfXRJl/cjTHGGGOMaTasuG8aqNZOnToVQDzaVg8nqioUhkWPR8Y8Tm34QgVAvVNwBH/WWWdtwpptHlhGqjNUK3hewnpyG88F662+8NUrQUO20Pxupb11Q+WdXHnllQBiLzNsK6EHBvUdzX6mUU3Vj7N6Y6C6zzUZ7Ieh3SrXt7D/qacHtXXXsugsE4+jahYq7sa0dl588UUA6R5Q2E+0/ev9mSozn6WhjXtaVOK02S5VrHnv4CfTVtv4cBZP18HQbpzqPxV5jTPC+5LGhlB7dVX9wzSYp84g6nee2zQFntfmq1/9KoxpiDb/4m6MMcYYY8zmJJPNIlOCq8dS9qmPdvPiPm7cOADAY489BqA4QhtH3aoOq2rOkTKVAqrNYURRwm1JEUBbOywzz4vaEYbbqDpQBVUft2l+clVV5XZeK1Ne/OAHPwAQ+cQFgM997nMAClXwNP/rqsDrGpKFCxcCiP03U1WjGqYeMEI0Uiq/Mw32aSp06ulG16Y8//zzAIALLrgg6TQY0yq54YYbAABXX301AOCAAw4o+J3tXeOO6HonKu26xgmI+y/XOfFYjaPCWdlevXoBiPstn6fsg7rWJWk2TGcOWA8q50xT7zVcH6O+51V5Z31DlZ/58xxpfZlXmgcb1u/VV18FEF8bY0qh3by4G2OMMcYYs1nIlGjjnrGNe6OYNWsWAGDUqFEA0qPF6Xb1ZUuVrj4FgMfSf2g5wTLff//9AJLrSVVefd6r32yNUEm4Hz95bQ477LBNWBPT3Hz3u98FgHyAizDk9FZbbQUARYEtqFBR/Xr//fcBxIoW+58q6lS62NaYPlC8ZkI9PVApfO211wDEnqe23377guMZgfGll14CYM8Pprz5/ve/DwC44447AAA777wzgFgtZv+gOq6279xOJZufQPzcpO9zfmqkVKr16qlG463ocWqXHm7TtNVGnWWjXTkVd9ZPPcypx6vw+aX147OQeegsnc4q81nHa2FMY2h3L+7GGGOMMcZsUjIZIFOC/XqCi+RGZVNXisf6Ngy9zehKe7VPpy9X2sESVZHDY4888shNX+AW4pFHHgFQrJQCxd45qJIuWbIEQGznx2O5//LlywHYpr09cfnllwOI2wQ/SVpEQvV8QYWd6yrY5mhXDwDDhw8HUNw+1eMDFXVGLeTvVNo4C2B1zLRF7rnnHgBx/AX2QbZ7Xb+ltuP03gTEyjKVaPXGRthfOevVp0+fgrR1xlvjqdA2HIgjwmpUdFXK+SznPYNp6jNdZ+RYz9DGndG8VXEnfNYxDd6vGOznlFNOgWk7rFixAr169cKy155Ezx7F70hF+3+2En32OBhVVVUFM1al0rSlrcYYY4wxxphmod0r7o3lpz/9KYBYEVQlEGjbNrA33nhj/n/a8bEJ0Xbw4osvbvZymfKECjzbEtU7qmBsW7RfVbtUVboOPfTQ/P9U3HQtBWHfpcca2ro7foBpj0yaNAkAMHLkSADFsUzYR/V76GlMI4emxWFQG3EeR6VaVXD2d6rk7KsAsMceewCI1W21L6e6z5kDKupqo69r0zTyeegtjdtYLtZTvzMN2rRPmDABpu1BxX3pv58uWXHfYvcDrbgbY4wxxhjTlvHi1EbS3tXktjybYFoOKnLqS1pVMI2sSqiyhV5n1JsEj02LtGil3bRnqAZPnDgRQOx5jWtF1BMM+0+oRLOfqp259muuKePvXO/ET+6v8Rz4e6jyc1u/fv0K6kN1Xo/R9Wrcrl5lWBf1qgPEtvg8huVjuekV66233gIAXHHFFTDtgEy2xMWpTdPMrbgbY4wxxhhTBlhxN8a0GGpHSu8LqmBxu/px5nH0wR6qYurxSZU15kGvMsaYWB2+6KKLAAB9+/YFUBwNlH0xXGeiMT3oLYbHatwFbqcCr/blTI+fXI8SzqxxG9edafRzRmdVLzNck8W06JWG9xR6n2Heoe28esNiuWmz/+KLLwJwRNR2RyZTmqvHJrqDtOJujDHGGGNMGdDqXtznzZuHk046Cb1790bPnj1x9NFH5+3FjDGFlHt/mThxIiZOnIjq6mpUV1dj9erVWL16NTZs2IANGzbkv69ZswZr1qxBbW0tamtr0blzZ3Tu3Bl9+/Yt+Mtms/m/ioqKgr/wt2w2ixUrVmDFihVYvnx53g7WGGOM2Siy2dL/mkCrMpVZuXIlDj44ckr//e9/Hx07dsTPf/5zHHjggXjttdfyi0qMMe4vxpjNB808vvnNbwIADjzwQADAkCFDCvaj2QsQm89oIEMuBKUZyvz58wGkBzmi6QkH1AsWLAAAnHbaaanlve+++wDEZnM0v1FzPA0ONXDgwII8uVidJkDcHi6I5zby4YcfAgCefvppAMCvfvWr1HIa01Ra1Yv7r371K7z77ruYPn06Ro8eDQA44ogjsMsuu+BnP/sZrr766hYuoTGth7bUX+jR5ZprrgFQ7J+dD0q+EDDKIz1e6P5A/GDmA1dt3ufOnVuQtzHGGLOx1GWyqCvBY0wp+9RHowIwPfnkk/iP//gPPPjggzj22GMLfrvnnntw6qmn4rnnnsN+++23UYUZM2YMAGD69OkF2w877DDMnj0b77333kala0xLsGbNmnw47ldffTW/uGnp0qXYeeedMWzYMPzzn/8sCgdeKm2xv/DFXV+yS31xD2cZVCnjsVykxiAu9al4xphC6C5yt912A4CCADIDBgwAEC/4ZF+jEs/XDV1szu1UwxcvXgwgXhjamD46bdo0APFiUi6uVVWf912WVbfz/sGyfvrpp/k8WM7XX38dgN09tncYgGnJ29NLDsC05U5jmicA00EHHYTBgwfj7rvvLvrt7rvvxogRI7Dffvth3bp1WLx4cUl/pLa2Fq+//jr23nvvorTHjBmD2bNn51eBG1MOdOnSBVOnTsV7772H//3f/81vP/fcc1FVVYUpU6agoqLC/cUYY4wxJdEoU5lMJoPTTjsNN9xwA6qqqvJulhYtWoS//e1v+ZeTe++9F+PHjy8pTY60ly5dinXr1uVH7CHc9sknn2CHHXZoTJGNaVH22WcffPe738V1112HY489FgsWLMB9992HG2+8MR9a3P0l5tJLLy34fuWVVwIoVuBZRw3QEgZm4TZ1LckBTaigGWNKQ9Xlyy+/PP//YYcdBiDuh6qsa/AztT/nfuyjZ5xxRqPLR3V+ypQpAGKXlMyLZeM9hfcHLSPvtVT9X3jhhXweP/zhDwEAJ554YqPLZ9owzRSAqdE27qeffjquueYa3H///fja174GAPjtb3+L6urqfIc57LDD8PjjjzcqXXYO9Y8KxA9n7mNMOfHjH/8YjzzyCMaNG4eVK1fiwAMPxLe+9a387+4vxhhjjCmFRr+477jjjhg9ejTuvvvu/Iv73XffjX333RfbbbcdgEgNS1IC64P2aPUtMgsDIBhTLlRWVmLy5MkYPXo0OnfujDvvvDOv/gDuL/Xxgx/8oOA7F9x27x7ZEVIV4/kMPVxQxaOyRqXt7bffBgBcfPHFm6vYxrQbqD4DwDnnnAMA2GWXXQAgP6tIO17avBP2X5oB0pUtPdk0Bar19PDC9TC0ec9IEBwNojRr1iwAwIwZMwAAt9xyS5PLZNo4rVVxByLV/YILLsDHH3+MdevW4fnnn8fNN9+c/33NmjWoqqoqKa2tt94aALDFFlugU6dOidPX3Ea3TcaUG4899hiA6KX63XffxbBhw/K/ub8YY4wxphQa5VWGLF68GAMHDsRVV12FNWvW4Morr8Qnn3ySH8lOmTKl0Ta7ADB69GhkMpkiLxmHHnooZs+ejdmzZze2qMa0OK+//jpGjx6NU089Fa+99hoWL16MN954I79GxP2ldH7yk58AAA4//HAAxWHXQ9MhKu40Hfr4448BRC4zjTHNx4QJEwDEfZFqN/vvL37xi2YrywUXXACg2JadM5WTJk1qtrKYtgG9yiye9Sp69ujR8P6ffYa+I/fcaK8yG6W49+3bF0cccQSmTZuGtWvX4vDDD8+/tAMbZ7MLACeccAIuueQSvPTSS3lvGTNnzsQ//vEPfOc739mYohrTomzYsAFnnHEGBg4ciF/84hf44IMPMHr0aHz729/G5MmTAbi/GGOMMaY0NkpxB4AHHngAJ5xwAoBocepJJ53U5MJ89tln2HPPPfHZZ5/hO9/5Djp27IgbbrgBNTU1eO2117DVVls1OQ9jmpMf/ehHuOKKK/DEE0/g4IMPBgBcddVV+MEPfoA///nP+NKXvrTRabfH/kJl7tBDDwUQL8DlbSy0oaW3iNWrVwOI/d1feOGFzVJWY4wxbZ+84v7uv0tX3LffvXn8uIccddRR6NOnD3r16oUvf/nLG5tMAT169MBTTz2FL3zhC7jyyisxceJE7L777nj66afb5EuIadu88soruPrqq3HeeeflX9qBKFLn6NGjcfbZZ+dDem8M7i/GGGNM+2KjFffq6moMHDgQRx11FO64445NXS5jjEnlrbfeAlDsVSf0404bd9r6c4bQGGOM2VTkFff3Xi9dcd9ut+a1cQeAP/zhD1i0aBFOP/30jU3CGGOMMcaY8qe1uoN84YUX8Prrr+OKK67AnnvuiQMPPLBJBTDGmMYyatQoAMB3v/vdgu3hBCI9Vtxwww3NVzBjjDFmM9Lo1/5JkyZhwoQJ6NevH+66667NUSZjjDHGGGPKhrpMtuS/prDRNu7GGGOMMca0Z2jjvuj9t0q2cd9q+Kjmt3E3xhhjjDHGILJdz25+G/emHW2MMcYYY4xpFqy4G2OMMcYY0xSayauMFXdjjDHGGGPKACvuxhhjjDHGNAUr7sYYY0z7pLa2Frfccgv22GMPdO/eHf3798cRRxyB5557rqWLZoxpQfzibowxxrQyLr74YkyYMAG77rorbrjhBvzP//wPZs2ahQMPPBDTp09v6eIZYxQq7qX8NQGbyhhjjDGtiOrqakyaNAknnHACfvOb3+S3n3jiiRg+fDjuvvtujBkzpgVLaIxRKntticoS/LJXZiqblI8Vd2OMMaYe5syZg0wmk/q3qdmwYQPWrFmD/v37F2zv168fstksunTpssnzNMaUB1bcjTHGmHrYaqutCpRvIHq5/va3v43Kykg9W716NVavXt1gWhUVFejTp0+9+3Tp0gX77LMPpkyZgv322w8HHHAAli9fjiuuuAJ9+vTB17/+9Y2vjDGmrPGLuzHGGFMP3bp1w2mnnVaw7dxzz8XKlSvx+OOPAwB+8pOf4LLLLmswrSFDhmDOnDkN7jdt2jScfPLJBfkOHz4c//rXvzB8+PDGVcAY02bwi7sxxhjTCO666y786le/ws9+9jMcfPDBAIDTTz8d+++/f4PHlmrm0qNHD+y8887Yb7/98MUvfhHz58/Htddei2OOOQb//Oc/0bdv3ybVwRhTnmTq6urqWroQxhhjTDnw2muvYezYsTjmmGNwzz33NCmtqqoqrFmzJv+9srISW2yxBaqrq7HnnnvioIMOwk033ZT//d1338XOO++Mb3/727juuuualLcxZtOwYsUK9OrVC1VVVehZwuLUxu6veHGqMcYYUwLLli3D8ccfj5EjR+L2228v+G3lypWYP39+g3+LFi3KH3PBBRdgwIAB+b/jjjsOAPDMM89gxowZ+PKXv1yQx/bbb4+ddtoJ//rXvzZ/ZY1pR/zyl7/E0KFD0blzZ+yzzz6t2uWqTWWMMcaYBqitrcWpp56K5cuX4+9//zu6du1a8Pv111/faBv37373uwU27Fy0umDBAgBATU1N0fEbNmxAdXX1xlbDGCP89re/xUUXXYRbbrkF++yzD2688UYcdthhmDlzJvr169fSxSvCL+7GGGNMA1x22WV47LHH8Ne//hXDhg0r+n1jbNxHjRqFUaNGFe0zcuRIAMB9992Hww8/PL/9lVdewcyZM+1VxphNyA033ICzzz4b48ePBwDccsst+POf/4zJkyfjkksuaeHSFWMbd2OMMaYe3njjDey+++74whe+gLPOOqvod/U4syk49NBD8fjjj+PYY4/FoYceik8//RQ33XQT1q9fj5dffhk77LDDJs/TmPbG+vXr0bVrV9x///045phj8tvHjRuH5cuX4+GHH24wjea2cbfibowxxtTDkiVLUFdXh6effhpPP/100e+b48X94YcfxvXXX4/77rsPjz76KCorK3HAAQfgiiuu8Eu7MZuIxYsXo6ampijYWf/+/fHOO+80Kq0VK1Zs0v3S8Iu7McYYUw8HHXQQmntyukuXLpg4cSImTpzYrPkaYxpHZWUltt56awwePLjkY7beeut88LbG4hd3Y4wxxhjT7ujbty8qKiryC8LJggULsPXWW5eURufOnfHBBx9g/fr1JedbWVmJzp07N6qsxC/uxhhjjDGm3VFZWYm99toLTzzxRN7Gvba2Fk888QTOO++8ktPp3LnzRr+INxa/uBtjjDHGmHbJRRddhHHjxmHvvffGmDFjcOONN2LVqlV5LzOtDb+4G2OMMcaYdsnJJ5+MRYsW4Yc//CHmz5+PPfbYA48++mjRgtXWgt1BGmOMMcYYUwZkW7oAxhhjjDHGmIbxi7sxxhhjjDFlgF/cjTHGGGOMKQP84m6MMcYYY0wZ4Bd3Y4wxxhhjygC/uBtjjDHGGFMG+MXdGGOMMcaYMsAv7sYYY4wxxpQBfnE3xhhjjDGmDPCLuzHGGGOMMWWAX9yNMcYYY4wpA/zibowxxhhjTBngF3djjDHGGGPKAL+4G2OMMcYYUwb4xd0YY4wxxpgywC/uxhhjjDHGlAF+cTfGGGOMMaYM+P8+sBhDCL/+MQAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Group comparison test between two groups\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,-1,0,0]],\n", - " t_con_moderator=None, device='cuda')\n", - "inference._contrast()\n", + "# Group comparison test between any two groups\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "# chi square statistics maps for group comparison test\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"1xschizophrenia_NoVS1xdepression_Yes_chi_sq\"),\n", + " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " threshold=1\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", ")" ] }, @@ -196,46 +301,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "[[0.94563486]]\n" + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" ] - } - ], - "source": [ - "# Test for existence of effect of study-level moderators\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,0]], device='cuda')\n", - "inference._contrast()\n", - "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", - "print(sample_size_p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "[[0.99838466]]\n" + "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" ] } ], "source": [ "# Test for existence of effect of study-level moderators\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,-1]], device='cuda')\n", - "inference._contrast()\n", - "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", - "print(effect_diff_p)" + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes\", \"standardized_avg_age\", \"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "sample_size_p = cbmr_res.tables[\"standardized_sample_sizes_p_values\"]\n", + "avg_age_p = cbmr_res.tables[\"standardized_avg_age_p_values\"]\n", + "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", + "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", + "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" ] }, { diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f7b937efa..00591c2e2 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -337,23 +337,23 @@ class CBMRInference(object): Results of optimized regression coefficients of CBMR, as well as their standard error in `tables`. Results of estimated spatial intensity function (per study) in `maps`. - t_con_group : :obj:`~bool` or obj:`~list` or obj:`~None`, optional + t_con_groups : :obj:`~bool` or obj:`~list` or obj:`~None`, optional Contrast matrix for homogeneity test or group comparison on estimated spatial intensity function. For boolean inputs, no statistical inference will be conducted for spatial intensity - if `t_con_group` is False, and spatial homogeneity test for groupwise intensity - function will be conducted if `t_con_group` is True. + if `t_con_groups` is False, and spatial homogeneity test for groupwise intensity + function will be conducted if `t_con_groups` is True. For list inputs, generialized linear hypothesis (GLH) testing will be conducted for - each element independently. We also allow any element of `t_con_group` in list type, + each element independently. We also allow any element of `t_con_groups` in list type, which represents GLH is conducted for all contrasts in this element simultaneously. Default is homogeneity test on group-wise estimated intensity function. - t_con_moderators : :obj:`~bool` or obj:`~list` or obj:`~None`, optional + t_con_moderatorss : :obj:`~bool` or obj:`~list` or obj:`~None`, optional Contrast matrix for testing the existence of one or more study-level moderator effects. For boolean inputs, no statistical inference will be conducted for study-level moderators - if `t_con_moderators` is False, and statistical inference on the effect of each study-level - moderators will be conducted if `t_con_group` is True. + if `t_con_moderatorss` is False, and statistical inference on the effect of each study-level + moderators will be conducted if `t_con_groups` is True. For list inputs, generialized linear hypothesis (GLH) testing will be conducted for - each element independently. We also allow any element of `t_con_moderators` in list type, + each element independently. We also allow any element of `t_con_moderatorss` in list type, which represents GLH is conducted for all contrasts in this element simultaneously. Default is statistical inference on the effect of each study-level moderators device: :obj:`string`, optional @@ -407,7 +407,7 @@ def create_regular_expressions(self): setattr(self, "{}_regular_expression".format(attr), reg_expr) - def create_contrast(self, contrast_name, type="group"): + def create_contrast(self, contrast_name, type="groups"): """Create contrast matrix for generalized hypothesis testing (GLH). (1) if `type` is "group", create contrast matrix for GLH on spatial intensity; @@ -432,8 +432,8 @@ def create_contrast(self, contrast_name, type="group"): if isinstance(contrast_name, str): contrast_name = [contrast_name] - contrast_matrix = list() - if type == "group": # contrast matrix for spatial intensity + contrast_matrix = {} + if type == "groups": # contrast matrix for spatial intensity for contrast in contrast_name: contrast_vector = np.zeros(self.n_groups) contrast_match = self.groups_regular_expression.match(contrast) @@ -447,9 +447,9 @@ def create_contrast(self, contrast_name, type="group"): contrast_vector[self.group_reference_dict[groups_contrast["second"]]] = int(contrast_match["operator"] + "1") else: # homogeneity test contrast_vector[self.group_reference_dict[contrast]] = 1 - contrast_matrix.append(contrast_vector) + contrast_matrix[contrast] = contrast_vector - elif type == "moderator": # contrast matrix for moderator effect + elif type == "moderators": # contrast matrix for moderator effect for contrast in contrast_name: contrast_vector = np.zeros(self.n_moderators) contrast_match = self.moderators_regular_expression.match(contrast) @@ -462,12 +462,11 @@ def create_contrast(self, contrast_name, type="group"): contrast_vector[self.moderator_reference_dict[moderators_contrast["second"]]] = int(moderators_contrast["operator"] + "1") else: # moderator effect contrast_vector[self.moderator_reference_dict[contrast]] = 1 - - contrast_matrix.append(contrast_vector) + contrast_matrix[contrast] = contrast_vector return contrast_matrix - def compute_contrast(self, t_con_group=None, t_con_moderator=None): + def compute_contrast(self, t_con_groups=None, t_con_moderators=None): """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. Estimate group-wise spatial regression coefficients and its standard error via inverse @@ -480,34 +479,43 @@ def compute_contrast(self, t_con_group=None, t_con_moderator=None): Parameters ---------- - t_con_group : :obj:`~list`, optional + t_con_groups : :obj:`~list`, optional Contrast matrix for GLH on group-wise spatial intensity estimation. Default is None (group-wise homogeneity test for all groups). - t_con_moderator : :obj:`~list`, optional + t_con_moderators : :obj:`~list`, optional Contrast matrix for GLH on moderator effects. Default is None (tests if moderator effects exist for all moderators). """ - self.t_con_group = t_con_group - self.t_con_moderator = t_con_moderator + self.t_con_groups = t_con_groups + self.t_con_moderators = t_con_moderators - if self.t_con_group is not False: + if self.t_con_groups is not False: # preprocess and standardize group contrast - self.t_con_group, self.t_con_group_name = self._preprocess_t_con_regressor(attr_list=["t_con_group", "groups", "n_groups"], type='groups') + self.t_con_groups, self.t_con_groups_name = self._preprocess_t_con_regressor(type="groups") # GLH test for group contrast self._glh_con_group() - if self.t_con_moderator is not False: + if self.t_con_moderators is not False: self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast - self.t_con_moderator, self.t_con_moderator_name = self._preprocess_t_con_regressor(attr_list=["t_con_moderator", "moderators", "n_moderators"], type='moderators') + self.t_con_moderators, self.t_con_moderators_name = self._preprocess_t_con_regressor(type="moderators") # GLH test for moderator contrast self._glh_con_moderator() - def _preprocess_t_con_regressor(self, attr_list, type): + def _preprocess_t_con_regressor(self, type): # regressor can be either groups or moderators - t_con_regressor, regressors, n_regressors = [getattr(self, attr) for attr in attr_list] + t_con_regressor = getattr(self, f"t_con_{type}") + n_regressors = getattr(self, f"n_{type}") + # if contrast matrix is a dictionary, convert it to list + if isinstance(t_con_regressor, dict): + t_con_regressor_name = list(t_con_regressor.keys()) + t_con_regressor = list(t_con_regressor.values()) + elif isinstance(t_con_regressor, (list, np.ndarray)): + for i in range(len(t_con_regressor)): + self.CBMRResults.metadata[f"GLH_{type}_{i}"] = t_con_regressor[i] + t_con_regressor_name = None # Conduct group-wise spatial homogeneity test by default - t_con_regressor = [np.eye(n_regressors)] if not self.t_con_group else [np.array(con_regressor) for con_regressor in t_con_regressor] + t_con_regressor = [np.eye(n_regressors)] if t_con_regressor is None else [np.array(con_regressor) for con_regressor in t_con_regressor] # make sure contrast matrix/vector is 2D t_con_regressor = [ con_regressor.reshape((1, -1)) if len(con_regressor.shape) == 1 else con_regressor @@ -534,8 +542,6 @@ def _preprocess_t_con_regressor(self, attr_list, type): raise ValueError( """One or more of contrast vector(s) in {type} contrast matrix are all zeros.""" ) - # name of GLH contrasts and save to `tables` later - t_con_regressor_name = self._name_of_con_regressor(t_con_regressor=t_con_regressor, regressors=regressors, type=type) # standardization (row sum 1) t_con_regressor = [ con_regressor / np.sum(np.abs(con_regressor), axis=1).reshape((-1, 1)) @@ -547,55 +553,9 @@ def _preprocess_t_con_regressor(self, attr_list, type): return t_con_regressor, t_con_regressor_name - def _name_of_con_regressor(self, t_con_regressor, regressors, type): - """Define the name of GLH contrasts on spatial intensity estimation. - - And the names will be displayed as keys of `CBMRResults.maps` (if `t_con_group` - exists). - """ - t_con_regressor_name = list() - for con_regressor in t_con_regressor: - con_regressor_name = list() - for num, idx in enumerate(con_regressor): - if np.sum(idx) != 0: # homogeneity test - nonzero_con_regressor_info = str() - nonzero_regressor_index = np.where(idx != 0)[0].tolist() - nonzero_regressor_name = [regressors[i] for i in nonzero_regressor_index] - nonzero_con = [int(idx[i]) for i in nonzero_regressor_index] - for i in range(len(nonzero_regressor_index)): - nonzero_con_regressor_info += ( - str(abs(nonzero_con[i])) + "x" + str(nonzero_regressor_name[i]) - ) - if type == 'groups': - con_regressor_name.append("homo_test_" + nonzero_con_regressor_info) - elif type == 'moderators': - con_regressor_name.append("ModeratorEffect_of_" + nonzero_con_regressor_info) - else: # group-comparison test - pos_regressor_idx, neg_regressor_idx = ( - np.where(idx > 0)[0].tolist(), - np.where(idx < 0)[0].tolist(), - ) - pos_regressor_name, neg_regressor_name = [regressors[i] for i in pos_regressor_idx], [ - regressors[i] for i in neg_regressor_idx - ] - pos_group_con, neg_group_con = [int(idx[i]) for i in pos_regressor_idx], [ - int(idx[i]) for i in neg_regressor_idx - ] - pos_con_regressor_info, neg_con_regressor_info = str(), str() - for i in range(len(pos_regressor_idx)): - pos_con_regressor_info += str(pos_group_con[i]) + "x" + str(pos_regressor_name[i]) - for i in range(len(neg_regressor_idx)): - neg_con_regressor_info += ( - str(abs(neg_group_con[i])) + "x" + str(neg_regressor_name[i]) - ) - con_regressor_name.append(pos_con_regressor_info + " - " + neg_con_regressor_info) - t_con_regressor_name.append(con_regressor_name) - - return t_con_regressor_name - def _glh_con_group(self): con_group_count = 0 - for con_group in self.t_con_group: + for con_group in self.t_con_groups: con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() con_group_involved = [self.groups[i] for i in con_group_involved_index] n_con_group_involved = len(con_group_involved) @@ -669,45 +629,40 @@ def _glh_con_group(self): (Cov_log_intensity, Cov_group_log_intensity), axis=0 ) # (m^2, n_voxels) # GLH on log_intensity (eta) - chi_sq_spatial = np.empty(shape=(0,)) - for j in range(n_brain_voxel): - Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) - V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) - CV_jC = simp_con_group @ V_j @ simp_con_group.T - CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_spatial_j = ( - Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j - ) - chi_sq_spatial = np.concatenate( - ( - chi_sq_spatial, - chi_sq_spatial_j.reshape( - 1, - ), - ), - axis=0, - ) + chi_sq_spatial = self._chi_square_log_intensity(m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) - - con_group_name = self.t_con_group_name[con_group_count] - if len(con_group_name) == 1: - self.CBMRResults.maps[con_group_name[0] + "_chi_sq"] = chi_sq_spatial - self.CBMRResults.maps[con_group_name[0] + "_p"] = p_vals_spatial + if self.t_con_groups_name: + self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_chi_square_values"] = chi_sq_spatial + self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_p_values"] = p_vals_spatial else: - self.CBMRResults.maps[ - "spatial_coef_GLH_" + str(con_group_count) + "_chi_sq" - ] = chi_sq_spatial - self.CBMRResults.maps[ - "spatial_coef_GLH_" + str(con_group_count) + "_p" - ] = p_vals_spatial - self.CBMRResults.metadata[ - "spatial_coef_GLH_" + str(con_group_count) - ] = con_group_name + self.CBMRResults.maps[f"GLH_groups_{con_group_count}_chi_square_values"] = chi_sq_spatial + self.CBMRResults.maps[f"GLH_groups_{con_group_count}_p_values"] = p_vals_spatial con_group_count += 1 - + + def _chi_square_log_intensity(self, m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity): + chi_sq_spatial = np.empty(shape=(0,)) + for j in range(n_brain_voxel): + Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) + V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) + CV_jC = simp_con_group @ V_j @ simp_con_group.T + CV_jC_inv = np.linalg.inv(CV_jC) + chi_sq_spatial_j = ( + Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + ) + chi_sq_spatial = np.concatenate( + ( + chi_sq_spatial, + chi_sq_spatial_j.reshape( + 1, + ), + ), + axis=0, + ) + return chi_sq_spatial + def _glh_con_moderator(self): con_moderator_count = 0 - for con_moderator in self.t_con_moderator: + for con_moderator in self.t_con_moderators: m_con_moderator, _ = con_moderator.shape moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) @@ -733,18 +688,10 @@ def _glh_con_moderator(self): chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) - con_moderator_name = self.t_con_moderator_name[con_moderator_count] - if len(con_moderator_name) == 1: - self.CBMRResults.tables[con_moderator_name[0] + "_chi_sq"] = chi_sq_moderator - self.CBMRResults.tables[con_moderator_name[0] + "_p"] = p_vals_moderator + if self.t_con_moderators_name: # None? + self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values"] = chi_sq_moderator + self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_p_values"] = p_vals_moderator else: - self.CBMRResults.tables[ - "moderator_coef_GLH_" + str(con_moderator_count) + "_chi_sq" - ] = chi_sq_moderator - self.CBMRResults.tables[ - "moderator_coef_GLH_" + str(con_moderator_count) + "_p" - ] = p_vals_moderator - self.CBMRResults.metadata[ - "moderator_coef_GLH_" + str(con_moderator_count) - ] = con_moderator_name - con_moderator_count += 1 + self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_chi_square_values"] = chi_sq_moderator + self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_p_values"] = p_vals_moderator + con_moderator_count += 1 \ No newline at end of file diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 767886688..35d7f404a 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -79,12 +79,13 @@ def init_spatial_weights(self): self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) def init_moderator_weights(self): - """Document this.""" + """Initialize the intercept and regression coefficients for moderators.""" self.moderators_linear = torch.nn.Linear( self.moderators_coef_dim, 1, bias=False ).double() torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) - + return + def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): """Document this.""" self.groups = groups @@ -250,14 +251,12 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci group_foci_per_study = torch.tensor( foci_per_study[group], dtype=torch.float64, device=self.device ) - group_spatial_coef = torch.tensor(self.spatial_coef_linears[group].weight, - dtype=torch.float64, device=self.device) - + group_spatial_coef = self.spatial_coef_linears[group].weight if self.moderators_coef_dim: group_moderators = torch.tensor( moderators_by_group[group], dtype=torch.float64, device=self.device ) - moderators_coef = torch.tensor(self.moderators_linear.weight, dtype=torch.float64, device=self.device) + moderators_coef = self.moderators_linear.weight else: group_moderators, moderators_coef = None, None @@ -337,7 +336,7 @@ def summary(self): # Extract optimized regression coefficients from model and store them in 'tables' tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(self.spatial_regression_coef, orient="index") maps = self.spatial_intensity_estimation - if self.moderators_coef_dim: + if self.moderators_coef_dim: tables["Moderators_Regression_Coef"] = pd.DataFrame(self.moderators_coef) tables["Moderators_Effect"] = pd.DataFrame.from_dict(self.moderators_effect, orient="index") @@ -361,7 +360,7 @@ def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, m n_involved_groups = len(involved_groups) involved_foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in involved_groups] involved_foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in involved_groups] - spatial_coef = [torch.tensor(self.spatial_coef_linears[group].weight.T, dtype=torch.float64, device=self.device) for group in involved_groups] + spatial_coef = [self.spatial_coef_linears[group].weight.T for group in involved_groups] spatial_coef = torch.stack(spatial_coef, dim=0) if self.moderators_coef_dim: involved_moderators_by_group = [torch.tensor( @@ -397,7 +396,7 @@ def FisherInfo_MultipleGroup_moderator(self, coef_spline_bases, moderators_by_gr """Document this.""" foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in self.groups] foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in self.groups] - spatial_coef = [torch.tensor(self.spatial_coef_linears[group].weight.T, dtype=torch.float64, device=self.device) for group in self.groups] + spatial_coef = [self.spatial_coef_linears[group].weight.T for group in self.groups] spatial_coef = torch.stack(spatial_coef, dim=0) if self.moderators_coef_dim: diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index eb761b8ba..1a9db5cee 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,11 +16,10 @@ def test_CBMREstimator(testdata_cbmr_simulated): model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e4, + tol=1e1, device="cpu" ) cbmr.fit(dataset=dset) -# ["standardized_sample_sizes", "standardized_avg_age"], def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) @@ -30,7 +29,7 @@ def test_CBMRInference(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, - model=models.ClusteredNegativeBinomialEstimator, + model=models.PoissonEstimator, penalty=False, lr=1e-1, tol=1e4, @@ -41,14 +40,12 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) - t_con_group = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type='group') - t_con_moderator = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type='moderator') - contrast_result = inference.compute_contrast(t_con_group=t_con_group, t_con_moderator=t_con_moderator) - # inference.summary() - - -# [[[1,0,0,0],[0,0,1,0]], [1, 0, 0, 0]] -# [[[1,0],[0,1]], [1, -1]] + t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type="groups") + t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") + contrast_result = inference.compute_contrast(t_con_groups=[[1,-1,0,0],[0,0,1,0]], t_con_moderators=[[1,-1,0,0],[0,0,1,0]]) + # self.maps.schizophrenia_Yes_p_values = ... + # self.maps.schizophrenia_Yes_chi_square_vals = ... + # self.tables.standardized_sample_sizes = ... def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index f2724faad..b6aadaf3a 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -137,13 +137,13 @@ def standardize_field(dataset, metadata): if len(numerical_metadata) == 0: raise ValueError("No numerical metadata found.") - moderators = dataset.annotations[numerical_metadata] + moderators = dataset.annotations[numerical_metadata] standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) if isinstance(metadata, str): column_name = "standardized_" + metadata elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in numerical_metadata] + column_name = ["standardized_" + moderator for moderator in numerical_metadata] dataset.annotations[column_name] = standardize_moderators return dataset From e842394100974b3b5af7d1f69c49a264a0295dbd Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:26:35 +0000 Subject: [PATCH 065/177] [skip CI][WIP] Tried standardized categorical covariates --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 48fefc57c..a94623419 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -322,7 +322,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" + "For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: 0.9243109811987764, 0.9461743884065033\n", + "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" ] } ], @@ -339,13 +340,6 @@ "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From a07d3597f77f858ba63e70ea32bc605a7fc8243a Mon Sep 17 00:00:00 2001 From: "Julio A. Peraza" <52050407+JulioAPeraza@users.noreply.github.com> Date: Wed, 11 Jan 2023 12:31:45 -0500 Subject: [PATCH 066/177] Raise deprecation warnings with Python 3.6 and 3.7 (#754) * Add deprecation warnings for Python 3.6 and 3.7 * Remove arrays from exclude in Github action * Run tests and minimum dependencies on python 3.8 * Run linting and publish on 3.8 * Ignore D401 warnings * Update setup.cfg * Update testing.yml --- .github/workflows/linting.yml | 2 +- .github/workflows/python-publish.yml | 2 +- .github/workflows/testing.yml | 19 ++++++++++++----- nimare/__init__.py | 32 ++++++++++++++++++++++++++++ setup.cfg | 2 ++ 5 files changed, 50 insertions(+), 7 deletions(-) diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml index ccb0050b1..a915c7136 100644 --- a/.github/workflows/linting.yml +++ b/.github/workflows/linting.yml @@ -36,7 +36,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest"] - python-version: ["3.7"] + python-version: ["3.8"] name: Style check defaults: run: diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 88d6b3450..bee4ac83d 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -17,7 +17,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v2 with: - python-version: '3.7' + python-version: '3.8' - name: Install dependencies run: | python -m pip install --upgrade pip diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index b87f82b56..d29d2e8ad 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -39,6 +39,15 @@ jobs: matrix: os: ["ubuntu-latest", "macos-latest"] python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"] + include: + # ubuntu-20.04 is used only to test python 3.6 + - os: "ubuntu-20.04" + python-version: "3.6" + exclude: + # ubuntu-latest does not support python 3.6 + - os: "ubuntu-latest" + python-version: "3.6" + defaults: run: shell: bash @@ -70,7 +79,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest"] - python-version: ["3.6"] + python-version: ["3.8"] defaults: run: shell: bash @@ -79,7 +88,7 @@ jobs: - name: 'Set up python' uses: actions/setup-python@v2 with: - python-version: 3.6 + python-version: 3.8 - name: 'Install NiMARE' shell: bash {0} run: pip install -e .[minimum,tests,peaks2maps-cpu] @@ -102,7 +111,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest"] - python-version: ["3.7"] + python-version: ["3.8"] defaults: run: shell: bash @@ -134,7 +143,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest"] - python-version: ["3.7"] + python-version: ["3.8"] defaults: run: shell: bash @@ -166,7 +175,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest"] - python-version: ["3.7"] + python-version: ["3.8"] defaults: run: shell: bash diff --git a/nimare/__init__.py b/nimare/__init__.py index dd66257fd..f8d3f91d0 100755 --- a/nimare/__init__.py +++ b/nimare/__init__.py @@ -1,5 +1,6 @@ """NiMARE: Neuroimaging Meta-Analysis Research Environment.""" import logging +import sys import warnings from ._version import get_versions @@ -40,3 +41,34 @@ ] del get_versions + + +def _py367_deprecation_warning(): + """Deprecation warnings message. + + Notes + ----- + Adapted from Nilearn. + """ + py36_warning = ( + "Python 3.6 and 3.7 support is deprecated and will be removed in release 0.1.0 of NiMARE. " + "Consider switching to Python 3.8, 3.9 or 3.10." + ) + warnings.filterwarnings("once", message=py36_warning) + warnings.warn(message=py36_warning, category=FutureWarning, stacklevel=3) + + +def _python_deprecation_warnings(): + """Raise deprecation warnings. + + Notes + ----- + Adapted from Nilearn. + """ + if sys.version_info.major == 3 and ( + sys.version_info.minor == 6 or sys.version_info.minor == 7 + ): + _py367_deprecation_warning() + + +_python_deprecation_warnings() diff --git a/setup.cfg b/setup.cfg index f1ced8cf4..ffadb41c8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -122,5 +122,7 @@ max-line-length = 99 exclude = *build/,_version.py putty-ignore = */__init__.py : +F401 +per-file-ignores = + */__init__.py:D401 ignore = E203,E402,E722,W503 docstring-convention = numpy From c87b134b8f86bb574581b07464fc6840a382fa26 Mon Sep 17 00:00:00 2001 From: James Kent Date: Thu, 12 Jan 2023 16:19:59 -0600 Subject: [PATCH 067/177] [MAINT] Fix various errors due to major version changes in dependencies (#757) * bump matplotlib version to handle new nilearn release * restrict numpy versions due to numba issue * make nibabel less than version 5.0 --- setup.cfg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.cfg b/setup.cfg index ffadb41c8..7b9df8d54 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,11 +43,11 @@ install_requires = fuzzywuzzy # nimare.annotate indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization - matplotlib>=3.0 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis + matplotlib>=3.3 # this is for nilearn, which doesn't include it in its reqs + nibabel<5.0.0,>=3.0.0 # I/O of niftis (less than version 5 until datatype fix: https://github.com/nipy/nibabel/releases/tag/5.0.0) nilearn>=0.7.1 numba # used by sparse - numpy + numpy<1.24,>=1.18 # for compatibility with numba https://github.com/numba/numba/issues/8615 pandas>=1.1.0 patsy pymare~=0.0.4rc2 # nimare.meta.ibma and stats From 12ff95b4b2dccff73700c6b52539c936b0d44989 Mon Sep 17 00:00:00 2001 From: "Julio A. Peraza" <52050407+JulioAPeraza@users.noreply.github.com> Date: Fri, 13 Jan 2023 09:53:37 -0500 Subject: [PATCH 068/177] Remove "dataset" `return_type` option from kernel transformers (#752) * Remove "dataset" `return_type` option from kernel transformers * Drop tests ma_map_reuse * Update test_meta_kernel.py * Update test_meta_kernel.py * Update nimare/meta/kernel.py Co-authored-by: Taylor Salo * Update nimare/meta/kernel.py Co-authored-by: Taylor Salo * Add versionchanged to child classes Co-authored-by: Taylor Salo --- nimare/meta/cbma/base.py | 51 ++------------ nimare/meta/kernel.py | 95 ++++++++------------------ nimare/tests/test_decode_continuous.py | 14 +++- nimare/tests/test_meta_ale.py | 60 ---------------- nimare/tests/test_meta_kernel.py | 7 +- 5 files changed, 51 insertions(+), 176 deletions(-) diff --git a/nimare/meta/cbma/base.py b/nimare/meta/cbma/base.py index 484f05bb4..d1856b718 100644 --- a/nimare/meta/cbma/base.py +++ b/nimare/meta/cbma/base.py @@ -1,7 +1,6 @@ """CBMA methods from the ALE and MKDA families.""" import logging from abc import abstractmethod -from hashlib import md5 import nibabel as nib import numpy as np @@ -92,8 +91,7 @@ def _preprocess_input(self, dataset): ---------- dataset : :obj:`~nimare.dataset.Dataset` In this method, the Dataset is used to (1) select the appropriate mask image, - (2) identify any pre-generated MA maps stored in its images attribute, - and (3) extract sample size metadata and place it into the coordinates input. + and (2) extract sample size metadata and place it into the coordinates input. Attributes ---------- @@ -111,17 +109,6 @@ def _preprocess_input(self, dataset): for name, (type_, _) in self._required_inputs.items(): if type_ == "coordinates": - # Try to load existing MA maps - if hasattr(self, "kernel_transformer"): - self.kernel_transformer._infer_names(affine=md5(mask_img.affine).hexdigest()) - if self.kernel_transformer.image_type in dataset.images.columns: - files = dataset.get_images( - ids=self.inputs_["id"], - imtype=self.kernel_transformer.image_type, - ) - if all(f is not None for f in files): - self.inputs_["ma_maps"] = files - # Calculate IJK matrix indices for target mask # Mask space is assumed to be the same as the Dataset's space # These indices are used directly by any KernelTransformer @@ -220,37 +207,13 @@ def _collect_ma_maps(self, coords_key="coordinates", maps_key="ma_maps"): Return a 4D sparse array of shape (n_studies, mask.shape) with MA maps. """ - if maps_key in self.inputs_.keys(): - LGR.debug(f"Loading pre-generated MA maps ({maps_key}).") - all_exp = [] - all_coords = [] - all_data = [] - for i_exp, img in enumerate(self.inputs_[maps_key]): - img_data = nib.load(img).get_fdata() - nonzero_idx = np.where(img_data != 0) - - all_exp.append(np.full(nonzero_idx[0].shape[0], i_exp)) - all_coords.append(np.vstack(nonzero_idx)) - all_data.append(img_data[nonzero_idx]) - - n_studies = len(self.inputs_[maps_key]) - shape = img_data.shape - kernel_shape = (n_studies,) + shape - - exp = np.hstack(all_exp) - coords = np.vstack((exp.flatten(), np.hstack(all_coords))) - data = np.hstack(all_data).flatten() - - ma_maps = sparse.COO(coords, data, shape=kernel_shape) + LGR.debug(f"Generating MA maps from coordinates ({coords_key}).") - else: - LGR.debug(f"Generating MA maps from coordinates ({coords_key}).") - - ma_maps = self.kernel_transformer.transform( - self.inputs_[coords_key], - masker=self.masker, - return_type="sparse", - ) + ma_maps = self.kernel_transformer.transform( + self.inputs_[coords_key], + masker=self.masker, + return_type="sparse", + ) return ma_maps diff --git a/nimare/meta/kernel.py b/nimare/meta/kernel.py index 3dbbafe7f..5cdea75b3 100644 --- a/nimare/meta/kernel.py +++ b/nimare/meta/kernel.py @@ -7,17 +7,14 @@ from __future__ import division import logging -import os -from hashlib import md5 import nibabel as nib import numpy as np import pandas as pd -import sparse from nimare.base import NiMAREBase from nimare.meta.utils import compute_ale_ma, compute_kda_ma, get_ale_kernel -from nimare.utils import _add_metadata_to_dataframe, _safe_transform, mm2vox +from nimare.utils import _add_metadata_to_dataframe, mm2vox LGR = logging.getLogger(__name__) @@ -25,6 +22,10 @@ class KernelTransformer(NiMAREBase): """Base class for modeled activation-generating methods in :mod:`~nimare.meta.kernel`. + .. versionchanged:: 0.0.13 + + - Remove "dataset" `return_type` option. + Coordinate-based meta-analyses leverage coordinates reported in neuroimaging papers to simulate the thresholded statistical maps from the original analyses. This generally involves convolving each coordinate with @@ -39,7 +40,7 @@ class KernelTransformer(NiMAREBase): """ def _infer_names(self, **kwargs): - """Determine filename pattern and image type for files created with this transformer. + """Determine filename pattern and image type. The parameters used to construct the filenames come from the transformer's parameters (attributes saved in ``__init__()``). @@ -53,7 +54,7 @@ def _infer_names(self, **kwargs): Attributes ---------- filename_pattern : str - Filename pattern for images that will be saved by the transformer. + Filename pattern for images. image_type : str Name of the corresponding column in the Dataset.images DataFrame. """ @@ -81,9 +82,9 @@ def transform(self, dataset, masker=None, return_type="image"): Mask to apply to MA maps. Required if ``dataset`` is a DataFrame. If None (and ``dataset`` is a Dataset), the Dataset's masker attribute will be used. Default is None. - return_type : {'sparse', 'array', 'image', 'dataset'}, optional - Whether to return a numpy array ('array'), a list of niimgs ('image'), - or a Dataset with MA images saved as files ('dataset'). + return_type : {'sparse', 'array', 'image'}, optional + Whether to return a sparse matrix ('sparse'), a numpy array ('array'), + or a list of niimgs ('image'). Default is 'image'. Returns @@ -97,19 +98,17 @@ def transform(self, dataset, masker=None, return_type="image"): contrast and V is voxel. If return_type is 'image', a list of modeled activation images (one for each of the Contrasts in the input dataset). - If return_type is 'dataset', a new Dataset object with modeled - activation images saved to files and referenced in the - Dataset.images attribute. Attributes ---------- filename_pattern : str - Filename pattern for MA maps that will be saved by the transformer. + Filename pattern for MA maps. If :meth:`_infer_names` is executed. image_type : str Name of the corresponding column in the Dataset.images DataFrame. + If :meth:`_infer_names` is executed. """ - if return_type not in ("sparse", "array", "image", "dataset"): - raise ValueError('Argument "return_type" must be "image", "array", or "dataset".') + if return_type not in ("sparse", "array", "image"): + raise ValueError('Argument "return_type" must be "image", "array", or "sparse".') if isinstance(dataset, pd.DataFrame): assert ( @@ -117,9 +116,6 @@ def transform(self, dataset, masker=None, return_type="image"): ), "Argument 'masker' must be provided if dataset is a DataFrame." mask = masker.mask_img coordinates = dataset - assert ( - return_type != "dataset" - ), "Input dataset must be a Dataset if return_type='dataset'." # Calculate IJK. Must assume that the masker is in same space, # but has different affine, from original IJK. @@ -129,24 +125,6 @@ def transform(self, dataset, masker=None, return_type="image"): mask = masker.mask_img coordinates = dataset.coordinates.copy() - # Determine MA map filenames. Must happen after parameters are set. - self._infer_names(affine=md5(mask.affine).hexdigest()) - - # Check for existing MA maps - # Use coordinates to get IDs instead of Dataset.ids bc of possible - # mismatch between full Dataset and contrasts with coordinates. - if self.image_type in dataset.images.columns: - files = dataset.get_images(ids=coordinates["id"].unique(), imtype=self.image_type) - if all(f is not None for f in files): - LGR.debug("Files already exist. Using them.") - if return_type == "array": - masked_data = _safe_transform(files, masker) - return masked_data - elif return_type == "image": - return [nib.load(f) for f in files] - elif return_type == "dataset": - return dataset.copy() - # Calculate IJK if not np.array_equal(mask.affine, dataset.masker.mask_img.affine): LGR.warning("Mask affine does not match Dataset affine. Assuming same space.") @@ -170,24 +148,13 @@ def transform(self, dataset, masker=None, return_type="image"): filter_func=np.mean, ) - # Generate the MA maps if they weren't already available as images if return_type == "array": mask_data = mask.get_fdata().astype(bool) elif return_type == "image": dtype = type(self.value) if hasattr(self, "value") else float mask_data = mask.get_fdata().astype(dtype) - elif return_type == "dataset": - if dataset.basepath is None: - raise ValueError( - "Dataset output path is not set. Set the path with Dataset.update_path()." - ) - elif not os.path.isdir(dataset.basepath): - raise ValueError( - "Output directory does not exist. Set the path to an existing folder with " - "Dataset.update_path()." - ) - dataset = dataset.copy() + # Generate the MA maps transformed_maps = self._transform(mask, coordinates) if return_type == "sparse": @@ -195,11 +162,8 @@ def transform(self, dataset, masker=None, return_type="image"): imgs = [] # Loop over exp ids since sparse._coo.core.COO is not iterable - for i_exp, id_ in enumerate(transformed_maps[1]): - if isinstance(transformed_maps[0][i_exp], sparse._coo.core.COO): - # This step is slow, but it is here just in case user want a - # return_type = "array", "image", or "dataset" - kernel_data = transformed_maps[0][i_exp].todense() + for i_exp, _ in enumerate(transformed_maps[1]): + kernel_data = transformed_maps[0][i_exp].todense() if return_type == "array": img = kernel_data[mask_data] @@ -208,11 +172,6 @@ def transform(self, dataset, masker=None, return_type="image"): kernel_data *= mask_data img = nib.Nifti1Image(kernel_data, mask.affine) imgs.append(img) - elif return_type == "dataset": - img = nib.Nifti1Image(kernel_data, mask.affine) - out_file = os.path.join(dataset.basepath, self.filename_pattern.format(id=id_)) - img.to_filename(out_file) - dataset.images.loc[dataset.images["id"] == id_, self.image_type] = out_file del kernel_data, transformed_maps @@ -220,14 +179,6 @@ def transform(self, dataset, masker=None, return_type="image"): return np.vstack(imgs) elif return_type == "image": return imgs - elif return_type == "dataset": - # Replace NaNs with Nones - dataset.images[self.image_type] = dataset.images[self.image_type].where( - dataset.images[self.image_type].notnull(), None - ) - # Infer relative path - dataset.images = dataset.images - return dataset def _transform(self, mask, coordinates): """Apply the kernel's unique transformer. @@ -264,6 +215,10 @@ class ALEKernel(KernelTransformer): will be determined on a study-wise basis based on the sample sizes available in the input, via the method described in :footcite:t:`eickhoff2012activation`. + .. versionchanged:: 0.0.13 + + - Remove "dataset" `return_type` option. + .. versionchanged:: 0.0.12 * Remove low-memory option in favor of sparse arrays for kernel transformers. @@ -326,6 +281,10 @@ def _transform(self, mask, coordinates): class KDAKernel(KernelTransformer): """Generate KDA modeled activation images from coordinates. + .. versionchanged:: 0.0.13 + + - Remove "dataset" `return_type` option. + .. versionchanged:: 0.0.12 * Remove low-memory option in favor of sparse arrays for kernel transformers. @@ -363,6 +322,10 @@ def _transform(self, mask, coordinates): class MKDAKernel(KDAKernel): """Generate MKDA modeled activation images from coordinates. + .. versionchanged:: 0.0.13 + + - Remove "dataset" `return_type` option. + .. versionchanged:: 0.0.12 * Remove low-memory option in favor of sparse arrays for kernel transformers. diff --git a/nimare/tests/test_decode_continuous.py b/nimare/tests/test_decode_continuous.py index c4a31738d..60786cd3a 100644 --- a/nimare/tests/test_decode_continuous.py +++ b/nimare/tests/test_decode_continuous.py @@ -2,6 +2,8 @@ Tests for nimare.decode.continuous.gclda_decode_map are in test_annotate_gclda. """ +import os + import pandas as pd import pytest @@ -29,6 +31,7 @@ def test_CorrelationDistributionDecoder_smoke(testdata_laird, tmp_path_factory): tmpdir = tmp_path_factory.mktemp("test_CorrelationDistributionDecoder") testdata_laird = testdata_laird.copy() + dset = testdata_laird.copy() features = testdata_laird.get_labels(ids=testdata_laird.ids[0])[:5] decoder = continuous.CorrelationDistributionDecoder(features=features) @@ -42,7 +45,16 @@ def test_CorrelationDistributionDecoder_smoke(testdata_laird, tmp_path_factory): # Then let's make some images to decode kern = kernel.MKDAKernel(r=10, value=1) - dset = kern.transform(testdata_laird, return_type="dataset") + kern._infer_names() # Determine MA map filenames + + imgs = kern.transform(testdata_laird, return_type="image") + for i_img, img in enumerate(imgs): + id_ = testdata_laird.ids[i_img] + out_file = os.path.join(testdata_laird.basepath, kern.filename_pattern.format(id=id_)) + + # Add file names to dset.images DataFrame + img.to_filename(out_file) + dset.images.loc[testdata_laird.images["id"] == id_, kern.image_type] = out_file # And now we have images we can use for decoding! decoder = continuous.CorrelationDistributionDecoder( diff --git a/nimare/tests/test_meta_ale.py b/nimare/tests/test_meta_ale.py index 97018818d..85e7571c8 100644 --- a/nimare/tests/test_meta_ale.py +++ b/nimare/tests/test_meta_ale.py @@ -1,5 +1,4 @@ """Test nimare.meta.ale (ALE/SCALE meta-analytic algorithms).""" -import logging import os import pickle @@ -15,65 +14,6 @@ from nimare.utils import vox2mm -def test_ALE_ma_map_reuse(testdata_cbma, tmp_path_factory, caplog): - """Test that MA maps are re-used when appropriate.""" - from nimare.meta import kernel - - tmpdir = tmp_path_factory.mktemp("test_ALE_ma_map_reuse") - testdata_cbma.update_path(tmpdir) - - # ALEKernel cannot extract sample_size from a Dataset, - # so we need to set it for this kernel and for the later meta-analyses. - kern = kernel.ALEKernel(sample_size=20) - dset = kern.transform(testdata_cbma, return_type="dataset") - - # The associated column should be in the new Dataset's images DataFrame - cols = dset.images.columns.tolist() - assert any(["ALEKernel" in col for col in cols]) - - # The Dataset without the images will generate them from scratch. - # If drop_invalid is False, then there should be an Exception, since two studies in the test - # dataset are missing coordinates. - meta = ale.ALE(kernel__sample_size=20) - with pytest.raises(Exception): - meta.fit(testdata_cbma, drop_invalid=False) - - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - meta.fit(testdata_cbma) - assert "Loading pre-generated MA maps" not in caplog.text - - # The Dataset with the images will re-use them, as evidenced by the logger message. - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - meta.fit(dset) - assert "Loading pre-generated MA maps" in caplog.text - - -def test_ALESubtraction_ma_map_reuse(testdata_cbma, tmp_path_factory, caplog): - """Test that MA maps are re-used when appropriate.""" - from nimare.meta import kernel - - tmpdir = tmp_path_factory.mktemp("test_ALESubtraction_ma_map_reuse") - testdata_cbma.update_path(tmpdir) - - # ALEKernel cannot extract sample_size from a Dataset, - # so we need to set it for this kernel and for the later meta-analyses. - kern = kernel.ALEKernel(sample_size=20) - dset = kern.transform(testdata_cbma, return_type="dataset") - - # The Dataset without the images will generate them from scratch. - sub_meta = ale.ALESubtraction(n_iters=10, kernel__sample_size=20) - - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - sub_meta.fit(testdata_cbma, testdata_cbma) - assert "Loading pre-generated MA maps" not in caplog.text - - # The Dataset with the images will re-use them, - # as evidenced by the logger message. - with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"): - sub_meta.fit(dset, dset) - assert "Loading pre-generated MA maps" in caplog.text - - def test_ALE_approximate_null_unit(testdata_cbma, tmp_path_factory): """Unit test for ALE with approximate null_method.""" tmpdir = tmp_path_factory.mktemp("test_ALE_approximate_null_unit") diff --git a/nimare/tests/test_meta_kernel.py b/nimare/tests/test_meta_kernel.py index 9fe416612..76125554e 100644 --- a/nimare/tests/test_meta_kernel.py +++ b/nimare/tests/test_meta_kernel.py @@ -11,8 +11,6 @@ @pytest.mark.parametrize( "kern, res, param, return_type, kwargs", [ - (kernel.ALEKernel, 1, "dataset", "dataset", {"sample_size": 20}), - (kernel.ALEKernel, 2, "dataset", "dataset", {"sample_size": 20}), (kernel.ALEKernel, 1, "dataset", "image", {"sample_size": 20}), (kernel.ALEKernel, 2, "dataset", "image", {"sample_size": 20}), (kernel.ALEKernel, 1, "dataframe", "image", {"sample_size": 20}), @@ -37,7 +35,6 @@ def test_kernel_peaks(testdata_cbma, tmp_path_factory, kern, res, param, return_ Notes ----- Remember that dataframe --> dataset won't work. - Only testing dataset --> dataset with ALEKernel because it takes a while. Test on multiple template resolutions. """ tmpdir = tmp_path_factory.mktemp("test_kernel_peaks") @@ -88,12 +85,12 @@ def test_kernel_peaks(testdata_cbma, tmp_path_factory, kern, res, param, return_ (kernel.KDAKernel, {"r": 4, "value": 1}), ], ) -def test_kernel_transform_attributes(testdata_cbma, kern, kwargs): +def test_kernel_transform_attributes(kern, kwargs): """Check that attributes are added at transform.""" kern_instance = kern(**kwargs) assert not hasattr(kern_instance, "filename_pattern") assert not hasattr(kern_instance, "image_type") - _ = kern_instance.transform(testdata_cbma, return_type="image") + kern_instance._infer_names() assert hasattr(kern_instance, "filename_pattern") assert hasattr(kern_instance, "image_type") From 252ab43172c2bcceb2ea1a51c287765357bab902 Mon Sep 17 00:00:00 2001 From: jdkent Date: Fri, 13 Jan 2023 21:10:25 +0000 Subject: [PATCH 069/177] [skip ci] Update CHANGELOG --- CHANGELOG.md | 51 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 50 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d7f7a7188..1e69127ec 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,9 +2,58 @@ All notable changes to NiMARE releases are documented in this page. -## [Unreleased](https://github.com/neurostuff/NiMARE/compare/0.0.12rc7...HEAD) +## [Unreleased](https://github.com/neurostuff/NiMARE/compare/0.0.13rc1...HEAD) +## [0.0.13rc1](https://github.com/neurostuff/NiMARE/compare/0.0.12rc7...0.0.13rc1) - 2023-01-13 + + +### What's Changed + +Testing release code + +#### 🛠 Breaking Changes + +- Remove Peaks2Maps from NiMARE by @tsalo in https://github.com/neurostuff/NiMARE/pull/644 +- Remove duecredit in favor of BibTeX references by @tsalo in https://github.com/neurostuff/NiMARE/pull/736 +- Switch from face+edge connectivity to face-only by @tsalo in https://github.com/neurostuff/NiMARE/pull/733 +- Remove conperm and scale CLI workflows by @tsalo in https://github.com/neurostuff/NiMARE/pull/740 + +#### 🎉 Exciting New Features + +- Add `tables` attribute to MetaResult class by @tsalo in https://github.com/neurostuff/NiMARE/pull/734 +- Add FocusFilter class for removing coordinates outside of a mask by @tsalo in https://github.com/neurostuff/NiMARE/pull/732 +- Add parallelization option to `CorrelationDecoder` and `CorrelationDistributionDecoder` by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/738 +- Append the top 3 words to LDA topic names by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/741 +- Enhance LDA annotator by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/742 + +#### 🐛 Bug Fixes + +- Shift centers of mass into clusters in Jackknife/FocusCounter by @tsalo in https://github.com/neurostuff/NiMARE/pull/735 +- fix a bug in conversion from z statistics to p values by @yifan0330 in https://github.com/neurostuff/NiMARE/pull/749 +- Remove "dataset" `return_type` option from kernel transformers by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/752 + +#### Other Changes + +- Fix import in download_neurosynth example by @PTDZ in https://github.com/neurostuff/NiMARE/pull/743 +- Optimize compute_kda_ma by @liuzhenqi77 in https://github.com/neurostuff/NiMARE/pull/745 +- Optimize dataset.get by @liuzhenqi77 in https://github.com/neurostuff/NiMARE/pull/746 +- Fix MACM analysis example by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/750 +- Remove upper bound for matplotlib version by @ghisvail in https://github.com/neurostuff/NiMARE/pull/751 +- Fix neurosyth download_abstracts example; inc biopython by @WillForan in https://github.com/neurostuff/NiMARE/pull/753 +- Raise deprecation warnings with Python 3.6 and 3.7 by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/754 +- [MAINT] Fix various errors due to major version changes in dependencies by @jdkent in https://github.com/neurostuff/NiMARE/pull/757 + +### New Contributors + +- @PTDZ made their first contribution in https://github.com/neurostuff/NiMARE/pull/743 +- @liuzhenqi77 made their first contribution in https://github.com/neurostuff/NiMARE/pull/745 +- @yifan0330 made their first contribution in https://github.com/neurostuff/NiMARE/pull/749 +- @ghisvail made their first contribution in https://github.com/neurostuff/NiMARE/pull/751 +- @WillForan made their first contribution in https://github.com/neurostuff/NiMARE/pull/753 + +**Full Changelog**: https://github.com/neurostuff/NiMARE/compare/0.0.12...0.0.13rc1 + ## [0.0.12rc7](https://github.com/neurostuff/NiMARE/compare/0.0.12rc6...0.0.12rc7) - 2022-06-14 Another release candidate to test a GitHub Action. From cb3b1acab967bf70da0f0922c21e9236ca614e26 Mon Sep 17 00:00:00 2001 From: "Julio A. Peraza" <52050407+JulioAPeraza@users.noreply.github.com> Date: Tue, 31 Jan 2023 14:48:42 -0500 Subject: [PATCH 070/177] Support nibabel 5.0.0 (#762) * Add header to `Nifti1Image` when passing an int64 array * @tsalo Apply suggestions from code review * Update test_annotate_gclda.py --- examples/03_annotation/04_plot_gclda.py | 2 +- examples/04_decoding/01_plot_discrete_decoders.py | 2 +- nimare/meta/utils.py | 2 +- nimare/tests/test_annotate_gclda.py | 4 ++-- nimare/tests/test_dataset.py | 2 +- nimare/tests/utils.py | 4 ++-- setup.cfg | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/03_annotation/04_plot_gclda.py b/examples/03_annotation/04_plot_gclda.py index 6b5098d99..f6da02b39 100644 --- a/examples/03_annotation/04_plot_gclda.py +++ b/examples/03_annotation/04_plot_gclda.py @@ -113,7 +113,7 @@ ############################################################################### # First we'll make an ROI -arr = np.zeros(dset.masker.mask_img.shape, int) +arr = np.zeros(dset.masker.mask_img.shape, np.int32) arr[65:75, 50:60, 50:60] = 1 mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine) plotting.plot_roi(mask_img, draw_cross=False) diff --git a/examples/04_decoding/01_plot_discrete_decoders.py b/examples/04_decoding/01_plot_discrete_decoders.py index 123eec0d3..c5d3495d1 100644 --- a/examples/04_decoding/01_plot_discrete_decoders.py +++ b/examples/04_decoding/01_plot_discrete_decoders.py @@ -34,7 +34,7 @@ # ----------------------------------------------------------------------------- # First we'll make an ROI -arr = np.zeros(dset.masker.mask_img.shape, int) +arr = np.zeros(dset.masker.mask_img.shape, np.int32) arr[65:75, 50:60, 50:60] = 1 mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine) plot_roi(mask_img, draw_cross=False) diff --git a/nimare/meta/utils.py b/nimare/meta/utils.py index 0c5bbc950..26b97f0ec 100755 --- a/nimare/meta/utils.py +++ b/nimare/meta/utils.py @@ -144,7 +144,7 @@ def _convolve_sphere(kernel, peaks): exp = np.hstack(all_exp) coords = np.vstack((exp.flatten(), np.hstack(all_coords))) - data = np.hstack(all_data).flatten() + data = np.hstack(all_data).flatten().astype(np.int32) kernel_data = sparse.COO(coords, data, shape=kernel_shape) return kernel_data diff --git a/nimare/tests/test_annotate_gclda.py b/nimare/tests/test_annotate_gclda.py index 0d5facda6..c66ea750e 100644 --- a/nimare/tests/test_annotate_gclda.py +++ b/nimare/tests/test_annotate_gclda.py @@ -36,7 +36,7 @@ def test_gclda_symmetric(testdata_laird): model.fit(n_iters=5, loglikely_freq=5) # Create ROI to decode - arr = np.zeros(testdata_laird.masker.mask_img.shape, int) + arr = np.zeros(testdata_laird.masker.mask_img.shape, np.int32) arr[40:44, 45:49, 40:44] = 1 mask_img = nib.Nifti1Image(arr, testdata_laird.masker.mask_img.affine) decoded_df, _ = decode.discrete.gclda_decode_roi(model, mask_img) @@ -70,7 +70,7 @@ def test_gclda_asymmetric(testdata_laird): model.fit(n_iters=5, loglikely_freq=5) # Create ROI to decode - arr = np.zeros(testdata_laird.masker.mask_img.shape, int) + arr = np.zeros(testdata_laird.masker.mask_img.shape, np.int32) arr[40:44, 45:49, 40:44] = 1 mask_img = nib.Nifti1Image(arr, testdata_laird.masker.mask_img.affine) decoded_df, _ = decode.discrete.gclda_decode_roi(model, mask_img) diff --git a/nimare/tests/test_dataset.py b/nimare/tests/test_dataset.py index 3f7007698..7bd14b7a0 100644 --- a/nimare/tests/test_dataset.py +++ b/nimare/tests/test_dataset.py @@ -37,7 +37,7 @@ def test_dataset_smoke(): with pytest.raises(ValueError): dset.get_studies_by_label("dog") - mask_data = np.zeros(dset.masker.mask_img.shape, int) + mask_data = np.zeros(dset.masker.mask_img.shape, np.int32) mask_data[40, 40, 40] = 1 mask_img = nib.Nifti1Image(mask_data, dset.masker.mask_img.affine) assert isinstance(dset.get_studies_by_mask(mask_img), list) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index b6aadaf3a..9e589f5bf 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -53,8 +53,8 @@ def _create_signal_mask(ground_truth_foci_ijks, mask): ) nonsig_prob_map = nonsig_prob_map[0].todense() - sig_map = nib.Nifti1Image((sig_prob_map == 1).astype(int), affine=mask.affine) - nonsig_map = nib.Nifti1Image((nonsig_prob_map == 0).astype(int), affine=mask.affine) + sig_map = nib.Nifti1Image((sig_prob_map == 1).astype(np.int32), affine=mask.affine) + nonsig_map = nib.Nifti1Image((nonsig_prob_map == 0).astype(np.int32), affine=mask.affine) return sig_map, nonsig_map diff --git a/setup.cfg b/setup.cfg index 7b9df8d54..11fae6460 100644 --- a/setup.cfg +++ b/setup.cfg @@ -44,7 +44,7 @@ install_requires = indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization matplotlib>=3.3 # this is for nilearn, which doesn't include it in its reqs - nibabel<5.0.0,>=3.0.0 # I/O of niftis (less than version 5 until datatype fix: https://github.com/nipy/nibabel/releases/tag/5.0.0) + nibabel>=3.0.0 # I/O of niftis nilearn>=0.7.1 numba # used by sparse numpy<1.24,>=1.18 # for compatibility with numba https://github.com/numba/numba/issues/8615 From d8918f6c1ca6119a78f9db484f43db6ad87e2622 Mon Sep 17 00:00:00 2001 From: "Julio A. Peraza" <52050407+JulioAPeraza@users.noreply.github.com> Date: Wed, 1 Feb 2023 16:15:49 -0500 Subject: [PATCH 071/177] Do not zero out one-tailed z-statistics for p-values > 0.5 (#693) * Do not zero out one-tailed z-statistics for p-values > 0.5 * add comment * Replace negative values in z with values estimated by CDF * Add test for p to z conversion * @yifan0330 Apply suggestions from code review * Random tests failing. Run black --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 2 +- nimare/decode/continuous.py | 1 - nimare/extract/utils.py | 2 +- nimare/io.py | 1 - nimare/meta/cbma/ale.py | 1 - nimare/meta/cbma/mkda.py | 1 - nimare/meta/kernel.py | 1 - nimare/meta/utils.py | 2 -- nimare/tests/test_estimator_performance.py | 1 + nimare/tests/test_transforms.py | 3 ++- nimare/transforms.py | 8 ++++---- 11 files changed, 9 insertions(+), 14 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index a94623419..982519b46 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -358,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" }, "vscode": { "interpreter": { diff --git a/nimare/decode/continuous.py b/nimare/decode/continuous.py index ce723a4f6..62c45a927 100755 --- a/nimare/decode/continuous.py +++ b/nimare/decode/continuous.py @@ -157,7 +157,6 @@ def __init__( target_image="z_desc-specificity", n_cores=1, ): - if meta_estimator is None: meta_estimator = MKDAChi2() else: diff --git a/nimare/extract/utils.py b/nimare/extract/utils.py index 710dccafe..66b59bcd5 100644 --- a/nimare/extract/utils.py +++ b/nimare/extract/utils.py @@ -126,7 +126,7 @@ def _get_dataset_dir(dataset_name, data_dir=None, default_paths=None): # If not, create a folder in the first writeable directory errors = [] - for (path, is_pre_dir) in paths: + for path, is_pre_dir in paths: if not is_pre_dir: path = os.path.join(path, dataset_name) diff --git a/nimare/io.py b/nimare/io.py index 9155f2f16..dba04a0b2 100644 --- a/nimare/io.py +++ b/nimare/io.py @@ -487,7 +487,6 @@ def convert_neurovault_to_dataset( dataset_dict = {} for coll_name, nv_coll in collection_ids.items(): - nv_url = f"https://neurovault.org/api/collections/{nv_coll}/images/?format=json" images = requests.get(nv_url).json() if "Not found" in images.get("detail", ""): diff --git a/nimare/meta/cbma/ale.py b/nimare/meta/cbma/ale.py index 80e5c97df..c7d71b654 100755 --- a/nimare/meta/cbma/ale.py +++ b/nimare/meta/cbma/ale.py @@ -235,7 +235,6 @@ def _compute_null_approximate(self, ma_maps): ale_hist = ma_hists[0, :].copy() for i_exp in range(1, ma_hists.shape[0]): - exp_hist = ma_hists[i_exp, :] # Find histogram bins with nonzero values for each histogram. diff --git a/nimare/meta/cbma/mkda.py b/nimare/meta/cbma/mkda.py index 57a8a9854..213305d0e 100644 --- a/nimare/meta/cbma/mkda.py +++ b/nimare/meta/cbma/mkda.py @@ -1211,7 +1211,6 @@ def _compute_null_approximate(self, ma_maps): stat_hist = ma_hists[0, :].copy() for i_exp in range(1, ma_hists.shape[0]): - exp_hist = ma_hists[i_exp, :] # Find histogram bins with nonzero values for each histogram. diff --git a/nimare/meta/kernel.py b/nimare/meta/kernel.py index 5cdea75b3..7d47576ce 100644 --- a/nimare/meta/kernel.py +++ b/nimare/meta/kernel.py @@ -304,7 +304,6 @@ def __init__(self, r=10, value=1): self.value = value def _transform(self, mask, coordinates): - ijks = coordinates[["i", "j", "k"]].values exp_idx = coordinates["id"].values transformed = compute_kda_ma( diff --git a/nimare/meta/utils.py b/nimare/meta/utils.py index 26b97f0ec..7360f8c4c 100755 --- a/nimare/meta/utils.py +++ b/nimare/meta/utils.py @@ -223,7 +223,6 @@ def compute_ale_ma(mask, ijks, kernel=None, exp_idx=None, sample_sizes=None, use all_coords = [] all_data = [] for i_exp, _ in enumerate(exp_idx_uniq): - # Index peaks by experiment curr_exp_idx = exp_idx == i_exp ijk = ijks[curr_exp_idx] @@ -271,7 +270,6 @@ def compute_ale_ma(mask, ijks, kernel=None, exp_idx=None, sample_sizes=None, use & (zlk >= 0) & (zhk >= 0) ): - ma_values[xl:xh, yl:yh, zl:zh] = np.maximum( ma_values[xl:xh, yl:yh, zl:zh], kernel[xlk:xhk, ylk:yhk, zlk:zhk] ) diff --git a/nimare/tests/test_estimator_performance.py b/nimare/tests/test_estimator_performance.py index 5f96e0551..1354277ae 100644 --- a/nimare/tests/test_estimator_performance.py +++ b/nimare/tests/test_estimator_performance.py @@ -24,6 +24,7 @@ # PRECOMPUTED FIXTURES # -------------------- + ########################################## # random state ########################################## diff --git a/nimare/tests/test_transforms.py b/nimare/tests/test_transforms.py index a574f9828..f77ff46be 100644 --- a/nimare/tests/test_transforms.py +++ b/nimare/tests/test_transforms.py @@ -247,7 +247,7 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma): @pytest.mark.parametrize( - "z,tail,expected_p", + "expected_z,tail,expected_p", [ (0.0, "two", 1.0), (0.0, "one", 0.5), @@ -264,3 +264,4 @@ def test_z_to_p(z, tail, expected_p): p = transforms.z_to_p(z, tail) assert np.all(np.isclose(p, expected_p)) + assert np.all(np.isclose(z, expected_z)) diff --git a/nimare/transforms.py b/nimare/transforms.py index 3aa6a279e..39f2d546a 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -368,7 +368,6 @@ def transform(self, dataset): coordinates_dict = {} for _, row in images_df.iterrows(): - if row["id"] in list(dataset.coordinates["id"]) and self.merge_strategy == "fill": continue @@ -679,12 +678,13 @@ def p_to_z(p, tail="two"): Z-statistics (unsigned) """ p = np.array(p) + + # Ensure that no p-values are converted to Inf/NaNs + p = np.clip(p, 1.0e-300, 1.0 - 1.0e-16) if tail == "two": z = stats.norm.isf(p / 2) elif tail == "one": - z = stats.norm.isf(p) - z = np.array(z) - z[z < 0] = 0 + z = np.abs(stats.norm.isf(p)) else: raise ValueError('Argument "tail" must be one of ["one", "two"]') From 7a70ed349d4203fbb528a068838c75e95a0a05ba Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Mon, 6 Feb 2023 17:40:30 -0500 Subject: [PATCH 072/177] Link to NeuroStars software support category instead of neuro questions (#768) * Link to software support category. * Add links to GitHub repo. * Update nilearn URL. --- .github/ISSUE_TEMPLATE/config.yml | 2 +- CONTRIBUTING.md | 4 ++-- README.md | 1 + docs/conf.py | 2 +- docs/index.rst | 4 ++++ 5 files changed, 9 insertions(+), 4 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 5606878c3..98375174a 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,4 +1,4 @@ contact_links: - name: Usage question - url: https://neurostars.org/tag/nimare + url: https://neurostars.org/tags/c/software-support/234/nimare about: Please ask questions about using NiMARE on NeuroStars. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 853e0831c..88033dc92 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -33,7 +33,7 @@ As stated in the code, severe or repeated violations by community members may re ## Asking questions about using NiMARE -Please direct usage-related questions to [NeuroStars][link_neurostars], with the ["nimare" tag][link_neurostars_nimare]. +Please direct usage-related questions to [NeuroStars][link_neurostars], with [the "Software Support" category and the "nimare" tag][link_neurostars_nimare]. The ``NiMARE`` developers follow NeuroStars, and will be able to answer your question there. ## Labels @@ -114,7 +114,7 @@ You're awesome. [link_labels]: https://github.com/neurostuff/NiMARE/labels [link_discussingissues]: https://help.github.com/articles/discussing-projects-in-issues-and-pull-requests [link_neurostars]: https://neurostars.org -[link_neurostars_nimare]: https://neurostars.org/tag/nimare +[link_neurostars_nimare]: https://neurostars.org/tags/c/software-support/234/nimare [link_pullrequest]: https://help.github.com/articles/creating-a-pull-request/ [link_fork]: https://help.github.com/articles/fork-a-repo/ diff --git a/README.md b/README.md index 5f8b18aee..04b409bc0 100755 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ A Python library for coordinate- and image-based meta-analysis. [![Latest Version](https://img.shields.io/pypi/v/nimare.svg)](https://pypi.python.org/pypi/nimare/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nimare.svg)](https://pypi.python.org/pypi/nimare/) +[![GitHub Repository](https://img.shields.io/badge/Source%20Code-neurostuff%2Fnimare-purple)](https://github.com/neurostuff/NiMARE) [![DOI](https://zenodo.org/badge/117724523.svg)](https://zenodo.org/badge/latestdoi/117724523) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Test Status](https://github.com/neurostuff/NiMARE/actions/workflows/testing.yml/badge.svg)](https://github.com/neurostuff/NiMARE/actions/workflows/testing.yml) diff --git a/docs/conf.py b/docs/conf.py index f755d1c8d..e56c5619c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -174,7 +174,7 @@ "matplotlib": ("https://matplotlib.org/", (None, "https://matplotlib.org/objects.inv")), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "nibabel": ("https://nipy.org/nibabel/", None), - "nilearn": ("http://nilearn.github.io/", None), + "nilearn": ("http://nilearn.github.io/stable/", None), "pymare": ("https://pymare.readthedocs.io/en/latest/", None), "skimage": ("https://scikit-image.org/docs/stable/", None), } diff --git a/docs/index.rst b/docs/index.rst index 71caa82de..087282874 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -16,6 +16,10 @@ To install NiMARE check out our `installation guide`_. :target: https://pypi.python.org/pypi/nimare/ :alt: PyPI - Python Version +.. image:: https://img.shields.io/badge/Source%20Code-neurostuff%2Fnimare-purple + :target: https://github.com/neurostuff/NiMARE + :alt: GitHub Repository + .. image:: https://zenodo.org/badge/117724523.svg :target: https://zenodo.org/badge/latestdoi/117724523 :alt: DOI From 6a986b28dafc311d49a7e91c408df92d28db8eaf Mon Sep 17 00:00:00 2001 From: "Julio A. Peraza" <52050407+JulioAPeraza@users.noreply.github.com> Date: Mon, 6 Feb 2023 18:20:06 -0500 Subject: [PATCH 073/177] Revert "Do not zero out one-tailed z-statistics for p-values > 0.5" (#769) * Revert "Do not zero out one-tailed z-statistics for p-values > 0.5 (#693)" This reverts commit 87964b887553ff475ec759bf7fc46c960d08adb3. * Solve `black` issues * Add a note about why we zero out negative z-scores --- docs/outputs.rst | 5 +++++ nimare/tests/test_transforms.py | 4 +--- nimare/transforms.py | 7 +++---- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/docs/outputs.rst b/docs/outputs.rst index 0690ee3e5..e35fb1357 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -33,6 +33,11 @@ Some of the values found in NiMARE include: - ``tau2``: Estimated between-study variance (IBMA only) - ``sigma2``: Estimated within-study variance (IBMA only) +.. note:: + For one-sided tests, p-values > 0.5 will have negative z-statistics. These values should not + be confused with significant negative results. As a result, in NiMARE, these values are + replaced by 0. + Next, a series of key/value pairs describe the methods applied to generate the map. - ``desc``: Description of the data type. Only used when multiple maps with the same data type are produced by the same method. diff --git a/nimare/tests/test_transforms.py b/nimare/tests/test_transforms.py index f77ff46be..ea196a3a3 100644 --- a/nimare/tests/test_transforms.py +++ b/nimare/tests/test_transforms.py @@ -247,7 +247,7 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma): @pytest.mark.parametrize( - "expected_z,tail,expected_p", + "z,tail,expected_p", [ (0.0, "two", 1.0), (0.0, "one", 0.5), @@ -262,6 +262,4 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma): def test_z_to_p(z, tail, expected_p): """Test z to p conversion.""" p = transforms.z_to_p(z, tail) - assert np.all(np.isclose(p, expected_p)) - assert np.all(np.isclose(z, expected_z)) diff --git a/nimare/transforms.py b/nimare/transforms.py index 39f2d546a..662b6e757 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -678,13 +678,12 @@ def p_to_z(p, tail="two"): Z-statistics (unsigned) """ p = np.array(p) - - # Ensure that no p-values are converted to Inf/NaNs - p = np.clip(p, 1.0e-300, 1.0 - 1.0e-16) if tail == "two": z = stats.norm.isf(p / 2) elif tail == "one": - z = np.abs(stats.norm.isf(p)) + z = stats.norm.isf(p) + z = np.array(z) + z[z < 0] = 0 else: raise ValueError('Argument "tail" must be one of ["one", "two"]') From ea43cec555b48144740e37244958fe5803486ae3 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 18 Jun 2022 12:22:30 +0100 Subject: [PATCH 074/177] create a design matrix function for cbmr --- nimare/meta/cbmr.py | 2 +- nimare/tests/conftest.py | 23 +++++++++++++++++++++++ nimare/utils.py | 13 +++++++++++++ 3 files changed, 37 insertions(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 00591c2e2..0952b7f5a 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -694,4 +694,4 @@ def _glh_con_moderator(self): else: self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_chi_square_values"] = chi_sq_moderator self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_p_values"] = p_vals_moderator - con_moderator_count += 1 \ No newline at end of file + con_moderator_count += 1 diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 800d5a854..71ae8a4e0 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -57,6 +57,29 @@ def testdata_cbma(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) return dset +@pytest.fixture(scope="session") +def testdata_cbmr(): + """Generate coordinate-based dataset for tests.""" + dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset = nimare.dataset.Dataset(dset_file) + + # Only retain one peak in each study in coordinates + # Otherwise centers of mass will be obscured in kernel tests by overlapping + # kernels + dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) + + n_rows = dset.annotations.shape[0] + dset.annotations["group_id"] = ["group_1"] * n_rows # group_id + dset.annotations[ + "sample_sizes" + ] = dset.metadata.sample_sizes # sample sizes as study-level covariates + dset.annotations["study_level_covariates"] = np.random.rand( + n_rows, 1 + ) # random study-level covariates + + return dset + + @pytest.fixture(scope="session") def testdata_cbma_full(): """Generate more complete coordinate-based dataset for tests. diff --git a/nimare/utils.py b/nimare/utils.py index 7e87ccafa..fe01a5186 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -18,7 +18,10 @@ from scipy import ndimage import patsy +<<<<<<< HEAD import sparse +======= +>>>>>>> f00c309 (create a design matrix function for cbmr) LGR = logging.getLogger(__name__) @@ -1161,6 +1164,7 @@ def _get_cluster_coms(labeled_cluster_arr): ) return cluster_coms +<<<<<<< HEAD def coef_spline_bases(axis_coords, spacing, margin): @@ -1178,6 +1182,15 @@ def coef_spline_bases(axis_coords, spacing, margin): coef_spline : 2-D ndarray (n_points x n_spline_bases) """ # create B-spline basis for x/y/z coordinate +======= + _, unique_row_indices = np.unique(ar_row_view, return_index=True) + ar_out = ar[unique_row_indices] + return ar_out + + +def coef_spline_bases(axis_coords, spacing, margin): + ## create B-spline basis for x/y/z coordinate +>>>>>>> f00c309 (create a design matrix function for cbmr) wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) design_matrix = patsy.dmatrix( From dac143fe38f5b65c7988ca5a62918447b6823bc7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:44:04 +0000 Subject: [PATCH 075/177] [skip CI][WIP] solve conflicts --- nimare/utils.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/nimare/utils.py b/nimare/utils.py index fe01a5186..bef4a6972 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1189,6 +1189,19 @@ def coef_spline_bases(axis_coords, spacing, margin): def coef_spline_bases(axis_coords, spacing, margin): + """ + Coefficient of cubic B-spline bases in any x/y/z direction + + Parameters + ---------- + axis_coords : value range in x/y/z direction + spacing: (equally spaced) knots spacing in x/y/z direction, + margin: extend the region where B-splines are constructed (min-margin, max_margin) + to avoid weakly-supported B-spline on the edge + Returns + ------- + coef_spline : 2-D ndarray (n_points x n_spline_bases) + """ ## create B-spline basis for x/y/z coordinate >>>>>>> f00c309 (create a design matrix function for cbmr) wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) @@ -1265,7 +1278,7 @@ def B_spline_bases(masker_voxels, spacing, margin=10): for bz in range(z_df): basis_index = bz + z_df * by + z_df * y_df * bx basis_coef = X[:, basis_index] - if np.max(basis_coef) >= 0.1: + if np.max(basis_coef) >= 0.1: support_basis.append(basis_index) X = X[:, support_basis] From 2f14d55142f75f6c102af91568c1c2771385e0a3 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 16 Jul 2022 19:10:08 +0100 Subject: [PATCH 076/177] update model structure --- nimare/meta/cbmr.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 0952b7f5a..984f5cd26 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -326,6 +326,21 @@ def _fit(self, dataset): return maps, tables + def _model_structure(self, model, penalty, device): + # beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect + beta_dim = 2627 + if hasattr(self, "moderators"): + gamma_dim = self.inputs_["moderators_array"].shape[1] + study_level_covariates = True + else: + gamma_dim = None + study_level_covariates = False + if model == 'Poisson': + cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, study_level_covariates=study_level_covariates, penalty=penalty) + if 'cuda' in device: + cbmr_model = cbmr_model.cuda() + + return cbmr_model class CBMRInference(object): """Statistical inference on outcomes (intensity estimation and study-level From 9d02762b45e895043de52395af539cf495db8b87 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Thu, 28 Jul 2022 12:27:11 +0100 Subject: [PATCH 077/177] use a sparse array instead of numpy --- nimare/utils.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/nimare/utils.py b/nimare/utils.py index bef4a6972..86e5702d5 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -18,10 +18,7 @@ from scipy import ndimage import patsy -<<<<<<< HEAD import sparse -======= ->>>>>>> f00c309 (create a design matrix function for cbmr) LGR = logging.getLogger(__name__) @@ -1255,6 +1252,7 @@ def B_spline_bases(masker_voxels, spacing, margin=10): z_spline_sparse = sparse.COO(z_spline_coords, z_spline[z_spline_coords]) # create spatial design matrix by tensor product of spline bases in 3 dimesion +<<<<<<< HEAD # Row sums of X are all 1=> There is no need to re-normalise X X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # remove the voxels outside brain mask @@ -1268,6 +1266,13 @@ def B_spline_bases(masker_voxels, spacing, margin=10): for z in zz if masker_voxels[x, y, z] == 1 ] +======= + X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # Row sums of X are all 1=> There is no need to re-normalise X + # remove the voxels outside brain mask + axis_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] + brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) + for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] +>>>>>>> 06f27f9 (use a sparse array instead of numpy) X = X[brain_voxels_index, :].todense() # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] From 514166f4584b63560f44abb65a21531b6cbe3c9d Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:49:39 +0000 Subject: [PATCH 078/177] [skip CI][WIP] solve conflict --- nimare/cli.py | 49 +++++++++++ nimare/meta/cbmr.py | 4 + nimare/meta/utils.py | 3 + nimare/tests/conftest.py | 13 ++- nimare/utils.py | 170 ++++++++++++++++++++++++++++++++++++ nimare/workflows/conperm.py | 88 +++++++++++++++++++ setup.cfg | 2 +- setup_BACKUP_7408.cfg | 129 +++++++++++++++++++++++++++ setup_BASE_7408.cfg | 134 ++++++++++++++++++++++++++++ setup_LOCAL_7408.cfg | 135 ++++++++++++++++++++++++++++ setup_REMOTE_7408.cfg | 124 ++++++++++++++++++++++++++ 11 files changed, 842 insertions(+), 9 deletions(-) create mode 100644 nimare/workflows/conperm.py create mode 100644 setup_BACKUP_7408.cfg create mode 100644 setup_BASE_7408.cfg create mode 100644 setup_LOCAL_7408.cfg create mode 100644 setup_REMOTE_7408.cfg diff --git a/nimare/cli.py b/nimare/cli.py index 847d6e732..c5993e27d 100644 --- a/nimare/cli.py +++ b/nimare/cli.py @@ -90,6 +90,55 @@ def _get_parser(): help=("Number of processes to use for meta-analysis. If -1, use all available cores."), ) +<<<<<<< HEAD +======= + # Contrast permutation workflow + conperm_parser = subparsers.add_parser( + "conperm", + help=( + "Meta-analysis of contrast maps using random effects and " + "two-sided inference with empirical (permutation-based) null " + "distribution and Family Wise Error multiple comparisons " + "correction. Input may be a list of 3D files or a single 4D " + "file." + ), + ) + conperm_parser.set_defaults(func=conperm_workflow) + conperm_parser.add_argument( + "contrast_images", + nargs="+", + metavar="FILE", + type=lambda x: _is_valid_file(parser, x), + help=("Data to analyze. May be a single 4D file or a list of 3D files."), + ) + conperm_parser.add_argument( + "--mask", + dest="mask_image", + metavar="FILE", + type=lambda x: _is_valid_file(parser, x), + help=("Mask file."), + default=None, + ) + conperm_parser.add_argument( + "--output_dir", + dest="output_dir", + metavar="PATH", + type=str, + help=("Output directory."), + default=".", + ) + conperm_parser.add_argument( + "--prefix", dest="prefix", type=str, help=("Common prefix for output maps."), default="" + ) + conperm_parser.add_argument( + "--n_iters", + dest="n_iters", + type=int, + help=("Number of iterations for permutation testing."), + default=10000, + ) + +>>>>>>> ab450fa ([skip ci][wip] fix conflict to merge) # MACM macm_parser = subparsers.add_parser( "macm", diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 984f5cd26..9bafa5376 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -3,10 +3,14 @@ import nibabel as nib import numpy as np import pandas as pd +<<<<<<< HEAD import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter from nimare.meta import models +======= +from nimare.utils import mm2vox, vox2idx, intensity2voxel +>>>>>>> 055370d ([skip CI][WIP] solve conflict) import torch import functorch import logging diff --git a/nimare/meta/utils.py b/nimare/meta/utils.py index 7360f8c4c..2a992d42c 100755 --- a/nimare/meta/utils.py +++ b/nimare/meta/utils.py @@ -120,6 +120,7 @@ def _convolve_sphere(kernel, peaks): counts = counts * value else: all_spheres = unique_rows(all_spheres) + counts = value # Mask coordinates beyond space idx = np.all( @@ -127,6 +128,8 @@ def _convolve_sphere(kernel, peaks): ) all_spheres = all_spheres[idx, :] + if sum_overlap: + counts = counts[idx] sphere_idx_inside_mask = np.where(mask_data[tuple(all_spheres.T)])[0] sphere_idx_filtered = all_spheres[sphere_idx_inside_mask, :].T diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 71ae8a4e0..aac87fee8 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -69,14 +69,11 @@ def testdata_cbmr(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) n_rows = dset.annotations.shape[0] - dset.annotations["group_id"] = ["group_1"] * n_rows # group_id - dset.annotations[ - "sample_sizes" - ] = dset.metadata.sample_sizes # sample sizes as study-level covariates - dset.annotations["study_level_covariates"] = np.random.rand( - n_rows, 1 - ) # random study-level covariates - + dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'dementia' for i in range(n_rows)] + dset.annotations['treatment'] = [False if i%2==0 else True for i in range(n_rows)] + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["avg_age"] = np.arange(n_rows) + return dset diff --git a/nimare/utils.py b/nimare/utils.py index 86e5702d5..b29bf2228 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1162,6 +1162,7 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms <<<<<<< HEAD +<<<<<<< HEAD def coef_spline_bases(axis_coords, spacing, margin): @@ -1180,6 +1181,11 @@ def coef_spline_bases(axis_coords, spacing, margin): """ # create B-spline basis for x/y/z coordinate ======= +======= +======= +<<<<<<< HEAD +>>>>>>> ab450fa ([skip ci][wip] fix conflict to merge) +>>>>>>> 055370d ([skip CI][WIP] solve conflict) _, unique_row_indices = np.unique(ar_row_view, return_index=True) ar_out = ar[unique_row_indices] return ar_out @@ -1307,6 +1313,7 @@ def index2vox(vals, masker_voxels): return voxel_array +<<<<<<< HEAD def dummy_encoding_moderators(dataset_annotations, moderators): for moderator in moderators: if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): @@ -1316,3 +1323,166 @@ def dummy_encoding_moderators(dataset_annotations, moderators): dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) moderators.append(category) # add dummy encoded moderators return dataset_annotations, moderators +======= +def intensity2voxel(intensity, masker_voxels): + masker_dim = masker_voxels.shape + xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] + yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] + zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] + + # correspondence between xyz coordinates and spatial intensity + brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) + brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) + + intensity_array = np.zeros(masker_dim) + for i in range(brain_voxel_intensity.shape[0]): + coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] + coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) + intensity_array[coord_x, coord_y, coord_z] = coord_intensity + + return intensity_array +======= + if return_counts: + _, unique_row_indices, counts = np.unique( + ar_row_view, return_index=True, return_counts=True + ) + + return ar[unique_row_indices], counts + else: + _, unique_row_indices = np.unique(ar_row_view, return_index=True) + + return ar[unique_row_indices] + + +def _cluster_nearest_neighbor(ijk, labels_index, labeled): + """Find the nearest neighbor for given points in the corresponding cluster. + + Parameters + ---------- + ijk : :obj:`numpy.ndarray` + (n_pts, 3) array of query points. + labels_index : :obj:`numpy.ndarray` + (n_pts,) array of corresponding cluster indices. + labeled : :obj:`numpy.ndarray` + 3D array with voxels labeled according to cluster index. + + Returns + ------- + nbrs : :obj:`numpy.ndarray` + (n_pts, 3) nearest neighbor points. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + labels = labeled[labeled > 0] + clusters_ijk = np.array(labeled.nonzero()).T + nbrs = np.zeros_like(ijk) + for ii, (lab, point) in enumerate(zip(labels_index, ijk)): + lab_ijk = clusters_ijk[labels == lab] + dist = np.linalg.norm(lab_ijk - point, axis=1) + nbrs[ii] = lab_ijk[np.argmin(dist)] + + return nbrs + + +def _get_cluster_coms(labeled_cluster_arr): + """Get the center of mass of each cluster in a labeled array. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + cluster_ids = np.unique(labeled_cluster_arr)[1:] + n_clusters = cluster_ids.size + + # Identify center of mass for each cluster + # This COM may fall outside the cluster, but it is a useful heuristic for identifying them + cluster_ids = np.arange(1, n_clusters + 1, dtype=int) + cluster_coms = ndimage.center_of_mass(labeled_cluster_arr, labeled_cluster_arr, cluster_ids) + cluster_coms = np.array(cluster_coms).astype(int) + + # NOTE: The following comes from Nilearn + # Determine if all subpeaks are within the cluster + # They may not be if the cluster is binary and has a shape where the COM is + # outside the cluster, like a donut. + coms_outside_clusters = ( + labeled_cluster_arr[cluster_coms[:, 0], cluster_coms[:, 1], cluster_coms[:, 2]] + != cluster_ids + ) + if np.any(coms_outside_clusters): + LGR.warning( + "Attention: At least one of the centers of mass falls outside of the cluster body. " + "Identifying the nearest in-cluster voxel." + ) + + # Replace centers of mass with their nearest neighbor points in the + # corresponding clusters. Note this is also equivalent to computing the + # centers of mass constrained to points within the cluster. + cluster_coms[coms_outside_clusters, :] = _cluster_nearest_neighbor( + cluster_coms[coms_outside_clusters, :], + cluster_ids[coms_outside_clusters], + labeled_cluster_arr, + ) + + return cluster_coms +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 +>>>>>>> 055370d ([skip CI][WIP] solve conflict) diff --git a/nimare/workflows/conperm.py b/nimare/workflows/conperm.py new file mode 100644 index 000000000..254edd742 --- /dev/null +++ b/nimare/workflows/conperm.py @@ -0,0 +1,88 @@ +"""Run a contrast permutation meta-analysis on a set of images.""" +import logging +import os +import pathlib + +import numpy as np +from nilearn.masking import apply_mask +from nilearn.mass_univariate import permuted_ols + +from nimare.results import MetaResult +from nimare.utils import get_template + +LGR = logging.getLogger(__name__) + + +def conperm_workflow(contrast_images, mask_image=None, output_dir=None, prefix="", n_iters=10000): + """Run a contrast permutation workflow.""" + from nimare import __version__ + + if mask_image is None: + target = "mni152_2mm" + mask_image = get_template(target, mask="brain") + + n_studies = len(contrast_images) + LGR.info("Loading contrast maps...") + z_data = apply_mask(contrast_images, mask_image) + + boilerplate = """ +A contrast permutation analysis was performed on a sample of {n_studies} +images with NiMARE {version} (RRID:SCR_017398; Salo et al., 2022a; Salo et al., 2022b). +A brain mask derived from the MNI 152 template (Fonov et al., 2009; Fonov et al., 2011) +was applied at 2x2x2mm resolution. The sign flipping +method used was implemented as described in Maumet & Nichols (2016), with +{n_iters} iterations used to estimate the null distribution. + +References +---------- +- Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., + Collins, D. L., & Brain Development Cooperative Group. (2011). + Unbiased average age-appropriate atlases for pediatric studies. + Neuroimage, 54(1), 313-327. +- Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L. + (2009). Unbiased nonlinear average age-appropriate brain templates from birth + to adulthood. NeuroImage, (47), S102. +- Maumet, C., & Nichols, T. E. (2016). Minimal Data Needed for Valid & Accurate + Image-Based fMRI Meta-Analysis. https://doi.org/10.1101/048249 +- Salo et al. (2022). NiMARE: Neuroimaging Meta-Analysis Research Environment. + NeuroLibre Reproducible Preprint Server, 1(1), 7, https://doi.org/10.55458/neurolibre.00007. +- Salo, Taylor, Yarkoni, Tal, Nichols, Thomas E., Poline, Jean-Baptiste, Kent, James D., + Gorgolewski, Krzysztof J., Glerean, Enrico, Bottenhorn, Katherine L., Bilgel, Murat, + Wright, Jessey, Reeders, Puck, Kimbler, Adam, Nielson, Dylan N., Yanes, Julio A., + Pérez, Alexandre, Oudyk, Kendra M., Jarecka, Dorota, Enge, Alexander, + Peraza, Julio A., ... Laird, Angela R. (2022). neurostuff/NiMARE: {version} + ({version}). Zenodo. https://doi.org/10.5281/zenodo.6642243. + **NOTE** Please replace this with the version-specific Zenodo reference in your manuscript. + """ + + LGR.info("Performing meta-analysis.") + log_p_map, t_map, _ = permuted_ols( + np.ones((z_data.shape[0], 1)), + z_data, + confounding_vars=None, + model_intercept=False, # modeled by tested_vars + n_perm=n_iters, + two_sided_test=True, + random_state=42, + n_jobs=1, + verbose=0, + ) + res = {"logp": log_p_map, "t": t_map} + # The t_test function will stand in for the Estimator in the results object + res = MetaResult(permuted_ols, mask=mask_image, maps=res, tables={}) + + boilerplate = boilerplate.format( + n_studies=n_studies, + n_iters=n_iters, + version=__version__, + ) + + if output_dir is None: + output_dir = os.getcwd() + else: + pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True) + + LGR.info("Saving output maps...") + res.save_maps(output_dir=output_dir, prefix=prefix) + LGR.info("Workflow completed.") + LGR.info(boilerplate) diff --git a/setup.cfg b/setup.cfg index 11fae6460..efcaa1dc0 100644 --- a/setup.cfg +++ b/setup.cfg @@ -49,7 +49,7 @@ install_requires = numba # used by sparse numpy<1.24,>=1.18 # for compatibility with numba https://github.com/numba/numba/issues/8615 pandas>=1.1.0 - patsy + patsy pymare~=0.0.4rc2 # nimare.meta.ibma and stats requests # nimare.extract scikit-learn # nimare.annotate and nimare.decode diff --git a/setup_BACKUP_7408.cfg b/setup_BACKUP_7408.cfg new file mode 100644 index 000000000..1933f95bf --- /dev/null +++ b/setup_BACKUP_7408.cfg @@ -0,0 +1,129 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy +<<<<<<< HEAD + pandas + patsy +======= + pandas>=1.1.0 +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_BASE_7408.cfg b/setup_BASE_7408.cfg new file mode 100644 index 000000000..6a4932af7 --- /dev/null +++ b/setup_BASE_7408.cfg @@ -0,0 +1,134 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +peaks2maps-cpu = + tensorflow>=2.0.0 + appdirs +peaks2maps-gpu = + tensorflow-gpu>=2.0.0 + appdirs +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +duecredit = + duecredit +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(duecredit)s + %(peaks2maps-cpu)s + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py,due.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_LOCAL_7408.cfg b/setup_LOCAL_7408.cfg new file mode 100644 index 000000000..7da488b1c --- /dev/null +++ b/setup_LOCAL_7408.cfg @@ -0,0 +1,135 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas + patsy + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +peaks2maps-cpu = + tensorflow>=2.0.0 + appdirs +peaks2maps-gpu = + tensorflow-gpu>=2.0.0 + appdirs +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +duecredit = + duecredit +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(duecredit)s + %(peaks2maps-cpu)s + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py,due.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy diff --git a/setup_REMOTE_7408.cfg b/setup_REMOTE_7408.cfg new file mode 100644 index 000000000..59d103597 --- /dev/null +++ b/setup_REMOTE_7408.cfg @@ -0,0 +1,124 @@ +[metadata] +url = https://github.com/neurostuff/NiMARE +license = MIT +author = NiMARE developers +author_email = tsalo006@fiu.edu +maintainer = Taylor Salo +maintainer_email = tsalo006@fiu.edu +description = NiMARE: Neuroimaging Meta-Analysis Research Environment +description-file = README.md +long_description = + NiMARE + ====== + NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package + for coordinate-based and image-based meta-analysis of neuroimaging data. + + License + ======= + `NiMARE` is licensed under the terms of the MIT license. See the file + 'LICENSE' for information on the history of this software, terms & conditions + for usage, and a DISCLAIMER OF ALL WARRANTIES. + + All trademarks referenced herein are property of their respective holders. + + Copyright (c) 2018--, NiMARE developers +long_description_content_type = text/x-rst +classifiers = + Development Status :: 3 - Alpha + Environment :: Console + Intended Audience :: Science/Research + License :: OSI Approved :: MIT License + Operating System :: OS Independent + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3.10 + Topic :: Scientific/Engineering + +[options] +python_requires = >= 3.6 +install_requires = + cognitiveatlas # nimare.annotate.cogat + fuzzywuzzy # nimare.annotate + indexed_gzip>=1.4.0 # working with gzipped niftis + joblib # parallelization + matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs + nibabel>=3.0.0 # I/O of niftis + nilearn>=0.7.1 + numba # used by sparse + numpy + pandas>=1.1.0 + pymare~=0.0.4rc2 # nimare.meta.ibma and stats + requests # nimare.extract + scikit-learn # nimare.annotate and nimare.decode + scipy + sparse>=0.13.0 # for kernel transformers + statsmodels!=0.13.2 # this version doesn't install properly + tqdm # progress bars throughout package +packages = find: +include_package_data = False + +[options.extras_require] +doc = + m2r + matplotlib + mistune<2 # just temporary until m2r addresses this issue + pillow + recommonmark + seaborn + sphinx>=3.5 + sphinx-argparse + sphinx-copybutton + sphinx_gallery==0.10.1 + sphinx_rtd_theme + sphinxcontrib-bibtex +tests = + codecov + coverage + coveralls + flake8-black + flake8-docstrings + flake8-isort + pytest + pytest-cov +minimum = + indexed_gzip==1.4 + nibabel==3.0 + nilearn==0.7.1 + numpy==1.18 + pandas==1.1 + pymare==0.0.4rc2 + scikit-learn==0.22 + scipy==1.5 # 1.6 drops Python 3.6 support +all = + %(doc)s + %(tests)s + +[options.entry_points] +console_scripts = + nimare = nimare.cli:_main + +[options.package_data] +* = + resources/* + resources/atlases/* + resources/templates/* + tests/data/* + tests/data/cognitive_atlas/* + +[versioneer] +VCS = git +style = pep440 +versionfile_source = nimare/_version.py +versionfile_build = nimare/_version.py +tag_prefix = +parentdir_prefix = + +[flake8] +max-line-length = 99 +exclude = *build/,_version.py +putty-ignore = + */__init__.py : +F401 +ignore = E203,E402,E722,W503 +docstring-convention = numpy From 8e237a11656bddf21f5ea1101784de742012cfcc Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:52:26 +0000 Subject: [PATCH 079/177] solve conflicts. --- nimare/meta/cbmr.py | 4 - nimare/utils.py | 203 +----------------------------------------- setup_BACKUP_7408.cfg | 129 --------------------------- setup_BASE_7408.cfg | 134 ---------------------------- setup_LOCAL_7408.cfg | 135 ---------------------------- setup_REMOTE_7408.cfg | 124 -------------------------- 6 files changed, 1 insertion(+), 728 deletions(-) delete mode 100644 setup_BACKUP_7408.cfg delete mode 100644 setup_BASE_7408.cfg delete mode 100644 setup_LOCAL_7408.cfg delete mode 100644 setup_REMOTE_7408.cfg diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 9bafa5376..984f5cd26 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -3,14 +3,10 @@ import nibabel as nib import numpy as np import pandas as pd -<<<<<<< HEAD import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter from nimare.meta import models -======= -from nimare.utils import mm2vox, vox2idx, intensity2voxel ->>>>>>> 055370d ([skip CI][WIP] solve conflict) import torch import functorch import logging diff --git a/nimare/utils.py b/nimare/utils.py index b29bf2228..9976ea2ba 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1161,34 +1161,6 @@ def _get_cluster_coms(labeled_cluster_arr): ) return cluster_coms -<<<<<<< HEAD -<<<<<<< HEAD - - -def coef_spline_bases(axis_coords, spacing, margin): - """ - Coefficient of cubic B-spline bases in any x/y/z direction - - Parameters - ---------- - axis_coords : value range in x/y/z direction - spacing: (equally spaced) knots spacing in x/y/z direction, - margin: extend the region where B-splines are constructed (min-margin, max_margin) - to avoid weakly-supported B-spline on the edge - Returns - ------- - coef_spline : 2-D ndarray (n_points x n_spline_bases) - """ - # create B-spline basis for x/y/z coordinate -======= -======= -======= -<<<<<<< HEAD ->>>>>>> ab450fa ([skip ci][wip] fix conflict to merge) ->>>>>>> 055370d ([skip CI][WIP] solve conflict) - _, unique_row_indices = np.unique(ar_row_view, return_index=True) - ar_out = ar[unique_row_indices] - return ar_out def coef_spline_bases(axis_coords, spacing, margin): @@ -1206,7 +1178,6 @@ def coef_spline_bases(axis_coords, spacing, margin): coef_spline : 2-D ndarray (n_points x n_spline_bases) """ ## create B-spline basis for x/y/z coordinate ->>>>>>> f00c309 (create a design matrix function for cbmr) wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) design_matrix = patsy.dmatrix( @@ -1258,7 +1229,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): z_spline_sparse = sparse.COO(z_spline_coords, z_spline[z_spline_coords]) # create spatial design matrix by tensor product of spline bases in 3 dimesion -<<<<<<< HEAD # Row sums of X are all 1=> There is no need to re-normalise X X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # remove the voxels outside brain mask @@ -1272,13 +1242,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): for z in zz if masker_voxels[x, y, z] == 1 ] -======= - X = np.kron(np.kron(x_spline_sparse, y_spline_sparse), z_spline_sparse) # Row sums of X are all 1=> There is no need to re-normalise X - # remove the voxels outside brain mask - axis_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] - brain_voxels_index = [(z - np.min(zz))+ axis_dim[2] * (y - np.min(yy))+ axis_dim[1] * axis_dim[2] * (x - np.min(xx)) - for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1] ->>>>>>> 06f27f9 (use a sparse array instead of numpy) X = X[brain_voxels_index, :].todense() # remove tensor product basis that have no support in the brain x_df, y_df, z_df = x_spline.shape[1], y_spline.shape[1], z_spline.shape[1] @@ -1313,7 +1276,6 @@ def index2vox(vals, masker_voxels): return voxel_array -<<<<<<< HEAD def dummy_encoding_moderators(dataset_annotations, moderators): for moderator in moderators: if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): @@ -1322,167 +1284,4 @@ def dummy_encoding_moderators(dataset_annotations, moderators): for category in categories_unique: dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) moderators.append(category) # add dummy encoded moderators - return dataset_annotations, moderators -======= -def intensity2voxel(intensity, masker_voxels): - masker_dim = masker_voxels.shape - xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] - yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] - zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] - - # correspondence between xyz coordinates and spatial intensity - brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) - brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) - - intensity_array = np.zeros(masker_dim) - for i in range(brain_voxel_intensity.shape[0]): - coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] - coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) - intensity_array[coord_x, coord_y, coord_z] = coord_intensity - - return intensity_array -======= - if return_counts: - _, unique_row_indices, counts = np.unique( - ar_row_view, return_index=True, return_counts=True - ) - - return ar[unique_row_indices], counts - else: - _, unique_row_indices = np.unique(ar_row_view, return_index=True) - - return ar[unique_row_indices] - - -def _cluster_nearest_neighbor(ijk, labels_index, labeled): - """Find the nearest neighbor for given points in the corresponding cluster. - - Parameters - ---------- - ijk : :obj:`numpy.ndarray` - (n_pts, 3) array of query points. - labels_index : :obj:`numpy.ndarray` - (n_pts,) array of corresponding cluster indices. - labeled : :obj:`numpy.ndarray` - 3D array with voxels labeled according to cluster index. - - Returns - ------- - nbrs : :obj:`numpy.ndarray` - (n_pts, 3) nearest neighbor points. - - This function is partially derived from Nilearn's code. - - License - ------- - New BSD License - - Copyright (c) 2007 - 2022 The nilearn developers. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - a. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - b. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - c. Neither the name of the nilearn developers nor the names of - its contributors may be used to endorse or promote products - derived from this software without specific prior written - permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH - DAMAGE. - """ - labels = labeled[labeled > 0] - clusters_ijk = np.array(labeled.nonzero()).T - nbrs = np.zeros_like(ijk) - for ii, (lab, point) in enumerate(zip(labels_index, ijk)): - lab_ijk = clusters_ijk[labels == lab] - dist = np.linalg.norm(lab_ijk - point, axis=1) - nbrs[ii] = lab_ijk[np.argmin(dist)] - - return nbrs - - -def _get_cluster_coms(labeled_cluster_arr): - """Get the center of mass of each cluster in a labeled array. - - This function is partially derived from Nilearn's code. - - License - ------- - New BSD License - - Copyright (c) 2007 - 2022 The nilearn developers. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - a. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - b. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - c. Neither the name of the nilearn developers nor the names of - its contributors may be used to endorse or promote products - derived from this software without specific prior written - permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH - DAMAGE. - """ - cluster_ids = np.unique(labeled_cluster_arr)[1:] - n_clusters = cluster_ids.size - - # Identify center of mass for each cluster - # This COM may fall outside the cluster, but it is a useful heuristic for identifying them - cluster_ids = np.arange(1, n_clusters + 1, dtype=int) - cluster_coms = ndimage.center_of_mass(labeled_cluster_arr, labeled_cluster_arr, cluster_ids) - cluster_coms = np.array(cluster_coms).astype(int) - - # NOTE: The following comes from Nilearn - # Determine if all subpeaks are within the cluster - # They may not be if the cluster is binary and has a shape where the COM is - # outside the cluster, like a donut. - coms_outside_clusters = ( - labeled_cluster_arr[cluster_coms[:, 0], cluster_coms[:, 1], cluster_coms[:, 2]] - != cluster_ids - ) - if np.any(coms_outside_clusters): - LGR.warning( - "Attention: At least one of the centers of mass falls outside of the cluster body. " - "Identifying the nearest in-cluster voxel." - ) - - # Replace centers of mass with their nearest neighbor points in the - # corresponding clusters. Note this is also equivalent to computing the - # centers of mass constrained to points within the cluster. - cluster_coms[coms_outside_clusters, :] = _cluster_nearest_neighbor( - cluster_coms[coms_outside_clusters, :], - cluster_ids[coms_outside_clusters], - labeled_cluster_arr, - ) - - return cluster_coms ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 ->>>>>>> 055370d ([skip CI][WIP] solve conflict) + return dataset_annotations, moderators \ No newline at end of file diff --git a/setup_BACKUP_7408.cfg b/setup_BACKUP_7408.cfg deleted file mode 100644 index 1933f95bf..000000000 --- a/setup_BACKUP_7408.cfg +++ /dev/null @@ -1,129 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy -<<<<<<< HEAD - pandas - patsy -======= - pandas>=1.1.0 ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_BASE_7408.cfg b/setup_BASE_7408.cfg deleted file mode 100644 index 6a4932af7..000000000 --- a/setup_BASE_7408.cfg +++ /dev/null @@ -1,134 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -peaks2maps-cpu = - tensorflow>=2.0.0 - appdirs -peaks2maps-gpu = - tensorflow-gpu>=2.0.0 - appdirs -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -duecredit = - duecredit -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(duecredit)s - %(peaks2maps-cpu)s - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py,due.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_LOCAL_7408.cfg b/setup_LOCAL_7408.cfg deleted file mode 100644 index 7da488b1c..000000000 --- a/setup_LOCAL_7408.cfg +++ /dev/null @@ -1,135 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas - patsy - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -peaks2maps-cpu = - tensorflow>=2.0.0 - appdirs -peaks2maps-gpu = - tensorflow-gpu>=2.0.0 - appdirs -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -duecredit = - duecredit -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(duecredit)s - %(peaks2maps-cpu)s - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py,due.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy diff --git a/setup_REMOTE_7408.cfg b/setup_REMOTE_7408.cfg deleted file mode 100644 index 59d103597..000000000 --- a/setup_REMOTE_7408.cfg +++ /dev/null @@ -1,124 +0,0 @@ -[metadata] -url = https://github.com/neurostuff/NiMARE -license = MIT -author = NiMARE developers -author_email = tsalo006@fiu.edu -maintainer = Taylor Salo -maintainer_email = tsalo006@fiu.edu -description = NiMARE: Neuroimaging Meta-Analysis Research Environment -description-file = README.md -long_description = - NiMARE - ====== - NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package - for coordinate-based and image-based meta-analysis of neuroimaging data. - - License - ======= - `NiMARE` is licensed under the terms of the MIT license. See the file - 'LICENSE' for information on the history of this software, terms & conditions - for usage, and a DISCLAIMER OF ALL WARRANTIES. - - All trademarks referenced herein are property of their respective holders. - - Copyright (c) 2018--, NiMARE developers -long_description_content_type = text/x-rst -classifiers = - Development Status :: 3 - Alpha - Environment :: Console - Intended Audience :: Science/Research - License :: OSI Approved :: MIT License - Operating System :: OS Independent - Programming Language :: Python :: 3.6 - Programming Language :: Python :: 3.7 - Programming Language :: Python :: 3.8 - Programming Language :: Python :: 3.9 - Programming Language :: Python :: 3.10 - Topic :: Scientific/Engineering - -[options] -python_requires = >= 3.6 -install_requires = - cognitiveatlas # nimare.annotate.cogat - fuzzywuzzy # nimare.annotate - indexed_gzip>=1.4.0 # working with gzipped niftis - joblib # parallelization - matplotlib<3.5 # this is for nilearn, which doesn't include it in its reqs - nibabel>=3.0.0 # I/O of niftis - nilearn>=0.7.1 - numba # used by sparse - numpy - pandas>=1.1.0 - pymare~=0.0.4rc2 # nimare.meta.ibma and stats - requests # nimare.extract - scikit-learn # nimare.annotate and nimare.decode - scipy - sparse>=0.13.0 # for kernel transformers - statsmodels!=0.13.2 # this version doesn't install properly - tqdm # progress bars throughout package -packages = find: -include_package_data = False - -[options.extras_require] -doc = - m2r - matplotlib - mistune<2 # just temporary until m2r addresses this issue - pillow - recommonmark - seaborn - sphinx>=3.5 - sphinx-argparse - sphinx-copybutton - sphinx_gallery==0.10.1 - sphinx_rtd_theme - sphinxcontrib-bibtex -tests = - codecov - coverage - coveralls - flake8-black - flake8-docstrings - flake8-isort - pytest - pytest-cov -minimum = - indexed_gzip==1.4 - nibabel==3.0 - nilearn==0.7.1 - numpy==1.18 - pandas==1.1 - pymare==0.0.4rc2 - scikit-learn==0.22 - scipy==1.5 # 1.6 drops Python 3.6 support -all = - %(doc)s - %(tests)s - -[options.entry_points] -console_scripts = - nimare = nimare.cli:_main - -[options.package_data] -* = - resources/* - resources/atlases/* - resources/templates/* - tests/data/* - tests/data/cognitive_atlas/* - -[versioneer] -VCS = git -style = pep440 -versionfile_source = nimare/_version.py -versionfile_build = nimare/_version.py -tag_prefix = -parentdir_prefix = - -[flake8] -max-line-length = 99 -exclude = *build/,_version.py -putty-ignore = - */__init__.py : +F401 -ignore = E203,E402,E722,W503 -docstring-convention = numpy From 5b4df2c7df6925fc353b34b903353ed9c89cacae Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 6 Aug 2022 17:56:22 +0100 Subject: [PATCH 080/177] [skip ci][wip] modify standardization of group moderators --- nimare/utils.py | 178 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 176 insertions(+), 2 deletions(-) diff --git a/nimare/utils.py b/nimare/utils.py index 9976ea2ba..dd5772c8f 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1162,7 +1162,6 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms - def coef_spline_bases(axis_coords, spacing, margin): """ Coefficient of cubic B-spline bases in any x/y/z direction @@ -1276,6 +1275,7 @@ def index2vox(vals, masker_voxels): return voxel_array +<<<<<<< HEAD def dummy_encoding_moderators(dataset_annotations, moderators): for moderator in moderators: if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): @@ -1284,4 +1284,178 @@ def dummy_encoding_moderators(dataset_annotations, moderators): for category in categories_unique: dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) moderators.append(category) # add dummy encoded moderators - return dataset_annotations, moderators \ No newline at end of file + return dataset_annotations, moderators +======= +def standardize_field(dataset, metadata): + # if isinstance(metadata, str): + # moderators = dataset.annotations[metadata] + # elif isinstance(metadata, list): + moderators = dataset.annotations[metadata] + standardize_moderators = moderators - np.mean(moderators, axis=0) + standardize_moderators /= np.std(standardize_moderators, axis=0) + if isinstance(metadata, str): + column_name = 'standardized_' + metadata + elif isinstance(metadata, list): + column_name = ['standardized_' + moderator for moderator in metadata] + dataset.annotations[column_name] = standardize_moderators + +<<<<<<< HEAD + # correspondence between xyz coordinates and spatial intensity + brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) + brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) + + intensity_array = np.zeros(masker_dim) + for i in range(brain_voxel_intensity.shape[0]): + coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] + coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) + intensity_array[coord_x, coord_y, coord_z] = coord_intensity + + return intensity_array +======= + if return_counts: + _, unique_row_indices, counts = np.unique( + ar_row_view, return_index=True, return_counts=True + ) + + return ar[unique_row_indices], counts + else: + _, unique_row_indices = np.unique(ar_row_view, return_index=True) + + return ar[unique_row_indices] + + +def _cluster_nearest_neighbor(ijk, labels_index, labeled): + """Find the nearest neighbor for given points in the corresponding cluster. + + Parameters + ---------- + ijk : :obj:`numpy.ndarray` + (n_pts, 3) array of query points. + labels_index : :obj:`numpy.ndarray` + (n_pts,) array of corresponding cluster indices. + labeled : :obj:`numpy.ndarray` + 3D array with voxels labeled according to cluster index. + + Returns + ------- + nbrs : :obj:`numpy.ndarray` + (n_pts, 3) nearest neighbor points. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + labels = labeled[labeled > 0] + clusters_ijk = np.array(labeled.nonzero()).T + nbrs = np.zeros_like(ijk) + for ii, (lab, point) in enumerate(zip(labels_index, ijk)): + lab_ijk = clusters_ijk[labels == lab] + dist = np.linalg.norm(lab_ijk - point, axis=1) + nbrs[ii] = lab_ijk[np.argmin(dist)] + + return nbrs + + +def _get_cluster_coms(labeled_cluster_arr): + """Get the center of mass of each cluster in a labeled array. + + This function is partially derived from Nilearn's code. + + License + ------- + New BSD License + + Copyright (c) 2007 - 2022 The nilearn developers. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the nilearn developers nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH + DAMAGE. + """ + cluster_ids = np.unique(labeled_cluster_arr)[1:] + n_clusters = cluster_ids.size + + # Identify center of mass for each cluster + # This COM may fall outside the cluster, but it is a useful heuristic for identifying them + cluster_ids = np.arange(1, n_clusters + 1, dtype=int) + cluster_coms = ndimage.center_of_mass(labeled_cluster_arr, labeled_cluster_arr, cluster_ids) + cluster_coms = np.array(cluster_coms).astype(int) + + # NOTE: The following comes from Nilearn + # Determine if all subpeaks are within the cluster + # They may not be if the cluster is binary and has a shape where the COM is + # outside the cluster, like a donut. + coms_outside_clusters = ( + labeled_cluster_arr[cluster_coms[:, 0], cluster_coms[:, 1], cluster_coms[:, 2]] + != cluster_ids + ) + if np.any(coms_outside_clusters): + LGR.warning( + "Attention: At least one of the centers of mass falls outside of the cluster body. " + "Identifying the nearest in-cluster voxel." + ) + + # Replace centers of mass with their nearest neighbor points in the + # corresponding clusters. Note this is also equivalent to computing the + # centers of mass constrained to points within the cluster. + cluster_coms[coms_outside_clusters, :] = _cluster_nearest_neighbor( + cluster_coms[coms_outside_clusters, :], + cluster_ids[coms_outside_clusters], + labeled_cluster_arr, + ) + + return cluster_coms +>>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 +======= + return dataset +>>>>>>> 48d4b57 ([skip ci][wip] modify standardization of group moderators) +>>>>>>> 7b9581b ([skip ci][wip] modify standardization of group moderators) From a4f67c06bf15247c42462d284905f07be199770e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 24 Sep 2022 16:27:42 +0100 Subject: [PATCH 081/177] [skip CI][wip] implement index2voxel function --- nimare/meta/cbmr.py | 2 +- nimare/utils.py | 164 +------------------------------------------- 2 files changed, 2 insertions(+), 164 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 984f5cd26..f558d9066 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -4,7 +4,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox +from nimare.utils import mm2vox, index2vox from nimare.diagnostics import FocusFilter from nimare.meta import models import torch diff --git a/nimare/utils.py b/nimare/utils.py index dd5772c8f..7510eb204 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1257,7 +1257,6 @@ def B_spline_bases(masker_voxels, spacing, margin=10): return X - def index2vox(vals, masker_voxels): xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] @@ -1275,7 +1274,6 @@ def index2vox(vals, masker_voxels): return voxel_array -<<<<<<< HEAD def dummy_encoding_moderators(dataset_annotations, moderators): for moderator in moderators: if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): @@ -1285,7 +1283,6 @@ def dummy_encoding_moderators(dataset_annotations, moderators): dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) moderators.append(category) # add dummy encoded moderators return dataset_annotations, moderators -======= def standardize_field(dataset, metadata): # if isinstance(metadata, str): # moderators = dataset.annotations[metadata] @@ -1299,163 +1296,4 @@ def standardize_field(dataset, metadata): column_name = ['standardized_' + moderator for moderator in metadata] dataset.annotations[column_name] = standardize_moderators -<<<<<<< HEAD - # correspondence between xyz coordinates and spatial intensity - brain_voxel_coord = np.array([[x,y,z] for x in xx for y in yy for z in zz if masker_voxels[x, y, z] == 1]) - brain_voxel_intensity = np.concatenate((brain_voxel_coord, intensity), axis=1) - - intensity_array = np.zeros(masker_dim) - for i in range(brain_voxel_intensity.shape[0]): - coord_x, coord_y, coord_z, coord_intensity = brain_voxel_intensity[i, :] - coord_x, coord_y, coord_z = coord_x.astype(int), coord_y.astype(int), coord_z.astype(int) - intensity_array[coord_x, coord_y, coord_z] = coord_intensity - - return intensity_array -======= - if return_counts: - _, unique_row_indices, counts = np.unique( - ar_row_view, return_index=True, return_counts=True - ) - - return ar[unique_row_indices], counts - else: - _, unique_row_indices = np.unique(ar_row_view, return_index=True) - - return ar[unique_row_indices] - - -def _cluster_nearest_neighbor(ijk, labels_index, labeled): - """Find the nearest neighbor for given points in the corresponding cluster. - - Parameters - ---------- - ijk : :obj:`numpy.ndarray` - (n_pts, 3) array of query points. - labels_index : :obj:`numpy.ndarray` - (n_pts,) array of corresponding cluster indices. - labeled : :obj:`numpy.ndarray` - 3D array with voxels labeled according to cluster index. - - Returns - ------- - nbrs : :obj:`numpy.ndarray` - (n_pts, 3) nearest neighbor points. - - This function is partially derived from Nilearn's code. - - License - ------- - New BSD License - - Copyright (c) 2007 - 2022 The nilearn developers. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - a. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - b. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - c. Neither the name of the nilearn developers nor the names of - its contributors may be used to endorse or promote products - derived from this software without specific prior written - permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH - DAMAGE. - """ - labels = labeled[labeled > 0] - clusters_ijk = np.array(labeled.nonzero()).T - nbrs = np.zeros_like(ijk) - for ii, (lab, point) in enumerate(zip(labels_index, ijk)): - lab_ijk = clusters_ijk[labels == lab] - dist = np.linalg.norm(lab_ijk - point, axis=1) - nbrs[ii] = lab_ijk[np.argmin(dist)] - - return nbrs - - -def _get_cluster_coms(labeled_cluster_arr): - """Get the center of mass of each cluster in a labeled array. - - This function is partially derived from Nilearn's code. - - License - ------- - New BSD License - - Copyright (c) 2007 - 2022 The nilearn developers. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - a. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - b. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - c. Neither the name of the nilearn developers nor the names of - its contributors may be used to endorse or promote products - derived from this software without specific prior written - permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH - DAMAGE. - """ - cluster_ids = np.unique(labeled_cluster_arr)[1:] - n_clusters = cluster_ids.size - - # Identify center of mass for each cluster - # This COM may fall outside the cluster, but it is a useful heuristic for identifying them - cluster_ids = np.arange(1, n_clusters + 1, dtype=int) - cluster_coms = ndimage.center_of_mass(labeled_cluster_arr, labeled_cluster_arr, cluster_ids) - cluster_coms = np.array(cluster_coms).astype(int) - - # NOTE: The following comes from Nilearn - # Determine if all subpeaks are within the cluster - # They may not be if the cluster is binary and has a shape where the COM is - # outside the cluster, like a donut. - coms_outside_clusters = ( - labeled_cluster_arr[cluster_coms[:, 0], cluster_coms[:, 1], cluster_coms[:, 2]] - != cluster_ids - ) - if np.any(coms_outside_clusters): - LGR.warning( - "Attention: At least one of the centers of mass falls outside of the cluster body. " - "Identifying the nearest in-cluster voxel." - ) - - # Replace centers of mass with their nearest neighbor points in the - # corresponding clusters. Note this is also equivalent to computing the - # centers of mass constrained to points within the cluster. - cluster_coms[coms_outside_clusters, :] = _cluster_nearest_neighbor( - cluster_coms[coms_outside_clusters, :], - cluster_ids[coms_outside_clusters], - labeled_cluster_arr, - ) - - return cluster_coms ->>>>>>> 87c3ce30c59382605fd141c6149be25be742be96 -======= - return dataset ->>>>>>> 48d4b57 ([skip ci][wip] modify standardization of group moderators) ->>>>>>> 7b9581b ([skip ci][wip] modify standardization of group moderators) + return dataset \ No newline at end of file From b6d912b034352b5382d249386013d5863224ef00 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 24 Sep 2022 23:03:56 +0100 Subject: [PATCH 082/177] [skip CI][wip] add implementation for SE of regression coefficient --- nimare/meta/cbmr.py | 2 +- nimare/tests/conftest.py | 2 +- nimare/utils.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f558d9066..984f5cd26 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -4,7 +4,7 @@ import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox, index2vox +from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter from nimare.meta import models import torch diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index aac87fee8..4789acd0c 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -60,7 +60,7 @@ def testdata_cbma(): @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" - dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") + dset_file = os.path.join(get_test_data_path(), "neurosynth.json") dset = nimare.dataset.Dataset(dset_file) # Only retain one peak in each study in coordinates diff --git a/nimare/utils.py b/nimare/utils.py index 7510eb204..c90790f45 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1296,4 +1296,4 @@ def standardize_field(dataset, metadata): column_name = ['standardized_' + moderator for moderator in metadata] dataset.annotations[column_name] = standardize_moderators - return dataset \ No newline at end of file + return dataset From 01aab8b780a2bb65266822b76ac61b824b6fb8a3 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 6 Nov 2022 23:12:18 +0000 Subject: [PATCH 083/177] [skip CI][wip] add a demonstration for CBMREstimator & CBMRInference --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 151 ++++++++++++++++ nimare/meta/cbmr.py | 173 ++++++++++++++++++- 2 files changed, 323 insertions(+), 1 deletion(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 982519b46..d6fb5efa6 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -34,15 +34,26 @@ "import nimare\n", "import os \n", "from nimare.dataset import Dataset\n", +<<<<<<< HEAD "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", "from nimare.tests.utils import standardize_field\n", "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", "from nimare.meta import models\n", +======= + "from nimare.utils import get_resource_path, standardize_field,index2vox\n", + "from nimare.meta.cbmr import CBMREstimator\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "from nilearn.plotting import plot_stat_map\n", "from nimare.generate import create_coordinate_dataset\n", "import nibabel as nib \n", "import numpy as np\n", +<<<<<<< HEAD "import scipy\n" +======= + "\n", + "import logging\n", + "import sys" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] }, { @@ -58,13 +69,18 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD "# data simulation\n", +======= + "# data simulation \n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", "# set up group columns: diagnosis & drug_status \n", "n_rows = dset.annotations.shape[0]\n", "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", +<<<<<<< HEAD "# set up moderators: sample sizes & avg_age\n", "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", @@ -79,6 +95,21 @@ "metadata": {}, "source": [ "## Estimate group-specific spatial intensity functions" +======= + "# set up `study-level moderators`: sample sizes & avg_age\n", + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", 'avg_age']) # standardisation\n", + "# load mask image from dataset\n", + "mask_img = dset.masker.mask_img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group-wise spatial intensity estimation" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] }, { @@ -91,6 +122,13 @@ "output_type": "stream", "text": [ "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", +<<<<<<< HEAD +======= + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", + " niimg = new_img_like(niimg, data, niimg.affine)\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", + " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", " anat_img = load_mni152_template()\n" ] @@ -98,7 +136,11 @@ { "data": { "text/plain": [ +<<<<<<< HEAD "" +======= + "" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] }, "execution_count": 3, @@ -107,7 +149,11 @@ }, { "data": { +<<<<<<< HEAD "image/png": 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", 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", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "text/plain": [ "
" ] @@ -117,6 +163,7 @@ } ], "source": [ +<<<<<<< HEAD "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", "cbmr = CBMREstimator(\n", " group_categories=[\"diagnosis\", \"drug_status\"],\n", @@ -131,6 +178,13 @@ "cbmr_res = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", +======= + "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", + " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -141,6 +195,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ +<<<<<<< HEAD +======= + "##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" ] }, @@ -153,6 +217,7 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", "INFO:nimare.meta.cbmr:depression_No = index_1\n", @@ -161,12 +226,20 @@ "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" +======= + "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", + " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] }, { "data": { "text/plain": [ +<<<<<<< HEAD "" +======= + "" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] }, "execution_count": 4, @@ -175,7 +248,11 @@ }, { "data": { +<<<<<<< HEAD "image/png": 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", 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", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) "text/plain": [ "
" ] @@ -185,6 +262,7 @@ } ], "source": [ +<<<<<<< HEAD "# homoogeneity test for each group\n", "inference = CBMRInference(\n", " CBMRResults=cbmr_res, device=\"cuda\"\n", @@ -198,6 +276,19 @@ " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", " threshold=30,\n", +======= + "from nimare.meta.cbmr import CBMRInference\n", + "# Group-wise spatial homogeneity test\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", + " t_con_moderator=None, device='cuda')\n", + "inference._contrast()\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"homo_test_1xschizophrenia_No_chi_sq\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=5\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ")" ] }, @@ -205,6 +296,7 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, +<<<<<<< HEAD "outputs": [ { "name": "stderr", @@ -289,6 +381,20 @@ " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", " threshold=0.5,\n", +======= + "outputs": [], + "source": [ + "# Group comparison test between two groups\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,-1,0,0]],\n", + " t_con_moderator=None, device='cuda')\n", + "inference._contrast()\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"1xschizophrenia_NoVS1xdepression_Yes_chi_sq\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=1\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ")" ] }, @@ -301,6 +407,7 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 7, "metadata": {}, "outputs": [ @@ -324,11 +431,22 @@ "text": [ "For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: 0.9243109811987764, 0.9461743884065033\n", "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" +======= + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.94563486]]\n" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] } ], "source": [ "# Test for existence of effect of study-level moderators\n", +<<<<<<< HEAD "inference = CBMRInference(\n", " CBMRResults=cbmr_res, device=\"cuda\"\n", ")\n", @@ -339,6 +457,35 @@ "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" +======= + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,0]], device='cuda')\n", + "inference._contrast()\n", + "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", + "print(sample_size_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.99838466]]\n" + ] + } + ], + "source": [ + "# Test for existence of effect of study-level moderators\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,-1]], device='cuda')\n", + "inference._contrast()\n", + "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", + "print(effect_diff_p)" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) ] } ], @@ -358,7 +505,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", +<<<<<<< HEAD "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" +======= + "version": "3.8.8" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) }, "vscode": { "interpreter": { diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 984f5cd26..b4bd9dce4 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -342,6 +342,177 @@ def _model_structure(self, model, penalty, device): return cbmr_model +<<<<<<< HEAD +======= + def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): + self.iter += 1 + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay + scheduler.step() + def closure(): + optimizer.zero_grad() + loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) + loss.backward() + return loss + loss = optimizer.step(closure) + # reset the L-BFGS params if NaN appears in coefficient of regression + if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): + all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() + for group in self.inputs_['all_group_study_id'].keys(): + beta_dim = model.all_beta_linears[group].weight.shape[1] + beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() + beta_linear_group.weight = torch.nn.Parameter(self.last_state['all_beta_linears.'+group+'.weight']) + all_beta_linears[group] = beta_linear_group + + if self.model == 'NB': + group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) + all_alpha_sqrt[group] = group_alpha_sqrt + elif self.model == 'clustered_NB': + group_alpha = torch.nn.Parameter(self.last_state['all_alpha.'+group]) + all_alpha[group] = group_alpha + + model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) + if self.model == 'NB': + model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) + elif self.model == 'clustered_NB': + model.all_alpha = torch.nn.ParameterDict(all_alpha) + + LGR.debug(f"Reset L-BFGS optimizer......") + else: + self.last_state = copy.deepcopy(model.state_dict()) # need to change the variable name? + + return loss + + def _optimizer(self, model, lr, tol, n_iter, device): + optimizer = torch.optim.LBFGS(model.parameters(), lr) + # load dataset info to torch.tensor + Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) + if self.moderators: + all_group_moderators_tensor = dict() + for group in self.inputs_['all_group_study_id'].keys(): + group_moderators_tensor = torch.tensor(self.inputs_['all_group_moderators'][group], dtype=torch.float64, device=device) + all_group_moderators_tensor[group] = group_moderators_tensor + else: + all_group_moderators_tensor = None + all_foci_per_voxel_tensor, all_foci_per_study_tensor = dict(), dict() + for group in self.inputs_['all_group_study_id'].keys(): + group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=device) + group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=device) + all_foci_per_voxel_tensor[group] = group_foci_per_voxel + all_foci_per_study_tensor[group] = group_foci_per_study + + if self.iter == 0: + prev_loss = torch.tensor(float('inf')) # initialization loss difference + + for i in range(n_iter): + loss = self._update(model, optimizer, Coef_spline_bases, all_group_moderators_tensor, all_foci_per_voxel_tensor, all_foci_per_study_tensor, prev_loss) + loss_diff = loss - prev_loss + LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") + if torch.abs(loss_diff) < tol: + break + prev_loss = loss + + return + + def _fit(self, dataset): + masker_voxels = self.inputs_['mask_img']._dataobj + Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) + P = Coef_spline_bases.shape[1] + self.inputs_['Coef_spline_bases'] = Coef_spline_bases + + cbmr_model = self._model_structure(self.model, self.penalty, self.device) + optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) + + maps, tables = dict(), dict() + Spatial_Regression_Coef, overdispersion_param = dict(), dict() + # beta: regression coef of spatial effect + for group in self.inputs_['all_group_study_id'].keys(): + group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight + group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) + Spatial_Regression_Coef[group] = group_beta_linear_weight + group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) + maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity#.reshape((1,-1)) + # overdispersion parameter: alpha + if self.model == 'NB': + alpha = cbmr_model.all_alpha_sqrt[group]**2 + alpha = alpha.cpu().detach().numpy() + overdispersion_param[group] = alpha + elif self.model == 'clustered_NB': + alpha = cbmr_model.all_alpha[group] + alpha = alpha.cpu().detach().numpy() + overdispersion_param[group] = alpha + tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') + if self.model == 'NB' or self.model == 'clustered_NB': + tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) + # study-level moderators + if self.moderators: + self.moderators_effect = dict() + self._gamma = cbmr_model.gamma_linear.weight + self._gamma = self._gamma.cpu().detach().numpy() + for group in self.inputs_['all_group_study_id'].keys(): + group_moderators = self.inputs_["all_group_moderators"][group] + group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) + self.moderators_effect[group] = group_moderators_effect + tables['Moderators_Regression_Coef'] = pd.DataFrame(self._gamma, columns=self.moderators) + else: + self._gamma = None + # standard error + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() + Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) + for group in self.inputs_['all_group_study_id'].keys(): + group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) + group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) + group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight + if self.moderators: + gamma = cbmr_model.gamma_linear.weight + group_moderators = self.inputs_["all_group_moderators"][group] + group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) + else: + gamma, group_moderators = None, None + if 'Overdispersion_Coef' in tables.keys(): + alpha = torch.tensor(tables['Overdispersion_Coef'].to_dict()['alpha'][group], dtype=torch.float64, device=self.device) + # a = -GLMCNB._log_likelihood_single_group(alpha, group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + if self.model == 'Poisson': + nll = lambda beta: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + elif self.model == 'NB': + nll = lambda beta: -GLMNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + elif self.model == 'clustered_NB': + nll = lambda beta: -GLMCNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + F = functorch.hessian(nll)(group_beta_linear_weight) + # Inference on regression coefficient of spatial effect + spatial_dim = group_beta_linear_weight.shape[1] + F_spatial_coef = F.reshape((spatial_dim, spatial_dim)) + Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) + Var_spatial_coef = np.diag(Cov_spatial_coef) + SE_spatial_coef = np.sqrt(Var_spatial_coef) + spatial_regression_coef_se[group] = SE_spatial_coef + + Var_log_spatial_intensity = np.einsum('ij,ji->i', self.inputs_['Coef_spline_bases'], Cov_spatial_coef @ self.inputs_['Coef_spline_bases'].T) + SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) + log_spatial_intensity_se[group] = SE_log_spatial_intensity + + group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'].reshape((-1)) + SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity + spatial_intensity_se[group] = SE_spatial_intensity + + tables['Spatial_Regression_Coef_SE'] = pd.DataFrame.from_dict(spatial_regression_coef_se, orient='index') + tables['Log_Spatial_Intensity_SE'] = pd.DataFrame.from_dict(log_spatial_intensity_se, orient='index') + tables['Spatial_Intensity_SE'] = pd.DataFrame.from_dict(spatial_intensity_se, orient='index') + + # Inference on regression coefficient of moderators + if self.moderators: + moderators_dim = gamma.shape[1] + nll = lambda gamma: -GLMPoisson._log_likelihood_single_group(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) + params = (gamma) + F_moderators_coef = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') + F_moderators_coef = F_moderators_coef.reshape((moderators_dim, moderators_dim)) + Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) + SE_moderators = np.sqrt(Var_moderators) + tables['Moderators_Regression_SE'] = pd.DataFrame(SE_moderators, columns=self.moderators) + + return maps, tables + +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) class CBMRInference(object): """Statistical inference on outcomes (intensity estimation and study-level moderator regressors) of CBMR. @@ -567,7 +738,7 @@ def _preprocess_t_con_regressor(self, type): t_con_regressor = [t_con_regressor[i] for i in uniq_con_regressor_idx[::-1]] return t_con_regressor, t_con_regressor_name - + def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_groups: From 5f732ab90ddc7bc99171a561d97b9238f75c4a64 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Wed, 21 Dec 2022 15:30:01 +0000 Subject: [PATCH 084/177] [skip CI][WIP] fix a bug in log-likelihood function of CNB model --- nimare/utils.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/nimare/utils.py b/nimare/utils.py index c90790f45..4826b1a1f 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1162,6 +1162,7 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms + def coef_spline_bases(axis_coords, spacing, margin): """ Coefficient of cubic B-spline bases in any x/y/z direction @@ -1169,14 +1170,14 @@ def coef_spline_bases(axis_coords, spacing, margin): Parameters ---------- axis_coords : value range in x/y/z direction - spacing: (equally spaced) knots spacing in x/y/z direction, + spacing: (equally spaced) knots spacing in x/y/z direction, margin: extend the region where B-splines are constructed (min-margin, max_margin) - to avoid weakly-supported B-spline on the edge + to avoid weakly-supported B-spline on the edge Returns ------- coef_spline : 2-D ndarray (n_points x n_spline_bases) """ - ## create B-spline basis for x/y/z coordinate + # create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) design_matrix = patsy.dmatrix( @@ -1251,7 +1252,7 @@ def B_spline_bases(masker_voxels, spacing, margin=10): for bz in range(z_df): basis_index = bz + z_df * by + z_df * y_df * bx basis_coef = X[:, basis_index] - if np.max(basis_coef) >= 0.1: + if np.max(basis_coef) >= 0.1: support_basis.append(basis_index) X = X[:, support_basis] @@ -1291,9 +1292,9 @@ def standardize_field(dataset, metadata): standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) if isinstance(metadata, str): - column_name = 'standardized_' + metadata + column_name = "standardized_" + metadata elif isinstance(metadata, list): - column_name = ['standardized_' + moderator for moderator in metadata] + column_name = ["standardized_" + moderator for moderator in metadata] dataset.annotations[column_name] = standardize_moderators return dataset From f745b63872bdf7aba128d6af53d364eb43a88ee9 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 10 Jan 2023 14:11:08 +0000 Subject: [PATCH 085/177] [skip CI][WIP] Update code according to comments --- nimare/meta/cbmr.py | 187 --------------------------------- nimare/tests/test_meta_cbmr.py | 13 +++ nimare/utils.py | 15 +-- 3 files changed, 14 insertions(+), 201 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index b4bd9dce4..860cbe68b 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -326,193 +326,6 @@ def _fit(self, dataset): return maps, tables - def _model_structure(self, model, penalty, device): - # beta_dim = self.inputs_['Coef_spline_bases'].shape[1] # regression coef of spatial effect - beta_dim = 2627 - if hasattr(self, "moderators"): - gamma_dim = self.inputs_["moderators_array"].shape[1] - study_level_covariates = True - else: - gamma_dim = None - study_level_covariates = False - if model == 'Poisson': - cbmr_model = GLMPoisson(beta_dim=beta_dim, gamma_dim=gamma_dim, study_level_covariates=study_level_covariates, penalty=penalty) - if 'cuda' in device: - cbmr_model = cbmr_model.cuda() - - return cbmr_model - -<<<<<<< HEAD -======= - def _update(self, model, optimizer, Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study, prev_loss, gamma=0.999): - self.iter += 1 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,gamma=gamma) # learning rate decay - scheduler.step() - def closure(): - optimizer.zero_grad() - loss = model(Coef_spline_bases, all_moderators, all_foci_per_voxel, all_foci_per_study) - loss.backward() - return loss - loss = optimizer.step(closure) - # reset the L-BFGS params if NaN appears in coefficient of regression - if any([torch.any(torch.isnan(model.all_beta_linears[group].weight)) for group in self.inputs_['all_group_study_id'].keys()]): - all_beta_linears, all_alpha_sqrt, all_alpha = dict(), dict(), dict() - for group in self.inputs_['all_group_study_id'].keys(): - beta_dim = model.all_beta_linears[group].weight.shape[1] - beta_linear_group = torch.nn.Linear(beta_dim, 1, bias=False).double() - beta_linear_group.weight = torch.nn.Parameter(self.last_state['all_beta_linears.'+group+'.weight']) - all_beta_linears[group] = beta_linear_group - - if self.model == 'NB': - group_alpha_sqrt = torch.nn.Parameter(self.last_state['all_alpha_sqrt.'+group]) - all_alpha_sqrt[group] = group_alpha_sqrt - elif self.model == 'clustered_NB': - group_alpha = torch.nn.Parameter(self.last_state['all_alpha.'+group]) - all_alpha[group] = group_alpha - - model.all_beta_linears = torch.nn.ModuleDict(all_beta_linears) - if self.model == 'NB': - model.all_alpha_sqrt = torch.nn.ParameterDict(all_alpha_sqrt) - elif self.model == 'clustered_NB': - model.all_alpha = torch.nn.ParameterDict(all_alpha) - - LGR.debug(f"Reset L-BFGS optimizer......") - else: - self.last_state = copy.deepcopy(model.state_dict()) # need to change the variable name? - - return loss - - def _optimizer(self, model, lr, tol, n_iter, device): - optimizer = torch.optim.LBFGS(model.parameters(), lr) - # load dataset info to torch.tensor - Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=device) - if self.moderators: - all_group_moderators_tensor = dict() - for group in self.inputs_['all_group_study_id'].keys(): - group_moderators_tensor = torch.tensor(self.inputs_['all_group_moderators'][group], dtype=torch.float64, device=device) - all_group_moderators_tensor[group] = group_moderators_tensor - else: - all_group_moderators_tensor = None - all_foci_per_voxel_tensor, all_foci_per_study_tensor = dict(), dict() - for group in self.inputs_['all_group_study_id'].keys(): - group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=device) - group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=device) - all_foci_per_voxel_tensor[group] = group_foci_per_voxel - all_foci_per_study_tensor[group] = group_foci_per_study - - if self.iter == 0: - prev_loss = torch.tensor(float('inf')) # initialization loss difference - - for i in range(n_iter): - loss = self._update(model, optimizer, Coef_spline_bases, all_group_moderators_tensor, all_foci_per_voxel_tensor, all_foci_per_study_tensor, prev_loss) - loss_diff = loss - prev_loss - LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") - if torch.abs(loss_diff) < tol: - break - prev_loss = loss - - return - - def _fit(self, dataset): - masker_voxels = self.inputs_['mask_img']._dataobj - Coef_spline_bases = B_spline_bases(masker_voxels=masker_voxels, spacing=self.spline_spacing) - P = Coef_spline_bases.shape[1] - self.inputs_['Coef_spline_bases'] = Coef_spline_bases - - cbmr_model = self._model_structure(self.model, self.penalty, self.device) - optimisation = self._optimizer(cbmr_model, self.lr, self.tol, self.n_iter, self.device) - - maps, tables = dict(), dict() - Spatial_Regression_Coef, overdispersion_param = dict(), dict() - # beta: regression coef of spatial effect - for group in self.inputs_['all_group_study_id'].keys(): - group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - group_beta_linear_weight = group_beta_linear_weight.cpu().detach().numpy().reshape((P,)) - Spatial_Regression_Coef[group] = group_beta_linear_weight - group_studywise_spatial_intensity = np.exp(np.matmul(Coef_spline_bases, group_beta_linear_weight)) - maps['Group_'+group+'_Studywise_Spatial_Intensity'] = group_studywise_spatial_intensity#.reshape((1,-1)) - # overdispersion parameter: alpha - if self.model == 'NB': - alpha = cbmr_model.all_alpha_sqrt[group]**2 - alpha = alpha.cpu().detach().numpy() - overdispersion_param[group] = alpha - elif self.model == 'clustered_NB': - alpha = cbmr_model.all_alpha[group] - alpha = alpha.cpu().detach().numpy() - overdispersion_param[group] = alpha - tables['Spatial_Regression_Coef'] = pd.DataFrame.from_dict(Spatial_Regression_Coef, orient='index') - if self.model == 'NB' or self.model == 'clustered_NB': - tables['Overdispersion_Coef'] = pd.DataFrame.from_dict(overdispersion_param, orient='index', columns=['alpha']) - # study-level moderators - if self.moderators: - self.moderators_effect = dict() - self._gamma = cbmr_model.gamma_linear.weight - self._gamma = self._gamma.cpu().detach().numpy() - for group in self.inputs_['all_group_study_id'].keys(): - group_moderators = self.inputs_["all_group_moderators"][group] - group_moderators_effect = np.exp(np.matmul(group_moderators, self._gamma.T)) - self.moderators_effect[group] = group_moderators_effect - tables['Moderators_Regression_Coef'] = pd.DataFrame(self._gamma, columns=self.moderators) - else: - self._gamma = None - # standard error - spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() - Coef_spline_bases = torch.tensor(self.inputs_['Coef_spline_bases'], dtype=torch.float64, device=self.device) - for group in self.inputs_['all_group_study_id'].keys(): - group_foci_per_voxel = torch.tensor(self.inputs_['all_foci_per_voxel'][group], dtype=torch.float64, device=self.device) - group_foci_per_study = torch.tensor(self.inputs_['all_foci_per_study'][group], dtype=torch.float64, device=self.device) - group_beta_linear_weight = cbmr_model.all_beta_linears[group].weight - if self.moderators: - gamma = cbmr_model.gamma_linear.weight - group_moderators = self.inputs_["all_group_moderators"][group] - group_moderators = torch.tensor(group_moderators, dtype=torch.float64, device=self.device) - else: - gamma, group_moderators = None, None - if 'Overdispersion_Coef' in tables.keys(): - alpha = torch.tensor(tables['Overdispersion_Coef'].to_dict()['alpha'][group], dtype=torch.float64, device=self.device) - # a = -GLMCNB._log_likelihood_single_group(alpha, group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - if self.model == 'Poisson': - nll = lambda beta: -GLMPoisson._log_likelihood_single_group(beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - elif self.model == 'NB': - nll = lambda beta: -GLMNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - elif self.model == 'clustered_NB': - nll = lambda beta: -GLMCNB._log_likelihood_single_group(alpha, beta, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - F = functorch.hessian(nll)(group_beta_linear_weight) - # Inference on regression coefficient of spatial effect - spatial_dim = group_beta_linear_weight.shape[1] - F_spatial_coef = F.reshape((spatial_dim, spatial_dim)) - Cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) - Var_spatial_coef = np.diag(Cov_spatial_coef) - SE_spatial_coef = np.sqrt(Var_spatial_coef) - spatial_regression_coef_se[group] = SE_spatial_coef - - Var_log_spatial_intensity = np.einsum('ij,ji->i', self.inputs_['Coef_spline_bases'], Cov_spatial_coef @ self.inputs_['Coef_spline_bases'].T) - SE_log_spatial_intensity = np.sqrt(Var_log_spatial_intensity) - log_spatial_intensity_se[group] = SE_log_spatial_intensity - - group_studywise_spatial_intensity = maps['Group_'+group+'_Studywise_Spatial_Intensity'].reshape((-1)) - SE_spatial_intensity = group_studywise_spatial_intensity * SE_log_spatial_intensity - spatial_intensity_se[group] = SE_spatial_intensity - - tables['Spatial_Regression_Coef_SE'] = pd.DataFrame.from_dict(spatial_regression_coef_se, orient='index') - tables['Log_Spatial_Intensity_SE'] = pd.DataFrame.from_dict(log_spatial_intensity_se, orient='index') - tables['Spatial_Intensity_SE'] = pd.DataFrame.from_dict(spatial_intensity_se, orient='index') - - # Inference on regression coefficient of moderators - if self.moderators: - moderators_dim = gamma.shape[1] - nll = lambda gamma: -GLMPoisson._log_likelihood_single_group(group_beta_linear_weight, gamma, Coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, self.device) - params = (gamma) - F_moderators_coef = torch.autograd.functional.hessian(nll, params, create_graph=False, vectorize=True, outer_jacobian_strategy='forward-mode') - F_moderators_coef = F_moderators_coef.reshape((moderators_dim, moderators_dim)) - Cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) - Var_moderators = np.diag(Cov_moderators_coef).reshape((1, moderators_dim)) - SE_moderators = np.sqrt(Var_moderators) - tables['Moderators_Regression_SE'] = pd.DataFrame(SE_moderators, columns=self.moderators) - - return maps, tables - ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) class CBMRInference(object): """Statistical inference on outcomes (intensity estimation and study-level moderator regressors) of CBMR. diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 1a9db5cee..913e417e7 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,6 +1,9 @@ from nimare.meta.cbmr import CBMREstimator, CBMRInference from nimare.tests.utils import standardize_field +<<<<<<< HEAD from nimare.meta import models +======= +>>>>>>> e86d28d ([skip CI][WIP] Update code according to comments) import logging import torch import numpy as np @@ -27,6 +30,7 @@ def test_CBMRInference(testdata_cbmr_simulated): dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], +<<<<<<< HEAD moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, model=models.PoissonEstimator, @@ -34,6 +38,15 @@ def test_CBMRInference(testdata_cbmr_simulated): lr=1e-1, tol=1e4, device="cpu", +======= + moderators=["standardized_sample_sizes", "standardized_avg_age"], + spline_spacing=10, + model="Poisson", + penalty=False, + lr=1e-1, + tol=1e6, + device="cuda", +>>>>>>> e86d28d ([skip CI][WIP] Update code according to comments) ) # ["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], cbmr_res = cbmr.fit(dataset=dset) diff --git a/nimare/utils.py b/nimare/utils.py index 4826b1a1f..064c415e9 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1284,17 +1284,4 @@ def dummy_encoding_moderators(dataset_annotations, moderators): dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) moderators.append(category) # add dummy encoded moderators return dataset_annotations, moderators -def standardize_field(dataset, metadata): - # if isinstance(metadata, str): - # moderators = dataset.annotations[metadata] - # elif isinstance(metadata, list): - moderators = dataset.annotations[metadata] - standardize_moderators = moderators - np.mean(moderators, axis=0) - standardize_moderators /= np.std(standardize_moderators, axis=0) - if isinstance(metadata, str): - column_name = "standardized_" + metadata - elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in metadata] - dataset.annotations[column_name] = standardize_moderators - - return dataset + From 2f10a965a26af032a95ae3787186b2afad118841 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 15 Jan 2023 04:57:08 +0000 Subject: [PATCH 086/177] refactor the optimizer functions into the model class --- nimare/tests/test_meta_cbmr.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 913e417e7..1a9db5cee 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,9 +1,6 @@ from nimare.meta.cbmr import CBMREstimator, CBMRInference from nimare.tests.utils import standardize_field -<<<<<<< HEAD from nimare.meta import models -======= ->>>>>>> e86d28d ([skip CI][WIP] Update code according to comments) import logging import torch import numpy as np @@ -30,7 +27,6 @@ def test_CBMRInference(testdata_cbmr_simulated): dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], -<<<<<<< HEAD moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, model=models.PoissonEstimator, @@ -38,15 +34,6 @@ def test_CBMRInference(testdata_cbmr_simulated): lr=1e-1, tol=1e4, device="cpu", -======= - moderators=["standardized_sample_sizes", "standardized_avg_age"], - spline_spacing=10, - model="Poisson", - penalty=False, - lr=1e-1, - tol=1e6, - device="cuda", ->>>>>>> e86d28d ([skip CI][WIP] Update code according to comments) ) # ["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], cbmr_res = cbmr.fit(dataset=dset) From 59432d9a55eb63ae2c87d333db8255a6a40f8729 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 16 Jan 2023 10:50:11 -0600 Subject: [PATCH 087/177] create a fit method for models --- nimare/meta/models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 35d7f404a..d217d6f2f 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -7,6 +7,7 @@ import logging import copy + LGR = logging.getLogger(__name__) class GeneralLinearModelEstimator(torch.nn.Module): def __init__( From 02c9c6997bd75f2e8dcca7687470485d6c7c9886 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 19 Jan 2023 22:48:53 +0000 Subject: [PATCH 088/177] allow categorical variables in CBMR --- nimare/meta/cbmr.py | 1 + nimare/tests/utils.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 860cbe68b..65fc6e4b6 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -14,6 +14,7 @@ import re + LGR = logging.getLogger(__name__) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 9e589f5bf..610f596ab 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -5,7 +5,11 @@ import nibabel as nib import numpy as np import pytest +<<<<<<< HEAD import logging +======= +import warnings +>>>>>>> 92ffce8 (allow categorical variables in CBMR) from nimare.meta.utils import compute_kda_ma From 116c8c279e7f18e0b7f9e63dd40d5b8b7c3fbbcd Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 24 Jan 2023 03:36:45 +0000 Subject: [PATCH 089/177] restruct code in CBMRInference --- nimare/meta/cbmr.py | 1 - 1 file changed, 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 65fc6e4b6..860cbe68b 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -14,7 +14,6 @@ import re - LGR = logging.getLogger(__name__) From 1913f606e571ce07f30b0b15b161a12320bb3e4c Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 22:21:52 +0000 Subject: [PATCH 090/177] [skip CI][WIP] update example file based on reconstructed code --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 143 +++++++++++++++++-- nimare/meta/cbmr.py | 4 + 2 files changed, 138 insertions(+), 9 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index d6fb5efa6..63b586577 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -35,18 +35,25 @@ "import os \n", "from nimare.dataset import Dataset\n", <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", "from nimare.tests.utils import standardize_field\n", "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", "from nimare.meta import models\n", +<<<<<<< HEAD ======= "from nimare.utils import get_resource_path, standardize_field,index2vox\n", "from nimare.meta.cbmr import CBMREstimator\n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "from nilearn.plotting import plot_stat_map\n", "from nimare.generate import create_coordinate_dataset\n", "import nibabel as nib \n", "import numpy as np\n", +<<<<<<< HEAD <<<<<<< HEAD "import scipy\n" ======= @@ -54,6 +61,9 @@ "import logging\n", "import sys" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "import scipy\n" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, { @@ -69,17 +79,22 @@ "metadata": {}, "outputs": [], "source": [ +<<<<<<< HEAD <<<<<<< HEAD "# data simulation\n", ======= "# data simulation \n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "# data simulation\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", "# set up group columns: diagnosis & drug_status \n", "n_rows = dset.annotations.shape[0]\n", "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", +<<<<<<< HEAD <<<<<<< HEAD "# set up moderators: sample sizes & avg_age\n", "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", @@ -97,19 +112,27 @@ "## Estimate group-specific spatial intensity functions" ======= "# set up `study-level moderators`: sample sizes & avg_age\n", +======= + "# set up moderators: sample sizes & avg_age\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", 'avg_age']) # standardisation\n", - "# load mask image from dataset\n", - "mask_img = dset.masker.mask_img" + "# categorical moderator: schizophrenia_subtype\n", + "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", + "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ +<<<<<<< HEAD "## Group-wise spatial intensity estimation" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "## Estimate group-specific spatial intensity functions" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, { @@ -123,12 +146,15 @@ "text": [ "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", <<<<<<< HEAD +<<<<<<< HEAD ======= "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", " niimg = new_img_like(niimg, data, niimg.affine)\n", "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", " anat_img = load_mni152_template()\n" ] @@ -136,11 +162,15 @@ { "data": { "text/plain": [ +<<<<<<< HEAD <<<<<<< HEAD "" ======= "" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, "execution_count": 3, @@ -149,11 +179,15 @@ }, { "data": { +<<<<<<< HEAD <<<<<<< HEAD "image/png": 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", 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", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "image/png": 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", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "text/plain": [ "
" ] @@ -164,6 +198,9 @@ ], "source": [ <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", "cbmr = CBMREstimator(\n", " group_categories=[\"diagnosis\", \"drug_status\"],\n", @@ -175,6 +212,7 @@ " tol=1,\n", " device=\"cpu\",\n", " )\n", +<<<<<<< HEAD "cbmr_res = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", @@ -185,6 +223,11 @@ "plot_stat_map(\n", " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -196,6 +239,7 @@ "metadata": {}, "source": [ <<<<<<< HEAD +<<<<<<< HEAD ======= "##" ] @@ -205,6 +249,8 @@ "metadata": {}, "source": [ >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" ] }, @@ -218,6 +264,9 @@ "output_type": "stream", "text": [ <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", "INFO:nimare.meta.cbmr:depression_No = index_1\n", @@ -226,20 +275,27 @@ "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" +<<<<<<< HEAD ======= "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, { "data": { "text/plain": [ +<<<<<<< HEAD <<<<<<< HEAD "" ======= "" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, "execution_count": 4, @@ -248,11 +304,15 @@ }, { "data": { +<<<<<<< HEAD <<<<<<< HEAD "image/png": 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", ======= "image/png": 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", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "image/png": 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9NfOgNsYYY4ypy9QZxX3q1Kl44okncraPHTs2Yy9mzLZk3LhxOOmkkzBt2jSce+65tZ0dY4zZYTz66KMAIpWY6jChXTYV6rZt2wKo2BUjbby5D5Vmqtb8TqWdyvXKlSuz0qTiThWcx6sNPBC5XNQgTuoWkmnsvvvuec/NgFNqy8+0Qrt6hfvwWJZDXU3yuvDa26tZw6Jgd5A1E9zrTsd98uTJebeffvrp7rib7cLxxx+PPffcEzfddBPOOeecCh/MxhhjjDG1TaI8HLoaY4wxpsHywgsvAIiUZlWoabtObyq0S+d3qsYVKe+VwW4HAzQtXLgQALBmzRoAkbJOMYVKPe3sly9fnjlX9+7dAUQzB1TKWR4q8W3atAEA9O7dO295alIOLc+qVauyvsfNIPDaH3roodXOg6l91qxZg+LiYkzv0A87JSsXANeXlWLM6gUoKSnJ1MuqYBt3Y4wxxhhj6gF1xlTGGGOMMdsHriGjrToVatph85PqNpVqelOJU9pDrzJE96H6rRP89BHPtKmWUw1X80W1mQciTy0al4NpavmYJtNQ/++aZj6jhHzebYDoWjEvtL/nLAZ/5ydnEHhvjjrqqJy0TP2h0dm4G2OMMcYYUx8pKtAdZCH7VIQ77sYYY0wDh8o01V96iykuLgaQ6/mETiGobsfZgoc+zQtRq8PtquIzj3GqPvMe+kPXY5gf9b8eF1lV04rLGxX8fKj/evq+17T5O9V/2r7bv7upCu64G2OMMcYYUwOSiURBwZVqGoDJHXdjjDGmgTJp0iQAQP/+/QFE9te09aatO1VfKvFUt2vidUV9oavazbwwTar+cWo5vbRw/xCWg2moD3WeU23hNU/Mc3XcA+v6AH6nrTv9u9O2nWkxr7xX559/fpXTNo0Hd9yNMcYYY4ypAYmiBBLJyge6NRkMA+64G2OMMQ0W+mGnWh2nZlMlprcVokp0RV5l4uzA4zoq3E47e02Ln1So86VJaC9O5Z3l476V+Z+P84STj9CuP8x33LVh3tSvO5V2bue9MqYi3HE3xhhjjDGmBiSLEkgWoLjbxt0YY4wxWfzpT38CAHTr1g1ApLQzKintrqkK06Zbbb6pDqvqTTtzKtvhOQqF+1Pd/uqrrwDk2qWTjRs3ZpUh3MZyMPqqnoP+66tjux7mEYiUcl5DQrVf1wdoOfXad+zYMSvPvHejRo2qVl5Nw8aRU40xxhhjTME8//zzOPbYY9GtWzckEgk88sgjFe4/e/ZsJBKJnL8VK1Zs13z++c9/xl577YUWLVpgv/32w+OPP571+1VXXYW99toLrVq1Qrt27TB8+HDMmTOneokVJZEo4A9FNet6W3E3xhhjGhht2rQBkOu3Xb2qcLt6aqE6TAW7pKQEQGTfzfPQZ3l4DlXvFW5n3nQWIM6envtxFiDcpuXSfavqLYczDqqSA8Dnn3+elQaVcyrmVPe5nWnrPSG8XkyD+9Vl1q1bhwMOOABnnnkmjj/++IKPW7BgQVb5amLXP3v2bJx++ulYunRp3t9feuklnHLKKZg4cSKOOeYYzJw5EyNHjsS8efOw7777AgD69u2LSZMmoVevXtiwYQN++9vf4sgjj8TChQszMyF1DSvuxhhjjDGmYI4++mhcc801+P73v1+l4zp16oQuXbpk/sKFxmVlZZg4cSJ69uyJli1b4oADDsBDDz1U7TzedtttOOqoozBu3DjsvffemDBhAgYOHJhxuwkAp556KoYPH45evXphn332wS233II1a9bgzTffrHJ6iWQi5Vmmsr8C7OArwoq7McYY08Bgh4if9BZDZZqqr+6nvtcJt1PB5ncq8fnOqaq2Kuncn7bhtHGnAq3KNJXaMM04FZtKOcuh9ueaJ/VUw+OooodpUhlnGnpO9Y7Dc3N2Qq8llXtV8BsiBx54IDZt2oR9990XV111Fb71rW9lfps4cSJmzJiBKVOmoE+fPnj++ecxevRodOzYEUOHDq1yWi+//DIuuuiirG0jRoyINevZvHkz7rrrLhQXF+OAAw6ocnrJogSSRQUsToU77sYYY4wxpo7StWtXTJkyBQcddBA2bdqEu+++G8OGDcOcOXMwcOBAbNq0Cddddx2eeuopDBkyBADQq1cvvPDCC7jzzjur1XFfsWIFOnfunLWtc+fOOXb1jz76KH7wgx9g/fr16Nq1K2bNmoUOHTpUv7DbGXfca4G//vWvAIDWrVsDyF1xrsrHF198AaBqK8y5Kr19+/Z5z6lpMopeVae9jKlvPPDAAwBybVjVb3Nc1Ee2pTFjxmz/zBpTBW6//fbM/3vuuSeASNWlms3vrMeMmEo1WFVz2mfTkwo/Sej5JU6l199Vied7inmMU7KZduhrnueMU9L5rmMaiqrjcb+H5VR7enrW4bXitVPVnrbxjKDKNJl33hvuH97Pn/3sZ3nzV1/o168f+vXrl/l+yCGHYNGiRfjtb3+L++67DwsXLsT69etxxBFHZB23efNmDBgwIPOd9RVI1ZNNmzZlbRs9ejSmTJlSpbwdfvjhmD9/PlavXo3f//73GDVqFObMmVNl+/tEMolEAbMlCWknVcUdd2OMMcYYs0MZNGgQXnjhBQDRYOaxxx5D9+7ds/YLB0rz58/P/D9nzhxceumlmD17dmZbuPC1S5cuWLlyZda5Vq5ciS5dumRta9WqFXr37o3evXvjm9/8Jvr06YN77rkHl112WY3Kt71wx90YY4xpAIRKts6y0i6bdtSqoHM/qoxUmNlpoocNVabDNNXvukYrjZvFouLMDhs92XC7epvRBY3hvlS9qV6rDbz6qdeZNG5XJZ+eYoAo0itRm35V2j/77DMA0YwCZ7ip1KuCH7dGoKExf/58dO3aFQDQv39/NG/eHMuWLavQLKZ3796Z/z/++GM0adIka1vIkCFD8PTTT+OCCy7IbJs1a1bGFCeOsrKyrFgBhWIb9wYAzVXY4Dmds9tuuwHIfUDoA4hwiu/ZZ58FkJrWiYP7sCLr1KVOk/LBwDy+9NJLAKLRLx80DgRh6ht//OMfAUQBWrTToJ9ETWb0dzJ58uTM//ry//GPf1yjvBtjTF1m7dq1WLhwYeb7kiVLMH/+fLRv3x677747LrvsMixfvhz33nsvAODWW29Fz549sc8++2Djxo24++678cwzz+Bf//oXgJTp8MUXX4wLL7wQZWVlOPTQQ1FSUoIXX3wRbdq0qZZp4tixYzF06FDcfPPN+O53v4sHHngAc+fOxV133QUg5dLy2muvxXHHHYeuXbti9erVuOOOO7B8+XKcdNJJ2+AqbR/ccTfGGGOMMQUzd+7cLBGR3lvGjBmDadOm4dNPP8WyZcsyv2/evBm/+MUvsHz5cuy0007Yf//98dRTT2WdY8KECejYsSMmTpyIxYsXo23bthg4cCAuv/zyauXxkEMOwcyZM3HFFVfg8ssvR58+ffDII49kfLgXFRXhv//9L6ZPn47Vq1djl112wcEHH4x///vf2GeffaqcHt09VrpfDRX3RHmcnGSqzdNPPw0gmqKjGkclj9OJ/NTpMJ1u5FQmj3/33XcBRKo4EKn5/fv3BxAtyAnDUQPR1B3RKT1+8nj+zqnLb3/727HlNqa2mDFjBoDshXOc6lQFne0rbnpbF9/pjFhFIdNVxY9ztafti3k477zzKi6oMRUQ+qfee++9AURuEPVZvn79egDI2PvSXINeODQgE4kzNQn/1zbC7Xy/6AwV2yhnhNV858svvwQQLe6kqQkQOXng4tp27dplnZvvQM5kM286A8fnQtwMXLhdyx7XjaKJD+2s+UyiVxPeG+0r8N689957mXOdf/75edMwtc+aNWtQXFyMf+z3DbSq4P1A1pWW4ti3XkdJSUm1gm1Zca8hG9elHyDlkd3ftwYfBAB46733ayNLxhhjjDFmB5JS3AvwKoP8HowKxR337QhH8Bzpc4Svbh+pCOh3juKpEFAp4SKhMCCELhyiAk8VhSN5VTL4XV1/8TsVEKoajz76aCbNY445puBrYcy25L777gMQKXisp7RnB3JVbw3DHqe4E52d0pmxcC2Kzlypyq8zWWHI9jAvdP+mil44C8dz2I7eKDpbBOTO+FL1VXfEOtOrdZnHcX++WypyBxmnbuvsM2E7YNtie2Z70ePDbbqPurUkzAvLp7Nher3yuYnksTqrx2uiMw4sJ4/jtaeyzjTiZtuNCXHH3RhjjDHGmBpgrzJ1nE1fp2zuElvTI+JEelRfFF1SDeesKrfaA3K0rfavSj4b2zi7W1UZmSeO/DVNVf+pCHB/lgWI7Clte2e2F1TWqaZpsCRVBUN1LC7AUlybqExpi2uvYVpqD6/nUHd2ce7e1H1eqP4zf2x/zMe5556b91ym8RCGd3/88ccBRCqwzvIwiJEq1KxfnOHlzK7OFKtNfLiNqNqtM79xtvBEbd4rUty5D49p0aIFindOv6/K85gmNGmKL9asy7Hlj2vDoXtAtVnXtSt0F8lrrG4tuZ3vV703PG94P03dJ5FIIJEsYHFqWc067pUb4xhjjDHGGGNqHSvuBfKHP/wBAHDKqBMBAInS9Op4Km15BlAcXVMRo1qtNnXqZUZRu3S1nw23qaofKuQVpcE88XcqASwDVYh169ZljqEKePfdd2elRbXgjDPOyJuWMXFQYVfbVlWk4mxm86FKutq2qlqu51I1TRX7itB9eKw+A+LKVVEaalcfehQBPBPW2KFiroq71kHWMT63+YzXQE3crjPI9PQCRMGbtK0o3M401PsZUfVb8xpuC9vO7h3bptL58uPUPpvTHtg2pd5naJFSvDu07gyUrkNZi5TN+cat0Sy3ztSF5dRgVnxfUknnMbxm6kFO192ocs97Z+oXyaIkkgUsTk2W10wzt+JujDHGGGNMPcCKewxTp04FAOyxxx4AgAEDBuTdr5yKWCJ3DKSjbtq5UQFRe1dVQDiq53k0fHRoA6+/qV9c2vGpz1pNW1UXnod+cz/99NNMmvT/26dPn6xzMg36s//www8BAGeeeWbONTIGAKZPnw4gqvM6y6SKG1XmyqKgFoL6aVZvNKSiCKuq0ms+49qb7qd+rbVd5zs2Lv+33XYbgEjVswLfuGCcD13HRLRusu2xra1evRpAFD27RYsWAHI9HVFtBqJ2SwU9bp0I30v8nefWeq9eacgXX3yR+b9r166ZfQYfkAqaU/T1qlR+XnsCALDq5TdSZfwsVZbmbVNe17p/7zup/fukXDi3bJOy11+3pTyTF5YpLCd/4zXj+5KqPCORd+jQIau8bLvqDYufvGdhjBZTfyg4AFM+E40qYMXdGGOMMcaYeoAVd4HK35577gkgWh3OkfLCJSn1uHfPlBKfKEsrf1TcA+WdKjXt3jg6V/+3cX5m1a6XhP6jK9oWnoOKRlwkR36q7R6VhE8++SQr70B0jdSekediJDuWk9d2zJgxefNqGh/33HMPgKi+UYnSeql25qo255uFiotuqOfS9SFaj1WpVNvXfMR5j9F1LXHnUM9SZ5+RajOJLSnb/0RZWmWnEp9Mz3Y1SSmdd949NceG315oGhdnn302AOCuu+4CEKng2nb4jmMbZJRSvrfoNUZt3fOt9dD6rLNXXLtCryz8nWnznaExTHT9Sai4Z/mET7+PE1tT6Wz+PDVr8OXCtBeYNWlf9CtTZeiwZEGqrLv3S6WzpW06veh9qrPXQKS+81pwRpvXku/RJUuWAIiiufL9SU89PF494zhGQ/3EirsxxhhjjDEmgxX3NA8//DAAYNdddwUQjaCpTmlENCrvHIV/9tlnmXNRnabKTaWDqoJ6cCHq4zbObrYiP+7qhUI9aaitu9rcMY8sF232uH+3bt0y51ZvOPQ2oJH2mCavLa/1CSeckFMO07C59957AUTKmyrs+TxEhN9JVWzbtR2pHbm2J1Xq46Iahr7V47zA6PY4LxtEj//x6aNT29enVNCir1PKIbak40c0TUdZbppuezunbGr/39mp9STT7rs/U14+V5jv3/3ud1lp/eQnP6kwb6Z+wvuuUbKpGi9fvhxA5BFm9913z9qP9Z8KvKrlIeqxhsoz7eT1/cO6yHPyvaPKu7Z/5jUkX4TT6rJhw4bMLHa+NqvvTyrq3M7I5SwH+wSLFi0CkBsdfVvm3dQeO8qrjDvuxhhjjGkwlKdNx5q2TnWgd+qQGnQ02zndGW+aXrDbIm36Kc4lOrRLmbyUrF0PY+oajb7j/sQTqVXn3bt3z9qukUT5naNwqg+0VQujr7Vv3x5ApDJQeVb/t2q/pz7Y1XOG2r6H6pyu0ldFg+dUW3dV+TVKHLezTGE5eSyvhSqSOtPA/fjJa3/UUUfBNFymTZuW+V+9xmj0UlXH1WOKRm9kG1I1MR9a51lfVe1X1PdyPqUxbp+4/Gh5NO2zT/9RavvG1DqZJiUpxW7DG88BALaUpPw8N0srfU269kil1+vAVJmSubEh4mz6CRX4MC/nnXde3vybus/kyZOzvse9V+j5ZLfddgOQWz+0vtNjCtss3w1A7vqQjz9O+VHXdsB3Ib2n8Dh6somLbaJ+z8Nt24OysrLM+cNysny8BnGRkwmvLWc5WE59FvGdyXvn9lfPKNDGPW/gnyrQ6DvuxhhjjGkAcJF2s5TA1HS3lKvijgO+AgCUbU67XW2WVtw7pgS78qK06UoBQdWMiSOZSCCZrLwOJWtYzxpdx/3Pf/4zgGj0TF/kcYqZbud39QwTenXhynKOukNb2HxpqPqm6req5lTyQyWE25ivOEU9TuFTRYRptmnTJqtMYTnV/j/OkwaPUd++VP/p752eAk466SSY+g+V9tAncZxNepw3ijgFS70jsY5VZCuqv6kNq6r5qurHrU3Jl3/1tKSza1p+/c7IzMkNqRm9zUvfAwB89MTLAIAvF6ds3tv3SdnQdh2S2q9Vl5S3q/JmKROBRCIRe+3iPPWEebHyV3/hu43QjpxROVkPONusPth1/RPrOH+n/TbtuYGoTVFpVwWeijPfKzrrxTRXrFgBIFpTpetMqGCH2/Q9uy345JNPMmuzwnISrgHTtqTl4rXltea7jm2NMxD04GNMRTS6jrsxxhhjGh50h1rWvDUAoGj3VECmNq1SA4XyzalF8WiSHrS3TpmalTZpDmNqSqIoiUQBi1MTZV6cWhC0p+aItnXrVMPmCF9t2SvzYsHjaPNNLxlANPLnKJqoZwlV2dROnd/VbzRH86Fqrn6hVQHk7zynRjlV1U1tDFVJCMuuXjq0XDoLoDMLnP2gWmPb9/oNfbNTXQvrYpwirjNbcSq42t1qfQ19LVfmqUFVPlXWiT4j8qHth22fdVpnvjRqZY5aKP6ot65YBgD4dF4qcvHatB/q8nQZ2vRIKXg7l+X6hc9R82W2rbJ1BgAwZcoUANG1sJ/pugVnkhlFFIhs13l/+bx+773U7I3OLOkn67s+v1m3870TOPNbUYwDIHpf8j1Mm2+FsVCYFo+jIh+eI4wxsq0oLS3NlIlrs4BotpizGnzW6fNJ197oteX+PXr0ABCp+jz+hRdeyKTJqOWekTaNpuNujDHGmIbLK6+9juLiYuzdpxcAoGynlKKeSKYH1hK4rDSt0Jc3SXXEaSNvTHVIFiWQLGBxarLMNu4V8uyzzwKIlAhVzNVGVhV3VeWIKmtUBoB4lTpO0VPUfp5qnNrYhr7jqa5wJM98adpxqOrIPKgyGKorTCPOXl6VPL3mqjKqPT3v3eGHH15h3k3d4O677wYQqWKqhgPxdqhsZzpjpDbuPGfc2pNwDUboeSIkLlKxtpG4iMD57NTjfL1r29Bzxc7CcZFd2j97k04p29huB6c+t6xLe2rqkuqYtOmT8r1NE4HydEclnIFQG3Z9Huk1zVdm3hdG47TyXrtMnToVANC3b9/YfXjP+Lym8s53hUZUVa9lVJf1OK5d4e9ApLjrjBlRm28+8+Nmgei1jWnwuLCdaz55zLYgmUzmVdzpHUsVcm7nM1CvJa8dZwlYHo2Bkq+PwD4M7/mZZ55Zs8KZekuD77gbY4wxpvHwytw3UFRUhEEH7pvaUJ7uYKdN0DJ+29Of5UXpgUAlopoxFZEo0B1kwop7Lo888kjmf9qOccTLEbJ6V1FVWBV3EqeghfbsHG2rNxUqyfm8N4RpUzng7xy185OqZah06MwB1RG1sa3MVzXzSLVS9w/LqSqh7kv1Mc6GUtU8nm/dupT9LqPRhfdz5MiRefNvao/p06cDyF7nAeTO4oTb1GOSrn9QtP6qsp3Pxj1uliyuLcRFX9V2qLMDIRqBWFVs9dChM1wZNZ+L7FqmvTn1PgAAsPv3Unko35hqI8n0orume+yVOm96/8zxwfOLeVHvIDrTEB4Tt6aA57jzzjtT6aefM1YBdyz0rsL7QyUXiOogP7mPvl/0faTqMesHz60zanxeA5XHMdD6FHqcyrdfXHTjMJ4IUZU/7l1XFcrLyzNlCMvJc+u7ns8IXru4Z47OEui90PUFQDSrH3rUMY2TBtlxN8YYY0zjppzKOm3cdYeEFXaz7bBXGWOMaaTMfDDlJeTUUSekNrRJ2bc23e9QAECiNGUby8AxpelFeGUtUqro3dPuq3Qtjam/cKZj7733BhDNOIWKu85CUYmmrfZHH30EIFKHddZZZ6P5SQ8qVIN5fHhs3DomVfdp461+z3VtmXpUC8+rHtU0/zWhSZMmmbyE5aTir1HRdYabMG+8F19+mYrDoOo58857FM4sMH1ed9aB//f//l81S2fqKw2q4/773/8eAHDQQQfl/MaGwIalLq60sfPBolPbCo8LH5h8sOnDlJ86Ja8PKZ1uZ4Pld3UXGW7jPpzWY8NneXVxnE5tMo88N6fn8r0YKjNv0AWtem3jHta8V0x7jz32yJyT9/icc87Jm6bZ8bC+K/nMzSpzi8Y6EmeipufUhXUhcS5ONVhTXIAiLYcS7he3yJRT6WoipLC9FbqAvSrEubjVafu46xHuE2dewWfWH/7wBwDAGWecsc3yb8w2p1zqOEPQs+4nKnYla0w+kkUo0KtMzdJpUB13Y4xpSEy//wEAwJjRpwIAypumFTh2PLi4Lm3TPuOPD+asNTDGGLP9SSQTSCQLWJxawD4V0aA67r179waQrVZRcdZgSCRuoVpF4c2BXBdyYXAWumYkugAlDqpWDElNJZPbmSbDLIeKO7cxDDUX/fAlzvLT/VZl7iF5ntAFFpBdzrhw9OoGU1X9OFd+PE4DwYRTlLzHpvZhoCXWT21DYf0kcTNcqjCrEq+L3eLU4nxwtomffCboAtm4BZjqCpHkC4DGfOtCP1XiNb+68FVnIAqlRYsWmWM4+6b51pm9uPKFxOVD7yfLYeV9+6LujfVZC0SOGPgO4PtEXTDqwmiijg6Imq3Q3CXcpmg7ZXvgu5Fpsc7y/aXtiA4L3njjjcy5BwwYkFXOfO9u+m9H2sQsoYo7/bcX0R1r6hx79029b95d8EFWOTnzrLONvFac8VZ3kLzW/K73gtdD3UyG5WE+wmBbpnHRoDruxhjTEJl23/2Z/3UwwM7CtrDpNcYYUz2SySSSBSxOTZZ6cWpG+dtvv/0AZL/AVAkiqjbp/hqQiZ96XD4Vneq2Kniqsqn6RmVZ1XIN5sD9QnWF27johfnnCJ5p6EKjuIU83K6dgrAMeg1U/dEFSKoqkjgXf/nyxhkA3vOzzjoLpnZgnVMFTu9/vjrDuqDqWJxbVu6vdSouuFeItmHCYzW/OmOkruk070DU5lXNVsWN8Hd1h0niVPEQzY+2bQ1mFRfcRdX9MK04F3u6fkBnRmzzvn1p3749gNz2E9471gPWTbZXbacaPEzflTyPto98gcviAimRjh07Aoie42zHfMcxD3HujFkPw5lXbtP2nPU+SftvT25al/U9k08GPGsmbiaLUmXs33fPcGcAwEefrMiZSVO3kJq3uICGGtCxotkMnot1wDQ+GkTH3RhjjDHGmNqi4ABMBexTEQ2i4057bFWWgGgkT7VB1eHKbDc5uqVCEBdyvSLiglGoisXRNUfl/K5T4sxTaPvdtm3brH14rLrb4vc4hV3zrITHxQWVYLnUzi/ODlnvRdz5wv95z82Oh+HuSZxaTHvOfPdP7cdVUVdlV1VArRus36H6pzbsal+qSrOmwdkqbetMM1wEqio9bd01+A3zwDyxDauKr4FnKlLcmYaqeXHedDSNuDUK4T4kTq3V/fXam20Dg53tuWdKAeY9pU10qDLrmiFtM/x88803AUQKbufOnbOO1/bN83FdVVgHmA/ed9qCU2kn9BjGd4TWG8LyhO86AJg7d27mfz13aJNPW/bElrT9+6ZUumXrUuvIyremZ8Sapdp6cue2qd9bpvPRbGco5UWpPO3WrQsAYMmyjzO/6bXidVi+fDkA4IsvvkilJ/dCXW/q8wTIvbZs96wTY8aMycmraZg0iI67McYYY4wxtUXBAZgK2Kci6nXHferUqQAi2/Z8vpI5Uo/z1Rxnb61KH/cvxCuL2vbqOXV7vtDwQG5IciqA+cJAc1+1tVXFTFWUOOVdbWsrmllQJU+94qiNcNy6grh7FKbNcnbv3h1AVAccan37M23aNADZdpdAbt3QsN3h7zqbpO1T7XDVblv3V0U7rFuqJDNNbVdqn81zUrnTdpnPZl7tx7V98Zxqh6sebtT7BAnVfbWLV7tyVd71GqotM89dkVeZymYW43zA87uDxWwbOLOq9auie6f1XNsQ3yuMl1GZXbbWt7Cusk5RHaYazrbHd4PaxzMtwjzyHVJRnAN9v/C3nXbaKeOXPVGatjn/clXq8/MVqWM3pBR/Ku5NOqXeK8mO6XcS3a0mIu9qCba7RNT+9JporAheW84wqCUA70FF/QpV51lO1gnTeKjXHXdjjDHGGGNqm0QymRnYVbZfTajXHfdevXoByPWlHqo+ajur9n38Xe2weS7a6FXm1z1UruN8TsfB3zlyVtWKo/FVq1blPX+4jeWgj1cNxsI0KsuTqnma1/A3taVVBZ32jFRddP2A2mCqqhKqMdzGc7EOmO3HjBkzAETejuKIU51C9J6yjrCeqnqmszlEbafzeUzR9OPCrKvqx9/jVPJ8dudUziqLoMryqb09883zsHz54lDwXBrVWT1aqOedymYC8/lzj4uQGqesx/mp5zmtvNcMXYfBuqDeWYAonojOfKn9NG3btW5qvaFazP3yRUzmjDQ/V69enZUvrhWLqye6PoYwj7QRz+ffvFOnTllpNW/ePApUlvYiQ9t2Ku8bPy9JXZMW6QjMzdO27m3Sa6iapde2BenpapOtW7dmrg2vtT57eH9YDr7L9V3H49leWF4gdwY7zmOeafjU6467McYYY4wxtU2yqEA/7o3Zxp1qOEfcVJNDxYijVPW8EOc/Wbfr6JbE+S8Of1NVW+1AVW3gKL1Lly5Z5VBFjYpCGMVUV6VToeM1UlWtIj/0+coZp5AAueq8Xju95qoA6WwGP6m6hGojy0ElguUz2w/apVbmiUntbfO1MapDWhd4bFwU07g1F3F23OFvWj+1Xqq9ua5vqczzVFjmuFko1tO49QG8DvydCh7hrFu+/Kjfdp0Z0FlFbXfapjVSJpDbhuOiyFY2k8e06Jnoxz/+cYX7m2zYFvlsVG9n+dRXvk9od85ZHX4nOuMSF49DZ4nCWWj+/8477wAAiouLAUQKvL774iIh63uH8UnYLsIZN27T6KNZHtDSyju9yGxZl7oGm9ek+gdNtqTt9TelZywYaVUjrAa88PKcjHcznWXUa6nvWV573kf1CrRiRcoGv6SkJHOM9jVYbtYJUwcocHEqathxr9nRxhhjjDHGmB1CvVTcp0yZAgAYPHgwgFyVJ1SMOPqmSk17ayrwRD1hxPlu1pFzPiVaowqquq2qg6qIcZ4puEKfI+xQXeQ5uI/6co5LuzL1VI8PlTZVMnUftVdUpV3VUu5HdTKfchKn+rBOnHvuuXnLY6oOPfZQxeP90PuuKjLJ5+kizqe0RvZV4jylUHHMZwuvPpEJZ+HiZhBUwVYf7Pm8QOnsQlwb1uiT+kmFUtcAhNdYZ+K0XemshpZfVVnmiecJ1X1dU8Jrp/e2MrW2oueIqZzJkycDiGYfeR/4XtN1UkD0ruPzlOow3x+77rorAGDZsmUAonVRWm+0vulMaFi/mCbrEOsz0Zm2fPEXgKiO8j1dUdwUbWP51kZtD3baaadMvplPnW3U5xbXCe2+++4AomvJe0MVndcxbKtfffUVgNx3OfPAOnLeeedtoxKaqpJIFugOsjEvTjXGGGOMqQpladOYrRuzBbzyrflFiKx9EjZUMLVLvey4qxLAEbbahQLx6gCVCvXQQFTZy6f+hmmHxPkpVz+sqkJxdK1KwSeffJKVdx4XehCgskE1hTaB3bp1yzqX+sONs02NU9PD8sbZ/au/eY0WSXiNuT8/1aNAODuing3y+bQ3NeMvf/kLgEjVi1ORibZH9bwU3nf10MJ7q55e1L+5KvJaZ/JF6tQ6rmso4tA8qGcqrXshbJOqaqtqqR6W1LuEtpkwz7xmcR54NM24aM/q3z4fcfnLF6U6JE4h1fvEmTLAs2UVwXpORZ31g3WSduthhFHWGa4H2m233QBEnk0+++wzAJF9Nb/THl09ran3tnyKNre1a9cOQO5aMI0sXJn//7h1YBV5j4o7dlvTtGnTTDnU2xKvHdsH38e81swz7wW/07adx4X3k2Xmc0nft9u7vKZy7A7SGGOMMaa6cHFpxi1k9kCTyntZnHlDUdrkKxkIQ1bcTS1TLzvuHI1+/vnnACJ/tfn8yqoNKZUKflKpjosQWkjkUCVOZarMkwvzqHbcVNE5+qbiRps3IJpR4LEcldPmnWnGqY2ap7joroWM6pm2+qqOO3dcXnifw5kU9WXLOmCb2W0H1SGqSKHNMxCpSaqeqeeXfMo0j1GFSmdO+Lsq1+pznWmxXuSLZqqeaeI8WMTNgOnsHAnbgvp+5znUFj8uIqp6sFFVM3ymaJRFXSeg/tn1O9Fno17LMB9x8RzU77Qq8rrWRtu8zsKZbO6++24AufFE4nyy5/PBz/cG6xrtqfn+4Dvi/fffB5DrbYawDld0T3ks2wPzwzqra8i0zuqaCJaT5+X+YR41mqy2++1FIpHIad/6vGJ+OZvRt29fAFG75r3QSKrqJQ7IXWOUN1Isojpz9tln17SIpookipJIFDD7nyiqWX+lXnbcjTHGGGMqIhM4iYp7Wjlvkg641Kx1qsPdrE3alLZ1ysQHLdKBopqkRYuiqKv0zn/fr9SNsjHbk3rZcdcRP1Uubs/ngaEyG+g4e+3KVLl8ftx1m6qMqg5zJM18q1K21157ZR3HUf03vvGNnHKqJ404tZ/Kh6qIOjOhKmVYzrgIsYXOXlT28FN74LDsmq/K7JZN5fz1r38FEHk+0HoY55FIZ1bU00W+tqGeheJUsspsqCuKGhgXa0HPyd85s8P6pnaqqqKHMxH0lU1PHZ07dwaQa48al0emydmOpUuXAgA+/vjjnDxrbAZdj6MzBWwrVAV1hkTvQTiToLOY2oZ17Y8qhtpOlTCtSZMmAQDOP//8vPs2Rqgm6ztEPR2pF58Q/sZ7w3vGOqpeZeKihDMvtMNWpTc85r333gMA9OzZM2vfiuKfhNvVrp7npV9z5jUsl3qw2V6zsK1atcq0Cz4r2f6prDO/Gsmc8Npru9Hj8q0pYx1QTzasC17vVXskCvTjXpCv9wpwb8cYY4wxDYZee6QW4mJT2swybZee3LktAKBph1THuU3a5KaoOGWy0qT7ngCAspZpE9omqc7w8s++rHARtzE7knrZcefInyvXOUrNZzutI/s4W8u473E2eHGRA8NjVHHmiJh22e+++y4AYMGCBQCAIUOGAAD69+8PIBqFqyqRb0St21Q9o/LHNF9++WUAQL9+/bLSpM2dlitfmfRaaB5UnWvVPD1TUrqVO6QTSSvwTVJKYMnabDv28NqqjTM/HT2u5tB3sPoHV1W4sjYQFxUx/E09VKjXElXUtQ2oQp/PFlw9mKg636lTJwBRnVdFWiOvaryBfLM8qs7ry76yCKN8plGRY6yKjz76KLPPm2++CSDXZ7Z6HGFeuB8VeHoNUR/t+TzBsBxqi66+49UWXr0/KfmUYXvFyIX3iveSSq+uEdH1CkDuTAyPZT2nnXjo+x2I7g2VdO6ns508j66BAYA99tgDQHZ07/AclXk1U1/yOnu955575pRTYyRsS5LJZN5ysp6zXLxWVMP5yVkyXmtdC6AzW+oPPjyXzrzrzEc4A2J2LMlksqD1kFVZM5mPetlxN8YYY4ypkLRtO23Vi9qlBuuJ5tmd8GSblFvGspapgXNZ89bp4zjwyHU1bYxiU5k80AZyv/32A5Drv1VVu/D/yjyYxBHnIUZVxXxqkaohapPP6GkrV64EADzzzDMAgNdffx0AMGzYMACR3ayq6PnURVVeaCM7e/ZsALk2gsyDRqjLFxFWv2vZVbFTW3Yq7Ymtab/smQVD2dUw7jxhuQjrAD0j2E626jz++OMAInvNuKifRJV1VYCUUJlWRVpV7cpsogn3i4uOGu7DfNEGdsCAAQByZ5fi6rz+TvLtp3W3spk+EtdmmAafAUBkN7xkyRIAwGuvvQYA+PTTTwFEaj0VQp21UHtanbHM5wuf6GyLzijE2S7HfQ+3s+y33347AOBnP/sZGisPP/wwgMhjmvr9jyOcBeNMi66tYlwQPvtZXzRiMNVhKuu03+bsLWeHwntI5Zj5Zt1j/rXdank0sqo+L6gmh57GVGHeHt5kysrKstLROBOc8VUvbur9h37b+TvvBa+T+uOv6H7rM0O9fLEOnXDCCVUrbIF8/fXXGD9+PP76179i1apVGDBgAG677TYcfPDBefc//fTTMX369Jzt/fv3xzvvvLNd8ggAf/7znzF+/HgsXboUffr0wQ033IDvfOc7AFLX/YorrsDjjz+OxYsXo7i4GMOHD8f111+fEwOnLmGHpMYYY4xpOJSXpf4SSSCRRHmzlqm/Vu1R3qo9Eu27pf467I5Eh91RtnNHlO3cEeUt2qC8RRugSTOgSTOs/uprfLLq89ouTZ3k7LPPxqxZs3DffffhrbfewpFHHonhw4dj+fLlefe/7bbb8Omnn2b+PvroI7Rv3x4nnXRStfMwe/Zs9OjRI/b3l156CaeccgrOOussvPHGGxg5ciRGjhyJt99+G0BqoDlv3jyMHz8e8+bNw1/+8hcsWLAAxx13XLXyQ8W9kL+aUK8Ud7W5UxVLI3EC0cheR+GVKUJKnHeZfCPiOP/R+bw2AMBBBx0EILJdXbRoEQDgwQcfBBCN7ukDdv/99weQ7cuWainPQZ+8qq7RNpDnIMwT7WDjlLZwe5yqqMe0Srvewpa0BxsNilGefT3atEqVF61aomTt+ry2hepdgdfC9n1VR/08x3lY0jgD3E8jefJ+5bOPVj/tcZ6XKvPepN4X8vlR5r5U2g855JCsfVU9VnVM1T7NS5hWXDRTbRvMt3pvUgWyoplCXn9GwqRy+sYbbwBARr2i+qc2wDy3RmpWe+SwPESfaaqkqvqn14VUVD7HZMj1RqRrJuI8d4Wz0LqGgfeCdvOMqEp1nJ9E7cv5bGXeeL6wfWs71XrNYzQWhNZFfeZo22Mewn0rm12vDuXl5Zk0Qzt05puzdroejddK4zYwj6tXrwYQXQ8q9sy7KvpA7syZxn7QZ014jbY1GzZswMMPP4y//e1v+J//+R8AwFVXXYV//OMfmDx5Mq655pqcY4qLi7M8/zzyyCP48ssvccYZZ2S2lZWV4YYbbsBdd92FFStWoG/fvhg/fjxOPPHEauXztttuw1FHHYVx48YBACZMmIBZs2Zh0qRJmDJlCoqLizFr1qysYyZNmoRBgwZh2bJlmWdrXaNeddyNMcYYY/Kxa9e0m8WytDDATnzaxr2cZpnl6U5t+nt5UXrAk7ZpLy/KHcSaiK1bt6K0tDRHVGvZsiVeeOGFgs5xzz33YPjw4ZmFzAAwceJEzJgxA1OmTEGfPn3w/PPPY/To0ejYsSOGDh1a5Xy+/PLLuOiii7K2jRgxAo888kjsMSUlJUgkEjmLtgshkUgiUcDC00QNo++64262P6qss9LSm0z6oVmeDnLx9fqU8msvE8YYY0zdonXr1hgyZAgmTJiAvffeG507d8Yf//hHvPzyy+jdu3elx3/yySf45z//iZkzZ2a2bdq0Cddddx2eeuqpjIe9Xr164YUXXsCdd95ZrY77ihUrMms4SOfOnTOxCJSNGzfi0ksvxSmnnJKZmaqL1KuOu04zq/kGp3rDKd/KFqXGLbyLWxSiU3gVhezWjqcu3tMpLi665SIzTs3xOJrB0D5rxIgRmXM9+eSTWWlq4ApO3TENzUNcHnW/sEz8XwNixZ27qmhY9TBNXUin5TWFw4VeGsSrsoWUamJCdHqc08jhMTr1HxeghagpBo9jvc63+JN1gSYyOv2sn3EwrwwRr67bgNxnjy741EVn+txgvqlg0Zwn38tDy8o0aHJHczhOATP/LD/PrWY9Wt4wDV0sqIuJeT/UTSvT0PtckYkh02/MC801mBZNKmjOpi54K3ru0VxD77e6AY1793E/1gF97ofth/eO+WVdI2yvbAdsS/pejQsole+9nbUPxaGybJGonDbF6cipjKhanv7OyKhU5L8sWZN5Lua7Llp2XhttBxoIUV3rquvdQoITss3x2jENXnN1mby9uO+++3DmmWeie/fuKCoqwsCBA3HKKadknGtUxPTp09G2bVuMHDkys23hwoVYv349jjjiiKx9N2/enHEoAGSbCJeWlmLTpk1Z20aPHo0pU6ZUuTxbtmzBqFGjUF5ejsmTJ1f5eMBeZUwDYKfm6Q5gWXYnJfOw5LRkMltpN8YYY0zdZc8998Rzzz2HdevWYc2aNejatStOPvlk9OrVq8LjysvLMXXqVPzoRz/KEuW4Tu+xxx5D9+7ds44J1wrMnz8/8/+cOXNw6aWXZrzmAdliR5cuXTJe+8jKlSszkW4JO+0ffvghnnnmmWqr7e645yFuFM6bT7UqHGnmczsG5KrdquRRXaPCQeWAn6oohYs245QspkHbKaahi024Svqtt97KOrcuDsy3cEUXmDEPPKe629I8qZpK8rna1CARzAOVij12rZo7pTjlM59ykG+BIGDFvVDoAhLIXZCsAYZUJSJsC9wvrs6ED12mReLcCmqdYh7U9aSqgGE733fffQEUvmBZ1TzOfHGx56pVq7LyEC7+YjAnulnlQj+mzQAszCfbvs528OXFTwZrCxd10Q0f0WvDtEaNGgUA+Pe//w0gWvTO+8K8qYob3kdVFHURsT4vdOZAZ2/02RXeL93WmBep6jOfiiLbHF09UnVV9RzIdbWqz/C4wH56L9XNIMmnfse5oFTlnc8EXayqQYWI1o0tW7bggH37Z37PiENbJbqp2hJTged3Xou0eLT6y5KcNqmz1kBucDqii4fVKkC3672Jm1EOz81tXBjL9q4zAzuq/bRq1QqtWrXCl19+iSeffBI33nhjhfs/99xzWLhwIc4666ys7f3790fz5s2xbNmyCs1iQlOcjz/+GE2aNIk1zxkyZAiefvppXHDBBZlts2bNypjiAFGn/YMPPsCzzz6b4ya4LuJejjHGGGOMKZgnn3wS5eXl6NevHxYuXIhx48Zhr732yniJueyyy7B8+XLce++9Wcfdc889GDx4cEZYIa1bt8bFF1+MCy+8EGVlZTj00ENRUlKCF198EW3atMGYMWOqnMexY8di6NChuPnmm/Hd734XDzzwAObOnYu77roLQKrTfuKJJ2LevHl49NFHUVpamrF/b9++fV5PWxWRLEoiWYCaXsg+FVEvO+4cjXLUzk8dtYbE2axzX6ppVMLUNpWBizga0+AUYZpxrqx0dK52ctyPQRo0cJOO3kMlU903ah408IOqKTryjwscE5aBlZoKBa9d3z1TAWIyKgivTWYFf+rzq7UMMpHfrj7ffdSyq6szUxihwh1nZ6pKrtq2xilwcYG5wn3UHaTaQKvqquHWdSozn+00gxbFtT9tM0zr5ZdfBpCyuQzTVMI6R3duDHhG5b1Pnz4AoucG660q8l9++WXWOdU2nMo7ED2LqLyrQqSKG9Uruo989tlnAUTPBD7L2I7DusH8MN9U0nVNgs50xQVli3OTGR5DKnPR25BRxV1neHnP2A44QxPOaOk54taIxbnxVbehfE7omol8a2H0XvLdQHSGW++1zuiE5+21x27Abt2RCNX19LsmY7vOPKniLuej0r5wyYc5eato7QvbBfsHuhZE7xfRd7k+/3SmIlTN2QbZbuNmUnaUU4eSkhJcdtll+Pjjj9G+fXuccMIJuPbaazNl//TTT7Fs2bKcYx5++GHcdtttec85YcIEdOzYERMnTsTixYvRtm1bDBw4EJdffnm18njIIYdg5syZuOKKK3D55ZejT58+eOSRRzKDhuXLl+Pvf/87AODAAw/MOvbZZ5/NBMGsa9TLjrsxxhhjjKkdRo0alTHHy8e0adNythUXF+dd3E8SiQTGjh2LsWPHFpSHYcOGYenSpRXuc9JJJ8UGeerRo8c2FQgSyURh7iCTNXPcUa867mr/paNxqlKhEsYRMFUpHfEy5LAGUGBwClUXqaxR6dCQx2G+qE7FKUlUTZi2hpzn77Qb5Ihb1RYgUtOobPAa0P5NvUBwO1WTfCN8IBrNM49hWfQaZJR2KiESWKk8vRj1tTfezEqboYX13vB+hgogr4GWq1APIY0d2raHD0+1F9fZFVWD4oIlaYCQfAqQKudE01Rlnufiwif+TvWZ5w29C1QWREw9pHCB0wcffJCVF/5OJYl1L7R51Xyz/TEQGn0Vs67zWrM+sy1R9aZyynKF7ZLXhCHo2TYZcEk97XB/rnM5/vjjAQB/+9vfstLgMzK8XzyW5eE1yBcgJsynBvNiGnEKZL5tjbkt69op1mtef75veJ1ZfyqyiY57tmuaOrPGeqaqOfPEeheek59sSzQ/OPjgg7PywnagnSfmPZ+azPdLojSYqS3Ntm1PcHaXG+i/nbPbRdldny1btuC1114DgMzCRc6WqdcWILomfGcTvpu5uDKuzxI326drRMJZTZ3V4j6892xjrBuNuf3UFjtqcWrNjjbGGGOMMcbsEOqV4p4vhDoQjTCpvoV+o2mDTpWMI1gq6lSzOVqlrTttUDVssHo4oeKRT6VSn65xiiYVMo6cObJn4ACWh4oZV1CHNu704Uy7XHqQ4Dk40mca6mkjbnW8em0JZzlY9q67pDxeJDamriXtDTM27U3S+Uxk+/XmdeK9oO0e0+a9oQoJRPdD1VO1mTb5UUU0RG3a42Zh1IuMeoSJ86AQpqHn0u3qk7h///5Z31nPCe9/2A7jvCqozT7PuXjxYgC5qhg9uvBZou07RMvB67xkyZKstBlKW9dssNxU09TjVHgOps/nnz43mG/NE7effPLJAICHHnoIQGRnH3qtUe9NlcVu0Dqj647Urjq8X7q+oTG3ZT7zWOeo7PL5TVWYz8hwxpfEzTjxOlMx1/eqem/j81lnh/gOyafssr6odySq2ow1oO829SIV1r+9+6TdDG5JP7u2Rs+wxBZ5nqVndzMRU7k9md3lefGVVzPvSuaR1yXOcxUQtRFeE15/XivOrOnsJPsCTIPH8XtFsVB4LK8/+zSsA7zW6t3N7DisuBtjjDHGGGMy1CvFXUfjVLM4mqUNnqrkQK4SpLbgH330EYBIrdJzUH1Q5Z6j3XxeazS/ek71sEDFmftxNK8BBPKVT7fxO5UMLZfaJ6s6o360w5mG3bql1J7kpnRkuTWp/CW3pNcJpFWNspb0PZ0qx7+efjarPGqXTyUwzv99uK/6lVY7a5MfXtvQXlPVLa2XRH3/q017Pl//4fnDfeI8WqgyxZX+VB7feOMNAJGNvvoLD8vFusJj42YC6K9dYxxQUVRlneUO2xzbrvqr5jOKStyCBQuy0mb7JBrlMp8tuc4Y6H3guh1Cu1u95kzrhBNOAADcf//9OWVQ+16tI/miZ4ZpaR2Ki7Ib7pvPrr+xoXbpar+sHkb4XgrrP+utem5hnYrzzMR7ql6GuL/6jg/vE2e9mQ8es88++wCI2iSjgFNp5gzacccdByDXdnzr1q1IpNdMJcrS9uBBYL/E1lR63CdTG9OzvAlkB/vj9jfffDOztoN55HX48MOUpxle6zCWgs70ch/2BzT+i7YPtUuP804T2rgzDbYZ3h/WCW03FUV1N9uHRCJZ2OJU9XZURay4G2OMMcYYUw+oV4r7mWeeCQD417/+BSDXhy0JlTBdic2RsHp/UE8u6odYR7v5Iv8p6qtW7d2IKp5Mi76g+/XrByA32iLVxnAbR9s8hufQfMf5tWce1a92j26donKtTa2oT65L2QSWrkr5rt66OR2Bc5eUIp9I27aXN03dD15brsjntacqoZ4omJfwflKZUNtAfmcdMfnJV28r83Me5zFFFVHeJ7WBD+u7+v/mOTVCJ9ds8Fz0Pc77r/U3n801Iw/Tk0VceehNhmmryqzrWmjfynUwQNQW9RrynKynbMPvvvsugEgppXLKth+nwAG5Pt41yiKPoUeP/fffPyuPauvM+3bYYYcBAObNm5dJi/lTf9M8Ru+DztwxTV5LXYsQ1o24NRW33HILAOCiiy5CYyGsW0DutaGyy/vA6xy+E+K8isRFIFeYhs7S8Xs+T2OcpeIn02D9pe03n9dsozw3lXi+v8L6UU71nGplnvKVV1HJPOCAAzL9CF07om057Gdo3Aj1VMVrpzNwek565IlTxyuaydf7Q/LVBbNjSBQVISnPwLj9aoIVd2OMMcYYY+oB9UpxJ3S4T3WKo1jacYeoUqT2oBzp096ao1dV2Wjfpsfl846gvlv1mMpUb1VC6EXmvffeyzpPuJ+q1zxGz5nPbzKQax/Xu2fK73Ric8qeseiryG62fHUqGtrmj1Or8Td+sjxVri0p1aBV75T/26Yt014Lmu+cdW61bWfeqODw2udThPgb7Xj12pqKUfvoEKpGGhFVbVm1LrHO8d6wLuWLisjf+Mk0qewOHDgQQFQ3GMU0zmtQPs8uhMc888wzAKIZOh7DqH5x51Q/7rTf5e+hz3iWPV+kxzANKqR8VvFZRhVfFXbaE4czh3H+t7XcbE/0aEPPPHGRMvnMmDt3bs5v+kzTuqD3k+gMnta/fBGn49JuDIwfPx4AcOyxxwKIf1foeyffuyTuGG2/GiuBv7MNUmlmO4+Lvg3krolivVblmedgBEu+27gGhF5zqBrrzPm2onXr1jmRhzUSOMsU5kHbAb/zWvFY9eqma0NIRe88Ra0B1He+zgawTk2YMKHSc5uasaO8ytTLjrsxxhhjGhFpM5jyZHpw2DTeFITB/iCLUZHuzL74yqs5i3ONqS/Uy467KmL8pB9i9VEe/hangnNkz1EqFQKq+hrhTW3jQ7VIbUg5ElZVW1W4OBtjfuqqfippYbm4j9q36bUiaku7a5eUzV1yQ0pdLPo65QFmy6I3o2MWpaJBrlma8sbxxYLUZ7OdU6pB9xZpjy9de6QOaJNtZ682xLwOtHtUpSi04eN9VDW3IuXVRFSk6FB505DUPEZ9c8epYaq45/MOwntMRY526LTL/s9//gMgPqKq2khTDQ9tg9XjA+sO6zzbnc6EqUcU/s41GBV5O4nzpqLPBF4bzuSxLVP1Vq9VYcwGndnQc2uaquYTjWzL+xpeQyqI6t1EbfrjvAXps06vcT6lWH+Lm5lsiMTFTND3j76v8l1Pvd9xMxeqAut7Sdu3zgaFsyx8/9B2m8dq5G5dM8ZZWPpUf/HFFwEAQ4cOzVuWmtK0adNMHph/5lV9rXNNVugrn9eMfQ1V5TXeiB6n17SyNgzkzq4wbe2D6NqXxuydaUdjxd0YY4wxBsA7/30fyWQyCsRUHgxEkjFdGVHpq7p41ZiqkEgW6A6yhmJEvey4M+og7cc4suSImP5XgUjRog2tqvOqFHEUrko71TYqHapS5UP9t+tImFDRY5o6+uZonsrZnDlzso4Ljx08eDCAeFt95ilj29815S2GkefoKSb5VUpF3/TflL3rV2/9N3OONUvSSvvC1L5rV6aUl+6DUsp6kxbZNtKaR1VqNGIjVRmqjVRTgUjJ2WOPlA0+r5H6ujf5qcgmVlVsVY/VBl4VW/V2onEMwmPoYWjIkCEAgJdeeglAFE+BM15Uf3Vm7OOPU56MWM9plxranVMt1uik+WbkwvyyrTOSotpvU7EP/aVrnAS2O7WTJ/Trvnr16qztVB5VkQvbuqbB33gM2xGvsZ4rTsHOZ6dPW12eg/eFdUBnutT+VutCnMofbotbJ9AYiHtH6DoSXiN9vofE2cHHeUTT2RI+a/mp96wQFVzt59VDjXo2YvtmvaPtO73RfP7551HHvQYkk8lY71gaHZie2fgZorORjAhLdKZQj9Png777K1rnxTrBa6fPL30em4ZDvey4G2OMMabx8ffHn8TXX3+NH558YrSxNO1OkSq8KOvlRamuzvMvzYkduBtTU2wqUwH0eczRKEfGGtUUiJRYKlxUyzg6VU80HIXzdzZyVZB0JJxPVaQyEad4VKbKxSmeVA5pewcAu+66a9Y+OqLnZ4e26ZmHLWnlJq2wJzam1O3y1Sklc+PitwEAX72bsjnc8Hlk30eat0nlo0W71Ii/w/4ptaFJl9SMSGLndKS59DSmen7hfaPKumLFCgC5kWO7d+8epZnepr7CWSdMxWjdDLcRVft0bYLuFxc1M5+NMu/ToYceCiCKycBZGKrErM+cMWP75e9sx1Ss1atDmG9GRi0qKsK4X6R8gU+dNj1zLpaLbZ11i3WN3me0POEsD2eN+Dxh/jV+gkbAVEWS5+HMAfMQqmZMl9eA7LXXXgByfYDHeWthmrRL5kwlrxcQtS8+W9WuVomLyKwqbz7VtrL1AY2Bm266CUA0A6X1Rp9/hNco9AeuXkbiZi5UDdfj8s0wAfmje/IYXQ/Ctsb2EGd3rf7M+W5Yvnx51u/bYt2D+m3nNabar2t5wuunUWkJZwbUxp1pxeVb+wj5YhpoO9a4MMy/XkPWKdNwqJcdd2OMMcY0Xm75/+4AkGvaogMUDoI5sDVme5FIJgpT3JM1W2xdrzvu6pmCNtFhw6VdGvelIvf++ynPKFSB1fOL+iemUkj1gSpDPrtMjnh1RKxKu9p96gr8uEhuhxxyCADgoYceyqTJbaoEUKHpWJyyh8vYsK9Pe9n5IqVyb1mRUhU3LFua+lyV+p0RvtrvvUcmrWTT9AMy7bedNu3NdkvZHtKbTHnzlJJXXpRdXrXN5XWh3ToftPnsYKlkUAGkEmsKY9SoUQCAu+66K7NNX3Rqd6r1OM4LBe+vno/tE4iicz7++OMAcl+qOuvC9kZ7TvU9TR/Mao8O5HpWKi0txfU3/gYA8Mtxv4Ay88E/A4hUM6bBeqp+nUO4D5VBPos0EvOqVauyyqXrBZiGxomgEh/+r8+e119/HUD0zOvVK9UeaaMc2v8DUdt57rnnAETRXLleAIjaGWc+eF/UflbVWpZL60ScPXH4W1z9akxo5E3O0PB68r6QfPEZ+JxVr2Vxyi3vpa5xUbt0/s5PquvhueMUZm7X9U56Lj4zwvVN+c6Xbxu/s87yWjINlpPXVuskrzHLmy9uCq+zri9RL0qqfsf5o9f91TIgLJfOfLJ8Gsk2bMemYVGvO+7GGGOMMcbUNvYqUwGqLmTst9O2neFKeyrs3JdKBe2mac9JpYzqhKqOJG6EHY7aK/NZrL+r3bwqASwD7Uup4oWjeW6jzS+P6bV7qpwZW/ZVKX/RW5anbNc3f5qyIVy7PHXclnVphbNLSsncqUfKXr1Jx8jOPNEyrX6mXWwlWqQj6rVMK+zN0qpc89S1nvXvl7PKS2WA6iLvBe+NekwIlUKqKPZVWzNC5UftsNV3tPoe1/gCOsvDesz2SJUdAP7xj38AiGawqA7zWPXixLZA9Zx+nqkmM6+sS2Gb4Dny2fgmtqZtU4MH6KmjTkj/mNo2+98pX9Khl6qwfBX5zKYqrtGBuZ/OunF7jx49srbTvztnIsIy81NnIZg2n22MRklPPLwuzBOVOb1vQHSftI7oc1VnCzVPaguss5Dh/2r/3pi8yhCuq+jbty+AXLWb10hjL4QKLffhDBLfH3FRtNVTEPfTNS5Mk3UgVKJ5DrZXXZelz2uei7M/rHv0HMe6ydkgtTsHcr2oMEIwnx28lkyjU6dOWXngObWcLBevbViHtR3rOfQdz+sSt96E6HqC8L3Gc+taHCru2i9iuU3Do1523I0xxhhjjKkrJJJFGUGzsv1qQr3suHOUzhEoR6n8HnoYoYrLUTNtYani8lxcvd6vXz8AuZHpdITN0bd6hgmP0RG9elxQbzJUS6gyqE1x6DEjLDeQq7RnVJW0m6zEptQ5Sz9PqXClJanrsHVjWkVN26nv3D2leDTfow8AoEn3lKcYqulAoKgXpaP5NUlHdUx7j6HrrZdfmwcgupbML681r4va3tK+kcpCOIOiNoDqe9wURmgnqes1FLWl5rGsl6GNKxApWvnWYvA3+iunhxR6YVGbVtYdtl+myTrD7WoLDOS36b3sgp+mtpWmlclwoob7pRX3YYemfMwPO+xbAIC5b6SiuVLpCr0kUd3+73//m/WbXiui9VVtWqnUU00L1T5VTnksVU0+8+bNm5e1nfeJzwhu5zoB9dGeuiTZqjeP1ecfP7V96vocJdyu3kxIY1TcjTEmjnrZcTfGGGMaKjSRoukUB1McrHFgyMFYXDAhIBrMchCsgpEGCVIXnkxbzaEIB5fhOTgo1DR4Dg64CQeqHNCrqNO7d28A0QA5HMzRnJVmdzyGaXNgSsGI4gHzQKEozqSV1zYcPHNwrKa1ep90MKrXWs1pea/U1SuQu/CV91MXEzOfrENmB5IsSv0Vsl8NcMe9sZBWEZOtUsp5UXHqYdaiWarxt0x/NumUtonvmHqAlrVM2ZSXNWsVnatJ+uGUVtjfejeKqgrkrgswprYZd+HPU/+kIwSjNK0Ebwk8YvCFna7XibL0izLtFemgAQcAAOa/9c52z68xxph6RjKZtW6qwv1qQL3suHO6lh1Eqg4czYeR0TgC1oUb6uKJx3Akzf05BUwFgdPJHBFzwQt/B3JH31xww5EwR9Vxo3KiC9d0gVK4QIeKhbrb2hFo6GU1ZdKFwbzWqhZxO/OuLuWASCVR8ww1IzIVE5rKqHKjAT20DeiiLd5f1nOayPzpT3/K2j/cR92VMk3WATXFYP2my1B1FcjjWReByORM3afVFNbL0ISLpj785CJaKoS6mJOwHDwXzYoOOuggAJH7yNClJp8HGuSGgZS4kI/XlgvvaUJIVZO/62LjEHUtxzrBaxC36JD3T4NWqeKYb1G/Kp6NMWT7ddddByCqD7y3+VycAvldZqqbVl3YqmZQeq/UL7qarXG/8F2j95efrKtxizfVBE7LxecG1fLw+a8BklSB1nOqyq3PO817vnLqu1pnM+KCX8UFY2TeNA/5ApTFOWLge5T9C9Yh0/Colx13Y4wxxhhj6gqJoqJM7JvK9qsJ9bLjTpWbtmscfedzH0YVmCNiKkVU9uheUG3uOGJWRYxpcPRNu7q33347cyxH8AMGDAAQqW26AC1U7IBcF1m6gE3dX4aj8djw8+mFolxQmuyQMoXJuHBMu8VL7pRSdMp2Tl3T0pZtU783TZXz0X89k3U9AGDffffNuhbqxlED92g5ee15L9SVGO9raO/H/1VxdyCmqjF69OjM/9OnTweQq7gRDVOuC4PZBgYOHAgA+Oc//wkgckPHBahAtPiUQYG0/cWpelRdqTxSgaerRrqPCxemc3Gm1hUASKRNZZKbgyAlZWmljouum6aV3mbpGTyp36HyxecM1S4ucue1YcC38Fpk5Ufsjnmd8gV44zY+R9h+eC3YjrhgvXPnzgCiax7nRjLfItBwAS4QzWjojIfaXGuwOVUY1Z1reE4NhtcYFXfCes53nbpo1c/wevI6qumiKrYaeIn1SZV5DYrGtEIlWhcp8xw8Rp8tuh/T+OyzzwDkukbWWdkwf7S153fOErHeq5MIvR7Mo75/mYdw5lffxcx3nNLO55m62tV7oc+R8H7G3XM9F+uMabjUy467McYYY4wxdQYvTo2HI2mOyqmy5QsTzH014AsVItp7UhFTW0G1+9TfOSLm6n8gUssYCEUVDx2FxwXEUBs8/T2fizVV0d5ZsBAAsG/vVP7Km6RtJFulFJxEedrGLqMytkzvl/r+zn9T10UV0rAcapNKNJCF5pHXnooB742uHwhVCXWRyX0c3rn6aB1XpU3tVHntGTiLAU+effZZAFHQGKpioV0ugwBRBdbw5KqWMS0GGAvbdpg32sCGdYX25gsXLsxs44LTxNa0Lem6rzK/lX2d+j/RPO0Ktk3qmVGWbiN0d9qnZ0rdf39xFJiJtuhU+aliHnrooQCAIUNSriU5G6HBobQth24tgWyVUL1K6H3hd9r2UqVU22XC7erCEcidedQ2HTc7qJ5ImKd8gYK0XMxP3LkbE1yf0KdPyj2vrovSNQYhvO+sJ2ojzTqmsx/85OwW62acfX3ozpf3m/lindLAhXHuQZk235msRwxIpGtjwnOzPJzpi5uFJrp2jJ+sm+F6GSD7OalrqtTGXffjbICq5Dq7wfOou9twH12bou2GdcY0XOplx90YY4wxxpg6QzJZoOLeCL3KUJ3jyJi2nPRaki+ACEfT9EpBxY9eH6ge0gaVCrOOoKn+cASdb1RPVYHKO/2pqnLOfGqQFuaV5WS54vISovtQCfzrBx8AiEbrRx85PLUD1cS0u8hH/vFYVhk4U0ElIFTjmD5H+synqiq8Npwh4bWmPaSqr7wn+TwmMH0N8xzOBJiqQXv3Bx54AECupwOdyerVqxcAoGfPngCAp59+GkDka1kVU95fIFKD+Mlzch/WDSpO/J3f2TaoZHXp0iUrzdAmm3W3qKgoM7NEO/bkxlTb3LQ0cmW68eNUiPAm6WBkTbumZoQYhAzp9SKJtHtI1mcAePnll1PHiE0388m2wfwuXbo0lR15fugzQMPLA5ESyOeGzjbxHJyFoHrJ/aji6bodVfLzlUc9lfBYtdXVWZp8s6HhecP/1fPXjTfeiMbKlVdeCSCazdL1CHpfwnefrkfQIIT6/lD7a6LvqzhvNECurTrrj3oQ02BuzD+f63yes85yDQtnWFkGIFKtuQ+P4TOD7+E4L27a1jjToLMG4Ttebdz12hBd+xF3zbmGgdeN9y7cX9+36kWH31lnTMOlXnbcjTHGGGOMqSskkslMPJDK9qsJ9bLjTjWco1wqCbRxCxUAXYW+YsUKAJF9NVdgc7RKG1wSF95dI5vl8/rAfFEB0JG9+sHWWQHa6nH0TTs/VerDbVSkqexR6aM6+EFaeecn883rpDaK6o0nVNZUPaO6oivsCcvH+8f9aL/MyHY8L/cP7fzUp7D6/TbV5wc/+AEA4MEHHwQQ3QfWBdrZUpGaPXs2gMjHOO+FqlGhUkVlnfdr//33BwAsW7Ys65NtgMoa77f6O2ZdYt0L62SoKJczAFlacS/9OrX+ZF1g/7748TeyrkeXg3uk0hicavPNWrVNlad5qk29+OKLmX3VFzrbONudtkcqilwHoxEX4/w7A7nqNT/VHl29T2hsB41mGWdvH+aHqKLOT/WBrWtSSL48qd/wOH/VjRHOUPG9pd5+1EYaiNoj92VdVFtu3m+16daZGH3v8HuoCms7CO3fgUhR12PZVrmd72k9D9t7PvS9q+q9erzRGUW2Taals2FhOeOuBdE2xHMwLV5T5on3hs9HvXfhsbr2g+e2bXvjoV523I0xxhhjjKkzJAr0KpNohF5l1OuFRu4M7UFVneIxtHvjCHfx4sVZ3zkipiKkUddUgcpnb05lUu11mSeOkKn6q2JGlY7qA5V75umqq67KpDVnzpysffjJc7zzzjtZabA8VBloW6y2iXH+l8PfiCplGmkztHUOv/NeMM+8f+rjF4jUE017W0fHbMycfPLJebc/9dRTAID//Oc/AKK6oB5deC9Yh8LZKdqdU2nWdQ86O6WeUNhWWLdUac+3BqNly5aZNRzlac8wybTXpNItUbv9+tP0uonNqXrctlfal/LGtA25rAcJ116oWqzrNThbNn78eIQwMuaJJ56IigjtvFkuXiOd4VAf66riqy9wjfaYLwon0RlHXm+dMeD9iPNkQ8LtPAfrgGfRIt58800AUTvRSKQ62xnCmWi2T37qM1Rnd3Q/rSdMM3zf8n7yHLTdZl1lu2We1q9fj+8d853UwdK+5sydl1lzRs9Q+dZ7qX080+D7RT3aUJHnOfieZnn4vuZsH69DRetMVGGPu5Yag0XvCa+L2rwDuTMFPDfbNeuIqUV2kDvImhnaGGOMMcYYY3YI9VJxJ2r3yk+OVoFcez7uQ8WPnjE0IiNtzIiOdlVhC1HlStUnnpv2ilSWqASceuqpWeejcnDAAQfkuQopBg8eHPtbeM6JEyfmzYP6oVX1Lp/3CLWh1civhGlRSeO15naqKjyeyke+KHmq6vJT/eqabc/w4SlPRLfccguA3NkZnY1SZReI7h/rHdV7ona2rAOsU6wL3E9tZUNbU6qSbdu2xZ//8gg++OADXHHBTwAAiZ1SqtrO3SOb2Y79U/+Xl6byX7xnSt1ssksqvkB50/QzoSjbp3m+sl9xxRUohMqUdnLJJZdk/r/ppptSZUi3SV5/Xht9dmm8CLUrrsi2Xe1p1ed33DoWolFQdV1MPp/x3Hb99dfn5KexwhmX++67D0C0/knXJIX1X6+52lXrveN+bDe6xoX1hG0vX/RbrSds73zm6+xQPn/+4bk4Y1xIFF2q8ToLx3e62tFz9pbvPuaRedaIsmE5eS5eC5290GvJc8T5wte+Aj/D+8n7oDNSnM1rzN6X6gpenGqMMcaYhk3aRCZRmhZ80p2abw0+CADw6rz/5D3MmMZKvey4c7TLUSrtZvN5lVEVR0fRVIgYZVFH3XER3pgHni+fqkg0spkqksz/2LFjKyz3tuCyyy4DECk36n9W/QLrjEJYTlX8dDuh1xjOhPAaq5eduKh5+ZRNnSHRPJjtB++XeiPRNRzqUQLIrVf0Cc8ZMB7D71Tc1E5VFa58fsKpPHONSHl5OcrTPtjLm6ftWvfYK7N/90NLss7Zaq9URNimu6ciwW5tmVLoaCc/cODAzL7vvvsugEhh255cfPHFAIDf/OY3AOIjpKq3Kr2G6sddZ87C33QffvL5p/b2cba/et4QnREwubz11lsAollYvVbhddV7wfuu959tRmeVdZaL95zPXs5y8jsQtUOmobOsfLbru7si1qxZkzmOqno+NIIq0+A7gmtxmCbLpTOHGlGWZQrLyX25Lc63uvYj+E6Lu/a8VzxPvrUhem7WCVMH2EE27vWy426MMcaY+g+V9kRp2qSnLD1YbNIs9hhjGjP1suOu9mAaoTG0g1MPJRzp6spsjr5p9xanPsSlHdp2qh0f0VE1f1eb1B0B01RFLe466awBEF0zVXCoKnC7Kj5q36i27UyD5wmVW26jBwG13zTbH1Vy2d5YpzTKabhGQRU51gUq7xq5WNV9tWXnd9aDUBX7739TUVHDKLvl9CbTKqW6JXsdmNm/befdssvZIv0saNk29b15yp71w49TNqU9evTI7MuosYxwuSMYN24cAGDy5MkA4j3txPlx10iMJFT5eK/jnnsaDVrVWV1/pLON4UwZz/2rX/2q8sI3UmjHfO+99wKIooWyrYVeSHQ9lnqF4afOluRbtwXkRtblvQ5nufSZr7PP6qWtkNmVoqKiTJ44E5cP5otpM2o4URt45kXbha6j0pmK8BimGff+0WvKT33XxV238PrwPvE3ziTatr0OkUwWqLjbxt0YY4wx9Yh+vVODdWxJm5elg6Ml0qZo5TEDCGMaO/Wy406bNapr9APOUWvomUKVZKqD6otW9+fvatOp3lZ0PyA3qqrakqp6Xxs2nZoHjY6nUeaY91DRUVt0Vd51ZkFnINQHMZUEno8KSaiI0GaS95z5o12i2XFQbeJ9p7LN7/xdPcUAkXrEe802o36feX+p5sf56+c6CtqaA8CHH36YdUxZWRmumXgDvvOd72T2Gbj/Ppn/y1q2yzpnedp7zEefrkofn6pz6jECiNr/fvvtlzd/25PzzjsPAPDrX/8aQHS9GdGWn7oWQWe8+BnOHvK5oFFw1ZuJqva8b2yn/NT4GBdccEE1Smxee+01ANHaLJ3JAnJnReJmYPSexnmd0XeFzqKE/2t9INxelbgbixYtQt++qXUmFc1OMz+LFi0CEJWXHqzUy1W+d3e+vOabidCZaFXctX+h59B1J6rE60wjEN1j7ss6cNppp+XNv9nxJIqKkCigbheyT0XUy467McYYYxoA6YBLmU9jTIXUy477e++9BwA46KCUuyiOWqnqhL5SOULnaFv9o6p9myrsqkzraF1H1ECkTukoXJUPfo+LVLk9YZqPPvoogFy1RT91VXz4myoXqtLpynheK157RgPkbAjPy+PCNQu8x6pUsE58//vfL/AKmOqi9zXOlzHrCv2Ih8dyNkXbmdqwqz0uj6ct/EcffQQgilAa2tuqvSi9SoQzPPPffi8nYiJRhZJ1TaMwh9dC/TTvSOJsw2+99VYAkTcNzpSpap7PF77aKMehaj1nwHifeM2YNr1bmepx++23AwCuueYaAMBhhx0GIJqRBKJ6y3VevDecqVYPTXxuVza7pSpzvjVlvM9qR58vsmtlrFu3LhPvgV6m2JYBYPXq1QAim2+2U66T4YwT6zXzoN5kNBow88wyhdeD1yjOtp37cs2cRmvlNed2the2RV0nFKb10ksvAYjqgKlDJJOF2a/bxt0YY4wx9YnlK1Zh7dq16Ldnj9QG6cyUW4E39Q27g4zn8ssvBwD88Y9/BBApSapoA9Eom0qYjvjj/JfH2a7FRRQN1Ub+r76lVcGrC9E+mQdeQ+ZRFXj1JADkqqGKXkNdP0BlhOfWFfr57qd6+/niiy8ARHXC7DhYvzUqoCrt4RoOKlVa93k/9RyESiI9RbzyyisAcmeE8vmxZvr9+6f8srN+sR5yxkB9LutsAH/PZ6fL9lIX2rSiduRXXnklgNzIkfzMF6tB2zDRtQicEfv8888BRFFezfaBEXoZzXjPPffM/Mb6yjanvtS5XddrEX0nqhcitpvw+cw6xPbKfakox8USqIh27dpl6hNn2BgtNCwn6ybXyVCd57NE128xL8wrv3PtCp9v9FYXXh9dt6PvTY2Szk/1FqORY5kmZw/CNGm7X2hUZtNwqZcdd2OMMcbUfxYvW57psHKwz4XuH3/8ca3ly5iqkkgWIVGAml7IPhVRrzvutGvt1KkTgFz/4ECuhxeN7kg1gXZw+TxgAFVbJU+lj6NrjuBVGdDRdm2g9rrqYYLXQ320A7meduJQv8BUOOiTVz3WqAoTXied8WAdMNsf2krzfvA+qlcKvnzV20x4DO8165f6ZQ/tZsPtVL+OOOIIAMCrr76alWa+2R+em0qcqsdaf7VdqnJPwrUbLA89XtVlrr766oL3/e1vfwsgt02ef/752zRPxpj6y/XXX4/LLrsMY8eOzbwn8nHrrbdi8uTJWLZsGTp06IATTzwREydO3K5rg/785z9j/PjxWLp0Kfr06YMbbog8i23ZsgVXXHEFHn/8cSxevBjFxcUYPnw4rr/+enTr1m275amm1OuOuzHGGNPYueiiiwAAkyZNymyjC8U4ExldQKomYRpIUAfodMEaQkGM56QpIwkXWwK5wpe6Au7atWtWmhwYh4NodvqYHy5K5TlUFOA5VFBiuWnuRfNRmoeGZrZMK86JhZ6b5dMAVOqaU92rvv/++5lz8B7XNV577TXceeed2H///Svcb+bMmfjlL3+JqVOn4pBDDsH777+P008/HYlEImPqVVVmz56N008/HUuXLs37+0svvYRTTjkFEydOxDHHHIOZM2di5MiRmDdvHvbdd1+sX78e8+bNw/jx43HAAQfgyy+/xNixY3Hcccdh7ty5Vc9QosDFqTVcv+HVH8YYY4wxpkqsXbsWP/zhD/H73/8+Y7UQx0svvYRvfetbOPXUU9GjRw8ceeSROOWUUzKzpkBqwDNx4kT07NkTLVu2xAEHHICHHnqo2vm77bbbcNRRR2HcuHHYe++9MWHCBAwcODAzwC0uLsasWbMwatQo9OvXD9/85jcxadIkvP7661i2bFm1093e1GvFnSPQp59+GkA06g3NYzjC5/S3hg3mCJnH0DUhR/E6jc4pfC6WYZoc3QPR6FrdPqqy8aMf/aiqRd7mMA9PPvkkgNzQ8uo+MzR70IA7NEXgvqrU0GSIC4t4LbkfF/Zp6PZQvVBzhbqqQjREdOEV6wYXjHJqkfeTplChS0GqYbyPulBMg3CxjmjQF9aRb37zmwCAF198MStPQFRvqNrFqWNqGqOB0rT8+cxxuI3PhYbChRdeWNtZMFUgNGF65plnsn6j0q5mCXHvSFWBuV2DaIXvPv7GfWkKp+4T2a75zFeXrOpMguehWey+++6bSfPtt98GkGuGp+VkWiynuoqOa/c8T1hOPgtYTjXt0wBL+k6Lcx+rgbTquknaT3/6U3z3u9/F8OHDM65J4zjkkEMwY8YMvPrqqxg0aBAWL16Mxx9/PKsfNHHiRMyYMQNTpkxBnz598Pzzz2P06NHo2LEjhg4dWuX8vfzyyzl9hBEjRuCRRx6JPaakpASJRCLvjFJl2MbdGGOMMcbUOR544AHMmzcvE8G1Mk499VSsXr0ahx56KMrLy7F161ace+65GY9wmzZtwnXXXYennnoKQ4YMAQD06tULL7zwAu68885qddxXrFiRWRdFOnfunPHao2zcuBGXXnopTjnllIyJWV2kQXTc33nnHQBRuPEw4AtRxU5t8ajGURXm6FsDNHEETTWR5w3Dn1M10BDFTIPH1iWYJ1Zy5pnXkuUM3d2pYs5yU8FQ9YXXSBcg8p5QKdHjQvgb7/m3v/3tapTWVAfWX95f3k8uEKZ6pIF8wilU/sZ7rXWgslDoVMuoXDFPDMjCgD/hvnvttVfecmie4oKp6KJyEi7YZDloH2tMbUOPLL179wYQtVdVmNVhA5/53J8dGNZxKttUrEN4LrYZqpY8hzpu4HNAXU1yP3Xdyg5XuAic+WRa2o7VNSPVbLXx1+CLqtCH7yP+rwvxmTbdX7JcavOurjZZBu5X173pfPTRRxg7dixmzZpV8MLS2bNn47rrrsPvfvc7DB48GAsXLsTYsWMxYcIEjB8/HgsXLsT69eszjgfI5s2bMWDAgMz3MFBeaWkpNm3alLVt9OjRmDJlSpXLtGXLFowaNQrl5eWYPHlylY8HkA7AVIgfdwdgMsYYY4wxO4DXX38dq1atwsCBAzPbSktL8fzzz2PSpEnYtGlTjggzfvx4/OhHP8LZZ58NICW0rlu3Dj/+8Y/xf//3f5mB0mOPPZYxWSahgDp//vzM/3PmzMGll16K2bNnZ7aFSnmXLl2yxBwgJe7Qxz9hp/3DDz/EM888U6fVdqCBdNx//vOfAwCmTp0KANhjjz0yv6k9LisHR7rq7lBXlqvNncKRd6jGaRocdVOp+MEPflDlMm5vmKe//OUvAKLrovbnoT0wyx53bahGaMhotWtWO0Fe83w27h9++CGA6J6bHcdPfvITAFGobb2/nLWhrbvaxAPRPY2zXSdqT67eGnSNSuiakdAmlWq8ql6q2rNuqzeNOHen4cuEwVHquk2qaTzMmzcPQLRuS2fM4tYS6ZoPVaLZ7vO5YKVyzHNS1dbAh7r+SxVsqv98F7AMPP/q1asz52L75j4892effZaVtnqHqcz9MPPEtVzhddHnlXqZ4TOD54671hoEiuXmvTvttNNQF/n2t7+Nt956K2vbGWecgb322guXXnpp3pnT9evX58x2hs/3/v37o3nz5li2bFmFZjGcQQJSMxNNmjTJ2hYyZMgQPP3001lB6GbNmpUxxQGiTvsHH3yAZ599NrNWr1okC/QqY8XdGGOMMcbsCFq3bp21QBhIiX277LJLZvtpp52G7t27Y+LEiQCAY489FrfccgsGDBiQMZUZP348jj32WBQVFaF169a4+OKLceGFF6KsrAyHHnooSkpK8OKLL6JNmzYYM2ZMlfM5duxYDB06FDfffDO++93v4oEHHsDcuXNx1113AUh12k888UTMmzcPjz76KEpLSzPmWO3bt8+J4F0ZiaIiJCox9+R+NaFBddzPPPNMAFHQECDyxcoRsK6sVz+yHPHyk6Ns2n5T2eMnz6urykN4juXLl1ezZDsO5rFnz54A4r3qhL/pNaGaQAWWKkqcTSHVCKopbDhUU0NfwPZyUXfg/dRZJ/VFHKovrAvqz5j7sA6xzXC7Ku/qqUn3B6I2q54s4pR39ahEtA3kU/cXLlyYs82Y2oSBcPhJO2EqyGwH7JywPetzXG3i1cNY+E5Qu3hd38T3rrZbVbd1RpzPEnqICteJcRvPzfxxH23PfPaoKsw86kww7dXDmWX1N6+KOsvPfHM7y6vrBZgWVeyKghjVF5YtW5alsF9xxRVIJBK44oorsHz5cnTs2BHHHnssrr322sw+EyZMQMeOHTFx4kQsXrwYbdu2xcCBAzMLWKvKIYccgpkzZ+KKK67A5Zdfjj59+uCRRx7JDC6WL1+Ov//97wCAAw88MOvYZ599FsOGDatWutubBtVxN8YYY4wxO5bQzjzf9yZNmuDKK6/ElVdeGXuORCKBsWPHYuzYsQWlOWzYsNjgS+Skk07CSSedlPe3Hj16xDolqBbJogIXp1pxzyFUZa+//noAkfrGUTNHyFQXOCKmIqi+x7mdx/NT9wNyvVCoJ426jK7y19Xy+fbltdBrqCvl+Z2zHtxfFU2qLlxU8stf/rJmhTLblJ/97GcAIlt3qkhUuHr06JG1PZ+NuNqqq50p6x+P1UiDrJdci6KqGhDZQjItteFV5Zy/qycInVFiff/ggw8yx9q23dRVaN/7xz/+EQCw2267Zf1OtVcjjVKRZhtk26M9N38Pva1QIWfbCWOqhOfi+5fvAm3f6rGMbY827+G7lNt0tk79tGvkWKalar96nKPNc/i8UB/2quJzX5aL5WEafMZobJPQFtuYOBpkx90YY4wxxpgdhhX3bQPV2unTpwOIRtvq4URVBSrM3M6RMY9TG75QAVDvFBzB0w1SXYZ5pDpDtYLXJSwnt/FasNzqC1+9ElRmC83vVtrrNlTeCSPn0csM60rogUF9R7OdaVRT9eOs3hio7nNNBtthaLfK9S1sf+rpQW3dNS86y8TjqJqFirsxdR0GyonzgMJ2ovVfn89UmfkuDW3c46ISx812qWLNZwc/eW61jQ9n8XQdDO3Gqf5Tkdc4I3wuaWwItVdX1T88B9PUGUT9zmsbp8Dz3pxyyikwpjIafMfdGGOMMcaY7UkimUSiAFePhexTEY2m405XQk8++SSA3AhtHHWrOqyqOUfKVAqoNocRRQm35YsAWtdhnnld1I4w3EbVgSqo+riN85Orqiq3V8ftk6l9rrjiCgDAjTfeCACZ4ByhCh7nf10VeF1DsmrVKgCR/2aqalTD1ANGiPoO5neeg22aCp16utG1Ka+88goAFLyAypi6wC233AIAuO666wAAhx12WNbvrO8ad0TXO1Fp1zVOQNR+uc6Jx2ocFc7KFhcXA4jaLd+nbIO61iXfbJjOHLAcVM55Tn3WcH2M+p5X5Z3lDVV+ps9rpOVlWnEebFi+N954A0B0b4wphEbTcTfGGGOMMWa7kCjQxj1hG/cq8f777wMA+vfvDyA+WpxuV1+2VOkqUgB47Omnn75tC7EDYJ4feughAPnLSVVefd6r32yNUEm4Hz95b0aMGLENS2J2NJdccgkAZAJv7LrrrpnfOnbsCCCarSFUqKh+LV68GECkaLH9qaJOpYt1jecHctdMqKcHKoUMoU3PU3369Mk6nhEY586dC8CeH0z9hj6x77nnHgDAPvvsAyBSi9k+qI6r7Tu3U8kOw8PzvUnf5/zUSKlU69VTjcZb0ePULj3cpudWG3XmjXblVNxZPvUwpx6vwveXlo/vQqahs3Q6q8x3XXX9k5vGTaPruBtjjDHGGLNNSSSARAH263lcJFcpmfJt6n2+/kFvM7rSXu3T6cuVdrBEVeTw2GOOOWbbZ7iWePTRRwHkKqVArncOqqSff/45gMjOj8dy/6+++gqAbdobE7/+9a8BRHWCnyQuIqF6vqDCznUVrHO0qweAXr16Acitn+rxgYo6oxbydyptnAWwOmYaIjNnzgQQxV9gG2S91/VbajtO701ApCxTiVZvbITtlbNe7dq1yzq3znhrPBXahgNRRFiNiq5KOd/lfGbwnPpO1xk5ljO0cWc0b1XcCd91PAefVwwWdOqpp8I0HNasWYPi4mJ8Of9ZtGmd20fK2f/rtWh34OEoKSnJmrEqlJotbTXGGGOMMcbsEBq94l5VfvOb3wCIFEFVAoGGbQN76623Zv6nHR+rEG0Hx40bt8PzZeonVOBZl6jeUQVj3aL9qtqlqtJ15JFHZv6n4qZrKQjbLj3W0Nbd8QNMY2Ty5MkAgL59+wLIjWXCNqrfQ09jGjk0Lg6D2ojzOCrVqoKzvVMlZ1sFgAMPPBBApG6rfTnVfc4cUFFXG31dm6aRz0NvadzGfLGc+p3noE37eeedB9PwoOL+xX+eK1hxb3/AUCvuxhhjjDHGNGS8OLWKNHY1uSHPJpjag4qc+pJWFUwjqxKqbKHXGfUmwWPjIi1aaTeNGarB48ePBxB5XuNaEfUEw/YTKtFsp2pnru2aa8r4O9c78ZP7azwH/h6q/NzWqVOnrPJQnddjdL0at6tXGZZFveoAkS0+j2H+mG96xXr33XcBABMmTIBpBCSSBS5OrZlmbsXdGGOMMcaYeoAVd2NMraF2pPS+oAoWt6sfZx5HH+yhKqYen1RZYxr0KmOMidThiy66CADQoUMHALnRQNkWw3UmGtOD3mJ4rMZd4HYq8GpfzvPxk+tRwpk1buO6M41+zuis6mWGa7J4Lnql4TOF3meYdmg7r96wmG/a7L/22msAHBG10ZFIFObqsYbuIK24G2OMMcYYUw+ocx335cuXY9SoUWjbti3atGmD733vexl7MWNMNvW9vYwfPx7jx4/H1q1bsXXrVqxfvx7r16/Hli1bsGXLlsz3DRs2YMOGDSgrK0NZWRlatGiBFi1aoEOHDll/yWQy81dUVJT1F/6WTCaxZs0arFmzBl999VXGDtYYY4ypFslk4X81oE6ZyqxduxaHH55ySn/55ZejadOm+O1vf4uhQ4di/vz5mUUlxhi3F2PM9oNmHj/5yU8AAEOHDgUA7LHHHln70ewFiMxnNJAhF4LSDGXFihUA4oMc0fSEA+qVK1cCAEaPHh2b3wceeABAZDZH8xs1x9PgUN26dctKk4vVaQLE7eGCeG4jH374IQDgueeeAwD87ne/i82nMTWlTnXcf/e73+GDDz7Aq6++ioMPPhgAcPTRR2PffffFzTffjOuuu66Wc2hM3aEhtRd6dJk4cSKAXP/sfFGyQ8Aoj/R4ofsD0YuZL1y1eV+2bFlW2sYYY0x1KU8kUV6Ax5hC9qmIKgVgevbZZ/G///u/+Mtf/oLvf//7Wb/NnDkTP/zhD/HSSy9hyJAh1crMoEGDAACvvvpq1vYRI0Zg0aJFWLhwYbXOa0xtsGHDhkw47jfeeCOzuOmLL77APvvsg549e+Lf//53TjjwQmmI7YUdd+1kF9pxD2cZVCnjsVykxiAuFal4xphs6C5y//33B4CsADJdu3YFEC34ZFujEs/uhi4253aq4atXrwYQLQytShudMWMGgGgxKRfXqqrP5y7zqtv5/GBeP/3000wazOebb74JwO4eGzsMwPT5e68WHIBpl70H7ZgATMOGDcNuu+2G+++/P+e3+++/H3vuuSeGDBmCTZs2YfXq1QX9kbKyMrz55ps46KCDcs49aNAgLFq0KLMK3Jj6QMuWLTF9+nQsXLgQ//d//5fZ/tOf/hQlJSWYNm0aioqK3F6MMcYYUxBVMpVJJBIYPXo0brnlFpSUlGTcLH322Wf417/+lemc/PGPf8QZZ5xR0Dk50v7iiy+wadOmzIg9hNs++eQT9OvXrypZNqZWGTx4MC655BLccMMN+P73v4+VK1figQcewK233poJLe72EnHZZZdlfb/mmmsA5CrwLKMGaAkDs3CbupbkgCZU0IwxhaHq8q9//evM/yNGjAAQtUNV1jX4mdqfcz+20dNPP73K+aM6P23aNACRS0qmxbzxmcLng+aRz1qq/nPmzMmk8atf/QoAcNJJJ1U5f6YBs4MCMFXZxv20007DxIkT8dBDD+Gss84CADz44IPYunVrpsGMGDECs2bNqtJ52TjUPyoQvZy5jzH1iauuugqPPvooxowZg7Vr12Lo0KH4+c9/nvnd7cUYY4wxhVDljvtee+2Fgw8+GPfff3+m437//ffjm9/8Jnr37g0gpYblUwIrgvZoFS0yCwMgGFNfaNasGaZOnYqDDz4YLVq0wB/+8IeM+gO4vVTEFVdckfWdC2533jllR0hVjNcz9HBBFY/KGpW29957DwAwbty47ZVtYxoNVJ8B4NxzzwUA7LvvvgCQmVWkHS9t3gnbL80A6cqWnmxqAtV6enjhehjavCckCI4GUXr//fcBAG+//TYAYMqUKTXOk2ng1FXFHUip7mPHjsXHH3+MTZs24ZVXXsGkSZMyv2/YsAElJSUFnatLly4AgPbt26N58+Z5p6+5jW6bjKlvPPnkkwBSneoPPvgAPXv2zPzm9mKMMcaYQqiSVxmyevVqdOvWDddeey02bNiAa665Bp988klmJDtt2rQq2+wCwMEHH4xEIpHjJePII4/EokWLsGjRoqpm1Zha580338TBBx+MH/7wh5g/fz5Wr16Nt956K7NGxO2lcG688UYAwFFHHQUgN+x6aDpExZ2mQx9//DGAlMtMY8yO47zzzgMQtUWq3Wy/t9122w7Ly9ixYwHk2rJzpnLy5Mk7LC+mYUCvMqvffwNtWreufP+vv0aHvgOq7VWmWop7hw4dcPTRR2PGjBnYuHEjjjrqqEynHaiezS4AnHjiifjlL3+JuXPnZrxlLFiwAM888wwuvvji6mTVmFply5YtOP3009GtWzfcdtttWLJkCQ4++GBceOGFmDp1KgC3F2OMMcYURrUUdwB4+OGHceKJJwJILU4dNWpUjTPz9ddfY8CAAfj6669x8cUXo2nTprjllltQWlqK+fPno2PHjjVOw5gdyZVXXokJEybg6aefxuGHHw4AuPbaa3HFFVfgsccew3e+851qn7sxthcqc0ceeSSAaAEuH2OhDS29Raxfvx5A5O/+ggsu2CF5NcYY0/DJKO4f/Kdwxb3PATvGj3vIsccei3bt2qG4uBjHHXdcdU+TRevWrTF79mz8z//8D6655hqMHz8eBxxwAJ577rkG2QkxDZt58+bhuuuuw/nnn5/ptAOpSJ0HH3wwzjnnnExI7+rg9mKMMcY0LqqtuG/duhXdunXDsccei3vuuWdb58sYY2J59913AeR61Qn9uNPGnbb+nCE0xhhjthUZxX3hm4Ur7r3337E27gDwyCOP4LPPPsNpp51W3VMYY4wxxhhT/6mr7iDnzJmDN998ExMmTMCAAQMwdOjQGmXAGGOqSv/+/QEAl1xySdb2cAKRHituueWWHZcxY4wxZjtS5W7/5MmTcd5556FTp0649957t0eejDHGGGOMqTeUJ5IF/9WEatu4G2OMMcYY05ihjftni98t2Ma9Y6/+O97G3RhjjDHGGIOU7Xpy+9u41+xoY4wxxhhjzA7BirsxxhhjjDE1YQd5lbHibowxxhhjTD3AirsxxhhjjDE1wYq7McYY0zgpKyvDlClTcOCBB2LnnXdG586dcfTRR+Oll16q7awZY2oRd9yNMcaYOsa4ceNw3nnnYb/99sMtt9yCX/ziF3j//fcxdOhQvPrqq7WdPWOMQsW9kL8aYFMZY4wxpg6xdetWTJ48GSeeeCLuu+++zPaTTjoJvXr1wv33349BgwbVYg6NMUp5IlFQcKXyRKJG6VhxN8YYYypg6dKlSCQSsX/bmi1btmDDhg3o3Llz1vZOnTohmUyiZcuW2zxNY0z9wIq7McYYUwEdO3bMUr6BVOf6wgsvRLNmzQAA69evx/r16ys9V1FREdq1a1fhPi1btsTgwYMxbdo0DBkyBIcddhi++uorTJgwAe3atcOPf/zj6hfGGLN92EGLU91xN8YYYyqgVatWGD16dNa2n/70p1i7di1mzZoFALjxxhtx9dVXV3quPfbYA0uXLq10vxkzZuDkk0/OSrdXr1548cUX0atXr6oVwBjTYHDH3RhjjKkC9957L373u9/h5ptvxuGHHw4AOO2003DooYdWemyhZi6tW7fGPvvsgyFDhuDb3/42VqxYgeuvvx4jR47Ev//9b3To0KFGZTDGbGMSidRfIfvVJJny8vLyGp3BGGOMaSTMnz8fhxxyCEaOHImZM2fW6FwlJSXYsGFD5nuzZs3Qvn17bN26FQMGDMCwYcNw++23Z37/4IMPsM8+++DCCy/EDTfcUKO0jTHbhjVr1qC4uBirli9DmzZtCtq/U/fdUVJSUtD+ihenGmOMMQXw5Zdf4oQTTkDfvn1x9913Z/22du1arFixotK/zz77LHPM2LFj0bVr18zf8ccfDwB4/vnn8fbbb+O4447LSqNPnz7Ye++98eKLL27/whrTiLjjjjvQo0cPtGjRAoMHD66ey1W7gzTGGGPqBmVlZfjhD3+Ir776Ck899RR22mmnrN9vuummKtu4X3LJJVk27Fy0unLlSgBAaWlpzvFbtmzB1q1bq1sMY4zw4IMP4qKLLsKUKVMwePBg3HrrrRgxYgQWLFiATp061Xb2cnDH3RhjjKmEq6++Gk8++ST++c9/omfPnjm/V8fGvX///ujfv3/OPn379gUAPPDAAzjqqKMy2+fNm4cFCxbYq4wx25BbbrkF55xzDs444wwAwJQpU/DYY49h6tSp+OUvf1nwecoTyQL9uFtxN8YYY7Ybb731FiZMmID/+Z//wapVqzBjxoys30ePHo1evXptM28v3/jGN3DEEUdg+vTpWLNmDY488kh8+umnuP3229GyZUtccMEF2yQdYxo7mzdvxuuvv47LLrsssy2ZTGL48OF4+eWXazFn8bjjbowxxlTA559/jvLycjz33HN47rnncn5XV5Hbgr/97W+46aab8MADD+CJJ55As2bNcNhhh2HChAno16/fNk/PmMbI6tWrUVpamhPsrHPnzvjvf/9bpXNt3lqKzVtzzdvy7VcT3HE3xhhjKmDYsGHY0Q7YWrZsifHjx2P8+PE7NF1jTNVo1qwZunTpgt12263gY7p06ZIJ3lZV3HE3xhhjjDGNjg4dOqCoqCizIJysXLkSXbp0KegcLVq0wJIlS7B58+aC023WrBlatGhRpbwSd9yNMcYYY0yjo1mzZvjGN76Bp59+GiNHjgSQ8iD19NNP4/zzzy/4PC1atKh2R7yquONujDHGGGMaJRdddBHGjBmDgw46CIMGDcKtt96KdevWZbzM1DXccTfGGGOMMY2Sk08+GZ999hl+9atfYcWKFTjwwAPxxBNP5CxYrSskynf0ihtjjDHGGGNMlamZF3hjjDHGGGPMDsEdd2OMMcYYY+oB7rgbY4wxxhhTD3DH3RhjjDHGmHqAO+7GGGOMMcbUA9xxN8YYY4wxph7gjrsxxhhjjDH1AHfcjTHGGGOMqQe4426MMcYYY0w9wB13Y4wxxhhj6gHuuBtjjDHGGFMPcMfdGGOMMcaYeoA77sYYY4wxxtQD3HE3xhhjjDGmHuCOuzHGGGOMMfUAd9yNMcYYY4ypB7jjbowxxhhjTD3g/wcMRHuFe7fOCgAAAABJRU5ErkJggg==", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "text/plain": [ "
" ] @@ -263,6 +323,9 @@ ], "source": [ <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "# homoogeneity test for each group\n", "inference = CBMRInference(\n", " CBMRResults=cbmr_res, device=\"cuda\"\n", @@ -270,6 +333,7 @@ "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", " \n", +<<<<<<< HEAD "plot_stat_map(\n", " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", " cut_coords=[0, 0, -8],\n", @@ -282,13 +346,19 @@ "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", " t_con_moderator=None, device='cuda')\n", "inference._contrast()\n", +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "plot_stat_map(\n", - " cbmr_res.get_map(\"homo_test_1xschizophrenia_No_chi_sq\"),\n", + " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", +<<<<<<< HEAD " threshold=5\n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + " threshold=30,\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ")" ] }, @@ -297,6 +367,9 @@ "execution_count": 5, "metadata": {}, <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "outputs": [ { "name": "stderr", @@ -353,6 +426,7 @@ "output_type": "display_data" } ], +<<<<<<< HEAD "source": [ "# Group comparison test between any two groups\n", "inference = CBMRInference(\n", @@ -383,18 +457,41 @@ " threshold=0.5,\n", ======= "outputs": [], +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "source": [ - "# Group comparison test between two groups\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,-1,0,0]],\n", - " t_con_moderator=None, device='cuda')\n", - "inference._contrast()\n", + "# Group comparison test between any two groups\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "# chi square statistics maps for group comparison test\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"1xschizophrenia_NoVS1xdepression_Yes_chi_sq\"),\n", + " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", +<<<<<<< HEAD " threshold=1\n", >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ")" ] }, @@ -407,6 +504,7 @@ }, { "cell_type": "code", +<<<<<<< HEAD <<<<<<< HEAD "execution_count": 7, "metadata": {}, @@ -433,20 +531,44 @@ "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" ======= "execution_count": 21, +======= + "execution_count": 6, +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ +<<<<<<< HEAD "[[0.94563486]]\n" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] } ], "source": [ "# Test for existence of effect of study-level moderators\n", <<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "inference = CBMRInference(\n", " CBMRResults=cbmr_res, device=\"cuda\"\n", ")\n", @@ -457,6 +579,7 @@ "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" +<<<<<<< HEAD ======= "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", " t_con_moderator=[[1,0]], device='cuda')\n", @@ -486,6 +609,8 @@ "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", "print(effect_diff_p)" >>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] } ], diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 860cbe68b..f6efa6065 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -551,7 +551,11 @@ def _preprocess_t_con_regressor(self, type): t_con_regressor = [t_con_regressor[i] for i in uniq_con_regressor_idx[::-1]] return t_con_regressor, t_con_regressor_name +<<<<<<< HEAD +======= + +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_groups: From 18bafd33b21e1fcb108d64ad6a4da235c57327de Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 11 Feb 2023 23:57:53 +0000 Subject: [PATCH 091/177] solve conflict --- nimare/tests/test_meta_cbmr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 1a9db5cee..5853d0475 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -27,7 +27,8 @@ def test_CBMRInference(testdata_cbmr_simulated): dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], + moderators=["standardized_sample_sizes", "standardized_avg_age" + , "schizophrenia_subtype"], spline_spacing=10, model=models.PoissonEstimator, penalty=False, From aa773d54062b06db5abced40e6a533e1e0eb503f Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 12 Feb 2023 00:16:43 +0000 Subject: [PATCH 092/177] [skip CI][WIP] solve conflicts --- nimare/cli.py | 49 ---------------------------------- nimare/meta/cbmr.py | 4 --- nimare/tests/test_meta_cbmr.py | 2 +- nimare/tests/utils.py | 4 --- 4 files changed, 1 insertion(+), 58 deletions(-) diff --git a/nimare/cli.py b/nimare/cli.py index c5993e27d..847d6e732 100644 --- a/nimare/cli.py +++ b/nimare/cli.py @@ -90,55 +90,6 @@ def _get_parser(): help=("Number of processes to use for meta-analysis. If -1, use all available cores."), ) -<<<<<<< HEAD -======= - # Contrast permutation workflow - conperm_parser = subparsers.add_parser( - "conperm", - help=( - "Meta-analysis of contrast maps using random effects and " - "two-sided inference with empirical (permutation-based) null " - "distribution and Family Wise Error multiple comparisons " - "correction. Input may be a list of 3D files or a single 4D " - "file." - ), - ) - conperm_parser.set_defaults(func=conperm_workflow) - conperm_parser.add_argument( - "contrast_images", - nargs="+", - metavar="FILE", - type=lambda x: _is_valid_file(parser, x), - help=("Data to analyze. May be a single 4D file or a list of 3D files."), - ) - conperm_parser.add_argument( - "--mask", - dest="mask_image", - metavar="FILE", - type=lambda x: _is_valid_file(parser, x), - help=("Mask file."), - default=None, - ) - conperm_parser.add_argument( - "--output_dir", - dest="output_dir", - metavar="PATH", - type=str, - help=("Output directory."), - default=".", - ) - conperm_parser.add_argument( - "--prefix", dest="prefix", type=str, help=("Common prefix for output maps."), default="" - ) - conperm_parser.add_argument( - "--n_iters", - dest="n_iters", - type=int, - help=("Number of iterations for permutation testing."), - default=10000, - ) - ->>>>>>> ab450fa ([skip ci][wip] fix conflict to merge) # MACM macm_parser = subparsers.add_parser( "macm", diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f6efa6065..860cbe68b 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -551,11 +551,7 @@ def _preprocess_t_con_regressor(self, type): t_con_regressor = [t_con_regressor[i] for i in uniq_con_regressor_idx[::-1]] return t_con_regressor, t_con_regressor_name -<<<<<<< HEAD -======= - ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_groups: diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 5853d0475..1a841f895 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,7 +16,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e1, + tol=1e4, device="cpu" ) cbmr.fit(dataset=dset) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 610f596ab..9e589f5bf 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -5,11 +5,7 @@ import nibabel as nib import numpy as np import pytest -<<<<<<< HEAD import logging -======= -import warnings ->>>>>>> 92ffce8 (allow categorical variables in CBMR) from nimare.meta.utils import compute_kda_ma From 8000f1c420b1a48c155a4f89650bc1115dafbd68 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 12 Feb 2023 16:27:53 +0000 Subject: [PATCH 093/177] solve conflicts --- nimare/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/nimare/utils.py b/nimare/utils.py index 064c415e9..9bd4ad7e0 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1162,7 +1162,6 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms - def coef_spline_bases(axis_coords, spacing, margin): """ Coefficient of cubic B-spline bases in any x/y/z direction From 6390ce0e154875e4fde0936ae1f4496f2798c9f7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 17 Feb 2023 15:57:52 +0000 Subject: [PATCH 094/177] [skip CI][WIP] work on example file --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 563 ++------------- .../02_meta-analyses/10_plot_cbmr_2.ipynb | 647 ++++++++++++++++++ nimare/tests/conftest.py | 3 +- nimare/tests/test_meta_cbmr.py | 4 +- nimare/utils.py | 12 +- 5 files changed, 725 insertions(+), 504 deletions(-) create mode 100644 examples/02_meta-analyses/10_plot_cbmr_2.ipynb diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 63b586577..92e6f1a8b 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -1,72 +1,38 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Coordinate-based meta-regression algorithms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A tour of CBMR algorithms in NiMARE.\n", + "# Coordinate-based meta-regression algorithms\n", "\n", + "A tour of CBMR algorithms in NiMARE\n", "This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" - ] - } - ], + "outputs": [], "source": [ - "import nimare\n", - "import os \n", - "from nimare.dataset import Dataset\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", + "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators, get_resource_path,index2vox\n", "from nimare.tests.utils import standardize_field\n", - "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", "from nimare.meta import models\n", -<<<<<<< HEAD -======= - "from nimare.utils import get_resource_path, standardize_field,index2vox\n", - "from nimare.meta.cbmr import CBMREstimator\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "\n", "from nilearn.plotting import plot_stat_map\n", "from nimare.generate import create_coordinate_dataset\n", - "import nibabel as nib \n", - "import numpy as np\n", -<<<<<<< HEAD -<<<<<<< HEAD - "import scipy\n" -======= + "import nibabel as nib\n", "\n", + "import numpy as np\n", + "import scipy\n", "import logging\n", "import sys" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "import scipy\n" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -75,51 +41,23 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ -<<<<<<< HEAD -<<<<<<< HEAD - "# data simulation\n", -======= - "# data simulation \n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= "# data simulation\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", "# set up group columns: diagnosis & drug_status \n", "n_rows = dset.annotations.shape[0]\n", "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", -<<<<<<< HEAD -<<<<<<< HEAD - "# set up moderators: sample sizes & avg_age\n", - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# categorical moderator: schizophrenia_subtype\n", - "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", - "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estimate group-specific spatial intensity functions" -======= - "# set up `study-level moderators`: sample sizes & avg_age\n", -======= "# set up moderators: sample sizes & avg_age\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# categorical moderator: schizophrenia_subtype\n", - "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", - "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" + "dset.annotations['schizophrenia_subtype'] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(n_rows/5)\n", + "# dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", + "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n" ] }, { @@ -127,297 +65,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ -<<<<<<< HEAD - "## Group-wise spatial intensity estimation" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "## Estimate group-specific spatial intensity functions" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "# Estimate group-specific spatial intensity function " ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", - " niimg = new_img_like(niimg, data, niimg.affine)\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", - " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" + "WARNING:nimare.tests.utils:Categorical metadata ['schizophrenia_subtype'] can't be standardized.\n", + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" ] - }, - { - "data": { - "text/plain": [ -<<<<<<< HEAD -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { -<<<<<<< HEAD -<<<<<<< HEAD - "image/png": 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", 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", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "image/png": 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", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", + "from nimare.meta.cbmr import CBMREstimator\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\", \"schizophrenia_subtype\"])\n", "cbmr = CBMREstimator(\n", - " group_categories=[\"diagnosis\", \"drug_status\"],\n", - " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\"],\n", - " spline_spacing=10,\n", - " model=models.PoissonEstimator,\n", - " penalty=False,\n", - " lr=1e-1,\n", - " tol=1,\n", - " device=\"cpu\",\n", - " )\n", -<<<<<<< HEAD - "cbmr_res = cbmr.fit(dataset=dset)\n", + " group_categories=[\"diagnosis\", \"drug_status\"],\n", + " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\", \"schizophrenia_subtype\"],\n", + " spline_spacing=10,\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1e-1,\n", + " tol=1e4,\n", + " device=\"cpu\"\n", + ")\n", + "cres = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", -======= - "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", - " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", - "cbmr_res = cbmr.fit(dataset=dset)\n", + " cres.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\"\n", + ")\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "cbmr_res = cbmr.fit(dataset=dset)\n", + " cres.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\"\n", + ")\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + " cres.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= - "##" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" + " cmap=\"RdBu_r\"\n", + ")\n", + "plot_stat_map(\n", + " cres.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\"\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" -<<<<<<< HEAD -======= - "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", - " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, { "data": { "text/plain": [ -<<<<<<< HEAD -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "dict_keys(['Group_schizophrenia_Yes_Studywise_Spatial_Intensity', 'Group_depression_Yes_Studywise_Spatial_Intensity', 'Group_schizophrenia_No_Studywise_Spatial_Intensity', 'Group_depression_No_Studywise_Spatial_Intensity'])" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { -<<<<<<< HEAD -<<<<<<< HEAD - "image/png": 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", -======= - "image/png": 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", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "image/png": 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", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "# homoogeneity test for each group\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - " \n", -<<<<<<< HEAD - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=30,\n", -======= - "from nimare.meta.cbmr import CBMRInference\n", - "# Group-wise spatial homogeneity test\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", - " t_con_moderator=None, device='cuda')\n", - "inference._contrast()\n", -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", -<<<<<<< HEAD - " threshold=5\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - " threshold=30,\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ")" + "cres.maps.keys()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -426,197 +168,27 @@ "output_type": "display_data" } ], -<<<<<<< HEAD - "source": [ - "# Group comparison test between any two groups\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "# chi square statistics maps for group comparison test\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", -======= - "outputs": [], -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) "source": [ - "# Group comparison test between any two groups\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "# chi square statistics maps for group comparison test\n", "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", + " cres.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", -<<<<<<< HEAD - " threshold=1\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + " threshold=1e-5,\n", ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generalized Linear Hypothesis (GLH) for study-level moderators" - ] - }, { "cell_type": "code", -<<<<<<< HEAD -<<<<<<< HEAD - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: 0.9243109811987764, 0.9461743884065033\n", - "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" -======= - "execution_count": 21, -======= - "execution_count": 6, ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ -<<<<<<< HEAD - "[[0.94563486]]\n" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - } - ], - "source": [ - "# Test for existence of effect of study-level moderators\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes\", \"standardized_avg_age\", \"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", - "sample_size_p = cbmr_res.tables[\"standardized_sample_sizes_p_values\"]\n", - "avg_age_p = cbmr_res.tables[\"standardized_avg_age_p_values\"]\n", - "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", - "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", - "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" -<<<<<<< HEAD -======= - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,0]], device='cuda')\n", - "inference._contrast()\n", - "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", - "print(sample_size_p)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.99838466]]\n" - ] - } - ], - "source": [ - "# Test for existence of effect of study-level moderators\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,-1]], device='cuda')\n", - "inference._contrast()\n", - "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", - "print(effect_diff_p)" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.8 ('torch': conda)", + "display_name": "torch", "language": "python", "name": "python3" }, @@ -630,12 +202,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", -<<<<<<< HEAD - "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" -======= "version": "3.8.8" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) }, + "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" @@ -643,5 +212,5 @@ } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 2 } diff --git a/examples/02_meta-analyses/10_plot_cbmr_2.ipynb b/examples/02_meta-analyses/10_plot_cbmr_2.ipynb new file mode 100644 index 000000000..63b586577 --- /dev/null +++ b/examples/02_meta-analyses/10_plot_cbmr_2.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Coordinate-based meta-regression algorithms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A tour of CBMR algorithms in NiMARE.\n", + "\n", + "This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "source": [ + "import nimare\n", + "import os \n", + "from nimare.dataset import Dataset\n", +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", + "from nimare.tests.utils import standardize_field\n", + "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", + "from nimare.meta import models\n", +<<<<<<< HEAD +======= + "from nimare.utils import get_resource_path, standardize_field,index2vox\n", + "from nimare.meta.cbmr import CBMREstimator\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "from nilearn.plotting import plot_stat_map\n", + "from nimare.generate import create_coordinate_dataset\n", + "import nibabel as nib \n", + "import numpy as np\n", +<<<<<<< HEAD +<<<<<<< HEAD + "import scipy\n" +======= + "\n", + "import logging\n", + "import sys" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "import scipy\n" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ +<<<<<<< HEAD +<<<<<<< HEAD + "# data simulation\n", +======= + "# data simulation \n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "# data simulation\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", + "# set up group columns: diagnosis & drug_status \n", + "n_rows = dset.annotations.shape[0]\n", + "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", + "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", + "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", +<<<<<<< HEAD +<<<<<<< HEAD + "# set up moderators: sample sizes & avg_age\n", + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "# categorical moderator: schizophrenia_subtype\n", + "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", + "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimate group-specific spatial intensity functions" +======= + "# set up `study-level moderators`: sample sizes & avg_age\n", +======= + "# set up moderators: sample sizes & avg_age\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "# categorical moderator: schizophrenia_subtype\n", + "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", + "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ +<<<<<<< HEAD + "## Group-wise spatial intensity estimation" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "## Estimate group-specific spatial intensity functions" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", +<<<<<<< HEAD +<<<<<<< HEAD +======= + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", + " niimg = new_img_like(niimg, data, niimg.affine)\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", + " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" + ] + }, + { + "data": { + "text/plain": [ +<<<<<<< HEAD +<<<<<<< HEAD + "" +======= + "" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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", 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", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "image/png": 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", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", + "cbmr = CBMREstimator(\n", + " group_categories=[\"diagnosis\", \"drug_status\"],\n", + " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\"],\n", + " spline_spacing=10,\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1e-1,\n", + " tol=1,\n", + " device=\"cpu\",\n", + " )\n", +<<<<<<< HEAD + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", +======= + "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", + " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "cbmr_res = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ +<<<<<<< HEAD +<<<<<<< HEAD +======= + "##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" +<<<<<<< HEAD +======= + "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", + " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + }, + { + "data": { + "text/plain": [ +<<<<<<< HEAD +<<<<<<< HEAD + "" +======= + "" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { +<<<<<<< HEAD +<<<<<<< HEAD + "image/png": 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", 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", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "image/png": 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", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "# homoogeneity test for each group\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + " \n", +<<<<<<< HEAD + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=30,\n", +======= + "from nimare.meta.cbmr import CBMRInference\n", + "# Group-wise spatial homogeneity test\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", + " t_con_moderator=None, device='cuda')\n", + "inference._contrast()\n", +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", +<<<<<<< HEAD + " threshold=5\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + " threshold=30,\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], +<<<<<<< HEAD + "source": [ + "# Group comparison test between any two groups\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "# chi square statistics maps for group comparison test\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", +======= + "outputs": [], +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "source": [ + "# Group comparison test between any two groups\n", + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "# chi square statistics maps for group comparison test\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", +<<<<<<< HEAD + " threshold=1\n", +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", + ")\n", + "plot_stat_map(\n", + " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " threshold=0.5,\n", +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) for study-level moderators" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD +<<<<<<< HEAD + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: 0.9243109811987764, 0.9461743884065033\n", + "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" +======= + "execution_count": 21, +======= + "execution_count": 6, +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ +<<<<<<< HEAD + "[[0.94563486]]\n" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= + "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + } + ], + "source": [ + "# Test for existence of effect of study-level moderators\n", +<<<<<<< HEAD +<<<<<<< HEAD +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + "inference = CBMRInference(\n", + " CBMRResults=cbmr_res, device=\"cuda\"\n", + ")\n", + "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes\", \"standardized_avg_age\", \"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "sample_size_p = cbmr_res.tables[\"standardized_sample_sizes_p_values\"]\n", + "avg_age_p = cbmr_res.tables[\"standardized_avg_age_p_values\"]\n", + "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", + "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", + "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" +<<<<<<< HEAD +======= + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,0]], device='cuda')\n", + "inference._contrast()\n", + "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", + "print(sample_size_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.99838466]]\n" + ] + } + ], + "source": [ + "# Test for existence of effect of study-level moderators\n", + "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", + " t_con_moderator=[[1,-1]], device='cuda')\n", + "inference._contrast()\n", + "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", + "print(effect_diff_p)" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) +======= +>>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.8 ('torch': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", +<<<<<<< HEAD + "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" +======= + "version": "3.8.8" +>>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) + }, + "vscode": { + "interpreter": { + "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 4789acd0c..e3e0749a2 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -158,7 +158,8 @@ def testdata_cbmr_simulated(): # set up moderators: sample sizes & avg_age dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) - dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)] + dset.annotations['schizophrenia_subtype'] = ["type1", "type2", "type3", "type4", "type5"] * int(n_rows/5) + # dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)] dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column return dset diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 1a841f895..2777d57f4 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,7 +16,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e4, + tol=1, device="cpu" ) cbmr.fit(dataset=dset) @@ -43,7 +43,7 @@ def test_CBMRInference(testdata_cbmr_simulated): ) t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type="groups") t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") - contrast_result = inference.compute_contrast(t_con_groups=[[1,-1,0,0],[0,0,1,0]], t_con_moderators=[[1,-1,0,0],[0,0,1,0]]) + contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) # self.maps.schizophrenia_Yes_p_values = ... # self.maps.schizophrenia_Yes_chi_square_vals = ... # self.tables.standardized_sample_sizes = ... diff --git a/nimare/utils.py b/nimare/utils.py index 9bd4ad7e0..937fe61fb 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1275,12 +1275,16 @@ def index2vox(vals, masker_voxels): return voxel_array def dummy_encoding_moderators(dataset_annotations, moderators): - for moderator in moderators: + new_moderators = moderators.copy() + for moderator in new_moderators: if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): - moderators.remove(moderator) # remove moderators that are dummy encoded + new_moderators.remove(moderator) # remove moderators that are dummy encoded categories_unique = dataset_annotations[moderator].unique().tolist() for category in categories_unique: dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) - moderators.append(category) # add dummy encoded moderators - return dataset_annotations, moderators + new_moderators.append(category) # add dummy encoded moderators + # remove last categorical moderator column as it encoded as the other dummy encoded columns being zero + dataset_annotations = dataset_annotations.drop([categories_unique[0]], axis=1) + new_moderators.remove(categories_unique[0]) + return dataset_annotations, new_moderators From b20dd74cf061e2573ce7c6cafa7cf0d129627149 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 20 Feb 2023 17:33:36 +0000 Subject: [PATCH 095/177] [skip CI][WIP] complete example file for cbmr. --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 524 +++++++++++++- .../02_meta-analyses/10_plot_cbmr_2.ipynb | 647 ------------------ nimare/meta/cbmr.py | 13 +- nimare/meta/models.py | 11 +- nimare/tests/test_meta_cbmr.py | 5 +- 5 files changed, 513 insertions(+), 687 deletions(-) delete mode 100644 examples/02_meta-analyses/10_plot_cbmr_2.ipynb diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 92e6f1a8b..2535a5d6d 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -13,9 +13,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], "source": [ "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators, get_resource_path,index2vox\n", "from nimare.tests.utils import standardize_field\n", @@ -41,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,11 +61,11 @@ "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", - "# set up moderators: sample sizes & avg_age\n", + "# set up continuous moderators: sample sizes & avg_age\n", "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted as groups)\n", "dset.annotations['schizophrenia_subtype'] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(n_rows/5)\n", - "# dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n" ] }, @@ -65,26 +74,82 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Estimate group-specific spatial intensity function " + "# Estimation of group-specific spatial intensity functions\n", + "Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a generative regression model that estimates a smooth intensity function and can have study-level moderators. It's developed with a spatial model to induce a smooth response and model the entire image jointly, and fitted with different variants of statistical distributions (Poisson, Negative Binomial (NB) or Clustered NB model) to find the most accurate but parsimonious model.\n", + "\n", + "CBMR framework can generate estimation of group-specific spatial internsity functions for multiple groups simultaneously, with different group-specific spatial regression coefficients. \n", + "\n", + "CBMR framework can also consider the effects of study-level moderators (e.g. sample size, year of publication) by estimating regression coefficients of moderators (shared by all groups). Note that moderators can only have global effects instead of localized effects within CBMR framework. In the scenario that there're multiple subgroups within a group, while one or more of them don't have enough number of studies to be inferred as a separate group, CBMR can interpret them as categorical study-level moderators. " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "WARNING:nimare.tests.utils:Categorical metadata ['schizophrenia_subtype'] can't be standardized.\n", - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "from nimare.meta.cbmr import CBMREstimator\n", - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\", \"schizophrenia_subtype\"])\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", "cbmr = CBMREstimator(\n", " group_categories=[\"diagnosis\", \"drug_status\"],\n", " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\", \"schizophrenia_subtype\"],\n", @@ -92,7 +157,7 @@ " model=models.PoissonEstimator,\n", " penalty=False,\n", " lr=1e-1,\n", - " tol=1e4,\n", + " tol=1e1,\n", " device=\"cpu\"\n", ")\n", "cres = cbmr.fit(dataset=dset)\n", @@ -100,66 +165,254 @@ " cres.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", - " cmap=\"RdBu_r\"\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_Yes\",\n", + " threshold=1e-4\n", ")\n", "plot_stat_map(\n", " cres.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", - " cmap=\"RdBu_r\"\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=1e-4\n", ")\n", "plot_stat_map(\n", " cres.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", - " cmap=\"RdBu_r\"\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=1e-4\n", ")\n", "plot_stat_map(\n", " cres.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", - " cmap=\"RdBu_r\"\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_No\",\n", + " threshold=1e-4\n", ")\n" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures correspond to group-specific spatial intensity map of four groups (\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"). Areas with stronger spatial intensity are highlighted. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups` can be generated by `create_contrast` function, with group names specified. " + ] + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type5 = index_2\n", + "INFO:nimare.meta.cbmr:type1 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type3 = index_5\n" + ] + }, { "data": { "text/plain": [ - "dict_keys(['Group_schizophrenia_Yes_Studywise_Spatial_Intensity', 'Group_depression_Yes_Studywise_Spatial_Intensity', 'Group_schizophrenia_No_Studywise_Spatial_Intensity', 'Group_depression_No_Studywise_Spatial_Intensity'])" + "" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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MH374Ab169cLhhx+OdevWJZ3byuS7777D888/Hxn9ds6cObjzzjtx//3345NPPsHMmTORk5ODE088Ee3atcN3332H66+/voJLLYQQQojqxBdffIEhQ4bEv99yyy0A/OjykyZNwu23346dO3fi2muvxdatWzFgwAC88cYbpTJhyYzFkGkE3dD9EPOdWZeScl2c+tRTT+HHH3/EqFGj0LdvX+Tl5eGDDz7AqFGjcPHFFyftv3HjRvTv3x/XXHMNLr74YvTs2RP9+/fH+vXr8f333+PPf/5zqNtHz/Nw/vnn4w9/+AMuvfRStG7dGmvXrsWTTz6J++67D/n5qS8QYHrDhw/HtGnTcO2116J3797o1asXtmzZgh9++AEPP/wwXnzxxfj+r7/+Opo2bYohQ4bgmGOOQdOmTbFx40Z89NFHeOqpp/Dqq6/G9500aRL279+Pk046CX369EGjRo2wbt06/Pvf/8af/vQnvPfeeymVcfLkyVi2bBlGjRqF/v37o2/fvli7di2eeuop3HfffSnb9FcUo0ePxqWXXopatcKb2NixY/Hf//4XN998M3r37o26deti1apVePDBB/HAAw8UOwshhBBCCFEUgwcPLtLhRSwWw+jRozF69OgKLFXZiHkpuvD48ssvE8waKgvP87BixYqUXSmK6s/cuXPRq1evyi6GEEIIIWoYubm5aNSoEUZktEOdWPEW6Hu8AowvWIVt27ahYcOGJc6v3GzchRBCCCGEEAeOSvPjLoQQQgghRHWgRDbuZUADd4ETTzwRV199dUr7Tp8+PdI/uxBCiNSYNGkSrrzySsyZMwfHH398ZRdHVEPYxkhmZiZatmyJU089Fffdd5+ik6cpaTdwty4YRdnp0qULrrjiipT2XbFihQbuQgghRJowevRodOzYEXl5efj0008xadIkfPTRR1iwYEGpvKeIcDJj/l+x+5Uxn7QbuIvy59lnn8Wzzz5b2cUQQgghRDlzxhlnxGd1rr76ajRr1gwPPvggXnvttdBglqJqo8WpQgghhBA1hIEDBwIAli1bVsklqV7Qxj2Vv7IgxV0IIYQQooawYsUKAEDjxo0rtyDVDJnKCCGEEEKIMrFt2zbk5OQgLy8Pn332Ge655x7UqVMHZ511VmUXTZQCDdyFEEIIIaopp5xySsL3Dh06YPLkyWjbtm0llah6IneQQgghhBCiTDz55JPo2rUrtm3bhgkTJuCDDz5AnTp1KrtYopSkPHBv1qwZsrOzkZeXdyDLI0SJyM7ORrNmzSq7GEIIIUSVpE+fPnGvMueccw4GDBiASy+9FIsXL0b9+vUruXTVhxhS8/hSVqfmKQ/c27Vrh8WLFyMnJ6eMWQpRfjRr1gzt2rWr7GIIIYQQVZ7MzEyMHTsWQ4YMwRNPPIFRo0ZVdpFECSmRqUy7du00SBJCCCGESFMGDx6MPn364LHHHsPIkSMVhKmckI27EEIIUc2ZMGEC3njjjaTtN910Exo0aFAJJRI1gdtuuw0XXHABJk2ahOuuu66yiyNKgAbuQgghRCUxfvz40O1XXHGFBu7igPHzn/8cnTt3xiOPPIJrrrkGmZll9S4uKsqPe8zzPK+MaQghhBBCpMSzzz4LAGjatCkAoG7dugm/c1iyc+dOAMDPfvazlNOeMWMGAOCggw4CAMSM6cLu3bsBAJs2bQIADB8+vERlF8KSm5uLRo0a4a66nZAdK355ap5XgHt2f49t27ahYcOGJc5PirsQQgghhBBlwFfcU7FxLxtS3IUQQghR7rz44osAgFatWgFA3Hd4RkZGwidV8YKCgoTj+Z2f8+bNAwCMGDEivg9NjY499tjQtAm/c8hj096zZw8AYN26dQCAiy66qER1FTUXKu73HdQJ2bHih+V5Xj5+v7P0insqLieFEEIIIYQQlYxMZYQQQghRZh5//HEAge16x44dAQC1a9dO2I8LIWmHnpWVBSBQwwlt3HNzcwEA7du3BwDcfffd8X369OmTcCzT5Cehqr9v376EtPPz8xPKQJfXzz//PIDAFv7GG28ssu5CVJQ7SCnuQgghhBBCpAFS3IUQQghRJC+//DIAoEWLFgAChdq1Sz/kkEMSjqHKzU+q2zxm//79AID69esDAGrV8ockeXl5AJJt4Gkjz/3dbdyHxzAtBhdiXvQqQ+WdcBaA6XCWgHWaPXt2fF/mwTQ2bNgAADjvvPMgai4ZKbqDLKtiLsVdCCGEEEKINKDSFfdJkybhyiuvxJw5c3D88cdXdnFENYPti2RmZqJly5Y49dRTcd9996FNmzaVWDohhKiaTJs2DQDQqFEjAIHtN9VmKtRU0YHAe8yaNWsABOo2sTbsVMGpcjPNXbt2AUhW3qmCu77ZuY378BhrR89yMk9+Ev7OMnNWoHXr1gACZd9N29rFv/322wCAbdu2AQDOP/98iJpDRdm4V/rAXYiKYPTo0ejYsSPy8vLw6aefYtKkSfjoo4+wYMGC+FSqEEIIIURVRgN3USM444wz4jM6V199NZo1a4YHH3wQr732Gi688MJKLp0QQlQNZs2aBSBQz63aTZWZn1THgcCunPtSvea+/J1qNvejmk0VnD7VXTUfCPf3biOj8hibBvNgnlT/WT9rA8/9WGZ+AkC9evUABDbu/KS6z0iwPJeDBg2CqP5kpmjjXtYATLJxFzWSgQMHAgCWLVtWySURQgghhEgNKe6iRrJixQoAQOPGjSu3IEIIUQWg1xSaDlI1pppso5pSqXZtv/fu3QsgsIunr3RiFXnef2kzTvt05km13Krq9rsLj2EaVNJZTuZJRZ5l5n6sJ+vAsrn1tFFZeQz34QwD1Xue2/79+0eWW6Q/FaW4a+AuagTbtm1DTk4O8vLy8Nlnn+Gee+5BnTp1cNZZZ1V20YQQQgiR5mhxqhDlyCmnnJLwvUOHDpg8eTLatm1bSSUSQgghhCgZGriLGsGTTz6Jrl27Ytu2bZgwYQI++OCDhKlPIYSoicyYMQMA0LJlSwDBAssGDRoAALZv3w4g2ZSE0CzEPZb70qSEn/y9WbNmAALTEqZJ8xUuHKVJDL/T1IbmK+62qGOYJk1/aArEwEo5OTkAApMZ1pvmPCyzW0/CctsAUUyD9d6xYweA4Fz/7Gc/S0pLpD+ZSNFUxit+n6LQwF3UCPr06RP3KnPOOedgwIABuPTSS7F48eKEKHxCCCGEEFUVDdxFjSMzMxNjx47FkCFD8MQTT2DUqFGVXSQhhKgUKFxYt4hUrJs2bQog0e0jECjQ7kJNKs9UwbnYlCp3ixYtAASKuVXFN2/eDCBYWGrTtQq3u43l4Hd+Mk0q7lHKu10gy9/tglo3bQvdRLI+duZBIlH1JiNFG/eMFPYp8vgyHS1EmjJ48GD06dMHjz32WPxGLYQQQghRlakyivuECRPwxhtvJG2/6aab4vZiQpQnt912Gy644AJMmjQJ1113XWUXRwghKozXX38dQKASUx0mtMumQn3wwQcDKNoVI228uQ+VZqrW/E6lncr1+vXrE/Kk4k4VnMdbG3ggcLlogzhZt5DMo127dqFpM+CUteVnXq5dvYX78FjWw7qa5HnhuZdXs+pFyu4gyya4V52B+/jx40O3X3HFFRq4iwPCz3/+c3Tu3BmPPPIIrrnmmiJvzEIIIYQQlU3Mc19dhRBCCFFt+eijjwAESrNVqGm7Tm8qtEvnd6rGRSnvxcFhBwM0LV26FACQm5sLIFDWKaZQqaed/Y8//hhPq02bNgCCmQMq5awPlfiGDRsCALp06RJan7LUw9Znw4YNCd+jZhB47gcMGFDqMojKJzc3F40aNcKzzbqhXkbxAuCugnwMz1mMbdu2xdtlSZCNuxBCCCGEEGlAlTGVEUIIIcSBgWvIaKtOhZp22Pykuk2lmt5UopR216sMsftQ/bYT/PQRz7ypllMNt+aL1mYeCDy12LgczNPWj3kyD+v/3eYZZpQQ5t0GCM4Vy0L7e85i8Hd+cgaB1+b0009PykukDzXOxl0IIYQQQoh0JDNFd5Cp7FMUGrgLIYQQ1Rwq01R/6S2mUaNGAJI9n9ApBNXtKFtw16d5Kmq1u92q+CxjlKrPsrv+0O0xLI/1vx4VWdXmFVU2KvhhWP/19H1v8+bvVP9p+y7/7qIkaOAuhBBCCCFEGciIxVIKrlTWAEwauAshhBDVlCeeeAIA0L17dwCB/TVtvWnrTtWXSjzV7bJ4XbG+0K3azbIwT6r+UWo5vbRwfxfWg3lYH+pM09rC2zKxzKVxD2zXB/A7bd3p35227cyLZeW1uuGGG0qct6g5aOAuhBBCCCFEGYhlxhDLKP5Ftywvw4AG7kIIIUS1hX7YqVZHqdlUielthVgluiivMlF24FEDFW6nnb3Ni59UqMPyJLQXp/LO+nHf4vzPR3nCCcO163fLHXVuWDbr151KO7fzWglRFBq4CyGEEEIIUQYyMmPISEFxl427EEIIIRJ46aWXAACtW7cGECjtjEpKu2uqwrTptjbfVIet6k07cyrbbhqpwv2pbm/duhVAsl06ycvLS6iDu431YPRVmwb915fGdt0tIxAo5TyHhGq/XR9g62nPffPmzRPKzGt34YUXlqqsonqjyKlCCCGEECKt6dChA2KxWNLf9ddfH7r/pEmTkvbNzs4ufQEyMxBL4Q+ZZRt6S3EXQgghqhkNGzYEkOy33XpV4XbrqYXqMBXsbdu2AQjsu5kOfZa7aVj13sLtLJudBYiyp+d+nAVwt9l62X1L6i2HMw5WJQeATZs2JeRB5ZyKOdV9bmfe9poQni/mwf1EyZgzZ07CzMiCBQtw6qmn4oILLog8pmHDhli8eHH8e1kXjlYEGrgLIYQQQoi0hiZH5IEHHkDnzp0xaNCgyGNisRhatWpVLvnHMmKIZabgVQaycRdCCCGEA9VeftJbDJVpqr52P+t7nXA7FWx+pxIflqZVL62Szv1pG04bdyrQVpmmEu3mGaViU3llPaz9uS2T9VTD46iiu3lSGWceNk3rHYdpc3bCnksq91bBF6Vn7969mDx5Mm655ZYiVfQdO3agffv2KCgoQK9evXD//fejR48epcozIzOGjBQG7hllHLirdQghhBBCiGrD9OnTsXXrVlxxxRWR+3Tr1g0TJkzAjBkzMHnyZBQUFKB///5YvXp1xRW0FEhxrwReffVVAECDBg0AJK84t8rH5s2bAZRshTlXpTdp0iQ0TZsno+ide+65Ja6PEOnE1KlTASTbsFq/zVFRH9mXhg8ffuALK0QJePzxx+P/d+7cGUCg6lLN5ne2Y0ZMpRpsVXPaZ9OTCj+J6/klSqW3v1sFlM8pljFKyWberq95phmlpPNZxzwsVh2P+t2tp7Wnp2cdniueO6va0zaeEVSZJ8vOa8P93et54403hpZPhPPMM8/gjDPOiHtVCqNfv37o169f/Hv//v1xxBFH4C9/+QvGjBlT4jxjGRmIpTBbEjP9pKRo4C6EEEIIIaoFK1euxDvvvINXXnmlRMdlZWWhZ8+eWLp06QEqWfmggbsQQghRDXCVbDvLSrts2lFbBZ37MXonFWaqy1z4Z5VpN0/rd91GK42axaLi3KZNGwCBJxtut95mXBtwq1pT9aZ6bW3grZ96O5PG7VbJp6cYIIj0SqxNv1XaN27cCCCYUeAMN5V6q+BHrREQqTFx4kS0aNEC//M//1Oi4/Lz8/H111/jzDPPLFW+FWXjroH7AYTmKuzwnJI89NBDASTfIOwNiHCK7/333wcADBkyJDJP7tOlS5eEtImdJuWNgWWcPXs2gGAqjzcaBYIQ6cYLL7wAIAjQYgcN9pNYkxn7Oxk/fnz8f/vwv/baa8tUdiGEECWnoKAAEydOxPDhw5NeJIcNG4Y2bdpg7NixAIDRo0fjhBNOQJcuXbB161Y8/PDDWLlyJa6++urKKHrKaOAuhBBCCCHSnnfeeQerVq3CVVddlfTbqlWrEmZrtmzZgmuuuQbr1q1D48aNcdxxx2H27Nno3r17qfKOZcodZNry7rvvAgA6duwIIFDjqOTZ6UE7HWbfEjmVySm/v//97wACVRwI1Hw2ODZONxy1myexU3p2IU/jxo0T6nTyySdH1luIymLy5MkAEhfO0STAKujsX1HT21GKu13sFgb3/fOf/5yQR9TicDtdP2LEiKIrKkSK8F5v2xpnXWl+QrMPa0IT1c6j2q67Leq7fQbaPsiolba/cNasKJgGTWW4gJXPwCjXlLYeUXVwB3xRx9hjeS6tm0eee1tmOzYQJee0006LnCWdOXNmwvdHH30Ujz76aAWUqnzRwF0IIYQQQogy4CvuKXiVQbgHo1TRwL2ceP311+P/28U9fHvmG751+0hFwH7nWyMVDi7Y4SIhNyCEXThEBZ6LXvgmbxci8bt1/cXvVGfoutKt51lnnVXMWRHiwMBZJ84UsZ26ypxVymwY9ijFnTBtYhU7VxWzM1dWtbczWm7IdrcsdP9mFT13Fo5pyI5eWKyrRiB5xpfqr3VHbGd6bVvmcdyfz5ai3EFyX6tuM02bJ/sB+xb7M/tL2KyYnUmwi0ptMCOWhfWz6r49X2FuInmsndXjObGzFawnj+O537VrV0IeUbPtQrho4C6EEEIIIUQZkFeZNOGJJ54AgITFDFHhnK3Kzf2s4mFtCC1htofF2SPaMvHN3+Zp1X8qAtyfdXHrfsMNN4TmLURZobJONc0GS7KqoKuORQVYiuoTxSltUf3Vzcvaw9s0rDu7KHdv1n2eq/6zfOx/LMd1110XmpaoOdxyyy3x///9738DAPpt/Sq+beeqHwEAGVl+m8lu2gjIAWp3ORrYCyw5qEu8fXGGlzO7dqaY6nLLli3j6Ue5NSR25tc+t2x/YL/n/kUp7tyHx9Be3qZp9+css/3d9mGq6wCwfv36hG127QrXDdDO3rq15HY+X+0MHNN1r6eo+sRiMcQyUlicWlC2gXvxxjhCCCGEEEKISkeKe4pMnDgRQKAoWCV6586d8X1pX863aypiVKutTR1/t/ZtxNqlW/tZd5tV9V2FvKg8WCb+zvqxDlQh3Hqy7n/7298S8qJacOWVV4bmJUQUVNitbatVpKJsZsOwSrq1bbVquU3LqmlWsS8Kuw+PtfeAqHoVlYe1q6cCTzQTVrOhYj57zNT4tmkLcxL2OaOlr/z2uHABAKDzz/11S/MP6pbkZYb3fjuDvGnTpnh6XN9l+4qF25mH9X5GrPrN/uCq31HBnaLSilL7ozzg8NOtpw1mxecllXQew3PG56pdX2PPA+vAayfSi4zMDGSksDg1wyubZi7FXQghhBBCiDRAinsEEyZMAAC0b98eANCzZ08AwRszVa4lS5YAANauXRs/lrZ1XDnOt27auVG1t/au1u6Vb/XW922YFwz7G4+hUkk7Ph5jfVnzk2/+LDPTYYhmt56tWrUCABx22GEJaTIP+n5fuXIlAIQGRBACAJ599lkAQZu3s0xWcWP/Ky4Kaiqwjds0rH1uURFWrUpvyxnV3+x+3B7V58OOjSr/n/70JwCBqicFvmbBOB8zjcru8p/1/n3+P49/CgD4/cH+bHHP81oAtYHXl++KR8+2NuN2dhYI+i0V9Kh1Inwu8Xembdu99UpDNm/eHP//kEMOSdgnakaM/cZ6UosqK8vC/d168jfer/i8pCrPSOTNmjVLqC/ztN6w+Mlr5sZoEelDygGYPNm4CyGEEEIIUe2R4m6g8te5c2cAwepwq5RR1eJ+CxcujKexZs0aAEDr1q0BBHZvfDu3/m+j/Mxau14SFlUtKtKaXWEfFcmRn9Z2j0oC6+R6DWDdrT0j02ratGlCPXluhw8fHlpWUfN45plnAATtjUqUbZdRappV6FxVPCq6oU3Lrg+x7djawlrb1zCivMfYdS1RaRTlWSrKPp7YGQN+lxeamsXVV18NAPjimntTPmbpP78GADQ97igAQEHBoXGvMdbWPUzZjoo6zLbItSv0ysLf2f/5zLAxTOz6E1dxtz7hoyK7bty4EUDgJYfb+ZzmMzJKeXefx1TfeS44o83xAp+jy5cvBxBEIOfzk2Xg8db+XjEa0hMp7kIIIYQQQog4UtwLefnllwEAbdu2BRC8QfMt3kZE4xs335RpZwcE6jTt3WhDR1XBenAh1sdtlN1sUX7crV2f9aRhbd2tzR3LSHWBdeD+VCfc8luvOTbSHvPkueW5Pu+885LqIao3zz33HIBAebMKe5SHCKuClcS23fYja0ce5V0iSiUnrm/1KC8wdnuUlw2SiqcaEnVOrJ95a9vLcj/11FMJx//qV79KOW9RM2jevHm8nXHm2KrlLtZrEpVn2snb5w/bItPkc8cq77at03uNS5RXmXXr1gEIVHr73OKz3NqncxY7rM/a5ycVdW6nZznWg2OCZcuWAUiOjh41eybSi4ryKqOBuxBCCCHw4jx/AN17+1Z/Q5PKK4sQIpwaP3B/4403AABt2rRJ2G4jifI738KpPtBWzY2+1qSJf7ejykDl2fq/tbZ41ge79Zxhbd9ddc6u0reKBtO0tu5W5bdR4riddXLryWN5LqwiaWcauB8/ee5PP/10iOrLpEmT4v9brzE2eqlVx63HFBu9kX3IeooIw7Z5tler9lus7+UwpTFqn6jy2PpE+Xu39S+KoiK7hqVpVT4q8G5ZRowYUWy+omoyfvz4MqdRUFCQ1PasIs1nA5C8PmT16tUAkvsBn4X0nsLjcnJ8DzhRsU2s33N3m1tmIHg2M02Wl2VhGXhPovLOMtGjHNN368k8mGZU5GRy6KGHJuTBMtl7EZ+ZvHbqf2lGijbuKKONe40fuAshhBACGNzMH8xmNmpaySURIv3IiMWQkVH8oDyjBCaRYdS4gfs//vEPAMHbM32RRylmdju/W88wrlcXriznW7drCxuWh1XfrPptVXMq+a4Swm0sV5SiHqXwWUWEeTZs2DChTm49rf1/lCcNHmP95VL9p7932iBecMEFEOkPlXbXJ3GUTXqUN4ooBct6R2IbK8pW1P5mbVitmm9V/ai1KWHlt56W7OyarX+Uoh7mQSZq36h7VdS5i/LU46Yv5S994bPtv2VIIysrK/4sYBvnfZz227Tn5v5AoLRbBZ6KM58rdtaLbZN26VxTZdeZUMF2t9n1MkwjaqaN23l/smtEaJfOtVluPQnt4m1fsvXi85cz+3zWMU+q//TgI0RR1LiBuxBCCCGAzg9dj6OOOgrTz/Bdg3bq5Q/2a7X1Xf1id2WVTIj0I5aZgVgKi1NjBVqcmhK0p+YbLaOa2uhpUZHaoqIq0uabXjKA4M2fb9HE2qBa5czaqfO79RvNt3lXNbd+oa0CyN+Zpo1yalU3a2MYZjfLulsvHbZedhbAzixw9oNqjWzf0xv6Zqe65rbFKEXcqsVRKrhdw2Hbq+truThPDVbls8o6sfeIMGz/Yd9nm7YzXzaiqp2Vs3m7dYny/W6VRWL7o/29uHUGAPD0008n5CE/01ULziS73s0YtbMoateuXeS6kNq1ayfdv9m2w54JnPktKsYBEDwv+RymzbeFEbuZF4+jmu6mwXLyGAv7gY1oHrUf68A6cW0WEMwWc1aD9zp7f7Jrb6KitXbo0AFAoOrz+I8++iieJ6OWa0Za1JiBuxBCCFHdmTzw8pT3/Xbko/jW+d7x9GMAAHN2NyryRVUIkUxGZgwZKSxOzSiQjXuRvP/++wACJcIq5tZG1iruVpUjVllz3/KjVOooRY80//pfAIDaC/worPt2+vOUWQf5ynbDw7sAAOocMwAAMKEwEhwQqCu82bJcNu8orOpI9cIqg666wjyi7OWtkmfPuVUZrT09r92QIUOKLLuoGvztb38DEKhiVg0HopVl9jM7Y2Rt3JlmlD23uwbD9TzhEhWp2PaRqIjAYXbqUb7eo7zF2PpEeZgK8/8epWbaaLJ2xsHasNv7kT2nYXVm2v/3f/8HQMp7ZTNhwgQAQNeuXcs13X379sXbA9VlzrDyGUPbcP4OBOq0nTEj1uab9/yoWSB6hmEePM7t57acPMb2Z9uX7FqyqP4RprjTE41VyLmd90DrAY7njqo/y2BjoISNETiG4TW/6qqrkvYRNYNqP3AXQgghqjs9134MANjVzV80+sriTSkfe+uNJwAAmpx+rr9hQ/HuR4UQicRSdAcZk+KezPTp0+P/03aMb7x8Q7beVawqbBV3EqWgufbsfNu23lSoJNs3+9jzDwAAbh75ckr1G3mVr0QPG35RfNtcM3NAdcTa2Bbnq5plpFpp93fraVVCu69dvW8/rZrH9Gh7yGh07vU855xzQssvKo9nn30WQOI6DyB5FsfdZj0m2fUPFtt+rbIdZuMeNUsW1ReivLVwu12DElZWG4E4Fovh8qP9KI/7163yj9+xFSgUKWO1Cmf6mvveJmq19WfVnp35NYBkf/duvexsoFXro+pvvYNE+cF2/7d9nGn85S9/ARDcZ6QCViz0rpKKn/+SUFBQEFeP2T6oEtsZNddWvLg4BrY9uR6nwvaLim7sxhMhVuWPilZsvciEzTSF1cGtJ4+xz3reI3juou45dpbAloV9kzPfQDCr73rUETWTajlwF0IIIWoS2cf9BADQ59ZcAEDWk+8ACKKhuvy8UJU/8vI+AICWF/h28R+u3VfuLwFC1BTkVaYGUO8dX636TYpKO3lswjwAwLWbdsW3HTdyGABgYd0e5VM4IUSpuPHsfgCA3Z/43pGWPTUDALBunu8JY+MPufF9s2v5N/A2fXx/1YcO8vvvZcf5A6qJC/dWQIlFusGZjiOOOAJAeGyB0pCfnx+5boSf9KBCNZjqMlD8OiY728wZJev33M4aWY9qbrrWo1rUmg3uxzxtmSy2TG49qfjbqOh2hpuwbFTkt2zZAiBZPWdZaU/vziwwf553toFf/vKXoeUX1ZdqNXD/61//CgA4/vjjk35jR2DHsi6ubGfnjcUGn7DwOE4jAsGNjR2fv/HzQCoavJFwWo8dn/W1i+Ps1CbLyBsMp+dsXdxjo86NXdBqz23UzZrXinkz9DQQXONrrrmmqNMgKhC2d0uYuVlxbtGiggbZ7fzk8WEP3ygXpzZYU1SAIlsPi7tf1LR8WQmrl627NVmzJktRLm7ttH3U+XD3sVP69j45ceJEAMCVV16ZSvVEOfL5+v1o0aIFMMQ3VzrxcN9DTN/1q4KdMgoXXDf13Rg++cVadO/eHVi+PWHBpxCi5GRkIkWvMmXLp1oN3NON9Z8vLNPx/zfju/j/951SGB/v+C5lSlMIUTb2LvoSALD6/a8AAIv/tQQA8J/14X6jAQCFfZk+Wjpk+4PyXww4DQDwzOylB6CkQgghyotYRgyxjBQWp6awT1FUq4F7ly7+oNVVwqg422BIJGqhWlHhzYFkF3Kuz1sb+MIuQDmQUPniAhyqb6w/3W8VpwwynLXrAgtIrGdUOHrrBtOq+lGu/HicDQTjTlHyGovKh4GW2OZsH3IXipKoGS6rclsl3i4Ui1KLw+BsEz95T7ALZKMWYFpXiCQsAJpf7nA3lGXBvXfYvmtnHfjJ2TdbbmvGEFU/lyhzAns9ec2lvB9YrHtje68FgDVtfXOrnOxOAIKZsd27dwO7gYULFyIWi8X7hX0+WUcHxJqtuKYnUc9L20/ZhvlsZF5ss3YBKT/psOCrr76Kp92zZ08AwbPOPrvpkIL1ZBvl/tbEJipgmVtPzjzb2UaeK854W3eQLAO/W3eYPB/WzaRbH5bDDbYlahbVauCeLjTdtgwAsGb15nJLc9cG32aufyv/kn66oYxzMUKIEnHN6X0BALtmTQcAbF25FUAxSrth20rfB/Xuwv5cf1vqLv2EEEJUHhkZGchIYXFqRr4Wp8aVv6OOOgpAuOs0q/5ZtcnubwMy8dMeF6aiU922Cl5xIdjLk9xcfwEc3+CZt11oFBUmndupIITVwZ4Dq/7YBUjWnR6JcvEXVjbOAPCa/+IXv4g4A+JAw5kQq8DZ6x/WZtgWrDoW5ZaV+9s2FRXcy8X2YcJjbXntjJF1TWfLDkTP6JUXYfcZWx7bt20wq6jgLlEBaIDiXezZ+4Js3iuGJk2aAEjuP+61Yztg22R/tf3UBg+zz0qmY/tHWOCyqEBKpHnz5gCC+zj7MdVvliHKnTHboTvzym22P9tPniu6PGZZqI5v3ry5yDq49bR157mxbiFt2aICGtqAjkXNZjAttgFR86gWA3chhBBCCCEqi5QDMKWwT1FUi4E77bGtsgQEb/JUG6w6XJztJt9uqRBEhVwvCmuf6/ENvnb5eaDIbuzb3H34o28jaMM62zf4KIXdltniHhcVVILn39r5Rc042GsRlZ77P6+5qHgY7p7YtsPvtOcMu37Wftwq6lblsiqgbRts32GqGFUua19qlWabB2erbF9nnq73lszMTEyf+z1q1aqFU2oXeq5qWAclpW7TwrDyDQvvN3UPKkx/a9K+LAftgq2aF+VNx9Yjao2Cuw+JUmvt/vbci/KBwc46d+4MILimtIl2Zy3tmiHbZ/g5f/58AIGC27Jly4Tjbf9meps2+WZcbhtgOXjdaQtOdZvQYxifEbbdENbHXecEAF988UX8f5u2tcm36je/85l+8MEHJ3xu3LgxoWxhZWDdqd4Te654Hn788UcAyap+VCBIO+sHJJ9b9nu2ieHDh0PUDKrFwF0IIYQQQojKIuUATCnsUxRpPXCfMGECgMC23drFAsFbcpSv5ih7a6v0cf9UvLJY216b5rbGhwEAmvcstNN7/psi6xnFiPMPj//fsDBgS7269RLKaxUzq6JEKe/Wlr+omQWr5NG+nivurY1w1LqCqGvk5k0PGm3a+CHi2QYUav3AM2nSJADJAUxs27Bhu93f2a6Yhu2f1g7X2m3b/ak6ha05sUoy87T+z619NtOkcmf7epjNvGs/Xrur7+Wi/am+qjiysZ9OzuIcAMC2lUEApows/9y0Ps73q91mgB+AqcExfhovL9meUGa3XtYLBrdb5d2eQ2vLbL1rhFHczGKUD3h+V7CY8oGqsL1/F3XtbDu3fYgzS4yXUZxdtm1vnMkCgjZFdZhqOPsenw3WRtz6kGcZ+QxhumGzAPb5wt9oL24VeHse2N/5bLcKPj3cuGWMuu/wnGRlZaFfm8J8vQKga1P/0yWWEfK5AysLElV8YtV51pNtQtQc0nrgLoQQQgghRGUTy8hALAXz6VT2KYq0Hrh36uT7p7W+1F3Vx9rOWvs+/m7tsJkWbfSK8+vuKtdRPqctTQadDAAYedVKAMBjE+ZFV9bh2p91BQB0u+b8+LaP9rUAANSt5deDPl5tFEW+nUeViRTn09b9zdrSWgWd9oxUXez6AWuDaVUVV+ngNqbFNiAOHJMnTwYQKE9RhKlOFntN2UbYTq16ZmdziLWdDvOYYvOPCrNuVT/+TlXNKtXWkwQQqHexWAzTv9+PzMxMnHPquQCAg3osBwC027TOL2vu1vhxnDKt1cKfQco61O/bz33qu4ytW9fP0/Vaw3NlozpbjxbW805xM4Fh/tyjIqRGKetRsR2YppT3smHXYbAtWO8sQBBPxM58Wftp2rbzmlFBt+2GNt/cLyxiMlVrfubk5CSUi3blUe3Ero8hLCNtxMP8m7do0SIhL5uGnRWy52PDhg0AghkI1oH3Ac4WsO4nH9YUwG5fSfd2+p8ZAPbvR6fmMXi7cplx4aeZFSmMYhurlZXwvUPmHqDQgyzXwyGWAdQGkFELyM7EJ6t3RnrME9WftB64CyGEEEIIUdlkZKbox70m27hTDecbN9VkVzGiKmA9L1g7UWK327dzEuW/2P3Nqtr2jX9L+xMAAJ2u9BWC37bwy7/+y1UAgL07fRWlbmNfAWh1vK8uNz/VD4M+fXWgVjRrlrgqnQodz5FV1YryQx9WzyiFBEhW5+25s+fcKkB2NoOfVExctZH1oPLC+okDB5Wm4jwxWXvbsD5Gdci2BR4bFcU0as1FlB23+5ttn7ZdWt/Rdn1LcZ6n3Dq75Z7x9drCX7MLFdHW/mdGs2Q7+61+mXasXgogWc2kChhWHuu33c4M2FlF2+9sn7Y2wUByH46KIlvcTB7zomeia6+9tsj9RSLsi7w3Wm9nYeprq1b++gnanXN2iN+JnXGJisdhbazdWWj+/803/rotel2heh2lekd5FGPeW7b4Acmo+rszbtxmo49GpWnbvZ1p2LbND4K2apX/HG7dujW6Ze8E6hcq+Pv2AjgIyPPz9aik79+X+L1Ypd3/6hVWJf7d7u8cg8Lb2969e+P1dmcCRCWT4uJUlHHgXrajhRBCCCGEEBVCWiruTz/9NACgb18/xLhVeVzFiG/ftHujvTUVeGI9YUT5brYKdZgSbaMKWnXbvulv6jwQAND2l763mTbbC+349hfahGf7qsoPddsBAD5c5tu/ZmUFb9pMmyvirS/nqLyLU0/t8a7SZpVMu4+1V7RKu1VLuR9VdKucANGqD9vEddddF1ofUXLosYcqHq+Hve7WywwJ83QR5VPaRva1RHlKoeIYZgtvfSITqtlRMwhWweZ+bJ/WNtYtn1WzraJoo0/aTyqUdg2Ae47tOh3br+yshq2/VWVZJqbjqvt2TQnPnb22xam1Rd1HRPGMHz8eQDD7yOvA5xrPr+thhM863k8Z+4IKbdu2bQEEyjLXRdl2Y9ubnQl12xfzZBuyfs7tTFtY/AUgaKN8ThcVN8X2sag1VMTOCLJt2z7IvLdv3w5kVy2Nc9OmTUmeedhGRowYUWnlqunEMlJ0B1mTF6cKIYQQQhwI+h1SOOjfEwRf8gpNYmgCk2QaQ7idZi58weD3wkWp8e8ZiS8vQkSRlgN3qwTwDZs2Xy5R6gCVCuuhgVhlL0z9dfN2ifJTbv2wWhVqbS1fLc9q0Trh+DVr1vhl35ebcJzrQYAqAdUU2gS2bp2YlvWHG2WbGqWmu/WNsvvnubIKTdQ55v78tN4A3NkR69mAeRTlV1+UjFdeeQVAoOpFqcjE9kfrecm97tZDC6+t9fTCWTM7Q2T7Iz/DInXaNm7XUERhy2A9U9m258I+aVVtq1paD0vWk5LtM26Zec6iPPDYPKNsfK1/+zCiyhcWpdolSiG114kzZYBmy4qC7ZyKOtsH2yTt1t3onmwzXA906KGHAgg8tDBCKG3d+Z326NbTmvXeFjY7xm2NGzcGkLwWjGWyHuCivBTZ7UXNrEUdGxWxO6oMTNv3UrMl9NjKpn79+knP26h6iopD7iCFEEIIISqYTlm+69vY/kLhKmwnLjIt/Jq0j1XYo5R3q7SHLU4tJFb422lH+AuO3160PnJfUX1Jy4E7VZ5NmzYBCPzVhvmVtTakVCr4SaU6KkJoKpFDLVEqU3GeXFhGa8dNFd1Getu6dWv8WM4o8FiqhbR5Z55RaqMtk7WBj/JAEQbz5jmO8q4TZV9v7WDdmRQbjZVtQDaz5Qdna6iKujbPQKDEWfXMen4JU6Z5jPUHbmdO+LtVrq3PdebFdhEWzdR6ponyNmFnwGxkURsTwe0L1vc707C2+HY2ySr0dq1NmMLI62FnOqL8s9vvxN4b7bl0yxEVz4G/29lPO/sSNbNnZ+FEIn/7298AJMcTifLJHuaDn88NtjXauvP5wWfEd999ByDZ2wxhGy7qmvJY9geWh23WriGzbdauiWA9mS73d8too8nafm+/23UmLBPPz65du4Dw4KVVjoyMjKRIsWwzV199daWVq6YSy8xALIXZ/1hm2cYraTlwF0IIIYQ4kMQDILmqeEaEeRiPKUIxD6UoW3mTFveJyR6+RpOWA3f7xk+Vi9vDPDAUZwMdZa9dnCoX5sfdbrMqo1WHqUbY1e3M6/DDD084jurccccdl1RP60kjSu23KgOxMxNWpXTrGRUhNtXZi+J8yFt7YLfutlzF2S2L4nn11VcBBBFSbTuM8khkZ1asp4uwvmE9C1lVjBRnQ11U1MCoWAs2Tf7OmR22N6ZtvctYO3sg8JVNTx0tW7YEEMw+Fee9iXlytmPFihUAgNWrVyeV2cZmsOtx7EwB+woVUztDYq+BO5NgZzFtH7Zrf9wIsm4eUfV383riiScAADfccEPovjURqsn2GWI9HVkvPi78jdeG14xt1HqViYoSzrKsW7cuYT83zgaP+fbbbwEAHTt2TNi3qPgn7nZrV8906WOdZXXrZT3Y2FmgqHgOUWs/0oG8vLyktWRa71V5xFL0456Sr/ci0GhHCCGEECKKFFT0KO8yHr9nJKrlHoxbVhPICQA8ChqZifb0fhAo4H+OagMAeG3equLrIKoNaTlw55s/V67zbTzMdtq+2UfZWkZ9j7LBi4oc6B5jFWe+EdMue+HChQCAxYsXAwD69esHAOjevTuAQEmwqkTYG7XdZtUzKn/M85NPPgEAdOvWLSFP2j/aeoXVyZ4LW4aSrg+I8nfvnltr48xPRY8rO/TBbP2DW1W4uD4QFRXR/c3al1qvJVZRt33AKvRhtuDWg4lV532vEUGbt4q0jbxq4w2EzfJYdd56bCkuwijvaVRDGavihx9+iO8zf/58AMk+s63HEZaF+1GBp9cQ66M9zBMM62Ft0a3veGsLb70/WcKUYXnFSIbXiteSMyB2jYhdrwAkz8TwWLZz2om7vt+B4NpQSed+draT6dg1MADQvn17AEG7IKl6NbO+5O3sdefOnZPqaW3Xo6IzkyjvUP7+u5FOFBQUxK+XOwMiKhZ3zUFx+5WFtBy4CyGEEEIcEAoKX1RpmuQq7nzBpG269evOF4EIld6Lsk8vTIeBF8OOiQ/36AM+q3bSvqLykKlMCLSBPOqoowAk+2+1qp37f3EeTKKI8hBjVcUwtciqIdYmv107PxLq+vW+S6f33nsPADB37lwAwODBgwEEdrNWRQ9TF63yQhvZmTNnAki2EWQZbIS6sIiw9rutu1XsonzBk6jIlVHpuPUibAP0jCA72ZLz73//G0Bg2x4V9ZNYZd2uvbC4yrRVpK2qXZxNNOF+UdFR3X1YLtrA9uzZE0Dy7FJUm7e/k7D9bNstbqaPFGeHy3sAENgNL1++HAAwZ84cAMDatWsBBGo9lVA7a8GZPOvXvihf+MTOttgZhSjb5ajv7nbW/fHHHwcA3HjjjaipvPzyywACj2nW738UrnrMmRa7topxQXjvZ3uxEYOpxFNZp593zt5ydsi9hlT1WW62PZbf9ltbH6uS2/sF1WTX05hVmK3HIxvV2LZhljEhhoK3HelERkZGkpcvtqHzzjuv0sqVLtx999245557ErZ169YNixYtqqQSFU1aDdyFEEIIIQ4oXuELaKH67e11nDhYxb3Q3jyuvFM5N4p7lCcYa9vuhSzIz6h7UMJvMZahsJw/7dkBAPDekpyi6yUi6dGjB955553499I4vJDiHoK1ubMqlo3ECQRv9lbpKk4RskR5lwlTQKL8R4d5bQCA448/HkBgu7ps2TIAwIsvvgggUDi6du0KADj66KMBJPqypVrKNOiT16prtA1kGoRloh1slNLmbo9SFe0xxfmvj/IRHea9g1jvCjwXsu8rOdbPc5SHJRtngPvZSJ68XmH20db+NMrzUnHem7g/1TKqgq7qz32ptPfv3z9hX6u8Wd/YVu2zZXHziopmavsGy229N1kFsqiZQp5/RsKkcvrVV18BAL755hsAwSyUtQFm2iw/87b2yG59iL2nWSXVeiSx54UUVT/FZEj2RmTXTEStH3Jnoe0aBl4L2s0zoirVcX4Sa1/OeyvLxvTc/m37qW3XPMbGgrBt0d5zbN9jGdx9bZuy23mfYx7Wjj7BK0sQrDut8DwvXm/3HIniqVWrVtw7WFUnrQbuQgghhBAHkhhf1qmC73EUd2OLbpX2gj2FC1utKV2UmVNBorqfUI5ahYu+6xS6XS1BHUTJWLJkCVq3bo3s7Gz069cPY8eOjYsiqRKLZSCWwsLTWKwGKe5CWI7M9mcaCnJ9Tz1HdW9cmcURQgghRBrRt29fTJo0Cd26dcPatWtxzz33YODAgViwYEE8bkZVIq0G7naaOSp0sTvlW9yi1OIWRlrsFF5RIbvt9LBdvGenQbnolovMuNCHx9EMZsGCBQCAoUOHxtN68803E/K0gSs4zck8bBmiymj3c+vE/21ALHtMcUE3irsW7vW0i4NF2eFCLxvEq7iFlNbEhNjpcU5Ru8fYqf+oAC3EmmLwOLbrsMWfbJc0keHUsW1DxbUllpUh4m1QIiD53mMXfPIc8NPeN1humhnRnIdmDWH72nNFkzuaw7399tsJ5Wf9mXaUOzy3f9o+aK+5NZmxblqZh73ORZkYMv+avNDcBtOiiQnN2awL3qLscWkCYq+3dQMa9ezjfmwD9r7v9h9eO5aXbY2wv7IfsC/Z52pUQKmwZ0WUCabtH3axujX9Ia45aJhv9YI8/35JFT6uvOftKvy9cHvEfcUqstyvIL/Q6YRj/5xJc1jroSZifLJnz54kl8mieM4444z4/0cffTT69u2L9u3b46WXXsIvfvGLlNORjbsQIRy6xvegkTv3cwDA/A99v/R5Wwp9VB/qvx33OOHcSiidEEIIIdKZgw8+GF27dsXSpUtLdJwG7iFEvYVTCaBa5b5pRi2MtGq3VfKorlHhoHLAT6souW/pUUoW86CbLebBMlAJ6NChAwDg66+/TkjbLg50lQ4eaxeYsQxM07rbsmWyaioJc7XJfaySQQWXnzZAjFVuSJTyKeWg/KELSCB5QbINMGQDMBH2Be4X1WaYnpsXiXIraNsUy2BduNm25PbzI488EkDqC5atmseZLy723LBhQ0IZXKWOwZzoZpUL/Zg3p1tZTvZ9O9vBReb8ZLA2N5w73fARe26Y14UXXggA+PDDDwEEi955XVg2q+K615HltTMJdgbELsi392LbhsKul91Wkxep2ns+F9+zz9HVIxVrq54Dya5W7T08KrCfvZbWzSAJU7+jXFBa5Z33BLtY1bpmJLZthC1Ct7NBdhG5nVF070suubm5aJFV6MK5Fu8vTlukjXuh8l6w2//cu71whnyn358K9ppZtcLBWmbtooddmdnBec6k/4lCjzSxELe3LvXr1498hovU2bFjB5YtW4b//d//reyihFK2Yb8QQgghhBBpyq233opZs2ZhxYoVmD17Ns4991xkZmbikksuKVE6GZkZKf+VhbRS3AnfpPnGnODGCeHKbZTNOvelmkYlzNqmMnARg7bY4BRunlGurKzNsLUR5H5NmjRJON7ODoQpmdZ9oy0D04xyT2dVmajAMW4dqMAcvH2Vv6HQpywoGPAz5qsqn3z/vb9/4bmjKlnUtWmR6x+z9e3pAIC5//gUAPDcBz+EHkOefq7In2s8rsIdZWdqlVxr2xqlwEUF5nL3se4grQ10VJAUHmdtv8Nspxm0KKr/2T7DvD755BMAiE+TRq1joVoI+CodEAQ8o/J+2GGHAQjuG+yzVpHfsmVLQprWNpzKOxDci6i820BSVnEbNGgQgMB95Pvvvw8guCewP1L9d9sGy8NyU0m3axKsjXVUULYoN5nuMaQ4F73VGau42xleXjP2A87QuDNaNo2oNWJRbnyt21DeJ+yaibC1MPZa8rlK7Ay3vdZ2RsemW1Twwai1K7ZP8ZyFuiqtUzjbyAiltYJZ3wK24ULlnQr73lz//OzZ6s865OclziJmZBU+awsVd9q6cxCXwbVBWcnDsrgPeCrv/F7oneST1TuRkZGBOnXqaP1XKVi9ejUuueQSbNq0Cc2bN8eAAQPw6aefxl3qVjXScuAuhBBCCCFEWZk6dWq5pBPLiKXmDjKjbI4902rgbt+k7ds4VSlXCaMqSFXKqtcM/0xlndsZnMKqi1TWqHSwLNzulovqVJSSRNWEeduQ8/yddoNUvazaAgRqGpUNngPau1ovENxO1STMvhUIVAyW0a1Lhww/r1i+ifhmV8AX0r9VoU/aTL9sXxWqk61bt/bTMddm06ZNKHjlWQDAmLvfCk1TlAzatrueUay9uJ1dsUF2ooIlMZ0o5d3dJ8qrim0DVnnr1KlTwu9Un5muG5SsuCBi1iZ25syZAHx/vm5Z+DtVT/Yd13belpv9j4HQ2rdvDyBo6zzX7NPsy1S9qZxa+1z3nDAEPfsm1SHraYf7c53Lz3/+cwDAjBkzEvLgPdK9XjyW9eE5sB56CMtpg3kxj6iATmHbarJyaFVktmuefz5veJ7Zftx+Zftt1L3d5mln1tjOrGrOMrmqpFX52ZfWrVsHAOjdu3dCWdgPrOLOslu7/LA2EaWsR3neYfviPZC/z5njOz5o1aoVlsKfLeuW5bf7WB0n4FOhT3XsMrMB+wrX5RQq8Pu2J3qfou16rcJPKuseFXjOdDpmFIHSXvRgsGnTpvE+xrZRk/tPZVFRi1Nl4y6EEEIIIUQakFaKe1gIdSB4w6T65vqNpg06VTK+4VNRp5rNt1XautMG1fp4tR5OqHiEqVTWt3GUokmFjKoJ1a2WLVsm1IeKWZcuXQAk2rjThzPtculBgmlQsWAe1tOGVb5YduvXPWGWI1Zom7+v0E5xL/3aGl/wtIct3J5Rl54R/HrxWtDmtsn81/1PACOltJcrVhF1sTbtUbMw1ouM9QhjbWjD/ILbtOx29gGm1b1794Tv1k0X+5zbD6O8Klibfab5feEaDLvegx5deC+x/dvF1oPnefny5Ql5MyKfXa/CenO2LsyLhj3vvP/Z+wbLbcvE7RdddBEAYNq0aQACO3vXa431zFFc7AbbZqzdsbWrdq+XXd9Qk23cOfPCNsfZWN6/GZqdzy93xpdEzTjxPFMxt89V672N92c7O8RnSJiyy/ZivSNR1WasAftss16kbPsL857Dc8Xnq73/8Fg+h1esWAEgeLbzWcky8rzs27cPXu3CZ7Vj4x6rXbjOI7vQw9xBfh/fs9Ws8yj0y+7lF573LOOv3ThLo4lFgqmFsW2PK+8Zidc2Pz8/fq6tdzdRcUhxF0IIIYQQQsRJK8Xdvo1TzaJSQBs8q5IDyUqQtQX/4QffSwnVKpsG396tck/FI8wzii2vTdN6WKDizP2ocKxfvz7huLD62W38TiXD1svaJ1t1xvrRTsWXejzKXOEnbd7jvmcLVQJu57nntaASuHPpkmLzEqUj35x7IFndsu2SWN//1qY9zNe/m767T5RHC7Y7trdjjz0WQKA8fvXVVwAC+1TrL9ytF9sVj42aCaC/dhvjgIqiVdZZb7fPse9af9W8R1G1XLx4cULe7J/ERrkMsyW3Mwb2OnDdDuGaGHvOmdd5550HAJgyZUpSHXjOrLcbliEseqabl21DUVF23X3D7PprGtYu3dov89qx3fG55LZ/tlvruYVtKsozE6+p9TLE/a3vePc6cdab5eAxPXr0ABD0SUYBp7rNGbSf/vSnAAKl3p4P1uHzzz+P/0a7ee5j70PM47XXXgOQPIvBtR0sI49buXIlevTwZzaosgPBjHH8OVf4md20cJZ6Z+F6sEKbdy+usBd69THeZZK+O37eXaUfcGzeDbm5uUn9pqio7uLAEItlpLY4NSbFXQghhBBCiGpPWinuV111FQDgrbd8u2frw5a4Spj1L0wFzHp/sJ5crB9iKhzWn25RdpjWV631xkGs4sm86Au6W7duAJKjLVJtdLfxbZvHMA1b7ijf6Syj9asdSizR3o5qgJdRkPA9yS6vMA+eeyogvDZ187Ua/kAR1m6L83Me5THFKqLsd9YG3m3v1v8307QROrlmg2nR9zhtYm27DLO5ZuRhKnJR9aE3GWsjaz2pENqAcx0MEPRFew6ZJlU/9uGFCxcCCJRSKqfs+6wn03N95lt/1PxuZ9Ho0ePoo49OKKO1deZ1GzhwIADgyy+/jOfF8ll/+zzGXgc7c8c8eS7tWgS3bUStqRg3bhwA4JZbbkFNwW1bQPK54UwvrwPPs/tMiPIqEhWB3MI87Cwdv4d5GuMsFT+ZB9svvSpx5oB9lGlTiefzyz4r+d1dx2aVdhtbgGkyD/5+zDHHAAjGEXbtSCwWiz/jYllB/4vVK/TURh/vhWo8o6ySzNr+7/vzCvPl2pnC7XE/7oUKu/U2438xftsj6Nu6LoC6+GhlbmhbEBVDLDMTGcVEt+V+ZUGKuxBCCCGEEGlAWinuhKvCqU7xDZp23C5WKbL2oHwLpx90vnVblY321/a4MO8I1h+uPaY41dsqIfQi8+233yak4+5n1WseY9MM85sMJNuVWiU09DiqEbQdpgJh96NqQHu9jETFnXlRRTmkaUOUlqe9FaU+tiZg7aNdqFjZiKjWltW2JbY59hnrAcJtp/yNn8yTym6vXr0ABMo0o5haG1dbtjB4zHvvvQcgUNZ4zKpVq4pM0/pxp/0uf3d9xrPuUZEerX0x71W8l1HFtwo77YndmcMo/9u23uxP9GhDzzxRkTJ5z/jiiy+SfrP3NNsW7PUkdgbPtr+wiNNRedcE7rzzTgDA2WefDSD6WWGfO2HPkqhjbP+1sRL4O/sgZ7TZz6OibwPJa6LYru3aD6Zx5JFHAgiebVwDQq85VI1rmVnaPn36JNXXzvRxFpppsgxHHHEEgOCeYyMP8zjrtz4dyMrKSooizTY1ZsyYSitXTaGivMqk5cBdCCGEEOKAUihOeZmOO8h6hQJVvUKB6WD/I7NxC/+zkf8iULvpjwCA/Nyt/qdZrGrhotR4gCcAMQqIKSx4FDWHtBy4W0WMn/RDbH2Uu79FqeB8s+dbKt/OqerbCG/WNt5Vi6wNKVWoKFWbKlyUjTE/7ap+KmluvbiPtW+z54pYW1qrukZ5GAGAJXl+uQ/LKrwetHE3rYrbaSfIlKziTrvH2l2Ojh877CR/JuS5D35Iyl+UnKLsWqm8uVFV3WOsb26rhhGruId5B2HfoCJHO3TaZf/3v/8FEB1R1dpIUw13bYP5G/sw7wFs8+x3dibMekTh71yDUZS3kyhvKvaewHPDmTz2Zare1muVG7PBzmzYtG2eVs0nNholr6t7Dqm2Wu8m1qY/yltQ1AxeVJnDfitynU01Iypmgn3+2OdV2Pm01ztq5sJ6CLLPJdu/7WyQO8vC5w+jqfJYG7nbrhnjjDd9qn/88ccAgEGDBiXUhc9l9zxFxQpgGjYPfmcZbGRV/u7PqG1BOlG/fv2ktS812TtTRSPFXQghhBCikvgBjbFx40YAiUGuOBimSMgXWj+IUwY6duwItOuMQw89FE3WzvOP2eYHePJ2+S/jBXt8AYGBCwkDOwHBgtf44li7SNUrSPxE2RY9irIRy0jRHWQZxYi0HLgz6iDtRPlmyY61cuXK+L5UtGjPZtV5qxTxLdwq7VTbqHRYlSoM67/dvgkTKnrM06onvClQOfvss88SjnOP7du3L4BoW32WyardViFkmWnvF6bUxhUd613G7hixIp7nzkZsnL3f/9y+fTtOuf1SAMBBLWYAAMZPWxSaFrku1gGAbN2jKMom1qrYtm3Y2Rir2FpvJzaOgXsMvTf169cPADB79mwAQTwFKmtUf+3M2OrVqwEk27O6dudUi2100rAZObe87OuMpGjtt6nYu/7SbZwE9jtrJ0/o1z0nJydhO1VBq3K6fd3mwd94DPsRz7FNK0rBDrPTp50v0+B1YRuwawzsmhjbFqJUfndb1DqBmkDUM8KuI+E5Kiq+RpQdfJRHNDtbQtWcn/aaRa2XcrH289ZDjfVsxP7Ndkfbd3qjYZ/kTBSQbKvOfsk82A+sJ6Qo71g2OjA9s/HTxc5GMiIsSeUclScHHXRQUjT3otYBifQkLQfuQgghhBBVnS2te/ovMC38Af4h+30F39vmf8aV+ELl3XX9GA/8VIw7SFE1kKlMEdDnMd92+WZso5oCQdRRKlxUy6gAWU80fAvn71TnrIJk1YowVdHa3lnFozhVLkrxpHLIKTwAaNu2bcI+Vj2xedjoclGKmF2pH2bLHz8HzBvFUOCrMBf191f3P/XPjwAA69atA5AYOXYGGqNly5YYeIevZIzp/x8AwBeP+369ZyzfWlxuwsFef3cbsWof22mUN5OoqJlhNsrsuwMGDAAQxGSgvSzVMbZnzpix//J39mMq1iyDG9OB5WZkVJafyhzT4nb2dbZ59jN6n7H1cSMac9aI9xOW38ZPsBEwrSLJdDhzwDK4qhnzdX1ZA8Dhhx8OINkHeJS3FuZJu2TOVPJ8AUG/572V5yhKtY+KyGxV3jDVtrj1ATWBRx55BEAwA2XbDduDnUXhOXL9ntt7fNTMhVXD7XFhM0xA8Jx1n7c8xq4HYV9jf4iyu7Z+2zkD9+OPPyb87rY/tteoKL5REUSt33aeY6r9di2Pm66NSks4M8BnmFXkD/R6jX379iXVl21KVB/ScuAuhBBCCFFZ9GlVG0Bt7P74dQDAhrm+28ndm3KxF0Ddpg2xF0CrAb6L2819LwYArK/d0n/JqNfGf3H1x/povM4PFJVg807nFzaYYSGx+MtuzTEnq8rEMmKpKe4ZZTOhSuuBu/VMQbs3982Ydmncl4rcd999ByBQ2K3nF+ufmEoh1QeqDGF2mXzjtZFSrdJuVW67Aj80khuA/v37AwCmTZsWz5PbrBJAhcaqLqmWyfr6dW0q4+eZLrNihfatVN4jVJX4zaYgsQzt27cHEKiNrk3lp9v9fA8aPAwA8JPjfD++x8/9HACwe5PvVSQzK62b9AHnwgsvBAD83//9X3ybVais3altx1FeKNh2bHrsn0AQnfPf//43gOBaUy22sy7sb7Txte2R6rm1RweSPSux3Bs2bAAQeKJhPZgWVTPmwXsElTg3D8J9qAzyXmQjMTNvex/hOWceNk4ElXj3f3vvmTt3LoDgntepUycAgY2ya/8PBLMYs2bNAhBEc+V6ASBQ2jnzweti7WetWst62TYRZU/s/hbVvmoSbD9s95yh4fnkdSFh8Rl4r7Zey+wMjPW1b9e4WLt0/s5Pqutu2lEKM7fzucSZNpsW7xnsoyQVf/X8zjbLc8k8WM8wDzVAcI5Z37C4Kf55Tox3kQp2poSUVxvPzc2NtwG3H4vqhUY5QgghhBAlgEr7rf87segdH/PNni4/cToAoOeN/wMA2DnkFwm7bW55FDzPQ7PdgUlSbH/hS4NcOqYF8ipTBFZd4Fs+bTtdVZgKO/elUtGmTRsAgX0clTK78pzfifXgYFUioHifxfZ3azdvlQDWgfalVPFcWzZuo82vPcZ6xLD1iPK/bP1nh6mNcai8I1F5j58Xc/Oh7nPD2f5swSdrEj0NJPrT9aGKsrqhr7hvrN0xoT70+yuKxlV+rB229R1tfY/b+AJ2lodthf2RKjsA/POf/wQQzGBRHeax1osT+wLVc/p5pprMslKpdvsE04iy8WXfPu644wAEbYvqPXG9VLn1K8pnNlVxlsv2LzvDxe0dOnRI2E7/7pyJcOvMTzsLwbx5b2PkSHri4XlhmaznKNdGntfJthF7X7WzhbZM1hbYzvi5/1v795rkVYZwXUXXrl0BJKvdPEc29oJ7f+Y+nEHisyAqirb1FMT97BoX5sk24KrFTIP91a7LsjMvTIuzP2x79BzHtsnZIGt3DiDJiwojBPPewXPJPFq0aJFQBqZp68l68dy6bbg8vMXEYrEkpd2uWykpe/bsiZ8H1ltUP9Jy4C6EEEIIUVms+fjrEu0/+ePVhZ9/AQDccKnvlOGwX14OANh2+MkAfN/xXAjsCoJZWVno3aLwpbbAKPCxsim4onyIZWQmeAUqar+ykJYDd2tvzbdUfqdKBAQKO9+aqaZRxWVaXL3erVs3AMmR6axSxrdv6xnGPca+0VuPC9bTC9USqgzWptj1mOHWG0hW2vkmb73oRNmwW9t3ltna/4XNLHyZ4x/bq3lhc/I405AYHCLS9r3w935tfYXku93+uadC5M6gsHysly23SA3XQ4xdr2GxttS2bbg2rkCgaIWtxeBv9FdODyn0wmJtWtkP2X+ZJ/svt1tbYCDappeq3vHHHw8g6BNffvllQhos45lnngkgaIdUl13f6lS3Fy1alPBbVD+y7dX2Uyr1tM911T6rnPJYqpq857E+3M7rxHsEt9O23/poB5LvDzzW3v/4afunXZ9jcbdbbyakJiruQggRRVoO3IUQQojqCk2kaDrFlym+rPHFkC9jUcGEgOBFlC/BVjCy5pDWhSfztuZQxA2GZAMZ2jyYBl+4CV9U+bLMMvAFvUuXLgCCF2T3ZY4mbzS74zHMmy+mFOcoHrAMXAgbZdLKc+u+PDdo0AA71gYiQWl44vlvAAAnvz0GAHDm4ytwEIBlR/xPqMlMfn4+Plzln9s+hxSaChWKXh//sDNeXpaTbUhUIBmZqfncr4mKu6iafLnRV9t4Iz3ioD0Jv9P2HbSr5/SepvmEEEIIkc5kZCS57Izcrwyk5cCd07UcIFJ14Nu8G9KcaoFd0GpdPPEYTjNzf04BU0HgdDLfiLnghb8DwRs78+bUPN+EqYBYhcMqGXbhml2g5C7QoWJh3W0xDZ4bu8jMLpSl+sCyM8hTWChuloemSbwegRlA4sA9VVh261IOCFQSa55hzYhE0bimMmxH1s0bz7vtA3bRFtsE2zlNZF566aWE/d19rLtS5sl+aE0x2L7pMtQuqubx7J9AYHJmF+kdc8wxAII28/nnvktRtt8TTjgBQLJ5h3Wd6ppw0dSHn1xES4XQLuYktl/SrIhmPHQf6brUZLlskBsGUuJCPp5bLrxnP6Wqyd/tYuOwOvNcsk2wb0YtOuT1s0GrrLobZnpnFc+aGLL9/vvvBxC0B17bKBenYe4ErUMBawZpzaDstbIBjazZGvdzn332+vKTbZXt3D53rAmcrRfvG1TL3fu/DZBk3VnaNO2zz97vbNnD6pmRkYE6DUvuDjKMdzcWmgHe8HcAwE//Vg9dCn/7pP6R8f3cPvPJ6p2F97qM+IwHn8ccX7ANiepHWg7chRBCCCGEqCrEMjOTvOlF7VcW0nLgTpWbtmt8+w5zH0YVjW/lVIqo7NEFnLW5oyphFTHmwbdv2tUtWLAgfizf4Hv27AkgUNvsAjRXsQOSXWTZBWzW/aWrCEaFn7dBZKwLOX5S1eLiQJ43lnHFihUJxwPAkUcGagCQ7MZxzjq/nr1b1UnY76l/+qvpeQ3o+ouzHe3a+deK19W19+P/VnFnmxCpcfnll8f/f/bZZwEkK27Ehim3C4PZB3r18iME/uc//wEQKNxcgAoE7YtBgWz/i1L12D6pPFKBp6tGtiF3YToXZ7Kt0F6Y7hLpLo19uXfv3gn1tcovCVtwyv5CtYuL3HluGPDNPRcu1u6Y5ykswBu38T7CfsRzwXscF6y3bNkSQHDOo9xIhi0CdRfgAsGMhp3xsDbXdnbCuuQMm8FjmjYYXk1U3AnbOZ911kWr/XTPJ8+jdWls7eBt4CXrQpjtxAZFY16uEm0XKVs3xPbeYvdjHpzpta6RWbYwlZ/KM79zlojt3jqJsOeDZbTPX5bBnfktKChA0x4dC78Fz/6yMDPHv+d1f+HN+LaTfuPn8UHuQZHX3FoVsM2I6ktaDtyFEEIIIYSoMmhxajR8k+ZbOVU2q2i7+9qAL1SIaO9JRSxKXSP2d6oOVPOAQC2jsmcVDx5DVTEqIIa1wbO/h7lYsyqaDfRi7fi4v1UR7SyBVUjdehSnTH6wfGtoGXnuqRjw2tj1A65CZF1kch+Fdy49to1bpc3aqfLcM4gZA568//77AIKgMVTFXLtcBgGiCmzDk1u1jHkxwJgNAGZtYN22QnvzpUuXJhzLvk879KFDhwJIVv+sra89T656SFt0qvxUMQcMGAAA6NevH4BgNsIGh7J92XVr6ZbNrTPPq70u/E7bXqqUtj62HtaFo1tnew7svcmqmNYTCcsUFijI1ovliUq7JsH1CYcddhiA5HVRdo2BC68724ldV8A2Zmc/+MnZLbbNKPt6N0AerzfLFRXwL8o9KPPmM5PtiH7N7doYN23WhzN9UbPQxK4d4yfbprteBkjs/zt27ECTQT8p/PbP0PRLy1MvLoz//8ApnwEABv/kHAB+kEK7NsX2G7YZUX1Jy4G7EEIIIYQQVYaMjBQV9xroVYbqHN+MactJG+mwACJ8m6ZXCip+9PpA9fCkjgcn5PVVTnjgFNrThb3VU1Wg8k5/qlY5Zzmt2s2ysp6sl1UpwpQouw+VQJbFvq1bLxB8e2cdOFNBtcVV45g/3/RZTquq8NxwhoTnmrMBVn3lNQnzmMD8ef65jzsTIEoG7d2nTp0KINnTg53J6tSpEwCgY0ff/vLdd98FEPhatoopry8QqEH8ZJrch22DKh5/53f2Dc4ItWrVKiFP1yabbZdtncd8/bUf8ZAqPbFKNLHeKIi7ruKTTz4BkGzTzTzZN1herhmx9w97D7Dh5YFACWS97GwT02D9qF5yP6redt2OVfLD6mM9lfBYazdtZ2nCZkPddN3/reevhx56CDWVu+66C0Awm2XXI9jr4j777HoEG4TQPj+4n5sGkPy8ivJGAyTbqrP9WH/zNpgby8/7Ou/nbLNcw8I+xzoAyWuleAzvGXz2RXlxs32NMw3urEHflpkA/Pp/tDoPBQUF+M/6DBx88MH4w5gzAACj7/wPypuc+f6sYN1O3/p1yOqY1E9sm2CbEdWXtBy4CyGEEEIIUVWIZWQgloKanso+RZGWA3faW1MBoJJAGzdXAbCr0NetWwcgsK/mCuy40l5QqEIUBgXq2cx/O19h7EdtZLMwrw8sFxUA+2Zv/WDbWQHa6lE5oZ2fVerdbVSkqexR6aPavWTJkoTzwXLzPFkbReuNx1XWrHpGdYVqi7UJZv14/bgf7ZcZ2c7aIrsef6xPYev3W5Seiy++GADw4osvAgiuA9sC7WypSM2cORNA4GOc18J6P3KVairrvF5HH300gMDDCz/ZB6is8Xqz/bFsbEt2LYe7zdrNM2/mwfpZTylWUWQ6LNPs2bPjeVlf6Ozj7He2P1JR5DoYGxnTKvBuvax6zU9rj269T7h2wW597P5h9sd2tsEq6vy0PrDtmhQSVibrNzwsCmhNhTNUfG5Zbz+89u5sCfsj92VbtLbcvN7WptvOxNjnDr+7yr3tB679OxAo6vZY9lVu53PapsP+HoZ97lr13nq8sTOK7JvMK2E2bH8w67t///6Ec/H94MvQqVMn/GqB79XJtVEvKxu/9mefm/bwFfe+PbKBnUBmm84ACvBlTkG8nLJtrzmk5cBdCCGEEEKIKkMsRa8ysRroVcZ6vaBSQAXXtQe16hSPod0bFcCZy74HAAzudHBontbONcpfuguVSWuvyzLxDZmqv1XMqNJRfaBiSJXq7rvvjuf12WefJezDT6bxzTffJOTB+lBloG2xtU2M8r/s/kasUmYjbbq2zu53XguWmdfPevkAAvXE5h0W9VGUjosuuih0+zvvvAMA+O9//wsgaAvWowuvBduQOztFu3MqzXbdg52dsp5Q2FfYtqzSHrYGg22a/Y2qHT+jonpGrSlhZFJ37YVVi+16Dc6W3XnnnQlpMjLm+eefj6Jw7bxtbAY7w2FnDqyKb32BW89SYVE4iZ1x5Pm2Mwa8HlGebIi7nWnYmREBzJ8/H0DQT2wkUjvb6cKZaPZPftp7qJ3dsfvZdsI83ectryfToP95tlX2W5aJ6jiPY548jnEP6BkqbL2XtY9nHny+WI82zJNp8DnN+vB5zZm1/Px8ePuD5/2Q9gcBOAjvrdiRMBbpcdcoAMDIg8YBAB6bMA9lZe1Cf/1e03n+THks078W2bv889ezTWegHvDhhqCNiEqkgtxBls3QRgghhBBCCFEhpKXiTqzdq31bB/y37cGdg2iKM5f5q9Kp+NEzBt+6adtOFu+iDWaiXZxV2FyscmXVJ9oe0l6RyhKVgEsvvTQhPSoHxxxzTFJepG/fvpG/uWmOHTs2tAw8lzaiqvUQ49qdWhtaG/mVMC8qaTzX3E5VhcdT+QiLkmdVXesxRBw4TjnlFADAuHG+omRnZ+xslFV2geD6sd1RvSfWzpZtgG2KbYH7WVtZ1yMGVUmuoaC6b+MHsP+xPrZvU7HjrBY9W7jt0tb9jjvuQCoUp7ST22+/Pf7/I488AiDokzz/LA/PGbHxIqxdcVG27daXuvX5HbWOhdgoqHZdTJjPeG574IEHkspTU+GMy9///ncAwfonuybJbf9RsTt43e21437sN3aNC9sJ+15Y9FvbTtjfec+3s0M2iriNFMsZ41Si6FKNt7NwTNPa0XP2ls8+ltF6WvPrlKxxZmVlxc+Fnb2oaFavXl2jvS9VFbQ4VQghhBCisnFeTgp2+wLUwDYHAdiFBbv8F+WcJt0AAIf97rcAgDFH+u51F77wKQDghTlrS5ztWxv8vBp97C98zc/zXyYab/JNZRrupivYJknHiupLWg7c+eZMBYF2s2FeZTIzMwHP6XTG9ykVoniURWMPGBXhjWXgW3mYqkhsZDOrSLL8N910U1HVLhd++1v/pkLlxvrmtX6B7YyCW0+r+NnthIonVRQqG9bLTlTUPFcZslH9rJoiDjy8XtYbiV3DYT1KAMntij7hOQPGY/idipu1U7UKV5ifcCrPXCPCvOkFx7ZTu0aDyiO3M/opcf240+49Ly8Pv710KPK/n4PMTr2T6l8e3HrrrQCAhx9+GEB0hFQ7Y2DPofW6Y2fO3N/sPvzk/c/a29t1SJaw7XZGQCTDGASchbXnyj2v9lrwutvrzz5jva3YWS5ec957OcsZn61G0A+Zh51l5b3dPrv5nTFZuB/rw+9U1cOwEVSZJp8RXIvDPFkvO3NoI8r6dUpeO0AaNmyY1OcqGrYLUclUkI17Wg7chRBCCCEqhILgZdbbX7h4d6//Yt+29k5gO7Clle/aNqd+B/+F5Wc3Ys2aNejdwQ8M1fL1WQCAea99BwCYmRO4OS6Of3ztm/mdn+8V5l0oLGZSwKgTdpiopqTlwN3ajtsIja4d3I4dOxLs1n9ymP/W/dHK3IQ0+PZNu7co9SEqb9e209rxEeslhb9bm9SKgHlaRc3av9ropa7Saf1fWxtCbreKj7VvtLbtzIPpuMott9GDgLXfFAceq+Syv7FN2Sinri24VeTYFqi8c70DPb5Ydd/asvM724Gr/i1atAhAcpRdKmxRfsLZ/mzUYLt/71Z+vWL79+D0rgPg7Su87+zx65i/zLeJz+xc9PqT0nLbbbcBAMaPHw8g2tNOlB93G/mYuJ5eeK2j7ns2GrRVZ+36Izvb6M6UMe0//OEPxVe+hkI75ueeew5AEC2Ufc31KmPXY1mvMPy0syVRqrGNrMtr7c5y2Xu+7TM8xj5XqaRTcedsVosWLRLKxJm4MFgu5s2o4cTawLMstl/YdVRRHpEsYc+gilLgZd9eRcjISFFxl427EEIIIUS50q+t7/qxYMu6+Da+oHuFiju2+y8TjQu/b257fEIaXzQ9Dnv37kX/s/zvVMkbv78CAPDq0s1IlWmF7iGHNfNf0L4cehyGDRuWeoVEtSAtB+60WaO6Rj/gfDN2PVP4KsNOWKz/28T9g7dta9Npva3Y/YDkqKrWltSq95Vh02nLYKPj2Shz1tbQ/d8q7NZrgVX1ib0GVDqYHhUSVxGhzSSvOctHu0RRcVDh4nWnss3v/N16igEClY/Xmn3G+n3m9aWaH+Wvn+soaGsOACtXrkw4xq6hIDYSpBt9EkhW0uIKY1bh2oxChR2FU+ioVThzVEGzQCNGjAAAjB49GkBwvmnLz0+7FsHOePHTnT20Pu15Dq03E6va87qxn/KT6fG4kSNHlqLGYs6cOQCCtVl2JgtInhWJmoGx1zTK64x9VthZFPf/KC8r3G6fm3a9F6No857StWtXAEXPTrM8y5YtS6iv9SIVVYaospYG6+v+QCnvc+bM0cC9ChHLzEQshZgyqexTFGk5cBdCCCGEOKDQsYU78C58QY8r7oXfOThv3mg5AOCHrEMSkqrV0n+BaNDON+k9qGWhKU8JFHeSd9c9ePXVV0t8nKgepOXA/dtvvwUAHH+8PyVFhYiqjquY1atXD2tRL0l1aNMmcWU5sQq7Vabt23rYG7WNwEhsGfg9KlLlgYR5vv766wCS1Rb7yTq5foKtMmM90tjZCcJzxXPPaIC0lWa6PM5ds8BrbO0y2SbOPffcFM+AKC32ukb5MmZboR9x91jOpth+Zm3YrT0uj6ctPJU5Rih17W2tnS29StgZHn63SrtVKNnW4lGYCxesRenqXoRHlQNFlG34Y489BiBQM62/evbDMF/4UesALFat5wwYrxPPGfOmdytROh5//HEAwL333gsAGDhwIIBgRhII2i29v/DacKbaemjifbu42S2rvCetKUNwna0dvZ3tsmuoODvE9sPYC4z3QC9T7MtAYBdP71HxAXThOhmmyXbNMlhvMjYasF/m0i/6zMzMjK+Z27VrF7A3p9RpWWbPnh1vA6IKkZGRmv26bNyFEEIIIcqZuOLueJUpSHyZ9eg6upikCnJ9ZX37Kv9ldu13JVfaRRVH7iCj+d3vfgcAeOGFFwAESpJVtIFku1X7xh/lv9x+2v3tSn1XbeT/1re0VfCqQrRPloHnkGW0Crz1JAAkq6EWew7t+gEqI0ybn9b2372e1tsPvQ+wTYiKg+2b14TXzyrt7hoOqnm27fN62jQIlUR6ivj0Uz+oiZ0RCvNjzfy7d+8OIGhfbIecMbCxG+xsAH8/2hfw4O3JZ0YJZY0V3pQPlDeZkmLtyO+66y4AyZEj+RkWq8H2YWLXInBGbNOmTQCCKK/iwMAIvYxm3Llz5/hvbK/sc9aXOrfb9VrEPhOtFyL2G/f+zDbE/sp9qaBHxRKwXqKorPM72xNn2NasWZNUT7ZNG3WVadv1WywLy8rvjN/g39+SY1GkSkZGRvwc16tXDyg/wT3lyMyiepKWA3chhBBCiIqGL+ZchM7vGQ0OBgBsqN8hwXS2zaZvAABrP/LFhq9e9d3UlsSPO+n6x1/jlltuKVW5xYEnlpEZtI9i9isLaT1wp10rfb1a/+BAsocXG93R2taFecAAUl8lD0RHYLTKgFvOyiJur1tYJuthgufDKiNAsqedKOyKeioc9MlrPdZYTz/uebIzHmwD4sBDW2leD15H65WCSrv1NuMew2vN9mUVN9du1t3OWA2nnnoqAODzzz9PyDNs9odpU4mz6rFtv7ZfWuUehc3Z22/cwpFaVTuK7z333JPyvo8++iiA5D55ww03lGuZhBCirIwdOxavvPIKFi1ahLp166J///548MEH0a1bt8hjJk2ahCuvvDJhW506deLjtqpIWg/chRBCiJoOVdgnnngivo0uFKNMZOwCUmsSZgMJ2hd0umB1oSDGNGnKSFxXo0Cy8GVdAR9yyCEJedIVtGt+R/McloeLUpmGFQWYhhWUWG+aezVp0iQI3ui8jFMtjRX4+cZq+59rGh/uv+B6HgoKCnDI9u/97TN87y/33/ceSsuRj9+ql+UUmDVrFq6//nr07t0b+/fvx+9+9zucdtppWLhwYaQoC/h9ZPHixfHvxYmRkcRSXJwa0+JUIYSoOHjTpVu4PYmzem+vj+HMM8+s6FIJIUSN5o033kj4PmnSJLRo0QJz587FSSedFHlcLBaLr4lIB9J64E6V4d133wUQvFG75jF8w+f0N79bN1Q8hq4J+RZv37w4hc/FMjZkMxCoB9bto1U2/vd//7ekVS53WIY333wTQHJoedaBqoVr9mAD7tAUgftapYZTT1xYxHPJ/bhg0YZud5URG6xK9n4Vh3Ufx7bBBaOtW7cGEFxPmkK5LgWphvE62oViNggX24gN+sI2csIJJwAAPv7444QyAUG7oWoX5eLVmsbYQGm2/nFbmQh4b6gO3HzzzZVdBFECXFX2vfcSFV4q7dbtadQzkn2Mn9xug2i5zz7+xn2pcjJv60KS93zeB+rXr59QRmtSR7PYI488Mp7nggULACSb4dl6Mi/Wk2Wyzyu333/y4240atQI3es1DBKiV5nafv1X12rl18eZTdizZw/WvTQZAHDfmHdQVqS2l46E2ZMi2LFjB9q3b4+CggL06tUL999/P3r06FHi/CrKxr1ser0QQgghhBBViIKCAowcORInnnhiwouepVu3bpgwYQJmzJiByZMno6CgAP3798fq1asrsLQlI60Vd/LNN/6q7aOOOgpAYsAXYhU7a4tHNY6qMN++bYAmKglUE5muu5CBqgHzsGGgeWxVgmXi4j+WmeeS9XTd+FnFnPWmgmHVF54juwCR14RKiT3Ohb/xmp988smlqK0oDTY8Oa8nFwhT4bKBfLjw2/2N19q2gSjXooRqGRU6lokBWRjwx9338MMPD62HLZN1/UrsovL5W/3jj2ngq3CeMZmhyiNEZcKBR5cuXQAE/dUqzNZhA+/53J828uyrVLapWLswLfYZ2p0zDeu4gfcB62qS+1nXrQyyRDt1t5zMy/ZjpmndX1obfxt8MUGhbxDUddneQlvpfJ6rLfE0Pc9D6899N9X/efKjpPNTUp72VpQ5jZrK9ddfjwULFuCjj4q+Dv369UO/fv3i3/v3748jjjgCf/nLXzBmzJiSZZqRkaIfd9m4CyGEEEIIgRtuuAGvv/46PvjgA7Rt27ZEx2ZlZaFnz55YunTpASpd2akWA/df//rXAIAJEyYAANq3bx//zdrj8i2ab+XW3aFdWW5t7ixUhV1beJsH1QQqFRdffHGJ63igYZleeeUVAMF5sfbnrj0w6x51bqjc8FgqG9aumZ9UdHjOw2zcV65cCSC45qLi+NWvfgUgCLdury9nbWjrbm3igeCaRtmuExsUhvtZxY7bA/vzANreUo23XiSsas+2bb1pRHkYWOb5azo6+3GMULDTDzJzww3nh+4vREXy5ZdfAgjWbdkZs6i1RHbNh1Wi2e/DXLBS/WaaVLVt4EO7/ovPAKZJ9Z/PAtaB6efkBNGM2L+5D9PeuHFjQt6sry1TlPthlmn79u34aLv7nNubdL86+OCD0dVbD+wDFr7+GQDg3Y0l99Muyobnebjxxhvx6quvYubMmejYsWOJ08jPz8fXX39dOgcDGSl6lZHiLoQQQgghajLXX389nn/+ecyYMQMNGjSIm1Y1atQo/qI2bNgwtGnTBmPHjgUAjB49GieccAK6dOmCrVu34uGHH8bKlStx9dVXlzj/WGYmYsWYe3K/slCtBu5XXXUVgCBoCBCsJqZqZlfWWz+yfNPnJ9++aftNZY+fTNcGjHFhGj/++GMpa1ZxsIx8U43yquP+Zs8JlRsqsFRRomwKqYRQTWFno5rq+gKWl4uqA6+nnXXi9QwLTsa2wH2sbTvbEPsMt1vl3XpqsvsDQZ+1niyilHfrUYnYPmDV/WVe07hXJnl/EFUFBkzjZ8+ePQEE3sDYD6jAsz/b+7i1ibcextxngrWLt+ub+Ny1/dZVt4HkGXHeS+ghyl0nxm1Mm+XjPrY/895j19OwjHYmODc3NyF9Nw/O6tWpUwf7Fvrrrha/twJl4fBHR2LkyJFlSqOmMn78eADA4MGDE7ZPnDgRV1xxBQBg1apVCbPAW7ZswTXXXIN169ahcePGOO644zB79mx07969oopdYqrVwF0IIYQQQtQ8ohwMuMycOTPh+6OPPpog9paJjMwUF6dKcU/CVWUfeOABAIH6xrdmvnVTXaDqRkXQ+h7ndh7PT7sfkOyFwnrSqMrYVf7uavmofXku7DnkObHniLMe3N8qmlRd6CFk1KhRZauUKFduvPFGAIGtO1UzKlwdOnRI2B5mI25t1a2dKdsfj+V+VErYLrkWxapqQOBNg3lZG16rnPN3pmUjRfKT7X3JkiXxY6W0i6oK1dsXXvC9nRx66KEJv1NZtpFGqUizD7Lv0XsLf3e9f1EhZ99xY6q4afH5y2eB7d/WYxn7Hm3e3Wcpt9nZOuun3UaOZV5W7bce5xifxL1fWB/2derUwZ71vnet0tq2D3p+LC655JJSHStqFtVy4C6EEEIIIUSFIcW9fKBa++yzzwII3rathxOrKlBh5naqxTzO2vC5CoD1TsE3+NIsdqhoWEaqM1QreF7cenIbzwXrbX3hW68ExdlC87uU9qoNlXdy7733Agi8zLCtuB4YrO9o9jMb1dT6cbaeL6juc00G+6Frt8r1Lex/zDvMW1FYWewsE4+jMucq7kJUdebMmQMgUMzt/Zj9xLZ/e3+mysxnqWvjHhWVOGq2y1WsgeDewU+mbW3j3Vk8uw6G3tuo/lORt3FGeF+ysSGstx2r+rtpMM+CggJkNXKiq5aCOXPmSHEXKVHtB+5CCCGEEEIcSGIZGYil4OoxlX2KosYM3IcPHw4Ace8PNkIb37qtOmxVcyoAVAqoNrsRRQm3hUUAreqwzDwv1o7Q3UbVgSqo9XEb5SfXqqrczmsl0os77rgDAPDQQw8BAHr16gUgUQWP8r9uFXi7hmTDhg0AAv/NVNWohlkPGC42Uiq/Mw32aSp01tONXZvy6aefAgBuuummsNMgRJVk3LhxAID7778fADBw4MCE39nebdwRu96JSrtd4wQE/ZfrnHisjaPCWdlGjfwgCOy3fJ6yD9q1LmGzYXbmgPWgcs407b2G62Os73mrvLO+rsrP/HmOdu3ahRZHDwAA/HbUtwCAsQ/MRCq0u+9a/O53v0tpXyGAGjRwF0IIIYQQ4oAQS9HGPSYb9xLx3XffAUDcR2dUtDi73fqypUpXlALAY+k/NJ1gmadNmwYgvJ5U5a3Pe+s320aoJNyPn7w2Q4cOLceaiIrm9ttvB4B4gAs35HTz5s0BBLM1hGoY1a/vv/8eQKBosf9ZRZ3KHtsa0weS10wwD6p5VArnzZsHIPA8ddhhhyUczwiMX3zxBQDIx7JIa6juPvPMMwCAHj16AAjUbfYPquPW9p3bqWTzEwiem/R9zk8bKZVqvfVUY+Ot2OOsXbq7zaZtbdRZNq5RoeLO+lkPc9bjlfv8svXbs2cP0Kp0wymp7aKk1LiBuxBCCCFEefLRuv0oKCjASVePAAA8fHh7AMDSGb5p3V9eXZywf++/3Ylf/OIXFVtIcWCJxYBYCvbrIS6SS0KNG7hbTxj0NmNX2lv7dPpypR2sVdxdqBKef/755Vn0SoF1eP311wEkKqVRkShZ/02bNgEIbAV5LPffunUrgMCm/eSTTy738ovK47e//W3SttGjRwMI2gQ/iY1ISBtY6/nCrj2h2sYoikCyrTqxXjLatWsHAPj6668BAIsWLQIQKG2cBZAyJqoTHDQ+//zzAIL4C+yD7FN2jRb7HvuHuwbEemOzduLEtQ1397MeXQj7oPVa425juWxcEe5rY7JwO5/tJCoOi2vjbiOsJ56jxLpGoUF7NSSWkeLAXYtThRBCCCEqnbeXbPFFqs6nIjs7G8f2OgkA8MeLPgcAzMhvj0svvbQyiyjSnJiXSoxYEefhhx8GECiCVgkEqrcN7GOPPRb/n0oLmxBtB2+77bYKL5dIT6jAsy3R9zpntti2qHpZu1SrBp522mnx/63iZm917Lv0WENbd8UPEDWR8ePHAwC6du0KIFk9Zx+1311PY5zFshG3bewEayPO4zgry/5P2N+pmrOvAsCxxx4LIFDarRcoqvucRaeibm307do0q7i73tK4jeViPe33WCyGQS38Y/Ys9AfuB12UPBMp0pvc3Fw0atQIm/87Cw0b1C9+/+070OSYQdi2bVvCGpFUkeIuhBBCCHEAmLWBjhcOxogRIyq7OKIaoIF7CanpanJ1nk0QlQcVOetL2qpg1p6VUGVzvc5YbxI8NirSopR2UZPhoPLOO+8EEHhe69SpE4BkTzDsP64SzX5q7cxtv+aaMv7O9U785P42ngN/d1V+bmvRokVCfajO22PsejVut15lWBfrVQcIbPF5DMvHctMr1sKFCwEAY8aMgagBVJCNe9mOFkIIIYQQQlQIUtyFEJWGtSO1noqoYHG79ePM4+iD3VXFrMcnq6wxD/ppF0IE6vAtt9wCAGjWrBmAoN9QbWZfdNeZ2JgejRs3TjjWxl3gdirw1r7cem/hehR3Zo3buO7MRj+nZyrrj51rspgWPd/wnsJI4MzbtZ233rBYbtrsz5kzB0AQrVbUEGKx1Fw9ltEdpBR3IYQQQggh0oAqp7j/+OOPuPnmm/HWW2+hoKAAQ4YMwaOPPhq3sxNCBKR7f6E97QMPPAAgUOSoblHNo72q9dXMT6qCrspu/bfT0wP3sXa1QgghRGnJbHcUMlPwEpNZODNTWqrUwH3Hjh0YMmQItm3bht/97nfIysrCo48+ikGDBmHevHlJgRKEqMmovwghDhQ08/jVr34FABg0aBAAoH379gn70ewFCMxnbCBDLgSlGcq6desABC/edIlHExmanvClev369QCAyy+/PLK8U6dOBRCYzdH8xprj0ZyFYkDr1q0T8uRidYoG3O4uiOc2snLlSgDArFmzAABPPfVUZDmFKCtVauD+1FNPYcmSJfj888/Ru3dvAMAZZ5yBI488En/84x9x//33V3IJhag6VKf+Qo8uY8eOBZDsn50PSg4IGOWRMwt2fyBZpbc276tWrUrIWwghhKjqlCgA0/vvv4+f/OQneOWVV3Duuecm/Pb888/jsssuw+zZs9GvX79SFaZPnz4AgM8//zxh+9ChQ7Fs2TIsXbq0VOkKURns3r0bPXv2BAB89dVXcfOPzZs3o0ePHujYsSM+/PDDJJOOVKmO/YUDdzvITnXg7s4yWKWMx3KRGoO4FKXiCSESoXnb0UcfDQAJAWQOOeQQAMGCT/Y1KvEcbtjF5txONTwnJwdAsDC0JH108uTJAAJzO5rRWVWf912W1W7n/YNlXbt2bTwPlnP+/PkA5O6xpsMATKkGVCrp/pYSLU4dPHgwDj30UEyZMiXptylTpqBz587o168f9uzZg5ycnJT+SEFBAebPn4/jjz8+Ke0+ffpg2bJl8VXgQqQDdevWxbPPPoulS5fi97//fXz79ddfj23btmHSpEnIzMxUfxFCCCFESpTIVCYWi+Hyyy/HuHHjsG3btribpY0bN+Ktt96KD05eeOEFXHnllSmlyTftzZs3Y8+ePfE3dhduW7NmDbp161aSIgtRqfTt2xe33347HnzwQZx77rlYv349pk6disceeyweWlz9JeC3v00MB37vvfcCSFbgWUcboMUNzMJt1rUkX2hcBU0IkRpWXR49enT8/6FDhwII+qFV1m3wM2t/zv3YR6+44ooSl4/q/KRJkwAELimZF8vGewrvD7aMvNdS9f/ss8/iefzhD38AAFxwwQUlLp8QZaXENu7Dhg3D2LFjMW3aNPziF78AALz44ovYv39/vMMMHToUb7/9donSZeew/lGB4OHMfYRIJ+6++268/vrrGD58OHbs2IFBgwbh17/+dfx39RchhBBCpEKJB+6HH344evfujSlTpsQH7lOmTMEJJ5yALl26APDVsDAlsCis+zcXLjJzAyAIkS7Url0bEyZMQO/evZGdnY2JEyfG1R9A/aUo7rjjjoTvXHBbv359AIEqxvPperigikdljUrbt99+CwC47bbbDlSxhagxUH0GgOuuuw4AcOSRRwJAfFaRdry0eSfsvzQD/P777wEEnmzKAtV6enjhehjavMdMEBwbROm7774DACxYsAAA8PTTT5e5TEKUB6XyKjNs2DDcdNNNWL16Nfbs2YNPP/0UTzzxRPz33bt3Y9u2bSml1apVKwBAkyZNUKdOndDpa26j2yYh0o0333wTgD+oXrJkCTp27Bj/Tf1FCCGEEKlQIq8yJCcnB61bt8Z9992H3bt3495778WaNWvib7KTJk0qsc0uAPTu3RuxWCzJS8Zpp52GZcuWYdmyZSUtqhCVzvz589G7d29cdtllmDdvHnJycvD111/H14iov6TOQw89BAA4/fTTASSHXXdNh6i403Ro9erVAHyXmUKIimPEiBEAgr5ItZv9909/+lOFleWmm24CkGzLzpnK8ePHV1hZRPWgor3KlEpxb9asGc444wxMnjwZeXl5OP300+ODdqB0NrsAcP7552PUqFH44osv4t4yFi9ejPfeew+33npraYoqRKWyb98+XHHFFWjdujX+9Kc/Yfny5ejduzduvvlmTJgwAYD6ixBCCCFSo1SKOwC8/PLLOP/88wH4i1MvvPDCMhdm+/bt6NmzJ7Zv345bb70VWVlZGDduHPLz8zFv3jw0b968zHkIUZHcddddGDNmDN59910MGTIEAHDffffhjjvuwL/+9S+ceeaZpU67JvYXKnOnnXYagGABLm9jrg0tvUXs2rULQODvfuTIkRVSViGEENWfKu3H3eXss89G48aN0ahRI/z0pz8tbTIJNGjQADNnzsRJJ52Ee++9F3feeSeOOeYYzJo1q1oOQkT15ssvv8T999+PG264IT5oB/xInb1798Y111wTD+ldGtRfhBBCiJpFqRX3/fv3o3Xr1jj77LPxzDPPlHe5hBAikoULFwJI9qrj+nGnjTtt/TlDKIQQQpQXaaO4T58+HRs3bsSwYcNKm4QQQgghhBAiRUq8OPWzzz7D/PnzMWbMGPTs2RODBg06EOUSQohIunfvDgC4/fbbE7a7E4j0WDFu3LiKK5gQQghxACmx4j5+/HiMGDECLVq0wHPPPXcgyiSEEEIIIYQwlNrGXQghhBBCiJpM2ti4CyGEEEIIISoODdyFEEIIIYRIAzRwF0IIIYQQIg3QwF0IIYQQQog0QAN3IYQQQggh0gAN3IUQQogqRkFBAZ5++mkce+yxqF+/Plq2bIkzzjgDs2fPruyiCSEqEQ3chRBCiCrGbbfdhhEjRuCoo47CuHHj8Jvf/AbfffcdBg0ahM8//7yyiyeEqCRKHDlVCCGEEAeO/fv3Y/z48Tj//PPx97//Pb79ggsuQKdOnTBlyhT06dOnEksohKgspLgLIYQQRbBixQrEYrHIv/Jm37592L17N1q2bJmwvUWLFsjIyEDdunXLPU8hRHogxV0IIYQogubNmyco34A/uL755ptRu3ZtAMCuXbuwa9euYtPKzMxE48aNi9ynbt266Nu3LyZNmoR+/fph4MCB2Lp1K8aMGYPGjRvj2muvLX1lhBBpjQbuQgghRBEcdNBBuPzyyxO2XX/99dixYwfefvttAMBDDz2Ee+65p9i02rdvjxUrVhS73+TJk3HRRRcl5NupUyd8/PHH6NSpU8kqIISoNmjgLoQQQpSA5557Dk899RT++Mc/YsiQIQCAYcOGYcCAAcUem6qZS4MGDdCjRw/069cPJ598MtatW4cHHngA55xzDj788EM0a9asTHUQQqQnMc/zvMouhBBCCJEOzJs3D/3798c555yD559/vkxpbdu2Dbt3745/r127Npo0aYL9+/ejZ8+eGDx4MB5//PH470uWLEGPHj1w880348EHHyxT3kKI8iE3NxeNGjXCtm3b0LBhw3Lf36LFqUIIIUQKbNmyBeeddx66du2Kv/3tbwm/7dixA+vWrSv2b+PGjfFjbrrpJhxyyCHxv5///OcAgA8++AALFizAT3/604Q8DjvsMBxxxBH4+OOPD3xlhahBPPnkk+jQoQOys7PRt2/fKu1yVaYyQgghRDEUFBTgsssuw9atW/HOO++gXr16Cb8/8sgjJbZxv/322xNs2Llodf369QCA/Pz8pOP37duH/fv3l7YaQgjDiy++iFtuuQVPP/00+vbti8ceewxDhw7F4sWL0aJFi8ouXhIauAshhBDFcM899+DNN9/Ef/7zH3Ts2DHp99LYuHfv3h3du3dP2qdr164AgKlTp+L000+Pb//yyy+xePFieZURohwZN24crrnmGlx55ZUAgKeffhr/+te/MGHCBIwaNaqSS5eMbNyFEEKIIvj6669xzDHH4KSTTsLVV1+d9Lv1OFMenHbaaXj77bdx7rnn4rTTTsPatWvx+OOPY+/evZg7dy66detW7nkKUdPYu3cv6tWrh2nTpuGcc86Jbx8+fDi2bt2KGTNmFJtGRdu4S3EXQgghimDTpk3wPA+zZs3CrFmzkn4/EAP3GTNm4JFHHsHUqVPxxhtvoHbt2hg4cCDGjBmjQbsQ5UROTg7y8/OTgp21bNkSixYtKlFaubm55bpfFBq4CyGEEEUwePBgVPTkdN26dXHnnXfizjvvrNB8hRAlo3bt2mjVqhUOPfTQlI9p1apVPHhbSdHAXQghhBBC1DiaNWuGzMzM+IJwsn79erRq1SqlNLKzs7F8+XLs3bs35Xxr166N7OzsEpWVaOAuhBBCCCFqHLVr18Zxxx2Hd999N27jXlBQgHfffRc33HBDyulkZ2eXeiBeUjRwF0IIIYQQNZJbbrkFw4cPx/HHH48+ffrgsccew86dO+NeZqoaGrgLIYQQQogayUUXXYSNGzfiD3/4A9atW4djjz0Wb7zxRtKC1aqC3EEKIYQQQgiRBmRUdgGEEEIIIYQQxaOBuxBCCCGEEGmABu5CCCGEEEKkARq4CyGEEEIIkQZo4C6EEEIIIUQaoIG7EEIIIYQQaYAG7kIIIYQQQqQBGrgLIYQQQgiRBmjgLoQQQgghRBqggbsQQgghhBBpgAbuQgghhBBCpAEauAshhBBCCJEGaOAuhBBCCCFEGqCBuxBCCCGEEGmABu5CCCGEEEKkARq4CyGEEEIIkQZo4C6EEEIIIUQa8P8B7QATXnS7rvIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "cres.maps.keys()" + "from nimare.meta.cbmr import CBMRInference\n", + "inference = CBMRInference(\n", + " CBMRResults=cres, device=\"cuda\"\n", + " )\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "\n", + "# generate chi-square maps for each group\n", + "plot_stat_map(\n", + " cres.get_map(\"schizophrenia_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.05)\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"schizophrenia_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=scipy.stats.norm.isf(0.05)\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"depression_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.05)\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"depression_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_No\",\n", + " threshold=scipy.stats.norm.isf(0.05)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures (displayed as z-statistics map) correspond to homogeneity test of group-specific spatial intensity for four groups. The null hypothesis assumes homogeneous spatial intensity over the whole brain, $H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is the number of voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values are highlighted (under significance level $0.05$). " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GLH testing for group comparisons among any two groups\n", + "\n", + "In the most basic scenario of group comparison test, contrast matrix `t_con_groups` can be generated by `create_contrast` function, with `contrast_name` specified as \"group1-group2\". " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type5 = index_2\n", + "INFO:nimare.meta.cbmr:type1 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type3 = index_5\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ouP9mz7bIT5T9WL6peBt+vcGKuzHGGGNqGEd0ODRzQ3FRZLpEcRGOPeIwTHx0Wmxexx5xWE5lFqs5w/bvc+YtyGl/Y6oSP7gbY4wxpsKMGzcOQNp2vU2bNgCA+vXrZ6TjQkjaoZeHVq1a4YYbbkh979atG4C0kl4R9tprr1SsmqlTpwJI28JfeumlFc7f1G5ydfWYV8EQTH5wN8YYY0yV8N/PC7HbbrsByH745uLUdevWAQDWrFmD75zYE985sSduvf2OVLrv9DyhZP+4QrYr6oUrVmLLli0ZeW/btm2ntMOYysIP7sYYY4wplaeeegoA0LRpUwBIPWyHdun7779/xj60XecnH5K5z9atWwGUKN0AUK9eySMJgwKpDTxt5MN9wm1lsfvuu6e8yvABnnAWgGVxloBtmjVrViot68U8VqxYAQA466yzcq6LqX0kc3QHWdHIp35wN8YYY0yN4LXZr+PNN9/M3Pb6G9hjjz0AAA0bNgSAlA/8/Px8AOmXBGNqOlX+4D5p0iQMGjQIc+fORZcuXaq6OqaWwf5F8vLy0KxZM3z3u9/Fb37zG7Rs2bIKa2eMMdWTJ598EkD6QZi231SbqVCHpib0HvP5558DSPtMJ2oKw4dpqtzMc+PGjQCylXeq4KFvdm5jGu6jdvSsJ8vUB3n+zjpzVqBFixYAMpV95s1ZBx6bGTNmAADWrl0LADj77LNh6g62cTdmJ3LTTTehTZs22LRpE15//XVMmjQJ//rXv/Duu++mFBljjDHGmOqMH9xNneDUU09Nzej89Kc/RZMmTXDbbbfhz3/+M84999wqrp0xxlQPXnnlFQBp9VzVbqrM/KQ6DqRt1JmW6jXT8neq2UxHNZsqOH2q68LRKH/vGhmV+2geLINlUv1n+1g268x0rDM/AaTMcmjjzk+q+4wEy2PZs2dPmNpPXo427hUNwFRRG3ljaiQnnFDiheDjjz+u4poYY4wxxuSGFXdTJ1m6dCkAYN99963aihhjTDWAXlNoOkjVmGqyRjWlUh3afm/evBlA2i6eLheJKvK8/tJmnPbpLJNquarq+j2E+zAPKumsJ8ukIs86Mx3byTawbmE7NSor92EazjBQveexPe6442LrbWo+laW4+8Hd1AnWrl2LVatWYdOmTZgzZw5uvPFG7L777jj99NOrumrGGGOMqeF4caoxO5FevXplfG/dujUeffRRHHDAAVVUI2OMMcaY8uEHd1MnuP/++9G+fXusXbsWEydOxKuvvlquwB3GGFMb+dOf/gQAaNasGYD0Akv6QV+/fj2AbFMSQrOQcF+mpUkJP/l7kyZNAKRNS5gnzVe4cJQmMfxOUxuar4Tb4vZhnjT9oSkQAyutWrUKQNpkhu2mOQ/rHLaTsN4aIIp5sN1fffUVgPSx/v73v5+Vl6n55CFHU5nYEL+54Qd3Uyfo1q1byqtM37590aNHD1xwwQVYtGhRKgKfMcYYY0x1xg/ups6Rl5eHMWPG4KSTTsJ9992Ha665pqqrZIwxVQKFC3WLSMX6W9/6FoBMt49AWoEOF2pSeaYKzsWmVLmbNm0KIK2Yqyq+evVqAOmFpZqvKtzhNtaD3/nJPKm4xynvukCWv+uC2jBvhW4i2R6debBIVLtJ5mjjnswhTan7V2hvY2ooJ554Irp164axY8emLtTGGGOMMdWZaqO4T5w4EdOnT8/aPnz48JS9mDE7k6uuugrnnHMOJk2ahIsvvriqq2OMMZXGc889ByCtElMdJrTLpkLdqFEjAKW7YqSNN9NQaaZqze9U2qlcL1++PKNMKu5Uwbm/2sADaZeLGsRJ3UKyjIMOOigybwacUlt+lhXa1StMw33ZDnU1yePCY2+vZrWLnN1BVkxwrz4P7uPHj4/cfuGFF/rB3ewSfvjDH+Lggw/GnXfeicGDB5d6YTbGGGOMqWoSxeGrqzHGGGNqLf/6178ApJVmVahpu05vKrRL53eqxqUp72XBxw4GaFq8eDEAYN26dQDSyjrFFCr1tLNftmxZKq+WLVsCSM8cUClne6jE77PPPgCAQw45JLI9FWmHtmfFihUZ3+NmEHjse/ToscN1MFXPunXr0LBhQ0xucij2SJYtAG4s2oaBqxZh7dq1qX5ZHmzjbowxxhhjTA2g2pjKGGOMMWbXwDVktFWnQk07bH5S3aZSTW8qcUp76FWGaBqq3zrBTx/xLJtqOdVwNV9Um3kg7alF43KwTG0fy2QZ6v9dy4wySojybgOkjxXrQvt7zmLwd35yBoHnpk+fPlllmZpDnbNxN8YYY4wxpiaSl6M7yFzSlIYf3I0xxphaDpVpqr/0FtOwYUMA2Z5P6BSC6nacLXjo0zwXtTrcrio+6xin6rPuoT903Yf1Uf/rcZFVtay4ulHBj0L919P3vZbN36n+0/bd/t1NefCDuzHGGGOMMRUgmUjkFFypogGY/OBujDHG1FLuu+8+AEDHjh0BpO2vaetNW3eqvlTiqW5XxOuK+kJXtZt1YZlU/ePUcnppYfoQtoNlqA915qm28Fon1nlH3APr+gB+p607/bvTtp1lsa48V8OGDSt32abu4Ad3Y4wxxhhjKkAiL4FEsuwX3Yq8DAN+cDfGGGNqLfTDTrU6Ts2mSkxvK0SV6NK8ysTZgcc9qHA77ey1LH5SoY4qk9BenMo728e0Zfmfj/OEE0Vo1x/WO+7YsG7q151KO7fzXBlTGn5wN8YYY4wxpgIk8xJI5qC428bdGGOMMRn83//9HwCgRYsWANJKO6OS0u6aqjBtutXmm+qwqt60M6eyHeaRK0xPdXvNmjUAsu3SyaZNmzLaEG5jOxh9VfOg//odsV0P6wiklXIeQ0K1X9cHaDv12O+3334Zdea5O/fcc3eorqZ248ipxhhjjDFmh7n//vvRunVr5Ofno6CgAG+88Uap6Z944gkcdthhyM/Px5FHHonnn38+4/fi4mJcd9112H///dGgQQP06tULH330UUaa1atXo3///thnn33QqFEjXHTRRakFwACwdOlSJBKJrL/XX389sk7Tpk1DIpFA3759d+wg5CWRyOEPeRV79LbibowxxtQy9tlnHwDZftvVqwq3q6cWqsNUsNeuXQsgbd/NfOizPMxD1XuF21k3nQWIs6dnOs4ChNu0XZq2vN5yOOOgKjkAfPnllxllUDmnYk51n9tZtp4TwuPFMpiupvD4449jxIgRmDBhAgoKCjB27Fj07t0bixYtirTbnzVrFs4//3yMGTMGp59+OqZOnYq+fftiwYIFOOKIIwAAt99+O+69915MnjwZbdq0wahRo9C7d2+8//77yM/PBwD0798fX3zxBWbMmIEtW7Zg0KBBGDJkCKZOnZpR3j/+8Q8cfvjhqe9hnyVLly7FlVdeiRNOOGFnHppdghV3Y4wxxhizQ9x9990YPHgwBg0ahI4dO2LChAnYY489MHHixMj099xzD/r06YOrrroKHTp0wOjRo3Hsscem3GEWFxdj7NixGDlyJL7//e/jqKOOwiOPPILPP/8czz77LABg4cKFmD59Oh566CEUFBSgR48eGDduHKZNm4bPP/88o7xvfetbaN68eepPX5y2bduG/v3748Ybb0Tbtm13+DgkkokSzzJl/eVgB18aVtyNMcaYWgbVXn7SWwyVaT68aDr1vU64nQo2v1OJj8pTVW1V0pmetuG0cacCrQ9YVKLDMuNUbCrlbIfan2ud1FMN96OKHpZJZZxlaJ7qHYd5U+nVY0nlXhX8msDmzZsxf/58/OpXv0ptSyaT6NWrF2bPnh25z+zZszFixIiMbb179049lC9ZsgSFhYXo1atX6veGDRuioKAAs2fPxnnnnYfZs2ejUaNG6NKlSypNr169kEwmMWfOHPzgBz9IbT/zzDOxadMmtG/fHldffTXOPPPMjLJvuukmNG3aFBdddBH++c9/7vCxSOYlkMzLYXEq/OBujDHGGGMqmVWrVmHbtm1o1qxZxvZmzZrhgw8+iNynsLAwMn1hYWHqd24rLY2a4dSrVw+NGzdOpdlrr71w11134fjjj0cymcRTTz2Fvn374tlnn009vP/rX//Cww8/jLfeemsHWl81+MG9CnjmmWcAAHvvvTeA7BXnqnysXr0aQPlWmHNVeuPGjSPz1DIZRS98SzWmNjJt2jQA2Tas6rc5Luojx9LAgQN3fWWNKQfjxo1L/X/wwQcDSKu6VLP5nf2YEVOpBqtqTvtselLhJwk9v8Sp9Pq7KvG8T7GOcUo2yw59zTPPOCWd9zqWoag6Hvd72E61p6dnHR4rHjtV7WkbzwWULJN157lh+vB8XnrppZH1M/E0adIkQ9nv2rUrPv/8c9xxxx0488wzsX79evz4xz/Ggw8+iCZNmlS4vEQyiUQOsyUJGSflxQ/uxhhjjDGm3DRp0gR5eXlYvnx5xvbly5ejefPmkfs0b9681PT8XL58Ofbff/+MNJ06dUqlWbFiRUYeW7duxerVq2PLBYCCggLMmDEDAPDxxx9j6dKlOOOMM1K/82WqXr16WLRoUeoFuDrhB3djjDGmFhAq2TrLSrts2lGrgs50ND+gwkx1mb7GVZkOy1S/6xqtNG4Wi4pzy5YtAaQ92XC7epsJbcBVtabqTfVabeDVT73OpHG7Kvn0FAOkI70StelXpX3lypUA0jMKnOGmUq8KftwagepI/fr10blzZ8ycOTPlRrGoqAgzZ87EsGHDIvfp3r07Zs6cicsuuyy1bcaMGejevTsAoE2bNmjevDlmzpyZelBft24d5syZg6FDh6byWLNmDebPn4/OnTsDAF588UUUFRWhoKAgtr5vvfVW6mXgsMMOwzvvvJPx+8iRI7F+/Xrcc889OPDAA8t1LGzjXguguQoHPKck2Rn0AqEXIMIpvpdeegkAcNJJJ8WWyTSHHHJIRt5Ep0l5YWAdZ82aBSA9lccLjQNBmJrGY489BiAdoEUfGvSTqMmM/k7Gjx+f+l9v/kOGDKlQ3Y0xpqYwYsQIDBw4EF26dEG3bt0wduxYbNiwAYMGDQIADBgwAC1btsSYMWMAAMOHD0fPnj1x11134Xvf+x6mTZuGefPm4YEHHgBQcj297LLLcPPNN6Ndu3Ypd5AtWrRIvRx06NABffr0weDBgzFhwgRs2bIFw4YNw3nnnZcKOjZ58mTUr18fxxxzDADg6aefxsSJE/HQQw8BAPLz81PuJwmfhXR7dcIP7juZTRs3VHUVjDHGGGMqhX79+mHlypW47rrrUFhYiE6dOmH69OmpxaWffvppxizJcccdh6lTp2LkyJG49tpr0a5dOzz77LMZD8tXX301NmzYgCFDhmDNmjXo0aMHpk+fnvLhDgBTpkzBsGHDcPLJJyOZTOKss87Cvffem1G30aNH45NPPkG9evVw2GGH4fHHH8fZZ5+9S44D3T2Wma6CinuiOE5OMjtE+OD+znvvA0ircVTyOJ3IT50O0+lGTmVy//ffL8k3jBBGNb9jx44A0gtywnDUQHrqjuiUHj+5P3/n1OXJJ58c2W5jqpJHH30UQObCOZoEqILO8RU3va2L73RGrLSQ6arix7na0/HFOnAa2JgdgX6wgRJFEki7QdRr+caNGwGk7YlprsGHLQ3IROJMTcL/dYxwO+8vOkPFMcoZYTXf+d///gcgvbiTpiZA2skDF9fuu+++GXnzHsiZbNZNZ+B4XYibgQu3a9vjHqNo4kN7bl6T6PWE54bmPMyP52bhwoWpvOLMTkzVs27dOjRs2BB/ObIz9izl/kA2bNuGM96Zj7Vr1+5QsC0r7hVk04aSC0j+niUXj/w99sTMmTMB1LzoZ8YYY4wxpvyUKO45eJVBtAejXPGD+07iueeeS/2vi3v4ps83fHX7SEVAv/MtngoBlRIuEgoDQujCISrwVFH4Jq9KBr+r6y9+pwJCVSNs5+mnn17GUTFm1/DHP/4RQFrBYz+lPTuQrXprGPY4xZ3o7JTOjIVrUXTmSlV+nckKQ7aHdaH7N1X0wlk45mE7eqPobBGQPeNL1VfdEetMr/Zl7sf0vLeU5g4yTt3W2WfCccCxxfHM8aL7h9s0jbq1JKwL26ezYXq8otxEcl+d1eMx0RkHtpP78dhTWQ89mYREnU9j/OBujDHGGGNMBbBXmWoOTWQe+sNkAGnbciA+nLOq3GoPyLdttX9Vomxs4+xuVWVknfjmr2Wq+k9FgOnZFiBtT2nbO7OroLJONU2DJakqGKpjcQGW4sZEWUpb3HgNy1J7eM1D3dnFuXtT93mh+s/6cfyxHhdffHFkXqbuEAabef755wGkVWCd5WEQI1Wo2b84w8uZXZ0pVpv4cBtRtVtnfuNs4YnavJemuDMN9+EiRs1T06stf9wYproOZNus69oVuovkMVa3ltzO+6ueG+Ybnk9T/UkkEkgkc1icWlSxB/eyjXGMMcYYY4wxVY4V9xzZ9NV2e/JEMuOTStiGDWlvMrQv59s1FTGq1WpTp15mFLVLV/vZcJuq+qFCXloZrBN/pxLANlCFCNvJttMnKsuiWkAfrsbkChV2tW1VRSrOZjYKVdLVtlXVcs1L1TRV7EtD03BfvQbEtau0MtSuPvQoAngmrK5DxVwVd+2D7GO8bvMar4GauF1nkOnpBUiv79KxonA7y1DvZ0TVb61ruE3HTlxecWp/nDcZfobt1GBWvF9SSec+PGbqQU7X3ahyz3NnahbJvCSSOSxOTRZXTDO34m6MMcYYY0wNwIp7DBMnTgQAXHDuWSUbEtHvOPSB+8UXX6S20baOK8f51k07Nyogau+qCgjf6vn2ruGjQ4VAf1O/uLTjU5+1WraqLsyHfnPDdrLt7dq1y8iTZdAt5ieffAIA+MlPfgJjopg8uWStCPu8zjKp4kaVuawoqLmgfprVGw0pLcKqqvRaz7jxpunUr7WO66h94+p/zz33AEirelbg6xaM86HrmIj2TY49jrVVq1YBSEfPVptxnZ0F0uOWCnrcOhHel/g789Z+r15pyOrVq1P/M3y9zozpbBXHjXpSi6sr68L0YTv5G48Z75dU5Rl9s0mTJhntZZnqDYufPGdhjBZTc8g5AFOxbdyNMcYYY4yp9VhxF2jLXpbSTg4++GAA6WimAPD5558DAFq0aAEgbffGt3P1fxvnZ1bteknoP7q0bWEeVDTiIjnyU233qCSwTaHXALZd7RmZFyPZsZ1UVQcOHBhZV1O70WBlAPDwww8DSPc3KlHaL+PUNFXocoluqHnp+hDtx6pUqu1rFHHeY3RdS1wepXmWirOPJzpjwO/2QlO3+OlPfwoAeOCBBwCklWUdO/SAwjHIKKW8b9FrjNq6Rynb2p+1L3LtCr2y8HeWzXuGxjDR9Seh4q4+4eOiEq9cuRJA2ksOt/M+zXtknPIe3o+pvvNYcEabx5L30SVLlgBIR3Pl/ZN14P5qf+8YDTUTK+7GGGOMMcaYFFbct5PlNaYsikveyo88vMR/O1UKIK1O096NSgdVBfXgQtTHbZzdbGl+3NWuTz1pqK272tyxjlQX2AampzoR1l+95mikPZZ5wAEHAACeeuopAMBZZ52V1Q5Te0iNKbJ9bFF5f2TKY1kKe5yHCFXBymPbruNI7cjjvEvEqeQk9K0e5wVGt8d52SC5eKohccdE/cyrbS/r/bvf/S5j/5///Oc5l21qDjzvattN1XjZsmUA0h5hDjrooIx07GdU4FUtD1GPNVSeaSev9x/2RebJ+44q79rXWdeQOK8yhYWFANIqvd63eBzUPp2z2FFjVu+fVNS5nZ7l2A4+E3z88ccAsqOjx82emZqFvcoYY4wxxhhjUtR5xZ3qX85Ku7Jdef9/x3cHAMxd8BYaN24MIK0yUHlW/7dqi6c+2NVzhtq+h+qcrtJXRYN5qq27qvwaJY7b2aYwyhz3pRKjiqTONDAdP6dPnw4A6NOnD0zt4Zv1JbNP7J3FHFvbxwrH2oD+52d8/8PkR7LUcfWYotEbOYZUTYxC+zz7q6r9ivpejlIa49LE1UfbE+fvXdtfGqVFdo3KU1U+KvBhXYYOHVpmuaZ6Mn78+IzvcfcVej458MADAWT3D+17qkjz3gBkrw/57LPPAGSPA94L6T2F+9GTTVxsE/V7Hm4jLJt25MyT9WVdWAfOClB5Z51atWqVkX/YTpbBPOMiJxMeW5bBOum1iPdMnjuPvxpGjjbuqKCNe51/cDfGGGOMMaYiJBMJJJNlP5Qny2ESGUWde3B/4oknAABnfO80AMAHH5XYnPHN99B2h5QkLI6xOYvbvl017Hpsp6xtL778SkbSsiI2qvqtqjmV/FAJ4TauWo9T1OMUPlVEWOY+++wDIL1aPvReQ5Ugzi89y+A+6i+X6j/9vdMG8ZxzzoGpwVBtYj+QMROnHQ8a8KOSf4LZr3vG3bc9y8w81DsS+1hptqL6m9qwqpqvqn7c2pRwX6KelnR2TVXMOEU9yoNMXFpV/dVGPy5PXR8T5m/lr+bCOBuEduSMysl+0LJlSwDZPth1/RP7OH+n/TbtuYH0mKLSrgo8FWfeV3TWi2XSLp1rqnSdCRXscJuul2EecTNt3E6FXdeI0C6da7PCdhLaxetY0nbx2PJY817HMqn+04OPMaVR5x7cjTHGGGOM2Zkk8pJI5LA4NVFUseWldebBnfbUfKNduOhDAOk3ZKoI/11W8pZ9YMvt3lPiFPbtUE0szQL1Oyf2BAC8+/7CjDJVOVM7dX5Xv9F8mw9Vc/ULrQogf2eeGuVUVTe1MYyym6W9u3rp0HbpLIDOLNB2kGqNbd9rJt+s/TJzQ4z6nYBs5wyU2sIDGD5su6eT7b/99p57S5KItxbt3yXFlz52VeVTZT1dvcwIi1Ho+KGCxj6tM18atTL0UBNVdtiWON/vqiwSHY/6u86MReU/YcKEjDLsZ7p6wZlkRhEF0rbrPL+8Xi9cWHIf0pkl/WR/1+s3+3bUPYEzv6XFOADS/twZXZw23wojdrMs7kc1PcyD9eQ+CseBRjSPS8c2sE1cmwWkZ4s5q8GZBF0XoGtv4qK1tm7dGkBa1ef+//rXv1JlMmq5Z6RNnXlwN8YYY4wxZleQzEsgmcPi1GSRbdxL5aWXXgKQViLU24PayKbs2wpL7AFbNNuvJKPtKqDa61JVTKmIgVqnKvwRHTsAANZv2FhqnTWyKtU4tbFlJDggra7wTZ6qiCrmcajqyDqoMhiqKywjzl5elTw95qoyqj09z91JJ51Uat1N1UIvMllwrJQxa4Vi9pftY6eUMXT5L4YBAMb9LlMB1r4GZHqeCImLVKxjJC4icJSdepyv9zhvMerJJs7DVJT/9zg1UyNi6oyD2rDrDIPayEe1mXkzGqeV96pl4sSJAID27dvHpuE54/WayjvvFRpRVb2WUV3W/Wgbzt+BtDqt60aI2nzzmh83C0TPMCyD+4XjXOvJfXQ861jStWRx4yNKcacnGlXIuZ0zA3oseeyo+rMOGgMlyhsVn2F4zn/yk59kpTF1g1r/4G6MMcYYY8yuJJGjO8iEFfdsnn322dT/tB3jGy/fkNW7iqrCZUYyK4pRE4OvKZNd2TXKe0NYNpUD/s63dn5SqQ6VDp05oDqiNrZl+aqm2k21UtNTGQCyVUJNq6v39VPVPOZH20NGowvPZ9++fSPrbyofRkZNnXWqRDI2EuznqsCnvMdsXxuR3K6QBVHlEnnb12KkNpT8dunPLwYAjH/gIQDRNu6qWukYKMsWXLfrGpQo5VsjEKuKrR46dIYrLv5CWFcdP+qlSrdr+9U7SJwf7PB/HePM4/e//z2A9HXGKmDlQu8qar8NpPsgP5lG7y96P1L1mP2DeVNN5n6hrXhZcQy0P1G1V3SMaR8N44kQVfnjohWrF5momaaoNoTt5D48Fuz/vEbw2MVdc3SWQOui6wuAtO1+6FHH1E1q5YO7McYYY4wxlYW9ylR34ux3M75v9/4iyvtee5SoBWXZuhtTE6A3mKz1H6kE27cXbc38Trg/k4fXtG0l+8Qp78bURTjT0aFDybopzjiFirvOQlGJpq32f//7XwBpdVhnnXU2mp/0oEI1mPuH+8atY1J1nzNK6vdcZ43Uo1qYr3pUi1uzwXQsU+ukaJ3CdlLx16joOsNNWDeei//9r2RdkKrnrCvPUTizwPJ53NkHfvazn0XW39ReatWD+4MPPggA6NKlS9ZvHAgcWOriSgd7LiHGdxZ6kdLpdg5Yfld3keE2puG0Hgc+26uL43Rqkxd+5s3puagbg15UFV3Qqsc27mLNc8WyGXoaSJ/jwYMHR5Zp6hZRrlFJnItTDdYUF6CIxJnNheniFplyKr0sEzyOt7gFo1H1UVMXLZPomNc6a6CmqDrq2NXvvGb94Q9/AAAMGjQosp3GGFNbSeYhR68yFSunVj247xIifEtHJqN9YHijTe0j6mBZXjaMqQmI6s1X3Sw/7bpbzCxVev/gJ0478mEyh2lIY4wxprJJJBNIJHNYnJpDmtKoVQ/uhxxyCIBMJYyKc7ioMiRuoVqUO6ZdDVUrhqTmlKSGcmaY5VBx5zaGoeYCHKpvbD/db5XlHpL5hC6wgMwgNLoYjmhgDlX140Kzcz8NBBNOUfIcGwNkq8VRcLaJn7wmcB+Or7gFmOoKUcsG0n2d40MX+sW5eyS68FVd1IXpdeyqOz9+cvZN660ze3Hti2trVF78ZDusvO9a1L2xXmuBtCMG3gN4P1EXjLowmqijA6JmK6HpibpDJdqP2Ye5OJNlsc/qAlJ+0mHBm2++mcr7mGOOyWinmsbwOLCd7KNMryY2cQHLwnZy5pnHlPBYccZb3UGyDvyu54LHQ91Mhu1hPcJgW6ZuUase3HcFa9aVmGs02nv7A6x6xLB6buooU6b9HwCg/3nnZv7Ah4eUsXrMA2GWd5lSqIIXaWOMMSZXkskkkjnMCie3eXEqHn74YQDAkUceCSDadVqoTgPZapOm14BMuxJd/KJquQZzYLpQXeE2Lnph/fkGzzJ0oVGcLS23U0GIUv5UDVH1RxcgqapI1AZZ6xDWjTMAPOcXXXQRTNUQzoRUFXEB1UI0kBLhGND+qjNG6pqO/TgsizNaqmar4kb4uwZXI3GqeIjWR8c2f9fPOBv+qPU9ZbnY0+uCbd4rh8aNGwPIVpfDc8d+wL7J8aprjzR4mN4rmY+Oj6jAZXGBlMh++5UENOR1nPcG3uNYhzh3xuyH4cwrt+l41k8eK7o8Zl2ojq9evbrUNoTt1Lbz2KhbSK1bXEBDlqGBE6NmM5gX+4Cpe9SKB3djjDHGGGOqipwDMOWQpjRqxYM77bFVWQLSb/JUG1QdLst2k2+3++6z3eYs5bouc5q/GBHT/jm6rFMbOg2+wrd6VSFCxbNRo0YZabivutuKCugStpfEedUJ94sLKsHjr3Z+cXbIei7i8gv/5zk3lQ/D3ae+P1yiqg65qERVTS0y5djYHliJi1KL1f0jx4l+5oDa20apYhxPal+qSrMqjZyt0jUZLDP03qIqPe3ONfgN68A6cQyriq+BZ0pT3FmGqnlx3nS0DO4XdS1Uu/c4tVbT67E3O4fJkycDAA4++GAA6XNKm+hw1lLXDOn1lJ9vv/02gLSC26xZs4z99d7A/LiuKuwDrAfPO23BqW4TegzjPUL7DWF7dHZv3rx5qf81b7XJV/Wb36n6897Jz5UrV2bULaoObDvVe6LHisdh2bJlALJV/bhAkHo9AbKPLcc9+8TAgQNh6ga14sHdGGOMMcaYqiLnAEwV9I5Wox/cJ06cCCBt2x7lK5lvyXG+muPsrfnGm1LhdDFqKry7VCpUC7lKPUZRVCUvzluErsCPCgPNtGprq4pZWX6i42xrS/Oyo0qeesVRG+G4dQVx5ygsm+1s2bIlgHQfcKj1Xc+kSZMAZAcwSZ2fuDHCRarJzMtNsS5OpUIf9slE9Bi6+55xALIV6bBvqZLMeuu4UvtsKllU7nRcRtnMq/24ji/mqXa46uFGvU+QUN1Xu3i1K1flXW3f1ZZZvWtEUZaXrTgf8PzuYDE7B6rC2r9KO3faz/WeyPsK42WUZZet/S3sq+xTVIephnPs8d6gNuIsi7COvIfExTkI89IxyHuhKvB6HDg26eFFFXyuOQvrqDNpekw0VgSPLVV8tQTgOShtTZ2q82wn+4SpO9ToB3djjDHGGGOqmkQyiUQOHtBySVMaNfrBvW3btgDSb6lU0kLVh2/q6qtY7d3UDpt50UZv2ReFAICWzZuW5Lc9/7TAnn0iimNsdTdu+ibjO9+g+easqhXfxlesWJHRllB14Da2gz5eNYoiy4iz7Sdl+bQNf1NbWlXQac9I1UXXD6gNpqoqodLBbcyLfcDsOh599FEAaeUpjgcfLpn9GPyTCwFE2bpv/84dRHFPKe2hMq9qfiJzRohEeUxRFS8uzLqqfvw9TiWPsjunclZWBFWOCbW3Z72ZD9sXFYeCeTGNKqS8BrAd6hVE1X2ta6jexkVIjVPW4/zUM08r7xVD12GwL6h3FgBo0qQJgPS9TGd22Z9p2659U/sN1WKmUx/mQFq15ueqVasy6kW78rh+wjrqjBPrSBvxKP/mTZs2zShL89BZIT0evL/yfss28DrA2YKw7UzDY8Njrdcenh+2g2XpvY77c7ywvWGZWn/tE6b2U6Mf3I0xxhhjjKlqknk5+nGvyzbuVMP5xk01OVSM+Jaqnhfi/Cfrdn27jQvzXirb99n4TXR0RMK39ObNm2e0QxU1KgphFFNdlU6FjscozhY4znuM5hunkADZ6rweOz3mqgCpL2J+UjEJ1Ua2g0oE22d2HVSayvLExO0P/aHEy8FPB5V4OSjO2+7lZNv2dROpIST2nBxb4UySjLff3nPv9iSZfUZnb8LftH9qv1R7c13fUpbnKSB7/YbOQrGfxq0P4Jjg71TwCFXAqPqo33adGdBZRR13OqbVJhjIHsNxUWTLmsljWfRMNGTIkFLTm0w4FnltpIId5xkJSN9PaHfOWR1+JzrjEhePQ2eJQs8n/P+9994DkPa6QmU6TvWO8yjGshmfhOMinHHjNo0+Gpen9nudaVi7di0A4NNPPwUAtGjRIqudcZ6ZdJYibl2XRnNVr0CFhYUZdQnrqTMg4UyAqWJyXJyKCj64V2xvY4wxxhhjTKVQIxX3CRMmAAAKCgoAZKs8oWLEt2+q1LS3pgJPmAeVrzjfzV9t3K5WNNhuP5eLz+lE9Bu+vkGrEqZqBFe78w07VBeZB9OoL2dVG9XONavKMcpHqLSpkqlp1F5RlXZVS5mO6qQqJ0C86sM+cfHFF0e2x5QfeuyhisfzoeddVeQUOjtF5b04e9YGiFkTsn3bhAdLIuVqn6HiqJ6Nwm3qqYGzcHEzCKpgqw/2KC9QOlMXN4Y1+qR+UqFUrxThMdaZOB1XPF+sk7ZfVVnWifmE6r6uKeGxU8W9LLW2tOuIKZvx48cDSM8+8jzwvqbrpID0vY7XU8a+4P3jgAMOAJBWlrkuSvuN9jedCQ37F8tkH1I/5zrrGhV/AUj3Ud6n9XofomMsbg0VUZVc46WwziybbQrrqG1nWs1bZ/W4Tuiggw4CkD6WPDdU0VlmOFbXrFkDIPtezjqwjwwdOjTrGJnKIZHM0R1kBRenWnE3xhhjjDGmBlAjFXdVAviGrXahQLw6QKVCPTQQVfZU/d2wqeQtfc/8+ohlu1r49TeZimScms23a1UIPv/884y6c7/QgwBVAqoptAmkfR5Rf7hxtqlxanqoeKj6oTaGqtDEHWOm56d6AwhnR9SzQZRPe1Mxnn76aQBpVS9ORSY6HnleJ/+xxBvNwB//KCN97KqKCMX9j1OnAUifbx2P/IyK1Kl9XNdQxKE27+qZSvteCMekqtqqWqqHJfUuoWMmrDPHg9YjTlmMs/FV//ZRxNUvKkp1SJxCqueJM2WAZ8tKg/2cijr7B/sk7dbD6J7sM1wPdOCBBwJIezZhhFDaV/M77dHV05p6b9P+Fm7bd999AWRHKdbIwmX5/49bB1aa96i4mW0lrg7Mm15qqJKHfZ1lMg/1tqTRWnk/5rHm/jwX/E7bdu4Xnk/Wi9clvd/GtdNUHpXlDtKKuzHGGGOMMTWAGqm48230yy+/BJD2VxvlV1ZtSKlU8JNKdVyE0LIih6aU9wYlb73rN2yMtWsty5ML66h23FTRNdIbbd6A9IwC9+VbOW3eWWac2qh1Uhv4OA8UUbBs9VUdl3dcXXiew5kU9WXLPmCb2Z0H1SGqSKHNM5BWk1Q902i7PEd/nDIVAPDj/heUXvB2xf3Bhyem8lTlWn2usyz2i6hopuxnGkm1LE8WGllUYyKEY0F9vzMPtcWPi4iqHmxU1QyvPxplke2M88+u34leG/VYhvWIi+egfqdVkde1NjrmdRbOZPLQQw8BSNup66yX+mSP8sHP+wb7Gu2pef/gPeLDDz8EkO1thrAPl3ZOuS/HA+vDPqtryLTP6poItpP5Mn1YR40mq+Nev+s6E9aJx0evJSyLdudhHjq+9XrF+nI2o3379hn78VxoJFX1EgdkrzGKixTLPvPTn/4UpnJJ5CWRyGH2P5FXsecVK+7GGGOMMcbUAGqk4q5v/FS5uD3KA0NZNtBx9tplqXJ8o173VVoVVm8xrFecD2S+SevqdpZ12GGHZezHt/rOnTtntVM9aWhd1IOEqog6M6EqZai86WyEpilr9qIsH/JqDxy2XetVlt2yKZtnnnkGQNqmU/thnEcinVlRTxc8z488OiVLweXY5WdeXl6GgleWDXVpUQO13nEeTfg7Z3bY39ROVVW2cCaCvrLpqaNZs2YAsu1R4+rIMjnbsXTpUgDAZ599llVnjc2g63F0poBjhaqgzpCoWhvOJOgspo5hXfujiqGOUyUs67777gMADBs2LDJtXYRqst5D1NORevEJ4W88Nzxn7KPqVSbKP3tYF9phq9Ib7rNw4UIAQJs2bTLSxo1JotcY7eP0a866hu1SDzaqSMfFc4hb+/Hxxx8DAI488kgA6fEDpMcFr5Uc/1TWWV+NZE547HXc6H5Ra8rYB9STDfuC13tVHYkc/bjn5Ou9FKy4G2OMMcYYUwOokTIl3/y5cp1vqVG20/pmH2drGfc9zgYvLnJguI8qznwjpl32+++/DwBYtGgRAKB79+4AgI4dOwJIv4WrKhH1Rq3bVD2j8scyZ8+eDQA49NBDM8qkzZ22K6pNeiy0DmWtD1B0ViDqfKqNMz8dPa7i0IZT/YOrKlzWGIiLihj+pval6rVEFXUdA2q3GmULrh5MVJ2n1wj2eVWkNfKqxhuImuVRdV49tpQVYZTXNCpyjFXx3//+N5Xm7bffBpDtM1s9jrAuTEcFnl5D1Ed7lCcYtkNt0dV3vM6kqPcnJUoZDtNu2liyf/4ee2alq0vwXPFcUulVLya6XgHInonhvuzntN0Ofb8D6XNDJZ3pdLaT+egaGABo1aoVgMzo3mEeZXk1U1/yOnt98MEHZ7VTbdfVZ3zcWiu9jjE926CzSyHs52wXj5XOJHKWjMda1wLozJb6gw/z0pl3nfkIZ0BM5ZJMJnN63sn1mSiOGvngbowxxhhjTHWhskxlatSDO20gaXOm/ltVtQv/L8uDSRxxfohVVYxSllQNUZt8Rk9bvnw5AODFF18EAMyfPx8AcOKJJwJI282qih6lLqryQhvZl19+GUC2jSDroBHqVPXWcqLaropdnC94Ehe5Mi6fsF2EfYCeEWwnW36ef/55AGl7zbion0SVdVWAlFCZVkVaVe2ybKIJ08VFRw3TsF60gT3mmGMAZM8uxfV5/Z1EpdO+W9ZMHynLDpfXACBtN7xkyRIAwNy5cwEAX3zxBYC0Wk+FUGct1J5WZyyjfOETnW3RGYU42+W47+F2tn3cuHEY/JMLUZd56qmnAKQ9pqnf/zhC9ZgzLbq2inFBeO1nf9GIwVSHqazTfpuzt5wdCs8hlWPWm32P9ddxq+1RlVyvF1STQ09jqjCrxyONaqx9WJVrzlipKh6Ww2OQWuO2fcZXvbip9x/6befvPBesg/rjL+186zVDvXyxD5111lmxeexM7r//ftxxxx0oLCzE0UcfjXHjxqFbt26x6Z944gmMGjUKS5cuRbt27XDbbbfhtNNOS/1eXFyM66+/Hg8++CDWrFmD448/HuPHj0e7du1SaVavXo1LL70Uf/nLX5BMJnHWWWfhnnvuSZ27RYsW4eKLL8b777+PtWvXokWLFrjgggtw/fXXZzw/rlmzBr/+9a/x9NNPY/Xq1WjVqhXGjh2bUZ/qhG3cjTHGGGPMDvH4449jxIgRuP7667FgwQIcffTR6N27N1asWBGZftasWTj//PNx0UUX4c0330Tfvn3Rt29fvPvuu6k0t99+O+69915MmDABc+bMwZ577onevXtnONTo378/3nvvPcyYMQPPPfccXn31VQwZMiT1+2677YYBAwbg73//OxYtWoSxY8fiwQcfxPXXX59Ks3nzZnz3u9/F0qVL8eSTT2LRokV48MEH0bJly3IfByruufxVhERxWZJzNWLixIkAgKOPPhpAtncZjcQJpNWC8toUleVNRu26Q/UtLmIht1O5UDWBtqtczU6lgG/39AF71FFHAcj0ZUu1lHnQJ6+qa7QNZB7q45mKgdoWqv0gkO1PVmcWcvVfH4d6+wCyPWWoDe6///1vAMBPfvKTcpVVl6Ey88knnwDIjtDH86rqGRULVWxL83Ch9qeqFqlHGvXwotECSZQfZf7P34477riM7zqWdYZBx6eq4GFZcdFMVXFnO9V7kyqQWpcou3NN8+abbwIA3nvvPQDZ6p/up5Gao7xyxXkSUhVX6830elxIadfjbdu24eLBFwEA8vfcOzZdbYazYLzGc1zQ61CcV5lQRdQ1DNyHdvOccaI6rudIr+dU03ld4LkNZ2iYh3qaYj9RO231oqIzaHFjL/Sprj7u4+67Woba0atXFvV8FfZZ1pv78D5LVT5uHRDzWLVqFYD0ejcq9jpbEJ4TXWOgs+ScAeG1kc8ElaEaFxQUoGvXrqkZ76KiIhx44IG49NJLcc0112Sl79evHzZs2IDnnnsute3b3/42OnXqhAkTJqC4uBgtWrTAFVdcgSuvvBJAibVAs2bNMGnSJJx33nlYuHAhOnbsiLlz56JLly4AgOnTp+O0007DZ599lhU1nowYMQJz587FP//5TwAl0ZvvuOMOfPDBB5HRgHNh3bp1aNiwId7/5QDsvXv9MtOv/2YzOt72CNauXZsaj+XBirsxxhhjjCk3mzdvxvz589GrV6/UtmQyiV69eqWcYCizZ8/OSA8AvXv3TqVfsmQJCgsLM9I0bNgQBQUFqTSzZ89Go0aNUg/tANCrVy8kk0nMmTMnstzFixdj+vTp6NmzZ2rbn//8Z3Tv3h2XXHIJmjVrhiOOOAK33HLLDgV1TCSSSCRz+EvUIRt3Y4wxtZfBF22fKSvariquKZmd3L3RflVVJWNMKaxatQrbtm1LrZUgzZo1wwcffBC5T2FhYWR6xgjgZ1lp6BmM1KtXD40bN06lIccddxwWLFiAb775BkOGDMFNN92U+u0///kPXnzxRfTv3x/PP/88Fi9ejJ///OfYsmVLhklNdaJGPbjrNHNc6OLQBVVZi1LLWhipqGlJaSG7depRF++pSQkX3XKRGacmuR/NYGgH1rt371ReL7zwQkaZGriCU3gsQ+sQV0dNF7ZJTSPijmVZQTfKOhfh+dSpe53udCCm8kNTJA3iVdZCSjWTIGr2wWnkcB91lxgXoIWoaY0uGIta/Mm+QBMZXVCmn3GwrgwRr67bgOxrj5oP6aIzvW6oCRDNeaKmUePGFZUnmsPNmDEjo/5sP/OOc4cXjk8dg3rO2Q41n+Iny9DzHHV93RGFq7aiwbRoFkGTGTVPLO26R3MOPd/qBjTu3sd0ar6hfRzINi8JgxYB6fHKccCxpPfVOHO7qHtFnAmmjg9drM66cFwQ1oHXxajjom3nsdFxoKa0agKorndzCU7IdvDYsYwwiF1YB1Nig79+/Xr8+9//xlVXXYU777wTV199NYCSc9O0aVM88MADyMvLQ+fOnbFs2TLccccd5X5wt1cZY4wxdYvi7Q842z/53RhTPWnSpAny8vJS3vHI8uXLUxFllebNm5eanp/Lly/PiDq7fPlydOrUKZVGF79u3boVq1evzir3wAMPBFASr2bbtm0YMmQIrrjiCuTl5WH//ffHbrvtlrF+r0OHDigsLMTmzZuzYhGUhh/cI4h7C+eBpVoVvmnGLYxUtVuVPKprVDioHPBTFaXwpMcpWSyDbrZYButAJaB169YAgHfeeScjb36yjlELV7STsQ7MU91taZ1UTSVRrjY1SATrQKWCnxogJmrhTdR3EqUcRC0QBKy45woXvwHpPq6LtHQmRV3/cSwwXVyfYX5hWSTOraD2qbiFZtqXwnF+xBFHAMg9IImqeZz54mJP3iRYh1Cp45QtbzJc8MeyuYCO9eTY19kOLijjJxevheHcuQiN6LFhWeeeey4ApBZhcdE7zwvrpipueB5VUeS51hkQXZCv12LtQ1Hnq6yZj7qEXvO5SJVjjoszqbqqeg5ku1rVa3hcYD89l+pmkESp33EuKFV55zWBKjHHs7pmJNpfwut+3CJzvUfojGJ4XQrhQlFdQBteR+KCOulCYLUKiFsorsp8lGtWdanJxcUc7zozUFkzWPXr10fnzp0xc+ZM9O3bF0BJ/WfOnBnrlrl79+6YOXMmLrvsstS2GTNmpIJQtmnTBs2bN8fMmTNTD+rr1q3DnDlzMHTo0FQea9aswfz589G5c2cAJS61i4qKUoHroigqKsKWLVtQVFSEvLw8HH/88Zg6dSqKiopSx+7DDz/E/vvvX66H9srETznGGGOMMWaHGDFiBAYOHIguXbqgW7duGDt2LDZs2IBBgwYBAAYMGICWLVtizJgxAIDhw4ejZ8+euOuuu/C9730P06ZNw7x58/DAAw8AKHkJueyyy3DzzTejXbt2aNOmDUaNGoUWLVqkXg46dOiAPn36YPDgwZgwYQK2bNmCYcOG4bzzzkt5lJkyZQp22203HHnkkdh9990xb948/OpXv0K/fv1SL3BDhw7Ffffdh+HDh+PSSy/FRx99hFtuuQW/+MUvyn0cknlJJHNQ03NJUxo18sFdXU6pG6co5SbOZp1pqaZRCVPbVAYu4luuBqcIy1R7vTgbbrWTYzoGaYhzrxilZGqgFK2DBn5QNUXf/OMCx4Rt4NsoVUMeO6qEVAioTK5evRpA+thRlSzr3IRo21kGlRuTG6HCHWdnqkqu2rbGKXBxgbnCNOrOU22g44KkcD+1/Y6ynWbQorjxp2OGZdFrweLFizPKVMI+R5WOAc+ovDNYCK8b7LeqyNPNn7pP5HHhmALS1yIq7xpIShU3elDgdPFLL70EIH1N4HjkOA77BuvDelNJ1zUJOtMVF5Qtzk0m3T+mTGO4f1HdtHtXxV1neHnOOA44QxPOaGkecWvEdFzEuQ3ldULXTESthdFrN+8NJM5VJdEZHc23tOCDcWtXdEzxmMW5Ki1tBojjgs8HuhZEzxeJc5WsanrU7BfHIMdt3ExKVcxc9evXDytXrsR1112HwsJCdOrUCdOnT08tLv30008zztlxxx2HqVOnYuTIkbj22mvRrl07PPvss6lZUgC4+uqrsWHDBgwZMgRr1qxBjx49MH369AxXwFOmTMGwYcNw8sknpwIw3Xvvvanf69Wrh9tuuw0ffvghiouL0apVKwwbNgyXX355Ks2BBx6IF154AZdffjmOOuootGzZEsOHD8cvf/nLXXnIKkSNfHA3xhhjjDHVg2HDhsWaxjBye8g555yDc845Jza/RCKBm266KcMDjNK4cWNMnTo19vd+/fqhX79+8ZXeTvfu3fH666+Xma4sEskEEjnEDEokS48MXhY16sFd36T1bZyqVKiE8Q2YqpS+8TLkMN+cuZ3qsKqLVNaodGjI47BefDPUN2CWQdWEZWvIef5Ou0G+cavaAqTVNCobPAYanIJv9NxO1STqDR9Iv82zjmFbSjsGQHYYZyoFVBepDnFaS8+NKvfhMdB25eohpK5D2/bQM4rai+vsiqpBaufMdMwnTnkP08R5VdE+oMpb27ZtM36n+sx8w6BkqqjrjJXaxPLm8tFHH2XUhb9TRWPfC21etd4cfwyE1qpVKwDpvs5jzf7MsUTVm2ND7XPDY8JAbhxfDLiknnaYnutcfvjDHwIA/vSnP2WUwWtkeL402AuPgXroIawn66+qLdP//OKflewgi1CzFqUWxXvtqs2oisx+zePPay2PM/tPaTbRcdd2LVNn1tjPVDVnndjvwjz5ybFE93xdu3bNqAvHgSrurHsuanKcsh7neUcDSvH3uXPnAkgvjORsmXptAdLHhPdswnszo27GPbPEzfbpGpFwVlNntZiG555jjH3D98LKp7IWpzoAkzHGGGOMMTWAGqW4qwpH+IZJ9S30G00bdKpkfIOlok41m2+rtHWnDar6eFUPJ1Q8olQq9ekap2hSIeObM9/saR/G9lAxO+SQQwBk2rjThzPtculBgnnwTZ9lqKeNuNXx6rUlnOVQDyFsp3q3YP0//fRTAGkPHDxOPBdU5Fk2zw1VSCB9PlQ9VZtpE40qoiFq0x43C6NeZNQjTJwHhbAMzUu3q0/ijh07ZnxnPyc8/+E4jPOqoDb7zPM///kPgGxVjB5deC3R8R2i7eBxXrJkSUbZBx10UEYZ6mWDalqUFw097rz+6XWD9dY6cTunkJ988kkA6Zmw0GuNeuYoK3aD9hm1O/75zwaXJIxz81gkCvx2xX3bxyWREPMOjvcWUZvgNY99jsour99UhXmN1NlOIH7Gif2birneV9V7G6/POjvEe0iUssv+ot6RqGoz1oDe29SLlPa/KO85PFa8v+r1h/vy/rR06VIA6XsJ75WsI49LnOcqID1GeEx4/HmsOLOms5OsA8vgfvweF8sk3JfHn/dX9gEea/XuZioPK+7GGGOMMcaYFDVKcde3capZfJulDZ6q5EC2EqS24P/9738BpNUqzYNv76rc8203yjOK1lfzVA8LVJyZjm/zGqggqn26jd+pZGi71D5Z1Rn1ox3lS502gjwmqrBru6kUfPLJJwCy7fKpBMb5vw/Tql9ptbM20fDYhvaaqm5pvyTq+19t2qN8/Yf5h2niPFqoMkUfvlQe33zzTQDpvqf+wsN2sa9w37iZAPpr1xgHVBRVWWe7wzHHsav+qnmNohK3aNGijLI5PolGuYyyJdcZAz0PXLdDaHerx5xlnXXWWQBKPDRoG9S+V/tIVPTMsCx+/nTQQESitu3bSbDvcXspEaprI2qXrvbL6mGE196w/7PfqucWvR4TjlueU/UyxPTqOz68XnPWm/XgPocffjiA9JhkFHAqzZxBO/PMMwFk247rjOobb7yR+o128xpFW2cW/vznPwPInsXg2g7WkfvxPsVjHcZS0JlepuHzgMZ/0fGhdulx3mlCG3eWwWsdzw/7hK6HKS2qu9k1JBLJ3BanJqy4G2OMMcYYU+upUYr7T37yEwDA3//+dwDZPmxJqITpSmy+Cav3B/Xkon6I9W03KvKfor5q1d6NqOLJsugL+tBDDwWQHW0x9GeqERi5D/PQesf5Tmcd1a92FGw789SIdKr08NhyRT6PPVUJ9UTBuoTnk8qE2gbyO/uIiSaq35bl5zzOY4rOjPA8qQ182N/V/7f2ISpMXLPBvOh7nOdf+2WUzTUjD1ORi2sPvcmojax6UiG0b+U6GCA9FvUYMk/2U47h999/H0BaKaVyyrETp8AB2f6oNcoi96FHj6OOOiqjjmrrzPN2wgknAAAWLFiQKov1U3/T3EfPg87cDRo4oOSfOJv2HElIvrWdsG8B2TPGVHZ5Hnhuw3tCnFeRuAjkCsvQWTp+j/I0xlkqfrIM9l/afvN6zTHKvKnE8/6l90p+D9exqdKusQWYJ8vg70cffTSA9HOErh3RsRw+Z2jcCPVUxWOnM3CaJz3yxKnjpc3k6/khUX3BVA6JvDwkc7hWVfR6ZsXdGGOMMcaYGkCNUtwJV4VTneJbLO24Q1QpUntQvoXT3ppvr6qy0b5N91O1PPxf1U21E41Ts1UJoReZhQsXZuQTplP1mvtonlFR7oBs+zhVQqP24zatD48V7Xq1DLVt535UUXjsoxQh/kY7Xj22pnTUPjqEqpFGRFVbVu1L7HM8N+oBIjyP/I2fLJPK7rHHHgsg3TcYxTTOa1CUZxfCfV588UUAaWWN+9DLUVye6sed9rv8PfQZz7bHRXpU+2Jeq3gto4qvCjvticOZwzj/29pujid6tKFnnrhImbxmzJs3L+s3vaZpX9DzSZ546mkAwDln/XB7htEqcPF2m88Eth8v1jG5XR1O1g3FfdSoUQCAM844A0D8vULvO1H3krh9dPxqrAT+zjFIpZnjPC76NpC9Jor9WpVn5sFImby3cQ0IveZQNWYZvM5369Ytq70608dZaObJOnTo0AFA+pqjkYc1EjjbFLZTxwG/81hxX/XqpmtDSGn3PEXvyeo7X2cD2KdGjx5dZt6mYtirjDHGGGOMMSZFjVTcVRHjJ/0Qq4/y8Lc4FZxv9nxL5ds5VX2N8Ka28aGKpTakfBOOU7WpwsXZGPNTV/VTSQvbxTRq36bHiqgtraqucR5Goo6F+qun3S5/p5KhNsTMh3aPqhSFNnw8j6rmlqa8mjSlKTpU3sKoquE+6ptb1TCiinuUP3WeYypytEOnXfa///1vAPERVdWum2p4aBusHh/Yd9jnOe50Jkw9ovB3rsGI8w8fta9u13UvnJ3iWKbqrV6rwpgNOrOheWuZquYTjUbJ8xoeQyqI6t1EbfrjvAVxv0mP/DGjPZde8vOMuqSUeLahePvndsX9nmf/iREjRqC2ExczQe8/er+K6gN6vuNs3lUF1vuSjm+dDQpnWXj/oe0299XI3bpmjLOw9Kn+2muvAQB69uyZ0Rbel8PjpLECWH/moWXoWiyNrKq+1rkmK/SVz/L5rKGqvMYb0f30mJY1hsP2MQ3L1mcQXftS2vXK7FwqS3GvkQ/uxhhjjDHGVBcSyRzdQeaQpjRq5IM7ow7Sfoxvlnwjpv9VIK1o0Z5N1XlVivgWrko71TYqHapSRaF+zPVNmFDRY5n69s23eSpnc+bMydgv3LegoCSyYJytfpxduioDrDNV8iilVu0s1b++qv6q6PLYacRGpqM6RzUVSCs5rVq1ApA+Rurr3kRTmk2sqtjaN3Q2RhVb9XaicQzCfehhqHv37gCAWbNmAUjHU6CyRvVXZ8Y+++wzANn2rKHdOdVijU4aNSMX1pf9l5EU1X6bin3oL13jJHDcqZ084fqPVatWZWynKqiKXDjWtQz+xn04jniMNa+4tTVRdvq01WUePC/sAzrTpdcC7QvMe9z9v8so8+LBFwEAeJVJ5FH9zexbtZ24e4SuI+FxjIqvQeLs4OM8oulsCa+1/NR7Xtx6qRC1n1cPNerZiOOb/Y627/RGwzHJewOQbavOcckyOA7UE1KcdyyNDkzPbPwM0dlIRoQlOlOo++n1Qe/9pa3zYp9gu/T6pddjU3uokQ/uxhhjjDHGVBdsKlMKtJ3m2yjfjDWqKZBWYqlwUS3j26l6ouFbOH+nOqcKkr4JR6mKanunikdZqlyc4knlkLZ3AHDAAQdkpNE3ei1DV6CrIqaqAxWFKFt+tTNnWiqeVNhVRWLeVFkLCwsBZEeObdmyZWofbtN6sU+Y0tHzH24jep7YT+O8mcRFzVTvBkD6PPXo0QNAOiYD+wjVMfZn9VDE3zmOqVirV4ew3oyMyvpTmWNe3M6xzr7FvkbvM9qecJaHs0a8nrD+Gj9BI2CqIsl8OHOgMRHCckNf1gBw2GGHAcj2AR7nRYZlakRjHi8gPb54bVW7WiUuIrOqvKra/v6hidi6dSsuuXhISTrWPRkdmbW2cueddwJIz0Bpv9HrH+FxDf2B6zU+7hiqGq77Rc0wAdHRPbmPrgfhWON4iLO7Vn/mvDcsW7Ys4/ew/7G/xkXxjfORrn7beYyp9utanjBfjUpLODOgNu4sK27c6DOC+u0HssexxoVh/bW97FOm9lAjH9yNMcYYY4ypLiSSidwU92TZZmalUaMf3NUzBe3ewjdj2qUxLRW5Dz/8EEBaYVfPL+qfmEoh1QeqDFE2w3zj1TdiVdpV5dYV+HGR3I477jgAwJNPPpkqk9tUCaBCo6pLrnVSX7+hTaUqG3psqJKqWq+2ucyHdutUG6PWEVDJoAKovuJN6Zx77rkAgAceeCC1Tc+j2p1qP47zQsG+o/lxfALp6JzPP/88gPS5plqssy7sU7Tn1P5I9Vzt0YHsNRas94oVKwCk106wHcyLqhnLYD9Vv84hTENlkNcijcTMsnWs8JizDI0TQSU+/F+vPfPnzweQvua1bdsWQNpGObT/B9Jj55VXXgGQjubK9QJAepxx5oPnRe1nVa1lu7RPxNkT87d77vsdtm3bhhHDL8X2xgHIzb91bUIjb3KGhseT54VExWfgdVa9lsUptzyXusZF7dL5Oz+prod5xynM3M77EmfaNC9eM8L1TVH5RW3jd/ZZHkuWwXZGeagB0seY7Y2Km8LjrOtL1Aubqt86U0I0vVoGhO3SmU+2TyPZhuPY1C5q9IO7McYYY4zJpmeP47Bpw3rk77l32YlNhbFXmVJQdYFv+bTtDFVhKuxMS6WCdtO0j6NSpivP+Z3EvWGHb+3qazpupbiqVtyuSgDbQPtSqnjh2zy30eZX91GPGNqOOP/Luio+Sm1U9YFqm6oHTMfvVBd5Lnhu1GNCqBRSRbGv2ooRKj9qh62+o9X3uMYX0Fke9hWOR6rsAPCXv/wFQHoGi+ow91UvThwLVM/p55lqMuvKvhSOCeYRZ+PLsd25c2cA6b5F9Z6EXqrC9pXmM5uquEYH1lkn9bzTunXrjO30786ZiLDN/NRZCJbNaxsjR9ITD48L66Seo0IbeZ4n7SN6XdXZQq2T2gLrjF/4f15eHn57731Z19i6BNdVtG/fHkC22s2xp566wusz03AGifeCuCja6imI6XSNC8tkHwiVaObB8arrsvR6zbw4+8O+R89x7JucDVK7cyDbiwojBPPawWPJMpo2bZpRB+ap7WS7eGzDPqzjWPPQezyPS9x6E6LrCcK+z7x1LQ4Vd30uKmn3cTC1D0dONcYYY4ypZfxx6jSr7ZVIIpmX819FqJGKu9pb8y2V30MPI1Rx+dZMNY0qLvPi6vVDDz0UQHZkOn3D5tu3eoYJ99E3evW4oJ5eqJZQZVCb4tBjRthuIFtp55u82srF2bCr7TvrrGpX1MwC84zzksNjybrwWLMMtb2lfSOVhXAGJU7Fj/McYKIJ7SR1vYaittTaN0IbVyCtaEWtxeBv9FdODyn0wqI2rew7HL8sk32G29UWGIi36aWq16VLFwDp/rtgwYKMPFjH0047DUC6H1LpCn2rU93+4IMPMn6LG0faX3WcUqmnmhaqfaqccl+qmrzmsT3czvPEawS307ZffbQD2dcH7qvXP37q+NT1OUq4Xb2ZkLriUcYYY3KhRj64G2OMMbUVmkjRdIovU3xZ44shX8biggkB6RdRvgSrsKLmkOrCk2WrORQJgyFpIEMtg3nwhZvwRZUvyyrqHHLIIQDSL8jhyxxN3mh2x31YNl9MKRhRPGAdKBTFmbTy2IYvz3w5VtNaPU/6MqrHWs1pea7U1SuQvfCV51MXE7Oe7EOmEknmlfzlkq4C+MHdGGOMMcaYipBMpjxhlZmuAtTIB3dO1/Jtl6oD3+bDkOZ8A9aFG+riifvwTZrpOQVMBYHTyXwj5oIX/g5kv31zap5vwnyrjnsrJ7pwTRcohQt0qFiouy3mwWOji8z0zZ/qA+vOIE9RobhZH5om8XyoKZMuDOaxVrWI21l3dSkHpFUSNc9QMyJTOqGpjCo3GtBDx4Au2uL5ZT+nicz//d//ZaQP06i7UpbJPqCmGOzfdBmqi6q5P8cnkDY500V6Rx99NIB0n3njjTcApPvvt7/9bQDZ5h3qOjU04aKpDz+5iJYKoS7mJDouaVZEMx66jwxdarJeGuSGgZS4kI/HlgvvOU6pavJ3XWwc1WYeS/YJjs24RYc8fxq0ShXHKNM7VTzrYsj2W265BUC6P/Dcxrk4jXKXqaaMagapZlB6rjSgkZqtMV1479Pzy0/21bjFm2oCp+3idYNqeXj91wBJqkBrnnrv0+ud1j2qnXqv1tmMuOBXccEYWTetQ1TAw7jF2ryP8vmCfcjUPmrkg7sxxhhjjDHVhUReHhIRAkhUuopQIx/cqXLTdo1v36ogAGkVjW/EVIqo7NEFnNrc8Y1ZFTGWwbdv2tW9++67qX35Bn/MMccASKttugAtVOyAbBdZuoBN3V+Gb+Nx4ec1iIy6kOMnVS0uDuRxYx2XLl2asT8AHHHEERllqRtHDdyj7eSx57lQV2I8r6G9H/9Xxd2BmMrHj370o9T/kydPBpCtuBENU64LgzkGjj32WADA3/72NwBphZsLUIF0/2JQIB1/caoe+yeVRyrwdNVI93HhwnQuzmRfob0w3SXSTRzHcteuXTPaq8oviVpwyvFCtYuL3HlsGPAtPBYhanfM4xQV4I3beB3h+OGx4DjigvVmzZoBSB/zODeSUYtAwwW4QHpGQ2c81OZaZydUYYyawWOeGgyvLiruhP2c9zp10aqf4fHkcVSXxqrYauAldSHMfqJB0VhWqETrImV1Q6zXFk3HMjjTq66RdVY2rB9t7fmds0Ts9+okQo8H66j3X9YhnPnVezHrHae083qmrnb1XOh1JDyfcedc82KfMbWXGvngbowxxhhjTLXBi1Pj4Zs038qpskWFCWZaDfhChYj2nlTE4tQ1or/zjZhqHpBWy6jsqeKhb+FxATHUBk9/j3KxpiqaBnqJs6FTFVFnCVQhDdtRljKp21kmjz0VA54bXT8QqhLqIpNpHN55x9E+rkqb2qny2DNwFgOevPTSSwDSQWOoioV2uQwCRBVYw5OrWsayGGBMA4CpDWzYV2hvvnjx4ox9OfZph967d28A2eqf2vrqcQrVQ9qiU+WnitmjRw8AQPfu3QGkZyM0OJSO5dCtZVi3sM06M6XuOWnbS5VS26PtUBeOYZv1GOi1SVVM9UTCOkUFCtJ2sT5xedcluD6hXbt2ALLXRekagxCed/YTtZFmH9PZD35ydot9M86+PnTny/PNesUF/ItzD8qyec9kP2JAIl0bE+bN9nCmL24WmujaMX6yb4brZYDM8a9rqvLz83HQASXXwzXr1mel42yAquQ6u8FjrO5uwzS6NkXHDfuMqb3UyAd3Y4wxxhhjqg3JZI6Kex30KkN1jm/GtOWk15KoACJ8m6ZXCip+9PpA9ZA2qFSYVRmg+sM36Ki3eqoKVN7pT1WVc9ZT1W7Wle1ku+LqEqJpqASyLvq2rl4g+PbONnCmgkpAqMaxfL7ps56qqvDYcIaEx5qzAaq+8pxEeUxg+RrmOZwJMOWD9u7Tpk0DkO3pQGey2rZtCwBo06YNAGDmzJkA0r6WVTHl+QXSahA/mSfTsG9QceLv/M6xQSWrefPmGWWGNtnsu+zr3Oedd94BkFbpiSrRRL1RkHBdxezZswFk23SzTI4N1pdrRvT6odcADS8PpJVAtktnm5gH20f1kumo4um6HVXyo9qjnkq4r9rq6ixN1GxomG/4v3r+uv3221FXuf766wGkZ7N0PYKel/Dep+sRNAih3j/U/pro/SrOGw2QbavO/qMexDSYG+vP6zqv5+yzXMPCMcc2AGnVmmm4D68ZvPfFeXHTscaZBp01YHsLupZ4fEKxKPnbvzfaO63UN9yLHu6aA4mS8t6YNz/1O4851zDwuPHchedI77fqRaeoqAjdu3VBQZdjHS21llMjH9yNMcYYY4ypLiSSSSRyUNNzSVMaNfLBnWo4FQAqCbRxCxUAXYVeWFgIIG1fzRXYfHulDS6JC++ukc2ivD6wXlQA9M1e/WDrrABt9fj2TTs/VerDbVSkqexR6aPa/dFHH2UcD9abx0ltFNUbT6isqXpGdUVX2BO2j+eP6Wi/zMh2aosc2vmpT2H1+212nPPOOw8A8PjjjwNInwf2BdrZUpF6+eWXAaR9jPNcqMeFUKmmss7zddRRRwFIe3jhJ8cAlTWeb/V3zL6kaznCbWo3z7JZBtunnlJUUWQ+rNOsWbNSZakvdI5xjjsdj1QUuQ5GIy7G+XcGstVrfqo9unqfCO2Cw/Zo+ij7Y51tUEWdn+oDW9ekkKg6qd/wOH/VdRHOUPG+pd5+1EYaSI9HpmVfVFtunm+16daZGL3v8HuoCus4CO3fgbSirvtyrHI779OaD8d7FHrfVfVePd7ojCLHJssKZ8O6HHN0uiAq7aq450i3ziXe5qjAvzb79dS54fVRz11YT137UVRUhIIuJR69nnzmTxkew0ztpEY+uBtjjDHGGFNtSOToVSZRB73KqNcLKgVUcEN7UFWnuA/t3viG+5///CfjO1UGKkJq5xrnLz2EyqTa67JOVFGo+qtiRpWO6gMVQ9bphhtuSJU1Z86cjDT8ZB7vvfdeRhlsD1UG2harbWKc/+XwN6JKmUbaDG2dw+88F6wzz596+QDS6omWHRX10ewY/fr1i9z+j3/8AwDw73//G0C6L6hHF54L9qFwdop251Sadd2Dzk6pJxSOFfYtVdqj1mCwT3O8UbXjZ1xUz7g1JYxMGq69ULVY12twtmzUqFEZeTIy5tlnn43SCO28NTaDznDozIGq+OoLXD1LRUXhJDrjyOOtMwY8H3GebEi4nXnozIgB3n77bQDpcaKRSHW2M4Qz0Ryf/NRrqM7uaDrtJywzvN/yfDIP2m6zr3Lcsk7q35xlcj+uOaNnqKj1XmofzzJ4f1GPNiyTefA+zfbwfv3ll1/i8A6HbS8kuN9v/z/BbTFea3QBYtyoOv7b3TI3JNL7hfFKtm7diiMP75hdn+A7+4mpIirJHWTFDG2MMcYYY4wxlUKNVNyJ2r3q2zqQbc/HNFT86BlDIzLSxoyoXZwqbCGqXKn6xLxpr0hliUrABRdckJEflYOjjz4acRQUFMT+FuY5ZsyYyDrwWGpEVfUQE9qdqg2tRn4lLItKGo81t1NV4f5UPqKi5Kmqqx5DzK6jV69eAIC7774bQPbsjM5GqbILpM8f+x3Ve6J2tuwD7FPsC0yntrKhRwyqVVxDQXVf4wdw/LE9OrZ5DeGsFj1bhP1S2z5y5EjkQllKO7n66qtT/995550A0mOSx5/10WuXxotQu+LSbNvVnlZ9fsetYyEaBVXXxUT5jOe2W2+9Nas+dRXOuPzxj38EkF7/pGuSwv4fF7uD513PHdNx3OgaF/YTjr2o6LfaTzjeec3X2SGNIq6RYjljnEsUXarxOgvHPNWOnrO3vPexjupprSoJz6euUYmiLntgqg54caoxxhhjTFURsRA1y0QmbpHqtu3bt5u+JLD9O114anqayAT5tWgWLMZttl9WfViX/3v2Ofz4xz8uuz2mVlAjH9z55kwFgXazUV5lVMXhp9qJMsqivnXHRXhjHZhflKpINLKZKpKs//Dhw0tt987gV7/6FYC0cqO+edUvsM4ohO1UxU+3EyqeVFF4jNXLTlzUvFAZ0qh+qqaYXQ/Pl3oj0TUc6lECyO5X9AnPGTDuw+9U3NROVe1zo/yEU3nmGhGWTS842k91jQaVR25n9FMS+nGn3Xtok7qruPLKKwEAd9xxB4D4CKk6Y6DHUL3u6MxZ+Jum4Sevf2pvr+uQlKjtOiNgsmEMAs7C6rEKj6ueC553Pf8cMzqrrLNcPOe89nKWk9+B9DhkGTrLymu73rv5nTFZmI7t4Xeq6lFoBFXmyXsE1+KwTLZLZw41omxNgP3CVDGVZONeIx/cjTHGGGOqjCz1O1NDL+ZLVHGm8k7hXRV4pitOxJtR5Kz2m1pNjXxwV9txjdAYvimrhxK+VesKer590+4tTn2IKzu07VQ7PqJeUvi72qRWBixTFbW446SzBkC2/2u1IeR2VXzUvlFt21kG8wmVW26jBwG13zS7HlVyOd7YpzTKaWibqYoc+wKVd41crOq+2rLzO/tBqP598MEHALKj7FJhi/MTzv6nUYM1fVgWo8YywmVlcNVVVwEAxo8fDyDe006cH3eNxEhCTy8813HXPY0Greqsrj/S2cZwpox5X3fddWU3vo5CG+ZHHnkEQDpaKMda6FVG12OpVxh+6mxJ1LotIDuyLs91OMul13wdM+qljf2HSjoVd85mNW3aNKNOnImLgvVi2YwaTtQGnnXRcaHrqGoCtm2vJiSTOSruFbNxt1cZY4wxxpidSKK4OOMPxUUlf0VbS/5S3zP/Etu2xv5h+19i2xYktm3B43/+G3ZvFB+UytROaqTiTps1Kl70A863/dAzhSrJVAfVF62m5+9q06neVjQdkB1VVW1JVb2vCptOrYNGx9Moc2prGP6vCrt6LVBVn6gPYiodzI8KSaiI0GaS55z1o12iqTyocPG8U9nmd/6unmKAtMrHc80xo36feX6p5sepYFxHQVtzAPjkk08y9tE1FEQjQYbRJ4Hs2Rz1vgGkx/+RRx4ZWb9dydChQwEAN910E4D08aYtPz91LYLOePEznD1Un/Y8hurNRFV7njeOU35qfIzLLrtsB1ps5s6dCyC9NktnsoDsWZG4GRg9p3FeZ/ReobMo4f/aHwi3631T13sxijavKe3btwdQ+uw06/Pxxx9ntFe9SMXVIa6u1Zm5c+diwIABVV0Ns51EXh4SOczU5JKmNGrkg7sxxhhjTKUQ2p2rzTo3I9rWPRb1WBPhVSZVPPMsKnkJ+9fbH+KZZ57JrRxT66iRD+4LFy4EAHTp0gVAWiGiqhMqZnxD59s238L5Xe3bVGFXZVrf1tWHNZAdgZGo8sHvcZEqdyUs87nnngOQrbboJ9sU+pVVZUY90ujsBOGx4rFnNEDOhjBf7heuWeA5VrtM9okf/OAHOR4Bs6PoeaXCqwoV+wr9iIf7cjZFx5nasKs9LvenLTyVOUYoDe1t1c6WXiV0hoffVWlXhZJ9TaMwh8dC86hM4mzDx44dCyCtZqq/eo7DKF/4cesAFFXrOQPG88RjxrLp3crsGOPGjQMA3HzzzQCAE044AUB6RhJI91uu8+K54Uy1emjidbus2S1V3qPWlPE8qx29znbpGirODrH/MPYC4z3QyxTHMpC2i6f3KI5TrpNhnuzXrIN6k9FowBqToDoxa9asVB8w1YhkMjf7dftxN8YYY4zZucx+Yx5WrlyJM793amobvb6kBHdqdgm+AIlinqPnl0RRxEtyyovMdpPVLbve3aypAHYHGc+1114LAHjssccApJUkVbSBbLtVfeOP81+un5peV+qHaiP/V9/SquBVh2ifrAOPIeuoCrx6EgCy1VBFj6GuH6Aywrz5qbb/4flUbz/0PsA+YSoP9m+eE54/VdrDNRxU87Tv83xqHoRKIj1FvP766wCyZ4Si/Fiz/I4dOwJI9y/2Q84YaOwGnQ3g7zrrBqTHS3UY04rakV9//fUAsiNH8jMqVoOOYaJrETgj9uWXXwJIR3k1uwZG6GU044MPPjj1G/srx5z6Uud2Xa9F9J6oXog4bsLrM/sQxyvTUkGPiyWgXqKorPM7+xNn2D7//POsdrJvatRV5q3rt1gX1pXfuXaF1zd6q6tO5BqZ2dROauSDuzHGGGNMZTBn3oLUi02XY44GEPhbT2RGMU056ysqw3Y9Tokv2pr1f2JrifnOuGnPYcSIETvWCLPLSSTzkMhBTc8lTWnU6Ad32rXS16v6BweyPbxodEe1rYvygAHkvkoeiI/AqMpAWM+qQu111cMEj4cqI0C2p5041C8wFQ765FWPNerpJzxOOuPBPmB2PbSV5vngeVSvFFTa1dtMuA/PNfuXKm4atVBjNXz3u98FALzxxhsZZUbN/jBvKnGqHmv/1XGpyj0J126wPfR4VZ258cYbc07729/+FkD2mBw2bNhOrZMxpmZz//3344477kBhYSGOPvpojBs3Dt26dYtN/8QTT2DUqFFYunQp2rVrh9tuuw2nnXZa6vfi4mJcf/31ePDBB7FmzRocf/zxGD9+PNq1a5dKs3r1alx66aX4y1/+gmQyibPOOgv33HNPKpbIpk2bcPHFF2P+/PlYuHAhTj/9dDz77LNZdZkyZQpuv/12fPTRR2jYsCFOPfVU3HHHHal7RnWjRj+4G2OMMXUdqrD33XdfahtdKMaZyOgCUjUJ00CC+oJOF6whFMSYJ00ZiS74VOFLXQHvv//+GWXyxTh8iaZ5DuvDRanMQ0UB5qGCEttNcy+aj9I8lMdr8ZJPUmXVq1cPjfYuER+KVVnP2y5sxSnxpBQlPrFdcf/903/HsGHDqq3a/vjjj2PEiBGYMGECCgoKMHbsWPTu3RuLFi1KCashs2bNwvnnn48xY8bg9NNPx9SpU9G3b18sWLAARxxxBICSoFL33nsvJk+ejDZt2mDUqFHo3bs33n///dQ579+/P7744gvMmDEDW7ZswaBBgzBkyBBMnToVQMk5bdCgAX7xi1/gqaeeiqz7a6+9hgEDBuC3v/0tzjjjDCxbtgwXX3wxBg8ejKeffrp8ByKR4+LUUqLj5kL1d1RqjDHGGGOqJXfffTcGDx6MQYMGoWPHjpgwYQL22GMPTJw4MTL9Pffcgz59+uCqq65Chw4dMHr0aBx77LGpF8/i4mKMHTsWI0eOxPe//30cddRReOSRR/D555+nFPOFCxdi+vTpeOihh1BQUIAePXpg3LhxmDZtWmoNxJ577onx48dj8ODBqTUPyuzZs9G6dWv84he/QJs2bdCjRw/87Gc/S83mVkdqtOLOt8+ZM2cCSL9Rh+YxfMPn9De/qxsq7kPXhHyj02l0TuFzsYyGbAbS6oG6fVRl48c//nF5m7zTYR1eeOEFANmh5dV9Zmj2oAF3OK3EtKrU0GSIg4rHkum4sE9Dt4fKiJorVFcFojai7uPYN7hgtEWLFgDS55OmUKFLQaphPI+6UEyDcLGPaNAX9pFvf/vbAEpUk7BOQLrfULWLc/GqpjEaKE3bH2WOw228LtQWLr/88qqugikHoQnTiy++mPEblXZ1WRp3j+QY4ye3axCt8N7H35iWpnAsW11I8prP6wBNHNSZBPOhektVFgDeffddANlmeNpOlsV2qqvouHHPfMJ28lrwzTffYPk336Tas/vuu6Npk+3mFduVc1Xis7zRkKItGekAILG1ZFt1Nk3bvHkz5s+fn+HiNZlMolevXpg9e3bkPrNnz866d/fu3Tv1UL5kyRIUFhaiV69eqd8bNmyIgoICzJ49G+eddx5mz56NRo0apdyCA0CvXr2QTCYxZ86cnF1Dd+/eHddeey2ef/55nHrqqVixYgWefPLJDLOdXKksG3cr7sYYY4wxptysWrUK27Zty/K+06xZs5RvfaWwsLDU9PwsK42a4dSrVw+NGzeOLTeK448/HlOmTEG/fv1Qv359NG/eHA0bNsT999+fcx6VTY1W3Ml7770HIB1uPAz4QlSxU1s8qnFUhfn2rQGaqCRQTWS+YfhzqgYsQ8NAc9/qBOvEgcI681iynaG7O1XM2W4qGKq+8BjpAkSeEyolul8If+M5P/nkk3egtWZH0PDkPJ9cIExlSgP5cOF3+BvPtfaBONeihGoZFTrWiQFZGPAnTHvYYYdFtkPrpK5fiS4qJ+GCTbaD9rHGVDWfffYZAOCQQw4BkB6vqjCrwwZe85meNvLs41S2qViHMC+OGdqCMw913MDrgLqaZDp13coHsnAROOvJsnQcM091f6k2/hp8URX68H7E/3UhfnFxMT79bFnK/eXWrVvR6agjM+rD2qWU922Z3mcSgeJe74AOMLuW999/H8OHD8d1112H3r1744svvsBVV12Fiy++GA8//HD5Mksmc/Tj7gBMxhhjjDGmkmnSpAny8vIyRBOgRESJsytv3rx5qen5uXz58pSpI7936tQplYZmmmTr1q1YvXp1bLlRjBkzBscffzyuuuoqAMBRRx2FPffcEyeccAJuvvnmjPKrC7Xiwf0Xv/gFAKQWQrRq1Sr1m9rj8i2ab+Xq7lBXlqvNncI371CN0zKoJlCpOO+888rdxl0N68RV1Dwuan8e2gOz7XHHhmoE96WyoXbN/KSiw2MeZeP+ySefAEifc1N5/PznPweQDreu55ezNrR1V5t4IH1O42zXidqTM50qdtweumYktEmlGq9eJFS1Z99Wbxpx7k7D2biPP/4YQPW2RTV1iwULFgBIr9vSGbO4tUS65kOVaI77KBesVL+ZJ1VtDXyo679Uwab6z3sB28D8V61alcqL45tpmPfKlSszylbvMGW5H2aduJYrPC56vaLary6Sd9ttN7y38APstddeaHXgARn7pJT3tPS+/YfcIq1WF+rXr4/OnTtj5syZ6Nu3L4CSvjRz5szY62H37t0xc+bMjOBwM2bMQPfu3QEAbdq0QfPmzTFz5szUg/q6deswZ84cDB06NJXHmjVrMH/+fHTu3BlAybqOoqIiFBQU5Fz/jRs3Zt0/9L6TM8kcvcpYcTfGGGOMMVXBiBEjMHDgQHTp0gXdunXD2LFjsWHDBgwaNAgAMGDAALRs2RJjxowBAAwfPhw9e/bEXXfdhe9973uYNm0a5s2bhwceeABAyYvPZZddhptvvhnt2rVLuYNs0aJF6uWgQ4cO6NOnDwYPHowJEyZgy5YtGDZsGM4777yUeASUmMJs3rwZq1evxvr16/HWW28BQOqF4IwzzsDgwYMxfvz4lKnMZZddhm7dumXkkwuJvDwkyjD3ZLqKUKse3H/yk58ASAcNAdK+WPkGrCvr1Y8s3/T5ybds2n7zzYyfzFcDxoQwj2XLlu1gyyoP1rFNmzYA4r3qhL/pMaFyQwWWKkqcTSGVEKoptGPk227oC9heLqoPPJ8668TzGRWcjH2BadS2nX2IY4bbVQFRT02aHkiPWfVkEae8q0clomMgSt1fvHhx1jZjqhIGTOPnMcccAyCtIHMcUIHneNbruNrEq4ex8J6gdvG6von3XR23qm7rjDivJTRbCNeJcRvzZv2YRsczrz26noZ11Jlg2quHM8vqb15nL9h+1nu33XbDilVfYsuWLWjZPNuveUmjS473/dOey1CiawL9+vXDypUrcd1116GwsBCdOnXC9OnTU9fgTz/9NGPm9bjjjsPUqVMxcuRIXHvttWjXrh2effbZDG9BV199NTZs2IAhQ4ZgzZo16NGjB6ZPn55xHqZMmYJhw4bh5JNPTgVguvfeezPqdtppp6Vm6oH0OGAfu/DCC7F+/Xrcd999uOKKK9CoUSN85zvfwW233bbzD9ROolY9uBtjjDHGmMpl2LBhsaYxL7/8cta2c845B+ecc05sfolEAjfddBNuuumm2DSNGzdOBVuKY+nSpaX+DgCXXnopLr300jLTlUkyL8fFqVbcswhV2VtvvRVAWn3j2xrfkKku8E2ZiqD6Hud27s9PTQdke6FQTxrVGV3lH66Wj0vLY6HHkMdEjxFnPZheFU2qLly8cs0111SsUWanwgscbd2pmlHhat26dcb2KBtxtVVXO1P2P+7LdFRt2C+5FkVVNSDtTYNlqQ2vKuf8nXlppEh+sr9/9NFHqX1t226qK1RvH3vsMQDAgQcemPE7lWWNNEpFmmOQY4/23Pw99LZChZxjJ4ypEubF+y/vBTq+1WMZxx5t3sN7KbfpbJ36adfIsSxL1X71OMf4JOH1Qn3Yq4rPtGwX27N+/Xpg/+0LJ8WW/am/v4Lzzz+/xqntpvKplQ/uxhhjjDHGVBpW3HcOVGsnT54MIP22rR5OVFWgwsztVIu5n9rwhQqAeqfgG/xPf/rTndiyXQPrSHWGagWPS9hObuOxYLvVF756JSjLFprfrbRXb3Rq8eabbwaQ9jLDvhJ6YFDf0RxnGtVU/Tir5wuq+1yTwXEY2q1yfQvHX+jpIcyLaF10lon7UZkLFXdjqjtz584FEO8BheNE+79en6ky814a2rjHRSWOm+1SxZrXDn4yb7WND2fxdB0MvbdR/acir3FGeF3S2BBqr66qf5gHy9QZRP3OY7tt2za8t/ADfPPNNzj2qMMRMnfuXJx//vkwpixq/YO7McYYY4wxu5JEMolEDq4ec0lTGnXmwX3gwIEAgBdeeAFAdoQ2vnWrOqyqORUAKgVUm8OIooTboiKAVndYZx4XtSMMt1F1oAqqPm7j/OSqqsrtPFemZjFy5EgAwO233w4AOPbYYwFkquBx/tdVgdc1JAy0Qf/NVNWohqkHjBCNlMrvzINjmgqderrRtSmvv/46gBKXZsbUFO6++24AwC233AIAOOGEEzJ+Z3/XuCO63olKu65xAtLjl+ucuK/GUeGsbMOGDQGkxy3vpxyDutYlajZMZw7YDirnzFOvNVwfo77nVXlne0OVn+XzGGl7WVaUB5vDO2yP5FxUkvau+3+Pa6+9NnV+jCmLOvPgbowxxhhjzC4hkaONe8I27uXiww8/BAB07NgRQHy0ON2uvmyp0pWmAHDfCy+8cOc2ohJgnZ988kkA0e2kKq8+79VvtkaoJEzHT56b3r1778SWmMrm6quvBoBUsI0DDkhHDNxvv/0ApGdrCBUqql//+c9/AKQVLY4/VdSpdLGvMX8ge82EenqgUsiAHPQ81a5du4z9GYFx3rx5AGCvD6ZGc+211wIAHn74YQDA4YeX2FpTLeb4oDqutu/cTiWbn0D6vknf5/zUSKlU69VTjcZb0f3ULj3cpnmrjTrrxjUqVNzZPvUwpx6vwvuXto/3Qpahs3Q6qxzC82FMrlTM0MYYY4wxxuRGcRFQXIQpjz+J/L0aVnVtzM4kkQASyRz+sl0kl6uY4igH3XUIepvRlfZqn05frrSDJaoih/uefvrpO7/CVcRzzz0HIFspBbK9c1Al/fLLLwGk7fy4L9OvWbMGgG3a6xIMpsE+wU8SF5FQPV9QYee6CvY52tUDQNu2bQFk90/1+EBF/Z133sn4nUobZwGsjJnaCAPYMP4CxyD7va7fUttxem8C0soylWj1xkY4Xjnrte+++2bkrTPeGk/lzTffTOXFSJgaFV2Vct7Lec1gnnpP1xk5tjO0cWc0b1XcCe91zIPXq6VLl+KH3z+j5Lc994apHaxbtw4NGzbE/956Cfvsnf2MlJV+/VfYt9NJWLt2bcaMVa5YcTfGGGOMqQSe/tNf/NBuKkSdV9zLyx133AEgrQiqEgjUbhvYsWPHpv6nHR+7EG0Hr7rqqkqvl6mZUIFnX6J6RxWMfYv2q2qXqkrXKaeckvqfipuupSAcu/RYQ1t3xw8wdZHx48cDANq3bw8gO5YJx6h+Dz2NaeTQuDgMaiPO/ahUqwrO8U6VnGMVADp16gQgrW6rfTnVfc4cUFFXG31dm6aRz0NvadzGerGd+p15cP3W0KFDYWofVNxX//uVnBX3xkf3tOJujDHGGGNMbabOeZWpKHVdTa7Nswmm6qAip76kVQXTyKqEKlvodUa9SXDfuEiLVtpNXYZq8KhRowCkPa9xrYh6guH4CZVojlO1M9dxzTVl/J3rnfjJ9BrPgb+HKj+3NW3aNKM9VOd1H12vxu3qVYZtUa86QNoWn/uwfqw3vWK9//77AIDRo0fD1AG4+DSXdBXAirsxxhhjjDE1ACvuxpgqQ+1I6X1BFSxuVz/O3I8+2ENVTD0+qbLGMuhVxhiTVodHjBgBAGjSpAmA7GigHIvhOhON6UFvMdxX4y5wOxV4tS9nfvzkepRwZo3buO5Mo58zOqt6meGaLOZFrzS8ptD7DMsObefVGxbrTZv9uXPnAoCjodY1EoncXD1W0B2kFXdjjDHGGGNqANXuwX3ZsmU499xz0ahRI+yzzz74/ve/n7IXM8ZkUtPHy6hRozBq1Chs3boVW7duxcaNG7Fx40Zs2bIFW7ZsSX3/+uuv8fXXX6OoqAhFRUXIz89Hfn4+mjRpkvGXTCZTf3l5eRl/4W/JZBLr1q3DunXrsGbNmpQdrDHGGLNDJJO5/1WAamUq89VXX+Gkk0qc0l977bXYbbfd8Nvf/hY9e/bEW2+9lVpUYozxeDHG7Dpo5vHzn/8cANCzZ08AQKtWrTLS0ewFSJvPaCBDLgSlGUphYSGA+CBHND3hC/Xy5csBAD/60Y9i6ztt2jQAabM5mt+oOZ4Gh2rRokVGmVysThMgbg8XxHMb+eSTTwAAr7zyCgDgd7/7XWw9jako1erB/Xe/+x0++ugjvPHGG+jatSsA4NRTT8URRxyBu+66C7fccksV19CY6kNtGi/06DJmzBgA2f7ZeaPkAwGjPNLjhaYH0jdm3nDV5v3TTz/NKNsYY4zZUYoTSRTn4DEmlzSlUa4ATC+99BK+853v4Omnn8YPfvCDjN+mTp2K/v37Y9asWejevfsOVaZbt24AgDfeeCNje+/evfHxxx9j8eLFO5SvMVXB119/nQrH/eabb6YWN61evRqHH3442rRpg3/+859Z4cBzpTaOFz6460N2rg/u4SyDKmXcl4vUGMSlNBXPGJMJ3UUeddRRAJARQGb//fcHkF7wybFGJZ6PG7rYnNuphq9atQpAemFoecboo48+CiC9mJSLa1XV53WXddXtvH6wrl988UWqDNbz7bffBmB3j3UdBmD6cuEbOQdg+laHbpUTgOnEE0/EgQceiClTpmT9NmXKFBx88MHo3r07vvnmG6xatSqnP1JUVIS3334bXbp0ycq7W7du+Pjjj1OrwI2pCTRo0ACTJ0/G4sWL8etf/zq1/ZJLLsHatWsxadIk5OXlebwYY4wxJifKZSqTSCTwox/9CHfffTfWrl2bcrO0cuVK/P3vf089nDz22GMYNGhQTnnyTXv16tX45ptvUm/sIdz2+eef49BDDy1PlY2pUgoKCnD11Vfjtttuww9+8AMsX74c06ZNw9ixY1OhxT1e0vzqV7/K+H7zzTcDyFbg2UYN0BIGZuE2dS3JF5pQQTPG5IaqyzfddFPq/969ewNIj0NV1jX4mdqfMx3H6IUXXlju+lGdnzRpEoC0S0qWxbrxmsLrg9aR11qq/nPmzEmVcd111wEAzjnnnHLXz9RiKikAU7lt3AcMGIAxY8bgySefxEUXXQQAePzxx7F169bUgOnduzdmzJhRrnw5ONQ/KpC+OTONMTWJG264Ac899xwGDhyIr776Cj179sQvfvGL1O8eL8YYY4zJhXI/uB922GHo2rUrpkyZknpwnzJlCr797W/jkEMOAVCihkUpgaVBe7TSFpmFARCMqSnUr18fEydORNeuXZGfn48//OEPKfUH8HgpjZEjR2Z854LbvfYqsSOkKsbjGXq4oIpHZY1K28KFCwEAV1111a6qtjF1BqrPAHDxxRcDAI444ggASM0q0o6XNu+E45dmgHRlS082FYFqPT28cD0Mbd4TEgRHgyh9+OGHAIB3330XADBhwoQK18nUcqqr4g6UqO7Dhw/HZ599hm+++Qavv/467rvvvtTvX3/9NdauXZtTXs2bNwcANG7cGLvvvnvk9DW30W2TMTWNF154AUDJQ/VHH32ENm3apH7zeDHGGGNMLpTLqwxZtWoVWrRogd/85jf4+uuvcfPNN+Pzzz9PvclOmjSp3Da7ANC1a1ckEoksLxmnnHIKPv74Y3z88cflraoxVc7bb7+Nrl27on///njrrbewatUqvPPOO6k1Ih4vuXP77bcDAPr06QMgO+x6aDpExZ2mQ5999hmAEpeZxpjKY+jQoQDSY5FqN8fvPffcU2l1GT58OIBsW3bOVI4fP77S6mJqB/Qqs+rDN7HP3nuXnX79ejRpf8wOe5XZIcW9SZMmOPXUU/Hoo49i06ZN6NOnT+qhHdgxm10AOPvss3HNNddg3rx5KW8ZixYtwosvvogrr7xyR6pqTJWyZcsWXHjhhWjRogXuueceLFmyBF27dsXll1+OiRMnAvB4McYYY0xu7JDiDgBPPfUUzj77bAAli1PPPffcCldm/fr1OOaYY7B+/XpceeWV2G233XD33Xdj27ZteOutt7DffvtVuAxjKpPrr78eo0ePxsyZM3HSSScBAH7zm99g5MiR+Otf/4rTTjtth/Oui+OFytwpp5wCIL0Al5ex0IaW3iI2btwIIO3v/rLLLquUuhpjjKn9pBT3j/6du+Le7ujK8eMecsYZZ2DfffdFw4YNceaZZ+5oNhnsvffeePnll/H//t//w80334xRo0bh6KOPxiuvvFIrH0JM7WbBggW45ZZbMGzYsNRDO1ASqbNr164YPHhwKqT3juDxYowxxtQtdlhx37p1K1q0aIEzzjgDDz/88M6ulzHGxPL+++8DyPaqE/px37p1Kzq0L/F09dzfXkjNEBpjjDE7i5Tivvjt3BX3Q46qXBt3AHj22WexcuVKDBgwYEezMMYYY4wxpuZTXd1BzpkzB2+//TZGjx6NY445Bj179qxQBYwxpry0PahlyT96ASwusWtPbP/ElhJPEVbbjTHG1AbK/dg/fvx4DB06FE2bNsUjjzyyK+pkjDHGGGNMjaE4kcz5ryLssI27McYYY4wxdRnauK/8z/s527jv17Zj5du4G2OMMcYYY1Biupnc9TbuFdvbGGOMMcYYUylYcTfGGGOMMaYiVJJXGSvuxhhjjDHG1ACsuBtjjDHGGFMRrLgbY4wxdZOioiJMmDABnTp1wl577YVmzZrh1FNPxaxZs6q6asaYKsQP7sYYY0w146qrrsLQoUNx5JFH4u6778YVV1yBDz/8ED179sQbb7xR1dUzxihU3HP5qwA2lTHGGGOqEVu3bsX48eNx9tln449//GNq+znnnIO2bdtiypQp6NatWxXW0BijFCcSOQVXKk4kKlSOFXdjjDGmFJYuXYpEIhH7t7PZsmULvv76azRr1ixje9OmTZFMJtGgQYOdXqYxpmZgxd0YY4wphf322y9D+QZKHq4vv/xy1K9fHwCwceNGbNy4scy88vLysO+++5aapkGDBigoKMCkSZPQvXt3nHDCCVizZg1Gjx6NfffdF0OGDNnxxhhjdg2VtDjVD+7GGGNMKey555740Y9+lLHtkksuwVdffYUZM2YAAG6//XbceOONZebVqlUrLF26tMx0jz76KPr165dRbtu2bfHaa6+hbdu25WuAMabW4Ad3Y4wxphw88sgj+N3vfoe77roLJ510EgBgwIAB6NGjR5n75mrmsvfee+Pwww9H9+7dcfLJJ6OwsBC33nor+vbti3/+859o0qRJhdpgjNnJJBIlf7mkq0gxxcXFxRXKwRhjjKkjvPXWWzjuuOPQt29fTJ06tUJ5rV27Fl9//XXqe/369dG4cWNs3boVxxxzDE488USMGzcu9ftHH32Eww8/HJdffjluu+22CpVtjNk5rFu3Dg0bNsSKZZ9in332ySl905YHYe3atTmlV7w41RhjjMmB//3vfzjrrLPQvn17PPTQQxm/ffXVVygsLCzzb+XKlal9hg8fjv333z/198Mf/hAA8Oqrr+Ldd9/FmWeemVFGu3bt0KFDB7z22mu7vrHG1CHuv/9+tG7dGvn5+SgoKNgxl6t2B2mMMcZUD4qKitC/f3+sWbMG//jHP7DHHntk/H7nnXeW28b96quvzrBh56LV5cuXAwC2bduWtf+WLVuwdevWHW2GMUZ4/PHHMWLECEyYMAEFBQUYO3YsevfujUWLFqFp06ZVXb0s/OBujDHGlMGNN96IF154AX/729/Qpk2brN93xMa9Y8eO6NixY1aa9u3bAwCmTZuGPn36pLYvWLAAixYtslcZY3Yid999NwYPHoxBgwYBACZMmIC//vWvmDhxIq655pqc8ylOJHP0427F3RhjjNllvPPOOxg9ejT+3//7f1ixYgUeffTRjN9/9KMfoW3btjvN20vnzp3x3e9+F5MnT8a6detwyimn4IsvvsC4cePQoEEDXHbZZTulHGPqOps3b8b8+fPxq1/9KrUtmUyiV69emD17dhXWLB4/uBtjjDGl8OWXX6K4uBivvPIKXnnllazf1VXkzuBPf/oT7rzzTkybNg3Tp09H/fr1ccIJJ2D06NE49NBDd3p5xtRFVq1ahW3btmUFO2vWrBk++OCDcuW1ees2bN6abd4Wla4i+MHdGGOMKYUTTzwRle2ArUGDBhg1ahRGjRpVqeUaY8pH/fr10bx5cxx44IE579O8efNU8Lby4gd3Y4wxxhhT52jSpAny8vJSC8LJ8uXL0bx585zyyM/Px5IlS7B58+acy61fvz7y8/PLVVfiB3djjDHGGFPnqF+/Pjp37oyZM2eib9++AEo8SM2cORPDhg3LOZ/8/PwdfhAvL35wN8YYY4wxdZIRI0Zg4MCB6NKlC7p164axY8diw4YNKS8z1Q0/uBtjjDHGmDpJv379sHLlSlx33XUoLCxEp06dMH369KwFq9WFRHFlr7gxxhhjjDHGlJuKeYE3xhhjjDHGVAp+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagB+cDfGGGOMMaYG4Ad3Y4wxxhhjagD/HzPdm83fxE9wAAAAAElFTkSuQmCC", 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", 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", 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", 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" ] @@ -169,21 +422,234 @@ } ], "source": [ + "inference = CBMRInference(\n", + " CBMRResults=cres, device=\"cuda\"\n", + " )\n", + "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "\n", + "# generate z-statistics maps for each group\n", "plot_stat_map(\n", - " cres.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", + " cres.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " threshold=1e-5,\n", - ")" + " title=\"schizophrenia_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.4)\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=scipy.stats.norm.isf(0.4)\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"depression_Yes-depression_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.4)\n", + ")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures (displayed as z-statistics map) correspond to group comparison test of spatial intensity for any two groups. The null hypothesis assumes spatial intensity estimations of two groups are equal at voxel level, $H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values (significant difference in spatial intensity estimation between two groups) are highlighted (under significance level $0.05$). \n", + "\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GLH testing with contrast matrix specified \n", + "\n", + "CBMR supports more flexible GLH test by specifying a contrast matrix. For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast` function. Multiple independent GLH tests can be conducted simultaneously by including multiple contrast vectors/matrices in `t_con_group`. \n", + "\n", + "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors) when it's represented as one of elements in `t_con_group` (datatype: list). Only if all of null hypotheses are rejected at voxel level, p-values are significant. For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing the equality of spatial intensity estimation among all of four groups (finding the consistent activation regions). Note that only $n-1$ contrast vectors are necessary for testing the equality of $n$ groups. \n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type5 = index_2\n", + "INFO:nimare.meta.cbmr:type1 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type3 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inference = CBMRInference(\n", + " CBMRResults=cres, device=\"cuda\"\n", + " )\n", + "contrast_result = inference.compute_contrast(t_con_groups=[[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]], t_con_moderators=False)\n", + "plot_stat_map(\n", + " cres.get_map(\"GLH_groups_0_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"GLH_groups_0\",\n", + " threshold=scipy.stats.norm.isf(0.4)\n", + ")\n", + "print(\"The contrast matrix of GLH_0 is {}\".format(cres.metadata[\"GLH_groups_0\"]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GLH testing for study-level moderators \n", + "\n", + "CBMR framework can estimate global study-level moderator effects, and allows inference on the existence of m . " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type5 = index_2\n", + "INFO:nimare.meta.cbmr:type1 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type3 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " standardized_sample_sizes standardized_avg_age type5 type1 \\\n", + "0 -0.00109 0.000588 -0.027104 -0.025923 \n", + "\n", + " type4 type3 \n", + "0 -0.026694 -0.027402 \n", + "P-values of moderator effects `sample_sizes` is 0.9130485642134478\n", + "P-value of moderator effects `avg_age` is 0.9529915576540059\n" + ] + } + ], + "source": [ + "inference = CBMRInference(\n", + " CBMRResults=cres, device=\"cuda\"\n", + ")\n", + "contrast_name = cres.estimator.moderators\n", + "t_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "print(cres.tables[\"Moderators_Regression_Coef\"])\n", + "print(\"P-values of moderator effects `sample_sizes` is {}\".format(cres.tables[\"standardized_sample_sizes_p_values\"]))\n", + "print(\"P-value of moderator effects `avg_age` is {}\".format(cres.tables[\"standardized_avg_age_p_values\"]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table shows the regression coefficients of study-level moderators, here, `sample_sizes` and `avg_age` are standardized in the preprocessing steps. Moderator effects of both `sample_size` and `avg_age` are not significant under significance level $0.05$. With reference to spatial intensity estimation of a chosen subtype, spatial intensity estimations of the other $4$ subtypes of schizophrenia are moderatored globally." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", + "INFO:nimare.meta.cbmr:depression_No = index_1\n", + "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", + "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type5 = index_2\n", + "INFO:nimare.meta.cbmr:type1 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type3 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P-values of difference in two moderator effectors (`sample_size-avg_age`) is 0.9054368009582764\n" + ] + } + ], + "source": [ + "inference = CBMRInference(\n", + " CBMRResults=cres, device=\"cuda\"\n", + ")\n", + "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "print(\"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(cres.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CBMR also allows flexible contrasts between study-level covariates. For example, we can write `contrast_name` (an input to `create_contrast` function) as `standardized_sample_sizes-standardized_avg_age` when exploring if the moderator effects of `sample_sizes` and `avg_age` are equivalent. " + ] } ], "metadata": { diff --git a/examples/02_meta-analyses/10_plot_cbmr_2.ipynb b/examples/02_meta-analyses/10_plot_cbmr_2.ipynb deleted file mode 100644 index 63b586577..000000000 --- a/examples/02_meta-analyses/10_plot_cbmr_2.ipynb +++ /dev/null @@ -1,647 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Coordinate-based meta-regression algorithms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A tour of CBMR algorithms in NiMARE.\n", - "\n", - "This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" - ] - } - ], - "source": [ - "import nimare\n", - "import os \n", - "from nimare.dataset import Dataset\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators\n", - "from nimare.tests.utils import standardize_field\n", - "from nimare.meta.cbmr import CBMREstimator, CBMRInference\n", - "from nimare.meta import models\n", -<<<<<<< HEAD -======= - "from nimare.utils import get_resource_path, standardize_field,index2vox\n", - "from nimare.meta.cbmr import CBMREstimator\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "from nilearn.plotting import plot_stat_map\n", - "from nimare.generate import create_coordinate_dataset\n", - "import nibabel as nib \n", - "import numpy as np\n", -<<<<<<< HEAD -<<<<<<< HEAD - "import scipy\n" -======= - "\n", - "import logging\n", - "import sys" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "import scipy\n" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ -<<<<<<< HEAD -<<<<<<< HEAD - "# data simulation\n", -======= - "# data simulation \n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "# data simulation\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", - "# set up group columns: diagnosis & drug_status \n", - "n_rows = dset.annotations.shape[0]\n", - "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", - "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", - "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", -<<<<<<< HEAD -<<<<<<< HEAD - "# set up moderators: sample sizes & avg_age\n", - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# categorical moderator: schizophrenia_subtype\n", - "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", - "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estimate group-specific spatial intensity functions" -======= - "# set up `study-level moderators`: sample sizes & avg_age\n", -======= - "# set up moderators: sample sizes & avg_age\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# categorical moderator: schizophrenia_subtype\n", - "dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)]\n", - "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ -<<<<<<< HEAD - "## Group-wise spatial intensity estimation" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "## Estimate group-specific spatial intensity functions" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg_conversions.py:296: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.\n", - " niimg = new_img_like(niimg, data, niimg.affine)\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", - " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" - ] - }, - { - "data": { - "text/plain": [ -<<<<<<< HEAD -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - 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", 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", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "image/png": 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", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", - "cbmr = CBMREstimator(\n", - " group_categories=[\"diagnosis\", \"drug_status\"],\n", - " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\"],\n", - " spline_spacing=10,\n", - " model=models.PoissonEstimator,\n", - " penalty=False,\n", - " lr=1e-1,\n", - " tol=1,\n", - " device=\"cpu\",\n", - " )\n", -<<<<<<< HEAD - "cbmr_res = cbmr.fit(dataset=dset)\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", -======= - "cbmr = CBMREstimator(group_names=['diagnosis', 'drug_status'], moderators=['standardized_sample_sizes', 'standardized_avg_age'], \n", - " spline_spacing=10, model='Poisson', penalty=False, lr=1e-1, tol=1, device='cuda')\n", - "cbmr_res = cbmr.fit(dataset=dset)\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "cbmr_res = cbmr.fit(dataset=dset)\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= - "##" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "## Generalized Linear Hypothesis (GLH) for Spatial homogeneity" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" -<<<<<<< HEAD -======= - "/gpfs2/well/nichols/users/pra123/NiMARE/nimare/meta/cbmr.py:416: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1666642975312/work/torch/csrc/utils/tensor_new.cpp:230.)\n", - " involved_spatial_coef = torch.tensor([self.CBMRResults.tables['Spatial_Regression_Coef'].to_numpy()[i, :].reshape((-1,1)) for i in GLH_involved_index], dtype=torch.float64, device=self.device)\n" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - { - "data": { - "text/plain": [ -<<<<<<< HEAD -<<<<<<< HEAD - "" -======= - "" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { -<<<<<<< HEAD -<<<<<<< HEAD - "image/png": 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", -======= - "image/png": 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", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "image/png": 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", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "# homoogeneity test for each group\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - " \n", -<<<<<<< HEAD - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=30,\n", -======= - "from nimare.meta.cbmr import CBMRInference\n", - "# Group-wise spatial homogeneity test\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=[[1,0,0,0]],\n", - " t_con_moderator=None, device='cuda')\n", - "inference._contrast()\n", -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", -<<<<<<< HEAD - " threshold=5\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - " threshold=30,\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], -<<<<<<< HEAD - "source": [ - "# Group comparison test between any two groups\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "# chi square statistics maps for group comparison test\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", -======= - "outputs": [], -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "source": [ - "# Group comparison test between any two groups\n", - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "# chi square statistics maps for group comparison test\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_Yes-schizophrenia_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", -<<<<<<< HEAD - " threshold=1\n", ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"schizophrenia_No-depression_Yes_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", - ")\n", - "plot_stat_map(\n", - " cbmr_res.get_map(\"depression_Yes-depression_No_chi_square_values\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " threshold=0.5,\n", ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generalized Linear Hypothesis (GLH) for study-level moderators" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD -<<<<<<< HEAD - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: 0.9243109811987764, 0.9461743884065033\n", - "For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: 0.8487350829759214\n" -======= - "execution_count": 21, -======= - "execution_count": 6, ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ -<<<<<<< HEAD - "[[0.94563486]]\n" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= - "0.9243109811987764 0.9461743884065033 0.8487350829759214\n" ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - } - ], - "source": [ - "# Test for existence of effect of study-level moderators\n", -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - "inference = CBMRInference(\n", - " CBMRResults=cbmr_res, device=\"cuda\"\n", - ")\n", - "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes\", \"standardized_avg_age\", \"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", - "sample_size_p = cbmr_res.tables[\"standardized_sample_sizes_p_values\"]\n", - "avg_age_p = cbmr_res.tables[\"standardized_avg_age_p_values\"]\n", - "moderators_diff_p = cbmr_res.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", - "print(f\"For hypothesis test for existence of effect of study-level moderators (sample_size and avg_age), the p values are: {sample_size_p}, {avg_age_p}\")\n", - "print(f\"For hypothesis test for difference between effect of study-level moderators (sample_size and avg_age), the p values are: {moderators_diff_p}\")" -<<<<<<< HEAD -======= - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,0]], device='cuda')\n", - "inference._contrast()\n", - "sample_size_p = cbmr_res.tables[\"Effect_of_1xstandardized_sample_sizes_p\"]\n", - "print(sample_size_p)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.99838466]]\n" - ] - } - ], - "source": [ - "# Test for existence of effect of study-level moderators\n", - "inference = CBMRInference(CBMRResults=cbmr_res, t_con_group=False,\n", - " t_con_moderator=[[1,-1]], device='cuda')\n", - "inference._contrast()\n", - "effect_diff_p = cbmr_res.tables[\"1xstandardized_sample_sizesVS1xstandardized_avg_age_p\"]\n", - "print(effect_diff_p)" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) -======= ->>>>>>> 53676d6 ([skip CI][WIP] update example file based on reconstructed code) - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.8 ('torch': conda)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", -<<<<<<< HEAD - "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" -======= - "version": "3.8.8" ->>>>>>> 82d56a4 ([skip CI][wip] add a demonstration for CBMREstimator & CBMRInference) - }, - "vscode": { - "interpreter": { - "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 860cbe68b..6661bc4d7 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -309,6 +309,7 @@ def _fit(self, dataset): """ init_weight_kwargs = { "groups": self.groups, + "moderators": self.moderators, "spatial_coef_dim": self.inputs_["coef_spline_bases"].shape[1], "moderators_coef_dim": len(self.moderators) if self.moderators else None, } @@ -630,12 +631,23 @@ def _glh_con_group(self): # GLH on log_intensity (eta) chi_sq_spatial = self._chi_square_log_intensity(m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) + # convert p-values to z-scores for visualization + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) + z_stats_spatial[z_stats_spatial < 0] = 0 + else: + z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial/2) + if con_group.shape[0] == 1: # GLH one test: Z statistics are signed + z_stats_spatial *= np.sign(Contrast_log_intensity.flatten()) + z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) if self.t_con_groups_name: self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_chi_square_values"] = chi_sq_spatial self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_p_values"] = p_vals_spatial + self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_z_statistics"] = z_stats_spatial else: self.CBMRResults.maps[f"GLH_groups_{con_group_count}_chi_square_values"] = chi_sq_spatial self.CBMRResults.maps[f"GLH_groups_{con_group_count}_p_values"] = p_vals_spatial + self.CBMRResults.maps[f"GLH_groups_{con_group_count}_z_statistics"] = z_stats_spatial con_group_count += 1 def _chi_square_log_intensity(self, m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity): @@ -686,7 +698,6 @@ def _glh_con_moderator(self): ) chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) - if self.t_con_moderators_name: # None? self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values"] = chi_sq_moderator self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_p_values"] = p_vals_moderator diff --git a/nimare/meta/models.py b/nimare/meta/models.py index d217d6f2f..c50e6bc45 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -14,7 +14,6 @@ def __init__( self, spatial_coef_dim=None, moderators_coef_dim=None, - groups=None, penalty=False, lr = 0.1, lr_decay=0.999, @@ -25,7 +24,6 @@ def __init__( super().__init__() self.spatial_coef_dim = spatial_coef_dim self.moderators_coef_dim = moderators_coef_dim - self.groups = groups self.penalty = penalty self.lr = lr self.lr_decay = lr_decay @@ -87,9 +85,10 @@ def init_moderator_weights(self): torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) return - def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): + def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): """Document this.""" self.groups = groups + self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim self.moderators_coef_dim = moderators_coef_dim self.init_spatial_weights() @@ -338,8 +337,8 @@ def summary(self): tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(self.spatial_regression_coef, orient="index") maps = self.spatial_intensity_estimation if self.moderators_coef_dim: - tables["Moderators_Regression_Coef"] = pd.DataFrame(self.moderators_coef) - tables["Moderators_Effect"] = pd.DataFrame.from_dict(self.moderators_effect, orient="index") + tables["Moderators_Regression_Coef"] = pd.DataFrame(data=self.moderators_coef, columns=self.moderators) + tables["Moderators_Effect"] = pd.DataFrame.from_dict(data=self.moderators_effect, orient="index") # Estimate standard error of regression coefficient and (Log-)spatial intensity and store them in 'tables' # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) @@ -353,7 +352,7 @@ def summary(self): self.spatial_intensity_se, orient="index" ) if self.moderators_coef_dim: - tables["Moderators_Regression_SE"] = pd.DataFrame(self.se_moderators) + tables["Moderators_Regression_SE"] = pd.DataFrame(data=self.se_moderators, columns=self.moderators) return maps, tables def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 2777d57f4..9ea95b5e3 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,7 +16,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1, + tol=1e4, device="cpu" ) cbmr.fit(dataset=dset) @@ -44,9 +44,6 @@ def test_CBMRInference(testdata_cbmr_simulated): t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type="groups") t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) - # self.maps.schizophrenia_Yes_p_values = ... - # self.maps.schizophrenia_Yes_chi_square_vals = ... - # self.tables.standardized_sample_sizes = ... def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) From d82d4851c11e8cc43423fb6d6273685ae854a45e Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 25 Feb 2023 19:01:30 +0000 Subject: [PATCH 096/177] [skip CI][WIP] implement an option to specify the reference subtype for categorical moderators. --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 778 ++++--------------- examples/02_meta-analyses/10_plot_cbmr.py | 313 ++++++++ nimare/meta/cbmr.py | 150 ++-- nimare/meta/models.py | 61 +- nimare/utils.py | 18 +- 5 files changed, 609 insertions(+), 711 deletions(-) create mode 100644 examples/02_meta-analyses/10_plot_cbmr.py diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 2535a5d6d..090126961 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -1,682 +1,208 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Coordinate-based meta-regression algorithms\n", - "\n", - "A tour of CBMR algorithms in NiMARE\n", - "This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 24 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" - ] - } - ], - "source": [ - "from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators, get_resource_path,index2vox\n", - "from nimare.tests.utils import standardize_field\n", - "from nimare.meta import models\n", - "\n", - "from nilearn.plotting import plot_stat_map\n", - "from nimare.generate import create_coordinate_dataset\n", - "import nibabel as nib\n", - "\n", - "import numpy as np\n", - "import scipy\n", - "import logging\n", - "import sys" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# data simulation\n", - "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", - "# set up group columns: diagnosis & drug_status \n", - "n_rows = dset.annotations.shape[0]\n", - "dset.annotations['diagnosis'] = [\"schizophrenia\" if i%2==0 else 'depression' for i in range(n_rows)]\n", - "dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)]\n", - "dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n", - "# set up continuous moderators: sample sizes & avg_age\n", - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] \n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted as groups)\n", - "dset.annotations['schizophrenia_subtype'] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(n_rows/5)\n", - "dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estimation of group-specific spatial intensity functions\n", - "Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a generative regression model that estimates a smooth intensity function and can have study-level moderators. It's developed with a spatial model to induce a smooth response and model the entire image jointly, and fitted with different variants of statistical distributions (Poisson, Negative Binomial (NB) or Clustered NB model) to find the most accurate but parsimonious model.\n", - "\n", - "CBMR framework can generate estimation of group-specific spatial internsity functions for multiple groups simultaneously, with different group-specific spatial regression coefficients. \n", - "\n", - "CBMR framework can also consider the effects of study-level moderators (e.g. sample size, year of publication) by estimating regression coefficients of moderators (shared by all groups). Note that moderators can only have global effects instead of localized effects within CBMR framework. In the scenario that there're multiple subgroups within a group, while one or more of them don't have enough number of studies to be inferred as a separate group, CBMR can interpret them as categorical study-level moderators. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] }, { - "data": { - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Coordinate-based meta-regression algorithms\n\nA tour of CBMR algorithms in NiMARE\n\nThis tutorial is intended to provide a brief description and example of the CBMR\nalgorithm implemented in NiMARE. For a more detailed introduction to the elements\nof a coordinate-based meta-regression, see other stuff.\n" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from nimare.tests.utils import standardize_field\nfrom nimare.meta import models\n\nfrom nilearn.plotting import plot_stat_map\nfrom nimare.generate import create_coordinate_dataset\n\nimport numpy as np\nimport scipy" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Dataset\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# data simulation\nground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n# set up group columns: diagnosis & drug_status\nn_rows = dset.annotations.shape[0]\ndset.annotations[\"diagnosis\"] = [\n \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n]\ndset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\ndset.annotations[\"drug_status\"] = (\n dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column\n# set up continuous moderators: sample sizes & avg_age\ndset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\ndset.annotations[\"avg_age\"] = np.arange(n_rows)\n# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n# as groups)\ndset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n n_rows / 5\n)\ndset.annotations[\"schizophrenia_subtype\"] = (\n dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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bm4vc3Fzs37+/VNqYCofNVGbVqlX4wx/+gJEjR+K9997D//73P+Tm5uK8885DrVq1MH78+ISHXwC4+eabMXXqVNx555248cYbsWDBAqxduxY1a9bE2WefjQYNGmDYsGF47733EvZbvXo1Fi5ciG+++QbTpk3DgQMH8POf/xy1atXC9OnTE+zLU+FPf/oTmjVrhh49emDx4sX44osvYl5XTjvtNDRv3hwLFy7E+PHjAQAtWrTAsGHDsH79esybNw+bNm1CnTp10LFjR1StWhXPP/88fvrpJwCRh/y77roLP/zwA7744gts374d2dnZuPDCC5GRkYHBgwcneX1Rvv/+e/zf//0fxo4di3/+85/47LPP8P333+Pcc89Fs2bNkJubm3RsS5Nly5Zh4sSJgdFv58yZg0GDBuGJJ57AzJkz8dFHH2Hjxo04//zz0aBBAyxbtgx33nlnCdfaMAzDMIyySrdu3bBhwwY8+OCDyM3NRcuWLTF16tTYAtQ1a9bE1i0CQIcOHTBx4kQMHDgQDzzwAJo2bYopU6bgzDPPBBAxx6W3wZYtWyaU9eGHH6JTp05Fql9GKIQMEXR90yEUcWZ9iBzWxakvvPACfvzxRwwYMADt2rXD3r178b///Q8DBgxA9+7dk9Jv2LABHTp0QO/evdG9e3ecc8456NChA9atW4fvvvsOzz//vK/bR8/zcN111+HBBx/EjTfeiOzsbPz0008YOXIkHn/8ceTlpb5AgPn17NkTkydPxu233442bdrg3HPPxZYtW/D999/j6aefxmuvvRZL/+6776J27dq46KKL0KJFC9SuXRsbNmzAp59+ihdeeAH/+Mc/YmnHjh2LgwcP4mc/+xnatm2LmjVrIjc3F//+97/x3HPPYfr06SnVcfz48VixYgUGDBiADh06oF27dvjpp5/wwgsv4PHHH0/Zpr+keOSRR3DjjTeiUiX/LjZkyBB8+eWXuPvuu9GmTRtUq1YNa9aswZNPPok//elPhc5CGIZhGIZRsejbt29MMVc++uijpG1du3aNmRMrjRo1SktnGCEvxVrPnz8/wayhtPA8D6tWrUrZlaJR/pk3bx7OPffc0q6GYRiGYRgVjO3bt6NmzZroE26AKqHCLdD3efkYlb8G27ZtK/JCWOAw2rgbhmEYhmEYhnHkKDU/7oZhGIZhGIZRHiiSjXsxsAd3A+effz5uu+22lNJOmTIl0D+7YRiGkRpjx45Fr169MGfOHLRu3bq0q2OUQ9jHSEZGBurVq4dLLrkEjz/+uEUnT1PS7sFdXTAaxeeUU07BLbfcklLaVatW2YO7YRiGYaQJjzzyCBo3boy9e/fi888/x9ixY/Hpp5/i66+/PqQAQIY/GaHIX6HpillO2j24G4efcePGYdy4caVdDcMwDMMwDjOXX355bFbntttuQ1ZWFp588km88847vsEsjbKNLU41DMMwDMOoIFx44YUAgBUrVpRyTcoXtHFP5a84mOJuGIZhGIZRQVi1ahUAoFatWqVbkXKGmcoYhmEYhmEYxWLbtm3YuHEj9u7di1mzZuHhhx9GlSpVcNVVV5V21YxDwB7cDcMwDMMwyimdO3dO+N6oUSOMHz8eJ554YinVqHxi7iANwzAMwzCMYjFy5Eiceuqp2LZtG8aMGYP//e9/qFKlSmlXyzhEUn5wz8rKQtWqVbF3794jWR/DKBJVq1ZFVlZWaVfDMAzDMMokbdu2jXmV6dKlCy644ALceOONWLp0KapXr17KtSs/hJCax5fiOjVP+cG9QYMGWLp0KTZu3FjMIg3j8JGVlYUGDRqUdjUMwzAMo8yTkZGBIUOG4KKLLsKIESMwYMCA0q6SUUSKZCrToEEDe0gyDMMwDMNIUzp16oS2bdti2LBhuOuuuywI02HCbNwNwzAMo5wzZswYTJ06NWl7v379UKNGjVKokVERuPfee9G1a1eMHTsWd9xxR2lXxygC9uBuGIZhGKXEqFGjfLffcsst9uBuHDF+9atf4eSTT8YzzzyD3r17IyOjuN7FjZLy4x7yPM8rZh6GYRiGYRgpMW7cOABA7dq1AQDVqlVL+J2PJbt27QIAXHPNNSnn/fbbbwMAjj76aABASEwX9uzZAwDYtGkTAKBnz55FqrthKNu3b0fNmjUxuFoTVA0Vvjx1r5ePh/d8h23btuGYY44pcnmmuBuGYRiGYRhGMYgo7qnYuBcPU9wNwzAMwzjsvPbaawCA+vXrA0DMd3g4HE74pCqen5+fsD+/83PBggUAgD59+sTS0NSoZcuWvnkTfucjj+a9b98+AEBubi4AoFu3bkVqq1FxoeL++NFNUDVU+GP5Xi8P/2/XoSvuqbicNAzDMAzDMAyjlDFTGcMwDMMwis3w4cMBxG3XGzduDADIzMxMSMeFkLRDr1y5MoC4Gk5o4759+3YAQMOGDQEADz30UCxN27ZtE/ZlnvwkVPUPHDiQkHdeXl5CHejyeuLEiQDitvC/+93vCmy7YZSUO0hT3A3DMAzDMAwjDTDF3TAMwzCMAnnzzTcBAHXr1gUQV6hdu/Tjjz8+YR+q3Pykus19Dh48CACoXr06AKBSpcgjyd69ewEk28DTRp7p3W1Mw32YF4MLsSx6laHyTjgLwHw4S8A2zZgxI5aWZTCP9evXAwCuvfZaGBWXcIruIIurmJvibhiGYRiGYRhpQKkr7mPHjkWvXr0wZ84ctG7durSrY5Qz2L9IRkYG6tWrh0suuQSPP/44TjjhhFKsnWEYRtlk8uTJAICaNWsCiNt+U22mQk0VHYh7j1m7di2AuLpN1IadKjhVbua5e/duAMnKO1Vw1zc7tzEN91E7etaTZfKT8HfWmbMC2dnZAOLKvpu32sV/8MEHAIBt27YBAK677joYFYeSsnEv9Qd3wygJHnnkETRu3Bh79+7F559/jrFjx+LTTz/F119/HZtKNQzDMAzDKMvYg7tRIbj88stjMzq33XYbsrKy8OSTT+Kdd97B9ddfX8q1MwzDKBt8/PHHAOLquardVJn5SXUciNuVMy3Va6bl71SzmY5qNlVw+lR31XzA39+7RkblPpoHy2CZVP/ZPrWBZzrWmZ8AcNRRRwGI27jzk+o+I8HyWHbs2BFG+ScjRRv34gZgMht3o0Jy4YUXAgBWrFhRyjUxDMMwDMNIDVPcjQrJqlWrAAC1atUq3YoYhmGUAeg1haaDVI2pJmtUUyrVru33/v37AcTt4ukrnagiz+svbcZpn84yqZarqq7fXbgP86CSznqyTCryrDPTsZ1sA+vmtlOjsnIfpuEMA9V7HtsOHToE1ttIf0pKcbcHd6NCsG3bNmzcuBF79+7FrFmz8PDDD6NKlSq46qqrSrtqhmEYhmGkObY41TAOI507d0743qhRI4wfPx4nnnhiKdXIMAzDMAyjaNiDu1EhGDlyJE499VRs27YNY8aMwf/+97+EqU/DMIyKyNtvvw0AqFevHoD4AssaNWoAAHbs2AEg2ZSE0CzE3ZdpaVLCT/6elZUFIG5awjxpvsKFozSJ4Xea2tB8xd0WtA/zpOkPTYEYWGnjxo0A4iYzbDfNeVhnt52E9dYAUcyD7d65cyeA+LG+5pprkvIy0p8MpGgq4xWepiDswd2oELRt2zbmVaZLly644IILcOONN2Lp0qUJUfgMwzAMwzDKKvbgblQ4MjIyMGTIEFx00UUYMWIEBgwYUNpVMgzDKBUoXKhbRCrWtWvXBpDo9hGIK9DuQk0qz1TBudiUKnfdunUBxBVzVcU3b94MIL6wVPNVhdvdxnrwOz+ZJxX3IOVdF8jyd11Q6+at0E0k26MzDyYSlW/CKdq4h1NIU+D+xdrbMNKUTp06oW3bthg2bFjsQm0YhmEYhlGWKTOK+5gxYzB16tSk7f369YvZixnG4eTee+9F165dMXbsWNxxxx2lXR3DMIwS49133wUQV4mpDhPaZVOhPvbYYwEU7IqRNt5MQ6WZqjW/U2mncr1u3bqEMqm4UwXn/moDD8RdLmoQJ3ULyTIaNGjgmzcDTqktP8ty7eoVpuG+bIe6muRx4bE3r2bli5TdQRZPcC87D+6jRo3y3X7LLbfYg7txRPjVr36Fk08+Gc888wx69+5d4IXZMAzDMAyjtAl57qurYRiGYRjllk8//RRAXGlWhZq26/SmQrt0fqdqXJDyXhh87GCApm+//RYAsH37dgBxZZ1iCpV62tn/+OOPsbxOOOEEAPGZAyrlbA+V+GOOOQYAcMopp/i2pzjt0PasX78+4XvQDAKP/QUXXHDIdTBKn+3bt6NmzZoYl3UajgoXLgDuzs9Dz41LsW3btli/LApm424YhmEYhmEYaUCZMZUxDMMwDOPIwDVktFWnQk07bH5S3aZSTW8qQUq761WGaBqq3zrBTx/xLJtqOdVwNV9Um3kg7qlF43KwTG0fy2QZ6v9dy/QzSvDzbgPEjxXrQvt7zmLwd35yBoHn5rLLLksqy0gfKpyNu2EYhmEYhmGkIxkpuoNMJU1B2IO7YRiGYZRzqExT/aW3mJo1awJI9nxCpxBUt4NswV2f5qmo1e52VfFZxyBVn3V3/aHrPqyP+l8PiqyqZQXVjQq+H+q/nr7vtWz+TvWftu/m390oCvbgbhiGYRiGYRjFIBwKpRRcqbgBmOzB3TAMwzDKKSNGjAAANG/eHEDc/pq23rR1p+pLJZ7qdnG8rqgvdFW7WReWSdU/SC2nlxamd2E7WIb6UGeeaguvdWKdD8U9sK4P4HfautO/O23bWRbrynPVt2/fIpdtVBzswd0wDMMwDMMwikEoI4RQuPAX3eK8DAP24G4YhmEY5Rb6YadaHaRmUyWmtxWiSnRBXmWC7MCDHlS4nXb2WhY/qVD7lUloL07lne1j2sL8zwd5wvHDtet36x10bFg39etOpZ3bea4MoyDswd0wDMMwDMMwikE4I4RwCoq72bgbhmEYhpHA66+/DgDIzs4GEFfaGZWUdtdUhWnTrTbfVIdV9aadOZVtN49UYXqq21u3bgWQbJdO9u7dm9AGdxvbweirmgf91x+K7bpbRyCulPMYEqr9uj5A26nHvk6dOgl15rm7/vrrD6muRvnGIqcahmEYhmEYpc7IkSPRqFEjVK1aFe3atcPs2bMLTP/GG2+gWbNmqFq1Ks466yz8+9//Tvj9rbfewqWXXoratWsjFAphwYIFSXl06tQJoVAo4e+OO+4oeuUzwgil8IeM4j16m+JuGIZhGOWMY445BkCy33b1qsLt6qmF6jAV7G3btgGI23czH/osd/NQ9V7hdtZNZwGC7OmZjrMA7jZtl6YtqrcczjioSg4AmzZtSiiDyjkVc6r73M6y9ZwQHi+WwXQVjddeew39+/fH6NGj0a5dOwwbNgw5OTlYunSpr/3/jBkzcMMNN2DIkCG46qqrMHHiRHTp0gXz58/HmWeeCSCyfuCCCy7A9ddfj969eweW3bt3bzzyyCOx75w9KYvYg7thGIZhGIZRqgwdOhS9e/dGr169AACjR4/Gv/71L4wZMwYDBgxISv/cc8/hsssuw7333gsAePTRR/HBBx9gxIgRGD16NADg17/+NQBg1apVBZZ91FFHoX79+sWqfygcQigjBa8yMBt3wzAMwzAcqPbyk95iqExT9dV06nudcDsVbH6nEu+Xp6raqqQzPdVN2rhTgVZlmkq0W2aQik2lnO1Q+3Otk3qq4X5U0d0yqYyzDM1TveMwb85O6LGkcq8KfkVi//79mDdvHu6///7YtnA4jM6dO2PmzJm++8ycORP9+/dP2JaTk4MpU6YUufwJEyZg/PjxqF+/Pq6++moMGjSoyKp7OCOEcAoP7mF7cDcMwzAMwzDSlY0bNyIvLw/16tVL2F6vXj0sWbLEd5/c3Fzf9Lm5uUUq+8Ybb0TDhg2RnZ2NhQsX4o9//COWLl2Kt956q2iNKCHswb0U+Mc//gEAqFGjBoDkFeeqfGzevBlA0VaYc1X6cccd55unlskoer/85S+L3B7DSCcmTZoEINmGVf02B0V95Fjq2bPnka+sYRSB4cOHx/4/+eSTAcRVXarZ/M5+zIipVINVNad9Nj2p8JO4nl+CVHr9XZV43qdYxyAlm2W7vuaZZ5CSznsdy1BUHQ/63W2n2tPTsw6PFY+dqva0jWcEVZbJuvPcML17Pn/3u9/51s8oPrfffnvs/7POOgvHH388Lr74YqxYsSI2jlIhFA4jlMJsSUjGSVGpePMxhmEYhmEYRpkhKysLGRkZWLduXcL2devWBdqe169fv0jpU6Vdu3YAgG+//bZY+RwpTHE3DMMwjHKAq2TrLCvtsmlHrQo609F7BxVmqsv0Na7KtFum+l3XaKVBs1hUnE844QQAcU823K7eZlwbcFWtqXpTvVYbePVTrzNp3K5KPj3FAPFIr0Rt+lVp37BhA4D4jAJnuKnUq4IftEagPJOZmYlWrVph2rRp6NKlC4DIuZ02bRr69u3ru0/79u0xbdo03HXXXbFtH3zwAdq3b1+sutBl5PHHH1+k/czGvRxAcxUOeE5JnnTSSQCSLxB6ASKc4vvwww8BABdddFFgmUxzyimnJORNdJqUFwbWccaMGQDiU3m80FggCCPdePXVVwHEA7ToQ4N+EjWZ0d/JqFGjYv/rzd+dejUMwzAKp3///ujZsydat26Ntm3bYtiwYdi1a1fMy0yPHj1wwgknYMiQIQCAfv36oWPHjnj22Wdx5ZVXYtKkSZg7dy7++te/xvLcvHkz1qxZg7Vr1wIAli5dCiCi1tevXx8rVqzAxIkTccUVV6B27dpYuHAh7r77bvzsZz/D2WefXcJHIDXswd0wDMMwDMMoVbp164YNGzbgwQcfRG5uLlq2bImpU6fGFqCuWbMmYbalQ4cOmDhxIgYOHIgHHngATZs2xZQpU2I+3AHgnXfeiT34A0D37t0BAIMHD8ZDDz2EzMxM/Pe//429JJx00km49tprMXDgwCLXP5RRMu4gQ16QnGQcMtOmTQMQn6KjGkclj9OJ/NTpMJ1u5FQm91+0aBGAuCoOxNX85s2bA4gvyHHDUQPxqTuiU3r85P78nVOXF198cWC7DaO0GD9+PIDEhXM0CVAFneMraHpbF9/pjFhBIdNVxQ9ytafji3Xo06dPwQ01jAIYMWJE7P/TTz8dQNwNol7Ld+/eDQAxe2Caa/AhSQMykSBTE/d/HSPczvuLzlBxjHJGWM13tmzZAiC+uJOmJkDcyQMX19aqVSshb94DOZPNuukMHK8LQTNw7nZte9BjFE18aIfNaxK9nvDc6LMCz83ixYtjeQWZixilz/bt21GzZk3886xWOLqA+wPZlZeHq7+ah23bth1SsC1T3A3DMAzDMAyjGEQU9xS8ysDfg1Gq2IP7YeLdd9+N/a+Le/imzzd8dftIRUC/8y2eCgGVEi4ScgNC6MIhKvBUUfgmr0oGv6vrL36nAkJVw23nVVddVchRMYwjw9///ncAcQWP/ZT27ECy6q1h2IMUd6KzUzoz5q5F0ZkrVfl1JssN2e7Whe7fVNFzZ+GYh9nRG4rOFgHAOS1bBqb/5NNPY/1JZ3q1L1MtZnreWwpyBxmkbuvsM+E44NjieOZ40f3dbZpG3VoS1oXt09kwnSH3cxPJfXVWj8dEZxzYTu7H+ymVdZYRNNtuGC724G4YhmEYhmEYxcC8yqQJtCmkbTkQHM5ZVW61B+Tbttq/Kn42tkF2t6oysk5889cyVf2nIsD0bghgtt1s74wjBZV1qmkaLElVQVcdCwqwFDQmClPagsarW5baw2se6s4uyN2bus9z1X/Wj+OP9bjjjjt88zIqDm7493//+9+RfzynD3qJ/fHC8zskfP9u1epY/+IML2d2daZYbeLdbUTVbp35DbKFJ2rzXpDizjTcp2rVqr55anq15Q8aw1TXgWSbdV27QneRDBSlbi25nfdXnYFjvu75NMo+oVAIoXAKi1Pzi/fgbgGYDMMwDMMwDCMNMMU9RV5++WUAcUVBlehdu3bF0tK+nG/XVMSoVqtNnXqZUdQuXe1n3W2q6rsKeUFlsE78ne1jG6hCuO1k2//2t78llEW1wHXBZBipQIVdbVtVkQqymfVDlXS1bVW1XPNSNU0V+4LQNNxXrwFB7SqoDLWrdz2KADYTVtGhYh7Kc+yk1V6bYyAcue43adQQAPDVN4uSAjXx2q8zyPT0AsTXd+lYUbidZaj3M6LqN8eDq34HBXcKyitI7Q/yJsNPt50azIr3Syrp3IfHTD3I6bobVe557oz0IpwRRjiFxalhr3iauSnuhmEYhmEYhpEGmOIewJgxYwAADRtGFIhzzjkHQLI/2uXLlwMAfvrpp9i+tK3jynG+ddPOjaq92ruq3Svf6vn2ruGjXYVAf1O/uLTjU5+1Wjbf/Fln5kO/uW476f+3adOmCXmyDPqzX716NQDg1ltvhWH4MW7cOADxPq+zTKq4cfwVFgU1FdRPs3qjIQVFWFWVXusZNN40nfq11nHtt29Q/Z977jkAcVXPFPiKBeN8hPbFZ0mpvnuhaN+uFLlXIeogicr7WdEANlPfey8WPVttxnV2FoiPWyroQetEeF/i78xb+716pSGbN2+O/c+w9DozprNVHDfqSS2orqwL07vt5G+8XvF+SVWekcizsrIS2ssy1RsWP3nO3BgtRvqQcgAmz2zcDcMwDMMwDKPcY4q7QOXv5JNPBhBfHa5KGVUtpmM0UwBYu3YtACA7OxtA3O6Nb+fq/zbIz6za9RLXf3RB29w8qGgERXLkp9ruUUlgm1yvAWy72jMyL0ayYzt5bHv27OlbV6Pi8dJLLwGI9zcqUdovg9Q0VehSiW6oeen6EO3Hagurtq9+BHmP0XUtQXkU5FkqyD6e6IwBv5sXmorFbbfdBgA4+P03sW2h/VElN6q451eN3N+8UOQTlZPvRfQao7bufsq29mfti1y7Qq8s/J3jn/cMjWGi609cxV19wgdFJd6wYQOAuJccbud9mvfIIOXdvR9Tfeex4Iw2nxd4H125ciWAeDRX3j9ZB+6v9vcWoyE9McXdMAzDMAzDMIwYprhHefPNNwEAJ554IoD4GzTf4jUiGt+4+aZMOzsgrk7T3o02dFQV1IMLUR+3QXazBflxV7s+9aShtu5qc8c6Ul1gG5ie6oRbf/Wao5H2WCaPLY/1tddem9QOo3zzyiuvAIgrb6qwB3mIUBWsKLbtOo7UjjzIu0SQSk5c3+pBXmB0e5CXDZKKpxoSdEzUz7za9rLeL7zwQsL+v/3tb1Mu26gY1KlTJ9bPOHOsarmLek2i8kw7eb3/sC8yT953VHnXvk7vNS5BXmVyc3MBxFV6vW/xXq726ZzF9huzev+kos7t9CzHdvCZYMWKFQCSo6MHzZ4Z6UVJeZWxB3fDMAzDKKeEdsbdGB7c8CMAIFwt8hAerhN1KFAl8qBZ9KXdhmGUNBX+wX3q1KkAgBNOOCFhu0YS5Xe+hVN9oK2aG33tuOOOAxBXGag8q/9btcVTH+zqOUNt3111Tlfpq6LBPNXWXVV+jRLH7WyT207uy2OhiqTONDAdP3nsL7vsMhjll7Fjx8b+V68xGr1U1XH1mKLRGzmG1FOEH9rn2V9V7VfU97Kf0hiUJqg+2p4gf+/a/oIoKLKrX56q8lGBd+vSp0+fQss1yiajRo0CANze6YxDziM/Pz+p76kizXsDkLw+5IcffgCQPA54L6T3FO63ceNGAMGxTdTvubvNrTMQvzczT9aXdWEdeE2i8s460aMc83fbyTKYZ1DkZHLSSScllME66bWI90yeOxt/aUaKNu4opo17hX9wNwzDMIzySv7uHfH/d2yN/hN9Ac4yEw3DOFyEQyGEw4U/lIeLYBLpR4V7cH/jjTcAxN+e6Ys8SDHT7fyunmFcry5cWc63btcW1q8MVd9U/VbVnEq+q4RwG+sVpKgHKXyqiLDMY445JqFNbjvV/j/Ikwb3UX+5VP/p7502iF27doWR/lBpd30SB9mkB3mjCFKw1DsS+1hBtqL6m9qwqpqvqn7Q2hS/+qunJZ1d0/YHKep+HmSC0gZdq4KOXZCnHjd/U/7SF97bikPlypVj9wL2cV7Hab9Ne26mB+JKuyrwVJx5X9FZL/ZN2qVzTZWuM6GC7W7T9TLMI2imjdt5fdI1IrRL59ost52EdvE6lrRdvP9yZp/3OpZJ9Z8efAyjICrcg7thGIZhVBRCVY+K/R8+ukb0M/LgPHTydJx11lm4qN2J0cSRh9m58+YVaHZmGEYyoYwwQiksTg3l2+LUlKA9Nd9oGdVUo6cFRWoLiqpIm296yQDib/58iyZqg6rKmdqpNzvttITfP581C0D8bd5VzdUvtCqA/J1laJRTVd3UxtDPbpZtVy8d2i6dBdCZBc5+UK0x2/f0hr7Zqa65fTFIEVe1OEgF1zUc2l9dX8uFeWpQlU+VdaLXCD90/HDss0/rzJdGVNVZOS3bbUuQ73dVFomOR/29sHUGADB69OiEMszPdNmCM8mudzNG7QT8+xYQ6dN+D+jhcDjW3/X6zb7td0/gzG9BMQ6A+P2S92HafCuM2M2yuB/VdDcP1pP7KBwHGtE8KB3bwDZxbRYQny3mrAavdXp90rU3QdFaGzVqBCCu6nP/Tz/9NFYmo5bbjLRRYR7cDcMwDKO807rlWQCA8L7og+nuLbHfKkXVd69S5MGzz40to9+jL6UhU9kN41AJZ4QQTmFxajjfbNwL5MMPPwQQVyJUMVcbWVXcVZUjqqy5b/lBKnWQolcj+pZd4+hIXnVrR3zCIj9RKTmvbZvExjkX2bnz5gGIv8mzXlp2EKo6Ur1QZdBVV1hGkL28Knl6zFVlVHt6nruLLrqowLobZYO//e1vAOKqmKrhQLCyzHGmM0Zq4848g+y53TUYrucJl6BIxTpGgiIC+9mp+/l6v/GGGyL/eCksAIyO5Vf+/veE/Pz8vwepmRpNVmcc1IZdr0d6TN08VIX861//CsCU99JmzJgxAIBTTz31sOZ74MCBWH+guswZVt5jaBvO34G4Oq0zZkRtvnnND5oFomcYlsH93HGu9eQ+Op51LOlasqDx4ae40xONKuTczmugeoDjsaPqzzpoDBS/WRA+w/Cc33rrrUlpjIpBuX9wNwzDMIwKQzhqBpkZedDOd1X0qjUS01SKCDReRuSBctacuSVUScMof4RSdAcZMsU9mSlTpsT+p+0Y33j5hqzeVVQVVsWdBPlbdu3Z+bat3lSoJMfqwjd4KutRZS50cH/C95iyHv30MqKnLRw/fa3PPQcAsHjpMgBxdURtbAvzVc06Uq3U9G471VZf0+rqff1UNY/50faQ0ejc89mlSxff+hulx7hx4wAkrvMAkmdx3G3qMUnXPyjaf9Wzi5+Ne9AsWdBYCPLWwu26BkXr+lt6XuG41c8U6HHzTdHKRuo+Nnps3brqMVQvVYXNEqp3kCA/2O7/OsaZx1/+8hcA8euMqYAlC72rqP32oeJ5XpJ6zP7BvHVGzbUVLyyOgfYn1+OUX7qg6MZuPBGiKn9QtGL1IuM30+TXBred3Efv9bxG8NgFXXN0lkDrousLgPisvutRx6iYlMsHd8MwDMOoiCxZthwA0OzUpgAAzxF44MkDb/S3WXPmphTkyzCMYMyrTDlGlfYQP/dH39IPRpVLKu9RhZ3Tmagcufh67tmLXoBPb9YMAPBj1AetYRglhCrrHNfc7uflhgqlzKqRW3r2BBBX3g0DiM90nH766QDiM06Hqrjv2bMnadZZZ6P5SQ8qVIOpLrv7Bq1jUjty2nir33OdNaKK7ueFST2qBa3ZYDqWqXVStE5uO6n4a1R0neEmrBsV+S1bIguGVT1nXWlP784ssHwed/aB//u///Otv1F+KVcP7i+++CIAoHXr1km/cSBwYKmLKx3svLBo8AmF+7kXTF7Y9GJa3GnMosBpPQ58tlcX2unUJuvICwyn5/xuDIWZN+iCVj22QRdrniuWzdDTQPwc9+7du5AjYJQU7O+Kn7lZYW7RgoIG6XZ+cn+/m2+Qi1MN1hQUoEjbobjpgm7+hws3f227mqypyVKQi1udtg86Hm4andLX6+TLL78MAOjVq1dRmmccAb79biUA4JSTT45vFPPLF0aNQvPmzUu6aoZRLglnIEWvMsUrp1w9uKcNavsaVdZD+yK2d+F9kQfWUF7k4dkLR2+8VaIPzT7qnGduvAyjVLiDileQTTsfhPN9POp4FAxk/YphGIaRVoTCIYTCKSxOTSFNQZSru8Qpp5wCIFEJo+KswZBI0EK1gsKbA8ku5NzgLPHAFxF0AUpJwAU4VN/YfrrfKsw9JMNZuy6wgMR2BoWjVzeYquoHufLjfhoIxp2i5Dk2Sh8GWqLaqmPIXShKgma4VOVWJV4XigWpxX5wtomfvCboAtmgBZjqCpEcaZXdxb126NjVWQd+chpf661mDEHtcwlqq55PnnNT3o8s6t5Yr7VA3BHD1998AyA+M8br6aJFixAKhWLjQu9PvA7rQlA1W3FNT4LulzpO2Yd5b2RZ7LO6gJSfdFjwxRdfxPI+55yIYwbe6/TezePAdrKPMr2a2AQFLHPbyZlnnW3kseKMt7qDZB34Xd1h8niom0m3PayHG2zLqFiUqwf3dIO2r6H86Cr4qMKO/ZELSn70M5QZGcyhynyQKOY8i2EYh42/vfQSKlWqhFt6/LroO6vnKMMwDCMtCYfDCKewODWcZ4tTY8rfWWdFIsb5uU5T9U/VJk2vAZn4qfv5qehUt1XBKywE++Fk+/btAOJv8CxbFxoFhUnndioIfm3QY6Dqjy5AUnd6JMjFn1/dOAPAc/6b3/wm4AgYRxoqd6rA6fn36zPsC6qOBbllZXrtU0HBvVx0DBPuq/XVGSN1Tad1B4Jn9A4XftcZrY+ObQ1mFRTcJSgADVC4iz29LpjNe8lw3HHHAUgeP+65Yz9g3+R41XGqwcP0Xsl8dHz4BS4LCqRE6tSpAyB+Hec4pvrNOgS5M2Y/dGdeuU3Hs37yWNHlMetCdXzz5s0FtsFtp7adx0bdQmrdggIaakDHgmYzmBf7gFHxKBcP7oZhGIZhGIZRWqQcgCmFNAVRLh7caY+tyhIQf5On2qDqcGG2m3y7pUIQFHK9IIKCUXBBaWxhWnQRaqhyxGY1zLf5jMoJn7HAS35lhxKD3qi7LX4PUti1zknZO/sFBZXg8Vc7v6AZBz0XQfm5//OcGyUPw90T7Tv8TntOv/On9uOqqKvKpSqg9g32bz9VjCqX2peq0qxlcLZKxzrLdL23ZGRkBJu7xI5PdNy6pm7cR8ey5OW3JoX1oF2wqnlB3nS0HUFrFNw0JEit1fR67I3DA4OdnRz1FMNzSptod9ZS1wzpmOHnwoULAcQV3Hr16iXsr+Ob+W3atAlAYh9gPXjeaQtOdZvQYxjvEdpvCNvjrnMCgLlz4xFeNW+1yVf1m995Tz/22GMTPjds2JBQN786sO1U74keKx6HH3/8EUCyqh8UCFJn/YDkY8txzz7RM+o61ij/lIsHd8MwDMMwDMMoLVIOwJRCmoJI6wf3MWPGAIjbtqtdLBB/Sw7y1Rxkb61KH9On4pVFbXuTbOTEnWNMSa8SeaPOpxIfXbTqVYoq7pWi3mkqRb0AuKpc9P+vvv4aQFwtVMVMVZQg5V1t+QuaWVAlj/b1XHGvNsJB6wqCzpFbNj1onHDCCQDifcBCrR95xo4dCyA5gIn2DQ3b7f7OfsU8dHyqHa7abWt6qk5+a05USWaZ6v9c7bOZJ5U7Het+NvN5eXl455//ROXKlXF5zqXRA8GZtOhlNhS9HjiKe5Ib14Dvrjcntku9YHC7Ku96DNWWWb1r+FHYzGKQD3h+t2Axhweqwnr9LujcaT/XMcR7BeNlFGaXrf2NM1lAvE9RHaYazrHHe4PaiLMswjryHsJ8/WYB9P7C32gvrgq8HgeOd97bVcGnhxu3jkHXHR4TjRXBY0sVXy0BeA4Keq5QdZ7tZJ8wKg5p/eBuGIZhGIZhGKVNKBxGKAXz6VTSFERaP7g3adIEQLIvdVf1UdtZte/j72qHzbxoo1eYX3dXuQ7yOU12R9+yj6oaVc5jQVeiykBUoaN26HF/bmf6sFOXUKL3HPp41SiKfDsvzAd1YT5t3d/UllYVdNozUnXR9QNqg6mqiqt0cBvzYh8wjhzjx48HEFeegghSnVz0nLKPsJ+qeqazOURtp/08pmj5QWHWVfXj71TVVKlWTxJAXDkLhUL457/+jYyMDFxx+eWJjacC72fjrkS3T3n7bQCJXmt4rDSqs3q0UM87hc0E+vlzD4qQGqSsB8V2YJ6mvBcPXYfBvqDeWYB4PBGd+VL7adq285xRQdd+Q7WY6fwiJlO15ufGjRsT6kW78qB+outjCOtIG3E//+Z169ZNKEvz0FkhPR7r168HEFe/2QZeBzhb4LadaXhseKz12sPzw3awLL3XcX+OF7bXLVPr7xcvwyjfpPWDu2EYhmEYhmGUNuGMFP24V2Qbd6rhfOOmmuwqRnxLVc8LaidKdLu+3ZIg/8Xub6pqJ9mBxjxKRJX0wsKei228q9bNmDkTQPyYUKHjd1XVCvJD79fOIIUESFbn9djpMVcFSGcz+EnFxFUb2Q4qEWyfceSg0lSYJya1t/UbY1SHtC9w36AopkFrLoLsuN3ftH9qv1Tf0bq+pTDPU26b3Xr/Z+rU2O/79+/HNb/4ReRLAcGWJr76KoD4dYsKJVVAv/qo33adGdBZRR13OqbVJhhIHsNBUWQLm8ljWfRMdPvttxeY3kiEY5HXRvV25qe+1q9fH0Dc7pyzQ/xOdMYlKB6H2li7s9D8/5totFZ6XaEyHaR6B3kUY9lbtmwBEFe23Rk3btPoo0F5ar/XmYZt27YBANasWQMAyM7OTmpnkGcmnaUIWtel0VzVK1Bubm5CXdx66gyIOxNglDIpLk5FMR/ci7e3YRiGYRiGYRglQloq7qNHjwYAtGvXDkCyyuMqRnz7pt0b7a2pwBP1hBHku1nfnP2UaI0qqOo2026Nvk1zey2uDveSVe1IhpG6fDZjBoDEN21dEa++nLVstXNNKipA+XCVNlUyNY3aK6rSrmop01FFV+UECFZ92CfuuOMO3/YYRYcee6ji8XzoeVcvM8TP00WQT2mN7KsEeUqh4uhnC68+kQnV7KAZBFWwmY79U21j3fqpmu0qilPfey8pQqTWjQqlrgFwj7Gu09FxpbMaWoaqsmwf83HVfV1TwmOn57Ywtbag64hROKNGjQIQn33keWB/4vF1PYzwXsfrKWNf8L5x4oknAogry1wXpf1G+5vOhLr9i2WyD6mfc51pCxoH7KO8TxcUN0VV+6A1VERVcvZt1oF1Ztlsk1tHbTvTat563eI6oQYNGgCIH0ueG6roLNMdq1u3bgWQfC9nHdhH+vTpk3SMjJIhFE7RHWQxF6ea4m4YhmEYhmEYaUBaKu6qBPANm2+rLkHqAJUK9dBAVNnzU3/dsl2C/JSrH1ZVoTZGo7GpQrB27dqEunM/14MAVQKqKbQJpH0eUX+4QbapQWq6294gu3/1Ic9zEHSMmZ6f6g3AnR1RzwYsoyD/t0bReOuttwDEVb0gFZnoeFTPS+55Vw8tPLfq6YWzZjpDpOORn36ROrWP6xqKILQO6plK+54Lx6Sq2qpaqocl9S6hY8atM49ZkAceLTPIxlf92/sRVD+/KNUuQQqpnifOlAE2W1YQ7OdU1Nk/2Cdpt+5G92Sf4Xqgk046CUDcswkjhNK+mt85e6ue1tR7m9/sGLfVqlULQPJaMNZJPcAFeSnS7QXNrAXtGxSxO6gOzJteaqiSu32dZTIP9bak0Vp5P+ax5v48F/xO23bu555P1ovXJb3fBrXTKDlKyh2kKe6GYRiGYRiGkQakpeLOt9FNUYWa/mr9/MpqlEMqFfykUh0UITSVyKFKkMpUmCcX1lHtuKmia6Q32rwB8RkF7su3ctq8s8wgtVHrpDbwQR4o/GDZPMZB3nWC7OvVDtadSVFftuwDZjN7+KA6RBXJtXkG4mqSqmfq+cVPmeY+qlDpzAl/V+Vafa6zLPYLjWYKJHumCfI2oTNgGllUYyK4Y0F9vzMPtcXX2SRV6HWtjZ/CqFEWdZ2A+mdPitwcRa+NeizdegTFc1C/06rI61obHfM6C2ck8re//Q1AcjyRIJ/sfj74ed9gX6M9Ne8fvEcsW7YMQLK3GcI+XNA55b4cD6wP+6yuIdM+q2si2E7my/RuHTWarI57/c4ydAaBx0evJSyLduduHjq+9XrF+nI249RTT03Yj+dCI6myru5sgq4xCooUyz5z2223wShZQhlhhFKY/Q9lFO95xRR3wzAMwzAMw0gD0lJx1zd+qlzc7ueBoTAb6CB77cJUOT8/7rpNVUZVh/kmravbWVazZs0S9uNbfatWrZLaqZ40gtR+VRmIzkyoSum2MyhCbKqzF4X5kFd7YLftWq/C7JaNwvnHP/4BIG7Tqf0wyCORzqyopwu/saGehVQVI4XZUBcUNTAo1oLmyd85s8P+pnaqqrK5MxH0lU1PHfXq1QOQbI8aVEeWydmOVatWAQB++OGHpDprbAZdj6MzBRwrVAV1hkTPgTuToLOYOoZ17Y8qhjpOFbesESNGAAD69u3rm7YiQjVZ7yHq6Ui9+LjwN54bnjP2UfUqExQlnHWhHbYqve4+ixcvBgA0btw4IW1B8U/c7WpXz3zp15x1ddulHmxUkQ6K5xC09mPFihUAgLPOOgtAfPwA8XHBayXHP5V11lcjmRMeex03up/fmjL2AfVkw75g671Kj1CKftxT8vVeAKa4G4ZhGIZhGEYakJYyJd/8uXKdb6l+ttP6Zh9kaxn0PcgGLyhyoLuPKs58I6Zd9qJFiwAAS5cuBQC0b98eANC8eXMA8bdwVSX83qh1m6pnVP5Y5sxopNXTTjstoUza3Gm7/Nqkx0LrUNT1AUH+7t1jqzbO/LToccWHNpzqH1xV4cLGQFBURPc3tS9VryWqqOsYUIXezxZcPZioOk+vEezzqkhr5FWNN+A3y6PqvHpsKSzCKK9pVOQYq+L777+PpVm4cCGAZJ/Z6nGEdWE6KvD0GqI+2v08wbAdaouuvuPVFl69Pyl+yrB5xUiG54rnkkqvrhHR9QpA8kyMRuOl7bbr+x2Inxsq6Uyns51+UX1Jw4YNAcT7BUnVq5n6ktfZ65NPPjmpnWq7HhSdmQR5h2J6tkFnl1zYz9kuHiuq4fzkLBmPta4F0Jkt9Qfv5qUz7zrz4c6AGCVLOBxO6XmnKGsm/UjLB3fDMAzDMAzDKCuUlKlMWj240waSNmfqv1VVO/f/wjyYBBHkIUZVRT+1SNUQtcln9LR169YBAKZPnw4AmDdvHgCgU6dOAOJ2s6qi+6mLqrzQRvajjz4CkGwjyDpohDq/iLD6Xduuil2QL3gSFLkyKB+3XYR9gJ4RzE626Pz73/8GELfX9Iv66aLKuipAiqtMqyKtqnZhNtGE6YKio7ppWC/awJ5zzjkAkmeXgvq8/k780mnfLWymjxRmh8trABC3G165ciUAYM6cOQCAn376CUBcradCqLMWak+rM5Z+vvCJzrbojEKQ7XLQd3c72z58+HAAwO9+9ztUVN58800AcY9p6vc/CFc95kyLrq1iXBBe+9lfNGIw1WEq67Tf5uwtZ4fcc0jlmPVm32P9ddxqe1Ql1+sF1WTX05gqzOrxSKMaax9W5ZozVqqKu+VonAnO+KoXN/X+Q7/t/J3ngnVQf/wFnW+9ZqiXL/aha6+9NjCPkmbkyJF4+umnkZubixYtWmD48OFo27ZtYPo33ngDgwYNwqpVq9C0aVM8+eSTuOKKK2K/e56HwYMH48UXX8TWrVtx/vnnY9SoUWjatGkszfz58/HHP/4Rc+bMQUZGBq699loMHTo0wTPRnDlzMGDAAMybNw+hUAht27bFU089hRYtWhyZA1FMzMbdMAzDMAzDOGK89tpr6N+/PwYPHoz58+ejRYsWyMnJwfr1633Tz5gxAzfccAN+85vf4IsvvkCXLl3QpUsXfP3117E0Tz31FJ5//nmMHj0as2bNwtFHH42cnJyYWeDatWvRuXNnnHLKKZg1axamTp2Kb775Brfcckssj507d+Kyyy5DgwYNMGvWLHz66aeoUaMGcnJyCnR+4AcV91T+ikPIK0xyLkOMGTMGAGJvQepdRiNxAnG1oKg2RYV5k1G7bld9C4pYyO1ULlRNoO0qV7NTKeDbPX3Ann322QASfdlSLWUe9Mmr6hptA5mH+nimYqC2hWo/CCT7k9WZBd23qF1NvX0AyZ4y1Ab3yy+/BADceuutRSqrIkNlZvXq1QCSI/TxvKp6plFQNTqi3/lW+1NVi9QjjXp40WiBxM+PMv/nbx06dEj4rmNZZxh0fKoK7pYVFM1UFXe2U703qQKpdfGzO9c0X3zxBQDgm2++AZCs/ul+rL+eR1dFD/IkpCqu1pvp9biQgq7HzPOuu+4KTFPe4SwYr/EcF/Q6FORVxp2F1jUM3Id285xxojqu50iv51TTeV3guXVnaJiHepriOVU7bfWiojNoQWPP9amuPu6D7rtahtrRq1cWlsk6u32W9eY+vM9SlQ9aB8Q8Nm7cCCC+3o2Kvc4WuOdE1xjoLDlnQHht5DOBq1CXJu3atUObNm1is+L5+fk46aST8Lvf/Q4DBgxISt+tWzfs2rUL7777bmzbeeedh5YtW2L06NHwPA/Z2dn4wx/+gHvuuQdAxKKgXr16GDt2LLp3746//vWvGDRoEH766afYsf/qq69w9tlnY/ny5TjllFMwd+5ctGnTBmvWrIlFF9Y0hbF9+3bUrFkTi/7YAzWqZBaafse+/Wj+5CvYtm1bbDwWBVPcDcMwDMMwjCPC/v37MW/ePHTu3Dm2LRwOo3PnzjFHGcrMmTMT0gNATk5OLP3KlSuRm5ubkKZmzZpo165dLM2+ffuQmZmZ8NLFF89PP/0UQMRBR+3atfHSSy9h//792LNnD1566SWcfvrpaNSoUZHaGQqFEQqn8Bcyd5CGgTOaN8cZzZvjxu7dcGP3bqVdHcMwDMMwEJlhyMvLi62nIPXq1YvFBVByc3MLTM/PgtL8/Oc/R25uLp5++mns378fW7Zsian7XBNUo0YNfPTRRxg/fjyqVauG6tWrY+rUqfjPf/5TZuPDlM1aBaDTzEGhi10XVIUtSi1sYaSipiUFhezWqUe1l1KTEi66ZYfi1CT3oxkMbbxycnJieb333nsJZWrgCk7hsQytQ1AdNZ3bJjWNCDqWhQXdKOxcuOdTp+4tZHrxoSmSBvEqbCGlmkkQNfvgNLK7j7pLDArQQtS0RheM+S3+ZL+kiYwuKNPPIFhXhohX121A8rVHzYd00Zn2WzUBojmP3zRq0Lhq3bo1gLg53AcffJBQf7afeQe5w3PHp45BPedsh5pP8ZNl6Hn2u75q36jIC801mBbNImgyo+aJBT1g0JxDz7e6AQ269zGdmm9oHweSzUvcoEVAfLxyHHAs6X01yNzO714RZIKp40MXq7MuHBeEdeB10e+4aNt5bHQcqCmtmgCq691UghOyHTx2LIPHXF0mV2TOOOMMjBs3Dv3798f999+PjIwM/P73v0e9evUS7k+/+c1vcP755+PVV19FXl4ennnmGVx55ZWYM2dOUv8oCPMqYxg+nHjCCZF/PHnYyj/ov90wDMMwjFIjKysLGRkZMQ96ZN26dbGos0r9+vULTM/PdevWJUSmXbduHVq2bBn7fuONN+LGG2/EunXrcPTRRyMUCmHo0KFo0qQJAGDixIlYtWoVZs6cGXuYnzhxImrVqoW3334b3bt3T7md9uDuQ9BbON9WqVa5b5pBCyNV7VYlj+oaFQ4qB/xURcldtBmkZLEMutliGawDlQDaVX311VcJefOTdfRbuKIBL1gH5qnutrROqqYSP1ebGiSCdaBSwU8NEOO38MbvOzHl4PDDxW9AvI/rIi2dSVHXfxwLTBfUZ5ifWxYJciuofSpooZn2JXecn3nmmQBSD0iiah5nvrjYk94PWAdXiWEwJ95AuOCPZXMBHevJsa+zHVxQxk8uXnPDuXMRGtFjw7Kuv/56AMAnn3wCIL7oneeFdVMV1z2PqijyXOsMiC7I12ux9iG/86XbtD9VJPSaz0WqHHNcnEnVVdVzINnVql7DgwL76blUN4PET/0OckGpyjuvCVSJOZ7VNSPRvuHODActMtdF5Dqj6F6XXLhQVBfQuteRoKBOuhBYrQKCFoqrMu83o6wuNbm4mONdZwbK0vjJzMxEq1atMG3aNHTp0gVApI3Tpk0LnFFr3749pk2blrBI/YMPPogFqmzcuDHq16+PadOmxR7Ut2/fjlmzZqFPnz5J+dGkZsyYMahatSouueQSAJExFA6HE84lv5fVoHBm424YhmEYhmEcMfr3748XX3wR48aNw+LFi9GnTx/s2rULvXr1AgD06NED999/fyx9v379MHXqVDz77LNYsmQJHnroIcydOzf2oB8KhXDXXXfhsccewzvvvIOvvvoKPXr0QHZ2duzlAIiY3M2fPx/Lli3DyJEj0bdvXwwZMiQmJF5yySXYsmUL7rzzTixevBjffPMNevXqhUqVKuGiiy4qUhvDGeGU/4pDWinuRF1OqRsnv7ekIJt1pqWaRiVMbVMZuIhvuRqcwi1T7fWCbLjVTo7pGKQhyL2in5KpgVK0Dhr4QdUUffMPChzjtoGqA1VDHjuqhFQIqExu3rwZQPzYUZUs7NwAQK2aUVtfmsJETWNC/J4X/Z4XVWKqxxVKIxlX4Q6yM1UlV21bgxS4oMBcbhp156k20EFBUrif2n772U4zaFHQ+NMxw7LokeDbb79NKFOhWgjEVToGPKPyzkAgvG5wzKoiTzd/6j6Rx4VjCohfi6i8ayApVdw6duwIADFXZx9++CGA+DWB45Hj2O0brA/rTSVd1ySojXVQULYgN5nuPiSNPBUfdlRx1xlenjOOA87QuDNamkfQGjEdF0FuQ3md0DUTfmth9Fzy3kCCXFUSndHRfAsKPhi0dkXHFI9ZkKvSgtRWjgs+H+haED1fJMhVsqrpfrNfHIMct0EzKWVVJe7WrRs2bNiABx98ELm5uWjZsiWmTp0aU8LXrFmTcF47dOiAiRMnYuDAgXjggQfQtGlTTJkyJTaTCgD33Xcfdu3ahdtvvx1bt27FBRdcgKlTpya4C549ezYGDx6MnTt3olmzZvjLX/6CX//617HfmzVrhn/+8594+OGH0b59e4TDYZxzzjmYOnVqgglOWSItH9wNwzAMwzCM9KFv376BpjGM7u7StWtXdO3aNTC/UCiERx55BI888khgmldeeaXQel1yySUx05niEAqHEEohZlAoXHBk8MJIqwd3fZPWt3GqUq4SxjdgqlL6xsuQw3xz5naqw6ouUlmj0qEhj9168a0vSEmiasKyNeQ8f6fdIN+4VW0B4moalQ0eAw1OwTd6bqdq4veGD8Tf5llHty0FHQMgOYwzlQKqi1SHsrOzASSfm02bNqHpyZEFJFTaQ1yEejBq0xlV2EMHo4F7DkTPfe1sGMnQtt31jKL24jq7omqQ2jkzHfMJUt7dNEFeVbQPqPLGBUX8neoz83WDkqmirjNWahPLG8fy5csT6sLfqaJx7Lg2r1pvjj8GQmvYsCGAeF/nseaY5lii6s2xofa57jFhIDeOLwZcUk87TM91Lr/61a8AAG+//XZCGbxGuudLg73wGKiHHsJ6sv6q2gYpkH7byqpyWBKoisx+zePPay2PM/tPQTbRQdd2LVNn1tjPVDVnndjv3Dz5ybFE93xt2rRJqAvHgSrurHsqanKQsh7keUcDSvH3OXPmAIgveuRsmXptAeLHhPdswnvzCVEnCkHPLEGzfbpGxJ3V1FktpuG55xhj36jI46e0KKnFqWbjbhiGYRiGYRhpQFop7qrCEb5hUn1z/UbTBp0qGd9gqahTzebbKm3daYOqPjzVwwkVDz+VSn26BimaVMj45sw3e9p+sT1UzBiC17Vxpw9n2uXSgwTz4Js+y1BPG0Gr49WvuzvLoR5C2E71bsH6r1mzBkDcAwePE88FFfnjora7x9WqFbdlp8J+MBrmeV9E6QgdiNoY7o3aK+7cGqnMSWfASEYVURe1aQ+ahVEvMuoRJsiDgluG5qXb1Sdx8+bNE76znxP2JXccBnlVUJt95vndd98BSFbF6NGF1xId3y7aDh7nlStXJpTdoEGDhDLUywbVND8vGnrcef3T6wbrrXXi9m7dIoHKJk+eDCA+E+Z6rVHPHIXFbtA+o3bHalftni9d31CRbdw588I+R2WX12+qwrx/6WwnEDzjxONMxVzvq+q9jddnnR3iPcRP2WV/Ue9IVLUZa0DvbepFSvufn/ccHiveX/X6w315f1q1ahWA+L2d90rWkcclyHMVEB8jPCY8/jxWnFnT2UnWgWVwP34PimXi7svjz/sr+wCPtXp3M0oOU9wNwzAMwzAMw4iRVoq7vo1TzeLbLG3w/CJdqRKktuDff/89gLhapXnw7V2Ve77t+nlG0fpqnuphgYoz0/FtXoMQ+LVPt/E7lQxtl9onqzqjfrT9fKnTRpDHRBV2bTeVgtWrVwNItsuP+ap2gyjRtp227HsjCkV4b2R2JX/rRgBA3raIWpK3JTJjUrnVlUn1NeJ9zrXXVHVL+yVR3/9q0+7n69/N300T5NFClSn656Xy+MUXXwCI9z31F+62i/2K+wbNBNBfu8Y4oKKoyjrb7Y45jl31V81rFJW4pUuXJpTN8Uk0yqWfLbnOGOh54LodQrtbPeYs69prrwUATJgwIakNat+rfcQveqZblvahoCi7blo/u/6Khtqlq/2yehjhtdft/+y36rlFr8eE54bnVL0MMb36jnfPE2e9WQ/uc8YZkRlQjklGAafSzBm0X/ziFwCSbcfVC9Xs2bNjv9Funmn0OsQy3nnnHQDJsxhc28E6cj/ep3is3VgKGjmdafg8oPFfdHyoXXqQdxrXxp1lcMzw/LBP6LixqOIlTygUTm1xasgUd8MwDMMwDMMo96SV4n7rrbcCAN5//30AyT5siauE6Upsvgmr9wf15KJ+iPVt1y/yn6K+atXejajiybLoQ/S0004DkBxt0fVVqhEYuQ/z0HoH+U5nHdWvth9sO/PUiHSq9PDYckU+jz1VCV+766h/duQl2rh7u6M27Tsidrl5/Ny5I7C+hn+/LczPeZDHFFVEOe7UBt7t7+r/W/sQFSau2WBe9D1OZUr7pZ/NNSMPU5ELag+9yaiNrHpSIbRv5ToYID4W9RgyT6p+HMOLFi0CEFdKqZxy7AQpcECyP2qNssh96NHj7LPPTqij2jrzvF144YUAgPnz58fKYv3U3zT30fOgM3csk8dS1yK4fSNoTcXQoUMBRAK4VBTcvgUkHxsquzwPPM7uPSHIq0hQBHKFZegsHb/7eRrjLBU/WQb7L22/qRJzjDJvKvG8f+m9kt/ddWyqtGtsAebJMvh7ixYtAMSfI3TtiI5l9zlD40aopyoeO52B0zzpkSdIHS9oJl/PD/HrC0bJEMrIQFiugUHpioMp7oZhGIZhGIaRBqSV4k64KpzqFN9iacftokqR2oPyLZz21nx7VZWN9m26n593BPXdqvsUpnqrEkIvMosXL07Ix02n6jX30Tz9/CYDyfZxqoQW5G9Z68NjRbteLUNt27mfq6IkFxY9VuGospoZjeB4dNROM1rPo67pF5yHkWQf7ULVSCOiqi2r9iX2OY4Z9QDh9lP+xk+WSWX33HPPBRDvG4xiqjauWjc/uM/06dMBxJU17kMvR0F5qh932u/yd9dnPNseFOlR7Yt5reK1jCq+Kuy0J3ZnDoP8b2u7OZ7o0YaeeYIiZfKaMXfu3KTf9JqmfUHPJ9EZPO1/fhGng8quCAwaNAgAcPXVVwMIvlfofcfvXhK0j45fjZXA3zkGqTRznAdF3waS10SxX6vyzDwYBZP3Nq4BodccqsYsg/eatm3bJrVXZ/o4C808WYfTTz8dQPyao5GHNRI42+S2U8cBv/NYcV/16qZrQwiPVyqelPSerL7zdTaAferRRx8tNG+jeJhXGcMwDMMwDMMwYqSl4q6KGD/ph1h9lLu/BangfLPnWyrfzqnqa4Q3tY131SK1IeWbcJCqTRUuyMaYn7qqn0qa2y6mUfs2PVZEbWlVdQ3yMOJ3LNRfPe12+TuVDLUhZj5+vogRTlTavczo+oWoAh+OKu8Zteom72skUZCiQ+XNjarq7qO+uVUNI6q4+3kH4digIkc7dNplf/nllwCCI6qqjTTVcNc2WD0+8BrAPs9xpzNh6hGFv3MNRkHeToK8qeg1gceGs1Mcy1S91WuVG7NBZzY0by1T1Xyi0Sh5Xt1jSAVRvZuoTX+Qt6CgGbygOvv9VtA6m/JGUMwEvf/o/crveOr5Dpq5UBVY70s6vnU2yJ1l4f2HttvcVyN365oxzsLSp/pnn30GAOjYsWNCW3hfdo9TUKwA5qFl6Fosjayqvta5Jsu9P7F8PmuoKs9jp9eBIMW9sDHsto9pWLY+g+jal4rsnamkKSnFPS0f3A3DMAzDMAyjrBAKp+gOsphiRFo+uDPqIO3H+GbJN2L6XwXiihbt2VSdV6WIb+GqtFNto9KhKpUf6sdc34QJFT2WqW/ffJuncjZr1qyE/dx927VrByDYVj/ILl2VAdaZKrmfUqt2lupfX1V/VXR57PwiNhIvqrQj2gyPCmylaN5VIgpMKBphNW/tskjy7FOT8jIKtolVFVv7hs7GqGKr3k40joG7Dz0MtW/fHgAwY8YMAPF4ClTWqP7qzNgPP/wAINme1bU7p1qs0Un9ZuTc+rL/MpKi2m9TsXf9pWucBI47tZMnXP+xcePGhO1UBVWRc8e6lsHfuA/HEY+x5hWkYPvZ6dNWl3nwvLAP6BoDvRZoXwhS+d1tQesEKgJB9whdR8Jj5BdfgwTZwQd5RNPZEqrm/NRzFrReykXt59VDjXo24vhmv6PtO73RcExyJgpItlXnuGQZHAfqCSnIO5ZGB6ZnNn666GwkI8ISnSnU/fT6oPd+v/suzzn7BNul1y+9Hhvlh7R8cDcMwzAMwzCMsoKZyhQAbaf5Nso3Y41qCsSjjlLholrGt1P1RMO3cP5OdU4VJH0T9lMV1fZOFY/CVLkgxZPKIW3vAODEE09MSKNv9FqGrkAPUsR0pb6fLb967mFaKp5U2FVFYt5UWXNzcwEkeg3q8ouId4W48h49RoyuSvWImeZbtLiC0PPvbiN6nthPg7yZBEXN9LNR5ti94IILAMRjMrCPUB1jf1YPRfyd45iKtXp1cOvNyKisP5U55sXtHOvslxxn9D6j7XEjGnPWiNcT1l/jJ2gETFUkmQ9nDjQmgluuemFq1qwZgGQf4EHeWlimRjTm8QLiY5nXVrWrVYIiMqvK66faFrY+oCLwzDPPAIjPQGm/YX8IspF2/YHrNT5o5kLVcN3Pb4YJ8I/uyX10PQjHGsdDkN21+jPnveHHH39M+N3tf+yvQVF8g3ykq992HmOq/bqWx81Xo9ISzgyojTvLCho3+ozgF9NAx7HGhWH9tb3sU0b5IS0f3A3DMAzDMEqbdm1aJ26gqJQQ1v7o2PfN0Zdzo/wRCodSU9zDhZuZFURaP7irZwravblvxrRLY1oqcsuWRWyhqbCr5xf1T0ylkOoDVQY/u0y+8eobsSrtqnLrCvygSG4dOnQAAEyePDlWJrepEkCFRlWXVOukvn5dm0pVNvTYUCVVtV5tc5lPw4YNAcTVxvz8fMyaMxcZGRlo3apVpBBPFHaKwLxYItEHuZHI9ddfDwD461//Gtum51HtTrUfB3mhYN/R/Ny1C4zO+e9//xtA/FxTLeasC8tmn6I9p/ZHqudqjw4kr7FgvdevXw8g7omG7WBeVM1YBvup+nV2YRoqg7wWaSRmlq1jhcecZWiciC3OzV49ZHHfefPmAYhf85o0aQIgbqPs2v8D8fH58ccfA4hHc+V6ASCutHPmg+dF7WdVrWW7tE8E2RO7vwX1r4qERt7kDA2PJ88L8YvPwOusei0LUm55LnWNi9ql83d+uhGv1ZuQKszczvsSZ9o0L14zOEZJKv7q+Z19lseSZbCdfh5qgPgxZnv94qbwOLtrT1JFZ0vcvDUGg3ut8ZstBeLt00i27jg2yhdp/eBuGIZhGIZR0pwfFctonkkHCcgT05yo0u5lRB63jql+dML2HY55n5HemFeZAlB1gW/5tO10VWEq7ExLpeKEE04AELePo1KmK8/5nagHBz9btMJ8FuvvajevSgDbQPtSqniuLRu30eZX91GPGNqOIP/LuireT21U9YFqm6oHTMfvVBd5Lnhu1GNC9erV8W3Uvy9VFB4z2vm3bNECAFBVzpfhj6v6qB22+o5W3+MaX0BnedhXOB6psgPAP//5TwDxGSyqw9xXvThxLFA9p59nqsmsK/uSOyaYR5CNL8d2q+hsDvsW1Xvieqly21eQz2yq4hodWGed1PNOo0aNErbTvztnItw281NnIVg2r22MHElPPDwurJN6jnJt5HmetI/odVVnC7VOagusM37u/2r/XpG8yhCuqzj11Ih3LFW7eYzUU5d7fWYaziDxXhAURVs9BTGdrnFhmewDrhLNPDhedV2WzrwwL87+sO/Rcxz7JmeD1O4cSPaiwgjBvHbwWLKMunXrJtSBeWo72S4eW7cPp+JRJ1Xc46frCdznCtZP1+JQcdfnIrbbKH+k5YO7YRiGYRhGqRE1z6TSHtoXeckI7d8d3R4NPhaOvghkRl+cKkXdPleKvORQgd++00xb0p1QOAOhcLCLcDddcUjLB3e1t+ZbKr9TJQLiKi7fmqmm0d6TeXH1+mmnnQYgOTKdKmV8+1bPMO4++kavHhfU0wvVEqoMalO8U6bUXK8SqrTzLV5t5YJs2NX2nXVW+z+/mQXmGeQlh8eSdeGxZhlqe0v7RioL7gxKkIr/WdQLw8UXXwyjcFw7SV2voagttfYN18YViCtafmsx+Bv9ldNDCr2wqE0r+w7HL8tkn+F2tQUGgm16qeq1bh1ZVMb+O3/+/IQ8WMcrrrgCQLwfUuly7Vupbi9ZsiTht6BxpJ4fdJxSqaea5qp9qpxyX6qavOaxPdzO88RrBLfTtl99tAPJ1wfuq9c/fur41PU5irtdvZmQiqi4G4ZhBJGWD+6GYRiGUV6hiRRNp/gyxZc1vhjyZSwomBAQfxHlS7AKK2oOqS48WbaaQxE3GJIGMtQymAdfuAlfVPmyrKLOKaecAiD+guy+zNHkjWZ33Idl88WUghHFA9aBQlGQSSuPrfvyXKNGjbhDhIPRl9N9kZfa8M6IOJi/M9ImUPCoGREM8o+KlJdfJfKC7VWOnJdjqh8dUd9DYdSPupWlIAXEzy3rxfOpi4lZT/YhowQJZ0T+UklXDOzB3TAMwzAMwzCKQzgce1ErNF0xSMsHd07X8k2bqgPf5t2Q5lQLdOGGunjiPpxmZnpOAVNB4HQyp4K54IW/A/E3dpbNqXm+CVMBUYVDlQxduKYLlNwFOlQs1N0W8+Cx0UVmulCW6gPrzsWffqG4WR+aJvF8qCmTLgzmsVa1iNtZd3UpB8RVEjXPUDMio2BcUxlVbjSgh44BXbTF88t+ThOZ119/PSG9m0bdlbJM9gE1xWD/pstQXVTN/Tk+gbjJmS7SaxFdyMw+M3v2bADx/nveeecBSDbvUNeprgkXTX34yUW0VAh1MSfRcUmzIprx0H2k61KT9dIgNwykxIV8PLZceM9xSlWTv+tiY78281iyT3BsBi065PnToFWq7vqZ3qniWRFDtj/xxBMA4v2B5zbIxamfy0w1ZVQzSDWD0nOlAY3UbI3p3Hufnl9+sq+yn+t9R03gtF28blAtd6//GiBJFWjNU+99er3Tuvu1088tJW3a83dH7u95m6KBzFjevkidM+pyh+iieXqd4dNY2P+xzG/MuPA+yucL9iGj/JGWD+6GYRiGYRiGUVYIZWQg5COA+KUrDmn54E6Vm7ZrfPv2cx9GFY1v5VSKqOzRBZza3FGVUEWMZfDtm3Z1X3/9dWxfvsGfc845AOJqmy5AcxU7INlFli5gU/eXriIYFH5eg8ioCzl+UtXi4kAeN9Zx1apVCfsDwJlnnplQlrpx1MA92k4ee54LdSXG8+ouwuX/qrizTxipcfPNN8f+HzduHIBkxY1omHJdGMwxcO655wIA/vOf/wCIK9xcgArE+xeDAun4C1L12D+pPFKBp6tGuo9zF6ZzcSb7Cu2F6S6R7tI4ltu0aZPQXlV+id+CU44Xql1c5M5jw4Bv7rFwUbtjHie/AG/cxusIxw+PBccRF6zXi9rL8pgHuZH0WwSqAWY4o6EzHmpzrbMT6pLTbwaPeWowvIqouBP2c97r1EWrfrrHk8dRXRqrYquBl9SFMPuJBkVjWa4SrYuU1Q2xXls0HcvgTK+6RtZZWbd+tLXnd84Ssd+rkwg9Hqyj3n9ZB3fmNz8/Px4ZNaqQe5Wjdv1HRa5R3lGR+nj7E+9VVObp7z32ncEFvfg5PL99ZPbvs5mfB55ztSpgnzHKL2n54G4YhmEYhmEYZQZbnBoM36T5Vk6VzS9MMNNqwBcqRLT3pCIWpK4R/Z2qA9U8IK6WUdlTxYP7UFUMCoihNnj6u5+LNVXRNNCL2vExvaqIOkugCqnbjsKUSd3OMnnsqRjw3Oj6AVchUheZTGPhnQ8d7eOqtKmdKo89A2cx4MmHH34IIB40hqqYa5fLIEBUgTU8uaplLIsBxjQAmNrAun2F9ubffvttwr4c+7RDz8nJAZCs/qmtrx4nVz2kLTpVfqqYF1xwAQCgffv2AOKzERocSsey69bSrZvbZp2ZUvectO2lSqnt0XaoC0e3zXoM9NqkKqZ6ImGd/AIFabtYn6C8KxJcn9C0aVMAyeuidI2BC887+4naSLOP6ewHPzm7xb4ZZF/vuvPl+Wa9ggL+BbkHZdm8Z7IfMSCRro1x82Z7ONMXNAtNdO0YP9k33fUyQOL4d9dUMSKqVznqpvXoyLNGBmel9kb9ukf9tudXjj4PFGGBIqO0zp03L2ltio4b9hmj/JKWD+6GYRiGYRiGUWYIh1NU3CugVxmqc3wzpi0nvZb4BRDh2zS9UlDxo9cHqoe0QaXCrMoA1R/a0/m91VNVoPJOf6qqnLOeqnazrmwn2xVUFxdNQyWQddG3dfUCwbd3toEzFVRbXDWO5fNNn/VUVYXHhjMkPNacDVD1lefEz2MCy9cwz+5MgFE0aO8+adIkAMmeHnQmq0mTJgCAxo0bAwCmTZsGIO5rWRVTnl8grgbxk3kyDfsGVTz+zu8cG1S86tevn1Cma5PNvsu+zn2++uorAHGVnqgSTdQbBXHXVcycORNAsk03y+TYYH25ZkSvH3oN0PDyQFwJZLt0tol5sH1UL5mOqreu21El36896qmE+6rdtM7S+M2Guvm6/6vnr6eeegoVlcGDBwOIz2bpegQ9L+69T9cjaBBCvX8wnZsHkHy/CvJGAyTbqrP/qAcxDebG+vO6zus5+yzXsHDMsQ1AfGaBabgPrxm89wV5cdOxxpkGnTVwx39+fj7e++C/OPbYY9GudWRtj5cZaTNThaI276gavbfSJp4KfUbUN3/A7EMCUbv31q1aAQAWL1mS5EWH39lnjPJLWj64G4ZhGIZhGEZZIRQOI5SCmp5KmoJIywd3quFUAKgk0MbNVQB0FXpubi6AuH01V2DzbZU2uCQovLvr6UTLVBt2KgD6Zq9+sHVWgLZ6VE5o56dKvbuNijSVPSp9VLuXL1+ecDxYbx4ntVFUbzyusqbqGdUVqi1qE8z28fwxHe2XGdlObZFdjz/qU1j9fhuHTvfu3QEAr732GoD4eWBfoJ0tFamPPvoIQNzHOM+Fej9ylSoq6zxfZ599NoC4hxd+cgxQWeP5Zv9j3diXdC2Hu03t5lk2y2D71FOKKorMh3Wa4UQ1VF/oHOMcdzoeqShyHYxGxlQF3m2Xqtf8VHt09T7h2gW77dH0fvbHOtugijo/1Qe2rkkhfnVSv+FB/qorIpyh4n1Lvf3w3LuzJRyPTMu+qLbcPN9q060zMXrf4XdXuddx4Nq/A3FFXfflWOV23qc1H453P/S+q+q9erzRGUWOTZals2FuOxOOBb3KxH6M3Ne8jOj1p7LMRnF8qQIfSuFhLqq8n96sme/Pk998s/A8jLQnLR/cDcMwDMMwDKPMEErRq0yoAnqVUa8XVAqo4Lr2oKpOcR/avVEB/O677xK+U2WgIqR2rkH+0l2oTKq9LutEFYWqvypmVOmoPlAxZJ0eeuihWFmzZs1KSMNP5vHNN98klMH2UGWgbbHaJgb5X3Z/I6qUaaRN19bZ/c5zwTrz/KmXDyCunmjZflEfjUOjW7duvtv/+9//AgC+/PJLAPG+oB5deC7Yh9zZKdqdU2nWdQ86O6WeUDhW2LdUafdbg8E+zfFG1Y6fQVE9g9aUMDKpu/ZC1WJdr8HZskGDBiXkyciY1113HQrCtfPW2Aw6w6EzB6riqy9w9SzlF4WT6Iwjj7fOGPB8BHmyIe525qEzIwawcOFCAPFxopFIdbbThTPRHJ/81Guozu5oOu0nLNO93/J8Mg/6n2df5bhlnaiOcz+Wyf245oyeofzWe6l9PMvg/UU92rBM5sH7NNvD+zVn1tTTGpC4zuSLBQvQqFEj1KoZ9d8etV2PKfFRlTzkyayVKuypKO6xnf095bCfGKVECbmDLJ6hjWEYhmEYhmEYJUJaKu5E7V71bR1ItudjGip+9IyhERlpH0jULk4VNhdVrlR9Yt60V6SyRCXgxhtvTMiPykGLFi18jkKEdu3aBf7m5jlkyBDfOvBYakRV9RDj2p2qDa1GfiUsi0oajzW3U1Xh/lQ+/KLkqaqrHkOMI0fnzp0BAEOHDgWQPDujs1Gq7ALx88d+R/WeqJ0t+wD7FPsC06mtrOsRg6ok11BQ3df4ARx/bI+ObV5DOKtFzxZuv9S2Dxw4EKlQmNJO7rvvvtj/zzzzDID4mOTxZ3302qXxItSuuCDbdvWlrj6/g9axEI2Cquti/HzGc9uf/vSnpPpUVDjj8ve//x1AfP2Trkly+39Q7A6edz13TMdxo2tc2E849vyi32o/4XjnNV9nhzSKuEaK5YxxKlF0qcbrLBzzVDt6zt7y3sc6qqc1v8jCzIvHQmcvSouK7IGpLFBSi1PLRm8zDMMwDMNIU7bt2IltO3ZGTF7cv3AlIFwJnvwlpfP7KwwvH/Dy8cabb6HqUUcXnt4oF6Sl4s63XSoItJv18yqjKo6+RVMhYpRFfesOivDGOjA/P1WRaGQzVSRZ/379+hXY7sPB/fffDyCu3KhvXvULrDMKbjtV8dPthIonVRQeY/WyExQ1z1WGNKqfqinGkYfnS72R6BoO9SgBJPcr+oTnDBj34XcqbmqnqgqXn59wKs9cI8Ky6QVH+6mu0aDyyO2MfkpcP+60e+c+R5J77rkHAPD0008DCI6QqjMGegzV647OnLm/aRp+8vqn9va6Dknx264zAkYyjEHAWVg9Vu5x1XPB867nn2NGZ5V1lovnnNdeznLyOxAfhyxDZ1l5bdd7N78zJgvTsT38TlXdD42gyjx5j+BaHJbJdunMoUaUZZvcdjItt+mYK2nYL4xSpoRs3NPywd0wDMMwDKOssW37duzatQvZxx9/+DPXRakBi1SN8k1aPrir7bhGaHTt4NRDCd+UdQU9375p9xakPgSV7dp2qh0fUS8p/F1tUksClqmKWtBx0lkDINn/tdoQcrsqPmrfqLbtLIP5uMott9GDgNpvGkceVXI53tinNMqpawuuihz7ApV3jVys6r7asvM7+4Grii1ZsgRAcpRdKmxBfsLZ/zRqsKZ3y2LUWEa4LAnuvfdeAMCoUaMABHvaCfLjrpGPievphec66Lqn0aBVndX1Rzrb6M6UMe8HH3yw8MZXUGjD/MorrwCIRwvlWHO9yuh6LPUKw0+dLQlSjTWyLs+1O8ul13wdM+qljf2HSjoVd85m1a1bN6FOnInzg/Vi2YwaTtQGnnXRcaHrqHSmwt2HZZb2/cds28sI4XCKirvZuBuGYRiGYZQZ1v70E2bPmYPZc+bg088+K7r9ukvUll2/v/7W26havebhrbhR5klLxZ02a1S86Aecb8SuZwpVkqkOqi9aTc/f1aZTva1oOiA5qqrakqp6Xxo2nVoHjY6nUebU1tD9XxV29Vqgqj5RH8RUOpgfFRJXEaHNJM8560e7RKPkoNrE805lm9/5u3qKAeIqH881x4z6feb5pZof5K+f6yhoaw4Aq1evTthH11AQjQTpRp8EktU09b4BxMf/WWed5Vu/I0mfPn0AAI888giA+PGmLT8/dS2Cznjx0509VJ/2PIbqzURVe543jlN+anyMu+666xBabMyZMwdAfG2WzmQBybMiQTMwek6DvM7ovUJnUdz/g7yscLveN3W9F6No85py6qmnAih4dpr1WbFiRUJ71YtUUB2C6uo3E6Ez0XqN0OeLI2X7PmfOHPTo0eOI5G0UnVBGBkIpxJRJJU1BpOWDu2EYhmEYRrrw+axZqFSpElq3ahXZQNW9IDt1/iZBnD6b8wX+8Y9/HKmqGmWctHxwX7x4MQCgdevWAOIKEVUdVzHjGzrftvkWzu9q36YKuyrT+rbu90atERiJKh/8HhSp8kjCMt99910AyWqLfrJNrp9gVWbUI43OThAeKx57RgPkbAjz5X7umgWeY7XLZJ/45S9/meIRMA4VPa9BvozZV+hH3N2Xsyk6ztSGXe1xuT9t4anMMUKpa2+rdrb0KqEzPPyuSrsqlOxrGoXZPRaaR0kSZBs+bNgwAHE1U/3Vcxz6+cIPWgegqFrPGTCeJx4zlk3vVsahMXz4cADAY489BgC48MILAcRnJIF4v+U6L54bzlSrhyZetwub3VKV2W9NGc+z2tHrbJcq15wdYv9h7AXGe6CXKY5lIG4XT+9RHKdcJ8M82a9ZB/Umo9GAWWe2yT0ePEZBtu1MyzVzGq21uMyYMSPWB4wyRDicmv16MW3c0/LB3TAMwzAMI20JUtrVlh1xpR0HgwNQGWUAcwcZzAMPPAAAePXVVwHElSRVtIFku1V94w/yXx5kuxYUUdRVG/m/+pZWBa8sRPtkHXgMWUdV4NWTAJCship6DHX9AJUR5q0r9P3Op3r7ofcB9gmj5GD/5jnh+VOl3V3DQaVK+z7Pp+ZBqCTSU8Tnn38OIHlGyM+PNctv3rw5gHj/Yj/kjIHGbtDZAP6us25AfLyUhTGtqB354MGDASRHjuSnX6wGHcNE1yJwRmzTpk0A4lFejSMDI/QymvHJJ58c+439lWNOfalzu67XInpPVC9EHDfu9Zl9iOOVaamgB8USUC9RVNb5nf2JM2xr165Naif7pkZdZd66fot1YV35nWtXeH2jtzr3+Oi6Hb1vapR0fqY6g1UYqUZmNson5lXGMAzDMAzjCFK/Xj2ckJ0d6CEG+QeB/IMIuX950b8D+xA6sA8jX56AKrXqlV4jjAIJhTNS/isOaam4E9q10ter+gcHkj28aHRHta3z84ABpL5KHgiOwKjKgFvP0kLtddXDBI+HKiNAsqedIHRFPRUO+uRVjzXq6cc9TjrjwT5gHHloK83zwfOoXimotKu3GXcfnmv2L1XcXLtZdzvVr0suuQQAMHv27IQy/WZ/mDeVOFWPtf/quFTlnrhrN9geerwqyzz88MMpp/3zn/8MIHlM9u3b97DWyTCM8s/IkSPx9NNPIzc3Fy1atMDw4cPRtm3bwPRvvPEGBg0ahFWrVqFp06Z48sknccUVV8R+9zwPgwcPxosvvoitW7fi/PPPx6hRo9C0adOkvPbt24d27drhyy+/xBdffIGWLVvGfnv99dfxxBNPYNmyZahTpw769u0bi5NRFknrB3fDMAzDqOj0798fADBixIjYNrpQDDKR0QWkahKmgQT1BZ0uWF0oiDFPmjIS19UokCx8qSvg46PRR1kmX4zdl2ia57A+XJTKPFQUYB4qKLHdNPei+SjNQ10zW5YV5MTCzbt+VGxQDzEklB9tC/fJj7ctdDByvP762jvo27dv7DynI6+99hr69++P0aNHo127dhg2bBhycnKwdOnSmPjqMmPGDNxwww0YMmQIrrrqKkycOBFdunTB/PnzceaZZwKIBJ56/vnnMW7cODRu3BiDBg1CTk4OFi1alOQo4L777kN2dnZsoTP5z3/+g5tuugnDhw/HpZdeisWLF6N3796oVq1a0QWKUIqLU4vqx18wUxnDMAzDMAzjiDF06FD07t0bvXr1QvPmzTF69GgcddRRGDNmjG/65557DpdddhnuvfdenH766Xj00Udx7rnnxl5OPc/DsGHDMHDgQFxzzTU4++yz8corr2Dt2rWYMmVKQl7/+c9/8P777/uuufn73/+OLl264I477kCTJk1w5ZVX4v7778eTTz5Z6hFxg0hrxZ1vn9OmTQMQf+t1zWP4hs/pb35XN1Tch64J+bam0+icwudiGQ3ZDMTVA3X7qMrGr3/966I2+bDDOrz33nsAkkPLq/tM1+xBA+7QFIFpVamhyRAXFvFYMh0X9mnodle9UHOFdFYg0g1deMW+wQWj2dnZAOLnk6ZQ7oIsqmE8j7pQTINwsY9o0Bf2kfPOOw8A8NlnnyXUCYj3G6p2QeqYmsZooDRtv585DrfxulBeuPvuu0u7CkYRcBXC6dOnJ/xGpV2VyKB7JMcYP7ldg2i59z7+xrQ0hVP3iRzXvObzOlC9evWEOqpJHZVZKq4A8PXXXwNINsPTdrIstlNdRQeNe+bjtpPXArZTTfv27duHk07Ijh6UfN/PUB6V9miQw6i6HjrgBHbbF7nHprtp2v79+zFv3rwEN7DhcBidO3fGzJkzffeZOXNm0v09Jycn9lC+cuVK5ObmonPnzrHfa9asiXbt2mHmzJno3r07gIhL2t69e2PKlCm+Abz27duXtL1atWr44YcfsHr1ajRq1CjldqZqv15cG3dT3A3DMAzDMIwjwsaNG5GXlxdbo0Tq1asX87+v5ObmFpienwWl8TwPt9xyC+64445Y3B8lJycHb731FqZNm4b8/HwsW7YMzz77LICyu4YurRV38s033wCIhxt3A74QVezUFo9qHFVhvn1rgCa+YVNNZL5u+HOqBixDw0Bz37IE68RBwDrzWLKdrrs7VczZbioYqr7wGOkCRJ4TKiW6nwt/4zm/+OKLD6G1xqGg4cl5PnlxozKlgXy48Nv9jeda+0CQa1FCtYwKHevEgCwM+OOmbdasmW87tE5B06K6qJy4CzbZDtrHGkZp88MPPwAATjnlFADx8aoKszps4DWf6Wkjzz5OZdtPvWReHDO0BWce6riB1wF1Ncl06rqVD2TuInDWk2XpOFbXjFTL1cZfgy+qQu/ej/i/LsT3PA8NTjopkshLtFkvTGGnup6/Of4gm9HqShiHzvDhw7Fjx44CA7717t0bK1aswFVXXYUDBw7gmGOOQb9+/fDQQw8FOiUJJBxO0Y+72bgbhmEYhmEYZZCsrCxkZGQkCCtARGihv32lfv36BabnZ0Fppk+fjpkzZ6JKlSqoVKlS7CW2devW6NmzJ4DIC+yTTz6JnTt3YvXq1cjNzY15umnSpElxmn3EKBeK++9//3sAiC1yaNiwYew3tcflWzTfytXdoa4sV5s7hW/erhqnZVBNoFJB26uyBOv01ltvAYgfF7U/d+2B2fagY0M1QkNGq12z2gnymPvZuK9evRpA/JwbJcdvf/tbAPFw63p+OWtDW3e1iQfi5zTIdp2oPbl6a9A1Kq5rRkKbVKrx6kVCVXv2bfWmEeTu1J2NW7FiBYD0t0U1yg/z588HEF+3pTNmQWuJdM2HKtEc934uWKl+M0+q2hr4UNd/qYJN9Z/3AraB+W/cuDGWF8c30zDvDRs2JJSt3mEKcz/MOnEtl3tc9Hp17LHHoknjxpEvAUp7XGGPtDG8N6qwb4ko7Pt/iFxDtnz5TSzf7HKiuGdmZqJVq1aYNm0aunTpAiDS36ZNmxZ4zWzfvj2mTZuWEEDugw8+QPv27QEAjRs3Rv369TFt2rSYa8ft27dj1qxZ6NOnDwDg+eefx2OPPRbbf+3atcjJycFrr72Gdu3aJZSXkZER60Ovvvoq2rdvH1uTlTLhFL3KFFNxLxcP7oZhGIZhGEbZpH///ujZsydat26Ntm3bYtiwYdi1axd69eoFAOjRowdOOOEEDBkyBADQr18/dOzYEc8++yyuvPJKTJo0CXPnzsVf//pXABFB5a677sJjjz2Gpk2bxtxBZmdnx14OGjRokFAHvgiefPLJOPHEEwFEXgInT56MTp06Ye/evXj55Zfxxhtv4OOPPy5yG0MZGQgVYu7JdMWhXD2433rrrQDiQUOAuC9Wqma6sl79yPJNn598y6btN5U9fjJfXVXuwjx+/PHHQ2xZycE6No6qB0Feddzf9JhQuaECSxUlyKaQSgjVFHdhCZDoC9i8XJQdeD511onn0y84GfsC06htO/sQxwy3q/Kunpo0PRAfs+rJIkh5V49KRMeAn7r/7bffJm0zjNKEAdP4ec455wCIK8gcB1TgOZ71Oq428ephzL0nqF28rm/ifVfHrarbOiPOawk9RLnrxLiNebN+TKPjmdceXU/DOupM8Pbt2xPyd8vgrF7CWjh6jYl5j4kGOaTSvivqke7H7wAAuxcvBAB8/2HEv/j08y5NUJnLC926dcOGDRvw4IMPIjc3Fy1btsTUqVNj1+k1a9YkzM526NABEydOxMCBA/HAAw+gadOmmDJlSoJHofvuuw+7du3C7bffjq1bt+KCCy7A1KlTA60Aghg3bhzuueceeJ6H9u3b46OPPiowMFRpU64e3A3DMAzDMIyyR9++fQNNYz766KOkbV27dkXXrl0D8wuFQnjkkUfwyCOPpFR+o0aNkhYvZ2VlBbqkLDLhjBQXp5rinoSryv7pT38CEFff+CbGN2SqC1TdqAiq73Fu5/781HRAshcK9aRRltFV/u5q+aC0PBZ6DHlM9Bhx1oPpVdGk6sJFJwMGDCheo4zDyu9+9zsAcVt3qmZUuOj3ltv9bMTVVl3tTNn/uC/TUZFhv+RaFFXVgLg3DZalNryqnPN35qWRIvnJ/r58+fLYvmbbbpRVqN6++uqrAICT6PUkCpVljTRKRZpjkGOP3lv4u+tthQo5x44bU8XNi/df3gt0fKvHMo49mjq491Ju09k69dOukWNZlqr96nGO8Unc64X6sK9SpUpSRNRYJNS8aFv2Rda45W2IxDHZsyzif37lf+YBAJZ264UbbrgBZ8MwCqZcPrgbhmEYhmEYRolhivvhgWrtuHHjAMTfttXDiaoKVJi5nWox91MbPlcBUO8UfIO/7bbbDmPLjgysI9UZqhU8Lm47uY3Hgu1WX/jqlaAwW2h+N6W9bEPlnXD1Pr3MsK+4HhjUdzTHmUY1VT/O6vmC6j7XZHAcunarXN/C8cey/bwV+dVFZ5m4H5U5V3E3jLLOnDlzAMQVc70ec5xo/9frM1Vm3ktdG/egqMRBs11qI85rBz+Zt9rGu7N4ug6G3tuo/lOR1zgjvC5pbAj1tqOqv5sHy8zPzwdCUftsVd6j0Obd2x+dxc+NrN3KXRCZWZ7TZA5uuOEG330Nw6XcP7gbhmEYhmEYxpEkFA4jlIKrx1TSFESFeXCns/333nsPQHKENr51qzqsqjkVACoFVJvdiKKE2/wigJZ1WGceF7UjdLdRdaAKqj5ug/zkqqrK7TxXRnoxcOBAAMBTTz0FADj33HMBJKrgQf7XVYHXNSTr168HEPffTFWNaph6wHDR6Hf8zjw4pqnQqacbXZvy+eefA4i4KzOMdGHo0KEAgCeeeAIAcOGFFyb8zv6ucUd0vROVdl3jBMTHL9c5cV+No8JZ2Zo1awKIj1veTzkGda2L32yYzhywHVTOmadea7g+Rn3Pq/LO9roqP8vnMdq9ezc2bdqEgwcPotmpTSNpMqKe5zIibcuvHPWqVjNiM39U/ciM4ILbu+GBBx7AFTCM1KgwD+6GYRiGYRiGcUQIpWjjHjIb9yKxbNkyAEDz5s0BBEeL0+3qy5YqXUEKAPe95ZZbDm8jSgDWefLkyQD820lVXn3eq99sjVBJmI6fPDc5OTmHsSVGSXPfffcBQCyQBgNdAIhFouNsDaEaRvXru+8iPo6paHH8qaJOZY99zY10p2smWAbVPCqFCxYsABD3PNW0adOE/RmBce7cuQBQLn0sGxWHBx54AADw0ksvAQDOOOMMAHF1m+OD6rjavnM7lWx+AvH7Jn2f81MjpVKtV081Gm9F91O7dHeb5q026qwb16hQcWf71MOcerxy71/aPt4Ldd1MKvB8GEaqVLgHd8MwDMMwjCMCF6mGo66UKzM407GRn2tHhKo3Q03wm9/8poQrZxxRQqH4+S8sXXGK8fwcdFcg6G1GV9qrfTp9udIOlqiK7O571VVXHf4KlxLvvvsugGSlFEhWGaiSbtoUiRBHW0Huy/Rbt24FYDbtFQkGymCfSIg4iOCIhOr5ggo711Wwz9GuHgCaNGkCILl/qg95KupfffVVwu9U2jgLYMqYUR6ZOHEigHj8BY5B9ntdv6W24/TeBMRnT6lEqzc2wvHKWa9atWol5K0z3hpP5YsvvojlxYiwGhVdlXLey3nNYJ56T9cZObbTtXFnNG9V3AGgU8eO0YPByKlRP+4HImnCuyPlVzrxdBjlg+3bt6NmzZrYsuBDHFMj+RkpKf2OnajV8iJs27YtYcYqVYq3tNUwDMMwDMMAAHz08ceYO29eRHkNheFlZMLLyER+ZjXkZ1bD6//7wh7ajWJR4RX3ovL0008DiCuCqgQC5dsGdtiwYbH/aUvILkTbwXvvvbfE62WkJ1Tg2Zeo3lEFY9+i/araparHpksvvTT2PxU3XUtBOHbpsYa27hY/wKiIjBo1CgBw6qmnAkiOZcIxqt9dT2NU1jXitsZOUBtx7sdZWVXBOd6pknOsAkDLli0BxBVy9QJFdZ8zB1TU1UZf16Zp5HPXWxq3sV5sp/v9gvPPjySmX/f8yD5Vq9eEUb6g4r75y49TVtyPa9HxkBV3s3E3DMMwDMM4jHz62Wexh386XujTp09pVskoJ9iDexGp6GpyeZ5NMEoPKnLqS1pVMI2sSqiyuV5n1JsE9w2KtGhKu1GR4UPloEGDAMQ9r3GtiHqC4fhxlWiOU7Uz13HNNWX8neud+Mn0Gs+Bv7sqP7fVrVs3oT1U53UfXa/G7epVhm1RrzpA3Baf+7B+rDe9Yi1atAgA8Oijj8KoAETNo1JKVwzMxt0wDMMwDMMw0gBT3A3DKDXUjpTeYlTB4nb148z96IPdVcXU45MqayyDXmUMw4irw/379wcAZGVlAYiPG6rNHIvuOhON6UFvMdxX4y5wOxV4tS9nfvzkehR3Zo3buO5Mo58zOqt6meGaLOZFrzS8ptD7DMt2befVGxbrTZv9OXPmAIhHqzUqCKFQaq4ei+kO0hR3wzAMwzAMw0gDytyD+48//ojrr78exx57LI455hhcc801MXsxwzASSffxMmjQIAwaNAgHDx7EwYMHsXv3buzevRsHDhzAgQMHYt/37NmDPXv2ID8/H/n5+ahatSqqVq2KrKyshL9wOBz7y8jISPhzfwuHw9i+fTu2b9+OrVu3xuxgDcMwDOOQCIdT/ysGZcpUZufOnbjooohT+gceeACVK1fGn//8Z3Ts2BELFiyILSoxDMPGi2EYRw6aefz2t78FAHSMBhZq2LBhQjqavQBx8xkNZMiFoDRDyc3NBRAc5IimJ3yhXrduHQDg5ptvDqzvpEmTAMTN5mh+o+Z4GhwqOzs7oUwuVqcJELe7C+K5jaxevRoA8PHHHwMAXnjhhcB6GkZxKVMP7i+88AKWL1+O2bNno02bNgCAyy+/HGeeeSaeffZZPPHEE6VcQ8MoO5Sn8UKPLkOGDAGQ7J+dN0o+EDDKIz1eaHogfmPmDVdt3tesWZNQtmEYhmEcKl4oDC8FjzGppCmIIgVg+vDDD/Hzn/8cb731Fn75y18m/DZx4kTcdNNNmDFjBtq3b39IlWnbti0AYPbs2Qnbc3JysGLFCnz77beHlK9hlAZ79uyJheP+4osvYoubNm/ejDPOOAONGzfGJ598khQOPFXK43jhg7s+ZKf64O7OMqhSxn25SI1BXApS8QzDSITuIs8++2wASAggc/zxxwOIL/jkWKMSz8cNXWzO7VTDN27cCCC+MLQoY3T8+PEA4otJubhWVX1ed1lX3c7rB+v6008/xcpgPRcuXAjA3D1WdBiAadPi2SkHYKp9ettDDsBUpMf+Tp064aSTTsKECROSfpswYQJOPvlktG/fHvv27cPGjRtT+iP5+flYuHAhWrdunZR327ZtsWLFitgqcMNIB6pVq4Zx48bh22+/xf/7f/8vtv3OO+/Etm3bMHbsWGRkZNh4MQzDMAwjJYpkKhMKhXDzzTdj6NCh2LZtW8zN0oYNG/D+++/HHk5effVV9OrVK6U8+aa9efNm7Nu3L/bG7sJta9euxWmnnVaUKhtGqdKuXTvcd999ePLJJ/HLX/4S69atw6RJkzBs2LBYaHEbL3Huv//+hO+PPfYYgGQFnm3UAC1uYBZuU9eSfKFxFTTDMFJD1eVHHnkk9n9OTg6A+DhUZV2Dn6n9OdNxjN5yyy1Frh/V+bFjxwKIu6RkWawbrym8Pmgdea2l6j9r1qxYGQ8++CAAoGvXrkWun1GOKaEATEW2ce/RoweGDBmCyZMn4ze/+Q0A4LXXXsPBgwdjAyYnJwcffPBBkfLl4FD/qED85sw0hpFOPPTQQ3j33XfRs2dP7Ny5Ex07dsTvf//72O82XgzDMAzDSIUiP7g3a9YMbdq0wYQJE2IP7hMmTMB5552HU045BUBEDfNTAguC9mgFLTJzAyAYRrqQmZmJMWPGoE2bNqhatSpefvnlmPoD2HgpiIEDByZ854Lb6tUjdoRUxXg8XQ8XVPGorFFpW7x4MQDg3nvvPVLVNowKA9VnALjjjjsAAGeeeSYAxGYVacdLm3fC8UszQLqypSeb4kC1nh5euB6GNu8hCYKjQZSWLVsGAPj6668BAKNHjy52nYxyTllV3IGI6t6vXz/88MMP2LdvHz7//HOMGDEi9vuePXuwbdu2lPKqX78+AOC4445DlSpVfKevuY1umwwj3XjvvfcARB6qly9fjsaNG8d+s/FiGIZhGEYqFMmrDNm4cSOys7Px+OOPY8+ePXjsscewdu3a2Jvs2LFji2yzCwBt2rRBKBRK8pJx6aWXYsWKFVixYkVRq2oYpc7ChQvRpk0b3HTTTViwYAE2btyIr776KrZGxMZL6jz11FMAgMsuuwxActh113SIijtNh3744QcAEZeZhmGUHH369AEQH4tUuzl+n3vuuRKrS79+/QAk27JzpnLUqFElVhejfECvMhuXfYFjatQoPP2OHcg69ZxD9ipzSIp7VlYWLr/8cowfPx579+7FZZddFntoBw7NZhcArrvuOgwYMABz586NectYunQppk+fjnvuuedQqmoYpcqBAwdwyy23IDs7G8899xxWrlyJNm3a4O6778aYMWMA2HgxDMMwDCM1DklxB4A333wT1113HYDI4tTrr7++2JXZsWMHzjnnHOzYsQP33HMPKleujKFDhyIvLw8LFixAnTp1il2GYZQkgwcPxqOPPopp06bhoosuAgA8/vjjGDhwIP71r3/hiiuuOOS8K+J4oTJ36aWXAogvwOVlzLWhpbeI3bt3A4j7u7/rrrtKpK6GYRhG+SemuC//MnXFvWmLkvHj7nL11VejVq1aqFmzJn7xi18cajYJ1KhRAx999BF+9rOf4bHHHsOgQYPQokULfPzxx+XyIcQo38yfPx9PPPEE+vbtG3toByKROtu0aYPevXvHQnofCjZeDMMwDKNicciK+8GDB5GdnY2rr74aL7300uGul2EYRiCLFi0CkOxVx/XjTht32vpzhtAwDMMwDhcxxf3bhakr7qecXbI27gAwZcoUbNiwAT169DjULAzDMAzDMAwj/Smr7iBnzZqFhQsX4tFHH8U555yDjh07FqsChmEYRaV58+YAgPvuuy9huzuBSI8VQ4cOLbmKGYZhGMYRpMiP/aNGjUKfPn1Qt25dvPLKK0eiToZhGIZhGIaRNnihcMp/xeGQbdwNwzAMwzAMoyJDG/cN3y1K2ca9TpPmJW/jbhiGYRiGYRgGIrbr4SNv4168vQ3DMAzDMAzDKBFMcTcMwzAMwzCM4lBCXmVMcTcMwzAMwzCMNMAUd8MwDMMwDMMoDqa4G4ZhGEbFJD8/H6NHj0bLli1RvXp11KtXD5dffjlmzJhR2lUzDKMUsQd3wzAMwyhj3HvvvejTpw/OOussDB06FH/4wx+wbNkydOzYEbNnzy7t6hmGoVBxT+WvGJipjGEYhmGUIQ4ePIhRo0bhuuuuw9///vfY9q5du6JJkyaYMGEC2rZtW4o1NAxD8UKhlIIreaFQscoxxd0wDMMwCmDVqlUIhUKBf4ebAwcOYM+ePahXr17C9rp16yIcDqNatWqHvUzDMNIDU9wNwzAMowDq1KmToHwDkYfru+++G5mZmQCA3bt3Y/fu3YXmlZGRgVq1ahWYplq1amjXrh3Gjh2L9u3b48ILL8TWrVvx6KOPolatWrj99tsPvTGGYRwZSmhxqj24G4ZhGEYBHH300bj55psTtt15553YuXMnPvjgAwDAU089hYcffrjQvBo2bIhVq1YVmm78+PHo1q1bQrlNmjTBZ599hiZNmhStAYZhlBvswd0wDMMwisArr7yCF154Ac8++ywuuugiAECPHj1wwQUXFLpvqmYuNWrUwBlnnIH27dvj4osvRm5uLv70pz+hS5cu+OSTT5CVlVWsNhiGcZgJhSJ/qaQrTjGe53nFysEwDMMwKggLFixAhw4d0KVLF0ycOLFYeW3btg179uyJfc/MzMRxxx2HgwcP4pxzzkGnTp0wfPjw2O/Lly/HGWecgbvvvhtPPvlksco2DOPwsH37dtSsWRPrf1yDY445JqX0dU9ogG3btqWUXrHFqYZhGIaRAlu2bMG1116LU089FX/7298Sftu5cydyc3ML/duwYUNsn379+uH444+P/f3qV78CAPzvf//D119/jV/84hcJZTRt2hSnn346PvvssyPfWMOoQIwcORKNGjVC1apV0a5du0NzuWruIA3DMAyjbJCfn4+bbroJW7duxX//+18cddRRCb8/88wzRbZxv++++xJs2Llodd26dQCAvLy8pP0PHDiAgwcPHmozDMMQXnvtNfTv3x+jR49Gu3btMGzYMOTk5GDp0qWoW7duaVcvCXtwNwzDMIxCePjhh/Hee+/hP//5Dxo3bpz0+6HYuDdv3hzNmzdPSnPqqacCACZNmoTLLrsstn3+/PlYunSpeZUxjMPI0KFD0bt3b/Tq1QsAMHr0aPzrX//CmDFjMGDAgJTz8ULhFP24m+JuGIZhGEeMr776Co8++ih+9rOfYf369Rg/fnzC7zfffDOaNGly2Ly9tGrVCpdccgnGjRuH7du349JLL8VPP/2E4cOHo1q1arjrrrsOSzmGUdHZv38/5s2bh/vvvz+2LRwOo3Pnzpg5c2Yp1iwYe3A3DMMwjALYtGkTPM/Dxx9/jI8//jjpd3UVeTh4++238cwzz2DSpEmYOnUqMjMzceGFF+LRRx/FaaeddtjLM4yKyMaNG5GXl5cU7KxevXpYsmRJkfLavmNnSvbr23fsLFK+ij24G4ZhGEYBdOrUCSXtgK1atWoYNGgQBg0aVKLlGoZRNDIzM1G/fn00jZq4pUL9+vVjwduKij24G4ZhGIZhGBWOrKwsZGRkxBaEk3Xr1qF+/fop5VG1alWsXLkS+/fvT7nczMxMVK1atUh1JfbgbhiGYRiGYVQ4MjMz0apVK0ybNg1dunQBEPEgNW3aNPTt2zflfKpWrXrID+JFxR7cDcMwDMMwjApJ//790bNnT7Ru3Rpt27bFsGHDsGvXrpiXmbKGPbgbhmEYhmEYFZJu3bphw4YNePDBB5Gbm4uWLVti6tSpSQtWywohr6RX3BiGYRiGYRiGUWSK5wXeMAzDMAzDMIwSwR7cDcMwDMMwDCMNsAd3wzAMwzAMw0gD7MHdMAzDMAzDMNIAe3A3DMMwDMMwjDTAHtwNwzAMwzAMIw2wB3fDMAzDMAzDSAPswd0wDMMwDMMw0gB7cDcMwzAMwzCMNMAe3A3DMAzDMAwjDbAHd8MwDMMwDMNIA+zB3TAMwzAMwzDSAHtwNwzDMAzDMIw0wB7cDcMwDMMwDCMNsAd3wzAMwzAMw0gD7MHdMAzDMAzDMNIAe3A3DMMwDMMwjDTg/wODALerD9fmeQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimation of group-specific spatial intensity functions\nUnlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a\ngenerative regression model that estimates a smooth intensity function and\ncan have study-level moderators. It's developed with a spatial model to\ninduce a smooth response and model the entire image jointly, and fitted with\ndifferent variants of statistical distributions (Poisson, Negative Binomial\n(NB) or Clustered NB model) to find the most accurate but parsimonious model.\n\nCBMR framework can generate estimation of group-specific spatial internsity\nfunctions for multiple groups simultaneously, with different group-specific\nspatial regression coefficients.\n\nCBMR framework can also consider the effects of study-level moderators\n(e.g. sample size, year of publication) by estimating regression coefficients\nof moderators (shared by all groups). Note that moderators can only have global\neffects instead of localized effects within CBMR framework. In the scenario\nthat there're multiple subgroups within a group, while one or more of them don't\nhave enough number of studies to be inferred as a separate group, CBMR can\ninterpret them as categorical study-level moderators.\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nimare.meta.cbmr import CBMREstimator\n", - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", - "cbmr = CBMREstimator(\n", - " group_categories=[\"diagnosis\", \"drug_status\"],\n", - " moderators=[\"standardized_sample_sizes\", \"standardized_avg_age\", \"schizophrenia_subtype\"],\n", - " spline_spacing=10,\n", - " model=models.PoissonEstimator,\n", - " penalty=False,\n", - " lr=1e-1,\n", - " tol=1e1,\n", - " device=\"cpu\"\n", - ")\n", - "cres = cbmr.fit(dataset=dset)\n", - "plot_stat_map(\n", - " cres.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", - " threshold=1e-4\n", - ")\n", - "plot_stat_map(\n", - " cres.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", - " threshold=1e-4\n", - ")\n", - "plot_stat_map(\n", - " cres.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", - " threshold=1e-4\n", - ")\n", - "plot_stat_map(\n", - " cres.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"depression_No\",\n", - " threshold=1e-4\n", - ")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures correspond to group-specific spatial intensity map of four groups (\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"). Areas with stronger spatial intensity are highlighted. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups` can be generated by `create_contrast` function, with group names specified. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type5 = index_2\n", - "INFO:nimare.meta.cbmr:type1 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type3 = index_5\n" - ] }, { - "data": { - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from nimare.meta.cbmr import CBMREstimator\n\ndset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\ncbmr = CBMREstimator(\n group_categories=[\"diagnosis\", \"drug_status\"],\n moderators=[\n \"standardized_sample_sizes\",\n \"standardized_avg_age\",\n \"schizophrenia_subtype:reference=type1\",\n ],\n spline_spacing=10,\n model=models.PoissonEstimator,\n penalty=False,\n lr=1e-1,\n tol=1e1,\n device=\"cpu\",\n)\nresults = cbmr.fit(dataset=dset)\nplot_stat_map(\n results.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_No\",\n threshold=1e-4,\n)" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures correspond to group-specific spatial intensity map of four groups\n(\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\").\nAreas with stronger spatial intensity are highlighted.\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\nIn the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with group names specified.\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from nimare.meta.cbmr import CBMRInference\nfrom nimare.correct import FWECorrector\n\ninference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_groups = inference.create_contrast(\n [\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\"\n)\ncontrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate chi-square maps for each group\nplot_stat_map(\n results.get_map(\"schizophrenia_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"schizophrenia_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"depression_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"depression_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_No\",\n threshold=scipy.stats.norm.isf(0.05),\n)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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MH374Ab169cLhhx+OdevWJZ3byuS7777D888/Hxn9ds6cObjzzjtx//3345NPPsHMmTORk5ODE088Ee3atcN3332H66+/voJLLYQQQojqxBdffIEhQ4bEv99yyy0A/OjykyZNwu23346dO3fi2muvxdatWzFgwAC88cYbpTJhyYzFkGkE3dD9EPOdWZeScl2c+tRTT+HHH3/EqFGj0LdvX+Tl5eGDDz7AqFGjcPHFFyftv3HjRvTv3x/XXHMNLr74YvTs2RP9+/fH+vXr8f333+PPf/5zqNtHz/Nw/vnn4w9/+AMuvfRStG7dGmvXrsWTTz6J++67D/n5qS8QYHrDhw/HtGnTcO2116J3797o1asXtmzZgh9++AEPP/wwXnzxxfj+r7/+Opo2bYohQ4bgmGOOQdOmTbFx40Z89NFHeOqpp/Dqq6/G9500aRL279+Pk046CX369EGjRo2wbt06/Pvf/8af/vQnvPfeeymVcfLkyVi2bBlGjRqF/v37o2/fvli7di2eeuop3HfffSnb9FcUo0ePxqWXXopatcKb2NixY/Hf//4XN998M3r37o26deti1apVePDBB/HAAw8UOwshhBBCCFEUgwcPLtLhRSwWw+jRozF69OgKLFXZiHkpuvD48ssvE8waKgvP87BixYqUXSmK6s/cuXPRq1evyi6GEEIIIWoYubm5aNSoEUZktEOdWPEW6Hu8AowvWIVt27ahYcOGJc6v3GzchRBCCCGEEAeOSvPjLoQQQgghRHWgRDbuZUADd4ETTzwRV199dUr7Tp8+PdI/uxBCiNSYNGkSrrzySsyZMwfHH398ZRdHVEPYxkhmZiZatmyJU089Fffdd5+ik6cpaTdwty4YRdnp0qULrrjiipT2XbFihQbuQgghRJowevRodOzYEXl5efj0008xadIkfPTRR1iwYEGpvKeIcDJj/l+x+5Uxn7QbuIvy59lnn8Wzzz5b2cUQQgghRDlzxhlnxGd1rr76ajRr1gwPPvggXnvttdBglqJqo8WpQgghhBA1hIEDBwIAli1bVsklqV7Qxj2Vv7IgxV0IIYQQooawYsUKAEDjxo0rtyDVDJnKCCGEEEKIMrFt2zbk5OQgLy8Pn332Ge655x7UqVMHZ511VmUXTZQCDdyFEEIIIaopp5xySsL3Dh06YPLkyWjbtm0llah6IneQQgghhBCiTDz55JPo2rUrtm3bhgkTJuCDDz5AnTp1KrtYopSkPHBv1qwZsrOzkZeXdyDLI0SJyM7ORrNmzSq7GEIIIUSVpE+fPnGvMueccw4GDBiASy+9FIsXL0b9+vUruXTVhxhS8/hSVqfmKQ/c27Vrh8WLFyMnJ6eMWQpRfjRr1gzt2rWr7GIIIYQQVZ7MzEyMHTsWQ4YMwRNPPIFRo0ZVdpFECSmRqUy7du00SBJCCCGESFMGDx6MPn364LHHHsPIkSMVhKmckI27EEIIUc2ZMGEC3njjjaTtN910Exo0aFAJJRI1gdtuuw0XXHABJk2ahOuuu66yiyNKgAbuQgghRCUxfvz40O1XXHGFBu7igPHzn/8cnTt3xiOPPIJrrrkGmZll9S4uKsqPe8zzPK+MaQghhBBCpMSzzz4LAGjatCkAoG7dugm/c1iyc+dOAMDPfvazlNOeMWMGAOCggw4CAMSM6cLu3bsBAJs2bQIADB8+vERlF8KSm5uLRo0a4a66nZAdK355ap5XgHt2f49t27ahYcOGJc5PirsQQgghhBBlwFfcU7FxLxtS3IUQQghR7rz44osAgFatWgFA3Hd4RkZGwidV8YKCgoTj+Z2f8+bNAwCMGDEivg9NjY499tjQtAm/c8hj096zZw8AYN26dQCAiy66qER1FTUXKu73HdQJ2bHih+V5Xj5+v7P0insqLieFEEIIIYQQlYxMZYQQQghRZh5//HEAge16x44dAQC1a9dO2I8LIWmHnpWVBSBQwwlt3HNzcwEA7du3BwDcfffd8X369OmTcCzT5Cehqr9v376EtPPz8xPKQJfXzz//PIDAFv7GG28ssu5CVJQ7SCnuQgghhBBCpAFS3IUQQghRJC+//DIAoEWLFgAChdq1Sz/kkEMSjqHKzU+q2zxm//79AID69esDAGrV8ockeXl5AJJt4Gkjz/3dbdyHxzAtBhdiXvQqQ+WdcBaA6XCWgHWaPXt2fF/mwTQ2bNgAADjvvPMgai4ZKbqDLKtiLsVdCCGEEEKINKDSFfdJkybhyiuvxJw5c3D88cdXdnFENYPti2RmZqJly5Y49dRTcd9996FNmzaVWDohhKiaTJs2DQDQqFEjAIHtN9VmKtRU0YHAe8yaNWsABOo2sTbsVMGpcjPNXbt2AUhW3qmCu77ZuY378BhrR89yMk9+Ev7OMnNWoHXr1gACZd9N29rFv/322wCAbdu2AQDOP/98iJpDRdm4V/rAXYiKYPTo0ejYsSPy8vLw6aefYtKkSfjoo4+wYMGC+FSqEEIIIURVRgN3USM444wz4jM6V199NZo1a4YHH3wQr732Gi688MJKLp0QQlQNZs2aBSBQz63aTZWZn1THgcCunPtSvea+/J1qNvejmk0VnD7VXTUfCPf3biOj8hibBvNgnlT/WT9rA8/9WGZ+AkC9evUABDbu/KS6z0iwPJeDBg2CqP5kpmjjXtYATLJxFzWSgQMHAgCWLVtWySURQgghhEgNKe6iRrJixQoAQOPGjSu3IEIIUQWg1xSaDlI1pppso5pSqXZtv/fu3QsgsIunr3RiFXnef2kzTvt05km13Krq9rsLj2EaVNJZTuZJRZ5l5n6sJ+vAsrn1tFFZeQz34QwD1Xue2/79+0eWW6Q/FaW4a+AuagTbtm1DTk4O8vLy8Nlnn+Gee+5BnTp1cNZZZ1V20YQQQgiR5mhxqhDlyCmnnJLwvUOHDpg8eTLatm1bSSUSQgghhCgZGriLGsGTTz6Jrl27Ytu2bZgwYQI++OCDhKlPIYSoicyYMQMA0LJlSwDBAssGDRoAALZv3w4g2ZSE0CzEPZb70qSEn/y9WbNmAALTEqZJ8xUuHKVJDL/T1IbmK+62qGOYJk1/aArEwEo5OTkAApMZ1pvmPCyzW0/CctsAUUyD9d6xYweA4Fz/7Gc/S0pLpD+ZSNFUxit+n6LQwF3UCPr06RP3KnPOOedgwIABuPTSS7F48eKEKHxCCCGEEFUVDdxFjSMzMxNjx47FkCFD8MQTT2DUqFGVXSQhhKgUKFxYt4hUrJs2bQog0e0jECjQ7kJNKs9UwbnYlCp3ixYtAASKuVXFN2/eDCBYWGrTtQq3u43l4Hd+Mk0q7lHKu10gy9/tglo3bQvdRLI+duZBIlH1JiNFG/eMFPYp8vgyHS1EmjJ48GD06dMHjz32WPxGLYQQQghRlakyivuECRPwxhtvJG2/6aab4vZiQpQnt912Gy644AJMmjQJ1113XWUXRwghKozXX38dQKASUx0mtMumQn3wwQcDKNoVI228uQ+VZqrW/E6lncr1+vXrE/Kk4k4VnMdbG3ggcLlogzhZt5DMo127dqFpM+CUteVnXq5dvYX78FjWw7qa5HnhuZdXs+pFyu4gyya4V52B+/jx40O3X3HFFRq4iwPCz3/+c3Tu3BmPPPIIrrnmmiJvzEIIIYQQlU3Mc19dhRBCCFFt+eijjwAESrNVqGm7Tm8qtEvnd6rGRSnvxcFhBwM0LV26FACQm5sLIFDWKaZQqaed/Y8//hhPq02bNgCCmQMq5awPlfiGDRsCALp06RJan7LUw9Znw4YNCd+jZhB47gcMGFDqMojKJzc3F40aNcKzzbqhXkbxAuCugnwMz1mMbdu2xdtlSZCNuxBCCCGEEGlAlTGVEUIIIcSBgWvIaKtOhZp22Pykuk2lmt5UopR216sMsftQ/bYT/PQRz7ypllMNt+aL1mYeCDy12LgczNPWj3kyD+v/3eYZZpQQ5t0GCM4Vy0L7e85i8Hd+cgaB1+b0009PykukDzXOxl0IIYQQQoh0JDNFd5Cp7FMUGrgLIYQQ1Rwq01R/6S2mUaNGAJI9n9ApBNXtKFtw16d5Kmq1u92q+CxjlKrPsrv+0O0xLI/1vx4VWdXmFVU2KvhhWP/19H1v8+bvVP9p+y7/7qIkaOAuhBBCCCFEGciIxVIKrlTWAEwauAshhBDVlCeeeAIA0L17dwCB/TVtvWnrTtWXSjzV7bJ4XbG+0K3azbIwT6r+UWo5vbRwfxfWg3lYH+pM09rC2zKxzKVxD2zXB/A7bd3p35227cyLZeW1uuGGG0qct6g5aOAuhBBCCCFEGYhlxhDLKP5Ftywvw4AG7kIIIUS1hX7YqVZHqdlUielthVgluiivMlF24FEDFW6nnb3Ni59UqMPyJLQXp/LO+nHf4vzPR3nCCcO163fLHXVuWDbr151KO7fzWglRFBq4CyGEEEIIUQYyMmPISEFxl427EEIIIRJ46aWXAACtW7cGECjtjEpKu2uqwrTptjbfVIet6k07cyrbbhqpwv2pbm/duhVAsl06ycvLS6iDu431YPRVmwb915fGdt0tIxAo5TyHhGq/XR9g62nPffPmzRPKzGt34YUXlqqsonqjyKlCCCGEECKt6dChA2KxWNLf9ddfH7r/pEmTkvbNzs4ufQEyMxBL4Q+ZZRt6S3EXQgghqhkNGzYEkOy33XpV4XbrqYXqMBXsbdu2AQjsu5kOfZa7aVj13sLtLJudBYiyp+d+nAVwt9l62X1L6i2HMw5WJQeATZs2JeRB5ZyKOdV9bmfe9poQni/mwf1EyZgzZ07CzMiCBQtw6qmn4oILLog8pmHDhli8eHH8e1kXjlYEGrgLIYQQQoi0hiZH5IEHHkDnzp0xaNCgyGNisRhatWpVLvnHMmKIZabgVQaycRdCCCGEA9VeftJbDJVpqr52P+t7nXA7FWx+pxIflqZVL62Szv1pG04bdyrQVpmmEu3mGaViU3llPaz9uS2T9VTD46iiu3lSGWceNk3rHYdpc3bCnksq91bBF6Vn7969mDx5Mm655ZYiVfQdO3agffv2KCgoQK9evXD//fejR48epcozIzOGjBQG7hllHLirdQghhBBCiGrD9OnTsXXrVlxxxRWR+3Tr1g0TJkzAjBkzMHnyZBQUFKB///5YvXp1xRW0FEhxrwReffVVAECDBg0AJK84t8rH5s2bAZRshTlXpTdp0iQ0TZsno+ide+65Ja6PEOnE1KlTASTbsFq/zVFRH9mXhg8ffuALK0QJePzxx+P/d+7cGUCg6lLN5ne2Y0ZMpRpsVXPaZ9OTCj+J6/klSqW3v1sFlM8pljFKyWberq95phmlpPNZxzwsVh2P+t2tp7Wnp2cdniueO6va0zaeEVSZJ8vOa8P93et54403hpZPhPPMM8/gjDPOiHtVCqNfv37o169f/Hv//v1xxBFH4C9/+QvGjBlT4jxjGRmIpTBbEjP9pKRo4C6EEEIIIaoFK1euxDvvvINXXnmlRMdlZWWhZ8+eWLp06QEqWfmggbsQQghRDXCVbDvLSrts2lFbBZ37MXonFWaqy1z4Z5VpN0/rd91GK42axaLi3KZNGwCBJxtut95mXBtwq1pT9aZ6bW3grZ96O5PG7VbJp6cYIIj0SqxNv1XaN27cCCCYUeAMN5V6q+BHrREQqTFx4kS0aNEC//M//1Oi4/Lz8/H111/jzDPPLFW+FWXjroH7AYTmKuzwnJI89NBDASTfIOwNiHCK7/333wcADBkyJDJP7tOlS5eEtImdJuWNgWWcPXs2gGAqjzcaBYIQ6cYLL7wAIAjQYgcN9pNYkxn7Oxk/fnz8f/vwv/baa8tUdiGEECWnoKAAEydOxPDhw5NeJIcNG4Y2bdpg7NixAIDRo0fjhBNOQJcuXbB161Y8/PDDWLlyJa6++urKKHrKaOAuhBBCCCHSnnfeeQerVq3CVVddlfTbqlWrEmZrtmzZgmuuuQbr1q1D48aNcdxxx2H27Nno3r17qfKOZcodZNry7rvvAgA6duwIIFDjqOTZ6UE7HWbfEjmVySm/v//97wACVRwI1Hw2ODZONxy1myexU3p2IU/jxo0T6nTyySdH1luIymLy5MkAEhfO0STAKujsX1HT21GKu13sFgb3/fOf/5yQR9TicDtdP2LEiKIrKkSK8F5v2xpnXWl+QrMPa0IT1c6j2q67Leq7fQbaPsiolba/cNasKJgGTWW4gJXPwCjXlLYeUXVwB3xRx9hjeS6tm0eee1tmOzYQJee0006LnCWdOXNmwvdHH30Ujz76aAWUqnzRwF0IIYQQQogy4CvuKXiVQbgHo1TRwL2ceP311+P/28U9fHvmG751+0hFwH7nWyMVDi7Y4SIhNyCEXThEBZ6LXvgmbxci8bt1/cXvVGfoutKt51lnnVXMWRHiwMBZJ84UsZ26ypxVymwY9ijFnTBtYhU7VxWzM1dWtbczWm7IdrcsdP9mFT13Fo5pyI5eWKyrRiB5xpfqr3VHbGd6bVvmcdyfz5ai3EFyX6tuM02bJ/sB+xb7M/tL2KyYnUmwi0ptMCOWhfWz6r49X2FuInmsndXjObGzFawnj+O537VrV0IeUbPtQrho4C6EEEIIIUQZkFeZNOGJJ54AgITFDFHhnK3Kzf2s4mFtCC1htofF2SPaMvHN3+Zp1X8qAtyfdXHrfsMNN4TmLURZobJONc0GS7KqoKuORQVYiuoTxSltUf3Vzcvaw9s0rDu7KHdv1n2eq/6zfOx/LMd1110XmpaoOdxyyy3x///9738DAPpt/Sq+beeqHwEAGVl+m8lu2gjIAWp3ORrYCyw5qEu8fXGGlzO7dqaY6nLLli3j6Ue5NSR25tc+t2x/YL/n/kUp7tyHx9Be3qZp9+css/3d9mGq6wCwfv36hG127QrXDdDO3rq15HY+X+0MHNN1r6eo+sRiMcQyUlicWlC2gXvxxjhCCCGEEEKISkeKe4pMnDgRQKAoWCV6586d8X1pX863aypiVKutTR1/t/ZtxNqlW/tZd5tV9V2FvKg8WCb+zvqxDlQh3Hqy7n/7298S8qJacOWVV4bmJUQUVNitbatVpKJsZsOwSrq1bbVquU3LqmlWsS8Kuw+PtfeAqHoVlYe1q6cCTzQTVrOhYj57zNT4tmkLcxL2OaOlr/z2uHABAKDzz/11S/MP6pbkZYb3fjuDvGnTpnh6XN9l+4qF25mH9X5GrPrN/uCq31HBnaLSilL7ozzg8NOtpw1mxecllXQew3PG56pdX2PPA+vAayfSi4zMDGSksDg1wyubZi7FXQghhBBCiDRAinsEEyZMAAC0b98eANCzZ08AwRszVa4lS5YAANauXRs/lrZ1XDnOt27auVG1t/au1u6Vb/XW922YFwz7G4+hUkk7Ph5jfVnzk2/+LDPTYYhmt56tWrUCABx22GEJaTIP+n5fuXIlAIQGRBACAJ599lkAQZu3s0xWcWP/Ky4Kaiqwjds0rH1uURFWrUpvyxnV3+x+3B7V58OOjSr/n/70JwCBqicFvmbBOB8zjcru8p/1/n3+P49/CgD4/cH+bHHP81oAtYHXl++KR8+2NuN2dhYI+i0V9Kh1Inwu8Xembdu99UpDNm/eHP//kEMOSdgnakaM/cZ6UosqK8vC/d168jfer/i8pCrPSOTNmjVLqC/ztN6w+Mlr5sZoEelDygGYPNm4CyGEEEIIUe2R4m6g8te5c2cAwepwq5RR1eJ+CxcujKexZs0aAEDr1q0BBHZvfDu3/m+j/Mxau14SFlUtKtKaXWEfFcmRn9Z2j0oC6+R6DWDdrT0j02ratGlCPXluhw8fHlpWUfN45plnAATtjUqUbZdRappV6FxVPCq6oU3Lrg+x7djawlrb1zCivMfYdS1RaRTlWSrKPp7YGQN+lxeamsXVV18NAPjimntTPmbpP78GADQ97igAQEHBoXGvMdbWPUzZjoo6zLbItSv0ysLf2f/5zLAxTOz6E1dxtz7hoyK7bty4EUDgJYfb+ZzmMzJKeXefx1TfeS44o83xAp+jy5cvBxBEIOfzk2Xg8db+XjEa0hMp7kIIIYQQQog4UtwLefnllwEAbdu2BRC8QfMt3kZE4xs335RpZwcE6jTt3WhDR1XBenAh1sdtlN1sUX7crV2f9aRhbd2tzR3LSHWBdeD+VCfc8luvOTbSHvPkueW5Pu+885LqIao3zz33HIBAebMKe5SHCKuClcS23fYja0ce5V0iSiUnrm/1KC8wdnuUlw2SiqcaEnVOrJ95a9vLcj/11FMJx//qV79KOW9RM2jevHm8nXHm2KrlLtZrEpVn2snb5w/bItPkc8cq77at03uNS5RXmXXr1gEIVHr73OKz3NqncxY7rM/a5ycVdW6nZznWg2OCZcuWAUiOjh41eybSi4ryKqOBuxBCCCHw4jx/AN17+1Z/Q5PKK4sQIpwaP3B/4403AABt2rRJ2G4jifI738KpPtBWzY2+1qSJf7ejykDl2fq/tbZ41ge79Zxhbd9ddc6u0reKBtO0tu5W5bdR4riddXLryWN5LqwiaWcauB8/ee5PP/10iOrLpEmT4v9brzE2eqlVx63HFBu9kX3IeooIw7Z5tler9lus7+UwpTFqn6jy2PpE+Xu39S+KoiK7hqVpVT4q8G5ZRowYUWy+omoyfvz4MqdRUFCQ1PasIs1nA5C8PmT16tUAkvsBn4X0nsLjcnJ8DzhRsU2s33N3m1tmIHg2M02Wl2VhGXhPovLOMtGjHNN368k8mGZU5GRy6KGHJuTBMtl7EZ+ZvHbqf2lGijbuKKONe40fuAshhBACGNzMH8xmNmpaySURIv3IiMWQkVH8oDyjBCaRYdS4gfs//vEPAMHbM32RRylmdju/W88wrlcXriznW7drCxuWh1XfrPptVXMq+a4Swm0sV5SiHqXwWUWEeTZs2DChTm49rf1/lCcNHmP95VL9p7932iBecMEFEOkPlXbXJ3GUTXqUN4ooBct6R2IbK8pW1P5mbVitmm9V/ai1KWHlt56W7OyarX+Uoh7mQSZq36h7VdS5i/LU46Yv5S994bPtv2VIIysrK/4sYBvnfZz227Tn5v5AoLRbBZ6KM58rdtaLbZN26VxTZdeZUMF2t9n1MkwjaqaN23l/smtEaJfOtVluPQnt4m1fsvXi85cz+3zWMU+q//TgI0RR1LiBuxBCCCGAzg9dj6OOOgrTz/Bdg3bq5Q/2a7X1Xf1id2WVTIj0I5aZgVgKi1NjBVqcmhK0p+YbLaOa2uhpUZHaoqIq0uabXjKA4M2fb9HE2qBa5czaqfO79RvNt3lXNbd+oa0CyN+Zpo1yalU3a2MYZjfLulsvHbZedhbAzixw9oNqjWzf0xv6Zqe65rbFKEXcqsVRKrhdw2Hbq+truThPDVbls8o6sfeIMGz/Yd9nm7YzXzaiqp2Vs3m7dYny/W6VRWL7o/29uHUGAPD0008n5CE/01ULziS73s0YtbMoateuXeS6kNq1ayfdv9m2w54JnPktKsYBEDwv+RymzbeFEbuZF4+jmu6mwXLyGAv7gY1oHrUf68A6cW0WEMwWc1aD9zp7f7Jrb6KitXbo0AFAoOrz+I8++iieJ6OWa0Za1JiBuxBCCFHdmTzw8pT3/Xbko/jW+d7x9GMAAHN2NyryRVUIkUxGZgwZKSxOzSiQjXuRvP/++wACJcIq5tZG1iruVpUjVllz3/KjVOooRY80//pfAIDaC/worPt2+vOUWQf5ynbDw7sAAOocMwAAMKEwEhwQqCu82bJcNu8orOpI9cIqg666wjyi7OWtkmfPuVUZrT09r92QIUOKLLuoGvztb38DEKhiVg0HopVl9jM7Y2Rt3JlmlD23uwbD9TzhEhWp2PaRqIjAYXbqUb7eo7zF2PpEeZgK8/8epWbaaLJ2xsHasNv7kT2nYXVm2v/3f/8HQMp7ZTNhwgQAQNeuXcs13X379sXbA9VlzrDyGUPbcP4OBOq0nTEj1uab9/yoWSB6hmEePM7t57acPMb2Z9uX7FqyqP4RprjTE41VyLmd90DrAY7njqo/y2BjoISNETiG4TW/6qqrkvYRNYNqP3AXQgghqjs9134MANjVzV80+sriTSkfe+uNJwAAmpx+rr9hQ/HuR4UQicRSdAcZk+KezPTp0+P/03aMb7x8Q7beVawqbBV3EqWgufbsfNu23lSoJNs3+9jzDwAAbh75ckr1G3mVr0QPG35RfNtcM3NAdcTa2Bbnq5plpFpp93fraVVCu69dvW8/rZrH9Gh7yGh07vU855xzQssvKo9nn30WQOI6DyB5FsfdZj0m2fUPFtt+rbIdZuMeNUsW1ReivLVwu12DElZWG4E4Fovh8qP9KI/7163yj9+xFSgUKWO1Cmf6mvveJmq19WfVnp35NYBkf/duvexsoFXro+pvvYNE+cF2/7d9nGn85S9/ARDcZ6QCViz0rpKKn/+SUFBQEFeP2T6oEtsZNddWvLg4BrY9uR6nwvaLim7sxhMhVuWPilZsvciEzTSF1cGtJ4+xz3reI3juou45dpbAloV9kzPfQDCr73rUETWTajlwF0IIIWoS2cf9BADQ59ZcAEDWk+8ACKKhuvy8UJU/8vI+AICWF/h28R+u3VfuLwFC1BTkVaYGUO8dX636TYpKO3lswjwAwLWbdsW3HTdyGABgYd0e5VM4IUSpuPHsfgCA3Z/43pGWPTUDALBunu8JY+MPufF9s2v5N/A2fXx/1YcO8vvvZcf5A6qJC/dWQIlFusGZjiOOOAJAeGyB0pCfnx+5boSf9KBCNZjqMlD8OiY728wZJev33M4aWY9qbrrWo1rUmg3uxzxtmSy2TG49qfjbqOh2hpuwbFTkt2zZAiBZPWdZaU/vziwwf553toFf/vKXoeUX1ZdqNXD/61//CgA4/vjjk35jR2DHsi6ubGfnjcUGn7DwOE4jAsGNjR2fv/HzQCoavJFwWo8dn/W1i+Ps1CbLyBsMp+dsXdxjo86NXdBqz23UzZrXinkz9DQQXONrrrmmqNMgKhC2d0uYuVlxbtGiggbZ7fzk8WEP3ygXpzZYU1SAIlsPi7tf1LR8WQmrl627NVmzJktRLm7ttH3U+XD3sVP69j45ceJEAMCVV16ZSvVEOfL5+v1o0aIFMMQ3VzrxcN9DTN/1q4KdMgoXXDf13Rg++cVadO/eHVi+PWHBpxCi5GRkIkWvMmXLp1oN3NON9Z8vLNPx/zfju/j/951SGB/v+C5lSlMIUTb2LvoSALD6/a8AAIv/tQQA8J/14X6jAQCFfZk+Wjpk+4PyXww4DQDwzOylB6CkQgghyotYRgyxjBQWp6awT1FUq4F7ly7+oNVVwqg422BIJGqhWlHhzYFkF3Kuz1sb+MIuQDmQUPniAhyqb6w/3W8VpwwynLXrAgtIrGdUOHrrBtOq+lGu/HicDQTjTlHyGovKh4GW2OZsH3IXipKoGS6rclsl3i4Ui1KLw+BsEz95T7ALZKMWYFpXiCQsAJpf7nA3lGXBvXfYvmtnHfjJ2TdbbmvGEFU/lyhzAns9ec2lvB9YrHtje68FgDVtfXOrnOxOAIKZsd27dwO7gYULFyIWi8X7hX0+WUcHxJqtuKYnUc9L20/ZhvlsZF5ss3YBKT/psOCrr76Kp92zZ08AwbPOPrvpkIL1ZBvl/tbEJipgmVtPzjzb2UaeK854W3eQLAO/W3eYPB/WzaRbH5bDDbYlahbVauCeLjTdtgwAsGb15nJLc9cG32aufyv/kn66oYxzMUKIEnHN6X0BALtmTQcAbF25FUAxSrth20rfB/Xuwv5cf1vqLv2EEEJUHhkZGchIYXFqRr4Wp8aVv6OOOgpAuOs0q/5ZtcnubwMy8dMeF6aiU922Cl5xIdjLk9xcfwEc3+CZt11oFBUmndupIITVwZ4Dq/7YBUjWnR6JcvEXVjbOAPCa/+IXv4g4A+JAw5kQq8DZ6x/WZtgWrDoW5ZaV+9s2FRXcy8X2YcJjbXntjJF1TWfLDkTP6JUXYfcZWx7bt20wq6jgLlEBaIDiXezZ+4Js3iuGJk2aAEjuP+61Yztg22R/tf3UBg+zz0qmY/tHWOCyqEBKpHnz5gCC+zj7MdVvliHKnTHboTvzym22P9tPniu6PGZZqI5v3ry5yDq49bR157mxbiFt2aICGtqAjkXNZjAttgFR86gWA3chhBBCCCEqi5QDMKWwT1FUi4E77bGtsgQEb/JUG6w6XJztJt9uqRBEhVwvCmuf6/ENvnb5eaDIbuzb3H34o28jaMM62zf4KIXdltniHhcVVILn39r5Rc042GsRlZ77P6+5qHgY7p7YtsPvtOcMu37Wftwq6lblsiqgbRts32GqGFUua19qlWabB2erbF9nnq73lszMTEyf+z1q1aqFU2oXeq5qWAclpW7TwrDyDQvvN3UPKkx/a9K+LAftgq2aF+VNx9Yjao2Cuw+JUmvt/vbci/KBwc46d+4MILimtIl2Zy3tmiHbZ/g5f/58AIGC27Jly4Tjbf9meps2+WZcbhtgOXjdaQtOdZvQYxifEbbdENbHXecEAF988UX8f5u2tcm36je/85l+8MEHJ3xu3LgxoWxhZWDdqd4Te654Hn788UcAyap+VCBIO+sHJJ9b9nu2ieHDh0PUDKrFwF0IIYQQQojKIuUATCnsUxRpPXCfMGECgMC23drFAsFbcpSv5ih7a6v0cf9UvLJY216b5rbGhwEAmvcstNN7/psi6xnFiPMPj//fsDBgS7269RLKaxUzq6JEKe/Wlr+omQWr5NG+nivurY1w1LqCqGvk5k0PGm3a+CHi2QYUav3AM2nSJADJAUxs27Bhu93f2a6Yhu2f1g7X2m3b/ak6ha05sUoy87T+z619NtOkcmf7epjNvGs/Xrur7+Wi/am+qjiysZ9OzuIcAMC2lUEApows/9y0Ps73q91mgB+AqcExfhovL9meUGa3XtYLBrdb5d2eQ2vLbL1rhFHczGKUD3h+V7CY8oGqsL1/F3XtbDu3fYgzS4yXUZxdtm1vnMkCgjZFdZhqOPsenw3WRtz6kGcZ+QxhumGzAPb5wt9oL24VeHse2N/5bLcKPj3cuGWMuu/wnGRlZaFfm8J8vQKga1P/0yWWEfK5AysLElV8YtV51pNtQtQc0nrgLoQQQgghRGUTy8hALAXz6VT2KYq0Hrh36uT7p7W+1F3Vx9rOWvs+/m7tsJkWbfSK8+vuKtdRPqctTQadDAAYedVKAMBjE+ZFV9bh2p91BQB0u+b8+LaP9rUAANSt5deDPl5tFEW+nUeViRTn09b9zdrSWgWd9oxUXez6AWuDaVUVV+ngNqbFNiAOHJMnTwYQKE9RhKlOFntN2UbYTq16ZmdziLWdDvOYYvOPCrNuVT/+TlXNKtXWkwQQqHexWAzTv9+PzMxMnHPquQCAg3osBwC027TOL2vu1vhxnDKt1cKfQco61O/bz33qu4ytW9fP0/Vaw3NlozpbjxbW805xM4Fh/tyjIqRGKetRsR2YppT3smHXYbAtWO8sQBBPxM58Wftp2rbzmlFBt+2GNt/cLyxiMlVrfubk5CSUi3blUe3Ero8hLCNtxMP8m7do0SIhL5uGnRWy52PDhg0AghkI1oH3Ac4WsO4nH9YUwG5fSfd2+p8ZAPbvR6fmMXi7cplx4aeZFSmMYhurlZXwvUPmHqDQgyzXwyGWAdQGkFELyM7EJ6t3RnrME9WftB64CyGEEEIIUdlkZKbox70m27hTDecbN9VkVzGiKmA9L1g7UWK327dzEuW/2P3Nqtr2jX9L+xMAAJ2u9BWC37bwy7/+y1UAgL07fRWlbmNfAWh1vK8uNz/VD4M+fXWgVjRrlrgqnQodz5FV1YryQx9WzyiFBEhW5+25s+fcKkB2NoOfVExctZH1oPLC+okDB5Wm4jwxWXvbsD5Gdci2BR4bFcU0as1FlB23+5ttn7ZdWt/Rdn1LcZ6n3Dq75Z7x9drCX7MLFdHW/mdGs2Q7+61+mXasXgogWc2kChhWHuu33c4M2FlF2+9sn7Y2wUByH46KIlvcTB7zomeia6+9tsj9RSLsi7w3Wm9nYeprq1b++gnanXN2iN+JnXGJisdhbazdWWj+/803/rotel2heh2lekd5FGPeW7b4Acmo+rszbtxmo49GpWnbvZ1p2LbND4K2apX/HG7dujW6Ze8E6hcq+Pv2AjgIyPPz9aik79+X+L1Ypd3/6hVWJf7d7u8cg8Lb2969e+P1dmcCRCWT4uJUlHHgXrajhRBCCCGEEBVCWiruTz/9NACgb18/xLhVeVzFiG/ftHujvTUVeGI9YUT5brYKdZgSbaMKWnXbvulv6jwQAND2l763mTbbC+349hfahGf7qsoPddsBAD5c5tu/ZmUFb9pMmyvirS/nqLyLU0/t8a7SZpVMu4+1V7RKu1VLuR9VdKucANGqD9vEddddF1ofUXLosYcqHq+Hve7WywwJ83QR5VPaRva1RHlKoeIYZgtvfSITqtlRMwhWweZ+bJ/WNtYtn1WzraJoo0/aTyqUdg2Ae47tOh3br+yshq2/VWVZJqbjqvt2TQnPnb22xam1Rd1HRPGMHz8eQDD7yOvA5xrPr+thhM863k8Z+4IKbdu2bQEEyjLXRdl2Y9ubnQl12xfzZBuyfs7tTFtY/AUgaKN8ThcVN8X2sag1VMTOCLJt2z7IvLdv3w5kVy2Nc9OmTUmeedhGRowYUWnlqunEMlJ0B1mTF6cKIYQQQhwI+h1SOOjfEwRf8gpNYmgCk2QaQ7idZi58weD3wkWp8e8ZiS8vQkSRlgN3qwTwDZs2Xy5R6gCVCuuhgVhlL0z9dfN2ifJTbv2wWhVqbS1fLc9q0Trh+DVr1vhl35ebcJzrQYAqAdUU2gS2bp2YlvWHG2WbGqWmu/WNsvvnubIKTdQ55v78tN4A3NkR69mAeRTlV1+UjFdeeQVAoOpFqcjE9kfrecm97tZDC6+t9fTCWTM7Q2T7Iz/DInXaNm7XUERhy2A9U9m258I+aVVtq1paD0vWk5LtM26Zec6iPPDYPKNsfK1/+zCiyhcWpdolSiG114kzZYBmy4qC7ZyKOtsH2yTt1t3onmwzXA906KGHAgg8tDBCKG3d+Z326NbTmvXeFjY7xm2NGzcGkLwWjGWyHuCivBTZ7UXNrEUdGxWxO6oMTNv3UrMl9NjKpn79+knP26h6iopD7iCFEEIIISqYTlm+69vY/kLhKmwnLjIt/Jq0j1XYo5R3q7SHLU4tJFb422lH+AuO3160PnJfUX1Jy4E7VZ5NmzYBCPzVhvmVtTakVCr4SaU6KkJoKpFDLVEqU3GeXFhGa8dNFd1Getu6dWv8WM4o8FiqhbR5Z55RaqMtk7WBj/JAEQbz5jmO8q4TZV9v7WDdmRQbjZVtQDaz5Qdna6iKujbPQKDEWfXMen4JU6Z5jPUHbmdO+LtVrq3PdebFdhEWzdR6ponyNmFnwGxkURsTwe0L1vc707C2+HY2ySr0dq1NmMLI62FnOqL8s9vvxN4b7bl0yxEVz4G/29lPO/sSNbNnZ+FEIn/7298AJMcTifLJHuaDn88NtjXauvP5wWfEd999ByDZ2wxhGy7qmvJY9geWh23WriGzbdauiWA9mS73d8too8nafm+/23UmLBPPz65du4Dw4KVVjoyMjKRIsWwzV199daWVq6YSy8xALIXZ/1hm2cYraTlwF0IIIYQ4kMQDILmqeEaEeRiPKUIxD6UoW3mTFveJyR6+RpOWA3f7xk+Vi9vDPDAUZwMdZa9dnCoX5sfdbrMqo1WHqUbY1e3M6/DDD084jurccccdl1RP60kjSu23KgOxMxNWpXTrGRUhNtXZi+J8yFt7YLfutlzF2S2L4nn11VcBBBFSbTuM8khkZ1asp4uwvmE9C1lVjBRnQ11U1MCoWAs2Tf7OmR22N6ZtvctYO3sg8JVNTx0tW7YEEMw+Fee9iXlytmPFihUAgNWrVyeV2cZmsOtx7EwB+woVUztDYq+BO5NgZzFtH7Zrf9wIsm4eUfV383riiScAADfccEPovjURqsn2GWI9HVkvPi78jdeG14xt1HqViYoSzrKsW7cuYT83zgaP+fbbbwEAHTt2TNi3qPgn7nZrV8906WOdZXXrZT3Y2FmgqHgOUWs/0oG8vLyktWRa71V5xFL0456Sr/ci0GhHCCGEECKKFFT0KO8yHr9nJKrlHoxbVhPICQA8ChqZifb0fhAo4H+OagMAeG3equLrIKoNaTlw55s/V67zbTzMdtq+2UfZWkZ9j7LBi4oc6B5jFWe+EdMue+HChQCAxYsXAwD69esHAOjevTuAQEmwqkTYG7XdZtUzKn/M85NPPgEAdOvWLSFP2j/aeoXVyZ4LW4aSrg+I8nfvnltr48xPRY8rO/TBbP2DW1W4uD4QFRXR/c3al1qvJVZRt33AKvRhtuDWg4lV532vEUGbt4q0jbxq4w2EzfJYdd56bCkuwijvaVRDGavihx9+iO8zf/58AMk+s63HEZaF+1GBp9cQ66M9zBMM62Ft0a3veGsLb70/WcKUYXnFSIbXiteSMyB2jYhdrwAkz8TwWLZz2om7vt+B4NpQSed+draT6dg1MADQvn17AEG7IKl6NbO+5O3sdefOnZPqaW3Xo6IzkyjvUP7+u5FOFBQUxK+XOwMiKhZ3zUFx+5WFtBy4CyGEEEIcEAoKX1RpmuQq7nzBpG269evOF4EIld6Lsk8vTIeBF8OOiQ/36AM+q3bSvqLykKlMCLSBPOqoowAk+2+1qp37f3EeTKKI8hBjVcUwtciqIdYmv107PxLq+vW+S6f33nsPADB37lwAwODBgwEEdrNWRQ9TF63yQhvZmTNnAki2EWQZbIS6sIiw9rutu1XsonzBk6jIlVHpuPUibAP0jCA72ZLz73//G0Bg2x4V9ZNYZd2uvbC4yrRVpK2qXZxNNOF+UdFR3X1YLtrA9uzZE0Dy7FJUm7e/k7D9bNstbqaPFGeHy3sAENgNL1++HAAwZ84cAMDatWsBBGo9lVA7a8GZPOvXvihf+MTOttgZhSjb5ajv7nbW/fHHHwcA3HjjjaipvPzyywACj2nW738UrnrMmRa7topxQXjvZ3uxEYOpxFNZp593zt5ydsi9hlT1WW62PZbf9ltbH6uS2/sF1WTX05hVmK3HIxvV2LZhljEhhoK3HelERkZGkpcvtqHzzjuv0sqVLtx999245557ErZ169YNixYtqqQSFU1aDdyFEEIIIQ4oXuELaKH67e11nDhYxb3Q3jyuvFM5N4p7lCcYa9vuhSzIz6h7UMJvMZahsJw/7dkBAPDekpyi6yUi6dGjB955553499I4vJDiHoK1ubMqlo3ECQRv9lbpKk4RskR5lwlTQKL8R4d5bQCA448/HkBgu7ps2TIAwIsvvgggUDi6du0KADj66KMBJPqypVrKNOiT16prtA1kGoRloh1slNLmbo9SFe0xxfmvj/IRHea9g1jvCjwXsu8rOdbPc5SHJRtngPvZSJ68XmH20db+NMrzUnHem7g/1TKqgq7qz32ptPfv3z9hX6u8Wd/YVu2zZXHziopmavsGy229N1kFsqiZQp5/RsKkcvrVV18BAL755hsAwSyUtQFm2iw/87b2yG59iL2nWSXVeiSx54UUVT/FZEj2RmTXTEStH3Jnoe0aBl4L2s0zoirVcX4Sa1/OeyvLxvTc/m37qW3XPMbGgrBt0d5zbN9jGdx9bZuy23mfYx7Wjj7BK0sQrDut8DwvXm/3HIniqVWrVtw7WFUnrQbuQgghhBAHkhhf1qmC73EUd2OLbpX2gj2FC1utKV2UmVNBorqfUI5ahYu+6xS6XS1BHUTJWLJkCVq3bo3s7Gz069cPY8eOjYsiqRKLZSCWwsLTWKwGKe5CWI7M9mcaCnJ9Tz1HdW9cmcURQgghRBrRt29fTJo0Cd26dcPatWtxzz33YODAgViwYEE8bkZVIq0G7naaOSp0sTvlW9yi1OIWRlrsFF5RIbvt9LBdvGenQbnolovMuNCHx9EMZsGCBQCAoUOHxtN68803E/K0gSs4zck8bBmiymj3c+vE/21ALHtMcUE3irsW7vW0i4NF2eFCLxvEq7iFlNbEhNjpcU5Ru8fYqf+oAC3EmmLwOLbrsMWfbJc0keHUsW1DxbUllpUh4m1QIiD53mMXfPIc8NPeN1humhnRnIdmDWH72nNFkzuaw7399tsJ5Wf9mXaUOzy3f9o+aK+5NZmxblqZh73ORZkYMv+avNDcBtOiiQnN2awL3qLscWkCYq+3dQMa9ezjfmwD9r7v9h9eO5aXbY2wv7IfsC/Z52pUQKmwZ0WUCabtH3axujX9Ia45aJhv9YI8/35JFT6uvOftKvy9cHvEfcUqstyvIL/Q6YRj/5xJc1jroSZifLJnz54kl8mieM4444z4/0cffTT69u2L9u3b46WXXsIvfvGLlNORjbsQIRy6xvegkTv3cwDA/A99v/R5Wwp9VB/qvx33OOHcSiidEEIIIdKZgw8+GF27dsXSpUtLdJwG7iFEvYVTCaBa5b5pRi2MtGq3VfKorlHhoHLAT6souW/pUUoW86CbLebBMlAJ6NChAwDg66+/TkjbLg50lQ4eaxeYsQxM07rbsmWyaioJc7XJfaySQQWXnzZAjFVuSJTyKeWg/KELSCB5QbINMGQDMBH2Be4X1WaYnpsXiXIraNsUy2BduNm25PbzI488EkDqC5atmseZLy723LBhQ0IZXKWOwZzoZpUL/Zg3p1tZTvZ9O9vBReb8ZLA2N5w73fARe26Y14UXXggA+PDDDwEEi955XVg2q+K615HltTMJdgbELsi392LbhsKul91Wkxep2ns+F9+zz9HVIxVrq54Dya5W7T08KrCfvZbWzSAJU7+jXFBa5Z33BLtY1bpmJLZthC1Ct7NBdhG5nVF070suubm5aJFV6MK5Fu8vTlukjXuh8l6w2//cu71whnyn358K9ppZtcLBWmbtooddmdnBec6k/4lCjzSxELe3LvXr1498hovU2bFjB5YtW4b//d//reyihFK2Yb8QQgghhBBpyq233opZs2ZhxYoVmD17Ns4991xkZmbikksuKVE6GZkZKf+VhbRS3AnfpPnGnODGCeHKbZTNOvelmkYlzNqmMnARg7bY4BRunlGurKzNsLUR5H5NmjRJON7ODoQpmdZ9oy0D04xyT2dVmajAMW4dqMAcvH2Vv6HQpywoGPAz5qsqn3z/vb9/4bmjKlnUtWmR6x+z9e3pAIC5//gUAPDcBz+EHkOefq7In2s8rsIdZWdqlVxr2xqlwEUF5nL3se4grQ10VJAUHmdtv8Nspxm0KKr/2T7DvD755BMAiE+TRq1joVoI+CodEAQ8o/J+2GGHAQjuG+yzVpHfsmVLQprWNpzKOxDci6i820BSVnEbNGgQgMB95Pvvvw8guCewP1L9d9sGy8NyU0m3axKsjXVUULYoN5nuMaQ4F73VGau42xleXjP2A87QuDNaNo2oNWJRbnyt21DeJ+yaibC1MPZa8rlK7Ay3vdZ2RsemW1Twwai1K7ZP8ZyFuiqtUzjbyAiltYJZ3wK24ULlnQr73lz//OzZ6s865OclziJmZBU+awsVd9q6cxCXwbVBWcnDsrgPeCrv/F7oneST1TuRkZGBOnXqaP1XKVi9ejUuueQSbNq0Cc2bN8eAAQPw6aefxl3qVjXScuAuhBBCCCFEWZk6dWq5pBPLiKXmDjKjbI4902rgbt+k7ds4VSlXCaMqSFXKqtcM/0xlndsZnMKqi1TWqHSwLNzulovqVJSSRNWEeduQ8/yddoNUvazaAgRqGpUNngPau1ovENxO1STMvhUIVAyW0a1Lhww/r1i+ifhmV8AX0r9VoU/aTL9sXxWqk61bt/bTMddm06ZNKHjlWQDAmLvfCk1TlAzatrueUay9uJ1dsUF2ooIlMZ0o5d3dJ8qrim0DVnnr1KlTwu9Un5muG5SsuCBi1iZ25syZAHx/vm5Z+DtVT/Yd13belpv9j4HQ2rdvDyBo6zzX7NPsy1S9qZxa+1z3nDAEPfsm1SHraYf7c53Lz3/+cwDAjBkzEvLgPdK9XjyW9eE5sB56CMtpg3kxj6iATmHbarJyaFVktmuefz5veJ7Zftx+Zftt1L3d5mln1tjOrGrOMrmqpFX52ZfWrVsHAOjdu3dCWdgPrOLOslu7/LA2EaWsR3neYfviPZC/z5njOz5o1aoVlsKfLeuW5bf7WB0n4FOhT3XsMrMB+wrX5RQq8Pu2J3qfou16rcJPKuseFXjOdDpmFIHSXvRgsGnTpvE+xrZRk/tPZVFRi1Nl4y6EEEIIIUQakFaKe1gIdSB4w6T65vqNpg06VTK+4VNRp5rNt1XautMG1fp4tR5OqHiEqVTWt3GUokmFjKoJ1a2WLVsm1IeKWZcuXQAk2rjThzPtculBgmlQsWAe1tOGVb5YduvXPWGWI1Zom7+v0E5xL/3aGl/wtIct3J5Rl54R/HrxWtDmtsn81/1PACOltJcrVhF1sTbtUbMw1ouM9QhjbWjD/ILbtOx29gGm1b1794Tv1k0X+5zbD6O8Klibfab5feEaDLvegx5deC+x/dvF1oPnefny5Ql5MyKfXa/CenO2LsyLhj3vvP/Z+wbLbcvE7RdddBEAYNq0aQACO3vXa431zFFc7AbbZqzdsbWrdq+XXd9Qk23cOfPCNsfZWN6/GZqdzy93xpdEzTjxPFMxt89V672N92c7O8RnSJiyy/ZivSNR1WasAftss16kbPsL857Dc8Xnq73/8Fg+h1esWAEgeLbzWcky8rzs27cPXu3CZ7Vj4x6rXbjOI7vQw9xBfh/fs9Ws8yj0y+7lF573LOOv3ThLo4lFgqmFsW2PK+8Zidc2Pz8/fq6tdzdRcUhxF0IIIYQQQsRJK8Xdvo1TzaJSQBs8q5IDyUqQtQX/4QffSwnVKpsG396tck/FI8wzii2vTdN6WKDizP2ocKxfvz7huLD62W38TiXD1svaJ1t1xvrRTsWXejzKXOEnbd7jvmcLVQJu57nntaASuHPpkmLzEqUj35x7IFndsu2SWN//1qY9zNe/m767T5RHC7Y7trdjjz0WQKA8fvXVVwAC+1TrL9ytF9sVj42aCaC/dhvjgIqiVdZZb7fPse9af9W8R1G1XLx4cULe7J/ERrkMsyW3Mwb2OnDdDuGaGHvOmdd5550HAJgyZUpSHXjOrLcbliEseqabl21DUVF23X3D7PprGtYu3dov89qx3fG55LZ/tlvruYVtKsozE6+p9TLE/a3vePc6cdab5eAxPXr0ABD0SUYBp7rNGbSf/vSnAAKl3p4P1uHzzz+P/0a7ee5j70PM47XXXgOQPIvBtR0sI49buXIlevTwZzaosgPBjHH8OVf4md20cJZ6Z+F6sEKbdy+usBd69THeZZK+O37eXaUfcGzeDbm5uUn9pqio7uLAEItlpLY4NSbFXQghhBBCiGpPWinuV111FQDgrbd8u2frw5a4Spj1L0wFzHp/sJ5crB9iKhzWn25RdpjWV631xkGs4sm86Au6W7duAJKjLVJtdLfxbZvHMA1b7ijf6Syj9asdSizR3o5qgJdRkPA9yS6vMA+eeyogvDZ187Ua/kAR1m6L83Me5THFKqLsd9YG3m3v1v8307QROrlmg2nR9zhtYm27DLO5ZuRhKnJR9aE3GWsjaz2pENqAcx0MEPRFew6ZJlU/9uGFCxcCCJRSKqfs+6wn03N95lt/1PxuZ9Ho0ePoo49OKKO1deZ1GzhwIADgyy+/jOfF8ll/+zzGXgc7c8c8eS7tWgS3bUStqRg3bhwA4JZbbkFNwW1bQPK54UwvrwPPs/tMiPIqEhWB3MI87Cwdv4d5GuMsFT+ZB9svvSpx5oB9lGlTiefzyz4r+d1dx2aVdhtbgGkyD/5+zDHHAAjGEXbtSCwWiz/jYllB/4vVK/TURh/vhWo8o6ySzNr+7/vzCvPl2pnC7XE/7oUKu/U2438xftsj6Nu6LoC6+GhlbmhbEBVDLDMTGcVEt+V+ZUGKuxBCCCGEEGlAWinuhKvCqU7xDZp23C5WKbL2oHwLpx90vnVblY321/a4MO8I1h+uPaY41dsqIfQi8+233yak4+5n1WseY9MM85sMJNuVWiU09DiqEbQdpgJh96NqQHu9jETFnXlRRTmkaUOUlqe9FaU+tiZg7aNdqFjZiKjWltW2JbY59hnrAcJtp/yNn8yTym6vXr0ABMo0o5haG1dbtjB4zHvvvQcgUNZ4zKpVq4pM0/pxp/0uf3d9xrPuUZEerX0x71W8l1HFtwo77YndmcMo/9u23uxP9GhDzzxRkTJ5z/jiiy+SfrP3NNsW7PUkdgbPtr+wiNNRedcE7rzzTgDA2WefDSD6WWGfO2HPkqhjbP+1sRL4O/sgZ7TZz6OibwPJa6LYru3aD6Zx5JFHAgiebVwDQq85VI1rmVnaPn36JNXXzvRxFpppsgxHHHEEgOCeYyMP8zjrtz4dyMrKSooizTY1ZsyYSitXTaGivMqk5cBdCCGEEOKAUihOeZmOO8h6hQJVvUKB6WD/I7NxC/+zkf8iULvpjwCA/Nyt/qdZrGrhotR4gCcAMQqIKSx4FDWHtBy4W0WMn/RDbH2Uu79FqeB8s+dbKt/OqerbCG/WNt5Vi6wNKVWoKFWbKlyUjTE/7ap+KmluvbiPtW+z54pYW1qrukZ5GAGAJXl+uQ/LKrwetHE3rYrbaSfIlKziTrvH2l2Ojh877CR/JuS5D35Iyl+UnKLsWqm8uVFV3WOsb26rhhGruId5B2HfoCJHO3TaZf/3v/8FEB1R1dpIUw13bYP5G/sw7wFs8+x3dibMekTh71yDUZS3kyhvKvaewHPDmTz2Zare1muVG7PBzmzYtG2eVs0nNholr6t7Dqm2Wu8m1qY/yltQ1AxeVJnDfitynU01Iypmgn3+2OdV2Pm01ztq5sJ6CLLPJdu/7WyQO8vC5w+jqfJYG7nbrhnjjDd9qn/88ccAgEGDBiXUhc9l9zxFxQpgGjYPfmcZbGRV/u7PqG1BOlG/fv2ktS812TtTRSPFXQghhBCikvgBjbFx40YAiUGuOBimSMgXWj+IUwY6duwItOuMQw89FE3WzvOP2eYHePJ2+S/jBXt8AYGBCwkDOwHBgtf44li7SNUrSPxE2RY9irIRy0jRHWQZxYi0HLgz6iDtRPlmyY61cuXK+L5UtGjPZtV5qxTxLdwq7VTbqHRYlSoM67/dvgkTKnrM06onvClQOfvss88SjnOP7du3L4BoW32WyardViFkmWnvF6bUxhUd613G7hixIp7nzkZsnL3f/9y+fTtOuf1SAMBBLWYAAMZPWxSaFrku1gGAbN2jKMom1qrYtm3Y2Rir2FpvJzaOgXsMvTf169cPADB79mwAQTwFKmtUf+3M2OrVqwEk27O6dudUi2100rAZObe87OuMpGjtt6nYu/7SbZwE9jtrJ0/o1z0nJydhO1VBq3K6fd3mwd94DPsRz7FNK0rBDrPTp50v0+B1YRuwawzsmhjbFqJUfndb1DqBmkDUM8KuI+E5Kiq+RpQdfJRHNDtbQtWcn/aaRa2XcrH289ZDjfVsxP7Ndkfbd3qjYZ/kTBSQbKvOfsk82A+sJ6Qo71g2OjA9s/HTxc5GMiIsSeUclScHHXRQUjT3otYBifQkLQfuQgghhBBVnS2te/ovMC38Af4h+30F39vmf8aV+ELl3XX9GA/8VIw7SFE1kKlMEdDnMd92+WZso5oCQdRRKlxUy6gAWU80fAvn71TnrIJk1YowVdHa3lnFozhVLkrxpHLIKTwAaNu2bcI+Vj2xedjoclGKmF2pH2bLHz8HzBvFUOCrMBf191f3P/XPjwAA69atA5AYOXYGGqNly5YYeIevZIzp/x8AwBeP+369ZyzfWlxuwsFef3cbsWof22mUN5OoqJlhNsrsuwMGDAAQxGSgvSzVMbZnzpix//J39mMq1iyDG9OB5WZkVJafyhzT4nb2dbZ59jN6n7H1cSMac9aI9xOW38ZPsBEwrSLJdDhzwDK4qhnzdX1ZA8Dhhx8OINkHeJS3FuZJu2TOVPJ8AUG/572V5yhKtY+KyGxV3jDVtrj1ATWBRx55BEAwA2XbDduDnUXhOXL9ntt7fNTMhVXD7XFhM0xA8Jx1n7c8xq4HYV9jf4iyu7Z+2zkD9+OPPyb87rY/tteoKL5REUSt33aeY6r9di2Pm66NSks4M8BnmFXkD/R6jX379iXVl21KVB/ScuAuhBBCCFFZ9GlVG0Bt7P74dQDAhrm+28ndm3KxF0Ddpg2xF0CrAb6L2819LwYArK/d0n/JqNfGf3H1x/povM4PFJVg807nFzaYYSGx+MtuzTEnq8rEMmKpKe4ZZTOhSuuBu/VMQbs3982Ydmncl4rcd999ByBQ2K3nF+ufmEoh1QeqDGF2mXzjtZFSrdJuVW67Aj80khuA/v37AwCmTZsWz5PbrBJAhcaqLqmWyfr6dW0q4+eZLrNihfatVN4jVJX4zaYgsQzt27cHEKiNrk3lp9v9fA8aPAwA8JPjfD++x8/9HACwe5PvVSQzK62b9AHnwgsvBAD83//9X3ybVais3altx1FeKNh2bHrsn0AQnfPf//43gOBaUy22sy7sb7Txte2R6rm1RweSPSux3Bs2bAAQeKJhPZgWVTPmwXsElTg3D8J9qAzyXmQjMTNvex/hOWceNk4ElXj3f3vvmTt3LoDgntepUycAgY2ya/8PBLMYs2bNAhBEc+V6ASBQ2jnzweti7WetWst62TYRZU/s/hbVvmoSbD9s95yh4fnkdSFh8Rl4r7Zey+wMjPW1b9e4WLt0/s5Pqutu2lEKM7fzucSZNpsW7xnsoyQVf/X8zjbLc8k8WM8wDzVAcI5Z37C4Kf55Tox3kQp2poSUVxvPzc2NtwG3H4vqhUY5QgghhBAlgEr7rf87segdH/PNni4/cToAoOeN/wMA2DnkFwm7bW55FDzPQ7PdgUlSbH/hS4NcOqYF8ipTBFZd4Fs+bTtdVZgKO/elUtGmTRsAgX0clTK78pzfifXgYFUioHifxfZ3azdvlQDWgfalVPFcWzZuo82vPcZ6xLD1iPK/bP1nh6mNcai8I1F5j58Xc/Oh7nPD2f5swSdrEj0NJPrT9aGKsrqhr7hvrN0xoT70+yuKxlV+rB229R1tfY/b+AJ2lodthf2RKjsA/POf/wQQzGBRHeax1osT+wLVc/p5pprMslKpdvsE04iy8WXfPu644wAEbYvqPXG9VLn1K8pnNlVxlsv2LzvDxe0dOnRI2E7/7pyJcOvMTzsLwbx5b2PkSHri4XlhmaznKNdGntfJthF7X7WzhbZM1hbYzvi5/1v795rkVYZwXUXXrl0BJKvdPEc29oJ7f+Y+nEHisyAqirb1FMT97BoX5sk24KrFTIP91a7LsjMvTIuzP2x79BzHtsnZIGt3DiDJiwojBPPewXPJPFq0aJFQBqZp68l68dy6bbg8vMXEYrEkpd2uWykpe/bsiZ8H1ltUP9Jy4C6EEEIIUVms+fjrEu0/+ePVhZ9/AQDccKnvlOGwX14OANh2+MkAfN/xXAjsCoJZWVno3aLwpbbAKPCxsim4onyIZWQmeAUqar+ykJYDd2tvzbdUfqdKBAQKO9+aqaZRxWVaXL3erVs3AMmR6axSxrdv6xnGPca+0VuPC9bTC9USqgzWptj1mOHWG0hW2vkmb73oRNmwW9t3ltna/4XNLHyZ4x/bq3lhc/I405AYHCLS9r3w935tfYXku93+uadC5M6gsHysly23SA3XQ4xdr2GxttS2bbg2rkCgaIWtxeBv9FdODyn0wmJtWtkP2X+ZJ/svt1tbYCDappeq3vHHHw8g6BNffvllQhos45lnngkgaIdUl13f6lS3Fy1alPBbVD+y7dX2Uyr1tM911T6rnPJYqpq857E+3M7rxHsEt9O23/poB5LvDzzW3v/4afunXZ9jcbdbbyakJiruQggRRVoO3IUQQojqCk2kaDrFlym+rPHFkC9jUcGEgOBFlC/BVjCy5pDWhSfztuZQxA2GZAMZ2jyYBl+4CV9U+bLMMvAFvUuXLgCCF2T3ZY4mbzS74zHMmy+mFOcoHrAMXAgbZdLKc+u+PDdo0AA71gYiQWl44vlvAAAnvz0GAHDm4ytwEIBlR/xPqMlMfn4+Plzln9s+hxSaChWKXh//sDNeXpaTbUhUIBmZqfncr4mKu6iafLnRV9t4Iz3ioD0Jv9P2HbSr5/SepvmEEEIIkc5kZCS57Izcrwyk5cCd07UcIFJ14Nu8G9KcaoFd0GpdPPEYTjNzf04BU0HgdDLfiLnghb8DwRs78+bUPN+EqYBYhcMqGXbhml2g5C7QoWJh3W0xDZ4bu8jMLpSl+sCyM8hTWChuloemSbwegRlA4sA9VVh261IOCFQSa55hzYhE0bimMmxH1s0bz7vtA3bRFtsE2zlNZF566aWE/d19rLtS5sl+aE0x2L7pMtQuqubx7J9AYHJmF+kdc8wxAII28/nnvktRtt8TTjgBQLJ5h3Wd6ppw0dSHn1xES4XQLuYktl/SrIhmPHQf6brUZLlskBsGUuJCPp5bLrxnP6Wqyd/tYuOwOvNcsk2wb0YtOuT1s0GrrLobZnpnFc+aGLL9/vvvBxC0B17bKBenYe4ErUMBawZpzaDstbIBjazZGvdzn332+vKTbZXt3D53rAmcrRfvG1TL3fu/DZBk3VnaNO2zz97vbNnD6pmRkYE6DUvuDjKMdzcWmgHe8HcAwE//Vg9dCn/7pP6R8f3cPvPJ6p2F97qM+IwHn8ccX7ANiepHWg7chRBCCCGEqCrEMjOTvOlF7VcW0nLgTpWbtmt8+w5zH0YVjW/lVIqo7NEFnLW5oyphFTHmwbdv2tUtWLAgfizf4Hv27AkgUNvsAjRXsQOSXWTZBWzW/aWrCEaFn7dBZKwLOX5S1eLiQJ43lnHFihUJxwPAkUcGagCQ7MZxzjq/nr1b1UnY76l/+qvpeQ3o+ouzHe3a+deK19W19+P/VnFnmxCpcfnll8f/f/bZZwEkK27Ehim3C4PZB3r18iME/uc//wEQKNxcgAoE7YtBgWz/i1L12D6pPFKBp6tGtiF3YToXZ7Kt0F6Y7hLpLo19uXfv3gn1tcovCVtwyv5CtYuL3HluGPDNPRcu1u6Y5ykswBu38T7CfsRzwXscF6y3bNkSQHDOo9xIhi0CdRfgAsGMhp3xsDbXdnbCuuQMm8FjmjYYXk1U3AnbOZ911kWr/XTPJ8+jdWls7eBt4CXrQpjtxAZFY16uEm0XKVs3xPbeYvdjHpzpta6RWbYwlZ/KM79zlojt3jqJsOeDZbTPX5bBnfktKChA0x4dC78Fz/6yMDPHv+d1f+HN+LaTfuPn8UHuQZHX3FoVsM2I6ktaDtyFEEIIIYSoMmhxajR8k+ZbOVU2q2i7+9qAL1SIaO9JRSxKXSP2d6oOVPOAQC2jsmcVDx5DVTEqIIa1wbO/h7lYsyqaDfRi7fi4v1UR7SyBVUjdehSnTH6wfGtoGXnuqRjw2tj1A65CZF1kch+Fdy49to1bpc3aqfLcM4gZA568//77AIKgMVTFXLtcBgGiCmzDk1u1jHkxwJgNAGZtYN22QnvzpUuXJhzLvk879KFDhwJIVv+sra89T656SFt0qvxUMQcMGAAA6NevH4BgNsIGh7J92XVr6ZbNrTPPq70u/E7bXqqUtj62HtaFo1tnew7svcmqmNYTCcsUFijI1ovliUq7JsH1CYcddhiA5HVRdo2BC68724ldV8A2Zmc/+MnZLbbNKPt6N0AerzfLFRXwL8o9KPPmM5PtiH7N7doYN23WhzN9UbPQxK4d4yfbprteBkjs/zt27ECTQT8p/PbP0PRLy1MvLoz//8ApnwEABv/kHAB+kEK7NsX2G7YZUX1Jy4G7EEIIIYQQVYaMjBQV9xroVYbqHN+MactJG+mwACJ8m6ZXCip+9PpA9fCkjgcn5PVVTnjgFNrThb3VU1Wg8k5/qlY5Zzmt2s2ysp6sl1UpwpQouw+VQJbFvq1bLxB8e2cdOFNBtcVV45g/3/RZTquq8NxwhoTnmrMBVn3lNQnzmMD8ef65jzsTIEoG7d2nTp0KINnTg53J6tSpEwCgY0ff/vLdd98FEPhatoopry8QqEH8ZJrch22DKh5/53f2Dc4ItWrVKiFP1yabbZdtncd8/bUf8ZAqPbFKNLHeKIi7ruKTTz4BkGzTzTzZN1herhmx9w97D7Dh5YFACWS97GwT02D9qF5yP6redt2OVfLD6mM9lfBYazdtZ2nCZkPddN3/reevhx56CDWVu+66C0Awm2XXI9jr4j777HoEG4TQPj+4n5sGkPy8ivJGAyTbqrP9WH/zNpgby8/7Ou/nbLNcw8I+xzoAyWuleAzvGXz2RXlxs32NMw3urEHflpkA/Pp/tDoPBQUF+M/6DBx88MH4w5gzAACj7/wPypuc+f6sYN1O3/p1yOqY1E9sm2CbEdWXtBy4CyGEEEIIUVWIZWQgloKanso+RZGWA3faW1MBoJJAGzdXAbCr0NetWwcgsK/mCuy40l5QqEIUBgXq2cx/O19h7EdtZLMwrw8sFxUA+2Zv/WDbWQHa6lE5oZ2fVerdbVSkqexR6aPavWTJkoTzwXLzPFkbReuNx1XWrHpGdYVqi7UJZv14/bgf7ZcZ2c7aIrsef6xPYev3W5Seiy++GADw4osvAgiuA9sC7WypSM2cORNA4GOc18J6P3KVairrvF5HH300gMDDCz/ZB6is8Xqz/bFsbEt2LYe7zdrNM2/mwfpZTylWUWQ6LNPs2bPjeVlf6Ozj7He2P1JR5DoYGxnTKvBuvax6zU9rj269T7h2wW597P5h9sd2tsEq6vy0PrDtmhQSVibrNzwsCmhNhTNUfG5Zbz+89u5sCfsj92VbtLbcvN7WptvOxNjnDr+7yr3tB679OxAo6vZY9lVu53PapsP+HoZ97lr13nq8sTOK7JvMK2E2bH8w67t///6Ec/H94MvQqVMn/GqB79XJtVEvKxu/9mefm/bwFfe+PbKBnUBmm84ACvBlTkG8nLJtrzmk5cBdCCGEEEKIKkMsRa8ysRroVcZ6vaBSQAXXtQe16hSPod0bFcCZy74HAAzudHBontbONcpfuguVSWuvyzLxDZmqv1XMqNJRfaBiSJXq7rvvjuf12WefJezDT6bxzTffJOTB+lBloG2xtU2M8r/s/kasUmYjbbq2zu53XguWmdfPevkAAvXE5h0W9VGUjosuuih0+zvvvAMA+O9//wsgaAvWowuvBduQOztFu3MqzXbdg52dsp5Q2FfYtqzSHrYGg22a/Y2qHT+jonpGrSlhZFJ37YVVi+16Dc6W3XnnnQlpMjLm+eefj6Jw7bxtbAY7w2FnDqyKb32BW89SYVE4iZ1x5Pm2Mwa8HlGebIi7nWnYmREBzJ8/H0DQT2wkUjvb6cKZaPZPftp7qJ3dsfvZdsI83ectryfToP95tlX2W5aJ6jiPY548jnEP6BkqbL2XtY9nHny+WI82zJNp8DnN+vB5zZm1/Px8ePuD5/2Q9gcBOAjvrdiRMBbpcdcoAMDIg8YBAB6bMA9lZe1Cf/1e03n+THks078W2bv889ezTWegHvDhhqCNiEqkgtxBls3QRgghhBBCCFEhpKXiTqzdq31bB/y37cGdg2iKM5f5q9Kp+NEzBt+6adtOFu+iDWaiXZxV2FyscmXVJ9oe0l6RyhKVgEsvvTQhPSoHxxxzTFJepG/fvpG/uWmOHTs2tAw8lzaiqvUQ49qdWhtaG/mVMC8qaTzX3E5VhcdT+QiLkmdVXesxRBw4TjnlFADAuHG+omRnZ+xslFV2geD6sd1RvSfWzpZtgG2KbYH7WVtZ1yMGVUmuoaC6b+MHsP+xPrZvU7HjrBY9W7jt0tb9jjvuQCoUp7ST22+/Pf7/I488AiDokzz/LA/PGbHxIqxdcVG27daXuvX5HbWOhdgoqHZdTJjPeG574IEHkspTU+GMy9///ncAwfonuybJbf9RsTt43e21437sN3aNC9sJ+15Y9FvbTtjfec+3s0M2iriNFMsZ41Si6FKNt7NwTNPa0XP2ls8+ltF6WvPrlKxxZmVlxc+Fnb2oaFavXl2jvS9VFbQ4VQghhBCisnFeTgp2+wLUwDYHAdiFBbv8F+WcJt0AAIf97rcAgDFH+u51F77wKQDghTlrS5ztWxv8vBp97C98zc/zXyYab/JNZRrupivYJknHiupLWg7c+eZMBYF2s2FeZTIzMwHP6XTG9ykVoniURWMPGBXhjWXgW3mYqkhsZDOrSLL8N910U1HVLhd++1v/pkLlxvrmtX6B7YyCW0+r+NnthIonVRQqG9bLTlTUPFcZslH9rJoiDjy8XtYbiV3DYT1KAMntij7hOQPGY/idipu1U7UKV5ifcCrPXCPCvOkFx7ZTu0aDyiO3M/opcf240+49Ly8Pv710KPK/n4PMTr2T6l8e3HrrrQCAhx9+GEB0hFQ7Y2DPofW6Y2fO3N/sPvzk/c/a29t1SJaw7XZGQCTDGASchbXnyj2v9lrwutvrzz5jva3YWS5ec957OcsZn61G0A+Zh51l5b3dPrv5nTFZuB/rw+9U1cOwEVSZJp8RXIvDPFkvO3NoI8r6dUpeO0AaNmyY1OcqGrYLUclUkI17Wg7chRBCCCEqhILgZdbbX7h4d6//Yt+29k5gO7Clle/aNqd+B/+F5Wc3Ys2aNejdwQ8M1fL1WQCAea99BwCYmRO4OS6Of3ztm/mdn+8V5l0oLGZSwKgTdpiopqTlwN3ajtsIja4d3I4dOxLs1n9ymP/W/dHK3IQ0+PZNu7co9SEqb9e209rxEeslhb9bm9SKgHlaRc3av9ropa7Saf1fWxtCbreKj7VvtLbtzIPpuMott9GDgLXfFAceq+Syv7FN2Sinri24VeTYFqi8c70DPb5Ydd/asvM724Gr/i1atAhAcpRdKmxRfsLZ/mzUYLt/71Z+vWL79+D0rgPg7Su87+zx65i/zLeJz+xc9PqT0nLbbbcBAMaPHw8g2tNOlB93G/mYuJ5eeK2j7ns2GrRVZ+36Izvb6M6UMe0//OEPxVe+hkI75ueeew5AEC2Ufc31KmPXY1mvMPy0syVRqrGNrMtr7c5y2Xu+7TM8xj5XqaRTcedsVosWLRLKxJm4MFgu5s2o4cTawLMstl/YdVRRHpEsYc+gilLgZd9eRcjISFFxl427EEIIIUS50q+t7/qxYMu6+Da+oHuFiju2+y8TjQu/b257fEIaXzQ9Dnv37kX/s/zvVMkbv78CAPDq0s1IlWmF7iGHNfNf0L4cehyGDRuWeoVEtSAtB+60WaO6Rj/gfDN2PVP4KsNOWKz/28T9g7dta9Npva3Y/YDkqKrWltSq95Vh02nLYKPj2Shz1tbQ/d8q7NZrgVX1ib0GVDqYHhUSVxGhzSSvOctHu0RRcVDh4nWnss3v/N16igEClY/Xmn3G+n3m9aWaH+Wvn+soaGsOACtXrkw4xq6hIDYSpBt9EkhW0uIKY1bh2oxChR2FU+ioVThzVEGzQCNGjAAAjB49GkBwvmnLz0+7FsHOePHTnT20Pu15Dq03E6va87qxn/KT6fG4kSNHlqLGYs6cOQCCtVl2JgtInhWJmoGx1zTK64x9VthZFPf/KC8r3G6fm3a9F6No857StWtXAEXPTrM8y5YtS6iv9SIVVYaospYG6+v+QCnvc+bM0cC9ChHLzEQshZgyqexTFGk5cBdCCCGEOKDQsYU78C58QY8r7oXfOThv3mg5AOCHrEMSkqrV0n+BaNDON+k9qGWhKU8JFHeSd9c9ePXVV0t8nKgepOXA/dtvvwUAHH+8PyVFhYiqjquY1atXD2tRL0l1aNMmcWU5sQq7Vabt23rYG7WNwEhsGfg9KlLlgYR5vv766wCS1Rb7yTq5foKtMmM90tjZCcJzxXPPaIC0lWa6PM5ds8BrbO0y2SbOPffcFM+AKC32ukb5MmZboR9x91jOpth+Zm3YrT0uj6ctPJU5Rih17W2tnS29StgZHn63SrtVKNnW4lGYCxesRenqXoRHlQNFlG34Y489BiBQM62/evbDMF/4UesALFat5wwYrxPPGfOmdytROh5//HEAwL333gsAGDhwIIBgRhII2i29v/DacKbaemjifbu42S2rvCetKUNwna0dvZ3tsmuoODvE9sPYC4z3QC9T7MtAYBdP71HxAXThOhmmyXbNMlhvMjYasF/m0i/6zMzMjK+Z27VrF7A3p9RpWWbPnh1vA6IKkZGRmv26bNyFEEIIIcqZuOLueJUpSHyZ9eg6upikCnJ9ZX37Kv9ldu13JVfaRRVH7iCj+d3vfgcAeOGFFwAESpJVtIFku1X7xh/lv9x+2v3tSn1XbeT/1re0VfCqQrRPloHnkGW0Crz1JAAkq6EWew7t+gEqI0ybn9b2372e1tsPvQ+wTYiKg+2b14TXzyrt7hoOqnm27fN62jQIlUR6ivj0Uz+oiZ0RCvNjzfy7d+8OIGhfbIecMbCxG+xsAH8/2hfw4O3JZ0YJZY0V3pQPlDeZkmLtyO+66y4AyZEj+RkWq8H2YWLXInBGbNOmTQCCKK/iwMAIvYxm3Llz5/hvbK/sc9aXOrfb9VrEPhOtFyL2G/f+zDbE/sp9qaBHxRKwXqKorPM72xNn2NasWZNUT7ZNG3WVadv1WywLy8rvjN/g39+SY1GkSkZGRvwc16tXDyg/wT3lyMyiepKWA3chhBBCiIqGL+ZchM7vGQ0OBgBsqN8hwXS2zaZvAABrP/LFhq9e9d3UlsSPO+n6x1/jlltuKVW5xYEnlpEZtI9i9isLaT1wp10rfb1a/+BAsocXG93R2taFecAAUl8lD0RHYLTKgFvOyiJur1tYJuthgufDKiNAsqedKOyKeioc9MlrPdZYTz/uebIzHmwD4sBDW2leD15H65WCSrv1NuMew2vN9mUVN9du1t3OWA2nnnoqAODzzz9PyDNs9odpU4mz6rFtv7ZfWuUehc3Z22/cwpFaVTuK7z333JPyvo8++iiA5D55ww03lGuZhBCirIwdOxavvPIKFi1ahLp166J///548MEH0a1bt8hjJk2ahCuvvDJhW506deLjtqpIWg/chRBCiJoOVdgnnngivo0uFKNMZOwCUmsSZgMJ2hd0umB1oSDGNGnKSFxXo0Cy8GVdAR9yyCEJedIVtGt+R/McloeLUpmGFQWYhhWUWG+aezVp0iQI3ui8jFMtjRX4+cZq+59rGh/uv+B6HgoKCnDI9u/97TN87y/33/ceSsuRj9+ql+UUmDVrFq6//nr07t0b+/fvx+9+9zucdtppWLhwYaQoC/h9ZPHixfHvxYmRkcRSXJwa0+JUIYSoOHjTpVu4PYmzem+vj+HMM8+s6FIJIUSN5o033kj4PmnSJLRo0QJz587FSSedFHlcLBaLr4lIB9J64E6V4d133wUQvFG75jF8w+f0N79bN1Q8hq4J+RZv37w4hc/FMjZkMxCoB9bto1U2/vd//7ekVS53WIY333wTQHJoedaBqoVr9mAD7tAUgftapYZTT1xYxHPJ/bhg0YZud5URG6xK9n4Vh3Ufx7bBBaOtW7cGEFxPmkK5LgWphvE62oViNggX24gN+sI2csIJJwAAPv7444QyAUG7oWoX5eLVmsbYQGm2/nFbmQh4b6gO3HzzzZVdBFECXFX2vfcSFV4q7dbtadQzkn2Mn9xug2i5zz7+xn2pcjJv60KS93zeB+rXr59QRmtSR7PYI488Mp7nggULACSb4dl6Mi/Wk2Wyzyu333/y4240atQI3es1DBKiV5nafv1X12rl18eZTdizZw/WvTQZAHDfmHdQVqS2l46E2ZMi2LFjB9q3b4+CggL06tUL999/P3r06FHi/CrKxr1ser0QQgghhBBViIKCAowcORInnnhiwouepVu3bpgwYQJmzJiByZMno6CgAP3798fq1asrsLQlI60Vd/LNN/6q7aOOOgpAYsAXYhU7a4tHNY6qMN++bYAmKglUE5muu5CBqgHzsGGgeWxVgmXi4j+WmeeS9XTd+FnFnPWmgmHVF54juwCR14RKiT3Ohb/xmp988smlqK0oDTY8Oa8nFwhT4bKBfLjw2/2N19q2gSjXooRqGRU6lokBWRjwx9338MMPD62HLZN1/UrsovL5W/3jj2ngq3CeMZmhyiNEZcKBR5cuXQAE/dUqzNZhA+/53J828uyrVLapWLswLfYZ2p0zDeu4gfcB62qS+1nXrQyyRDt1t5zMy/ZjpmndX1obfxt8MUGhbxDUddneQlvpfJ6rLfE0Pc9D6899N9X/efKjpPNTUp72VpQ5jZrK9ddfjwULFuCjj4q+Dv369UO/fv3i3/v3748jjjgCf/nLXzBmzJiSZZqRkaIfd9m4CyGEEEIIgRtuuAGvv/46PvjgA7Rt27ZEx2ZlZaFnz55YunTpASpd2akWA/df//rXAIAJEyYAANq3bx//zdrj8i2ab+XW3aFdWW5t7ixUhV1beJsH1QQqFRdffHGJ63igYZleeeUVAMF5sfbnrj0w6x51bqjc8FgqG9aumZ9UdHjOw2zcV65cCSC45qLi+NWvfgUgCLdury9nbWjrbm3igeCaRtmuExsUhvtZxY7bA/vzANreUo23XiSsas+2bb1pRHkYWOb5azo6+3GMULDTDzJzww3nh+4vREXy5ZdfAgjWbdkZs6i1RHbNh1Wi2e/DXLBS/WaaVLVt4EO7/ovPAKZJ9Z/PAtaB6efkBNGM2L+5D9PeuHFjQt6sry1TlPthlmn79u34aLv7nNubdL86+OCD0dVbD+wDFr7+GQDg3Y0l99Muyobnebjxxhvx6quvYubMmejYsWOJ08jPz8fXX39dOgcDGSl6lZHiLoQQQgghajLXX389nn/+ecyYMQMNGjSIm1Y1atQo/qI2bNgwtGnTBmPHjgUAjB49GieccAK6dOmCrVu34uGHH8bKlStx9dVXlzj/WGYmYsWYe3K/slCtBu5XXXUVgCBoCBCsJqZqZlfWWz+yfNPnJ9++aftNZY+fTNcGjHFhGj/++GMpa1ZxsIx8U43yquP+Zs8JlRsqsFRRomwKqYRQTWFno5rq+gKWl4uqA6+nnXXi9QwLTsa2wH2sbTvbEPsMt1vl3XpqsvsDQZ+1niyilHfrUYnYPmDV/WVe07hXJnl/EFUFBkzjZ8+ePQEE3sDYD6jAsz/b+7i1ibcextxngrWLt+ub+Ny1/dZVt4HkGXHeS+ghyl0nxm1Mm+XjPrY/895j19OwjHYmODc3NyF9Nw/O6tWpUwf7Fvrrrha/twJl4fBHR2LkyJFlSqOmMn78eADA4MGDE7ZPnDgRV1xxBQBg1apVCbPAW7ZswTXXXIN169ahcePGOO644zB79mx07969oopdYqrVwF0IIYQQQtQ8ohwMuMycOTPh+6OPPpog9paJjMwUF6dKcU/CVWUfeOABAIH6xrdmvnVTXaDqRkXQ+h7ndh7PT7sfkOyFwnrSqMrYVf7uavmofXku7DnkObHniLMe3N8qmlRd6CFk1KhRZauUKFduvPFGAIGtO1UzKlwdOnRI2B5mI25t1a2dKdsfj+V+VErYLrkWxapqQOBNg3lZG16rnPN3pmUjRfKT7X3JkiXxY6W0i6oK1dsXXvC9nRx66KEJv1NZtpFGqUizD7Lv0XsLf3e9f1EhZ99xY6q4afH5y2eB7d/WYxn7Hm3e3Wcpt9nZOuun3UaOZV5W7bce5xifxL1fWB/2derUwZ71vnet0tq2D3p+LC655JJSHStqFtVy4C6EEEIIIUSFIcW9fKBa++yzzwII3rathxOrKlBh5naqxTzO2vC5CoD1TsE3+NIsdqhoWEaqM1QreF7cenIbzwXrbX3hW68ExdlC87uU9qoNlXdy7733Agi8zLCtuB4YrO9o9jMb1dT6cbaeL6juc00G+6Frt8r1Lex/zDvMW1FYWewsE4+jMucq7kJUdebMmQMgUMzt/Zj9xLZ/e3+mysxnqWvjHhWVOGq2y1WsgeDewU+mbW3j3Vk8uw6G3tuo/lORt3FGeF+ysSGstx2r+rtpMM+CggJkNXKiq5aCOXPmSHEXKVHtB+5CCCGEEEIcSGIZGYil4OoxlX2KosYM3IcPHw4Ace8PNkIb37qtOmxVcyoAVAqoNrsRRQm3hUUAreqwzDwv1o7Q3UbVgSqo9XEb5SfXqqrczmsl0os77rgDAPDQQw8BAHr16gUgUQWP8r9uFXi7hmTDhg0AAv/NVNWohlkPGC42Uiq/Mw32aSp01tONXZvy6aefAgBuuummsNMgRJVk3LhxAID7778fADBw4MCE39nebdwRu96JSrtd4wQE/ZfrnHisjaPCWdlGjfwgCOy3fJ6yD9q1LmGzYXbmgPWgcs407b2G62Os73mrvLO+rsrP/HmOdu3ahRZHDwAA/HbUtwCAsQ/MRCq0u+9a/O53v0tpXyGAGjRwF0IIIYQQ4oAQS9HGPSYb9xLx3XffAUDcR2dUtDi73fqypUpXlALAY+k/NJ1gmadNmwYgvJ5U5a3Pe+s320aoJNyPn7w2Q4cOLceaiIrm9ttvB4B4gAs35HTz5s0BBLM1hGoY1a/vv/8eQKBosf9ZRZ3KHtsa0weS10wwD6p5VArnzZsHIPA8ddhhhyUczwiMX3zxBQDIx7JIa6juPvPMMwCAHj16AAjUbfYPquPW9p3bqWTzEwiem/R9zk8bKZVqvfVUY+Ot2OOsXbq7zaZtbdRZNq5RoeLO+lkPc9bjlfv8svXbs2cP0Kp0wymp7aKk1LiBuxBCCCFEefLRuv0oKCjASVePAAA8fHh7AMDSGb5p3V9eXZywf++/3Ylf/OIXFVtIcWCJxYBYCvbrIS6SS0KNG7hbTxj0NmNX2lv7dPpypR2sVdxdqBKef/755Vn0SoF1eP311wEkKqVRkShZ/02bNgEIbAV5LPffunUrgMCm/eSTTy738ovK47e//W3SttGjRwMI2gQ/iY1ISBtY6/nCrj2h2sYoikCyrTqxXjLatWsHAPj6668BAIsWLQIQKG2cBZAyJqoTHDQ+//zzAIL4C+yD7FN2jRb7HvuHuwbEemOzduLEtQ1397MeXQj7oPVa425juWxcEe5rY7JwO5/tJCoOi2vjbiOsJ56jxLpGoUF7NSSWkeLAXYtThRBCCCEqnbeXbPFFqs6nIjs7G8f2OgkA8MeLPgcAzMhvj0svvbQyiyjSnJiXSoxYEefhhx8GECiCVgkEqrcN7GOPPRb/n0oLmxBtB2+77bYKL5dIT6jAsy3R9zpntti2qHpZu1SrBp522mnx/63iZm917Lv0WENbd8UPEDWR8ePHAwC6du0KIFk9Zx+1311PY5zFshG3bewEayPO4zgry/5P2N+pmrOvAsCxxx4LIFDarRcoqvucRaeibm307do0q7i73tK4jeViPe33WCyGQS38Y/Ys9AfuB12UPBMp0pvc3Fw0atQIm/87Cw0b1C9+/+070OSYQdi2bVvCGpFUkeIuhBBCCHEAmLWBjhcOxogRIyq7OKIaoIF7CanpanJ1nk0QlQcVOetL2qpg1p6VUGVzvc5YbxI8NirSopR2UZPhoPLOO+8EEHhe69SpE4BkTzDsP64SzX5q7cxtv+aaMv7O9U785P42ngN/d1V+bmvRokVCfajO22PsejVut15lWBfrVQcIbPF5DMvHctMr1sKFCwEAY8aMgagBVJCNe9mOFkIIIYQQQlQIUtyFEJWGtSO1noqoYHG79ePM4+iD3VXFrMcnq6wxD/ppF0IE6vAtt9wCAGjWrBmAoN9QbWZfdNeZ2JgejRs3TjjWxl3gdirw1r7cem/hehR3Zo3buO7MRj+nZyrrj51rspgWPd/wnsJI4MzbtZ233rBYbtrsz5kzB0AQrVbUEGKx1Fw9ltEdpBR3IYQQQggh0oAqp7j/+OOPuPnmm/HWW2+hoKAAQ4YMwaOPPhq3sxNCBKR7f6E97QMPPAAgUOSoblHNo72q9dXMT6qCrspu/bfT0wP3sXa1QgghRGnJbHcUMlPwEpNZODNTWqrUwH3Hjh0YMmQItm3bht/97nfIysrCo48+ikGDBmHevHlJgRKEqMmovwghDhQ08/jVr34FABg0aBAAoH379gn70ewFCMxnbCBDLgSlGcq6desABC/edIlHExmanvClev369QCAyy+/PLK8U6dOBRCYzdH8xprj0ZyFYkDr1q0T8uRidYoG3O4uiOc2snLlSgDArFmzAABPPfVUZDmFKCtVauD+1FNPYcmSJfj888/Ru3dvAMAZZ5yBI488En/84x9x//33V3IJhag6VKf+Qo8uY8eOBZDsn50PSg4IGOWRMwt2fyBZpbc276tWrUrIWwghhKjqlCgA0/vvv4+f/OQneOWVV3Duuecm/Pb888/jsssuw+zZs9GvX79SFaZPnz4AgM8//zxh+9ChQ7Fs2TIsXbq0VOkKURns3r0bPXv2BAB89dVXcfOPzZs3o0ePHujYsSM+/PDDJJOOVKmO/YUDdzvITnXg7s4yWKWMx3KRGoO4FKXiCSESoXnb0UcfDQAJAWQOOeQQAMGCT/Y1KvEcbtjF5txONTwnJwdAsDC0JH108uTJAAJzO5rRWVWf912W1W7n/YNlXbt2bTwPlnP+/PkA5O6xpsMATKkGVCrp/pYSLU4dPHgwDj30UEyZMiXptylTpqBz587o168f9uzZg5ycnJT+SEFBAebPn4/jjz8+Ke0+ffpg2bJl8VXgQqQDdevWxbPPPoulS5fi97//fXz79ddfj23btmHSpEnIzMxUfxFCCCFESpTIVCYWi+Hyyy/HuHHjsG3btribpY0bN+Ktt96KD05eeOEFXHnllSmlyTftzZs3Y8+ePfE3dhduW7NmDbp161aSIgtRqfTt2xe33347HnzwQZx77rlYv349pk6disceeyweWlz9JeC3v00MB37vvfcCSFbgWUcboMUNzMJt1rUkX2hcBU0IkRpWXR49enT8/6FDhwII+qFV1m3wM2t/zv3YR6+44ooSl4/q/KRJkwAELimZF8vGewrvD7aMvNdS9f/ss8/iefzhD38AAFxwwQUlLp8QZaXENu7Dhg3D2LFjMW3aNPziF78AALz44ovYv39/vMMMHToUb7/9donSZeew/lGB4OHMfYRIJ+6++268/vrrGD58OHbs2IFBgwbh17/+dfx39RchhBBCpEKJB+6HH344evfujSlTpsQH7lOmTMEJJ5yALl26APDVsDAlsCis+zcXLjJzAyAIkS7Url0bEyZMQO/evZGdnY2JEyfG1R9A/aUo7rjjjoTvXHBbv359AIEqxvPperigikdljUrbt99+CwC47bbbDlSxhagxUH0GgOuuuw4AcOSRRwJAfFaRdry0eSfsvzQD/P777wEEnmzKAtV6enjhehjavMdMEBwbROm7774DACxYsAAA8PTTT5e5TEKUB6XyKjNs2DDcdNNNWL16Nfbs2YNPP/0UTzzxRPz33bt3Y9u2bSml1apVKwBAkyZNUKdOndDpa26j2yYh0o0333wTgD+oXrJkCTp27Bj/Tf1FCCGEEKlQIq8yJCcnB61bt8Z9992H3bt3495778WaNWvib7KTJk0qsc0uAPTu3RuxWCzJS8Zpp52GZcuWYdmyZSUtqhCVzvz589G7d29cdtllmDdvHnJycvD111/H14iov6TOQw89BAA4/fTTASSHXXdNh6i403Ro9erVAHyXmUKIimPEiBEAgr5ItZv9909/+lOFleWmm24CkGzLzpnK8ePHV1hZRPWgor3KlEpxb9asGc444wxMnjwZeXl5OP300+ODdqB0NrsAcP7552PUqFH44osv4t4yFi9ejPfeew+33npraYoqRKWyb98+XHHFFWjdujX+9Kc/Yfny5ejduzduvvlmTJgwAYD6ixBCCCFSo1SKOwC8/PLLOP/88wH4i1MvvPDCMhdm+/bt6NmzJ7Zv345bb70VWVlZGDduHPLz8zFv3jw0b968zHkIUZHcddddGDNmDN59910MGTIEAHDffffhjjvuwL/+9S+ceeaZpU67JvYXKnOnnXYagGABLm9jrg0tvUXs2rULQODvfuTIkRVSViGEENWfKu3H3eXss89G48aN0ahRI/z0pz8tbTIJNGjQADNnzsRJJ52Ee++9F3feeSeOOeYYzJo1q1oOQkT15ssvv8T999+PG264IT5oB/xInb1798Y111wTD+ldGtRfhBBCiJpFqRX3/fv3o3Xr1jj77LPxzDPPlHe5hBAikoULFwJI9qrj+nGnjTtt/TlDKIQQQpQXaaO4T58+HRs3bsSwYcNKm4QQQgghhBAiRUq8OPWzzz7D/PnzMWbMGPTs2RODBg06EOUSQohIunfvDgC4/fbbE7a7E4j0WDFu3LiKK5gQQghxACmx4j5+/HiMGDECLVq0wHPPPXcgyiSEEEIIIYQwlNrGXQghhBBCiJpM2ti4CyGEEEIIISoODdyFEEIIIYRIAzRwF0IIIYQQIg3QwF0IIYQQQog0QAN3IYQQQggh0gAN3IUQQogqRkFBAZ5++mkce+yxqF+/Plq2bIkzzjgDs2fPruyiCSEqEQ3chRBCiCrGbbfdhhEjRuCoo47CuHHj8Jvf/AbfffcdBg0ahM8//7yyiyeEqCRKHDlVCCGEEAeO/fv3Y/z48Tj//PPx97//Pb79ggsuQKdOnTBlyhT06dOnEksohKgspLgLIYQQRbBixQrEYrHIv/Jm37592L17N1q2bJmwvUWLFsjIyEDdunXLPU8hRHogxV0IIYQogubNmyco34A/uL755ptRu3ZtAMCuXbuwa9euYtPKzMxE48aNi9ynbt266Nu3LyZNmoR+/fph4MCB2Lp1K8aMGYPGjRvj2muvLX1lhBBpjQbuQgghRBEcdNBBuPzyyxO2XX/99dixYwfefvttAMBDDz2Ee+65p9i02rdvjxUrVhS73+TJk3HRRRcl5NupUyd8/PHH6NSpU8kqIISoNmjgLoQQQpSA5557Dk899RT++Mc/YsiQIQCAYcOGYcCAAcUem6qZS4MGDdCjRw/069cPJ598MtatW4cHHngA55xzDj788EM0a9asTHUQQqQnMc/zvMouhBBCCJEOzJs3D/3798c555yD559/vkxpbdu2Dbt3745/r127Npo0aYL9+/ejZ8+eGDx4MB5//PH470uWLEGPHj1w880348EHHyxT3kKI8iE3NxeNGjXCtm3b0LBhw3Lf36LFqUIIIUQKbNmyBeeddx66du2Kv/3tbwm/7dixA+vWrSv2b+PGjfFjbrrpJhxyyCHxv5///OcAgA8++AALFizAT3/604Q8DjvsMBxxxBH4+OOPD3xlhahBPPnkk+jQoQOys7PRt2/fKu1yVaYyQgghRDEUFBTgsssuw9atW/HOO++gXr16Cb8/8sgjJbZxv/322xNs2Llodf369QCA/Pz8pOP37duH/fv3l7YaQgjDiy++iFtuuQVPP/00+vbti8ceewxDhw7F4sWL0aJFi8ouXhIauAshhBDFcM899+DNN9/Ef/7zH3Ts2DHp99LYuHfv3h3du3dP2qdr164AgKlTp+L000+Pb//yyy+xePFieZURohwZN24crrnmGlx55ZUAgKeffhr/+te/MGHCBIwaNaqSS5eMbNyFEEKIIvj6669xzDHH4KSTTsLVV1+d9Lv1OFMenHbaaXj77bdx7rnn4rTTTsPatWvx+OOPY+/evZg7dy66detW7nkKUdPYu3cv6tWrh2nTpuGcc86Jbx8+fDi2bt2KGTNmFJtGRdu4S3EXQgghimDTpk3wPA+zZs3CrFmzkn4/EAP3GTNm4JFHHsHUqVPxxhtvoHbt2hg4cCDGjBmjQbsQ5UROTg7y8/OTgp21bNkSixYtKlFaubm55bpfFBq4CyGEEEUwePBgVPTkdN26dXHnnXfizjvvrNB8hRAlo3bt2mjVqhUOPfTQlI9p1apVPHhbSdHAXQghhBBC1DiaNWuGzMzM+IJwsn79erRq1SqlNLKzs7F8+XLs3bs35Xxr166N7OzsEpWVaOAuhBBCCCFqHLVr18Zxxx2Hd999N27jXlBQgHfffRc33HBDyulkZ2eXeiBeUjRwF0IIIYQQNZJbbrkFw4cPx/HHH48+ffrgsccew86dO+NeZqoaGrgLIYQQQogayUUXXYSNGzfiD3/4A9atW4djjz0Wb7zxRtKC1aqC3EEKIYQQQgiRBmRUdgGEEEIIIYQQxaOBuxBCCCGEEGmABu5CCCGEEEKkARq4CyGEEEIIkQZo4C6EEEIIIUQaoIG7EEIIIYQQaYAG7kIIIYQQQqQBGrgLIYQQQgiRBmjgLoQQQgghRBqggbsQQgghhBBpgAbuQgghhBBCpAEauAshhBBCCJEGaOAuhBBCCCFEGqCBuxBCCCGEEGmABu5CCCGEEEKkARq4CyGEEEIIkQZo4C6EEEIIIUQa8P8B7QATXnS7rvIAAAAASUVORK5CYII=", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures (displayed as z-statistics map) correspond to homogeneity test of\ngroup-specific spatial intensity for four groups. The null hypothesis assumes\nhomogeneous spatial intensity over the whole brain,\n$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\nthe number of voxels within brain mask, $j$ is the index of voxel. Areas with\nsignificant p-values are highlighted (under significance level $0.05$).\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nimare.meta.cbmr import CBMRInference\n", - "inference = CBMRInference(\n", - " CBMRResults=cres, device=\"cuda\"\n", - " )\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "\n", - "# generate chi-square maps for each group\n", - "plot_stat_map(\n", - " cres.get_map(\"schizophrenia_Yes_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", - " threshold=scipy.stats.norm.isf(0.05)\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"schizophrenia_No_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", - " threshold=scipy.stats.norm.isf(0.05)\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"depression_Yes_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", - " threshold=scipy.stats.norm.isf(0.05)\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"depression_No_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"depression_No\",\n", - " threshold=scipy.stats.norm.isf(0.05)\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures (displayed as z-statistics map) correspond to homogeneity test of group-specific spatial intensity for four groups. The null hypothesis assumes homogeneous spatial intensity over the whole brain, $H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is the number of voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values are highlighted (under significance level $0.05$). " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GLH testing for group comparisons among any two groups\n", - "\n", - "In the most basic scenario of group comparison test, contrast matrix `t_con_groups` can be generated by `create_contrast` function, with `contrast_name` specified as \"group1-group2\". " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type5 = index_2\n", - "INFO:nimare.meta.cbmr:type1 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type3 = index_5\n" - ] }, { - "data": { - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GLH testing for group comparisons among any two groups\nIn the most basic scenario of group comparison test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with `contrast_name` specified as\n\"group1-group2\".\n\n" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_groups = inference.create_contrast(\n [\n \"schizophrenia_Yes-schizophrenia_No\",\n \"schizophrenia_No-depression_Yes\",\n \"depression_Yes-depression_No\",\n ],\n type=\"groups\",\n)\ncontrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate z-statistics maps for each group\nplot_stat_map(\n results.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n results.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n results.get_map(\"depression_Yes-depression_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=scipy.stats.norm.isf(0.4),\n)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Four figures (displayed as z-statistics map) correspond to group comparison\ntest of spatial intensity for any two groups. The null hypothesis assumes\nspatial intensity estimations of two groups are equal at voxel level,\n$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\nwithin brain mask, $j$ is the index of voxel. Areas with significant p-values\n(significant difference in spatial intensity estimation between two groups)\nare highlighted (under significance level $0.05$).\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GLH testing with contrast matrix specified\nCBMR supports more flexible GLH test by specifying a contrast matrix.\nFor example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\nrepresented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\nfunction. Multiple independent GLH tests can be conducted simultaneously by\nincluding multiple contrast vectors/matrices in `t_con_group`.\n\nCBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\nwhen it's represented as one of elements in `t_con_group` (datatype: list).\nOnly if all of null hypotheses are rejected at voxel level, p-values are significant.\nFor example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\nthe equality of spatial intensity estimation among all of four groups (finding the\nconsistent activation regions). Note that only $n-1$ contrast vectors are necessary\nfor testing the equality of $n$ groups.\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inference = CBMRInference(\n", - " CBMRResults=cres, device=\"cuda\"\n", - " )\n", - "t_con_groups = inference.create_contrast([\"schizophrenia_Yes-schizophrenia_No\", \"schizophrenia_No-depression_Yes\", \"depression_Yes-depression_No\"], type=\"groups\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "\n", - "# generate z-statistics maps for each group\n", - "plot_stat_map(\n", - " cres.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", - " threshold=scipy.stats.norm.isf(0.4)\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", - " threshold=scipy.stats.norm.isf(0.4)\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"depression_Yes-depression_No_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", - " threshold=scipy.stats.norm.isf(0.4)\n", - ")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures (displayed as z-statistics map) correspond to group comparison test of spatial intensity for any two groups. The null hypothesis assumes spatial intensity estimations of two groups are equal at voxel level, $H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values (significant difference in spatial intensity estimation between two groups) are highlighted (under significance level $0.05$). \n", - "\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GLH testing with contrast matrix specified \n", - "\n", - "CBMR supports more flexible GLH test by specifying a contrast matrix. For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast` function. Multiple independent GLH tests can be conducted simultaneously by including multiple contrast vectors/matrices in `t_con_group`. \n", - "\n", - "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors) when it's represented as one of elements in `t_con_group` (datatype: list). Only if all of null hypotheses are rejected at voxel level, p-values are significant. For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing the equality of spatial intensity estimation among all of four groups (finding the consistent activation regions). Note that only $n-1$ contrast vectors are necessary for testing the equality of $n$ groups. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type5 = index_2\n", - "INFO:nimare.meta.cbmr:type1 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type3 = index_5\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\ncontrast_result = inference.compute_contrast(\n t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n)\nplot_stat_map(\n results.get_map(\"GLH_groups_0_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"GLH_groups_0\",\n threshold=scipy.stats.norm.isf(0.4),\n)\nprint(\"The contrast matrix of GLH_0 is {}\".format(results.metadata[\"GLH_groups_0\"]))" + ] }, { - "data": { - "image/png": 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79u1xRronWqdqt0BfVVWJMZWzUV5eHmtSXhvNw/3LBmTHHXdEmzZtcra1a9cOv//979G5c2c88sgjTXLQbowxxhhjmhcbLXJqsXLOOefghBNOwLvvvot58+ahU6dOGDhwYNYjy8UXX9zYRTTGGGOMMY1InWzc60GLGrjfdNNNiZFdFUZkffLJJ9GtWzfsueeeGDx4MABgxowZuOeee3DjjTdi8eLFG6y8TZV77723oP0WLlyI888/fwOXxhhjio9x48bh5JNPxqRJk+rlgs6YJNjGSElJCbp27Yrvfve7+NWvftVgEdrNxqVFDdyPOeaYbICj2mBjf/HFF/Hiiy9uwFJtONStY0PB1dm1MXPmTA/cjTHGmEbkqquuykbq/Ne//oVx48bhtddew4cfflinCKGmZkpSmb9a96tnPi1q4N4cF2k2BhvqhcAYY4wxDcuhhx6andU59dRT0alTJ9xwww34y1/+Ehug0TRtvDjVGGOMMaaFMHToUADA9OnTG7kkzQvauBfyVx9alOJujDHGGNOSoe/yDh06NG5Bmhk2lTHGGGOMMfWivLwcCxcuREVFBd566y1ceeWVaN26Nb73ve81dtHMeuCBuzHGGGNMM+XAAw/M+d67d288+OCD2HrrrRupRM0Tu4M0xhhjjDH14s4778T222+P8vJyjB07Fq+88gpat27d2MUy60nBA/dOnTqhrKwMFRUVG7I8xtRKWVlZwf74jTHGmJbM4MGDs15lRowYgSFDhuBHP/oRpk6dis0226yRS9d8SKEwjy/19ctX8MC9Z8+emDp1KhYuXFjPLI2pH506dULPnj0buxjGGGNMUVFSUoLrrrsO+++/P+644w5ceOGFjV0kU0fqZCrTs2dPD5iMMcYYY4qU4cOHY/Dgwbj11ltx7rnnOghTA2Ebd2OMMaaZM3bsWDz//PN528855xy0a9euEUpkWgLnn38+jj32WIwbNw6nn356YxfH1AEP3I0xxphGYsyYMbHbTzrpJA/czQbjqKOOQr9+/XDzzTfjtNNOQ0lJfb2Lm43lxz1VVVVVVc80jDHGGGMK4r777gMAbLnllgCANm3a5PzOYcny5csBAN///vcLTvuZZ54BAGy66aYAgJSYLqxcuRIAsGjRIgDAyJEj61R2Y5SlS5eiffv2uLxNX5Slal+eWlFViStX/gfl5eXYfPPN65yfFXdjjDHGGGPqQUZxL8TGvX5YcTfGGGNMg/Poo48CALp16wYAWd/h6XQ655OqeGVlZc7x/M7PyZMnAwDOOOOM7D40Ndp9991j0yb8ziGPpr1q1SoAwPz58wEAxx9/fJ3qalouVNx/tWlflKVqH5ZXVK3D/y1ff8W9EJeTxhhjjDHGmEbGpjLGGGOMqTe33347gMh2vU+fPgCA0tLSnP24EJJ26K1atQIQqeGENu5Lly4FAPTq1QsAcMUVV2T3GTx4cM6xTJOfhKr+mjVrctJet25dThno8vrhhx8GENnCn3XWWTXW3ZiN5Q7SirsxxhhjjDFFgBV3Y4wxxtTIE088AQDo0qULgEihDu3St9pqq5xjqHLzk+o2j1m7di0AYLPNNgMAbLJJZkhSUVEBIN8Gnjby3D/cxn14DNNicCHmRa8yVN4JZwGYDmcJWKc33ngjuy/zYBpfffUVAODoo4+GabmkC3QHWV/F3Iq7McYYY4wxRUCjK+7jxo3DySefjEmTJmGvvfZq7OKYZgbbFykpKUHXrl3x3e9+F7/61a/Qo0ePRiydMcY0TR5//HEAQPv27QFEtt9Um6lQU0UHIu8xc+fOBRCp20Rt2KmCU+VmmitWrACQr7xTBQ99s3Mb9+ExakfPcjJPfhL+zjJzVqB79+4AImU/TFvt4l988UUAQHl5OQDgmGOOgWk5bCwb90YfuBuzMbjqqqvQp08fVFRU4F//+hfGjRuH1157DR9++GF2KtUYY4wxpinjgbtpERx66KHZGZ1TTz0VnTp1wg033IC//OUvOO644xq5dMYY0zSYOHEigEg9V7WbKjM/qY4DkV0596V6zX35O9Vs7kc1myo4faqHaj4Q7+9dI6PyGE2DeTBPqv+sn9rAcz+WmZ8A0LZtWwCRjTs/qe4zEizP5bBhw2CaPyUF2rjXNwCTbdxNi2To0KEAgOnTpzdySYwxxhhjCsOKu2mRzJw5EwDQoUOHxi2IMcY0Aeg1haaDVI2pJmtUUyrVoe336tWrAUR28fSVTlSR5/2XNuO0T2eeVMtVVdfvITyGaVBJZzmZJxV5lpn7sZ6sA8sW1lOjsvIY7sMZBqr3PLf77rtvYrlN8bOxFHcP3E2LoLy8HAsXLkRFRQXeeustXHnllWjdujW+973vNXbRjDHGGFPkeHGqMQ3IgQcemPO9d+/eePDBB7H11ls3UomMMcYYY+qGB+6mRXDnnXdi++23R3l5OcaOHYtXXnklZ+rTGGNaIs888wwAoGvXrgCiBZbt2rUDAHzzzTcA8k1JCM1CwmO5L01K+MnfO3XqBCAyLWGaNF/hwlGaxPA7TW1ovhJuSzqGadL0h6ZADKy0cOFCAJHJDOtNcx6WOawnYbk1QBTTYL2XLVsGIDrX3//+9/PSMsVPCQo0lamqfZ+a8MDdtAgGDx6c9SozYsQIDBkyBD/60Y8wderUnCh8xhhjjDFNFQ/cTYujpKQE1113Hfbff3/ccccduPDCCxu7SMYY0yhQuFC3iFSst9xySwC5bh+BSIEOF2pSeaYKzsWmVLm7dOkCIFLMVRVfvHgxgGhhqaarCne4jeXgd34yTSruScq7LpDl77qgNkxboZtI1kdnHiwSNW/SBdq4pwvYp8bj63W0MUXK8OHDMXjwYNx6663ZG7UxxhhjTFOmySjuY8eOxfPPP5+3/ZxzzsnaixnTkJx//vk49thjMW7cOJx++umNXRxjjNloPPvsswAilZjqMKFdNhXqLbbYAkDNrhhp4819qDRTteZ3Ku1Urr/88sucPKm4UwXn8WoDD0QuFzWIk7qFZB49e/aMTZsBp9SWn3mFdvUK9+GxrIe6muR54bm3V7PmRcHuIOsnuDedgfuYMWNit5900kkeuJsNwlFHHYV+/frh5ptvxmmnnVbjjdkYY4wxprFJVYWvrsYYY4xptrz22msAIqVZFWrartObCu3S+Z2qcU3Ke21w2MEATdOmTQMALF26FECkrFNMoVJPO/s5c+Zk0+rRoweAaOaASjnrQyV+8803BwBsu+22sfWpTz20Pl999VXO96QZBJ77IUOGrHcZTOOzdOlStG/fHvd12gFt07ULgCsq12HkwqkoLy/Ptsu6YBt3Y4wxxhhjioAmYypjjDHGmA0D15DRVp0KNe2w+Ul1m0o1vakkKe2hVxmi+1D91gl++ohn3lTLqYar+aLazAORpxaNy8E8tX7Mk3mo/3fNM84oIc67DRCdK5aF9vecxeDv/OQMAq/NIYcckpeXKR5anI27McYYY4wxxUhJge4gC9mnJjxwN8YYY5o5VKap/tJbTPv27QHkez6hUwiq20m24KFP80LU6nC7qvgsY5Kqz7KH/tD1GJZH/a8nRVbVvJLKRgU/DvVfT9/3mjd/p/pP23f7dzd1wQN3Y4wxxhhj6kE6lSoouFJ9AzB54G6MMcY0U+644w4AwM477wwgsr+mrTdt3an6Uomnul0fryvqC13VbpaFeVL1T1LL6aWF+4ewHsxDfagzTbWF1zKxzOvjHljXB/A7bd3p35227cyLZeW1GjVqVJ3zNi0HD9yNMcYYY4ypB6mSFFLp2l906/MyDHjgbowxxjRb6IedanWSmk2VmN5WiCrRNXmVSbIDTxqocDvt7DUvflKhjsuT0F6cyjvrx31r8z+f5AknjtCuPyx30rlh2dSvO5V2bue1MqYmPHA3xhhjjDGmHqRLUkgXoLjbxt0YY4wxOTz22GMAgO7duwOIlHZGJaXdNVVh2nSrzTfVYVW9aWdOZTtMo1C4P9XtJUuWAMi3SycVFRU5dQi3sR6Mvqpp0H/9+tiuh2UEIqWc55BQ7df1AVpPPfedO3fOKTOv3XHHHbdeZTXNG0dONcYYY4wxRc8rr7yCww8/HN27d0cqlcLTTz9d4/4nnXQSUqlU3t8uu+xS98xL0kgV8IeS+g29rbgbY4wxzYzNN98cQL7fdvWqwu3qqYXqMBXs8vJyAJF9N9Ohz/IwDVXvFW5n2XQWIMmenvtxFiDcpvXSfevqLYczDqqSA8CiRYty8qByTsWc6j63M2+9JoTni3lwP1N3li9fjgEDBuCUU07BUUcdVev+t912G66//vrs97Vr12LAgAE49thjN2Qx64UH7sYYY4wxpug59NBDceihhxa8f/v27bOLowHg6aefxtdff42TTz65znmn0imkSgrwKgPbuBtjjDEmgGovP+kthso0VV/dT32vE26ngs3vVOLj0lRVW5V07k/bcNq4U4FWZZpKdJhnkopNpZz1UPtzLZN6quFxVNHDPKmMMw9NU73jMG3OTui5pHKvCr7Z+Pzxj3/EgQceiF69etX52HRJCukCBu5pD9yNMcYYY4xZf+bOnYu///3vePjhhxu7KDXigXsj8NRTTwEA2rVrByB/xbkqH4sXLwZQtxXmXJXesWPH2DQ1T0bRO/LII+tcH2OKiUceeQRAvg2r+m1OivrIvjRy5MgNX1hj6sDtt9+e/b9fv34AIlWXaja/sx0zYirVYFXNaZ9NTyr8JKHnlySVXn9XJZ7PKZYxSclm3qGveaaZpKTzWcc8FFXHk34P66n29PSsw3PFc6eqPW3jGUGVebLsvDbcP7yeZ511Vmz5TMNx3333YYsttsCIESPW6/hUOo1UAbMlKekndcXzMcYYY4wxpsVSVVWFsWPH4ic/+UmOi9OmiBV3Y4wxphkQKtk6y0q7bNpRq4LO/Ri9kwoz1WX6GldlOsxT/a5rtNKkWSwqzj169AAQebLhdvU2E9qAq2pN1ZvqtdrAq596nUnjdlXy6SkGQM5ixvBYTZvK+YIFCwBEMwqc4eYAURX8pDUCZsMxceJETJs2DT/96U/XOw3buDcDaK7CDs8pyW222QZA/g1Cb0CEU3wvv/wyAGD//fdPzJP7bLvttjlpE50m5Y2BZXzjjTcARFN5vNE4EIQpNv70pz8BiAK06KBBP4mazOjvZMyYMdn/9eH/s5/9rF5lN8YYU3eWLVuGadOmZb/PmDEDkydPRseOHdGzZ09cdNFFmDNnDu6///6c4/74xz9i7733Rv/+/Td2keuMB+7GGGOMMaboeeedd3LEzdGjRwPIrEkaN24c5s2bh9mzZ+ccU15ejieeeAK33XZbvfJOldgdZNEyfvx4AECfPn0ARGoclTydHtTpMJ1u5FQmp/weeOABAJEqDkRq/s477wwgUv7UVivJbRWn9HQhT4cOHXLqdMABByTW25jG4sEHHwSQu3COJgGqoLN/JU1vJynuutgtDu7729/+NiePpMXhOl1/xhln1FxRYwqE93pta5x1pfkJzT7UhCapnSe13XBb0nd9BmofLCsry9nO/sJZs5pgGjSV4QJWPgOTXFNqPZLqEJrnJB2jx/JcqptHnnsts44NTN0ZPnx44iwpAIwbNy5vW/v27bFixYoNWKqGxQN3Y4wxxhhj6kFGcS/AqwziPRgVigfuDcSzzz6b/V8X9/DtmW/46vaRioB+51sjFQ4u2OEioTAghC4cogLPRS98k9eFSPyurr/4neoMXVeG9fze975Xy1kxZsPAWSfOFLGdhsqcKmUahj1JcSdMm6hiF6piOnOlqr3OaIUh28Oy0P2bKnrhLBzTsB29UdRVI5A/40v1V90R60yvtmUex/35bKnJHST3VXWbaWqe7AfsW+zP7C9xs2I6k6CLSjWYEcvC+qm6r+crzk0kj9VZPZ4Tna1gPXkczz1VXuaRNNtuTIgH7sYYY4wxxtQDe5UpEu644w4AkW05kBzOWVVu7qeKh9oQKnG2h7XZI2qZ+Oavear6T0WA+7MuYd1HjRoVm7cx9YXKOtU0DZakqmCojiUFWErqE7UpbUn9NcxL7eE1DXVnl+TuTd3nheo/y8f+x3KcfvrpsWmZlgMX4gHA3/72NwCRCrxmzRrs17MtsFU3IFXt+rAkc79/Z97KbD9g++IML2d2daaY6Xbt2jWbZ5JbQ6Izv/rc0v7Afs/9a1LcuQ+Pob28pqn7c5ZZf9c+THUdAL788sucbbp2hesGaGevbi25nc9XnYFjuuH1NE2fVCqFVLqAxamV9Ru4OwCTMcYYY4wxRYAV9wK59957AUSKgirRy5cvz+5L+3K+XVMRo1qtNnX8Xe3biNqlq/1suE1V/VAhrykPlom/s36sA1WIsJ6s+z333JOTF9WCk08+OTYvY5Kgwq62rapIJdnMxqFKutq2qlquaamapop9Teg+PFbvAUn1qikPtaunAk88E9ayoWJ+8E5UxNtg9cdvAwDSm2ZsrDfp2hMAkEq1zt63eY9XLzPcrjPIixYtyubJ9V3aVxRuZx5JkSpV/WZ/CNXvpOBOSWklqf1JHnD4GdZTg1nxeUklncfwnPG5qutr9DywDrx2prhIl6SRLmBxarqqfpq5FXdjjDHGGGOKACvuCYwdOxYA0KtXLwDAwIEDAURvzFS5Pv/8cwDAvHnzssfSto4rx/nWTTs3qvZq76p2r3yrV9+3cV4w9DceQ6WSdnw8Rn1Z81NVF6bDEM1hPbt16wYA2G677XLSZB70/T5r1iwAwCmnnAJj4rjvvvsARG1eZ5lUcWP/qy0KaiGwjWsaap9bU4RVVem1nEn9Tffj9qQ+H3dsUvkZTISqnhX4lgXjfFQu+Sra9tlnAIC2W2fu3Ztsk7l3V1VVZfse+9rChQsBRNGz1WZcZ2eBqN9SQU9aJ8LnEn9n2tru1SsNWbx4cfb/rbbaKmefpBkx9hv1pJZUVpaF+4f15G88Z3xeUpVnJPJOnTrl1Jd5qjcsfvKahTFaTPFQcACmKtu4G2OMMcYY0+yx4i5Q+evXrx+AaHW4KmVUtbjfxx9/nE1j7ty5AIDu3bsDiOze+Hau/m+T/MyqXS+Ji6qWFGlNV9gnRXLkp9ruUUlgnUKvAay72jMyrS233DKnnjy3I0eOjC2raXn88Y9/BBC1NypR2i6T1DRV6EJVPCm6oaal60O0HastrNq+xpHkPUbXtSSlUZNnqST7eKIzBvxuLzQti1NPPRUAUPHPe7PblkybAwAo65iJwv3U+//Ni5z69ddf40eD+wLVsUGAzqjaNBNJ+7l3P8s+E+KU7aSow2yLXLtCryz8nXnzmaExTHT9Sai4q0/4pMiuCxYsABB5yeF2Pqf5jExS3sPnMdV3ngvOaPNc8jk6Y8YMAFEEcj4/WQYer/b3jtFQnFhxN8YYY4wxxmSx4l7NE088AQDYeuutAURv0HyL14hofOPmmzLt7IBInaa9G23oqCqoBxeiPm5VvRjYM6N6vDF1ZV75k+z61JOG2rqrzR3LSHWBdeD+VCfC8qvXHI20xzx5bnmujz766Lx6mObN/fffDyBS3lRhT/IQoSpYXWzbtR+pHXmSd4kklZyEvtWTvMDo9iQvG6QQTzUk6Zyon3m17WW577rrrpzjf/7znxect2keLFiwAHPmZFR4eoTp2bNn4v4dOnTIU8tD1GsSlWfayevzh22RafK5o8q7tnWWNSTJq8z8+fMBRCq9Prf4LFf7dM5ix/VZfX5SUed2epZjPTgmmD59OoD86OhJs2emuNhYXmU8cDfGGGOaKeu+jhanrqvIvLxt0imzOBWzMx+jjhgCAFj4xP3AHGDaZ/8FALTv1wMA0GGffQEA+/bZDeizJZ76V2QaaozZuLT4gfvzzz8PAOjRo0fOdo0kyu98C6fdOm3VwuhrHTtmlHGqDFSe1f+t2uJxe/dUxoNLqm11hNXSzPFV5Rl7uCG9MvaJH3wQqXO6Sl8VDaptauuuKr9GieN21imsJ4/luVBFUmcauB8/ee4POeQQmObLuHHjsv+r1xiNXqrquHpM0eiN7EPqKSIObfNsr6r2K+p7OU5pTNonLM//HHlgZt91wdqV6giWdz/zck56an9fiLecmiK7xqWpKh8V+PA8nHHGGbXma5omY8aMAQCc2Cl5n6VLl2Y9n9SFLbbYIttn+WwA8teHfPHFFwDy+yWfhfSewuPoySYpton6PQ+3EebNZzPTZHlZFpaB9yQq7ywTPcox/bCezINpJkVOJttss01OHiyT3ov4zOS1c/8rMgq0cUc9bdxb/MDdGGOMaa6kSsuy/2+9f8atcatdMwr76N0zA8//XHsZAGDiw1MAABx27jkkEwioXe+MieQmPfpu8PIaU6ykUymk07UPytN1MImMo8UN3P/85z8DiN6e6Ys8STHT7fyunmFCry5cWc637tAWNi4PfnaozNwk06syivu6b77O/L78m5zjS7bMlJkKHgA88+aHOeVKUtTVC4eWibDMm2++eU6dwnqq/X+SJw0eo/5yqf7T3zttEI899liY4odKe+iTOMkmPckbRZKCpd6R2MZqshXV39SGVdV8VfWT1qbElb+kpASnjzgAAJBam7HnR0VGAcTqSC1EaabvsC/f/eSLsfUMy56kvifdq5LOXZKnnjB9K3/FC59tWXuYBqS0tDRrv017biDqU1TaVYGn4sznis56sW3SLp1rqnSdCRXscJuul2EaSTNt3M77k64RoV0612aF9SS0i9e+pPXi85cz+3zWMU+q/127MsqtMcm0uIG7McYY01Jo1b1P9v9U18z/tzzy9+xAdtddd0V6fGbR5HtLMi+ZHUszA9DdVuaKTjTtMsbkkypJI1XA4tRUpRenFgTtqflGy6imGj0tKVJbUlRF2nzTSwYQvfnzLZqoDWqeckaf1asyNndVKzM27ZUVmc9Uuvq4ymrVPDhe/UKrAsjfaQuvUU5VdVMbwzi7WdZdvXRovXQWQCPScfaDao1t34sb+manuha2xSRFXNXiJBVc13Boew19LdfmqUFVPlXWid4j4igpKcHpxx6aKdPqTP9NVyvsldX9uGrtamYcpV39mWod75UmTnFP8v2uyiLR/qi/17bOAADuvvvunDzsZ7ppwZnk0LtZZLv+dd7+rVq1yptZqgvdunWLfSZwXVdNMQ6A6HnJ5zBtvhVG7GZePI5qepgGnzM8RmE/0IjmSfuxDqwT12YB0WwxZzV4r9P7k669SYrW2rt3bwCRqs/jX3vttWyejFruGWnTYgbuxhhjTHNnr4rPAQDrFmUGt1XbbJf9bfzHc/DJJ59kv5/WG8DKT/DqusxAsXPrzAB5mzaZl7j222RMWkraV7tnLKl+eY2PDWhMiyZdkkK6gMWp6UrbuNfIyy9nvDVQiVDFXG1kVXFXVY5QpaKqHr7lJ6nUSYoembc28xbfoyyTZpreLlrlKn2p6t8r09FqeqorfJNnuTTvJFR1pHrB2YO4Vf7MI0klVCVPz7na/qs9Pa/d/vvvX2PZTdPgnnvuARCpYqqGA/nXnLCf6YyR2rgzzSR77nANRuh5IiQpUrH2kaSIwCzT6Ud9N5N3tZeY1AquScmsVVnLNSprq39nf90kP8pxVTrXi5XWNyxbkpqp0WR1xkFt2PV+pOc0rs5M+/e//z0AK++NzdixYwEA22+/fUH7t23bNjtTmmlr8ep0bZSWlmZtw6k+A5E6rTNmRG2+ec9PmgWiZxjmwePCfs59uQ+P0f6sfUk9qSX1jzjFnZ5oVCHndt4DOQPItNm/qfqzDBoDJW6MwDEMr/kpp5ySt49pGTT7gbsxxhjT3BlUmfG9vrbab/vK+dWuFbfqnXhMae+dAAD9T9gLANBzZkalb71FZkDadfDOmTR69AMAVLWuHrzmxwA0psWTKtAdZMqKez5PP/109n/ajvGNl2/I6l1FVWFV3IkqA3wrD+3Z+bat3lSoJMd5b8jJuyyzH6o/U7IflfZHxr+V3faD7+5T/WOmvLNX5vqdV6VS4XaWkWql7h/WU231dV9dva+fquYxPdoeMhpdeD1HjBgRW37TeNx3330Actd5APmzOOE29bik6x8Ubb/q2SXOxj1pliypLyR5a+F2pn0G/bFXe3+iwr6uPONhggFvKpctqS5c9WxetblBerMtosSrvcqwL1M11PgLYVn1HKqXqtpmCXmOdV0PCe95SWsKmMbvfvc7ANF9xirgxoXeVQrx88/92MYKPSaOioqKbFsIbcVrimMA5Len0ONU3H5J0Y3DeCJEVf6kaMXqRSZupimuDmE9eYw+63mPoPKedM/RWQItC/smZ76BaFY/9KhjWibNcuBujDHGtCToPYZ+20vaZYQPvjACAJbkHvPAe3PQt29fDP1x5oWra/VLKF82U20zpiyVrTOmHbOW5b4cG2Mi7FWmBdKtKmObV5XKKATz1mXUbd4oly7N3FT5Nv+DA/bOHptaU23PW5VRKnq1zlzauZX59rTGmPWnioOaNdU332ovT1WrM+pb5YqMMrZ6aUahK908Y16Q2iSjoqU33TxIKzPIYl9OVafFflxVksnrj395qeErYooWznTstFPG1CUutkAStNX+738zpjWZ50nrGo6IaNWqVdaDCtVgPo+A2tcx6WwzZ5TU77nOGlFFV5/t4Ta1D1clnfsxTy2TomUK60nFX6Oi6ww3Ydn4LP/668z6F1XPWVZeo3BmgfnzvLMN/M///E9s+U3zpVkN3P/whz8AAPbaa6+839gR2LHURaF2dt5YeIOozQUbF5sAkWsodnz+xk+dokxyW8ebAjssv9fm5i4uDdZXF9rp1CbLyBsMp+e0LuGxSedGF7TquU26WfNaMW+Gngaia3zaaafVXnmzUWB7V+LMzWpzi5YUNEi385PHxz18k1ycarCmpABFWo/GJKyf1l1N1tRkKcnFrU7bJ52PcB+d0tf75L333gsAOPnkk+tSPdMAvLWgCl26dEGvrTKDvZIOXTI/BJFTsSTeNOUf05ZU94sSaf9LscUWWwBYnnXba4yJJ12CAr3K1C+fZjVwLzY6VGRsYVNrMgMf2sqWtOHq9S3jDquZ6gAZVfUMqWuMiefOR57N+T7qqEyE1E2qB0j0+lSyZebFM922XfX3as9WbbfIHlvVqlrp5ItLdZTV1NrMC3fVJoUpocYYYxqXVDqFVLqAxakF7FMTzWrgvu222wLIVcw41aTBkEjSQrWawpsD+S7kwuAsUeCLDOu7GIjTaRrKmWGW0bdLwWmw/nS/VZt7SIazDl1gAbn1TApHr24wVdVPcuXH4zQQTDhFyWtsGh8GWmL71D4UZwubNMOlKrcq8bpQLEktjoOzTfzkPUEXyCYtwFRXiCRpin1DEN47tO/qrAM/Odum5VYzhqT6hSTVVa8nr7mV9w2LujcO77X/rax2sdg642iAwY1WrFiBVCqV54KR/UKfT7wP60JQNVsJTU+SnpfaT9mG+WxkXmyzuoCUn3RY8P7772fTHjhwIIBopkmf3XRIwXqyjXJ/NbFJClgW1pMzzzrbyHPFhazqDpJl4He9Fjwf6mYyrA/LEQbbMi2LZjVwLzZSqzM3p8qliwFEATNS1Ta0e/bK2C++NS/e9/XYv76c/f+Uw3N9nU+aWe0KrA62j8aYunP3XyZm+1lpaSlOPHAQAKCkeiYN6WqTsNLMS/ADL7yR96LBwcOpBw8GAFStqR4keObMGGOKgnQ6jXQBi1PT67w4Nav87brrrgDiXaep+qdqk+6vAZn4qcfFqehUt1XBW19bWS5KVVdTNYWq5jF8g2ferJe6v0yacaCCEFcHPQeq/ugCJJY/ydZWXzLiysYZAF7zn/70pwlnwGxo2A5VgdPrH9dm2BZUHUtyy8r9tU0lBfcK0T5MeKyWV2eM1DWdlh2I+nwh4eNLS0uz9xF1h5lE3H1Gy6N9W4NZJQV3SQpAA9TuYk/vC7Z53zjQ3lz7T3jt2A7YNtlftZ/y2sW5VA3T0f4RF7gsKZAS6dy5M4DoPs5+TPWbZeBzRxd7sh2GM6/cpv1ZP3mu6PKYZaE6vnjx4hrrENZT685zo24htWxJAQ01oGNNsxlMy2sOWi7NYuBujDHGGGNMY1FwAKYC9qmJZjFwpz22KktA9CZPtUHV4dpsN/l2S4UgKeR6TSQFoyAMhZ4NiU6FsiKjBBy4S8Zl1Ov/WZhTptD2e/yU/wCIlAoN66xv8EkKu5Y5r6zBcUlBJXj+1c4vacZBr0VSeuH/vOZm48Nw90TbDr/TnjPu+qn9uCrqqnKpCqhtg+07ThWjyqX2pao0ax6crdK+zjxD7y2hSv/c+//JszNnvVq3bo127dply8Q+zLwfnDgFAPDj/feormh+ECvCcjAPVfOSvOloPZLWKIT7kCS1VvfXc28aBgY769cvE8mU15Q20eGspa4Z0j7DzylTMm2OCm7Xrl1zjtf+zfS41ipsAywHrzttwaluE3oM4zNC2w1hfcJnHQC888472f81bbXJV/Wb3/lMz3jNiT4XLFiQU7a4MrDuVO+Jniuehzlz5gDIV/WTAkHqrB+Qf27Z79kmRo4cCdMyaBYDd2OMMcYYYxqLggMwFbBPTRT1wH3s2LEAItt2tYsForfkJF/NSfbWqvRx/0K8sqhtr6aZ9ZfcJrNCPtWq2q6tLKMAVK7I2KdXra1ekb5iCQDg2/0yXmRe+WxeXhnoWUJtbVUxUxUlSXlXW/6aZhZUyaN9PVfcq41w0rqCpGsU5s169ujRA0DUBhxqfcMzbtw4APkBTLRtaNju8He2K6ah/VPtcNVuW/dXRTtsW6okM0/1f6722UxTZ69InM282o9r/2KaaoerHm64/+NvfJSTZ+jNSe3i1a5clXc9h2rLrN414qhtZjHJBzy/O1hMw0BVWNtXTddO27n2Ic4sMV5GbXbZ2t44kwVEbYrqMNVw9j0+G9RGnHkRlpHPEKYbNwugzxf+xmehKvB6Htg3+WxXBZ8ebsIyJt13eE40VgTPLVV8tQTgNahpXKHqPOvJNmFaDkU9cDfGGGOMMaaxSaXTSBVgPl3IPjVR1AP3vn37Asj3pR6qPmo7q/Z9/F3tsJkWbfRq8+seKtdJPqeV8tKMTWFVq8z+W1SHN0+tzagTVdWh0yu/yfhiT1dvH7pDdwDAo3Pn5uXJetDHq0ZR5Nt5bT6oa/NpG/6mtrSqoNOekaqLrh9QG0xVVUKlg9uYFtuA2XA8+OCDACLlKYkk1SlErynbCNupqmc6m0PUdjrOY4rmnxRmXVU//p6kkqsnCSBSzrQNh9FMRx62X2af6uBKAFBV3ecfffmdnHRYv7g4FDxXGtVZPVqo553aZgLj/LknRUhNUtaTYjswTSvv9UPXYbAtqHcWIIonojNfaj9N23ZeMyro2m6oFnO/uIjJVK35SR/yLBftypPaia6PISwjbcTj/Jt36dIlJy9NQ2eF9Hx89VUmICLVb9aB9wHOFoR15z48NzzXeu/h9WE9mJc+60pLS9F1kwoAy6srlynrR1+tyJvBTvKYZ5o/RT1wN8YYY4wxprFJlxTox70l27hTDecbN9XkUDHiW6p6Xkjyn6zb9e2WJPkvDn9TVVvf+FVtWFKa8ZTScZPq1fq0Ca+oftOu9jpTsmmmnqHdq65Kp0LHc6SqWk1+6OPqmaSQAPnqvJ47PeeqAOlsBj+pmIRqI+tB5YX1MxsOKk21eWJSe9u4PkZ1SNsCj02KYpq05iLJjjv8Tduntku1N9f1LbV5ngrrnDQLldNOq6I+lFq3Juc8UJmjgkeoAsaVR/2268yAzipqv9M+rTbBQH4fTooiW9tMHvOiZ6Kf/exnNe5vcmFf5L1RvZ3Fqa/dunUDENmdc1aH34nOuCTF41Ab63AWmv9/9FFmjQa9rlCZTlK9kzyKMW9GAGe/CGfcuE2jjyalqe1eZxrKy8sBALNnzwYAdO/ePa+eSZ6ZdJYiaV1XGM11+C4Z+/fUujVAVTmwKphNrPYqtUvnMgBleO2zeXkzIOFMgGlkClycinoO3Ot3tDHGGGOMMWajUJSK+9133w0A2HvvvQHkqzyhYsS3b9q90d6aCjxRTxhJvpv1zTlOidaogqpu65u+qohVbao91zCPNtWqSmmmLlUl+XZ0TIMr4tWXc1LetamnenyotKmSqfuovaIq7aqWcj+qk6qcAMmqD9vE6aefHlsfU3fosYcqHq+HXnf1MkPiPF0k+ZTWyL5KkqcUKo5xtvDqE5lwFi5pBkEV7NAHe5hHmK7OLsR5l3nu7Y9RUVGBY4YMyB5XJTNlVCh1DUB4jnWdjvYrndXQ+qsqy/oxnVDd1zUlPHd6bWtTa2u6j5jaGTNmDIBo9pHXgc81nt/QwwifdbyfMvYFFdqtt94aQKQsc12UthttbzoTGrYv5sk2pH7OdaYtLv4CELVRPqdripuiqn3SGiqiKjnbNsvAMjNv1ikso9ad+2raet/iOqGePXsCyLfDr41FixblPctZBraRM844o05pmoYjlS7QHWRLXpxqjDHGGFOsvDltPvr06YNu6WoxLfyx2lRm4idfYMmSJRu9bKZpUpQDd1UC+IatdqFAsjpApUI9NBBV9uLU3zDvkCQ/5eqHVVUoqgyLWnepLnzms+PajH1fVXUnfvXTL3LKDkQqAdUU2gTSPo+oP9wk29QkNT2sb5LdP8+VKjRJ55j781NViHB2RD0bMI+a/N+auvHkk08CiFS9JBWZaH9Uz0vhdVcPLby26ulF/ZurIq9tJi5Sp7ZxXUORhJZBPVNp2wthn1RVO1QtHxr/dp46qN4ltM+EZeY5S/LAo3km2fiqf/s4ksoXF6U6JEkh1evEmTLAs2U1wXZORZ3tg22SduthdE+2Ga4H2mabTARuejZhhFDaW/M77dHV05p6b4ubHeO2Dh2qY5TIWjCWST3AJXkp0u01zawlHZsUsTupDEybXmqokodtnXkyDfW2pNFa+TzmuebxvBatWrUCapiEWrlyZbZcvC/p8zapnmbjYXeQxhhjjDEtgEUlW+C///0vgEigopmOMSFFOXDnW+yiRRnvK/RXG+dXVqMcUqngJ5XqpAihhUQOVZJUpto8ubCMasf9xvQvAeRHegunzjijwGP5Vk6bd+aZpDZqmdQGPskDRRzMm+c4ybtOkn292sGGMynqy5ZtwDazDQfVIapIoc0zEKlJqp6p55c4ZZrHqEKlMyf8XZVr9bnOvNgu4qKZqmeaJG8TSTNgOjtHwr6gvt+Zhtri62ySKvS61iZOYdQoi7pOQP2z63ei90Y9l2E5kuI58Hed/dTZl6SZPZ2FM7ncc889APLjiST5ZI/zwc/nBtsabd35/OAz4rPPPgOQ722GsA3XdE15LPsDy8M2q2vItM3qmgjWk+ly/7CMGk1W+71+Zx46g8Dzo/cS5hUOopP6t96vWF7OZmy//fY5x/FaaCRV9RIH5K8xSooUyzZz6qmnwmxcUiVppAqY/U+V1G+8Yq8yxhhjjDHGFAFFqbjrGz9VLm6P88BQmw10kr12bapcnB933aYqo6rDfJPW1e3Ma8cdd8w5jm/1e+65Z1491ZNGktqvKgPRmQlVKcN6JkWILXT2ojYf8qxTeD3VY4baL5v156mnngIQ2XRqO0zySKQzK+rpIq5vqGchVcVIbTbUNUUNTIq1oGnyd87ssL2pnaqqbOFMBH1l01NH165dAeTboyaVkXlytmPmzJkAgC+++CKvzBqbQdfj6EwB+wpVQZ0h0WsQziToLKb2YV37o4qh9lMlzOuOO+4AAIwaNSp235YI1WR9hqinI/XiE8LfeG14zdhG1atMUpRwlmX+/Pk5+4VxNnjMJ598AgDo06dPzr41xT8Jt6tdPdOlj3WWNayXerBRRTopnkPS2o/p06cDAHbddVcAUf8Bon7BeyX7P5V1llcjmROee+03elzcmjK2AfVkw7bg9V6NR6pAP+4F+XqvASvuxhhjjDHGFAFFKVPyzZ8r1/mWGmc7rW/2SbaWSd+TbPCSIgeGx6jizDdi2mV//PHHAICpU6cCAPbZZx8AwM477wwgegtXVSLujVq3qXpG5Y95vvnmmwCAHXbYISdP2txpveLqpOdCy1DX9QFJ/u7Dc6s2zvx09Lj6QxtO9Q+uqnBtfSApKmL4m9qXqtcSVdS1D6hCH2cLrh5MVJ2n1wi2eVWkNfKqxhuIm+VRdV49ttQWYZT3NCpyjFXBRWsAMGXKFAD5PrPV4wjLwv2owNNriPpoj/MEw3qoLbr6jldbePX+pMQpw/aKkQ+vFa8llV5dI6LrFYD8mRgey3ZO2+3Q9zsQXRsq6dxPZzuZjq6BAYBevTLRQMPo3mEatXk1U1/yOnvdr1+/vHqq7XpSdGaS5B2K+7MOOrsUwnbOevFcUQ3nJ2fJeK51LYDObKk/+DAtnXnXmY9wBsRsXNLpdEHjnbqsmYyjKAfuxhhjjDHGNBU2lqlMUQ3caQNJmzP136qqXfh/bR5MkkjyEKOqYpxapGqI2uQzetqXX2a8xrz00ksAgHfffRcAMHz4cACR3ayq6HHqoiovtJGdMGECgHwbQZZBI9TFRYTV71p3VeySfMGTpMiVSemE9SJsA/SMYDvZuvO3v/0NQGSvGRf1M0SVdVWAlFCZVkVaVe3abKIJ90uKjhruw3LRBnbgwIEA8meXktq8/k7i9tO2W9tMH6nNDpf3ACCyG54xYwYAYNKkSQCAefPmAYjUeiqEOmuh9rQ6YxnnC5/obIvOKCTZLid9D7ez7rfffjsA4KyzzkJL5YknngAQeUzjea5NqQvVY8606NqquXPnAoju/WwvGjGY6jCVddpvc/aWs0PhNaRyzHKz7bH82m+1PqqS6/2CanLoaUwVZvV4pFGNtQ2rcs0ZK1XFw3w0zgRnfNWLm3r/od92/s5rwTKoP/6arrfeM9TLF9vQ0UcfnZhGc+SVV17BTTfdhHfffRfz5s3DU089hREjRiTuP2/ePPziF7/AO++8g2nTpuHss8/GrbfeutHKuz7Yxt0YY4wxxhQ9y5cvx4ABA3DnnXcWtP+qVavQuXNnXHLJJRgwYEC98qbiXshffSgqxV1t7lTF0kicQPRmr0pXbYqQkuRdJu6NOMl/dJzXBgDYa6+9AES2q1zN/uijjwKI3u7pA3a33XYDkOvLlmop06BPXlXXaBvINAjLRDvYJKUt3J6kKuoxtfmvT/IRHee9g6h3BZ4L2/fVHfXznORhSeMMcD+N5MnrFWcfrfanSZ6XavPepN4X4vwoc18q7fvuu2/Ovqq8qTqmap+WJcwrKZqp9g2WW703qQJZ00whzz8jYVI5ff/99wEAH330EYBI/VMbYKbN8msUyBC9V+k9TZVUVf/0vJCa6ueYDPneiHTNRNL6oXAWWtcw8FrQbp4RVamO85OofTnvrSwb0wv7t/ZTbdc8RmNBaFvUe472PZYh3FfblG7nfY55qB29emXRPEM7dJabs3a6Ho3nSuM2sCwLFy7MOR9U7FlmVfTDc6RxJpJ84IfnqCVx6KGH4tBDDy14/969e+O2224DAIwdO3ZDFatBKaqBuzHGGGOMMU2NVCqNVAELT1Mpu4M0xhhjjDGm2VNUirtOMyeFLg6nfGtblFrbwkhFp/BqCtmt08O6eE+nuLjolovMODXH42gG8+GHHwIADj744GxaL7zwQk6eGriCU3fMQ8uQVEbdL6wT/9eAWHpMbUE3arsW4fXUxcE63elATHWHC700iFdtCynVxITo9DinkcNjdOo/KUALUVMMXTAWt/iTbYEmMjr9rJ9JsKwMEa+u24D8e48u+NRFZ3rfYLlpZkRzHpo1xO2r54omdzSHe/HFF3PKz/oz7SR3eGH/1D6o11xNZtRNK/PQ61yTiSHzb8kLzTWYFk0qaM6mLnhruu/RXEOvt7oBTXr2cT+2Ab3vh/2H147lDYMWAVF/ZT9gX9LnalJAqbhnRZIJpvYPXayupj+EZeB9Me68aN15brQfaCBEda2rrncLCU7IevDcMQ+ec3WZbDYe9ipjjDHGGGNMEeCBewxJb+F8W6VaFb5pJi2MVLVblTyqa1Q4qBzwUxWlcNFmkpLFPOhmi3noYpPevXsDAD744IOctHVxYNzCFV1gxjIwTXW3pWVSNZXEudrUIBEsA5UKfmqAGFVuSJLyGaccxC0QBKy4FwpdQAL5C5I1wJCqRIR9gfsltZlwgRbzIkluBbVNsQzqwk3bUtjP+/fvD6DwBcuq5nHmi4s9v/rqq5wyhEodgznRzSoX+jFvBmBhOdn3dbaDi8z5yWBtYTh3uuEjem6Y13HHHQcAePXVVwFEi955XVg2VXHD66iKoi4i1vuFzhzo7I3eu8Lrpdta8iJVvedz8T37HF09UnVV9RzId7Wq9/CkwH56LdXNIIlTv5NcUKryznuCLlZV14xE20bcInSdDdJnhM4o6sJRwoWi3F9nrYHkoE66eFitAnS7XpukGeUwbW7jwlj2d50ZaMn9p7njUY4xxhhjjCl6li1bhmnTpmW/z5gxA5MnT0bHjh3Rs2dPXHTRRZgzZw7uv//+7D6TJ0/OHrtgwQJMnjwZpaWl2YjyhZIuSSNdgJpeyD41UZQDd76N8o1Z3TjFKbdJNuvcl2oalTC1TWXgIr7lanCKMM8kV1b6dq52ctyPQRo0cJO+vYeKgbpv1DJo4AdVU/TNPylwTFgHqg5UDXnuqBJSIaAySfdjPHdUJWu6Np0qM+c9vbo64EZV9XluVa2qdMmc/ynzl+cdb5IJFe4kO1NVctW2NUmBSwrMFe6j7iDVBjopSAqPU9vvONtpBi1K6n/aZ5jXm2++CQDZm3/SOhaqhUCk0jHgGZX37bbbDkB032CfVUX+66+/zklTbcPZp4DoXkTlXQNJqeI2bNgwAJH7yJdffhlAdE9gf2Q/DtsGy8NyU0nXNQk605UUlC3JTWZ4DKnNRW9zRhV3neHlNWM/4AxNOKOlaSStEUty46tuQ3mf0DUTcWth9Fry2UB0hluvtc7oaLo1BR9MWruifYrnLMlVaU1rX9gvOD7QtSB6vYg+y/X+pzMVoWrOPsh+mzSTUtuanebOO++8g/333z/7ffTo0QCAkSNHYty4cZg3bx5mz56dcwyD8gGZAJgPP/wwevXqhZkzZ26UMteVohy4G2OMMcYYEzJ8+PAaX/rHjRuXt62hRIJUOlWYO8h0zZHBa6OoBu76Jq1v41SlQiWMb8BUpfSNlyGHNYAC1WFVF6msUenQkMdhuahOJSlJVE2Yt4ac5++0G+Qbt6otQKSmUdngOaD9m3qB4HaqJnFv+ED0Ns8yhnWp6RwA+WGcqRRQXaQ61L17dwDRtemUqlbPU0B6Weaarvs6MyOSohpcllHaU+1zbZxbutpQG7RtDz2jqL24zq6oGpQULEkDhMQpQKqcE81TlXmm1bdv35zfqT4z3TAoWW1BxNQmdsKECQCAzz//PKcs/J0qGvtOaPOq5Wb/YyC0Xr16AYjaOs81+zT7ElVv9g21zw3PCUPQs38x4JJ62uH+XOdy1FFHAQCeeeaZnDx4jwyvF49lfXgO4gLEhOXUYF7MI0mBjNvWkvuyqshs1zz/vNfyPLP91GQTnXRv1zx1Zo3tTFVzlontLkyTn+xL8+fPBwAMGjQopyzsBzpwYtkLUZOTlPUkzztsX+qVZdKkSQCAbt26AYhmy9RrCxCdEz6zCZ/NPXr0yCmLjlmSZvt0jUg4q6mzWtyH1559jG2jJfefxmJjLU61H3djjDHGGGOKgKJS3ONCqAPRGybVt9BvNG3QqZLxDZaKOtVsvq3S1p02qOrjVT2cUPGIU6nUp2uSokmFjG/OfLPv2rVrTn2omG277bYAcm3c6cOZdrn0IME0+KbPPNTTRtLqePXaEs5yqIcQ1lO9W7D8tCujBw6eJ14LKvKpdVGelcuWZD6XZpT31CbV3nA2qfZWUb2f2kybeFQRDVGb9qRZGPUiox5hkjwohHloWrpdfRJzkRC/h4uPgOj6h/0wyauC2uwzzf/85z8A8lUxenThvUT7d4jWg+d5xowZOXn37NkzJw/1skE1Lc6Lhp533v/0vsFya5m4/fjjjwcAPP744wCimbDQa4165qgtdoO2GbU7Vrvq8Hrp+oaW3Jc588I2R2WX92+qwnx+6WwnkDzjxPNMxVyfq+q9jfdnnR3iMyRO2WV7Ue9IVLUZa0CfbepFSttfnPccnis+X/X+w2P5fKLdMp/tfFayjDwvSZ6rgKiP8Jzw/PNccWZNZydZBubB4/g9KZZJeCzPP5+vbAM81+rdzWw8rLgbY4wxxhhjshSV4q5v41Sz+DZLGzxVyYF8JUhtwf/73/8CiNQqTYNv76rc8203zjOKllfTVA8LVJy5H9/mv/zyy5zj4uqn2/idSobWS+2TVZ1RP9pxvtRpI8hzogq71ptKwaxZswDk2+VTCZy1ujRb7723yihL6baZtNG62ra2deb7lC9zPaIkRX41GdjmQntNVbe0XRL1/a827XG+/sP0w32SPFqoMrX77rsDiJTH999/H0DU9tRfeFgvtisemzQTQH/tGuOAiqIq66x32OfYd9VfNe9RVOKmTp2akzf7J9Eol3G25DpjoNeB63YI7W71nDOvo48+GgDw0EMP5dVB7Xu1jcRFzwzz0jaUFGU33DfOrr+loXbpar+sHkZ47w3bP9utem7R+zHhteE1VS9D3F99x4fXibPeLAeP2WWXXQBEfZJRwKk0cwbtiCOOAJBvO64zqm+//Xb2N9rNaxRtnVn4y1/+AiB/FoNrO1hGHsfnFM91GEtBI6dzH44HNP6L9g+1S0/yThPauDMP9hleH7YJ7Tc1RXU3G4ZUKl3Y4tSUFXdjjDHGGGOaPUWluJ9yyikAgH/84x8A8n3YklAJ05XYfBNW7w/qyUX9EOvbblzkP0V91aq9G1HFk3nRF/QOO+wAID/aItXGcBvftnkM09ByJ/lOZxnVr3YcrDvT1Ih0qvTw3HJFPs89VQn1RLHJJpvg3QXrqs9XplyLF39ZXfeMsqO2gmwjJp64dlubn/MkjymqiLLfqQ182N7V/7e2ISpMXLPBtOh7nMqUtss4m2tGHqYil1QfepNRG1n1pEJo38p1MEDUF/UcMk22U/bhjz/+GECklFI5Zd9JUuCAfH/UGmWRx9Cjx2677ZZTRrV15nUbOnQoAOC9997L5sXyqb9pHqPXQWfumCfPpa5FCNtG0pqKW265BUDkj7klELYtIP/cUNnldeB5Dp8JSV5FkiKQK8xDZ+n4Pc7TGGep+Mk82H5p+837Nfso06YSz+eXPiv5PVzHpkq7xhZgmsyDvw8YMABANI7QtSPal8NxhsaNUE9VPHc6A6dp0iNPkjpe00y+Xh8S1xbMxiFVUoK03AOT9qsPVtyNMcYYY4wpAopKcSdcFU51im+xtOMOUaVI7UH5Fk57a769qspG+zY9Ls47gvpu1WNqU71VCaEXmU8++SQnnXA/Va95jKYZ5zcZyLePUyW0Jn/LWh6eK9r1ah5q287jqKLw3McpQvyNdrzc58fDd4+tl8lF7aNDqBppRFS1ZdW2xDbHa6MeIMLryN/4yTyp7O6xxx4AorbBKKZJXoPiPLsQHvPSSy8BiJQ1HkMvR0lpqh932u/y99BnPOueFOlR7Yt5r+K9jCq+Kuy0Jw5nDpP8b2u92Z/o0YaeeZIiZfKe8c477+T9pvc0bQt6PYnO4Gn7i4s4nZR3S+DSSy8FABx++OEAkp8V+tyJe5YkHaP9V2Ml8Hf2QSrN7OdJ0beB/DVRbNeqPDON/v37A4iebVwDQq85VI2ZB581gwcPzquvzvRxFpppsgw77bQTgOieo5GHNRI46xTWU/sBv/Nc8Vj16qZrQ0hNzzxFn8nqO19nA9imrr766lrTNvXDXmWMMcYYY4wxWYpScVdFjJ/0Q6w+ysPfklRwvtnzLZVv51T1NcKb2saHapHakPJNOEnVpgqXZGPMT13VTyUtrBf3Ufs2PVdEbWlVdU3yMBJ3LtRfPe12+TuVDLUhZjq0e1SlKLTh43X80bCMfSKWLsx8LstV9008NSk6VN7CqKrhMeqbW9Uwoop7nHcQXmMqcrRDp132v//9bwDJEVXVRppqeGgbrB4f2HbY5tnvdCZMPaLwd67BqMnbSZI3Fb0n8Nxwdop9maq3eq0KYzbozIamrXmqmk80GiWva3gOqSCqdxO16U/yFpQ0g5dU5rjfalpn09xIipmgzx99XsWdT73eSTMXqgLrc0n7t84GhbMsfP7QdpvHauRuXTPGWVj6VH/99dcBAMOGDcupC5/L4XlKihXANDQPXYulkVXV1zrXZIW+8pk/xxqqyvPc6X0gSXGvrQ+H9eM+zFvHILr2pSV7Z9rYbCzFvSgH7sYYY4wxxjQVUukC3UHWU4woyoE7ow7Sfoxvlnwjpv9VIFK0aM+m6rwqRXwLV6WdahuVDlWp4lA/5vomTKjoMU99++bbPJWzt956K+e48Ni9994bQLKtfpJduioDLDNV8jilVu0s1b++qv6q6PLcacRG7vfNN9/g0IEZBTK1KnNtq7bIKDlVX0XXGADSm2fsFNfOyii1m/QakFdeU7NNrKrY2jZ0NkYVW/V2onEMwmPoYWifffYBALzxxhsAongKVNao/urM2BdffAEg3541tDunWqzRSeNm5MLysv0ykqLab1OxD/2la5wE9ju1kydc/7Fw4cKc7VQFVZEL+7rmwd94DPsRz7GmlaRgx9np01aXafC6sA3oGgO9F2hbSFL5w21J6wRaAknPCF1HwnMUF1+DJNnBJ3lE09kSqub81GuWtF4qRO3n1UONejZi/2a7o+07vdGwT3ImCsi3VWe/ZB7sB+oJKck7lkYHpmc2fobobCQjwhKdKdTj9P6gz/6a1nmxTbBeev/S+7FpPhTlwN0YY4wxxpimgk1laoC203wb5ZuxRjUFoqijVLiolvHtVD3R8C2cv1OdUwVJ34TjVEW1vVPFozZVLknxpHJI2zsA2HrrrXP20Td6zUNXoCcpYrpSP86WXz33cF8qnlTYVUVi2lRZ58+fDyA3cmxqbUY9wOrqGYLV1X74V2U+U5syomq1ilqS6xHF5KLXP9xG9DqxnSZ5M0mKmhlno8y+O2TIEABRTAa2EapjbM/qoYi/sx9TsVavDmG5GRmV5acyx7S4nX2d7ZL9jN5ntD5hRGPOGvF+wvJr/ASNgKmKJNPhzIHGRAjzDX1ZA8COO+4IIN8HeJK3FuapEY15voCoL/Peqna1SlJEZlV541Tb2tYHtARuvvlmANEMlLYbtockG+nQH7je45NmLlQN1+PiZpiA+OiePEbXg7CvsT8k2V2rP3M+G+bMmZPze9j+2F6Tovgm+UhXv+08x1T7dS1PmK5GpSWcGVAbd+aV1G90jBAX00D7scaFYfm1vmxTpvlQlAN3Y4wxxhhjmgqpdKowxT1du5lZTRT1wF09U9DuLXwzpl0a96Ui99lnnwGIFHb1/KL+iakUUn2gyhBnl8k3Xn0jVqVdVW5dgZ8UyW3fffcFADz++OPZPLlNlQAqNKq6FFom9fUb2lSqsqHnhiqpqvVqm8t0evXqBQA4cLc+UaJrqr3etMqkmSqrngEprfZqsdkWAIA3v8jYOA8ZMhAmmeOOOw4A8Pvf/z67Ta+j2p1qO07yQsG2o+mxfwJRdM6//e1vACJlmWqxzrqwTdGeU9sj1XO1Rwfy11iw3F999RWAyBMN68G0qJoxD7ZT9escwn2oDPJepJGYmbf2FZ5z5qFxIqjEh//rvefdd98FEN3z+vbtCyCyUQ7t/4Gof06cOBFAFM2V6wWASGnnzAevi9rPqlrLemmbSLInDn9Lal8tCY28yRkank9eFxIXn4H3WfValqTc8lrqGhe1S+fv/KS6HqadpDBzO59LnGnTtHjPYB8lhfir53e2WZ5L5sF6xnmoAaJzzPrGxU3hedb1JepFSdVvnSkhur9aBoT10plP1k8j2Yb92DQvinrgbowxxhjTWBy8U7W5bSoNYCU+r2jTqOUxjYe9ytSAqgt8y6dtZ6gKU2HnvlQqevToASCyj6NSpivP+Z0kvWGHb+21+SzW39VuXpUA1oH2pVTxwrd5bqPNrx6jHjG0Hkn+l3VVfJzaqOoD1TZVD7gfv1Nd5LXgtZk0a3H2fO21dXXE2nR1U21VfT2qbdknfVGeU15TGKHyo3bY6jtafY9rfAGd5WFbYX+kyg4Af/3rXwFEM1hUh3msenFiX6B6Tj/PVJNZVralsE8wjSQbX/btPffcE0Ck0FG9J6GXqrB+NfnMpiqu0YF11kk97/Tu3TtnO/27cyYirDM/dRaCefPexsiR9MTD88Iyqeeo0Eae10nbiN5XdbZQy6S2wDrjF/6v9u8tyasM4bqK7bffHkC+2s1zpJ66wvsz9+EMEp8FSVG01VMQ99M1LsyTbSBUopkG+6uuy9KZF6bF2R+2PXqOY9vkbJDanQP5XlQYIZj3Dp5L5tGlS5ecMjBNrSfrxXMbtmHtxyGtWrXKe8bzvCStNyG6niB8rjEvXYtDxV3HRay3aX4U5cDdGGOMMaaxqVycealeOz8zUN5my24AgFTPXQAAX1bGu581zY9UugSpdLKL8HC/+lCUA3e1t+ZbKr9TJQIiFZdvzVTTaO/JtLh6fYcddgCQH5lO37D59q2eYcJj9I1ePS6opxeqJVQZ1KY49JgR1hvIV9r5Jq+2ckk27Gr7zjKrkh03s8A0k7zk8FyyLDzXzENtb2nf2LZtW8xZV51utbjJ8i1dsjS23KYwQjtJXa+hqC21to3QxhWIFK24tRj8jf7K6SGFXljUppVth/2XebLNcLvaAgPJNr1U9fbaay8AUft97733ctJgGQ877DAAUfum0hX6Vqe6/emnn+b8ltSPtL1qP6VSTzUtVPtUOeWxVDV5z2N9uJ3XifcIbqdtv/poB/LvDzxW73/81Fk2XZ+jhNvVmwlpiYq7McYkUZQDd2OMMaa5QhMpmk7xZYova3wx5MtYUjAhIHoR5UuwCitqDqkuPJm3mkORMBiSBjLUPJgGX7gJX1T5sqyizrbbZgLx8QU5fJmjyRvN7ngM8+aLKQUjigcsA4WiJJNWntvw5Zkvx23atEGqtNot7tKMKFi5InO+S6sdKpR03yPxXFdWVqLj2oxpW6qqCh03AT5evCbP1SuQv/CV11MXE7OcbENmI5IuyfwVsl898MDdGGOMMcaY+pBOZ/4K2a8eFOXAndO1fNOm6sC3+TCkOdUCXbihLp54DN+kuT+ngKkgcDqZb8Rc8MLfgeiNnXlzap5vwlRAVOFQJUMXrukCpXCBDhULdbfFNHhudJGZLqKh+sCyM8hTXChuloemSbweasqkC4N5rlUt4naWXV3KAZFKouYZakZkaiY0lVHlRgN6aB/QRVu8vmznNJF57LHHcvYP91F3pcyTbUBNMdi+6TJUF1XzePZPIDI500V6AwYMABC1mbfffhtA1H6/9a1vAcg371DXqeHCNJr68JOLaKkQ6mJOov2SZkU046H7yNClJsulQW4YSIkL+XhuufCe/ZSqJn/XxcZxdea5ZJtg30xadMjrp0GrVHGMM71TxbMlhmy/9tprAUTtgdc2ycVpnMtMNWVUM0g1g9JrpQGN1GyN+4XPPr2+/GRbZTvX546awGm9eN+gWh7e/zVAkirQmqY++/R+p2WPq2fOs3qLjE176fYZV8SVyyNzPQDoUjEXqAAWbbpNzrnusHZJ5v9ql8dYl2nnO7cvA9pn6lvVOnM/e+WjWbF9JoTPUY4v2IZM86MoB+7GGGOMMcY0FVIlJUjFCCBx+9WHohy4U+Wm7RrfvuPch1FF41s5lSIqe3QBpzZ3VCVUEWMefPumXd2HH36YPZZv8AMHZt7AqbbpAjR1JaUusnQBm7q/DBXBpPDzGkRGXcjxk6oWFwfyvLGMM2fOzDkeAPr375+TF1UIXcSn9WA9ee55LdSVGK9raO/H/1VxZ5swhXHCCSdk/7/vvvsA5CtuRMOUq3tP9oE99tgDAPD3v/8dQKRwcwEqELUvBgXS/pek6rF9UnmkAk9XjXQfFy5M5+JMthXaC9NdIt2lsS8PGjQop76q/JK4BafsL1S7uMid54YB38JzEaJ2xzxPcQHeuI33EfYfngv2Iy5Y79o142ea5zzJjWTcItBwAS4QzWjojIfaXOvshLrkjJvBY5oaDK8lKu6E7ZzPOnXRqp/h+eR5VJfGqthq4CV1Icx2okHRmFeoROsiZXVDrPcW3Y95cKZXXSPrrGxYPtra8ztnidju1UmEng+WUZ+/LEM48xs+i1+bviBz32m1DTp37ozO65ZkfluTO6bouDpjAz9nXVuUlpZiYaod1q1bh27p6mBr1Yp7qiq6hlXrorImXXO1KmCbMc2Xohy4G2OMMcYY02Tw4tRk+CbNt3KqbHFhgrmvBnyhQkR7TypiSeoa0d/55k01D4jUMip7qnjwGKqKSQEx1AZPf49zsaYqmgZ6UTs+7q8qos4SqEIa1qM2ZVK3M0+eeyoGvDa6fiBUiNRFJvdxeOf1R9u4Km1qp8pzz8BZDHjy8ssvA4iCxlAVC+1yGQSIKrCGJ1e1jHkxwJgGAFMb2LCt0N582rRpOcey79MO/eCDDwaQr/6pra+ep1A9pC06VX6qmEOGDAEA7LPPPgCi2QgNDqV9OXRrGZYtrLPOTKl7Ttr2UqXU+mg91IVjWGc9B3pvUhVTPZGwTHGBgrReLE9S2i0Jrk/YbrvtAOSvi9I1BiG87mwnaiPNNqazH/zk7BbbZpJ9fejOl9eb5UoK+JfkHpR585nJdsSARLo2Jkyb9eFMX9IsNNG1Y/xk2wzXywDROR+yU+a59fykT7K/hR57Fm7SIZN2q45YunQp+rSpbr+Vmc/um6wEKlfi8/JKtG3bFlXZmffqtXmp6L7yxrTMDN0mm2ySd821L7LfsM2Y5ktRDtyNMcYYY4xpMqTTBSruLdCrDNU5vhnTlpNeS+ICiPBtml4pqPjR6wPVQ9qgUmFWZYDqD+3p4t7qqSpQeac/VVXOWU5Vu1lW1pP1SipLiO5DJZBl0bd19QLBt3fWgTMVVBRCNY75802f5VRVheeGMyQ815wNUPWV1yTOYwLz1zDP4UyAqRu0d3/kkUcA5Ht60Jmsvn37AgD69OkDABg/fjyAyNeyKqa8vkCkBvGTaXIftg2qePyd39k3OCPUrVu3nDxDm2y2XbZ1HvPBBx8AiFR6oko0UW8UJFxX8eabbwLIt+lmnuwbLC/XjOj9Q+8BGl4eiJRA1ktnm5gG60f1kvtR9dZ1O6rkx9VHPZXwWLWb1lmauNnQMN3wf/X8deONN6KlcvnllwOIZrN0PYJel/DZp+sRNAihPj+4X5gGkP+8SvJGA+TbqrP9qAcxDebG8vO+zvs52yzXsLDPsQ5ANLPAfXgM7xl89iV5cdO+xpkGnTXI9v9qu/ND9sysY3nj83l554asWrUKn67KzAYM7pnxDJVal+kD27dLA1gOVFWfv3T1upFg4BdeC33eqhcdfmebMc2Xohy4G2OMMcYY01RIpdNIFaCmF7JPTRTlwJ1qON9GqSTQxi1UAHQV+vz58wFE9tVcgc23VdrgkqTw7hrZLM7rA8tFBUDf7NUPts4K0FaPygnt/FSpD7dRkaayR6WPavfnn3+ecz5Ybp4ntVFUbzyhsqbqGdUVqi1qE8z68fpxP9ovM7Kd2iKHHn/Up7D6/Tbrzw9+8AMAwKOPPgogug5sC7SzpSI1YcIEAJGPcV4L9X4UKtVU1nm9dtttNwCRhxd+sg9QWeP1Zvtj2diWdC1HuE3t5pk382D91FOKKopMh2V64403snmpL3T2cfY77Y9UFLkORiNjqgIf1kvVa36qPbp6nwjtgsP66P5x9sc626CKOj/VB7auSSFxZVK/4Un+qlsinKHic0u9/fDah7Ml7I/cl21Rbbl5vdWmW2di9LnD76Fyr/0gtH8HIkVdj2Vf5XY+pzUd9vc49Lmr6r16vNEZRfZN5qWzYTpDkarMHP/tfl0ArERVKgWsrcBi2rhL2f41cxGWL1+OA3bM3AvU6wxKqu8BpdF1CK9nktcbls+27S2Hohy4G2OMMcYY02RIFehVJtUCvcqo1wsqBVRwQ3tQVad4DO3eqAD+5z//yflOlYGKkNq5JvlLD6Eyqfa6LBPfkKn6q2JGlY7qAxVDlumKK67I5vXWW2/l7MNPpvHRRx/l5MH6UGWgbTHP4fBdMmrkm9Pm59Q3VLbVJ68qZRppM7R1Dr/zWrDMvH7q5QOI1BPNOy7qo1k/jj/++Njt//znPwEA//73vwFEbUE9uvBaUOEKZ6dod06lWdc96OyUekJhX2HbUqU9bg0G2zT7G1U7fiZF9UxaU8LIpOHaC1WLdb0GZ8suvfTSnDQZGfOYY45BTYR23hqbQWc4dOZAVXz1Ba6epeKicBKdceT51hkDXo8kTzYk3M40dGbEAFOmTAEQ9RONRKqznSGciWb/5KfeQ3V2R/fTdsI8w+ctryfToP95tlX2W5aJ6jiPY548jmvO6Bkqbr2X2sczDz5f1KMN82QafE6zPnxec2ZNPa1lvb5UVo8pxP86I0ksTLfPO5epVCp7fNWKTHpVq6vj0JRm1PV0u2zVcq6nzhQwbfZrthHTiGwkd5D1M7QxxhhjjDHGbBSKUnEnaveqb+tAvj0f96HiR88YGpGR9oFE33ZVYQtR5UrVJ6ZNe0UqS1QCfvSjH+WkR+VgwIABMWchw9577534W5jmddddF1sGnsv9+2XslbEyc3727ZVRuKtaR3Z3b1Wr8GpDq5FfCfOiksZzze1UVXg8lY+4KHmq6qrHELPhOPDAAwEAt9xyC4B8zxE6G6XKLhBdP7Y7qvdE7WzZBtim2Ba4n9rKhl4YqEpyDQXVfY0fwP7H+mjf5j2Es1r0bBG2S637JZdcgkKoTWknF1xwQfb/m2++GUCkGPL8szx679J4EWpXXJNtu9rTqs/vpHUsRKOg6rqYOJ/x3Hb99dfnlaelwhmXBx54AEC0/knXJIXtPyl2B6+7Xjvux36ja1zYTtj34qLfajthf+c9X2eHNIq4RorljHEhUXSpxussHNNUO3rO3vLZxzKqp7WaIgvXxJo1a7LnkmmoL/zaCK8nr4POSHE2ryV7X2oqeHGqMcYYY0xTpjqwEtZUm8xUu3VEqm3s7lWbZAbgfE2uWrk85zMc0vXcIjNwn70m3wTKtFyKcuDON2cqCLSbjfMqoyqOvkVTIWKURX3rTorwxjIwvThVkWhkM1UkWf5zzjmnxno3BBdddBGASLlhGfbvm1EfKhfPz9k/vdkW1f9ETUUVulbZyG/I2U6oeFJF4TlWLztJUfNCZUij+qmaYjY8vF7qjUTXcKhHCSDf3po+4TkDxmP4nYqb2qmqfW6cn3Aqz1wjwrzpBSfO8wOQ70GK2xn9lIR+3Gn3zmM2JL/85S8BADfddBOA5AipOmOg51C97ujMWfib7sNP3v/U3l7XISlx23VGwOTDGASchc3zdBKcV70WvO56/dlndFZZZ7l4zXnv5SwnvwNRP2QeOsvKe7s+u/mdMVm4H+vD71TV49AIqkyTzwiuxWGerJfOHGpEWdYprGchrF27Nm8coT7yCyW8rnqd2CZME2Aj2bgX5cDdGGOMMWajQ9GA77fVi1JR7R6Si1STeHf2YrRq1Qq7t60WIKuPw9rqxablUXCpkuoFq1u3awW0LsMnCze8MGCaPkU5cFfbcY3QGNrBqYcSvlXrymy+fdPuLUl9SMo7tO1UOz6iXlL4u9qkbgyY53d2yJyzyoUZ//WV32SUzlRZxo4xVV3WyuANUZUYVXCoKnC7Kj5q36i27VR2mE6o3HIbPQio/abZ8KiSy/7GNqVRTkNbcFXk2BaovGvkYlX31Zad39kOQlXs008/BZAfZZcKW5KfcLY/jRqs+4d5MWosI1xuDM4//3wAwJgxYwAke9pJ8uOukRhJ6OmF1zrpvqfRoFWd1fVHOtsYzpQx7csuu6z2yrdQaMd8//33A4iihbKvhV5IdD2WeoXhp86WJNlya2RdXutwlkvv+dpn1Esb2w+VdCrunM3q0qVLTpk4ExcHy8W8GTWcqA08y6L9QtdR6UxFXdBzys/1nSFetWpVtnycSbRtexMinS5QcbeNuzHGGGPMBueNqXOw2WabYUCXui00VVKtMi+vqU2qlfdqxZ3uIQGgcmlGfU+V5ZsdmpZLUQ7cabNGxYt+wPlGHHqmoMqgq93VF63uz9/VplN9Jet+QH5UVbUlVfW+MWw6s0rZ2uqIh5zmoy/ZTau9ALTKKDhvTs+oF23atMmqOmqLrsq7zizoDIT6IKaSwPSokISKCG0mec1ZD9olmo0HFS5edyrb/M7f1VMMEKl8vNbsM+r3mdeXan6Sv36uo6CtOQDMmjUr5xhdQ0E0EqR6ftDZHPW+AUT9f9ddd40t34bkjDPOAABcddVVAKLzTVt+fupaBJ3x4mc4e6g+7XkO1ZuJqva8buyn/NT4GOeee+561NhMmjQJQLQ2S2eygPxZkaQZGL2mSV5nNC6AzqKE/2t7INyuz01d78Uo2rynbL/99gBqnp1meaZPn55TX/UilVSGpLLGzUQUEtW3pKQk0ePWqlWrgPWYaF+5cmU2DbaBE088se4JmQ1CqqQka6VQ2371oSgH7sYYY4wxjQYDMfGzngsO1dYdAKpWZcSBdOXGF/dM06UoB+6ffPIJAGCvvfYCEClEVHVCxYxv6Hzb5ls4v6t9myrsqo7r27q+UQP5ERiJKh/8nhSpckNy1NCBmX8qMucs1bY6Il3176lNtwAAvPdVrv1yuCpelRmeG1Xp1DaQ54rnntEAORvCdHlcuGaB11jtMtkmjjzyyAJqb+qDXlcqvKpcsa3Qj3h4LGdTtJ+pDbva4/J42sJTmWOE0tDeVu1s6VVCZ3j4XZV2VSjZ1jQKc3gu6uqnuSFJsg2/9dZbAURqpvqrZz+M84VfiLIY7qeePXideM6YN71bmfXj9ttvBwBcc801AIChQ4cCiGYkgajdcp0Xrw1nqtVDE+/btc1uqfIet6aM11nt6HW2i+1GPT6x/TD2AuM90MsU+zIQ2cXT5pv9lOtkmCbbtc4Ya4Rn9ZTEOoXno5B1aSUlJdk1cxqtNXPOC+tbIVVVVXjjjTcARG3ANCHS6cLs123jbowxxhiz8aiqVtizfts3qV7Ina55WLVbj+qghl9nzIBA0Y827muiF6GsCp+q30DPbCTsDjKZiy++GADwpz/9CUCkJKmiDeTbreobv/onVjVBVXNVG/X48H/1La0KXqNG+2R9S6oXyLTZLOf7W3Myyo2qkeEMhaqhip5DXT9AZYRp81Nt/8Prqd5+6H2AbcJsPNi+NSqgKu3hGg4qVdr2eT01DUIlkZ4i/vWvfwHIXz8S58ea+e+8884AovbFdsgZA/W5rLMB/F1n3YDoftAUI/iqHfnll18OID9yJD/De5nOHuo6FV2LwBmxRYsyi+oY5dVsGBihl9GM+/Xrl/2N7ZV9Tn2pc7uu1yL6TFQvROw34f2ZbYj9lftSQU+KJaBeoqis8zvbE2fYGC00rCfbpkZdZdrqyYhlYVn5nWtXeH+jt7rw/MSt21HS6XT2HPMzU9b194D27rvvFhyV2TRfinLgbowxxhjTWExdvBodO3ZE52qNIbUu809VLWpqalXmRaGyojpi6trVuTsEx6fbZF4QqjZpPBM8UzipdAlSBajphexTE0U9cKddK329qn9wIN/Di0Z3VNu6pDfpQlfJA8kRGFUZCMu5sWnVLeN3mhFUf3DA3gCAlz+cCSBSZXg+VBkB8j3tJKF+galw0CevRsdTTz/hedIZD7YBs+GhrTSvB6+jeqWg0q7eZsJjtH2p4hbazYbbqX5997vfBQC8/fbbOXnGzf4wbSpxqh5r+9V+qco9CddusD70eNWUufLKKwve9ze/+Q2A/D45atSoBi2TMcYUwp133ombbroJ8+fPx4ABA3D77bdj8ODBsfuOGzcOJ598cs621q1b53gEO+mkk3Dffffl7HPwwQfj+eefb/jCNxBFPXA3xhhjWjqjR48GANxxxx3ZbXShmGQiowtI1SRMAwnqCzpdsIZQEGOaNGUkoatRIF/4UlfAW221VU6efDEOX6JpnsPycFEq01BRgGmooMR609yLJrg0Dw3NbJlXOp3GImxeLTSU5S58r46g2qM0I0Kk1qwENgFSKzLpV1Z7jKHCnt4sk2aqdaSupztnXFmOeXo8Ro0ahdGjd0JL5tFHH8Xo0aNx9913Y++998att96Kgw8+GFOnTs0KuMrmm2+OqVOnZr/HCY2HHHII7r333ux3dVpSMKkCF6fWc82CB+4GADDho1mNXQRjjDHGmFhuueUWnHbaaVkV/e6778Zzzz2HsWPH4sILL4w9JpVKZdc7JNG6deta92lKFPXAnSrD+PHjAURv1KF5DN/wOf3N7+qGisfQNSHf4vXtjFP4XCyjIZuBSD1Qt4+qbPzkJz+pa5UbHJbhhRdeAJAfWl7dZ4ZmDxpwh6YI3FeVGk5PcWERzyX348I+Dd0eKiNqrsA2YDY86j6ObYMLRrt37w4gup40hQpdClIN43XUhWIahIttRIO+sI1861vfAgC8/vrrOWUConZD1S7JxauaxmigNK1/nDkOt/G+0Fw477zzGrsIpg6EJkwvvfRSzm9U2tVladIzkn2Mn9yuQbTCZx9/4740hWPe6kKS93zeBzbbbLOcMqpJHVXV/v37Z/P88MMPAeSb4Wk9mRfrqS6Ok/o90wnryXsB66mmfatWrUKfstVACZBekXlOVq7MtW1nEJ6SzpkxR9WmGXPdd+Yuz5r+fWdQxm3zqFED0dJZvXo13n333RxXsul0GgceeCDefPPNxOOWLVuGXr16obKyEnvssQeuvfZa7LLLLjn7TJgwAV26dEGHDh3wne98B9dcc012PFMXNpaNu30MGWOMMcaYJsvChQuxbt267Don0rVr16wPf2WHHXbA2LFj8cwzz+DBBx9EZWUl9t13X3zxxRfZfQ455BDcf//9GD9+PG644QZMnDgRhx56aMExLBqDolbcyUcffQQgCjceBnwhqtipLR7VOKrCfPtWWye+YVNNZLrhYgeqBsxDw0Dz2KYEy8ROwTLzXLKeobs7VcxZbyoYqr7wHOkCRF4TKiV6XAh/4zU/4IAD1qO2Zn3Q8OS8nlwgTGVKA/lw4Xf4G6+1toEk16KEahkVOpaJAVkY8Cfcd8cdd4yth5ZJXb8SXVROwgWbrAftY41pbDg42XbbjCMC9ldVmNVhA+/53J828mzjVLbjghAxLfYZ2oIzDXXcwPuAuprkfuq6lQO0cBE4y8m8tB8zTXV/qTb+GnxRFfrwecT/dSF+VVUV+myeBkpTSH1dPStfXj0LV+2TPdU6k2+6Q2b24IuSLhl3meXRLHY4sDTrzz777IN99tkn+33ffffFTjvthN/97ne4+uqrAQA/+MEPsr/vuuuu2G233dCvXz9MmDCh7uOLdLpAP+7108ytuBtjjDHGmCZLp06dUFJSkiPOABmxplD79FatWmHgwIGYNm1a4j59+/ZFp06datynsWkWivvZZ58NABg7diwAoFevXtnf1B6Xb9F8K1d3h7qyXG3uFL55h2qc5kE1gUpF+IbXVGCZnnzySQDReVH789AemHVPOjdUIzRktNo1q50gz3mcjfusWZlFtLzmZuPx85//HEAUaluvL2dtaOuuNvFAdE2TbNeJ2pNzP1XsuD10zUhok0o1Xr1IqGrPtq3eNJLcnYazcdOnTwdgN4mm6fDee+8BiNZt6YxZ0loiXfOhSjT7fZwLVqrfTJOqtgY+1PVfqmBT/eezgHVg+gsXLsymxf7NfZj2ggULcvJW7zC1uR9mmaiCh+dF71ehl5n0isx9cO2COQCAqmqbdnqNSW+RKe9/050zeaypyI4NWG9euxNPPBEmQ2lpKfbcc0+MHz8eI0aMAJBps+PHjy/4vrtu3Tp88MEHOOywwxL3+eKLL7Bo0aLs2qg6kS7Qq0w9FfdmMXA3xhhjjDHNl9GjR2PkyJHYa6+9MHjwYNx6661Yvnx51svMiSeeiB49euC6664DAFx11VX41re+hW233RZLlizBTTfdhFmzZuHUU08FkBGdrrzyShx99NHo1q0bpk+fjgsuuADbbrstDj744DqXL1VSkl10XNt+9aFZDdxPOeUUAFHQECDyxUrVTFfWqx9ZvvHyk2/ZtP2mssdPpqurykOYxpw5c9azZhsPlrFPnz4Akr3qhL/pOaFyQwWWKkqSTSGVEKoptGOkmhr6AraXi6YDr6fOOvF6xgUnY1vgPmrbzjbEPsPtqryrpybdH4j6rHqySFLe1aMS0T4Qp+435WlV0zJhwDR+DhyY8UxCBZn9gAo8+7Pex9UmXj2Mhc8EtYvX9U187mq/VXVbZ8R5L6EKGq4T4zamzfJxH+3PvPfoehqWUWeCly5dmpN+mAdn9Vq3bo2ebdYBqMS62Rn79HXVinuqrLoO7TJrfWaiU/U5W5lV2letWoUhXVJA+1a4/YX3stfM5HL88cdjwYIFuOyyyzB//nzsvvvueP7557P3+tmzZ+fM8H799dc47bTTMH/+fHTo0AF77rkn3njjDey8884AMm1gypQpuO+++7BkyRJ0794dBx10EK6++ur19+W+EWhWA3djjDHGGNM8GTVqVKJpzIQJE3K+/+Y3v8kRcpU2bdpkXWE3COmSAhenWnHPI1Rlr7/+egCR+sa3Zr5NUV2g6kZFUH2PczuP56fuB+R7oVBPGk0ZXeUfrpZP2pfnQs8hz4meI856cH9VNKm6cBFKUmAF0zicddZZACJbd6pmVLh69+6dsz3ORlxt1dXOlO2Px3I/qilsl1yLoqoaEHnTYF5qw6vKOX9nWhopkp9s759//nn2WNu2m6bKueeeCwD405/+BADYZpttcn6nsqyRRqlIsw+y79Gem7+H3laokLPvhDFVwrT4/OWzQPu3eixj36PNe/gs5TadrVM/7Ro5lnmp2q8e5+jPO7xfqA/71q1bI12R8a61elFmxnjdskx6pdXeY6o6Zmzwv1nwDdauXYsBW20GbL4ZVv7jIQDAY3N3xg9/+EOcu9N+MKYmmuXA3RhjjDHGmI2GFfeGgWrtfffdByB621YPJ6oqUGHmdqrFPE5t+EIFQL1T8A2eCyKaMiwj1RmqFTwvYT25jeeC9VZf+OqVoDZbaH630t60ofJOrrnmGgCRlxm2ldADg/qOZj/TqKbqx1k9X1Dd55oM9sPQbpXrW9j/mHect6K4sugsE4+jMhcq7sY0dSZNmgQg1wMKEPUL9hNt/3p/psrMZ2lo454UlThptitUrIHo3sFPpq228eEsnq6Dod041X8q8hpnhPcljQ2h3nZU9Q/TYJ7hvSTdptpTXY/emeP6ZuLLfPL12hwPahX/zDxjy2dklPpJy5bjhz/8IYypjWY/cDfGGGOMMWZDkkqnkSrA1WMh+9REixm4jxw5EgCyCxE0QhvfulUdVtWcCgCVAqrNYURRwm1xEUCbOiwzz4vaEYbbqDpQBVUft0l+clVV5XZeK1NcXHLJJQCAG2+8EQCwxx57AMhVwZP8r6sCr2tIvvrqKwCR/2aqalTD1ANGiEZK5XemwT5NhU493ejalH/9618AgHPOOSfuNBjTJLnlllsAANdeey0AYOjQoTm/s71r3BFd70SlXdc4AVH/5TonHqtxVDgr2759ewBRv+XzlH1Q17rEzYbpzAHrQeWcaeq9hutj1Pe8Ku+sb6jyM3+eoxUrVmAR2mDt2rXov92eAIBPy6syaazJ1IO3wYHt1wBIY9nUzD3rgbK+uPjii3ELjCmMFjNwN8YYY4wxZoOQKtDGPWUb9zrx2WefAUDWj2dStDjdrr5sqdLVpADw2JNOOqlhK7ERYJkff/xxAPH1pCqvPu/Vb7ZGqCTcj5+8NusT+MA0HS644AIAyAbB2HrrrbO/de7cGUA0W0OohlH9+s9//gMgUrTY/1RRp7LHtsb0gfw1E8yDah6VwsmTJwOIPE9tt912OcczAuM777wDIPLSYUwxcvHFFwMA/vjHPwIAdtllFwCRus3+QXVcbd+5nUo2P4HouUnf5/zUSKlU69VTjcZb0ePULj3cpmmrjTrLxjUqVNxZP/Uwpx6vwueX1o/PwsrKSmDLLjnnCcifVQ7h9TCmUFrcwN0YY4wxZkPw6rSvanT9XLk8M9j/c2Vv/PSnP91YxTIbg1QKSBVgvx7jIrlO2VTFOehuQdDbjK60V/t0+nKlHSxRFTk89nvf+17DF7iRePbZZwHkK6VAvncOqqSLFi0CENkK8ljuv2TJEgC2aW9JXHXVVQCiNqHR6eIiEgL5ni+osHNdBdsc7eoBoG/fvgDy26f6kKei/sEHH+T8TqWNswBWxkxz5OGHHwYQxV9gH2S71/VbajtO701ApCxTiVZvbIT9lbNeHTp0yElbZ7w1nsr777+fTYsRYTUquirlfJbznsE09ZmuM3KsZ2jjzmjeqrgTPuuYBu9XM2fOxHG7dAIAbDLgIJjmwdKlS9G+fXt8PfllbN4uf4yUt/83y9Bh9/1RXl6eM2NVKPVb2mqMMcYYYwrisY8WetBu6kWLV9zryk033QQgUgRVCQSatw3srbfemv2f04FsQrQdPP/88zd6uUxxQgWebYnqHVUwti3ar6pdqipdBx0UPRCpuOlaCsK+S481tHV3/ADTEhkzZgwAYPvttweQH8uEfVS/h57GqKxrxG2NnaA24jyOSrWq4OzvVMnZVwFg9913BxCp22qmQnWfMwdU1NVGX9emaeTz0Fsat7FcrKd+Zxpcv3XGGWfAND+ouC/+98SCFfeOA4ZZcTfGGGOMMaY548WpdaSlq8nNeTbBNB5U5NSXtKpgGlmVUGULvc6oNwkemxRp0Uq7aclQDb700ksBRJ7XuFZEPcGw/4RKNPup2plrv+aaMv7O9U785P4az4G/hyo/t3Xp0iWnPlTn9Rhdr8bt6lWGdVGvOkBki89jWD6Wm16xPv74YwDA1VdfDdMCSKULXJxaP83cirsxxhhjjDFFgBV3Y0yjoXak9L6gCha3qx9nHkcf7KEqph6fVFljHvQqY4yJ1OHRo0cDADp1qvaCUt1vqDazL4brTDSmB73F8FiNu8DtVODVvpzp8ZPrUcKZNW7jujONfs7orOplhmuymBa90vCeQu8zzDu0nVdvWCw3bfYnTZoEIIpWa1oIqVRhrh7r6Q7SirsxxhhjjDFFQJMbuM+ZMwfHHXcctthiC2y++eb4/ve/n7UXM8bkUuz95dJLL8Wll16KtWvXYu3atVixYgVWrFiBNWvWYM2aNdnvK1euxMqVK1FZWYnKykqUlZWhrKwMnTp1yvlLp9PZv5KSkpy/8Ld0Oo2lS5di6dKlWLJkSdYO1hhjjFkv0unC/+pBkzKVWbZsGfbfP+OU/uKLL0arVq3wm9/8BsOGDcPkyZOzi0qMMe4vxpgNB808fv7znwMAhg0bBgDo1atXzn40ewEi8xkNZMiFoDRDmT9/PoDkIEc0PeEL9ZdffgkAOOGEExLL+8gjjwCIzOZofqPmeBocqnv37jl5crE6TYC4PVwQz21k1qxZAICJEycCAO66667EchpTX5rUwP2uu+7C559/jrfffhuDBg0CABx66KHo378/fv3rX+Paa69t5BIa03RoTv2FHl2uu+46APn+2fmg5ICAUR7p8UL3B6IHMx+4avM+e/bsnLyNMcaY9aUqlUZVAR5jCtmnJuoUgOnll1/Gd77zHTz55JM48sgjc357+OGH8eMf/xhvvPEG9tlnn/UqzODBgwEAb7/9ds72gw8+GNOnT8e0adPWK11jGoOVK1dmw3G///772cVNixcvxi677II+ffrg1VdfzQsHXijNsb9w4K6D7EIH7uEsgyplPJaL1BjEpSYVzxiTC91F7rbbbgCQE0Bmq622AhAt+GRfoxLP4YYuNud2quELFy4EEC0MrUsfffDBBwFEi0m5uFZVfd53WVbdzvsHyzpv3rxsHiznlClTANjdY0uHAZgWffJ2wQGYttxp8MYJwDR8+HBss802eOihh/J+e+ihh9CvXz/ss88+WLVqFRYuXFjQH6msrMSUKVOw11575aU9ePBgTJ8+PbsK3JhioE2bNrjvvvswbdo0/N///V92+5lnnony8nKMGzcOJSUl7i/GGGOMKYg6mcqkUimccMIJuOWWW1BeXp51s7RgwQL84x//yA5O/vSnP+Hkk08uKE2+aS9evBirVq3KvrGHcNvcuXOxww471KXIxjQqe++9Ny644ALccMMNOPLII/Hll1/ikUcewa233poNLe7+EnHRRRflfL/mmmsA5CvwrKMGaAkDs3CbupbkC02ooBljCkPV5auuuir7/8EHHwwg6oeqrGvwM7U/537soyeddFKdy0d1fty4cQAil5TMi2XjPYX3By0j77VU/d96661sHpdddhkA4Nhjj61z+UwzZiMFYKqzjfuJJ56I6667Do8//jh++tOfAgAeffRRrF27NtthDj74YLz44ot1SpedQ/2jAtHDmfsYU0xcccUVePbZZzFy5EgsW7YMw4YNw9lnn5393f3FGGOMMYVQ54H7jjvuiEGDBuGhhx7KDtwfeughfOtb38K2224LIKOGxSmBNUF7tJoWmYUBEIwpFkpLSzF27FgMGjQIZWVluPfee7PqD+D+UhOXXHJJzncuuN1ss4wdIVUxns/QwwVVPCprVNo++eQTAMD555+/oYptTIuB6jMAnH766QCA/v37A0B2VpF2vLR5J+y/NAOkK1t6sqkPVOvp4YXrYWjznpIgOBpE6bPPPgMAfPjhhwCAu+++u95lMs2cpqq4AxnV/ZxzzsEXX3yBVatW4V//+hfuuOOO7O8rV65EeXl5QWl169YNANCxY0e0bt06dvqa2+i2yZhi44UXXgCQGVR//vnn6NOnT/Y39xdjjDHGFEKdvMqQhQsXonv37vjVr36FlStX4pprrsHcuXOzb7Ljxo2rs80uAAwaNAipVCrPS8ZBBx2E6dOnY/r06XUtqjGNzpQpUzBo0CD8+Mc/xuTJk7Fw4UJ88MEH2TUi7i+Fc+ONNwIADjnkEAD5YddD0yEq7jQd+uKLLwBkXGYaYzYeZ5xxBoCoL1LtZv+97bbbNlpZzjnnHAD5tuycqRwzZsxGK4tpHtCrzMLP3sfm7drVvv8336DT9gPX26vMeinunTp1wqGHHooHH3wQFRUVOOSQQ7KDdmD9bHYB4JhjjsGFF16Id955J+stY+rUqXjppZfwy1/+cn2KakyjsmbNGpx00kno3r07brvtNsyYMQODBg3Ceeedh7FjxwJwfzHGGGNMYayX4g4ATzzxBI455hgAmcWpxx13XL0L880332DgwIH45ptv8Mtf/hKtWrXCLbfcgnXr1mHy5Mno3LlzvfMwZmNy+eWX4+qrr8b48eOx//77AwB+9atf4ZJLLsFzzz2Hww47bL3Tbon9hcrcQQcdBCBagMvbWGhDS28RK1asABD5uz/33HM3SlmNMcY0f6i4F6qg13V/Zb0t5A8//HB06NAB7du3xxFHHLG+yeTQrl07TJgwAfvttx+uueYaXHrppRgwYAAmTpzYLAchpnnz3nvv4dprr8WoUaOyg3YgE6lz0KBBOO2007IhvdcH9xdjjDGmZbHeivvatWvRvXt3HH744fjjH//Y0OUyxphEPv74YwD5XnVCP+60caetP2cIjTHGmIaiaBT3p59+GgsWLMCJJ564vkkYY4wxxhhjCqTOi1PfeustTJkyBVdffTUGDhyIYcOGbYhyGWNMIjvvvDMA4IILLsjZHk4g0mPFLbfcsvEKZowxxmxA6qy4jxkzBmeccQa6dOmC+++/f0OUyRhjjDHGGCOst427McYYY4wxLZmisXE3xhhjjDHGbDw8cDfGGGOMMaYI8MDdGGOMMcaYIsADd2OMMcYYY4oAD9yNMcYYY4wpAjxwN8YYY5oYlZWVuPvuu7H77rtjs802Q9euXXHooYfijTfeaOyiGWMaEQ/cjTHGmCbG+eefjzPOOAO77rorbrnlFvziF7/AZ599hmHDhuHtt99u7OIZYxqJOkdONcYYY8yGY+3atRgzZgyOOeYYPPDAA9ntxx57LPr27YuHHnoIgwcPbsQSGmMaCyvuxhhjTA3MnDkTqVQq8a+hWbNmDVauXImuXbvmbO/SpQvS6TTatGnT4HkaY4oDK+7GGGNMDXTu3DlH+QYyg+vzzjsPpaWlAIAVK1ZgxYoVtaZVUlKCDh061LhPmzZtsPfee2PcuHHYZ599MHToUCxZsgRXX301OnTogJ/97GfrXxljTFHjgbsxxhhTA5tuuilOOOGEnG1nnnkmli1bhhdffBEAcOONN+LKK6+sNa1evXph5syZte734IMP4vjjj8/Jt2/fvnj99dfRt2/fulXAGNNs8MDdGGOMqQP3338/7rrrLvz617/G/vvvDwA48cQTMWTIkFqPLdTMpV27dthll12wzz774IADDsD8+fNx/fXXY8SIEXj11VfRqVOnetXBGFOcpKqqqqoauxDGGGNMMTB58mTsu+++GDFiBB5++OF6pVVeXo6VK1dmv5eWlqJjx45Yu3YtBg4ciOHDh+P222/P/v75559jl112wXnnnYcbbrihXnkbYxqGpUuXon379igvL8fmm2/e4PsrXpxqjDHGFMDXX3+No48+Gttvvz3uueeenN+WLVuG+fPn1/q3YMGC7DHnnHMOttpqq+zfUUcdBQB45ZVX8OGHH+KII47IyWO77bbDTjvthNdff33DV9aYFsSdd96J3r17o6ysDHvvvXeTdrlqUxljjDGmFiorK/HjH/8YS5YswT//+U+0bds25/ebb765zjbuF1xwQY4NOxetfvnllwCAdevW5R2/Zs0arF27dn2rYYwRHn30UYwePRp333039t57b9x66604+OCDMXXqVHTp0qWxi5eHB+7GGGNMLVx55ZV44YUX8Pe//x19+vTJ+319bNx33nln7Lzzznn7bL/99gCARx55BIccckh2+3vvvYepU6faq4wxDcgtt9yC0047DSeffDIA4O6778Zzzz2HsWPH4sILL2zk0uVjG3djjDGmBj744AMMGDAA++23H0499dS839XjTENw0EEH4cUXX8SRRx6Jgw46CPPmzcPtt9+O1atX491338UOO+zQ4Hka09JYvXo12rZti8cffxwjRozIbh85ciSWLFmCZ555ptY0NraNuxV3Y4wxpgYWLVqEqqoqTJw4ERMnTsz7fUMM3J955hncfPPNeOSRR/D888+jtLQUQ4cOxdVXX+1BuzENxMKFC7Fu3bq8YGddu3bFp59+Wqe0li5d2qD7JeGBuzHGGFMDw4cPx8aenG7Tpg0uvfRSXHrppRs1X2NM3SgtLUW3bt2wzTbbFHxMt27dssHb6ooH7sYYY4wxpsXRqVMnlJSUZBeEky+//BLdunUrKI2ysjLMmDEDq1evLjjf0tJSlJWV1amsxAN3Y4wxxhjT4igtLcWee+6J8ePHZ23cKysrMX78eIwaNargdMrKytZ7IF5XPHA3xhhjjDEtktGjR2PkyJHYa6+9MHjwYNx6661Yvnx51stMU8MDd2OMMcYY0yI5/vjjsWDBAlx22WWYP38+dt99dzz//PN5C1abCnYHaYwxxhhjTBGQbuwCGGOMMcYYY2rHA3djjDHGGGOKAA/cjTHGGGOMKQI8cDfGGGOMMaYI8MDdGGOMMcaYIsADd2OMMcYYY4oAD9yNMcYYY4wpAjxwN8YYY4wxpgjwwN0YY4wxxpgiwAN3Y4wxxhhjigAP3I0xxhhjjCkCPHA3xhhjjDGmCPDA3RhjjDHGmCLAA3djjDHGGGOKAA/cjTHGGGOMKQI8cDfGGGOMMaYI8MDdGGOMMcaYIuD/A+htHYvGUcWLAAAAAElFTkSuQmCC", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GLH testing for study-level moderators\nCBMR framework can estimate global study-level moderator effects,\nand allows inference on the existence of m.\n\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inference = CBMRInference(\n", - " CBMRResults=cres, device=\"cuda\"\n", - " )\n", - "contrast_result = inference.compute_contrast(t_con_groups=[[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]], t_con_moderators=False)\n", - "plot_stat_map(\n", - " cres.get_map(\"GLH_groups_0_z_statistics\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"GLH_groups_0\",\n", - " threshold=scipy.stats.norm.isf(0.4)\n", - ")\n", - "print(\"The contrast matrix of GLH_0 is {}\".format(cres.metadata[\"GLH_groups_0\"]))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GLH testing for study-level moderators \n", - "\n", - "CBMR framework can estimate global study-level moderator effects, and allows inference on the existence of m . " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type5 = index_2\n", - "INFO:nimare.meta.cbmr:type1 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type3 = index_5\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\ncontrast_name = results.estimator.moderators\nt_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\ncontrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\nprint(results.tables[\"Moderators_Regression_Coef\"])\nprint(\n \"P-values of moderator effects `sample_sizes` is {}\".format(\n results.tables[\"standardized_sample_sizes_p_values\"]\n )\n)\nprint(\n \"P-value of moderator effects `avg_age` is {}\".format(\n results.tables[\"standardized_avg_age_p_values\"]\n )\n)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " standardized_sample_sizes standardized_avg_age type5 type1 \\\n", - "0 -0.00109 0.000588 -0.027104 -0.025923 \n", - "\n", - " type4 type3 \n", - "0 -0.026694 -0.027402 \n", - "P-values of moderator effects `sample_sizes` is 0.9130485642134478\n", - "P-value of moderator effects `avg_age` is 0.9529915576540059\n" - ] - } - ], - "source": [ - "inference = CBMRInference(\n", - " CBMRResults=cres, device=\"cuda\"\n", - ")\n", - "contrast_name = cres.estimator.moderators\n", - "t_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", - "print(cres.tables[\"Moderators_Regression_Coef\"])\n", - "print(\"P-values of moderator effects `sample_sizes` is {}\".format(cres.tables[\"standardized_sample_sizes_p_values\"]))\n", - "print(\"P-value of moderator effects `avg_age` is {}\".format(cres.tables[\"standardized_avg_age_p_values\"]))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This table shows the regression coefficients of study-level moderators, here, `sample_sizes` and `avg_age` are standardized in the preprocessing steps. Moderator effects of both `sample_size` and `avg_age` are not significant under significance level $0.05$. With reference to spatial intensity estimation of a chosen subtype, spatial intensity estimations of the other $4$ subtypes of schizophrenia are moderatored globally." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table shows the regression coefficients of study-level moderators, here,\n`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\nModerator effects of both `sample_size` and `avg_age` are not significant under\nsignificance level $0.05$. With reference to spatial intensity estimation of\na chosen subtype, spatial intensity estimations of the other $4$ subtypes of\nschizophrenia are moderatored globally.\n\n" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:schizophrenia_No = index_0\n", - "INFO:nimare.meta.cbmr:depression_No = index_1\n", - "INFO:nimare.meta.cbmr:depression_Yes = index_2\n", - "INFO:nimare.meta.cbmr:schizophrenia_Yes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type5 = index_2\n", - "INFO:nimare.meta.cbmr:type1 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type3 = index_5\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_moderators = inference.create_contrast(\n [\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\"\n)\ncontrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\nprint(\n \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n results.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n )\n)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "P-values of difference in two moderator effectors (`sample_size-avg_age`) is 0.9054368009582764\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CBMR also allows flexible contrasts between study-level covariates.\nFor example, we can write `contrast_name` (an input to `create_contrast`\nfunction) as `standardized_sample_sizes-standardized_avg_age` when exploring\nif the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" } - ], - "source": [ - "inference = CBMRInference(\n", - " CBMRResults=cres, device=\"cuda\"\n", - ")\n", - "t_con_moderators = inference.create_contrast([\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", - "print(\"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(cres.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CBMR also allows flexible contrasts between study-level covariates. For example, we can write `contrast_name` (an input to `create_contrast` function) as `standardized_sample_sizes-standardized_avg_age` when exploring if the moderator effects of `sample_sizes` and `avg_age` are equivalent. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py new file mode 100644 index 000000000..73c5dd4cd --- /dev/null +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -0,0 +1,313 @@ +""" + +=========================================== +Coordinate-based meta-regression algorithms +=========================================== + +A tour of CBMR algorithms in NiMARE + +This tutorial is intended to provide a brief description and example of the CBMR +algorithm implemented in NiMARE. For a more detailed introduction to the elements +of a coordinate-based meta-regression, see other stuff. +""" +from nimare.tests.utils import standardize_field +from nimare.meta import models + +from nilearn.plotting import plot_stat_map +from nimare.generate import create_coordinate_dataset + +import numpy as np +import scipy + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- + +# data simulation +ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000) +# set up group columns: diagnosis & drug_status +n_rows = dset.annotations.shape[0] +dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) +] +dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] +dset.annotations["drug_status"] = ( + dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) +) # random shuffle drug_status column +# set up continuous moderators: sample sizes & avg_age +dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] +dset.annotations["avg_age"] = np.arange(n_rows) +# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted +# as groups) +dset.annotations["schizophrenia_subtype"] = ["type1", "type2", "type3", "type4", "type5"] * int( + n_rows / 5 +) +dset.annotations["schizophrenia_subtype"] = ( + dset.annotations["schizophrenia_subtype"].sample(frac=1).reset_index(drop=True) +) # random shuffle drug_status column + +############################################################################### +# Estimation of group-specific spatial intensity functions +# ----------------------------------------------------------------------------- +# Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a +# generative regression model that estimates a smooth intensity function and +# can have study-level moderators. It's developed with a spatial model to +# induce a smooth response and model the entire image jointly, and fitted with +# different variants of statistical distributions (Poisson, Negative Binomial +# (NB) or Clustered NB model) to find the most accurate but parsimonious model. +# +# CBMR framework can generate estimation of group-specific spatial internsity +# functions for multiple groups simultaneously, with different group-specific +# spatial regression coefficients. +# +# CBMR framework can also consider the effects of study-level moderators +# (e.g. sample size, year of publication) by estimating regression coefficients +# of moderators (shared by all groups). Note that moderators can only have global +# effects instead of localized effects within CBMR framework. In the scenario +# that there're multiple subgroups within a group, while one or more of them don't +# have enough number of studies to be inferred as a separate group, CBMR can +# interpret them as categorical study-level moderators. +from nimare.meta.cbmr import CBMREstimator + +dset = standardize_field(dataset=dset, metadata=["sample_sizes", "avg_age"]) +cbmr = CBMREstimator( + group_categories=["diagnosis", "drug_status"], + moderators=[ + "standardized_sample_sizes", + "standardized_avg_age", + "schizophrenia_subtype:reference=type1", + ], + spline_spacing=10, + model=models.PoissonEstimator, + penalty=False, + lr=1e-1, + tol=1e1, + device="cpu", +) +results = cbmr.fit(dataset=dset) +plot_stat_map( + results.get_map("Group_schizophrenia_Yes_Studywise_Spatial_Intensity"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_Yes", + threshold=1e-4, +) +plot_stat_map( + results.get_map("Group_schizophrenia_No_Studywise_Spatial_Intensity"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_No", + threshold=1e-4, +) +plot_stat_map( + results.get_map("Group_depression_Yes_Studywise_Spatial_Intensity"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="depression_Yes", + threshold=1e-4, +) +plot_stat_map( + results.get_map("Group_depression_No_Studywise_Spatial_Intensity"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="depression_No", + threshold=1e-4, +) + +############################################################################### +# Four figures correspond to group-specific spatial intensity map of four groups +# ("schizophrenia_Yes", "schizophrenia_No", "depression_Yes", "depression_No"). +# Areas with stronger spatial intensity are highlighted. + +############################################################################### +# Generalized Linear Hypothesis (GLH) testing for spatial homogeneity +# ----------------------------------------------------------------------------- +# In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups` +# can be generated by `create_contrast` function, with group names specified. +from nimare.meta.cbmr import CBMRInference +from nimare.correct import FWECorrector + +inference = CBMRInference(CBMRResults=results, device="cuda") +t_con_groups = inference.create_contrast( + ["schizophrenia_Yes", "schizophrenia_No", "depression_Yes", "depression_No"], type="groups" +) +contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) + +# generate chi-square maps for each group +plot_stat_map( + results.get_map("schizophrenia_Yes_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_Yes", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + results.get_map("schizophrenia_No_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_No", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + results.get_map("depression_Yes_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="depression_Yes", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + results.get_map("depression_No_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="depression_No", + threshold=scipy.stats.norm.isf(0.05), +) + +############################################################################### +# Four figures (displayed as z-statistics map) correspond to homogeneity test of +# group-specific spatial intensity for four groups. The null hypothesis assumes +# homogeneous spatial intensity over the whole brain, +# $H_0: \mu_j = \mu_0 = sum(n_{\text{foci}})/N$, $j=1, \cdots, N$, where $N$ is +# the number of voxels within brain mask, $j$ is the index of voxel. Areas with +# significant p-values are highlighted (under significance level $0.05$). + +############################################################################### +# GLH testing for group comparisons among any two groups +# ----------------------------------------------------------------------------- +# In the most basic scenario of group comparison test, contrast matrix `t_con_groups` +# can be generated by `create_contrast` function, with `contrast_name` specified as +# "group1-group2". +inference = CBMRInference(CBMRResults=results, device="cuda") +t_con_groups = inference.create_contrast( + [ + "schizophrenia_Yes-schizophrenia_No", + "schizophrenia_No-depression_Yes", + "depression_Yes-depression_No", + ], + type="groups", +) +contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) + +# generate z-statistics maps for each group +plot_stat_map( + results.get_map("schizophrenia_Yes-schizophrenia_No_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_Yes", + threshold=scipy.stats.norm.isf(0.4), +) + +plot_stat_map( + results.get_map("schizophrenia_No-depression_Yes_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="schizophrenia_No", + threshold=scipy.stats.norm.isf(0.4), +) + +plot_stat_map( + results.get_map("depression_Yes-depression_No_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="depression_Yes", + threshold=scipy.stats.norm.isf(0.4), +) +############################################################################### +# Four figures (displayed as z-statistics map) correspond to group comparison +# test of spatial intensity for any two groups. The null hypothesis assumes +# spatial intensity estimations of two groups are equal at voxel level, +# $H_0: \mu_{1j}=\mu_{2j}$, $j=1, \cdots, N$, where $N$ is the number of voxels +# within brain mask, $j$ is the index of voxel. Areas with significant p-values +# (significant difference in spatial intensity estimation between two groups) +# are highlighted (under significance level $0.05$). + +############################################################################### +# GLH testing with contrast matrix specified +# ----------------------------------------------------------------------------- +# CBMR supports more flexible GLH test by specifying a contrast matrix. +# For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be +# represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast` +# function. Multiple independent GLH tests can be conducted simultaneously by +# including multiple contrast vectors/matrices in `t_con_group`. +# +# CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors) +# when it's represented as one of elements in `t_con_group` (datatype: list). +# Only if all of null hypotheses are rejected at voxel level, p-values are significant. +# For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing +# the equality of spatial intensity estimation among all of four groups (finding the +# consistent activation regions). Note that only $n-1$ contrast vectors are necessary +# for testing the equality of $n$ groups. + +inference = CBMRInference(CBMRResults=results, device="cuda") +contrast_result = inference.compute_contrast( + t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False +) +plot_stat_map( + results.get_map("GLH_groups_0_z_statistics"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="GLH_groups_0", + threshold=scipy.stats.norm.isf(0.4), +) +print("The contrast matrix of GLH_0 is {}".format(results.metadata["GLH_groups_0"])) + +############################################################################### +# GLH testing for study-level moderators +# ----------------------------------------------------------------------------- +# CBMR framework can estimate global study-level moderator effects, +# and allows inference on the existence of m. +inference = CBMRInference(CBMRResults=results, device="cuda") +contrast_name = results.estimator.moderators +t_con_moderators = inference.create_contrast(contrast_name, type="moderators") +contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) +print(results.tables["Moderators_Regression_Coef"]) +print( + "P-values of moderator effects `sample_sizes` is {}".format( + results.tables["standardized_sample_sizes_p_values"] + ) +) +print( + "P-value of moderator effects `avg_age` is {}".format( + results.tables["standardized_avg_age_p_values"] + ) +) + +############################################################################### +# This table shows the regression coefficients of study-level moderators, here, +# `sample_sizes` and `avg_age` are standardized in the preprocessing steps. +# Moderator effects of both `sample_size` and `avg_age` are not significant under +# significance level $0.05$. With reference to spatial intensity estimation of +# a chosen subtype, spatial intensity estimations of the other $4$ subtypes of +# schizophrenia are moderatored globally. + +inference = CBMRInference(CBMRResults=results, device="cuda") +t_con_moderators = inference.create_contrast( + ["standardized_sample_sizes-standardized_avg_age"], type="moderators" +) +contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) +print( + "P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}".format( + results.tables["standardized_sample_sizes-standardized_avg_age_p_values"] + ) +) + +############################################################################### +# CBMR also allows flexible contrasts between study-level covariates. +# For example, we can write `contrast_name` (an input to `create_contrast` +# function) as `standardized_sample_sizes-standardized_avg_age` when exploring +# if the moderator effects of `sample_sizes` and `avg_age` are equivalent. diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 6661bc4d7..a1cec4bb0 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -2,15 +2,12 @@ from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators import nibabel as nib import numpy as np -import pandas as pd import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter from nimare.meta import models import torch -import functorch import logging -import copy import re @@ -151,8 +148,10 @@ def _preprocess_input(self, dataset): ---------- dataset : :obj:`~nimare.dataset.Dataset` In this method, the Dataset is used to (1) select the appropriate mask image, - (2) categorize studies into multiple groups according to group categories in annotations, - (3) summarize group-wise study id, moderators (if exist), foci per voxel, foci per study, + (2) categorize studies into multiple groups according to group categories in + annotations, + (3) summarize group-wise study id, moderators (if exist), foci per voxel, foci + per study, (4) extract sample size metadata and use it as one of study-level moderators. Attributes @@ -200,7 +199,8 @@ def _preprocess_input(self, dataset): elif isinstance(self.group_categories, str): if self.group_categories not in valid_dset_annotations.columns: raise ValueError( - f"Category_names: {self.group_categories} does not exist in the dataset" + f"""Category_names: {self.group_categories} does not exist + in the dataset""" ) else: unique_groups = list( @@ -220,7 +220,8 @@ def _preprocess_input(self, dataset): ) if missing_categories: raise ValueError( - f"Category_names: {missing_categories} do/does not exist in the dataset." + f"""Category_names: {missing_categories} do/does not exist in + the dataset.""" ) unique_groups = ( valid_dset_annotations[self.group_categories] @@ -327,6 +328,7 @@ def _fit(self, dataset): return maps, tables + class CBMRInference(object): """Statistical inference on outcomes (intensity estimation and study-level moderator regressors) of CBMR. @@ -350,8 +352,8 @@ class CBMRInference(object): t_con_moderatorss : :obj:`~bool` or obj:`~list` or obj:`~None`, optional Contrast matrix for testing the existence of one or more study-level moderator effects. For boolean inputs, no statistical inference will be conducted for study-level moderators - if `t_con_moderatorss` is False, and statistical inference on the effect of each study-level - moderators will be conducted if `t_con_groups` is True. + if `t_con_moderatorss` is False, and statistical inference on the effect of each + study-level moderators will be conducted if `t_con_groups` is True. For list inputs, generialized linear hypothesis (GLH) testing will be conducted for each element independently. We also allow any element of `t_con_moderatorss` in list type, which represents GLH is conducted for all contrasts in this element simultaneously. @@ -396,8 +398,8 @@ def create_regular_expressions(self): >>> self.groups_regular_expression.match("group1 - group2").groupdict() """ - operator = '(\\ ?(?P[+-]?)\\ ??)' - for attr in ['groups', 'moderators']: + operator = "(\\ ?(?P[+-]?)\\ ??)" + for attr in ["groups", "moderators"]: groups = getattr(self, attr) first_group, second_group = [ f"(?P<{order}>{'|'.join([re.escape(g) for g in groups])})" @@ -406,7 +408,7 @@ def create_regular_expressions(self): reg_expr = re.compile(first_group + "(" + operator + second_group + "?)") setattr(self, "{}_regular_expression".format(attr), reg_expr) - + def create_contrast(self, contrast_name, type="groups"): """Create contrast matrix for generalized hypothesis testing (GLH). @@ -419,9 +421,9 @@ def create_contrast(self, contrast_name, type="groups"): (2) if `type` is "moderator", create contrast matrix for GLH on study-level moderators; if `contrast_name` begins with 'moderator_', followed by a valid moderator name, we create a contrast matrix for testing if the effect of this moderator exists; - if `contrast_name` comes in the form of "moderator1VSmoderator2", with valid moderator names - "modeator1" and "moderator2", we create a contrast matrix for testing if the effect of - these two moderators are different. + if `contrast_name` comes in the form of "moderator1VSmoderator2", with valid moderator + names "modeator1" and "moderator2", we create a contrast matrix for testing if the + effect of these two moderators are different. Parameters ---------- @@ -429,7 +431,7 @@ def create_contrast(self, contrast_name, type="groups"): Name of contrast in GLH. """ self.create_regular_expressions() - + if isinstance(contrast_name, str): contrast_name = [contrast_name] contrast_matrix = {} @@ -444,8 +446,10 @@ def create_contrast(self, contrast_name, type="groups"): # create contrast matrix if all(groups_contrast.values()): # group comparison contrast_vector[self.group_reference_dict[groups_contrast["first"]]] = 1 - contrast_vector[self.group_reference_dict[groups_contrast["second"]]] = int(contrast_match["operator"] + "1") - else: # homogeneity test + contrast_vector[self.group_reference_dict[groups_contrast["second"]]] = int( + contrast_match["operator"] + "1" + ) + else: # homogeneity test contrast_vector[self.group_reference_dict[contrast]] = 1 contrast_matrix[contrast] = contrast_vector @@ -458,9 +462,13 @@ def create_contrast(self, contrast_name, type="groups"): moderators_contrast = contrast_match.groupdict() if all(moderators_contrast.values()): # moderator comparison moderator_groups = list(map(moderators_contrast.get, ["first", "second"])) - contrast_vector[self.moderator_reference_dict[moderators_contrast["first"]]] = 1 - contrast_vector[self.moderator_reference_dict[moderators_contrast["second"]]] = int(moderators_contrast["operator"] + "1") - else: # moderator effect + contrast_vector[ + self.moderator_reference_dict[moderators_contrast["first"]] + ] = 1 + contrast_vector[ + self.moderator_reference_dict[moderators_contrast["second"]] + ] = int(moderators_contrast["operator"] + "1") + else: # moderator effect contrast_vector[self.moderator_reference_dict[contrast]] = 1 contrast_matrix[contrast] = contrast_vector @@ -492,13 +500,17 @@ def compute_contrast(self, t_con_groups=None, t_con_moderators=None): if self.t_con_groups is not False: # preprocess and standardize group contrast - self.t_con_groups, self.t_con_groups_name = self._preprocess_t_con_regressor(type="groups") + self.t_con_groups, self.t_con_groups_name = self._preprocess_t_con_regressor( + type="groups" + ) # GLH test for group contrast self._glh_con_group() if self.t_con_moderators is not False: self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast - self.t_con_moderators, self.t_con_moderators_name = self._preprocess_t_con_regressor(type="moderators") + self.t_con_moderators, self.t_con_moderators_name = self._preprocess_t_con_regressor( + type="moderators" + ) # GLH test for moderator contrast self._glh_con_moderator() @@ -515,7 +527,11 @@ def _preprocess_t_con_regressor(self, type): self.CBMRResults.metadata[f"GLH_{type}_{i}"] = t_con_regressor[i] t_con_regressor_name = None # Conduct group-wise spatial homogeneity test by default - t_con_regressor = [np.eye(n_regressors)] if t_con_regressor is None else [np.array(con_regressor) for con_regressor in t_con_regressor] + t_con_regressor = ( + [np.eye(n_regressors)] + if t_con_regressor is None + else [np.array(con_regressor) for con_regressor in t_con_regressor] + ) # make sure contrast matrix/vector is 2D t_con_regressor = [ con_regressor.reshape((1, -1)) if len(con_regressor.shape) == 1 else con_regressor @@ -527,11 +543,13 @@ def _preprocess_t_con_regressor(self, type): [con_regressor.shape[1] != n_regressors for con_regressor in t_con_regressor] )[0].tolist() raise ValueError( - f"""The shape of {str(wrong_con_regressor_idx)}th contrast vector(s) in contrast matrix doesn't match with {type}.""" + f"""The shape of {str(wrong_con_regressor_idx)}th contrast vector(s) in contrast + matrix doesn't match with {type}.""" ) # remove zero rows in contrast matrix (if exist) con_regressor_zero_row = [ - np.where(np.sum(np.abs(con_regressor), axis=1) == 0)[0] for con_regressor in t_con_regressor + np.where(np.sum(np.abs(con_regressor), axis=1) == 0)[0] + for con_regressor in t_con_regressor ] if np.any([len(zero_row) > 0 for zero_row in con_regressor_zero_row]): t_con_regressor = [ @@ -540,7 +558,8 @@ def _preprocess_t_con_regressor(self, type): ] if np.any([con_regressor.shape[0] == 0 for con_regressor in t_con_regressor]): raise ValueError( - """One or more of contrast vector(s) in {type} contrast matrix are all zeros.""" + """One or more of contrast vector(s) in {type} contrast matrix are + all zeros.""" ) # standardization (row sum 1) t_con_regressor = [ @@ -550,9 +569,9 @@ def _preprocess_t_con_regressor(self, type): # remove duplicate rows in contrast matrix (after standardization) uniq_con_regressor_idx = np.unique(t_con_regressor, axis=0, return_index=True)[1].tolist() t_con_regressor = [t_con_regressor[i] for i in uniq_con_regressor_idx[::-1]] - + return t_con_regressor, t_con_regressor_name - + def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_groups: @@ -629,37 +648,60 @@ def _glh_con_group(self): (Cov_log_intensity, Cov_group_log_intensity), axis=0 ) # (m^2, n_voxels) # GLH on log_intensity (eta) - chi_sq_spatial = self._chi_square_log_intensity(m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity) + chi_sq_spatial = self._chi_square_log_intensity( + m, + n_brain_voxel, + n_con_group_involved, + simp_con_group, + Cov_log_intensity, + Contrast_log_intensity, + ) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) # convert p-values to z-scores for visualization - if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) z_stats_spatial[z_stats_spatial < 0] = 0 - else: - z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial/2) - if con_group.shape[0] == 1: # GLH one test: Z statistics are signed + else: + z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) + if con_group.shape[0] == 1: # GLH one test: Z statistics are signed z_stats_spatial *= np.sign(Contrast_log_intensity.flatten()) z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) if self.t_con_groups_name: - self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_chi_square_values"] = chi_sq_spatial - self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_p_values"] = p_vals_spatial - self.CBMRResults.maps[f"{self.t_con_groups_name[con_group_count]}_z_statistics"] = z_stats_spatial + self.CBMRResults.maps[ + f"{self.t_con_groups_name[con_group_count]}_chi_square_values" + ] = chi_sq_spatial + self.CBMRResults.maps[ + f"{self.t_con_groups_name[con_group_count]}_p_values" + ] = p_vals_spatial + self.CBMRResults.maps[ + f"{self.t_con_groups_name[con_group_count]}_z_statistics" + ] = z_stats_spatial else: - self.CBMRResults.maps[f"GLH_groups_{con_group_count}_chi_square_values"] = chi_sq_spatial + self.CBMRResults.maps[ + f"GLH_groups_{con_group_count}_chi_square_values" + ] = chi_sq_spatial self.CBMRResults.maps[f"GLH_groups_{con_group_count}_p_values"] = p_vals_spatial - self.CBMRResults.maps[f"GLH_groups_{con_group_count}_z_statistics"] = z_stats_spatial + self.CBMRResults.maps[ + f"GLH_groups_{con_group_count}_z_statistics" + ] = z_stats_spatial con_group_count += 1 - - def _chi_square_log_intensity(self, m, n_brain_voxel, n_con_group_involved, simp_con_group, Cov_log_intensity, Contrast_log_intensity): + + def _chi_square_log_intensity( + self, + m, + n_brain_voxel, + n_con_group_involved, + simp_con_group, + Cov_log_intensity, + Contrast_log_intensity, + ): chi_sq_spatial = np.empty(shape=(0,)) for j in range(n_brain_voxel): Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) CV_jC = simp_con_group @ V_j @ simp_con_group.T CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_spatial_j = ( - Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j - ) + chi_sq_spatial_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j chi_sq_spatial = np.concatenate( ( chi_sq_spatial, @@ -670,7 +712,7 @@ def _chi_square_log_intensity(self, m, n_brain_voxel, n_con_group_involved, simp axis=0, ) return chi_sq_spatial - + def _glh_con_moderator(self): con_moderator_count = 0 for con_moderator in self.t_con_moderators: @@ -698,10 +740,18 @@ def _glh_con_moderator(self): ) chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) - if self.t_con_moderators_name: # None? - self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values"] = chi_sq_moderator - self.CBMRResults.tables[f"{self.t_con_moderators_name[con_moderator_count]}_p_values"] = p_vals_moderator + if self.t_con_moderators_name: # None? + self.CBMRResults.tables[ + f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values" + ] = chi_sq_moderator + self.CBMRResults.tables[ + f"{self.t_con_moderators_name[con_moderator_count]}_p_values" + ] = p_vals_moderator else: - self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_chi_square_values"] = chi_sq_moderator - self.CBMRResults.tables[f"GLH_moderators_{con_moderator_count}_p_values"] = p_vals_moderator + self.CBMRResults.tables[ + f"GLH_moderators_{con_moderator_count}_chi_square_values" + ] = chi_sq_moderator + self.CBMRResults.tables[ + f"GLH_moderators_{con_moderator_count}_p_values" + ] = p_vals_moderator con_moderator_count += 1 diff --git a/nimare/meta/models.py b/nimare/meta/models.py index c50e6bc45..039e20fb5 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -10,6 +10,7 @@ LGR = logging.getLogger(__name__) class GeneralLinearModelEstimator(torch.nn.Module): + def __init__( self, spatial_coef_dim=None, @@ -20,7 +21,7 @@ def __init__( n_iter=1000, tol=1e-2, device="cpu", - ): + ): super().__init__() self.spatial_coef_dim = spatial_coef_dim self.moderators_coef_dim = moderators_coef_dim @@ -49,7 +50,7 @@ def __init__( self.log_spatial_intensity_se = None self.spatial_intensity_se = None self.se_moderators = None - + @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): """Document this.""" @@ -83,8 +84,8 @@ def init_moderator_weights(self): self.moderators_coef_dim, 1, bias=False ).double() torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) - return - + return + def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): """Document this.""" self.groups = groups @@ -94,7 +95,7 @@ def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim self.init_spatial_weights() if moderators_coef_dim: self.init_moderator_weights() - + def _update( self, optimizer, @@ -107,7 +108,7 @@ def _update( """One iteration in optimization with L-BFGS. Adjust learning rate based on the number of iteration (with learning rate decay parameter - `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous + `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous iteration) if NaN occurs. """ self.iter += 1 @@ -155,10 +156,10 @@ def closure(): else: self.last_state = copy.deepcopy( self.state_dict() - ) + ) return loss - + def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): optimizer = torch.optim.LBFGS(self.parameters(), self.lr) # load dataset info to torch.tensor @@ -224,7 +225,7 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): # Estimate group-specific spatial intensity group_spatial_intensity_estimation = np.exp(np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)) spatial_intensity_estimation["Group_" + group + "_Studywise_Spatial_Intensity"] = group_spatial_intensity_estimation - + # Extract optimized regression coefficient of study-level moderators from the model if self.moderators_coef_dim: moderators_effect = dict() @@ -259,7 +260,7 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci moderators_coef = self.moderators_linear.weight else: group_moderators, moderators_coef = None, None - + ll_single_group_kwargs = { "moderators_coef": moderators_coef if self.moderators_coef_dim else None, "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), @@ -313,7 +314,7 @@ def nll_moderators_coef(moderators_coef): cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) var_moderators = np.diag(cov_moderators_coef).reshape((1, self.moderators_coef_dim)) se_moderators = np.sqrt(var_moderators) - else: + else: se_moderators = None self.spatial_regression_coef_se = spatial_regression_coef_se @@ -336,10 +337,10 @@ def summary(self): # Extract optimized regression coefficients from model and store them in 'tables' tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(self.spatial_regression_coef, orient="index") maps = self.spatial_intensity_estimation - if self.moderators_coef_dim: + if self.moderators_coef_dim: tables["Moderators_Regression_Coef"] = pd.DataFrame(data=self.moderators_coef, columns=self.moderators) tables["Moderators_Effect"] = pd.DataFrame.from_dict(data=self.moderators_effect, orient="index") - + # Estimate standard error of regression coefficient and (Log-)spatial intensity and store them in 'tables' # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( @@ -369,7 +370,7 @@ def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, m moderators_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) else: involved_moderators_by_group, moderators_coef = None, None - + ll_mult_group_kwargs = { "moderator_coef": moderators_coef, "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), @@ -378,7 +379,7 @@ def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, m "moderators": involved_moderators_by_group, "device": self.device } - + if hasattr(self, "overdispersion"): ll_mult_group_kwargs['overdispersion_coef'] = [self.overdispersion[group] for group in involved_groups] # create a negative log-likelihood function @@ -386,19 +387,19 @@ def nll_spatial_coef(spatial_coef): return -self._log_likelihood_mult_group( spatial_coef=spatial_coef, **ll_mult_group_kwargs, ) - + h = functorch.hessian(nll_spatial_coef)(spatial_coef) h = h.view(n_involved_groups * self.spatial_coef_dim, -1) return h.detach().cpu().numpy() - + def FisherInfo_MultipleGroup_moderator(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """Document this.""" foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in self.groups] foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in self.groups] spatial_coef = [self.spatial_coef_linears[group].weight.T for group in self.groups] spatial_coef = torch.stack(spatial_coef, dim=0) - + if self.moderators_coef_dim: moderators_by_group = [torch.tensor( moderators_by_group[group], dtype=torch.float64, device=self.device @@ -406,7 +407,7 @@ def FisherInfo_MultipleGroup_moderator(self, coef_spline_bases, moderators_by_gr moderator_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) else: moderators_by_group, moderator_coef = None, None - + ll_mult_group_kwargs = { "spatial_coef": spatial_coef, "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), @@ -422,12 +423,12 @@ def nll_moderator_coef(moderator_coef): return -self._log_likelihood_mult_group( moderator_coef=moderator_coef, **ll_mult_group_kwargs, ) - + h = functorch.hessian(nll_moderator_coef)(moderator_coef) h = h.view(self.moderators_coef_dim, self.moderators_coef_dim) return h.detach().cpu().numpy() - + class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) @@ -461,7 +462,7 @@ def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_vox overdispersion_param[group] = group_overdispersion tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( overdispersion_param, orient="index", columns=["overdispersion"]) - + return maps, tables class PoissonEstimator(GeneralLinearModelEstimator): @@ -591,7 +592,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) group_eig_vals = torch.real( torch.linalg.eigvals(group_F) - ) + ) del group_F group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) del group_eig_vals @@ -630,7 +631,7 @@ def _log_likelihood_single_group( group_foci_per_voxel, group_foci_per_study, device="cpu", - ): + ): v = 1 / group_overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) @@ -757,7 +758,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) moderators_coef, group_moderators = None, None group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] - + nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_overdispersion, group_spatial_coef, @@ -871,8 +872,8 @@ def _log_likelihood_mult_group( - torch.sum((foci_per_study[i] + v[i]) * torch.log(mu_sum_per_study[i] + v[i])) + torch.sum(foci_per_voxel[i] * log_spatial_intensity[i]) + torch.sum(foci_per_study[i] * log_moderator_effect[i]) - ) - + ) + return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): @@ -894,7 +895,8 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) coef_spline_bases, group_moderators, group_foci_per_voxel, - group_foci_per_study) + group_foci_per_study + ) log_l += group_log_l if self.penalty: @@ -910,7 +912,6 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_foci_per_voxel = foci_per_voxel[group] group_foci_per_study = foci_per_study[group] group_moderators = moderators[group] - nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_overdispersion, group_spatial_coef, @@ -922,7 +923,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) ) group_F = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=True - ) + ) group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) del group_F diff --git a/nimare/utils.py b/nimare/utils.py index 937fe61fb..ad80a1084 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1275,16 +1275,24 @@ def index2vox(vals, masker_voxels): return voxel_array def dummy_encoding_moderators(dataset_annotations, moderators): - new_moderators = moderators.copy() - for moderator in new_moderators: + new_moderators = [] + for moderator in moderators.copy(): + if len(moderator.split(":reference=")) == 2: + moderator, reference_subtype = moderator.split(":reference=") if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): - new_moderators.remove(moderator) # remove moderators that are dummy encoded categories_unique = dataset_annotations[moderator].unique().tolist() + # sort categories alphabetically + categories_unique = sorted(categories_unique, key=str.lower) + if "reference_subtype" in locals(): + # remove reference subgroup from list and add it to the first position + categories_unique.remove(reference_subtype) + categories_unique.insert(0, reference_subtype) for category in categories_unique: dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) - new_moderators.append(category) # add dummy encoded moderators # remove last categorical moderator column as it encoded as the other dummy encoded columns being zero dataset_annotations = dataset_annotations.drop([categories_unique[0]], axis=1) - new_moderators.remove(categories_unique[0]) + new_moderators.extend(categories_unique[1:]) # add dummy encoded moderators (except from the reference subgroup) + else: + new_moderators.append(moderator) return dataset_annotations, new_moderators From c73bdbb18835837a574881d16654e9688b9b22b1 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 27 Feb 2023 22:22:20 +0000 Subject: [PATCH 097/177] [skip CI][WIP] rewrite cbmr example in py file. --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 321 +++++++++++++++++-- nimare/tests/test_meta_cbmr.py | 10 +- 2 files changed, 305 insertions(+), 26 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 090126961..ed8c0aee9 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -15,7 +15,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Coordinate-based meta-regression algorithms\n\nA tour of CBMR algorithms in NiMARE\n\nThis tutorial is intended to provide a brief description and example of the CBMR\nalgorithm implemented in NiMARE. For a more detailed introduction to the elements\nof a coordinate-based meta-regression, see other stuff.\n" + "\n", + "# Coordinate-based meta-regression algorithms\n", + "\n", + "A tour of CBMR algorithms in NiMARE\n", + "\n", + "This tutorial is intended to provide a brief description and example of the CBMR\n", + "algorithm implemented in NiMARE. For a more detailed introduction to the elements\n", + "of a coordinate-based meta-regression, see other stuff.\n" ] }, { @@ -26,14 +33,22 @@ }, "outputs": [], "source": [ - "from nimare.tests.utils import standardize_field\nfrom nimare.meta import models\n\nfrom nilearn.plotting import plot_stat_map\nfrom nimare.generate import create_coordinate_dataset\n\nimport numpy as np\nimport scipy" + "from nimare.tests.utils import standardize_field\n", + "from nimare.meta import models\n", + "\n", + "from nilearn.plotting import plot_stat_map\n", + "from nimare.generate import create_coordinate_dataset\n", + "\n", + "import numpy as np\n", + "import scipy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load Dataset\n\n" + "## Load Dataset\n", + "\n" ] }, { @@ -44,14 +59,54 @@ }, "outputs": [], "source": [ - "# data simulation\nground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n# set up group columns: diagnosis & drug_status\nn_rows = dset.annotations.shape[0]\ndset.annotations[\"diagnosis\"] = [\n \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n]\ndset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\ndset.annotations[\"drug_status\"] = (\n dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column\n# set up continuous moderators: sample sizes & avg_age\ndset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\ndset.annotations[\"avg_age\"] = np.arange(n_rows)\n# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n# as groups)\ndset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n n_rows / 5\n)\ndset.annotations[\"schizophrenia_subtype\"] = (\n dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column" + "# data simulation\n", + "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", + "# set up group columns: diagnosis & drug_status\n", + "n_rows = dset.annotations.shape[0]\n", + "dset.annotations[\"diagnosis\"] = [\n", + " \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n", + "]\n", + "dset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\n", + "dset.annotations[\"drug_status\"] = (\n", + " dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n", + ") # random shuffle drug_status column\n", + "# set up continuous moderators: sample sizes & avg_age\n", + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n", + "# as groups)\n", + "dset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n", + " n_rows / 5\n", + ")\n", + "dset.annotations[\"schizophrenia_subtype\"] = (\n", + " dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n", + ") # random shuffle drug_status column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Estimation of group-specific spatial intensity functions\nUnlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a\ngenerative regression model that estimates a smooth intensity function and\ncan have study-level moderators. It's developed with a spatial model to\ninduce a smooth response and model the entire image jointly, and fitted with\ndifferent variants of statistical distributions (Poisson, Negative Binomial\n(NB) or Clustered NB model) to find the most accurate but parsimonious model.\n\nCBMR framework can generate estimation of group-specific spatial internsity\nfunctions for multiple groups simultaneously, with different group-specific\nspatial regression coefficients.\n\nCBMR framework can also consider the effects of study-level moderators\n(e.g. sample size, year of publication) by estimating regression coefficients\nof moderators (shared by all groups). Note that moderators can only have global\neffects instead of localized effects within CBMR framework. In the scenario\nthat there're multiple subgroups within a group, while one or more of them don't\nhave enough number of studies to be inferred as a separate group, CBMR can\ninterpret them as categorical study-level moderators.\n\n" + "## Estimation of group-specific spatial intensity functions\n", + "Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a\n", + "generative regression model that estimates a smooth intensity function and\n", + "can have study-level moderators. It's developed with a spatial model to\n", + "induce a smooth response and model the entire image jointly, and fitted with\n", + "different variants of statistical distributions (Poisson, Negative Binomial\n", + "(NB) or Clustered NB model) to find the most accurate but parsimonious model.\n", + "\n", + "CBMR framework can generate estimation of group-specific spatial internsity\n", + "functions for multiple groups simultaneously, with different group-specific\n", + "spatial regression coefficients.\n", + "\n", + "CBMR framework can also consider the effects of study-level moderators\n", + "(e.g. sample size, year of publication) by estimating regression coefficients\n", + "of moderators (shared by all groups). Note that moderators can only have global\n", + "effects instead of localized effects within CBMR framework. In the scenario\n", + "that there're multiple subgroups within a group, while one or more of them don't\n", + "have enough number of studies to be inferred as a separate group, CBMR can\n", + "interpret them as categorical study-level moderators.\n", + "\n" ] }, { @@ -62,21 +117,76 @@ }, "outputs": [], "source": [ - "from nimare.meta.cbmr import CBMREstimator\n\ndset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\ncbmr = CBMREstimator(\n group_categories=[\"diagnosis\", \"drug_status\"],\n moderators=[\n \"standardized_sample_sizes\",\n \"standardized_avg_age\",\n \"schizophrenia_subtype:reference=type1\",\n ],\n spline_spacing=10,\n model=models.PoissonEstimator,\n penalty=False,\n lr=1e-1,\n tol=1e1,\n device=\"cpu\",\n)\nresults = cbmr.fit(dataset=dset)\nplot_stat_map(\n results.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_No\",\n threshold=1e-4,\n)" + "from nimare.meta.cbmr import CBMREstimator\n", + "\n", + "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", + "cbmr = CBMREstimator(\n", + " group_categories=[\"diagnosis\", \"drug_status\"],\n", + " moderators=[\n", + " \"standardized_sample_sizes\",\n", + " \"standardized_avg_age\",\n", + " \"schizophrenia_subtype:reference=type1\",\n", + " ],\n", + " spline_spacing=10,\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1e-1,\n", + " tol=1e1,\n", + " device=\"cpu\",\n", + ")\n", + "results = cbmr.fit(dataset=dset)\n", + "plot_stat_map(\n", + " results.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_Yes\",\n", + " threshold=1e-4,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=1e-4,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=1e-4,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_No\",\n", + " threshold=1e-4,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures correspond to group-specific spatial intensity map of four groups\n(\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\").\nAreas with stronger spatial intensity are highlighted.\n\n" + "Four figures correspond to group-specific spatial intensity map of four groups\n", + "(\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\").\n", + "Areas with stronger spatial intensity are highlighted.\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\nIn the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with group names specified.\n\n" + "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\n", + "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\n", + "can be generated by `create_contrast` function, with group names specified.\n", + "\n" ] }, { @@ -87,21 +197,75 @@ }, "outputs": [], "source": [ - "from nimare.meta.cbmr import CBMRInference\nfrom nimare.correct import FWECorrector\n\ninference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_groups = inference.create_contrast(\n [\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\"\n)\ncontrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate chi-square maps for each group\nplot_stat_map(\n results.get_map(\"schizophrenia_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"schizophrenia_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"depression_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n results.get_map(\"depression_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_No\",\n threshold=scipy.stats.norm.isf(0.05),\n)" + "from nimare.meta.cbmr import CBMRInference\n", + "from nimare.correct import FWECorrector\n", + "\n", + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "t_con_groups = inference.create_contrast(\n", + " [\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\"\n", + ")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "\n", + "# generate chi-square maps for each group\n", + "plot_stat_map(\n", + " results.get_map(\"schizophrenia_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"schizophrenia_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"depression_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"depression_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_No\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to homogeneity test of\ngroup-specific spatial intensity for four groups. The null hypothesis assumes\nhomogeneous spatial intensity over the whole brain,\n$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\nthe number of voxels within brain mask, $j$ is the index of voxel. Areas with\nsignificant p-values are highlighted (under significance level $0.05$).\n\n" + "Four figures (displayed as z-statistics map) correspond to homogeneity test of\n", + "group-specific spatial intensity for four groups. The null hypothesis assumes\n", + "homogeneous spatial intensity over the whole brain,\n", + "$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\n", + "the number of voxels within brain mask, $j$ is the index of voxel. Areas with\n", + "significant p-values are highlighted (under significance level $0.05$).\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for group comparisons among any two groups\nIn the most basic scenario of group comparison test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with `contrast_name` specified as\n\"group1-group2\".\n\n" + "## GLH testing for group comparisons among any two groups\n", + "In the most basic scenario of group comparison test, contrast matrix `t_con_groups`\n", + "can be generated by `create_contrast` function, with `contrast_name` specified as\n", + "\"group1-group2\".\n", + "\n" ] }, { @@ -112,21 +276,79 @@ }, "outputs": [], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_groups = inference.create_contrast(\n [\n \"schizophrenia_Yes-schizophrenia_No\",\n \"schizophrenia_No-depression_Yes\",\n \"depression_Yes-depression_No\",\n ],\n type=\"groups\",\n)\ncontrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate z-statistics maps for each group\nplot_stat_map(\n results.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_Yes\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n results.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"schizophrenia_No\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n results.get_map(\"depression_Yes-depression_No_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"depression_Yes\",\n threshold=scipy.stats.norm.isf(0.4),\n)" + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "t_con_groups = inference.create_contrast(\n", + " [\n", + " \"schizophrenia_Yes-schizophrenia_No\",\n", + " \"schizophrenia_No-depression_Yes\",\n", + " \"depression_Yes-depression_No\",\n", + " ],\n", + " type=\"groups\",\n", + ")\n", + "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "\n", + "# generate z-statistics maps for each group\n", + "plot_stat_map(\n", + " results.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"schizophrenia_No\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"depression_Yes-depression_No_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"depression_Yes\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to group comparison\ntest of spatial intensity for any two groups. The null hypothesis assumes\nspatial intensity estimations of two groups are equal at voxel level,\n$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\nwithin brain mask, $j$ is the index of voxel. Areas with significant p-values\n(significant difference in spatial intensity estimation between two groups)\nare highlighted (under significance level $0.05$).\n\n" + "Four figures (displayed as z-statistics map) correspond to group comparison\n", + "test of spatial intensity for any two groups. The null hypothesis assumes\n", + "spatial intensity estimations of two groups are equal at voxel level,\n", + "$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\n", + "within brain mask, $j$ is the index of voxel. Areas with significant p-values\n", + "(significant difference in spatial intensity estimation between two groups)\n", + "are highlighted (under significance level $0.05$).\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing with contrast matrix specified\nCBMR supports more flexible GLH test by specifying a contrast matrix.\nFor example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\nrepresented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\nfunction. Multiple independent GLH tests can be conducted simultaneously by\nincluding multiple contrast vectors/matrices in `t_con_group`.\n\nCBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\nwhen it's represented as one of elements in `t_con_group` (datatype: list).\nOnly if all of null hypotheses are rejected at voxel level, p-values are significant.\nFor example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\nthe equality of spatial intensity estimation among all of four groups (finding the\nconsistent activation regions). Note that only $n-1$ contrast vectors are necessary\nfor testing the equality of $n$ groups.\n\n" + "## GLH testing with contrast matrix specified\n", + "CBMR supports more flexible GLH test by specifying a contrast matrix.\n", + "For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\n", + "represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\n", + "function. Multiple independent GLH tests can be conducted simultaneously by\n", + "including multiple contrast vectors/matrices in `t_con_group`.\n", + "\n", + "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\n", + "when it's represented as one of elements in `t_con_group` (datatype: list).\n", + "Only if all of null hypotheses are rejected at voxel level, p-values are significant.\n", + "For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\n", + "the equality of spatial intensity estimation among all of four groups (finding the\n", + "consistent activation regions). Note that only $n-1$ contrast vectors are necessary\n", + "for testing the equality of $n$ groups.\n", + "\n" ] }, { @@ -137,14 +359,29 @@ }, "outputs": [], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\ncontrast_result = inference.compute_contrast(\n t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n)\nplot_stat_map(\n results.get_map(\"GLH_groups_0_z_statistics\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"GLH_groups_0\",\n threshold=scipy.stats.norm.isf(0.4),\n)\nprint(\"The contrast matrix of GLH_0 is {}\".format(results.metadata[\"GLH_groups_0\"]))" + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "contrast_result = inference.compute_contrast(\n", + " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"GLH_groups_0_z_statistics\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"GLH_groups_0\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + ")\n", + "print(\"The contrast matrix of GLH_0 is {}\".format(results.metadata[\"GLH_groups_0\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for study-level moderators\nCBMR framework can estimate global study-level moderator effects,\nand allows inference on the existence of m.\n\n" + "## GLH testing for study-level moderators\n", + "CBMR framework can estimate global study-level moderator effects,\n", + "and allows inference on the existence of m.\n", + "\n" ] }, { @@ -155,14 +392,34 @@ }, "outputs": [], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\ncontrast_name = results.estimator.moderators\nt_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\ncontrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\nprint(results.tables[\"Moderators_Regression_Coef\"])\nprint(\n \"P-values of moderator effects `sample_sizes` is {}\".format(\n results.tables[\"standardized_sample_sizes_p_values\"]\n )\n)\nprint(\n \"P-value of moderator effects `avg_age` is {}\".format(\n results.tables[\"standardized_avg_age_p_values\"]\n )\n)" + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "contrast_name = results.estimator.moderators\n", + "t_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "print(results.tables[\"Moderators_Regression_Coef\"])\n", + "print(\n", + " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", + " results.tables[\"standardized_sample_sizes_p_values\"]\n", + " )\n", + ")\n", + "print(\n", + " \"P-value of moderator effects `avg_age` is {}\".format(\n", + " results.tables[\"standardized_avg_age_p_values\"]\n", + " )\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This table shows the regression coefficients of study-level moderators, here,\n`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\nModerator effects of both `sample_size` and `avg_age` are not significant under\nsignificance level $0.05$. With reference to spatial intensity estimation of\na chosen subtype, spatial intensity estimations of the other $4$ subtypes of\nschizophrenia are moderatored globally.\n\n" + "This table shows the regression coefficients of study-level moderators, here,\n", + "`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\n", + "Moderator effects of both `sample_size` and `avg_age` are not significant under\n", + "significance level $0.05$. With reference to spatial intensity estimation of\n", + "a chosen subtype, spatial intensity estimations of the other $4$ subtypes of\n", + "schizophrenia are moderatored globally.\n", + "\n" ] }, { @@ -173,20 +430,33 @@ }, "outputs": [], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\nt_con_moderators = inference.create_contrast(\n [\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\"\n)\ncontrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\nprint(\n \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n results.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n )\n)" + "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "t_con_moderators = inference.create_contrast(\n", + " [\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\"\n", + ")\n", + "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "print(\n", + " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", + " results.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", + " )\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "CBMR also allows flexible contrasts between study-level covariates.\nFor example, we can write `contrast_name` (an input to `create_contrast`\nfunction) as `standardized_sample_sizes-standardized_avg_age` when exploring\nif the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n\n" + "CBMR also allows flexible contrasts between study-level covariates.\n", + "For example, we can write `contrast_name` (an input to `create_contrast`\n", + "function) as `standardized_sample_sizes-standardized_avg_age` when exploring\n", + "if the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n", + "\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "torch", "language": "python", "name": "python3" }, @@ -200,9 +470,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" + }, + "vscode": { + "interpreter": { + "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" + } } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 9ea95b5e3..353c20c85 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -4,6 +4,7 @@ import logging import torch import numpy as np +from nimare.correct import FDRCorrector def test_CBMREstimator(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) @@ -41,9 +42,12 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) - t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_Yes-schizophrenia_No"], type="groups") - t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") - contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) + t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_No"], type="groups") + # t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") + contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) + + corr = FDRCorrector(method="indep", alpha=0.05) + cres = corr.transform(cbmr_res) def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) From c0049e087e88190eee01094cb44773b9f0992f84 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 12 Mar 2023 14:57:22 +0000 Subject: [PATCH 098/177] [skip CI][WIP] modify corrector class to be consistent with cbmr outputs --- nimare/correct.py | 56 ++++++++++++++++++++++------------ nimare/meta/cbmr.py | 29 +++++++++--------- nimare/meta/models.py | 2 +- nimare/tests/test_meta_cbmr.py | 7 +++-- 4 files changed, 57 insertions(+), 37 deletions(-) diff --git a/nimare/correct.py b/nimare/correct.py index 14485cd4a..e2ec4e137 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -4,6 +4,7 @@ from abc import ABCMeta, abstractproperty import numpy as np +import re from pymare.stats import bonferroni, fdr from nimare.results import MetaResult @@ -80,24 +81,35 @@ def _collect_inputs(self, result): f"\tAvailable native methods: {', '.join(corr_methods)}\n" f"\tAvailable estimator methods: {', '.join(est_methods)}" ) - + for rm in self._required_maps: + print(rm) # Check required maps + # for cbmr approach, we have customized name for groupwise p maps + p_map_cbmr = tuple([m for m in result.maps.keys() if re.search("p_", m)]) + if len(p_map_cbmr) > 0: + self._required_maps = p_map_cbmr for rm in self._required_maps: if result.maps.get(rm) is None: raise ValueError( f"{type(self)} requires '{rm}' maps to be present in the MetaResult, " "but none were found." ) - - def _generate_secondary_maps(self, result, corr_maps): + + + def _generate_secondary_maps(self, result, corr_maps, rm): """Generate corrected version of z and log-p maps if they exist.""" - p = corr_maps["p"] - if "z" in result.maps: - corr_maps["z"] = p_to_z(p) * np.sign(result.maps["z"]) + p = corr_maps[rm] + + if rm == "p": + z_map_name, logp_map_name = "z", "logp" + else: + z_map_name, logp_map_name = rm.replace("p_", "z_"), rm.replace("p_", "logp_") + if z_map_name in result.maps: + corr_maps[z_map_name] = p_to_z(p) * np.sign(result.maps[z_map_name]) - if "logp" in result.maps: - corr_maps["logp"] = -np.log10(p) + if logp_map_name in result.maps: + corr_maps[logp_map_name] = -np.log10(p) return corr_maps @@ -215,22 +227,26 @@ def _transform(self, result, method): An empty dictionary meant to contain any tables (pandas DataFrames) produced by the correction procedure. """ - p = result.maps["p"] + # Create a dictionary of the corrected results + corr_maps = {} + for rm in self._required_maps: + p = result.maps[rm] - # Find NaNs in the p value map, and mask them out - nonnan_mask = ~np.isnan(p) - p_corr = np.empty_like(p) - p_no_nans = p[nonnan_mask] + # Find NaNs in the p value map, and mask them out + nonnan_mask = ~np.isnan(p) + p_corr = np.empty_like(p) + p_no_nans = p[nonnan_mask] - # Call the correction method - p_corr_no_nans, tables = getattr(self, method)(p_no_nans) + # Call the correction method + p_corr_no_nans, tables = getattr(self, method)(p_no_nans) - # Unmask the corrected p values based on the NaN mask - p_corr[nonnan_mask] = p_corr_no_nans + # Unmask the corrected p values based on the NaN mask + p_corr[nonnan_mask] = p_corr_no_nans - # Create a dictionary of the corrected results - corr_maps = {"p": p_corr} - self._generate_secondary_maps(result, corr_maps) + # Create a dictionary of the corrected results + corr_maps[rm] = p_corr + self._generate_secondary_maps(result, corr_maps, rm) + return corr_maps, tables diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index a1cec4bb0..8ee7870de 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -192,10 +192,10 @@ def _preprocess_input(self, dataset): ] studies_by_group = dict() if self.group_categories is None: - studies_by_group["default"] = ( + studies_by_group["Default"] = ( valid_dset_annotations["study_id"].unique().tolist() ) - unique_groups = ["default"] + unique_groups = ["Default"] elif isinstance(self.group_categories, str): if self.group_categories not in valid_dset_annotations.columns: raise ValueError( @@ -213,7 +213,7 @@ def _preprocess_input(self, dataset): group_study_id = valid_dset_annotations.loc[group_study_id_bool][ "study_id" ] - studies_by_group[group] = group_study_id.unique().tolist() + studies_by_group[group.capitalize()] = group_study_id.unique().tolist() elif isinstance(self.group_categories, list): missing_categories = set(self.group_categories) - set( dataset.annotations.columns @@ -235,7 +235,8 @@ def _preprocess_input(self, dataset): group_study_id = valid_dset_annotations.loc[group_study_id_bool][ "study_id" ] - studies_by_group["_".join(group)] = group_study_id.unique().tolist() + camelcase_group = "".join([g.capitalize() for g in group]) + studies_by_group[camelcase_group] = group_study_id.unique().tolist() self.inputs_["studies_by_group"] = studies_by_group self.groups = list(self.inputs_["studies_by_group"].keys()) # collect studywise moderators if specficed @@ -597,7 +598,7 @@ def _glh_con_group(self): np.sum(group_foci_per_voxel) / (n_voxels * n_study) ) group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps["Group_" + group + "_Studywise_Spatial_Intensity"] + self.CBMRResults.maps[group + "_Studywise_Spatial_Intensity"] ) group_log_intensity_per_voxel = ( group_log_intensity_per_voxel - group_null_log_spatial_intensity @@ -610,7 +611,7 @@ def _glh_con_group(self): involved_log_intensity_per_voxel = list() for group in con_group_involved: group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps["Group_" + group + "_Studywise_Spatial_Intensity"] + self.CBMRResults.maps[group + "_Studywise_Spatial_Intensity"] ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) involved_log_intensity_per_voxel = np.stack( @@ -668,21 +669,21 @@ def _glh_con_group(self): z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) if self.t_con_groups_name: self.CBMRResults.maps[ - f"{self.t_con_groups_name[con_group_count]}_chi_square_values" + f"chi_square_{self.t_con_groups_name[con_group_count]}" ] = chi_sq_spatial self.CBMRResults.maps[ - f"{self.t_con_groups_name[con_group_count]}_p_values" + f"p_{self.t_con_groups_name[con_group_count]}" ] = p_vals_spatial self.CBMRResults.maps[ - f"{self.t_con_groups_name[con_group_count]}_z_statistics" + f"z_{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: self.CBMRResults.maps[ - f"GLH_groups_{con_group_count}_chi_square_values" + f"chi_square_GLH_groups_{con_group_count}" ] = chi_sq_spatial - self.CBMRResults.maps[f"GLH_groups_{con_group_count}_p_values"] = p_vals_spatial + self.CBMRResults.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial self.CBMRResults.maps[ - f"GLH_groups_{con_group_count}_z_statistics" + f"z_GLH_groups_{con_group_count}" ] = z_stats_spatial con_group_count += 1 @@ -745,13 +746,13 @@ def _glh_con_moderator(self): f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values" ] = chi_sq_moderator self.CBMRResults.tables[ - f"{self.t_con_moderators_name[con_moderator_count]}_p_values" + f"p_{self.t_con_moderators_name[con_moderator_count]}" ] = p_vals_moderator else: self.CBMRResults.tables[ f"GLH_moderators_{con_moderator_count}_chi_square_values" ] = chi_sq_moderator self.CBMRResults.tables[ - f"GLH_moderators_{con_moderator_count}_p_values" + f"p_GLH_moderators_{con_moderator_count}" ] = p_vals_moderator con_moderator_count += 1 diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 039e20fb5..677e2af09 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -224,7 +224,7 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): spatial_regression_coef[group] = group_spatial_coef_linear_weight # Estimate group-specific spatial intensity group_spatial_intensity_estimation = np.exp(np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)) - spatial_intensity_estimation["Group_" + group + "_Studywise_Spatial_Intensity"] = group_spatial_intensity_estimation + spatial_intensity_estimation[group + "_Studywise_Spatial_Intensity"] = group_spatial_intensity_estimation # Extract optimized regression coefficient of study-level moderators from the model if self.moderators_coef_dim: diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 353c20c85..a768b96e7 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -11,7 +11,7 @@ def test_CBMREstimator(testdata_cbmr_simulated): """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) cbmr = CBMREstimator( - group_categories=["diagnosis", "drug_status"], + group_categories= ["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=10, model=models.PoissonEstimator, @@ -42,12 +42,15 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) - t_con_groups = inference.create_contrast(["schizophrenia_Yes", "schizophrenia_No"], type="groups") + t_con_groups = inference.create_contrast(["SchizophreniaYes", "SchizophreniaNo"], type="groups") # t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) corr = FDRCorrector(method="indep", alpha=0.05) cres = corr.transform(cbmr_res) + + corr = FDRCorrector(method="indep", alpha=0.05) + cres2 = corr.transform(cres) def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) From 650cda4b82a5e6bb9d889f3eca743efaf42016c3 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Tue, 14 Mar 2023 16:30:16 +0000 Subject: [PATCH 099/177] [skip CI][WIP] add FDR/FWE correction methods to test --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 584 +++++++++++++++++-- examples/02_meta-analyses/10_plot_cbmr.py | 137 ++++- nimare/correct.py | 4 +- nimare/meta/cbmr.py | 24 +- nimare/meta/models.py | 2 +- nimare/tests/test_meta_cbmr.py | 20 +- 6 files changed, 662 insertions(+), 109 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index ed8c0aee9..3f0e10a37 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -111,11 +111,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from nimare.meta.cbmr import CBMREstimator\n", "\n", @@ -131,40 +189,40 @@ " model=models.PoissonEstimator,\n", " penalty=False,\n", " lr=1e-1,\n", - " tol=1e1,\n", + " tol=1e3,\n", " device=\"cpu\",\n", ")\n", "results = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", - " results.get_map(\"Group_schizophrenia_Yes_Studywise_Spatial_Intensity\"),\n", + " results.get_map(\"SpatialIntensity_group-SchizophreniaYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", + " title=\"SchizophreniaYes\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"Group_schizophrenia_No_Studywise_Spatial_Intensity\"),\n", + " results.get_map(\"SpatialIntensity_group-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", + " title=\"SchizophreniaNo\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"Group_depression_Yes_Studywise_Spatial_Intensity\"),\n", + " results.get_map(\"SpatialIntensity_group-DepressionYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", + " title=\"DepressionYes\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"Group_depression_No_Studywise_Spatial_Intensity\"),\n", + " results.get_map(\"SpatialIntensity_group-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"depression_No\",\n", + " title=\"DepressionNo\",\n", " threshold=1e-4,\n", ")" ] @@ -191,55 +249,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", + "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", + "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", + "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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GjBiBd999Fxs3bkSPHj3Qv39//OY3v8GMGTOqs5g46aSTMHDgQKxevRr9+/fP8CJDvvzyy7Q5xr333osf//jHOOqoozBr1iy8+eabSCQSOOSQQ9C8eXO8+OKLeOihhyLzevTRR/Hvf/8bb7/9Nr7//nsMGTIE3bt3x3fffYeLL764WusVxezZs3Haaafh6aefxgsvvIBZs2bhiy++wNq1a9GxY0fsu+++2HnnndG/f//tYj5y22234amnnsJzzz2Hiy66CAsWLMA+++yDPn364J577sHll19eqfRWrFiBP/3pT7juuusif//222/xv//7vxgzZgz+8Y9/4L333sO3336LfffdF3369MGiRYtwxhlnVEfVjDHGGFOHmTp1Kg4++OD0d445Ro4ciTFjxuCqq67C2rVr8ctf/hIrV67EAQccgJdffnmbTFgKEwkU5rBmrxCJlNPpbaRaB+633HILpk6dihEjRmCfffbB8OHD0aJFC6xatQoffvghxo8fj4ceegjr1q1LH7Nw4UIMGjQIl112GU466SQcdthh2Lp1KxYsWICHHnoIEyZMqM4iAkip3kDKm8lZZ50Vuc+kSZPSA/fS0lIce+yxuPDCC3HWWWdhxIgRAIDPP/8cjz/+OB555JHYRY933XUXpk6diksvvRRDhgzB2rVr8eSTT+Kaa66pMTvrl156CXvvvTcuv/xyHHbYYTjssMOwefNmLFy4EP/4xz/wwgsvRNqDVwdPP/00VqxYgWuvvRb9+/fHXnvthalTp+LCCy9EIpGo9MAdSM18/OpXv0LLli0jfx87dixmz56Nq6++GsOGDcOQIUPw/fff46GHHsIf/vCH7b6uwBhjjDG1z0EHHVSuU4pEIoGbbroJN910Uw2Wqmokkjm62fj444+x3377be/y1BsmTpyIgw46CF27di3XlaSpu3z00UfYd999a7sYxhhjjKmjrFq1Ci1atMAFBZ3ROFGxBfrGZClGlc5HSUkJmjdvXun8anRxqjHGGGOMMWbbqFF3kMYYY4wxxtQ3KmXjXgWsuBtjjDE1zJgxY5BIJDB16tTaLoqpp7CN8W+HHXZAx44dcdZZZzmWSR5jxX07EV7FbIwxxhhTG9x0003o1q0bNmzYgPfffx9jxozBu+++ixkzZmyT9xQTTWEi9VfhflXMxwN3Y4wxxph6ypFHHomBAwcCAM4991y0adMGd9xxB1566aVKB0I0tY9NZYwxxhhjGgjDhw8HkIrzYqoP2rjn8lcVrLgbY4wxxjQQ5s6dCwBo1apV7RaknmFTGWOMMcYYUyVKSkpQXFyMDRs24IMPPsCNN96Ixo0b4+ijj67topltwAN3Y4wxxph6yqGHHprxvWvXrhg7diw6depUSyWqn9SUO8icB+5t2rRBkyZNsGHDhiplaEw+0KRJE7Rp06a2i2GMMcZUiQcffBC9evVCSUkJRo8ejbfffhuNGzeu7WKZbSTngXvnzp0xc+ZMFBcXb8/yGFMnaNOmDTp37lzbxTDGGGOqxODBg9NeZY477jgccMAB+NnPfoaZM2di5513ruXS1R8SyM3jS9X09kqaynTu3NmDGWOMMcaYPKSwsBC33XYbDj74YDzwwAO4+uqra7tIppLYHaQxxhhjTAPhoIMOwuDBg3HffffZ/LkasTtIY4wxpp4zevRovPzyy1nbL730UjRr1qwWSmQaAldeeSVOPvlkjBkzBueff35tF8dUAg/cjTHGmFpi1KhRkdvPOussD9zNduOEE05Ajx49cNddd+G8885DYWFVvYubmvLjnkgmk8kqpmGMMcYYkxNPPPEEAOAHP/gBAKBp06YZv3NYsnbtWgDAT37yk5zTHj9+PABgp512AgAkxCxh/fr1AIBly5YBAEaOHFmpshujrFq1Ci1atMD1TbujSaJiC/QNyVLcuP4blJSUoHnz5pXOz4q7McYYY4wxVSCluOfix71qWHE3xhhjTLXz3HPPAQDat28PAGnf4QUFBRmfVMVLS0szjud3fk6bNg0AcMEFF6T3oalR//79I9Mm/M4hj6a9ceNGAMCiRYsAAKeeemql6moaLlTc/7BTdzRJVDws35Dcit+t3XbF3V5ljDHGGGOMyQNsKmOMMcaYKnP//fcDCGzXu3XrBgAoKirK2I8LIWmH3qhRIwCBGk5o475q1SoAQJcuXQAAN9xwQ3qfwYMHZxzLNPlJqOpv3rw5I+2tW7dmlIGxap5++mkAgS38JZdcUm7djcnV1WNhFUMwWXE3xhhjjDEmD7Dibowxxphy+fvf/w4AaNu2LYBAoQ7bpe+6664Zx1Dl5ifVbR6zZcsWAMDOO+8MANhhh9SQhEGB1AaeNvLcP7yN+/AYptWkSZOMvOhVhso74SwA0+EsAes0efLk9L7Mg2ksWbIEAHDiiSfCNFwKcnQHWVXF3Iq7McYYY4wxeUCtK+5jxozB2WefjSlTpmDgwIG1XRxTz2D7IoWFhWjXrh0OO+ww/OEPf0DHjh1rsXTGGFM3ef755wEALVq0ABDYflNtpkJNFR0IvMcsXLgQQKBuE7VhpwpOlZtprlu3DkC28k4VPOybndu4D49RO3qWk3nyk/B3lpmzAh06dAAQKPvhtNUu/rXXXgMAlJSUAABOOukkmIZDTdm41/rA3Zia4KabbkK3bt2wYcMGvP/++xgzZgzeffddzJgxIz2VaowxxhhTl/HA3TQIjjzyyPSMzrnnnos2bdrgjjvuwEsvvYRTTjmllktnjDF1g7feegtAoJ6r2k2VmZ9Ux4HArpz7Ur3mvvydajb3o5pNFZw+1cNqPhDt710jo/IYTYN5ME+q/6yf2sBzP5aZnwCw4447Aghs3PlJdZ+RYHkuDzzwQJj6T2GONu5VDcBkG3fTIBk+fDgAYPbs2bVcEmOMMcaY3LDibhokc+fOBQC0atWqdgtijDF1AHpNoekgVWOqyRrVlEp12PZ706ZNAAK7ePpKJ6rI8/5Lm3HapzNPquWqquv3MDyGaVBJZzmZJxV5lpn7sZ6sA8sWrqdGZeUx3IczDFTveW6HDRsWW26T/9SU4u6Bu2kQlJSUoLi4GBs2bMAHH3yAG2+8EY0bN8bRRx9d20UzxhhjTJ7jxanGVCOHHnpoxveuXbti7Nix6NSpUy2VyBhjjDGmcnjgbhoEDz74IHr16oWSkhKMHj0ab7/9dsbUpzHGNETGjx8PAGjXrh2AYIFls2bNAACrV68GkG1KQmgWEj6W+9KkhJ/8vU2bNgAC0xKmSfMVLhylSQy/09SG5ivhbXHHME2a/tAUiIGViouLAQQmM6w3zXlY5nA9CcutAaKYBuu9Zs0aAMG5/slPfpKVlsl/CpGjqUyy4n3KwwN30yAYPHhw2qvMcccdhwMOOAA/+9nPMHPmzIwofMYYY4wxdRUP3E2Do7CwELfddhsOPvhgPPDAA7j66qtru0jGGFMrULhQt4hUrH/wgx8AyHT7CAQKdHihJpVnquBcbEqVu23btgACxVxV8eXLlwMIFpZquqpwh7exHPzOT6ZJxT1OedcFsvxdF9SG01boJpL10ZkHi0T1m4IcbdwLctin3OOrdLQxecpBBx2EwYMH47777kvfqI0xxhhj6jJ1RnEfPXo0Xn755aztl156adpezJjq5Morr8TJJ5+MMWPG4Pzzz6/t4hhjTI0xYcIEAIFKTHWY0C6bCnXLli0BlO+KkTbe3IdKM1VrfqfSTuV68eLFGXlScacKzuPVBh4IXC5qECd1C8k8OnfuHJk2A06pLT/zCtvVK9yHx7Ie6mqS54Xn3l7N6hc5u4OsmuBedwbuo0aNitx+1llneeButgsnnHACevTogbvuugvnnXdeuTdmY4wxxpjaJpEMv7oaY4wxpt7y7rvvAgiUZlWoabtObyq0S+d3qsblKe8VwWEHAzR9/fXXAIBVq1YBCJR1iilU6mln/91336XT6tixI4Bg5oBKOetDJb558+YAgJ49e0bWpyr10PosWbIk43vcDALP/QEHHLDNZTC1z6pVq9CiRQs80aY3diyoWABcV7oVI4tnoqSkJN0uK4Nt3I0xxhhjjMkD6oypjDHGGGO2D1xDRlt1KtS0w+Yn1W0q1fSmEqe0h73KEN2H6rdO8NNHPPOmWk41XM0X1WYeCDy1aFwO5qn1Y5477bQTGk9/BQCwcdZ0AMDaRSkPNI1bptLccfc9Umntf1JG2lHebYDgXLEstL/nLAZ/5ydnEHhtjjjiCJj8pcHZuBtjjDHGGJOPFOboDjKXfcrDA3djjDGmnkNlmuovvcW0aNECQLbnEzqFoLodZwse9mmuCnncEjqNcspPljFO1WfZw/7Q9RiWR/2vx0VWzYVEIpFW8ON+D+dJ3/eaN3+n+k/bd/t3N5XBA3djjDHGNBjav/8UAGD6c+8AAP5vwteR+511SFcAQJ+fzkxva3ZoymxmfZvdt2MJTT5SkEjkFFypqgGYPHA3xhhj6ikPPPAAAKBv374AAvtr2nrT1p2qL5V4qttV8bqivtBV7WZZmCdV/zi1nF5auH8Y1oN5qA91pqm28NWJrg/gd9q60787bdt5flhWXquLL754u5XR5D8euBtjjDGm3rPP0qnA0qn44N5/AwCe/XhRufuPeWMuAOAn36xMb/tRizL3fUdacTeZJAoTSBRU/KJblZdhwAN3Y4wxpt5CP+xUq+PUbKrE9OhCNMppeV5l4uzA4wYq3E47e82Ln1Soo/IktBen8s76cd9EIgEsjSzGNhG27dcZBS0ny6Z+3am0czuvlTHl4YG7McYYY+o9372WCj5VkdKujJ+zMv3/buPeBgDsMfBQAMDaH1h5NykKChMoyEFxt427McYYYzL461//CgDo0KEDgEBpZ1RS2l1TFaZHGLVDpzqsqjftzKlsh9PIFe5PpX7lypUAsu3SyYYNGzLqEN7GejD6qqbRsmVLNKpU6XKDZQYCtV/XB2g99dzvsssuGWXmtTvllFO2Q4lNvuPIqcYYY4ypt+zfbB36bF2IeZNmY96k2VVKa/57CzD/vQUoXb4Ipcsrp9yb7UvXrl2RSCSy/i666KLI/ceMGZO1b5MmTba9AIUFSOTwh8KqDb2tuBtjjDH1jObNU4so1W+7elXhdvXUQnWYCnZJSQmAwLab6dBneTgNVe8VbmfZdBYgzp6e+3EWILxN65Wxb7PIYlQLS5cuTSvnVMwZnZbbeV70mhCeL9af+5nKMWXKlIx1FjNmzMBhhx2Gk08+OfaY5s2bY+bMwN1nVReO1gQeuBtjjDGm3pLclDKn2bI+PohSpSktS8t2C3UGmhyR22+/HT169MCBBx4Ye0wikUD79u2rJf9EQQKJwhy8ysA27sYYY4wJQbWXn/QWQ2Waqq/up77XCbdT7eZ3KvFRaap6qUo696dtOO3FqUCrMk0lOpxnnIpN5bUyEVK3hc2bN2flrd5xeD44O6HnkrMD/IzymmMqx6ZNmzB27Fhcfvnl5aroa9asQZcuXVBaWop9990Xt956K/r167dNeRYUJlCQw8C9wAN3Y4wxxphoCpqnBszt+5e5W/x6+Tan1W6vlKpb0LpdasPS7ftiYLaNcePGYeXKlTjrrLNi9+nduzdGjx6NvffeGyUlJbjrrrswbNgwfPbZZ+jUqVPNFbaSeOBeC7z44osAgGbNUoZ3uuJclY/ly1M3mcqsMOeq9NatW0emqXkyit7xxx9f6foYk088++yzAAJVjH1AfVDHRX1kXxo5cuT2L6wxleD+++9P/9+jRw8AgapLNZvf2Y4ZMZVqsKrmtM+mz3F+krDnlziVXn9XBZTPKZaRfVGVbOYd9jXPNHXfzGddZjmqkx133DHtWYfniueOZaPt+7JlywAEEVRZRpad14b7h6/nJZdcst3qUB957LHHcOSRR6a9KkUxdOhQDB06NP192LBh2GOPPfDII4/g5ptvrnSeiYICJHKYLUlUcRbIA3djjDHG1Fs+XF6A7t27o9tRgwAAh7w1HwDwxtJ15R0WSdcRAwAAn61tWsGepraYN28eXn/9dbzwwguVOq5Ro0YYMGAAvv766+1UsurBA3djjDGmHhBWsnWWlR5LaEetCjr3Y/ROKum0iefCP1XRw3mq33X+xs+4WSwqzh07dgQQeLLhdvU2E7YBV9WaqjfV6zgb+Kqy6667pv9Xm35V2pcuTYVs5YwCZ7ip1KtHnLg1AiY3Hn/8cbRt2xb/8z//U6njtm7diunTp+Ooo47apnxt414PoLkKXUNxSnK33XYDEHRuXciiNxhOI06cOBEAcPDBB8fmyX169uyZkTbRaVLeGFjGyZMnAwim8nijcSAIk28888wzAIIALTpo0E+iJjNxi9tGjRqV/l/NaH75y19WqezGmOpnpwOPBQCMuCP1vevfUs+7x/4d7dv99P1TLxJ9Thmc3tbqiBMBAMu2VyFNlSgtLcXjjz+OkSNHZr1InnnmmejYsSNuu+02AMBNN92E/fffHz179sTKlStx5513Yt68eTj33HNro+g544G7McYYY4zJe15//XXMnz8f55xzTtZv8+fPz5itWbFiBc477zwsWrQIrVq1wn777YfJkyejb9++25R3orBm3EEmktvbV1ID5I033gAQTNFRjaOSx7dAfup0mL4lciqTx3/++ecAAlUcCNR8NjguPg2HowaCqTuiU3r85PH8nVOXhxxySGy9jaktxo4dCyBz4RxNAlRBZ/+Km97WxXc6I1ZeWHdV8eNc7Wn/YhkuuOCC8itqTDk88MAD6f/32GMPAIGrRb2Xr1uXsu+mD2uaa7Rrl/KWogGZiPaX8POL/2sf4XY+X3SGin2UM8JqvrNixQoAweJOmpoAgZMHLq5t1apVRtp8BnImm2UrLS1Fs8WfpfKd90WqHCtSJi0FO7dMHdO5Vyq/TvtmzLxp3eOGUTTxWbx4MYDgnrRoUSriKq+NjhV4bb744ot0WhdffHFkHqb2WbVqFVq0aIF/7LUfdirn+UDWbt2KY6Z/hJKSkm0KtmXF3RhjjDHGmCqQUtxz8CpTRS9HHrhXExMmTEj/r4t7+KZP9UDdPlIR0O98i6dCQKWEi4TCQSh04RAVeKoofJNXJYPf1fUXv1MBoaoRrufRRx9dwVkxZvvwl7/8BUCg4LGd0p4dyFa9NQx7nOJOdHZKZ8bCa1F05kpVfp3JCodsD5eF7t9U0QvPwjEN29EbRWeLgOwZX6q+6o5YZ3q1LfM47s9nS3nuIMPqdvh3nX0m7AfsW+zP7C96fHib7qNuLQnLsmXLFqz4Qe/U77vskXF/2FqWxwbuLwtyeW50Vo/nRGccWE8ex3NPZZ1lipttNyaMB+7GGGOMMcZUAXuVyRNoUxhezBAXzllVbrUH5Nu22r8qUTa2cXa3qjKyTHzz1zxV/aciwP1Zl3DdbXtnthdU1qmmabAkVQXDruriAizF9QlVJuPc3kUplHEeojQNdWcX5+5N3eeF1X+Wj/2P5Tj//PMj0zINh8svvzz9/7/+9S8AgQqsszwMYqQKNdsXZ3g5s6szxWoTH95GVO3Wmd84W3iiNu/lKe7ch8c0adIkMk3dX2354/ow1XUg22Zd1660aNECQHCO1a0lt/P5qteG6Yavp6n7JBIJJApyWJxaWrWBe8XGOMYYY4wxxphax4p7jjz++OMAAkVBlei1a9em96V9Od+uqYhRrVYPE+plRlG7dLWfDW9TVT+skJeXB8vE31k/1oEqRLierPuf//znjLyoFpx99tmReRkTBxV2tW1VRSrOZjYKVdLZbjUseVxaqqapYl8eug+P1XtAXL3Ky0Pt6sMeRQDPhDV0qJir4q5tkG2M923e4zVQE7frDDI9vQDB+i7tKwq3Mw/1fkZU/dayhrdp34lLK0rtb14yt2ynVBqrW/XImqkL1zMczOqgHq0zjkWiAMBG/GfB2vQ5Uw9yuu5GlXteO5NfFBQWoCCHxakFyapp5lbcjTHGGGOMyQOsuMcwevRoAECXLl0AAAMGDACQ7Y921qxZAIDvv/8+fSxt67hynG/dtHOjAqL2rqqA8K2eb/waPjqsLOhv6heXdnw8Rn1Z81NVF6ZDv7nhetL/7+67756RJvOgP/t58+YBQGRABGMA4IknngAQtHmdZVLFjf2voiioucA2HueDnZQXYVVVei1nXH/T/dSvtfbrqGPjyv+nP/0JQKDqWYFvWDDOh65jIto22ffY14qLiwEE0bPVZlxnZ4Gg31JBj1snwucSf2fa2u7VKw1Zvnx5+v9dd901Y5+4GTH2m02bNqFjaZl6vrXMdn5DWR22lHlR21A2s1xUZrNfWIS2KbN1fPj9hnQ5169fHyjt8llcXIw2bdpk1JdlUG9Y/OQ1C8doMflDzgGYkrZxN8YYY4wxpt5jxV2g8tejRw8AwepwVcqoanE/RjMFgIULFwIAOnToACBYQU5FQ/3fxvmZVbteEvYfXd62cBpUNOIiOfIzbLsHBMo76xT2GsC6qz0j02IkO9aT53bkyJGRZTUNj8ceewxA0N6oRGm7jFPTVKHLJbqhpqXrQ7Qdq1Kptq9RxHmP0XUtcWmU51kqzj6e6IwBv9sLTcPi3HPPBQA8+uijAAIVXPsOn3Hsg4xSyucWvcaorXuUsq3tWdsi167QKwt/Z958ZmgME11/Elbc1Sd8XFTipUtTUVFLSkrQsXVZv9qUyjetuJeWzV6VfaafzCET/MHti0Ibdg5VNrM/HrPPbgCAp9/9PB3Nlc9PeurhuVT7e8doyE+suBtjjDHGGGPSWHEv4+9//zsAoFOnTgACm3C+xWtENNr18U2ZdnZAoE5zFTqVDqoK6sGFqI/bOLvZ8vy4q12fetJQW3e1uWMZqdSzDtyfswjh8qvXHI20xzx5bnmuTzzxxKx6mPrNk08+CSBQ3lRhj/MQoepyZWzbtR+pHbn2J1Xq4qIahn2rx3mB0e1xXjZILp5qSNw5UT/zYdvecLkfeuihjOMvvPDCnPM2+QOvu0bJ5jPsu+++AxB4hOncuXPGfmxnVOBVLQ+jHms4g0s7eX3+sC0yTT53VHnXts6yhonzKrNo0SIAgUqf6hfl98PqpHPnzukxwezZswFkR0ePmz0z+UVNeZXxwN0YY4wx9Z6hnVIvH+lFqWUmMWkTmc1lwRHL9k+WLVYtaBTYyiQz3D4iy0Qm2NGDcbN9aPAD95dffhkA0LFjx4ztGkmU32lLTvWBtmrh6GutW6f8ulJloPKs/m/VFk99sKvnDLV9D6tzukpfFQ2mqbbuqvJrlDhuZ53C9eSxPBeqSOpMA/fjJ8/9EUccAVN/GTNmTPp/9Rqj0UtVHVePKRq9kX1I1cQotM2zvarar6jv5SilMW6fuPJofeL8vWv9y6O8yK5RaarKRwU+XJYLLrigwnxN3WTUqFEZ3+OeK/R8sttuKZtsbR/a9lq2bAkg6LN8NgDZ60MWLFgAILsf8FlI7yk8jp5s4mKbqN/z8DbCvPlsZppx68BqAp5bznKwTHov4jOT1879L8/I0cYdVbRxb/ADd2OMMcY0PBIFZeLZDmWDel0sTiU+vD1Xc7Yyxf2AzqkFrP/5bv22F9TkBQWJBAoKKm4fBZUwiYyiwQ3c//a3vwEIVAL6Io9TzHQ7v6tnmPDbPFfp880/bAsblYeqb6p+q2pOJT+shHAbyxWnqMcpfKqIMM/mzZtn1ClcT7X/j/OkwWPUty/Vf/p7pw3iySefDJP/UGkP+ySOs0mP80YRZwOq3pHYxsqzFdXfeIwq0Zq22u1GRR/W8qunJZ1d0/rHKepRHmTi9o27V8WduzhPPeH0rfzlL3y2EdqRMyon2wFnm9UHu65/Yhvn77RDZ6RwIOhTVNpVgafizOeKznoxT9qlc02VrjOhgh3eputlmIb2h9pi06ZN6XPNZx37Gmcg6MHHmPJocAN3Y4wxxjQ8pizaiKKiIuzTKjX0STQqE+hKt0YfUKbII/R7Iln2Eh7zHpDgyzlfFGzr3mBIFBYgkcPi1ESpF6fmBO2p+UbLqKZ8G1db9oq8WPA42nzTSwYQvPnzLZqoDaoqZ2qnzu/qN5oKQ1hBUL/QqgDyd6apUU5VdVMbwyi7WdZdvXRovXQWQGcWOPtBtca27/kNfbNTXQu3xThFXNXiOBVc7W61vYZjH1TkqUFVPlXWid4jotD+w77PNq0zXxq1UmflNO9wXeJ8v8cpi9of9feK1hkAwMMPP5yRh/1M1y04kxz2bkbbdV5f3q+/+OILANkzS/rJ9q73b7btqGcCZ37Li3EABM9LPodp860wYjfz4nFU08NpsJw8RmE/SO2/qtzybQ8KCgqyzi37ddeuXQEEsxcs67vvvps+nlHLPSNtGszA3RhjjDHmmy3NsXbtWuzVMvNlN7FDmZvGLWWL56m4FxRmJ1JJJX1415S56RtfLd2WIps8oKAwgYIcFqcWlNrGvVwmTpwIIFAiVDFXG1lV3FWVI6qshd/y41TqOEVPUft5qnFqY8tIcECgrvBNnuXSvONQ1ZFlUGUwrK4wjzh7eVXy9Jyryqj29Lx2Bx98cLllN3WDP//5zwACVUzVcCBeWWY/0xkjtXFPP2Bj7LnDazDCnifCxEUq1j4SFxE4yk49ztd7nLcYrU+ch6ko/+9xaqZGxNQZB7Vh1/uRntOoOjPtRx99FGcPStkZb/r6UwDATif/v8hyme3H6NGjAQC9evWK3YfXjPdrKu98VmhEVfVaxrVIehzXrvB3IFDcdcaMqOc03vPjZoHoGYZ58LhwP9dy8hjtz9qXUmnVvKeZoqKi9CwB66MxUKLGCBzD8Jqfc845272spm5S7wfuxhhjjDHKR0s2Z7wkD2ybGvwnyvy2R9m+JxO5qaVpW3eUvUDb1r3ek8jRHWTCins248aNS/9P2zG+bdOGTL2rqCqsijuJU9DC9uxUHNWbCpXkKO8N4bypHPB3KiD8pGoZVjp05oDqiNrYVuSrmmWkWqn7h+upKqHuq5Eb9VPVPKa3du1aAECrVq0AZF7P4447LrL8pvZ44oknAGSu8wCyZ3HC29Rjkq5/ULT9qrIdZeMeN0sW1xfivLVoP9TZgTAagVhVbPXQoTNccfEXwmXVc6heqiqaJVTvIHF+sMP/h/v42bun7jsL//ocAODrf34GAFh36V8AAMcsnJF1Xsz2gd5VMu23U7AN8pP76PNFn0f8ZMwFtg+mrTNqvF8DFccx0PYU9jgVtV9cdONwPBGiKn9ctGLmGec5bnuTSCSyZgm0LLq+AAhm9cMedUzDpF4O3I0xxhhjKsPHxZlC3b67lA2cw2p5MlpBT8S8+JuGg73KGGOMqTOc0z31uPjioWcBAI+8ODNyv38muqb/fzg5d3sXq0HyyCOPAAD22GMPAMGMU1hx11koKtGMXP3tt98CCJR1nXXW2Wh+0lsU1WAeHz42bh2TqvucUVJ/7jprpB7VwumqR7W4NRvcj3lqmZSoeA3Vwfr167FixQoA2eo5y8prFJ5Z4HnmeWcb+N///d/tUk5Td6lXA/f/+7//AwAMHDgw6zd2BN581MWVdnadsq7IBVv4hskbm95M+alT8nqT0ul2dlh+V3eR4W3ch9OX7Pisry6O06lNlpFpc3ou6sFQkXmDLmjVcxt3s+a1Yt5dunRJp8lrfN5550XmaWoetnclytwszuWcLsaMM1HTNHVhXZg4F6carCkuQJHWQwnvF7fIlGYFUW4dw7C/xS0YjSqPmrponiTOxa2aDMWdj7hyGFPf+XRF9uJ1eqJBadnLg23XTRkFhcjRq0zV8qlXA3djjDHVy7k/3B0AMPv+BwHEK+1RnF+mvlt5N8bUdxIFCSQKclicmsM+5VGvBu49e/YEkKmEUXHWYEgkbqFaeeHNgey38HBwFrpmJLoAJQ4q7QxJTSVTQzkvW7YMQKbizm0MQ80FOFTfWH+636rIPSTTofpNwvWMC0evbjBV1Y9z5cfjNBBMeCqW19jUPgy0xPapfSjcPkncDJeq3KrE62K3OLU4Cs428ZP3BF0gq+1TXVLqzFJUADSWWxf6xbl7JLrwtbwZCO27OuvAT86+abl1Zi+ufqbuou6N9V4LBI4Y+Azg80RdMOrCaKKODoiarYRNT+Kel9qO2Yb5bGRebLO6gJSfdFjwySefpNMeMGBARj312c3zwHqyr3F/NbGJC1jGen60JPV87FpYFsSpTHmn95iKbN0bN26cTlOvBc+HBmIK14flCAfbMg2LejVwN8YYU72s//htAMC9//fxNqdh5d0YU98pKChAQQ6LUwu2enFqWvnba6+9AES7TlP1T9Um3V8DMvFTj4tS0aluq4KnKpuqb1SWVS3nJ+vA/cLqCrdx0QvLzzd45qELjeJsabmdaktUHfQcqPqjC5BUVSRxLv6iysYZAF7zX/ziFzC1A9ucKnB6/aPaDNuCqmNxblm5v7apuOBeYbQPEx6r5dUZI3VNp2UHgj6varYGsyH8Xd1hkjhVPIyWR/u2BrOKC+6i6n44r4pm5kzt0Lp1awDZ/Sd87dgO2DbZX7WfavAwfVYyHe0fUYHL4gIpkV122QVAcB9nP+YzjmWIc2fMNhyeeeU27c/6yXNFl8csC9eDLV++vNw6hOtZWlqKb0p3xvr169GvWeX8tJeWlsau/9LAiVGzGawn24BpeNSLgbsxxhhjjDG1Rc4BmHLYpzzqxcCd9tiqLAHBmzzVBlWH47wlqPJOhSAu5Hp5xAWj0EARfLvW4Ct801cVImz73bJly4x9eKy624oK6BJVtjh7/PBxcQFtWC8qGXFKu+ZVUXrh/3nNTc3z6KOPZnyPU4tpUxp1/dR+XBV1VblUBdS2wfatqhgQ9CeWR2ePNNQ74WyV9nXmGfbeoio97c41+A3LwDKxD6uKr4FnylPcmQfTVBUvbjaDx8etUfjliJR3rq9vfzkr723FJjNVg8HOevToASC4prSJDs9a6poh7TP8/PTTTwEECm67du0yjtf+zfS4rip8X2c52KbohYzqNqHHMD4jWBZ9RrA+4WcdAEydOjX9v6atNvnqDpPf+Uzns5OfS5cuzShbVBlY9xYtWgCJsvtbosxGHvKcK9vOz08//TTrWqjrTb2fANnnlv2ebWLkyJEwDYN6MXA3xhhjjDGmtsg5AFMO+5RHXg/cR48eDSCwbY/ylcy35DhfzXH21qr0cf9cvLKoba+mqdujQsMD2X6aqQBGhYHmvmprq54iKvITHWdbW97Mgip56hVHbYTj1hXEXaNw3qxnx44dAQRt4Jxzzoktn6kexowZAyA7gIm2DQ3bHf5dZ5O0f6odrtpt6/6qaIfblirJzFP7lXquYZpU7rRfRtnMq/249i+mqXa46uFGPX2QsLqvdvEac0KVdz2Hasus3jXSdVo0DwAwd+JcVDfzf5/qr51vGV3taddnqApr+yrPI5C2c+1DfK4wXobajGt70vYWbqtsU1SHqYaz7/HZoPbxzIuwjHyGxMU5CKelfZDPQlXg9Tywb/LZrgo+15yFyxi+78xc1xhNmzZF54KVZRlkBp2i0v7O3BJ8/vnnaNy4cZYlAK9BeeMKVedZT7YJ03DI64G7McYYY4wxtU2ioACJHMync9mnPPJ64N69e3cA2b7Uw8qt2s6qfR9/VztspkUbvYr8uoeV6zif03Hwd745q/LMt/ElS5ZEph/exnrQx6tGUWQeFZWpIp+24d/UllYVdNozUnXR9QNqg6mqSljp4DamxTZgth9jx44FEChPcagSpyoakH1N2UbYTlU909kcoqHUozymaP5xYdZV9ePvcSp5lN05lbOKIqiyfmpvz3IzHdYvKg4F09Koziwn7wHqeaeimUC9R25Z+h0A4MWvl6O62XGXlD/urXM+SpWh237Vnkd9RNdhsC2odxYgiCeiM19qP03bdm2b2m5o8839oiImU7XmZ3FxcUa5aFceF89A18cQlpGeX6L8m7dt2zYjL01DYyTo+eDzlc9b1oH3Ac4WhOvOfdauXYsv0AhFRUXoUZQ6T/Qy85/v1qevD+vBvPRZx2vFPsj6hvPU8kfFyzD1m7weuBtjjDHGGFPbFBTm6Me9Idu4Uw3nGzfV5LC9H99S1fNCnP9k3a5vt0Q9U4QVgLhorPrGr2oDFY727dtn1EMVNSoK4SimuiqdCh3Pkapq5fmhj6pnnEICZKvzeu70nKsCpLMZ/KRiElYbWQ8qEayf2X5QaarIE5Pa20b1MapD2hZ4bFwU07g1F3F23OHftH1qu1R7c13fUpHnqXCd42ah2E7j1gfwPPB3qpuEKmBUedRvu84M6Kyi9jvt0zwPyU2rUN2c0DvlDWrnfnumymilvVKwL/LeqN7OotRXPk9od85ZHX4nOuMSF49DZ4nCs9D8/7PPPgMQ+EynMh2nesd5FGPejE/CfhGeceM2jT4al6a2e51pKCkpAQDMnz8fANChQ4esesZ5ZmrUqBG+3rhjury6BodoNFf1CrRo0aKMsoTLqTMg4ZkAU8vkuDgVVRy4V+1oY4wxxhhjTI2Ql4r7ww8/DAAYMmQIgGwFKryKnG/fVKlpb803YaKeMOJ8N+ubc5QSTaVLVQPdV9+gVQlTNYKr3fmGHVYXmQb3UV/OcXlXpJ7q8eHZDFUydR+1V1SlXdVS7kd1UpUTIF71YZs4//zzI+tjKg899lDF4/XQ664qMonydBHnU1oj+ypxnlKoOEbZwqtPZMJZuLgZBFWw1Qd7lBconV2I68MafVI/qVDqGoDwOdaZOO1XOquh9VdVlmViOmkPF81SdujH90zZ5VaHrXu/01IK++OzUvfjCw6tcpINglGjRgEIZh/Zhvlc03VSQPCs4/2UsS/4/OjUqROAQFnmuihtN9redCY03L6YJ9sQ2zPRmbao+AtA0Eb5nC4vbor2sbg1VERVco2XwjIzb9YpXEatO/fVtPW+xXVCnTt3BhCcS14bqujMM9xXV65cCSD7Wc4ysI1ccMEFWefI1AyJghzdQVZxcaoVd2OMMcYYY/KAvFTcVQngG7bahQLx6gCVCvXQQFTZi1J/w3mHifNTrn5YVYXj27UqBAsXLswoO48LexCgSkA1hTZ2tM8j6g83zh4/Tk0P1zfO7l/9zWu0SMJzzP35qd4AwrMj6tkgyqe9qRovvPACgEDVi1ORifZH9bwUvu7qoYXXVj29qH9zVeS1zajderhcam8e5xmKaBnUM5W2vTDsk6pqq2qpHpbUu4T2mXCZec7iPPBonnE2vurfXtmhYyo6Z7dDyzw3VUFx/+VPegEA2h51dKpsH6RUXs6UAZ4tKw+2cyrqbB9sk7RbD0f3ZJvheqDddtsNQODZhBFCaV/N77RHV09r6r0tanaM21q1Ss3W6FowjSwct96ronVg5XmPqmgtGYkrA9Omlxqq5OH+zjyZBvsp09BorXwe81zzeF4LfqdtO48LX0+Wi/clfd7G1dPUHHYHaYwxxhhTD9l/3ZfAvC+xfOon2Apg3ZKVaAmgsGgH/KBsnxY9UoEGmw0cBiCBCQvLdyZhGgZ5OXDn2+iyZcsABP5qo/zKqg0plQp+UqmOixCaS+RQRfdVW/Y4Ty4so9pxU0XXSG+0eQOCGQUey7dy2rwzzzi1UcsUF901l7d65q2+quPSjisLr3N4JkV92bINlBc50FQOqkNUkcIeTYBATVL1TD2/RCnTPEYVKp054e+qXKvPdebFdhEVzVQ908R5m4ibAdPZORLuC+r7nWmoLX5cRFT1YKOqZvieolEWdZ2A+mfX70TvjXoun5wyD0VFRTjhiOEAgAuKU8rjqOe/RHkcskvgBar/ySnvMZ1PPR4AMPrj7zP21Vk4k8mf//xnANnxROJ8sof7Gq87nxtsa7Sn5vODz4ivvvoKQLa3GcI2rOunwvdxHsv+wPKwzeoaMm2zuu6E9WS63D9cRo0mq/1ev+s6E5aJ50fvJcyLdufhNLR/6/2K5eVsRq9evTKOqyzNmjXLqqdGimWbOffcc7cpD7PtJAoLkMhh9j9RWLXxSl4O3I0xxhhj8o3BC94GAIy/4m8AgEnF2YKjculZMwEAB553JgDgnfWttlPpTD6QlwN3feOnysXtUR4YKrKBjrPXrkiVi/LjrttUZVR1mGqErm5nXn369Mk4jm/1++0X+EBWO1emEaf2q8pAdGZCVcpwPeMixOY6e1GRD3m1Bw7XXctVkd2yqZgXX3wRQGDTqe0wziORzqyop4uovqGehVQVI3EzKeX5rdZ9tA9omvydMztsb2qnqipbeCaCvrLpqaNdu3YAsu1R48rIPDnbMXfuXADAggULssqssRl0PY7OFLCvUBXUGRK9BuGZhHXr1mHsulQdRl50BgDglgM+AQCsnJ2KrMpgIjt1TCm3zXvvnj6+qO8gAMCjr34cWf9wXg888AAA4OKLL47ctyFCNVmfIerpSH2uh+FvVN7ZbtlG1atMXJRwloV22Kr0ho/54osvAADdunXL2Le8+Cfh7WpXz3Tp15xlDddLPdioIh0XzyFu7cfs2bMBAHvttReAoP8AgSrPeyX7P5V1llcjmVcXW7ZsyfJkw7bg9V61RyJHP+45+XovB492jDHGGLPN7N64zJwxmTIn6bIXnSJsBJKlAMoWWW4GkCgbtBTsABQA80ozB9z1lSN2LAa2FmPijeMB5Ka0kz+N+RQAcEOftwAAh56cUt7/OeP72GNM/SUvB+588+fKdb7dRtlO65t9nBeVuO9xNniq2kXlqYoz34hpl/35558DAGbOTE2DDR06FADQt29fAIGSoKpE1Bu1blP1jMof8/zPf/4DAOjdu3dGnrR/1HpF1UnPhZahsusD4vzdh8+t2jjz09Hjqg5tONU/uKrCFfWBuKiI4d/UvlS9qqiirn1AFfooW3D1NKPqPL1GsM2rIq2RVzXeQNQsj6rz6rEl7v5DeE+jGspYFd9++216n08/TT3E1We2ehxhWbgfFXh6DWG94mIjhOuxZcsWjP6izMa3qGfqHO3TNytGw4YNG4AVwezF1nlTIusZpQzbK0Y2vFa8llR6dY2IrlcAsmdieCzbOW23w77fgeDaUEnnfjrbyXSaNGkCJLM9uuVKIpHI6g/qS15nr3v06JHeV2cbdJ2M3sdInHco7t+lSxcAQX/QtT5A0M55bniuqIbzs1mzZsDW4sj6V4WNGzdmzXyEZ0BMzVJQUJDTeKcyayajyMuBuzHGGGPqCKVli4zDi41Ly15iS+UFf4fUwBhcnxn9DlvvWDDhdQDA+DkrtzmNr/+RMlNrNWhQ2Zam8TubGsemMhHQBpI2Z7oyW1W78P8VeTCJI85DjKqKUWqRqiFqk8/oaYsXLwYAvPnmmwCAjz76CABw0EEHAQjsZlVFj1IXVXmhjeykSZMAZNsIsgwaoS4qIqx+17qrYhfnC57ERa6MSydcL8I2QM8ItpOtPP/6178ABPaacVE/iSrruvZCCSvTqkirqq1rF+LgfnHRUcP7sFy0gR0wYACA7NmluDavv5Oo/bTtVjTTRyqyw+U9AAjshufMmQMAmDIlpWp//31q2pxqPRVCnbXgTJ76tS/PFz7R2RadUYizXY77Ht7Out9///0AgEsuuQQNlb///e8AAo9p6vc/jrB6zJkWXVvFuCC897O9aMRgKvFU1mm/zdlbzg4lk0lg2wX3jDagKrneL6gmhz2NqcKsnpk0qrG2YfYPjaGgHnLC+WicCc74qhe3qCjL1UlY4VUvX2xDJ5544nbJuz5xww034MYbb8zY1rt3b3z5ZfketGqLvBq4G2OMMaZukeCL5pbQixxf4rZEB/hKlA00uxSmxKT5yZbbr4C1yF4FS4Ek8NqEqg8Cx76XEuL6L0y9tKPpnlVO06To168fXn/99fT3bXF4YcU9ArW5UxVLI3ECgYqgSldFipAS510mSgGJ8x8d5bUBAAYOHAggsF3lavbnnnsOQPB2Tx+we++9N4BMX7ZUS5kGffKqukbbQKZBWCbawcYpbeHtcaqiHlOR/3rdrnbLUbaF6l2B58L2fZVH/TzHeVjSOAPcTyN58nqF7aOJ2p/GeV6qyHuTem2I8qPMfam0Dxs2LGNfVd5UHVO1T8sSzisumqn2DZZbvTepjX55M4U8/4yESeX0k09S0+ifffYZgED9Uxtgpn3aAf0y0v3b+zOz8tJ7ld7TWBZVNTUyrqZTXv0ckyHbG5GumYhbPxSehdY1DLwWtJtnRFWq+vwkal/OeyvLxvSqyxf/+vXrs57XWgZdsxXeV9uUbud9jnmoHb16ZdE8w3Fi2L45a6fr0XiuGjduDJTNVGwPNm/eHOsDP3yOTMXssMMOae9AdZ2qDfuNMcYY06BJbt2KpL5wlW4t+ytNq+9RfLN5Z8xc1zj293wnuWkDkps2oGT1JpSsjp59qCylm7egdLODl1Uns2bNQocOHdC9e3ecfvrpaTeplSGRKECiIIe/RANS3I0xxlQNVdory5kH9wcAjH5tajWUxhhjapchQ4ZgzJgx6N27N77//nvceOONGD58OGbMmJGOm1GXyKuBu04zx4Uu5idQ8aLUihZGKjqFV940oU4P6+I9neLiolsuMuPUHI+jGcyMGTMAACNGjEin9corr2TkqYErOHXHPLQMcWXU/cJ14v8aEEuPqSjoRkXXInw9dXGwTnc6EFPlSbvukyBeFS2kVBMTomYfnEYOH6NT/3EBWoiaYuiCsajFn2wLNJHRBWX6GQfLyhDx4SlzovceXfDJc8BPvW+w3DQzojkPzRqi9tVzRZM7msO99tprGeWvaOpczZfCeehiQSB1LdRkRt20smx6ncszMWT+DXmhuQbTolkKzdnUBW959z2agKgZl7oBjXv2cT+2Ab3vl5aWpsO8JysjApepjuvWrct6rsYFlIp6VsSZYGr/0MXqavpDWAbeF6POi/ZvnhvtB4WFhUjsmBr4tWlbZsZZCf/tyiG7pNJo1DblJ3/d4nXp8rKNqMtkUzFHHnlk+v+9994bQ4YMQZcuXfDXv/4Vv/jFL3JOxzbuxhhjqp1xH30DADhuv+457X/Gj8oWwNHl38bUwOPsA1PKfZKDKE7/hqeBy/5/8KV3qlBiY4ypOVq2bIlevXrh66+/rtRxHrhHEPcWzrddqlXhN824hZGqdquSR3WNCgcVMH6qohRetBkX2IF50M0W82AZqAR07doVADB9+vSMtHVxYFgp5LFhdTpcBqap7ra0TKqmkihXm9xHlQwqFfzUADGq3JA45TNKOYhaIAhYcc8VuoAEshcka4AhDcBE2Be4X1ybYXrhvEicW0FtUyyDunDTthTu53vumRpw5rpgWdU8znxxseeSJUsyyhBW6hjMiW5WudCPeXO6leVk39fZDi4y5yeDtYXDudMNH9Fzw7xOOeUUAMA776QGzVz0HueajseHr2N1BUViG9B7V/h66baGvEhV7/lcfM8+R1ePnP1R9RzIdrWq9/C4wH7qXEHdDJKM/lIJpT1RkNn+CgsL0/1ZXTMSbRtRi9B1BlCfETqjGL4vhaFrR+6vs9ZAfFAnXTycTCaxtHUfFBYWotP+ZS5dP9/2QEy9f9w1VYfOKUcVP9iyJt3fdWagIfefqrJmzRrMnj0bP//5z2u7KJF4caoxxhhjjGmQXHHFFXjrrbcwd+5cTJ48GccffzwKCwtx2mmnVSqdgsKCnP+qQl7Kk3yT5huzunGKUonibNa5L9U0KmFqm8rARXT/pMEpwnnGubLSt3O1k+N+rVu3zjheZweilExV0bQMTDPOPZ2qMnGBY8J1oOpA1ZDnjioh1Qcqk3Q/xnNHVbKiaxNG666uzkxuhBVuqnbaZlTJVdvWOAUuLjBXeB+1p1Yb6LggKTxObb+jbKcZtCiu/2mfYV7/+c9/ACA9TRq3jiXc5qjSMeAZlffdd98dQHDfYLtVRX7FihUZaaptOPsUENyLqLxrIClV3A488EAAgfvIiRMnAgCenDgNQNAfdUbvnMNSNvMaFTO5KTVrQr02sUPZjBiV1PCsV0Hm/SXOTWa4zqQiF731GVXcdYaX14z9gDM04RktTSNujVicG19eM/Y93id0zURFwdLA9StsH/xMBLPceq11TQvJJfhg3NoV7VM8Z3GuSsubbWI/5fhA14Lo9QKAzkcdkPpn9LTYdCuix/HDAQDvFKcCL7Vs2TJrJqW6ZskaEgsWLMBpp52GZcuWYZdddsEBBxyA999/P+1St66RlwN3Y4wxxhhjqsqzzz5bLekkChJIVBDdmPtVhbwauOubtL6NU5UKK2F8A6Yqpeo1wz9rAAWqw6ouUlmj0qEhj8Pl6rJ1cSrNzak3/O+bdc/Ig6oJ89aQ8/yd9oxU61RtAQI1jao3zwHt3zSkPLdTNYmybwUCFYNlDL/Nl3cOgECp4bFU/akuUh3q0CG1Ql6vjSr34XOg9crVQ0hDh7btYc8oai+usysaZCcuWBLTiVPew/vEeVXRNqDKW/fumX2I6jPTDQclqyiImNrETpo0CUDKn2+4LPydKhrbXtjmVcvN/sdAaF26dAEQtHWea7Zn9iXOXrFvqH1u+JwwBD37F9Uh9bTD/bnO5YQTTgAAjB8/PiMP3iPT1ytZNmNSWjbjwgiYpZn3CHoSSZQ9TRLJ4Lwnk5l2xnEBnaK2NeS+rCoy2zXbIO+1bCdsP+F+pf027t6ueerMGtsZny2EZYpVJcuU9fQV5sxMWbqfLEuiqKgIO+yQPbvCsueiJscp63Ged9i+eA/k71OmTAGAdBAezpap1xYgOCd8ZhM+mzt27JhRltLSUqz64RkoKCjAbQ+nnmu/Pf+ZrLoo/7Nr6hwf/MeTAQBT26c8ZBUgWDvGa88+xrbRkPtPbVFTi1Nt426MMcYYY0wekFeKe1QIdSB4w6T6FvYbTRt0qmRUuqioU83m2ypt3WmDqj5e1cMJFY+Oc94GACx77z/p3/7733mp8m5IlXeXPVNv4e2GDwYArBx4IoBAIeObM9/s27Vrl1EfKmY9e/YEkGnjTh/OtMulBwmmQcWCeainjbjV8eq1JTzLoR5CeG7UuwXLz0hk9MDB68hrQUWeefPaUIUEguuh6mmUD2qTDa+NXjsg26Y9bhZGvcioRxi1oY3yC65p6Xb1Sdy3b9+M7+qmi9c/rDLFeVVQm32m+c03KTeJut6DHl14L1Hf5WG0HjzPc+bMyci7c+fOGXmwL7PeVNOivGjoeef9T+8bLLeWidtPPfVUAMDzzz8PIJgJU6816YiYXIsjirt6CYlC1/FEqeq6vqEh92Xe89jmqOzy/k1VmPdIne0E4meceJ6pmOtzVb238f6ss0M77rgjuu2wGsCKdB7htqBrIBKNUs/eZGHqs6RkedazTb1IqWeYKO85PFd8tuv9h8fy+TR37lwAwbOEz0rO8PK8xHmuAoI+wnPC889zxZk1nZ1s1KgRNh13GVatWoX7Xkt5hlk9PRWfZcv6sjVhbVum89lpnyEAgMnrWqBRo0ZohOD88/nKNsA2ot7dTM1hxd0YY4wxxhiTJq8Ud30bp5rFt1na4EVFCFT1UG3Bv/32WwCBWqVp8O1dlfuWU/4GAJj468cBAOPnrIyvwL9Tb/aHP/ERAOCHV6ZsaduceGaqbK0z8+bb/OLFizOSiaqfbuN3KhlaL7VPVnVG/WhH+VKnjSDPiSrsTJt5UimYNy81E6F2+VQC4/zfh/fVCJVqZ22i4bkN22uquqWeP4j6/leb9ihf/+H0w/vEebQIK1MA0L9/fwCB8vjJJ58ACNqexm4I14tthcfGzQTQX7vGOOCslCrrrHe4z3G2iHkR3qOoxM2cOTMjb/ZPolEuNdorkD1joNeB63YI7W71nDOvE09Mzfw99dRTAICzD90vdWBp5swmyVLYeb3LtifD1zRRILtGR9kNly/Krr+hoXbpar/Ma8d2x3tvuP2z3apvcb0fE14b3lOp2PJ47p/hO76NtoXge0K8DFFpf+rt6VnqNmfQjj32WADZtuM6o/rhhx+mfxs0aFDGPnofYh4vvfQSgOxZDK7t6NevX8ZxfE7xXIdjKehML/eh+q3xX9TevHnz5ijZ66jUeegwJNY7DT93bRHkwT7D68M2of2mvKjuZvuQSBTktjg1YcXdGGOMMcaYek9eKe7nnHMOAODVV18FkO3DloSVMI2kyTdh9f6gnlzUB7W+7bYrSSkE7/4h5UaoXKVdeHVJ6k351StTXh3u7phalV/6wzMz8qIv6N69ewPIjrZItTG8jW/bPIZpqF/ZON/pPF/qVzsKnkOmqRHpVOnhueWKfJ57qhK8Nqr8hK8nlQmqDFRT+J1txEQTFbGyIj/ncR5TVBHldVIb+LDyw2urabJcVJi4ZoNp0fc4r7+2yyhbeUYepiIXVx96k1EbWdZTZ5to38p1MEDQF/UcMk22U/bhzz//HECglFI5Zd+JU+CAbH/U/K6zaPSOs/fee2eUUW2ded2GDx+OctGZFLVdFv/cAPDkpJT9rtr6apmB+DUV99xzDwDg8ssvL7989Yhw2wKyzw2VXV47XtvwMyHOq0hcBHKFeegsHb+vX78e+EGmap8IzdyyPUz+bkNZ+92EwsJC9O3bN32/Zh9l2p9+mmovfH6paszv4XVsqrRrjBKmyTz4+z777AMgGEfo2hHty+FxhsaNUE9VPHc6A6dp0iNPnDoe9fylsq7Xh0S1BVMzJAoLURATmVr3qwpW3I0xxhhjjMkD8kpxJ1wVTnWKb7G04w6jkRnVHpRv4bS35turqmy0b0vbYX6b8s/8zJTvq1odLPss5c0CP8wsM6EXmS+++CKjzOH9VL3mMSROVSVqH6dKaHn+lrU8PFe069U81Ladx1FF4bmPUoT4G+149Tqa8lH76DBUjTQiKvtOXNRLtjleG/UAEb6O/I2fzJPK87777gsgaBuMYhrnNSjKswvhMW+++SaAQFnjMfRyFJem+nGn1yr+HvYZz7rHRXpU+2Leq3gvo4qvCjvticMzh3H+t7Xe7E/0aEPPPHGRMtP3jNXzyn4Qn+s7lPn551oB2rSXRUd99N/vZ5WN9dU2FBV3Ia5cDckf9bXXXgsAOOaYYwDEz5DqupQoZTbuGO2/GiuBv7MPUmlmP6/oWaK0atUqS3lmGnvuuSeA4NnGNSD0mkPVmO2f9/nBgwdn5aMzfZyFZposwx577AEguOdo5GGNBM57VbgP6nogfue54rHq1Y37qyVAec88RZ/JGiFXZwPYpm6++eYK0zZVo6a8yuTlwN0YY4wxdYP0guSQmdR/Fm5Mm0caY6qPvBy4qyLGT/ohVh/l4d9UAVO/yXxL5ds5VX0qYGkVaO3qaqvPlrUp2+5GMTbG/NRV/VTSgKBe3Eft2/RcEbWlVdU1zsNIeJvaAvNY2u3ydyoZakPMdGj3qEpR2IaP11HV3PKUVxNQnqJD5S0cVTV8jEYiVDWMqOIe5R2E15hKM+3QaZf93//+F0B8RFW1kaYaHrYNVo8PbDts81SD2Q713qDtm2swyvN2EueDXO3KeW44O8W+TNWbfUijJgPZMxuatuapaj7RaJSqlqcHYby+Zd+psD/z7mfpXdetW4fCwsIsNTZuBi+uzFG/lbfOpr4RFzNBnz/6vIo6n3q942YuVAXW2SHt3zobFEenTp3Sx2rkbl0zxllY+lR/7733AAAHHnhgRl34XA6fJ7ZbbVtMQ/PQtVgaWZW/c0aNa7LCvvKZP8caqsprvBE9Ts9pRX04XD/uw7x13ZCufWnI3plqGivuxhhjjKn7lL3UfZzy8phedGlMQyJRkKM7yCqKEXk5cGfUQdqP8c2Sb8T0vwoEihbt2VSdV9/OfAtXpZ1qW9p7RFH1rdhu3LIsgp2oiuovm8rZBx98ACDTrpvlHTIkFWktzlY/zi5dlQEqBlTJo5RatbNU//qq+quiy3OvERu5H9VGqqlA8EDo0qULgOAcqa97E015NrGqYmvb0NkYVWzV24nGXQgfwyn0oUOHAgAmT54MIIinQGWNCrrOjC1YsABAtj1r2O6c9qYanTRqRi5cXrZf+ppWe3wq9mF/6Rongf1O7eQJ138UFxdnbKcqqIpcuK9rHvyNx7Af8RxrWnEKdvpeQaVdfA3fP+6t9IzezjvvjKKioqyZLr0XaFsoT+WPa5sNycZd1VKi60h4jqLia5A4O/goz1Lh7zyO91p+6jVLJBLA5sz7Lr2YEbWfVw816tmI/Zs24rR9ZyRS9kk+G4BsW3X2S+bBfsA8mGecdyzWk/2GddK6AdmzkT169Mj4XWcK9Ti9P+izv7x1XmwTrJfev/R+bOoPeTlwN8YYY0wdocBDCWNsKlMOtJ3m2yjfjDWqKRAosVS4qJbx7VQ90fAtnL9TnVMFqVHXPgCAU/unjn9uWuUV3//ZNaUOth44AACwIsa+TRVPKoe0vQNS9oThffSNXt/sdQV6nCKmK/XD6qmWT22aqXhSYVcViWlTZV20aBGA7MixHTt2TB/DbVoutglTPnr9w9uIXicqPHHeTHT/8myUeZ0OOOAAAEFMBrYRqmNsz+qhiL+zH1OxVq8O4XIzMirLT2WOaXE7+zrbFtsavc9ofcKzPJw14v2E5df4CRoBUxVJpsOZA42JEM437MsaAPr0Sd2T1Ad4nLcW5qkRjSdMeDu9L9W7li1bomnTpll2tUpcRGZVedXndvi3OHWyIXDXXXcBCGagtN3o/Y/wHIX9ges9Pm7mQtVwPS5qhglIPWd3bZ/pgYrPJF0Pwr7G/hBnd63+zPls+O677zJ+D7c/9m+ekzgvS4r6bec5ptqva3nC6WpUWsKZAbVxZ15x/UZnRaJiGmg/1tgILL/Wl23K1B/ycuBujDHGmFqmzJxq2rLyA/sZ0xBIFCRyU9wLcnOlGkdeD9zVMwXt3sJvxrRL475U5L76KuWHnQo7367VUwO/Uymk+vBFUUrVGnDRoQCA5857KudyH7JLSrUads2RAIDveh8GAGgS89atXnSGDRsGAHj++efTaXKbKgFUaFR10QiG6qlCV6pz/7BNpSobPDf9f5A69t15qzLqwbTUNpfp0G6damOUHSyVDM6uqK94Uz6nnHIKAODRRx9Nb9PrqHanquzEeaFg29H02D+BIDrnv/71LwDBtaZarLMubFO059T2SPVc7dGB7DUWLPeSJUsABGsnWA+mRdWMebCdql/nMNyHyiDvRRqJmXmzXrpegHlolEcq8eH/VaX76KOPAAT3vO7duwMIbJTD9v9A0HfeeustAEE0V64XAIJ+xpkPXhe1n1W1lvXSNhFnTxz+La59NSQ08iZnaHg+eV1IVHwG3md5zeI8i2m0bF3jonbp/D312SSjfOpNKM5DGJ9LnGljmlTqec8Ir2+KSi9qG7+zzfJcMg/WM8pDDRCcY9Y3Km4Kz7OuL1EvSqp+60wJ0f3VMiBcL535ZP14DMsW7semfpHXA3djjDHG1A6TZi+3ym5MGfYqUw6qLvAtn7adYVWYCjv3pVJBu2nax1Ep05Xn/E70DXvB/qcBAP5wf+otfvY/PkrvO++TlHq4texFvdcPU3avvU5KhUht+sOjU2Uo8+POt25VAlgH2pdSxQu/zXMbbX71GPWIofWI87+sq+Kj1Ma+zcrKQUGiNPX9gM5lCl/ZdOo/P/02owxUF3kteG3UY0JYKaSKYl+1VSOs/KgdtvqOVt/jGl9AZ3nYVtgfqbIDwD/+8Q8AwQwW1WEeq16c2BeontPPM9VklpVtKdwnmEacjS/79n777QcgaFtU70nYS1W4fuX5zKYqrtGBddZJPe907do1Yzv9u3MmIlxnfuosBPPmvY2RI+mJh+eFZVLPUWEbeV4nbSN6X1V/3VomtQXWGb/w/2r/3pC8yhCuq+jVqxeAbLWb50g9dYXvz9yHM0h8FsRFPmXf036sa1yYJ9tAWIlmGuyvui5L79dMi7M/bHv0HMe2ydkgtTsHsr2oMEIw7x08l8yjbdu2GWVgmlpP1ovnNtyGtR9rGhq3gOclbr0J0fUE4eca09a1OFTcdVzEepv6R14O3I0xxhhjjKkrJAoKkSgozGm/qpCXA3e1t+ZbKr+HPYxQxeVbM9U0qrhMi6vXe/fuDSA7Mp2+Yae9y5R9XzD8DADAvsMPS+e999LvMsq9Q7uUijC3acqWe+sm2gGmPqmWUGVQm+Kwx4xwvYFspZ1v8morF2fDrrbvVBBUyQ5/Zx4fL00ds28bmf5JZiplzIvnmnmo7S3tG6kshGdQ1AZQy21yI2wnSTUoTtlUW2ptG1S8CBWtqLUY/I3+yukhhV5Y1KaV/ZD9l3myzXC72gID8Ta9VPUGDhwIIOgTH3/8cUYaLONRRx0FIGiHVLrCvtWpbn/55ZcZv8X1I22v2k+p1FNNC6t9qpzyWKqavOexPtzO68R7BLfTtp/nMOyVS+8PPFbvf/zU/qnrc5TwdvVmQhqi4m6MMXHk5cDdGGOMqa/QRIqmU3yZ4ssaXwz5MhYXTAgIXkTTwQPFNbCaQ6oLT+at5lAkHAxJAxlqHkyDL9yEL6p8WVZRp2fPngCCF+TwyxxN3mh2x2OYN19MKRhRPGAZKBTFBT/iuQ2/PPPlWE1r9Trpy6iea3WTymulrl6B7IWvvJ66mJjlZBsyNUhBYeovl/2qgAfu1cxnaJfupDt0zLwJpaOZ1k7Rtj+MtJi0QmaMMcaYBkRBQeovl/2qQF4O3Dldy7ddqg58mw+HNOcbsC7cUBdPPIZv0ulBdtkUMBUETifzjZgLXvg7kP32zal5vgnzrTrurZzowjVdoBReoEPFQt1tMQ2eG11kpm/+VB9YdgbUiArFzfIEpkmB6UAUPLc816oWcTvLri7lgEAlUfMMNSMy5RM2lVHlRgN6aB/QRVtsE2znNJH561//mrF/eB91V8o82QbUFIPtmy5DdVE1j2f/BAKTM12kt88++wAI2syHH34IILif7L///gCyzTvUdWrYhIumPvzkIloqhLqYk2i/pFkRzXjoPjLsUpPl0iA3DKTEhXw8t1x4z35KVZO/62LjqDrzXLJNsG/GLTrk9dOgVao4RpneqeLZEEO233rrrQCC9sBrG+fiNMplpjoUUDNINYPSa6UBjdRsjfuFn316ffnJthq3eFNN4LRevG9QLQ/f/zVAkirQmqY++/R+p2WPqqc+q3U2Iy74lZ5rrb+WISpAWZwjBj5HOb5gGzL1j7wcuBtjjDHGGFNXSBQWIhEhgETtVxXycuBOlZu2a3z7jnIfRhWNb8RUiqjs0QWc2tzxjVkVMebBt2/a1c2YMSN9LN/gBwwYACBQ23QBWlixA7JdZOkCNnV/GX4bjws/r0Fk1IUcP6lqcXEgzxvLOHfu3IzjAWDPPfdEJqK4x5jMsJ4897wW6kqM1zVs78f/VXF3IKbKccYZZ6T/f+KJJwBkK25Ew5TrwmD2gX333RcA8O9//xtAoHBzASoQtC8GBdL+F6fqsX1SeaQCT1eNdB8XXpjOxZlsK7QXprtEuktjXx40aFBGfVX5JVELTtlfqHZxkTvPDQO+hc9FGLU75nlShS68jfcR9h+eC/YjLlhv164dgOCcx7mRjFoEGl6ACwQzGjrjoTbXOjuhCmPUDB7T1GB4DVFxJ2znfNapi1b9DJ9Pnkd1aayKrQZeUhfCbCcaFI15hZVoXaSsboj13qL7MQ/O9KprZJ2VDZePtvb8zlkitnt1Z6nng2XU5y/LEJ751Wcxyx2ntPN+pq529VrofSR8PeOuuabFNmPqL3k5cDfGGGOMMabO4MWp8fBNmm/lVNmiwgRzXw34QoWI9p5UxOLUNaK/842Yah4QqGVU9lTx0LfwuIAYaoOnv0e5WFMVTQO9xNnQqYqoswSqkIbrwTf/b5Gyw92toCw8e5ni/vbcwEVfOE+eeyoGvDa6fiCsSqiLTO7j8M7bjrZxVdrUTpXnnoGzGPBk4sSJAIKgMVTFwna5DAJEFVjDk6taxrwYYEwDgKkNbLit0N7866+/zjiWfZ926CNGjACQrf6pra+ep7B6SFt0qvxUMQ844AAAwNChQwEEsxEaHEr7ctitZbhs4TrrzJS656RtL1VKrY/WQ104huus50DvTapiqicSlikqUJDWi+WJS7shwfUJu+++O4DsdVG6xiAMrzvbidpIs43p7Ac/ObvFthlnXx9258vrzXLFBfyLcw/KvPnMZDtiQCJdGxNOm/XhTF/cLDTRtWP8ZNsMr5cBMvu/rqlSG3fdj7MBqpLr7AbTUXe34X10bYr2G7YZU3/Jy4G7McYYY4wxdYaCghwV9wboVYbqHN+MactJryVRAUT4Nk2vFFT86PWB6iFtUKkw6xs01R++QUe91VNVoPJOf6qqnLOcqnazrKwn6xVXljC6D5VAlkXf1tULBN/eWQfOVFAJCKtxzJ9v+izntzuklPfiZanrwnPDGRKea84GqPrKaxLlMYH5a5jn8EyAqRy0d3/22WcBZHs60Jms7t27AwC6desGAHjjjTcABL6WVTHl9QUCNYifTJP7sG1QceLv/M6+QSWrffv2GXmGbbLZdtnWecz06dMBBCo9USWaqDcKEl5X8Z///AdAtk0382TfYHm5ZkTvH3oP0PDyQKAEsl4628Q0WD+ql9yPKp6u21ElP6o+6qmEx6qtrs7SRM2GhtMN/6+ev/74xz+ioXL99dcDCGazdD2CXpfws0/XI2gQQn1+qP010edVnDcaINtWne1HPYhpMDeWn/d13s/ZZrmGhX2OdQAC1Zr78BjeM/jsi/Pipn2NMw06axDu/2rjrueG6NqPuHPONQw8b7x24f31eatedPidbcbUX/Jy4G6MMcYYY0xdIVFQgEQOanou+5RHXg7cqYbzLZdKAm3cwgqArkJftGgRgMC+miuw+bZKG1wSF95dI5tFeX1guagA6Ju9+sHWWQHa6vHtm3Z+qtSHt1GRprJHpY9q96xZszLOB8vN86Q2iuqNJ6ysqXpGdUVX2BPWj9eP+9F+mZHt1BY5bOenPoXV77fZdn76058CAJ577jkAwXVgW6CdLRWpSZMmAQh8jPNaqBoVVqqorPN67b333gACDy/8ZB+gssbrrf6O2ZZ0LUd4m9rNM2/mwfqppxRVFJkOyzR58uR0XuoLnX2c/U77IxVFroPRiItx/p2BbPWan2qPrt4nwnbB4fro/lH2xzrboIo6P9UHtq5JIVFlUr/hcf6qGyKcoeJzS739qI00EPRH7su2GLblPqxPqj++PnNJlk23zsToc4ffw6qw9oOw/TsQKOp6LPsqt/M5remwv0ehz11V79Xjjc4osm8yL50NC9cz7lyQuBgQzIvnlGXiteH9Ua9d+Fhd+8G0bdvecMjLgbsxxhhjjDF1hkSOXmUSDdCrjHq9oFJABTdsD6rqFI+h3RvfcL/55puM73wjpiKkdq5x/tLDUJlUe12WiW/IVP1VMaNKR/WBiiHLdMMNN6Tz+uCDDzL24SfT+OyzzzLyYH2oMtC2WG0T4/wvh38jqpRppM2wrXP4O68Fy8zrp14+gEA90byjoj6abePUU0+N3P76668DAP773/8CCNqCenThtWAbCs9O0e6cSrOue9DZKfWEwr7CtqVKe9QaDLZp9jeqdvyMi+oZt6aEkUnDay9ULdb1Gpwtu/baazPSZGTMk046CeURtvPW2Aw6w6EzB6riqy9w9SwVFYWT6Iwjz7fOGPB6xHmyIeHtTENnRgzw6aefAgj6iUYi1dnOMLvNezf1CWDNZ6l4I2u+S/W7ea+k2tK+nVP34uYD9gOKgc9b9Ev357h2wjzDz1teT15/2m6zrbLfcnZc/ZszTx7HNWf0DBW13kvt45kHny/q0YZ5Mg0+p1kfPq85s6ae1oDsdSZ6r9CZMn7X+Cncrp5+1OYdyJ4pYNrs12wjphapIXeQVTO0McYYY4wxxtQIeam4E7V71bd1INs3K/eh4kfPGBqRkTZmRN92VWELo8qVqk9Mm/aKVJaoBPzsZz/LSI/KwT777BNxFlIMGTIk9rdwmrfddltkGdQPrap3Ud4j1IZWI78S5kUljeea26mq8HgqH1FR8lTVVY8hZvtx6KGHAgDuueceANmzMzobpcouEFw/tjuq90TtbNkG2KbYFrif2sqGbU2pSnINBdV9jR/A/sf6aN/mPYSzWvRsEW6XWvff//73yIWKlHZy1VVXpf+/6667AAR9kuef5dF7l8aLULvi8mzb1Z5WfX7HrWMhGgVV18VE+Yzntttvvz2rPA0Vzrj85S9/ARCsf9I1SeH2H+UrP1c2bNiQtcaF7YR9Lyr6rbYT9nfe83V2SKOIa6RYzhjnEkWXarzOwjFNtaPn7C2ffSyjelqLiizMtHgudAaYeas3mThf+DpW4Gf4evI66IwUZ/MasveluoIXpxpjjDGmUuxWMhMAsGDsUwCAy+55N+djLz0rFZRs35+fBHw9C4t7HlTt5TPGVI28HLjzbZdvqbSbjfIqoyqOvkVTIWKURX3rjovwxjIwvShVkWhkM1UkWf5LL7203HpXB7/97W8BBMqN+p9Vv8A6oxCupyp+up1Q8aSKwnOsXnbiouaFVT2N6qdqitn+8HqpNxJdw6EeJYDsdkWf8JwB4zH8TsVN7VRV4YryE07lmWtEmDe94MR5flAPUtzO6Kck7Meddu88ZntyxRVXAADuvPNOAPERUnXGQM+het3RmbPwb7oPP3n/U3v7ONtfTTeMzgiYbBiDgLOweq7izve2smXLlizFnfdeznLyOxD0Q7YxnWXlvV2f3fzOmCzcj/Xhd6rqUWgEVabJZwTX4jBP1ktnDjWiLOsUrif35bY43+o6juAzTWcFdD0X04laG6Jps02YOkAN2bjn5cDdGGOMMQG9V34GrARmPjIWAPDA059VOo0/jUktcLx+95Q5GboeUF3FM8ZUE3k5cFd7MI3QGLaDUw8lfNPVldl8+6bdm77V8ntc3mHbTrXjI/pWzd/VJrUmYJ6qqMWdJ501ALL9X6sNIbertxy1b1TbdubBdMLKLbfRgwDTKM8ThqleVMllf2Ob0iinYVtwVeTYFqi8a+RiVffVlp3f2Q7CqtiXX34JIDvKLhW2OD/hbH8aNVj3D+fFqLGMcFkTXHnllQCAUaNGAYj3tBPnx10jMZKwysdrHXff02jQqs7q+iOdbQzPlDHt6667ruLKN1Box/zkk08CCKKF6tqC7YFG1uW1Ds9y6T1f+4x6aWP7oZJOxZ2zWW3btgUQtBvOxEXBcjFvRg0nagPPsmi/0HVUrFO4X2ick7jnj6594ac+6+LOW3hGhdeYv3Em0bbtdYiCghwVd9u4G2OMMQ2S/Td9DWwCPn/4bwCAR16cWeU0V3+beoHugWXA2mUo3qlzldM0xlQPeTlwp80aFS/6Aedba9gzhSrJVAfVF63uz9/VplO9reh+QHZUVbUlVfW+Nmw6tQwaHU+jzKmtYfh/Vdh5rM4s6AyE+iCmksD0qJCEFRHaTPKas3y0SzQ1B9UmXncq2/zO39VTDBCoR7zW7DPq95nXl2p+nL9+rqOgrTkAzJs3L+MYXUNBNPqhen5QNU09RgBB/99rr70iy7c9ueCCCwAAN910E4DgfNOWn5+6FkFnvPgZVm/Vp73a3qrCTnjd2E/5qfExLrvssm2osZkyZQqAYG1WTaDPCp1FCf+v7YFwuz43db0Xo2jzntKrVy8A5c9OszyzZ6cW2LJ9qxepuDLElTUqdovOROs9QscXmoauO1ElXmcageAeyX3ZBs4888zI8puaJ1FYiEQOMWVy2ac88nLgbowxxhhg0etvA6gepZ00bpkyd1tQ2DbrRRYA2m5chLZtKXq1xNtzVlZb3saY8snLgfsXX3wBABg4cCCA4K2Vqk74RsM3dL5tq39UtW9ThV2VaX1b1zdqIDsCI1F7XH6Pi1S5PWGeEyZMAJCtluunrooP/6bKhap0ujKe54rnntEAORvCdHlceM0Cr7EqFWwTxx9/fI5nwGwrel3jfBmzrdCPePhYzqZoP1MbdvXXz+NpC09ljhFKw/a2ai9KrxI6w8PvOkBRG3G2NY3CHD4XUYOcmiLONvy+++4DEKiZ6q+e/TDKF37cOgBF1XrOgPE68Zwxb3q3MtvG/fffDwC45ZZb0H075hM1wxVlz817NNeU8TrzeLYLne1S5ZqzQ2w/jL3AeA/0MsW+DAR28bT5Zj/lOhmmyXbNMqg3GY0GzDKzTuFzwXFFnG079+WaOY3WynsKt7O+7Iu6Tiic1+TJkwEEbcDUIQoKcrNft427McYY0zAZ27wXhg8fDtz5drWlucvAPQEAy2V7m/WpYD/YWiamJBx83Zg0dgcZzzXXXAMAeOaZZwAESpIq2kC23aq+8cf5L4+zXYuLKBpWG/m/+pZWBa8uRPtkGXgOWUZV4NWTAJCthip6DnX9AJURpq0r9KOup3r7ofcBtglTc7B9a1RAVdrDazioVGnb5/XUNAjXNtBTxPvvvw8ge0YorIKrT+W+ffsCCNoX2yFnDNTnss4G8HeddQOC/lIX+rSiduTXX389gOzIkfyMitWgfZjoWgTOiC1btgxAEOXVbB8YoZfRjKubgoKCnNZisQ+xDbG/si1RUY6LJaBeoqis8zvbE2fYGC0UyO63GnWVaev6LZaFZeV3rl3h/Y3e6sL9Xdft6HNTo6TzU73FaHRb5snZg3CetN3PNSqzqb/k5cDdGGOMMQG97v4VevTogX8ft+2B/K753Y8BAEv2Oynr5RkAEltSg9skTajKBqpDO6UGsu/Os5MA03BJFBQikYOanss+5ZHXA3fatdLXq/oHB7I9vGh0R7Wti/KAAeS+Sh6Ij8CoyoC+bdcGaq+rHiZ4PlQZAbI97cSh0VepcNAnr3qsUU8/4fOkMx5sA2b7Q1tpXg9eR/U0QqVdvc2Ej+G1ZvtSxS28riG8nerXYYcdBgD48MMPM/KMmv1h2lTiVD3W9qv9UpV7El67wfrQ41Vd5sYbb8x533vvvRdAdp+8+OKLq7VMxhhTVW677Ta88MIL+PLLL9G0aVMMGzYMd9xxB3r37h17zJgxY3D22WdnbGvcuHGNRMHeVvJ64G6MMcY0dC6//HIAwAMPPIA9778CADDjktzNlC4/P+XoodNZvwQAzEdmIMG26xcACCntpMzG/Ys1qRc7ddtKU0aigaJU+FJXwLvuuiuA4CWZL8bhl2ia59AlLBelMg0VBZiGCkoUq2juRfNRmoeGzWyZV5wTC02b9dMAVBocTd2rfvXVV+k0eI1NPG+99RYuuugiDBo0CFu2bME111yDww8/HJ9//nmsKAukXIvPnBl4ZapIjIwlkePi1CquDfHA3RhjjDHG5DUvv/xyxvcxY8agbdu2+Oijj/CjH/0o9rhEIpFeE5EP5PXAnW+gb7zxBoDgrTdsHsM3fE5/a9hgviHzGLom5Fu8vnlxCp+LZTRkMxC8XavbR27n95///OeVrXK1wzK88sorALJDy6v7zLDZgwbcoSkC99WgLZx64sIinkvux4V9Gro9rF6ouYJViJpDF16xbXDBaIcOHQAE15OmUGGXglTDeB11oZgG4WIb0aAvbCP7778/AOC9997LKBMQtBuqdnHqmJrGaKA0rX+UOQ638b5QX/j1r39d20UwlSBswnR+Dor7Zef0BwD0+NWvAACzkz8o64tr0LhxY3RvtAbYAmBzmelaaZm5Z6NU/0yWKYd8/rFPsT9T5VT3ieo+kvcBukFUZxJMh2axe+65Z7oOM2bMAJBthqeuWZkX+7u6io7r90wn/IznvYD1VNM+DbCkz7Q497Ech/B3m6RVDZ09iWPNmjXo0qULSktLse++++LWW29Fv379Kp1fTdm425eTMcYYY4ypN5SWluKyyy7DD3/4w4wXPaV3794YPXo0xo8fj7Fjx6K0tBTDhg3DggULarC0lSOvFXfy2WefAQjCjYcDvhBV7Phmq8GDqArz7VsDNPENmmoi0w0vZKBqoCGKmQePrUuwTFz8xzLzXLKeYXd3qpiz3lQwVH3hOdIFiLwmVEr0uDD8jdf8kEMO2Ybamm1Bw5PzenKBMNUjDeTDhd/h33ittQ3EuRYlVMuoXLFMDMjCgD/hffv06RNZDy1TXDAVXVROwgs2WQ8qPMbUNsOeuAkAMHlkEJjr8LYp5Xqvk1JqYrcLLwAAfLPDrqnn1eb16Xv+hg0bgJ3K7v9bypwY7JBq88myvjB7M1XozJlt9hnagjdv3hxAtuMG3gfYBzXYmbpupZvE8CJw3oeYl/Zjdc1INVuDRGnwRVXow88j/q8L8Zk33V+yXmrzru6nWQfuV5cHjfnCRRddhBkzZuDdd98td7+hQ4di6NCh6e/Dhg3DHnvsgUceeQQ333xz5TItKMjRj7tt3I0xxhhjjMHFF1+MCRMm4O2330anTp0qdWyjRo0wYMAAfP3119updFWnXgzcf1Vmnzd69GgAQJcuXdK/qT0u36L5pqvuDnVludrcKXzzDqtxmgffummD99Of/rTSddzesEwvvPACgOC8qP152B6YdY87N1QjNGS02jWrnSDPeZSN+7x58wAE19zUHBdeeCGAINS2Xl/O2tDWXW3igeCaxtmuE7UnV28NukYl7JqR0CaVaryqXqras22HvWmEP5XwbByDo9gm1dQVPv74YwDAH+4/Ob2t6a6pGdXGAw4EAHxbkFpz0gipdt+z0Spg50Ik15c9wzapy+KyWaYy2/bi4uKMWTIqx+xbVLU18KGu/1IFm7PVfBZw7RnTLy4uTqfF/s19mPbSpUsz8lbvMBW5H2aZuJYr/OzT+5V6meE9g2nHrdvSIFCsN6/dmWeeCZM7yWQSl1xyCV588UVMmjQJ3bp1q3QaW7duxfTp03HUUUdVvgAFOXqVseJujDHGGGMaMhdddBGefvppjB8/Hs2aNUubVrVo0SL9onbmmWeiY8eOuO222wAAN910E/bff3/07NkTK1euxJ133ol58+bh3HPPrXT+icJCJCow9+R+VaFeDdzPOeccAEHQECBYTcw3YNq5aXhvqgZ84+Un37Jp+01lj59MV1eVh2Ea33333TbWrOZgGfmmGudVJ/ybnhOqCVRgqaLE2RRSjaCaws5GNTXsC9heLuoOvJ4666S+iMOKHNuC+jPmPmxD7DPcrsq7emrS/YGgz6onizjlXT0qEe0DUep+XZ5WNQ0TBkzj54ABAwCUzaYu56zqehQVFaHrptR9P7k6dQ+mTTuVwbQXDH6WKe4bN27MeCbwHk6bdl3fxOeu9ltVt3VGnPcSeogKrxPjNqbNtTXcR/sz7z26noZl1Jlg2quHZ5bV37wq6qw/y83trC/PGZV25jV9+nQAwTUzlWPUqFEAgIMOOihj++OPP46zzjoLADB//vyMWeAVK1bgvPPOw6JFi9CqVSvst99+mDx5Mvr27VtTxa409WrgbowxxhhjGh5xDgbCTJo0KeP7vffemyH2VomCwhwXp1pxzyKsyt5+++0AAvWNb818Q6Z6xjdiKoLqe5zbeTw/dT8g2wuFetKoy+gqf10tH7Uvz4WeQ10pz++c9eD+qmhSdaGHkKuvvrpqlTLVyiWXXAIgsHWnikSFq2vXrhnbo2zE1VZd7UzZ/nisRhpku+RaFFXVAKBnz54ZefGT5VLlnL+rJwidUWJ7nzVrVvpY27abuspll10GAHjmmWcAALvttlv6twHNNgJbgNJ1qzMPotK+Q5mP8zJvMijz3z5nawusXr0aLVu2zPC2QoWcfSccUwXInpXjs0D7t3osY9+jzXv4WcptOlunftp5DLczL1X71eMc45OE7xcsvyruOnPIerE+zIP3GI1twmtlTHnUy4G7McYYY4wxNYYV9+qBau0TTzwBIHjbVg8nfLNX/6rczjdjHqc2fGEFQL1T8A1+WxY71DQsI9UZqhU8L+F6chvPBeutvvDVP25FttD8bqW9bkPlndxyyy0AAi8zbCthDwy89mwr7Gca1VT9OKs3Bqr7XJPBfhi2W+X6FvY/9fSgtu5aFp1l4nFUzcKKuzF1nSlTpgAQDyhcq0VFvWxAkSgqs+emylyQ+lzSpAMKCgrQDMGzNGzjHheVOG62SxVr3jv4ybTVNj48i6frYGg3TvWfirzGGeF9SWNDqL26qv7hNJinziDqd96D4hR4XpvTTjsNxlREvR+4G2OMMcYYsz1JFBQgkYOrx1z2KY8GM3AfOXIkAOCVV14BkB2hjW/dqg6ras43ZSoFVJvDEUUJt0VFAK3rsMw8L2pHGN5G1YEqqPq4jfOTq6oqt/Namfzi97//PQDgj3/8IwBg3333BZCpgsf5X1cFXteQLFmyBEDgv5mqGtUw7kclLIxGSuV3psE+TYVOPd3o2pT3338fAHDppZdGnQZj6iT33HMPAODWW28FAAwfPhyTF27FjjvuiP6MblzmLeabLc1Tz4DNst5p7bK00q5rnICg/3KdE4/VOCqclW3RogWAoN/yeco+qGtdombDdCaX/ZbKOdPUew3Xx6jveVXeWd+wys/8eQ/R+jKvOA82rN8nn3wCILg2xuRCgxm4G2OMMcYYs11I5GjjnrCNe6X46quvACDto1MVd6LbqQio3/byFAAeS/+h+QTL/PzzzwOIridVefV5r36zNUIl4X785LUZMWJENdbE1DRXXXUVAKQDXIRDTu+ySypSI2drCBUqql/ffPMNgEDRYv9TRZ1KF9sa0wey10yopwcqhdOmTQMQeJ7afffdM45nBMapU6cCsOcHk99cc801AIDHHnsMANCvXz8AmQOJjRs3ptVx3t/Zj7idSjY/geC5Sd/n/NRIqVTrmSbt7jXeih6ndunhbZq22qizbLQrp+LO+qmHOfV4FX5+af34LGQeOkuns8p81vFaGFMZGtzA3RhjjDEBU5ZsTZuKRblvNcbkQCKRNjercL+qZJPMxWN9PYbeZjJW2iPbPp2+XHlzI6oih489+uijq7/AtcSECRMAZCulQLZ3Dqqky5YtAxDY+fFY7r9y5UoAtmlvSNx0000AgjbBTxIXkZCDCV1rwnUVbHO0qweA7t27A8hun+rxgYo6oxbydyptnAWwOmbqI08//TSAIP4C+yDbva7fUttxem8CAmWZSrR6YyPsr5z1alVmY8+0dcZb46nQNhwIIsJqVHRVyvks5z2DaeozXWfkWM/wCw2jeaviTvisYxq8X82dOxcA8LOf/Qym/rBq1Sq0aNECK6ZNRPNm2WOkrP1Xr0Gr/gejpKQkY8YqV6q2tNUYY4wxxhhTIzR4xb2y3HnnnQACRVCVQKB+28Ded9996f9px8cmRNvBK6+8ssbLZfITKvBsS1TvqIKxbdF+Ve1SVek6/PDD0/9TcdO1FIR9lx5raOvu+AGmITJq1CgAQK9evQBkxzJhH9XvYU9jGjk0Lg6D2ojzOCrVqoKzv1MlZ18FgP79+wMI1G21L6e6z5kDKupqo69r0zTyedhbGrexXKynfmcatGm/4IILYOofVNyX//etnBX31vscaMXdGGOMMcaY+owXp1aShq4m1+fZBFN7UJGj8kZFS1UwjaxKqLKFvc6oNwkeGxdp0Uq7achQDb722msBBJ7XuFZEPcGw/4SVaPZTtTPXfs01Zfyd6534yf01ngN/D6v83Na2bduM+lCd12N0vRq3q1cZ1kW96gCBLT6PYflYbnrF+vzzzwEAN998M0wDIFGQ4+LUqmnmVtyNMcYYY4zJA6y4G2NqDbUjpfcFVbC4Xf048zj6YA+rYurxSZU15kGvMsaYQB2+/PLLAQBt2rQBkB0NlH0xvM5EY3rQWwyP1bgL3E4FXu3LmR4/uR4lPLPGbVx3ptHPGZ1VvcxwTRbTolca3lPofYZ5h23n1RsWy02b/SlTpgBwRNQGRyKRm6vHKrqDtOJujDHGGGNMHlDnFPfvvvsOv/71r/Hqq6+itLQUBx98MO699960nZ0xJiDf+wvtaW+//XYAgSJHdYtqHu1VqZLT9pWfVAXDKrv6jqanB+6jdrXGGGPMtlLYeS8U5uAlprBsZmZbqVMD9zVr1uDgg1NO6a+55ho0atQI9957Lw488EBMmzYtvajEGOP+YozZftDM48ILLwQAHHjggQCALl26ZOxHsxcgMJ/RQIZcCEozlEWLFgGID3JE0xO+VC9evBgAcMYZZ8SW99lnnwUQmM3R/EbN8TQ4VIcOHTLy5GJ1igbcHl4Qz21k3rx5AIC33noLAPDQQw/FltOYqlKnBu4PPfQQZs2ahQ8//BCDBg0CABx55JHYc889cffdd+PWW2+t5RIaU3eoT/2FHl1uu+02ANn+2fmg5ICAUR45s6D7A9kqvdq8z58/PyNvY4wxpq5TqQBMEydOxI9//GO88MILOP744zN+e/rpp3H66adj8uTJGDp06DYVZvDgwQCADz/8MGP7iBEjMHv2bHz99dfblK4xtcH69evT4bg/+eSTtPnH8uXL0a9fP3Tr1g3vvPNOlklHrtTH/sKBuw6ycx24h2cZVCnjsVykxiAu5al4xphMaN629957A0BGAJldd90VQLDgk32NSjyHG7rYnNuphhcXFwMIFoZWpo+OHTsWQGBuRzM6VfV532VZdTvvHyzr999/n86D5fz0008B2N1jQ4cBmHINqFTZ/ZVKLU496KCDsNtuu+Gpp57K+u2pp55Cjx49MHToUGzcuBHFxcU5/ZHS0lJ8+umnGDhwYFbagwcPxuzZs9OrwI3JB5o2bYonnngCX3/9NX73u9+lt1900UUoKSnBmDFjUFhY6P5ijDHGmJyolKlMIpHAGWecgXvuuQclJSVpN0tLly7Fq6++mh6cPPPMMzj77LNzSpNv2suXL8fGjRvTb+xhuG3hwoXo3bt3ZYpsTK0yZMgQXHXVVbjjjjtw/PHHY/HixXj22Wdx3333pUOLu78E/Pa3v834fssttwDIVuBZRw3QEg7Mwm3qWpIvNGEFzRiTG6ou33TTTen/R4wYASDoh6qsa/AztT/nfuyjZ511VqXLR3V+zJgxAAKXlMyLZeM9hfcHLSPvtVT9P/jgg3Qe1113HQDg5JNPrnT5jKkqlbZxP/PMM3Hbbbfh+eefxy9+8QsAwHPPPYctW7akO8yIESPw2muvVSpddg71jwoED2fuY0w+ccMNN2DChAkYOXIk1qxZgwMPPBC/+tWv0r+7vxhjjDEmFyo9cO/Tpw8GDRqEp556Kj1wf+qpp7D//vujZ8+eAFJqWJQSWB7q/i0MF5mFAyAYky8UFRVh9OjRGDRoEJo0aYLHH388rf4A7i/l8fvf/z7jOxfc7rzzzgACVYznM+zhgioelTUqbV988QUA4Morr9xexTamwUD1GQDOP/98AMCee+4JAOlZRdrx0uadsP/SDPCbb74BEHiyqQpU6+nhhethaPOekCA4GkTpq6++AgDMmDEDAPDwww9XuUzGVAfb5FXmzDPPxKWXXooFCxZg48aNeP/99/HAAw+kf1+/fj1KSkpySqt9+/YAgNatW6Nx48aR09fcRrdNxuQbr7zyCoDUoHrWrFno1q1b+jf3F2OMMcbkQqW8ypDi4mJ06NABf/jDH7B+/XrccsstWLhwYfpNdsyYMZW22QWAQYMGIZFIZHnJOPzwwzF79mzMnj27skU1ptb59NNPMWjQIJx++umYNm0aiouLMX369PQaEfeX3PnjH/8IADjiiCMAZIddD5sOUXGn6dCCBQsApFxmGmNqjgsuuABA0BepdrP//ulPf6qxslx66aUAsm3ZOVM5atSoGiuLqR/UtFeZbVLc27RpgyOPPBJjx47Fhg0bcMQRR6QH7cC22ewCwEknnYSrr74aU6dOTXvLmDlzJt58801cccUV21JUY2qVzZs346yzzkKHDh3wpz/9CXPmzMGgQYPw61//GqNHjwbg/mKMMcaY3NgmxR0A/v73v+Okk04CkFqcesopp1S5MKtXr8aAAQOwevVqXHHFFWjUqBHuuecebN26FdOmTcMuu+xS5TyMqUmuv/563HzzzXjjjTdw8MEHAwD+8Ic/4Pe//z3++c9/4qijjtrmtBtif6Eyd/jhhwMIFuDyNha2oaW3iHXr1gEI/N1fdtllNVJWY4wx9Z867cc9zDHHHINWrVqhRYsWOPbYY7c1mQyaNWuGSZMm4Uc/+hFuueUWXHvttdhnn33w1ltv1ctBiKnffPzxx7j11ltx8cUXpwftQCpS56BBg3DeeeelQ3pvC+4vxhhjTMNimxX3LVu2oEOHDjjmmGPw2GOPVXe5jDEmls8//xxAtledsB932rjT1p8zhMYYY0x1kTeK+7hx47B06VKceeaZ25qEMcYYY4wxJkcqvTj1gw8+wKeffoqbb74ZAwYMwIEHHrg9ymWMMbH07dsXAHDVVVdlbA9PINJjxT333FNzBTPGGGO2I5VW3EeNGoULLrgAbdu2xZNPPrk9ymSMMcYYY4wRttnG3RhjjDHGmIZM3ti4G2OMMcYYY2oOD9yNMcYYY4zJAzxwN8YYY4wxJg/wwN0YY4wxxpg8wAN3Y4wxxhhj8gAP3I0xxpg6RmlpKR5++GH0798fO++8M9q1a4cjjzwSkydPru2iGWNqEQ/cjTHGmDrGlVdeiQsuuAB77bUX7rnnHvzmN7/BV199hQMPPBAffvhhbRfPGFNLVDpyqjHGGGO2H1u2bMGoUaNw0kkn4S9/+Ut6+8knn4zu3bvjqaeewuDBg2uxhMaY2sKKuzHGGFMOc+fORSKRiP2rbjZv3oz169ejXbt2Gdvbtm2LgoICNG3atNrzNMbkB1bcjTHGmHLYZZddMpRvIDW4/vWvf42ioiIAwLp167Bu3boK0yosLESrVq3K3adp06YYMmQIxowZg6FDh2L48OFYuXIlbr75ZrRq1Qq//OUvt70yxpi8xgN3Y4wxphx22mknnHHGGRnbLrroIqxZswavvfYaAOCPf/wjbrzxxgrT6tKlC+bOnVvhfmPHjsWpp56akW/37t3x3nvvoXv37pWrgDGm3uCBuzHGGFMJnnzySTz00EO4++67cfDBBwMAzjzzTBxwwAEVHpurmUuzZs3Qr18/DB06FIcccggWLVqE22+/HccddxzeeecdtGnTpkp1MMbkJ4lkMpms7UIYY4wx+cC0adMwbNgwHHfccXj66aerlFZJSQnWr1+f/l5UVITWrVtjy5YtGDBgAA466CDcf//96d9nzZqFfv364de//jXuuOOOKuVtjKkeVq1ahRYtWqCkpATNmzev9v0VL041xhhjcmDFihU48cQT0atXL/z5z3/O+G3NmjVYtGhRhX9Lly5NH3PppZdi1113Tf+dcMIJAIC3334bM2bMwLHHHpuRx+6774499tgD77333vavrDENiAcffBBdu3ZFkyZNMGTIkDrtctWmMsYYY0wFlJaW4vTTT8fKlSvx+uuvY8cdd8z4/a677qq0jftVV12VYcPORauLFy8GAGzdujXr+M2bN2PLli3bWg1jjPDcc8/h8ssvx8MPP4whQ4bgvvvuw4gRIzBz5ky0bdu2touXhQfuxhhjTAXceOONeOWVV/Dvf/8b3bp1y/p9W2zc+/bti759+2bt06tXLwDAs88+iyOOOCK9/eOPP8bMmTPtVcaYauSee+7Beeedh7PPPhsA8PDDD+Of//wnRo8ejauvvrqWS5eNbdyNMcaYcpg+fTr22Wcf/OhHP8K5556b9bt6nKkODj/8cLz22ms4/vjjcfjhh+P777/H/fffj02bNuGjjz5C7969qz1PYxoamzZtwo477ojnn38exx13XHr7yJEjsXLlSowfP77CNGraxt2KuzHGGFMOy5YtQzKZxFtvvYW33nor6/ftMXAfP3487rrrLjz77LN4+eWXUVRUhOHDh+Pmm2/2oN2YaqK4uBhbt27NCnbWrl07fPnll5VKa9WqVdW6XxweuBtjjDHlcNBBB6GmJ6ebNm2Ka6+9Ftdee22N5muMqRxFRUVo3749dtttt5yPad++fTp4W2XxwN0YY4wxxjQ42rRpg8LCwvSCcLJ48WK0b98+pzSaNGmCOXPmYNOmTTnnW1RUhCZNmlSqrMQDd2OMMcYY0+AoKirCfvvthzfeeCNt415aWoo33ngDF198cc7pNGnSZJsH4pXFA3djjDHGGNMgufzyyzFy5EgMHDgQgwcPxn333Ye1a9emvczUNTxwN8YYY4wxDZJTTz0VS5cuxXXXXYdFixahf//+ePnll7MWrNYV7A7SGGOMMcaYPKCgtgtgjDHGGGOMqRgP3I0xxhhjjMkDPHA3xhhjjDEmD/DA3RhjjDHGmDzAA3djjDHGGGPyAA/cjTHGGGOMyQM8cDfGGGOMMSYP8MDdGGOMMcaYPMADd2OMMcYYY/IAD9yNMcYYY4zJAzxwN8YYY4wxJg/wwN0YY4wxxpg8wAN3Y4wxxhhj8gAP3I0xxhhjjMkDPHA3xhhjjDEmD/DA3RhjjDHGmDzAA3djjDHGGGPygP8fS3X8aJ3K6lEAAAAASUVORK5CYII=", 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", 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nnXfi//7v/zBp0iS88sorWL58Ofbaay+0bdsWb731Fi677LJq1bm6lJWV4eqrr8bo0aPRvHnznN+XL1+OE044IXM9TzvtNHzxxRfYbrvt0LdvXyxZsgTHHXfcBn25MMYYY0zD5f33388K9nn++ecDAIYPH47S0lJceOGFWLZsGU477TQsXLgQgwcPxnPPPbdOJiwliQRKRHTOux8SaWfX68gGGbifdNJJANILGRcvXowZM2bgvvvuw5NPPon//ve/sQsk33nnHey0004477zz8Itf/AJDhw4FAMycORPjxo3D448/jpdeeinnuPnz52OPPfbADTfcgP333x8tW7bEZ599hpEjR+Lee++tdvnnzp2LQYMGYcSIETj22GPRp08fDBo0CLNnz8bXX3+Nv/3tb3jooYcy+998882YNm0a9txzTwwZMgSbbropZsyYgYcffhh/+ctfsl4cLr30Uhx22GEYMGAAfvazn6Fx48aYNm0a/vnPf+Kmm27K69YyH6effjreeOMNnH766Rg6dCgaNWqEqVOnYuTIkbjllltqtGK5UO677z5cfPHFec2UAOCVV15Bv3798Mc//hH77LMPdt55Z8ybNw/3338/rr766oLraowxxhhTXfbaa69KnWEkEglceeWVBYmmGwuJVIHuPT788EPstttuG7o81WLo0KEYO3YsSktLcfLJJ9d1ccwG4oMPPohdBGuMMcYYU1csXrwYrVq1whnJzmiSqNoCfVWqHKPKv8eiRYsKcgOu1Ikfd2OMMcYYY0z1qBM/7sYYY4wxxtQXqmXjXgOsuBtjjDG1TGlpKRKJRCYquDHrG7Yx/jVq1AidOnXCSSed5BgqRUxRK+7jxo3LcRtpjDHGGGPSXHnllejWrRtWrlyJt99+G6WlpXjjjTcwadKkdfKeYvJTkkj/VblfDfMp6oG7McYYY4yJ58ADD8Tuu+8OADj11FPRpk0b3HDDDfjvf/+Lo48+uo5LZ6qLTWWMMcYYYxoIQ4YMAQBMnTq1jktSv6CNeyF/NcGKuzHGGGNMA+Hbb78FALRu3bpuC1LPsKmMMcYYY4ypEYsWLcK8efOwcuVKvPPOO7jiiivQpEkTHHzwwXVdNLMOeOBujDHGGFNP2XfffbO+d+3aFWPGjMFWW21VRyWqn9SWO8iCB+5t2rRB06ZNsXLlyhplaEx1aNq0Kdq0aVPXxTDGGGOKkttvvx09e/bEokWLMHr0aLz22mto0qRJXRfLrCMFD9w7d+6MKVOmYN68eRuyPMZk0aZNG3Tu3Lmui2GMMcYUJf379894lTnssMMwePBgHH/88ZgyZQo222yzOi5d/SGBwjy+1NSJebVMZTp37uxBlDHGGGNMEVJSUoLrrrsOe++9N2677TZcdNFFdV0kU03sDtIYY4wxpoGw1157oX///hg5cqTNn9cjdgdpjDHG1HNGjx6N5557Lmf7OeecgxYtWtRBiUxD4IILLsBRRx2F0tJSnH766XVdHFMNPHA3xhhj6ohRo0bl3X7SSSd54G42GEcccQS22WYb3HTTTRgxYgRKSmrqXdzUlh/3RCqVStUwDWOMMcaYgrj33nsBAD/5yU8AAM2aNcv6ncOSZcuWAQAOPfTQgtN+8sknAQCbbropACAhZgkrVqwAAMyfPx8AMHz48GqV3Rhl8eLFaNWqFf7UrDuaJqq2QF+ZKscVK77GokWL0LJly2rnZ8XdGGOMMcaYGpBW3Avx414zrLgbY4wxZr3z8MMPAwDat28PABnf4clkMuuTqnh5eXnW8fzOzwkTJgAAzjjjjMw+NDXadddd86ZN+J1DHk171apVAIBZs2YBAI455phq1dU0XKi4X7NpdzRNVD0sX5laiz8uW3fF3V5ljDHGGGOMKQJsKmOMMcaYGnPrrbcCiGzXu3XrBgBo3Lhx1n5cCEk79E022QRApIYT2rgvXrwYANClSxcAwOWXX57Zp3///lnHMk1+Eqr6a9asyUp77dq1WWVgrJp//etfACJb+LPPPrvSuhtTqKvHkhqGYLLibowxxhhjTBFgxd0YY4wxlfLoo48CANq2bQsgUqhDu/QOHTpkHUOVm59Ut3lMWVkZAGCzzTYDADRqlB6SMCiQ2sDTRp77h9u4D49hWk2bNs3Ki15lqLwTzgIwHc4SsE7jx4/P7Ms8mMacOXMAAEceeSRMwyVZoDvImirmVtyNMcYYY4wpAupccS8tLcXJJ5+M9957D7vvvntdF8fUM9i+SElJCdq1a4ef//znuOaaa9CpU6c6LJ0xxmycPPLIIwCAVq1aAYhsv6k2U6Gmig5E3mNmzJgBIFK3idqwUwWnys00ly9fDiBXeacKHvpm5zbuw2PUjp7lZJ78JPydZeasQMeOHQFEyn6YttrFv/jiiwCARYsWAQB+9atfwTQcasvGvc4H7sbUBldeeSW6deuGlStX4u2330ZpaSneeOMNTJo0KTOVaowxxhizMeOBu2kQHHjggZkZnVNPPRVt2rTBDTfcgP/+9784+uij67h0xhizcTBu3DgAkXquajdVZn5SHQciu3LuS/Wa+/J3qtncj2o2VXD6VA/VfCC/v3eNjMpjNA3mwTyp/rN+agPP/VhmfgJA8+bNAUQ27vykus9IsDyXQ4cOhan/lBRo417TAEy2cTcNkiFDhgAApk6dWsclMcYYY4wpDCvupkHy7bffAgBat25dtwUxxpiNAHpNoekgVWOqyRrVlEp1aPu9evVqAJFdPH2lE1Xkef+lzTjt05kn1XJV1fV7CI9hGlTSWU7mSUWeZeZ+rCfrwLKF9dSorDyG+3CGgeo9z+2gQYNiy22Kn9pS3D1wNw2CRYsWYd68eVi5ciXeeecdXHHFFWjSpAkOPvjgui6aMcYYY4ocL041Zj2y7777Zn3v2rUrxowZg6222qqOSmSMMcYYUz08cDcNgttvvx09e/bEokWLMHr0aLz22mtZU5/GGNMQefLJJwEA7dq1AxAtsGzRogUAYMmSJQByTUkIzULCY7kvTUr4yd/btGkDIDItYZo0X+HCUZrE8DtNbWi+Em6LO4Zp0vSHpkAMrDRv3jwAkckM601zHpY5rCdhuTVAFNNgvZcuXQogOteHHnpoTlqm+ClBgaYyqar3qQwP3E2DoH///hmvMocddhgGDx6M448/HlOmTMmKwmeMMcYYs7HigbtpcJSUlOC6667D3nvvjdtuuw0XXXRRXRfJGGPqBAoX6haRivVPfvITANluH4FIgQ4XalJ5pgrOxaZUudu2bQsgUsxVFf/xxx8BRAtLNV1VuMNtLAe/85NpUnGPU951gSx/1wW1YdoK3USyPjrzYJGofpMs0MY9WcA+lR5fo6ONKVL22msv9O/fHyNHjszcqI0xxhhjNmY2GsV99OjReO6553K2n3POORl7MWPWJxdccAGOOuoolJaW4vTTT6/r4hhjTK3x9NNPA4hUYqrDhHbZVKg333xzAJW7YqSNN/eh0kzVmt+ptFO5nj17dlaeVNypgvN4tYEHIpeLGsRJ3UIyj86dO+dNmwGn1JafeYV29Qr34bGsh7qa5HnhubdXs/pFwe4gaya4bzwD91GjRuXdftJJJ3ngbjYIRxxxBLbZZhvcdNNNGDFiRKU3ZmOMMcaYuiaRCl9djTHGGFNveeONNwBESrMq1LRdpzcV2qXzO1XjypT3quCwgwGavvrqKwDA4sWLAUTKOsUUKvW0s58+fXomrU6dOgGIZg6olLM+VOJbtmwJAOjRo0fe+tSkHlqfOXPmZH2Pm0HguR88ePA6l8HUPYsXL0arVq1wb5teaJ6sWgBcXr4Ww+dNwaJFizLtsjrYxt0YY4wxxpgiYKMxlTHGGGPMhoFryGirToWadtj8pLpNpZreVOKU9tCrDNF9qH7rBD99xDNvquVUw9V8UW3mgchTi8blYJ5aP+bJPFKpFJp8+lI6/8Xz0+VvnPYos8lWaXV+Zceds9LO590GiM4Vy0L7e85i8Hd+cgaB1+aAAw6AKV4anI27McYYY4wxxUhJge4gC9mnMjxwN8YYY+o5VKap/tJbTKtWrQDkej6hUwiq23G24KFPc1XI45bQaZRTfrKMcao+yx76Q9djWB71vx4XWbUQG/dEIpFR8ON+D/Ok73vNm79T/aftu/27m+rggbsxxhhjGgxdFnwGLPgMC994FQDw+YsTAQDzPk+bymzWLm1G03mvbQEA7YYOygyWygYfX7uFNUVDMpEoKLhSTQMweeBujDHG1FNuu+02AMAOO+wAILK/pq03bd2p+lKJp7pdE68r6gtd1W6WhXlS9Y9Ty+mlhfuHsB7MQ32oM021hV+f6PoAfqetO/2707ad54dl5bU666yzNlgZTfHjgbsxxhhj6j39Wq0Gln6D6U8+AQAYf+fbAICx85Zn7/jNwvTn22m3kyMmTcv81Lvi08q7URIlCSSShZle1QQP3I0xxph6Cv2wU62OU7OpEtPbCtEop5V5lYmzA48bqHA77ew1L35Soc6XJ6G9OJV31o/7pvOam7cc60Jo2x9nL8+8WTb1606lndt5rYypDA/cjTHGGFPvWTnhNQDA+/98B0AepT2Gfz79Veb/8zqkXTf26LUbAGDZT7Zdn0U0RUyyJIFkAYq7bdyNMcYYk8W///1vAEDHjh0BREo7o5LS7pqqMD3CqB061WFVvWlnTmU7TKNQuD+V+oULFwLItUsnK1euzKpDuI31YPRVTYP+69c3LDMQqf26PkDrqed+yy23zCozr93RRx+9QcpsihtHTjXGGGNMvWfO+59jzvuf44U5y/DCnGXrlMbEJz7HxCc+R9kPX6Dshy/WcwlNTejatSsSiUTO35lnnpl3/9LS0px9mzZtuu4FKEkiUcAfSmo29LbibowxxtQzWrZsCSDXb7t6VeF29dRCdZgK9qJFiwBEtt1Mhz7LwzRUvVe4nWXTWYA4e3rux1mAcJvWS/dt1qwZsq331w9z587NKOdUzKnuczvPi14TwvPF+nM/Uz3ee++9rHUWkyZNws9//nMcddRRsce0bNkSU6ZMyXyv6cLR2sADd2OMMcbUe9YsX1njNF6emzYZOnjJwvSGDfE2YNYJmhyR66+/Httssw2GDh0ae0wikUD79u3XS/6JZAKJkgK8ysA27sYYY4wJoNrLT3qLoTJN1Vf3U9/rhNupYPM7lfh8aap6qUo696dtOO3FqUCrMk0lOswzTsWm8sp6MI/1zZo1a3LyVu84PB+cndBzydkBfubzmmOqx+rVqzFmzBicf/75laroS5cuRZcuXVBeXo6+ffvi2muvRe/evWP3r4xkSQLJAgbuSQ/cjTHGGGMqp9U2nSr+e3+d0/hFh3SgqmSLzWteILPBeOKJJ7Bw4UKcdNJJsfv06tULo0ePxs4774xFixbhpptuwqBBg/Dpp59iq622qr3CVhMP3OuAxx9/HADQokULALkrzlX5+PHHHwFUb4U5V6VvscUWedPUPBlF7/DDD692fYwpJh566CEAkSrGPqA+qOOiPrIvDR8+fMMX1phqcOutt2b+32abbQBEqi7VbH5nO2bEVKrBqprTPps+x/lJQs8vcSq9/q4KKJ9TLCP7oirZzDv0Nc80dV991jGP9U3z5s0znnV4rnjuWDbavs+fPx9AFEGVZWTZeW24f3g9zz777A1S/vrK3XffjQMPPDDjVSkfAwcOxMCBAzPfBw0ahO233x533nknrrrqqmrnmUgmkShgtiQh/aS6eOBujDHGmHrP5n3Tvtd/c+AkAMDd/5ta7TS679sVANCoU/f0hnVzTmM2IN999x1eeuklPPbYY9U6bpNNNkGfPn3w1VdfVb1zHeKBuzHGGFMPCJVsnWWlxxLaUauCzv0YvZNKOm3iufBPVfQwT/W7zt/4GTeLRcW5U6e0KQs92XC7epsJbcBVtabqTfU6zgZ+fdGhQ4ccm35V2ufOTUds5YwCZ7ip1KtHnLg1AqYw7rnnHrRt2xa/+MUvqnXc2rVr8cknn+Cggw5ap3xt414PoLkKXUNxSnLrrbcGEHVuXciiNxhO8b366qsAgL333js2T+7To0ePrLSJTpPyxsAyjh8/HkA0lccbjQNBmGLjwQcfBBAFaNFBg34SNZnR38moUaMy/6sZzWmnnVajshtj1j/Ldj0YjRo1wo5npmXyc9r9DwDw19KJlR7322N2yPy/zfG/BAD82LL7BiqlqQnl5eW45557MHz48JwXyWHDhqFTp0647rrrAABXXnkl9thjD/To0QMLFy7EjTfeiO+++w6nnnpqXRS9YDxwN8YYY4wxRc9LL72E77//HqecckrOb99//33WbM2CBQswYsQIzJo1C61bt8Zuu+2G8ePHY4cddsg5thASJbXjDjKRipOTzDrz8ssvA4im6KjGUcnjWyA/dTpM3xI5lcnjP/vsMwCRKg5Eaj4bHBfkhOGogWjqjuiUHj95PH/n1OXPfvaz2HobU1eMGTMGQPbCOZoEqILO/hU3va2L73RGrLKw7qrix7na0/7FMpxxxhmVV9SYSrjtttsy/2+//fYAIjeIei9fvjztj5w+rGmu0a5dOwC5AZmI9pfw+cX/tY9wO58vOkPFPsoZYTXfWbBgAYBocSdNTYDIyQMX17Zu3TorbT4DOZPNspWXl6PF/HTgnTVffgwAWPHdN1nlbrpV53Qa3SP3gIva7ZhT97hhFE18Zs+eDSC6J82aNQtAdG10rMBrM3ny5ExaZ511Vt48TN2zePFitGrVCk/ttBs2reT5QJatXYtDPvkAixYtWqdgW1bcjTHGGGOMqQFpxb0ArzIor3KfyvDAfT3x9NNPZ/7XxT1806d6oG4fqQjod77FUyGgUsJFQmEQCl04RAWeKgrf5FXJ4Hd1/cXvVECoaoT1PPjgg6s4K8ZsGO6//34AkYLHdkp7diBX9dYw7HGKO9HZKZ0ZC9ei6MyVqvw6kxWGbA/LQvdvquiFs3BMw3b0RtHZIiB3xpeqr7oj1plebcs8jvvz2VKZO8hQ3Q5/19lnwn7AvsX+zP6ix4fbdB91a0lYlrKyMixotU369/7boqSkBMmB2edrZcX+y8MEUqnMudFZPZ4TnXFgPXkczz2VdZYpbrbdmBAP3I0xxhhjjKkB9ipTJNCmMFzMEBfOWVVutQfk27bavyr5bGzj7G5VZWSZ+Oavear6T0WA+4dho1l3296ZDQWVdappGixJVcHQVV1cgKW4PqHKZJzbu3wKZZyHKE1D3dnFuXtT93mh+s/ysf+xHKeffnretEzD4fzzz8/8/+yzzwKIVGCd5WEQI1Wo2b44w8uZXZ0pVpv4cBtRtVtnfuNs4YnavFemuHMfHtO0adO8aer+assf14eprgO5Nuu6dqVVq1YAonOsbi25nc9XvTZMN7yeZuMnkUggkSxgcWp5zQbuVRvjGGOMMcYYY+ocK+4Fcs899wCIFAVVopcti8Kn0b6cb9dUxKhWq4cJ9TKjqF262s+G21TVDxXyyvJgmfg768c6UIUI68m633XXXVl5US04+eST8+ZlTBxU2NW2VRWpOJvZfKiSznarYcnj0lI1TRX7ytB9eKzeA+LqVVkealcfehQBPBPW0KFiroq7tkG2Md63eY/XQE3crjPI9PQCROu7tK8o3M481PsZUfVbyxpu074Tl1ac2h8Xz4GfYT01mNXy5ctx0I4dKwqUBLAKb01bljln6kFO192ocs9rZ4qLZEkSyQIWpyZTNdPMrbgbY4wxxhhTBFhxj2H06NEAgC5dugAA+vTpAyDXH+2XX34JAJg5c2bmWNrWceU437pp50YFRO1dVQHhWz3f+DV8dKgQ6G/qF5d2fDxGfVnzU1UXpkO/uWE96f932223zUqTedCf/XfffQcAeQMiGAMA9957L4Cozesskypu7H9VRUEtBLbxOB/spLIIq6rSaznj+pvup36ttV/nOzau/H/9618BRKqeFfiGBeN86Domom2TfY99bd68eQCi6NlqM66zs0DUb6mgx60T4XOJvzNtbffqlYb8+OOPmf87dOiQtU/cjBj7TehJrcPqmcAyAGsqbOjLKtafJSvOVZP0c3NaSduserKcK1aswIG9OwDYHEhVlLHic+BWaTv2t6Yty4lYrt6w+MlrFsZoMcVDwQGYUrZxN8YYY4wxpt5jxV2g8rfNNtsAiFaHq1JGVYv7MZopAMyYMQMA0LFj2uaNK8ipaKj/2zg/s2rXS0L/0ZVtC9OgohEXyZGfartH5Z11Cr0GsO5qz8i0GMmO9eS5HT58eN6ymobH3XffDSBqb1SitF3GqWmq0IWqeFx0Q01L14doO1alUm1f8xHnPUbXtcSlUZlnqTj7eKIzBvxuLzQNi1NPPRUA8I9//ANApIJr3+Ezjn2QUUr53KLXGLV1z6dsa3vWtsi1K/TKwt+ZN58ZGsNE15+Eirv6hI+LSjx37lwAaS85fX6SANYiUtpXpuuUKqvwBc/nZEUeWzWeha3aVnxLNgJAm/zNcpT2BGe+Kvb45ptvMtFc+fykpx6eS7W/d4yG4sSKuzHGGGOMMSaDFfcKHn30UQDAVlttBSCyCedbvEZEo70b35RpZwdE6jRXoVPpoKqgHlyI+riNs5utzI+72vWpJw21dVebO5aRSj3rwP05ixCWX73maKQ95slzy3N95JFH5tTD1G/uu+8+AJHypgp7nIcIVZerY9uu/UjtyOO8S8Sp5CT0rR7nBUa3x3nZIIV4qiFx50T9zKttL8v997//Pev43/72twXnbYoHXneNks1n2PTp0wFEHmE6d+6ctR/bGRV4VctD1GMNZ3BpJ6/PH7ZFpsnnjirv2tZZ1pA4rzKzZs0CEKn06X6Rf4Z6Q9C5c+fMmGDq1KkAcqOjx82emeKitrzKeOBujDHGmIYDzVvKKxZ287PCdCZBc5vVaYEhs1gViEZNCQkupS8yFXkM7rwZ0Lkn/v32F+ur9KaB0+AH7s899xwAoFOnTlnbNZIov9OWnOoDbdXC6GtbbLEFgEhloPKs/m/VFk99sKvnDLV9D9U5XaWvigbTVFt3Vfk1Shy3s05hPXksz4UqkjrTwP34yXN/wAEHwNRfSktLM/+r1xiNXqrquHpM0eiN7EOqJuZD2zzbq6r9ivpezqc0xu0TVx6tT5y/d61/ZVQW2TVfmqryUYEPy3LGGWdUma/ZOBk1alTW97jnSps2bQAAW2+9NYDc9qFtb/PNNwcQ9Vk+G4Dc9SHTpk0DkNsP+Cyk9xQeR082cbFN1O95uI0wbz6bmSbLW9k9YkOy9dZbZ2Y5WCa9F/GZyWvn/ldkFGjjjhrauDf4gbsxxhhjGiAVSnpGUY8zZSuPBIVEqkJwQ+XmLZlFqimbwTQUkokEksmqB+XJaphE5qPBDdz/85//AIhUAvoij1PMdDu/q2eY0KsLV+nzzT+0hc2Xh6pvqn6rak4lP1RCuI3lilPU4xQ+VSGYZ8uWLbPqFNZT7f/jPGnwGPXtS/Wf/t5pg3jUUUfBFD9U2kOfxHE26XHeKOJsQNU7EttYZbai+huPUSVa01a73XzRh7X86mlJZ9e0/nGKej4PMnH7xt2r4s5dnKeeMH0rf8ULn22EduSMysl2wNlm9cGu65/Yxvk77dAZKRyI+hSVdlXgqTjzuaKzXsyTdulcU6XrTDg7EG7T9TJMI/9MW7xHqA3F6tWrM+eazzr2Nc5A0IOPMZXR4AbuxhhjjGnAJCuGPo0qzOQaN836OVW+7gP7FMUxqvcVivvQ7mmXkC9+Pnud0zYbN4mSJBIFLE5NlHtxakHQnppvtIxqyrdxtWWvyosFj6PNN71kANGbP9+iidqgqnKmdur8rn6jqTCEqrn6hVYFkL8zTY1yqqqb2hjms5tl3dVLh9ZLZwF0ZoGzH1RrbPte3NA3O9W1sC3GKeKqFsep4Gp3q+01jH1QlacGVflUWSd6j8iH9h/2fbZpnfnSqJU6K6d5h3WJ8/2uyiLR/qi/V7XOAADuuOOOrDzsZ3rjgjPJoXcz2q7z+vJ+PXnyZAC5M0v6yfau92+27XzPBM78VhbjAIiel3wO0+ZbYcRu5sXjqKaHabCcPEZhP0jvX3eRSZPJZM7sc9euXQFEsxcs6xtvvJE5jlHLPSNtGszA3RhjjDHm6zWbYdmyZdhp84qX8YqPjK172eoq08jxIlMVtnWv9yRLEkgWsDg1WW4b90p59dVXAURKhCrmaiOriruqckSVtfAtP06ljlP0FLWfpxqnNraMBAdE6grf5FkuzTsOVR1ZBlUGQ3WFecTZy6uSp+dcVUa1p+e123vvvSstu9k4uOuuuwBEqpiq4UC8ssx+pjNGauPONOPsucM1GKHniZC4SMXaR+IiAuezU4/z9R7nLUbrE+dhKp//9zg1UyNi6oyD2rDr/UjPab46M+1//OMfOHWfnQEAq7+aCABoeoBV+Npm9OjRAICePXvG7sNrxvs1lXc+KzSiqnot41okPY5rV/g7ECnuOmNG1HMa7/lxs0D0DMM8eFzYz7WcPEb7s/aldFq158+dNG7cODNLwPpoDJR8YwSOYXjNTznllA1eVrNxUu8H7sYYY4wxygdz1mS9JO/erkKM2qTiZaYGtu45VCjuQ7q2wpCufXDvKx+tv7TNRkGiQHeQCSvuuTzxxBOZ/2k7xrdt2pCpdxVVhVVxJ3EKWmjPTsVRvalQSc7nvSHMm8oBf6cCwk+qlqHSoTMHVEfUxrYqX9UsI9VK3T+sp6qEuq9GbtRPVfOY3rJlywAArVunF/OE1/Owww7LW35Td9x7770Astd5ALmzOOE29Zik6x8Ubb+qbOezcY+bJYvrC3HeWrQf6uxAiEYgVhVbPXToDFdc/IWwrHoO1UtVVbOE6h0kzg92+H/Yx0/tn46s+eMzabvqr59ND0AWnnsnAGC/zz/IOS9mw0DvKtn222nYBvnJffT5os8jfjLmAtsH09YZNd6vgarjGGh7Cj1O5dsvLrpxGE+EqMofF62YecZ5jqstEolE7LXQ9QVANKsfetQxDZN6OXA3xhhjjKkOH87NNuXru2XFwLk82C626tW2dTf1FnuVMcYYs9Fw6q5tAQDf3Xs/AOCmW9/Ou99jia6Z/+9Ifbuhi9UgufPO9OzG9ttvDyCacQoVd52FohLNyNU//PADgEhZ11lnnY3mJ71FUQ3m8eGxceuYVN3njJL6c9dZI/WoFqarHtXi1mxwP+apZVLyxWtYnyxYsCBHPWdZeY3CmQWeZ553toH/+7//26DlNBsf9Wrg/s9//hMAsPvuu+f8xo7Am4+6uNLOrlPWVblgC2+YvLHpzZSfOiWvNymdbmeH5Xd1Fxlu4z6cvmTHZ311cZxObbKMTJvTc/keDFWZN+iCVj23cTdrXivm3aVLl0yavMYjRozIm6epfdjelXzmZnEu53QxZpyJmqapC+tC4lycarCmuABFWg8l3C9ukSnNCvK5dQxhf4tbMJqvPGrqonmSOBe3ajIUdz7iymFMfWfiAvaHZKYf7dSq4nlXobwzgqqVd5MsQYFeZWqWT70auBtjjFm/nLbvrgCAH+5O+3KPU9rzcXqF+m7l3RhT30kkE0gkC1icWsA+lVGvBu49evQAkK2EUXHWYEgkbqFaZeHNgVwXcmFwFrpmJLoAJQ4q7QxJTSVTQznPnz8fQLbizm0MQ80FOFTfWH+636rKPSTTofpNwnrGhaNXN5iq6se58uNxGggmnIrlNTZ1DwMtsX1qHwrbJ4mb4VKVW5V4XewWpxbng7NN/OQ9QRfIavtUl5Q6s5QvABrLrQv94tw9El34WtkMhPZdnXXgJ2fftNw6sxdXP7Pxou6N9V4LRI4Y+Azg80RdMOrCaKKODoiarYSmJ3HPS23HbMN8NjIvtlldQMpPOiz46KPII0ufPn2y6qnPbp4H1pN9jfuriU1cwDLW84O56edj1+TC9A6F+mdP5C4W12vB86GBmML6sBxhsC3TsKhXA3djjDHrlxUfjQMATBrz4TqnYeXdGFPfSSaTSBawODW51otTM8rfTjvtBCC/6zRV/1Rt0v01IBM/9bh8KjrVbVXwVGVT9Y3Ksqrl/GQduF+ornDbggULssrPN3jmoQuN4mxpuZ1qS7466DlQ9UcXIKmqSOJc/OUrG2cAeM1/85vfwNQNbHOqwOn1z9dm2BZUHYtzy8r9tU3FBfcK0T5MeKyWV2eM1DWdlh2I+ryq2RrMhvB3dYdJ4lTxEC2P9m0NZhUX3EXV/TCvqmbmTN2wxRZbAMjtP+G1Yztg22R/1X6qwcP0Wcl0tH/kC1wWF0iJbLnllgCi+zj7MZ9xLEOcO2O24XDmldu0P+snzxVdHrMsXA/2448/VlqHsJ7l5eX4urwlVqxYgd4Vy75SFZ5nElX0mbFTf8Rnn32GRo0a5az/0sCJ+WYzWE+2AdPwqBcDd2OMMcYYY+qKggMwFbBPZdSLgTvtsVVZAqI3eaoNqg7HeUtQ5Z0KQVzI9cqIC0ahgSL4dq3BV/imrypEaPu9+eabZ+3DY9XdVr6ALvnKFmePHx4XF9CG9aKSEae0a15VpRf+z2tuap9//OMfWd/j1GLalOa7fmo/roq6qlyqAmrbYPtWVQyI+hPLo7NHGuqdcLZK+zrzDL23qEpPu3MNfsMysEzsw6ria+CZyhR35sE0VcWLm83g8XFrFE7bP+2d6+u/3AgAeGra4pwyVBebzNQMBjvbZpttAETXlDbR4aylrhnSPsPPiRMnAogU3Hbt2mUdr/2b6XFdVXhfZznYpuiFjOo2occwPiNYFn1GsD7hsw4A3n///cz/mrba5Ks7TH7nM53PTn7OnTs3q2z5ysC6t2rVCkhU3N8qbNjpZSYDbdsrPidOnJhzLdT1pt5PgNxzy37PNjF8+HCYhkG9GLgbY4wxxhhTVxQcgKmAfSqjqAfuo0ePBhDZtufzlcy35DhfzXH21qr0cf9CvLKoba+mqdvzhYYHcv00UwHMFwaa+6qtrXqKqMpPdJxtbWUzC6rkqVcctRGOW1cQd43CvFnPTp06AYjawCmnnBJbPrN+KC0tBZAbwETbhobtDn/X2STtn2qHq3bbur8q2mHbUiWZeWq/Us81TJPKnfbLfDbzaj+u/Ytpqh2uerhRTx8kVPfVLl5jTqjyrudQbZnVu0amTjO+AQB88/JUrG8+PuoAAMAu/3luvaddn6EqrO2rMo9A2s61D/G5wngZajOu7UnbW9hW2aaoDlMNZ9/js0Ht45kXYRn5DImLcxCmpX2Qz0JV4PU8sG/y2a4KPtechWUM7ztTljdBs2bN0JleZsIoqwCQTJ+3179dhM8++wxNmjTJsQTgNahsXKHqPOvJNmEaDkU9cDfGGGOMMaauSSSTSBRgPl3IPpVR1AP37t27A8j1pR4qt2o7q/Z9/F3tsJkWbfSq8useKtdxPqfj4O98c1blmW/jc+bMyZt+uI31oI9XjaLIPKoqU1U+bcPf1JZWFXTaM1J10fUDaoOpqkqodHAb02IbMBuOMWPGAIiUpzhUiVMVDci9pmwjbKeqnulsDtFQ6vk8pmj+cWHWVfXj73EqeT67cypnVUVQZf3U3p7lZjqsX744FExLozqznLwHqOedqmYC9R5ZNvt7AMBjU+ZjfdO8bVrdXPPR/9Jl7HPges+jPqLrMNgW1DsLEMUT0ZkvtZ+mbbu2TW03tPnmfvkiJlO15ue8efOyykW78rh4Bro+hrCM9PySz79527Zts/LSNDRGgp4PPl/5vGUdeB/gbEFYd+6zbNkyTMYmmXNN727p67Uqc31YD+alzzoezz7I+oZ5avnzxcsw9ZuiHrgbY4wxxhhT1yRLCvTj3pBt3KmG842banJo78e3VPW8EOc/Wbfr2y1RzxShAhAXjVXf+FVtoMLRvn37rHqookZFIYxiqqvSqdDxHKmqVpkf+nz1jFNIgFx1Xs+dnnNVgHQ2g59UTEK1kfWgEsH6mQ0HlaaqPDGpvW2+PkZ1SNsCj42LYhq35iLOjjv8Tduntku1N9f1LVV5ngrrHDcLxXYatz6A54G/U90kVAHzlUf9tuvMgM4qar/TPs3zsHbprLz1Xh90HLxzuuxW2qsF+yLvjertLJ/6yucJ7c45q8PvRGdc4uJx6CxROAvN/z/99FMAkc90KtNxqnecRzHmTQWb/SKcceM2jT4al6a2e51pWLRoEQDg++/TM04dO3bMqWecZyadpYhb16XRXNUr0KxZs7LKEpZTZ0DCmQBTxxS4OBU1HLjX7GhjjDHGGGNMrVCUivsdd9wBABgwYACAXAUqXEXOt2+q1LS3pgJP1BNGnO9mfXPOp0RT6VLVQPfVN2hVwlSN4Gp3vmGH6iLT4D7qyzku76rUUz0+nM1QJVP3UXtFVdpVLeV+VCdVOQHiVR+2idNPPz1vfUz1occeqni8HnrdVUUm+TxdxPmU1si+SpynFCqO+Wzh1Scy4Sxc3AyCKtjqgz2fFyidXYjrwxp9Uj+pUOoagPAc60yc9iud1dD6qyrLMjGdjIeLTdL98MB26XP8v9nZswDrwrmn7AoAaLLznjVOqyExatQoANHsI9swn2u6TgqInnW8nzL2BZ8fW221FYBIWea6KG032t50JjRsX8yTbYjtmehMW774C0DURvmcrixuivaxuDVURFVyjZfCMjNv1ikso9ad+2raet/iOqHOnTsDiM4lrw1VdOYZ9tWFCxcCyH2WswxsI2eccUbOOTK1QyJZoDvIhrw41RhjjDGmWNmzUxNg9QxgdYWY2KQZunZKv/i8+vWqSo40DZWiHLirEsA3bLULBeLVASoV6qGBqLKXT/0N8w6J81OuflhVhePbtSoEM2bMyCo7jws9CFAloJpCm0Da5xH1hxtnjx+npof1jbP7V3/zGi2S8Bxzf36qN4BwdkQ9G+TzaW9qxmOPPQYgUvXiVGSi/VE9L4XXXT208Nqqpxf1b66KvLYZtVsPy6X25nGeoYiWQT1TadsLYZ9UVVtVS/WwpN4ltM+EZeY5i/PAo3nG2fiqf3ulUad0dM4e+3VLb7h/Ut79CuG4fmk1t+sxBwMA7ng1bQPd6PUpmX08WxYP2zkVdbYPtknarYfRPdlmuB5o6623BhB5NmGEUNpX8zvt0dXTmnpvyzc7xm2tW7cGkLsWTCMLx633qmodWGXeo6paS0biysC06aWGKnnY35kn02A/ZRoarZXPY55rHp++FvEzWeH1ZLl4X9LnbVw9Te1hd5DGGGOMMfWII7dtAWA55j3/LABg4vtfAQDKVqYH/5tVmKYBwOA9egMAWu6VfuF98tPZtVhSs7FSlAN3vsXOn5/2L0x/tfn8yqoNKZUKflKpjosQWkjkUEX3VVv2OE8uLKPacVNF10hvtHkDohkFHsu3ctq8M884tVHLFBfdtZC3euatvqrj0o4rC69zOJOivmzZBiqLHGiqB9UhqkihRxMgUpNUPVPPL/mUaR6jCpXOnPB3Va7V5zrzYrvIF81UPdPEeZuImwHT2TkS9gX1/c401BY/LiKqerBRVTO8p2iURV0noP7Z9TvRe6OeyzGfLUfjxo1x+IF7AAB+My99/7n7f4VHUj371zsCALY5/pcAgHu/ye6nOgtnsrnrrrsA5MYTifPJHvY1Xnc+N9jWaE/N5wefEV988QWAXG8zhG1Y10+F93Eey/7A8rDN6hoybbO67oT1ZLrcPyyjRpPVfq/fdZ0Jy8Tzo/cS5kW78zAN7d96v2J5OZvRs2dPANn3kerQokWLnHpqpFi2mVNPPXWd8zHrRqIkiUQBs/+JkpqNV4py4G6MMcYYUyyMGLQNgLWYevvdAIBb/vlh1QeVTgQA/OmatHi3z3GnAADGfr1wQxTRFAlFOXDXN36qXNyezwNDVTbQcfbaValy+fy46zZVGVUdphqhq9uZ13bbbZd1HN/qd9ttt5x6qieNOLVfVQaiMxOqUob1jIsQW+jsRVU+5NUeOKy7lqsqu2VTNY8//jiAyKZT22GcRyKdWVFPF/n6hnoWUlWMxM2kVOa3WvfRPqBp8nfO7LC9qZ2qqmzhTAR9ZdNTR7t27QDk2qPGlZF5crbj22+/BQBMmzYtp8wam0HX4+hMAfsKVUGdIdFrEM4kLF++HA8gbes77HfHAAD+NPhjAMDib2dm1aVF53SdN99+m8y2Jjv0BwD8Y+yneesf5nXbbbcBAM4666y8+zZEqCbrM0Q9HanP9RD+RuWd7ZZtVL3KxEUJZ1noY1yV3vCYyZMnAwC6deuWtW9l8U/C7WpXz3Tp15xlDeulHmxUkY6L5xC39mPq1PTM0k477QQg6j9ApMrzXsn+T2Wd5dVI5uuLsrKyHE82bAte71V3JAr0416Qr/dK8GjHGGOMMWYDsnDs8wAKVNqFD0e9DgDYZ6ft0xsab7veymWKj6IcuPPNnyvX+Xabz3Za3+zjvKjEfY+zwVPVLl+eqjjzjZh22Z999hkAYMqUtGeFgQMHAgB22GEHAJGSoKpEvjdq3abqGZU/5vnWW28BAHr16pWVJ+0ftV756qTnQstQ3fUBcf7uw3OrNs78dPS4mkMbTvUPrqpwVX0gLipi+Jval6pXFVXUtQ+oQp/PFlw9zag6T68RbPOqSGvkVY03kG+WR9V59dgSd/8hvKdRDWWsih9++CGzz8SJ6elz9ZmtHkdYFu5HBZ5eQ1ivuNgIYT3Kyspwzzcs5I5o3LgxTjq+LwDgwTc+zZ7BWxbNXqydOTFvPfMpw/aKkQuvFa8llV5dI6LrFYDcmRgey3ZO2+3Q9zsQXRsq6dxPZzuZjq6BAYAuXboAyI7uHaZRlVcz9SWvs9fbbBPN6uhsg66T0fsYifMOxf1ZB/aHfPVkO2e9eK6ohvNzfUf4XrFiRU4kZF6vcAbE1C7JZLKg8U511kzmoygH7sYYY4zZONg6lRajkEoPhrduyV/WIFEeDIxTQCpZApQDSDYCEsD3qc1rsaS1z15bps/Jmw+MX+c0npqWFqZ2HP8+AOCQ09Ii32Pvf13D0pn1iU1l8kAbSNqcqf9WVe3C/6vyYBJHnIcYVRXzqUWqhqhNPqOnzZ6ddvH0yiuvAAA++OADAMBee+0FILKbVRU9n7qoygttZMeOHQsg10aQZdAIdfkiwup3rbsqdnG+4Elc5Mq4dMJ6EbYBekawnWz1efbZtFsy2mvGRf0kqqzr2gslVKZVkVZVW9cuxMH94qKjhvuwXLSB7dOnD4Dc2aW4Nq+/k3z7adutaqaPVGWHy3sAENkNf/NNWgJ/7733AAAzZ6ZtzqnWUyHUWQvO5Klf+8p84ZNwtuXuF97LmXWIs12O+x5uZ91vvfVWAMDZZ5+Nhsqjjz4KIPKYpn7/4wjVY8606NoqxgXhvZ/tRSMGU4mnsk77bc7ecnYolUoBkbvxahO2AVXJ9X5BNTn0NKYKs3pm0qjGOmOoyjVnrNRDTpiPxpngjK96cUunObfqk7COhAqvevliGzryyCM3WP71hcsvvxxXXHFF1rZevXrh888/r6MSVU5RDdyNMcYYs5FRXvGyF7r3LKfQVGHWlqwQsDZJDyxTyYYx/CibOx0A8OB7M6vYs2qmj08vmO34q1k1Tstk07t3b7z00kuZ7+vi8MKKex7U5k5VLI3ECUQqgipdVSlCSpx3mXwKSJz/6HxeGwBg9913BxDZrnI1+8MPPwwgertP+4AFdt55ZwDZvmypljIN+uRVdY22gUyDsEy0g41T2sLtcaqiHlOV/3rdrnbL+WwL1bsCz4Xt+6qP+nmO87CkcQa4n0by5PUK7aOJ2p/GeV6qynuTem3I50eZ+1JpHzRoUNa+qrypb2xV+7QsYV5x0Uy1b7Dc6r1JbfQrmynk+WckTCqnH330EQDg00/THlyo/qkNMNM+bnDvrHT/8/YUKHqv0nsay6KqpkbG1XQqq59jMuR6I9I1E3Hrh8JZaF3DwGtBu3lGVKWqz0+i9uW8t7JsTK+mvvjXrl2bEzVc7zna91iGcF9tU7qd9znmoXb06pVF8wzjxLB9c9ZO16PxXDVp0gSYX52zUT3WrFkT6wM/PEemaho1apTxDrSxU7NhvzHGGGMaJJ3K56NLchESqRQSqVRaZa/4S5WtQapsDVDxl/newEgkSzKzDTVl1eLVWLV4NVJr0n9m/fHll1+iY8eO6N69O0444YSMm9TqkEgkkUgW8JdoQIq7McaYmqFKuzHGNGQGDBiA0tJS9OrVCzNnzsQVV1yBIUOGYNKkSevdI9D6oKgG7jrNHBe6OHRBVdWi1KoWRio6hVfZNKFOD+viPZ3i4qJbLjLj1ByPoxnMpEmTAAD7779/Jq3nn38+K08NXMGpO+ahZYgro+4X1on/a0AsPaaqoBtVXYvweuriYJ3udCCm6pNx3SdBvKpaSKkmJkTNPjiNHB6jU/9xAVqImmLogrF8iz/ZFmgiowvK9DMOlpUh4sMpc6L3Hl3wyXPAT71vsNw0M6I5D80a8u2r54omdzSHe/HFF7PKX9XUuZovhXnoYkFdTMzroW5aWTa9zpWZGDL/hrzQXINp0SyF5mzqgrey+x5NQNSMS92Axj37uB/bQNZ9v0L0TfHahR5kyrO3xanOy5cvz3muxgWUyvesiDPB1P6hi9XV9IewDLwv5jsv2r95brQflJSUINkqbU7ziw5pc85nZi7NW95CaNUlbfaXbLE5AGD58m8y5WUbUZfJpmoOPPDAzP8777wzBgwYgC5duuDf//43fvOb3xScjm3cjTHGrHee+CDtQu6w3boDAMaMSwsCcREXTxxasR6mYgFilns/ACkOojj9G04DV/x/+39fr3G5jTGmNth8883Rs2dPfPXVV9U6zgP3PMS9hfNtl2pV+KYZtzBS1W5V8qiuUeGgAsZPVZTCh15cYAfmQTdbzINloBLQtWtXAMAnn3ySlbYuDgyVQh6rAS9YBqap7ra0TKqmknyuNrmPKhlUKvipAWJUuSFxymc+5SDfAkHAinuh0AUkkLsgWQMMaQAmwr7A/eLaDNML8yKq/hFtUyyDunDTthT28x133BFA4QuWVc3jzBcXe86ZMyerDKFSx2BOdLPKhX7Mm9OtLCf7vs52cJE5PxmsLQznTjd8RM8N8zr66KMBAK+/nh40c9G7DtB1MW54HddXUCS2Ab13hddLtzXkRap6z+fie/Y5unrk7I+q50Cuq1W9h8cF9lPnCupmkKRSqYzivi5MT/4EixcvRklJ1J/VNSPRtpFvEbrOBukzQmcUw/tSCF07cn+dtQbigzrp4uFUKoV5HfuipKQE3fZOB3TCvz7Nm28htN89PZv2n4mz0Lx5c/zkJz/J9HedGWjI/aemLF26FFOnTsWvf/3rui5KXrw41RhjjDHGNEh+//vfY9y4cfj2228xfvx4HH744SgpKcFxxx1XrXSSJcmC/2pCUcqTfJPmG7O6ccqnEsXZrHNfqmlUwtQ2lYGL6P5Jg1OEeca5stK3c7WT435bbLFF1vE6O5BPyVQVTcvANOPc06kqExc4JqwDVQeqhjx3VAmpPlCZpPsxnjuqklVdmxCtu7o6M4URKtxU7bTNqJKraxryKnCID8wV7qP21GoDHRckhcep7Xc+22kGLYrrf9pnmNdbb70FAJlp0rh1LGGbo0rHgGdU3rfddlsA0X2D7VYV+QULFmSlqbbh7FNAdC+i8q6BpFRxGzp0KIDIfeSrr74KALjv1QkAov7Ifsw8T/l52mY+46O7wotFeVm2zJrjnzsR9Ntk9v0lzk1mWGdSlYve+owq7jrDy2vGfsAZmnBGS9OIWyMW58aX14x9j/eJQtdMkIxtO/t2xfelS5fmKNRE17SQQoIPxq1d0T7FcxbnqrSy2Sb2U44PdC2IXi8A6H7E3ul/1kFxP2qntGvXnwxKR0xtn2gfO5OyvmbJGhLTpk3Dcccdh/nz52PLLbfE4MGD8fbbb2dc6m5sFOXA3RhjjDHGmJry0EMPrZd0EskEElVEN+Z+NaGoBu76Jq1v41SlQiWMb8BUpVS9ZvhnDaBAdVjVRSprVDo05HFYLtp2xylJVE2Yt4ac5++0Z6Rap2oLEKlpVL15Dmj/pl4guJ2qST77ViB6m2cZw7pUdg6ASKnhsVT9qS5SHerYsSOA3Gujyn14DrRehXoIaejQtj30jKL24jq7okF24oIlMZ045T3cJ86rirYBVd66d++e9TvVZ6YbBiWrKoiY2sSOHTsWQNqfb1gW/k4VjW0vtHnVcrP/MRBaly5p+1a2dZ5rtmf2Jare7BtqnxueE4agZ/+iOqSedrg/17kcccQRAIAnn3wyKw/eI3P6UKpiRpBKu/jiTvEpUqHIU3lPn49sO+O4gE75tjXkvqwqMts12yDvtWwnbD9hv9J+G3dv1zx1Zo3tjM8Wstlmm6HdlpUMIURpTzSqaBcVC5aXL1+e6QequLPshajJccp6nOcdti/eA/n7e++9BwCZIDycLVOvLUB0TvjMJnw2d+rUKass5eXlWLzniUgmk7jujvRz7Q+nP5hTF4VK+8CrTgQAvF2Svo8kEY0veO3Zx9g2GnL/qStqa3GqbdyNMcYYY4wpAopKcc8XQh2I3jCpvoV+o2mDTpWMb/hU1Klm822Vtu60QVU7PvVwQsWj0zevpdP7ZGLmtyXfzwYANNo0/Wbcps8OAICmuwwGAMzeNP32TIWMb858s2/Xrl1WfaiY9ejRA0C2jTt9ONMulx4kmAYVC+ahnjbiVser15ZwlkM9hPDcqHcLlp+RyOiBg9eR14KKPPPmtaEKCUTXQ9XTfD6oTS68NnrtgFyb9rhZGPUiox5h1IY2n19wTUu3q0/iHXbYIeu7uuni9Q9VpjivCmqzzzS//jrtJlFtR+nRhfcS9V0eovXgef7mm2+y8u7cuXNWHuzLrDfVtHxeNPS88/6n9w2WW8vE7ccccwwA4JFHHgEQzYRlvNZUqOUJXkeuxVG/3DznlUSH1HU8+VR1Xd/QkPsy73lsc1R2ef+mKsx7pM52AvEzTjzPVPH1uare23h/1tmh5s2bA6lFWXlk+WrXtQ8l6efD3S9+kIk1oM829SKlnmHyec/hueKzXe8/PJbPp2+//RZA9Czhs5IzvDwvcZ6rgKiP8Jzw/PNccWZNZyc32WQTrD7sXCxevBgjX+wJIBozlK2oWBPWdvNMPpv2ScegGL90M2yyySbYBNH55/OVbYBtRL27mdrDirsxxhhjjDEmQ1Ep7vo2TjWLb7O0wcu32l3VQ7UF/+GHHwBEapWmwbd3Ve5bjRsNAHj1j2nV6slvFlZSg1cAABee+yEAYOvjjwUAzGrfB0CkODNvvs3Pnj07K5V89dNt/E4lQ+ul9smqzqgf7Xy+1GkjyHOiCjvTZp5UCr777rt0/cUun0pgnP/7cF+NUKl21iY/PLehvaaqW+r5g6jvf7Vpz+frP0w/3CfOo0WoTAHArrvuCiBSHj/66CMAUdvT2A1hvdhWeGzcTAD9tWuMA85KqbLOeod9jn2XeRHeo6jETZkyJStv9k+iUS412iuQO2Og14HrdgjtbvWcM68jjzwSAPDAAw8AAE7ed7f0gRXeZFJV+YPm9a5QWFPhNU0kZdf8UXbD8uWz629oqF262i/z2rHd8d4btn+2W/XcovdjwmvDe6p6GeL+Wb7jt5BZkVBxp3Jf4VnomU+mY+HChWjatGkmCjjVbc6g/fKXvwSQazuuM6rvvvtu5rd+/fpl7aP3Iebx3//+F0DuLAbXdvTu3TvrOD6neK7DWAo608t9qH5r/Be1N2/ZsiUW7XRQWuXvOCDWOw0/O7SI8mCf4fVhm9B+U1lUd7NhSCSShS1OTVhxN8YYY4wxpt5TVIr7KaecAgB44YUXAOT6sCWhEqaRNPkmrN4f1JOL+qDWt902378DAHjynH8BAF6eG3npqIo/jxwPADh/ZVrJ2OaCtM/n6Y3aZeVFX9C9evUCkBttkWpjuI1v2zyGaahf2Tjf6Txf6lc7HzyHTFMj0qnSw3PLFfk891QleG1U+QmvJ5UJqgxUU/idbcTkJ1/Eyqr8nMd5TFFFlNdJbeBD5YfXVtNkuagwcc0G06LvcV5/bZf5bOUZeZiKXFx96E1GbWRZT51ton0r18EAUV/Uc8g02U7Zhz/77DMAkVJK5ZR9J06BA3L9UfO7zqLRO87OO++cVUa1deZ1GzJkSFbZaeOuZLyDqLcQ3muT0T33vrETK37KtvXVMgPxaypuvvlmAMD555+ftzz1kbBtAbnnhsourx2vbfhMiPMqEheBXGEeOkvH7ytWrAC2SLfrREme2ZaSdLsY/8NSNG7cGO3atcvYfvN+zT7KtKnE8/mlqjG/h+vYVGnXGCVMk3nw91122QVANI7QtSPal8NxhsaNUE9VPHc6A6dp0hNUnDqe7/lLZV2vD8nXFkztkCgpQVKsF+L2qwlW3I0xxhhjjCkCikpxJ1wVTnWKb7G04w7RyIxqD8q3cNpb8+1VVTbat/G4lZPTdurVUdqVr/6XVsS6HFfhIaNru6w8CL3ITJ48OavM4X6qXvMYEqeqErWPUyW0Mn/LWh6eK9r1ah5q287jqKLw3OdThPgb7Xj1OprKUfvoEKpGGhGVfScu6iXbHK+NeoAIryN/4yfzpPLct29fAFHbYBTTOK9B+Ty7EB7zyivptSVU1ngMvRzFpal+3Om1ir+HPuNZ97hIj2pfzHsV72VU8VVhpz1xOHMY539b683+RI829MwTFykzc89Y8h3ykWhUcU+kHbMq7BV2m3e/8F7mGNZX21C+uAtx5WpI/qgvvfRSAMAhhxwCIH6GVNel5FNm447R/quxEvg7+yCVZvbzqp4lSuvWrXOUZ6ax4447AoiebVwDQq85VI3Z/nmf79+/f04+OtPHWWimyTJsv/32AKJ7jkYe1kjgvFeFfVDXA/E7zxWPVa9u3F8tASp75in6TNYIuTobwDZ11VVXVZm2qRm15VWmKAfuxhhjjNk4yJjIlEQv/W9NW5YxjzTGrD+KcuCuihg/6YdYfZSHv6kCpn6T+ZbKt3Oq+hrhbdnMbDV5Xfjvd2kFb+9F87PqQfRNWlf1U0kL68V91L5NzxVRW1pVXeM8jITb1BaYx9Jul79TyVAbYqZDu0dVikIbPl5HVXMrU15NRGWKDpW3MKpqeIxGIlQ1jKjins87CK8xlWbaodMu++OPPwYQH1FVbaSphoe2werxgW2HbZ5qMNuh3hu0fXMNRmXeTuJ8kKtdOc8NZ6fYl6l6sw9p1GQgd2ZD09Y8Vc0nGo2y9dIfsitDzweZ65utsD/45uTMrmwzqsbGzeDFlTnfb5Wts6lvxMVM0OePPq/ynU+93nEzF6oC6+yQ9m+dDYpjq622yhyrkbt1zRhnYelT/c033wQADB06NKsufC6H54n3I21bTEPz0LVYGlmVv3NGjWuyQl/5zJ9jDVXlNd6IHqfntKo+HNaP+zBvXTeka18asnem2saKuzHGGGM2XvhyV/H54dz0wJyLLo1pSCSSBbqDrKEYUZQDd0YdpP0Y3yz5Rkz/q0CkaNGeTdV59e3Mt3BV2qm2ZTwzrE+7y2R+bxfqL5vK2TvvpD3ahHbdLO+AAQMAxNvqx9mlqzJAxYAqeT6lVu0s1b++qv6q6PLca8RG7ke1kWoqED0QunRJR53lOVJf9yY/ldnEqoqtbUNnY1SxVW8nGnchPIZT6AMHDgQAjB+f9rTEeApU1qig68zYtGnTAOTas4Z257Q31eik+WbkwvKy/TKSotrjU7EP/aVrnAT2O7WTJ1z/MW/evKztVAVVkQv7uubB33gM+xHPsaYVp2CvaLNtOv15X2b/IIOze1/5CJttthkaN26cM9Ol9wJtC9pmQnv9uLbZkGzcVS0luo6E5yhffA0SZwefz7NU+J3H8V7LT71miUQCWJN936UXM6L28+qhRj0bsX/TRpy27/RGwz7JZwOQa6vOfsk82A+YB/OM847FerLfsE5aNyB3NpIRYYk+0/U4vT/os7+ydV5sE6yX3r/0fmzqD0U5cDfGGGNMHVPxMjezkRV2Y2wqUwm0nebbKN+MNaopECmxVLiolvHtVD3R8C2cv1OdUwWpwzadalyPE/fcKl3+9ukZhDj7NlU8qRzS9g5I2xOG++gbvb7Z6wr0OEVMV+qH6qmWT22aqXhSYVcViWlTZZ01axaA3MixnTpF55rbtFxsE6Zy9PqH24heJyo8cd5MdP/KbJR5nQYPHgwgisnANkJ1jO1ZPRTxd/ZjKtbq1SEsNyOjsvxU5pgWt7Ovs22xrdH7jNYnnOXhrBHvJyy/xk/QCJiqSDIdzhxoTIQw39CXNQBst912AHJ9gMd5a2GeGtH46adfy+xL9Y7nqlWrVmjevHmsah8XkVlVXvW5Hf4Wp042BG666SYA0QyUthu9/xGeo9AfuN7j42YuVA3X4/LNMAHp52yHDmmFnG2Sx+h6ELYf9oc4u2v1Z85nw/Tp07N+D9sf+zfPSZyXJUX9tvMcU+3XtTxhuhqVlnBmQG3cmVdcv9FZkXwxDbQfa2wEll/ryzZl6g9FOXA3xhhjTN3y7syVOe5jjWmoJJKJwhT3ZGGuVOMo6oG7eqag3Vv4Zky7NO5LRe6LL74AECnsfLtWTw38TqWQ6kOTHdO2uSfs8T8AwANvT692+Xc4YU8AwHet0lHimsa8dasXnUGDBgEAHnnkkUxa3KZKABUaVV00gqF6qtCV6tw/tKlUZaNX82xf6m98tzirHkxLbXOZDu3WqTbms4OlksHZFfUVbyrn6KOPBgD84x//yGzT66h2p6rsxHmhYNvR9Ng/gSg657PPPgsgutZUi3XWhf2N9pzaHqmeqz06kLvGguWeM2cOgGjtBOvBtKiaMQ+2U/XrHMJ9qAzyXqSRmJk366XrBZiHRnmkEh/+ryrdBx98ACC653Xv3h1AZKMc2v8DUd8ZN24cgCiaK9cLAFE/48wHr4vaz6pay3ppm4izJw5/i2tfDQmNvMkZGp5PXheSLz4D77O8ZnGexTRatq5xUbt0/s5Pquth2nEKM7fzucSZNk2L94xwfVO+9PJt43e2WZ5L5sF65vNQA0TnmPXNFzeF51nXl6gXJVW/daaE6P5qGRDWS2c+WT8ew7KF/djUL4p64G6MMcYYY0xdY68ylaDqAt/yadsZqsJU2LkvlQraTdM+jkqZrjznd8I37E8bpfPqe+7BAIAVf0qr349NmR9b7l90SCte/c//GQBg858fDgCYUaEy8K1blQDWgTa4VPHCt3luo82vHqMeMVQpiPO/rKvi86mNO7SoKEeqPOtzcOcKha9iAdMzE3/IKgPVRV4LXhv1mBAqhVRR7Ku2ZoTKj9phq+9o9T2u8QV0lodthf2RKjsAPPXUUwCiGSyqwzxWvTixL1A9p59nqsksK9tS2CeYRpyNL/v2brvtBiBqW1TvSeilKqxfZT6zqYprdGCddVLPO127ds3aTv/unIkI68xPnYVg3ry3MXIkPfGslPuNeo4KbeR5nbSN6H1V/XVrmdQWWGf8wv/V/r0heZUhXFfRs2dPALlqN8+ReuoK78/chzNIfBbERT5l39N+rGtcmCfbQKhEMw32V12XpfdrpsXZH7Y9eo5j2+RskNqdA7leVBghmPcOnkvm0bZt26wyME2tJ+vFcxu2Ye3HmobGLeB5iVtvQnQ9QfhcY9q6FoeKu46LWG9T/yjKgbsxxhhjjDEbC4lkCRLJkoL2qwlFOXBXe2u+pfJ76GGEKi7fmqmmUcVlWly93qtXhb25RKbTN2y+fX/Z60AAwE9Hpu1jd/74k0zeS6envb40bpl+Q26z204AgCa7/jR97OrNKvJIvylTLaHKoDbFoceMsN5ArtLON3m1lYuzYVfbdyoIqmSH35kHg270bSPTP1TgE9l58VwzD7W9pX0jlYVwBkVtALXcpjBCO0mqQXHKptpSa9sIbVyBSNHKtxaDv9FfOb1R0AuL2rSyH7L/Mk+2GW5XW2Ag3qaXqt7uu+8OIOoTH374YVYaLONBBx0EIGqHVLpC3+pUtz///POs3+L6kbZX7adU6qmmhWqfKqeZdTcVqibveawPt/M68R7B7bTt5zkMFxvq/YHHsjw8J/zU/qnrc5Rwu3ozIQ1RcTfGmDiKcuBujDHG1FdoIkXTKb5M8WWNL4Z8GYsLJgREL6J8CVbXwGoOqS48mbeaQ5EwGJIGMtQ8mAZfuAlfVPmyrKJOjx49AEQvyOHLHE3eaHbHY5g3X0wpGFE8YBkoFMUFP+K5DV+e+XKsprV6nfRlVM+1uknltVJXr0DuwldeT11MzHKyDZlaJFmSCahZ5X41wAP39cSUDumIpegwINNJM35lKzrvd4xmupYKcj2zy2aExZQVMmOMMcY0IJLJ9F8h+9WAohy4c7qWb7tUHThQDkOa8w1YF26oiycewzdp7s8pYCoInE7mGzEXvPB3IPftm1PzfBPmW3XcWznRhWu6QClcoEPFQt1tMQ2eG11kpm/+VB9YdgZ5yheKm+WJTJOy3UEqPLc816oWcTvLri7lgEglUfMMNSMylROayqhyowE9tA/ooi22CbZzmsj8+9//zto/3EfdlTJPtgE1xWD7pstQXVTN49k/gcjkTBfp7bLLLgCiNvPuu+8CiO4ne+yxB4Bc8w51nRqacNHUh59cREuFUBdzEu2XNCuiGQ/dR4YuNVkuDXLDQEpcyMdzy4X37KdUNfm7LjbOV2eeS7YJ9s24RYe8fhq0ShXHfKZ3qng2xJDt1157LYCoPfDaxrk4zecyUx0KqBmkmkHptdKARmq2xv3CZ59eX36yrcYt3lQTOK0X7xtUy8P7vwZIUgVa09Rnn97vtOz56qnPap3NiAt+peda669lyBegLM4RA5+jHF+wDZn6R1EO3I0xxhhjjNlYSJSUIJFHAMm3X00oyoE7VW7arvHtO5/7MKpofCOmUkRljy7g1OaOb8yqiDEPvn3Trm7SpEmZY/kG36dPHwCR2qYL0ELFDsh1kaUL2NT9Zfg2Hhd+XoPIqAs5flLV4uJAnjeW8dtvv806HgB23HFHZBOjuIvpDOvJc89roa7EeF1Dez/+r4q7AzFVjxNPPDHz/7333gsgV3EjGqZcFwazD/Tt2xcA8L//pYOSUeHmAlQgal8MCqT9L07VY/uk8kgFnq4a6T4uXJjOxZlsK7QXprtEuktjX+7Xr19WfVX5JfkWnLK/UO3iIneeGwZ8C89FiNod8zypQhdu432E/Yfngv2IC9bbtWsHIDrncW4k8y0CDRfgAtGMhs54qM21zk6owphvBo9pajC8hqi4E7ZzPuvURat+hueT51FdGqtiq4GX1IUw24kGRWNeoRKti5TVDbHeW3Q/5sGZXnWNrLOyYfloa8/vnCViu1d3lno+WEZ9/rIM4cyvPotZ7jilnfczdbWr10LvI+H1jLvmmhbbjKm/FOXA3RhjjDHGmI0GL06Nh2/SfCunypYvTDD31YAvVIho70lFLE5dI/o734ip5gGRWkZlTxUPfQuPC4ihNnj6ez4Xa6qiaaCXOBs6VRF1lkAV0rAefPP/AWk73K2TFeHZK5T2176NXPSFefLcUzHgtdH1A6EqoS4yuY/DO6872sZVaVM7VZ57Bs5iwJNXX30VQBQ0hqpYaJfLIEBUgTU8uaplzIsBxjQAmNrAhm2F9uZfffVV1rHs+7RD33///QHkqn9q66vnKVQPaYtOlZ8q5uDBgwEAAwcOBBDNRmhwKO3LoVvLsGxhnXVmSt1z0raXKqXWR+uhLhzDOus50HuTqpjqiYRlyhcoSOvF8sSl3ZDg+oRtt90WQO66KF1jEMLrznaiNtJsYzr7wU/ObrFtxtnXh+58eb1ZrriAf3HuQZk3n5lsRwxIpGtjwrRZH870xc1CE107xk+2zXC9DJDd/3VNldq4636cDVCVXGc3mI66uw330bUp2m/YZkz9pSgH7sYYY4wxxmw0JJMFKu4N0KsM1Tm+GdOWk15L8gUQ4ds0vVJQ8aPXB6qHtEGlwqxv0FR/+Aad762eqgKVd/pTVeWc5VS1m2VlPVmvuLKE6D5UAlkWfVtXLxB8e2cdOFNBJSBU45g/3/RZzh8apZX3efPT14XnhjMkPNecDVD1ldckn8cE5q9hnsOZAFM9aO/+0EMPAcj1dKAzWd27dwcAdOvWDQDw8ssvA4h8LatiyusLRGoQP5km92HboOLE3/mdfYNKVvv27bPyDG2y2XbZ1nnMJ5+kg6RRpSeqRBP1RkHCdRVvvfUWgFybbubJvsHycs2I3j/0HqDh5YFICWS9dLaJabB+VC+5H1U8XbejSn6++qinEh6rtro6S5NvNjRMN/xfPX/9+c9/RkPlT3/6E4BoNkvXI+h1CZ99uh5BgxDq80Ptr4k+r+K80QC5tupsP+pBTIO5sfy8r/N+zjbLNSzsc6wDEKnW3IfH8J7BZ1+cFzfta5xp0FmDsP+rjbueG6JrP+LOOdcw8Lzx2oX76/NWvejwO9uMqb8U5cDdGGOMMcaYjYVEMolEAWp6IftURlEO3KmG8y2XSgJt3EIFQFehz5o1C0BkX80V2HxbpQ0uiQvvrpHN8nl9YLmoAOibvfrB1lkB2urx7Zt2fqrUh9uoSFPZo9JHtfvLL7/MOh8sN8+T2iiqN55QWVP1jOqKrrAnrB+vH/ej/TIj26ktcmjnpz6F1e+3WXeOPfZYAMDDDz8MILoObAu0s6UiNXbsWACRj3FeC1WjQqWKyjqv18477wwg8vDCT/YBKmu83urvmG1J13KE29RunnkzD9ZPPaWoosh0WKbx48dn8lJf6Ozj7HfaH6koch2MRlyM8+8M5KrX/FR7dPU+EdoFh/XR/fPZH+tsgyrq/FQf2LomheQrk/oNj/NX3RDhDBWfW+rtR22kgag/cl+2RbXl5vVWm26didHnDr+HqrD2g9D+HYgUdT2WfZXb+ZzWdNjf86HPXVXv1eONziiybzIvnQ0L6xl3LkhcDAjmxXPKMvHa8P6o1y48Vtd+MG3btjccinLgbowxxhhjzEZDokCvMokG6FVGvV5QKaCCG9qDqjrFY2j3xjfcr7/+Ous734ipCKmda5y/9BAqk2qvyzLxDZmqvypmVOmoPlAxZJkuv/zyTF7vvPNO1j78ZBqffvppVh6sD1UG2harbWKc/+XwN6JKmUbaDG2dw++8Fiwzr596+QAi9UTzzhf10awbxxxzTN7tL730EgDg448/BhC1BfXowmvBNhTOTtHunEqzrnvQ2Sn1hMK+wralSnu+NRhs0+xvVO34GRfVM25NCSOThmsvVC3W9RqcLbv00kuz0mRkzF/96leojNDOW2Mz6AyHzhyoiq++wNWzVL4onERnHHm+dcaA1yPOkw0JtzMNnRkxwMSJEwFE/UQjkepsZwhnoteuXYvBbRMANsHaBXOAZkCqrOI6tdgcwBKMX9w8c+3Yn+PaCfMMn7e8nkyDtttsq+y3LJP6N2eePI5rzugZKt96L7WPZx58vqhHG+bJNPicZn34vObMmnpaA3LXmei9QmfK+F3jp3C7evpRm3cgd6aAabNfs42YOqSW3EHWzNDGGGOMMcYYUysUpeJO1O5V39aBXHs+7kPFj54xNCIjbcyIvu2qwhaiypWqT0yb9opUlqgEHH/88VnpUTnYZZdd8pyFNAMGDIj9LUzzuuuuy1sG9UOr6l0+7xFqQ6uRXwnzopLGc83tVFV4PJWPfFHyVNVVjyFmw7HvvvsCAG6++WYAubMzOhulyi4QXT+2O6r3RO1s2QbYptgWuJ/ayoa2plQluYaC6r7GD2D/Y320b/MewlkterYI26XW/ZJLLkEhVKW0kwsvvDDz/0033QQg6pM8/yyP3rs0XoTaFVdm2672tOrzO24dC9EoqLouJp/PeG67/vrrc8rTUOGMy/333w8gWv+ka5LC9p8/dkd+/+mkrKws0290jQvbCftevui32k7Y33nP19khjSKukWI5Y1xIFF2q8ToLxzTVjp6zt3z2sYzqaS1fZGGmxXOhM8DMW73JxPnC17ECP8PryeugM1KczWvI3pc2Frw41RhjjDHrxNbfjAMANHp+bGbbex+nnS8sm5Me/CVL0gOItjulzcb2OHAPAEDTPQ4E1s7F9JL4xaDGmLqhKAfufNvlWyrtZvN5lVEVR9+iqRAxyqK+dcdFeGMZmF4+VZFoZDNVJFn+c845p9J6rw/+8Ic/AIiUG/U/q36BdUYhrKcqfrqdUPGkisJzrF524qLmhaqeRvVTNcVseHi91BuJruFQjxJAbruiT3jOgPEYfqfipnaqqnDl8xNO5ZlrRJg3veDEeX5QD1LczuinJPTjTrt3HrMh+f3vfw8AuPHGGwHER0jVGQM9h+p1R2fOwt90H37y/qf29nG2v5puiM4ImFwYg4CzsHqu4s73ulJWVpajuPPey1lOfgeifsg2prOsvLfrs5vfGZOF+7E+/E5VPR8aQZVp8hnBtTjMk/XSmUONKMs6hfXkvtwW51tdxxF8pumsgK7nYjr51oZo2mwTZiOglmzci3LgbowxxpiI3puuAMpXYNELjwEAnr38aQDAC3OWVXZYmglpc8UTv14IAOjbtMJkbZdD13s5jTE1oygH7moPphEaQzs49VDCN11dmc23b9q96Vstv8flHdp2qh0f0bdq/q42qbUB81RFLe486awBkOv/Wm0IuV295ah9o9q2Mw+mEyq33EYPAtn2m6Y2UCWX/Y1tSqOchrbgqsixLVB518jFqu6rLTu/sx2Eqtjnn38OIDfKLhW2OD/hbH8aNVj3D/Ni1FhGuKwNLrjgAgDAqFGjAMR72onz466RGEmo8vFax933NBq0qrO6/khnG8OZMqZ92WWXVV35BgrtmO+77z4AUbTQVatWAZvGHrZe0Mi6vNbhLJfe87XPqJc2th8q6VTcOZvVtm1bAFG74UxcPlgu5s2o4URt4FkW7Re6jop1CvuFxjmJe/7o2hd+6rMu7ryFMyq8n/I3ziTatn0jIpksUHG3jbsxxhjTINmzxTIAyzDj3scBANdc9dI6pzXmzbQN/DYHpd0vIt4fgjGmjijKgTtt1qh40Q8431pDzxSqJFMdVF+0uj9/V5tO9bai+wG5UVXVllTV+7qw6dQyaHQ8jTKntobh/6qw81idWdAZCPVBTCWB6VEhCRUR2kzymrN8tEs0tQfVJl53Ktv8zt/VUwwQqUe81uwz6veZ15dqfpy/fq6joK05AHz33XdZx+gaCqLRD9Xzg6pp6jECiPr/TjvtlLd8G5IzzjgDAHDllVcCiM43bfn5qWsRdMaLn+Hsofq0V9tbVdgJrxv7KT81Psa55567DjU27733HoBobdaGIuxv+qzQWZTwf20PhNv1uanrvRhFm/eUnj17Aqh8dprlmTp1KoCofasXqbgyxJU1X+wWnYnWe4SOLzQNXXeiSrzONALRPZL7sg0MGzYsb/lN7ZMoKUGigJgyhexTGUU5cDfGGGMMMOfpJwHUTGlXUnncHBtjNg6KcuA+efJkAMDuu+8OIHprpaoTKmZ8Q+fbtvpHVfs2VdhVmda3dX2jBnIjMBK1x+X3uEiVGxLm+fTT6QVMqpbrp66KD39T5UJVOl0Zz3PFc89ogJwNYbo8LlyzwGusSgXbxOGHH17gGTDril7XOF/GbCv0Ix4ey9kU7Wdqw67++nk8beGpzDFCaWhvq/ai9CqhMzz8rkq72oizrWkU5vBcaBq1SZxt+MiRIwFEaqb6q2c/zOcLP24dgKJqPWfAeJ14zpg3vVuZdePWW28FAFx99dXotQHzyTfDpSpzvjVlvM48nu1CZ7tUuebsENsPYy8w3gO9TLEvA5FdPG2+2U+5ToZpsl2zDOpNRqMBs8ysU3guOK6Is23nvlwzp9FaeU/hdtaXfVHXCYV5jR8/HkDUBsxGRDJZmP26bdyNMcaYhsk9JZ0xZMgQAC+stzRbdk0PkBfG/N5u5XS02zIJJJJA2zZ46Yt56y1vY4oWu4OM5+KLLwYAPPjggwAiJUkVbSDXblXf+OP8l8fZrsVFFA3VRv6vvqVVwdsYon2yDDyHLKMq8OpJAMhVQxU9h7p+gMoI09YV+vmup3r7ofcBtglTe7B9a1RAVdrDNRxUqrTt83pqGoRrG+gp4u233waQOyMUquDqU3mHHXYAELUvtkPOGKjPZZ0N4O866wZE/WVj6NOK2pH/6U9/ApAbOZKf+WI1aB8muhaBM2Lz588HEEV5NRsGRuhlNOP1TTKZzLkf57MJZx9iG2J/5b5UlONiCaiXKCrr/M72xBk2RgsFcvutRl1l2rp+i2VhWfmda1d4f6O3urC/67odfW5qlHR+qrcYjSTMPDl7EOZJ2/1CozKb+ktRDtyNMcYYE9HzL7/DNttsg/8dtu6B/H57TPrltlm/fQEAuuS/w6rpAIAERZmazfgbU69IJEuQKEBNL2SfyijqgTvtWunrVf2DA7keXjS6o9rW5fOAARS+Sh6Ij8CoyoC+bdcFaq+rHiZ4PlQZAXI97cSh0VepcNAnr3qsUU8/4XnSGQ+2AbPhoa00rwevo3oaodKu3mbCY3it2b5UcQvtZsPtVL9+/vOfAwDefffdrDzzzf4wbSpxqh5r+9V+qco9CddusD70eLUxc8UVVxS87y233AIgt0+eddZZ67VMxhhTU6677jo89thj+Pzzz9GsWTMMGjQIN9xwA3r1il8JUlpaipNPPjlrW5MmTWolCva6UtQDd2OMMaahc/755wMAbrvtNux46+8BAJPOLtxM6aid0gs5e51+PADghyad0i+m5eVIJBJouyptupGIWYxJd6wUxPgCTVNGEi62BHKFL3UF3KFDBwDRSzJfjMOXaJrnsAxclMo0VBRgGiooUayiuRfNR2keGprZMq84JxaaNuunAag0OJq6V/3iiy8yafAam3jGjRuHM888E/369UNZWRkuvvhi7Lfffvjss89iRVkg7Vp8ypQpme9ViZGxJApcnJrw4lRjjDHGGNOAee6557K+l5aWom3btvjggw/w05/+NPa4RCKRWRNRDBT1wJ1voC+//DKA6K03NI/hGz6nvzVsMN+QeQxdE/ItXt+8OIXPxTIashmI3q7V7SO38/uvf/3r6lZ5vcMyPP/88wByQ8ur+8zQ7EED7tAUgftq0BZOPXFhEc8l9+PCPg3dHqoXaq5gFaL20IVXbBtcMNqxY0cA0fWkKVToUpBqGK+jLhTTIFxsIxr0hW1kjz32AAC8+eabWWUConZD1S5OHVPTGA2UpvXPZ47Dbbwv1BfOO++8ui6CqQahCdPpBSju+7VNq5ADrzoRAPB9pwHpvrh0aZYKnChL988UzT3ZB5LpPsHnH/sU+zNVTnWfyH7Nez7vA3SDqM4kmA7NYnfcccdMHSZNmgQg1wxPXbMyL/Z3dRUd1++ZTviM572A9VTTPg2wpM+0OPexHIfwd5uk1QydPYlj6dKl6NKlC8rLy9G3b19ce+216N27d7Xzqy0bdy8tMcYYY4wx9Yby8nKce+652HPPPbNe9JRevXph9OjRePLJJzFmzBiUl5dj0KBBmDZtWi2WtnoUteJOPv30UwBRuPEw4AtRxU5t8ajGURXm27cGaOIbNNVEphsuZKBqoCGKmQeP3Zhgmbj4j2XmuWQ9Q3d3qpiz3lQwVH3hOdIFiLwmVEr0uBD+xmv+s5/9bB1qa9YFDU/O68kFwlSPNJAPF36Hv/FaaxuIcy1KqJZRuWKZGJCFAX/Cfbfbbru89dAyxQVT0UXlJFywyXpQ4TGmrhl075UAgPHD8wfmAoD9/nI0AGD6Dgeln1crVmTu+StXrkSf1muBNUBqTbainKpQhKeupt1w9sw2+wxtwVu2bAkg13ED7wPqapL7qetWukkMF4HzPsS8tB+ra0aq2RokSoMvqkIfPo/4vy7EZ950f8l6qc27up9mHbjfxjxoLBbOPPNMTJo0CW+88Ual+w0cOBADBw7MfB80aBC233573Hnnnbjqqquql2kyWaAfd9u4G2OMMcYYg7POOgtPP/00XnvtNWy11VbVOnaTTTZBnz598NVXX22g0tWcejFw/93vfgcAGD16NACgS5cumd/UHpdv0XzTVXeHurJcbe4UvnmHapzmwbdu2uAde+yx1a7jhoZleuyxxwBE50Xtz0N7YNY97txQjdCQ0WrXrHaCPOf5bNy/++47ANE1N7XHb3/7WwBRqG29vpy1oa272sQD0TWNs10nak+u3hp0jUrompHQJpVqvKpeqtqzbXO/OHeRJJyNY3AU26SajYUPP/wQAHDSz7pmtm3WLn1f77Lf7gCAeYOHAwA2QdTu16xZgz6t1gAoQ/nyZVlpJhpVzDJVeMWYN29e1iwZlWP2LaraGvhQ13+pgs3Zaj4LuPaM6c+bF0VqZf/mPkx77ty5WXmrd5iq3A+zTFzLFT779H6lXmZ4z2Daceu2NAgU681rN2zYMJjCSaVSOPvss/H4449j7Nix6NatW7XTWLt2LT755BMcdNBB1S9AskCvMlbcjTHGGGNMQ+bMM8/Ev/71Lzz55JNo0aJFxrSqVatWmRe1YcOGoVOnTrjuuusAAFdeeSX22GMP9OjRAwsXLsSNN96I7777Dqeeemq180+UlEQLt6vYrybUq4H7KaecAiAKGgJEq4n5Bkw7Nw3vTdWAb7z85Fs2bb+p7PGT6eqq8hCmMX369HWsWe3BMvJNNc6rTvibnhOqCVRgqaLE2RRSjaCaws5GNTX0BWwvFxsPvJ4666S+iENFjm1B/RlzH7Yh9hluV+VdPTXp/kDUZ9WTRZzyrh6ViPaBfOr+xjytahomDJjGzz59+mD3LdJte8KqVulZ1RUrMmtRmjVrhk4rfgA2A8orFOBUecX6jkYVgfdov1uhuK9atSrrmcB7OG3adX0Tn7vab1Xd1hlx3kvoISpcJ8ZtTJv14T7an3nv0fU0LKPOBNNePZxZVn/zqqiz/iw3t7O+PGdU2pnXJ598AiC6ZqZ6jBo1CgCw1157ZW2/5557cNJJJwEAvv/++6xZ4AULFmDEiBGYNWsWWrdujd122w3jx4/HDjvsUFvFrjb1auBujDHGGGMaHnEOBkLGjh2b9f2WW27JEntrRLKkwMWpVtxzCFXZ66+/HkCkvvGtmW/IVM/4RkxFUH2PczuP56fuB+R6oVBPGhszuspfV8vn25fnQs+hrpTnd856cH9VNKm60EPIRRddVLNKmfXK2WefDSCydaeKRIWra9euWdvz2YirrbrambL98ViNNMh2ybUoqqoBQI8ePbLyCm14wzS0TOoJQmeU2N6//PLLzLG2bTcbK+eeey4A4MEHH8R/ZgBbb701gOh5tWrVKuzUZDGwGkitzg7zTqU9Y9te0Qe+WdsKS5Ysweabb57lbYUKOftOGFMFyJ2V47NA+7d6LGPfo817+CzlNp2tUz/tPIbbmZeq/epxjvFJwvsFy6+Ku84csl6sD/PgPUZjm/BaGVMZ9XLgbowxxhhjTK1hxX39QLX23nvvBRC9bauHE77Zq39VbuebMY9TG75QAVDvFHyDX5fFDrUNy/jggw8CiNQKnpewntzGc8F6qy989Y9blS00v1tp37ih8k6uvvpqAJGXGbaV0AMDrz3bCvuZRjVVP87qjYHqPtdksB+Gdqtc38L+p54e1NZdy6KzTDyOqlmouBuzsfPee+8BiPeAAgCoUNYzSjvXllQMNOY02wrJZBItED1LQxv3uKjEcbNdqljz3sFPpq228WGZdR0M7cap/lOR1zgjvC9pbAi1V1fVP0yDeeoMon7nPShOgee1Oe6442BMVdT7gbsxxhhjjDEbkkQyiUQBrh4L2acyGszAffjwtL/a559/HkBuhDa+das6rKo535SpFFBtDiOKEm7LFwF0Y4dl5nlRO8JwG1UHqqDq4zbOT66qqtzOa2WKi0suuQQA8Oc//xkA0LdvXwDZKnic/3VV4HUNyZw5cwBE/pupqlEN435UwkI0Uiq/Mw32aSp06ulG16a8/fbbAIBzzjkn32kwZqPk5ptvBgBce+21AIAhQ4ZEPzatiNtR8fXrspbpZ8AaWe+0fH5Gadc1TkDUf7nOicdqHBXOyrZq1QpA1G/5PGUf1LUu+WbDdOaA/ZbKOdPUew3Xx6jveVXeWd9Q5Wf+vIdofZlXnAcb1u+jjz4CEF0bYwqhwQzcjTHGGGOM2SAkCrRxT9jGvVp88cUXAJDx0amKO9HtVATUb3tlCgCPpf/QYoJlfuSRRwDkrydVefV5r36zNUIl4X785LXZf//912NNTG1z4YUXAkAmwEUYcnrLLbcEEM3WECpUVL++/vprAJGixf6nijqVLrY1pg/krplQTw9UCidMmAAg8jy17bbbZh3PCIzvv/8+AHt+MMXNxRdfDAC4++67AQC9e/dGOnZqxKpVqzLqOO/v7EfcTiWbn0D03KTvc35qpFSq9UyTdvcab0WPU7v0cJumrTbqLBvtyqm4s37qYU49XoXPL60fn4XMQ2fpdFaZzzpeC2OqQ4MbuBtjjDEm4r3ZazKmYvnctxpjCiCRyAQnq3K/mmSTKsRjfT2G3mZ0pb3ap9OXK29uRFXk8NiDDz54/Re4jnj66acB5CqlQK53Dqqk8+fPBxDZ+fFY7r9w4UIAtmlvSFx55ZUAojbBTxIXkZCDCV1rwnUVbHO0qweA7t27A8htn+rxgYo6oxbydyptnAWwOmbqI//6178ARPEX2AfZ7nX9ltqO03sTECnLVKLVGxthf+WsV+vWrbPS1hlvjadC23AgHREWyI2Krko5n+W8ZzBNfabrjBzrGb7QMJq3Ku6EzzqmwfvVt99+CwA4/vjjYeoPixcvRqtWrbBgwqto2SJ3jJSz/5KlaL3r3li0aFHWjFWh1GxpqzHGGGOMMaZWaPCKe3W58cYbAUSKoCqBQP22gR05cmTmf9rxsQnRdvCCCy6o9XKZ4oQKPNsS1TuqYGxbtF9Vu1RVuvbbb7/M/1TcdC0FYd+lxxraujt+gGmIjBo1CgDQs2dPALmxTNhH9XvoaUwjh8bFYVAbcR5HpVpVcPZ3quTsqwCw6667AojUbbUvp7rPmQMq6mqjr2vTNPJ56C2N21gu1lO/Mw3atJ9xxhkw9Q8q7j9+PK5gxX2LXYZacTfGGGOMMaY+48Wp1aShq8n1eTbB1B1U5Ki8UdFSFUwjqxKqbKHXGfUmwWPjIi1aaTcNGarBl156KYDI8xrXiqgnGPafUIlmP1U7c+3XXFPG37neiZ/cX+M58PdQ5ee2tm3bZtWH6rweo+vVuF29yrAu6lUHiGzxeQzLx3LTK9Znn30GALjqqqtgGgCJZIGLU2ummVtxN8YYY4wxpgiw4m6MqTPUjpTeF1TB4nb148zj6IM9VMXU45Mqa8yDXmWMMZE6fP755wMA2rRpAyA3Gij7YrjORGN60FsMj9W4C9xOBV7ty5keP7keJZxZ4zauO9Po54zOql5muCaLadErDe8p9D7DvEPbefWGxXLTZv+9994D4IioDY5EojBXjzV0B2nF3RhjjDHGmCJgo1Pcp0+fjvPOOw8vvPACysvLsffee+OWW27J2NkZYyKKvb/Qnvb6668HEClyVLeo5tFelSo5bV/5SVUwVNnVdzQ9PXAftas1xhhj1pWSzjuhpAAvMSUVMzPrykY1cF+6dCn23jvtlP7iiy/GJptsgltuuQVDhw7FhAkTMotKjDHuL8aYDQfNPH77298CAIYOHQoA6NKlS9Z+NHsBIvMZDWTIhaA0Q5k1axaA+CBHND3hS/Xs2bMBACeeeGJseR966CEAkdkczW/UHE+DQ3Xs2DErTy5Wp2jA7eGCeG4j3333HQBg3LhxAIC///3vseU0pqZsVAP3v//97/jyyy/x7rvvol+/fgCAAw88EDvuuCP+8pe/4Nprr63jEhqz8VCf+gs9ulx33XUAcv2z80HJAQGjPHJmQfcHclV6tXn//vvvs/I2xhhjNnaqFYDp1VdfxT777IPHHnsMhx9+eNZv//rXv3DCCSdg/PjxGDhw4DoVpn///gCAd999N2v7/vvvj6lTp+Krr75ap3SNqQtWrFiRCcf90UcfZcw/fvzxR/Tu3RvdunXD66+/nmPSUSj1sb9w4K6D7EIH7uEsgyplPJaL1BjEpTIVzxiTDc3bdt55ZwDICiDToUMHANGCT/Y1KvEcbuhic26nGj5v3jwA0cLQ6vTRMWPGAIjM7WhGp6o+77ssq27n/YNlnTlzZiYPlnPixIkA7O6xocMATIUGVKru/kq1Fqfutdde2HrrrfHAAw/k/PbAAw9gm222wcCBA7Fq1SrMmzevoD9SXl6OiRMnYvfdd89Ju3///pg6dWpmFbgxxUCzZs1w77334quvvsIf//jHzPYzzzwTixYtQmlpKUpKStxfjDHGGFMQ1TKVSSQSOPHEE3HzzTdj0aJFGTdLc+fOxQsvvJAZnDz44IM4+eSTC0qTb9o//vgjVq1alXljD+G2GTNmoFevXtUpsjF1yoABA3DhhRfihhtuwOGHH47Zs2fjoYcewsiRIzOhxd1fIv7whz9kfb/66qsB5CrwrKMGaAkDs3CbupbkC02ooBljCkPV5SuvvDLz//777w8g6oeqrGvwM7U/537soyeddFK1y0d1vrS0FEDkkpJ5sWy8p/D+oGXkvZaq/zvvvJPJ47LLLgMAHHXUUdUunzE1pdo27sOGDcN1112HRx55BL/5zW8AAA8//DDKysoyHWb//ffHiy++WK102TnUPyoQPZy5jzHFxOWXX46nn34aw4cPx9KlSzF06FD87ne/y/zu/mKMMcaYQqj2wH277bZDv3798MADD2QG7g888AD22GMP9OjRA0BaDcunBFaGun8L4SKzMACCMcVC48aNMXr0aPTr1w9NmzbFPffck1F/APeXyrjkkkuyvnPB7WabbQYgUsV4PkMPF1TxqKxRaZs8eTIA4IILLthQxTamwUD1GQBOP/10AMCOO+4IAJlZRdrx0uadsP/SDPDrr78GEHmyqQlU6+nhhethaPOekCA4GkTpiy++AABMmjQJAHDHHXfUuEzGrA/WyavMsGHDcM4552DatGlYtWoV3n77bdx2222Z31esWIFFixYVlFb79u0BAFtssQWaNGmSd/qa2+i2yZhi4/nnnweQHlR/+eWX6NatW+Y39xdjjDHGFEK1vMqQefPmoWPHjrjmmmuwYsUKXH311ZgxY0bmTba0tLTaNrsA0K9fPyQSiRwvGfvttx+mTp2KqVOnVreoxtQ5EydORL9+/XDCCSdgwoQJmDdvHj755JPMGhH3l8L585//DAA44IADAOSGXQ9Nh6i403Ro2rRpANIuM40xtccZZ5wBIOqLVLvZf//617/WWlnOOeccALm27JypHDVqVK2VxdQPaturzDop7m3atMGBBx6IMWPGYOXKlTjggAMyg3Zg3Wx2AeBXv/oVLrroIrz//vsZbxlTpkzBK6+8gt///vfrUlRj6pQ1a9bgpJNOQseOHfHXv/4V33zzDfr164fzzjsPo0ePBuD+YowxxpjCWCfFHQAeffRR/OpXvwKQXpx69NFH17gwS5YsQZ8+fbBkyRL8/ve/xyabbIKbb74Za9euxYQJE7DlllvWOA9japM//elPuOqqq/Dyyy9j7733BgBcc801uOSSS/DMM8/goIMOWue0G2J/oTK33377AYgW4PI2FtrQ0lvE8uXLAUT+7s8999xaKasxxpj6z0btxz3kkEMOQevWrdGqVSv88pe/XNdksmjRogXGjh2Ln/70p7j66qtx6aWXYpdddsG4cePq5SDE1G8+/PBDXHvttTjrrLMyg3YgHamzX79+GDFiRCak97rg/mKMMcY0LNZZcS8rK0PHjh1xyCGH4O67717f5TLGmFg+++wzALledUI/7rRxp60/ZwiNMcaY9UXRKO5PPPEE5s6di2HDhq1rEsYYY4wxxpgCqfbi1HfeeQcTJ07EVVddhT59+mDo0KEbolzGGBPLDjvsAAC48MILs7aHE4j0WHHzzTfXXsGMMcaYDUi1FfdRo0bhjDPOQNu2bXHfffdtiDIZY4wxxhhjhHW2cTfGGGOMMaYhUzQ27sYYY4wxxpjawwN3Y4wxxhhjigAP3I0xxhhjjCkCPHA3xhhjjDGmCPDA3RhjjDHGmCLAA3djjDFmI6O8vBx33HEHdt11V2y22WZo164dDjzwQIwfP76ui2aMqUM8cDfGGGM2Mi644AKcccYZ2GmnnXDzzTfj//2//4cvvvgCQ4cOxbvvvlvXxTPG1BHVjpxqjDHGmA1HWVkZRo0ahV/96le4//77M9uPOuoodO/eHQ888AD69+9fhyU0xtQVVtyNMcaYSvj222+RSCRi/9Y3a9aswYoVK9CuXbus7W3btkUymUSzZs3We57GmOLAirsxxhhTCVtuuWWW8g2kB9fnnXceGjduDABYvnw5li9fXmVaJSUlaN26daX7NGvWDAMGDEBpaSkGDhyIIUOGYOHChbjqqqvQunVrnHbaaeteGWNMUeOBuzHGGFMJm266KU488cSsbWeeeSaWLl2KF198EQDw5z//GVdccUWVaXXp0gXffvttlfuNGTMGxxxzTFa+3bt3x5tvvonu3btXrwLGmHqDB+7GGGNMNbjvvvvw97//HX/5y1+w9957AwCGDRuGwYMHV3lsoWYuLVq0QO/evTFw4ED87Gc/w6xZs3D99dfjsMMOw+uvv442bdrUqA7GmOIkkUqlUnVdCGOMMaYYmDBhAgYNGoTDDjsM//rXv2qU1qJFi7BixYrM98aNG2OLLbZAWVkZ+vTpg7322gu33npr5vcvv/wSvXv3xnnnnYcbbrihRnkbY9YPixcvRqtWrbBo0SK0bNlyve+veHGqMcYYUwALFizAkUceiZ49e+Kuu+7K+m3p0qWYNWtWlX9z587NHHPOOeegQ4cOmb8jjjgCAPDaa69h0qRJ+OUvf5mVx7bbbovtt98eb7755oavrDENiNtvvx1du3ZF06ZNMWDAgI3a5apNZYwxxpgqKC8vxwknnICFCxfipZdeQvPmzbN+v+mmm6pt437hhRdm2bBz0ers2bMBAGvXrs05fs2aNSgrK1vXahhjhIcffhjnn38+7rjjDgwYMAAjR47E/vvvjylTpqBt27Z1XbwcPHA3xhhjquCKK67A888/j//973/o1q1bzu/rYuO+ww47YIcddsjZp2fPngCAhx56CAcccEBm+4cffogpU6bYq4wx65Gbb74ZI0aMwMknnwwAuOOOO/DMM89g9OjRuOiii+q4dLnYxt0YY4yphE8++QS77LILfvrTn+LUU0/N+V09zqwP9ttvP7z44os4/PDDsd9++2HmzJm49dZbsXr1anzwwQfo1avXes/TmIbG6tWr0bx5czzyyCM47LDDMtuHDx+OhQsX4sknn6wyjdq2cbfibowxxlTC/PnzkUqlMG7cOIwbNy7n9w0xcH/yySdx00034aGHHsJzzz2Hxo0bY8iQIbjqqqs8aDdmPTFv3jysXbs2J9hZu3bt8Pnnn1crrcWLF6/X/eLwwN0YY4yphL322gu1PTndrFkzXHrppbj00ktrNV9jTPVo3Lgx2rdvj6233rrgY9q3b58J3lZdPHA3xhhjjDENjjZt2qCkpCSzIJzMnj0b7du3LyiNpk2b4ptvvsHq1asLzrdx48Zo2rRptcpKPHA3xhhjjDENjsaNG2O33XbDyy+/nLFxLy8vx8svv4yzzjqr4HSaNm26zgPx6uKBuzHGGGOMaZCcf/75GD58OHbffXf0798fI0eOxLJlyzJeZjY2PHA3xhhjjDENkmOOOQZz587FZZddhlmzZmHXXXfFc889l7NgdWPB7iCNMcYYY4wpApJ1XQBjjDHGGGNM1XjgbowxxhhjTBHggbsxxhhjjDFFgAfuxhhjjDHGFAEeuBtjjDHGGFMEeOBujDHGGGNMEeCBuzHGGGOMMUWAB+7GGGOMMcYUAR64G2OMMcYYUwR44G6MMcYYY0wR4IG7McYYY4wxRYAH7sYYY4wxxhQBHrgbY4wxxhhTBHjgbowxxhhjTBHggbsxxhhjjDFFgAfuxhhjjDHGFAEeuBtjjDHGGFME/H/06TJSlRbOegAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from nimare.meta.cbmr import CBMRInference\n", "from nimare.correct import FWECorrector\n", "\n", "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "t_con_groups = inference.create_contrast(\n", - " [\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\"], type=\"groups\"\n", + " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], type=\"groups\"\n", ")\n", "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", "\n", - "# generate chi-square maps for each group\n", + "# generate z-score maps for group-wise spatial homogeneity test\n", "plot_stat_map(\n", - " results.get_map(\"schizophrenia_Yes_z_statistics\"),\n", + " results.get_map(\"z_group-SchizophreniaYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", + " title=\"SchizophreniaYes\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"schizophrenia_No_z_statistics\"),\n", + " results.get_map(\"z_group-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", + " title=\"SchizophreniaNo\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"depression_Yes_z_statistics\"),\n", + " results.get_map(\"z_group-DepressionYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", + " title=\"DepressionYes\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"depression_No_z_statistics\"),\n", + " results.get_map(\"z_group-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"depression_No\",\n", + " title=\"DepressionNo\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")" ] @@ -257,6 +384,133 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\n", + "The default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg.py:63: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n", + " warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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9+nQMGzYsbt/99tsPALBw4cKU8m7cuDEAYPHixYG/c3uTJk0SfivqxSDs9xYtWgAApk+fnvR4tgtQ/HolKwPz79atW+jLkl+Gn376KdpOS5YsCdwvqP2OO+44XHXVVQnbb7vtNqxdu7aIkieWA4jvI6zHyJEjMXLkyCKP9QmrBxfw1q1bF3Xr1k3ok8na85prrsE111yTtB716tXDihUr8PHHH2PAgAG46aabMHr0aOTn52POnDmYNGkSBg8eHLe4dOjQoejWrRsuvPBCvPvuu9iyZQtmzZqFCRMmYPDgwSl5aClNP1+2bFngMRs3bsTee+8dmqdzLvois3nz5rjf2GZBL7Y+/rm7/vrr8dZbb+HKK6/ElVdeiZUrV2Lq1KkYN24cxowZE/diYBiGYVRe3nrrLaxfvx5XXHFF6D6XXHIJsrOzcfzxx8M5h4KCAlx77bW4++67i51fXl4etm3blvL+1atXjwp1xSUtQot26NABAPD9998H/q4vCrfddhv++c9/4rnnnsMnn3ySkrJeUpI92Aapq6n8npFROBHyxhtvJDzQ+IQFtCoOQWVg/gsWLMBnn32W9PjiPmD7tGnTJnBQ3X///cVON6iPsB4ffPBB0ofX7OzsYuWVjK1btyZsYzm++uorfP311ykff+utt+LFF1/EWWedhVNOOQXHHXccOnfujDvuuAMXX3xxVI3esWMHLrroIjz22GM466yzcPLJJ6Nz58448cQTceedd+K0007D559/Xqp6Jevnu+KBmG0WFsyNzJw5M/r/t99+i3bt2uG0007D6aefjq5du+LCCy/EhRdeiOnTp6Nr167Iz8/f6WU1DMMwKhYvv/wyevToERWlgpgyZQoeffRRPP/88+jcuTN+/vln9OvXDw899BDuvffelPPKy8vD3jVqIhepB/vcZ599sGjRopI9vKdqU1Nei1Nr167tNm7c6Jxz7uijj075+AkTJjjnCj27+Nsvvvhi51yh/XsqZaRnj4MOOijw93HjxjnnnLvwwgsTbH/Vmwf/aOMe5J0EgJs0aZJzzrkjjjgi5bacP3++c84FBqoK+qONe5DtOO273n777ZTz79Wrl3POudGjRwf+fuaZZzrndo2Ne0ZGhlu8eLFzzrmLLrooup2LaM8555yU86ON+5o1awJ/r1WrlnPOuc2bN8fZZLOcQcdceumlzrngxaPF+dt9993drbfe6pxzbtWqVUn3rVWrlnvqqaecc87NnDkz7jfnEm3Pd0U/T9Ymuo8uHl2wYIFzzrl69eqVqs3atWvn5s6d65xzrm/fvkXubzbuhmEY6c3ixYtdRkaGe+utt5Lud/zxx0cdcZBXX33V1ahRw23fvj3l/HJycgrv9Wji+mC/Iv8uRRMHwOXk5JSofhXej/tTTz2FmjVr4osvvsCMGTNSPu7OO+8EAFx++eVo1qxZdPuUKVNQUFCA7t27o2nTpkWmQ1vaiy++OOG3+vXro3v37tixY0eRynRxmDRpEgDg7LPPTvkY2mMXZYqRCrNmzcL69evRpUsX1K1bN6Vjpk2bBgA47bTTUKtWrYTfL7roolKXK4y7774bzZs3x7JlyzB27Njo9pK0I6lfvz5OPvnkhO2sx+eff56y0vzJJ5+goKAAZ5xxBnbbreSTXFu3bsVTTz2FFStWoGHDhmjQoEHovhs3bsRdd92FHTt24JBDDiky7fLo58kozbnz+f777/Hcc88BQErtYBiGYaQ3Q4YMQcOGDQNjtfjk5uZGZ3cJY6q4IsyEg6iBDNSIpPBXyhBKFfbBvWXLlhg9ejSuuuoqbNq0CVdeeWWxjp87dy7efPNNVKtWDXfccUd0+2+//YZXXnkFNWrUwLBhw1CvXr244xo0aBC34O25557D9u3b8be//Q1HHnlkdHu1atUwcOBA7LHHHhg3blyonW9JePHFF7Fq1SrccccduPrqqxMWVmRmZqJbt244+OCDo9uefvppbNmyBVdffTUuuOCChDRPOeWUlKOKbtu2DU888QRq166NcePGoWXLlgn7NG7cGJdddln0+y+//IKJEyeiTp06eOqpp+IGQ48ePQIDI5WWRo0a4d///jceeughFBQUoE+fPnGmEGPHjsV3332Hyy67DPfcc09g/Y899lgce+yxgek/+eSTcf2jRYsW0UA+fBhMhRUrVmDw4MFo2bIlRo0aFRgUrHXr1jjnnHOi388666y4yKLkiCOOQKNGjaIBmQDgsssui+sLpEePHsjIyMCvv/5aZBnLo58n46mnnkJubi6efPLJwIf36tWr49xzz43a3NeoUQM33nhjQqCoSCSC0047DQBSagfDMAwjfdmxYweGDBmC3r17JwhlvXr1wl133RX9fuaZZ2LQoEEYPXo0Fi1ahEmTJuHee+/FmWeeGRgUscKQqjS/K01lhgwZ4oYMGeKGDRvm3nzzTffdd99Fpynmz58f6Jc5lfwOO+wwt337dpebmxvni7xmzZpu2rRpzrnCAEzvvfeeGzVqlJs+fbrLy8tLCMB01113OecKA9N8+OGHbuTIkdGgPvPnzw8NTFNSUxkArnPnzm716tXOucIAPO+9954bPny4++ijj9y6deucc4m+xC+88EK3detW55xz8+bNcyNHjnTvvfde0gBMYSYokUjEDRs2zDnnXF5envv888/dyJEj3ZgxY9y3337rtm/f7r766qu4Y1q2bOl+++0355xzCxYsiAad2r59uxs4cKBzruSmMm+88YYbMmSIGzp0qBs7dqybM2dO1Df8ihUrXLdu3QKPb9OmTdQMZOXKle7DDz90w4cPdxMmTIgGP+rXr190fz8A05dffhkNwDR+/Pho0KagoF5FmYVkZWVFgw5t3LjRffrpp27EiBHurbfecj/99JNzLt58i+4Qf/31V/f222+74cOHu48//jha55tvvjm675tvvhlt83HjxrkRI0a46dOnu+3bt7uCggJ33nnnJYybIDeNO7ufl8ZUBoD785//HG3zn376yY0fPz4a1IvmczQNq1OnjnPOua1bt7rp06dH+6offCsVsxszlTEMw0hfJk6c6ICY+bBPly5dXO/evaPf8/Pzo0EAs7Ky3H777eeuu+469/vvvxcrT5rK/DXSzN2Y0aLIv79GCl2Rl9RUpkI8uJNt27a57Oxs980337ghQ4a4nj17xtkRlyS/MWPGOOece/zxx+O2V6tWzd14441uxowZbsOGDW7z5s1uwYIF7uWXX3YdOnRISOf00093kyZNcr///rvLy8tzP/30k3vsscfcXnvtlbDvznhwBwr9ez/22GPu22+/dZs2bXKbNm1yCxYscG+++abr1auX23PPPROOOfTQQ90rr7zifv31V7d161a3cuVK9+mnn7qbb745zqd9UQ/u/DvzzDPdO++841auXBlNb9asWe6xxx4LbKdmzZq54cOHuzVr1rjc3Fw3Z84c16tXr+gDcUkf3ElBQYFbt26d++GHH9zIkSPd5ZdfnhDASv9q167t7r77bvfll1+6DRs2uNzcXPfLL7+4Dz74wPXt2zcuyJJfztq1a7tnn33WLVu2zOXl5bkffvjB3XLLLYF9MpWH1IyMDHf55Ze7jz76yGVnZ7utW7e6ZcuWuc8++8zde++9bv/994/u2759e/fPf/7TzZw5061cudJt2bLFLVq0yI0fP96dfPLJcemecMIJbuDAgW7OnDnRdmfk2qCXXufC/avvzH5e2gd3oDDo1bPPPuvmz5/vcnNzXU5OTvTcn3feedEATJmZma5v375uzJgxbsGCBW7Tpk1u3bp1bu7cue7ee+91devWTam/2YO7YRiGURzK+sE94lxqhjxz5syJm0I3jMpI8+bNsXjxYkyZMgUnnXRSeRfHKGNmz56NI444oryLYRiGYaQJGzZsQJ06ddA3oxl2jxRtgb7V7cCgHUuRk5OD2rVrFzu/CmvjbhiGYRiGYRhGjLTw424YhmEYhmEYFZXMSASZIVFa4/ZD0fskwxR3wzAMwyhjhg4dikgkgi+//LK8i2JUUtjH+LfbbruhSZMmuOKKK7B8+fLyLp5RQkxxNwyPJUuWJLjfNAzDMIx05cEHH0TLli2Rl5eHGTNmYOjQoZg2bRrmzZtXssidRiCZkcK/IvcrZT724G4YhmEYhlFJ6dGjB4466igAwFVXXYX69evj8ccfx9tvvx0Y98Wo2JipjGEYhmEYRhXhhBNOAAAsXLiwnEtSuaCNeyp/pcEUd8MwDMMwjCrC4sWLAQB169Yt34JUMsxUxjAMwzAMwygVOTk5yM7ORl5eHmbOnIkHHngAu+++O84444zyLppRAuzB3TAMwzAMo5JyyimnxH1v0aIFhg8fjqZNm5ZTiSonZeUOMuUH9/r16yMrKwt5eXmlytAwDKMikpWVhfr165d3MQzDMHYqzz33HNq2bYucnBwMHjwY//vf/7D77ruXd7GMEpLyg3uzZs0wf/58ZGdn78ryGIZhlAv169dHs2bNyrsYhmEYO5VOnTpFvcr07NkTxx9/PC655BLMnz8fNWvWLOfSVR4iSM3jS2kdThfLVKZZs2Z2YzMMwzAMw0hDMjMz0b9/f5x00kl49tlnceedd5Z3kYxiYu4gDcMwDMMwqghdu3ZFp06d8PTTT5v5807E3EEahmEYRiVn8ODBmDBhQsL2fv36oVatWuVQIqMqcPvtt+P888/H0KFDce2115Z3cYxiYA/uhmEYhlFODBo0KHD7FVdcYQ/uxi7jnHPOQevWrfHkk0/i6quvRmZmab2LG2Xlxz3inHOlTMMwDMMwDCMlhg0bBgDYe++9AQA1atSI+52PJZs3bwYAnHXWWSmnPX78eADAnnvuCQCIiFnCli1bAABr164FAPTu3btYZTcMZcOGDahTpw7uq9EKWZGiLdDz3A48sOUX5OTkoHbt2sXOzxR3wzAMwzAMwygFhYp7Kn7cS4cp7oZhGIZh7HRee+01AMA+++wDAFHf4RkZGXGfVMV37NgRdzy/83Pu3LkAgL59+0b3oanR4YcfHpg24Xc+8mjaW7duBQCsXLkSAHDhhRcWq65G1YWK+yN7tkJWpOjH8jy3HX/fXHLF3bzKGIZhGIZhGEYaYKYyhmEYhmGUmoEDBwKI2a63bNkSAFC9evW4/bgQknbo1apVAxBTwwlt3Dds2AAAaN68OQDg/vvvj+7TqVOnuGOZJj8JVf38/Py4tLdv3x5XBsaqGTlyJICYLfyNN96YtO6Gkaqrx8xShmAyxd0wDMMwDMMw0gBT3A3DMAzDSMrYsWMBAA0bNgQQU6h9u/R999037hiq3Pykus1jCgoKAAA1a9YEAOy2W+EjCYMCqQ08beS5v7+N+/AYppWVlRWXF73KUHknnAVgOpwlYJ2mT58e3Zd5MI3Vq1cDAM4991wYVZeMFN1BllYxN8XdMAzDMAzDMNKAclfchw4dij59+mDWrFk46qijyrs4RiWD/YtkZmaiUaNGOPXUU/HII4+gSZMm5Vg6wzCMismYMWMAAHXq1AEQs/2m2kyFmio6EPMes2LFCgAxdZuoDTtVcKrcTDM3NxdAovJOFdz3zc5t3IfHqB09y8k8+Un4O8vMWYHGjRsDiCn7ftpqFz9p0iQAQE5ODgDgvPPOg1F1KCsb93J/cDeMsuDBBx9Ey5YtkZeXhxkzZmDo0KGYNm0a5s2bF51KNQzDMAzDqMjYg7tRJejRo0d0Rueqq65C/fr18fjjj+Ptt9/GBRdcUM6lMwzDqBhMnToVQEw9V7WbKjM/qY4DMbty7kv1mvvyd6rZ3I9qNlVw+lT31Xwg2N+7RkblMZoG82CeVP9ZP7WB534sMz8BYI899gAQs3HnJ9V9RoJlW3bp0gVG5SczRRv30gZgMht3o0pywgknAAAWLlxYziUxDMMwDMNIDVPcjSrJ4sWLAQB169Yt34IYhmFUAOg1haaDVI2pJmtUUyrVvu33tm3bAMTs4ukrnagiz+svbcZpn848qZarqq7ffXgM06CSznIyTyryLDP3Yz1ZB5bNr6dGZeUx3IczDFTv2bbHHntsaLmN9KesFHd7cDeqBDk5OcjOzkZeXh5mzpyJBx54ALvvvjvOOOOM8i6aYRiGYRhpji1ONYydyCmnnBL3vUWLFhg+fDiaNm1aTiUyDMMwDMMoHvbgblQJnnvuObRt2xY5OTkYPHgw/ve//8VNfRqGYVRFxo8fDwBo1KgRgNgCy1q1agEANm7cCCDRlITQLMQ/lvvSpISf/L1+/foAYqYlTJPmK1w4SpMYfqepDc1X/G1hxzBNmv7QFIiBlbKzswHETGZYb5rzsMx+PQnLrQGimAbrvWnTJgCxtj7rrLMS0jLSn0ykaCrjit4nGfbgblQJOnXqFPUq07NnTxx//PG45JJLMH/+/LgofIZhGIZhGBUVe3A3qhyZmZno378/TjrpJDz77LO48847y7tIhmEY5QKFC3WLSMV67733BhDv9hGIKdD+Qk0qz1TBudiUKnfDhg0BxBRzVcXXrVsHILawVNNVhdvfxnLwOz+ZJhX3MOVdF8jyd11Q66et0E0k66MzDyYSVW4yUrRxz0hhn6THl+pow0hTunbtik6dOuHpp5+OXqgNwzAMwzAqMhVGcR88eDAmTJiQsL1fv35RezHD2JncfvvtOP/88zF06FBce+215V0cwzCMMuPdd98FEFOJqQ4T2mVTod5rr70AJHfFSBtv7kOlmao1v1Npp3K9atWquDypuFMF5/FqAw/EXC5qECd1C8k8mjVrFpg2A06pLT/z8u3qFe7DY1kPdTXJdmHbm1ezykXK7iBLJ7hXnAf3QYMGBW6/4oor7MHd2CWcc845aN26NZ588klcffXVSS/MhmEYhmEY5U3E+a+uhmEYhmFUWqZNmwYgpjSrQk3bdXpToV06v1M1Tqa8FwUfOxig6eeffwYAbNiwAUBMWaeYQqWedvbLly+PptWkSRMAsZkDKuWsD5X42rVrAwDatGkTWJ/S1EPrs3r16rjvYTMIbPvjjz++xGUwyp8NGzagTp06GFb/AOyRUbQAmLtjO3pnz0dOTk60XxYHs3E3DMMwDMMwjDSgwpjKGIZhGIaxa+AaMtqqU6GmHTY/qW5TqaY3lTCl3fcqQ3Qfqt86wU8f8cybajnVcDVfVJt5IOapReNyME+tH/Pcc889kfVz4exD9geFNuerv1pYmH6NwvSbnNgeAFD9kr/HpR3k3QaItRXLQvt7zmLwd35yBoHn5rTTToORvlQ5G3fDMAzDMAzDSEcyU3QHmco+ybAHd8MwDMOo5FCZpvpLbzF16tQBkOj5hE4hqG6H2YL7Ps1VIQ9bQqdRTvnJMoap+iy77w9dj2F51P96WGTVVIhEIlEFP+x3P0/6vte8+TvVf9q+m393ozjYg7thGIZhGFWGfWaMAABMuOMNAEDX+Z8jEomgFmIvCZFIBPfUagcA6PPJ3OixLV58rUzLaqQPGZFISsGVShuAyR7cDcMwDKOS8uyzzwIA2rUrfAil/TVtvWnrTtWXSjzV7dJ4XVFf6Kp2syzMk6p/mFpOLy3c34f1YB7qQ51pqi08j1Hf8iVxD6zrA/idtu70707bdubFsvJc3XDDDcXO26g62IO7YRiGYRiVniMKlgJLlmJ0v9EAgJ6LZgYuriUPb/w++j9NZbIfvA4AUP8fz+/CkhrpSCQzgkhG0S+6pXkZBuzB3TAMwzAqLfTDTrU6TM2mSkyPLkSV6GReZcLswMMeVLiddvaaFz+pUAflSWgvTuWd9fNNX7A2sBiBZUxm0+6XzS93WNuwbOrXnUo7t/NcGUYy7MHdMAzDMIxKz6JXCpX2k7+eVKLANwDw0X+/AABccM9OK5ZRScjIjCAjBcXdbNwNwzAMw4jj9ddfBwA0btwYQExpZ1RS2l1TFaZNt9qhUx1W1Zt25lS2/TRShftT3V6/fj2ARLt0kpeXF1cHfxvrweirmsZee+0VjThZEvt1VeC3bt0abUNCtV/XB2g9te0bNGgQV2aeuwsuuKDY5TQqPxY51TAMwzCMSs934+fju/HzS5XG+Ytn4qyfPsVeS7/Avmu+3UklM3YG999/PyKRSNzfgQceGLp/fn4+HnzwQbRu3RpZWVlo3759NBhWicjMQCSFP2SW7tHbFHfDMAzDqGTQFET9tqtXFW5XTy1Uh6lg5+TkAIjZdjMd+iz301D1XuF2lk1nAcLs6bkfZwH8bVov3devX7Vq1YpcIMgZB1XJAWDt2rXY64//16xZE1XOqZgzOi23M289J4TtxTxKasZjAAcffDA++uij6HedtfG55557MHz4cPz3v//FgQceiIkTJ+Lss8/G9OnT0aFDh7IobomwB3fDMAzDMCo923akHnQpjCf3OxYA8PjXgws35JU6SWMnsttuu2GfffZJad9XX30Vf//733H66acDAPr27YuPPvoITz31FIYPH17svCMZEUQyU/AqA7NxNwzDMAzDg2ovP+kthso0VV/dT32vE26ngs3vVOKD0lRVW5V07k/bcNq4U4FWZZpKtJ9nmIpNpZz1YB66L8uknmp4HFV0P0+f/Pz8hLzVOw7T5uyEtiVnB/iZzEWlkZwFCxagcePGyMrKwjHHHIP+/fujWbNmgftu3boVWVlZcdtq1KiBadOmlSjvjMwIMlJ4cM+wB3fDMAzDMIzktD2uaanT6NayLgDgq9yaRbqMNMqWzp07Y+jQoTjggAPw22+/4YEHHsAJJ5yAefPmoVatWgn7d+/eHQMGDMCJJ56I1q1bY/LkyRg3blyFP6/24F4OvPnmmwAQ7Ui64lyVj3Xr1gEo3gpzrkqvV69eYJqaJ6PonX322cWuj2GkE6NHF7qEUxtWvViHRX3kWOrdu/euL6xhFIOBAwdG/2/dujWAmKpLNZvf2Y8ZMZVqsKrmtM+mz3F+Et+GOEyl199Vied9imXkWFQlm3n7vuaZpu6r97p69ephORJRdTzsd7WV9m3q6VmHbcW2U9V+7dpCR/KMoMo8WXaeG+7vn88bb7wxsHxGjB49ekT/P+yww9C5c2c0b94cr7/+Oq688sqE/Z955hlcffXVOPDAAxGJRNC6dWv06dMHgwcPLlH+kYwMRFKYLYm40pls2YO7YRiGYRiVngP7/B8AYHiLzgCAi5fOKnYaHW85FQCwbOcVy9hF7LXXXmjbti1+/vnnwN8bNGiAt956C3l5eVi7di0aN26MO++8E61atSrjkhYPe3A3DMMwjEqAr2TrLCs9ltCOWhV07sfonVSYqSzT17gq036eqkrzN36GzWJRcW7SpAmAmCcbbldvM74NuKrWVL2pXofZwLMcOpPGsqmST08xZN99943+rzb9qrSvWbMGQGxGgTPcVOrVI07YGgGjeGzatAkLFy7E5ZdfnnS/rKwsNGnSBPn5+Rg7dmyJ/eebjXslgOYqHPCcktxvv/0AJF4g9AJEOI34ySefAABOOumk0Dy5T5s2beLSJjpNygsDyzh9+nQAsak8XmgsEISRbowaNQpALECLPjToJ1GTGf2dDBo0KPq/3vyvueaaUpXdMIydT94Jl2O33XbD5cMLH5i/Pb8nAOC1yYsAAA+sL/TLfnvtQwEAFx7eCABw5N+6gWGmtp1/e+g1wShfbrvtNpx55plo3rw5VqxYgfvuuw+ZmZm4+OKLAQC9evVCkyZN0L9/fwDAzJkzsXz5chx++OFYvnw57r//fuzYsQN33HFHeVajSOzB3TAMwzAMw0hrli1bhosvvhhr165FgwYNcPzxx2PGjBnR2aKlS5fGzdbk5eXhnnvuwS+//IKaNWvi9NNPx6uvvpowu5IqkcyycQcZcfbquNOZPHkygNgUHdU4KnmcTuSnTofpdCOnMnn8999/DyCmigMxNb9du3YAYgty/HDUQGzqjuiUHj95PH/n1OWf/vSn0HobRnlBn7v+wjmaBKiCzvEVNr2ti+90RixZuHRV8cNc7en4Yhn69u2bvKKGkYRnn302+v9BBx0EIOYGUa/lubm5ABD1eU1zjUaNClVmDchEdLz49y/+r2OE23l/0RkqjlHOCKv5zu+//w4gtriTpiZAzMkDF9fWrVs3Lm3eAzmTzbLpDNxuu+2G3b/+oHCfvQvbZHOTwxPcSgbVPewxiiY+q1atAhC7Jq1cuRJA7NzoswLPzQ8//BBN64YbbgjMwyh/NmzYgDp16uCdQ4/EnknuD2Tz9u0489vZyMnJKVGwLVPcDcMwDMMwDKMUFCruKXiVQbAHo1SxB/edxLvvvhv9Xxf38E2fb/jq9pGKgH7nWzwVAiolnPbxA0LowiEq8FRR+CavSga/q+svfqcCQlXDr+cZZ5xRRKsYxq7h1VdfBRBT8NhPac8OJKreGoY9THEnOjulM2P+WhSduVKVX2ey/JDtflno/k0VPX8WjmmYHb2h6GwRkDjjS9VX3RHrTK/2ZR7H/XlvSeYOMkzd1tlnwnHAscXxzPGix/vbdB91a0lYFtbPnw3bcmj3+Pbavj3QTSSP1Vk9tonOOLCePI5tT2WdeYTNthuGjz24G4ZhGIZhGEYpMK8yaQJtCmlbDoSHc1aVW+0B+bat9q9KkI1tmN2tqowsE9/8NU9V/6kIcH8/bDTrbrZ3xq6CyjrVNA2WpKqgr46FBVgKGxOqTIa5vQtSKMM8RGka6s4uzN2bus/z1X+Wj+OP5bj22msD0zKqDrfcckv0//fffx9ATAXWWR4GMVKFmv2LM7yc2dWZYrWJ97cRVbt15jfMFp6ozXsyxZ378BiGstc0dX+15Q8bw1TXgUSbdV27UqdOHQCxNla3ltzO+6ueG6brn0+j4hOJRBDJSGFx6o7SPbgXbYxjGIZhGIZhGEa5Y4p7igwZMgRATFFQJXrz5s3RfWlfzrdrKmJUq9XDhHqZUdQuXe1n/W2q6vsKebI8WCb+zvqxDlQh/Hqy7i+99FJcXlQL+vTpE5iXYYRBhV1tW1WRCrOZDUKVdLVtVbVc01I1TRX7ZOg+PFavAWH1SpaH2tX7HkUAmwmr6lAxV8Vd+yD7GK/bvMZroCZu1xlkenoBYuu7dKwo3M481PsZUfVby+pv07ETllaY2h8Wz4Gffj01mBXvl1TSeQzbTD3I6bobVe557oz0IiMzAxkpLE7NcKXTzE1xNwzDMAzDMIw0wBT3EAYPHgwAaN68OQCgQ4cOABL90S5YsAAA8Ntvv0WPpW0dV47zrZt2blRA1N5VFRC+1fPtXcNH+wqB/qZ+cWnH5/usDcpbVRemQ7+5fj3p/3f//fePS5N50J/9kiVLAAB/+ctfYBhBDBs2DECsz+sskypuHH9FRUFNBfXTrN5oSLIIq6rSaznDxpvup36tdVwHHRtW/meeeQZATNUzBb5qwTgfuo6JaN/k2ONYy87OBhCLnq024zo7C8TGLRX0sHUivC/xd6at/V690pB169ZF/993333j9gmbEeO4UU9qYWVlWbi/X0/+xjbj/ZKqPAP41K9fP66+zFO9YfGT58yP0WKkDykHYHJm424YhmEYhmEYlR5T3AUqf61btwYQWx2uShlVLe7HaKYAsGLFCgBA48aNAcTs3vh2rv5vw/zMql0v8f1HJ9vmp0FFIyySIz/Vdo9KAuvkew1g3dWekWkxkh3rybbt3bt3YFmNqsfLL78MINbfqERpvwxT01ShSyW6oaal60O0H6tSqbavQYR5j9F1LWFpJPMsFWYfT3TGgN/NC03V4qqrrgIA/Oc//wEQU5Z17PAexzHIKKW8b9FrjNq6Bynb2p+1L3LtCr2y8HfmzXuGxjDR9Se+4q4+4cOiEq9ZswZAzEsOt/M+zXtkmPLu34+pvrMtOKPNtuR9dNGiRQBi0Vx5/2QZeLza31uMhvTEFHfDMAzDMAzDMKKY4v4HY8eOBQA0bdoUQOwNmm/xGhGNb9x8U6adHRBTp2nvRqWDqoJ6cCHq4zbMbjaZH3e161NPGmrrrjZ3LCPVBdaB+1Od8MuvXnM00h7zZNuyrc8999yEehiVm1deeQVATHlThT3MQ4SqYMWxbddxpHbkYd4lwlRy4vtWD/MCo9vDvGyQVDzVkLA2UT/zatvLcj///PNxx1933XUp522kDzzvatvNe9jy5csBxDzCNGvWLG4/9jMq8KqW+6jHGirPtJPX+w/7ItPkfUeVd+3rLKtPmFeZlStXAoip9HrfYjuofTpnsYPGrN4/qahzOz3LsR58Jli4cCGAxOjoYbNnRnphXmUMwzAMwzDKiAMja7BPzoLyLoZhJKXKK+4TJkwAADRp0iRuu0YS5Xe+hVN9oK2aH32tXr16AGIqA5Vn9X+rtnjqg109Z6jtu6/O6Sp9VTSYptq6q8qvUeK4nXXy68lj2RaqSOpMA/fjJ9v+tNNOg1F5GTp0aPR/9Rqj0UtVHVePKRq9kWNI1cQgtM+zv6rar6jv5SClMWyfsPJofcL8vWv9k5EssmtQmqryUYH3y9K3b98i8zUqJoMGDYr7HnZfoeeT/fbbD0Bi/9C+p4o07w1A4vqQZcuWAUgcB7wX0nsKj6Mnm7DYJur33N9GmDfvzUyT5WVZWAZek3Jzc4HCWxOWLVsW9SjH9P16Mg+mGRY5mbBtqe6zTHot4j2T587GX5qRoo07SmnjXuUf3A3DMAzDMOZs3tPMVowSkxGJICOj6IfyjGKYRAZR5R7c33jjDQCxt2f6Ig9TzHQ7v6tnGN+rC1eW8wLg28IG5aHqm6rfqppTyfeVEG5jucIU9TCFTxUR5lm7du24Ovn1VPv/ME8aPEb95VL9p7932iCef/75MNIfKu2+T+Iwm/QwbxRhCpZ6R2IfS3bT1d/UhlXVfFX1w9amBJVfPS3p7JrWP0xRD/IgE7Zv2LUqrO3CPPX46Zvyl77w3kZoR86onOwHnG1WH+y6/ol9nL/Tfpv23EBsTFFpVwWeijPvKzrrxTxpl841VbrOhAq2v03XyzCNsJk2buf1SdeI0C6da7P8ehLaxetY0nqxbdnWvNcxT6r/9OBjGMmocg/uhmEYhmEYhrEziWRmIJLC4tTIjtItL60yD+60p+YbLaOaavS0sEhtYVEVafNNLxlA7M2fb9FEbVBVOVM7dX5Xv9F8m/dVc/ULrQogf2eaGuVUVTe1MQyym2Xd1UuH1ktnAXRmgbMfVGvM9j29oW92qmt+XwxTxFUtDlPB1e5W+6vva7moKW9V+VRZJ3qNCELHD8c++7TOfGnUSp2V07z9uoT5fldlkeh41N+LWmcAAC+88EJcHuZnumLBmWTfuxlt13l+eb3+4YcfACTOLOkn+7tev9m3g+4JnPlNFuMAiN0veR+mzbfCiN3Mi8dRTffTYDl5jMJxoBHNw/ZjHVgnrs0CYrPFnNXgtU6vT7r2Jixaa4sWLQDEVH0eP23atGiejFpuM9JGlXlwNwzDMAzDMIxdQUZmBBkpLE7N2GE27kn55JNPAMSUCFXM1UZWFXdV5Ygqa/5bfphKHaboKWo/TzVObWwZCQ6IqSt8k2e5NO8wVHVkGVQZ9NUV5hFmL69Knra5qoxqT89zd9JJJyUtu1ExeOmllwDEVDFVw4FwZZnjTGeM1MadaYbZc/trMHzPEz5hkYp1jIRFBA6yUw/z9R7mLUbrE+ZhKsj/e5iaqRExdcZBbdj1eqRtGlRnpv2f//wHV55wIABg84xJAIDafR4KLJex6xg8eDAAoG3btqH78Jzxek3lnfcKjaiqXsuoLutxtA3n70BMndYZM6I237zmh80C0TMM8+Bx/jjXcvIYHc86lnQtWdj4CFLc6YlGFXJu5zVQ25JtR9WfZdAYKEHPCHyG4Tn/y1/+krCPUTWo9A/uhmEYhmEYhrEriaToDjJiinsib731VvR/2o7xjZdvyOpdRVVhVdxJmILm27PzbVu9qVBJDvLe4OdN5YC/862dn1QtfaVDZw6ojqiNbVG+qllGqpW6v19PVQl1X129r5+q5jE92h4yGp1/Pnv27BlYfqP8GDZsGID4dR5A4iyOv009Jun6B0X7ryrbQTbuYbNkYWMhzFuLjkOdHfDRCMSqYquHDp3hCou/4JdV21C9VBU1S6jeQcL8YPv/+2P8imYAsB1z73gQAPD2pEUAgDV/eRUA8LxbnNAuxq6B3lXUfhuI9UF+ch+9v+j9SNVj9g+mrTNqvq14UXEMtD/5HqeC9guLbuzHEyGq8odFK1YvMkEzTUF18OvJY/Rez2sE2y7smqOzBFoWXV8AxGb1fY86RtWkUj64G4ZhGIZhGEZZYV5lDMMwjApD7waFit/4KwpnWH5/8EZkZGSg8RmFimNzmN1tWfHiiy8CAA466CAAsRknX3HXWSgq0bTV/vXXXwHE1GGdddbZaH7SgwrVYB7vHxu2jknVfc4oqd9znTVSj2p+uupRLWzNBvdjnlomRcvk15OKv0ZF1xluwrLxXPz+++8AEtVzlpXnyJ9ZYP5sd/aBv/71r4HlNyovlerB/b///S8A4Kijjkr4jQOBA0tdXOlg1ynrolyw+RdMXtj0YspPnZLXi5ROt3PA8ru6i/S3cR9O63Hgs766OE6nNllGps3puaAbQ1HmDbqgVds27GLNc8W8GXoaiJ3jq6++OjBPo+xhf1eCzM2KcosWFjRIt/NTF9b5hLk41WBNYQGKtB6Kv1/YIlNOpQe5dfTheAtbMBpUHjV10TxJmItbnbYPa4+wcvh58po1ZMgQAECfPn0C9zcMw6isZGQiRa8ypcunUj24G4ZhGDuXq47bHwAw6axbAQDrHrgh9EHeMAyjqhLJiCCSkcLi1BT2SUalenBv06YNgHgljIqzBkMiYQvVkoU3BxJdyPnBWeiakegClDCoWjEkNZVMDeXMMMu+4s5tDEPNBThU31h/ut8qyj0k0/FdYAHx9QwLR69uMFXVD3Plx+M0EIw/RclzbJQ/DLTE/qljyO+fJGyGS1VuVeJ1oViYWhwEZ5v4yWuCLpDV/qkuKXVmKSgAGsutC/3C3D0SXfiabAZCx67OOvCTs29abp3ZC6tfGEELZfnJepjyvmtR98Z6rQVijhh4D+D9RF0w6sJooo4OiJqt+KYnYfdL7cfsw7w3Mi/2WV1Ayk86LPjqq6+iaXfo0CGunnrvZjuwnuyj3F9NbMIClvn15MyzzjayrTjjre4gWQZ+13PB9lA3k359WA4/2JZRtahUD+6GYRjGzmXFayMAAEvuuLZI8cEwDKOqkpGRgYwUFqdmbLfFqVHl79BDDwUQrAip+qdqk+6vAZn4qccF3ciobquCpyqbqm9UllUt12AO3M9XV7iNi15Yfr7BMw9daBRmS8vtVBCC6qBtoOqPLkBSVZGEufgLKhtnAHjOr7zyShjlA/ucKnB6/oP6DPuCqmNhblm5v/apsOBePjqGCY/V8uqMkbqm07IDsTGvarYqboS/qztMEqaK+2h5dGxrMKuw4C5hAWj8tuB2nR3T64LZvJcN9erVA5A4fvxzx37AvsnxquNUg4fpvZLp6PgIClwWFkiJNGjQAEDsOs5xzHscyxDmzph92J955TYdz/rJtqLLY5aF6vi6deuS1sGvp9adbaNuIbVsYQENNaBjstkMpsU+YFQ9KsWDu2EYhmEYhmGUFykHYEphn2RUigd32mOrsgTE3uSpNqg6HLbISpV3KgRhIdeTERaMQgNF8O1ag6/wrV5VCN/2e6+99orbh8equ62ggC5BZQubEvePCwsqwXqpnV+YHbKei7D0/P95zo2y5z//+U/c9zC1mPacQedP7cdVUVeVS1VA7Rvs30GqGMeT2peq0qx5cLZKxzrz9L23qEpPu3MNfsMysEwcw6ria+CZZIo781A1L8ybjuYRtkaB+3w77MvCL3d3DFVrtUza9sbOgcHOWrduDSB2TmkT7c9a6pohHTP8/OabbwDEFNxGjRrFHa/jm+lxXZXfB1gOnnfaglPdJvQYxnuE9hvC+vj3OgD48ssvo/9r2mqTr+o3v/OeznsnP9esWRNXtqAysO5U74m2Fdth+fLlABJV/bBAkHo9ARLbluOefaJ3794wqgaV4sHdMAzDMAzDMMqLlAMwpbBPMtL6wX3w4MEAYrbtQb6S+ZYc5qs5zN5alT7un4pXFrXt1TR1e1BoeCDRTzMVwKAw0NxXbW3VU0RRfqLDbGuTzSyokqdecdRGOGxdQdg58vNmPZs0aQIg1gcs8MuuZ+jQoQASA5ho39Cw3f7vOpuk41PtcNVuW/dXRdvvW6okM08dV2qfzTSp3Om4DLKZV/txHV9MU+1w1cONep8gvrqvdvFqV67Ku7ah2jKrdw3SZ//C682Qu/sWLrpCOGE+4Pldg8X80OfPAICDhrydJFVDoSqs/SuZRyDt5zqGeF9hvIyi7LK1v/l9lX2K6jDVcI493hvURpx5EZaR95CwOAd+WjoGeS9UBV7bgWOT93ZV8LnmzC9j2HWHbaKxIti2VPHVEoDnINlzharzrCf7hFF1SOsHd8MwDMMwDMMobyIZGYikYD6dyj7JSOsH91atWgFI9KXuqz5qO6v2ffxd7bCZFm30ivLr7ivXYT6nw+DvfHNW1Ypv46tXrw5M39/GetDHq0ZRZB5Flakon7b+b2pLqwo67Rmpuuj6AbXBVFXFVzq4jWmxDxi7juHDhwOIKU9hhKlOPnpO2UfYT1U909kcorbTQR5TNP+wMOuq+vH3MJU8yO6cyllREVRZP7W3Z7mZDusXFIeCaWlUZ/VooZ53ipoJ1Gtk7oIf/9ivZcK5DVPWw/zUM00q76fv1xBG8dF1GOwL6p0FiMUT0ZkvtZ+mbbv2Te03VIu5X1DEZKrW/MzOzo4rF+3Kw/qJro8hLCNtxIP8mzds2DAuL01DZ4W0PXh/5f2WdeB1gLMFft25D9uGba3XHp4f1oN56b2Ox3O8sL5+nlr+oHgZRuUmrR/cDcMwDMMwDKO8ychM0Y97VbZxpxrON26qyb69H99S1fNCmP9k3a5vtyTMf7H/m6ra+savagPf0vfZZ5+4eqiiRkXBj2Kqq9Kp0LGNVFVL5oc+qJ5hCgmQqM5r22mbqwKksxn8pGLiq42sB5UI1s/YdVBpKsoTk9rbBo0xqkPaF3hsWBTTsDUXYXbc/m/aP7Vfqr25rm8pyvOUX+ewWSj207D1AWwH/k4Fj1AFDCqP+m3XmQGdVdRxp2Oa7bD998KyZNXNShjDYVFki5rJ23DrkwCARh/2T7qfEQzHIq+N6u0sSH3l/YR255zV4XeiMy5h8Th0lsifheb/3333HYCY1xUq02Gqd5hHMebN+CQcF/6MG7dp9NGwNLXf60xDTk4OAGDp0qUAgMaNGyfUM8wzk85ShK3r0miu6hVo5cqVcWXxy6kzIP5MgFHOpLg4FaV8cC/d0YZhGIZhGIZhlAlpqbi/8MILAIDOnTsDSFR5/FXkfPumSk17ayrwRD1hhPlu1jfnICVaowqquq1v+qoihnmm4Gp3vmH76iLT4D7qyzks76LUUz3eV9pUydR91F5RlXZVS7kf1UlVToBw1Yd94tprrw2sj1F86LGHKh7Ph553VZFJkKeLMJ/SGtlXCfOUQsUxyBZefSITzsKFzSCogq0+2IO8QOnsQtgY1uiT+kmFUtcA+G2sM3E6rnRWQ+uvqizLxHR4nvdoWqjUzrjgfgDA8a8+HG07PbdFqbWs/9l/ORIA8PLXhb6y+x4LIwUGDRoEIDb7yPPA+5qukwJi9zpeTxn7gvePpk2bAogpy1wXpf1G+5vOhPr9i3myD6mfc51pC4q/AMT6KO/TyeKm6BgLW0NFVCXXeCksM/Nmnfwyat25r6at1y2uE2rWrBmAWFvy3FBFZ57+WF2/fj2AxHs5y8A+0rdv34Q2MsqGSEaK7iBLuTjVFHfDMAzDMAzDSAPSUnFXJYBv2GoXCoSrA1Qq1EMDUWUvSP318/YJ81OuflhVhePbtSoEK1asiCs7j/M9CFAloJpCm0Da5xH1hxtmmxqmpvv1DbP7V3/zGi2SsI25Pz/VG4A/O6KeDYJ82hulY9y4cQBiql6Yikx0PKrnJf+8q4cWnlv19KL+zVWR1z4TFKlT+7iuoQhDy6CeqbTv+XBMqqqtqqV6WFLvEjpm/DKzzcI88GieYTa+6t9eeWd7c+y22264uGOhErsiMzPhWhbmP1zzavVMYbTdj268qvC4P+rFmTLAZsuSwX5ORZ39g32Sdut+dE/2Ga4H2m+//QDEPJswQijtq/md9ujqaU29twXNjnFb3bp1ASSuBdPIwkX5/w9bB5bMe1RRa8lIWBmYNr3UUCX3+zrzZBrqbUmjtfJ+zLbm8TwX/E7bdh7nn0+Wi9clvd+G1dMoO8wdpGEYhmEYRiXkuGprgPVr8NOgYcgGsPyL3wAAO7bHHsD3ObDQhOagS04EAHy4x6FlXk6j4pGWD+58G127di2AmL/aIL+yakNKpYKfVKrDIoSmEjlU0X3Vlj3MkwvLqHbcVNE10htt3oDYjAKP5Vs5bd6ZZ5jaqGUKi+6ayls981Zf1WFph5WF59mfSVFftuwDySIHGsWD6hBVJN+jCRBTk1Q9U88vQco0j1GFSmdO+Lsq1+pznXmxXwRFM1XPNGHeJsJmwHR2jvhjQX2/Mw21xQ+LiKoebFTV9K8pGmVR1wmof3b9TvTaqG3Jchx1e08AwLS7/4l6AH79f9cllNOf/fzhuseix/c55wAAwCFjBgAAFv7vh7gy6CycEc9LL70EIDGeSJhPdn+s8RzxvsG+Rntq3j94j/jpp58AJHqbIezDun7Kv47zWI4Hlod9VteQaZ/VNRGsJ9Pl/n4ZNZqsjnv9rutMWCa2j15LmBftzv00dHzr9Yrl5WxG27Zt444rLrVq1Uqop0aKZZ+56qqrSpSHUXIimRmIpDD7H8ks3fNKWj64G4ZhGIZhpBvHR5YD24Bnz3oYAHDxT1OxdetWHIDYQmG+GKz946XrwTOvAwDc+e/Cl4qv2nYr41IbFYm0fHDXN36qXNwe5IGhKBvoMHvtolS5ID/uuk1VRlWHqUbo6nbmdeCBB8Ydx7f6I488MqGe6kkjTO1XlYHozISqlH49wyLEpjp7UZQPebUH9uuu5SrKbtkomjfffBNAzKZT+2GYRyKdWVFPF0FjQz0LqSpGirKhThY1MCzWgqbJ3zmzw/6mdqqqsvkzEfSVTU8djRo1ApBojxpWRubJ2Y7FixcDAJYtW5ZQZo3NoOtxdKaAY4WqoM6Q6DnwZxJyc3MxIrfQXrnPyPsBANmTJgIA1nyzCACQkVlYh70PbgEAuGLSA9HjX/6xsC9Nn/JdYP39vJ599lkAwA033BC4b1WEarLeQ9TTkXrx8eFvVN7Zb9lH1atMWJRwloV22Kr0+sf88EPhzErLli3j9k0W/8Tfrnb1TJd+zVlWv17qwUYV6bB4DmFrPxYuXAgAOPTQQvMUjh8gpsrzWsnxT2Wd5dVI5kFEIpFoHfS4sJn+goKCBE827Au23qv8iKToxz0lX+9JsKcdwzAMwzCMMmD6rc8BAM6c817KAQRPee8FZGZm4vMrbgYAnPrG0QCACavtIb0qkpYP7nzz58p1vqUG2U7rm32YF5Ww72E2eGGRA/1j9G2Zb8S0y/7+++8BAPPnzwcAHHPMMQCAdu3aAYgpCapKBL1R6zZVz6j8Mc/PP/8cAHDAAQfE5Un7R61XUJ20LbQMxV0fEObv3m9btXHmp0WPKz204VT/4KoKFzUGwqIi+r+pfal6VVFFXceAKvRBtuDqaUbVeXqNYJ9XRVojr2q8gaBZHlXn1WNLURFGeU2jGspYFb/++mt0n2+++QZAos9s9TjCsnA/KvD0GsJ6hcVG8OtRUFCAl75c/kchDylso1OPSIjRkJeXByxL9P6kBCnD5hUjEZ4rnksqvbpGRNcrAIkzMTyW/Zy2277vdyB2bqikcz+d7WQ6ugYGAJo3bw4gPrq3n0ZRXs3Ul7zOXrdu3Tqhnmq7HhadmYR5h+L+rAPHQ1A92c9ZL7YV1XB+8gF9r732Au9UNWvWjI49XZ+n/uCZlt5Jt27dmjDz4c+AGGVLRkZGSs87xVkzGURaPrgbhmEYhmGkC51/mwb8Bsx/6fkEN82psuP5R7B9+3as/XhS4Yb9u+68AhqlxkxlAqANJG3OdGW2qnb+/0V5MAkjzEOMqopBapGqIWqTz+hpq1atAgB8/PHHAIDZs2cDALp27QogZjerKnqQuqjKC21kp0yZAiDRRpBl0Ah1QRFh9bvWXRW7MF/wJCxyZVg6fr0I+wA9I5idbPF5//33AcTsNcOifhJV1nXtheIr06pIq6qtaxfC4H5h0VH9fVgu2sB26NABQOLsUlif199J0H7ad4ua6SNF2eHyGgDE7IYXLSq0MZ81axYA4LffCt3JUa2nQqizFmpPqzOWQb7wic626IxCmO1y2Hd/O+s+cOBAAMCNN96IqsrYsWMBxDymqd//MHz1mDMturaKcUF47Wd/0YjBVOKprNN+m7O3nB3yzyGVY5abfY/l13Gr9VGVXK8XVJN9T2OqMKvHI41qrH1Y7ck5i6Qecvx8NM4EZ3zVi1uc95/C4YlGjRpFf+e5YBnUH39R59tXeNXLF/vQueeemzSNysSgQYMwaNCg6Bqhgw8+GP/4xz/Qo0ePwP3z8/PRv39/DBs2DMuXL8cBBxyAxx9/HKeddloZlrr4WORUwzAMwzCMXcivk2fj18mzd0pay6b9iGXTftwpaVUmmjZtisceewyzZ8/Gl19+iZNPPhlnnXUWvvsueHH8PffcgxdffBEDBw7E999/j2uvvRZnn302vvrqqxLlT8U9lb/SkFaKu9rcqYqlkTiB2Ju9Kl1FKUJKmHeZoDfiMP/RQV4bAOCoo44CELNd5Wr21157DUDs7Z4+YA877DAA8b5sqZYyDfrkVXWNtoFMg7BMtIMNU9r87WGqoh5TlP963a52y0G2hepdgW1h9n3FR/08h3lY0jgD3E8jefJ8+fbRRO1PwzwvFeW9Sb0vBPlR5r5U2o899ti4fVV5U9/YqvZpWfy8wqKZ6thgudV7kyqQyWYK2f6MhEnllDcb3qSo/qkNMNPWSM1qj+zXh+g1TZVUVf+0XUiy+llMhkRvRLpmImz9kD8LrWsYeC5oN8+IqlTH+UnUvpzXVpaN6fnjW8ep9mseo7EgtC/qNUfHHsvg76t9SrfzOsc81I5evbJonr4dOsvNWTtdj8a28uM25HjHZmdnx7UHFXuWWRV9v438+2V+fn6oD3y/jaoKZ555Ztz3Rx55BIMGDcKMGTNw8MEHJ+z/6quv4u9//ztOP/10AEDfvn3x0Ucf4amnnsLw4cPLpMwlIa0e3A3DMAzDMNKNbZu2Fb1TirjtyYVGo1AAeOONN7B58+ao4w9l69atCcJgjRo1MG3atBLlGYlkIJLCwtNIpAop7oZhGIZhGIYRxLfffotjjjkGeXl5qFmzJt58882o1zyle/fuGDBgAE488US0bt0akydPxrhx4yr8rF9aPbjrNHNY6GJ/yreoRalFLYxUdAovWchunR7WxXs6xcVFt1xkxqk5HkczmHnz5gEo7HRk4sSJcXlq4ApO3TEPLUNYGXU/v078XwNi6TFFBd0o6lz451MXB+t0pwViKj5c6KVBvIpaSKkmJkSnxzmN7B+jU/9hAVqImmLogrGgxZ/sCzSR0eln/QyDZWWIeHXdBiRee3TBpy460+sGy031h+Y8NGsI2lfbiiZ3NIebNGlSXPlZf6Yd5g7PH586BvWcq8mMumllHnqek5kYMv+qvNBcg2nRpILmbOqCN9l1j+Yaer7VDWjYvY/7sQ/odd8fPzx3LK8ftAiIjVeOA44lva+GBZQKuleEmWDq+NDF6mr6Q1gGXheD2kXrzrbRceAHQqzTsmE0Pe6nrndTCU5YUFCAhu0LF6vPy82N5sE2V5fJVY0DDjgAc+fORU5ODsaMGYPevXtj6tSpgQ/vzzzzDK6++moceOCBiEQiaN26Nfr06YPBgweXKO+y8ipji1MNwzAMwzCMtKd69epo06YNjjzySPTv3x/t27fHM888E7hvgwYN8NZbb2Hz5s1YsmQJfvzxR9SsWROtWrUqUd62ODWAsLdwvq1SrfLfNMMWRqrarUoe1TUqHFQO+KmKkr9oM0zJYh50s8U8WAYqAS1atABQOOXjp62LA4MWrugCM5aBaaq7LS2TqqkkyNWmBolgGahU8FMDxKhyQ8KUzyDlIGiBIGCKe6rQBSSQuCBZAwxpACbCscD9wvqMv0CLeZEwt4Lap1gGdeGmfckf54cccgiA1Bcsq5rHmS8u9ly9enVcGXyljsGc6GaVC/2YNwOwsJwc+zrbwUXm/GSwNj+cO93wEW0b5nXBBRcAAD799FMAsUXvPC8sm6q4/nlURVEXEev1QmcOdPZGr13++dJtFX26elei13wuvueYo6tHKtaqngOJrlb1Gh4W2E/PpboZJEHqd5gLSlXeeU3QxarqmpFo3whahK6zQXqP0BlF/7rkw4Wi3F9nrYHwoE66eNi3Cmh4TKEb2o1IfO7wlXn/05+Z27ZtG6acfysA4Mz37wYA7L117+h415mBqjx+fHbs2BHtZ2FkZWWhSZMmyM/Px9ixY6PXzoqKPeUYhmEYhmEYac1dd92FHj16oFmzZti4cSNGjhyJKVOmRE2Je/XqhSZNmqB///4AgJkzZ2L58uU4/PDDsXz5ctx///3YsWMH7rjjjhLln5GZgYwU1PRU9klGWj64822Ub8zqxilIuQ2zWee+VNOohKltKgMX0f2TBqfw8wxzZaVv52onx/3q1asXd7zODgQpmeq+UcvANMPc0+mbf1jgGL8OVB2oGrLtqBJSfaAySfdjbDuqkkWdGx+tu7o6M1LDV7jD7ExVyVXb1jAFLiwwl7+PuoNUG+iwICk8Tm2/g2ynGbQobPzpmGFen3/+OQDg559/jstT8fscVToGPKPyvv/++wOIXTfYb1WR//333+PSVNtwjikgdi2i8q6BpFRx69KlC4CY+8hPPvkEQOyawPHIcez3DZaH5aaSrmsSdKYrLChbmJtM/xhSlIveyowq7jrDy3PGccAZGn9GS9MIWyMW5sZX3YbyOqFrJoLWwui55L2B6Ay3nmud0dF0kwUfDFu7omOKbRbmqjTZ2heOCz4f6FoQPV8AsOm4SwEAc48rDO6z96j/JFz/dKbCV8133313XNe70I3z57vvj4yMDOxVI3Empag1O5WZ1atXo1evXvjtt99Qp04dHHbYYZg4cSJOPfVUAMDSpUvj+k5eXh7uuece/PLLL6hZsyZOP/10vPrqq9HxVVFJywd3wzAMwzAMwyAvv/xy0t8ZQZ506dIF33///U7LP5IRSc0dZEbyyOBFkVYP7vomrW/jVKV8JYxvwFSlVL1myGENoEB1WNVFKmtUOjTksV8uqlNhShLf6pi3hpzn77QbpOqlagsQU9OobLANaP+mXiC4napJ0Bs+EHubZxn9uiRrAyAxjDMVPqqLVIcaN24MIPHcqHLvt4HWK1UPIVUd2rb7nlHUXlxnV1QNCguWpAFCghQgVc6J5qnKPNPioiH+TvWZ6fpByYoKIqY2sbyoL1iwIK4s/J0qGvueb/Oq5eb4YyC05s2bA4j1dbY1+zPHElVvjg21z/XbhCHoOb4YcEk97XB/rnM555xzAADjx4+Py4PXSP988VjWh20QFCDGL6cG82IeYQGdgrZV5bGsKjL7Nduf11q2M/uP2kT728Ku7Zqnzqyxn6lqzjKx3/lp8pNjaeXKlQCAjh07xpWF40AVd5Y9FTU5TFkP87zD/sVrIH+fNWsWAGCfffYBEJstU68tQKxNeM8mvDc3adIkriz+M8vJox8CAAw75TIAQOtxg0LXiLB99hn2LwDA6itvL0zH24fnnmOMfaMqj5/ywrzKGIZhGIZhGIYRJa0U96AQ6kDsDZPqm+83mjboVMn4BktFnWo231Zp604bVPXxqh5OqHg0WVhoN/rr+A/B99yf5ywHAOyWVdjMbf58BABg6x8rw/l2TYWMb858s2/UqFFcfaiYtWnTBkC8jTt9ONMulx4kmAYVC+ahnjbCVser1xa1D/Nh26h3C5Z/6dKlAGIeOHgeeS6oyDNvnhuqkEDsfKh6qjbTRjCqiPqoTXvYLIx6kVGPMGpDG+QXXNPS7eqTmD54+Z39nPD8+ypTmFcFtdlnmr/88guAxPUe9OjCa4n6LvfRerCdFy1aFJd3s2bN4vJQLxtU04K8aGi78/qn1w2WW8vE7RdeeCEAYMyYMQBiM2G+1xr1zFFU7AbtM2p3rHbV/vnS9Q1VeSzzmsc+R2WX12+qwrxG6mwnED7jxHamYq73VfXexuuzzg7xHhKk7LK/qHckqtqMNaD3NvUipf0vyHsO24r3dr3+8FjenxYvXgwgdi/hvZJlZLuEea4CYmOEbcL2Z1txZk1nJ6tVq4Z1TY/Chg0b0OWLd7BixQq0/ex1AEDOwsLnhd33is0a7nPycQCAL668HdWqVUM1xNqf91f2Aba1enczyg5T3A3DMAzDMAzDiJJWiru+jVPN4tssbfBUJQcSlSC1Bf/1118BxNQqTYNv76rc15w4CABw51XDAQDX//hh9M28rryxF/xx7PcnnQEA6PJ8PwDA6v1PAhBTnJk33+ZXrVoVV5ag+uk2fqeSofVS+2RVZ9SPdpAvddoIsk1UYWfazJNKwZIlSwAk2uVTCQzzf+/vq36l1c7aCIZt69trqrqlnj+I+v5Xm/YgX/9++v4+YR4tfGUKAA4//HAAMeXxq6++AhDre+ov3K8X+wqPDZsJoL92jXFARVGVddbbH3Mcu+qvmtcoKnHz58+Py5vjk2iUyyBbcp0x0PPAdTuEdrfa5szr3HPPBQCMGDEioQ5q36t9JCh6pp+X9qGwKLv+vkF2/VUNtUtX+2WeO/Y7Xnv9/s9+q55b9HpMeG54TtXLEPdX3/H+eeKsN8vBYw4++GAAsTHJKOBUtzmD9uc//xlAou24zqh+8cUX0d9oN69RtHVm4e233waQOIvBtR0sI4/jfYpt7cdS0Jle7kP1W+O/6Pjw7dJ/73l93D0zF7Fr0K9/fO7r5cExw/PDPqHjJllUd2PXEIlkpLY4NWKKu2EYhmEYhmFUetJKcf/LX/4CAPjwww8BJPqwJb4Spv6F+Sas3h/Uk4v6Ida3Xf7+8DWFKtX1P36YUF71VcuyHPrJuwCACR1OAQAc+eXJcWkyL/qCPuCAAwAkRluk2uhv49s2j2EaWu4w3+kso/rVDoJtyDQ1Ip0qPWxbrshn21OVUE8ULIt/PqlMUGWgmsLv7CNGMEERK4vycx7mMUUVUZ4ntYH3lR/1/619iEoT12wwLfoe5/nXfhlkc83Iw1TkwupDbzJqI6ueVAjtW7kOBoiNRW3D6MzcH/2UY5guyKiUUjnl2AnzDw0k+qPWKIs8hh49DjvssLgyqq0zz9sJJ5wAAJgzZ040L5ZP/U3zGD0POnPHPNmWuhbB7xthayoGDBgAALjllltQVfD7FpDYNlR2eR7Yzv49IcyrSFgEcoV56Cwdvwd5GuMsFT+ZB/svbb95veYYZdpU4nn/UtWY3/11bKq0a2wBpsk8+Hv79u0BxJ4jdO2IjmX/OUPjRqinKradzsBpmvTIE6aOB91/qazr+SFBfcEoGyKZmciQa2DYfqXBFHfDMAzDMAzDSAPSSnEnXBVOdYpvsbTj9lGlSO1B+RZOe2u+varKRvs2Hlf380Kl/cp5HwCIVx3Ud6vmxe1HflkYhrfezNEAgOyOF8TtT+hF5ocffohLx99P1Wseo+0Q5DcZSLSPUyU0mb9lLQ/bina9mofatvM4qihs+yBFiL/Rjlfb1kiO2kf7UDXSiKhqy6p9iX2O50Y9QPjnkb/xk3lS2T3iiELPS+wbjGIa5jUoyLML4TEff/wxgJiyxmPo5SgsTfXjTvtd/u77jGfdwyI9qn0xr1W8llHFV4Wd9sT+zGGY/22tN8cTPdrQM09YpExeM7788suE3/Sapn1BzyfRGTztf0ERp8Pyrgrce++9AIAzzzwTQPgMqa5LCVJmw47R8auxEvg7xyCVZo7zsOjbQOKaKPZrVZ6ZxiGHHAIgdm/jGhB6zaFqzDx4ne/UqVNCfXWmj7PQTJNlOOiggwDErjkaeVgjgbNOfj11HPA724rHqlc3XRtCkt3zFL0nq+98nQ1gn3rooYeKTNsoHeZVxjAMwzAMwzCMKGmpuKsixk/6IVYf5f5vYSo43+z5lsq3c6r6GuFt05I/FN9DE5VstSHlm3CYqr3lt1UJafjH8VNX9VNJ8+vFfdS+TduKqC2tqq5hHkb8bWoLzGNpt8vfqWSoDTHTod2jKkW+DR/Po6q5yZRXI0YyRYfKmx9V1T9GfXOrGkZUcQ/yDsJzTEWOdui0y/76668BhEdUVRtpquG+bbB6fGDfYZ+n+q0zYeoRhb9zDUYybydh3lT0msC24ewUxzJVb44hjZoMJM5saNqap6r5RKNR8rz6bUgFUb2bqE1/mLegsBm8sDIH/ZZsnU1lIyxmgt5/9H4V1J56vsNmLlQF1tkhHd86G+TPsvD+Q9ttHquRu3XNGGdh6VP9s88+A1AYkt6vC+/LfjuFxQpgGpqHrsXSyKr8nTNqXJPl+8pn/nzWUFVe443ocdqmRY1hv37ch3nruiFd+1KVvTOVNWWluKflg7thGIZhGIZhVBQiGSm6gyylGJGWD+6MOkj7Mb5Z8o2Y/leBmKJFezZV51Up4lu4Ku1U26IqY1b1uPSCUD/m+iZMMiUtffvm2zyVs5kzZwKIt+tmeTt37gwg3FY/zC5dlQGWmSp5kFKrdpbqX19Vf1V02fYasZH7UW2kmgrElJzmzZsDiLWR+ro3gklmE6sqtvYNnY1RxVa9nWjcBf8Yehg65phjAADTp08HEIunQGWN6q/OjC1btgxAoj2rb3dOtVijkwbNyPnlZf9lJEW136Zi7/tL1zgJHHdqJ0+4/iM7OztuO1VBVeT8sa558Dcew3HENta0whTsIDt92uoyDZ4X9gGd6dJrgfaFMJXf3xa2TqAqEHaP0HUkbKOg+BokzA4+yLOU/53H8VrLTz1nye59RO3n1UONejbi+Ga/o+07vdFwTPLeACTaqnNcMg+OA/WEFOYdS6MD0zMbP310NpIRYYne0/U4vT7ovT/ZOi/2CdZLr196PTYqD2n54G4YhmEYhmEYFQUzlUkCbaf5Nso3Y41qCsSUWCpcVMv4dqqeaPgWzt+pzqmCVOuIQmWbelmQqqi2d6p4jG5/OgDgrv8V+inODbFvU8WTyiFt7wCgadOmcfvoG72+2esK9DBFTFfqB9nyq50596XiSYVdVSSmTZV15cqVABIjxzZp0iR6DLdpudgnjOTo+fe3ET1PVHjCvJmERc0MslHmeTr++OMBxGIysI9QHWN/Vg9F/J3jmIq1enXwy83IqCw/lTmmxe0c6+xb7Gv0PqP18Wd5OGvE6wnLr/ETNAKmKpJMhzMHGhPBz9f3ZQ0ABx54IIBEH+Bh3lqYp0Y0ZnsBsfHFa6va1SphEZlV5Q1SbYtaH1AVePLJJwHEZqC03+j1j7CNfH/geo0Pm7lQNVyPC5phAmL3Wf9+y2N0PQjHGsdDmN21+jPnvWH58uVxv/v9j/01LIpvmI909dvONqbar2t5/HQ1Ki3hzIDauDOvsHGjzwhBMQ10HLO+GuVc68s+ZVQe0vLB3TAMwzAMwzAqCpGMSGqKe0bRZmbJSOsHd/VMQbs3/82Ydmncl4rcTz/9BCCmsKvnF/VPTKWQ6sP8WoWR3bb8rS8AoM6/B0Xz5BuvvhFrhNErbj0RALCkbqF/5ayQt271onPssccCAMaMGRPNk9tUCaBCo6pLWJlUeVdfv75NpSobbBsqMFRJVa1X21ymQ7t1qo1BdrBUMqgAqq94IzkXXFAYK+A///lPdJueR7U71X4c5oWCfUfT4/gEYtE533//fQCxc021WGdd2Kdoz6n9keq52qMDiWssWO7Vq1cDiK2dYD2YFlUz5sF+qn6dfbgPlUFeizQSM/PWscI2Zx4a5ZFKvP+/qnSzZ88GELvmtWrVCkDMRtm3/wdiY2fq1KkAYtFcuV4AiI0zznzwvKj9rKq1rJf2iTB7Yv+3sP5VldDIm5yhYXvyvJCg+Ay8zvKchXkWU1/7usZF7dL5Oz+prvtphynM3M77EmfaNC1eM/z1TUHpBW3jd/ZZtiXzYD2DPNQAsTZmfYPiprCddX2JelFS9VtnSojur5YBfr105pP100i2/jg2Khdp/eBuGIZhGIZhGOWNeZVJgqoLfMunbaevClNh575UKmg3Tfs4KmW68pzfib5hH/Hk3wEAn17UCwDQ4NWXQ30Wv3b4/wGIKe25l90BACj4Q2XgfqoEsA60L6WK57/NcxttfvUY9Yih9Qjzv6yr4oPURlUfqLapesD9+J3qIs8Fz416TPCVQqoo5qu2dPjKj9phq+9o9T2u8QV0lod9heORKjsAvPPOOwBiM1hUh3msenHiWKB6Tj/PVJNZVvYlf0wwjTAbX47tI488EkCsb1G9J76XKr9+yXxmUxXX6MA666Sed1q0aBG3nf7dORPh15mfOgvBvHltY+RIeuLJk+uNeo7ybeR5nrSP6HVV/XVrmdQWWGf8/P/V/r0qeZUhXFfRtm1bAIlqN9tIPXX512fuwxkk3gvComirpyDup2tcmCf7gK9EMw2OV12XpddrpsXZH/Y9eo5j3+RskNqdA4leVBghmNcOtiXzaNiwYVwZmKbWk/Vi2/p9WMexpqHPAGyXsPUmRNcT+Pc1pq1rcai463MR621UPtLywd0wDMMwDMMwKgqRjExEMjJT2q80pOWDu9pb8y2V330PI1Rx+dZMNY0qLtPi6vUDDii0XdfIdPqGzbfvedUK7WLrDS20GW753TvRvNd+WxgFcresQrXh/733j8K8mhXao0fEDpBqCVUGtSn2PWb49QYSlfZoVFaxlQuzYVfbdyoIqmT731WFD/NdzbZkWdjWzENtb2nfSGXBn0EJU/HDPAcYwfh2klSDwpRNtaXWvuHbuAIxRStoLQZ/o79yekihFxa1aWXf4fhlnuwz3K62wEC4TS9VvaOOOgpArP/OmTMnLg2W8fTTC70/sR9S6fJ9q1Pd/vHHH+N+CxtH2l91nFKpp5rmq32qnPJYqpq85rE+3M7zxGsEt9O2X320A4nXBx7L8rBN+KnjU9fnKP529WZCqqLibhiGEUZaPrgbhmEYRmWFJlI0neLLFF/W+GLIl7GwYEJA7EWUL8EqrKg5pLrwZN5qDkX8YEgayFDzYBp84SZ8UeXLsoo6bdq0ARB7QfZf5mjyRrM7HsO8+WJKwYjiActAoSgs+BHb1n955suxmtbqedKXUW1rNafluVJXr0DiwleeT11MzHKyDxllSEZm4V8q+5UCe3Dfyfx84OnRQbrbYYXNy8Gbw2im5VM0wzAMwzAMY1eQkVH4l8p+pSAtH9w5Xcu3XaoOfJv3Q5rzDVgXbqiLJx7DN2nuzylgKgicTuYbMRe88Hcg8e2bU/N8E+ZbddhbOdGFa7pAyV+gQ8VC3W0xDbaNLjLTN3+qDyw7gzwFheJmeWiaxPOhpky6MJhtrWoRt7Ps6lIOiKkkap6hZkRGcnxTGVVuNKCHjgFdtMXzy35OE5nXX389bn9/H3VXyjzZB9QUg/2bLkN1UTWP5/gEYiZnukivffv2AGJ95osvvgAQ679HH300gETzDnWd6ptw0dSHn1xES4VQF3MSHZc0K6IZD91H+i41WS4NcsNASlzIx7blwnuOU6qa/F0XGwfVmW3JPsGxGbbokOdPg1ap4hhkeqeKZ1UM2f7oo48CiPUHntswF6dBLjPVlFHNINUMSs+VBjRSszXu59/79Pzyk301bPGmmsBpvXjdoFruX/81QJIq0Jqm3vv0eqdlD6qn3qt1NiMs+FVYMEaWTcsQFKAszBED76N8vmAfMiofafngbhiGYRiGYRgVhUhmJiIBAkjQfqUhLR/cqXLTdo1v30Huw6ii8Y2YShGVPbqAU5s7vjGrIsY8+PZNu7p58+ZFj+UbfIcOHQDE1DZdgOYrdkCiiyxdwKbuL/238bDw8xpERl3I8ZOqFhcHst1YxsWLF8cdDwCHHHJIXF7qxlED92g92fY8F+pKjOfVt/fj/6q4WyCm4nHZZZdF/x82bBiARMWNaJhyXRjMMXDEEUcAAD744AMAMYWbC1CBWP9iUCAdf2GqHvsnlUcq8HTVSPdx/sJ0Ls5kX6G9MN0l0l0ax3LHjh3j6qvKLwlacMrxQrWLi9zZNgz45reFj9ods51UofO38TrC8cO24DjigvVGjRoBiLV5mBvJoEWg/gJcIDajoTMeanOtsxOqMAbN4DFNDYZXFRV3wn7Oe526aNVPvz3ZjurSWBVbDbykLoTZTzQoGvPylWhdpKxuiPXaovsxD870qmtknZX1y0dbe37nLBH7vbqz1PZgGfX+yzL4M796L2a5w5R2Xs/U1a6eC72O+Ocz7JxrWuwzRuUlLR/cDcMwDMMwDKPCYItTw+GbNN/KqbIFhQnmvhrwhQoR7T2piIWpa0R/5xsx1TwgppZR2VPFQ9/CwwJiqA2e/h7kYk1VNA30EmZDpyqizhKoQurXoyhlUrczT7Y9FQOeG10/4KsS6iKT+1h455KjfVyVNrVTZdszcBYDnnzyyScAYkFjqIr5drkMAkQVWMOTq1rGvBhgTAOAqQ2s31dob/7zzz/HHcuxTzv07t27A0hU/9TWV9vJVw9pi06Vnyrm8ccfDwA45phjAMRmIzQ4lI5l362lXza/zjozpe45adtLlVLro/VQF45+nbUN9NqkKqZ6ImGZggIFab1YnrC0qxJcn7D//vsDSFwXpWsMfHje2U/URpp9TGc/+MnZLfbNMPt6350vzzfLFRbwL8w9KPPmPZP9iAGJdG2Mnzbrw5m+sFloomvH+Mm+6a+XAeLHv66pUht33Y+zAaqS6+wG01F3t/4+ujZFxw37jFF5ScsHd8MwDMMwDMOoMGRkpKi4V0GvMlTn+GZMW056LQkKIMK3aXqloOJHrw9UD2mDSoVZ36Cp/vANOuitnqoClXf6U1XlnOVUtZtlZT1Zr7Cy+Og+VAJZFn1bVy8QfHtnHThTQSXAV+OYP9/0WU5VVdg2nCFhW3M2QNVXnpMgjwnMX8M8+zMBRvGgvfvo0aMBJHo60JmsVq1aAQBatmwJAJg8eTKAmK9lVUx5foGYGsRPpsl92DeoOPF3fufYoJK1zz77xOXp22Sz77Kv85hvv/0WQEylJ6pEE/VGQfx1FZ9//jmARJtu5smxwfJyzYheP/QaoOHlgZgSyHrpbBPTYP2oXnI/qni6bkeV/KD6qKcSHqu2ujpLEzQb6qfr/6+ev5544glUVe677z4AsdksXY+g58W/9+l6BA1CqPcPtb8mer8K80YDJNqqs/+oBzEN5sby87rO6zn7LNewcMyxDkBMteY+PIbXDN77wry46VjjTIPOGvjjX23ctW2Irv0Ia3OuYWC78dz5++v9Vr3o8Dv7jFF5ScsHd8MwDMMwDMOoKEQyMhBJQU1PZZ9kpOWDO9VwvuVSSaCNm68A6Cr0lStXAojZV3MFNt9WaYNLwsK7a2SzIK8PLBcVAH2zVz/YOitAWz2+fdPOT5V6fxsVaSp7VPqodi9YsCCuPVhutpPaKKo3Hl9ZU/WM6oqusCesH88f96P9MiPbqS2yb+enPoXV77dRci666CIAwGuvvQYgdh7YF2hnS0VqypQpAGI+xnkuVI3ylSoq6zxfhx12GICYhxd+cgxQWeP5Vn/H7Eu6lsPfpnbzzJt5sH7qKUUVRabDMk2fPj2al/pC5xjnuNPxSEWR62A04mKYf2cgUb3mp9qjq/cJ3y7Yr4/uH2R/rLMNqqjzU31g65oUElQm9Rse5q+6KsIZKt631NuP2kgDsfHIfdkX1Zab51ttunUmRu87/O6rwjoOfPt3IKao67Ecq9zO+7Smw/EehN53Vb1Xjzc6o8ixybx0NsyvZ1hbkLAYEMyLbcoy8dzw+qjnzj9W134wbbNtrzqk5YO7YRiGYRiGYVQYIil6lYlUQa8y6vWCSgEVXN8eVNUpHkO7N77h/vLLL3Hf+UZMRUjtXMP8pftQmVR7XZaJb8hU/VUxo0pH9YGKIct0//33R/OaOXNm3D78ZBrfffddXB6sD1UG2harbWKY/2X/N6JKmUba9G2d/e88Fywzz596+QBi6onmHRT10SgZF154YeD2jz76CADw9ddfA4j1BfXownPBPuTPTtHunEqzrnvQ2Sn1hMKxwr6lSnvQGgz2aY43qnb8DIvqGbamhJFJ/bUXqhbreg3Olt17771xaTIy5nnnnYdk+HbeGptBZzh05kBVfPUFrp6lgqJwEp1xZHvrjAHPR5gnG+JvZxo6M2IA33zzDYDYONFIpDrb6dN4TmEfawzg1w8L7xPrfl6LGgC2b/sjFkPHQiW/2SUXApvW4LvdW0THc1g/YZ7+/Zbnk+efttvsqxy3nB1X/+bMk8dxzRk9QwWt91L7eObB+4t6tGGeTIP3adaH92vOrKmnNSBxnYleK3SmjN81fgq3q6cftXkHEmcKmDbHNfuIUY6UkTvI0hnaGIZhGIZhGIZRJqSl4k7U7lXf1oFEez7uQ8WPnjE0IiNtzIi+7arC5qPKlapPTJv2ilSWqARccsklcelROWjfvn1AKxTSuXPn0N/8NPv37x9YBvVDq+pdkPcItaHVyK+EeVFJY1tzO1UVHk/lIyhKnqq66jHE2HWccsopAIABAwYASJyd0dkoVXaB2Pljv6N6T9TOln2AfYp9gfupraxva0pVkmsoqO5r/ACOP9ZHxzavIZzVomcLv19q3e+55x6kQlFKO7njjjui/z/55JMAYmOS7c/y6LVL40WoXXEy23a1p1Wf32HrWIhGQdV1MUE+47ntscceSyhPVYUzLq+++iqA2PonXZPk9/8gX/mpkpeXl7DGhf2EYy8o+q32E453XvN1dkijiGukWM4YpxJFl2q8zsIxTbWj5+wt730so3paC4oszLTYFjoDzLzVm0yYL3x9VuCnfz55HnRGirN5Vdn7UkXBFqcahmEYhlEs9ls7DwAw7ZpHAABrBz0D4I+HyMuPSXCLCAArUPiwuPK2QleCnZ+5C1izDr81OLQMS24YRiqk5YM733Z5AaLdbJBXGVVx9C2aChGjLOpbd1iEN5aB6QWpikQjm6kiyfL369cvab13BnfddReAmHKj/mfVL7DOKPj1VMVPtxMqnlRR2MbqZScsap5/g9GofqqmGLseni/1RqJrONSjBJDYr+gTnjNgPIbfqbipnaoqXEF+wqk8c40I86YXnDDPD+pBitsZ/ZT4ftxp985jdiW33XYbAOCf//wngPAIqTpjoG2oXnd05sz/TffhJ69/am8fZvur6frojICRCGMQcBZW2yqoXXXtlJ5/jpmgdi8oKEhQ3Hnt5SwnvwOxccg8dJaV13a9d/M7Y7JwP9aH36mqB6ERVJkm7xFci8M8WS+dOdSIsqyTX0/uy21hvtX1OYL3tLC257liOkFrQzRt9gmjAlBGNu5p+eBuGIZhGEaMtiu/AFYCYy8tNGXrMO1NALEH0VTY9uCDqF69OlaOH1q44YqDdnYxDcMoJWn54K72YBqh0beDUw8lfNPVldl8+6bdm77V8ntY3r5tp9rxEX2r5u9qk1oWME9V1MLaSWcNgET/12pDyO2q+Kh9o9q2Mw+m4yu33EYPAkwjmScMY+eiSi7HG/uURjn1bcFVkWNfoPKukYtV3Vdbdn5nP/BVsR9//BFAYpRdKmxhfsLZ/zRqsO7v58WosYxwWRbcfvvtAIBBgwYBCPe0E+bHXSMxEl/l47kOu+5pNGhVZ3X9kc42+jNlTPsf//hH0ZWvotCO+ZVXXgEQixaqawuAxPVY6hWGnzpbErRuC0iMrMtz7c9y6TVfx4x6aWP/oZJOxZ2zWQ0bNowrE2figmC5mDejhhO1gWdZdFzoOiqdqfCPYZ5h9x9tU37qvS6s3fxZEJ4n/saZRLNtr0BkZKSouJuNu2EYhmFUSTqt/RJYC4y6+iUAwPEz3wFQusWpv/9UuOAx0djNMIzyJi0f3GmzRsWLfsD51up7plAlmeqg+qLV/fm72nSqtxXdD0iMqqq2pKrel4dNp5ZBo+NplDm1NfT/V4Wdx+rMgs5AqA9iKglMjwqJr4jQZpLnnOUrznSwsXOg2sTzTmWb3/m7eooBYuoRzzXHjPp95vmlmh/mr5/rKGhrDgBLliyJO0bXUBCNfqieH1RNU48RQGz8H3po2S/m69u3LwDgwQcfBBBrb9ry81PXIuiMFz999VZ92qvtrSrshOeN45SfGh/jpptuKkGNjVmzZgGIrc3y0VlmnZXSGRg9p/490R9veq/QWRT/f+0PhNv1vqnrvRhFm9eUtm3bAkg+O83yLFy4MK6+6kUqrAxhZQ2aidCZaL1G6POFpqHrTlSJ15lGIHaN5L7sA7169Qosv1H2RDIzEUkhpkwq+yQjLR/cDcMwDMMA5vzrLQBA67f+k3TxZnHYo2HtoncyDKNcSMsH9x9++AEAcNRRRwGIvbVS1fEVM76h821b/aOqfZsq7KpM69u6vlEDiREYiSof/B4WqXJXwjzfffddAIlqi37qqnj/N1UuVKXTlfFsK7Y9owFyNoTp8jh/zQLPsSoV7BNnn312ii1glBQ9r2G+jNlX6EfcP5YPGDrO1IZd7XF5PG3hqcwxQqlvHqD2ovQqoTM8/K5KuyqU7GsahdlvC02jLAmzDX/66acBxNRM9VfPcRjkCz9sHYCiaj1nwHie2GbMm96tjJIxcOBAAMDDDz+Mrn9sc85FZySBWL+lAs9zw5lq9dDE67avagfNcKnKHLSmjOdZ7eh1tkuVa84Osf8w9gLjPdDLFMcyELOLp803xynXyTBN9muWQb3JaDRglpl18tuCbRRm2859uWZOo7Wyzbmd9eVY1HVCfl7Tp08HEOsDRgUiIyM1+3WzcTcMwzCMqsmU007DCSecsFPTbHLq8QCAtTs1VcOo5Jg7yHDuvvtuAMCoUaMAxJQkVbSBRLtVfeMP818eZrsWFlHUVxv5v/qWVgWvIkT7ZBnYhiyjKvDqSQBIVEMVbUNdP0BlhGnrCv2g86nefuh9gH3CKDvYvzUqoCrt/hoOKlXa93k+NQ1CJZGeImbMmAEgcUbIV8HVp3K7du0AxPoX+yFnDNTnss4G8HeddQNi46UijGlF7cjvu68wyI5GjuRnUKwGHcNE1yJwRmzt2sJHPkZ5NXYNjNDLaMatW7eO/sb+yjGnvtS5Xddr+WRkZCRcjzWCqn99Zh/ieOW+VJTDYgmolygq6/zO/sQZNkYL9evJvqlRV5m2rt9iWVhWfufaFV7f6K3Obx9dt6P3TY2Szk/1FqMLiJknZw/8PGm7n2pUZqPykpYP7oZhGIZh7Fw2/f36wn8eeS7h5dkwjOREMjIRSUFNT2WfZKT1gzvtWunrVf2DA4keXjS6o9rWBXnAAFJfJQ+ER2BUZaA07rp2Fmqvqx4m2B6qjACJnnbCUL/AVDjok1c91qinH7+ddMaDfcDY9dBWmueD51G9UlBpV28z/jE81+xfqrj5drP+dqpfp556KgDgiy++iMszSDVk2lTiVD3W/qvjUpV74q/dYH3o8aoi88ADD6S877/+9S8AiWPyhhtu2KllMgzDKA/69++PcePG4ccff0SNGjVw7LHH4vHHH8cBBxxQ3kULJa0f3A3DMAyjqnPLLbcAAJ599tnoNrpQDDOR8ReQ/nh6TwBAm7fHJLh5DHMFShesPhTE+AJNU0aigaJU+FJXwPvuu29cnnwx9l+iaZ7D8nBRKtNQUYBpqKDEetPci+ajNA/1zWyZV5gTC02b9dMAVOqaU92r/vTTT9E0eI6NncvUqVNx/fXXo2PHjigoKMDdd9+Nbt264fvvvw8VckOJpLg4NWKLUw3DMAzDMAyjWEyYMCHu+9ChQ9GwYUPMnj0bJ554YjmVKjlp/eDON9DJkycDiL31+uYxfMPn9LeGDeYbMo+ha0K+xes0OqfwuVhGQzYDsbdrdfvI7fx++eWXF7fKOx2WYeLEiQASQ8ur+0zf7EED7tAUgfuqUkOTIS4sYltyPy7s09Dtvnqh5gqmQpQduvCKfYMLRhs3bgwgdj5pCuW7FKQaxvOoC8U0CBf7iAZ9YR85+uijAQCfffZZXJmAWL+hahemjqlpjAZK0/oHmeNwG68LlYWbb765vItgFAPfhOnjjz+O+41Ku++ydPZR3QEA7We8h/z8fOTl5UXHoqrA3K5BtPx7H3/jvlQs1X0ixzWv+bwO0A2iOpNgOjSLPeSQQ6J5zps3D0CiGZ66ZmVeHO/qKjps3DMdv568FrCeatqnAZb0nhbmPpbPIfzdTNLKHp1xKQ5lZeNeOr3eMAzDMAzDMNKcHTt24KabbsJxxx0X93JY0UhrxZ189913AGLhxv2AL0QVO7XFoxpHVZhv3xqgiW/QVBOZrh/+nKqBhihmHjy2IsEycfEfy8y2ZD19d3eqmLPeVDBUfWEb6QJEnhMqJXqcD3/jOf/Tn/5UgtoaJUHDk/N8coEw1SMN5MOF3/5vPNfaB8JcixKqZVSuWCYGZGHAH3/fAw88MLAeWqawYCq6qJz4CzZZD6o1hlHeLFu2DADQpk0bALHxWr16dXzRsQcAoOfEpwAAi/Pzo/crXvO5P23k2cepbPuBmgjHHMcMbcGZhjpu4HVAXU1yP3XdSjeJ/iJwlpN56ThW14xUszVIlAZfVIXevx/xf12Iz7zp/pL1Upt3dT/NOnA/njujbLn++usxb948TJs2rWQJZGSk6MfdbNwNwzAMwzAMo0TccMMNePfdd/G///0PTZs2Le/iJKVSPLj/7W9/AwAMHjwYANC8efPob2qPy7dovumqu0NdWa42dwrfvH01TvPgWzeViosuuqjYddzVsEzjxo0DEGsXtT/37YFZ97C2oRqhIaPVrlntBNnmQTbuS5YsARA750bZcd111wGIhdrW88tZG9q6q008EDunYbbrRO3J1VuDrlHxXTMS2qRSjVfVS1V79m31phHm7tSfjWNwFLNJNSoKc+bMAQAcMm44ACAfQN3WDbEDwNmTngYALKtT6HmmGmL9Xtd8qBLNcR/kgpXKMccWVW0NfKjrv1TBpvrPewHXnjH97OzsaFoc39yHaa9ZsyYub/UOU5T7YZaJa7n8dtHrlXqZ4TWDaYet29IgUKw3z12vXr1g7Fqcc7jxxhvx5ptvYsqUKWjZsmXJE8tI0auMKe6GYRiGYRiGUTyuv/56jBw5EuPHj0etWrWi5lh16tRJeOEsikhmJiJFmHtyv9JQqR7c//KXvwCIBQ0BYiuD+QasK+vVjyzfePnJt2zaflPZ4yfT1VXlPkxj+fLlJaxZ2cEy8q0zzKuO/5u2CdUEKrBUUcJsCqlGUE3hwKGa6vsCNi8XFQeeT511Ul/EviLHvqD+jLkP+xDHDLer8q6emnR/IDZm1ZNFmPKuHpWIjoEgdf/nn39O2GYY5QkDpvGzQ4cO2IjC2dS1+X/Mqm7ZEl2LwvGs13H1uqIexvx7gtrF6/om3nd13Kq6rTPivJbQQ5S/TozbmDbLx310PPPao+tpWEadCaa9uj+zrP7mVVFn/Vlubmd9db0A8/r2228BxM6ZsesZNGgQAKBr165x24cMGYIrrrii7AuUApXqwd0wDMMwDMMwUiHMKUGJyMhMcXGqKe4J+KrsY489BiCmvvGtmW/IVBf4RkxFUH2PczuP56fuByR6oVBPGhUZXeWvq+WD9mVbaBvqSnl+56wH91dFk6oLPYTceeedpauUsVO58cYbAcRs3akiUeFq0aJF3PYgG3G1VVc7U/Y/HquRBtkvuRZFVTUg5k2DeakNryrn/F09QeiMEvv7ggULoseabbtRUbnpppsAAKNGjQIA7LfffnG/U+3VSKNUpDkGOfZoz83ffW8rVMg5dvyYKn5avP/yXqDjWz2WcezR5t2/l3Kbztapn3aNHMu8VO1Xj3OMT+JfL9SHvar43Jf1Yn2YB68xGtuE58owklEpH9wNwzAMwzAMo8wwxX3nQLV22LBhAGJv2+rhRFUFKszczjdjHqc2fL4CoN4p+AZ/1VVX7cSa7RpYRqozVCvYLn49uY1twXqrL3z1SlCULTS/m9JesaHyTh5++GEAMS8z7Cu+Bwaee/YVjjONaqp+nNUbA9V9rsngOPTtVrm+heNPPT2orbuWRWeZeBxVM19xN4yKzqxZswCEe0DhONH+r9dnqsy8l/o27mFRicNmu1Sx5rWDn0xbbeP9WTxdB0O7car/VOQ1zgivSxobQu3VVfX302CeOoOo39m2YQo8z83FF18MwyiKSv/gbhiGYRiGYRi7kkhGBiIpuHpMZZ9kVJkH9969ewMAJk6cCCAxQhvfulUdVtWcb8pUCqg2+xFFCbcFRQCt6LDMbBe1I/S3UXWgCqo+bsP85Kqqyu08V0Z6cc899wAAnnjiCQDAEUccASBeBQ/zv64KvK4hWb16NYCY/2aqalTD1AOGj0ZK5XemwTFNhU493ejalBkzZgAA+vXrF9QMhlEhGTBgAADg0UcfBQCccMIJcb+zv2vcEV3vRKVd1zgBsfHLdU48VuOocFa2Tp06AGLjlvdTjkFd6xI0G6YzB6wHlXOmqdcaro9R3/OqvLO+vsrP/NlGWl/mFebBhvX76quvAMTOjWGkQpV5cDcMwzAMwzCMXUFGm6OR8ceLYtL9/nAxWlKq3IP7Tz/9BABo164dgPBocbpdfdlSpUumAPDYiuoLNBks85gxYwAE15OqvPq8V7/ZGqGScD9+8tx07959J9bEKGvuuOMOAED//v0BIC58dIMGDQDEZmsIFSqqX7/88guAmKLF8aeKOpUu9jWmDySumVBPD1QK586dCyDmeWr//fePO54RGL/88ksA5vnBSG/uvvtuAMDLL78MADj44IMBxNRijg+q42r7zu1Usmt7Dyq8b9L3OT81UirVevVUo/FW9Di1S/e3adpqo86y0a6cijvrpx7m1OOVf//S+vFeyDx0lk5nlXmv47kwjOJQOkMbwzAMwzAMwzDKhIjbqd7n0w96m9GV9mqfTl+utIMlqiL7x55xxhk7v8DlxLvvvgsgUSkFEr1zUCVdu3YtgJidH4/l/uvXrwdgNu1ViQcffBBArE/wk4RFJFTPF1TYua6CfY529QDQqlUrAIn9Uz0+UFFn1EL+TqWNswCmjhmVkZEjRwKIxV/gGGS/1/VbajtO701ATFmmEq3e2AjHK2e96tatG5e2znhrPBXahgOFEWGBxKjoqpTzXs5rBtPUe7rOyLGevo07o3mr4k54r2MavF4tXrwYAHDJJZfAqDxs2LABderUQU5OTtwM1M7aXzHF3TAMwzAMwzDSgCqvuBeXf/7znwBiiqAqgUDltoF9+umno//Tjo9diLaDt99+e5mXy0hPqMCzL1G9owrGvkX7VbVLVaWrW7du0f+puOlaCsKxS481tHW3+AFGVWTQoEEAgLZt2wJIjGXCMarffU9jGjk0LA6D2ojzOCrVqoJzvFMl51gFgMMPPxxATN1W+3Kq+5w5oKKuNvq6Nk0jn/ve0riN5WI99TvToE173759YVQ+THE3DMMwDMMwDCOBKudVprRUdTW5Ms8mGOUHFTn1Ja0qmEZWJVTZfK8z6k2Cx4ZFWjSl3ajKUA2+9957AcQ8r3GtiHqC4fjxlWiOU7Uz13HNNWX8neud+Mn9NZ4Df/dVfm5r2LBhXH2ozusxul6N29WrDOuiXnWAmC0+j2H5WG56xfr+++8BAA899BAMY2dhirthGIZhGIZhpAGmuBuGUW6oHSm9L6iCxe3qx5nH0Qe7r4qpxydV1pgHvcoYhhFTh2+55RYAQP369QEkRgPlWPTXmWhMD3qL4bEad4HbqcCrfTnT4yfXo/gza9zGdWca/ZzRWdXLDNdkMS16peE1hd5nmLdvO6/esFhu2uzPmjULgEVENXYNprgbhmEYhmEYRhpQ4R7cly9fjgsuuAB77bUXateujbPOOitqL2YYRjzpPl7uvfde3HvvvSgoKEBBQQFyc3ORm5uL/Px85OfnR79v2bIFW7ZswY4dO7Bjxw5kZWUhKysL9evXj/vLyMiI/mVmZsb9+b9lZGRgw4YN2LBhA9avXx+1gzUMwzCMikyFMpXZtGkTTjrpJOTk5ODuu+9GtWrV8K9//QtdunTB3Llzo4tKDMOw8WIYxq6DZh7XXXcdAKBLly4AgObNm8ftR7MXIGY+o4EMuRCUZigrV64EEB7kiKYnfKFetWoVAOCyyy4LLe/o0aMBxMzmaH6j5ngaHKpx48ZxeXKxOk2AuN1fEM9tZMmSJQCAqVOnAgCef/750HIaRmmpUA/uzz//PBYsWIAvvvgCHTt2BAD06NEDhxxyCJ566ik8+uij5VxCw6g4VKbxQo8u/fv3B5Don503Sj4QMMojPV7o/kDsxswbrtq8L126NC5vwzAMw6joFCsA0yeffIKTTz4Z48aNw9lnnx3328iRI3HppZdi+vTpOOaYY0pUmE6dOgEAvvjii7jt3bt3x8KFC/Hzzz+XKF3DKA+2bNkSDcf91VdfRRc3rVu3DgcffDBatmyJTz/9NCEceKpUxvHCB3d9yE71wd2fZVCljMdykRqDuCRT8QzDiIfuIg877DAAiAsgs++++wKILfjkWKMSz8cNXWzO7VTDs7OzAcQWhhZnjA4fPhxAbDEpF9eqqs/rLsuq23n9YFl/++23aB4s5zfffAPA3D1WdSp0AKauXbtiv/32w4gRIxJ+GzFiBFq3bo1jjjkGW7duRXZ2dkp/ZMeOHfjmm29w1FFHJaTdqVMnLFy4MLoK3DDSgRo1amDYsGH4+eef8fe//z26/frrr0dOTg6GDh2KzMxMGy+GYRiGYaREsUxlIpEILrvsMgwYMAA5OTlRN0tr1qzBhx9+GH04GTVqFPr06ZNSmnzTXrduHbZu3Rp9Y/fhthUrVuCAAw4oTpENo1zp3Lkz7rjjDjz++OM4++yzsWrVKowePRpPP/10NLS4jZcYd911V9z3hx9+GECiAs86aoAWPzALt6lrSb7Q+AqaYRipoerygw8+GP2/e/fuAGLjUJV1DX6m9ufcj2P0iiuuKHb5qM4PHToUQMwlJfNi2XhN4fVBy8hrLVX/mTNnRvP4xz/+AQA4//zzi10+wygtxbZx79WrF/r3748xY8bgyiuvBAC89tprKCgoiA6Y7t27Y9KkScVKl4ND/aMCsZsz9zGMdOL+++/Hu+++i969e2PTpk3o0qUL/va3v0V/t/FiGIZhGEYqFPvB/cADD0THjh0xYsSI6IP7iBEjcPTRR6NNmzYACtWwICUwGbRHS7bIzA+AYBjpQvXq1TF48GB07NgRWVlZGDJkSFT9AWy8JOOee+6J+84FtzVr1gQQU8XYnr6HC6p4VNaotP3www8AgNtvv31XFdswqgxUnwHg2muvBQAccsghABCdVaQdL23eCccvzQDpypaebEoD1Xp6eOF6GNq8+9dgIDGI0k8//QQAmDdvHgDghRdeKHWZDGNnUCKvMr169UK/fv2wbNkybN26FTNmzMCzzz4b/X3Lli3IyclJKa199tkHAFCvXj3svvvugdPX3Ea3TYaRbkycOBFA4UP1ggUL0LJly+hvNl4MwzAMw0iFYnmVIdnZ2WjcuDEeeeQRbNmyBQ8//DBWrFgRfZMdOnRosW12AaBjx46IRCIJXjK6deuGhQsXYuHChcUtqmGUO9988w06duyISy+9FHPnzkV2dja+/fbb6BoRGy+p88QTTwAATjvtNACJYdd90yEq7jQdWrZsGYBCl5mGYZQdffv2BRAbi1S7OX6feeaZMitLv379ACTasnOmctCgQWVWFqNyUNZeZUqkuNevXx89evTA8OHDkZeXh9NOOy360A6UzGYXAM477zzceeed+PLLL6PeMubPn4+PP/4Yt912W0mKahjlSn5+Pq644go0btwYzzzzDBYtWoSOHTvi5ptvxuDBgwHYeDEMwzAMIzVKpLgDwNixY3HeeecBKFycesEFF5S6MBs3bkSHDh2wceNG3HbbbahWrRoGDBiA7du3Y+7cuWjQoEGp8zCMsuS+++7DQw89hMmTJ+Okk04CADzyyCO455578N577+H0008vcdpVcbxQmevWrRuA2AJcXsZ8G1p6i8jNzQUQ83d/0003lUlZDcMwjMpPhfbj7nPmmWeibt26qFOnDv785z+XNJk4atWqhSlTpuDEE0/Eww8/jHvvvRft27fH1KlTK+VDiFG5mTNnDh599FHccMMN0Yd2oDBSZ8eOHXH11VdHQ3qXBBsvhmEYhlG1KLHiXlBQgMaNG+PMM8/Eyy+/vLPLZRiGEcr3338PINGrju/HnTbutPXnDKFhGIZh7CzSRnF/6623sGbNGvTq1aukSRiGYRiGYRiGkSLFXpw6c+ZMfPPNN3jooYfQoUMHdOnSZVeUyzAMI5R27doBAO6444647f4EIj1WDBgwoOwKZhiGYRi7kGIr7oMGDULfvn3RsGFDvPLKK7uiTIZhGIZhGIZhCCW2cTcMwzAMwzCMqkza2LgbhmEYhmEYhlF22IO7YRiGYRiGYaQB9uBuGIZhGIZhGGmAPbgbhmEYhmEYRhpgD+6GYRiGYRiGkQbYg7thGIZhVDB27NiBF154AYcffjhq1qyJRo0aoUePHpg+fXp5F80wjHLEHtwNwzAMo4Jx++23o2/fvjj00EMxYMAA3Hrrrfjpp5/QpUsXfPHFF+VdPMMwyoliR041DMMwDGPXUVBQgEGDBuG8887Dq6++Gt1+/vnno1WrVhgxYgQ6depUjiU0DKO8MMXdMAzDMJKwePFiRCKR0L+dTX5+PrZs2YJGjRrFbW/YsCEyMjJQo0aNnZ6nYRjpgSnuhmEYhpGEBg0axCnfQOHD9c0334zq1asDAHJzc5Gbm1tkWpmZmahbt27SfWrUqIHOnTtj6NChOOaYY3DCCSdg/fr1eOihh1C3bl1cc801Ja+MYRhpjT24G4ZhGEYS9txzT1x22WVx266//nps2rQJkyZNAgA88cQTeOCBB4pMq3nz5li8eHGR+w0fPhwXXnhhXL6tWrXCZ599hlatWhWvAoZhVBrswd0wDMMwisErr7yC559/Hk899RROOukkAECvXr1w/PHHF3lsqmYutWrVwsEHH4xjjjkGf/rTn7By5Uo89thj6NmzJz799FPUr1+/VHUwDCM9iTjnXHkXwjAMwzDSgblz5+LYY49Fz549MXLkyFKllZOTgy1btkS/V69eHfXq1UNBQQE6dOiArl27YuDAgdHfFyxYgIMPPhg333wzHn/88VLlbRjGzmHDhg2oU6cOcnJyULt27Z2+v2KLUw3DMAwjBX7//Xece+65aNu2LV566aW43zZt2oSVK1cW+bdmzZroMf369cO+++4b/TvnnHMAAP/73/8wb948/PnPf47LY//998dBBx2Ezz77bNdX1jCqEM899xxatGiBrKwsdO7cuUK7XDVTGcMwDMMogh07duDSSy/F+vXr8dFHH2GPPfaI+/3JJ58sto37HXfcEWfDzkWrq1atAgBs37494fj8/HwUFBSUtBqGYQivvfYabrnlFrzwwgvo3Lkznn76aXTv3h3z589Hw4YNy7t4CdiDu2EYhmEUwQMPPICJEyfigw8+QMuWLRN+L4mNe7t27dCuXbuEfdq2bQsAGD16NE477bTo9jlz5mD+/PnmVcYwdiIDBgzA1VdfjT59+gAAXnjhBbz33nsYPHgw7rzzznIuXSJm424YhmEYSfj222/Rvn17nHjiibjqqqsSflePMzuDbt26YdKkSTj77LPRrVs3/Pbbbxg4cCC2bduG2bNn44ADDtjpeRpGVWPbtm3YY489MGbMGPTs2TO6vXfv3li/fj3Gjx9fZBplbeNuirthGIZhJGHt2rVwzmHq1KmYOnVqwu+74sF9/PjxePLJJzF69GhMmDAB1atXxwknnICHHnrIHtoNYyeRnZ2N7du3JwQ7a9SoEX788cdipbVhw4adul8Y9uBuGIZhGEno2rUrynpyukaNGrj33ntx7733lmm+hmEUj+rVq2OfffbBfvvtl/Ix++yzTzR4W3GxB3fDMAzDMAyjylG/fn1kZmZGF4STVatWYZ999kkpjaysLCxatAjbtm1LOd/q1asjKyurWGUl9uBuGIZhGIZhVDmqV6+OI488EpMnT47auO/YsQOTJ0/GDTfckHI6WVlZJX4QLy724G4YhmEYhmFUSW655Rb07t0bRx11FDp16oSnn34amzdvjnqZqWjYg7thGIZhGIZRJbnwwguxZs0a/OMf/8DKlStx+OGHY8KECQkLVisK5g7SMAzDMAzDMNKAjPIugGEYhmEYhmEYRWMP7oZhGIZhGIaRBtiDu2EYhmEYhmGkAfbgbhiGYRiGYRhpgD24G4ZhGIZhGEYaYA/uhmEYhmEYhpEG2IO7YRiGYRiGYaQB9uBuGIZhGIZhGGmAPbgbhmEYhmEYRhpgD+6GYRiGYRiGkQbYg7thGIZhGIZhpAH24G4YhmEYhmEYaYA9uBuGYRiGYRhGGmAP7oZhGIZhGIaRBtiDu2EYhmEYhmGkAfbgbhiGYRiGYRhpgD24G4ZhGIZhGEYa8P8By9AKCb57WEEAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from nimare.correct import FDRCorrector\n", + "corr = FDRCorrector(method=\"indep\", alpha=0.05)\n", + "cres = corr.transform(results)\n", + "\n", + "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"FDRcorrecred-SchizophreniaYes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"FDRcorrecred-SchizophreniaNo\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"FDRcorrecred-DepressionYes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"FDRcorrecred-DepressionNo\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After FDR correction (via BH procedure), areas with stronger spatial intensity\n", + "are more stringent, (the number of voxels with significant p-values is reduced).\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -270,18 +524,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", + "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", + "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", + "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "t_con_groups = inference.create_contrast(\n", " [\n", - " \"schizophrenia_Yes-schizophrenia_No\",\n", - " \"schizophrenia_No-depression_Yes\",\n", - " \"depression_Yes-depression_No\",\n", + " \"SchizophreniaYes-SchizophreniaNo\",\n", + " \"SchizophreniaNo-DepressionYes\",\n", + " \"DepressionYes-DepressionNo\",\n", " ],\n", " type=\"groups\",\n", ")\n", @@ -289,29 +602,29 @@ "\n", "# generate z-statistics maps for each group\n", "plot_stat_map(\n", - " results.get_map(\"schizophrenia_Yes-schizophrenia_No_z_statistics\"),\n", + " results.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_Yes\",\n", + " title=\"SchizophreniaYes-SchizophreniaNo\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"schizophrenia_No-depression_Yes_z_statistics\"),\n", + " results.get_map(\"z_group-SchizophreniaNo-DepressionYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"schizophrenia_No\",\n", + " title=\"SchizophreniaNo-DepressionYes\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"depression_Yes-depression_No_z_statistics\"),\n", + " results.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"depression_Yes\",\n", + " title=\"DepressionYes-DepressionNo\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")" ] @@ -330,6 +643,77 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform family-wise error rate (FWE) correction on group comparison tests\n", + "The default setting is performing Bonferroni FWE correction.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n", + " get_mask_bounds(new_img_like(img, not_mask, affine))\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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74zqeEFa+XPz9738PBx10UE4b69+pp54aQghh6tSpoUWLFjm/1a5dO/zhD39Y7f5QnvUpr7zySgghhO7du2e2bbvttuHXX38NS5cuDUceeWTOObvnnnsy4ztOJ75G9erVK8dG+49//GNYunRpWLRoUWjYsGFme3ztHTBgQNh0000zv/3+97/PHKPXR/LDDz/kpMc/9ul777031KxZM7O9Xr164YsvvgghhNC5c+c11s6HHnpoXh+oWbNmeOSRR0IIIVx33XU5v+mD+1577RWWL18eJk+enFP+tAf3/v37hxBCePbZZ3Ou8fvvv39YtGhRWLp0aV5fS/vzg7sxq88aeXBPQy9At956awghq64DCI899lgIIWQexrp27RpCyL1ZU13YbbfdUi/uaajC+uabb4YQQrj66qtLrV+nTp1CCCsfRpMWDL388sshhBD++c9/Jt48fv/73yemy4cYVe+AlQ+6fDCOX174V79+/VBcXBxGjBiR2damTZsQQggzZswIm2yySan1YrvNnDkzceHfvffeG0II4cILL0w8ng+jxxxzTGbbwIEDQwghcfFc586dU/tESX+1a9cOTzzxRFi+fHnOOV2+fHn4/PPPQ5cuXfKOKc/5jc/F2WefnffbFVdcEULIV7LL+uB+9tlnhxBC+Pbbb3POy1lnnRVCWPlil3Tc0KFDQwi5C7H54L5s2bKcB1/+devWLYQQwn//+9/EMaovYADCAw88EELIv3FXdP9c0+0c//3444/h559/Tv2dD3AhhDB//vwcVfjoo48OIYTw0ksvJR57zDHHhBBCuO+++/L621577VXmMqb91alTJ/PQFLN06dLwwQcfJJ7DUaNGhRBC4lhI+luV/lDWB/dbbrklhBDCBx98kPOgfeONN4YQQnjuuefyjqlZs2aYOnVqCCGE/fbbL7Od16gFCxaELbbYIu+4Pn36hBBC+Ne//pXZxmtv2hhh22p/Iscff3zeMS1atAghhDBkyJC8WSQAoWXLliGElbNMa6ud+Ve7du3MzFO8XR/cAWRmQuJretKDO2fAFyxYkOcQAMjO7jz22GNlKqMf3I1ZfdaqO8hFixblfE9aoNq+fXuMGTMGs2fPBgB8+umnme0fffQRdthhBzRs2BCzZ8/G6NGjE/MpyR1kvMirRo0a6NSpEwDgscceK7VetLV/8cUXExcMPfPMMzj++OMT7fanT5+Or776qsT0+/fvn7etU6dOqFmzJt5+++1EW9eZM2di7Nix2HPPPVG7dm0UFxfjD3/4AwCgT58+eW1eEu+//37iwr9DDjkEAPDKK68kHvfJJ5/g0ksvRdu2bfHaa69hgw02wL777gsAeOGFF/L2f+eddzB37lzUqVOnzGUDgOLiYpx99tm47bbbcPzxx6N9+/Zo06YNGjRogH322QcvvPAC9ttvP1x22WUAyn9+Y5IWJv7www8AkGo3XRLt27fHww8/jNmzZ+PII4/MOS/sL88991zisc8++yxat26NDh06oHfv3jm/jRgxIlOumD59+uCaa65J7Iu//fZbom/r0upXUf0zpqLauU6dOjjqqKOwxx57YIsttsjY1W+44YaoW7cuttxyy7wFzQDw1Vdf4dVXX8WJJ56IRx99NCe2RFn6PbBy/USc3uGHH46HHnoI1157LQYNGoTly5eXuR4xc+fOxVFHHYW99toLxxxzDPbff3+0bt0aderUwYEHHogDDzwQV1xxRWatzDbbbIPmzZtj3rx5ePHFF8ucz6r2h5I46aSTcO2112LcuHE44YQTctqgpP7+22+/4aWXXsJll12GDh06YPDgwTm/v/vuu/jll1/yjuvTpw9OPvnkxP4+efLkxDFSUv1WrFiB119/PW87+8Rrr72WGO9hxIgRWLhwYU6fiOtWUe3csGFDHHnkkdh1112x2WaboXr16pk84vVLadx444047rjj8I9//AOPP/44lixZkrgf781vv/124vh55plncOWVV652/AxjTNlZIw/uZV2xPnjwYCxfvhx77703ateujU022QS77bYbnnjiicw+33zzDebPn5+5gPACUZKf9kGDBpWpDFtttRU23nhjzJkzJ/FmoDRs2BAAMGnSpMTfub1Ro0Z5v02ZMqXU9JP22X777QEA5513Hs4777wSj69Tpw6mT5+O7bbbDgAwfvz4UvMsSxlZhunTp5d4fN26dQGsbNdatWph1qxZqR5AJk+enPfgnuRb+LXXXkO/fv1yto0fPx533nkn7rzzTgBAq1atcOONN+Koo47CpZdeihdffBGDBw8u9/mNSQoOtnDhQgBArVq1ypVW48aN0bdvX1SrVg0nnnhiXtyC1elXkydPLvEYph0zY8YMrFixIm97afWrqP4ZUxHtfPLJJ+Oxxx4rcdHzpptumvjgAQC//vprzidh3Xr37p33whTDfg8Ad911F9q3b48DDjgAAwcOzCxafPPNN9GrVy8sWLAAwMoxcvfdd+el9fjjj2fECjJy5MiMv3l62erWrRv2339/3HHHHejbty+mTJmSGfcTJkxILWsSq9of0th7773Rs2dPLFiwAEcddVReu6/t/p4W6K+k+s2aNQu//fZb3nb2idtuuw233XZbYrrAysWwSkW18+WXX566oLWsjBo1Ci+99BJOOukknH/++XjggQcS91udc2WMWTOskQf3srJgwQKMHDkSrVq1wr777psJuhQ/lIcQ8Pnnn6Ndu3aoXr16iYGXKpskBYaUxTNEkupBJWX48OH4+uuvy318eUgrI8tQmuu5IUOGrFb+Z511Vt62SZMm5T24K8OHD8cxxxyDIUOGoE2bNvjjH/+Yp9SVl5LOZXnYeOON0b9/f9SrVw9du3bFwIEDK60sJOnhoSysif65unVr3Lhxpl9eeumlePPNNzFt2rRMX/7000+x3377oVq1auVOm3V76623MHPmzNT9ODsIrHwIO/DAA7H//vvjyCOPRKdOnXDggQfikEMOwT/+8Q906NAB48aNwyabbJLY3wcOHJj34B6zYsUKfPLJJzj44IMxZswYbLfddujcuTP++9//lrt+cZoVRf369dGvXz/UqlULXbp0SZ0VLYl1ob+Xdi385JNPyi2MVEQ777PPPrjnnnvwyy+/4Nxzz8XAgQMxY8aMzEvGtGnTEl9ekrjppptwwgkn4Jprrin3jCSp6HNljCmdSn1wB1ZeAFu1aoX27dsnPrgDK2++nTt3xl577ZVR3iviwX327NkoKirCVltthc033xzz588vcX+qhWkhqKnGTJs2bbXLRqgWDRo0CJdcckmZjqGLTLq5q4gy7Ljjjrjyyisxd+7cUvefM2cOlixZgnr16iWaRwArH7iUVXm4IiEEfPTRR2jTpk1GAS3v+V0TPPPMM2jRogUefvhhPPLII4n7rE6/SjuG20ubJVldVqV/ViSHH344atWqhbvuuitRNdxhhx1WOW3W7fHHH081l0nj008/zTyAb7311rjvvvtwyimn4NZbb8VJJ52EyZMnr1Z/X7x4MYYMGYLtttsu09857lenzqtDzZo18dprr2HbbbfF1VdfjTfffDNxv+nTp2PXXXdFkyZN8N133+X9Xgj9/bXXXsM999yzRvNK4thjjwUA/Otf/8LTTz+d81vt2rXL7G4UAEaPHo0XXngBp5xySmr03cq45xljSqbS/LiT2M69Q4cOmDFjBsaNG5ezD2+Axx13HHbZZRcsWrQIw4YNW+28V6xYkVFAS5vmj8t64oknZpSXmNNOOy1nv4pgwIABWLZsGY444ghssEHZ3rPef/99AMCf/vQn/O53v1vtMtCfPm8apbFs2bKM+t6lS5e83w8++GBstdVWq10uZccddwSQvYmU9/xWNDfffDOOO+44fPjhhyU+1LK//OlPf0r8vaR+1bJly0y9Y04++WQAJZuUVQSr0j8rki233BJAsjlEhw4dyvUgo5S336fx888/48YbbwQA7LHHHquVVoz2959++gnfffcdttxyS5x44okVlk9Z+e9//4t9990XzzzzDO66667U/Urq7xtuuGGm7En9/ZBDDkn0Vb62+ntF9YlVpaT+nnZfKombb74Zy5cvx9///ndsvPHGeb+zPQ899NDEdl8T9zxjTMmsMw/u++23H1q1apU4TTxkyBAsW7YMF154IQDg888/X+UFX8odd9yRCUjBhYykRo0aOYE9Bg4ciJEjR6Jp06a4+eabc/Y95phjcNxxx2HhwoXo2bNnhZQNWKl49OzZE02bNkWfPn1Qr169vH2aNWuWE1hl6NCh+PDDD1G/fn089thjeRfkJk2alOsB4j//+Q+Kiopw9913J96watasieOPPz7HzrFHjx4AVk7H0vYWWGnbW9JNPY3NN98cQ4YMwfHHH58XOKRatWo4++yzcdRRR2H58uU5gVLKc34rki5duuC6667D+PHjceKJJ5bYX1988UXMmDEDHTp0wLnnnpvz28UXX4w2bdpg6tSp6Nu3b96xNWrUwIMPPoiNNtoos23vvffGRRddhBUrVmTOw5piVfpnRcKFfaeddlpOP2/YsGHqDEdZ6du3L0aNGoXTTjsN1157baJN8X777Yf99tsv8/3888/PqJAxhx9+OACUOWDcXnvthXfeeQeHHHJInjK/wQYb4Prrr0fLli3x66+/4q233sr8dvvttwMA7rnnHuy55545x9WqVSuzcL2iueqqq3DGGWfg888/z+vDyhNPPIGioiKcfPLJmXYBVo7j2267Ddtuuy2+/PLLRHO3TTfdFPfcc09OUK9DDz0UXbp0QVFRUeI6mYrkiy++wLvvvov27duje/fuiesq9tprL3Tu3HmN5M/+fvbZZ+e8KO+222644447yp3emDFj0KdPH9SvXx8nnHBC3u8TJ07EG2+8gc022wz3339/Tp777rsvunbtimXLluGhhx5ahdoYY1aFSjeVodcJroRPUkx+/fVXfP311/j9738PoPS3+/bt25d4Ae/du3dGOfn4449x9dVX484778SAAQMwdOhQjB07FnXr1kWLFi1Qq1atjMoBAKeeeioGDBiAf/3rXzj22GMxYsQING7cGO3bt8fSpUtx9tlnY8aMGeVuh5K49NJLsf322+OEE07AoYceihEjRmDKlCn43e9+h+bNm2OnnXbCa6+9ljOdf/rpp+ODDz7AKaecgs6dO2PQoEFYsmQJmjVrhpYtW+LKK6/Et99+W6b8x48fjz/96U/o3bs3XnnlFYwdOxajR4/Gr7/+ikaNGmHvvffGJptsgpYtW2bUv+effx7HHnssunTpgu+++w4ffPABli1bhgMPPBATJkzAZ599hnbt2pWrHdq2bYuXX34ZCxcuxFdffYXp06dj0003xR577IGmTZtmHtC/+eabzDHlPb8VBReuTZ8+Hf/5z38S97n99tsxZswYFBUV4dRTT8Xrr7+Oxx57DOeddx5++OEH7Lrrrth7772xcOFC/OlPf0q0EX/99dfRokULjB8/Hh9//DE233xzHHjggahZsyZuueWWUj0ZVQSr0j8riv79++Pbb79FmzZtMG7cOHz66aeoXbs2DjjgAIwYMQKffvop9t9//1VKe/ny5TjmmGPwzjvv4JZbbsFFF12EkSNHYtasWahbty5atmyJ+vXr47LLLss8ZF5wwQV45JFHMGrUKIwePRrLli3DrrvuipYtW2Lx4sV5L/xpVKtWDYcccggOOeQQzJkzB8OGDcOsWbNQp04dtGzZEttssw2WLl2K8847LyeS8DPPPIPWrVvjkksuwbBhw/DZZ5/hxx9/xDbbbIOWLVti8uTJaNWq1Sq1R0l069YNADBv3rzUF6a//e1vmDNnDn788Uecf/756NWrF15//XV8+umn+PHHH7H33ntj1113xYwZMzJKrvLss8/iuOOOQ6dOnTBkyBBss802+L//+z9Ur14dF1xwwVox2TjttNPw9ttv48ILL8Qpp5yCESNGYPr06dh8882x1157oXHjxrjvvvvwzjvvVHjeTz75JK688kocddRRGDNmDIYOHYo6deqgY8eOeO2119C2bdvEF8eSuPnmm3HyyScnKu7AypfRTz75BGeeeSY6duyIzz77DFtvvTU6deqEDTbYAFdccUWp61uMMRVIWf1GrokATPx74oknMse2bt06cZ/7778/s48GZuFfWfy4hxByoobyr3379pnIjUuWLAnTpk0L7733XvjLX/6St+92220XHn300TB58uSwZMmSMGvWrPDKK6+ENm3a5O1LH7oDBgxIrT99WpfURtWrVw+nn356eP/998Ps2bPDkiVLwtSpU8Onn34arrvuurDTTjvlHbPJJpuEa6+9NowYMSL8+uuvYcGCBeG7774LDzzwQGjWrFleuyVF2oz/dthhh9C9e/cwZsyYUFRUFObPn5+JHnvCCSfkBOcAVga4ueqqq8L3338fiouLw9SpU0P37t3DZpttVm4fxQDCPvvsE66//vrw4YcfhgkTJoSioqJQVFQUxo4dG5566qnQrl271GPLen5LOhf0na4BvpL8izOdklD/0c2bNw/PPfdc+OmnnzJlfPrppxN9UMdl2WabbcLTTz8dZs6cGRYvXhyGDx+e5xM6HqMTJ04scfxoP1gT/bOi2hlA2GKLLcJDDz0UJkyYEBYvXhzGjRsXunXrFjbaaKMy9TOmm9b/N9tss/DPf/4zfPnll2HBggWhqKgoTJgwIbz11luha9euOZGfjzjiiPD444+Hb775JsydOzcsWrQofP/99+Gxxx5LPI9pfzVq1AidOnUK3bp1C4MGDQqTJ08OxcXFYdGiRWHUqFGhR48eicGw+HfkkUeGt956K8yePTsUFxeHKVOmhP79++fEWljV/pDUpmVBz0G7du1Cv379ws8//xyWLFkSJk2aFB566KHEoEdxWXbdddfw6quvhjlz5oRff/01fPrpp+GPf/xj3jGlXXtvuOGGEEK+//SS2oR/tWrVChdddFEYNGhQmDdvXiguLg6TJ08OAwYMCFdeeWVo1KjRGmlnAKFRo0bh2WefDT/++GMoKioKo0aNCldffXWoXr164rhK8uOufwwcltQewMqYAnfddVcYO3ZsKC4uDnPnzg1vv/12OPjgg8vcpwH7cTemIqjQB3f/+c9/a+cv7eHWf/5bH//KKi74b93+84O7MatPpdu4G2OMMcYYY0rHD+7GGGOMMcYUAH5wN8YYY4xZz+jVqxeqVauW+dtggw3QqFEjnHXWWfa9X8BUulcZY0z5+eijj1YrgI8xhcRTTz2Fp556qrKLYUxBcvPNN6Np06YoLi7G559/jl69emHQoEH49ttvUbt27couniknfnA3xhhjjFlPOeyww9C6dWsAwDnnnIO6devijjvuQP/+/RODJJp1G5vKGGOMMcZUETp06ABgZYwWU3j4wd0YY4wxpoowadIkAFgjwQfNmsemMsYYY4wx6ynz58/H7NmzUVxcjCFDhuCmm25CrVq1cMQRR1R20cwq4Ad3Y4wxxpj1lD/84Q8537fffns8++yz2HbbbSupRGZ1KPODe926dVG7dm0UFxevyfIYY4wxZj2jdu3aqFu3bmUXo0ry0EMPYeedd8b8+fPRs2dPfPzxx6hVq1ZlF8usImV+cG/cuDHGjBmD2bNnr8nyGGOMMes9/fv3x0033YRnnnkGzZs3r+zirHHq1q2Lxo0bV3YxqiRt27bNeJU55phj0L59e5xyyikYM2YMNtlkk0ounSkv5TKVady4sQeeMcYYs5qMHDkSALDrrrti7733ruTSmKpCjRo10K1bNxxwwAHo3r07rrnmmsoukikn9ipjjDHGGFNF6NSpE9q2bYv77rvP5s8FiBenGmOMMZVEz5498fbbb+dtv/TSS7HppptWQolMVeCqq67CiSeeiF69euGCCy6o7OKYcuAHd2OMMaaS6NGjR+L2s846yw/uZo1x3HHHoVmzZrj77rtx7rnnokaNGpVdJFNGqoUQQmUXwhhjjDFVg6eeegoAsNVWWwEANtpoo5zf+Vjy66+/AgCOPvroMqfdr18/AMDvfvc7AEC1atVyfl+8eDEAYM6cOQCAM888s1xlN6aysY27McYYY4wxBYAVd2OMMcZUOC+88AIAoEGDBgCQ8R1evXr1nE+q4itWrMg5nt/5OWLECABA165dM/vQ1Khly5aJaRN+5yOPpr1kyRIAwIwZMwAAJ510Urnqaszawoq7McYYY4wxBYAVd2OMMcasNg8++CCArO1606ZNAQA1a9bM2Y8LIWmHvuGGGwLIquGENu4LFiwAAPzyyy8AgC+++CKzT9u2bQEAW2yxBQBgs802y0mbUNVfunRpTtrLly/P2e+3334DAEycOBFA1hb+4osvTqm1MWsXK+7GGGOMMcYUAHYHaYwxxpgS6du3LwCgXr16ALIqeWyXvs022+QcQ5Wbn1S3ecyyZcsAAJtssgkAYIMNVj6SMCiQ2sDTRp77x9u4D49hWrVr187Ji15lqLwTzgIwHc4SsE6DBw/O7Ms8mMasWbMAAMcffzyMWdNYcTfGGGOMMaYAqHTFvVevXvjzn/+MoUOHonXr1pVdHLOewf5FatSogfr16+Pggw/GrbfeikaNGlVi6YwxZt3k5ZdfBgBsvvnmAIDGjRsDyKrNVKhjG3F6j5k+fTqArLpN1IadKjhVbqZZVFQEIF95pwoe+2bnNu7DY9SOnuVknvwk/J1l5qxAw4YNAWSV/Thtzjqwbd577z0AwPz58wEAJ5xwAoypaCr9wd2YtcHNN9+Mpk2bori4GJ9//jl69eqFQYMG4dtvv81MpRpjjDHGrMv4wd1UCQ477LDMjM4555yDunXr4o477kD//v3RpUuXSi6dMcasG3z00UcAsuq5qt1UmflJdRzI2pVzX6rX3Je/U83mflSzqYLTp7p6fEny966RUXmMpsE8mCfVf9ZPbeC5H8vMTwDYeOONAWRt3PlJdZ+RYNmWHTt2hDEVhW3cTZWkQ4cOAIDx48dXckmMMcYYY8qGFXdTJZk0aRIAYMstt6zcghhjzDoAvabQdJCqMdVkjWpKpTq2/aYPdNrF01c6UUWe11/ajNM+nXlSLVdVXb/H8BimQSWd5WSeVORZZu7HerIOLFtcT43KymO4D2cYqN6zbffbb7/UchtTVvzgbqoE8+fPx+zZs1FcXIwhQ4bgpptuQq1atXDEEUdUdtGMMcYYY8qEH9xNleAPf/hDzvftt98ezz77LLbddttKKpExxhhjTPnwg7upEjz00EPYeeedMX/+fPTs2RMff/xxztSnMcZURfr16wcAqF+/PoDsAstNN90UALBw4UIA+aYkhGYh8bHclyYl/OTvdevWBZA1LWGaNF/hwlGaxPA7TW1ovhJvSzuGadL0h6ZADKw0e/ZsAFmTGdab5jwsc1xPwnJrgCimwXovWrQIQLatjz766Ly0jCkrfnA3VYK2bdtmvMocc8wxaN++PU455RSMGTMmJwqfMcYYY8y6ih/cTZWjRo0a6NatGw444AB0794d11xzTWUXyRhjKgUKF+oWkYr1VlttBSDX7SOQVaDjhZpUnqmCc7EpVe569eoByCrmqorPnTsXQHZhqaarCne8jeXgd34yTSruacq7LpDl77qgNk5boZtI1kdnHiwSmYrA7iBNlaRTp05o27Yt7rvvvsyF2hhjjDFmXWadUdx79uyJt99+O2/7pZdemrEXM6Yiueqqq3DiiSeiV69euOCCCyq7OMYYs9Z44403AGRVYqrDhHbZVKi32GILACW7YqSNN/eh0kzVmt+ptFO5njlzZk6eVNypgvN4tYEHsi4XNYiTuoVkHo0bN05MmwGn1JafecV29Qr34bGsh7qaZLuw7e3VzKwK68yDe48ePRK3n3XWWX5wN2uE4447Ds2aNcPdd9+Nc889t8QLszHGGGNMZVMtxK+uxhhjjFlvGTRoEICs0qwKNW3X6U2Fdun8TtW4JOW9NPjYwQBN48aNAwAsWLAAQFZZp5hCpZ529tOmTcuk1ahRIwDZmQMq5awPlfjNNtsMALDjjjsm1md16qH1mTVrVs73tBkEtn379u1XuQym6mEbd2OMMcYYYwqAdcZUxhhjjDFrBq4ho606FWraYfOT6jaVanpTSVPaY68yRPeh+q0T/PQRz7ypllMNV/NFtZkHsp5aNC4H89T6MU/mof7fNc8ko4Qk7zZAtq1YFtrfcxaDv/OTMwg8N4ceemheXsYoVtyNMcYYY4wpAKy4G2OMMes5VKap/tJbzOabbw4g3/MJnUJQ3U6zBY99mpdFrY63q4rPMqap+ix77A9dj2F51P96WmRVzSutbFTwk1D/9fR9r3nzd6r/tH23f3dTHqy4G2OMMcYYUwBYcTfGGGPWU7p37w4AaN68OYCs/TVtvWnrTtWXSjzV7dXxuqK+0FXtZlmYJ1X/NLWcXlq4fwzrwTzUhzrTVFt4LRPLvCrugXV9AL/T1p3+3WnbzrxYVp6riy66qNx5m6qDFXdjjDHGGGMKACvuxhhjzHoK/bBTrU5Ts6kS09sKUSW6JK8yaXbgaao9t9POXvPiJxXqpDwJ7cWpvLN+3Lc0//NpnnCSiO3643KntQ3Lpn7dqbRzO8+VMSVhxd0YY4wxxpgCwIq7McYYs57x4osvAgAaNmwIIKu0Myop7a6pCtOmW22+qQ6r6k07cyrbcRplhftT3f7ll18A5Nulk+Li4pw6xNtYD0Zf1TTov35VbNfjMgJZpZxtSKj26/oArae2/dZbb51TZp67Ll26rFJZzfqNFXdjjDHGGGMKACvuxhhjzHrGZpttBiDfb7t6VeF29dRCdZgK9vz58wFk7buZDn2Wx2moeq9wO8umswBp9vTcj7MA8Tatl+5bXm85nHFQlRwA5syZk5MHlXMq5lT3uZ156zkhbC/mwf2MScKKuzHGGGOMMQWAFXdjjDFmPYNqLz/pLYbKNFVf3U99rxNup4LN71Tik9JUVVuVdO5P23DauFOBVmWaSnScZ5qKTaWc9VD7cy2TeqrhcVTR4zypjDMPTVO94zBtzk5oW1K5VwXfmCTcO4wxxhhjjCkArLhXAq+++ioAYNNNNwWQv+JclY+5c+cCKN8Kc65Kr1OnTmKamiej6B177LHlro8xhcTzzz8PIN+GVf02p0V95Fg688wz13xhjSkHDz74YOb/Zs2aAciqulSz+Z39mBFTqQarak77bHpS4SeJPb+kqfT6uyrxvE+xjGlKNvOOfc0zzTQlnfc65qGoOp72e1xPtaenZx22FdtOVXvaxjOCKvNk2XluuH98Pi+++OLE8pmqhxV3Y4wxxhhjCgAr7sYYY8x6QKxk6ywr7bJpR60KOvdj9E4qzFSX6Wtclek4T/W7rtFK02axqDg3atQIQNaTDbert5nYBlxVa6reVK/VBl791OtMGrerkk9PMUA20itRm35V2n/++WcA2RkFznBTqVcFP22NgDGAH9zXKDRX4YDnlOR2220HIP8CoRcgwim+AQMGAAAOOOCA1Dy5z4477piTNtFpUl4YWMbBgwcDyE7l8ULjQBCm0OjTpw+AbIAWfWjQT6ImM/o76dGjR+Z/vfmfd955q1V2Y4wxJgmbyhhjjDHGGFMAWHFfA3zwwQcAgKZNmwLIqnFU8nR6UKfDdLqRU5mc8nvmmWcAZFVxIKvmN2/eHEBW+YvDUcd5Ep3S04U8W265ZU6dDjrooNR6G1NZPPvsswByF87RJEAVdI6vtOntNMVdF7slwX0feOCBnDzSFofrdH3Xrl1LrqgxZYTXeu1rnHWl+QnNPtSEJq2fp/XdeFvad70H6hisXbt2znaOF86alQTToKkMF7DyHpjmmlLrkVaH2Dwn7Rg9lm2pbh7Z9lpmfTYwJgkr7sYYY4wxxhQAVtwriDfeeCPzvy7u4dsz3/DV7SMVAf3Ot3cqHFyww0VCcUAIXThEBZ6LXvgmrwuR+F1df/E71Rm6rozrecQRR5TSKsasGTjrxJki9tNYmVOlTMOwpynuhGkTVexiVUxnrlS11xmtOGR7XBa6f1NFL56FYxq2ozeKumoE8md8qf6qO2Kd6dW+zOO4P+8tJbmD5L6qbjNNzZPjgGOL45njJWlWTGcSdFGpBjNiWVg/Vfe1vZLcRPJYndVjm+hsBevJ49j2RUVFOXmkzbYbE2PF3RhjjDHGmALAivtq0r17dwBZ23IgPZyzqtzcTxUPtSFUkmwPS7NH1DLxzV/zVPWfigD3Z13iul900UWJeRuzulBZp5qmwZJUFYzVsbQAS2ljojSlLW28xnmpPbymoe7s0ty9qfu8WP1n+Tj+WI4LLrggMS1Tdbjiiisy///vf/8DkFWBdZaHNuCqULN/cYaXM7s6U8x069evn0kzza0h0ZlfvW/peGCZuX9Jijv34TG0l9c0dX/OMuvvOoaprgPAzJkzc7bp2hWuG2Abq1tLbuf9Vc8N043PpzHEirsxxhhjjDEFgBX3MvLkk08CyCoKqkT/+uuvmX1pX863aypiVKvVpo6/q30bUbt0tZ+Nt6mqHyvkJeXBMvF31o91oAoR15N1f/zxx3Pyolrw5z//OTEvY9Kgwq62rapIpdnMJqFKutq2qlquaamapop9Seg+PFavAWn1KikPtaunAk88E1a1oWKuirv2QfYxXrd5jVcvM9yuM8hz5szJ5Mn1XTpWFG5nHur9jKj6rWWNt+nYSUsrTe1P84DDz7ieGsyK90sq6TyGbcb7qq6v0XZgHXjujEnCirsxxhhjjDEFgBX3FHr27AkAaNKkCQCgVatWALJvzFS5xo4dCwD46aefMsfSto4rx/nWTTs3KiBq76oKCN/q1fdtkhcM/Y3HUGWhHR+PUV/W/FTVhekwRHNczwYNGgAAdtppp5w0mQd9v0+ePBkA8Je//AXGJPHUU08ByPZ5nWVSxY3jr7QoqGWBfVzTUPvckiKsqkqv5Uwbb7oft6eN+aRj08p///33A8iqelbgqxaM86HrmIj2TY49jrXZs2cDyEbPVptxnZ0FsuOWCnraOhHel/g709Z+r15pyNy5czP/b7PNNjn7pM2IcdyoJ7W0srIs3D+uJ39jm/F+SVWekcjr1q2bU1/mqd6w+MlzFsdoMUax4m6MMcYYY0wBYMVdoPLXrFkzANnV4aqUUdXift99910mjenTpwMAGjZsCCBr98a3c/V/m+ZnVu16SVJUtbRIa7rCPi2SIz/Vdo9KAusUew1g3dWekWlttdVWOfVk25555pmJZTVVjyeeeAJAtr9RidJ+maamqUIXq+Jp0Q01LV0fov1YlUq1fU0izXuMrmtJS6Mkz1Jp9vFEZwz43V5oqhbnnHMOAOCxxx4DkB9BlH1PI6fOmzcPQPa+Ra8xauuepGynRR1mX+TaFXpl4e/Mm/cMjWGi609ixV19wqdFdv35558BZL3kcDvv07xHpinv8f2Y6jvbgjPabEveRydOnAggG4Gc90+Wgcer/b1jNJiSsOJujDHGGGNMAWDF/f/Tt29fAMC2224LIPsGzbd4jYjGN26+KdPODsiq07R3o9JBVUE9uBD1cZtmN1uSH3e161NPGmrrrjZ3LCPVBdaB+1OdiMuvXnM00h7zZNuyrY8//vi8epj1m6effhpAVnlThT3NQ4SqYOWxbddxpHbkad4l0lRyEvtWT/MCo9vTvGyQsniqIWlton7m1baX5X744Ydzjv/rX/9a5rxN4cDzrrbdvIdNmzYNQNYjTOPGjXP2Yz+jAq9qeYx6rKHyTDt5vf+wLzJN3ndUede+zrLGpHmVmTFjBoCsSq/3LbaD2qdzFjtpzOr9k4o6t9OzHOvBZ4Lx48cDyI+OnjZ7ZkwSVtyNMcYYY4wpAKq84v72228DABo1apSzXSOJ8jvfwqk+0FYtjr5Wp04dAFmVgcqz+r9VWzz1wa6eM9T2PVbndJW+KhpMU23dVeXXKHHczjrF9eSxbAtVJHWmgfvxk21/6KGHwqy/9OrVK/O/eo3R6KWqjqvHFI3eyDGkamIS2ufZX1XtV9T3cpLSmLZPWnm0Pmn+3rX+JVFSZNekNFXlowIfl6Vr166l5mvWTXr06JHzPe2+Qs8n2223HYD8/qF9TxVp3huA/PUhU6dOBZA/DngvpPcUHkdPNmmxTdTvebyNMG/em5kmy8uysAy8JlF5Z5noUY7px/VkHkwzLXIyYdsyD5ZJr0W8Z/LcefyZJKy4G2OMMcYYUwBUOcX9pZdeApB9e6Yv8jTFTLfzu3qGib26cGU537pjW9ikPFR9U/VbVXMq+bESwm0sV5qinqbwqSLCPDfbbLOcOsX1VPv/NE8aPEb95VL9p7932iCeeOKJMIUPlfbYJ3GaTXqaN4o0BUu9I7GPlWQrqr+pDauq+arqp61NSSq/elrS2TWtf5qinuRBJm3ftGtVWtuleeqJ07fyV7jw3kZoR86onOwHnG1WH+y6/ol9nL/Tfpv23EB2TFFpVwWeijPvKzrrxTxpl841VbrOhAp2vE3XyzCNtJk2buf1SdeI0C6da7PiehLaxetY0nqxbdnWvNcxT6r/9OBjTElYcTfGGGOMMaYAqDKKO+2p+UbLqKYaPS0tUltaVEXafNNLBpB98+dbNFEbVFXO1E6d39VvNN/mY9Vc/UKrAsjfmaZGOVXVTW0Mk+xmWXf10qH10lkAnVng7AfVGtu+Fzb0zU51Le6LaYq4qsVpKrja3Wp/jX0tl+apQVU+VdaJXiOS0PHDsc8+rTNfGrVSZ+U077guab7fVVkkOh7199LWGQDAI488kpOH/UyvW3AmOfZuRtt1nl9er0ePHg0gf2ZJP9nf9frNvp10T+DMb0kxDoDs/ZL3Ydp8K4zYzbx4HNX0OA2Wk8coHAca0TxtP9aBdeLaLCA7W8xZDV7r9Pqka2/SorVuv/32ALKqPo8fNGhQJk9GLfeMtLHibowxxhhjTAGw3ivuAwYMAJBVIlQxVxtZVdxVlSOqrMVv+WkqdZqip6j9PNU4tbFlJDggq67wTZ7l0rzTUNWRZVBlMFZXmEeavbwqedrmqjKqPT3P3QEHHFBi2c26weOPPw4gq4qpGg6kK8scZzpjpDbuTDPNnjtegxF7nohJi1SsYyQtInCSnXqar/c0bzFanzQPU0n+39PUTI2IqTMOasOu1yNt06Q6M21G47TyXrn07NkTALDzzjun7sNzxus1lXfeKzSiqnoto7qsx9E2nL8DWXVaZ8yI2nzzmp82C0TPMMyDx8XjXMvJY3Q861jStWRp4yNJcacnGlXIuZ3XQG1Lth1Vf5ZBY6AkPSPwGYbn/C9/+UvePqZqYMXdGGOMMcaYAmC9VNxfe+21zP+0HeMbL9+Q1buKqsKquJM0BS22Z+fbtnpToZKc5L0hzpvKAX/nWzs/qVrGSofOHFAdURvb0nxVs4xUK3X/uJ6qEuq+unpfP1XNY3q0PWQ0uvh8HnPMMYnlN5XHU089BSB3nQeQP4sTb1OPSbr+QdH+q8p2ko172ixZ2lhI89ai41BnB2I0ArGq2OqhQ2e40uIvxGXVNlQvVaXNEqp3kDQ/2PH/OsaZxqOPPgoge52xCrh2oXcVtd8Gsn2Qn9xH7y96P1L1mP2DaeuMWmwrXlocA+1PsceppP3SohvH8USIqvxp0YrVi0zSTFNSHeJ68hi91/MawbZLu+boLIGWRdcXANlZ/dijjqmaWHE3xhhjjDGmAFgvFXdjjDFmfYUzHbvtthuA7IxTrLjrLBSVaNpq//jjjwCy6rDOOutsND/pQYVqMI+Pj01bx6TqPmeU1O+5zhqpR7U4XfWolrZmg/sxTy2TomWK60nFX6Oi6ww3Ydl4LubNmwcgXz1nWXmO4pkF5s92Zx84//zzE8tv1l/Wqwf3//73vwCA1q1b5/3GgcCBpS6udLDrlHVpLtjiCyYvbHox5adOyetFSqfbOWD5Xd1Fxtu4D6f1OPBZX10cp1ObLCPT5vRc0o2hNPMGXdCqbZt2sea5Yt4MPQ1kz/G5556bmKdZ+7C/K0nmZqW5RUsLGqTb+akL62LSXJxqsKa0AEVaDyXeL22RKafSk9w6xnC8pS0YTSqPmrponiTNxa1O26e1R7xPmnkFr1lPPvkkAODPf/5zYj2NMcasHjaVMcYYY4wxpgBYrxT3HXfcEUCuEkbFWYMhkbSFaiWFNwfyXcjFwVnompHoApQ0qFoxJDWVTA3lzDDLseLObQxDzQU4VN9Yf7rfKs09JNOJXWABufVMC0evbjBV1U9z5cfjNBBMPEXJc2wqHwZaYv/UMRT3T5I2w6UqtyrxulAsTS1OgrNN/OQ1QRfIpi3AVFeIJCkAGsutC/3S3D0SXfha0gyEjl2ddeAnZ9+03Dqzl1a/tLompcVP1sPK+5pF3RvrtRbIOmLgPYD3E3XBqAujiTo6IGq2EpuepN0vtR+zD/PeyLzYZ3UBKT/psGD48OGZtFu1apVTT713sx1YT/ZR7q8mNmkBy+J6cuZZZxvZVpzxVneQLAO/67lge6ibybg+LEccbMtULay4G2OMMcYYUwCsF4o7lb8999wTQLLrNFX/VG3S/TUgEz/1uCQVneq2Kniqsqn6RmVZ1XIN5sD9YnWF27joheXnGzzz0IVGaba03E4FIakO2gaq/ugCJFUVSZqLv6SycQaA5/zss8+GqRzY51SB0/Of1GfYF1QdS3PLyv21T6UF94rRMUx4rJZXZ4zUNZ2WHciOeVWzVXEj/F3dYZI0VTxGy6NjW4NZpQV3SQtAE7dFmos9vS7Y5n3tUKdOHQD54yc+d+wH7JscrzpONXiY3iuZjo6PpMBlaYGUyNZbbw0gex3nOOY9jmVIc2fMfhjPvHKbjmf9ZFvR5THLQnV87ty5JdYhrqfWnW2jbiG1bGkBDTWgY0mzGUyLfcBUPay4G2OMMcYYUwCsF4o77bFVWQKyb/JUG1QdLs12k2+3VAjSQq6XRFowClWx+HatwVf4Vq8qRGz7vcUWW+Tsw2PV3VZSQJeksqXZ48fHpQWVYL3Uzi/NDlnPRVp68f8852btw3D3JE0tpj1n0vlT+3FV1FXlUhVQ+wb7d5IqxvGk9qWqNGsenK3Ssc48Y+8tqtLT7lyD37AMLBPHsKr4GnimJMWdeaial+ZNR/NIW6MQ70PS1FrdX9veVAwMdtasWTMA2XNKm+h41lLXDOmY4efIkSMBZBXc+vXr5xyv45vpcV1V3AdYDp532oJT3Sb0GMZ7hPYbwvrE9zoA+PLLLzP/a9pqk6/qN7/zns57Jz9//vnnnLIllYF1p3pPtK3YDtOmTQOQr+qnBYLU6wmQ37Yc9+wTZ555JkzVwIq7McYYY4wxBUBBK+49e/YEkLVtT/KVzLfkNF/NafbWqvRx/7J4ZVHbXk1TtyeFhgfy/TRTAUwKA8191dZWFbPS/ESn2daWNLOgSp56xVEb4bR1BWnnKM6b9WzUqBGAbB9wqPU1T69evQDkBzDRvqFhu+PfdTZJx6fa4ardtu6vinbct1RJZp46rtQ+m2lSudNxmWQzr/bjOr6Yptrhqocb9T5BYnVf7eLVrlyVd21DtWVW7xpJlDazmOYDnt8dLKZioCqs/aukc6f9XMcQ7yuMl1GaXbb2t7ivsk9RHaYazrHHe4PaiDMvwjLyHpIW5yBOS8cg74WqwGs7cGzy3q4KPtecxWVMu+6wTTRWBNuWKr5aAvAclPRcoeo868k+YaoOVtyNMcYYY4wpAApacd9hhx0A5PtSj1UftZ1V+z7+rnbYTIs2eqX5dY+V6zSf02nwd745q2rFt/FZs2Ylph9vYz3o41WjKDKP0spUmk/b+De1pVUFnfaMVF10/YDaYKqqEisd3Ma02AfMmuPZZ58FkFWe0khTnWL0nLKPsJ+qeqazOURtp5M8pmj+aWHWVfXj72kqeZLdOZWz0iKosn5qb89yMx3WLykOBdPSqM7q0UI975Q2E5jkzz0tQmqasp7mp55pWnlfPXQdBvuCemcBsvFEdOZL7adp2659U/sN1WLulxQxmao1P2fPnp1TLtqVp/UTXR9DWEbaiCf5N69Xr15OXpqGzgppe/D+yvst68DrAGcL4rpzH7YN21qvPTw/rAfz0nsdj+d4YX3jPLX8SfEyzPqNFXdjjDHGGGMKgIJW3KmG842banKsGPEtVT0vpPlP1u36dkvS/BfHv6mqrW/8qjbwLb1BgwY59VBFjYpCHMVUV6VToWMbqapWkh/6pHqmKSRAvjqvbadtrgqQzmbwk4pJrDayHlQiWD+z5qDSVJonJrW3TRpjVIe0L/DYtCimaWsu0uy449+0f2q/VHtzXd9SmuepuM5ps1Dsp2nrA9gO/J0KHqEKmFQe9duuMwM6q6jjTse02gQD+WM4LYpsaTN5zIueic4777wS9ze5cCzy2qjezpLUV95PaHfOWR1+JzrjkhaPQ2eJ4llo/j9q1CgAWa8rVKbTVO80j2LMm/FJOC7iGTdu0+ijaWlqv9eZhvnz5wMApkyZAgBo2LBhXj3TPDPpLEXaui6N5qpegWbMmJFTlricOgMSzwSYqoEVd2OMMcYYYwqAglTcH3nkEQDAPvvsAyBf5YkVI759U6WmvTUVeKKeMNJ8N+ubc5ISrVEFVd3WN31VEdM8U3C1O9+wY3WRaXAf9eWclndp6qkeHyttqmTqPmqvqEq7qqXcj+qkKidAuurDPnHBBRck1seUH3rsoYrH86HnXVVkkuTpIs2ntEb2VdI8pVBxTLKFV5/IhLNwaTMIqmCrD/YkL1A6u5A2hjX6pH5SodQ1AHEb60ycjiud1dD6qyrLMjGdWN3XNSVsOz23pam1JV1HTOn06NEDQHb2keeB9zVdJwVk73W8njL2Be8f2267LYCsssx1UdpvtL/pTGjcv5gn+5D6OdeZtqT4C0C2j/I+XVLcFB1jaWuoiKrkGi+FZWberFNcRq0799W09brFdUKNGzcGkG1Lnhuq6MwzHqu//PILgPx7OcvAPtK1a9e8NjLrF1bcjTHGGGOMKQAKUnFXJYBv2GoXCqSrA1Qq1EMDUWUvSf2N845J81OuflhVheLbtSoE06dPzyk7j4s9CFAloJpCm0Da5xH1h5tmm5qmpsf1TbP7V3/zGi2SsI25Pz/VG0A8O6KeDZJ82pvV45VXXgGQVfXSVGSi41E9L8XnXT208Nyqpxf1b66KvPaZpEid2sd1DUUaWgb1TKV9L4ZjUlVtVS3Vw5J6l9AxE5eZbZbmgUfzTLPxVf/2SaSVLylKdUyaQqrniTNlgGfLSoL9nIo6+wf7JO3W4+ie7DNcD7TddtsByHo2YYRQ2lfzO+3R1dOaem9Lmh3jti233BJA/lowjSxcmv//tHVgJXmPKm0tGUkrA9Omlxqq5HFfZ55MQ70tabRW3o/Z1jye54LfadvO4+LzyXLxuqT327R6mvUPK+7GGGOMMcYUAAWpuPNtdM6cOQCy/mqT/MqqDSmVCn5SqU6LEFqWyKFKmspUmicXllHtuKmia6Q32rwB2RkFHsu3ctq8M880tVHLlBbdtSxv9cxbfVWnpZ1WFp7neCZFfdmyD9hmtuKgOkQVKbZ5BrJqkqpn6vklSZnmMapQ6cwJf1flWn2uMy/2i6RopuqZJs3bRNoMmM7OkXgsqO93pqG2+GkRUdWDjaqa8TVFoyzqOgH1z67fiV4btS3jcqTFc1C/06rI61obHfM6C2dyefzxxwHkxxNJ88me5IOf9w32NdpT8/7Be8QPP/wAIN/bDGEfLumc8liOB5aHfVbXkGmf1TURrCfT5f5xGTWarI57/a7rTFgmto9eS5gX7c7jNHR86/WK5eVsxs4775xzHM+FRlJVL3FA/hqjtEix7DPnnHMOzPqJFXdjjDHGGGMKgIJU3PWNnyoXtyd5YCjNBjrNXrs0VS7Jj7tuU5VR1WG+Sevqdua166675hzHt/rf//73efVUTxppar+qDERnJlSljOuZFiG2rLMXpfmQV3vguO5artLslk3pvPrqqwCyNp3aD9M8EunMinq6SBob6llIVTFSmg11SVED02ItaJr8nTM77G9qp6oqWzwTQV/Z9NRRv359APn2qGllZJ6c7Zg0aRIAYOrUqXll1tgMuh5HZwo4VqgK6gyJnoN4JkFnMXUM69ofVQx1nCpxXt27dwcAXHTRRYn7VkWoJus9RD0dqRefGP7Gc8Nzxj6qXmXSooSzLLTDVqU3Pmb06NEAgKZNm+bsW1L8k3i72tUzXfo1Z1njeqkHG1Wk0+I5pK39GD9+PABgzz33BJAdP0B2XPBayfFPZZ3l1UjmhG2v40aPS1pTxj6gnmzYF7zea/3HirsxxhhjjDEFQEHKlHzz58p1vqUm2U7rm32arWXa9zQbvLTIgfExqjjzjZh22d999x0AYMyYMQCAdu3aAQCaN28OIPsWrqpE0hu1blP1jMof8/zss88AALvssktOnrS503ol1UnbQstQ3vUBaf7u47ZVG2d+Onrc6kMbTvUPrqpwaWMgLSpi/Jval6rXElXUdQyoQp9kC64eTFSdp9cI9nlVpDXyqsYbSJrlUXVePbaUFmGU1zQqcoxV8eOPP2b2GTlyJIB8n9nqcYRl4X5U4Ok1RH20J3mCYT3UFl19x6stvHp/UpKUYXvFyIfniueSSq+uEdH1CkD+TAyPZT+n7Xbs+x3Inhsq6dxPZzuZjq6BAYAmTZoAyI3uHadRmlcz9SWvs9fNmjXLq6farqdFZyZp3qG4P+ugs0sx7OesF9uKajg/OUvGtta1ADqzpf7g47R05l1nPuIZELN+YsXdGGOMMcaYAqBaKM3QeB2CNpC0OVObNhK/5VOx0LfutFXsJE0J0Lf0snhb0WiR06ZNA5B9Yx46dCgA4KeffsqpV6dOnQBk7WbT3rTj/NXGnTayAwcOBJBvI9imTZuc4xo1agQg37NGElp3VU1JmjedtKiKqlbGanqaz2nW65tvvgFgO9ny8L///Q9A1q5aPRPxfGq0YfV3rj7Z1e8wkK+Uq1qmEUhVYUyz605CxwvHVatWrQDkzy4lRUFO+j3te0za9SVNaU+LtJo008U6T5w4EUD2+sF6Uq3nMVTctSy6VijJFz5Jixyd5llIvT+ltV28Xa8bF198cV45qgp9+/YFkPWYxjFFm+qSlHais638ZP9h9E7uR3/vPNeqDtN+m3D8xOeQ5539gX2S5df7VFpMAV13of0nLgu94hD1oqTKu45zvZ+qRyeWKVaymQdnz7UsPE/03MOy0G878+L6ApZB/fEnzVZrDAX1vMPyz549GwBw/PHH56VhChsr7sYYY4wxxhQABWXjrjZ3qmJpJE4gX+0m5VHPkvJKiwwIpPuPTvLaAACtW7cGkLVd5Wr2F154AUBWKaEP2L322gtAri9bvuEzDfrk1dXqtA1kGoRloh1smr16vF1X6ae1cWn+69N8RCd57yDqXYFtYfu+8qN+ntM8LGmcAVXi2Yd4vpLso1UFS/O8VJr3JvW+kORHmftSad9vv/1y9lVbffWNrWpfmlKX1AZ6rF6j1HuTKpBJ6wMI25+RMKmcDh8+HAAwatQoAFn1T2c1mLZGalZ75Lg+RK9pLIvOlGhkXE2npPo5JkO+NyKdhUxbPxTPQOkaBp4LzkJTYaeqz0+i9uW8tuoMWzy+dZxqv+YxGgtC+6Jec3TsxbN4aV5hdDuvc8xDZ/HUK4vmGduhs9ycddD1aGwrjdvAslANZ3twVpll1sjRcRvpbHja7F7cRmb9woq7McYYY4wxBYAf3I0xxhhjjCkACspURqeZ00IXx1O+aYvBSluUmubCMG3hSxI6PawLPXWKi4tuuUiVU3M8jmYw3377LQCgc+fOmbTeeeednDx1ER+n7piHliGtjLpfXCf+r4vc9JjSgm6Udi7i86kBOtIWLZqyQ5dmGsSrtIWUamJCdHqc08jxMTr1n7Yok6gphi4YS1r8yb5AExmdftbPNFhWLjRT121A/rVHXdTqojO9brDcNDOiOQ/NGpL21baiyR3N4d57772c8rP+TDvNHV48PnUM6jlXkxl108o89DyXZGLI/KtyQCYNpkWTCpqzqQvekq57NNfQ861uQNPufdyPfUCv+/H44bljeeOgRUB2vHIccCzpfTUtoFTSvSLNBDNt0TXbVE1/CMvA62JSu2jd2TY6DjQQoi7CVde7ZQlOqAvAmQfbXF0mm/UPK+7GGGOMMcYUAAUlT6a9hfNtlWpV/KaZtjBS1W5V8qiuUeGgcsBPVZTiRZtpShbzoJst5qGLTbbffnsAWdeG6vJLXWvFx+oCM5aBaaprLC2TqqkkydWjBolgGahU8FMDxKhyQ9KUzyTlIGmBIGDFvazQBSSQvyBZAwypSkQ4FtR1G9H04ryIqn9E+xTLoC4etS/F43yPPfYAUPYFy6rmceaLiz1nzZqVU4ZYqWMwJ7pZ5UI/5s0ALCwnx77OdnCROT/pbi52fcsgTUTbhnl16dIFAPDJJ58AyC5653lh2VTFjc+jKoq6iFivFzpzoLM3eu2Kz5duq8qLVPWaz8X3HHN08UnVVdVzID+AmF7D0wL7pbls1XtLkvrNcqlDBlXeeU3QxapJro6B/L6RtAg9zUUz99UZRV04SrhQlPvrrDWQHtRJFw+rVYBu13OTNqMcp81tXBjL8a4zA1V5/KzvWHE3xhhjjDGmAChIeZJvo3xjVjdOScptms0696WaRiVMbVMZ4IdvuVSP1V4USHdlpW/naifH/RikQcNC69t7rBio+0YtA9NMc0+nb/5pgWPiOlB1oGrItqNKqIF86H6MbUdVsrRzE6N1V1dnpmzECneanakquWrbmqbAqeKVZI+q7iDVBlpVNA23rrbfSbbTDFyWNv50zDCvzz77DAAwbty4nDyVuM9RpWPAMyrvO+20E4DsdYP9VhX5efPm5aSptuFxoBdei6i8ayApVdw6duwIIOs+csCAAQCy1wSOR47juG+wPCw3lXRdk6AzXXrutUxJ50THewHFBqxwVHHXGV6eM44DztDEM1qaRtoasTQ3vuo2lNcJXTORtBZGzyXvDURnuPVc64yOppsUfDBNtdb90oJZadlKWvvCccHnA10LoueLlBZsUGcqYtWcY1CDOOlMSmlrdkzhY8XdGGOMMcaYAqCgFHd9k9a3capSsRLGN2CqUvrGO336dAD5ARSoDqu6SGWNSgfLwu1xuahOpSlJVE2YNxUAtTun3SDfuFVtAbJqGpUNtgHt39QLBLdTNUl6wweyb/MsY1yXktoAyA/jTKWA6iLVoYYNGwLIPzeq3MdtoPUqq4eQqg5t22PPKGovrrMrqgalBUvSACFJCpAq50TzVGWeae2www45v1N9ZrpxULLSgoipTezAgQMBAGPHjs0pC3+nisa+lxQCXb1qMBBakyZNAGT7Otua/Zljiao3x4ba58ZtwrDvHF8MuKSedrg/17kcd9xxAIB+/frl5MFrZHy+eCzrwzZIChATl1ODeTGPNAUyaVtVHsuqIrNfs/15rWU7s/+UZBOddm3XPHVmjf1MVXOWif0uTpOfHEszZswAALRp0yanLBwHqriz7GVRk9OU9TTPO+xf6pVl6NChAIAGDRoAyM6WqdcWINsmvGcT3psbNWqUUxZ9Zkmb7dM1IvGsps5qcR+ee44x9o2qPH7Wd6y4G2OMMcYYUwAUlOKeFEIdyL5hUn2L/UbTBp0qGd9gqahTzebbKm3daYOqPl7VwwkVjySVSn26pimaVMj45sw3+/r16+fUh4rZjjvuCCDXxp0+nGmXSw8STINv+sxDPW2krY5Xry3xLId6CGE91bsFyz9lyhQAWQ8cbCeeCyryzJvnhiokkD0fqp6qzbRJRhXRGLVpT5uFUS8y6hEmzYNCnIempdvVJ3Hz5s1zvrOfE57/eBymeVVQm32mOWHCBAD5qhg9uvBaouM7RuvBdp44cWJO3o0bN87JQ71sUE1L8qKh7c7rn143WG4tE7efdNJJAICXX34ZQHYmLPZao545SovdoH1G7Y7Vrjo+X7q+oSqPZV7z2Oeo7PL6TVWY10id7QTSZ5zYzlTM9b6q3tt4fdbZId5DkpRd9hf1jkRVm7EG9N6mXqS0/yV5z2Fb8f6q1x8ey/vTpEmTAGTvJbxXsoxslzTPVUB2jLBN2P5sK86s6ewky8A8eBy/p8UyiY9l+/P+yj7Atlbvbmb9w4q7McYYY4wxBUBBKe76Nk41i2+ztMFTlRzIV4LUFvzHH38EkFWrNA2+vatyz7fdJM8oWl5NUz0sUHHmfnybnzlzZs5xSfXTbfxOJUPrpfbJqs6oH+0kX+q0EWSbqMKu9aZSMHnyZAD5dvlUAtP838f7ql9ptbM2ybBtY3tNVbe0XxL1/a827Um+/uP0433SPFqoMtWyZUsAWeVx+PDhALJ9T/2Fx/ViX+GxaTMB9NeuMQ6oKKqyznrHY45jV/1V8xpFJW7MmDE5eXN8Eo1ymWRLrjMGeh64bofQ7lbbnHkdf/zxAIDnnnsurw5q36t9JCl6ZpyX9qG0KLvxvkl2/VUNtUtX+2X1MMJrb9z/2W/Vc4tejwnPDc+pehni/uo7Pj5PnPVmOXjM7rvvDiA7JhkFnEozZ9COOuooAPm24zqj+sUXX2R+o928RtHWmYX+/fsDyJ/F4NoOlpHH8T7Fto5jKehML/fh84DGf9HxoXbpad5pYht35sExw/PDPqHjpqSo7qawseJujDHGGGNMAVBQivtf/vIXAMC7774LIN+HLYmVMF2JzTdh9f6gnlzUD7G+7SZF/lPUV63auxFVPJkXfUHvsssuAPKjLVJtjLfxbZvHMA0td5rvdJZR/WonwbozTY1Ip0oP25Yr8tn2VCXUEwXLEp9PKhNqG8jv7CMmmaR+W5qf8zSPKaqI8jypDXzc39X/t/YhKkxcs8G06Huc51/7ZZLNNSMPU5FLqw+9yaiNrHpSIbRv5ToYIDsWtQ2ZJvspx/B3330HIKuUUjnl2ElT4IB8f9QaZZHH0KPHXnvtlVNGtXXmeevQoQMAYNiwYZm8WD71N81j9DzozB3zZFvqWoS4b6StqbjnnnsAAFdccQWqCnHfAvLbhsouzwPbOb4npHkVSYtArjAPnaXj9yRPY5yl4ifzYP+l7Tev1xyjTJtKPO9feq/k93gdmyrtGluAaTIP/t6iRQsA2ecIXTuiYzl+ztC4Eeqpim2nM3CaJj3ypKnjJc3k6/khSX3BrF9YcTfGGGOMMaYAKCjFnXBVONUpvsXSjjtGlSK1B+VbOO2t+faqKhvt2/S4JO8I6rtVjylN9VYlhF5kRo8enZNOvJ+q1zxG00zymwzk28epElqSv2UtD9uKdr2ah9q28ziqKGz7JEWIv9GOV9vWlIzaR8dQNdKIqGrLqn2JfY7nRj1AxOeRv/GTeVLZ3XvvvQFk+wajmKZ5DUry7EJ4zIcffgggq6zxGHo5SktT/bjTfpe/xz7jWfe0SI9qX8xrFa9lVPFVYac9cTxzmOZ/W+vN8USPNvTMkxYpk9eML7/8Mu83vaZpX9DzSXQGT/tfUsTptLyrAtdddx0A4MgjjwSQfq/Q+07SvSTtGB2/GiuBv3MMUmnmOE+Lvg3kr4liv1blmWnsscceALL3Nq4BodccqsbMg9f5tm3b5tVXZ/o4C800WYbddtsNQPaao5GHNRI46xTXU8cBv7OteKx6ddO1IaSke56i92T1na+zAexTt9xyS6lpm8LAirsxxhhjjDEFQEEq7qqI8ZN+iNVHefxbmgrON3u+pfLtnKq+RnhT2/hYLVIbUr4Jp6naVOHSbIz5qav6qaTF9eI+at+mbUXUllZV1zQPI0ltof7qabfL36lkqA0x06HdoypFsQ0fz6OquSUpryZLSYoOlbc4qmp8jPrmVjWMqOKe5B2E55iKHO3QaZf99ddfA0iPqKo20lTDY9tg9fjAvsM+z3GnM2HqEYW/cw1GSd5O0ryp6DWBbcPZKY5lqt7qtSqO2aAzG5q25qlqPtFolDyvcRtSQVTvJmrTn+YtKG0GL63MSb+VtM5mfSMtZoLef/R+ldSeer7TZi5UBdb7ko5vnQ2KZ1l4/6HtNo/VyN26ZoyzsPSp/umnnwIAOnbsmFMX3pfjdkqLFcA0NA9di6WRVdXXOtdkxb7ymT+fNVSV13gjepy2aWljOK4f92He+gyia1+qsnem9ZWqc0U0xhhjjDGmgClIxZ1RB2k/xjdLvhHT/yqQVbRoz6bqvCpFfAtXpZ1qG5UOVamSUD/m+iZMqOgxT3375ts8lbMhQ4bkHBcfu88++wBIt9VPs0tXZYBlpkqepNSqnaX611fVXxVdtp1GbOR+VBuppgJZJadJkyYAsm2kvu5NMiXZxKqKrX1DZ2NUsVVvJxrHID6GHobatWsHABg8eDCAbDwFKmtUf3VmbOrUqQDy7Vlju3OqxRqdNGlGLi4v+y8jKar9NhX72F+6xknguFM7ecL1H7Nnz87ZTlVQFbl4rGse/I3HcByxjTWtNAU7yU6ftrpMg+eFfUBnuvRaoH0hTeWPt6WtE6gKpN0jdB0J2ygpvgZJs4NP84imsyW81vJTz1naeqkYtZ9XDzXq2Yjjm/2Otu/0RsMxyXsDkG+rznHJPDgO1BNSmncsjQ5Mz2z8jNHZSEaEJTpTqMfp9UHv/SWt82KfYL30+qXXY7P+YMXdGGOMMcaYAqAgFXfaTvNtlG/GGtUUyCqxVLiolvHtVD3R8C2cv1OdUwVJ34STVEW1vVPFozRVLk3xpHJI2zsA2HbbbXP20Td6zUNXoKcpYrpSP8mWX+3MuS8VTyrsqiIxbaqsM2bMAJAfObZRo0aZY7hNy8U+YUpGz3+8jeh5Yj9N82aSFjUzyUaZ56l9+/YAsjEZ2EeojrE/q4ci/s5xTMVavTrE5WZkVJafyhzT4naOdfYt9jV6n9H6xLM8nDXi9YTl1/gJGgFTFUmmw5kDjYkQ5xv7sgaAXXfdFUC+D/A0by3MUyMas72A7PjitVXtapW0iMyq8iaptqWtD6gK3H333QCyM1Dab/T6R9hGsT9wvcanzVyoGq7HJc0wAcnRPXmMrgfhWON4SLO7Vn/mvDdMmzYt5/e4/7G/pkXxTfORrn7b2cZU+3UtT5yuRqUlnBlQG3fmlTZu9BkhKaaBjmONC8Pya33Zp8z6gxV3Y4wxxhhjCoCCVNyJeqag3Vv8Zky7NO5LRe6HH34AkFXY1fOL+iemUkj1gSpDkl0m33j1jViVdlW5dQV+WiS3/fbbDwDw8ssvZ/LkNlUCqNCo6lLWMqmv39imUpUNbRuqpKrWq20u06HdOtXGJDtYKhlUANVXvCmZLl26AAAee+yxzDY9j2p3qv04zQsF+46mx/EJZKNz/u9//wOQPddUi3XWhX2K9pzaH6meqz06kL/GguWeNWsWgOzaCdaDaVE1Yx7sp+rXOYb7UBnktUgjMTNvHStsc+ahcSKoxMf/67Xnq6++ApC95u2www4AsjbKsf0/kB07H330EYBsNFeuFwCy44wzHzwvaj+rai3rpX0izZ44/i2tf1UlNPImZ2jYnjwvJCk+A6+z6rUsTbnludQ1LmqXzt/5SXU9TjtNYeZ23pc406Zp8ZoRr29KSi9pG7+zz7ItmQfrmeShBsi2MeubFDeF7azrS9SLkqrfOlNCdH+1DIjrpTOfrJ9Gso3HsVm/sOJujDHGGGNMAVCQiruqC3zLp21nrApTYee+VCpoN037OCpluvKc30naG3b81l6az2L9Xe3mVQlgHWhfShUvfpvnNtr86jHqEUPrkeZ/WVfFJ6mNqj5QbVP1gPvxO9VFngueG/WYECuFVFHsq3b1iJUftcNW39Hqe1zjC+gsD/sKxyNVdgB4/fXXAWRnsKgO81j14sSxQPWcfp6pJrOs7EvxmGAaaTa+HNu///3vAWT7FtV7EnupiutXks9squIaHVhnndTzzvbbb5+znf7dORMR15mfOgvBvHltY+RIeuJhu7BM6jkqtpHnedI+otdVnS3UMqktsM74xf+r/XtV8ipDuK5i5513BpCvdrON1FNXfH3mPpxB4r0gLYq2egrifrrGhXmyD8RKNNPgeNV1WXq9Zlqc/WHfo+c49k3OBqndOZDvRYURgnntYFsyj3r16uWUgWlqPVkvtm3ch3Ucaxp6j2e7pK03IbqeIL6vMW1di0PFXZ+LWG+z/mHF3RhjjDHGmAKgIBV3tbfmWyq/xx5GqOLyrZlqGlVcpsXV67vssguA/Mh0+obNt2/1DBMfo2/06nFBPb1QLaHKoDbFsceMuN5AvtLON3m1lUuzYVfbd5ZZleykmQWmmeYlh23JsrCtmYfa3tK+kcpCPIOSpuKneQ4wycR2krpeQ1Fbau0bsY0rkFW0ktZi8Df6K6eHFHphUZtW9h2OX+bJPsPtagsMpNv0UtVr3bo1gGz/HTZsWE4aLOPhhx8OINsPqXTFvtWpbn///fc5v6WNI+2vOk6p1FNNi9U+VU55LFVNXvNYH27neeI1gttp268+2oH86wOP1esfP3V86vocJd6u3kxIVVTcjTEmjYJ8cDfGGGPWV2giRdMpvkzxZY0vhnwZSwsmBGRfRPkSrMKKmkOqC0/mreZQJA6GpIEMNQ+mwRduwhdVviyrqLPjjjsCyL4gxy9zNHmj2R2PYd58MaVgRPGAZaBQlGbSyraNX575cqymtXqe9GVU21rNaXmu1NUrkL/wledTFxOznOxDZv3DpjLGGGOMMcYUAAWpuHO6lm+7VB34Nh+HNOcbsC7cUBdPPIZv0tyfU8BUEDidzDdiLnjh70D+2zen5vkmzLfqtLdyogvXdIFSvECHioW622IabBtdZKZv/lQfWHYGeUoKxc3y0DSJ50NNmXRhMNta1SJuZ9nVpRyQVUnUPEPNiEzJxKYyqtxoQA8dA7poi+eX/ZwmMi+++GLO/vE+6q6UebIPqCkG+zddhuqiah7P8QlkTc50kV6LFi0AZPvMF198ASDbf/fdd18A+eYd6jo1NuGiqQ8/uYiWCqEu5iQ6LmlWRDMeuo+MXWqyXBrkhoGUuJCPbcuF9xynVDX5uy42Tqoz25J9gmMzbdEhz58GrVLFMcn0ThXPqhiy/bbbbgOQ7Q88t2kuTpNcZqopo5pBqhmUnisNaKRma9wvvvfp+eUn+2ra4k01gdN68bpBtTy+/muAJFWgNU299+n1TsueVE+9V+tsRlrwq7RgjCybliEpQFmaIwbeR/l8wT5k1j+suBtjjDHGGFMAFKTiTpWbtmt8+05yH0YVjW/EVIqo7NEFnNrc8Y1ZFTHmwbdv2tV9++23mWP5Bt+qVSsAWbVNF6DFih2Q7yJLF7Cp+8v4bTwt/LwGkVEXcvykqsXFgWw3lnHSpEk5xwPAHnvskZOXunHUwD1aT7Y9z4W6EuN5je39+L8q7g7EVD5OO+20zP9PPfUUgHzFjWiYcl0YzDGw9957AwDeeustAFmFmwtQgWz/YlAgHX9pqh77J5VHKvB01Uj3cfHCdC7OZF+hvTDdJdJdGsdymzZtcuqryi9JWnDK8UK1i4vc2TYM+Ba3RYzaHbOdkgK8cRuvIxw/bAuOIy5Yr1+/PoBsm6e5kUxaBBovwAWyMxo646E21zo7oQpj0gwe09RgeFVRcSfs57zXqYtW/Yzbk+2oLo1VsdXAS+pCmP1Eg6Ixr1iJ1kXK6oZYry26H/PgTK+6RtZZ2bh8tLXnd84Ssd+rkwhtD5ZR778sQzzzq/diljtNaef1TF3t6rnQ60h8PtPOuabFPmPWX6y4G2OMMcYYUwAUpOLON2m+lVNlSwoTzH014AsVItp7UhFLU9eI/s43Yqp5QFYto7Knioe+hacFxFAbPP09ycWaqmga6CXNhk5VRJ0lUIU0rkdpyqRuZ55seyoGPDe6fiBWJdRFJvdxeOdVR/u4Km1qp8q2Z+AsBjwZMGAAgGzQGKpisV0ugwBRBdbw5KqWMS8GGNMAYGoDG/cV2puPGzcu51iOfdqhd+7cGUC++qe2vtpOsXpIW3Sq/FQx27dvDwBo164dgOxshAaH0rEcu7WMyxbXWWem1D0nbXupUmp9tB7qwjGus7aBXptUxVRPJCxTUqAgrRfLk5Z2VYLrE3baaScA+euidI1BDM87+4naSLOP6ewHPzm7xb6ZZl8fu/Pl+Wa50gL+pbkHZd68Z7IfMSCRro2J02Z9ONOXNgtNdO0YP9k34/UyQO741zVVauOu+3E2QFVynd1gOuruNt5H16bouGGfMesvVtyNMcYYY4wpAApScac6xzdj2nLSa0lSABG+TdMrBRU/en2gekgbVCrM+gZN9Ydv0Elv9VQVqLzTn6oq5yynqt0sK+vJeqWVJUb3oRLIsujbunqB4Ns768CZCioBsRrH/Pmmz3KqqsK24QwJ25qzAaq+8pwkeUxg/hrmOZ4JMOWD9u7PP/88gHxPBzqTtcMOOwAAmjZtCgD44IMPAGR9LatiyvMLZNUgfjJN7sO+QcWJv/M7xwaVrAYNGuTkGdtks++yr/OYb775BkBWpSeqRBP1RkHidRWfffYZgHybbubJscHycs2IXj/0GqDh5YGsEsh66WwT02D9qF5yP6p4um5Hlfyk+qinEh6rtro6S5M0GxqnG/+vnr/uvPNOVFVuuOEGANnZLF2PoOclvvfpegQNQqj3D7W/Jnq/SvNGA+TbqrP/qAcxDebG8vO6zus5+yzXsHDMsQ5AVrXmPjyG1wze+9K8uOlY40yDzhrE419t3LVtiK79SGtzrmFgu/Hcxfvr/Va96PA7+4xZf7HibowxxhhjTAFQkIo71XC+5VJJoI1brADoKvQZM2YAyNpXcwU231Zpg0vSwrtrZLMkrw8sFxUAfbNXP9g6K0BbPb59085Plfp4GxVpKntU+qh2jx07Nqc9WG62k9ooqjeeWFlT9Yzqiq6wJ6wfzx/3o/0yI9upLXJs56c+hdXvt1l1Tj75ZADACy+8ACB7HtgXaGdLRWrgwIEAsj7GeS5UjYqVKirrPF977bUXgKyHF35yDFBZ4/lWf8fsS7qWI96mdvPMm3mwfuopRRVFpsMyDR48OJOX+kLnGOe40/FIRZHrYDTiYpp/ZyBfvean2qOr94nYLjiuj+6fZH+ssw2qqPNTfWDrmhSSVCb1G57mr7oqwhkq3rfU24/aSAPZ8ch92RfVlpvnW226dSZG7zv8HqvCOg5i+3cgq6jrsRyr3M77tKbD8Z6E3ndVvVePNzqjyLHJvHQ2LK5nWluQtBgQzIttyjLx3PD6qOcuPlbXfjBt27ZXHay4G2OMMcYYUwAUpOKuXi+oFFDBje1BVZ3iMbR74xvuhAkTcr7zjZiKkNq5pvlLj6Eyqfa6LBPfkKn6q2JGlY7qAxVDlunGG2/M5DVkyJCcffjJNEaNGpWTB+tDlYG2xWqbmOZ/Of6NqFKmkTZjW+f4O88Fy8zzp14+gKx6onknRX00q8ZJJ52UuP39998HAHz99dcAsn1BPbrwXLAPxbNTtDun0qzrHnR2Sj2hcKywb6nSnrQGg32a442qHT/TonqmrSlhZNJ47YWqxbpeg7Nl1113XU6ajIx5wgknoCRiO2+NzaAzHDpzoCq++gJXz1JJUTiJzjiyvXXGgOcjzZMNibczDZ0ZMcDIkSMBZMeJRiLV2c4YzkRzfPJTr6E6u6P7aT9hnvH9lueTadB2m32V45ZlUv/mzJPHcc0ZPUMlrfdS+3jmwfuLerRhnkyD92nWh/drzqyppzUgf52JXivS2lLjp+g5YbuozTuQP1PAtDmu2UfM+o8Vd2OMMcYYYwqAglTcidq96ts6kG/Px32o+NEzhkZkpI0Z0bddVdhiVLlS9Ylp016RyhKVgFNOOSUnPSoHLVq0SGiFleyzzz6pv8VpduvWLbEM6odW1bsk7xFqQ6uRXwnzopLGtuZ2qio8nspHUpQ8VXXVY4hZc/zhD38AANxzzz0A8mdndDZKlV0ge/7Y76jeE7WzZR9gn2Jf4H5qKxvbmlKV5BoKqvsaP4Djj/XRsc1rCGe16Nki7pda92uvvRZloTSlnVx99dWZ/++++24A2THJ9md59Nql8SLUrrgk23a1p1Wf32nrWIhGQdV1MUk+47nt9ttvzytPVYUzLs888wyA7PonXZMU9/+02B0873ruuB/Hja5xYT/h2EuKfqv9hOOd13ydHdIo4hopljPGZYmiSzVeZ+GYptrRc/aW9z6WUT2tJUUWZlpsC5290LZkGmm+8PVZgZ/x+eR50BkpzuZVZe9LVQ0r7sYYY4wxxhQABam4822Xb6m0m03yKqMqjr5FUyFilEV9606L8MYyML0kVZFoZDNVJFn+Sy+9tMR6VwT/+Mc/AGSVG/U/q36BdUYhrqcqfrqdUPGkisI2Vi87aVHzYmVIo/qpmmLWPDxf6o1E13CoRwkgv1/RJzxnwHgMv1NxUztVVbiS/IRTeeYaEeZNLzhpnh/UgxS3M/opif240+6dx6xJ/va3vwEA7rrrLgDpEVJ1xkDbUL3u6MxZ/Jvuw09e/9TePs32V9ON0RkBkw9jEHAWVtsqblc9Fzzvev45ZnRWWWe5eM557eUsJ78D2XHIPHSWldd2vXfzO2OycD/Wh9+pqiehEVSZJu8RXIvDPFkvnTnUiLKsU1xP7sttab7V9TmC97S0tue5YjpJa0M0bfYJU3Ww4m6MMcYYY0wBUJCKu9qDaYTG2A5OPZTwTVdXZvPtm3ZvaepDWt6xbafa8RF9q+bvapO6NmCeqqiltZPOGgD5/q/VhpDbVfFR+0a1bWceTCdWbrmNHgTUftOseVTJ5Xhjn9Iop7EtuCpy7AtU3jVysar7asvO7+wHsSr2/fffA8iPskuFLc1POPufRg3W/eO8GDWWES7XBldddRUAoEePHgDSPe2k+XHXSIwkVvl4rtOuexoNWtVZXX+ks43xTBnTvv7660uvfBWFdsxPP/00gGy0UI612AuJrsdSrzD81NmSpHVbQH5kXZ7reJZLr/k6ZtRLG/sPlXQq7pzNqlevXk6ZOBOXBMvFvBk1nKgNPMui40LXUelMRXwM80y7/2ib8lPvdWntFs+o8DzxN84k2ra96mHF3RhjjDHGmAKgIBV32qxR8aIfcL61xp4pVEmmOqi+aHV//q42neptRfcD8qOqqi2pqveVYdOpZdDoeBplTm0N4/9VYVevBarqE/VBTCWB6VEhiRUR2kzynLN8tEs0aw+qTTzvVLb5nb+rpxggqx7xXHPMqN9nnl+q+Wn++rmOgrbmADB58uScY3QNBdHoh+r5QdU09RgBZMf/nnvumVi+NUnXrl0BADfffDOAbHvTlp+fuhZBZ7z4Gc8eqk97tb1VhZ3wvHGc8lPjY1x22WWrUGMzdOhQANm1WTqTBeTPiqTNwOg5TfM6o/cKnUWJ/9f+QLhd75u63otRtHlN2XnnnQGUPDvN8owfPz6nvupFKq0MaWVNmonQmWi9Rujzhaah605UideZRiB7jrkv+8AZZ5yRWH6z/mLF3RhjjDHGmAKgIBX30aNHAwBat24NIPvWSlUnVsz4hs63bfWPqvZtqrCrMq1v6/pGDeRHYCSqfPB7WqTKNQnzfOONNwDkqy36qavi499UuVCVTlfGs63Y9owGyNkQpsvj4jULPMeqVLBPHHvssWVsAbOq6HlN82XMvkI/4vGxnE3RcaY27GqPy+NpC09ljhFKY3tbtRelVwmd4eF3VdpVoWRf0yjMcVtoGmuTNNvw++67D0BWzVR/9RyHSb7w09YBKKrWcwaM54ltxrzp3cqsGg8++CAA4N///jcAoEOHDgCyM5JAtt9ynRfPDWeq1UMTr9ulzW6pypy0poznWe3odbZLlWvODrH/MPYC4z3QyxTHMpC1i6fNN8cp18kwTfZrlkG9yWg0YJaZdYrbg22UZtvOfblmTqO1ss25nfXlWNR1QnFegwcPBpDtA6bqYcXdGGOMMcaYAqAgFfd//vOfAIA+ffoAyCpJqmgD+Xar+saf5r88zXYtLaJorDbyf/UtrQreuhDtk2VgG7KMqsCrJwEgXw1VtA11/QCVEaatK/STzqd6+6H3AfYJs/Zg/9aogKq0x2s4qFRp3+f51DQIlUR6ivj8888B5M8IJfmxZv7NmzcHkO1f7IecMVCfyzobwN911g3Ijpd1YUwrakd+ww03AMiPHMnPpFgNOoaJrkXgjNicOXMAZKO8mjUDI/QymnGzZs0yv7G/csypL3Vu1/VaRO+J6oWI4ya+PrMPcbxyXyrKabEE1EsUlXV+Z3/iDBujhcb1ZN/UqKtMW9dvsSwsK79z7Qqvb/RWF7ePrtvR+6ZGSeeneovRSMLMk7MHcZ603S9rVGaz/mLF3RhjjDHGmAKgIBV3QrtW+npV/+BAvocXje6otnVJHjCAsq+SB9IjMKoyoG/blYHa66qHCbaHKiNAvqedNNQvMBUO+uRVjzXq6SduJ53xYB8wax7aSvN88DyqVwoq7eptJj6G55r9SxW32G423k716+CDDwYAfPHFFzl5Js3+MG0qcaoea//VcanKPYnXbrA+9Hi1LnPTTTeVed97770XQP6YvOiiiyq0TMYYY8pGQT+4G2OMMVWdK664AgDQvXv3zDa6UEwzkdEFpGoSpoEE9QWdLlhjKIgxTZoyknixJZAvfKkr4G222SYnT74Yxy/RNM9hebgolWmoKMA0VFBivWnuRfNRmofGZrbMK82JhabN+mkAKnXNqe5Vf/jhh0waPMfG2FTGGGOMMcaYAqBaWA9ixX/wwQcA8hebAflBRzRssAYeomtCvsWrCQmn8LlYRkM2x+WgWQBNBXTKvXPnzqta5QrnnXfeAZC/uIb10gV8QH7AHZoiqCs/tjFNhqZNmwYg25a6aFVDt8fqBduU5TjooINWtcqmnFDN40IznreddtoJANCwYUMA2fPF/eI+QzWMY4AqFvuCBuHS7WoKw3H96aef5nyPy0HVrl27djl5E12wXlpgtCRzHLpvYzlOP/30xGONWVt8+OGHALIKO6/Pem/ThZK8R6oKrEHTdDuQHfPqtpeqv7qQpIkbr/UcR0QXO/M+tMcee2T2+fbbbwFk7w2sJxdfEy4cZz3VVTTbQ10zMh3WIanurI9u17ZUV8d6b2Q92S4HHnggjFGsuBtjjDHGGFMArBc27qNGjQKQDTceB3whGqZZbfGoMPPNl2/fGqCJb9BcWKlvzEBWNdAQxcyDx65LsExc/Mcysy1Zz9jdnSrmrDcVDB6jbrZ0ASLPCZVPPS6Gv/GcW3Ffe2h4cp5PLhCmkqeBfLjwO/4tTWlPcy1KqJZRuWKZGJCFAX/ifXfdddfEemiZ0iYfdVE5iRdssh5UCI2pbKZOnQoA2HHHHQFkx6sqzOqwgdd87k+lmX2cyjYV6ximxTFDW3CmoY4beB1QV5PcT1230k1ivAic5WReOo7VNSPVbrXx1+CLqtDH9yP+rwvxmTdVe9ZLbd7V1SbrwP147oxJwoq7McYYY4wxBcB6obhfcsklAICePXsCAJo0aZL5TW3c+RbNN111d6gry6kip4Uy55t3rMZpHnzrplJx8sknl7uOaxqW6ZVXXgGQbRfWS9UHIFv3tLahGqEhozUAhtoJss2TbNwnT54MIHvOzdrjr3/9K4BsqG09v5y1oa27rpcAsueU51pnwogGhVFvDbpGJXbNSLg+g2q8ql6q2rNvqzeNNHen8Wwcg6PYTaJZVxg2bBiA7LotnTFLW0uk7lBViea4T3LBSuWYaVLV1nUkzIP3RlWwqf7zXsA6MP3Zs2dn0uL45j5M++eff87JW73DlOZ+mGXiWq64XfR6pV5meM1g2mltrUGgWG+euzPOOAPGKFbcjTHGGGOMKQDWC8Wd/OUvfwGQDRoCZH2x8g2Ydm4a3puqAd94+cm3bNp+U9njJ9PVgDExTIPeVNZlWMamTZsCyFcb43pqMCR+Uk2gAksVJc2mkGoE1RTaMVJNjX0BX3755atTPVOB8HzqrJP6Io4VOfYF9WfMfdiHOGa4XZV3KnXcrvsD2TGrnizSlHfmrd5kdAwkqfvjxo3L22ZMZcKAafxs1aoVgHwvZ1TgOZ71Oq428fzOdOJ7gtrF6/om3nd13Kq6rTPivJbQQ1S8TozbmDbLx310PPPao+tpWEadCaa9ejyzrP7mVVFn/Vlubmd9db0A8/rmm28AZM+ZMUlYcTfGGGOMMaYAWK8UdxKrsrfffjuArPrGt2a+IVNd4BsxFUGqDXwL53Yez0/dD8j3QqGeNNZldJW/rpZP2pdtoW2oK+X5nbMe3F8VTaou9BByzTXXrF6lTIVy8cUXA8jaulNFosK1/fbb52xPshFXW3W1M2X/0zgKVLDYL7kWJcnnOr1pMC+14VXlXOMsaKRIfrK/jx07NnOsbdvNuspll10GAOjTpw8AYLvttsv5nWqvRhqlIs0xyLFHe27+HntboULOscOZa6KzcrwX6PhWj2Uce7R5j++l3KazdeqnXSPHMi9V+9XjHP3Cx9cLll8Vd5051PgvzIPXGN4Tp0+fDiB7rowpCSvuxhhjjDHGFADrpeIeQ7X2qaeeApB921YPJ6oqUGHmdr4Z8zi14YsVAPVOwTf4c845pwJrtmZgGanOUK1gu8T15Da2BeutvvDVK0FpttD8bqV93YbKO/n3v/8NIOtlhn0l9sCgvqM5znjOVe3m7+qNgeo+12RwHMZ2q1zfwvGnnh7U1l3LorNMPI6qWay4G7OuM3ToUADpHlA4TrT/6/WZKjPvpbGNu8ZE0HuhznapYs1rBz+ZttrGx7N4ug6GduNU/6nIa5wRXpc0NoTaq6vqH6fBPHUGUb+zbdMUeJ6bP/3pTzCmNKy4G2OMMcYYUwCs94o7OfPMMwEA77zzDoD8CG1861Z1WFVzvilTKaDaHEcUJdyWFAF0XYdlZruoHWG8jaoDVVD1cZvmJ1dVVW7nuTKFxbXXXgsAuPPOOwEAe++9N4BcFTzN/7oq8LqGZNasWQCy/pupqlENUw8YMRopld+ZBsc0FTr1dKNrUz7//HMAwKWXXprUDMask9xzzz0AgNtuuw0A0KFDh5zf2d817oiud6LSrmucgOz45TonHqtxVDgru/nmmwPIjlveTzkGda1L0myYzhywHlTOmaZea7g+Rn3Pq/LO+sYqP/NnG2l9mVeaBxvWb/jw4QCy58aYsmDF3RhjjDHGmAKgWkhyF7IeQ08YzZs3B5AeHVGVePVlq1EXYzs/NumcOXMAACeccEIF12Lt8fLLLwPIrqyP24ltom2lfrO1rQj34+d3330HIN922hQm3bp1AwBsu+22mW1bb701gOxsDaFCpV4WqGipTSn7FpWuX375JSd9APi///u/nDTZ/1TNGzFiBICs56mddtopJy9GYPzyyy8B2PODWT944oknAAC77747gPRo2Wr7rko2P4HsvYD3PvpA10ipVOu33HJLAFm7e94LOGb1nsLtHIsA0Lp1awBZ5V/v0bqd1wrOELN+6mGO9eK9L75/sV785Aw168/68TqnCv2oUaMAAGeffTaMKS9W3I0xxhhjjCkAqpzirtDbjK60V/t0vnVTlSCqIsfHHnHEERVf4ErijTfeAJCvlAL53jmoKlB1oULDY7k/lQ/btFcdbr75ZgDZPsFPkhaRUD1fUHGnasY+R7t6ANhhhx0A5PdP9fhARZ1RC/k7lTeqfP/85z/LWVtj1n169+4NIBt/gWOQ/V7Xb6niTu9NQFZZphKt3tgIxyvVcCrvTFtncTWeCm3DgWxEWI2KrjO9vJfzmsE09Z7O6wLTYT3j2WZG81bFnfBexzR4vZo0aRIA4JRTToExq4oVd2OMMcYYYwqAKq+4l5e77roLQFYRVCUQWL9tYO+7777M/7TjYxeiT9qrrrpqrZfLFCZU4NmXqN5RBWPfom0sPUFwvKnSdcghh2T+p+KmaykIxy491tDW3fEDTFWkR48eAICdd94ZQH4sE45R/R57GtPIoWlxGNRGnMdRqVYVnOOdKjnHKgC0bNkSQFbdVi9QVPc5c0BFXW30qcxrxGdee2JvadzGcrGe+p1p/PDDDwCArl27wpjVxYq7McYYY4wxBYAVd2NMpUHFnaoX15pQsaMKRqVNbUqpjlFlO+iggzJpq4937quq4Lhx4wAAxx9/fMVVzJgC5brrrgOQ9bzGtSIcYxpJNVaiOdY4I02lXX3Eq694rnfidq4po4rO/Tg7NnXq1Eye9FpVr149AFm1ntcMrrXieOc1hvtxOz81oipnkuP1bFp3jfcyYcIEAFlPabfccguMqSisuBtjjDHGGFMAVJnIqcaYdQ+1I6X3BSpY6rdd1TAeRx/ssSqmHp9UWWMe9CpjjMmqw1dccQUAoG7dugDyo4FyLMbrTKg4c3zSWwyPpVcYjmdVwdW+nOnxk4p7HCGZ26jya/RzRmdVLzNU0pkWvdLwmqJ+3mPbefWGxXJzRnDo0KEAHBHVrBmsuBtjjDHGGFMArHMP7tOmTUOXLl2wxRZbYLPNNsPRRx+dsRczxuRS6OPluuuuw3XXXYdly5Zh2bJlKCoqQlFREZYuXYqlS5dmvi9evBiLFy/GihUrsGLFCtSuXRu1a9dG3bp1c/6qV6+e+atRo0bOX/xb9erVsWDBAixYsAC//PJLxsbWGGOMWZdZp0xlFi1ahAMOOADz58/HP//5T2y44Ya499570bFjR4wYMSKzYMUY4/FijFlz0Mzjr3/9KwCgY8eOAIAmTZrk7EezFyBrPqOBDLkQlGYoM2bMAJAe5IimJ3yhnjlzJgDgtNNOSy3v888/DyBrNkfzGzXH0+BQDRs2zMmTC2F1AXwcyI3byOTJkwEAH330EQDg4YcfTi2nMavLOvXg/vDDD2Ps2LH44osv0KZNGwDAYYcdhj322AP/+c9/cNttt1VyCY1Zd1ifxgt9p3fr1g1Avn923ij5QMAoj/R4ofsD2Rszb7hq8z5lypScvI0xxph1nXK5gxwwYAAOPPBAvPLKKzj22GNzfuvduzdOPfVUDB48GO3atVulwrRt2xYA8MUXX+Rs79y5M8aPH59x22ZMIbB48eJMOO7hw4dnFjfNnTsXu+++O5o2bYpPPvkkLxx4WVkfxwsf3PUhu6wP7vEsgyplPJaL1BjEpSQVzxiTC91F7rXXXgCyajkAbLPNNgCyCz451qjE83FDF5tzO9Xw2bNnA8guDC3PGH322WcBZBeTcnGtqvq87rKsup3XD5b1p59+yuTBco4cORKA3T2atUu5bNw7deqE7bbbDs8991zeb8899xyaNWuGdu3aYcmSJZg9e3aZ/siKFSswcuRItG7dOi/ttm3bYvz48ZlV4MYUAhtttBGeeuopjBs3Dv/6178y2y+88ELMnz8fvXr1Qo0aNTxejDHGGFMmymUqU61aNZx22mm45557MH/+/IybpZ9//hnvvvtu5uGkT58++POf/1ymNPmmPXfuXCxZsiTzxh7DbdOnT8cuu+xSniIbU6nss88+uPrqq3HHHXfg2GOPxcyZM/H888/jvvvuy4QW93jJ8o9//CPn+7///W8A+Qo860gbWarqcfh1blPXknyhiRU0Y0zZUHWZQdSAlbN9QHYcqrLOMchPtT/nfhyjZ511VrnLR3W+V69eALIuKZmXBnfj9UHLyGstVf8hQ4Zk8rj++usBACeeeGK5y2fM6lJuG/czzjgD3bp1w8svv4yzzz4bAPDCCy9g2bJlmQHTuXNnvPfee+VKl4ND/aMC2Zsz9zGmkLjxxhvxxhtv4Mwzz8SiRYvQsWNHXHLJJZnfPV6MMcYYUxbK/eC+6667ok2bNnjuuecyD+7PPfcc9t13X+y4444AVqphSUpgSdAeraRFZnEABGMKhZo1a6Jnz55o06YNateujSeffDKj/gAeLyVx7bXX5nzngttNNtkEQFYVY3vGHi6o4lFZo9I2evRoAMBVV121poptTJWB6jMAXHDBBQCAPfbYAwAys4q0L6fNO+H4pRkgXdnSk83qQLWeHl64HoY27/E1GMgPovTDDz8AAL799lsAwCOPPLLaZTKmIlglrzJnnHEGLr30UkydOhVLlizB559/ju7du2d+X7x4MebPn1+mtBo0aAAAqFOnDmrVqpU4fc1tdNtkTKHxzjvvAFj5UD127Fg0bdo085vHizHGGGPKQrm8ypDZs2ejYcOGuPXWW7F48WL8+9//xvTp0zNvsr169Sq3zS4AtGnTBtWqVcvzknHIIYdg/PjxGD9+fHmLakylM3LkSLRp0wannnoqRowYgdmzZ+Obb77JrBHxeCk7d955JwDg0EMPBZAfdj02HaLiTtOhqVOnAljpMtMYs/bo2rUrgOxYpNrN8Xv//fevtbJceumlAPJt2TlT2aNHj7VWFmNWhVVS3OvWrYvDDjsMzz77LIqLi3HooYdmHtqBVbPZBYATTjgB11xzDb788suMt4wxY8bgww8/xN/+9rdVKaoxlcrSpUtx1llnoWHDhrj//vsxceJEtGnTBpdffjl69uwJwOPFGGOMMWVjlRR3AOjbty9OOOEEACsXp3bp0mW1C7Nw4UK0atUKCxcuxN/+9jdsuOGGuOeee7B8+XKMGDECW2+99WrnYcza5IYbbsAtt9yCDz74AAcccAAA4NZbb8W1116LN998E4cffvgqp10VxwuVuUMOOQRAdgEuL2OxDS29RRQVFQHI+ru/7LLL1kpZjTHGmIpmlR/cf/vtNzRo0AArVqzAjBkzMlNgq8vUqVNx+eWX491338WKFSvQqVMn3HvvvZmFr8YUCsOGDcM+++yDrl274oEHHshsX758Odq1a4dp06Zh1KhRmdDcq0JVGy9+cDfGGFOVWeUH92XLlqFhw4Y48sgj8cQTT1R0uYwxJpXvvvsOQL5XndiPO23caevPGUJjjDGmUClX5NSY1157DT///DPOOOOMiiyPMcYYY4wxJoFyK+5DhgzByJEjccstt6Bu3boYNmzYmiqbMcaUyNVXX53zPb6c0WPFPffcs1bLZIwxxqwpyq249+jRA127dkW9evXw9NNPr4kyGWOMMcYYY4RVtnE3xhhjjDHGrD1W2cbdGGOMMcYYs/bwg7sxxhhjjDEFgB/cjTHGGGOMKQD84G6MMcYYY0wB4Ad3Y4wxxhhjCgA/uBtjjDHrGCtWrMAjjzyCli1bYpNNNkH9+vVx2GGHYfDgwZVdNGNMJWJ3kMYYY8w6xpVXXol77rkHp512Gjp06IBffvkFjz76KKZMmYJPP/0Ubdu2rewiGmMqAT+4G2OMMesQy5Ytw2abbYY//vGPeOmllzLbJ06ciB122AGXXHIJ7r///kosoTGmsrCpjDHGGFMCkyZNQrVq1VL/KpqlS5di8eLFqF+/fs72evXqoXr16thoo40qPE9jTGGwQWUXwBhjjFmX2XrrrfHMM8/kbFu6dCkuv/xy1KxZEwBQVFSEoqKiUtOqUaMGttxyyxL32WijjbDPPvugV69eaNeuXcZU5pZbbsGWW26J8847b9UrY4wpaGwqY4wxxpSTCy+8EI8++ijee+89HHDAAbjxxhtx0003lXpckyZNMGnSpFL3GzduHE466SQMGzYss22HHXbA//73P+yyyy6rU3RjTAHjB3djjDGmHDz99NM488wz8Z///AdXXHEFAGDChAmYMGFCqcdutNFG2H///Uvdb+bMmbjqqquw2Wab4aCDDsKMGTNw++23Y+ONN8Ynn3yCunXrrnY9jDGFhx/cjTHGmDIyYsQI7LfffjjmmGPQu3fv1Upr/vz5WLx4ceZ7zZo1UadOHSxbtgytWrVCp06d8OCDD2Z+Hzt2LHbffXdcfvnluOOOO1Yrb2NMYeIHd2OMMaYMzJs3D61bt8amm26KwYMHY+ONN878tmjRIixatKjUNGrUqIGtt94aAHDWWWfhqaeeyvzWsWNHDBw4EB9++CEOOuggvPvuuzj44INzjm/RogU23XRTDBo0qIJqZYwpJLw41RhjjCmFFStW4NRTT8Uvv/yC999/P+ehHQDuvvvuctu4X3311TjttNMyv3HR6syZMwEAy5cvzzt+6dKlWLZs2apWwxhT4PjB3RhjjCmFm266Ce+88w7eeustNG3aNO/3M844A+3bty81ndiVY/PmzdG8efO8fXbeeWcAwPPPP49DDz00s33YsGEYM2aMvcoYU4WxqYwxxhhTAt988w1atGiB//u//8M555yT93usmlcUhxxyCN577z0ce+yxOOSQQ/DTTz/hwQcfxG+//YavvvrKnmWMqaL4wd0YY4wpgYEDB+KAAw5I/X1N3EYXL16Mu+++G88//zwmTpyImjVrokOHDrjlllvQsmXLCs/PGFMY+MHdGGOMMcaYAqB6ZRfAGGOMMcYYUzp+cDfGGGOMMaYA8IO7McYYY4wxBYAf3I0xxhhjjCkA/OBujDHGGGNMAeAHd2OMMcYYYwoAP7gbY4wxxhhTAPjB3RhjjDHGmALAD+7GGGOMMcYUAH5wN8YYY4wxpgDwg7sxxhhjjDEFgB/cjTHGGGOMKQD84G6MMcYYY0wB4Ad3Y4wxxhhjCgA/uBtjjDHGGFMA+MHdGGOMMcaYAsAP7sYYY4wxxhQAfnA3xhhjjDGmAPCDuzHGGGOMMQWAH9yNMcYYY4wpAPzgbowxxhhjTAHgB3djjDHGGGMKgP8HOTBk5880USoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from nimare.correct import FWECorrector\n", + "corr = FWECorrector(method=\"bonferroni\")\n", + "cres = corr.transform(results)\n", + "\n", + "\n", + "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo_corr-FWE_method-bonferroni\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"FWEcorrecred-SchizophreniaYes-SchizophreniaNo\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bonferroni correction is a very conservative FWE correction methods, especially\n", + "because most functional imaging data have some degree of spatial correlation\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -353,18 +737,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", + "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", + "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", + "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "contrast_result = inference.compute_contrast(\n", " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"GLH_groups_0_z_statistics\"),\n", + " results.get_map(\"z_GLH_groups_0\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -386,11 +806,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", + "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", + "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", + "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " standardized_sample_sizes standardized_avg_age type2 type3 \\\n", + "0 0.001238 0.005385 -0.023627 -0.023361 \n", + "\n", + " type4 type5 \n", + "0 -0.042416 -0.045277 \n", + "P-values of moderator effects `sample_sizes` is p_value\n", + "0 0.901471\n", + "P-value of moderator effects `avg_age` is p_value\n", + "0 0.590164\n" + ] + } + ], "source": [ "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "contrast_name = results.estimator.moderators\n", @@ -399,12 +853,12 @@ "print(results.tables[\"Moderators_Regression_Coef\"])\n", "print(\n", " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", - " results.tables[\"standardized_sample_sizes_p_values\"]\n", + " results.tables[\"p_standardized_sample_sizes\"]\n", " )\n", ")\n", "print(\n", " \"P-value of moderator effects `avg_age` is {}\".format(\n", - " results.tables[\"standardized_avg_age_p_values\"]\n", + " results.tables[\"p_standardized_avg_age\"]\n", " )\n", ")" ] @@ -424,11 +878,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", + "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", + "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", + "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", + "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", + "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p_value\n", + "0 0.771564\n" + ] + } + ], "source": [ "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "t_con_moderators = inference.create_contrast(\n", @@ -437,7 +918,7 @@ "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", "print(\n", " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", - " results.tables[\"standardized_sample_sizes-standardized_avg_age_p_values\"]\n", + " results.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", " )\n", ")" ] @@ -456,7 +937,7 @@ ], "metadata": { "kernelspec": { - "display_name": "torch", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -470,12 +951,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" - }, - "vscode": { - "interpreter": { - "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" - } + "version": "3.8.8" } }, "nbformat": 4, diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 73c5dd4cd..5d3e0e012 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -81,40 +81,40 @@ model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e1, + tol=1e3, device="cpu", ) results = cbmr.fit(dataset=dset) plot_stat_map( - results.get_map("Group_schizophrenia_Yes_Studywise_Spatial_Intensity"), + results.get_map("SpatialIntensity_group-SchizophreniaYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_Yes", + title="SchizophreniaYes", threshold=1e-4, ) plot_stat_map( - results.get_map("Group_schizophrenia_No_Studywise_Spatial_Intensity"), + results.get_map("SpatialIntensity_group-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_No", + title="SchizophreniaNo", threshold=1e-4, ) plot_stat_map( - results.get_map("Group_depression_Yes_Studywise_Spatial_Intensity"), + results.get_map("SpatialIntensity_group-DepressionYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="depression_Yes", + title="DepressionYes", threshold=1e-4, ) plot_stat_map( - results.get_map("Group_depression_No_Studywise_Spatial_Intensity"), + results.get_map("SpatialIntensity_group-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="depression_No", + title="DepressionNo", threshold=1e-4, ) @@ -133,44 +133,44 @@ inference = CBMRInference(CBMRResults=results, device="cuda") t_con_groups = inference.create_contrast( - ["schizophrenia_Yes", "schizophrenia_No", "depression_Yes", "depression_No"], type="groups" + ["SchizophreniaYes", "SchizophreniaNo", "DepressionYes", "DepressionNo"], type="groups" ) contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) -# generate chi-square maps for each group +# generate z-score maps for group-wise spatial homogeneity test plot_stat_map( - results.get_map("schizophrenia_Yes_z_statistics"), + results.get_map("z_group-SchizophreniaYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_Yes", + title="SchizophreniaYes", threshold=scipy.stats.norm.isf(0.05), ) plot_stat_map( - results.get_map("schizophrenia_No_z_statistics"), + results.get_map("z_group-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_No", + title="SchizophreniaNo", threshold=scipy.stats.norm.isf(0.05), ) plot_stat_map( - results.get_map("depression_Yes_z_statistics"), + results.get_map("z_group-DepressionYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="depression_Yes", + title="DepressionYes", threshold=scipy.stats.norm.isf(0.05), ) plot_stat_map( - results.get_map("depression_No_z_statistics"), + results.get_map("z_group-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="depression_No", + title="DepressionNo", threshold=scipy.stats.norm.isf(0.05), ) @@ -182,6 +182,55 @@ # the number of voxels within brain mask, $j$ is the index of voxel. Areas with # significant p-values are highlighted (under significance level $0.05$). +############################################################################### +# Perform fasle discovery rate (FDR) correction on spatial homogeneity test +# ----------------------------------------------------------------------------- +# The default FDR correction method is "indep", using Benjamini-Hochberg(BH) procedure. +from nimare.correct import FDRCorrector +corr = FDRCorrector(method="indep", alpha=0.05) +cres = corr.transform(results) + +# generate FDR corrected z-score maps for group-wise spatial homogeneity test +plot_stat_map( + cres.get_map("z_group-SchizophreniaYes_corr-FDR_method-indep"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="FDRcorrecred-SchizophreniaYes", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + cres.get_map("z_group-SchizophreniaNo_corr-FDR_method-indep"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="FDRcorrecred-SchizophreniaNo", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + cres.get_map("z_group-DepressionYes_corr-FDR_method-indep"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="FDRcorrecred-DepressionYes", + threshold=scipy.stats.norm.isf(0.05), +) + +plot_stat_map( + cres.get_map("z_group-DepressionNo_corr-FDR_method-indep"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="FDRcorrecred-DepressionNo", + threshold=scipy.stats.norm.isf(0.05), +) + +############################################################################### +# After FDR correction (via BH procedure), areas with stronger spatial intensity +# are more stringent, (the number of voxels with significant p-values is reduced). + ############################################################################### # GLH testing for group comparisons among any two groups # ----------------------------------------------------------------------------- @@ -191,9 +240,9 @@ inference = CBMRInference(CBMRResults=results, device="cuda") t_con_groups = inference.create_contrast( [ - "schizophrenia_Yes-schizophrenia_No", - "schizophrenia_No-depression_Yes", - "depression_Yes-depression_No", + "SchizophreniaYes-SchizophreniaNo", + "SchizophreniaNo-DepressionYes", + "DepressionYes-DepressionNo", ], type="groups", ) @@ -201,29 +250,29 @@ # generate z-statistics maps for each group plot_stat_map( - results.get_map("schizophrenia_Yes-schizophrenia_No_z_statistics"), + results.get_map("z_group-SchizophreniaYes-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_Yes", + title="SchizophreniaYes-SchizophreniaNo", threshold=scipy.stats.norm.isf(0.4), ) plot_stat_map( - results.get_map("schizophrenia_No-depression_Yes_z_statistics"), + results.get_map("z_group-SchizophreniaNo-DepressionYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="schizophrenia_No", + title="SchizophreniaNo-DepressionYes", threshold=scipy.stats.norm.isf(0.4), ) plot_stat_map( - results.get_map("depression_Yes-depression_No_z_statistics"), + results.get_map("z_group-DepressionYes-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="depression_Yes", + title="DepressionYes-DepressionNo", threshold=scipy.stats.norm.isf(0.4), ) ############################################################################### @@ -235,6 +284,30 @@ # (significant difference in spatial intensity estimation between two groups) # are highlighted (under significance level $0.05$). +############################################################################### +# Perform family-wise error rate (FWE) correction on group comparison tests +# ----------------------------------------------------------------------------- +# The default setting is performing Bonferroni FWE correction. +from nimare.correct import FWECorrector +corr = FWECorrector(method="bonferroni") +cres = corr.transform(results) + + +# generate FDR corrected z-score maps for group-wise spatial homogeneity test +plot_stat_map( + cres.get_map("z_group-SchizophreniaYes-SchizophreniaNo_corr-FWE_method-bonferroni"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="FWEcorrecred-SchizophreniaYes-SchizophreniaNo", + threshold=scipy.stats.norm.isf(0.05), +) + +############################################################################### +# Bonferroni correction is a very conservative FWE correction methods, especially +# because most functional imaging data have some degree of spatial correlation + + ############################################################################### # GLH testing with contrast matrix specified # ----------------------------------------------------------------------------- @@ -257,7 +330,7 @@ t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False ) plot_stat_map( - results.get_map("GLH_groups_0_z_statistics"), + results.get_map("z_GLH_groups_0"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -278,12 +351,12 @@ print(results.tables["Moderators_Regression_Coef"]) print( "P-values of moderator effects `sample_sizes` is {}".format( - results.tables["standardized_sample_sizes_p_values"] + results.tables["p_standardized_sample_sizes"] ) ) print( "P-value of moderator effects `avg_age` is {}".format( - results.tables["standardized_avg_age_p_values"] + results.tables["p_standardized_avg_age"] ) ) @@ -302,7 +375,7 @@ contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) print( "P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}".format( - results.tables["standardized_sample_sizes-standardized_avg_age_p_values"] + results.tables["p_standardized_sample_sizes-standardized_avg_age"] ) ) diff --git a/nimare/correct.py b/nimare/correct.py index e2ec4e137..d6ebec82f 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -81,11 +81,9 @@ def _collect_inputs(self, result): f"\tAvailable native methods: {', '.join(corr_methods)}\n" f"\tAvailable estimator methods: {', '.join(est_methods)}" ) - for rm in self._required_maps: - print(rm) # Check required maps # for cbmr approach, we have customized name for groupwise p maps - p_map_cbmr = tuple([m for m in result.maps.keys() if re.search("p_", m)]) + p_map_cbmr = tuple([m for m in result.maps.keys() if m.startswith("p_") and "_corr-" not in m]) if len(p_map_cbmr) > 0: self._required_maps = p_map_cbmr for rm in self._required_maps: diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 8ee7870de..3b5d5c0a4 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -2,6 +2,7 @@ from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators import nibabel as nib import numpy as np +import pandas as pd import scipy from nimare.utils import mm2vox from nimare.diagnostics import FocusFilter @@ -598,7 +599,7 @@ def _glh_con_group(self): np.sum(group_foci_per_voxel) / (n_voxels * n_study) ) group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[group + "_Studywise_Spatial_Intensity"] + self.CBMRResults.maps["SpatialIntensity_group-" + group] ) group_log_intensity_per_voxel = ( group_log_intensity_per_voxel - group_null_log_spatial_intensity @@ -611,7 +612,7 @@ def _glh_con_group(self): involved_log_intensity_per_voxel = list() for group in con_group_involved: group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps[group + "_Studywise_Spatial_Intensity"] + self.CBMRResults.maps["SpatialIntensity_group-" + group] ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) involved_log_intensity_per_voxel = np.stack( @@ -669,13 +670,13 @@ def _glh_con_group(self): z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) if self.t_con_groups_name: self.CBMRResults.maps[ - f"chi_square_{self.t_con_groups_name[con_group_count]}" + f"chi_square_group-{self.t_con_groups_name[con_group_count]}" ] = chi_sq_spatial self.CBMRResults.maps[ - f"p_{self.t_con_groups_name[con_group_count]}" + f"p_group-{self.t_con_groups_name[con_group_count]}" ] = p_vals_spatial self.CBMRResults.maps[ - f"z_{self.t_con_groups_name[con_group_count]}" + f"z_group-{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: self.CBMRResults.maps[ @@ -739,20 +740,19 @@ def _glh_con_moderator(self): @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) @ Contrast_moderator_coef ) - chi_sq_moderator = chi_sq_moderator.item() p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) if self.t_con_moderators_name: # None? self.CBMRResults.tables[ - f"{self.t_con_moderators_name[con_moderator_count]}_chi_square_values" - ] = chi_sq_moderator + f"chi_square_{self.t_con_moderators_name[con_moderator_count]}" + ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) self.CBMRResults.tables[ f"p_{self.t_con_moderators_name[con_moderator_count]}" - ] = p_vals_moderator + ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p_value"]) else: self.CBMRResults.tables[ - f"GLH_moderators_{con_moderator_count}_chi_square_values" - ] = chi_sq_moderator + f"chi_square_GLH_moderators_{con_moderator_count}" + ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) self.CBMRResults.tables[ f"p_GLH_moderators_{con_moderator_count}" - ] = p_vals_moderator + ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p_value"]) con_moderator_count += 1 diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 677e2af09..56c91a7c0 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -224,7 +224,7 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): spatial_regression_coef[group] = group_spatial_coef_linear_weight # Estimate group-specific spatial intensity group_spatial_intensity_estimation = np.exp(np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)) - spatial_intensity_estimation[group + "_Studywise_Spatial_Intensity"] = group_spatial_intensity_estimation + spatial_intensity_estimation["SpatialIntensity_group-" + group] = group_spatial_intensity_estimation # Extract optimized regression coefficient of study-level moderators from the model if self.moderators_coef_dim: diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index a768b96e7..f31366238 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -4,7 +4,7 @@ import logging import torch import numpy as np -from nimare.correct import FDRCorrector +from nimare.correct import FDRCorrector, FWECorrector def test_CBMREstimator(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) @@ -42,15 +42,21 @@ def test_CBMRInference(testdata_cbmr_simulated): inference = CBMRInference( CBMRResults=cbmr_res, device="cuda" ) - t_con_groups = inference.create_contrast(["SchizophreniaYes", "SchizophreniaNo"], type="groups") - # t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") - contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) + t_con_groups = inference.create_contrast( + [ + "SchizophreniaYes-SchizophreniaNo", + "SchizophreniaNo-DepressionYes", + "DepressionYes-DepressionNo", + ], + type="groups", + ) + # t_con_groups = inference.create_contrast(["SchizophreniaYes", "SchizophreniaNo"], type="groups") + t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") + contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=t_con_moderators) - corr = FDRCorrector(method="indep", alpha=0.05) + corr = FWECorrector(method="bonferroni") cres = corr.transform(cbmr_res) - corr = FDRCorrector(method="indep", alpha=0.05) - cres2 = corr.transform(cres) def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) From 9904123a8f9cd38163c1d2cc9de5a395deb7646b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 19 Mar 2023 23:30:01 +0000 Subject: [PATCH 100/177] add testing cases with more coverage for CBMREstimator --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 9 ++++-- nimare/meta/models.py | 18 +++++------ nimare/tests/test_meta_cbmr.py | 32 +++++++++++++++----- 3 files changed, 40 insertions(+), 19 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 3f0e10a37..616d4a7cb 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -937,7 +937,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "torch", "language": "python", "name": "python3" }, @@ -951,7 +951,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" + }, + "vscode": { + "interpreter": { + "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" + } } }, "nbformat": 4, diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 56c91a7c0..13069d7a9 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -32,12 +32,6 @@ def __init__( self.tol = tol self.device = device - # initialization for spatial regression coefficients - if self.spatial_coef_dim and self.groups: - self.init_spatial_weights() - # initialization for regression coefficients of moderators - if self.moderators_coef_dim: - self.init_moderator_weights() # initialization for iteration set up self.iter = 0 @@ -124,6 +118,12 @@ def closure(): loss = optimizer.step(closure) scheduler.step() + if torch.isnan(loss): + raise ValueError( + f"""The current learing rate {str(self.lr)} or choice of model gives rise to + NaN log-likelihood, please try Poisson model or adjust learning rate to a smaller + value.""" + ) # reset the L-BFGS params if NaN appears in coefficient of regression if any( [ @@ -433,8 +433,6 @@ class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) - if self.groups: - self.init_overdispersion_weights() def init_overdispersion_weights(self): """Document this.""" @@ -447,9 +445,9 @@ def init_overdispersion_weights(self): overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) self.overdispersion = torch.nn.ParameterDict(overdispersion) - def init_weights(self, groups, spatial_coef_dim, moderators_coef_dim): + def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): """Document this.""" - super().init_weights(groups, spatial_coef_dim, moderators_coef_dim) + super().init_weights(groups, moderators, spatial_coef_dim, moderators_coef_dim) self.init_overdispersion_weights() def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index f31366238..92f80cf63 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,26 +1,44 @@ +import nimare from nimare.meta.cbmr import CBMREstimator, CBMRInference from nimare.tests.utils import standardize_field from nimare.meta import models import logging import torch +import pytest import numpy as np from nimare.correct import FDRCorrector, FWECorrector -def test_CBMREstimator(testdata_cbmr_simulated): +# @pytest.mark.parametrize( +# "group_categories, spline_spacing, model", +# [ +# (None, 10, models.PoissonEstimator), +# ("diagnosis", 10, models.PoissonEstimator), +# (["diagnosis", "drug_status"], 10, models.PoissonEstimator), +# ] +# ) +@pytest.mark.parametrize("group_categories", [["diagnosis", "drug_status"]]) +@pytest.mark.parametrize("spline_spacing", [10, 5]) +@pytest.mark.parametrize("model",[models.NegativeBinomialEstimator]) + +def test_CBMREstimator(testdata_cbmr_simulated, group_categories, spline_spacing, model): logging.getLogger().setLevel(logging.DEBUG) - """Unit test for CBMR estimator.""" + LGR = logging.getLogger(__name__) + """Unit test for CBMR estimator.""" dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) + LGR.debug("group_categories: {}, spline_spacing: {}, model: {}".format(group_categories, spline_spacing, model)) cbmr = CBMREstimator( - group_categories= ["diagnosis", "drug_status"], + group_categories= group_categories, moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], - spline_spacing=10, - model=models.PoissonEstimator, + spline_spacing=spline_spacing, + model=model, penalty=False, lr=1e-1, - tol=1e4, + tol=1, device="cpu" ) - cbmr.fit(dataset=dset) + res = cbmr.fit(dataset=dset) + assert isinstance(res, nimare.results.MetaResult) + def test_CBMRInference(testdata_cbmr_simulated): logging.getLogger().setLevel(logging.DEBUG) From f517b9f2c455f92b73be3ea0d209f003b86bdb86 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 20 Mar 2023 16:03:31 +0000 Subject: [PATCH 101/177] [skip CI] [WIP] added new changes --- nimare/tests/test_meta_cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 92f80cf63..c24f7340c 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -16,7 +16,7 @@ # (["diagnosis", "drug_status"], 10, models.PoissonEstimator), # ] # ) -@pytest.mark.parametrize("group_categories", [["diagnosis", "drug_status"]]) +@pytest.mark.parametrize("group_categories", [None, ["diagnosis", "drug_status"]]) @pytest.mark.parametrize("spline_spacing", [10, 5]) @pytest.mark.parametrize("model",[models.NegativeBinomialEstimator]) From e9f2cae2d3feaf666e4967d361d973885c739c61 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 20 Mar 2023 14:39:36 -0500 Subject: [PATCH 102/177] run black and isort --- nimare/correct.py | 11 +- nimare/meta/cbmr.py | 36 +++-- nimare/meta/models.py | 268 +++++++++++++++++++++------------ nimare/tests/conftest.py | 101 +++++++++---- nimare/tests/test_meta_cbmr.py | 204 ++++++++++++++----------- nimare/tests/utils.py | 17 ++- nimare/utils.py | 23 ++- 7 files changed, 404 insertions(+), 256 deletions(-) diff --git a/nimare/correct.py b/nimare/correct.py index d6ebec82f..3c24a4d12 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -1,10 +1,10 @@ """Multiple comparisons correction methods.""" import inspect import logging +import re from abc import ABCMeta, abstractproperty import numpy as np -import re from pymare.stats import bonferroni, fdr from nimare.results import MetaResult @@ -83,7 +83,9 @@ def _collect_inputs(self, result): ) # Check required maps # for cbmr approach, we have customized name for groupwise p maps - p_map_cbmr = tuple([m for m in result.maps.keys() if m.startswith("p_") and "_corr-" not in m]) + p_map_cbmr = tuple( + [m for m in result.maps.keys() if m.startswith("p_") and "_corr-" not in m] + ) if len(p_map_cbmr) > 0: self._required_maps = p_map_cbmr for rm in self._required_maps: @@ -92,8 +94,7 @@ def _collect_inputs(self, result): f"{type(self)} requires '{rm}' maps to be present in the MetaResult, " "but none were found." ) - - + def _generate_secondary_maps(self, result, corr_maps, rm): """Generate corrected version of z and log-p maps if they exist.""" @@ -244,7 +245,7 @@ def _transform(self, result, method): # Create a dictionary of the corrected results corr_maps[rm] = p_corr self._generate_secondary_maps(result, corr_maps, rm) - + return corr_maps, tables diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 3b5d5c0a4..58f28da2a 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,16 +1,17 @@ -from nimare.base import Estimator -from nimare.utils import get_masker, B_spline_bases, dummy_encoding_moderators +"""Cla.""" +import logging +import re + import nibabel as nib import numpy as np import pandas as pd import scipy -from nimare.utils import mm2vox -from nimare.diagnostics import FocusFilter -from nimare.meta import models import torch -import logging -import re +from nimare.base import Estimator +from nimare.diagnostics import FocusFilter +from nimare.meta import models +from nimare.utils import B_spline_bases, dummy_encoding_moderators, get_masker, mm2vox LGR = logging.getLogger(__name__) @@ -332,8 +333,9 @@ def _fit(self, dataset): class CBMRInference(object): - """Statistical inference on outcomes (intensity estimation and study-level - moderator regressors) of CBMR. + """Statistical inference on outcomes of CBMR. + + (intensity estimation and study-level moderator regressors) Parameters ---------- @@ -463,7 +465,7 @@ def create_contrast(self, contrast_name, type="groups"): raise ValueError(f"{contrast} is not a valid contrast.") moderators_contrast = contrast_match.groupdict() if all(moderators_contrast.values()): # moderator comparison - moderator_groups = list(map(moderators_contrast.get, ["first", "second"])) + _ = list(map(moderators_contrast.get, ["first", "second"])) contrast_vector[ self.moderator_reference_dict[moderators_contrast["first"]] ] = 1 @@ -679,13 +681,9 @@ def _glh_con_group(self): f"z_group-{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: - self.CBMRResults.maps[ - f"chi_square_GLH_groups_{con_group_count}" - ] = chi_sq_spatial + self.CBMRResults.maps[f"chi_square_GLH_groups_{con_group_count}"] = chi_sq_spatial self.CBMRResults.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial - self.CBMRResults.maps[ - f"z_GLH_groups_{con_group_count}" - ] = z_stats_spatial + self.CBMRResults.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial con_group_count += 1 def _chi_square_log_intensity( @@ -752,7 +750,7 @@ def _glh_con_moderator(self): self.CBMRResults.tables[ f"chi_square_GLH_moderators_{con_moderator_count}" ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) - self.CBMRResults.tables[ - f"p_GLH_moderators_{con_moderator_count}" - ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p_value"]) + self.CBMRResults.tables[f"p_GLH_moderators_{con_moderator_count}"] = pd.DataFrame( + data=np.array(p_vals_moderator), columns=["p_value"] + ) con_moderator_count += 1 diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 13069d7a9..5394135c4 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1,22 +1,22 @@ - import abc -import torch -import numpy as np -import pandas as pd -import functorch -import logging import copy +import logging +import functorch +import numpy as np +import pandas as pd +import torch LGR = logging.getLogger(__name__) -class GeneralLinearModelEstimator(torch.nn.Module): + +class GeneralLinearModelEstimator(torch.nn.Module): def __init__( self, spatial_coef_dim=None, moderators_coef_dim=None, penalty=False, - lr = 0.1, + lr=0.1, lr_decay=0.999, n_iter=1000, tol=1e-2, @@ -74,9 +74,7 @@ def init_spatial_weights(self): def init_moderator_weights(self): """Initialize the intercept and regression coefficients for moderators.""" - self.moderators_linear = torch.nn.Linear( - self.moderators_coef_dim, 1, bias=False - ).double() + self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) return @@ -138,7 +136,9 @@ def closure(): ) spatial_coef_linears, overdispersion = dict(), dict() for group in self.groups: - group_spatial_linear = torch.nn.Linear(self.spatial_coef_dim, 1, bias=False).double() + group_spatial_linear = torch.nn.Linear( + self.spatial_coef_dim, 1, bias=False + ).double() group_spatial_linear.weight = torch.nn.Parameter( self.last_state["spatial_coef_linears." + group + ".weight"] ) @@ -154,9 +154,7 @@ def closure(): self.overdispersion = torch.nn.ParameterDict(overdispersion) LGR.debug("Reset L-BFGS optimizer......") else: - self.last_state = copy.deepcopy( - self.state_dict() - ) + self.last_state = copy.deepcopy(self.state_dict()) return loss @@ -210,7 +208,9 @@ def fit(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_s """Fit the model.""" self._optimizer(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) self.extract_optimized_params(coef_spline_bases, moderators_by_group) - self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + self.standard_error_estimation( + coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study + ) return @@ -220,11 +220,17 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): for group in self.groups: # Extract optimized spatial regression coefficients from the model group_spatial_coef_linear_weight = self.spatial_coef_linears[group].weight - group_spatial_coef_linear_weight = group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() + group_spatial_coef_linear_weight = ( + group_spatial_coef_linear_weight.cpu().detach().numpy().flatten() + ) spatial_regression_coef[group] = group_spatial_coef_linear_weight # Estimate group-specific spatial intensity - group_spatial_intensity_estimation = np.exp(np.matmul(coef_spline_bases, group_spatial_coef_linear_weight)) - spatial_intensity_estimation["SpatialIntensity_group-" + group] = group_spatial_intensity_estimation + group_spatial_intensity_estimation = np.exp( + np.matmul(coef_spline_bases, group_spatial_coef_linear_weight) + ) + spatial_intensity_estimation[ + "SpatialIntensity_group-" + group + ] = group_spatial_intensity_estimation # Extract optimized regression coefficient of study-level moderators from the model if self.moderators_coef_dim: @@ -243,12 +249,19 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): self.moderators_coef = moderators_coef self.moderators_effect = moderators_effect - def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + def standard_error_estimation( + self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study + ): """Document this.""" - spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = dict(), dict(), dict() + spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( + dict(), + dict(), + dict(), + ) for group in self.groups: group_foci_per_voxel = torch.tensor( - foci_per_voxel[group], dtype=torch.float64, device=self.device) + foci_per_voxel[group], dtype=torch.float64, device=self.device + ) group_foci_per_study = torch.tensor( foci_per_study[group], dtype=torch.float64, device=self.device ) @@ -263,7 +276,9 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci ll_single_group_kwargs = { "moderators_coef": moderators_coef if self.moderators_coef_dim else None, - "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "coef_spline_bases": torch.tensor( + coef_spline_bases, dtype=torch.float64, device=self.device + ), "group_moderators": group_moderators if self.moderators_coef_dim else None, "group_foci_per_voxel": group_foci_per_voxel, "group_foci_per_study": group_foci_per_study, @@ -271,12 +286,14 @@ def standard_error_estimation(self, coef_spline_bases, moderators_by_group, foci } if hasattr(self, "overdispersion"): - ll_single_group_kwargs['group_overdispersion'] = self.overdispersion[group] + ll_single_group_kwargs["group_overdispersion"] = self.overdispersion[group] # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( - group_spatial_coef=group_spatial_coef, **ll_single_group_kwargs, + group_spatial_coef=group_spatial_coef, + **ll_single_group_kwargs, ) + F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) F_spatial_coef = F_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) @@ -294,7 +311,7 @@ def nll_spatial_coef(group_spatial_coef): group_studywise_spatial_intensity = np.exp( np.matmul(coef_spline_bases, group_spatial_coef.detach().cpu().numpy().T) - ).flatten() + ).flatten() se_spatial_intensity = group_studywise_spatial_intensity * se_log_spatial_intensity spatial_intensity_se[group] = se_spatial_intensity @@ -307,10 +324,14 @@ def nll_spatial_coef(group_spatial_coef): def nll_moderators_coef(moderators_coef): return -self._log_likelihood_single_group( - moderators_coef=moderators_coef, **ll_single_group_kwargs, + moderators_coef=moderators_coef, + **ll_single_group_kwargs, ) + F_moderators_coef = functorch.hessian(nll_moderators_coef)(moderators_coef) - F_moderators_coef = F_moderators_coef.reshape((self.moderators_coef_dim, self.moderators_coef_dim)) + F_moderators_coef = F_moderators_coef.reshape( + (self.moderators_coef_dim, self.moderators_coef_dim) + ) cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) var_moderators = np.diag(cov_moderators_coef).reshape((1, self.moderators_coef_dim)) se_moderators = np.sqrt(var_moderators) @@ -335,11 +356,17 @@ def summary(self): raise ValueError("Run fit first") tables = dict() # Extract optimized regression coefficients from model and store them in 'tables' - tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict(self.spatial_regression_coef, orient="index") + tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( + self.spatial_regression_coef, orient="index" + ) maps = self.spatial_intensity_estimation if self.moderators_coef_dim: - tables["Moderators_Regression_Coef"] = pd.DataFrame(data=self.moderators_coef, columns=self.moderators) - tables["Moderators_Effect"] = pd.DataFrame.from_dict(data=self.moderators_effect, orient="index") + tables["Moderators_Regression_Coef"] = pd.DataFrame( + data=self.moderators_coef, columns=self.moderators + ) + tables["Moderators_Effect"] = pd.DataFrame.from_dict( + data=self.moderators_effect, orient="index" + ) # Estimate standard error of regression coefficient and (Log-)spatial intensity and store them in 'tables' # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) @@ -353,39 +380,62 @@ def summary(self): self.spatial_intensity_se, orient="index" ) if self.moderators_coef_dim: - tables["Moderators_Regression_SE"] = pd.DataFrame(data=self.se_moderators, columns=self.moderators) + tables["Moderators_Regression_SE"] = pd.DataFrame( + data=self.se_moderators, columns=self.moderators + ) return maps, tables - def FisherInfo_MultipleGroup_spatial(self, involved_groups, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + def FisherInfo_MultipleGroup_spatial( + self, + involved_groups, + coef_spline_bases, + moderators_by_group, + foci_per_voxel, + foci_per_study, + ): """Document this.""" n_involved_groups = len(involved_groups) - involved_foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in involved_groups] - involved_foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in involved_groups] + involved_foci_per_voxel = [ + torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) + for group in involved_groups + ] + involved_foci_per_study = [ + torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) + for group in involved_groups + ] spatial_coef = [self.spatial_coef_linears[group].weight.T for group in involved_groups] spatial_coef = torch.stack(spatial_coef, dim=0) if self.moderators_coef_dim: - involved_moderators_by_group = [torch.tensor( - moderators_by_group[group], dtype=torch.float64, device=self.device - ) for group in involved_groups] - moderators_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) + involved_moderators_by_group = [ + torch.tensor(moderators_by_group[group], dtype=torch.float64, device=self.device) + for group in involved_groups + ] + moderators_coef = torch.tensor( + self.moderators_coef.T, dtype=torch.float64, device=self.device + ) else: involved_moderators_by_group, moderators_coef = None, None ll_mult_group_kwargs = { "moderator_coef": moderators_coef, - "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "coef_spline_bases": torch.tensor( + coef_spline_bases, dtype=torch.float64, device=self.device + ), "foci_per_voxel": involved_foci_per_voxel, "foci_per_study": involved_foci_per_study, "moderators": involved_moderators_by_group, - "device": self.device + "device": self.device, } if hasattr(self, "overdispersion"): - ll_mult_group_kwargs['overdispersion_coef'] = [self.overdispersion[group] for group in involved_groups] + ll_mult_group_kwargs["overdispersion_coef"] = [ + self.overdispersion[group] for group in involved_groups + ] # create a negative log-likelihood function def nll_spatial_coef(spatial_coef): return -self._log_likelihood_mult_group( - spatial_coef=spatial_coef, **ll_mult_group_kwargs, + spatial_coef=spatial_coef, + **ll_mult_group_kwargs, ) h = functorch.hessian(nll_spatial_coef)(spatial_coef) @@ -393,35 +443,51 @@ def nll_spatial_coef(spatial_coef): return h.detach().cpu().numpy() - def FisherInfo_MultipleGroup_moderator(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + def FisherInfo_MultipleGroup_moderator( + self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study + ): """Document this.""" - foci_per_voxel = [torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in self.groups] - foci_per_study = [torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) for group in self.groups] + foci_per_voxel = [ + torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) + for group in self.groups + ] + foci_per_study = [ + torch.tensor(foci_per_study[group], dtype=torch.float64, device=self.device) + for group in self.groups + ] spatial_coef = [self.spatial_coef_linears[group].weight.T for group in self.groups] spatial_coef = torch.stack(spatial_coef, dim=0) if self.moderators_coef_dim: - moderators_by_group = [torch.tensor( - moderators_by_group[group], dtype=torch.float64, device=self.device - ) for group in self.groups] - moderator_coef = torch.tensor(self.moderators_coef.T, dtype=torch.float64, device=self.device) + moderators_by_group = [ + torch.tensor(moderators_by_group[group], dtype=torch.float64, device=self.device) + for group in self.groups + ] + moderator_coef = torch.tensor( + self.moderators_coef.T, dtype=torch.float64, device=self.device + ) else: moderators_by_group, moderator_coef = None, None ll_mult_group_kwargs = { "spatial_coef": spatial_coef, - "coef_spline_bases": torch.tensor(coef_spline_bases, dtype=torch.float64, device=self.device), + "coef_spline_bases": torch.tensor( + coef_spline_bases, dtype=torch.float64, device=self.device + ), "foci_per_voxel": foci_per_voxel, "foci_per_study": foci_per_study, "moderators": moderators_by_group, - "device": self.device + "device": self.device, } if hasattr(self, "overdispersion"): - ll_mult_group_kwargs['overdispersion_coef'] = [self.overdispersion[group] for group in self.groups] + ll_mult_group_kwargs["overdispersion_coef"] = [ + self.overdispersion[group] for group in self.groups + ] # create a negative log-likelihood function w.r.t moderator coefficients def nll_moderator_coef(moderator_coef): return -self._log_likelihood_mult_group( - moderator_coef=moderator_coef, **ll_mult_group_kwargs, + moderator_coef=moderator_coef, + **ll_mult_group_kwargs, ) h = functorch.hessian(nll_moderator_coef)(moderator_coef) @@ -429,6 +495,7 @@ def nll_moderator_coef(moderator_coef): return h.detach().cpu().numpy() + class OverdispersionModelEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) @@ -442,7 +509,9 @@ def init_overdispersion_weights(self): overdispersion_init_group = torch.tensor(1e-2).double() if self.square_root: overdispersion_init_group = torch.sqrt(overdispersion_init_group) - overdispersion[group] = torch.nn.Parameter(overdispersion_init_group, requires_grad=True) + overdispersion[group] = torch.nn.Parameter( + overdispersion_init_group, requires_grad=True + ) self.overdispersion = torch.nn.ParameterDict(overdispersion) def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): @@ -450,19 +519,25 @@ def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim super().init_weights(groups, moderators, spatial_coef_dim, moderators_coef_dim) self.init_overdispersion_weights() - def inference_outcome(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + def inference_outcome( + self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study + ): """Document this.""" - maps, tables = super().inference_outcome(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) + maps, tables = super().inference_outcome( + coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study + ) overdispersion_param = dict() for group in self.groups: group_overdispersion = self.overdispersion[group] group_overdispersion = group_overdispersion.cpu().detach().numpy() overdispersion_param[group] = group_overdispersion tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( - overdispersion_param, orient="index", columns=["overdispersion"]) + overdispersion_param, orient="index", columns=["overdispersion"] + ) return maps, tables + class PoissonEstimator(GeneralLinearModelEstimator): def __init__(self, **kwargs): super().__init__(**kwargs) @@ -475,7 +550,7 @@ def _log_likelihood_single_group( group_moderators, group_foci_per_voxel, group_foci_per_study, - device="cpu" + device="cpu", ): log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) @@ -510,7 +585,8 @@ def _log_likelihood_mult_group( torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] spatial_intensity = [ - torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity + torch.exp(group_log_spatial_intensity) + for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: log_moderator_effect = [ @@ -552,12 +628,13 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) else: moderators_coef, group_moderators = None, None group_log_l = self._log_likelihood_single_group( - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study) + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) log_l += group_log_l if self.penalty: @@ -588,9 +665,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) outer_jacobian_strategy="forward-mode", ) group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real( - torch.linalg.eigvals(group_F) - ) + group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) del group_F group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) del group_eig_vals @@ -600,7 +675,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): def __init__(self, **kwargs): - kwargs['square_root'] = True + kwargs["square_root"] = True super().__init__(**kwargs) def _three_term(self, y, r): @@ -672,7 +747,8 @@ def _log_likelihood_mult_group( torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] spatial_intensity = [ - torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity + torch.exp(group_log_spatial_intensity) + for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: log_moderator_effect = [ @@ -704,10 +780,7 @@ def _log_likelihood_mult_group( ] p = [ numerators[i] - / ( - v[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]) - + denominators[i] - ) + / (v[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]) + denominators[i]) for i in range(n_groups) ] r = [v[i] * denominators[i] / numerators[i] for i in range(n_groups)] @@ -739,7 +812,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) coef_spline_bases, group_moderators, group_foci_per_voxel, - group_foci_per_study + group_foci_per_study, ) log_l += group_log_l @@ -766,7 +839,9 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_foci_per_voxel, group_foci_per_study, ) - group_F = torch.autograd.functional.hessian(nll, group_spatial_coef, create_graph=True) + group_F = torch.autograd.functional.hessian( + nll, group_spatial_coef, create_graph=True + ) group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) del group_F @@ -779,7 +854,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): def __init__(self, **kwargs): - kwargs['square_root'] = False + kwargs["square_root"] = False super().__init__(**kwargs) def _log_likelihood_single_group( @@ -831,13 +906,14 @@ def _log_likelihood_mult_group( device="cpu", ): n_groups = len(foci_per_voxel) - v = [1 / group_overdispersion_coef for group_overdispersion_coef in overdispersion_coef] + v = [1 / group_overdispersion_coef for group_overdispersion_coef in overdispersion_coef] # estimated intensity and log estimated intensity log_spatial_intensity = [ torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) ] spatial_intensity = [ - torch.exp(group_log_spatial_intensity) for group_log_spatial_intensity in log_spatial_intensity + torch.exp(group_log_spatial_intensity) + for group_log_spatial_intensity in log_spatial_intensity ] if moderator_coef is not None: log_moderator_effect = [ @@ -858,18 +934,20 @@ def _log_likelihood_mult_group( torch.exp(group_log_moderator_effect) for group_log_moderator_effect in log_moderator_effect ] - mu_sum_per_study = [torch.sum(spatial_intensity[i]) * moderator_effect[i] for i in range(n_groups)] + mu_sum_per_study = [ + torch.sum(spatial_intensity[i]) * moderator_effect[i] for i in range(n_groups) + ] n_study_list = [group_foci_per_study.shape[0] for group_foci_per_study in foci_per_study] log_l = 0 for i in range(n_groups): log_l += ( - n_study_list[i] * v[i] * torch.log(v[i]) - - n_study_list[i] * torch.lgamma(v[i]) - + torch.sum(torch.lgamma(foci_per_study[i] + v[i])) - - torch.sum((foci_per_study[i] + v[i]) * torch.log(mu_sum_per_study[i] + v[i])) - + torch.sum(foci_per_voxel[i] * log_spatial_intensity[i]) - + torch.sum(foci_per_study[i] * log_moderator_effect[i]) + n_study_list[i] * v[i] * torch.log(v[i]) + - n_study_list[i] * torch.lgamma(v[i]) + + torch.sum(torch.lgamma(foci_per_study[i] + v[i])) + - torch.sum((foci_per_study[i] + v[i]) * torch.log(mu_sum_per_study[i] + v[i])) + + torch.sum(foci_per_voxel[i] * log_spatial_intensity[i]) + + torch.sum(foci_per_study[i] * log_moderator_effect[i]) ) return log_l @@ -887,14 +965,14 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) else: moderators_coef, group_moderators = None, None group_log_l = self._log_likelihood_single_group( - group_overdispersion, - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study - ) + group_overdispersion, + group_spatial_coef, + moderators_coef, + coef_spline_bases, + group_moderators, + group_foci_per_voxel, + group_foci_per_study, + ) log_l += group_log_l if self.penalty: diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index e3e0749a2..2b0e33746 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -1,17 +1,19 @@ """Generate fixtures for tests.""" import os +import random from shutil import copyfile + import nibabel as nib import numpy as np -import pandas as pd +import pandas as pd import pytest from nilearn.image import resample_img import nimare +from nimare.generate import create_coordinate_dataset from nimare.tests.utils import get_test_data_path from nimare.utils import get_resource_path -from nimare.generate import create_coordinate_dataset -import random + # Only enable the following once in a while for a check for SettingWithCopyWarnings # pd.options.mode.chained_assignment = "raise" @@ -57,6 +59,7 @@ def testdata_cbma(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) return dset + @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" @@ -69,11 +72,13 @@ def testdata_cbmr(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'dementia' for i in range(n_rows)] - dset.annotations['treatment'] = [False if i%2==0 else True for i in range(n_rows)] - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "dementia" for i in range(n_rows) + ] + dset.annotations["treatment"] = [False if i % 2 == 0 else True for i in range(n_rows)] + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) - + return dset @@ -87,6 +92,7 @@ def testdata_cbma_full(): dset = nimare.dataset.Dataset(dset_file) return dset + @pytest.fixture(scope="session") def testdata_cbmr(): """Generate coordinate-based dataset for tests.""" @@ -99,14 +105,19 @@ def testdata_cbmr(): dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) # set up group columns & moderators n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] - dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] - dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) + ] + dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] + dset.annotations["drug_status"] = ( + dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) + ) # random shuffle drug_status column + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) - + return dset + @pytest.fixture(scope="session") def testdata_cbmr_full(): """Generate more complete coordinate-based dataset for tests. @@ -117,53 +128,77 @@ def testdata_cbmr_full(): dset = nimare.dataset.Dataset(dset_file) # set up group columns & moderators n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] - dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] - dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) + ] + dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] + dset.annotations["drug_status"] = ( + dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) + ) # random shuffle drug_status column + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) return dset + @pytest.fixture(scope="session") def testdata_cbmr_laird(): """Generate more complete coordinate-based dataset for tests. Same as above, except returns all coords, not just one per study. """ - dset_file = os.path.join(get_test_data_path(), "neurosynth_laird_studies.json") + dset_file = os.path.join(get_test_data_path(), "neurosynth_laird_studies.json") dset = nimare.dataset.Dataset(dset_file) # set up group columns & moderators n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] - dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] - dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column - if 'year' in dset.metadata.columns: - dset.annotations["publication_year"] = [dset.metadata['year'][i] for i in range(n_rows)] + dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) + ] + dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] + dset.annotations["drug_status"] = ( + dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) + ) # random shuffle drug_status column + if "year" in dset.metadata.columns: + dset.annotations["publication_year"] = [dset.metadata["year"][i] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) return dset + @pytest.fixture(scope="session") def testdata_cbmr_simulated(): - """Simulate coordinate-based dataset for tests. - """ - # simulate - ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000) - # set up group columns: diagnosis & drug_status + """Simulate coordinate-based dataset for tests.""" + # simulate + ground_truth_foci, dset = create_coordinate_dataset( + foci=10, sample_size=(20, 40), n_studies=1000 + ) + # set up group columns: diagnosis & drug_status n_rows = dset.annotations.shape[0] - dset.annotations['diagnosis'] = ["schizophrenia" if i%2==0 else 'depression' for i in range(n_rows)] - dset.annotations['drug_status'] = ['Yes' if i%2==0 else 'No' for i in range(n_rows)] - dset.annotations['drug_status'] = dset.annotations['drug_status'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + dset.annotations["diagnosis"] = [ + "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) + ] + dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] + dset.annotations["drug_status"] = ( + dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) + ) # random shuffle drug_status column # set up moderators: sample sizes & avg_age - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] + dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] dset.annotations["avg_age"] = np.arange(n_rows) - dset.annotations['schizophrenia_subtype'] = ["type1", "type2", "type3", "type4", "type5"] * int(n_rows/5) + dset.annotations["schizophrenia_subtype"] = [ + "type1", + "type2", + "type3", + "type4", + "type5", + ] * int(n_rows / 5) # dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)] - dset.annotations['schizophrenia_subtype'] = dset.annotations['schizophrenia_subtype'].sample(frac=1).reset_index(drop=True) # random shuffle drug_status column + dset.annotations["schizophrenia_subtype"] = ( + dset.annotations["schizophrenia_subtype"].sample(frac=1).reset_index(drop=True) + ) # random shuffle drug_status column return dset + @pytest.fixture(scope="session") def testdata_laird(): """Load data from dataset into global variables.""" diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index c24f7340c..3e3bee03e 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -1,82 +1,108 @@ -import nimare -from nimare.meta.cbmr import CBMREstimator, CBMRInference -from nimare.tests.utils import standardize_field -from nimare.meta import models +"""Tests for CBMR meta-analytic methods.""" import logging -import torch + import pytest -import numpy as np +import torch + +import nimare from nimare.correct import FDRCorrector, FWECorrector +from nimare.meta import models +from nimare.meta.cbmr import CBMREstimator, CBMRInference +from nimare.tests.utils import standardize_field -# @pytest.mark.parametrize( -# "group_categories, spline_spacing, model", -# [ -# (None, 10, models.PoissonEstimator), -# ("diagnosis", 10, models.PoissonEstimator), -# (["diagnosis", "drug_status"], 10, models.PoissonEstimator), -# ] -# ) -@pytest.mark.parametrize("group_categories", [None, ["diagnosis", "drug_status"]]) -@pytest.mark.parametrize("spline_spacing", [10, 5]) -@pytest.mark.parametrize("model",[models.NegativeBinomialEstimator]) - -def test_CBMREstimator(testdata_cbmr_simulated, group_categories, spline_spacing, model): - logging.getLogger().setLevel(logging.DEBUG) - LGR = logging.getLogger(__name__) - """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) - LGR.debug("group_categories: {}, spline_spacing: {}, model: {}".format(group_categories, spline_spacing, model)) +# numba has a lot of debug messages that are not useful for testing +logging.getLogger("numba").setLevel(logging.WARNING) +# indexed_gzip has a few debug messages that are not useful for testing +logging.getLogger("indexed_gzip").setLevel(logging.WARNING) + + +@pytest.fixture( + scope="session", + params=[ + pytest.param(models.PoissonEstimator, id="Poisson"), + pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), + pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), + ], +) +def model(request): + """CBMR models.""" + return request.param + + +@pytest.fixture(scope="session") +def cbmr_result(testdata_cbmr_simulated, model): + """Test CBMR estimator.""" + dset = standardize_field( + dataset=testdata_cbmr_simulated, + metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"], + ) cbmr = CBMREstimator( - group_categories= group_categories, + group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], - spline_spacing=spline_spacing, + spline_spacing=50, model=model, penalty=False, lr=1e-1, - tol=1, - device="cpu" + tol=1e5, + device="cpu", ) res = cbmr.fit(dataset=dset) assert isinstance(res, nimare.results.MetaResult) + return res -def test_CBMRInference(testdata_cbmr_simulated): - logging.getLogger().setLevel(logging.DEBUG) - """Unit test for CBMR estimator.""" - dset = standardize_field(dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"]) - cbmr = CBMREstimator( - group_categories=["diagnosis", "drug_status"], - moderators=["standardized_sample_sizes", "standardized_avg_age" - , "schizophrenia_subtype"], - spline_spacing=10, - model=models.PoissonEstimator, - penalty=False, - lr=1e-1, - tol=1e4, - device="cpu", +@pytest.fixture(scope="session") +def inference_results(testdata_cbmr_simulated, cbmr_result): + """Test inference results for CBMR estimator.""" + inference = CBMRInference(CBMRResults=cbmr_result, device="cuda") + t_con_groups = inference.create_contrast( + [ + "DepressionYes-DepressionNo", + ], + type="groups", ) - # ["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], - cbmr_res = cbmr.fit(dataset=dset) - inference = CBMRInference( - CBMRResults=cbmr_res, device="cuda" + t_con_moderators = inference.create_contrast( + ["standardized_sample_sizes"], + type="moderators", ) - t_con_groups = inference.create_contrast( - [ - "SchizophreniaYes-SchizophreniaNo", - "SchizophreniaNo-DepressionYes", - "DepressionYes-DepressionNo", - ], - type="groups", + contrast_result = inference.compute_contrast( + t_con_groups=t_con_groups, t_con_moderators=t_con_moderators ) - # t_con_groups = inference.create_contrast(["SchizophreniaYes", "SchizophreniaNo"], type="groups") - t_con_moderators = inference.create_contrast(["standardized_sample_sizes", "standardized_sample_sizes-standardized_avg_age"], type="moderators") - contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=t_con_moderators) - - corr = FWECorrector(method="bonferroni") - cres = corr.transform(cbmr_res) - + + return contrast_result + + +@pytest.fixture( + scope="session", + params=[ + pytest.param(FWECorrector(method="bonferroni"), id="bonferroni"), + pytest.param(FDRCorrector(method="indep"), id="indep"), + pytest.param(FDRCorrector(method="negcorr"), id="negcorr"), + ], +) +def corrector(request): + """Corrector classes.""" + return request.param + + +def test_cbmr_estimator(cbmr_result): + """Unit test for CBMR estimator.""" + assert isinstance(cbmr_result, nimare.results.MetaResult) + + +def test_cbmr_inference(inference_results): + """Unit test for CBMR inference.""" + assert isinstance(inference_results, nimare.results.MetaResult) + + +def test_cbmr_correctors(inference_results, corrector): + """Unit test for Correctors that work with CBMR.""" + corrected_results = corrector.transform(inference_results) + assert isinstance(corrected_results, nimare.results.MetaResult) + def test_CBMREstimator_update(testdata_cbmr_simulated): + """Unit test for CBMR estimator update function.""" cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) @@ -87,16 +113,18 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): groups=cbmr.groups, penalty=cbmr.penalty, device=cbmr.device, - ) - + ) + optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) # load dataset info to torch.tensor - coef_spline_bases = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) + _ = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) if cbmr.moderators: moderators_by_group_tensor = dict() for group in cbmr_model.groups: moderators_tensor = torch.tensor( - cbmr_model.inputs_["moderators_by_group"][group], dtype=torch.float64, device=cbmr.device + cbmr_model.inputs_["moderators_by_group"][group], + dtype=torch.float64, + device=cbmr.device, ) moderators_by_group_tensor[group] = moderators_tensor else: @@ -114,32 +142,28 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) if cbmr.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - - loss = cbmr._update( - cbmr_model, - optimizer, - torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss, - ) - - # deliberately set the first spatial coefficient to nan - nan_coef = torch.tensor(cbmr_model.spatial_coef_linears['default'].weight) - nan_coef[:, 0] = float('nan') - cbmr_model.spatial_coef_linears['default'].weight = torch.nn.Parameter(nan_coef) - - loss = cbmr._update( - cbmr_model, - optimizer, - torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss, - ) - + _ = cbmr._update( + cbmr_model, + optimizer, + torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) + # deliberately set the first spatial coefficient to nan + nan_coef = torch.tensor(cbmr_model.spatial_coef_linears["default"].weight) + nan_coef[:, 0] = float("nan") + cbmr_model.spatial_coef_linears["default"].weight = torch.nn.Parameter(nan_coef) + _ = cbmr._update( + cbmr_model, + optimizer, + torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 9e589f5bf..22a77d372 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -1,11 +1,11 @@ """Utility functions for testing nimare.""" +import logging import os.path as op from contextlib import ExitStack as does_not_raise import nibabel as nib import numpy as np import pytest -import logging from nimare.meta.utils import compute_kda_ma @@ -15,6 +15,7 @@ LGR = logging.getLogger(__name__) + def get_test_data_path(): """Return the path to test datasets, terminated with separator. @@ -128,22 +129,26 @@ def standardize_field(dataset, metadata): # moderators = dataset.annotations[metadata] categorical_metadata, numerical_metadata = [], [] for metadata_name in metadata: - if np.array_equal(dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(str)): + if np.array_equal( + dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(str) + ): categorical_metadata.append(metadata_name) - elif np.array_equal(dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(float)): + elif np.array_equal( + dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(float) + ): numerical_metadata.append(metadata_name) if len(categorical_metadata) > 0: LGR.warning(f"Categorical metadata {categorical_metadata} can't be standardized.") if len(numerical_metadata) == 0: raise ValueError("No numerical metadata found.") - - moderators = dataset.annotations[numerical_metadata] + + moderators = dataset.annotations[numerical_metadata] standardize_moderators = moderators - np.mean(moderators, axis=0) standardize_moderators /= np.std(standardize_moderators, axis=0) if isinstance(metadata, str): column_name = "standardized_" + metadata elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in numerical_metadata] + column_name = ["standardized_" + moderator for moderator in numerical_metadata] dataset.annotations[column_name] = standardize_moderators return dataset diff --git a/nimare/utils.py b/nimare/utils.py index ad80a1084..fea726915 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -14,11 +14,10 @@ import nibabel as nib import numpy as np import pandas as pd -from nilearn.input_data import NiftiMasker -from scipy import ndimage - import patsy import sparse +from nilearn.input_data import NiftiMasker +from scipy import ndimage LGR = logging.getLogger(__name__) @@ -1162,6 +1161,7 @@ def _get_cluster_coms(labeled_cluster_arr): return cluster_coms + def coef_spline_bases(axis_coords, spacing, margin): """ Coefficient of cubic B-spline bases in any x/y/z direction @@ -1257,6 +1257,7 @@ def B_spline_bases(masker_voxels, spacing, margin=10): return X + def index2vox(vals, masker_voxels): xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] @@ -1274,25 +1275,31 @@ def index2vox(vals, masker_voxels): return voxel_array + def dummy_encoding_moderators(dataset_annotations, moderators): new_moderators = [] for moderator in moderators.copy(): if len(moderator.split(":reference=")) == 2: moderator, reference_subtype = moderator.split(":reference=") - if np.array_equal(dataset_annotations[moderator], dataset_annotations[moderator].astype(str)): + if np.array_equal( + dataset_annotations[moderator], dataset_annotations[moderator].astype(str) + ): categories_unique = dataset_annotations[moderator].unique().tolist() # sort categories alphabetically categories_unique = sorted(categories_unique, key=str.lower) if "reference_subtype" in locals(): # remove reference subgroup from list and add it to the first position - categories_unique.remove(reference_subtype) + categories_unique.remove(reference_subtype) categories_unique.insert(0, reference_subtype) for category in categories_unique: - dataset_annotations[category] = (dataset_annotations[moderator] == category).astype(int) + dataset_annotations[category] = ( + dataset_annotations[moderator] == category + ).astype(int) # remove last categorical moderator column as it encoded as the other dummy encoded columns being zero dataset_annotations = dataset_annotations.drop([categories_unique[0]], axis=1) - new_moderators.extend(categories_unique[1:]) # add dummy encoded moderators (except from the reference subgroup) + new_moderators.extend( + categories_unique[1:] + ) # add dummy encoded moderators (except from the reference subgroup) else: new_moderators.append(moderator) return dataset_annotations, new_moderators - From 8efc82ae342ac36bde02ee1a5abbeb802ef28ec2 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 24 Mar 2023 15:23:34 -0500 Subject: [PATCH 103/177] wip: working through refactor --- nimare/correct.py | 2 - nimare/meta/cbmr.py | 238 +++++++++++++++++++++++++------------------- nimare/utils.py | 13 ++- 3 files changed, 146 insertions(+), 107 deletions(-) diff --git a/nimare/correct.py b/nimare/correct.py index 3c24a4d12..8f843349c 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -1,7 +1,6 @@ """Multiple comparisons correction methods.""" import inspect import logging -import re from abc import ABCMeta, abstractproperty import numpy as np @@ -97,7 +96,6 @@ def _collect_inputs(self, result): def _generate_secondary_maps(self, result, corr_maps, rm): """Generate corrected version of z and log-p maps if they exist.""" - p = corr_maps[rm] if rm == "p": diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 58f28da2a..e830ad3a0 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,6 +1,7 @@ """Cla.""" import logging import re +from functools import wraps import nibabel as nib import numpy as np @@ -11,7 +12,7 @@ from nimare.base import Estimator from nimare.diagnostics import FocusFilter from nimare.meta import models -from nimare.utils import B_spline_bases, dummy_encoding_moderators, get_masker, mm2vox +from nimare.utils import b_spline_bases, dummy_encoding_moderators, get_masker, mm2vox LGR = logging.getLogger(__name__) @@ -179,7 +180,7 @@ def _preprocess_input(self, dataset): self.inputs_["mask_img"] = mask_img # generate spatial matrix of coefficient of cubic B-spline bases in x,y,z dimension - coef_spline_bases = B_spline_bases( + coef_spline_bases = b_spline_bases( masker_voxels=mask_img._dataobj, spacing=self.spline_spacing ) self.inputs_["coef_spline_bases"] = coef_spline_bases @@ -339,7 +340,7 @@ class CBMRInference(object): Parameters ---------- - CBMRResults : :obj:`~nimare.results.MetaResult` + result : :obj:`~nimare.cbmr.CBMREstimator` Results of optimized regression coefficients of CBMR, as well as their standard error in `tables`. Results of estimated spatial intensity function (per study) in `maps`. @@ -353,7 +354,7 @@ class CBMRInference(object): each element independently. We also allow any element of `t_con_groups` in list type, which represents GLH is conducted for all contrasts in this element simultaneously. Default is homogeneity test on group-wise estimated intensity function. - t_con_moderatorss : :obj:`~bool` or obj:`~list` or obj:`~None`, optional + t_con_moderators : :obj:`~bool` or obj:`~list` or obj:`~None`, optional Contrast matrix for testing the existence of one or more study-level moderator effects. For boolean inputs, no statistical inference will be conducted for study-level moderators if `t_con_moderatorss` is False, and statistical inference on the effect of each @@ -367,33 +368,72 @@ class CBMRInference(object): Default is 'cpu'. """ - def __init__(self, CBMRResults, device="cpu"): + def __init__(self, device="cpu"): self.device = device - self.CBMRResults = CBMRResults - self.groups = self.CBMRResults.estimator.groups - self.n_groups = len(self.groups) - self.moderators = self.CBMRResults.estimator.moderators - # visialize group/moderator names and their indices in contrast array + # device check + if self.device == "cuda" and not torch.cuda.is_available(): + LGR.debug("cuda not found, use device 'cpu'") + self.device = "cpu" + self.result = None + self.groups = None + self.moderators = None + self.n_groups = None + self.n_moderators = None + + def _check_fit(fn): + """Check if CBMRInference instance has been fit.""" + + @wraps(fn) + def wrapper(self, *args, **kwargs): + if self.result is None: + raise ValueError("CBMRInference instance has not been fit.") + return fn(self, *args, **kwargs) + + return wrapper + + def fit(self, result): + """Fit CBMRInference instance. + + Parameters + ---------- + result : :obj:`~nimare.cbmr.CBMREstimator` + Results of optimized regression coefficients of CBMR, as well as their + standard error in `tables`. Results of estimated spatial intensity function + (per study) in `maps`. + """ + self.result = result.copy() + self.groups = result.groups + self.moderators = result.moderators + self.n_groups = result.n_groups + self.n_moderators = result.n_moderators + + self.create_regular_expressions() + self.group_reference_dict, self.moderator_reference_dict = dict(), dict() - LGR.info("Group Reference in contrast array") for i in range(self.n_groups): self.group_reference_dict[self.groups[i]] = i - LGR.info(f"{self.groups[i]} = index_{i}") if self.moderators: self.n_moderators = len(self.moderators) - LGR.info("Moderator Reference in contrast array") for j in range(self.n_moderators): self.moderator_reference_dict[self.moderators[j]] = j LGR.info(f"{self.moderators[j]} = index_{j}") - # device check - if self.device == "cuda" and not torch.cuda.is_available(): - LGR.debug("cuda not found, use device 'cpu'") - self.device = "cpu" + @_check_fit + def display(self): + """Display Groups and Moderator names and order.""" + # visialize group/moderator names and their indices in contrast array + LGR.info("Group Reference in contrast array") + for group, index in self.group_reference_dict.items(): + LGR.info(f"{group} = index_{index}") + if self.moderators: + LGR.info("Moderator Reference in contrast array") + for moderator, index in self.moderator_reference_dict.items(): + LGR.info(f"{moderator} = index_{index}") def create_regular_expressions(self): """ Create regular expressions for parsing contrast names. + creates the following attributes: self.groups_regular_expression: regular expression for parsing group names self.moderators_regular_expression: regular expression for parsing moderator names @@ -401,7 +441,6 @@ def create_regular_expressions(self): usage: >>> self.groups_regular_expression.match("group1 - group2").groupdict() """ - operator = "(\\ ?(?P[+-]?)\\ ??)" for attr in ["groups", "moderators"]: groups = getattr(self, attr) @@ -413,16 +452,17 @@ def create_regular_expressions(self): setattr(self, "{}_regular_expression".format(attr), reg_expr) - def create_contrast(self, contrast_name, type="groups"): + @_check_fit + def create_contrast(self, contrast_name, source="groups"): """Create contrast matrix for generalized hypothesis testing (GLH). - (1) if `type` is "group", create contrast matrix for GLH on spatial intensity; + (1) if `source` is "group", create contrast matrix for GLH on spatial intensity; if `contrast_name` begins with 'homo_test_', followed by a valid group name, create a contrast matrix for one-group homogeneity test on spatial intensity; if `contrast_name` comes in the form of "group1VSgroup2", with valid group names "group1" and "group2", create a contrast matrix for group comparison on estimated group spatial intensity; - (2) if `type` is "moderator", create contrast matrix for GLH on study-level moderators; + (2) if `source` is "moderator", create contrast matrix for GLH on study-level moderators; if `contrast_name` begins with 'moderator_', followed by a valid moderator name, we create a contrast matrix for testing if the effect of this moderator exists; if `contrast_name` comes in the form of "moderator1VSmoderator2", with valid moderator @@ -434,12 +474,10 @@ def create_contrast(self, contrast_name, type="groups"): contrast_name : :obj:`~string` Name of contrast in GLH. """ - self.create_regular_expressions() - if isinstance(contrast_name, str): contrast_name = [contrast_name] contrast_matrix = {} - if type == "groups": # contrast matrix for spatial intensity + if source == "groups": # contrast matrix for spatial intensity for contrast in contrast_name: contrast_vector = np.zeros(self.n_groups) contrast_match = self.groups_regular_expression.match(contrast) @@ -457,7 +495,7 @@ def create_contrast(self, contrast_name, type="groups"): contrast_vector[self.group_reference_dict[contrast]] = 1 contrast_matrix[contrast] = contrast_vector - elif type == "moderators": # contrast matrix for moderator effect + elif source == "moderators": # contrast matrix for moderator effect for contrast in contrast_name: contrast_vector = np.zeros(self.n_moderators) contrast_match = self.moderators_regular_expression.match(contrast) @@ -478,7 +516,8 @@ def create_contrast(self, contrast_name, type="groups"): return contrast_matrix - def compute_contrast(self, t_con_groups=None, t_con_moderators=None): + @_check_fit + def transform(self, t_con_groups=None, t_con_moderators=None): """Conduct generalized linear hypothesis (GLH) testing on CBMR estimates. Estimate group-wise spatial regression coefficients and its standard error via inverse @@ -498,37 +537,44 @@ def compute_contrast(self, t_con_groups=None, t_con_moderators=None): Contrast matrix for GLH on moderator effects. Default is None (tests if moderator effects exist for all moderators). """ - self.t_con_groups = t_con_groups self.t_con_moderators = t_con_moderators - if self.t_con_groups is not False: + if self.t_con_groups: # preprocess and standardize group contrast self.t_con_groups, self.t_con_groups_name = self._preprocess_t_con_regressor( - type="groups" + source="groups" ) # GLH test for group contrast self._glh_con_group() - if self.t_con_moderators is not False: + if self.t_con_moderators: self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast self.t_con_moderators, self.t_con_moderators_name = self._preprocess_t_con_regressor( - type="moderators" + source="moderators" ) # GLH test for moderator contrast self._glh_con_moderator() - def _preprocess_t_con_regressor(self, type): + return self.result + + def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): + """Fit and transform.""" + self.fit(result) + return self.transform(t_con_groups, t_con_moderators) + + @_check_fit + def _preprocess_t_con_regressor(self, source): # regressor can be either groups or moderators - t_con_regressor = getattr(self, f"t_con_{type}") - n_regressors = getattr(self, f"n_{type}") + t_con_regressor = getattr(self, f"t_con_{source}") + n_regressors = getattr(self, f"n_{source}") # if contrast matrix is a dictionary, convert it to list if isinstance(t_con_regressor, dict): t_con_regressor_name = list(t_con_regressor.keys()) t_con_regressor = list(t_con_regressor.values()) elif isinstance(t_con_regressor, (list, np.ndarray)): for i in range(len(t_con_regressor)): - self.CBMRResults.metadata[f"GLH_{type}_{i}"] = t_con_regressor[i] + self.result.metadata[f"GLH_{source}_{i}"] = t_con_regressor[i] t_con_regressor_name = None # Conduct group-wise spatial homogeneity test by default t_con_regressor = ( @@ -548,7 +594,7 @@ def _preprocess_t_con_regressor(self, type): )[0].tolist() raise ValueError( f"""The shape of {str(wrong_con_regressor_idx)}th contrast vector(s) in contrast - matrix doesn't match with {type}.""" + matrix doesn't match with {source}.""" ) # remove zero rows in contrast matrix (if exist) con_regressor_zero_row = [ @@ -562,7 +608,7 @@ def _preprocess_t_con_regressor(self, type): ] if np.any([con_regressor.shape[0] == 0 for con_regressor in t_con_regressor]): raise ValueError( - """One or more of contrast vector(s) in {type} contrast matrix are + f"""One or more of contrast vector(s) in {source} contrast matrix are all zeros.""" ) # standardization (row sum 1) @@ -576,6 +622,7 @@ def _preprocess_t_con_regressor(self, type): return t_con_regressor, t_con_regressor_name + @_check_fit def _glh_con_group(self): con_group_count = 0 for con_group in self.t_con_groups: @@ -587,12 +634,8 @@ def _glh_con_group(self): if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test involved_log_intensity_per_voxel = list() for group in con_group_involved: - group_foci_per_voxel = self.CBMRResults.estimator.inputs_["foci_per_voxel"][ - group - ] - group_foci_per_study = self.CBMRResults.estimator.inputs_["foci_per_study"][ - group - ] + group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] + group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] n_voxels, n_study = ( group_foci_per_voxel.shape[0], group_foci_per_study.shape[0], @@ -601,7 +644,7 @@ def _glh_con_group(self): np.sum(group_foci_per_voxel) / (n_voxels * n_study) ) group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps["SpatialIntensity_group-" + group] + self.result.maps["spatialIntensity_group-" + group] ) group_log_intensity_per_voxel = ( group_log_intensity_per_voxel - group_null_log_spatial_intensity @@ -614,42 +657,38 @@ def _glh_con_group(self): involved_log_intensity_per_voxel = list() for group in con_group_involved: group_log_intensity_per_voxel = np.log( - self.CBMRResults.maps["SpatialIntensity_group-" + group] + self.result.maps["spatialIntensity_group-" + group] ) involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) involved_log_intensity_per_voxel = np.stack( involved_log_intensity_per_voxel, axis=0 ) - Contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) - m, n_brain_voxel = Contrast_log_intensity.shape + contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) + m, n_brain_voxel = contrast_log_intensity.shape # Correlation of involved group-wise spatial coef moderators_by_group = ( - self.CBMRResults.estimator.inputs_["moderators_by_group"] - if self.moderators - else None + self.result.estimator.inputs_["moderators_by_group"] if self.moderators else None ) - F_spatial_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_spatial( + f_spatial_coef = self.estimator.model.fisher_info_multiple_group_spatial( con_group_involved, - self.CBMRResults.estimator.inputs_["coef_spline_bases"], + self.estimator.inputs_["coef_spline_bases"], moderators_by_group, - self.CBMRResults.estimator.inputs_["foci_per_voxel"], - self.CBMRResults.estimator.inputs_["foci_per_study"], - ) - Cov_spatial_coef = np.linalg.inv(F_spatial_coef) - spatial_coef_dim = ( - self.CBMRResults.tables["Spatial_Regression_Coef"].to_numpy().shape[1] + self.estimator.inputs_["foci_per_voxel"], + self.estimator.inputs_["foci_per_study"], ) - Cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) + cov_spatial_coef = np.linalg.inv(f_spatial_coef) + spatial_coef_dim = self.result.tables["spatial_regression_coef"].to_numpy().shape[1] + cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) for k in range(n_con_group_involved): for s in range(n_con_group_involved): - Cov_beta_ks = Cov_spatial_coef[ + cov_beta_ks = cov_spatial_coef[ k * spatial_coef_dim : (k + 1) * spatial_coef_dim, s * spatial_coef_dim : (s + 1) * spatial_coef_dim, ] - X = self.CBMRResults.estimator.inputs_["coef_spline_bases"] - Cov_group_log_intensity = (X.dot(Cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) - Cov_log_intensity = np.concatenate( - (Cov_log_intensity, Cov_group_log_intensity), axis=0 + X = self.estimator.inputs_["coef_spline_bases"] + cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + cov_log_intensity = np.concatenate( + (cov_log_intensity, cov_group_log_intensity), axis=0 ) # (m^2, n_voxels) # GLH on log_intensity (eta) chi_sq_spatial = self._chi_square_log_intensity( @@ -657,8 +696,8 @@ def _glh_con_group(self): n_brain_voxel, n_con_group_involved, simp_con_group, - Cov_log_intensity, - Contrast_log_intensity, + cov_log_intensity, + contrast_log_intensity, ) p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) # convert p-values to z-scores for visualization @@ -668,22 +707,22 @@ def _glh_con_group(self): else: z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) if con_group.shape[0] == 1: # GLH one test: Z statistics are signed - z_stats_spatial *= np.sign(Contrast_log_intensity.flatten()) + z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) if self.t_con_groups_name: - self.CBMRResults.maps[ - f"chi_square_group-{self.t_con_groups_name[con_group_count]}" + self.result.maps[ + f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" ] = chi_sq_spatial - self.CBMRResults.maps[ + self.result.maps[ f"p_group-{self.t_con_groups_name[con_group_count]}" ] = p_vals_spatial - self.CBMRResults.maps[ + self.result.maps[ f"z_group-{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: - self.CBMRResults.maps[f"chi_square_GLH_groups_{con_group_count}"] = chi_sq_spatial - self.CBMRResults.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial - self.CBMRResults.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial + self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial + self.result.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial + self.result.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial con_group_count += 1 def _chi_square_log_intensity( @@ -692,16 +731,16 @@ def _chi_square_log_intensity( n_brain_voxel, n_con_group_involved, simp_con_group, - Cov_log_intensity, - Contrast_log_intensity, + cov_log_intensity, + contrast_log_intensity, ): chi_sq_spatial = np.empty(shape=(0,)) for j in range(n_brain_voxel): - Contrast_log_intensity_j = Contrast_log_intensity[:, j].reshape(m, 1) - V_j = Cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) - CV_jC = simp_con_group @ V_j @ simp_con_group.T - CV_jC_inv = np.linalg.inv(CV_jC) - chi_sq_spatial_j = Contrast_log_intensity_j.T @ CV_jC_inv @ Contrast_log_intensity_j + contrast_log_intensity_j = contrast_log_intensity[:, j].reshape(m, 1) + v_j = cov_log_intensity[:, j].reshape((n_con_group_involved, n_con_group_involved)) + cv_jc = simp_con_group @ v_j @ simp_con_group.T + cv_jc_inv = np.linalg.inv(cv_jc) + chi_sq_spatial_j = contrast_log_intensity_j.T @ cv_jc_inv @ contrast_log_intensity_j chi_sq_spatial = np.concatenate( ( chi_sq_spatial, @@ -713,44 +752,43 @@ def _chi_square_log_intensity( ) return chi_sq_spatial + @_check_fit def _glh_con_moderator(self): con_moderator_count = 0 for con_moderator in self.t_con_moderators: m_con_moderator, _ = con_moderator.shape - moderator_coef = self.CBMRResults.tables["Moderators_Regression_Coef"].to_numpy().T - Contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) + moderator_coef = self.result.tables["moderators_regression_coef"].to_numpy().T + contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) moderators_by_group = ( - self.CBMRResults.estimator.inputs_["moderators_by_group"] - if self.moderators - else None + self.result.estimator.inputs_["moderators_by_group"] if self.moderators else None ) - F_moderator_coef = self.CBMRResults.estimator.model.FisherInfo_MultipleGroup_moderator( - self.CBMRResults.estimator.inputs_["coef_spline_bases"], + f_moderator_coef = self.result.estimator.model.fisher_info_multiple_group_moderator( + self.result.estimator.inputs_["coef_spline_bases"], moderators_by_group, - self.CBMRResults.estimator.inputs_["foci_per_voxel"], - self.CBMRResults.estimator.inputs_["foci_per_study"], + self.result.estimator.inputs_["foci_per_voxel"], + self.result.estimator.inputs_["foci_per_study"], ) - Cov_moderator_coef = np.linalg.inv(F_moderator_coef) + cov_moderator_coef = np.linalg.inv(f_moderator_coef) chi_sq_moderator = ( - Contrast_moderator_coef.T - @ np.linalg.inv(con_moderator @ Cov_moderator_coef @ con_moderator.T) - @ Contrast_moderator_coef + contrast_moderator_coef.T + @ np.linalg.inv(con_moderator @ cov_moderator_coef @ con_moderator.T) + @ contrast_moderator_coef ) p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) if self.t_con_moderators_name: # None? - self.CBMRResults.tables[ + self.result.tables[ f"chi_square_{self.t_con_moderators_name[con_moderator_count]}" ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) - self.CBMRResults.tables[ + self.result.tables[ f"p_{self.t_con_moderators_name[con_moderator_count]}" - ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p_value"]) + ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p"]) else: - self.CBMRResults.tables[ + self.result.tables[ f"chi_square_GLH_moderators_{con_moderator_count}" ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) - self.CBMRResults.tables[f"p_GLH_moderators_{con_moderator_count}"] = pd.DataFrame( - data=np.array(p_vals_moderator), columns=["p_value"] + self.result.tables[f"p_GLH_moderators_{con_moderator_count}"] = pd.DataFrame( + data=np.array(p_vals_moderator), columns=["p"] ) con_moderator_count += 1 diff --git a/nimare/utils.py b/nimare/utils.py index fea726915..00eadbf36 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1164,7 +1164,7 @@ def _get_cluster_coms(labeled_cluster_arr): def coef_spline_bases(axis_coords, spacing, margin): """ - Coefficient of cubic B-spline bases in any x/y/z direction + Coefficient of cubic B-spline bases in any x/y/z direction. Parameters ---------- @@ -1178,7 +1178,7 @@ def coef_spline_bases(axis_coords, spacing, margin): """ # create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) - knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) + # knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) design_matrix = patsy.dmatrix( "bs(x, knots=knots, degree=3,include_intercept=False)", data={"x": wider_axis_coords}, @@ -1193,8 +1193,8 @@ def coef_spline_bases(axis_coords, spacing, margin): return coef_spline -def B_spline_bases(masker_voxels, spacing, margin=10): - """Cubic B-spline bases for spatial intensity +def b_spline_bases(masker_voxels, spacing, margin=10): + """Cubic B-spline bases for spatial intensity. The whole coefficient matrix is constructed by taking tensor product of all B-spline bases coefficient matrix in three direction. @@ -1259,6 +1259,7 @@ def B_spline_bases(masker_voxels, spacing, margin=10): def index2vox(vals, masker_voxels): + """Document This Function.""" xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] @@ -1277,6 +1278,7 @@ def index2vox(vals, masker_voxels): def dummy_encoding_moderators(dataset_annotations, moderators): + """Document This Function.""" new_moderators = [] for moderator in moderators.copy(): if len(moderator.split(":reference=")) == 2: @@ -1295,7 +1297,8 @@ def dummy_encoding_moderators(dataset_annotations, moderators): dataset_annotations[category] = ( dataset_annotations[moderator] == category ).astype(int) - # remove last categorical moderator column as it encoded as the other dummy encoded columns being zero + # remove last categorical moderator column as it encoded + # as the other dummy encoded columns being zero dataset_annotations = dataset_annotations.drop([categories_unique[0]], axis=1) new_moderators.extend( categories_unique[1:] From 15df47c1838cab2f6f69d8b0c449060844794d45 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 24 Mar 2023 15:23:55 -0500 Subject: [PATCH 104/177] more refactor --- nimare/meta/models.py | 86 +++++++++++++++++++++++++++---------------- 1 file changed, 54 insertions(+), 32 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 5394135c4..a9d7076da 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1,6 +1,8 @@ +"""CBMR Models.""" import abc import copy import logging +from functools import partial import functorch import numpy as np @@ -10,7 +12,14 @@ LGR = logging.getLogger(__name__) +def opposite(fn): + """Return the opposite of a function.""" + return lambda x: -fn(x) + + class GeneralLinearModelEstimator(torch.nn.Module): + """Base class for GLM estimators.""" + def __init__( self, spatial_coef_dim=None, @@ -229,7 +238,7 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): np.matmul(coef_spline_bases, group_spatial_coef_linear_weight) ) spatial_intensity_estimation[ - "SpatialIntensity_group-" + group + "spatialIntensity_group-" + group ] = group_spatial_intensity_estimation # Extract optimized regression coefficient of study-level moderators from the model @@ -287,6 +296,7 @@ def standard_error_estimation( if hasattr(self, "overdispersion"): ll_single_group_kwargs["group_overdispersion"] = self.overdispersion[group] + # create a negative log-likelihood function def nll_spatial_coef(group_spatial_coef): return -self._log_likelihood_single_group( @@ -294,9 +304,9 @@ def nll_spatial_coef(group_spatial_coef): **ll_single_group_kwargs, ) - F_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) - F_spatial_coef = F_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - cov_spatial_coef = np.linalg.inv(F_spatial_coef.detach().numpy()) + f_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) + f_spatial_coef = f_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + cov_spatial_coef = np.linalg.inv(f_spatial_coef.detach().numpy()) var_spatial_coef = np.diag(cov_spatial_coef) se_spatial_coef = np.sqrt(var_spatial_coef) spatial_regression_coef_se[group] = se_spatial_coef @@ -328,11 +338,11 @@ def nll_moderators_coef(moderators_coef): **ll_single_group_kwargs, ) - F_moderators_coef = functorch.hessian(nll_moderators_coef)(moderators_coef) - F_moderators_coef = F_moderators_coef.reshape( + f_moderators_coef = functorch.hessian(nll_moderators_coef)(moderators_coef) + f_moderators_coef = f_moderators_coef.reshape( (self.moderators_coef_dim, self.moderators_coef_dim) ) - cov_moderators_coef = np.linalg.inv(F_moderators_coef.detach().numpy()) + cov_moderators_coef = np.linalg.inv(f_moderators_coef.detach().numpy()) var_moderators = np.diag(cov_moderators_coef).reshape((1, self.moderators_coef_dim)) se_moderators = np.sqrt(var_moderators) else: @@ -356,36 +366,36 @@ def summary(self): raise ValueError("Run fit first") tables = dict() # Extract optimized regression coefficients from model and store them in 'tables' - tables["Spatial_Regression_Coef"] = pd.DataFrame.from_dict( + tables["spatial_regression_coef"] = pd.DataFrame.from_dict( self.spatial_regression_coef, orient="index" ) maps = self.spatial_intensity_estimation if self.moderators_coef_dim: - tables["Moderators_Regression_Coef"] = pd.DataFrame( + tables["moderators_regression_Coef"] = pd.DataFrame( data=self.moderators_coef, columns=self.moderators ) - tables["Moderators_Effect"] = pd.DataFrame.from_dict( + tables["moderators_effect"] = pd.DataFrame.from_dict( data=self.moderators_effect, orient="index" ) - # Estimate standard error of regression coefficient and (Log-)spatial intensity and store them in 'tables' - # spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se, se_moderators = self.standard_error_estimation(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) - tables["Spatial_Regression_Coef_SE"] = pd.DataFrame.from_dict( + # Estimate standard error of regression coefficient and (Log-)spatial intensity and store + # them in 'tables' + tables["spatial_regression_coef_se"] = pd.DataFrame.from_dict( self.spatial_regression_coef_se, orient="index" ) - tables["Log_Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + tables["log_spatial_intensity_se"] = pd.DataFrame.from_dict( self.log_spatial_intensity_se, orient="index" ) - tables["Spatial_Intensity_SE"] = pd.DataFrame.from_dict( + tables["spatial_intensity_se"] = pd.DataFrame.from_dict( self.spatial_intensity_se, orient="index" ) if self.moderators_coef_dim: - tables["Moderators_Regression_SE"] = pd.DataFrame( + tables["moderators_regression_se"] = pd.DataFrame( data=self.se_moderators, columns=self.moderators ) return maps, tables - def FisherInfo_MultipleGroup_spatial( + def fisher_info_multiple_group_spatial( self, involved_groups, coef_spline_bases, @@ -431,6 +441,7 @@ def FisherInfo_MultipleGroup_spatial( ll_mult_group_kwargs["overdispersion_coef"] = [ self.overdispersion[group] for group in involved_groups ] + # create a negative log-likelihood function def nll_spatial_coef(spatial_coef): return -self._log_likelihood_mult_group( @@ -443,7 +454,7 @@ def nll_spatial_coef(spatial_coef): return h.detach().cpu().numpy() - def FisherInfo_MultipleGroup_moderator( + def fisher_info_multiple_group_moderator( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): """Document this.""" @@ -483,6 +494,7 @@ def FisherInfo_MultipleGroup_moderator( ll_mult_group_kwargs["overdispersion_coef"] = [ self.overdispersion[group] for group in self.groups ] + # create a negative log-likelihood function w.r.t moderator coefficients def nll_moderator_coef(moderator_coef): return -self._log_likelihood_mult_group( @@ -497,6 +509,8 @@ def nll_moderator_coef(moderator_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): + """Document this.""" + def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) @@ -531,7 +545,7 @@ def inference_outcome( group_overdispersion = self.overdispersion[group] group_overdispersion = group_overdispersion.cpu().detach().numpy() overdispersion_param[group] = group_overdispersion - tables["Overdispersion_Coef"] = pd.DataFrame.from_dict( + tables["overdispersion_coef"] = pd.DataFrame.from_dict( overdispersion_param, orient="index", columns=["overdispersion"] ) @@ -539,6 +553,8 @@ def inference_outcome( class PoissonEstimator(GeneralLinearModelEstimator): + """Document this.""" + def __init__(self, **kwargs): super().__init__(**kwargs) @@ -617,6 +633,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Document this.""" log_l = 0 for group in self.groups: group_spatial_coef = self.spatial_coef_linears[group].weight @@ -648,7 +665,6 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_moderators = moderators[group] else: moderators_coef, group_moderators = None, None - nll = lambda group_spatial_coef: -self._log_likelihood_single_group( group_spatial_coef, moderators_coef, @@ -657,16 +673,16 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_foci_per_voxel, group_foci_per_study, ) - group_F = torch.autograd.functional.hessian( + group_f = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=False, vectorize=True, outer_jacobian_strategy="forward-mode", ) - group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) - del group_F + group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) + del group_f group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) del group_eig_vals log_l += group_firth_penalty @@ -674,6 +690,8 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): + """Document this.""" + def __init__(self, **kwargs): kwargs["square_root"] = True super().__init__(**kwargs) @@ -794,6 +812,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Document this.""" log_l = 0 for group in self.groups: group_overdispersion = self.overdispersion[group] ** 2 @@ -839,12 +858,12 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_foci_per_voxel, group_foci_per_study, ) - group_F = torch.autograd.functional.hessian( + group_f = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=True ) - group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) - del group_F + group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) + del group_f group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) del group_eig_vals log_l += group_firth_penalty @@ -853,6 +872,8 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): + """Document this.""" + def __init__(self, **kwargs): kwargs["square_root"] = False super().__init__(**kwargs) @@ -953,6 +974,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Document this.""" log_l = 0 for group in self.groups: group_overdispersion = self.overdispersion[group] @@ -997,12 +1019,12 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) group_foci_per_voxel, group_foci_per_study, ) - group_F = torch.autograd.functional.hessian( + group_f = torch.autograd.functional.hessian( nll, group_spatial_coef, create_graph=True ) - group_F = group_F.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_F)) - del group_F + group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) + del group_f group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) del group_eig_vals log_l += group_firth_penalty From 45004c7a4d9fa466f3361490986edd6733e976a9 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 24 Mar 2023 15:34:09 -0500 Subject: [PATCH 105/177] remove debug info --- pyproject.toml | 2 -- 1 file changed, 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e06bc216a..f90b323dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,8 +29,6 @@ markers = [ "performance_estimators: mark tests that measure estimator performance", "performance_correctors: mark tests that measure corrector performance", ] -log_cli = true -log_cli_level = "DEBUG" [tool.isort] profile = "black" From 88ecc314fe414ac4ab32b3ecddbf5129bbddc984 Mon Sep 17 00:00:00 2001 From: James Kent Date: Fri, 24 Mar 2023 18:27:58 -0500 Subject: [PATCH 106/177] fix errors --- nimare/correct.py | 3 --- nimare/meta/cbmr.py | 49 +++++++++++++++++++--------------- nimare/meta/models.py | 2 +- nimare/tests/test_meta_cbmr.py | 9 ++++--- nimare/tests/utils.py | 1 + nimare/utils.py | 4 ++- 6 files changed, 38 insertions(+), 30 deletions(-) diff --git a/nimare/correct.py b/nimare/correct.py index 3298415f6..364b9b2c1 100644 --- a/nimare/correct.py +++ b/nimare/correct.py @@ -249,9 +249,6 @@ def _transform(self, result, method): corr_maps[rm] = p_corr self._generate_secondary_maps(result, corr_maps, rm) - # Create a dictionary of the corrected results - corr_maps = {"p": p_corr} - self._generate_secondary_maps(result, corr_maps) return corr_maps, tables, description diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e830ad3a0..afd0a8937 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -9,8 +9,8 @@ import scipy import torch -from nimare.base import Estimator from nimare.diagnostics import FocusFilter +from nimare.estimator import Estimator from nimare.meta import models from nimare.utils import b_spline_bases, dummy_encoding_moderators, get_masker, mm2vox @@ -140,6 +140,18 @@ def __init__( # Initialize optimisation parameters self.iter = 0 + def _generate_description(self): + """Generate a description of the Estimator instance. + + Returns + ------- + description : :obj:`str` + Description of the Estimator instance. + """ + description = "CBMR!!!" + + return description + def _preprocess_input(self, dataset): """Mask required input images using either the Dataset's mask or the Estimator's. @@ -330,7 +342,7 @@ def _fit(self, dataset): maps, tables = self.model.summary() - return maps, tables + return maps, tables, self._generate_description() class CBMRInference(object): @@ -377,8 +389,6 @@ def __init__(self, device="cpu"): self.result = None self.groups = None self.moderators = None - self.n_groups = None - self.n_moderators = None def _check_fit(fn): """Check if CBMRInference instance has been fit.""" @@ -402,19 +412,17 @@ def fit(self, result): (per study) in `maps`. """ self.result = result.copy() - self.groups = result.groups - self.moderators = result.moderators - self.n_groups = result.n_groups - self.n_moderators = result.n_moderators + self.estimator = self.result.estimator + self.groups = self.result.estimator.groups + self.moderators = self.result.estimator.moderators self.create_regular_expressions() self.group_reference_dict, self.moderator_reference_dict = dict(), dict() - for i in range(self.n_groups): + for i in range(len(self.groups)): self.group_reference_dict[self.groups[i]] = i if self.moderators: - self.n_moderators = len(self.moderators) - for j in range(self.n_moderators): + for j in range(len(self.moderators)): self.moderator_reference_dict[self.moderators[j]] = j LGR.info(f"{self.moderators[j]} = index_{j}") @@ -479,7 +487,7 @@ def create_contrast(self, contrast_name, source="groups"): contrast_matrix = {} if source == "groups": # contrast matrix for spatial intensity for contrast in contrast_name: - contrast_vector = np.zeros(self.n_groups) + contrast_vector = np.zeros(len(self.groups)) contrast_match = self.groups_regular_expression.match(contrast) # check validity of contrast name if contrast_match is None: @@ -497,7 +505,7 @@ def create_contrast(self, contrast_name, source="groups"): elif source == "moderators": # contrast matrix for moderator effect for contrast in contrast_name: - contrast_vector = np.zeros(self.n_moderators) + contrast_vector = np.zeros(len(self.moderators)) contrast_match = self.moderators_regular_expression.match(contrast) if contrast_match is None: raise ValueError(f"{contrast} is not a valid contrast.") @@ -548,7 +556,6 @@ def transform(self, t_con_groups=None, t_con_moderators=None): # GLH test for group contrast self._glh_con_group() if self.t_con_moderators: - self.n_moderators = len(self.moderators) # preprocess and standardize moderator contrast self.t_con_moderators, self.t_con_moderators_name = self._preprocess_t_con_regressor( source="moderators" @@ -567,7 +574,7 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): def _preprocess_t_con_regressor(self, source): # regressor can be either groups or moderators t_con_regressor = getattr(self, f"t_con_{source}") - n_regressors = getattr(self, f"n_{source}") + n_regressors = len(getattr(self, f"{source}")) # if contrast matrix is a dictionary, convert it to list if isinstance(t_con_regressor, dict): t_con_regressor_name = list(t_con_regressor.keys()) @@ -667,7 +674,7 @@ def _glh_con_group(self): m, n_brain_voxel = contrast_log_intensity.shape # Correlation of involved group-wise spatial coef moderators_by_group = ( - self.result.estimator.inputs_["moderators_by_group"] if self.moderators else None + self.estimator.inputs_["moderators_by_group"] if self.moderators else None ) f_spatial_coef = self.estimator.model.fisher_info_multiple_group_spatial( con_group_involved, @@ -761,13 +768,13 @@ def _glh_con_moderator(self): contrast_moderator_coef = np.matmul(con_moderator, moderator_coef) moderators_by_group = ( - self.result.estimator.inputs_["moderators_by_group"] if self.moderators else None + self.estimator.inputs_["moderators_by_group"] if self.moderators else None ) - f_moderator_coef = self.result.estimator.model.fisher_info_multiple_group_moderator( - self.result.estimator.inputs_["coef_spline_bases"], + f_moderator_coef = self.estimator.model.fisher_info_multiple_group_moderator( + self.estimator.inputs_["coef_spline_bases"], moderators_by_group, - self.result.estimator.inputs_["foci_per_voxel"], - self.result.estimator.inputs_["foci_per_study"], + self.estimator.inputs_["foci_per_voxel"], + self.estimator.inputs_["foci_per_study"], ) cov_moderator_coef = np.linalg.inv(f_moderator_coef) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index a9d7076da..52e7bcfcb 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -371,7 +371,7 @@ def summary(self): ) maps = self.spatial_intensity_estimation if self.moderators_coef_dim: - tables["moderators_regression_Coef"] = pd.DataFrame( + tables["moderators_regression_coef"] = pd.DataFrame( data=self.moderators_coef, columns=self.moderators ) tables["moderators_effect"] = pd.DataFrame.from_dict( diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 3e3bee03e..b60705cb5 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -54,18 +54,19 @@ def cbmr_result(testdata_cbmr_simulated, model): @pytest.fixture(scope="session") def inference_results(testdata_cbmr_simulated, cbmr_result): """Test inference results for CBMR estimator.""" - inference = CBMRInference(CBMRResults=cbmr_result, device="cuda") + inference = CBMRInference(device="cuda") + inference.fit(cbmr_result) t_con_groups = inference.create_contrast( [ "DepressionYes-DepressionNo", ], - type="groups", + source="groups", ) t_con_moderators = inference.create_contrast( ["standardized_sample_sizes"], - type="moderators", + source="moderators", ) - contrast_result = inference.compute_contrast( + contrast_result = inference.transform( t_con_groups=t_con_groups, t_con_moderators=t_con_moderators ) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index 22a77d372..a0b2bc71c 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -126,6 +126,7 @@ def _transform_res(meta, meta_res, corr): def standardize_field(dataset, metadata): + """Document This.""" # moderators = dataset.annotations[metadata] categorical_metadata, numerical_metadata = [], [] for metadata_name in metadata: diff --git a/nimare/utils.py b/nimare/utils.py index bd5a98731..09f3b9dff 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1855,7 +1855,9 @@ def coef_spline_bases(axis_coords, spacing, margin): """ # create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) - # knots = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing) + knots = np.arange( # noqa: F841 + np.min(axis_coords) - margin, np.max(axis_coords) + margin, step=spacing + ) design_matrix = patsy.dmatrix( "bs(x, knots=knots, degree=3,include_intercept=False)", data={"x": wider_axis_coords}, From d90b73ec0505fad1605b1a13362c42bc6c5d12c1 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:28:43 -0500 Subject: [PATCH 107/177] test firth penalty --- nimare/meta/models.py | 175 +++++++++++++++------------------ nimare/tests/test_meta_cbmr.py | 21 ++++ 2 files changed, 102 insertions(+), 94 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 52e7bcfcb..db9f4e79f 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -2,7 +2,7 @@ import abc import copy import logging -from functools import partial +from functools import partial, wraps import functorch import numpy as np @@ -12,14 +12,11 @@ LGR = logging.getLogger(__name__) -def opposite(fn): - """Return the opposite of a function.""" - return lambda x: -fn(x) - - class GeneralLinearModelEstimator(torch.nn.Module): """Base class for GLM estimators.""" + _hessian_kwargs = {} + def __init__( self, spatial_coef_dim=None, @@ -507,10 +504,61 @@ def nll_moderator_coef(moderator_coef): return h.detach().cpu().numpy() + def firth_penalty( + self, + foci_per_voxel, + foci_per_study, + moderators, + coef_spline_bases, + overdispersion=False, + ): + """Document this.""" + group_firth_penalty = 0 + for group in self.groups: + partial_kwargs = {"coef_spline_bases": coef_spline_bases} + if overdispersion: + partial_kwargs["group_overdispersion"] = self.overdispersion[group] + if getattr(self, 'square_root', False): + partial_kwargs["group_overdispersion"] = partial_kwargs["group_overdispersion"] ** 2 + partial_kwargs["group_foci_per_voxel"] = foci_per_voxel[group] + partial_kwargs["group_foci_per_study"] = foci_per_study[group] + if self.moderators_coef_dim: + moderators_coef = self.moderators_linear.weight + group_moderators = moderators[group] + else: + moderators_coef, group_moderators = None, None + partial_kwargs["moderators_coef"] = moderators_coef + partial_kwargs["group_moderators"] = group_moderators + + # create a negative log-likelihood function w.r.t spatial coefficients + def nll_spatial_coef(group_spatial_coef): + return -self._log_likelihood_single_group( + group_spatial_coef=group_spatial_coef, + **partial_kwargs, + ) + + group_spatial_coef = self.spatial_coef_linears[group].weight + group_f = torch.autograd.functional.hessian( + nll_spatial_coef, + group_spatial_coef, + **self._hessian_kwargs, + ) + + group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) + group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) + del group_f + group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) + del group_eig_vals + group_firth_penalty += group_firth_penalty + + return group_firth_penalty + class OverdispersionModelEstimator(GeneralLinearModelEstimator): """Document this.""" + _hessian_kwargs = {"create_graph": True} + def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) super().__init__(**kwargs) @@ -555,6 +603,12 @@ def inference_outcome( class PoissonEstimator(GeneralLinearModelEstimator): """Document this.""" + _hessian_kwargs = { + 'create_graph': False, + 'vectorize': True, + 'outer_jacobian_strategy': "forward-mode", + } + def __init__(self, **kwargs): super().__init__(**kwargs) @@ -656,36 +710,13 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) if self.penalty: # Firth-type penalty - for group in self.groups: - group_spatial_coef = self.spatial_coef_linears[group].weight - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - if self.moderators_coef_dim: - moderators_coef = self.moderators_linear.weight - group_moderators = moderators[group] - else: - moderators_coef, group_moderators = None, None - nll = lambda group_spatial_coef: -self._log_likelihood_single_group( - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - group_f = torch.autograd.functional.hessian( - nll, - group_spatial_coef, - create_graph=False, - vectorize=True, - outer_jacobian_strategy="forward-mode", - ) - group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) - del group_f - group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) - del group_eig_vals - log_l += group_firth_penalty + log_l += self.firth_penalty( + foci_per_voxel, + foci_per_study, + moderators, + coef_spline_bases, + overdispersion=False, + ) return -log_l @@ -838,35 +869,13 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) if self.penalty: # Firth-type penalty - for group in self.groups: - group_overdispersion = self.overdispersion[group] ** 2 - group_spatial_coef = self.spatial_coef_linears[group].weight - if self.moderators_coef_dim: - moderators_coef = self.moderators_linear.weight - group_moderators = moderators[group] - else: - moderators_coef, group_moderators = None, None - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - - nll = lambda group_spatial_coef: -self._log_likelihood_single_group( - group_overdispersion, - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - group_f = torch.autograd.functional.hessian( - nll, group_spatial_coef, create_graph=True - ) - group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) - del group_f - group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) - del group_eig_vals - log_l += group_firth_penalty + log_l += self.firth_penalty( + foci_per_voxel, + foci_per_study, + moderators, + coef_spline_bases, + overdispersion=True, + ) return -log_l @@ -999,34 +1008,12 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) if self.penalty: # Firth-type penalty - for group in self.groups: - group_overdispersion = self.overdispersion[group] - group_spatial_coef = self.spatial_coef_linears[group].weight - if self.moderators_coef_dim: - moderators_coef = self.moderators_linear.weight - group_moderators = moderators[group] - else: - moderators_coef, group_moderators = None, None - group_foci_per_voxel = foci_per_voxel[group] - group_foci_per_study = foci_per_study[group] - group_moderators = moderators[group] - nll = lambda group_spatial_coef: -self._log_likelihood_single_group( - group_overdispersion, - group_spatial_coef, - moderators_coef, - coef_spline_bases, - group_moderators, - group_foci_per_voxel, - group_foci_per_study, - ) - group_f = torch.autograd.functional.hessian( - nll, group_spatial_coef, create_graph=True - ) - group_f = group_f.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) - group_eig_vals = torch.real(torch.linalg.eigvals(group_f)) - del group_f - group_firth_penalty = 0.5 * torch.sum(torch.log(group_eig_vals)) - del group_eig_vals - log_l += group_firth_penalty + log_l += self.firth_penalty( + foci_per_voxel, + foci_per_study, + moderators, + coef_spline_bases, + overdispersion=True, + ) return -log_l diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index b60705cb5..5636b2647 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -102,6 +102,27 @@ def test_cbmr_correctors(inference_results, corrector): assert isinstance(corrected_results, nimare.results.MetaResult) +def test_firth_penalty(testdata_cbmr_simulated): + """Unit test for Firth penalty.""" + + dset = standardize_field( + dataset=testdata_cbmr_simulated, + metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"], + ) + cbmr = CBMREstimator( + group_categories=["diagnosis", "drug_status"], + moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], + spline_spacing=100, + model=models.ClusteredNegativeBinomialEstimator, + penalty=True, + lr=1e-1, + tol=1e7, + device="cpu", + ) + res = cbmr.fit(dataset=dset) + assert isinstance(res, nimare.results.MetaResult) + + def test_CBMREstimator_update(testdata_cbmr_simulated): """Unit test for CBMR estimator update function.""" cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) From 5fd240268328a2faa0ddf42efaa8fbe62e08364c Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:29:21 -0500 Subject: [PATCH 108/177] black formating --- nimare/meta/models.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index db9f4e79f..7c85b37ed 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -518,8 +518,10 @@ def firth_penalty( partial_kwargs = {"coef_spline_bases": coef_spline_bases} if overdispersion: partial_kwargs["group_overdispersion"] = self.overdispersion[group] - if getattr(self, 'square_root', False): - partial_kwargs["group_overdispersion"] = partial_kwargs["group_overdispersion"] ** 2 + if getattr(self, "square_root", False): + partial_kwargs["group_overdispersion"] = ( + partial_kwargs["group_overdispersion"] ** 2 + ) partial_kwargs["group_foci_per_voxel"] = foci_per_voxel[group] partial_kwargs["group_foci_per_study"] = foci_per_study[group] if self.moderators_coef_dim: @@ -604,9 +606,9 @@ class PoissonEstimator(GeneralLinearModelEstimator): """Document this.""" _hessian_kwargs = { - 'create_graph': False, - 'vectorize': True, - 'outer_jacobian_strategy': "forward-mode", + "create_graph": False, + "vectorize": True, + "outer_jacobian_strategy": "forward-mode", } def __init__(self, **kwargs): From addd0babbf9862d293285b5a7bb1436b9963f030 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:30:14 -0500 Subject: [PATCH 109/177] more formatting --- nimare/meta/models.py | 1 - nimare/tests/test_meta_cbmr.py | 1 - 2 files changed, 2 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 7c85b37ed..e3083a3bf 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -2,7 +2,6 @@ import abc import copy import logging -from functools import partial, wraps import functorch import numpy as np diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 5636b2647..d2b7a23c0 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -104,7 +104,6 @@ def test_cbmr_correctors(inference_results, corrector): def test_firth_penalty(testdata_cbmr_simulated): """Unit test for Firth penalty.""" - dset = standardize_field( dataset=testdata_cbmr_simulated, metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"], From 618a2eeab55f81a9e258509173b22cea8a1f3428 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:46:30 -0500 Subject: [PATCH 110/177] remove peaks2maps --- .github/workflows/testing.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index d29d2e8ad..7809e5f58 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -59,7 +59,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: 'Install NiMARE' shell: bash {0} - run: pip install -e .[tests,peaks2maps-cpu] + run: pip install -e .[tests] - name: 'Run tests' shell: bash {0} run: make unittest @@ -91,7 +91,7 @@ jobs: python-version: 3.8 - name: 'Install NiMARE' shell: bash {0} - run: pip install -e .[minimum,tests,peaks2maps-cpu] + run: pip install -e .[minimum,tests] - name: 'Run tests' shell: bash {0} run: make unittest @@ -123,7 +123,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: 'Install NiMARE' shell: bash {0} - run: pip install -e .[tests,peaks2maps-cpu] + run: pip install -e .[tests] - name: 'Run tests' shell: bash {0} run: make test_performance_estimators @@ -155,7 +155,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: 'Install NiMARE' shell: bash {0} - run: pip install -e .[tests,peaks2maps-cpu] + run: pip install -e .[tests] - name: 'Run tests' shell: bash {0} run: make test_performance_correctors @@ -187,7 +187,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: 'Install NiMARE' shell: bash {0} - run: pip install -e .[tests,peaks2maps-cpu] + run: pip install -e .[tests] - name: 'Run tests' shell: bash {0} run: make test_performance_smoke From d3d813faf4303e11c096f07e8079e5fd8f00110e Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:46:52 -0500 Subject: [PATCH 111/177] remove redundant def --- nimare/tests/conftest.py | 25 ------------------------- 1 file changed, 25 deletions(-) diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index 771cf63e3..c201fd293 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -1,12 +1,10 @@ """Generate fixtures for tests.""" import json import os -import random from shutil import copyfile import nibabel as nib import numpy as np -import pandas as pd import pytest from nilearn.image import resample_img from requests import request @@ -62,28 +60,6 @@ def testdata_cbma(): return dset -@pytest.fixture(scope="session") -def testdata_cbmr(): - """Generate coordinate-based dataset for tests.""" - dset_file = os.path.join(get_test_data_path(), "neurosynth.json") - dset = nimare.dataset.Dataset(dset_file) - - # Only retain one peak in each study in coordinates - # Otherwise centers of mass will be obscured in kernel tests by overlapping - # kernels - dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) - - n_rows = dset.annotations.shape[0] - dset.annotations["diagnosis"] = [ - "schizophrenia" if i % 2 == 0 else "dementia" for i in range(n_rows) - ] - dset.annotations["treatment"] = [False if i % 2 == 0 else True for i in range(n_rows)] - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] - dset.annotations["avg_age"] = np.arange(n_rows) - - return dset - - @pytest.fixture(scope="session") def testdata_cbma_full(): """Generate more complete coordinate-based dataset for tests. @@ -193,7 +169,6 @@ def testdata_cbmr_simulated(): "type4", "type5", ] * int(n_rows / 5) - # dset.annotations['schizophrenia_subtype'] = ['type1' if i%2==0 else 'type2' for i in range(n_rows)] dset.annotations["schizophrenia_subtype"] = ( dset.annotations["schizophrenia_subtype"].sample(frac=1).reset_index(drop=True) ) # random shuffle drug_status column From 7d477d27994b8c859b37591edf1d5e96091d6910 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:47:35 -0500 Subject: [PATCH 112/177] change documentation line --- nimare/meta/cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index afd0a8937..886627112 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,4 +1,4 @@ -"""Cla.""" +"""Document This.""" import logging import re from functools import wraps From 1fc008d0da4cad3122e43c9451ffac8f0f8a1f97 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:47:53 -0500 Subject: [PATCH 113/177] move patsy into function --- nimare/utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/nimare/utils.py b/nimare/utils.py index 09f3b9dff..eb5419354 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -17,7 +17,6 @@ import nibabel as nib import numpy as np import pandas as pd -import patsy import sparse from nilearn._utils import check_niimg_3d from nilearn._utils.niimg import _safe_get_data @@ -1853,6 +1852,8 @@ def coef_spline_bases(axis_coords, spacing, margin): ------- coef_spline : 2-D ndarray (n_points x n_spline_bases) """ + import patsy + # create B-spline basis for x/y/z coordinate wider_axis_coords = np.arange(np.min(axis_coords) - margin, np.max(axis_coords) + margin) knots = np.arange( # noqa: F841 From 448f3766cefddaacce895a9495ee2a502b742751 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 09:48:13 -0500 Subject: [PATCH 114/177] add necessary installs --- setup.cfg | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index ca9ce1018..34a9af2f0 100644 --- a/setup.cfg +++ b/setup.cfg @@ -49,13 +49,14 @@ install_requires = numba # used by sparse numpy<1.24,>=1.18 # for compatibility with numba https://github.com/numba/numba/issues/8615 pandas>=1.1.0 - patsy + patsy # for cbmr pymare~=0.0.4rc2 # nimare.meta.ibma and stats requests # nimare.extract scikit-learn # nimare.annotate and nimare.decode scipy sparse>=0.13.0 # for kernel transformers statsmodels!=0.13.2 # this version doesn't install properly + torch # for cbmr models tqdm # progress bars throughout package packages = find: include_package_data = False @@ -82,7 +83,7 @@ tests = flake8-docstrings flake8-isort pytest - pytest-cov + pytest-cov minimum = indexed_gzip==1.4 matplotlib==3.3.4 From 13b90f9b4d163502d4164f5a5bbd2e1cfa838ed9 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 10:12:43 -0500 Subject: [PATCH 115/177] update example notebook with api --- examples/02_meta-analyses/10_plot_cbmr.py | 67 +++++++---------------- 1 file changed, 20 insertions(+), 47 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 5d3e0e012..4b5b56d7d 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -1,5 +1,7 @@ """ +.. _metas_cbmr: + =========================================== Coordinate-based meta-regression algorithms =========================================== @@ -10,14 +12,13 @@ algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, see other stuff. """ -from nimare.tests.utils import standardize_field -from nimare.meta import models - -from nilearn.plotting import plot_stat_map -from nimare.generate import create_coordinate_dataset - import numpy as np import scipy +from nilearn.plotting import plot_stat_map + +from nimare.generate import create_coordinate_dataset +from nimare.meta import models +from nimare.tests.utils import standardize_field ############################################################################### # Load Dataset @@ -129,13 +130,13 @@ # In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups` # can be generated by `create_contrast` function, with group names specified. from nimare.meta.cbmr import CBMRInference -from nimare.correct import FWECorrector -inference = CBMRInference(CBMRResults=results, device="cuda") +inference = CBMRInference(device="cuda") +inference.fit(result=results) t_con_groups = inference.create_contrast( - ["SchizophreniaYes", "SchizophreniaNo", "DepressionYes", "DepressionNo"], type="groups" + ["SchizophreniaYes", "SchizophreniaNo", "DepressionYes", "DepressionNo"], source="groups" ) -contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) +contrast_result = inference.fit_transform(t_con_groups=t_con_groups, t_con_moderators=False) # generate z-score maps for group-wise spatial homogeneity test plot_stat_map( @@ -187,6 +188,7 @@ # ----------------------------------------------------------------------------- # The default FDR correction method is "indep", using Benjamini-Hochberg(BH) procedure. from nimare.correct import FDRCorrector + corr = FDRCorrector(method="indep", alpha=0.05) cres = corr.transform(results) @@ -237,16 +239,15 @@ # In the most basic scenario of group comparison test, contrast matrix `t_con_groups` # can be generated by `create_contrast` function, with `contrast_name` specified as # "group1-group2". -inference = CBMRInference(CBMRResults=results, device="cuda") t_con_groups = inference.create_contrast( [ "SchizophreniaYes-SchizophreniaNo", "SchizophreniaNo-DepressionYes", "DepressionYes-DepressionNo", ], - type="groups", + source="groups", ) -contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False) +contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False) # generate z-statistics maps for each group plot_stat_map( @@ -284,29 +285,6 @@ # (significant difference in spatial intensity estimation between two groups) # are highlighted (under significance level $0.05$). -############################################################################### -# Perform family-wise error rate (FWE) correction on group comparison tests -# ----------------------------------------------------------------------------- -# The default setting is performing Bonferroni FWE correction. -from nimare.correct import FWECorrector -corr = FWECorrector(method="bonferroni") -cres = corr.transform(results) - - -# generate FDR corrected z-score maps for group-wise spatial homogeneity test -plot_stat_map( - cres.get_map("z_group-SchizophreniaYes-SchizophreniaNo_corr-FWE_method-bonferroni"), - cut_coords=[0, 0, -8], - draw_cross=False, - cmap="RdBu_r", - title="FWEcorrecred-SchizophreniaYes-SchizophreniaNo", - threshold=scipy.stats.norm.isf(0.05), -) - -############################################################################### -# Bonferroni correction is a very conservative FWE correction methods, especially -# because most functional imaging data have some degree of spatial correlation - ############################################################################### # GLH testing with contrast matrix specified @@ -325,8 +303,7 @@ # consistent activation regions). Note that only $n-1$ contrast vectors are necessary # for testing the equality of $n$ groups. -inference = CBMRInference(CBMRResults=results, device="cuda") -contrast_result = inference.compute_contrast( +contrast_result = inference.transform( t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False ) plot_stat_map( @@ -344,10 +321,9 @@ # ----------------------------------------------------------------------------- # CBMR framework can estimate global study-level moderator effects, # and allows inference on the existence of m. -inference = CBMRInference(CBMRResults=results, device="cuda") contrast_name = results.estimator.moderators -t_con_moderators = inference.create_contrast(contrast_name, type="moderators") -contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) +t_con_moderators = inference.create_contrast(contrast_name, source="moderators") +contrast_result = inference.transform(t_con_moderators=t_con_moderators) print(results.tables["Moderators_Regression_Coef"]) print( "P-values of moderator effects `sample_sizes` is {}".format( @@ -355,9 +331,7 @@ ) ) print( - "P-value of moderator effects `avg_age` is {}".format( - results.tables["p_standardized_avg_age"] - ) + "P-value of moderator effects `avg_age` is {}".format(results.tables["p_standardized_avg_age"]) ) ############################################################################### @@ -368,11 +342,10 @@ # a chosen subtype, spatial intensity estimations of the other $4$ subtypes of # schizophrenia are moderatored globally. -inference = CBMRInference(CBMRResults=results, device="cuda") t_con_moderators = inference.create_contrast( - ["standardized_sample_sizes-standardized_avg_age"], type="moderators" + ["standardized_sample_sizes-standardized_avg_age"], source="moderators" ) -contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators) +contrast_result = inference.transform(t_con_moderators=t_con_moderators) print( "P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}".format( results.tables["p_standardized_sample_sizes-standardized_avg_age"] From 5ac3b0c89bc743b96ac018f0ad481ccbb5fca492 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 10:16:56 -0500 Subject: [PATCH 116/177] increase spacing and tolerance --- nimare/tests/test_meta_cbmr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index d2b7a23c0..50de8d7d1 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -39,11 +39,11 @@ def cbmr_result(testdata_cbmr_simulated, model): cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], - spline_spacing=50, + spline_spacing=200, model=model, penalty=False, lr=1e-1, - tol=1e5, + tol=1e7, device="cpu", ) res = cbmr.fit(dataset=dset) From 2aa090669266ce6e0ec10ca0dd2cfdfe41a4ead2 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 16:27:27 -0500 Subject: [PATCH 117/177] fix estimator name --- nimare/tests/test_meta_cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 50de8d7d1..ed44b5193 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -124,7 +124,7 @@ def test_firth_penalty(testdata_cbmr_simulated): def test_CBMREstimator_update(testdata_cbmr_simulated): """Unit test for CBMR estimator update function.""" - cbmr = CBMREstimator(model=models.ClusteredNegativeBinomial, lr=1e-4) + cbmr = CBMREstimator(model=models.ClusteredNegativeBinomialEstimator, lr=1e-4) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) cbmr._preprocess_input(testdata_cbmr_simulated) From 1645c40ac41ce438b2e82f773cee12c815f8e0d5 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 16:30:39 -0500 Subject: [PATCH 118/177] sync utils with main --- nimare/meta/utils.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/nimare/meta/utils.py b/nimare/meta/utils.py index 2a992d42c..7360f8c4c 100755 --- a/nimare/meta/utils.py +++ b/nimare/meta/utils.py @@ -120,7 +120,6 @@ def _convolve_sphere(kernel, peaks): counts = counts * value else: all_spheres = unique_rows(all_spheres) - counts = value # Mask coordinates beyond space idx = np.all( @@ -128,8 +127,6 @@ def _convolve_sphere(kernel, peaks): ) all_spheres = all_spheres[idx, :] - if sum_overlap: - counts = counts[idx] sphere_idx_inside_mask = np.where(mask_data[tuple(all_spheres.T)])[0] sphere_idx_filtered = all_spheres[sphere_idx_inside_mask, :].T From 0b453f6fa19ffb25042082039275ef94b9c9a7e6 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 16:35:46 -0500 Subject: [PATCH 119/177] update to main on z_to_p test --- nimare/tests/test_transforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/tests/test_transforms.py b/nimare/tests/test_transforms.py index ea196a3a3..7f3928837 100644 --- a/nimare/tests/test_transforms.py +++ b/nimare/tests/test_transforms.py @@ -256,10 +256,10 @@ def test_ddimages_to_coordinates_merge_strategy(testdata_ibma): (-1.959963, "one", 0.975), (-1.959963, "two", 0.05), ([0.0, 1.959963, -1.959963], "two", [1.0, 0.05, 0.05]), - ([0.0, 1.959963, -1.959963], "one", [1.0, 0.025, 0.975]), ], ) def test_z_to_p(z, tail, expected_p): """Test z to p conversion.""" p = transforms.z_to_p(z, tail) + assert np.all(np.isclose(p, expected_p)) From f605cd1e2eea7e9024a9b7edca635ddd6e1df64f Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 16:37:34 -0500 Subject: [PATCH 120/177] remove conperm workflow --- nimare/workflows/conperm.py | 88 ------------------------------------- 1 file changed, 88 deletions(-) delete mode 100644 nimare/workflows/conperm.py diff --git a/nimare/workflows/conperm.py b/nimare/workflows/conperm.py deleted file mode 100644 index 254edd742..000000000 --- a/nimare/workflows/conperm.py +++ /dev/null @@ -1,88 +0,0 @@ -"""Run a contrast permutation meta-analysis on a set of images.""" -import logging -import os -import pathlib - -import numpy as np -from nilearn.masking import apply_mask -from nilearn.mass_univariate import permuted_ols - -from nimare.results import MetaResult -from nimare.utils import get_template - -LGR = logging.getLogger(__name__) - - -def conperm_workflow(contrast_images, mask_image=None, output_dir=None, prefix="", n_iters=10000): - """Run a contrast permutation workflow.""" - from nimare import __version__ - - if mask_image is None: - target = "mni152_2mm" - mask_image = get_template(target, mask="brain") - - n_studies = len(contrast_images) - LGR.info("Loading contrast maps...") - z_data = apply_mask(contrast_images, mask_image) - - boilerplate = """ -A contrast permutation analysis was performed on a sample of {n_studies} -images with NiMARE {version} (RRID:SCR_017398; Salo et al., 2022a; Salo et al., 2022b). -A brain mask derived from the MNI 152 template (Fonov et al., 2009; Fonov et al., 2011) -was applied at 2x2x2mm resolution. The sign flipping -method used was implemented as described in Maumet & Nichols (2016), with -{n_iters} iterations used to estimate the null distribution. - -References ----------- -- Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., - Collins, D. L., & Brain Development Cooperative Group. (2011). - Unbiased average age-appropriate atlases for pediatric studies. - Neuroimage, 54(1), 313-327. -- Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L. - (2009). Unbiased nonlinear average age-appropriate brain templates from birth - to adulthood. NeuroImage, (47), S102. -- Maumet, C., & Nichols, T. E. (2016). Minimal Data Needed for Valid & Accurate - Image-Based fMRI Meta-Analysis. https://doi.org/10.1101/048249 -- Salo et al. (2022). NiMARE: Neuroimaging Meta-Analysis Research Environment. - NeuroLibre Reproducible Preprint Server, 1(1), 7, https://doi.org/10.55458/neurolibre.00007. -- Salo, Taylor, Yarkoni, Tal, Nichols, Thomas E., Poline, Jean-Baptiste, Kent, James D., - Gorgolewski, Krzysztof J., Glerean, Enrico, Bottenhorn, Katherine L., Bilgel, Murat, - Wright, Jessey, Reeders, Puck, Kimbler, Adam, Nielson, Dylan N., Yanes, Julio A., - Pérez, Alexandre, Oudyk, Kendra M., Jarecka, Dorota, Enge, Alexander, - Peraza, Julio A., ... Laird, Angela R. (2022). neurostuff/NiMARE: {version} - ({version}). Zenodo. https://doi.org/10.5281/zenodo.6642243. - **NOTE** Please replace this with the version-specific Zenodo reference in your manuscript. - """ - - LGR.info("Performing meta-analysis.") - log_p_map, t_map, _ = permuted_ols( - np.ones((z_data.shape[0], 1)), - z_data, - confounding_vars=None, - model_intercept=False, # modeled by tested_vars - n_perm=n_iters, - two_sided_test=True, - random_state=42, - n_jobs=1, - verbose=0, - ) - res = {"logp": log_p_map, "t": t_map} - # The t_test function will stand in for the Estimator in the results object - res = MetaResult(permuted_ols, mask=mask_image, maps=res, tables={}) - - boilerplate = boilerplate.format( - n_studies=n_studies, - n_iters=n_iters, - version=__version__, - ) - - if output_dir is None: - output_dir = os.getcwd() - else: - pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True) - - LGR.info("Saving output maps...") - res.save_maps(output_dir=output_dir, prefix=prefix) - LGR.info("Workflow completed.") - LGR.info(boilerplate) From 5446f44a8cfc546795fb1ee283bdf356e679ae12 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 16:38:50 -0500 Subject: [PATCH 121/177] remove whitespace --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 34a9af2f0..0325ce406 100644 --- a/setup.cfg +++ b/setup.cfg @@ -83,7 +83,7 @@ tests = flake8-docstrings flake8-isort pytest - pytest-cov + pytest-cov minimum = indexed_gzip==1.4 matplotlib==3.3.4 From 45f2ee6290ef5fa6b34e51b396e3f61dd7c75ccd Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 17:04:43 -0500 Subject: [PATCH 122/177] fix some errors --- examples/02_meta-analyses/10_plot_cbmr.py | 40 +++++++++++------------ 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 4b5b56d7d..a579b5975 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -78,7 +78,7 @@ "standardized_avg_age", "schizophrenia_subtype:reference=type1", ], - spline_spacing=10, + spline_spacing=100, # a reasonable choice is 10, 100 is for speed model=models.PoissonEstimator, penalty=False, lr=1e-1, @@ -87,41 +87,41 @@ ) results = cbmr.fit(dataset=dset) plot_stat_map( - results.get_map("SpatialIntensity_group-SchizophreniaYes"), + results.get_map("spatialIntensity_group-SchizophreniaYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="SchizophreniaYes", + title="Schizophrenia with drug treatment", threshold=1e-4, ) plot_stat_map( - results.get_map("SpatialIntensity_group-SchizophreniaNo"), + results.get_map("spatialIntensity_group-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="SchizophreniaNo", + title="Schizophrenia without drug treatment", threshold=1e-4, ) plot_stat_map( - results.get_map("SpatialIntensity_group-DepressionYes"), + results.get_map("spatialIntensity_group-DepressionYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="DepressionYes", + title="Depression with drug treatment", threshold=1e-4, ) plot_stat_map( - results.get_map("SpatialIntensity_group-DepressionNo"), + results.get_map("spatialIntensity_group-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="DepressionNo", + title="Depression without drug treatment", threshold=1e-4, ) ############################################################################### # Four figures correspond to group-specific spatial intensity map of four groups -# ("schizophrenia_Yes", "schizophrenia_No", "depression_Yes", "depression_No"). +# ("schizophreniaYes", "schizophreniaNo", "depressionYes", "depressionNo"). # Areas with stronger spatial intensity are highlighted. ############################################################################### @@ -136,7 +136,7 @@ t_con_groups = inference.create_contrast( ["SchizophreniaYes", "SchizophreniaNo", "DepressionYes", "DepressionNo"], source="groups" ) -contrast_result = inference.fit_transform(t_con_groups=t_con_groups, t_con_moderators=False) +contrast_result = inference.transform(t_con_groups=t_con_groups) # generate z-score maps for group-wise spatial homogeneity test plot_stat_map( @@ -198,7 +198,7 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="FDRcorrecred-SchizophreniaYes", + title="Schizophrenia with drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), ) @@ -207,7 +207,7 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="FDRcorrecred-SchizophreniaNo", + title="Schizophrenia without drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), ) @@ -216,7 +216,7 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="FDRcorrecred-DepressionYes", + title="Depression with drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), ) @@ -225,7 +225,7 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="FDRcorrecred-DepressionNo", + title="Depression without drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), ) @@ -242,7 +242,7 @@ t_con_groups = inference.create_contrast( [ "SchizophreniaYes-SchizophreniaNo", - "SchizophreniaNo-DepressionYes", + "SchizophreniaNo-DepressionNo", "DepressionYes-DepressionNo", ], source="groups", @@ -255,16 +255,16 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="SchizophreniaYes-SchizophreniaNo", + title="Drug Treatment Effect for Schizophrenia", threshold=scipy.stats.norm.isf(0.4), ) plot_stat_map( - results.get_map("z_group-SchizophreniaNo-DepressionYes"), + results.get_map("z_group-SchizophreniaNo-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="SchizophreniaNo-DepressionYes", + title="Untreated Schizophrenia vs. Untreated Depression", threshold=scipy.stats.norm.isf(0.4), ) @@ -273,7 +273,7 @@ cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", - title="DepressionYes-DepressionNo", + title="Drug Treatment Effect for Depression", threshold=scipy.stats.norm.isf(0.4), ) ############################################################################### From 30e34e2b5aea3c5644235ee6f8f984bc0d1b0a39 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 17:05:05 -0500 Subject: [PATCH 123/177] make explicit where to document --- nimare/meta/cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 886627112..e332cbab0 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -148,7 +148,7 @@ def _generate_description(self): description : :obj:`str` Description of the Estimator instance. """ - description = "CBMR!!!" + description = "Document this (insert description of how this estimator was fit)" return description From f26282e8a9af435abba94b55e0c263ca6db7f8c7 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sun, 26 Mar 2023 17:30:02 -0500 Subject: [PATCH 124/177] make StandardizeField a transformer --- examples/02_meta-analyses/10_plot_cbmr.py | 5 +-- nimare/tests/test_meta_cbmr.py | 13 ++++---- nimare/transforms.py | 39 +++++++++++++++++++++++ 3 files changed, 48 insertions(+), 9 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index a579b5975..93aa57e1e 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -18,7 +18,7 @@ from nimare.generate import create_coordinate_dataset from nimare.meta import models -from nimare.tests.utils import standardize_field +from nimare.transforms import StandardizeField ############################################################################### # Load Dataset @@ -70,7 +70,8 @@ # interpret them as categorical study-level moderators. from nimare.meta.cbmr import CBMREstimator -dset = standardize_field(dataset=dset, metadata=["sample_sizes", "avg_age"]) +dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform(dset) + cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], moderators=[ diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index ed44b5193..999234595 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -8,7 +8,7 @@ from nimare.correct import FDRCorrector, FWECorrector from nimare.meta import models from nimare.meta.cbmr import CBMREstimator, CBMRInference -from nimare.tests.utils import standardize_field +from nimare.transforms import StandardizeField # numba has a lot of debug messages that are not useful for testing logging.getLogger("numba").setLevel(logging.WARNING) @@ -32,10 +32,10 @@ def model(request): @pytest.fixture(scope="session") def cbmr_result(testdata_cbmr_simulated, model): """Test CBMR estimator.""" - dset = standardize_field( - dataset=testdata_cbmr_simulated, - metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"], + dset = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( + testdata_cbmr_simulated ) + cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], @@ -104,9 +104,8 @@ def test_cbmr_correctors(inference_results, corrector): def test_firth_penalty(testdata_cbmr_simulated): """Unit test for Firth penalty.""" - dset = standardize_field( - dataset=testdata_cbmr_simulated, - metadata=["sample_sizes", "avg_age", "schizophrenia_subtype"], + dset = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( + testdata_cbmr_simulated ) cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], diff --git a/nimare/transforms.py b/nimare/transforms.py index 86a50c43c..1ffe24701 100644 --- a/nimare/transforms.py +++ b/nimare/transforms.py @@ -475,6 +475,45 @@ def transform(self, dataset): return new_dataset +class StandardizeField(NiMAREBase): + """Standardize metadata fields.""" + + def __init__(self, fields): + self.fields = fields # the fields to be standardized + + def transform(self, dataset): + """Standardize metadata fields.""" + # update a copy of the dataset + dataset = dataset.copy() + + categorical_metadata, numerical_metadata = [], [] + for metadata_name in self.fields: + if np.array_equal( + dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(str) + ): + categorical_metadata.append(metadata_name) + elif np.array_equal( + dataset.annotations[metadata_name], + dataset.annotations[metadata_name].astype(float), + ): + numerical_metadata.append(metadata_name) + if len(categorical_metadata) > 0: + LGR.warning(f"Categorical metadata {categorical_metadata} can't be standardized.") + if len(numerical_metadata) == 0: + raise ValueError("No numerical metadata found.") + + moderators = dataset.annotations[numerical_metadata] + standardize_moderators = moderators - np.mean(moderators, axis=0) + standardize_moderators /= np.std(standardize_moderators, axis=0) + if isinstance(self.fields, str): + column_name = "standardized_" + self.fields + elif isinstance(self.fields, list): + column_name = ["standardized_" + moderator for moderator in numerical_metadata] + dataset.annotations[column_name] = standardize_moderators + + return dataset + + def sample_sizes_to_dof(sample_sizes): """Calculate degrees of freedom from a list of sample sizes using a simple heuristic. From ba12a8005825a7f547527f2f9857f8b9471c5034 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 27 Mar 2023 13:25:44 -0500 Subject: [PATCH 125/177] add functorch for python 3.6 --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 0325ce406..7049f2a17 100644 --- a/setup.cfg +++ b/setup.cfg @@ -40,6 +40,7 @@ classifiers = python_requires = >= 3.6 install_requires = cognitiveatlas # nimare.annotate.cogat + functorch; python_version<"3.7" # for cbmr models fuzzywuzzy # nimare.annotate indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization From 38fb4e5d2173055a8c5476901e2b8d83671b3d58 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 27 Mar 2023 13:40:21 -0500 Subject: [PATCH 126/177] try to use older version of functorch --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 7049f2a17..46cfa5ca8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -40,7 +40,7 @@ classifiers = python_requires = >= 3.6 install_requires = cognitiveatlas # nimare.annotate.cogat - functorch; python_version<"3.7" # for cbmr models + functorch==0.2.1; python_version<"3.7" # for cbmr models fuzzywuzzy # nimare.annotate indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization From 2307161ecfd3f513ab6d8ef33713807614ca0f93 Mon Sep 17 00:00:00 2001 From: James Kent Date: Mon, 27 Mar 2023 13:44:23 -0500 Subject: [PATCH 127/177] loosen restriction --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 46cfa5ca8..3a00c3d8e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -40,7 +40,7 @@ classifiers = python_requires = >= 3.6 install_requires = cognitiveatlas # nimare.annotate.cogat - functorch==0.2.1; python_version<"3.7" # for cbmr models + functorch~=0.2; python_version<"3.7" # for cbmr models fuzzywuzzy # nimare.annotate indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization From d3865731f8c26254dcf6e8cf29f4d0577830c813 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 1 Apr 2023 19:40:47 +0100 Subject: [PATCH 128/177] fix bugs in cbmr example file --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 357 ++++++------------- examples/02_meta-analyses/10_plot_cbmr.py | 28 +- nimare/tests/test_meta_cbmr.py | 1 + 3 files changed, 117 insertions(+), 269 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index 616d4a7cb..e3862c0d7 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 43, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -15,6 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "\n", "# Coordinate-based meta-regression algorithms\n", "\n", @@ -27,20 +28,19 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from nimare.tests.utils import standardize_field\n", - "from nimare.meta import models\n", - "\n", + "import numpy as np\n", + "import scipy\n", "from nilearn.plotting import plot_stat_map\n", - "from nimare.generate import create_coordinate_dataset\n", "\n", - "import numpy as np\n", - "import scipy" + "from nimare.generate import create_coordinate_dataset\n", + "from nimare.meta import models\n", + "from nimare.transforms import StandardizeField" ] }, { @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -120,22 +120,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", + "WARNING:nimare.utils:Citation not found.\n", + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 46, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -177,7 +180,8 @@ "source": [ "from nimare.meta.cbmr import CBMREstimator\n", "\n", - "dset = standardize_field(dataset=dset, metadata=[\"sample_sizes\", \"avg_age\"])\n", + "dset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n", + "\n", "cbmr = CBMREstimator(\n", " group_categories=[\"diagnosis\", \"drug_status\"],\n", " moderators=[\n", @@ -185,7 +189,7 @@ " \"standardized_avg_age\",\n", " \"schizophrenia_subtype:reference=type1\",\n", " ],\n", - " spline_spacing=10,\n", + " spline_spacing=100, # a reasonable choice is 10, 100 is for speed\n", " model=models.PoissonEstimator,\n", " penalty=False,\n", " lr=1e-1,\n", @@ -194,35 +198,35 @@ ")\n", "results = cbmr.fit(dataset=dset)\n", "plot_stat_map(\n", - " results.get_map(\"SpatialIntensity_group-SchizophreniaYes\"),\n", + " results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaYes\",\n", + " title=\"Schizophrenia with drug treatment\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"SpatialIntensity_group-SchizophreniaNo\"),\n", + " results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaNo\",\n", + " title=\"Schizophrenia without drug treatment\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"SpatialIntensity_group-DepressionYes\"),\n", + " results.get_map(\"spatialIntensity_group-DepressionYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"DepressionYes\",\n", + " title=\"Depression with drug treatment\",\n", " threshold=1e-4,\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"SpatialIntensity_group-DepressionNo\"),\n", + " results.get_map(\"spatialIntensity_group-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"DepressionNo\",\n", + " title=\"Depression without drug treatment\",\n", " threshold=1e-4,\n", ")" ] @@ -232,7 +236,7 @@ "metadata": {}, "source": [ "Four figures correspond to group-specific spatial intensity map of four groups\n", - "(\"schizophrenia_Yes\", \"schizophrenia_No\", \"depression_Yes\", \"depression_No\").\n", + "(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\n", "Areas with stronger spatial intensity are highlighted.\n", "\n" ] @@ -249,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -258,12 +262,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", - "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", - "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", - "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", + "WARNING:nimare.utils:Citation not found.\n", "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", "INFO:nimare.meta.cbmr:type2 = index_2\n", @@ -275,16 +274,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -325,17 +324,17 @@ ], "source": [ "from nimare.meta.cbmr import CBMRInference\n", - "from nimare.correct import FWECorrector\n", "\n", - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", + "inference = CBMRInference(device=\"cuda\")\n", + "inference.fit(result=results)\n", "t_con_groups = inference.create_contrast(\n", - " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], type=\"groups\"\n", + " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n", ")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups)\n", "\n", "# generate z-score maps for group-wise spatial homogeneity test\n", "plot_stat_map(\n", - " results.get_map(\"z_group-SchizophreniaYes\"),\n", + " contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -344,7 +343,7 @@ ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"z_group-SchizophreniaNo\"),\n", + " contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -353,7 +352,7 @@ ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"z_group-DepressionYes\"),\n", + " contrast_result.get_map(\"z_group-DepressionYes\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -362,7 +361,7 @@ ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"z_group-DepressionNo\"),\n", + " contrast_result.get_map(\"z_group-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", @@ -395,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -404,6 +403,7 @@ "name": "stderr", "output_type": "stream", "text": [ + "WARNING:nimare.utils:Citation not found.\n", "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg.py:63: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n", " warn(\n" ] @@ -411,16 +411,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 48, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -461,8 +461,9 @@ ], "source": [ "from nimare.correct import FDRCorrector\n", + "\n", "corr = FDRCorrector(method=\"indep\", alpha=0.05)\n", - "cres = corr.transform(results)\n", + "cres = corr.transform(contrast_result)\n", "\n", "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", "plot_stat_map(\n", @@ -470,7 +471,7 @@ " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"FDRcorrecred-SchizophreniaYes\",\n", + " title=\"Schizophrenia with drug treatment (FDR corrected)\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", @@ -479,7 +480,7 @@ " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"FDRcorrecred-SchizophreniaNo\",\n", + " title=\"Schizophrenia without drug treatment (FDR corrected)\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", @@ -488,7 +489,7 @@ " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"FDRcorrecred-DepressionYes\",\n", + " title=\"Depression with drug treatment (FDR corrected)\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")\n", "\n", @@ -497,7 +498,7 @@ " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"FDRcorrecred-DepressionNo\",\n", + " title=\"Depression without drug treatment (FDR corrected)\",\n", " threshold=scipy.stats.norm.isf(0.05),\n", ")" ] @@ -524,42 +525,24 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", - "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", - "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", - "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -589,42 +572,41 @@ } ], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "t_con_groups = inference.create_contrast(\n", " [\n", " \"SchizophreniaYes-SchizophreniaNo\",\n", - " \"SchizophreniaNo-DepressionYes\",\n", + " \"SchizophreniaNo-DepressionNo\",\n", " \"DepressionYes-DepressionNo\",\n", " ],\n", - " type=\"groups\",\n", + " source=\"groups\",\n", ")\n", - "contrast_result = inference.compute_contrast(t_con_groups=t_con_groups, t_con_moderators=False)\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)\n", "\n", "# generate z-statistics maps for each group\n", "plot_stat_map(\n", - " results.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", + " contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaYes-SchizophreniaNo\",\n", + " title=\"Drug Treatment Effect for Schizophrenia\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"z_group-SchizophreniaNo-DepressionYes\"),\n", + " contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaNo-DepressionYes\",\n", + " title=\"Untreated Schizophrenia vs. Untreated Depression\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")\n", "\n", "plot_stat_map(\n", - " results.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", + " contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", - " title=\"DepressionYes-DepressionNo\",\n", + " title=\"Drug Treatment Effect for Depression\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")" ] @@ -643,77 +625,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform family-wise error rate (FWE) correction on group comparison tests\n", - "The default setting is performing Bonferroni FWE correction.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n", - " get_mask_bounds(new_img_like(img, not_mask, affine))\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from nimare.correct import FWECorrector\n", - "corr = FWECorrector(method=\"bonferroni\")\n", - "cres = corr.transform(results)\n", - "\n", - "\n", - "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo_corr-FWE_method-bonferroni\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"FWEcorrecred-SchizophreniaYes-SchizophreniaNo\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Bonferroni correction is a very conservative FWE correction methods, especially\n", - "because most functional imaging data have some degree of spatial correlation\n", - "\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -737,29 +648,11 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", - "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", - "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", - "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -769,7 +662,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -779,19 +672,18 @@ } ], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", - "contrast_result = inference.compute_contrast(\n", + "contrast_result = inference.transform(\n", " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", ")\n", "plot_stat_map(\n", - " results.get_map(\"z_GLH_groups_0\"),\n", + " contrast_result.get_map(\"z_GLH_groups_0\"),\n", " cut_coords=[0, 0, -8],\n", " draw_cross=False,\n", " cmap=\"RdBu_r\",\n", " title=\"GLH_groups_0\",\n", " threshold=scipy.stats.norm.isf(0.4),\n", ")\n", - "print(\"The contrast matrix of GLH_0 is {}\".format(results.metadata[\"GLH_groups_0\"]))" + "print(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" ] }, { @@ -806,60 +698,39 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", - "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", - "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", - "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ " standardized_sample_sizes standardized_avg_age type2 type3 \\\n", - "0 0.001238 0.005385 -0.023627 -0.023361 \n", + "0 0.000018 -0.003071 -0.190215 -0.186201 \n", "\n", " type4 type5 \n", - "0 -0.042416 -0.045277 \n", - "P-values of moderator effects `sample_sizes` is p_value\n", - "0 0.901471\n", - "P-value of moderator effects `avg_age` is p_value\n", - "0 0.590164\n" + "0 -0.185405 -0.184005 \n", + "P-values of moderator effects `sample_sizes` is p\n", + "0 0.998586\n", + "P-value of moderator effects `avg_age` is p\n", + "0 0.755084\n" ] } ], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "contrast_name = results.estimator.moderators\n", - "t_con_moderators = inference.create_contrast(contrast_name, type=\"moderators\")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", - "print(results.tables[\"Moderators_Regression_Coef\"])\n", + "t_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\n", + "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", + "print(contrast_result.tables[\"moderators_regression_coef\"])\n", "print(\n", " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", - " results.tables[\"p_standardized_sample_sizes\"]\n", + " contrast_result.tables[\"p_standardized_sample_sizes\"]\n", " )\n", ")\n", "print(\n", - " \"P-value of moderator effects `avg_age` is {}\".format(\n", - " results.tables[\"p_standardized_avg_age\"]\n", - " )\n", + " \"P-value of moderator effects `avg_age` is {}\".format(contrast_result.tables[\"p_standardized_avg_age\"])\n", ")" ] }, @@ -878,47 +749,28 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:Group Reference in contrast array\n", - "INFO:nimare.meta.cbmr:SchizophreniaNo = index_0\n", - "INFO:nimare.meta.cbmr:DepressionNo = index_1\n", - "INFO:nimare.meta.cbmr:DepressionYes = index_2\n", - "INFO:nimare.meta.cbmr:SchizophreniaYes = index_3\n", - "INFO:nimare.meta.cbmr:Moderator Reference in contrast array\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p_value\n", - "0 0.771564\n" + "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p\n", + "0 0.823866\n" ] } ], "source": [ - "inference = CBMRInference(CBMRResults=results, device=\"cuda\")\n", "t_con_moderators = inference.create_contrast(\n", - " [\"standardized_sample_sizes-standardized_avg_age\"], type=\"moderators\"\n", + " [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n", ")\n", - "contrast_result = inference.compute_contrast(t_con_groups=False, t_con_moderators=t_con_moderators)\n", + "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", "print(\n", " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", - " results.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", + " contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", " )\n", ")" ] @@ -937,7 +789,7 @@ ], "metadata": { "kernelspec": { - "display_name": "torch", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -951,12 +803,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]" - }, - "vscode": { - "interpreter": { - "hash": "1822150571db9db4b0bedbbf655c662224d8f689079b98305ee946f83c67882c" - } + "version": "3.8.8" } }, "nbformat": 4, diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 93aa57e1e..dc055a71c 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -141,7 +141,7 @@ # generate z-score maps for group-wise spatial homogeneity test plot_stat_map( - results.get_map("z_group-SchizophreniaYes"), + contrast_result.get_map("z_group-SchizophreniaYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -150,7 +150,7 @@ ) plot_stat_map( - results.get_map("z_group-SchizophreniaNo"), + contrast_result.get_map("z_group-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -159,7 +159,7 @@ ) plot_stat_map( - results.get_map("z_group-DepressionYes"), + contrast_result.get_map("z_group-DepressionYes"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -168,7 +168,7 @@ ) plot_stat_map( - results.get_map("z_group-DepressionNo"), + contrast_result.get_map("z_group-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -191,7 +191,7 @@ from nimare.correct import FDRCorrector corr = FDRCorrector(method="indep", alpha=0.05) -cres = corr.transform(results) +cres = corr.transform(contrast_result) # generate FDR corrected z-score maps for group-wise spatial homogeneity test plot_stat_map( @@ -252,7 +252,7 @@ # generate z-statistics maps for each group plot_stat_map( - results.get_map("z_group-SchizophreniaYes-SchizophreniaNo"), + contrast_result.get_map("z_group-SchizophreniaYes-SchizophreniaNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -261,7 +261,7 @@ ) plot_stat_map( - results.get_map("z_group-SchizophreniaNo-DepressionNo"), + contrast_result.get_map("z_group-SchizophreniaNo-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -270,7 +270,7 @@ ) plot_stat_map( - results.get_map("z_group-DepressionYes-DepressionNo"), + contrast_result.get_map("z_group-DepressionYes-DepressionNo"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", @@ -308,14 +308,14 @@ t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False ) plot_stat_map( - results.get_map("z_GLH_groups_0"), + contrast_result.get_map("z_GLH_groups_0"), cut_coords=[0, 0, -8], draw_cross=False, cmap="RdBu_r", title="GLH_groups_0", threshold=scipy.stats.norm.isf(0.4), ) -print("The contrast matrix of GLH_0 is {}".format(results.metadata["GLH_groups_0"])) +print("The contrast matrix of GLH_0 is {}".format(contrast_result.metadata["GLH_groups_0"])) ############################################################################### # GLH testing for study-level moderators @@ -325,14 +325,14 @@ contrast_name = results.estimator.moderators t_con_moderators = inference.create_contrast(contrast_name, source="moderators") contrast_result = inference.transform(t_con_moderators=t_con_moderators) -print(results.tables["Moderators_Regression_Coef"]) +print(contrast_result.tables["moderators_regression_coef"]) print( "P-values of moderator effects `sample_sizes` is {}".format( - results.tables["p_standardized_sample_sizes"] + contrast_result.tables["p_standardized_sample_sizes"] ) ) print( - "P-value of moderator effects `avg_age` is {}".format(results.tables["p_standardized_avg_age"]) + "P-value of moderator effects `avg_age` is {}".format(contrast_result.tables["p_standardized_avg_age"]) ) ############################################################################### @@ -349,7 +349,7 @@ contrast_result = inference.transform(t_con_moderators=t_con_moderators) print( "P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}".format( - results.tables["p_standardized_sample_sizes-standardized_avg_age"] + contrast_result.tables["p_standardized_sample_sizes-standardized_avg_age"] ) ) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 999234595..4a1e1341e 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -48,6 +48,7 @@ def cbmr_result(testdata_cbmr_simulated, model): ) res = cbmr.fit(dataset=dset) assert isinstance(res, nimare.results.MetaResult) + return res From 52f830bc0a2e0db132219139e2209570f9e6c2a3 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 1 Apr 2023 23:22:23 +0100 Subject: [PATCH 129/177] [skip CI][WIP] fix bugs in testing function for cbmr_update --- nimare/tests/test_meta_cbmr.py | 76 +++++++++++++++++++--------------- 1 file changed, 43 insertions(+), 33 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 4a1e1341e..9007042b4 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -124,26 +124,37 @@ def test_firth_penalty(testdata_cbmr_simulated): def test_CBMREstimator_update(testdata_cbmr_simulated): """Unit test for CBMR estimator update function.""" - cbmr = CBMREstimator(model=models.ClusteredNegativeBinomialEstimator, lr=1e-4) + testdata_cbmr_simulated = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( + testdata_cbmr_simulated + ) + cbmr = CBMREstimator( + moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], + model=models.PoissonEstimator, + lr=1e-4) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) cbmr._preprocess_input(testdata_cbmr_simulated) - cbmr_model = cbmr.model( - spatial_coef_dim=cbmr.inputs_["coef_spline_bases"].shape[1], - moderators_coef_dim=len(cbmr.moderators) if cbmr.moderators else None, - groups=cbmr.groups, - penalty=cbmr.penalty, - device=cbmr.device, - ) - - optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + + # fit the model + init_weight_kwargs = { + "groups": cbmr.groups, + "moderators": cbmr.moderators, + "spatial_coef_dim": cbmr.inputs_["coef_spline_bases"].shape[1], + "moderators_coef_dim": len(cbmr.moderators) if cbmr.moderators else None} + + cbmr.model.init_weights(**init_weight_kwargs) + + moderators_by_group = cbmr.inputs_["moderators_by_group"] if cbmr.moderators else None + # cbmr.model._optimizer(cbmr.inputs_["coef_spline_bases"], moderators_by_group, cbmr.inputs_["foci_per_voxel"], cbmr.inputs_["foci_per_study"]) + optimizer = torch.optim.LBFGS(cbmr.model.parameters(), cbmr.lr) + # load dataset info to torch.tensor - _ = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) + # _ = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) if cbmr.moderators: moderators_by_group_tensor = dict() - for group in cbmr_model.groups: + for group in cbmr.model.groups: moderators_tensor = torch.tensor( - cbmr_model.inputs_["moderators_by_group"][group], + cbmr.inputs_["moderators_by_group"][group], dtype=torch.float64, device=cbmr.device, ) @@ -151,7 +162,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): else: moderators_by_group_tensor = None foci_per_voxel_tensor, foci_per_study_tensor = dict(), dict() - for group in cbmr_model.groups: + for group in cbmr.model.groups: group_foci_per_voxel_tensor = torch.tensor( cbmr.inputs_["foci_per_voxel"][group], dtype=torch.float64, device=cbmr.device ) @@ -160,31 +171,30 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): ) foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor foci_per_study_tensor[group] = group_foci_per_study_tensor - optimizer = torch.optim.LBFGS(cbmr_model.parameters(), cbmr.lr) + if cbmr.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - _ = cbmr._update( - cbmr_model, + cbmr.model._update( optimizer, torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), moderators_by_group_tensor, foci_per_voxel_tensor, foci_per_study_tensor, - prev_loss, - ) - + prev_loss) # deliberately set the first spatial coefficient to nan - nan_coef = torch.tensor(cbmr_model.spatial_coef_linears["default"].weight) - nan_coef[:, 0] = float("nan") - cbmr_model.spatial_coef_linears["default"].weight = torch.nn.Parameter(nan_coef) - - _ = cbmr._update( - cbmr_model, - optimizer, - torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss, - ) + for group in cbmr.model.groups: + nan_coef = torch.tensor(cbmr.model.spatial_coef_linears[group].weight) + nan_coef[:, 0] = float("nan") + cbmr.model.spatial_coef_linears[group].weight = torch.nn.Parameter(nan_coef) + + # Expect exceptions when one of the spatial coefficients is nan. + with pytest.raises(ValueError): + cbmr.model._update( + optimizer, + torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) From 2b5613976b76a08cae6793e8e826530f52f29d20 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 2 Apr 2023 15:46:40 +0100 Subject: [PATCH 130/177] add documentation for models.py --- nimare/meta/models.py | 246 +++++++++++++++++++++++++++++---- nimare/tests/test_meta_cbmr.py | 2 +- 2 files changed, 222 insertions(+), 26 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index e3083a3bf..45bf2bf4a 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -12,7 +12,27 @@ class GeneralLinearModelEstimator(torch.nn.Module): - """Base class for GLM estimators.""" + """Base class for GLM estimators. + + Parameters + ---------- + spatial_coef_dim : :obj:`int` + Number of spatial B-spline bases. Default is None. + moderators_coef_dim : :obj:`int`, optional + Number of study-level moderators. Default is None. + penalty : :obj:`bool` + Whether to Firth-type regularization term. Default is False. + lr : :obj:`float` + Learning rate. Default is 0.1. + lr_decay : :obj:`float` + Learning rate decay for each iteration. Default is 0.999. + n_iter : :obj:`int` + Maximum number of iterations. Default is 1000. + tol : :obj:`float` + Tolerance for convergence. Default is 1e-2. + device : :obj:`str` + Device to use for computations. Default is "cpu". + """ _hessian_kwargs = {} @@ -52,21 +72,42 @@ def __init__( @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): - """Document this.""" + """Log-likelihood of a single group. + + Returns + ------- + torch.Tensor + Value of the log-likelihood of a single group. + """ return @abc.abstractmethod def _log_likelihood_mult_group(self, **kwargs): - """Document this.""" + """Total log-likelihood of all groups in the dataset. + + Returns + ------- + torch.Tensor + Value of total log-likelihood of all groups in the dataset. + """ return @abc.abstractmethod def forward(self, **kwargs): - """Document this.""" + """Define the loss function (nagetive log-likelihood function) + for each model. + + Returns + ------- + torch.Tensor + Value of the log-likelihood of a single group. + """ return def init_spatial_weights(self): - """Document this.""" + """Initialization for spatial regression coefficients. + Default is uniform distribution between -0.01 and 0.01. + """ # initialization for spatial regression coefficients spatial_coef_linears = dict() for group in self.groups: @@ -78,13 +119,16 @@ def init_spatial_weights(self): self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) def init_moderator_weights(self): - """Initialize the intercept and regression coefficients for moderators.""" + """Initialize the intercept and regression coefficients for moderators. + Default is uniform distribution between -0.01 and 0.01. + """ self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() torch.nn.init.uniform_(self.moderators_linear.weight, a=-0.01, b=0.01) return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Document this.""" + """Initialize the regression coefficients for spatial struture and study-level moderators. + """ self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim @@ -107,6 +151,26 @@ def _update( Adjust learning rate based on the number of iteration (with learning rate decay parameter `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous iteration) if NaN occurs. + + Parameters + ---------- + optimizer : :obj:`torch.optim.lbfgs.LBFGS` + L-BFGS optimizer. + coef_spline_bases : :obj:`torch.Tensor` + Coefficient of B-spline bases evaluated at each voxel. + moderators : :obj:`dict`, optional + Dictionary of group-wise study-level moderators. Default is None. + foci_per_voxel : :obj:`dict` + Dictionary of group-wise number of foci per voxel. + foci_per_study : :obj:`dict` + Dictionary of group-wise number of foci per study. + prev_loss : :obj:`torch.Tensor` + Value of the loss function of the previous iteration. + + Returns + ------- + torch.Tensor + Updated value of the loss (negative log-likelihood) function. """ self.iter += 1 scheduler = torch.optim.lr_scheduler.ExponentialLR( @@ -164,6 +228,20 @@ def closure(): return loss def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """ + Optimize the loss (negative log-likelihood) function with L-BFGS. + + Parameters + ---------- + coef_spline_bases : :obj:`numpy.ndarray` + Coefficient of B-spline bases evaluated at each voxel. + moderators_by_group : :obj:`dict`, optional + Dictionary of group-wise study-level moderators. + foci_per_voxel : :obj:`dict` + Dictionary of group-wise number of foci per voxel. + foci_per_study : :obj:`dict` + Dictionary of group-wise number of foci per study. + """ optimizer = torch.optim.LBFGS(self.parameters(), self.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( @@ -210,7 +288,7 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc return def fit(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): - """Fit the model.""" + """Fit the model and estimate standard error of estimates.""" self._optimizer(coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study) self.extract_optimized_params(coef_spline_bases, moderators_by_group) self.standard_error_estimation( @@ -220,7 +298,7 @@ def fit(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_s return def extract_optimized_params(self, coef_spline_bases, moderators_by_group): - """Document this.""" + """Extract optimized regression coefficient of study-level moderators from the model.""" spatial_regression_coef, spatial_intensity_estimation = dict(), dict() for group in self.groups: # Extract optimized spatial regression coefficients from the model @@ -257,7 +335,13 @@ def extract_optimized_params(self, coef_spline_bases, moderators_by_group): def standard_error_estimation( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): - """Document this.""" + """Estimate standard error of estimates. + + For spatial regression coefficients, we estimate its covariance matrix using Fisher + Information Matrix and then take the square root of the diagonal elements. + For log spatial intensity, we use the delta method to estimate its standard error. + For models with over-dispersion parameter, we also estimate its standard error. + """ spatial_regression_coef_se, log_spatial_intensity_se, spatial_intensity_se = ( dict(), dict(), @@ -350,7 +434,12 @@ def nll_moderators_coef(moderators_coef): self.se_moderators = se_moderators def summary(self): - """Document this.""" + """Summarize the main results of the fitted model. + + Summarize optimized regression coefficients from model and store in `tables`, + summarize standard error of regression coefficient and (Log-)spatial intensity + and store in `results`. + """ params = ( self.spatial_regression_coef, self.spatial_intensity_estimation, @@ -399,7 +488,29 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """Document this.""" + """ Estimate the Fisher information matrix of spatial regression + coeffcients for multiple groups. + + Fisher information matrix is estimated by negative Hessian of the log-likelihood. + + Parameters + ---------- + involved_groups : :obj:`list` + Group names involved in generalized linear hypothesis (GLH) testing in `CBMRInference`. + coef_spline_bases : :obj:`numpy.ndarray` + Coefficient of B-spline bases evaluated at each voxel. + moderators_by_group : :obj:`dict`, optional + Dictionary of group-wise study-level moderators. Default is None. + foci_per_voxel : :obj:`dict` + Dictionary of group-wise number of foci per voxel. + foci_per_study : :obj:`dict` + Dictionary of group-wise number of foci per study. + + Returns + ------- + numpy.ndarray + Fisher information matrix of spatial regression coefficients (for involved groups). + """ n_involved_groups = len(involved_groups) involved_foci_per_voxel = [ torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) @@ -453,7 +564,26 @@ def nll_spatial_coef(spatial_coef): def fisher_info_multiple_group_moderator( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): - """Document this.""" + """Estimate the Fisher information matrix of regression coefficients of moderators. + + Fisher information matrix is estimated by negative Hessian of the log-likelihood. + + Parameters + ---------- + coef_spline_bases : :obj:`numpy.ndarray` + Coefficient of B-spline bases evaluated at each voxel. + moderators_by_group : :obj:`dict`, optional + Dictionary of group-wise study-level moderators. Default is None. + foci_per_voxel : :obj:`dict` + Dictionary of group-wise number of foci per voxel. + foci_per_study : :obj:`dict` + Dictionary of group-wise number of foci per study. + + Returns + ------- + numpy.ndarray + Fisher information matrix of study-level moderator regressors. + """ foci_per_voxel = [ torch.tensor(foci_per_voxel[group], dtype=torch.float64, device=self.device) for group in self.groups @@ -511,7 +641,26 @@ def firth_penalty( coef_spline_bases, overdispersion=False, ): - """Document this.""" + """Compute Firth's penalized log-likelihood. + + Parameters + ---------- + foci_per_voxel : :obj:`dict` + Dictionary of group-wise number of foci per voxel. + foci_per_study : :obj:`dict` + Dictionary of group-wise number of foci per study. + moderators : :obj:`dict`, optional + Dictionary of group-wise study-level moderators. Default is None. + coef_spline_bases : :obj:`torch.Tensor` + Coefficient of B-spline bases evaluated at each voxel. + overdispersion : :obj:`bool` + Whether the model contains overdispersion parameter. Default is False. + + Returns + ------- + torch.Tensor + Firth-type regularization term. + """ group_firth_penalty = 0 for group in self.groups: partial_kwargs = {"coef_spline_bases": coef_spline_bases} @@ -556,8 +705,7 @@ def nll_spatial_coef(group_spatial_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): - """Document this.""" - + """Base class for CBMR models with over-dispersion parameter.""" _hessian_kwargs = {"create_graph": True} def __init__(self, **kwargs): @@ -565,7 +713,9 @@ def __init__(self, **kwargs): super().__init__(**kwargs) def init_overdispersion_weights(self): - """Document this.""" + """Initialize weights for overdispersion parameters. + Default is 1e-2. + """ overdispersion = dict() for group in self.groups: # initialization for alpha @@ -578,14 +728,17 @@ def init_overdispersion_weights(self): self.overdispersion = torch.nn.ParameterDict(overdispersion) def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Document this.""" + """Initialize weights for spatial and study-level moderator coefficients. + """ super().init_weights(groups, moderators, spatial_coef_dim, moderators_coef_dim) self.init_overdispersion_weights() def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): - """Document this.""" + """Summarize inference outcome into `maps` and `tables`. + Add optimized overdispersion parameter to the tables. + """ maps, tables = super().inference_outcome( coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ) @@ -602,7 +755,14 @@ def inference_outcome( class PoissonEstimator(GeneralLinearModelEstimator): - """Document this.""" + """CBMR framework with Poisson model. + + Poisson model is the most basic model for Coordinate-based Meta-regression (CBMR). + It's based on the assumption that foci arise from a realisation of a (continues) + inhomogeneous Poisson process, so that the (discrete) voxel-wise foci counts will + be independently distributed as Poisson random variables, with rate equal to the + integral of the (true, unobserved, continous) intensity function over each voxels. + """ _hessian_kwargs = { "create_graph": False, @@ -688,7 +848,14 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Document this.""" + """Define the loss function (nagetive log-likelihood function) for Poisson model. + Model refactorization is applied to reduce the dimensionality of variables. + + Returns + ------- + torch.Tensor + Loss (nagative log-likelihood) of Poisson model at current iteration. + """ log_l = 0 for group in self.groups: group_spatial_coef = self.spatial_coef_linears[group].weight @@ -722,7 +889,13 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): - """Document this.""" + """CBMR framework with Negative Binomial (NB) model. + + Negative Binomial (NB) model is a generalized Poisson model with overdispersion. + It's a more flexible model, but more difficult to estimate. In practice, foci + counts often display over-dispersion (the variance of response variable + substantially exceeeds the mean), which is not captured by Poisson model. + """ def __init__(self, **kwargs): kwargs["square_root"] = True @@ -844,7 +1017,15 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Document this.""" + """Define the loss function (nagetive log-likelihood function) for Negative + Binomial (NB) model. Model refactorization is applied to reduce the dimensionality + of variables. + + Returns + ------- + torch.Tensor + Loss (nagative log-likelihood) of NB model at current iteration. + """ log_l = 0 for group in self.groups: group_overdispersion = self.overdispersion[group] ** 2 @@ -882,7 +1063,14 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): - """Document this.""" + """CBMR framework with Clustered Negative Binomial (Clustered NB) model. + + Clustered NB model can also accommodate over-dispersion in foci counts. + In NB model, the latent Gamma random variable introduces indepdentent variation + at each voxel. While in Clustered NB model, we assert the random effects are not + independent voxelwise effects, but rather latent characteristics of each study, + and represent a shared effect over the entire brain for a given study. + """ def __init__(self, **kwargs): kwargs["square_root"] = False @@ -984,7 +1172,15 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Document this.""" + """Define the loss function (nagetive log-likelihood function) for Clustered + Negative Binomial (Clustered NB) model. + Model refactorization is applied to reduce the dimensionality of variables. + + Returns + ------- + torch.Tensor + Loss (nagative log-likelihood) of Poisson model at current iteration. + """ log_l = 0 for group in self.groups: group_overdispersion = self.overdispersion[group] diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 9007042b4..045dd2cab 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -41,7 +41,7 @@ def cbmr_result(testdata_cbmr_simulated, model): moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=200, model=model, - penalty=False, + penalty=True, lr=1e-1, tol=1e7, device="cpu", From 69f1b87f0ed890b8d569652e8c01272c85a5fcfd Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 2 Apr 2023 16:49:25 +0100 Subject: [PATCH 131/177] add documentation for cbmr.py --- nimare/meta/cbmr.py | 60 +++++++++++++++++++++++++++++++++- nimare/meta/models.py | 2 +- nimare/tests/test_meta_cbmr.py | 2 +- 3 files changed, 61 insertions(+), 3 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index e332cbab0..c8b390292 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -572,6 +572,24 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): @_check_fit def _preprocess_t_con_regressor(self, source): + """Preprocess contrast vector/matrix for GLH testing. + With the following steps: + (1) Remove groups not involved in contrast; + (2) Standardize contrast matrix (row sum to 1); + (3) Remove duplicate rows in contrast matrix. + Parameters + ---------- + source : :obj:`~string` + Source of contrast matrix, either "groups" or "moderators". + + Returns + ------- + t_con_regressor : :obj:`~list` + Preprocessed contrast vector/matrix for inference on + spatial intensity or study-level moderators. + t_con_regressor_name : :obj:`~list` + Name of contrast vector/matrix for spatial intensity + """ # regressor can be either groups or moderators t_con_regressor = getattr(self, f"t_con_{source}") n_regressors = len(getattr(self, f"{source}")) @@ -631,6 +649,13 @@ def _preprocess_t_con_regressor(self, source): @_check_fit def _glh_con_group(self): + """Conduct Generalized linear hypothesis (GLH) testing for + group-wise spatial intensity estimation. + + GLH testing allows flexible hypothesis testings on spatial + intensity, including group-wise spatial homogeneity test and + group comparison test. + """ con_group_count = 0 for con_group in self.t_con_groups: con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() @@ -740,7 +765,32 @@ def _chi_square_log_intensity( simp_con_group, cov_log_intensity, contrast_log_intensity, - ): + ): + """ + Calculate chi-square statistics for GLH on group-wise log intensity function, + as an intermediate steps for GLH testings. + + Parameters + ---------- + m : :obj:`int` + Number of independent GLH tests. + n_brain_voxel : :obj:`int` + Number of voxels within the brain mask. + n_con_group_involved : :obj:`int` + Number of groups involved in the GLH test. + simp_con_group : :obj:`numpy.ndarray` + Simplified contrast matrix for the GLH test. + cov_log_intensity : :obj:`numpy.ndarray` + Covariance matrix of log intensity estimation. + contrast_log_intensity : :obj:`numpy.ndarray` + The product of contrast matrix and log intensity estimation. + + Returns + ------- + chi_sq_spatial : :obj:`numpy.ndarray` + Voxel-wise chi-square statistics for GLH tests on group-wise spatial + intensity estimations. + """ chi_sq_spatial = np.empty(shape=(0,)) for j in range(n_brain_voxel): contrast_log_intensity_j = contrast_log_intensity[:, j].reshape(m, 1) @@ -761,6 +811,14 @@ def _chi_square_log_intensity( @_check_fit def _glh_con_moderator(self): + """Conduct Generalized linear hypothesis (GLH) testing for + study-level moderators. + + GLH testing allows flexible hypothesis testings on regression + coefficients of study-level moderators, including testing for + the existence of moderator effects and difference in moderator + effects across multiple moderator effects. + """ con_moderator_count = 0 for con_moderator in self.t_con_moderators: m_con_moderator, _ = con_moderator.shape diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 45bf2bf4a..1f9b21458 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1063,7 +1063,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): - """CBMR framework with Clustered Negative Binomial (Clustered NB) model. + """CBMR framework with Clustered Negative Binomial (Clustered NB) model. Clustered NB model can also accommodate over-dispersion in foci counts. In NB model, the latent Gamma random variable introduces indepdentent variation diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 045dd2cab..9007042b4 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -41,7 +41,7 @@ def cbmr_result(testdata_cbmr_simulated, model): moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=200, model=model, - penalty=True, + penalty=False, lr=1e-1, tol=1e7, device="cpu", From 18ae03f76694134de8f1999ff528dfcf242971a4 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 3 Apr 2023 13:16:10 +0100 Subject: [PATCH 132/177] add documentation for utils.py --- nimare/utils.py | 52 ++++++++++++++++++++++++------------------------- 1 file changed, 25 insertions(+), 27 deletions(-) diff --git a/nimare/utils.py b/nimare/utils.py index eb5419354..d7f499279 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1881,14 +1881,17 @@ def b_spline_bases(masker_voxels, spacing, margin=10): Parameters ---------- - masker_voxels : matrix with element either 0 or 1, indicating if it's within brain mask, - spacing: (equally spaced) knots spacing in x/y/z direction, - margin: extend the region where B-splines are constructed (min-margin, max_margin) - to avoid weakly-supported B-spline on the edge + masker_voxels : :obj:`numpy.ndarray` + matrix with element either 0 or 1, indicating if it's within brain mask, + spacing : :obj:`int` + (equally spaced) knots spacing in x/y/z direction, + margin : :obj:`int` + extend the region where B-splines are constructed (min-margin, max_margin) + to avoid weakly-supported B-spline on the edge Returns ------- - X : 2-D ndarray (n_voxel x n_spline_bases) - only keeps with within-brain voxels + X : :obj:`numpy.ndarray` + 2-D ndarray (n_voxel x n_spline_bases) only keeps with within-brain voxels """ # dim_mask = masker_voxels.shape # n_brain_voxel = np.sum(masker_voxels) @@ -1937,28 +1940,23 @@ def b_spline_bases(masker_voxels, spacing, margin=10): return X - -def index2vox(vals, masker_voxels): - """Document This Function.""" - xx = np.where(np.apply_over_axes(np.sum, masker_voxels, [1, 2]) > 0)[0] - yy = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 2]) > 0)[1] - zz = np.where(np.apply_over_axes(np.sum, masker_voxels, [0, 1]) > 0)[2] - image_dim = [xx.shape[0], yy.shape[0], zz.shape[0]] - voxel_array = np.zeros(shape=masker_voxels.shape) - index_count = 0 - for i in range(image_dim[0]): - for j in range(image_dim[1]): - for k in range(image_dim[2]): - x, y, z = xx[i], yy[j], zz[k] - if masker_voxels[x, y, z] == 1: - voxel_array[x, y, z] = vals[index_count] - index_count += 1 - - return voxel_array - - def dummy_encoding_moderators(dataset_annotations, moderators): - """Document This Function.""" + """Convert categorical moderators to dummy encoded variables. + + Parameters + ---------- + dataset_annotations : :obj:`pandas.DataFrame` + Annotations of the dataset. + moderators : :obj:`list` + Study-level moderators to be considered into CBMR framework. + + Returns + ------- + dataset_annotations : :obj:`pandas.DataFrame` + Annotations of the dataset with dummy encoded moderator columns. + new_moderators : :obj:`list` + List of study-level moderators after dummy encoding. + """ new_moderators = [] for moderator in moderators.copy(): if len(moderator.split(":reference=")) == 2: From 02db35cd72731fb62927931c63e3ea6bac11beab Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 3 Apr 2023 13:53:06 +0100 Subject: [PATCH 133/177] add description for CBMREstimator --- nimare/meta/cbmr.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index c8b390292..c2893e431 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -148,7 +148,14 @@ def _generate_description(self): description : :obj:`str` Description of the Estimator instance. """ - description = "Document this (insert description of how this estimator was fit)" + description = """CBMR is a meta-regression framework that can explicitly model + group-wise spatial intensity function, and consider the effect of + study-level moderators. It consists of two components: (1) a spatial + model that makes use of a spline parameterization to induce a smooth + response; (2) a generalized linear model (Poisson, Negative Binomial + (NB), Clustered NB) to model group-wise spatial intensity function). + CBMR is fitted via maximizing the log-likelihood function with L-BFGS + algorithm.""" return description From 42e12cd05ed6cbe76a744a6220ab3dde92370f29 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 3 Apr 2023 16:58:54 +0100 Subject: [PATCH 134/177] change lr to a smaller value --- nimare/tests/test_meta_cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 9007042b4..d99ccdbbf 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -42,7 +42,7 @@ def cbmr_result(testdata_cbmr_simulated, model): spline_spacing=200, model=model, penalty=False, - lr=1e-1, + lr=1e-2, tol=1e7, device="cpu", ) From 2643ecb20b94f537f4b8d0aad3d35608b6227efe Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 3 Apr 2023 22:01:45 +0100 Subject: [PATCH 135/177] edit description function and add reference. --- nimare/meta/cbmr.py | 52 +++++++++++++++++++++++++++++++-- nimare/resources/references.bib | 31 ++++++++++++++++++++ nimare/tests/test_meta_cbmr.py | 3 +- 3 files changed, 83 insertions(+), 3 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index c2893e431..d37a1ec46 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -9,13 +9,14 @@ import scipy import torch +from nimare import _version from nimare.diagnostics import FocusFilter from nimare.estimator import Estimator from nimare.meta import models from nimare.utils import b_spline_bases, dummy_encoding_moderators, get_masker, mm2vox LGR = logging.getLogger(__name__) - +__version__ = _version.get_versions()["version"] class CBMREstimator(Estimator): """Coordinate-based meta-regression with a spatial model. @@ -156,7 +157,54 @@ def _generate_description(self): (NB), Clustered NB) to model group-wise spatial intensity function). CBMR is fitted via maximizing the log-likelihood function with L-BFGS algorithm.""" - + if self.moderators: + moderators_str = f"""and accommodate the following study-level moderators: {', '.join(self.moderators)}""" + else: + moderators_str = "" + if self.model.penalty: + penalty_str = " Firth-type penalty is applied to ensure convergence." + else: + penalty_str = "" + + if type(self.model).__name__ == "PoissonEstimator": + model_str = (" Here, Poisson model \\citep{eisenberg1966general} is the most basic CBMR model. " + "It's based on the assumption that foci arise from a realisation of a (continues) " + "inhomogeneous Poisson process, so that the (discrete) voxel-wise foci counts will " + "be independently distributed as Poisson random variables, with rate equal to the " + "integral of the (true, unobserved, continous) intensity function over each voxels." + ) + elif type(self.model).__name__ == "NegativeBinomialEstimator": + model_str = (" Negative Binomial (NB) model \\citep{barndorff1969negative} is a generalized " + "Poisson model with over-dispersion. " + "It's a more flexible model, but more difficult to estimate. In practice, foci" + "counts often display over-dispersion (the variance of response variable" + "substantially exceeeds the mean), which is not captured by Poisson model." + ) + elif type(self.model).__name__ == "ClusteredNegativeBinomialEstimator": + model_str = ( + " Clustered NB model \\citep{geoffroy2001poisson} can also accommodate " + "over-dispersion in foci counts. " + "In NB model, the latent random variable introduces indepdentent variation" + "at each voxel. While in Clustered NB model, we assert the random effects are not " + "independent voxelwise effects, but rather latent characteristics of each study, " + "and represent a shared effect over the entire brain for a given study." + ) + + model_description = ( + f"CBMR is a meta-regression framework that was performed with NiMARE {__version__}. " + f"{type(self.model).__name__} model was used to model group-wise spatial intensity " + f"functions {moderators_str}." + model_str + ) + + optimization_description = ( + "CBMR is fitted via maximizing the log-likelihood function with L-BFGS algorithm, with " + f"learning rate {self.lr}, learning rate decay {self.lr_decay} and tolerance {self.tol}." + + penalty_str + f" The optimization is run on {self.device}." + f" The input dataset included {self.inputs_['coordinates'].shape[0]} foci from " + f"{len(self.inputs_['id'])} experiments." + ) + + description = model_description + "\n" + optimization_description return description def _preprocess_input(self, dataset): diff --git a/nimare/resources/references.bib b/nimare/resources/references.bib index 547338271..2e9381590 100644 --- a/nimare/resources/references.bib +++ b/nimare/resources/references.bib @@ -487,3 +487,34 @@ @article{zhang2009cluster url={https://doi.org/10.1016/j.neuroimage.2008.08.017}, doi={10.1016/j.neuroimage.2008.08.017} } + +@article{eisenberg1966general, + title={A general use of the Poisson approximation for binomial events, with application to bacterial endocarditis data}, + author={Eisenberg, Herbert B and Geoghagen, Randolph RM and Walsh, John E}, + journal={Biometrics}, + pages={74--82}, + year={1966}, + publisher={JSTOR} +} + +@article{barndorff1969negative, + title={Negative binomial processes}, + author={Barndorff-Nielsen, Ole and Yeo, GF}, + journal={Journal of Applied Probability}, + volume={6}, + number={3}, + pages={633--647}, + year={1969}, + publisher={Cambridge University Press} +} + +@article{geoffroy2001poisson, + title={A Poisson-gamma model for two-stage cluster sampling data}, + author={Geoffroy, Pedro and Weerakkody, Govinda}, + journal={Journal of Statistical Computation and Simulation}, + volume={68}, + number={2}, + pages={161--172}, + year={2001}, + publisher={Taylor \& Francis} +} diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index d99ccdbbf..b22ad51db 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -47,8 +47,9 @@ def cbmr_result(testdata_cbmr_simulated, model): device="cpu", ) res = cbmr.fit(dataset=dset) + # a = res.description_ assert isinstance(res, nimare.results.MetaResult) - + # assert isinstance(results.description_, str) return res From 109897f8d73253e412f6f1c18859e684fd763caf Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 3 Apr 2023 22:12:37 +0100 Subject: [PATCH 136/177] check if result.__description is a string. --- nimare/tests/test_meta_cbmr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index b22ad51db..3df155a0b 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -47,9 +47,9 @@ def cbmr_result(testdata_cbmr_simulated, model): device="cpu", ) res = cbmr.fit(dataset=dset) - # a = res.description_ assert isinstance(res, nimare.results.MetaResult) - # assert isinstance(results.description_, str) + assert isinstance(res.description_, str) + return res From 1dd229962d326c7f8f6830e701c3664f6985d12b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 6 Apr 2023 11:45:22 +0100 Subject: [PATCH 137/177] resolve merge conflict --- nimare/tests/test_meta_cbmr.py | 1 + 1 file changed, 1 insertion(+) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 3df155a0b..394a1e5cf 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -199,3 +199,4 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): foci_per_study_tensor, prev_loss, ) + From 0f4678147d94d2d0740258f5d915ec70dcb7388a Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 6 Apr 2023 14:26:18 +0100 Subject: [PATCH 138/177] set random seed --- nimare/meta/models.py | 1 + nimare/tests/conftest.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 1f9b21458..db847be54 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -242,6 +242,7 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc foci_per_study : :obj:`dict` Dictionary of group-wise number of foci per study. """ + torch.manual_seed(100) optimizer = torch.optim.LBFGS(self.parameters(), self.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index c201fd293..eea352bb8 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -148,7 +148,7 @@ def testdata_cbmr_simulated(): """Simulate coordinate-based dataset for tests.""" # simulate ground_truth_foci, dset = create_coordinate_dataset( - foci=10, sample_size=(20, 40), n_studies=1000 + foci=10, sample_size=(20, 40), n_studies=1000, seed=100 ) # set up group columns: diagnosis & drug_status n_rows = dset.annotations.shape[0] From f4d4b499d3d88764b6ee60a324057e7d6a5531ef Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 7 Apr 2023 15:53:33 +0100 Subject: [PATCH 139/177] simplify the log-likelihood function of NB model --- nimare/meta/models.py | 61 ++++++++++++---------------------- nimare/tests/test_meta_cbmr.py | 11 ++++-- 2 files changed, 31 insertions(+), 41 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index db847be54..e70734cd0 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -34,7 +34,11 @@ class GeneralLinearModelEstimator(torch.nn.Module): Device to use for computations. Default is "cpu". """ - _hessian_kwargs = {} + _hessian_kwargs = { + "create_graph": False, + "vectorize": True, + "outer_jacobian_strategy": "forward-mode", + } def __init__( self, @@ -707,7 +711,6 @@ def nll_spatial_coef(group_spatial_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): """Base class for CBMR models with over-dispersion parameter.""" - _hessian_kwargs = {"create_graph": True} def __init__(self, **kwargs): self.square_root = kwargs.pop("square_root", False) @@ -765,12 +768,6 @@ class PoissonEstimator(GeneralLinearModelEstimator): integral of the (true, unobserved, continous) intensity function over each voxels. """ - _hessian_kwargs = { - "create_graph": False, - "vectorize": True, - "outer_jacobian_strategy": "forward-mode", - } - def __init__(self, **kwargs): super().__init__(**kwargs) @@ -901,7 +898,7 @@ class NegativeBinomialEstimator(OverdispersionModelEstimator): def __init__(self, **kwargs): kwargs["square_root"] = True super().__init__(**kwargs) - + def _three_term(self, y, r): max_foci = torch.max(y).to(dtype=torch.int64, device=self.device) sum_three_term = 0 @@ -917,7 +914,7 @@ def _three_term(self, y, r): ) return sum_three_term - + def _log_likelihood_single_group( self, group_overdispersion, @@ -941,16 +938,12 @@ def _log_likelihood_single_group( [0] * n_study, dtype=torch.float64, device=device ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) - numerator = mu_spatial**2 * torch.sum(mu_moderators**2) - denominator = mu_spatial**2 * torch.sum(mu_moderators) ** 2 - # estimated_sum_alpha = alpha * numerator / denominator - - p = numerator / (v * mu_spatial * torch.sum(mu_moderators) + numerator) - r = v * denominator / numerator - - log_l = self._three_term(group_foci_per_voxel, r) + torch.sum( - r * torch.log(1 - p) + group_foci_per_voxel * torch.log(p) - ) + # parameter of a NB variable to approximate a sum of NB variables + r = 1/group_overdispersion * torch.sum(mu_moderators)**2 / torch.sum(mu_moderators**2) + p = 1 / (1 + torch.sum(mu_moderators) / (group_overdispersion * mu_spatial * torch.sum(mu_moderators**2))) + # log-likelihood (moment matching approach) + log_l = torch.sum(torch.lgamma(group_foci_per_voxel+r) - torch.lgamma(group_foci_per_voxel+1) \ + - torch.lgamma(r) + r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) return log_l @@ -993,28 +986,18 @@ def _log_likelihood_mult_group( torch.exp(group_log_moderator_effect) for group_log_moderator_effect in log_moderator_effect ] - - numerators = [ - spatial_intensity[i] ** 2 * torch.sum(moderator_effect[i] ** 2) - for i in range(n_groups) - ] - denominators = [ - spatial_intensity[i] ** 2 * torch.sum(moderator_effect[i]) ** 2 - for i in range(n_groups) - ] - p = [ - numerators[i] - / (v[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]) + denominators[i]) - for i in range(n_groups) - ] - r = [v[i] * denominators[i] / numerators[i] for i in range(n_groups)] + # After similification, we have: + # r' = 1/alpha * sum(mu^Z_i)^2 / sum((mu^Z_i)^2) + # p'_j = 1 / (1 + sum(mu^Z_i) / (alpha * mu^X_j * sum((mu^Z_i)^2) + r = [1/overdispersion_coef[i] * torch.sum(moderator_effect[i])**2 / torch.sum(moderator_effect[i]**2) for i in range(n_groups)] + p_frac = [torch.sum(moderator_effect[i]) / (overdispersion_coef[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]**2)) for i in range(n_groups)] + p = [1 / (1 + p_frac[i]) for i in range(n_groups)] log_l = 0 for i in range(n_groups): - log_l += self._three_term(foci_per_voxel[i], r[i]) + torch.sum( - r[i] * torch.log(1 - p[i]) + foci_per_voxel[i] * torch.log(p[i]) - ) - + group_log_l = torch.sum(torch.lgamma(foci_per_voxel[i]+r[i]) - torch.lgamma(foci_per_voxel[i]+1) - torch.lgamma(r[i]) + r[i]*torch.log(1-p[i]) + foci_per_voxel[i]*torch.log(p[i])) + log_l += group_log_l + return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 394a1e5cf..84b505963 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,15 +15,22 @@ # indexed_gzip has a few debug messages that are not useful for testing logging.getLogger("indexed_gzip").setLevel(logging.WARNING) +# @pytest.fixture( +# scope="session", +# params=[ +# pytest.param(models.PoissonEstimator, id="Poisson"), +# pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), +# pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), +# ], +# ) @pytest.fixture( scope="session", params=[ pytest.param(models.PoissonEstimator, id="Poisson"), - pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), - pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), ], ) + def model(request): """CBMR models.""" return request.param From 383dc22a5abbd1388add9b9f5496beacdd0ab805 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Fri, 7 Apr 2023 15:54:06 +0100 Subject: [PATCH 140/177] simplify the log-likelihood function of NB model --- nimare/tests/test_meta_cbmr.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 84b505963..4048600c9 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,19 +15,12 @@ # indexed_gzip has a few debug messages that are not useful for testing logging.getLogger("indexed_gzip").setLevel(logging.WARNING) -# @pytest.fixture( -# scope="session", -# params=[ -# pytest.param(models.PoissonEstimator, id="Poisson"), -# pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), -# pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), -# ], -# ) - @pytest.fixture( scope="session", params=[ pytest.param(models.PoissonEstimator, id="Poisson"), + pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), + pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), ], ) From 1171c30dff0029b363f802dce84f9f7881690ba0 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 9 Apr 2023 15:08:48 +0100 Subject: [PATCH 141/177] implement wald test for CBMRInference --- nimare/meta/cbmr.py | 290 ++++++++++++++++++++++++--------- nimare/tests/test_meta_cbmr.py | 22 ++- 2 files changed, 224 insertions(+), 88 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index d37a1ec46..39165030d 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -711,6 +711,8 @@ def _glh_con_group(self): intensity, including group-wise spatial homogeneity test and group comparison test. """ + X = self.estimator.inputs_["coef_spline_bases"] + n_brain_voxel, spatial_coef_dim = X.shape con_group_count = 0 for con_group in self.t_con_groups: con_group_involved_index = np.where(np.any(con_group != 0, axis=0))[0].tolist() @@ -718,41 +720,7 @@ def _glh_con_group(self): n_con_group_involved = len(con_group_involved) # Simplify contrast matrix by removing irrelevant columns simp_con_group = con_group[:, ~np.all(con_group == 0, axis=0)] - if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test - involved_log_intensity_per_voxel = list() - for group in con_group_involved: - group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] - group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] - n_voxels, n_study = ( - group_foci_per_voxel.shape[0], - group_foci_per_study.shape[0], - ) - group_null_log_spatial_intensity = np.log( - np.sum(group_foci_per_voxel) / (n_voxels * n_study) - ) - group_log_intensity_per_voxel = np.log( - self.result.maps["spatialIntensity_group-" + group] - ) - group_log_intensity_per_voxel = ( - group_log_intensity_per_voxel - group_null_log_spatial_intensity - ) - involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack( - involved_log_intensity_per_voxel, axis=0 - ) - else: # GLH: group comparison - involved_log_intensity_per_voxel = list() - for group in con_group_involved: - group_log_intensity_per_voxel = np.log( - self.result.maps["spatialIntensity_group-" + group] - ) - involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack( - involved_log_intensity_per_voxel, axis=0 - ) - contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) - m, n_brain_voxel = contrast_log_intensity.shape - # Correlation of involved group-wise spatial coef + # Covariance of involved group-wise spatial coef (either one or multiple groups) moderators_by_group = ( self.estimator.inputs_["moderators_by_group"] if self.moderators else None ) @@ -764,42 +732,86 @@ def _glh_con_group(self): self.estimator.inputs_["foci_per_study"], ) cov_spatial_coef = np.linalg.inv(f_spatial_coef) - spatial_coef_dim = self.result.tables["spatial_regression_coef"].to_numpy().shape[1] - cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) - for k in range(n_con_group_involved): - for s in range(n_con_group_involved): - cov_beta_ks = cov_spatial_coef[ + # compute numerator: contrast vector * group-wise log spatial intensity + involved_log_intensity_per_voxel = list() + for group in con_group_involved: + group_log_intensity_per_voxel = np.log( + self.result.maps["spatialIntensity_group-" + group] + ) + if np.all(np.count_nonzero(con_group, axis=1) == 1):# GLH: homogeneity test + group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] + group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] + n_voxels, n_study = ( + group_foci_per_voxel.shape[0], + group_foci_per_study.shape[0], + ) + group_null_log_spatial_intensity = np.log( + np.sum(group_foci_per_voxel) / (n_voxels * n_study) + ) + group_log_intensity_per_voxel -= group_null_log_spatial_intensity + involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + involved_log_intensity_per_voxel = np.stack( + involved_log_intensity_per_voxel, axis=0 + ) + contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) + + # check if a single hypothesis is tested or GLH tests (with multiple contrasts) are conducted + m, _ = con_group.shape + if m == 1: # a single contrast vector, use Wald test + var_log_intensity = [] + for k in range(n_con_group_involved): + cov_spatial_coef_k = cov_spatial_coef[ + k * spatial_coef_dim : (k + 1) * spatial_coef_dim, k * spatial_coef_dim : (k + 1) * spatial_coef_dim, - s * spatial_coef_dim : (s + 1) * spatial_coef_dim, ] - X = self.estimator.inputs_["coef_spline_bases"] - cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) - cov_log_intensity = np.concatenate( - (cov_log_intensity, cov_group_log_intensity), axis=0 - ) # (m^2, n_voxels) - # GLH on log_intensity (eta) - chi_sq_spatial = self._chi_square_log_intensity( - m, - n_brain_voxel, - n_con_group_involved, - simp_con_group, - cov_log_intensity, - contrast_log_intensity, - ) - p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) - # convert p-values to z-scores for visualization - if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test - z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) - z_stats_spatial[z_stats_spatial < 0] = 0 - else: - z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) - if con_group.shape[0] == 1: # GLH one test: Z statistics are signed - z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) - z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) + var_log_intensity_k = np.sum(np.multiply(X @ cov_spatial_coef_k, X), axis=1) + var_log_intensity.append(var_log_intensity_k) + var_log_intensity = np.stack(var_log_intensity, axis=0) + involved_var_log_intensity = simp_con_group**2 @ var_log_intensity + involved_std_log_intensity = np.sqrt(involved_var_log_intensity) + # Conduct Wald test (Z test) + z_stats_spatial = contrast_log_intensity / involved_std_log_intensity + if n_con_group_involved == 1: # one-tailed test + p_vals_spatial = scipy.stats.norm.sf(z_stats_spatial) # shape: (1, n_voxels) + else: # two-tailed test + p_vals_spatial = scipy.stats.norm.sf(abs(z_stats_spatial))*2 # shape: (1, n_voxels) + else: # GLH tests (with multiple contrasts) + cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) + for k in range(n_con_group_involved): + for s in range(n_con_group_involved): + cov_beta_ks = cov_spatial_coef[ + k * spatial_coef_dim : (k + 1) * spatial_coef_dim, + s * spatial_coef_dim : (s + 1) * spatial_coef_dim, + ] + cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + cov_log_intensity = np.concatenate( + (cov_log_intensity, cov_group_log_intensity), axis=0 + ) # (m^2, n_voxels) + # GLH on log_intensity (eta) + chi_sq_spatial = self._chi_square_log_intensity( + m, + n_brain_voxel, + n_con_group_involved, + simp_con_group, + cov_log_intensity, + contrast_log_intensity, + ) + p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) + # convert p-values to z-scores for visualization + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) + z_stats_spatial[z_stats_spatial < 0] = 0 + else: + z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) + if con_group.shape[0] == 1: # GLH one test: Z statistics are signed + z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) + z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) + # save results if self.t_con_groups_name: - self.result.maps[ - f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" - ] = chi_sq_spatial + if m > 1: # GLH tests (with multiple contrasts) + self.result.maps[ + f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" + ] = chi_sq_spatial self.result.maps[ f"p_group-{self.t_con_groups_name[con_group_count]}" ] = p_vals_spatial @@ -807,10 +819,108 @@ def _glh_con_group(self): f"z_group-{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: - self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial + if m > 1: # GLH tests (with multiple contrasts) + self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial self.result.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial self.result.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial con_group_count += 1 + + + + # if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + # involved_log_intensity_per_voxel = list() + # for group in con_group_involved: + # group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] + # group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] + # n_voxels, n_study = ( + # group_foci_per_voxel.shape[0], + # group_foci_per_study.shape[0], + # ) + # group_null_log_spatial_intensity = np.log( + # np.sum(group_foci_per_voxel) / (n_voxels * n_study) + # ) + # group_log_intensity_per_voxel = np.log( + # self.result.maps["spatialIntensity_group-" + group] + # ) + # group_log_intensity_per_voxel = ( + # group_log_intensity_per_voxel - group_null_log_spatial_intensity + # ) + # involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + # involved_log_intensity_per_voxel = np.stack( + # involved_log_intensity_per_voxel, axis=0 + # ) + # else: # GLH: group comparison + # involved_log_intensity_per_voxel = list() + # for group in con_group_involved: + # group_log_intensity_per_voxel = np.log( + # self.result.maps["spatialIntensity_group-" + group] + # ) + # involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) + # involved_log_intensity_per_voxel = np.stack( + # involved_log_intensity_per_voxel, axis=0 + # ) + # contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) + # m, n_brain_voxel = contrast_log_intensity.shape + # # Correlation of involved group-wise spatial coef + # moderators_by_group = ( + # self.estimator.inputs_["moderators_by_group"] if self.moderators else None + # ) + # f_spatial_coef = self.estimator.model.fisher_info_multiple_group_spatial( + # con_group_involved, + # self.estimator.inputs_["coef_spline_bases"], + # moderators_by_group, + # self.estimator.inputs_["foci_per_voxel"], + # self.estimator.inputs_["foci_per_study"], + # ) + # cov_spatial_coef = np.linalg.inv(f_spatial_coef) + # spatial_coef_dim = self.result.tables["spatial_regression_coef"].to_numpy().shape[1] + # cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) + + # for k in range(n_con_group_involved): + # for s in range(n_con_group_involved): + # cov_beta_ks = cov_spatial_coef[ + # k * spatial_coef_dim : (k + 1) * spatial_coef_dim, + # s * spatial_coef_dim : (s + 1) * spatial_coef_dim, + # ] + # X = self.estimator.inputs_["coef_spline_bases"] + # cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + # cov_log_intensity = np.concatenate( + # (cov_log_intensity, cov_group_log_intensity), axis=0 + # ) # (m^2, n_voxels) + # # GLH on log_intensity (eta) + # chi_sq_spatial = self._chi_square_log_intensity( + # m, + # n_brain_voxel, + # n_con_group_involved, + # simp_con_group, + # cov_log_intensity, + # contrast_log_intensity, + # ) + # p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) + # # convert p-values to z-scores for visualization + # if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test + # z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) + # z_stats_spatial[z_stats_spatial < 0] = 0 + # else: + # z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) + # if con_group.shape[0] == 1: # GLH one test: Z statistics are signed + # z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) + # z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) + # if self.t_con_groups_name: + # self.result.maps[ + # f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" + # ] = chi_sq_spatial + # self.result.maps[ + # f"p_group-{self.t_con_groups_name[con_group_count]}" + # ] = p_vals_spatial + # self.result.maps[ + # f"z_group-{self.t_con_groups_name[con_group_count]}" + # ] = z_stats_spatial + # else: + # self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial + # self.result.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial + # self.result.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial + # con_group_count += 1 def _chi_square_log_intensity( self, @@ -891,24 +1001,42 @@ def _glh_con_moderator(self): ) cov_moderator_coef = np.linalg.inv(f_moderator_coef) - chi_sq_moderator = ( - contrast_moderator_coef.T - @ np.linalg.inv(con_moderator @ cov_moderator_coef @ con_moderator.T) - @ contrast_moderator_coef - ) - p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) + if m_con_moderator == 1: # a single contrast vector, use Wald test + var_moderator_coef = np.diag(cov_moderator_coef) + involved_var_moderator_coef = con_moderator**2 @ var_moderator_coef + involved_std_moderator_coef = np.sqrt(involved_var_moderator_coef) + # Conduct Wald test (Z test) + z_stats_moderator = contrast_moderator_coef / involved_std_moderator_coef + p_vals_moderator = scipy.stats.norm.sf(abs(z_stats_moderator))*2 # two-tailed test + else: # GLH test (multiple contrast vectors) + chi_sq_moderator = ( + contrast_moderator_coef.T + @ np.linalg.inv(con_moderator @ cov_moderator_coef @ con_moderator.T) + @ contrast_moderator_coef + ) + p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) + z_stats_moderator = scipy.stats.norm.isf(p_vals_moderator / 2) + if self.t_con_moderators_name: # None? - self.result.tables[ - f"chi_square_{self.t_con_moderators_name[con_moderator_count]}" - ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) + if m_con_moderator > 1: + self.result.tables[ + f"chi_square_{self.t_con_moderators_name[con_moderator_count]}" + ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) self.result.tables[ f"p_{self.t_con_moderators_name[con_moderator_count]}" ] = pd.DataFrame(data=np.array(p_vals_moderator), columns=["p"]) - else: self.result.tables[ - f"chi_square_GLH_moderators_{con_moderator_count}" - ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) + f"z_{self.t_con_moderators_name[con_moderator_count]}" + ] = pd.DataFrame(data=np.array(z_stats_moderator), columns=["z"]) + else: + if m_con_moderator > 1: + self.result.tables[ + f"chi_square_GLH_moderators_{con_moderator_count}" + ] = pd.DataFrame(data=np.array(chi_sq_moderator), columns=["chi_square"]) self.result.tables[f"p_GLH_moderators_{con_moderator_count}"] = pd.DataFrame( data=np.array(p_vals_moderator), columns=["p"] ) + self.result.tables[f"z_GLH_moderators_{con_moderator_count}"] = pd.DataFrame( + data=np.array(z_stats_moderator), columns=["z"] + ) con_moderator_count += 1 diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 4048600c9..8a45deebc 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,12 +15,19 @@ # indexed_gzip has a few debug messages that are not useful for testing logging.getLogger("indexed_gzip").setLevel(logging.WARNING) +# @pytest.fixture( +# scope="session", +# params=[ +# pytest.param(models.PoissonEstimator, id="Poisson"), +# pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), +# pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), +# ], +# ) + @pytest.fixture( scope="session", params=[ pytest.param(models.PoissonEstimator, id="Poisson"), - pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), - pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), ], ) @@ -64,12 +71,13 @@ def inference_results(testdata_cbmr_simulated, cbmr_result): ], source="groups", ) - t_con_moderators = inference.create_contrast( - ["standardized_sample_sizes"], - source="moderators", - ) + # t_con_moderators = inference.create_contrast( + # ["standardized_sample_sizes-standardized_avg_age"], + # source="moderators", + # ) + t_con_moderators = [[[1,-1,0,0,0,0],[1,0,-1,0,0,0]]] contrast_result = inference.transform( - t_con_groups=t_con_groups, t_con_moderators=t_con_moderators + t_con_groups=False, t_con_moderators=t_con_moderators ) return contrast_result From 7529a6f8ad2cce586974e27318822aa808fc85e8 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 9 Apr 2023 15:09:55 +0100 Subject: [PATCH 142/177] edit testing function for cbmr --- nimare/tests/test_meta_cbmr.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 8a45deebc..b948ee2da 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,19 +15,12 @@ # indexed_gzip has a few debug messages that are not useful for testing logging.getLogger("indexed_gzip").setLevel(logging.WARNING) -# @pytest.fixture( -# scope="session", -# params=[ -# pytest.param(models.PoissonEstimator, id="Poisson"), -# pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), -# pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), -# ], -# ) - @pytest.fixture( scope="session", params=[ pytest.param(models.PoissonEstimator, id="Poisson"), + pytest.param(models.NegativeBinomialEstimator, id="NegativeBinomial"), + pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), ], ) @@ -71,11 +64,10 @@ def inference_results(testdata_cbmr_simulated, cbmr_result): ], source="groups", ) - # t_con_moderators = inference.create_contrast( - # ["standardized_sample_sizes-standardized_avg_age"], - # source="moderators", - # ) - t_con_moderators = [[[1,-1,0,0,0,0],[1,0,-1,0,0,0]]] + t_con_moderators = inference.create_contrast( + ["standardized_sample_sizes-standardized_avg_age"], + source="moderators", + ) contrast_result = inference.transform( t_con_groups=False, t_con_moderators=t_con_moderators ) From 0696e0534cb42537e4945974771faeebacd1bb1c Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sun, 9 Apr 2023 16:38:48 +0100 Subject: [PATCH 143/177] edit testing function for correctors --- nimare/tests/test_meta_cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index b948ee2da..366fdfc1c 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -69,7 +69,7 @@ def inference_results(testdata_cbmr_simulated, cbmr_result): source="moderators", ) contrast_result = inference.transform( - t_con_groups=False, t_con_moderators=t_con_moderators + t_con_groups=t_con_groups, t_con_moderators=t_con_moderators ) return contrast_result From 1f47e5f0002a916fbdc93cb836a96aea5e03074d Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 16:42:06 +0100 Subject: [PATCH 144/177] fix linting error --- examples/01_datasets/05_plot_nimads.py | 8 +- examples/02_meta-analyses/10_plot_cbmr.py | 4 +- nimare/meta/cbmr.py | 210 +++++++--------------- nimare/meta/models.py | 121 ++++++++----- nimare/tests/test_meta_cbmr.py | 43 +++-- nimare/utils.py | 5 +- 6 files changed, 165 insertions(+), 226 deletions(-) diff --git a/examples/01_datasets/05_plot_nimads.py b/examples/01_datasets/05_plot_nimads.py index 207fa1901..60291ec55 100644 --- a/examples/01_datasets/05_plot_nimads.py +++ b/examples/01_datasets/05_plot_nimads.py @@ -25,12 +25,8 @@ def download_file(url): return response.json() -nimads_studyset = download_file( - "https://neurostore.org/api/studysets/Cv2LLUqG76W9?nested=true" -) -nimads_annotation = download_file( - "https://neurostore.org/api/annotations/76PyNqoTNEsE" -) +nimads_studyset = download_file("https://neurostore.org/api/studysets/Cv2LLUqG76W9?nested=true") +nimads_annotation = download_file("https://neurostore.org/api/annotations/76PyNqoTNEsE") ############################################################################### diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index dc055a71c..4638854c9 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -332,7 +332,9 @@ ) ) print( - "P-value of moderator effects `avg_age` is {}".format(contrast_result.tables["p_standardized_avg_age"]) + "P-value of moderator effects `avg_age` is {}".format( + contrast_result.tables["p_standardized_avg_age"] + ) ) ############################################################################### diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 39165030d..b3f846c05 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -18,6 +18,7 @@ LGR = logging.getLogger(__name__) __version__ = _version.get_versions()["version"] + class CBMREstimator(Estimator): """Coordinate-based meta-regression with a spatial model. @@ -150,7 +151,7 @@ def _generate_description(self): Description of the Estimator instance. """ description = """CBMR is a meta-regression framework that can explicitly model - group-wise spatial intensity function, and consider the effect of + group-wise spatial intensity function, and consider the effect of study-level moderators. It consists of two components: (1) a spatial model that makes use of a spline parameterization to induce a smooth response; (2) a generalized linear model (Poisson, Negative Binomial @@ -158,7 +159,8 @@ def _generate_description(self): CBMR is fitted via maximizing the log-likelihood function with L-BFGS algorithm.""" if self.moderators: - moderators_str = f"""and accommodate the following study-level moderators: {', '.join(self.moderators)}""" + moderators_str = f"""and accommodate the following study-level moderators: + {', '.join(self.moderators)}""" else: moderators_str = "" if self.model.penalty: @@ -167,44 +169,48 @@ def _generate_description(self): penalty_str = "" if type(self.model).__name__ == "PoissonEstimator": - model_str = (" Here, Poisson model \\citep{eisenberg1966general} is the most basic CBMR model. " - "It's based on the assumption that foci arise from a realisation of a (continues) " - "inhomogeneous Poisson process, so that the (discrete) voxel-wise foci counts will " - "be independently distributed as Poisson random variables, with rate equal to the " - "integral of the (true, unobserved, continous) intensity function over each voxels." + model_str = ( + " Here, Poisson model \\citep{eisenberg1966general} is the most basic CBMR model. " + "It's based on the assumption that foci arise from a realisation of a (continues) " + "inhomogeneous Poisson process, so that the (discrete) voxel-wise foci counts will" + " be independently distributed as Poisson random variables, with rate equal to the" + " integral of (true, unobserved, continous) intensity function over each voxels" ) elif type(self.model).__name__ == "NegativeBinomialEstimator": - model_str = (" Negative Binomial (NB) model \\citep{barndorff1969negative} is a generalized " - "Poisson model with over-dispersion. " - "It's a more flexible model, but more difficult to estimate. In practice, foci" - "counts often display over-dispersion (the variance of response variable" - "substantially exceeeds the mean), which is not captured by Poisson model." + model_str = ( + " Negative Binomial (NB) model \\citep{barndorff1969negative} is a generalized " + "Poisson model with over-dispersion. " + "It's a more flexible model, but more difficult to estimate. In practice, foci" + "counts often display over-dispersion (the variance of response variable" + "substantially exceeeds the mean), which is not captured by Poisson model." ) elif type(self.model).__name__ == "ClusteredNegativeBinomialEstimator": model_str = ( - " Clustered NB model \\citep{geoffroy2001poisson} can also accommodate " + " Clustered NB model \\citep{geoffroy2001poisson} can also accommodate " "over-dispersion in foci counts. " "In NB model, the latent random variable introduces indepdentent variation" "at each voxel. While in Clustered NB model, we assert the random effects are not " "independent voxelwise effects, but rather latent characteristics of each study, " "and represent a shared effect over the entire brain for a given study." ) - + model_description = ( f"CBMR is a meta-regression framework that was performed with NiMARE {__version__}. " f"{type(self.model).__name__} model was used to model group-wise spatial intensity " f"functions {moderators_str}." + model_str ) - + optimization_description = ( - "CBMR is fitted via maximizing the log-likelihood function with L-BFGS algorithm, with " - f"learning rate {self.lr}, learning rate decay {self.lr_decay} and tolerance {self.tol}." - + penalty_str + f" The optimization is run on {self.device}." + "CBMR is fitted via maximizing the log-likelihood function with L-BFGS algorithm, with" + f" learning rate {self.lr}, learning rate decay {self.lr_decay} and " + + "tolerance {self.tol}." + + penalty_str + + f" The optimization is run on {self.device}." f" The input dataset included {self.inputs_['coordinates'].shape[0]} foci from " f"{len(self.inputs_['id'])} experiments." ) - - description = model_description + "\n" + optimization_description + + description = model_description + "\n" + optimization_description return description def _preprocess_input(self, dataset): @@ -636,11 +642,11 @@ def _preprocess_t_con_regressor(self, source): ---------- source : :obj:`~string` Source of contrast matrix, either "groups" or "moderators". - + Returns ------- t_con_regressor : :obj:`~list` - Preprocessed contrast vector/matrix for inference on + Preprocessed contrast vector/matrix for inference on spatial intensity or study-level moderators. t_con_regressor_name : :obj:`~list` Name of contrast vector/matrix for spatial intensity @@ -706,9 +712,9 @@ def _preprocess_t_con_regressor(self, source): def _glh_con_group(self): """Conduct Generalized linear hypothesis (GLH) testing for group-wise spatial intensity estimation. - - GLH testing allows flexible hypothesis testings on spatial - intensity, including group-wise spatial homogeneity test and + + GLH testing allows flexible hypothesis testings on spatial + intensity, including group-wise spatial homogeneity test and group comparison test. """ X = self.estimator.inputs_["coef_spline_bases"] @@ -738,7 +744,7 @@ def _glh_con_group(self): group_log_intensity_per_voxel = np.log( self.result.maps["spatialIntensity_group-" + group] ) - if np.all(np.count_nonzero(con_group, axis=1) == 1):# GLH: homogeneity test + if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] n_voxels, n_study = ( @@ -750,14 +756,13 @@ def _glh_con_group(self): ) group_log_intensity_per_voxel -= group_null_log_spatial_intensity involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - involved_log_intensity_per_voxel = np.stack( - involved_log_intensity_per_voxel, axis=0 - ) + involved_log_intensity_per_voxel = np.stack(involved_log_intensity_per_voxel, axis=0) contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) - - # check if a single hypothesis is tested or GLH tests (with multiple contrasts) are conducted + + # check if a single hypothesis is tested or GLH tests + # (with multiple contrasts) are conducted m, _ = con_group.shape - if m == 1: # a single contrast vector, use Wald test + if m == 1: # a single contrast vector, use Wald test var_log_intensity = [] for k in range(n_con_group_involved): cov_spatial_coef_k = cov_spatial_coef[ @@ -771,11 +776,13 @@ def _glh_con_group(self): involved_std_log_intensity = np.sqrt(involved_var_log_intensity) # Conduct Wald test (Z test) z_stats_spatial = contrast_log_intensity / involved_std_log_intensity - if n_con_group_involved == 1: # one-tailed test - p_vals_spatial = scipy.stats.norm.sf(z_stats_spatial) # shape: (1, n_voxels) - else: # two-tailed test - p_vals_spatial = scipy.stats.norm.sf(abs(z_stats_spatial))*2 # shape: (1, n_voxels) - else: # GLH tests (with multiple contrasts) + if n_con_group_involved == 1: # one-tailed test + p_vals_spatial = scipy.stats.norm.sf(z_stats_spatial) # shape: (1, n_voxels) + else: # two-tailed test + p_vals_spatial = ( + scipy.stats.norm.sf(abs(z_stats_spatial)) * 2 + ) # shape: (1, n_voxels) + else: # GLH tests (with multiple contrasts) cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) for k in range(n_con_group_involved): for s in range(n_con_group_involved): @@ -783,7 +790,9 @@ def _glh_con_group(self): k * spatial_coef_dim : (k + 1) * spatial_coef_dim, s * spatial_coef_dim : (s + 1) * spatial_coef_dim, ] - cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + cov_group_log_intensity = ( + (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) + ) cov_log_intensity = np.concatenate( (cov_log_intensity, cov_group_log_intensity), axis=0 ) # (m^2, n_voxels) @@ -806,9 +815,9 @@ def _glh_con_group(self): if con_group.shape[0] == 1: # GLH one test: Z statistics are signed z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) - # save results + # save results if self.t_con_groups_name: - if m > 1: # GLH tests (with multiple contrasts) + if m > 1: # GLH tests (with multiple contrasts) self.result.maps[ f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" ] = chi_sq_spatial @@ -819,108 +828,11 @@ def _glh_con_group(self): f"z_group-{self.t_con_groups_name[con_group_count]}" ] = z_stats_spatial else: - if m > 1: # GLH tests (with multiple contrasts) + if m > 1: # GLH tests (with multiple contrasts) self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial self.result.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial self.result.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial con_group_count += 1 - - - - # if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test - # involved_log_intensity_per_voxel = list() - # for group in con_group_involved: - # group_foci_per_voxel = self.estimator.inputs_["foci_per_voxel"][group] - # group_foci_per_study = self.estimator.inputs_["foci_per_study"][group] - # n_voxels, n_study = ( - # group_foci_per_voxel.shape[0], - # group_foci_per_study.shape[0], - # ) - # group_null_log_spatial_intensity = np.log( - # np.sum(group_foci_per_voxel) / (n_voxels * n_study) - # ) - # group_log_intensity_per_voxel = np.log( - # self.result.maps["spatialIntensity_group-" + group] - # ) - # group_log_intensity_per_voxel = ( - # group_log_intensity_per_voxel - group_null_log_spatial_intensity - # ) - # involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - # involved_log_intensity_per_voxel = np.stack( - # involved_log_intensity_per_voxel, axis=0 - # ) - # else: # GLH: group comparison - # involved_log_intensity_per_voxel = list() - # for group in con_group_involved: - # group_log_intensity_per_voxel = np.log( - # self.result.maps["spatialIntensity_group-" + group] - # ) - # involved_log_intensity_per_voxel.append(group_log_intensity_per_voxel) - # involved_log_intensity_per_voxel = np.stack( - # involved_log_intensity_per_voxel, axis=0 - # ) - # contrast_log_intensity = np.matmul(simp_con_group, involved_log_intensity_per_voxel) - # m, n_brain_voxel = contrast_log_intensity.shape - # # Correlation of involved group-wise spatial coef - # moderators_by_group = ( - # self.estimator.inputs_["moderators_by_group"] if self.moderators else None - # ) - # f_spatial_coef = self.estimator.model.fisher_info_multiple_group_spatial( - # con_group_involved, - # self.estimator.inputs_["coef_spline_bases"], - # moderators_by_group, - # self.estimator.inputs_["foci_per_voxel"], - # self.estimator.inputs_["foci_per_study"], - # ) - # cov_spatial_coef = np.linalg.inv(f_spatial_coef) - # spatial_coef_dim = self.result.tables["spatial_regression_coef"].to_numpy().shape[1] - # cov_log_intensity = np.empty(shape=(0, n_brain_voxel)) - - # for k in range(n_con_group_involved): - # for s in range(n_con_group_involved): - # cov_beta_ks = cov_spatial_coef[ - # k * spatial_coef_dim : (k + 1) * spatial_coef_dim, - # s * spatial_coef_dim : (s + 1) * spatial_coef_dim, - # ] - # X = self.estimator.inputs_["coef_spline_bases"] - # cov_group_log_intensity = (X.dot(cov_beta_ks) * X).sum(axis=1).reshape((1, -1)) - # cov_log_intensity = np.concatenate( - # (cov_log_intensity, cov_group_log_intensity), axis=0 - # ) # (m^2, n_voxels) - # # GLH on log_intensity (eta) - # chi_sq_spatial = self._chi_square_log_intensity( - # m, - # n_brain_voxel, - # n_con_group_involved, - # simp_con_group, - # cov_log_intensity, - # contrast_log_intensity, - # ) - # p_vals_spatial = 1 - scipy.stats.chi2.cdf(chi_sq_spatial, df=m) - # # convert p-values to z-scores for visualization - # if np.all(np.count_nonzero(con_group, axis=1) == 1): # GLH: homogeneity test - # z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial) - # z_stats_spatial[z_stats_spatial < 0] = 0 - # else: - # z_stats_spatial = scipy.stats.norm.isf(p_vals_spatial / 2) - # if con_group.shape[0] == 1: # GLH one test: Z statistics are signed - # z_stats_spatial *= np.sign(contrast_log_intensity.flatten()) - # z_stats_spatial = np.clip(z_stats_spatial, a_min=-10, a_max=10) - # if self.t_con_groups_name: - # self.result.maps[ - # f"chiSquare_group-{self.t_con_groups_name[con_group_count]}" - # ] = chi_sq_spatial - # self.result.maps[ - # f"p_group-{self.t_con_groups_name[con_group_count]}" - # ] = p_vals_spatial - # self.result.maps[ - # f"z_group-{self.t_con_groups_name[con_group_count]}" - # ] = z_stats_spatial - # else: - # self.result.maps[f"chiSquare_GLH_groups_{con_group_count}"] = chi_sq_spatial - # self.result.maps[f"p_GLH_groups_{con_group_count}"] = p_vals_spatial - # self.result.maps[f"z_GLH_groups_{con_group_count}"] = z_stats_spatial - # con_group_count += 1 def _chi_square_log_intensity( self, @@ -930,11 +842,11 @@ def _chi_square_log_intensity( simp_con_group, cov_log_intensity, contrast_log_intensity, - ): + ): """ Calculate chi-square statistics for GLH on group-wise log intensity function, - as an intermediate steps for GLH testings. - + as an intermediate steps for GLH testings. + Parameters ---------- m : :obj:`int` @@ -949,11 +861,11 @@ def _chi_square_log_intensity( Covariance matrix of log intensity estimation. contrast_log_intensity : :obj:`numpy.ndarray` The product of contrast matrix and log intensity estimation. - + Returns ------- chi_sq_spatial : :obj:`numpy.ndarray` - Voxel-wise chi-square statistics for GLH tests on group-wise spatial + Voxel-wise chi-square statistics for GLH tests on group-wise spatial intensity estimations. """ chi_sq_spatial = np.empty(shape=(0,)) @@ -978,7 +890,7 @@ def _chi_square_log_intensity( def _glh_con_moderator(self): """Conduct Generalized linear hypothesis (GLH) testing for study-level moderators. - + GLH testing allows flexible hypothesis testings on regression coefficients of study-level moderators, including testing for the existence of moderator effects and difference in moderator @@ -1001,14 +913,16 @@ def _glh_con_moderator(self): ) cov_moderator_coef = np.linalg.inv(f_moderator_coef) - if m_con_moderator == 1: # a single contrast vector, use Wald test + if m_con_moderator == 1: # a single contrast vector, use Wald test var_moderator_coef = np.diag(cov_moderator_coef) involved_var_moderator_coef = con_moderator**2 @ var_moderator_coef involved_std_moderator_coef = np.sqrt(involved_var_moderator_coef) # Conduct Wald test (Z test) z_stats_moderator = contrast_moderator_coef / involved_std_moderator_coef - p_vals_moderator = scipy.stats.norm.sf(abs(z_stats_moderator))*2 # two-tailed test - else: # GLH test (multiple contrast vectors) + p_vals_moderator = ( + scipy.stats.norm.sf(abs(z_stats_moderator)) * 2 + ) # two-tailed test + else: # GLH test (multiple contrast vectors) chi_sq_moderator = ( contrast_moderator_coef.T @ np.linalg.inv(con_moderator @ cov_moderator_coef @ con_moderator.T) @@ -1016,7 +930,7 @@ def _glh_con_moderator(self): ) p_vals_moderator = 1 - scipy.stats.chi2.cdf(chi_sq_moderator, df=m_con_moderator) z_stats_moderator = scipy.stats.norm.isf(p_vals_moderator / 2) - + if self.t_con_moderators_name: # None? if m_con_moderator > 1: self.result.tables[ diff --git a/nimare/meta/models.py b/nimare/meta/models.py index e70734cd0..2c3a63d32 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -13,7 +13,7 @@ class GeneralLinearModelEstimator(torch.nn.Module): """Base class for GLM estimators. - + Parameters ---------- spatial_coef_dim : :obj:`int` @@ -77,7 +77,7 @@ def __init__( @abc.abstractmethod def _log_likelihood_single_group(self, **kwargs): """Log-likelihood of a single group. - + Returns ------- torch.Tensor @@ -88,7 +88,7 @@ def _log_likelihood_single_group(self, **kwargs): @abc.abstractmethod def _log_likelihood_mult_group(self, **kwargs): """Total log-likelihood of all groups in the dataset. - + Returns ------- torch.Tensor @@ -100,7 +100,7 @@ def _log_likelihood_mult_group(self, **kwargs): def forward(self, **kwargs): """Define the loss function (nagetive log-likelihood function) for each model. - + Returns ------- torch.Tensor @@ -131,8 +131,8 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture and study-level moderators. - """ + """Initialize the regression coefficients for spatial struture + and study-level moderators.""" self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim @@ -155,7 +155,7 @@ def _update( Adjust learning rate based on the number of iteration (with learning rate decay parameter `lr_decay`, default value is 0.999). Reset L-BFGS optimizer (as params in the previous iteration) if NaN occurs. - + Parameters ---------- optimizer : :obj:`torch.optim.lbfgs.LBFGS` @@ -170,7 +170,7 @@ def _update( Dictionary of group-wise number of foci per study. prev_loss : :obj:`torch.Tensor` Value of the loss function of the previous iteration. - + Returns ------- torch.Tensor @@ -234,7 +234,7 @@ def closure(): def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): """ Optimize the loss (negative log-likelihood) function with L-BFGS. - + Parameters ---------- coef_spline_bases : :obj:`numpy.ndarray` @@ -246,7 +246,7 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc foci_per_study : :obj:`dict` Dictionary of group-wise number of foci per study. """ - torch.manual_seed(100) + torch.manual_seed(100) optimizer = torch.optim.LBFGS(self.parameters(), self.lr) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( @@ -341,7 +341,7 @@ def standard_error_estimation( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): """Estimate standard error of estimates. - + For spatial regression coefficients, we estimate its covariance matrix using Fisher Information Matrix and then take the square root of the diagonal elements. For log spatial intensity, we use the delta method to estimate its standard error. @@ -440,10 +440,10 @@ def nll_moderators_coef(moderators_coef): def summary(self): """Summarize the main results of the fitted model. - + Summarize optimized regression coefficients from model and store in `tables`, - summarize standard error of regression coefficient and (Log-)spatial intensity - and store in `results`. + summarize standard error of regression coefficient and (Log-)spatial intensity + and store in `results`. """ params = ( self.spatial_regression_coef, @@ -493,11 +493,11 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """ Estimate the Fisher information matrix of spatial regression + """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups. - + Fisher information matrix is estimated by negative Hessian of the log-likelihood. - + Parameters ---------- involved_groups : :obj:`list` @@ -510,7 +510,7 @@ def fisher_info_multiple_group_spatial( Dictionary of group-wise number of foci per voxel. foci_per_study : :obj:`dict` Dictionary of group-wise number of foci per study. - + Returns ------- numpy.ndarray @@ -570,9 +570,9 @@ def fisher_info_multiple_group_moderator( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): """Estimate the Fisher information matrix of regression coefficients of moderators. - + Fisher information matrix is estimated by negative Hessian of the log-likelihood. - + Parameters ---------- coef_spline_bases : :obj:`numpy.ndarray` @@ -583,7 +583,7 @@ def fisher_info_multiple_group_moderator( Dictionary of group-wise number of foci per voxel. foci_per_study : :obj:`dict` Dictionary of group-wise number of foci per study. - + Returns ------- numpy.ndarray @@ -647,7 +647,7 @@ def firth_penalty( overdispersion=False, ): """Compute Firth's penalized log-likelihood. - + Parameters ---------- foci_per_voxel : :obj:`dict` @@ -660,7 +660,7 @@ def firth_penalty( Coefficient of B-spline bases evaluated at each voxel. overdispersion : :obj:`bool` Whether the model contains overdispersion parameter. Default is False. - + Returns ------- torch.Tensor @@ -732,8 +732,7 @@ def init_overdispersion_weights(self): self.overdispersion = torch.nn.ParameterDict(overdispersion) def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize weights for spatial and study-level moderator coefficients. - """ + """Initialize weights for spatial and study-level moderator coefficients.""" super().init_weights(groups, moderators, spatial_coef_dim, moderators_coef_dim) self.init_overdispersion_weights() @@ -760,7 +759,7 @@ def inference_outcome( class PoissonEstimator(GeneralLinearModelEstimator): """CBMR framework with Poisson model. - + Poisson model is the most basic model for Coordinate-based Meta-regression (CBMR). It's based on the assumption that foci arise from a realisation of a (continues) inhomogeneous Poisson process, so that the (discrete) voxel-wise foci counts will @@ -848,7 +847,7 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Poisson model. Model refactorization is applied to reduce the dimensionality of variables. - + Returns ------- torch.Tensor @@ -888,17 +887,17 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Negative Binomial (NB) model. - + Negative Binomial (NB) model is a generalized Poisson model with overdispersion. - It's a more flexible model, but more difficult to estimate. In practice, foci - counts often display over-dispersion (the variance of response variable + It's a more flexible model, but more difficult to estimate. In practice, foci + counts often display over-dispersion (the variance of response variable substantially exceeeds the mean), which is not captured by Poisson model. """ def __init__(self, **kwargs): kwargs["square_root"] = True super().__init__(**kwargs) - + def _three_term(self, y, r): max_foci = torch.max(y).to(dtype=torch.int64, device=self.device) sum_three_term = 0 @@ -914,7 +913,7 @@ def _three_term(self, y, r): ) return sum_three_term - + def _log_likelihood_single_group( self, group_overdispersion, @@ -926,7 +925,6 @@ def _log_likelihood_single_group( group_foci_per_study, device="cpu", ): - v = 1 / group_overdispersion log_mu_spatial = torch.matmul(coef_spline_bases, group_spatial_coef.T) mu_spatial = torch.exp(log_mu_spatial) if moderators_coef is not None: @@ -939,11 +937,25 @@ def _log_likelihood_single_group( ).reshape((-1, 1)) mu_moderators = torch.exp(log_mu_moderators) # parameter of a NB variable to approximate a sum of NB variables - r = 1/group_overdispersion * torch.sum(mu_moderators)**2 / torch.sum(mu_moderators**2) - p = 1 / (1 + torch.sum(mu_moderators) / (group_overdispersion * mu_spatial * torch.sum(mu_moderators**2))) + r = ( + 1 + / group_overdispersion + * torch.sum(mu_moderators) ** 2 + / torch.sum(mu_moderators**2) + ) + p = 1 / ( + 1 + + torch.sum(mu_moderators) + / (group_overdispersion * mu_spatial * torch.sum(mu_moderators**2)) + ) # log-likelihood (moment matching approach) - log_l = torch.sum(torch.lgamma(group_foci_per_voxel+r) - torch.lgamma(group_foci_per_voxel+1) \ - - torch.lgamma(r) + r*torch.log(1-p) + group_foci_per_voxel*torch.log(p)) + log_l = torch.sum( + torch.lgamma(group_foci_per_voxel + r) + - torch.lgamma(group_foci_per_voxel + 1) + - torch.lgamma(r) + + r * torch.log(1 - p) + + group_foci_per_voxel * torch.log(p) + ) return log_l @@ -958,7 +970,6 @@ def _log_likelihood_mult_group( moderators=None, device="cpu", ): - v = [1 / overdispersion_params for overdispersion_params in overdispersion_coef] n_groups = len(foci_per_voxel) log_spatial_intensity = [ torch.matmul(coef_spline_bases, spatial_coef[i, :, :]) for i in range(n_groups) @@ -989,22 +1000,38 @@ def _log_likelihood_mult_group( # After similification, we have: # r' = 1/alpha * sum(mu^Z_i)^2 / sum((mu^Z_i)^2) # p'_j = 1 / (1 + sum(mu^Z_i) / (alpha * mu^X_j * sum((mu^Z_i)^2) - r = [1/overdispersion_coef[i] * torch.sum(moderator_effect[i])**2 / torch.sum(moderator_effect[i]**2) for i in range(n_groups)] - p_frac = [torch.sum(moderator_effect[i]) / (overdispersion_coef[i] * spatial_intensity[i] * torch.sum(moderator_effect[i]**2)) for i in range(n_groups)] + r = [ + 1 + / overdispersion_coef[i] + * torch.sum(moderator_effect[i]) ** 2 + / torch.sum(moderator_effect[i] ** 2) + for i in range(n_groups) + ] + p_frac = [ + torch.sum(moderator_effect[i]) + / (overdispersion_coef[i] * spatial_intensity[i] * torch.sum(moderator_effect[i] ** 2)) + for i in range(n_groups) + ] p = [1 / (1 + p_frac[i]) for i in range(n_groups)] log_l = 0 for i in range(n_groups): - group_log_l = torch.sum(torch.lgamma(foci_per_voxel[i]+r[i]) - torch.lgamma(foci_per_voxel[i]+1) - torch.lgamma(r[i]) + r[i]*torch.log(1-p[i]) + foci_per_voxel[i]*torch.log(p[i])) + group_log_l = torch.sum( + torch.lgamma(foci_per_voxel[i] + r[i]) + - torch.lgamma(foci_per_voxel[i] + 1) + - torch.lgamma(r[i]) + + r[i] * torch.log(1 - p[i]) + + foci_per_voxel[i] * torch.log(p[i]) + ) log_l += group_log_l - + return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Negative + """Define the loss function (nagetive log-likelihood function) for Negative Binomial (NB) model. Model refactorization is applied to reduce the dimensionality of variables. - + Returns ------- torch.Tensor @@ -1048,8 +1075,8 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Clustered Negative Binomial (Clustered NB) model. - - Clustered NB model can also accommodate over-dispersion in foci counts. + + Clustered NB model can also accommodate over-dispersion in foci counts. In NB model, the latent Gamma random variable introduces indepdentent variation at each voxel. While in Clustered NB model, we assert the random effects are not independent voxelwise effects, but rather latent characteristics of each study, @@ -1159,7 +1186,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) """Define the loss function (nagetive log-likelihood function) for Clustered Negative Binomial (Clustered NB) model. Model refactorization is applied to reduce the dimensionality of variables. - + Returns ------- torch.Tensor diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 366fdfc1c..4f7fa1047 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -15,6 +15,7 @@ # indexed_gzip has a few debug messages that are not useful for testing logging.getLogger("indexed_gzip").setLevel(logging.WARNING) + @pytest.fixture( scope="session", params=[ @@ -23,7 +24,6 @@ pytest.param(models.ClusteredNegativeBinomialEstimator, id="ClusteredNegativeBinomial"), ], ) - def model(request): """CBMR models.""" return request.param @@ -49,7 +49,7 @@ def cbmr_result(testdata_cbmr_simulated, model): res = cbmr.fit(dataset=dset) assert isinstance(res, nimare.results.MetaResult) assert isinstance(res.description_, str) - + return res @@ -125,32 +125,29 @@ def test_firth_penalty(testdata_cbmr_simulated): def test_CBMREstimator_update(testdata_cbmr_simulated): """Unit test for CBMR estimator update function.""" - testdata_cbmr_simulated = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( - testdata_cbmr_simulated - ) + testdata_cbmr_simulated = StandardizeField( + fields=["sample_sizes", "avg_age", "schizophrenia_subtype"] + ).transform(testdata_cbmr_simulated) cbmr = CBMREstimator( moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], - model=models.PoissonEstimator, - lr=1e-4) + model=models.PoissonEstimator, + lr=1e-4, + ) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) cbmr._preprocess_input(testdata_cbmr_simulated) - + # fit the model init_weight_kwargs = { - "groups": cbmr.groups, - "moderators": cbmr.moderators, - "spatial_coef_dim": cbmr.inputs_["coef_spline_bases"].shape[1], - "moderators_coef_dim": len(cbmr.moderators) if cbmr.moderators else None} - + "groups": cbmr.groups, + "moderators": cbmr.moderators, + "spatial_coef_dim": cbmr.inputs_["coef_spline_bases"].shape[1], + "moderators_coef_dim": len(cbmr.moderators) if cbmr.moderators else None, + } + cbmr.model.init_weights(**init_weight_kwargs) - - moderators_by_group = cbmr.inputs_["moderators_by_group"] if cbmr.moderators else None - # cbmr.model._optimizer(cbmr.inputs_["coef_spline_bases"], moderators_by_group, cbmr.inputs_["foci_per_voxel"], cbmr.inputs_["foci_per_study"]) optimizer = torch.optim.LBFGS(cbmr.model.parameters(), cbmr.lr) - # load dataset info to torch.tensor - # _ = torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device) if cbmr.moderators: moderators_by_group_tensor = dict() for group in cbmr.model.groups: @@ -172,7 +169,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): ) foci_per_voxel_tensor[group] = group_foci_per_voxel_tensor foci_per_study_tensor[group] = group_foci_per_study_tensor - + if cbmr.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference @@ -182,7 +179,8 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): moderators_by_group_tensor, foci_per_voxel_tensor, foci_per_study_tensor, - prev_loss) + prev_loss, + ) # deliberately set the first spatial coefficient to nan for group in cbmr.model.groups: nan_coef = torch.tensor(cbmr.model.spatial_coef_linears[group].weight) @@ -193,10 +191,11 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): with pytest.raises(ValueError): cbmr.model._update( optimizer, - torch.tensor(cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device), + torch.tensor( + cbmr.inputs_["coef_spline_bases"], dtype=torch.float64, device=cbmr.device + ), moderators_by_group_tensor, foci_per_voxel_tensor, foci_per_study_tensor, prev_loss, ) - diff --git a/nimare/utils.py b/nimare/utils.py index d7f499279..b248cb4bc 100755 --- a/nimare/utils.py +++ b/nimare/utils.py @@ -1940,16 +1940,17 @@ def b_spline_bases(masker_voxels, spacing, margin=10): return X + def dummy_encoding_moderators(dataset_annotations, moderators): """Convert categorical moderators to dummy encoded variables. - + Parameters ---------- dataset_annotations : :obj:`pandas.DataFrame` Annotations of the dataset. moderators : :obj:`list` Study-level moderators to be considered into CBMR framework. - + Returns ------- dataset_annotations : :obj:`pandas.DataFrame` From 5651c07c8b276f404811890e57204028ead97893 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 16:50:18 +0100 Subject: [PATCH 145/177] fix linting error --- nimare/meta/cbmr.py | 4 ++++ nimare/meta/models.py | 12 ++++++++++++ 2 files changed, 16 insertions(+) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index b3f846c05..fc5025fce 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -633,6 +633,7 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): @_check_fit def _preprocess_t_con_regressor(self, source): + """Preprocess contrast vector/matrix for GLH testing. With the following steps: (1) Remove groups not involved in contrast; @@ -710,6 +711,7 @@ def _preprocess_t_con_regressor(self, source): @_check_fit def _glh_con_group(self): + """Conduct Generalized linear hypothesis (GLH) testing for group-wise spatial intensity estimation. @@ -843,6 +845,7 @@ def _chi_square_log_intensity( cov_log_intensity, contrast_log_intensity, ): + """ Calculate chi-square statistics for GLH on group-wise log intensity function, as an intermediate steps for GLH testings. @@ -888,6 +891,7 @@ def _chi_square_log_intensity( @_check_fit def _glh_con_moderator(self): + """Conduct Generalized linear hypothesis (GLH) testing for study-level moderators. diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 2c3a63d32..83889b7af 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -98,6 +98,7 @@ def _log_likelihood_mult_group(self, **kwargs): @abc.abstractmethod def forward(self, **kwargs): + """Define the loss function (nagetive log-likelihood function) for each model. @@ -109,6 +110,7 @@ def forward(self, **kwargs): return def init_spatial_weights(self): + """Initialization for spatial regression coefficients. Default is uniform distribution between -0.01 and 0.01. """ @@ -123,6 +125,7 @@ def init_spatial_weights(self): self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) def init_moderator_weights(self): + """Initialize the intercept and regression coefficients for moderators. Default is uniform distribution between -0.01 and 0.01. """ @@ -131,6 +134,7 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): + """Initialize the regression coefficients for spatial struture and study-level moderators.""" self.groups = groups @@ -150,6 +154,7 @@ def _update( foci_per_study, prev_loss, ): + """One iteration in optimization with L-BFGS. Adjust learning rate based on the number of iteration (with learning rate decay parameter @@ -232,6 +237,7 @@ def closure(): return loss def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): + """ Optimize the loss (negative log-likelihood) function with L-BFGS. @@ -493,6 +499,7 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): + """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups. @@ -710,6 +717,7 @@ def nll_spatial_coef(group_spatial_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): + """Base class for CBMR models with over-dispersion parameter.""" def __init__(self, **kwargs): @@ -739,6 +747,7 @@ def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): + """Summarize inference outcome into `maps` and `tables`. Add optimized overdispersion parameter to the tables. """ @@ -845,6 +854,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Define the loss function (nagetive log-likelihood function) for Poisson model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1028,6 +1038,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Define the loss function (nagetive log-likelihood function) for Negative Binomial (NB) model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1183,6 +1194,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): + """Define the loss function (nagetive log-likelihood function) for Clustered Negative Binomial (Clustered NB) model. Model refactorization is applied to reduce the dimensionality of variables. From 7be16326c12d10ad99a862379c0f50519b1fae3c Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:02:14 +0100 Subject: [PATCH 146/177] fix linting error --- nimare/meta/cbmr.py | 8 ++++---- nimare/meta/models.py | 24 ++++++++++++------------ 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index fc5025fce..7c62a9046 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -633,7 +633,7 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): @_check_fit def _preprocess_t_con_regressor(self, source): - + """Preprocess contrast vector/matrix for GLH testing. With the following steps: (1) Remove groups not involved in contrast; @@ -711,7 +711,7 @@ def _preprocess_t_con_regressor(self, source): @_check_fit def _glh_con_group(self): - + """Conduct Generalized linear hypothesis (GLH) testing for group-wise spatial intensity estimation. @@ -845,7 +845,7 @@ def _chi_square_log_intensity( cov_log_intensity, contrast_log_intensity, ): - + """ Calculate chi-square statistics for GLH on group-wise log intensity function, as an intermediate steps for GLH testings. @@ -891,7 +891,7 @@ def _chi_square_log_intensity( @_check_fit def _glh_con_moderator(self): - + """Conduct Generalized linear hypothesis (GLH) testing for study-level moderators. diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 83889b7af..ae723f965 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -98,7 +98,7 @@ def _log_likelihood_mult_group(self, **kwargs): @abc.abstractmethod def forward(self, **kwargs): - + """Define the loss function (nagetive log-likelihood function) for each model. @@ -110,7 +110,7 @@ def forward(self, **kwargs): return def init_spatial_weights(self): - + """Initialization for spatial regression coefficients. Default is uniform distribution between -0.01 and 0.01. """ @@ -125,7 +125,7 @@ def init_spatial_weights(self): self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) def init_moderator_weights(self): - + """Initialize the intercept and regression coefficients for moderators. Default is uniform distribution between -0.01 and 0.01. """ @@ -134,7 +134,7 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - + """Initialize the regression coefficients for spatial struture and study-level moderators.""" self.groups = groups @@ -154,7 +154,7 @@ def _update( foci_per_study, prev_loss, ): - + """One iteration in optimization with L-BFGS. Adjust learning rate based on the number of iteration (with learning rate decay parameter @@ -237,7 +237,7 @@ def closure(): return loss def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): - + """ Optimize the loss (negative log-likelihood) function with L-BFGS. @@ -499,7 +499,7 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - + """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups. @@ -717,7 +717,7 @@ def nll_spatial_coef(group_spatial_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): - + """Base class for CBMR models with over-dispersion parameter.""" def __init__(self, **kwargs): @@ -747,7 +747,7 @@ def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): - + """Summarize inference outcome into `maps` and `tables`. Add optimized overdispersion parameter to the tables. """ @@ -854,7 +854,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - + """Define the loss function (nagetive log-likelihood function) for Poisson model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1038,7 +1038,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - + """Define the loss function (nagetive log-likelihood function) for Negative Binomial (NB) model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1194,7 +1194,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - + """Define the loss function (nagetive log-likelihood function) for Clustered Negative Binomial (Clustered NB) model. Model refactorization is applied to reduce the dimensionality of variables. From 395dae27ce823f962cafaa6ca69a4ba2e25b2716 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:18:57 +0100 Subject: [PATCH 147/177] fixed linting error --- nimare/meta/models.py | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index ae723f965..8fac09f04 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -98,7 +98,6 @@ def _log_likelihood_mult_group(self, **kwargs): @abc.abstractmethod def forward(self, **kwargs): - """Define the loss function (nagetive log-likelihood function) for each model. @@ -110,7 +109,6 @@ def forward(self, **kwargs): return def init_spatial_weights(self): - """Initialization for spatial regression coefficients. Default is uniform distribution between -0.01 and 0.01. """ @@ -125,7 +123,6 @@ def init_spatial_weights(self): self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) def init_moderator_weights(self): - """Initialize the intercept and regression coefficients for moderators. Default is uniform distribution between -0.01 and 0.01. """ @@ -134,7 +131,6 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture and study-level moderators.""" self.groups = groups @@ -154,7 +150,6 @@ def _update( foci_per_study, prev_loss, ): - """One iteration in optimization with L-BFGS. Adjust learning rate based on the number of iteration (with learning rate decay parameter @@ -499,7 +494,6 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups. @@ -717,7 +711,6 @@ def nll_spatial_coef(group_spatial_coef): class OverdispersionModelEstimator(GeneralLinearModelEstimator): - """Base class for CBMR models with over-dispersion parameter.""" def __init__(self, **kwargs): @@ -747,7 +740,6 @@ def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): - """Summarize inference outcome into `maps` and `tables`. Add optimized overdispersion parameter to the tables. """ @@ -854,7 +846,6 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Poisson model. Model refactorization is applied to reduce the dimensionality of variables. @@ -897,7 +888,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Negative Binomial (NB) model. - + Negative Binomial (NB) model is a generalized Poisson model with overdispersion. It's a more flexible model, but more difficult to estimate. In practice, foci counts often display over-dispersion (the variance of response variable @@ -1038,7 +1029,6 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Negative Binomial (NB) model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1086,7 +1076,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Clustered Negative Binomial (Clustered NB) model. - + Clustered NB model can also accommodate over-dispersion in foci counts. In NB model, the latent Gamma random variable introduces indepdentent variation at each voxel. While in Clustered NB model, we assert the random effects are not @@ -1194,7 +1184,6 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Clustered Negative Binomial (Clustered NB) model. Model refactorization is applied to reduce the dimensionality of variables. From c938ec38f87aeb750844009d82f686d714e33980 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:29:32 +0100 Subject: [PATCH 148/177] fix linting error --- nimare/meta/models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 8fac09f04..4a3defb2d 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1185,7 +1185,8 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Clustered - Negative Binomial (Clustered NB) model. + Negative Binomial (Clustered NB) model; + Model refactorization is applied to reduce the dimensionality of variables. Returns From 931d2c1767b9c3526b9be546a707b1960c1bf2fa Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:39:07 +0100 Subject: [PATCH 149/177] fix linting error --- nimare/meta/models.py | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 4a3defb2d..e226379ac 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -98,8 +98,7 @@ def _log_likelihood_mult_group(self, **kwargs): @abc.abstractmethod def forward(self, **kwargs): - """Define the loss function (nagetive log-likelihood function) - for each model. + """Define the loss function (nagetive log-likelihood function) for each model. Returns ------- @@ -110,6 +109,7 @@ def forward(self, **kwargs): def init_spatial_weights(self): """Initialization for spatial regression coefficients. + Default is uniform distribution between -0.01 and 0.01. """ # initialization for spatial regression coefficients @@ -124,6 +124,7 @@ def init_spatial_weights(self): def init_moderator_weights(self): """Initialize the intercept and regression coefficients for moderators. + Default is uniform distribution between -0.01 and 0.01. """ self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() @@ -131,8 +132,9 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture - and study-level moderators.""" + """Initialize the regression coefficients for spatial struture and study-level + + moderators.""" self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim @@ -232,7 +234,6 @@ def closure(): return loss def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study): - """ Optimize the loss (negative log-likelihood) function with L-BFGS. @@ -494,8 +495,8 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """Estimate the Fisher information matrix of spatial regression - coeffcients for multiple groups. + """Estimate the Fisher information matrix of spatial regression coeffcients for multiple + groups, Fisher information matrix is estimated by negative Hessian of the log-likelihood. @@ -719,6 +720,7 @@ def __init__(self, **kwargs): def init_overdispersion_weights(self): """Initialize weights for overdispersion parameters. + Default is 1e-2. """ overdispersion = dict() @@ -741,6 +743,7 @@ def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): """Summarize inference outcome into `maps` and `tables`. + Add optimized overdispersion parameter to the tables. """ maps, tables = super().inference_outcome( @@ -847,6 +850,7 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Poisson model. + Model refactorization is applied to reduce the dimensionality of variables. Returns @@ -888,7 +892,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class NegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Negative Binomial (NB) model. - + Negative Binomial (NB) model is a generalized Poisson model with overdispersion. It's a more flexible model, but more difficult to estimate. In practice, foci counts often display over-dispersion (the variance of response variable @@ -1030,8 +1034,9 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Negative - Binomial (NB) model. Model refactorization is applied to reduce the dimensionality - of variables. + Binomial (NB) model, + + Model refactorization is applied to reduce the dimensionality of variables. Returns ------- @@ -1076,7 +1081,7 @@ def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study) class ClusteredNegativeBinomialEstimator(OverdispersionModelEstimator): """CBMR framework with Clustered Negative Binomial (Clustered NB) model. - + Clustered NB model can also accommodate over-dispersion in foci counts. In NB model, the latent Gamma random variable introduces indepdentent variation at each voxel. While in Clustered NB model, we assert the random effects are not @@ -1185,7 +1190,7 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Clustered - Negative Binomial (Clustered NB) model; + Negative Binomial (Clustered NB) model, Model refactorization is applied to reduce the dimensionality of variables. From 73fe525de424669c132cabbf3d6e86325de7f91d Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:46:11 +0100 Subject: [PATCH 150/177] fix linting error --- nimare/meta/models.py | 29 ++++++++++++----------------- 1 file changed, 12 insertions(+), 17 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index e226379ac..9f8631735 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -108,8 +108,8 @@ def forward(self, **kwargs): return def init_spatial_weights(self): - """Initialization for spatial regression coefficients. - + """Initialization for spatial regression coefficients, + Default is uniform distribution between -0.01 and 0.01. """ # initialization for spatial regression coefficients @@ -124,7 +124,7 @@ def init_spatial_weights(self): def init_moderator_weights(self): """Initialize the intercept and regression coefficients for moderators. - + Default is uniform distribution between -0.01 and 0.01. """ self.moderators_linear = torch.nn.Linear(self.moderators_coef_dim, 1, bias=False).double() @@ -132,9 +132,7 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture and study-level - - moderators.""" + """Initialize the regression coefficients for spatial struture and study-level moderators.""" self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim @@ -495,8 +493,7 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """Estimate the Fisher information matrix of spatial regression coeffcients for multiple - groups, + """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups, Fisher information matrix is estimated by negative Hessian of the log-likelihood. @@ -720,7 +717,7 @@ def __init__(self, **kwargs): def init_overdispersion_weights(self): """Initialize weights for overdispersion parameters. - + Default is 1e-2. """ overdispersion = dict() @@ -743,7 +740,7 @@ def inference_outcome( self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study ): """Summarize inference outcome into `maps` and `tables`. - + Add optimized overdispersion parameter to the tables. """ maps, tables = super().inference_outcome( @@ -850,7 +847,7 @@ def _log_likelihood_mult_group( def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): """Define the loss function (nagetive log-likelihood function) for Poisson model. - + Model refactorization is applied to reduce the dimensionality of variables. Returns @@ -1033,9 +1030,8 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Negative - Binomial (NB) model, - + """Define the loss function (nagetive log-likelihood function) for NB model, + Model refactorization is applied to reduce the dimensionality of variables. Returns @@ -1189,9 +1185,8 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Clustered - Negative Binomial (Clustered NB) model, - + """Define the loss function (nagetive log-likelihood function) for Clustered NB model, + Model refactorization is applied to reduce the dimensionality of variables. Returns From 37456248a79b4d21665f3da9a90c492f30895af3 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 17:55:52 +0100 Subject: [PATCH 151/177] fix linting error --- nimare/meta/cbmr.py | 19 ++++++++----------- nimare/meta/models.py | 11 ++++++----- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 7c62a9046..85f90ce81 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -633,12 +633,13 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): @_check_fit def _preprocess_t_con_regressor(self, source): - """Preprocess contrast vector/matrix for GLH testing. - With the following steps: + + Follow the steps below: (1) Remove groups not involved in contrast; (2) Standardize contrast matrix (row sum to 1); (3) Remove duplicate rows in contrast matrix. + Parameters ---------- source : :obj:`~string` @@ -711,9 +712,8 @@ def _preprocess_t_con_regressor(self, source): @_check_fit def _glh_con_group(self): - - """Conduct Generalized linear hypothesis (GLH) testing for - group-wise spatial intensity estimation. + """Conduct Generalized linear hypothesis (GLH) testing for group-wise spatial intensity + estimation. GLH testing allows flexible hypothesis testings on spatial intensity, including group-wise spatial homogeneity test and @@ -845,10 +845,9 @@ def _chi_square_log_intensity( cov_log_intensity, contrast_log_intensity, ): - """ - Calculate chi-square statistics for GLH on group-wise log intensity function, - as an intermediate steps for GLH testings. + Calculate chi-square statistics for GLH on group-wise log intensity function. + It is an intermediate steps for GLH testings. Parameters ---------- @@ -891,9 +890,7 @@ def _chi_square_log_intensity( @_check_fit def _glh_con_moderator(self): - - """Conduct Generalized linear hypothesis (GLH) testing for - study-level moderators. + """Conduct Generalized linear hypothesis (GLH) testing for study-level moderators. GLH testing allows flexible hypothesis testings on regression coefficients of study-level moderators, including testing for diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 9f8631735..4744fe4ca 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -108,7 +108,7 @@ def forward(self, **kwargs): return def init_spatial_weights(self): - """Initialization for spatial regression coefficients, + """Initialize spatial regression coefficients. Default is uniform distribution between -0.01 and 0.01. """ @@ -132,7 +132,8 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture and study-level moderators.""" + """Initialize the regression coefficients for spatial struture and study-level + moderators.""" self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim @@ -493,7 +494,7 @@ def fisher_info_multiple_group_spatial( foci_per_voxel, foci_per_study, ): - """Estimate the Fisher information matrix of spatial regression coeffcients for multiple groups, + """Estimate the Fisher information matrix of spatial regression coeffcients. Fisher information matrix is estimated by negative Hessian of the log-likelihood. @@ -1030,7 +1031,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for NB model, + """Define the loss function (nagetive log-likelihood function) for NB model. Model refactorization is applied to reduce the dimensionality of variables. @@ -1185,7 +1186,7 @@ def _log_likelihood_mult_group( return log_l def forward(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study): - """Define the loss function (nagetive log-likelihood function) for Clustered NB model, + """Define the loss function (nagetive log-likelihood function) for Clustered NB model. Model refactorization is applied to reduce the dimensionality of variables. From da7577bb4ac1774f00e03e5e9f3f4ac0f1cd10f3 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 18:01:10 +0100 Subject: [PATCH 152/177] fix linting error --- nimare/meta/cbmr.py | 8 ++++---- nimare/meta/models.py | 3 +-- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 85f90ce81..f39731967 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -634,12 +634,12 @@ def fit_transform(self, result, t_con_groups=None, t_con_moderators=None): @_check_fit def _preprocess_t_con_regressor(self, source): """Preprocess contrast vector/matrix for GLH testing. - + Follow the steps below: (1) Remove groups not involved in contrast; (2) Standardize contrast matrix (row sum to 1); (3) Remove duplicate rows in contrast matrix. - + Parameters ---------- source : :obj:`~string` @@ -712,8 +712,7 @@ def _preprocess_t_con_regressor(self, source): @_check_fit def _glh_con_group(self): - """Conduct Generalized linear hypothesis (GLH) testing for group-wise spatial intensity - estimation. + """Conduct GLH testing for group-wise spatial intensity estimation. GLH testing allows flexible hypothesis testings on spatial intensity, including group-wise spatial homogeneity test and @@ -847,6 +846,7 @@ def _chi_square_log_intensity( ): """ Calculate chi-square statistics for GLH on group-wise log intensity function. + It is an intermediate steps for GLH testings. Parameters diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 4744fe4ca..b179618cb 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -132,8 +132,7 @@ def init_moderator_weights(self): return def init_weights(self, groups, moderators, spatial_coef_dim, moderators_coef_dim): - """Initialize the regression coefficients for spatial struture and study-level - moderators.""" + """Initialize regression coefficients of spatial struture and study-level moderators.""" self.groups = groups self.moderators = moderators self.spatial_coef_dim = spatial_coef_dim From 6e79adc9b66e729cba8d6b3c3eba7c7d1e99ca9b Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Mon, 10 Apr 2023 18:06:30 +0100 Subject: [PATCH 153/177] fix linting error --- nimare/meta/cbmr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index f39731967..c5390a319 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -846,7 +846,7 @@ def _chi_square_log_intensity( ): """ Calculate chi-square statistics for GLH on group-wise log intensity function. - + It is an intermediate steps for GLH testings. Parameters From 2b8827a17b958cbdf7bf153a279d7bc26eac891c Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 27 Apr 2023 20:26:59 +0100 Subject: [PATCH 154/177] remove unused test datasets --- nimare/tests/conftest.py | 49 ---------------------------------------- 1 file changed, 49 deletions(-) diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index eea352bb8..a4470276d 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -70,55 +70,6 @@ def testdata_cbma_full(): dset = nimare.dataset.Dataset(dset_file) return dset - -@pytest.fixture(scope="session") -def testdata_cbmr(): - """Generate coordinate-based dataset for tests.""" - dset_file = os.path.join(get_test_data_path(), "test_pain_dataset.json") - dset = nimare.dataset.Dataset(dset_file) - - # Only retain one peak in each study in coordinates - # Otherwise centers of mass will be obscured in kernel tests by overlapping - # kernels - dset.coordinates = dset.coordinates.drop_duplicates(subset=["id"]) - # set up group columns & moderators - n_rows = dset.annotations.shape[0] - dset.annotations["diagnosis"] = [ - "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) - ] - dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] - dset.annotations["drug_status"] = ( - dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) - ) # random shuffle drug_status column - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] - dset.annotations["avg_age"] = np.arange(n_rows) - - return dset - - -@pytest.fixture(scope="session") -def testdata_cbmr_full(): - """Generate more complete coordinate-based dataset for tests. - - Same as above, except returns all coords, not just one per study. - """ - dset_file = os.path.join(get_test_data_path(), "neurosynth_dset.json") - dset = nimare.dataset.Dataset(dset_file) - # set up group columns & moderators - n_rows = dset.annotations.shape[0] - dset.annotations["diagnosis"] = [ - "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) - ] - dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] - dset.annotations["drug_status"] = ( - dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) - ) # random shuffle drug_status column - dset.annotations["sample_sizes"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)] - dset.annotations["avg_age"] = np.arange(n_rows) - - return dset - - @pytest.fixture(scope="session") def testdata_cbmr_laird(): """Generate more complete coordinate-based dataset for tests. From da2fbe2f6a528bd69d41db6eb9192e79739aedd5 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 27 Apr 2023 20:42:58 +0100 Subject: [PATCH 155/177] fix linter error --- nimare/tests/conftest.py | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py index a4470276d..dd974e6a9 100644 --- a/nimare/tests/conftest.py +++ b/nimare/tests/conftest.py @@ -70,29 +70,6 @@ def testdata_cbma_full(): dset = nimare.dataset.Dataset(dset_file) return dset -@pytest.fixture(scope="session") -def testdata_cbmr_laird(): - """Generate more complete coordinate-based dataset for tests. - - Same as above, except returns all coords, not just one per study. - """ - dset_file = os.path.join(get_test_data_path(), "neurosynth_laird_studies.json") - dset = nimare.dataset.Dataset(dset_file) - # set up group columns & moderators - n_rows = dset.annotations.shape[0] - dset.annotations["diagnosis"] = [ - "schizophrenia" if i % 2 == 0 else "depression" for i in range(n_rows) - ] - dset.annotations["drug_status"] = ["Yes" if i % 2 == 0 else "No" for i in range(n_rows)] - dset.annotations["drug_status"] = ( - dset.annotations["drug_status"].sample(frac=1).reset_index(drop=True) - ) # random shuffle drug_status column - if "year" in dset.metadata.columns: - dset.annotations["publication_year"] = [dset.metadata["year"][i] for i in range(n_rows)] - dset.annotations["avg_age"] = np.arange(n_rows) - - return dset - @pytest.fixture(scope="session") def testdata_cbmr_simulated(): From c28869518577178df130a281dc3aef6ba9cc3a09 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 27 Apr 2023 20:58:46 +0100 Subject: [PATCH 156/177] add cbmr to docs/api.rst --- docs/api.rst | 1 + nimare/meta/cbmr.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/api.rst b/docs/api.rst index 39f4261b9..ee8f94deb 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -42,6 +42,7 @@ For more information about the components of coordinate-based meta-analysis in N meta.cbma.mkda meta.cbma.base meta.kernel + meta.cbmr .. _api_results_ref: diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index c5390a319..5d41a0c5e 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,4 +1,4 @@ -"""Document This.""" +"""Coordinate-based meta-regression (CBMR) framework for estimation and statistcial inference.""" import logging import re from functools import wraps From cabdfce57bb67506563d03c1683ed8c56b6eaff4 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 27 Apr 2023 21:52:17 +0100 Subject: [PATCH 157/177] edit example file for cbmr --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 660 ++----------------- examples/02_meta-analyses/10_plot_cbmr.py | 50 +- 2 files changed, 71 insertions(+), 639 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index e3862c0d7..de31f2102 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, @@ -15,775 +15,197 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", - "# Coordinate-based meta-regression algorithms\n", - "\n", - "A tour of CBMR algorithms in NiMARE\n", - "\n", - "This tutorial is intended to provide a brief description and example of the CBMR\n", - "algorithm implemented in NiMARE. For a more detailed introduction to the elements\n", - "of a coordinate-based meta-regression, see other stuff.\n" + "\n\n# Coordinate-based meta-regression algorithms\n\nA tour of Coordinate-based meta-regression (CBMR) algorithms in NiMARE\n\nCBMR is a generative framework to approximate smooth activation intensity function\nand investigate the effect of study-level moderators (e.g., year of pubilication,\nsample size, subtype of stimuli). CBMR considers three stochastic models (Poisson,\nNegative Binomial (NB) and Clustered NB) for modeling the random variation in foci,\nand allows flexible statistical inference for either spatial homogeneity tests or\ngroup comparison tests. It is a computationally efficient approach with\ngood statistical interpretability to model the locations of activation foci.\n\nThis tutorial is intended to provide a brief description and example of the CBMR\nalgorithm implemented in NiMARE.\n\nFor a more detailed introduction to the elements of a coordinate-based meta-regression, \nsee other stuff.\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import numpy as np\n", - "import scipy\n", - "from nilearn.plotting import plot_stat_map\n", - "\n", - "from nimare.generate import create_coordinate_dataset\n", - "from nimare.meta import models\n", - "from nimare.transforms import StandardizeField" + "import numpy as np\nimport scipy\nfrom nilearn.plotting import plot_stat_map\n\nfrom nimare.generate import create_coordinate_dataset\nfrom nimare.meta import models\nfrom nimare.transforms import StandardizeField" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load Dataset\n", - "\n" + "## Load Dataset\nHere, we're going to simulate a dataset (using `nimare.generate.create_coordinate_dataset`)\nthat includes 100 studies, each with 10 reported foci and sample size varying between\n20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression)\nand drug status (Yes or No). We also add two continuous study-level moderators (sample size and \naverage age) and a categorical study-level moderator (schizophrenia subtype).\n\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# data simulation\n", - "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", - "# set up group columns: diagnosis & drug_status\n", - "n_rows = dset.annotations.shape[0]\n", - "dset.annotations[\"diagnosis\"] = [\n", - " \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n", - "]\n", - "dset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\n", - "dset.annotations[\"drug_status\"] = (\n", - " dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n", - ") # random shuffle drug_status column\n", - "# set up continuous moderators: sample sizes & avg_age\n", - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n", - "# as groups)\n", - "dset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n", - " n_rows / 5\n", - ")\n", - "dset.annotations[\"schizophrenia_subtype\"] = (\n", - " dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n", - ") # random shuffle drug_status column" + "# data simulation\nground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n# set up group columns: diagnosis & drug_status\nn_rows = dset.annotations.shape[0]\ndset.annotations[\"diagnosis\"] = [\n \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n]\ndset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\ndset.annotations[\"drug_status\"] = (\n dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column\n# set up continuous moderators: sample sizes & avg_age\ndset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\ndset.annotations[\"avg_age\"] = np.arange(n_rows)\n# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n# as groups)\ndset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n n_rows / 5\n)\ndset.annotations[\"schizophrenia_subtype\"] = (\n dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Estimation of group-specific spatial intensity functions\n", - "Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a\n", - "generative regression model that estimates a smooth intensity function and\n", - "can have study-level moderators. It's developed with a spatial model to\n", - "induce a smooth response and model the entire image jointly, and fitted with\n", - "different variants of statistical distributions (Poisson, Negative Binomial\n", - "(NB) or Clustered NB model) to find the most accurate but parsimonious model.\n", - "\n", - "CBMR framework can generate estimation of group-specific spatial internsity\n", - "functions for multiple groups simultaneously, with different group-specific\n", - "spatial regression coefficients.\n", - "\n", - "CBMR framework can also consider the effects of study-level moderators\n", - "(e.g. sample size, year of publication) by estimating regression coefficients\n", - "of moderators (shared by all groups). Note that moderators can only have global\n", - "effects instead of localized effects within CBMR framework. In the scenario\n", - "that there're multiple subgroups within a group, while one or more of them don't\n", - "have enough number of studies to be inferred as a separate group, CBMR can\n", - "interpret them as categorical study-level moderators.\n", - "\n" + "## Estimation of group-specific spatial intensity functions\nCBMR can generate estimation of group-specific spatial internsity\nfunctions for multiple groups simultaneously, with different group-specific\nspatial regression coefficients.\n\nCBMR can also consider the effects of study-level moderators\n(e.g. sample size, year of publication) by estimating regression coefficients\nof moderators (shared by all groups).\n\nNote that study-level moderators can only have global effects instead of localized\neffects within CBMR framework. In the scenario that there're multiple subgroups\nwithin a group, while one or more of them don't have enough number of studies to be\ninferred as a separate group, CBMR can interpret them as categorical study-level moderators.\n\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n", - "WARNING:nimare.utils:Citation not found.\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from nimare.meta.cbmr import CBMREstimator\n", - "\n", - "dset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n", - "\n", - "cbmr = CBMREstimator(\n", - " group_categories=[\"diagnosis\", \"drug_status\"],\n", - " moderators=[\n", - " \"standardized_sample_sizes\",\n", - " \"standardized_avg_age\",\n", - " \"schizophrenia_subtype:reference=type1\",\n", - " ],\n", - " spline_spacing=100, # a reasonable choice is 10, 100 is for speed\n", - " model=models.PoissonEstimator,\n", - " penalty=False,\n", - " lr=1e-1,\n", - " tol=1e3,\n", - " device=\"cpu\",\n", - ")\n", - "results = cbmr.fit(dataset=dset)\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia with drug treatment\",\n", - " threshold=1e-4,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia without drug treatment\",\n", - " threshold=1e-4,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-DepressionYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression with drug treatment\",\n", - " threshold=1e-4,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression without drug treatment\",\n", - " threshold=1e-4,\n", - ")" + "from nimare.meta.cbmr import CBMREstimator\n\ndset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n\ncbmr = CBMREstimator(\n group_categories=[\"diagnosis\", \"drug_status\"],\n moderators=[\n \"standardized_sample_sizes\",\n \"standardized_avg_age\",\n \"schizophrenia_subtype:reference=type1\",\n ],\n spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed\n model=models.PoissonEstimator,\n penalty=False,\n lr=1e-1,\n tol=1e3, # a reasonable choice is 1e-1 or 1e-2, 1e3 is for speed\n device=\"cpu\", # \"cuda\" if you have GPU\n)\nresults = cbmr.fit(dataset=dset)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia with drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia without drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-DepressionYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression with drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression without drug treatment\",\n threshold=1e-4,\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures correspond to group-specific spatial intensity map of four groups\n", - "(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\n", - "Areas with stronger spatial intensity are highlighted.\n", - "\n" + "Four figures correspond to group-specific spatial intensity map of four groups\n(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\nAreas with stronger spatial intensity are highlighted.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\n", - "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\n", - "can be generated by `create_contrast` function, with group names specified.\n", - "\n" + "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\nIn the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with group names specified.\n\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:nimare.utils:Citation not found.\n", - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from nimare.meta.cbmr import CBMRInference\n", - "\n", - "inference = CBMRInference(device=\"cuda\")\n", - "inference.fit(result=results)\n", - "t_con_groups = inference.create_contrast(\n", - " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n", - ")\n", - "contrast_result = inference.transform(t_con_groups=t_con_groups)\n", - "\n", - "# generate z-score maps for group-wise spatial homogeneity test\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaYes\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaNo\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"DepressionYes\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"DepressionNo\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")" + "from nimare.meta.cbmr import CBMRInference\n\ninference = CBMRInference(device=\"cuda\")\ninference.fit(result=results)\nt_con_groups = inference.create_contrast(\n [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n)\ncontrast_result = inference.transform(t_con_groups=t_con_groups)\n\n# generate z-score maps for group-wise spatial homogeneity test\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"SchizophreniaYes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"SchizophreniaNo\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"DepressionYes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"DepressionNo\",\n threshold=scipy.stats.norm.isf(0.05),\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to homogeneity test of\n", - "group-specific spatial intensity for four groups. The null hypothesis assumes\n", - "homogeneous spatial intensity over the whole brain,\n", - "$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\n", - "the number of voxels within brain mask, $j$ is the index of voxel. Areas with\n", - "significant p-values are highlighted (under significance level $0.05$).\n", - "\n" + "Four figures (displayed as z-statistics map) correspond to homogeneity test of\ngroup-specific spatial intensity for four groups. The null hypothesis assumes\nhomogeneous spatial intensity over the whole brain,\n$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\nthe number of voxels within brain mask, $j$ is the index of voxel. Areas with\nsignificant p-values are highlighted (under significance level $0.05$).\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\n", - "The default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n", - "\n" + "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\nThe default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:nimare.utils:Citation not found.\n", - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/_utils/niimg.py:63: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n", - " warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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3xhhz0kknJV2ewsJCs3LlSmOMMZdeemnUdQBg9thjD9OzZ0/v8913322MMeadd94xOTk53vyRI0ea4uJiY4wxhx9+eKva6cSJE73jOv7442O+/81vfmOMMeabb74xw4YNi/ruhhtuMMYY8/TTT0fN//Of/+zVyaKioqjv8vLyzH777RdTrxL9Lralrrb1nsa/5557zhhjou5Bzf198cUXRlGC/PWvfzXdu3c3U6dONQsXLjTPPfecycnJMf/85z8TrnPRRReZ2267zXz22Wdm7ty55qqrrjKpqanmyy+/3Iglbx0d9uAOwBxwwAFm8eLFMeuuXbvW/Otf/zJ9+vSJWn633XYzxhhTXFwcdUNM9Mcf2vnz50c9aAL2IWPNmjWmtrY26rtkH9xzc3PN7NmzjTHGnH766VHfLVq0yBhjzMEHHxyz3hFHHGGMMWbu3LlR8/mA9OSTT8as061bN1NRUWEaGxvNgAEDvPl8GHj//ffjrmNM7I8xz8nKlStNZmZmzHp33nmnMSb2xYJ/d911lzHGmKOOOmqjnOfg35lnnmmMMeaCCy5IavmioqKYfRx11FHGGPtQVl1dbZ544gnvuwsuuMAYY3944l2btj64B18O+MeXu0QPgfKPP76TJk1KWD+MSfzgfumllzbbRlrz4P6Xv/zFGGPMQw89FLN8fn6+KS8vj1uWRH+teXDfddddY77/9a9/bYwx5rnnnou7Pq/5XXfdlVR5hg0bZowx5vnnn2/VueJxrFu3zhQUFER9l5eXZxobG40xxpxxxhkx677wwgvGGGPGjh3bruPiuWpoaIh6WeQfX0qC+0mmLif64z35nXfeSbhMS8RrV8a0/OB+9tlnG2OMee2115pdPxQKmaFDh5r77rvPGGMFkEgkkvQxXn755XH3E+8vKyvLVFZWmoaGBjNy5MiY73mPeeutt6Lmt9RO+eAuRQX+ffXVV8YYE/Nyyr8vv/zS1NfXm+7duxsAJjU11axdu9YYY8yYMWNaPK6W7tdtqattvafxj/ehiy++OKnrqA/uiuTwww83Z5xxRtS8Y445xpx88smt2s62225rbrzxxo4sWofSoXaQ7777Lrbaaiscfvjh+MUvfoExY8Zgxx13RGFhIc477zwce+yx2G+//TB37lwAwEEHHQQAePrpp1FRUZH0fqZPn+6FgZDGxkYsXLgQu+66K7p3747i4uKktxcKhTB58mRst912uPPOO6Pi/AYOHIjBgwdj1apVcUNDpk6ditLSUgwfPhy9e/fGypUro76fMmVKzDpr167FW2+9haOPPhr77LNPzDJvvfVW3HXWrFmDvn37xj2G//3vf6iuro6Z/4tf/AKA7Q6Kx/vvv4+LLroIY8aMwcsvvxz1XUee57333hvjxo1D//79kZGRgVAo5B1LS/GPZNGiRVi8eDH22GMPpKeno7a2FuPGjQNgQx8++eSTqFAVftfRXf7xrg/rdKLrI9l3330BtFw/EvHqq68mtZ9kYEzxc889F/NdWVkZ3nrrLRx77LEdtj+yfPlyfPHFFzHzk6mzgHUVkWy11VY47LDDsNVWWyE7OxvhcNjrIk22nkk+//zzGDes8vJyrF27Fj169IhbHxYsWAAguj6057gWL17s1bEgra13LcFYcYYsNUeicMPW3MuD8DqZBLkS4s1/8MEHcc4557RqP/zd+fe//93isrvuuiuysrIwc+bMuMYKTzzxBO655x7svffeCIVCMWVsqZ3G+75nz57YeeedMXfu3LhhkQDw4YcfYpdddsGuu+6Kt956C6NHj0ZhYSFmzZqFzz77rMXjaom21NX23tPWrl0LADF5AxQlWfbaay88+OCDmDt3LkaMGIGvv/4aH3zwAe64446kt9HU1IT169c3O45kU9PhmSvq6+vx8ssvew+B+fn5OPHEE3HLLbegd+/euPfee72bwsCBAwEgKiY5GZYuXRp3/vr16wEgZvBiS/ztb3/zXBQuu+yyqO84iHTx4sUJ11+8eDEKCwvRv3//mAf3ROtxcFC8QarNHV/37t3jfhdvYCZgY8wB+5DUHD169GhVOYDkznNeXh5efPFFHHjggQmXkQPBmmPGjBk49dRTvTj3cePG4bvvvsPq1asxffp0jBs3zotz32+//VBeXo4vv/wy6e0nQ7zz0tq611K9Yv1IRKLr3Rb40JfIEagj95XMdllnJ0+e7A1ai4essxMmTMAf//jHuIPFgdbVsyCJbEMrKirQo0ePuN/z4TVYH9p6XEDH3/MSwYGR3G5zdLRHP4+bD3ASvihkZGRgp512wjbbbIOzzz4bH330ER577LGk99Oa3x2200TtsaysDOvWrUNBQQEKCwtjyt5S24n3PevJiBEjWkz4xnPW1t/SRLSlrrb3nlZeXg4AKCgoSL6gihLgyiuvRHl5ObbeemtEIhE0Njbir3/9K04++eSktzFhwgRUVFTg+OOPb9W+a2pqUFdXl/TyaWlpyMjIaNU+yAZPOVdWVoZ///vfWL58OV599VXsv//+yMzMjKsOJwsHmHUEp5xyCq644grMmTMHJ5xwQpu23dLNtbW0pQyJBvPwISaROkbiJcXqiPN822234cADD8T06dNx/fXXY/bs2Vi3bh2amppw8MEH46233mpVxrzp06fj1FNPxbhx4/DNN99ghx12wP333+99B1ilPTMzEz179sRrr73WofUF6Pjr3RZqa2vbtF6ih9pNQUt19vXXX495EQ5SUlLi/X/CCSfg0ksvxZIlS/DHP/4RH3/8MVavXo2Ghgakpqairq6uzZkZW6o/ydaHthxXsmXoKMrKygC0/SWnPeyyyy4AbPK+eMgXhcsuuwz/+Mc/8K9//QvTpk3bYC+YLdHc9W+pncZrA6wnK1aswJtvvtns+s0JSu2hPXW1rfClcWPnelG2HJ599lk89dRTXgTFrFmzcPHFF6Nfv35JuaJNnjzZG5zeGqeqmpoadM/MQRWSz0HQp08fLFy4sE0P7xstV/S7775rd5iSgoKCAlRXV3sK37BhwzZWMaIYM2YMHnroIZSWluLII4/0frSCUKmOZ59H+F085W3w4MH49ttvE67TkhLeXpYuXYqtttoKl156aUIla0Ny9NFHo6GhAUceeWSMijd06NBWb49uMuPGjcPXX3+NcDjsPbB/8sknqKmp8R7cgY3rjNEaVqxYga233hqDBw/GDz/8EPN9c/WtOfjGn5OTE/d7KnPxyjJw4MC4ZYm3zoaE6vLDDz+csKtewi74c889F6+99lrUd22pZxuCthzXxoYuJRu7mzgvL89zcUjWtnLChAk46KCDcMghh+D666/HmWeemdR6P//8M7bZZhsMGzYMs2fPbnbZlu7/eXl5KCws9GxuOwLWk5KSkqR7NTr6t7QtdbW997TCwkIAbbOUVhQAuPzyy3HllVfixBNPBADssMMOWLx4MW699dYWH9ynTJmCs846C88995wXTpcsdXV1qEIjTkZ/pCXhsl6HJjxVvAx1dXVtenDfaPLbVlttBcAqEHxL/9///gcA+O1vf4vs7OyNVRQAQP/+/fHyyy8jJSUFJ5xwQtz4UcDeEBcvXoxevXp5PrhBDjvsMHTr1g0//fRTXGUiXndLYWGhZ02XrO1ZW2FcfnOxhRuSwsJClJeXx+16b21XFGBjh5csWYI99tgDv/zlL9HU1OQ9nNfW1npx7m2Jb6+rq0NKysZ5l2WMaHP1oy2sWLECgO1mlwwfPhyDBg2Kmc86GC+OPS8vr9Vl4ctDW89lW+osf/TjhZQkqmftLWdr2Zhtsa11+bvvvkN9fT1Gjhy5AUqVmNtvvx05OTn47LPP8MknnyS93pVXXgkA+L//+7+4dTse/N05++yzW1z2iy++QFVVFXbddVfvNyzIKaecAsC2oY7qiVu2bBl++OEHbLvttkmPy/jiiy9QWlqKnXfeGbvttluLy7dU99tSV9t7T9tmm20AxNrGKkqyVFVVxfQqRyKRFnssn376aZx++ul4+umnm81R0BKZCCMzlMRfOx+9O+zB/S9/+Qv+/ve/x1W3+vXr5w0EevXVV70BjzNnzsS7776L3r1748EHH0RWVlbUeoMHD8b222/fUUX0yMjIwMsvv4y+ffvisssua9GP/J577gEA3HHHHVExfb1798Y//vEPAIjxmicnnHBC1A0rEongzjvvRE5ODqZOnbrBM43efvvtqKqqwoQJE+LehNPS0nDssceif//+G2T/c+fORbdu3WJu5hdffHHcF6FkmDFjBjIyMnDqqafi+++/j+qunT59OgYOHIjDDjus1fHty5cvR+/evduUSKm1TJw4ETU1NTj55JOj4v9TUlK8+tEWZs6cicrKShx66KEYNWqUN7979+54+OGHEYlE4paltrYWp556qjfADLDd5bfffjvy8vJaVQaqlG19+HvhhRfw3Xff4ZRTTsE111wT16N+r732wl577eV95ou3fBjbZ599cPnll2+QcraWthxXW2lrXa6qqsJXX32Ffv36tZgkriMYMmSIp3RVVFQkrZqTWbNm4aWXXkJqampMMqpEPPzww1i9ejUOO+wwXHTRRTHf77777t4AyaqqKjz66KOIRCL417/+FfUbNXz4cM+n/+67725VuVviL3/5CyKRCF544QXstNNOMd9369YNZ511lvc5mLzskUceiXmJycvLw3777ed9LikpQV1dHYYNGxY3fK4tdbW997QxY8Z44ouitIVf/epX+Otf/4r//ve/WLRoEV566SXccccdUc8+V111FU499VTv8+TJk3Hqqafi9ttvx+67747i4mIUFxfHjcDYbEjWfqYlO0jaDhpjzI8//mhefPFFM3nyZPPee++Z2tpaY4y1TOzXr1/Uev369fO80EtKSszLL79snnnmGfP555+bhoYGc9FFF3nLtsXqLp7t1SmnnGKMMaa8vNxMnDgx7t+f/vQnb/lwOGz++9//GmOMKS0tNS+88IJ58cUXTVlZmTHGmBdffDHGC5hlueeee0xjY6OZNm2amTx5spk/f74xxpilS5eagQMHRq1DO8hEdlm0GAvOa+mcANbfmlZcc+fONa+88oqZPHmymTFjhlm/fr0xxpiddtqpw88zAHPSSSd59WLGjBnmqaeeMrNnzzYNDQ3m9ttvN8Yktg1M9EcbSZ7f4HdB7+1Edm+J7CDp9Tp//nzzxBNPmIceeijKSjLe+Zf7bc2xnHfeecYYa/X37rvvmsmTJ5sFCxaY0tJSz/c4kR1kc9ulLVtVVZV5/fXXzWuvvWbWrFljPvjgA/Phhx/GPfY//OEPXlneeecdM3nyZDNv3jyzdu1a8/jjjxtjjPntb3+b9LHNmjXLGGPMp59+ah599FHz0EMPmV/96lfN1pXg31ZbbeW1leLiYvPWW2+ZJ5980rzxxhued3bw3jB8+HCvLs+ePdur342Njebvf/+7MSbWHjE9Pd3b1rRp08wjjzxiHnroIbPnnnsmdU2buxaJ2nJrj6ulc5VoPy3V5eb+rrvuOmNMYm90kmxdILy3PvbYY+all14y3333nWepOWfOnLjWoMnsb8cddzSNjY2mqqrK9O7dO6kyjR071rt/z58/30yZMsW88sorXj6G4P0wJyfHzJw507tmzzzzjJk6daqpqqoyxsS3JW2pndIOUtp4Bv9uvvlmr01+/vnn5plnnjHPPvus+eKLL0x9fb0pLS2NWj4SiZgXX3zRGGNMTU2Nefvtt81TTz1l3nvvPVNRURGTN+SVV14xxtj8JI899ph56KGHzGmnndbmugq07Z4G2BwqxiRn0ck/tYNUJOXl5eaiiy4ygwYNMhkZGWbo0KHmz3/+s/cMaoytf8HnhERtMJjPpiXKysoMAHNOaJC5MFzU4t85oUEGgHcPai0d9uDevXt3c/LJJ5vHH3/cfP3112b16tWmrq7OlJSUmPfff99cdtllJisrK+66OTk55pprrjGzZs0ylZWVpry83Hz//ffm7rvvjko+0VEPlMlcEPlDGYlEzIUXXmi++OILU1FRYSoqKsxnn31mzj333LgJpoJlGT9+vPnyyy9NVVWVWb16tXnsscdM//79Y9bZUA/uvDHee++9Zs6cOaaqqsqUlZWZH374wUyePNn85je/iZuAqSMe3AGYQw891Hz00UemrKzMrF271rz11ltmv/32a9PDLuD7chtjzLHHHhv1XXp6uqmurjbGGHPFFVckXX7AejbffffdZvHixaauri7meDr6wR2wfskff/yxqaysNGvWrDEvvfSSGTlyZMK6kMyDOwBz6aWXmrlz55ra2lqzZMkS849//MNkZmYmPHYA5phjjjGffPKJV5bnn3/eDB8+3Dz44IPGGBOVVCWZa/Tiiy+a1atXm4aGhqj6lKznf15enrn66qvN559/bsrLy01VVZVZsGCBef311825557reVjzb+TIkeaVV14xxcXFpqKiwnzxxRfmrLPOMkBiX/Ndd93VvPnmm6a0tNR7iOQ53xAP7q09rrY+uLdUl5v7GzBggKmvrzdTp06N+z1Jti5I+LvwzTffmIkTJ5qjjjqq2SR9yezv+eefN8YYc9tttyVdrqKiInPfffeZBQsWmJqaGlNSUmJmzpxprrnmmpi8IllZWebaa681s2fPNtXV1aasrMy899575sQTT2x13QCSe3AHYPbdd1/zzDPPmKVLl5ra2lqzevVqM2vWLHP33XebfffdN2b5UChkTj31VDN9+nRTWlpqqqurzYIFC8yUKVNi9tWzZ0/z2GOPmeXLl5v6+vq4db21bRBo/T0NgLnmmmuMMcYcffTRSV8/fXBXNhc29oN7yJjkAvO+/PJL7LrrrsksqsAOsBo3bhyKioo22Mh/RdnQhMNhfPPNN9hmm23Qr1+/Zh0mlC2HF198EUcccQQGDhyo11zZ4Pzwww/IyclBUVERGhuTc+b44osvosIBFWVTUV5ejvz8fJwbHoT0UMsR6LWmCfc3LUFZWVmrQ1GBjTg4VVGUzZehQ4fGxEOnpaXh73//O7bbbju88847+gDXhbj22msRDodj8looSkdz1FFHYeutt8Z1112X9EO7onRl9MFdURQcd9xxWLlyJT788ENMmTIFr732GhYuXIhLL70Uq1evxgUXXLCpi6hsRL777js89thjOPfcczWTpbJBue666/Dtt9+2mGtEUTZ3IqFQ0n/tQR/cFUXBO++8gxdffBF9+/bF4Ycfjv333x/V1dW47777MGrUqIR2qcqWy5lnnomcnBz11VY2KKNGjcKOO+64WSS229KYNGkSQqGQ95eSkoL+/fvjtNNOS5gRWtn82WgJmLoa+++//6YugqIkzeeff46TTjppUxdDURRF6WBuuukmDBkyBDU1Nfjkk08wadIkfPDBB5g9e3abEgAp8YmE7F+Ly7VzP/rgriiKoiiKsoVy6KGHYvTo0QCAs846Cz169MBtt92GV199tU2JEJVNi4bKKIqiKIqidBGYaG/+/PmbuCRbFhsrxl0Vd0VRFEVRlC7CokWLAACFhYWbtiBbGBoqoyiKoiiKorSLsrIylJSUoKamBp9++iluvPFGpKen44gjjtjURVPagD64K4qiKIqibKEcdNBBUZ+Liorw5JNPYsCAAZuoRFsmyYbBRLCRQmV69OiBjIwM1NTUtGuHiqIoiqIobSUjIwM9evTY1MXoNPzrX//CiBEjUFZWhkcffRTvvfce0tPTN3WxlDaS9IP7oEGDMGfOHJSUlGzI8iiKoijKFs+rr76KG2+8EU888QS23XbbTV2cTkWPHj0waNCgTV2MTsOYMWM8V5mjjjoK++yzD0466STMmTMHOTk5m7h0Ww4hJOf40j69vZWhMoMGDdLGoiiKoijt5JtvvgEAbL311hg1atQmLo3SVYhEIrj11lux//77495778WVV165qYuktBK1g1QURVEURekijBs3DmPGjMFdd92l4c8diNpBKoqiKMoWzqOPPoo33ngjZv5FF12E3NzcTVAipStw+eWX47jjjsOkSZPw+9//flMXR2kF+uCuKIqiKJuI+++/P+780047TR/clQ3GMcccg2HDhmHChAn43e9+h0ikve7iysbycQ8ZY0w7t6EoiqIoipIUjz32GACge/fuAIDMzMyo7/lYUllZCQD49a9/nfS2X3nlFQBAdnY2ACAkwhKqq6sBAGvWrAEAjB8/vlVlVxRJeXk58vPzcX3mUGSEWo5ArzFNuLF6AcrKypCXl9fq/aniriiKoiiKoijtwCruyfi4tw9V3BVFURRF6XCeeeYZAECfPn0AwPMOD4fDUVOq4k1NTVHr8zOns2bNAgCce+653jIMNdp5553jbpvwMx955LZra2sBAMXFxQCAE044oVXHqnRdqLj/NXsoMkItP5bXmEb8uVIVd0VRFEVRlIQ8MvpYAMDhA+zDUtH+1t66757bAQC+3fawTVMwRWkF+uCuKIqiKEq7ueeeewD4setDhgwBAKSlpUUtx4GQjENPTU0F4KvhhDHu5eXlAIDBgwcDAG644QZvmTFjxkSty21ySmSsezxycnK8XDWTJ08G4MfCX3jhhS2ur3RtkrV6jLQzBZM+uCuKoiiKssXTJD5HUu0jUEqGfbHYoX4JEAK+MH02cskUJXn0wV1RFEVRlGZ54YUXAAC9evUC4Kvkwbj0vn37Rq1DlZvTxsbGqHUaGhoAWKUbAFJS7CMJkwLJGHjGyHP54Dwuw3W4rYyMjKh9JUMoFPJ6CXhMH330kfc991FfXw8AWLVqFQDg2GOPTXofypZHOEk7yPZmPtUHd0VRFEVRugzhVPvoFHFKeyQry87PdgMFK1vexpgeAJCKxvWlQE4WXl1QtQFKqiixbPIH90mTJuH000/HzJkzMXr06E1dHGULg/WLRCIR9O7dGwcffDD++te/on///puwdIqiKJsnzz//PAAgPz8fALzYb6rNjFOnig747jHLly8H4HumExnDThWcajm3WVVlH4Kl8k4VPBivznlchuvIOPpgOVuiurra6xXo168fAF/Z97ddE7XOoEGD8PbbbwMAysrKAAC/+c1vkt6n0vnRGHdF6UBuuukmDBkyBDU1Nfjkk08wadIkfPDBB5g9e7bXlaooiqIkZv5hNlnR9BL7YP37r17ehKVpPQxRiKTZh3rGtocyrOL+eWV2zMtGIhrL7KDVpko7cHaHmmUAAFNbA6QD/63VOHllw6AP7kqX4NBDD/V6dM466yz06NEDt912G1599VUcf/zxm7h0iqIomwczZswA4KvnUu0Okp2d7anjgB9XzmX5EMx4eH5PNZvLUc2mAk9PdamSx/N7l24xXEduI6iYJ6KhocErM8vGMnMKAFlZWUB589vq06ePdy7Hjh3b4r6Vzk8kyRj39iZg0gd3pUuy77774rbbbsP8+fM3dVEURVE2S/q9cB8AoPjLn1EJoKLBPjD/4bvXvIfxzgQfqiJpbgBrtu1t9WLbk2DbhqVAOdAkFPemGhcY32AHrO601obNmKYmlC/4H/JO/0u7y68ogD64K12URYsWAQAKCws3bUEURVE2A+iawtDBzMzMZpenEh9Usuvq6gD4cfH0YSdSkef9l/HojE+nWwvVcqmqN+fJznW4Dar4ySjugK/m8xhYNm/95M1povjoo4+w1157tW1lpVOgiruidCBlZWUoKSlBTU0NPv30U9x4441IT0/HEUccsamLpiiKslkx8ttXAQAljfYhtr7aPq2WN0gn9M5FZsSF/WTbF4e0PDtwNZxTYBdI8FC+c0oJUF0CAGgstdaPTevX2alT3Osr7UtHU31D1JQM++gZrPzoGfS+7J/tPxBls0QHpypKB3LQQQdFfS4qKsKTTz6JAQMGbKISKYqiKIqitA59cFe6BP/6178wYsQIlJWV4dFHH8V7772XdNepoijKlsorr7wCAOjduzd2qZoLAKCvinGKe1O9nVbzc2BwaDDWndaLDE9hOA2n/L5Hjx4A/PATbo8DSmkbyZAYfmaoDcNXgvMSrcNtZmZmIt/5t6fn2f2m5Tr/9twCOy0LIzc3136XloYxBfUAqtGwYrW3Pyru9WVOaV9vB+c2VNe5c2VDdJrcuTJN0b0US645AwAw6OZHoWxZRJBkqIxpeZnm0Ad3pUswZswYz1XmqKOOwj777IOTTjoJc+bMicrCpyiKoiiKsrmiD+5KlyMSieDWW2/F/vvvj3vvvRdXXnnlpi6SoijKJiEnJwf7D7ZKeP1Sp5BHopOyUz2ubrRSYWZmpqe0U00HfLWbKjgHm3LAa69evQD4irlUxdeuXWvL4VRruV0q88HBqZzHcvAzp9xmZmYmujn/9qweduBtWrcCAEBF/12watUq9OgRXebGVbMAAA1rVnj7qy2x26tZw9h22z/RWGMV98a66Nh2Ku6hcPQ5nfeHEwEAW909BcqWQTjJGPdwEss0u3671laUTsq4ceMwZswY3HXXXd6NWlEURVEUZXNms1HcH330Ubzxxhsx8y+66CIv5kxROpLLL78cxx13HCZNmoTf//73m7o4iqIoG42pU6cCsMmEpi+pQkZGBkanOV/zVPtoEHLKu3FKe12TnSay0WXMOhVxquBU4PmZSjtV8ZUrVwIAKioqAPiKO5V5ri9j4AE/yZNM4iRtIVeuXIne6XY7Gd3tM0Wk0PYAmJQUL+FUQ0MD8pZ9BdQCdU5pr1lZ4u2vstj6t9eW2rI2VFnhp7HO7q/JnSuODyA8l2EXBB1KJhha6VQkbQfZzku/2Ty433///XHnn3baafrgrmwQjjnmGAwbNgwTJkzA7373u6gBT4qiKIqiKJsbm/zB/bTTTsNpp522qYuhbKE0V7/C4TDmzZu3cQukKIqyCfnggw8A+M4uVKjr6+sRyrL+5qFUO2Wsu1SHU1NTm02ClCwMUywvt/HiVNyprFNMoVJfVVUVs401a6wCnpVlHWKo4lNxT09Px6uHnwcA+M229pizernET05xbwjsIy0tDY2rl9lyFBfbci3zXWWqV62z01Kr9NdXumRRTnGXLjKMbec55Dll9tYvj/oFAGDUy2/FHJvSuUjax11j3BVFURRFURRly2eTK+6KoiiKomxYOIasoKAAgK9Q19XVedNQhnWXCblY90iGVd4jqVb5zmR8dkAxDIdj9T+pxlP9DsamA8D69eujykC1nH7vMnxRxswD8Ox8ZV4O7rOurg4DM+3yeQPy7LH36QYAqBmxH1zBEAqFkLF4JgCg1sW2U2mvWObHuK9f7noFym2vQEONi6mvi59VljHtEedoEwpHf6by/slB4wAAe/xvetztKJs/XS7GXVEURVEURVE6IxsrVEYf3BVFURRlC4fKNP3XMzOtl3l+fj4AF+tetajV2w1mUZUKuVTY5Xwq85yyjFKxp9LOsgeT5sl1WB6q8mlpaQnLbpzSriidCX1wVxRFURQF4czsqGlqtktUlGMfmPNTO6fzVlGWLT9DZdJ79wUA1IvlGtfYwajVy1cBACpdiEz5z+u9ZdaviA6VqWlwg2HFOwrDIaiupoqQGYYfpWRGf1Y6L+FQKKnkSu1NwKQP7oqiKIqyhXLvvfcCALbddlsAfiw448sZ656TkwN0cC46qt9SYednloXqPx1upFrO5SsrK6OWD8Lj4D4YN89tJkKWaVNz77334oILLtjUxVA2Y/TBXVEURVEULM8YgMzMTORkWJvctFz7MJxRaB+Ue6R1LlV4yt6/BQD8ajtrA5nd304j3W2ypUSKOwelli+1NpXrFpd5y6wus0p7uVPamZSq0T34U2Gn4p7mBqOmuvmZzjaS8zMq3YDbDH0c6+yEIiFv8HGzy6niriiKoihKPHr1sl7lVKsTqdlUv+no0hzBuPagqwydXCSJHlQ4n3H2MqMqp9K/PZ6TDTOkUnlvLra9NWXc2PB6KUoi9MFdURRFURSPcK5NUJReYAd+ZnW3D8M98+3D/a09dgYAXFUya6OXrTWMzLUP7wWD7YtB7qDeAIBVg/ZAXl6et1zGwk8BAOtXudj24rUAfMW9eJ0fQ7Tc2T+W1TevuFNRp4Wm/zkcNT/DKfeZbnuZf7gRr/zhRgDAr4u/a+uhK5uAcCSEcBKKu8a4K4qiKIoSxbPPPgsA6NevHwBfaWdW0upqm/mT6jUdYVqjPNfW1kYp29JVpiW4PJX6devWAYiNdSfMtMpjCM7jcTALa3Ox7a0tp6JsTuiDu6IoiqJsoexRyEjueny+PnZQZzzCOQUAgEiBTVSU1ddO8weuAQAMK7UPy9f22g0AcNu6rzuotB3D5H1PAQAc4ZT2vEG2/HSTqRbLN7qES9Wr1wHwkyyVrLIhOj9X+y8BVNz/smqm9/JDGKZD20pjDK4s3NnOc+9DOSlU3MNRn2uccl/d6L84TelpBxSfuPr7JI5aAYBly5bhT3/6E15//XVUVVVhq622wsSJEzF69Oi4y7/44ou4//77MWvWLNTW1mK77bbDDTfcgEMOOaT1O4+EEYoTxhVDqH0DofXBXVEURVG2MIKhICQrKysmwygdWuI5tSQLY9C5jZbUe87nA67sBWBsu1yfyzGePThPHldzpKamtjumfeXKld6+qfZT5Wd22nix+MqGo7S0FHvvvTf2339/vP766+jZsyd++uknFBYWJlznvffew8EHH4xbbrkFBQUFmDhxIn71q1/h008/xS677LIRS588+uCuKIqiKFsopsYf2LltWhOQAnzb0K3Zddb33g5NTU3IXrkEAJDdpzsAIH+wjQEfuNwOdC2tjz8YdVPD2PbCIQUA/Nj2SM/+cZdvLLM9CVWrSgEAlavs8a2stQ/iVNkB4PrlH2PNmjVJl+VvpbO8Fw2uV11djUd3Pdr+3xhfgQd8FV5Jjttuuw0DBw7ExIkTvXlDhgxpdp277ror6vMtt9yCV155Bf/5z39a/eAeCocQiiThKgONcVcURVEUJYCv9sY+XPfs2dNTu7kcp9J7PRnKyqxdotymVLWlks7lGWLCGHcq9vyesBeB+wvOk8s2R3DZpqYmtEYXLysr83oGGJsv9y3dcXg+unfvHjVf6VheffVVHHLIITjuuOMwY8YM9O/fH+eddx5+97vfJb2NpqYmrF+/Ht26Nf9yG49wJIRwEg/uYX1wVxRFURQlyNgU60neVOk/VIab7INmn9I5AIA1vbZvdhuRQmtNmNnXKta5g6xi3K3EPrhuXWmTHL0w6ggAwDlLPumQsreVp/c5CQDwiz7WDaegyMa45zj/9vn5W3uDdQEga/k3AIDKtTZDas0am5Sqyh1fsVPab179OUpKSjq0rNes+BT19fW4eeA+AIDqRsa4+w/1QfVdaZkFCxbg/vvvxyWXXIKrr74aM2fOxB/+8AekpaVh/PjxSW1jwoQJqKiowPHHH7+BS9t29MF9E/DSSy8BAHJzcwEAn//6XAC+PVSea6wDn7UZ79autdZUralIdBTgW6NUU+RofmbRO/roo1t9PIrSmZgyZQoAXxVjG5Ae1Gwr+VdOAOCnLGeiFKYu/8WPX2zgEitKctxzzz3e/2dvl9/i8osXL7YZU+GrwVI1bw1SpZfKssygSvg7RcU9kZLNbKhBr3luMxnFvS0qKvcRdLiR8fR01mGMP88dy8bYd4bKVFTYwa/JKu/33HMPLrzwwjaVvSvR1NSE0aNH45ZbbgEA7LLLLpg9ezYeeOCBpB7cJ0+ejBtvvBGvvPJKm/z0Q+HkBqeG2pmlVx/cFUVRFGULo6naxmmHgmEhDTbWOuQejEflhgFTibmh3nG3sX7AKNTU1KBbqY1tz3WuMrWl9sGz3inu2/xoxaUHB+/prXth8ZcddizJslWOi20fWgAAyBloH75SesWPbW8qd8ezzh5PtXPLKXFKe2n9hg9pOe3jKVGDdB/f+yTvO3rExw4zVuLRt29fbLvttlHzttlmG7zwwgstrjtlyhScddZZeO6553DQQQdtqCJ2CPrgvhG4Km1o3PlMyJDmGfZHqxDVp14MAOjV1yoiXz/3KABgp+feSHrfe1f/CAD4+bXpAIAfXvoBAJD39L1Jb0NROis3ZW4FwE8/LpOjBMMR5Xe0b6v22qeLBXY/5mE3mGzaLrvb5bPtj29atn14SM/jNMNOXTKbIbc/2QFHpiixtNUpZe3atZ76S6WRCnNr3FoSlYdTGT8vY93797cP2PRi53zpNhPsDeC8ZBT3oJtMa2L4U1NTPacYwM/0SmRMv1TaV69eDcDvUWAPN5X6ls7x5pLVdXNn7733xpw5c6LmzZ07F4MHD252vaeffhpnnHEGpkyZgsMPP7zN+9cY9y0AhqtsTKZNmwYA2Gor+8CCZaviLseuRt4QeFP66KOPAPhdebzRbM7xXooSj6effnpTF0FRNhlNVfbhN5TiJ0gKpdiH21BTdFjYXn36AqjA9zU5cbcV6d4HAJDRzyrUeeutU01jjVXcG+ucMr1gnbfOPX1GAQD+sPKrdhxFcrxxyFkAgLGMbR/M2PaeAFp2k6kpXe+mVnEvd9lM/172zUYfSHrK+096v7/z5s3bqPvu7Pzxj3/EXnvthVtuuQXHH388PvvsMzz44IN48MEHvWWuuuoqLFu2DI8//jgAGx4zfvx4/POf/8Tuu++O4mI7NiQzMzPmBW1zQR/cO5jrM4Z5//upkKOXaemFjMofFT3G0nK6/GYbE1+w3QhvnfRtx8TdVlOVvSHVlVvFgmma+zZfBEXplPw+VAQAnksE25rs3ZLqevB/jjVpFKmrIy5pRqobPNYkpoR2YCHXfsNpKVHTlRMu8pbN7G47wVNcoptIoXvQcIMCU3b6RYvHrCiKogC77bYbXnrpJVx11VW46aabMGTIENx11104+eSTvWVWrFiBJUuWeJ8ffPBBNDQ04Pzzz8f555/vzR8/fjwmTZrUqv2HImoHqbQBWmO1NLAoI8N238suPQ5a5SAeJi545513AAAHHnhgB5dYUdrPk09q+ImiBGlySZFCkRpvXijVqu9ScW9082uR6g0A5W8BAJT12REAkLt+HQAgxwlCDZXOFrHe/n4EX2K3WWwV/3855X1RlY2vv3XtV15Prwwx4e+SNFOoqfGPgbx+lP8CPLrArlc41CqkuYPsC3BGP6u0l/bf1YbJBNZvampCkzueeteDUOdi9ssCse38jQz+pspQH36W4Tc8lzJRE0N/uD4HtcrQIaX1HHHEETjiiCMSfi8fxqdPn75hC7QB0Af3dvKPnOEAfCsnqa4ng1T/OKV7hT+1jTmSYW+y4excbxuTP1sAAN7AjB3S7U2zfJ2dctDN5powQ1E6gjGPXOf9zx/LoFvM7AutQ0xGzPgSX2mHcHX22qfnzRzdyNkzxthGv4fMzk9x7VVOASDi/KvZlsPZToFXpV1RFKVTYRX3JFxl0L7wK31w30Lo2dMqDMnG43EwDBUBKgHSzooZ32hdOXXqVG8bzb3VKsqG5IknngDg9xSxngaVOamUBRXEzZ27774bgK/ABa3oeBxnn332xi+YslnD+zkA1K2n4u4/SIRT7fcp9XVR6zH2faeeWUBZOYrzh3ttiy++tbW1KO+9MyKRCHrXWMeaPNfeqLSbgHLlhXouXAfAf0m+rrvNRnn9qs+j6jXgt1UqzmzPbN9BC9cROf4L8KDedsxW4VDbQ+xlSnWx+XIwa87P1sK1utyWra7cnqvacnteKlyMe2NjY9zfVJ4bKQ7wd1Xea3icXI+/p1WuV4T7kOcjeD0VheiDu6IoiqIoiqK0A3WV2cy5N39kq9eRlnSx9nMiVCbTvrWn5TibuTzbrU5buXCW7+4aqY5+w2+qWAfAV11qy60ywNg9KgJ8w+dn2lNRoaAiQKWAqakB4N57raXkBRdckNTxK0probJOxU0mS5KqYFAdi5dgaes7L0YkEsF3f7jdzouKSXUKoRuEmhaOf3MNh6MHj4dTOXUhMu5zaraNu2VoW1qeVQXTcv02FM5yITKuLYdzCgDE2ufxOIPHw/aXkpKCM3a2CmPTOms7l/FLVeO7Ipdccon3/2uvvQYAGPXt6968iBsg3eRi0qlZh8K27obSbJ1tzBnqWTKWlZXZea7ehcNh9HMuLal1VhHPddszcdRpDtYLO8cZhqTd3s8aKlyx0k9gJm0P2e7r6uo8f/OiLPt7uF2+b6HYfbhV2unbnuGyo0a6943ZrjEGTetL7XZFbHu9829n9tKmpiavDVJdB4CVK1dGzZOJDelGQvc2aWvJ+fx95XESbjd4PZXNn1AohFCC342o5Zra9+Cu+XQVRVEURVEUpROginuSvNh7OwBAWYLBnb6aHpzLD8Z9F38QKhUITjMy7WXxE7lYZYFKe2q+U+fyu3t7aqywb+x882+qXAcAqF1nR/9TcWfsXqLYQs6nEkCFgOplZWWltw5VwIcffhiAr8ZTLTj99NOhKK2BCruMbZWKlEzkEhyAKqHaFlTpt/rHBQiHw5h76d3echHPvjV66n0vbFrZI8bBqGku5pbT1CyrXqblOtXNKe2RvAJvm+Fc+z/b8r1vfRV1XM0lXjHG4Pd72MQiDauW2TK7DJe0nKxZY9XSwbdOSrgdZcuEinndev+eHUl11qTVVmE2TlnOcEpxKN3W2X5rvwcAfFqZ68VZ896flZWFGdU2nnt3p7ynu/YZL8MnFfeI65FKW2p/k7LX2t+Whwda5X1tnW2f1cLhISfFtrft3e/gYJforMdI//cvf0gPN7UKe4orV0nBVt5vGttUXV0dMtkj7WLbmQGWv4/1rgiNjY3efWPNmjXe/vi7yN86/l5SSec6PGcsg3TToUIvlXteO6VzEY6Evd+HZpcz7dPMVXFXFEVRFEVRlE6AKu4JePTRRwEAA/7+LwDxUqbbz3VNIfG9vw3//+hlfKXdbiTbTfOccpdZaFWPrB7W4SWje66b2ri5iFPn7p/+vbevESNsMiZPXXQZ4WpLbQa2OqfIM3aPCgDf/Dnlmz+VAioHTNG8YsUKb599+tgR+8OHD4/aJpUNer8vXrwYAHDGGWdAUeLx2GOPAfCVLKp8Xg+Sm7J+s7dH+ie3Jo05ABTd+ntP9Vp25X1R33kKu/ucyKaVPWNpTgmksp5emOumtqcszeVEiAR6yphoiW2Z7ZDtTqaM5zJnjbLKIpX2hhKb7a+yeC0AoKrYtv/qNVa5m3mYn3+B2SHZC3fQd5/HPTdK54bZNxsqfaelRt7vXaw7Y9LpPJPOzKoZVjnes3cegHQ88fFPnsd60Onoo/IspKWlYXSfQTH7p/pOlT8lowSAP2YrfbktX481ts2XV0b/RhEq7nn5dv/5g+2Wuw3v4S1TMMwq7Gl9B9p99RoQVVbAtpvcFd8AAGor7e9ZfZW733i/j/b+ccuaL1FbW4vGxkbvXkRVHfDbJ+9X/L2kKs9M5D162DKyx5DtW7phccprxqnSuUg6AZPRGHdFURRFURRF2eJRxV3w1TGHAAC2XuNivJlExcXnZYo4WMalx49xj0Yq7UwCU5gerbRndrdKe1YPq9xl9bJKXUZPlxbdjZQHFnjb5qj1UZlWSahaZ5U3b9S8y1rH8EGq41LRlLF7VBKWL18OwPepBYBhw4bZ8jjFklNuq3v37lH7oqo6fvz4BGdI6Wo88sgjAPz6RiVK1kvp2iDj1mUGw+D/8ju5LRkfL8eisI2ne7HsTjF0MbcZrt2mF1iVMrO7VQQz3DS9h223jGMPF/b09vXQR/PjnheWTfYgnD3aKouNq63SXl9i3S0qllk1s3KFVfyqVlmlvXKVbb9VJdXeNpiMrdzFFD9YuDUAoM7d2y4omxO3TErn4qyzzgIA/Himn3sj5DkhOcXdqduhcPT8cIatR02Ztk737t07JtY92BY/rre9S3sGlPcQncucq1Ikw7YXuiplFK4D4NfNPNcD1FgX3R5T3ZivzO52vZy+tuc5b0hfb5msQVZhj/S2ivvPqX1tD4ExXvtfvXo1sl1se6Pwb/dcZeL03PH+EBxnQ/Wd54L5T+gmw9/RhQsXAvAzkPP3k049XD8Yfw9ojobOiiruiqIoiqIoiqJ4qOLuWHjpKQB8Na2+0k5TMhrc1L51ZziFwh/4bj/Tgz0zTnJGGSNLBS87i8qdVSSye1l1I7efjYnN7m/j43LcNMWpCR9V5MTsw1MPS60yXrvOxsgxE1ydTNMuYttlJjiqC4zZoyLQz/njAr6SzildZYKZ9gBfjfjqjBsAAAV/+jsA4NfF38Uch7Jl8/jjjwPwXWOkwi79y6VKTloT2y4VdRlH7uUwEG5P7BFjjC3bqT+l4u4URE9pt6oblXbGsTOL48OfLEqYxTWRi8zZo+y6VNprV9qxJpXLrG87FfeKFVZpr1hplbzKVVYZLK3wMzCWulwOdMiikwbjey8MFwEA7mlaFLcsStcjKyvLc0yhh3lrx5NsTIJlKy624z/Wrl2LovREa0RTXV3t9WLHa6syKzMVdc7PybG/0ex1Zo/1/Pm2h435UhLd35TOycZyldEHd0VRFEXZQqkt9wenMlQm4kJAmYiJcNBqiguVoT3k3r3t9KOyjGb3tbzH9gCAjIwMdOdAV5fUKc+F3dDWmC+42c6ymGGdTXXRZWICMw7yzu5jH4Yz+/b2lknpWwQA+GBdBoYMGZKwfE3r10XtiwN3G7zES5vvy4iikC7/4L7u4T8D8B0gGmqsMtVQ7ZRAp7Ab16C9LHBuBHymU63Y3oPez36GVPs51XOhiB8jm9Pb3tiotOcOtDcmb6R8bxs/WL/MljGoznmZJSutj3NNqbsZVkQr7iwLlQJuQ7p4UC3n/G7drHpI9Ty4LpUY6Q1PVeHfOx4BANi/p11u4B42Tnf5zecCAAq2s444WUf/EcqWx6RJk7z/pWsM6xDVLamO87P0a5c+yNwe58dD1vmUlBQsuuoB7/seLoadCjunGbkuj4LXXjkGxcW09yywnzkWpbfrIXNjUai0P/D+3JjYdRmjT8775WgAQMOqpXZKpd2phxVOaa90Snv5Uqu0r19h233ZSvtgsrLW3sdW1/rnlX7ZW/3zUgBAfoLzc//99+Pcc8+F0jm5//77AQB7deA2jTGeYwrbLH8bgNhxTpsC7ptx5CUlto2wvMmwdOlSDB5scyTwmILHyX1wm7LnTx7/wIH2N5yx8SxT8F4E+L+ZvHba/joZSca4o50x7l3+wV1RFEVRtlTqKutj5oXdw0VqdvQDpqfIu0GqmU5xp2q+T+9BANLx7tLaFve7utcOWLx4MXbr67aRZcWxcLYNKUvrVgoAaHTWh/VO/Za9AFTcOahVhp4BwPuVec2+tO+VXwOgBnWr7D7loNSGavsyK0NKFaU1hEMhhMMtP5SHm0mslwxd7sH9ueeeAwD8uq9toGndrTrGm0Vjnb3JGfHG7GdLdA3dqeaNTnE3cbrYZMa4FJERNcNzkbFdgFm9CgAA2f2t60R6XztSPqW/dW/5qMRub926dXafAXV/vyw7r+Zn+ybPGHd2ARI62sj4RHnTY+xxXp69yXK0fFC1oErAkfJUGbitf29rHXp+2d9uY/B+VnXoM8a6WORss409H0Nt9yqvzXHHHQel80OlPehJnCgmXSrtLSlYcowG62VzSp/8LjU11YtjB3yFPdu121zXQ8aYduZVyHLuFpmuvbaktD/00XxEIhGkpaUlPH5+Pu+gnQAADcVL7NQp7TUJlPayn11M+wp7jktcTPty1+6L3XT4fVd5bbpHAnedeM48qvx1XphnY0OQlpbmxW8znhvw2+HSpbanKNFYjo4mJSUlZrwMY9ube6BPRCQS8eLSBwywv8PB4yQcAybbknRtY49g//62t3ntWuv6xrbG8WG9e/vhP4qSiC734K4oiqIoXQUm2gpCxf3d/Q7BDjvsgL6P3AUg1i6SancoJXq6T5/+AMrwQ30+WuLLikz7ABvqh1AohFEDC+w+XLx5So0dSJ1WY186jXiJD7mXgXCWFYDCuXb9l3+yL609e/ZESzC2nVMmXqqn4u5ecFVxV9pDKBL22lCzyzXp4NSkeOONNwD4b7SRni7WtMm5xTRROXeqsbiBpeW4rrWK6IbeWJdY4eMAoHBqtP9zmpdZ0b7BZ7pBOllu0E2kp30rZ0z755U2lrax0XYl8m3+3AO29/ZVv8A6tDBTYr0bfCM9cTNF/BXf+KXqJh1jpJIA+PHu0qXjue0PBgAcsYPt0hywl40V7DPGKuzpI3cGAPyYZXsSGtfb9XhteK1++ctfQul80Jud8ZxBNT2RIs56J2PXZUy7dJuR9TXotdxSnG1TU1NUe6Dinu/aa0ah7Uny8in0EEp7zwRKu2u/j89cjNTUVOTk5MSMJaE6yHKfte9IO3+lVdob11i1sHoFfdqj3WPKf7ax7Oud0r66JFppX+bG6Ozw4DVobGyMUtFlPH1L4wwA4IEH7FgAXgf1md68YG9l376+tzmzdjZHWlpamxRpkpGREfWbwN7geHWoo6GinpvrMhS7nl9m+W4PxhjvGHhMHMsFwMsiy14N3uvk/UmOveGUsexcvqioCICv6nP9Dz74wNsns5Zrj7TSZR7cFUVRFGVLJ/znqwAA9TRYqPdfYJuEMDVqyrMAgGoX+hmjuKdFzw85pxi4B9Ah3QFUAiuyByddvi8rs+2Da0ah9zBcUlKCMb0ZpioUd6fyv7+i3pomVBr89NNPSe3LxrYD9UttSAtDSL0Y9wq7z9o6jXFX2k84EvJ6s5pdrklj3Jtl2rRpAHwlgm+y39XaG892vQdFLZ8ZdllAXRdhSrZVlevKbXce7aMa63lTjI4hDwXUi0hadHdjqtuWp7g7W6yUbrarzxt04xS7nwxjCO0++LZOhaNxpZ85lRkUa9bYkfQ1ZbWufC72LhTtSf3XPmMAAH8u/ixq21Q6qV5IZTCorjAu78Eh+wAAdsq36xywv72J9919OACgcJcdAQArtzvUlpsqarWfzREA9ip0zjhNLk5/+pNoLLVKozrObP48/PDDAHyPdqmGA359klB5Yt2QbjLS510q9FI1BnxnJEk4HMbSPz8IAOif6Zctz7WNTPcQw0zGjG3P6Ga76hnTntknvtL+wIwfvLYh1W3punHGnq7XaeXPAICGNVZVq15u3aG8mPZiGxObrNI+8t4rEAqFonogZAw7yyLPYVCBlQoir8uDD9rzp8r7puXRRx8FAIwYMWITl8RCdTpRVmIZ880xVInyFwTJyMjw1gu2c65LFZxuMu2JrzfGeOvHU9zpjiUVcs7nPZDtj2Wk0s5eAh6PzIESrxeEzzC85meccUabj0/p3GzxD+6KoiiKsqUTudZaG9e5l7d65yYTDOek4u6tI2yPvRBR2h4LBT4muYx7EO/TYB9AlyGrzeWfWZbmXuzD4kWx3nsoT5Z9e4cBGDSstC/ATeVWcafSzlBS37/dvVCo4q60g1CSdpAhVdxjefnllwEAhw9MxT55wHeR/t4bL9+Q+Wa7IGTVtPQi63jSN3MWACCcbd+I0wrX2eUrrNJFn/dGN5XuM1GKO1V7Nw27t2oOsonkW0U97KbFhTbO1Rtp7qZ8a+d057pF9rNznACAyhVWkathjLt307bHyWyQjN+lCc4d/XcH4CeeaBRuF3I9AOiZbv8f5PytD3a+7D13sIpAr12s0p6xzSh7XP12tedBxPkNrnHOGavs8dS646l2ll0AULXa/l/7v+MBAMP/9SyUzYvHHnsMgK8yEdmLE5xH5Y0KvBwnIZFKu8wZEC/GXapWwW3IeHb7v1PcPbcnZkS194JMp7R7Y1Gcawynd7/9tbdPqv1SxW5oaMD40QO9fTITKt1jqpzSXrnCPmhULLPT9cutwk6lfe0aq+ytdP7sdI8Zcc/laGpqQmNjY8Lj5zmm+ieVyeDYgERjCriNf//73wB8BVRVwI0L/cE3lyymrHMysyiR9SnoOBVvOZndmATziZBolb99g//C4XDMMVRWVnrfsx3wnsf6z/sPlXd535JTvpDI9sW2yZ5vwI/d5zVXui5b5IO7oiiKonQlqB5TtKF/e33AEli6Fqe672hnLG2NqR6GxAM4dXXGo0ec4r6zE6EAYFZDy4NjO5pROdVATioaS+0LcGMZY9vXR029xISV0QkKVXFX2oO6ynQA9UvmAgC27e3HUv+cvUuz66zsa1XivnmLAQBNFesAAJFKq2Sn1jlVscENpJHWVQFlgEkrvKlL+RzOKQAArO0m4hITxOQOMTbOtX79Qjt1zhNU5wCg2inTNaXsArTla3I34lR3A6ZHddhliaTKSBgLn+Ep7c7buruvcOQNsD0GBUNtJrnCEVYByHW+7Cu3sf7tnmIhjqf3vOm2rMvt8axf4lTGYnuTrVnjuwJUl9rzTUuz4VCU9iM92wE/Q6qXX8FlSM3sztj2AgCxCWDufmd2Uvs8eWfbI9UQ6Cmj4l7l6n7FUsa02/YslfaytVTao33ah919eVJlULYM2NOxjbvnpqSkIP7okU1H0FudyHEWjPGWvucyJwiVdy4X3K435quDnWxkmaoDY7Ko+LNnjTHqVN5lzD7LRkW+tNS2b6me83gZTx/sWeD+qcKzDpxzzjltOTylE7NFPbg/9NBDAIDRo226cCxaGLMMGxYrv7RpSmaQTEcjb1K8KXmD6zZ+kToEOaC1I84tr/Hvfve7dm9L6RhohSaRP8JAy7ZorCPSylHO55Trx6tbiSxOU1NTUZfksSVDOBz29sF98sGEXentsdxLhvT09Jh9kkRp6GW3fbzrJZeVXfr8zHvWxIkTAQCnn356ew5HaQUp1/4ZBgHFnTHu7nNFg389paIcaXAhpI3OzMDFentKu4h1l2S45dMoZNX5dW+7HBtawkypnH6+tuPbwn49DYBKNLqESJ7ivs6GkNaWOjcZF9teWx6dMbWmKX6oqKK0hnAESbrKtG8/W9SDO8l//B8AgPUuA2lOjR+b1tsleUjpWwQAWJm/VdxtrM63bg8N2c6vXcTa9a20ijykdVVqmvf/l+vs6eUI8m7durXqOIa4GPB6EQdb8bP1r61ycbAAULXKjqTnDYk3b8Ibb06KcVM33ynrEafApzlHjfQ8eljbN/7cAQXetnIHWr/1vOHWkSetyCntrrcCQvnotuhjW6bFPwIA1s63WfXW/+w8qlfY3ozKlfY6VZX4ykbNevuDwh8fc8Xt9gt9cFfawPzL7gEADHH1PBjjzgypjHGn61OGy7OQ0s12/YcL7H3lXzPmJLXP3+3l7iWu/TYGFHe6xrAtS6W9cpVtE77SbtvWajctb2jnL4CiKIrSIYTCIYTCLT+4J7NMc2xRD+5bbeUewr+flvQ6iZR2ab3YFpicQQ5ASQRVq/Jy+yDbr8173rTES9akbHkw0RLrrWxDwYGiJFEPl1SkpRIvB4olUovjwd614ECvjiBYv/k/y83ybqxBg2lpaTHnhD12MoRA9uxJq8d4JOotk9eT11yV9w1L0N6YrjEUaxqE0l4ZiFuvlq4yTAzmFOf8VtbXJmGLnFbti2Rh93+4yoY+MlPqLi5k1Ashdd7w88K9vTrLQavBdjSmWxNQmA5TV4P9eg6G8QS5CjQ6UwZmRmVse81aO9+LbS+3L8BebLub0pyhPs7hy+RpgC/Gyd5GhrZwIKu0g+Q9iJ+lHSafFaTNJODfY1iOYLItpWuxRT1ZpV57LQCg1Clm9c5znR7sAJBb6eLE3E2lex/boNcOGN2qfa3KG2r3GSemrVr4kydL/2rr5dyn3Ho517vsifRop3MM49mpsgO+Ss1Y8IaaaMU9JSMlasqsrvzMrK4ZLq43u5e9cWT1sb0EuYN6e9vKGDQEAJA62CrtPBdSaS+Y/ToAoOInq7Svm2uPr3Se7cYsX2rPfTk9qeOoiGX1dh5/hPgD8/tQEQDgAbMIipIszJTKGPeMgI87e5nS8+z9g0o7p3SBYow74Cvn8Rg/pgiAr7BzSpUdaFlpL3ftuqSOSrtt12vd51H/vjquw4aiKIqycQmHwwnDyqKWa9TBqZ7yN7Id2+AbPpUjPpBTPeZUqlTxFDUOVJHxoFIVlMllOivyXKjS3jXgC6ocOMZpvPpthMWp7Nliu5O2kVxeDmqTy8eLJZdtuKOgKgb4bV6q2RuaxsZG79zwXHAqk1klSu4ixxcE72kykY6M5Zf3No153zgw7DIcDqPeqei0/q0RSnswxp2KO0V4huPWuYcNb0nnupLoIYSx8ExE2FjnHGwq/Rj3NPd/Wp4VaJqosGc4xT09WnEfkmKFKgTrZyqsV3wj0LDSJWVzcfSm1t9XU5VV1hvd7zgzpFa7hIT8TGGLzjs1QhA6a+bzMccq6zzg13c5pkXaQrLdcH6ihIbcB+8jUl0PzuO2Wht6q2w56BOWoiiKoiiKorSDpBMwJbFMc2wRD+7du9su7LLF9q27RlgI1pb7b+bMmJbrplnuTX1AT9tVHXHpy38M2dAQvt3SnilRyvXmSJSMIm/F1wD8uLz6dbYLnSPimYiISYiqV9nlqkoqo44T8GP1GCITzJYH+KExHISammAQKq3vsjmwd6CN00/tP8zb1vptDozadsipAv2q7GDaurlfAQDWfW8H7635zrr7rP3JHsfahfY4fq6OtrRj9/+53/zHbted42CsNB0zVq5cCWXTwHT3RNZrfmY8ZzB2Wqq/UoGXvVRUtGS8vEwERDUqnipGlSs9Pd1LKEa7Uw7GBoD0fFvetLysqCntW5ko7aVvV0TtM7jflJQUHDHCJmpqcLatDcLyMZhgLPkQGTtlG9n531d7x864YKnmxXPTAWIt9hKNUQguQ6RSKHsLubw890rHwGRnw4YNQ8oN1wAAmuqboqaM16a6HlTc+T/dU2SMe4yrSlm0QxGh3zt/Z5iQsCGguDe4sFQ6uaRmW4U9JcOq4MzGSkMHKu8Ii54xZwBBz3haMTfW+PbJVPo59X3bbbuiTTLthWvKok0PeK6GDh2K1audJauLU2ddD4bArnHuNfn5+VFFlfdC1v9ly+w9YO1aG+4q71/8LHv6g/cY9vBJ1znWifHjx0PpGmwRD+6KoiiKoiiKsqlIOgFTEss0R6d+cH/00UcBAOsv/hsAINOdjOxq+0Ze4KwRaZFo/2eMW/TA1ez+VnlPc6PRhxda1TucW2CnsEp0SYF1rmkuXlbG9hausglajLOibHLJnOqd0t5UzsRDZVHTKqewV6+hahDdk0CVHfD9aJuka4BT2KXSTsu7rB5WVczsaY8zxyntWQNsz0OKU9rXD98v4fH2XGsHn9bO+wYAsO5b+3n111ZpX/19CQDgZzcIdVGVvT60tjvzs+ds2d354kj9eHHLdO3o378/AL8OaKr1Dc+kSZMARMddArEqk0zbHfyeahG3QRVLJjmRKjGncnmqTvHGnEglua6uzlPcOeXg7OD/qbm2TdAOkveAqYts+4sXMx+MH/c8pEutclez2qpsVU5pryxe661XQQvUNbbOV65pfjDqro/e4B2XTC/P+VJ5l+eQ89mLwV6s5lxlWupZTOQBz8+aLKZjKCgoAGCvB1Vv3vMb3EB+qufVzcS4++4pzBjqFGAhuPOqR8qjfd7lvhlf31Dj3xvqq2y9Ss2yvzWRTHs/SMlwOVRSaZSQEvWZ8OHGePuMjqtvqvN7cxqc+k7Fnyq/VNq93073G1QjMqbW1dWhZ8+eUeXg+De2HyDxfYdtj8uy/g8ePBiAr+KzzfF+RjeZ5p4rpDrPNsk6oXQdOvWDu6IoiqIoiqJsakLhMEJJhE8ns0xzdOoH96FDrQ3h/5xyGwnZKZX3Uhfz1yNgjVhXwdHvLlFRZbQCn+3e1DO6WVUsklcAAAhnW9/WghVWRQ6luERLvAB0VQgkZOLo93o3+l2OfK8rd2qAGPles8bG53nxeDFKuzuGmsTxo6kZ0Zc2nOrijvNEWvdWKu3BONj8lbYnIZHSXjzLKo8/ubjdec6p4Ig3bfZTOQJfxjVLh5/gPCq2rAPKhuPJJ58E4CtPiUikOgWR15SKFBUofpZuDIliR0k8xxS5f2NMrOIesINMy7ZtOjXbjvcIO79pZnxsLLF1TjpJAFY5O2lHaxPZsHwRAKBuje1potLOMSqVK33v52qnsFessvNkLDunez19m7cOzxXPgTxXbE/SeUf2BCbK9hpU3hNlSE2krCdyyuI2VXlvH/FyI1D9pmpc4ynu0fHbAHDCe09i3bp1AIAFCxYAAHr16uVNXzqo+Z5L6fNu3L68rK2VfgLC1Gxb1rQcW19TMlwPXAKlnQp7ojACKu5U2qm8A4EYezfl7zx/M/3fUPs9eyH8GHd7HKtWrfLUb3qsU11nfhXA7xXmMlTS2cso7z1si3SCWbXK/jbK3zquz/bCmPjgPmWm9Xh1Qtmy6dQP7oqiKIqiKIqyqQlHkvRx78ox7sxexnhQLx7PKe9MshJvVH1fKgTV0VPGxlGJzii0b9kpWRwR71SDtFg1EQCa6vy3XyoCDWLEe916+3YuR7xTFagurXafo2PZK+ujPWeDDgB0B6CSGHbnhK4ZqU5Z5GeZzj2zj03nHulpY8crRowFAFBfj6ek1S+2Cvv6n6xyUzJ7MQBg5bc2tvfHlfb4flhvy3/kf++LOlcyhl06VtCVhN62gK9QUIlgHVA2HFSapOOLRDolyFhswFeH+B0VJq4r8yQkmp/I/z2odPG7YCw4NTOvnQRsucJpTFLmykS/6axcV4ZoJyNu9/jRRfbYls4D4I9ZocJevdpOK1fZ9lBV4ivuTJxGZb20Plpppxd3PBWcSN926bDDcy6986VXPomX0EnGz8t4eOm2lQjui85EZ599drPLK9GUl5djzDsvAADWu3lNrCPu50DGuAezgebk5Hhx1cE8BIBtH0e9/QhSUlLw/P7xHUpYUxrdPZ1ONqnM2lrtq+AplW4shVPhI2lU3qm4u1wAEbZFp7gnCCMwHEfhxdX79ZaKf4OXPZaKuy0nXWQqXWx7ZWP0OeI5S09PR1mZ7QFfssS6QvXrZ3OYBx1eEjkzyd4t6Z5FeO75G8fP3EdxsfW0Z1mA2F4tqvzBngBlE5Pk4FS088G9fWsriqIoiqIoirJR6JSK+wMPPAAA2H333QEA5zvf78JC659MFQ+wb6c3993d+8x4P76r910a7YHrx+q5+PI1zm3GeTp7alyC0d+NQcVdxN3RQ55v/zL+jjHt1S6Gvaw+WnWTSnskoG5lesqhc9Zxn6hmpGRYJSAtx5afintWrwK7rUIb457iFHepeAYVhII579pyLZwPACj9wSrta+ZYpXF+cbTSfvJ7T0RtI5FaSvWBKjpjDYMqHmMF5ch61onf//73UDoGOvZQqeX1kG4m0mWGxHMp4bWW6jzVpHhx8UBipxTmV4gXCy89keMRTo0E/ndKINu4y+z49JdLAfiqGPfhZRVdb2PYmY+hxhuzYjVR6WxRHci/sM45YHE8DpX2ctczuOekGwFEn2PphS/de2Svhjx+GRPL68jt8DoHt8n9M85WXlupMMrsufHuI0ry3H///QCiex/bQvfu3T2FdsCAAQB8ZblvXzu+qSt774dCIc+bnbHt69fbdhxsR7wOnMdlpQIv71scJzRo0CAA/m8ec9FQRec+g22VYxNk7zfLwDpy7rnntuXQlQ4gFE7SDrIrD05VFEVRlK4ExRiZfZGCDqcUei6f9w6AaEGrJY56+xGkp6fjmf1Oifu93FdOPe0gA6EyGQyVcSFaziCBYZt8eJEhM4mQFpQm4F3ZyP17oTIu9NXtmyEyZe4FmaKYFPIUpTPQKR/cPaVLOEnwbTVIeno6rls50/t8fa/d4m6zt/MY9zxx3Q0gPd8qTWnr7LZ5k5Ges9JjNrgNzwWmMtpXnsqbvKmUx4x4Z7Y7d/zu/sY4XfuduymKGNOIUxTpnkGP6oxCq2qnFNhR7swYW9pzW/tZqKr5iz/1tlm3zCrtZfNsNriSOc6nfYlVcuY4pf2PP75pj1sogIQqBBU+TqlCEKoZgH+NpftIc6qq0jpefPFFAIn99KXDi2yPwVhQIPq6Sy9xXlsqupzPOGvpD871ZZ2Jl6mT5QpmEJU+1UHCwtUilMYen+g4VVn3TKVVx5oq1tljcW5R/hiWaHeoigpfPS8VDxJs8wc88/eo4wmeU56zRA48RPo+y16qoL99wnMiehdkbG8i5TxRZml5ndhTBmhvWXOwnrfFs5vjgQYOHAjAdzZhhlD2JPEznVK6oluJMcars3TboUoerOtsa3wZkm5LPHdcp7TU9srxXHN9Xgt+Zmw71wtma2W5eG9kG5P3QGXToXaQiqIoiqIAAA6c/z4wH6hyohFfNMMxyrudUnFvD8e8MxE9e/bEv3c8Iu4+OOUgz8yAEUQmxa9Kil3RCjvLzc8kFI6vvNN6Mq7iXkfhzD7w0ipZJqGKFcXs95fOfVsffJVOQ6d8cOfb6Jo1Nqa6Rw/riEKFMIjMcnjjKqu+/7XPGABAWtg5X3AF56fMGwEdXejxnKhbj5njmur9t3LPscYp7rLbTqptVNx5U6Ef72+nPwbAf/t++VCrTGUHykD1Xd56WF4vK2S2U7VdjHsk38bWlQ60PRGhBF7PDcsXetssm2+V9tL51ou2dME6AMBcpyReu+rzuNsgieYTGQcb7EmRXrasAxoz23FQHaKKFIx5Bnw1iSqwVJ0SKdPBdaRCJXtO+L1U/fi9dG9gvYiXzTToTFMnMiWaOBK8725ht1VTE53tkJwxbnu7rUU/2LJW2vZZX+WmldGuUGz/8VyuON3uvivtZ5GtMahcyyyLcpyA9GeXn4m8N8pzCfjXVrpiyOy4svdT9r4kcp3pyvHUyfDwww8DaJ97Fn83WH8ZT82YaWYKnTt3LoBYt5muQFpamnd+5L2E54Nx54DfHuRYG3m/Yptkb8aIESOi1uO1kJlU2U6CvWhyjJFsc4y7Z50566yzkj8BSocQioQTjn+MXq59zyud8sFdURRFUboSKRk2hJCDpyNpDO2KH+vekfrxOd9MRWpqKu7d5pCoffAFmOYINU3+CybV7DT3UiqTn7HYYc+atfnwASrtTZ5Jg/+dLI9vhRmtuMtpTQf0SijKxqZTPrhLFYcxn5wfz4FBqk1ssGzAmZHomwvW2TdgKnJUzT3F3S0nu+8aqv03KQ6Qqaq1UxnDTgcJluGKnz+MKqOME+WxbLNoBgBgwpCx3rKyW5Tl5M2dPQbpBS62Pc+OnA87xZ3njOfJ85f9/n8AgMrly7xtly9cAQAoXWCV2Z9cTPulxVZpl+4xRM6XKp6Exxu8njJePhi/rLSPl156CYCv9krFVsabcz7rCj9TPZJOIsF1pbOQVMdJSzHUzcXhynIbY7zxIp5DU6CHzHswaIx+5GEPhFTZTL2rlw22DE1ufEtjtXOREt7W7L4PPizI8uy9994AgEWLFgEAli61jjbB80D1lddHOu3wnMgMq1QFZQ+JvAbBkAHZiynbsHR+koqhbKeS4L7uvfdeAMAFF1wQd9muCGPa23N/47q8NrxmvXv3BhDrKiPbpizLlkgoFIoZ+zF/vh3LtcMOOwDw2w/gtwveK/v06QPAV9Z57tgWeW4Jz71sN3K94G8o/2ebkk42vA/reK9NRyhJH/ekvN6bQZ92FEVRFGUzh2GOEZcojFamVKojCV6OOpJrVnyK4uJi/HvnXwNAIPTMlqE6IINLhV1O5aNLC6YyHtxF8PVaWiVL5b1OCHV8eb568XvJ7VRRNiM65YM73/zp28631Hix0zLbWIyiJwbysEGn8Yv19q021cWIh6qjYzKpuDPGvSZO/Cp92GUsO5X3obedB8B/w992W+vswrdwqUx7GRHjCNap7vDoJpPqlHb60HNKpf21pfZ4ts2z+2LMXf7K2QCA2tVWaV//s581ct3idQCA4uVWbVjoYnhlORMp74ng8jJGOnjNZIwzp5o9rv0whlP6g0v3kURe3FLhlgpW8DteR+5DupZIRV3WLanQx4sFlw4m9fX13g85p8FMj8y7QOXcNNjPhx56aNQ+PNejaqtUGrrLcNAc7zlC1a+L28Vvp2zLvKdRkWOuip9//tlb55tvvgHgXy/pOMLy8RxxOSrwdA2RHu3xnGB43mUsuvSOl7Hw0v1JEq+3TQcHxsJr1RFjeKgSUzlm7LZU0nltGDPN5RK5g3VmmpqaYs4t6/LgwYMBxPYuBWE957nhuaIazil7yXiuuZzMj0CkH3xwW/xOqvK8Xu31+lfaTjgcTup5J9lnokR0ygd3RVEURekK7FNtBz/XZtkHxxSnvNMP3XNr2fCCu8eZn7+A7Oxs3LXNLwEAdU0uJDXwkp4opt3/LKyLWyi/72STOMZdCnFyylfDPy2YljB8S1HaiobKxIExkEv+eAcAYIZTuKhW/f6rlwHEjwuTjTRV3ETSwlKJ543AzXfxqRxIIwfIMFY12FVY2UjXmGj3GCrw53xpY4pnzrRON+++azOSfvHFFwCAcePGAfAz3MWLQSXyOFIynaKZ7ZRp59/OGHe6yQzqbr19+TbvxaY6pb162XIAwPolq7x9lS226vZ8F/d/zYpPo8ol/b6lkk4S9YJI5S94PWX8HlVQOiNonGzree211wD48ZryvCe6PtIRJlG8eTBW1vM+d9ee38n4zZZ+VLlcc9lRpTNNfn4+9nz8L9hll13wzl7H2GUCCWOY4ZjKu6mzCjVVshiV2LnOhJgN1nOjCYvFkg9lkBlHeY55DwCAIUOGAAAWLrROT7x/rFhhx55QradCKHstZDyt7LGM54VPZG+L9ICXPZxyPfk5OJ914J577gEAXHjhheiqvPDCCwCcY1p1Cwu3gPQhX77c3tOZvZP1hW2Jy7HeU6Fn/DZ9xNk71BkxxiRUrr3xXUIVDyrZMs8Ee3z5OyR7m9mO6NvO73ktWAa2TU6bU2blfVq6fLEOHXvssc2dii2av/3tb7jqqqtw0UUX4a677oq7zHfffYfrrrsOX3zxBRYvXow777wTF1988UYtZ2vpVA/uiqIoitKVCGfbcJnU7HV2mmUfGlMynCjjlPdEItSG5MbiT6NevhjykZaWhpsH7gPAj2Wnos4X2Lb2EEjlPThPSlqXz3vHexFNZG2qbJnMnDkT//73v7Hjjjs2u1xVVRWGDh2K4447Dn/84x/btU9V3OPAN97+7kYlkyg8MfpoALENO173Xc90u42clHDUNKMFyyqptMuuuGjHCMbTRpfzvK9ftdtwb/yjR48G4MeuMtb9mWeeAeC/3dMDlhUxNXDjy4xEH0d6nlUNMgqjM6WG87pHTeFC66gc5BV/C8CPba9YZlWWssVl3r6Wr7ErLa+JVkelSipVtUQ3zEQe0TJLZhDpGU51ROP7Wo/0eZYqj/QBpwLF5WQmT16vePHR0qddKu9ScZfKs1yeSlU8H2Uv62++dVDaa6+9vGXZC8YsxsH/6yvteWiqtvGrfdcvAAAsyy6KKsuCsM2sOCjDxrqnulCGSKYbV+J6u5hDId05PaXV+TG1vM+wLfP4E/V2AP75ZyZMKqdfffUVAKsgAb76J1V8bpvnSmaBDCJ796RbDMsiyy0z48rtNHd8mpMhNjtme+A14rVg3DwzqjIPCqdE+vvz3sqycXvB9p2ox2VTEXSL4X2O9Z3llk5q8rh5TME4dNZvjgmT49F4rmQWcJalpMRmG+c5pGLP6y0VfSC254yf5b1SbqMrUlFRgZNPPhkPPfQQbr755maX3W233bDbbjaPzZVXXrkxitduOtWDu6IoiqJ0JcJZTnTxlHf7kMdYdyrvKc6QgEJV+zS99vPnJe97D5F8GZMDLOULuAx9S2RYEEyGxIGf8mVQY9i7Lueffz4OP/xwHHTQQS0+uHckoVA4JlQy0XLtoVM+uBdl2cYtFe16oYLHQypcVKqZQII3Pw74kdApIlwXrQrVNVGh9/fdUV2DiaC6Hvy/+q5rAQDvuix4XjzrWnupry20KtzXlVR0orfpxbavsC4yFcusMlD2s3+jXOQyv/61xMbiq0KmdDY43mR9IMa9W5lzgii1db1p/To7rbBT+DbOUfCBKpJXAABIL7DLp+XZB6yMQqvg15Y7xTOwz+pU2ujZKcMLrlv2UauPSVEUpaszZcoUfPnll97Yny2RTvXgvqUl2ZFdXEz0wEFm7JqjGsFQmtmzZyfcJpeRyVkShZC0R5WQCVwSDWKMlwgnSEuDVINd+DIJkOzu3NLqyMaAlmZ8AaPqlahuJEq5TaQ1I18eg+vwmsuwG1lXiAzFkAPG4qlsrAsMkYnX/dwamCJeWrcVpcZZuJ1wXwxrCJKoXTHkbtiwYQCAt99+G4B/rnn8DCuSg4tJsH3KNiivuQyZkTat3Ie8zvEG3cm60ZUHmgeTaYXdINBwlX2hTMuzIS70dU9xoaMMyaIIRZGK4Rryeksb0Hh2oMHlWAe4HTmoGfCvHZX1YNIiwG+vvCewLclwPBneJbcfbOeJwnJk+5CD1WXoD2EZeF+Md17ksfPcyHbAbUlrYy4nrXeTSU7I4+C54z54zqVlclfi559/xkUXXYS33347bojthkZj3Juh+1b2Rkbv9CYXr+p9dkq8aYyt/EwPHXZTmQmVnyVeVkWxrwgzqgZUNBIbB28v1n07HQnAd8FpK4zTB4AertyLEix78fbOTabQxuVKp4Lt0u2PQu2aYgBAZfEaAEC5U9qLXSZZADhvsaqBSueGzk6lgcypvV0dr11XETVNKbNtAb2K4m7ri/ruSEtLw3aFpQCArJ7rAAB15c7j2cXM0zO+sc5/0GkstQ3RH5djl7l7u8MAANf8/EEbjk5RFKXr8cUXX2DVqlUYNWqUN6+xsRHvvfce7r33XtTW1m7QzLL64B6HLU1NlW/XVAKKiooAAN9+aweKsqLJwYHxkJWypZTxbUkEIOMWpZJBpYJTmSBGKjckkRIaTzngstKCcEurIxsKWkAC/qBUOUhL9qTIekc1jcvJuia3F9wXSWQrKOuUrHOcL+tSUKnafvvtAXTcgOU33ngjqgxBpa5Xr17Yrl+H7CYKWj5ygC0Qa8Mnzw1jfo8//ngAwPvvvw/AH/TO68LzwmvC9YPXUSqKchCx7H1hWdjmZe8Nr3O86yXndeUQvOA9/8O19tzs7WLdI24gflqubX/eIGhnAZwailbceT5lkrNEif3ktZQ2gySe+s3Yc9YPbksq77wnUCVme5bWjETWjXiD0GVvkPyNkD2KcuAo4UBRGYcfvI/I5HREJqmT517Ol3aRiXqUg9vmPA6MZXuXv+ldsf0ceOCB3nMTOf3007H11lvjT3/60wZ9aN+Y6FOOoiiKoiiK0qnJzc31BBuSnZ2N7t27e/NPPfVU9O/fH7feeisA+0L0/fffe/8vW7YMs2bNQk5ODrbaaqtW7T8cCXt5O1parj10ygf3Xjv0AeCnGGeKcoazMESmKU6ojCScYMSoH27jQmNEiAy7vVMraSFn3+pTAvZyaS58xvfXbYz6/OguvwbgW0ie9vEUAH6SBr4dylh4zu+d7r895veOVhVlzGCkuz1nM8usUpCREa2mNK6xcfW1q2yipUo3KLV8qVVQfg6khpdxr1TWGZe7Zo0NLaBCQGWS9mNU3mk/lkj1j6fASxVXWp0pyRFUuBPFmUolV8a2JlLgpOIVLx5V2kHKGGiposl06zL2O17sNJMWxVMGr1/+MQDgsa328+ZVl1rlucrZnVatsqEv95QsRjyCda68vByRnfYFAKS6xE257r5kRD2WnwEALmTGG9Du7h1P7fd/AIAT3p3oLVpWZq1ZqbxTeSNScRs7diwA3z5y2rRpAPz7DNsj23GwbvAY2d6opMsxCbKnS157WaZ410S2983FUnBTIHtZU1NTEc51se5ZdJdxvRrZ9tqkZdv2KGPc2aPCayTV30Q2vtI2lPcJOWYi3lgYeS3520DkWBV5rWWPjtxucD7nJVKt5XLcJxNTJbIqbW4sDNsFY/Xlb6Ic40Hkb7m8/8meiqBqzjbIdivV+mC5n9z3FFuOEPASgHuaFiU8lq7GkiVLourP8uXLscsuu3ifJ0yYgAkTJmDs2LGYPn36Jihhy3TKB3dFURRFURRFaQ758C0/FxUVdZhIEAqHkrODbGeStE714M631x47WtcE4yns8RWtpsbYN+aYZROs21hnVQaq+UyDLtOi11U4xdcp7XWVvlqV4ezfckujbeDyU+0+mECK06ljT7bbFomjIldZ1Y1v3FTIevf24wbzBlgVhnF4VAQY/xbp3teWf3VD1PwhxiZY4qDUKjcodf1yq0aUOCVweWDwLWMAqXrKtM9EpnGmUlBaapVMKkH9+tngYKlGSOU+eA6kd28ilUWJhrHtQWcUGS8uXSakGpQoWZJMEBJPuZLKOZH7lMo8tzV06NCo76k+c7tMxAXEjveQPVaNjY1YG7B1LXVt+dOd9owqC/dNFe1Px+0PAJj03vdR5X/is0UwxuDU3YYAADKk2sZegUA3aZje22nOlrbEthWqpZm1tnyvHHQGAOCINx/yzglT0LN9MeGSdNrh8hw7c8wxx9htvvIKAP/cUbkPXi+uy/sJz0Eihx4qhDKZF/eRSIGMN68rt2WpIufk5OB7k4PU1FQMzbW9oml59jqn5dppqot1pzUwlXfZc0Zk+5D3ULZz1jOpmrOtsd4Ft8npTz/9BAAoLra/L0x0I91U5IMT7znx1GRJImWd+5BONTwf0pWF9oF9+tje6eHDh0d9H+xh4zmhak8KCgrw71E2EaR8fJOd+0kEBCS0lJZZcmlrzc/buwSMGzOLrrLxBqdu6hwNiqIoiqIoiqIkQadS3PlWnj1yu+SWb4ozqprzOEq9vi562QanGro41SanvNdXOeeFmui06PWVVu2qK7dv73XrfSWTKnytS+yS7RT4bi4englZapziTttIvonz7XqhUwqomPW7824AQO7wbt6+cvsXAADOSLXKRu7WdtnUoq0BANOX2vJKVa5h4VJblpU2xr2y2Mahr19hY9uptP/xxzcBWOWDioZ0CKEaJ90tqAguWWJTw/fqZS0pqeYx9p2KPNUWOhQw9hbwlT6pnsqYaSU+UhENIpW5eHGWQKyLjHSESeSgENyH3JacLz2Jt91226jP8+bNi1qe1z+oviVyVQjG7Jc3+MvP/8NvAQBhUbfo6HJ6oa2nDcW2HsdzdwqFQnjkI1u2M/eyA5syUpync6qLMU7zXZIiGVZxTMtxsbHZts6nr7B1Pjti7xVUT9869GwA8Mp99Bv/9nq6eM569+4dVW55bjn/hBNOAAA8//zzAPyesKBrjXTmkGqs3LasMzLuWMZVB6+XHN/Qldsy73l0Y6Gym5eX5yf8coo3M6h6dYh+7u535r9HXuht94R3J3rnmYq57N2SHuq8P7O3kz2t/A2Jp4KzvrDc7D2lqs1cA/xt4G+bdJGS9U96zwfPFdV7ef+RWVkXLVpkz4/7LaHbEsvI85LIuQqwbWTC8IMAANmubbKnY5jr+ZAqeKpQyxMlZowEjo3fhcO0sXZjS2hnLXrsYudvGS4qnQVV3BVFURRFURRF8ehUijvfgL/Ksuob34SpFKxyjiiev3LI/58qFNdZvNg6RVAROHiwVQ+otHvTWjtNrbFv9abaTpvc50a3PhV4Ku8AULe+Mmoe1fi6CqfiO0Wkocb5zNZFx+bz7XrI21btTv/UOTtsbWNRs/v4intOf6ekD7Cx7Cl9iwAAn1Za1XvQoN5R56ZX+QJbFi+23aoqFSuselexypa1OE5iKcIYQSrrUmGnKsJ9UqnhuafLBWMnqQRKpTQ4ApzLSl9pGWetxIfnNhivKdUt6fxBZHY/GdMu41Hl9oPLJHK0YFumOrbzzjsD8JXHr776CoBf96RfePC4WFe4bryegCsXTvfiVafue6Ld1jWnAwDG/vd1AEDPbW3bqhhh62tuuvVWP2PnEQCAR74q9nqLgn7VbyyuRk1NDY7ewS4XSrNlzQ/6QecyTtmWgZ7cdAhJX2XvITku9j0nxcW3u9j3qYeeAwA4/LUHvONavnw5gnBMjDznVFCPPfZYAMBTTz0VcwwyvlfWkXjZM4P7knUoUZbd4LLSd7srIt1FeE7Ky8sR7l4AwHeXScsTfu4ig2pqoInl5eXF3I8Jrw2vqXQZ4vJsezL7J+CPOWF94TrbbWd7ydkmmeGb6jZ70I480iYnlLHjskf1s88+875j3LzMoi17Fl599VUAsb0YHNvBMnI9/k6xnfD3aeIetqeKSQ+7uWlhajjqc0auizPPcdmERdumSs7r5SWDDEjwfAYIu946KrUcL8PP4QTzlY1LKBRObnBqSBV3RVEURVEURdni6VSK+xlnWGeFt956C4D/Ni/fzoMZDaW/MGN7+eZOZWDSR8sA+CPJm5rosGHfsvmWvFtft36NVR2aKq1CnVZlY1Mzq/14bH5Hpd1X5Z1aL5xq6GBDp5uQeNumb296gVW2s3oVePtK7WEV9ZQ+gwAAb660626zjVXgpa9s42p7vPUlKwH4ntWVK23ZVjpF78xv/otE8BxymzIjnfTopfJHhZPnngqIdKLgtQteT6r5jOulmsLPrCNKfOJlrEykfifKI8DrJhVRXicZAx90kJH+37IOUdUfPXp01LboPc7rL5XbeDHXzKBHRS7R8dAejA4zI//xBACgors9nnTn0BDJsG0lkmb3lZ1iy3rmLkUAgBnrc2LOYVpaGt5ZXOmOrxDr1q3DscO28c+Hp5o65Z3qaZ5tI+n56a4MLgvxCnuvy17vciQ4de7dX50HADjgP/d5ZaCjx4477mjXdb0T0vWH123ffa0P/ZdffumVj71o0m+a68jrIJ1KuE/WGTkWIVg3Eo2puOOOOwAAl1xyCboKzHFBgufmo1X2nrldtr02sX7uTuEVGVQB6+AlY9gTwTFGspeOn9kWg70tjHvnlPtgu6aDGO/XbKPcNpX4kSNHAoh1n+Jn7js4T7ZvHie3yX3w+5122gmA/xwhx+zItuyNM3Cx7D3TmU/F7qewwF6HbJdTJbuXbcuZhe4+UmB/q9IL7XVjb1uKd/2cL77rmbNfuszD7l4TYvtiuwtrDPvmRCgSQTiJ7KyhJJZpDlXcFUVRFEVRFKUT0KkUd8JR4VR2+WbMOPYgUimScbl8C2e8Nd+6g7GXgB/ftiDFxsNFMpybSY592x8esYo1Vfbg/6lU550ab1x8PD97XvF10ZkWGStFhS+S4Y43y3mz5/sZE+nT/p95dp/bbDMi7nnov26OPR8uU2rlChvbXrXKxbY7xX11bbQy1pzfMs8h98FzRTcCwnMvY9u5HlUUnvt4ihC/YxyvvI5K88gsqEGoWMmMqDKWVSr07DHhtZEOEMHryO845T6p7I4aNQqAXzc+/thmOE3kGhTP2YVwnXfffReAr6xxHboccZu9bjwTALD6r5Ps8s4NKnWpbRspmYxFdeq+a59ZzpFqrBtXMr3Cjx2WPRyZmZl4bbnx6uuxO7i4Wqee0imEalx6gXW6yHSONoyT9WLfV9p7C2PfPz7qfAB+j9nBz9+OhQttTD6deRJlymR6788//zzmO+nxLeuCvJ6EqqesQ/HyLiQqV1fyc7/22msBAL/61a8AxM8UCsS/HydDampqTPuVuRL4PdsglWa2c7l+sFdbOrjQoSg4biK4Daah/+GHHwAAc+bY3yd6qbPHhvtguxkzZkzMscmePsboc5sswzbb2B4v3nNk5mGZCZzHJHvvOxPXXnst/vKXv2zqYmzxbCxXmc5bExVFURSli0JbSD8Bk3vIzYwenKpJeNrPMwec7v0/0J1fb3CqC6krGGwFKy8ZYl87YJ3GEek97OdIYa/oqRPg1hZslZQFKgUu2lcGw4YA/wWM0//85z/JHKLSieiUD+5Ub846wMZuNtXaiksnGDSU2M+NjYCB9W4vdSu7mLCQi1kPjSyw0wz7Nv7xaucV797OqerLDG9882fj+KnReR9nFPoxmnbTngq1FVy56FBD5xp6yTsPeQj/eS++Ld3emKm4v7vMd1Bpcv9TVaRSQQZU2RHy9S62vdZlsqteHR3bXuZiZxnvK1WYIPJcUGXhuaLTBr+nkiGdKrgdxj1KdSkY80qvaanmNqe8Kj7N/TBQeQtmVQ2uI725pRpGpOIezx2E15iKHOPQGZf99ddfA0icUVXGSNPNIhgbzO/Yhll3+EPHOG3pmBK60mYqXuC2k3nfs/a46fogHoTYQ5bl2u/Y3rZHKdKzPwDgfz/XxsSV89y8Nq/MldH+iO8/1J6PSK5tp+kFdpqW63o1cu2PdXqePRbPfWapjUeWcc3v/OZSAMB5306NUWllNkpe1+A5pNoq3U147bnNRG5BUpmXPRCyrcf7Lt4yWyqJcibI35+MjAwgOlF1UsRr/9IhKNg7FJxPWBaZeRTwf3+YL4Tr8hrKNsnfDPbC0lP9ww8/BACMHTsWQGzvXvA8JcoVwG3IfcixWDKzKr/n+CmOydrQGGNi6nrwesneF44jkOOGeG+RvWPKhkcVd0VRFEXpoozKtC9js2rz435PsSmc6exOXTIv3w4yOgGQ0nYyAucw3yU3KnQJr3L72gf8/MEFAIC8Ihu2mjdsAAAgxb3Ip/QdAgBYkT3Ye2loicLV3wMAmpz5Be2oTVMjegaWC1GQpLgXqBujzzgS1zz6alL7U9pHKJykHWQ7xYhO+eA+aJB1TqlfMhdAIHZcerBTyW6MjZH0/FBZ0d1I7tHuJhiGVQ5CsOp2OGI/L4zYOHqpUsVD+pgvrHPrZrryOLMUboONWcZ0UuWiqvjpe58CiI7r5rq77747AP8tu6jBKusNK208L91kqoqtclexYp39vMaqJ6X1Lj6wMVo5i6fUSP9vfqY7jFT9paLL45YZG7kcXQ143ICv5AwePBiArzasXLkypnxKLIliZoPfyXhy6dMu/dxlvLIcTxJUerkOHYb23HNPAMBHH30EAPj5558B+Moa1V/WddappUttxl8Zz0qVDPDVYtm+sgI+6kFYXtbfeRfdDgBYTv9l5+hCxd0w07Ebm9JQbY87p4a5H2w9P9Ap7++s9u8VHP9RUlISVYaBhxwCABgxpMDuy/Wu5ec45T0vOiwiLceuz4e1FKe8p621vQrMwnjfDkd4+7ildFbc46fSGC9vAtVIXhfWAdnTJe8Fsi4kUvmD82Td7Eox7lIt5f9yHEk4HAba0MkYzHwt7+myt4T3Wk7lNUsmzl7Gz0uHGulsxPbNesfYd7rRMDyEvw1AbKw6xz9xH7zXSCekRO5YMjtw3759WzzO1iLPtezZ7uheJu2R3rLolA/uiqIoirIl4xkdhHPjfl9cOBKpqanIW2QfblOc4p7qYrBTNca9wwiew2x3XjML7UtGbj8rFuS6JId5w62wmFZkB8GuK9ozJqFWkPw51uq2fpkNE1q/yE+gtnCZDdOheURNqRUFGutESJW75rSuzSi0L/gZ3TlQvhuUDY+GyjQDY6frF9ksitInvaHSqk2eP3qd7wUb49iSye5FF8vOTIZO2aLLA/2WB+YW2M9uYFA4Ym+q8yN9vH3wbVnG3km1ieqJfOv2yppA8aRyGIy9GzBgQNQyg2uswt7gFHZOK7wbwTo7LalyU3vOyupt2a5cOB1AbBbDoMoiyydjmql4UmGXyhkVDaqsxS7unsoIj7N///7eOpwnHQRYJ5TmkXUzOI/I68R6KnuX4jmDAM3HKPM67bPPPgD8nAysI1THWJ+lQxG/Z5w6FWuWIZjTgeVesWJFVPmpzHFbnE8lnnVruweuBgD8dN6ttgx//C0AYNS/n7fH6RR39ujRHaqhxip5Oe7+k+56BA9wynukex8cMmRnAMBfnnk36hyx9+m7HHtfqc+27lA78n7jpqnC+znFecx7zjeL3XVe6eeVIFcX2n3fus6OI5AZjXm+AL99UdWUcbUSPqDI2Hep8sZTbRNlW01mwN6WwoQJEwC4Hih3ihsaGrz7sLz/tZZ4vRdSDZeZR9nWZEZe9roEXai4DnutuE22NbbJRHHX0redvw3Lli2L+j5Y/1hfE2Xxldsk0red55hqvxzL01aMMd6+NtV4DdYrZcugUz64K4qiKMqWzOdNVsHdvbABMKvwU6hX3OVCLrwzxXuJo22pC21Txb3dpMWJcc9wCZeyellxIXegvT6p/YcBAFb2HRVjrQsAPZa4UNc5swAAS2fZAfkrZ9mXk6Xf+YLc7HIrApy76MOEdql8MaFosXbtWjw97hQAwKAs+6J07s2tOVqlrYTCoeQU93a2yU794L5+ySoAQO06G+9G5b2u3CoEdZX2Dbqh2n/jbhLx7t7NjXGsbsAJs5ylOgU+w2U74+fUfKeAOSV+cLZVuDkoBAio8iE7b16TVRul37XMFCoVEJnJba+99gIAPP/8896+xu9pPZibKqxq1rDSnpvGUqewL7efq1Ywtt3GjVevseeq1Pk+l9VHZ7uUXr9Bf3upbFCZ4c2KKqlU67kN6ebBuHXGKceLg6UaTwVQesUrzXP88ccDAB588EFvnryOMu6U9VJ290oXCtZnuT2OXQD87JyvvfYaAP9ab7311gBie11YpxjjK+sj1XPGvrIMQKxSxnKvWmXbAsdO8Di4LcbTch+1d9qMncsusRk8f8609bffcue44rqt66upuLueJzfNrrLqYxbzN1T5diB//rUdk3Lbf78A4Kt9Msvj1FK7rSPdeQpluB5Bp4BzYCKzLIddd37ITbHc74lodNfrTwU2c+Rx/3sEAPD993YgHMcLAH47Y88Hr4sczyDVWt4DZJ1IFE8c/C5R/epKMIY76H9eXl7unU9eFwCIP3Q1PsaYmAdA6bUvx7jIuHR+zynVdSDWTSiRQxh7DtjTJrfFe0ZwfFO87cWbx8+sszyX3AePM55DDeDXWR5vW3OEGGNi1P629pSsX78+bm8pvwPiZ7JVtkw69YO7oiiKomzJNNXwRSp+yAaNFSKp0Up7xCnDkcCD+nW9beKiv6yauSGKusUSVNzT3fnNcDHuWX3sC39qbxsKt6Db9jEJpwCg51wbFlf6pX1JXzrjOwDAT5/YEKTpLmz1xuJPvYfwnQNCRGs4/N3HvRfwQ9yAd2XDo64yzeDFULt47dp1toJWu4EbHMBR67qZqLwDsYM6wq5bgxkJqbyn59k3fTbOzEIXd+2UeMbApxfkRn2OBBwrqIrRrmuwU+N5ow2lOn/2FKdi10Zna6XFkzGuzMZlfXXHc8U4PzsqB7Y0rV9nj7PcTqtW22n1KufXXuy8rNdEx7aXN7gY4YZoZUy6ytTHuZHwDZ/KBNU2qg1SCeBnqouMYaeKRGVB+ukCvoqiXrXtI6j88DxLNwXpHsNzTsWHU86X3vp0hKHKDvjJQBjrTscGrst9UnnjDyDVc/o8Dx06NKqsrEtBhYvbkGNLCNXjXXfdFYBft6jeE8Z+46JjAQBL7nrBltnFuPdbRXcZ58pTQ5cZO+XYG06zKv2EKWku/p1tOdLTlmF2ja3zzHrKnogHi4u9Y05JScHJw223fK67h/B+JrtrTaOvWjcWV0aVf+ZM+xAnrxvgXydZR9gtL+uM9JSWKifhfOmeAsTGv3dFBZFZfUeM8O/xtbW13nnjOWpoaEBbPE/iKe/SKYj1QY5xkaEZwR4RboPtNZFjGu/X3BZ7Tln36BzHh0/2BsWLO2c757aZIZj3Dp5L7qNXr15RZeA25XHyuGRei5YIZqdlmeKFzCSiurraG08Q/F1j+eRYHCruMisxj1vZ8uiUD+6KoiiK0hUwlbQ+zIn7vae4u3ApT3FPUx/3jiJ4DinyUcyjc0vE+bVLei54HwCw9hMb277o7dkAgI8/t4NqT5n/AQDgALT+JUHZvAiFI57g2tJy7aFTPrh7fuDOIomqcZVQkRm3TTUZAKpljDu91tdbtTjbKVX5qS7Ot4CKu51mFK6P+pyWZxuv70bjd5F5Kaiz3JRxqMw6SKXdTb0MqSKGzfCt22VUZYZVL1MsgMYqxvfbKeP+a9c5Z5dVtgehwrlM8Bytq3Ye8S62/bzvXrdlELGK3n4Cn6UKn8glh+oIlXY6BVDZCCrqgB/fSGUhGFefSMVP5BygxCcYJ0k1KJGyKV2RuC7rQjDGFfAVrXhjMfgdsxjSf58uMjKmlXWHP2jcJ+sM51NdY/ZdIDbrKqGqN3r0aAB+/f3yyy+jtsEyHnbYYQD8ephyrFXen9vHuswwZpz3ll5CcffG2lRRefcV92ynwmc6z3eGRWznHGi+cWpaULFmuzDGYPK8KlRXV+PMXazynp3gB6EpqLi7noEap7jPuebfAIBdH/8rgGh1UKreVFlZHp4TTmX7lONzJMH50s3EK3sXVNwVRVES0Skf3BVFURRlS4UhUkVFRWgKuUHNqU2eWMKwxJqaGoTSqLS7cCUX686ByZHAOxNfiYwxMWFN0qpThnnIcCgSTIbEbTA0Ru6D25DKMkPd+LIsRZ2ttrLmC7R+DL7MMeSNYXdch/vmoHMKRhQPWAYKRdLGNniuoxR3Z7nK8Ni07lYI+Kg8C0OHDkWaW7ffehvWV/7dLLv/GdZv/4OZNgzm9EUfo7GxEcYYb18UDfjiG4SiBZelqCEHE/NlmXVI2YiEI/YvmeXaQad+cJdKe8UqO13tlPYSF8++NhDXTlWsXhgVMKVxTkq04l7ottXNxc1net1kVODtlIkP0nJ8ddjzWnaKu/SMD3uDiaJvuIRxqvSJ5rTR+UUH/enpHd3gFDw67NSU2inj/ytX2R+ByjXxY9sVRUmOI9+ZBAB4fv/xAIBqp2rz3tLD3XdiYt4DLlcy/j3bPUyYWvv5xK2t8n7fR827Jz3yVTHS09NxyrZFdjuud65JeMwHy9G30v7As+2/d7L1rT/oOfV8VhRFaTXhsP1LZrl20Ckf3NldGz9xudIeqD5QZWGSp3ipuKn6cAAblQ9pHcl1ZBIXhlhwO5xPtUZaygG+SiLDM4KJd5SWCYbKSOWGUxkCxesiB23x+jIEhSEyzz77bNTywWVYn7hN7pN1QIZiUJGjZai0CuT6QTcHDmzjsdLmcaedrA0i68xnn30GwK+/e+yxB4DY8A6ZOC0YwrWhCdr/sVwyyQ0TKbWXeJavPJesE2ybiQYd8vrJJFxS3Y0XeicVz66Yrv2WW24BYMPMpiIXeXl5OLCXPXcf+jbfaGxs9EIuPStQCkJxXGWovjc1NcWEQclrJRMaybA1Lsc6AMReX05ZV1m35OBNGQIn1WXeN6iWB+//LJcMm+S6cpsyGZi838myh8PhqF4L+uSnOGEukt/dK0cw8Vj9Ausas+rzOQCA75zS/n/z3kc4HEZTU1PM8csyxEtQlsiIgb+jVO1Zh5Qtj0754K4oiqIoiqIomwuhSCRmjGKi5dpDp3xwp61TvrN7rCm1U4bEMESm2HVRXzTvXU8VoEUS4/L4mSrCDjvsAACYsv9pAIDCVG7TnuhubtuFFS6Jgws5Sc9zClSerz6kZbvQGMbEuTAbb9S/G6xKT0+GzBDON2JwVqMLkYnq/nbJXuSAuFrvHFmFgiEzPFdl9dGhMhwcSPWRCsmiRYvssQRsBLfffvuockkbR5m4xyu/Uwxov0eVVVqJUVUJxvvxf6m4ayKm5jkvVAQAuM8sAgCccsop3nePPfYYgFjFjcg05XJgMGNLR40aBQB4/XU7wJkKNwegAn796tmzJwC/DrD9JVL1qLpSVaYCT6tG2sex9wfwB5uyrhQV2XNQWmotUdn2eW/Ybbfdoo5XKr9EDs4988uX0djYiIdG20GrtFlkWF6PlXbazUvQFGy3LqkV268Lect2nzPcAPRTRtiQmcnzqrzzTnWO7Wfx4sW4Z7FtR+fsao81xw1k5/YBPyynzoXK9F6wDgCwti46thmITT7DHg3Z4xGMuQ5uQ55LmbgpuC9uk+eXx9cVFXfCet69e3fPjCAtLS+6rdLcgPbCniWoMwkIbC8Sx3RAJl5ifZK2nDIpGq9dUHGXg5S5Da4j7y1yOe6DPb0ySZLslQ2Wj7/p/MxeIt5rpJ0lkfc12fMoPdk9t55cOz+cW2DLnp6JSCSCXuULAABr59kY92UzbUbU/Wb+FykpKTZm3v3WSfWcx0/VPNg+5P2ZU7kt1hlly6VTPrgriqIoiqIoymaDDk5NDN+kqSpX1do340o3OIzWhrQ7KykpiUn4QoWIo9jnzp0LwFeQTnh3IgA/Pm7imOMABJRpN3g13ynWea4s2SWB5CpOceeAVcbG8Y09JcMpJRz9nxZ9MUPCf9e44/EGqdYF0oXLgXBuWlPmklBV2fJRaS915ea5+t2sV+12mNzKxf9ShZAKKRAbG5lImZTzqYjw3FMx4LXhPqmiB1UJqiCcx2WCadqVWKi0X5oyBIDfVh4wi2JSaEulTcap8twzcRYTnkybNg2AnzSGqlgwWcrSpUsBAL179wYQm55cqmXcV0FBAYDYBGAyBjZYV2gxOW/evKh12faZzImZBaX6J2N95XkKqoerVq3CMW8+6Kn8dXV1WPaXSfZ/12457RFHcWdPGXvRmsQA9Cw32PS3Q+05T+1nr+Oj02fHXJeamhr888OFiEQiOH+UbWO5gR4q2lHWllultYfrsezj7glvHvNHAMA+T9zsHbM8BzI+WKqY0omEZWRPSrxEbjLGPdG2uxLLllm1dvjw4Z7ibkxuVGKjUIq7v7LnVmRQDTqiMFa7oaHBU3Vl7wen7N1imwy2YyA2Lh3wrzfbPu/lbHP8PpE9KPdN5Zn1iAmJ5NiY4LZ5POzpk8cjYRm4Pqesm/GynwJA2I0d8OydcwoAABkRG+PesMK6uaz9wfYGzl5kexkLy8piVHLZu8FzLO1ug8vw2GVb5LZZZ5Qtl0754K4oiqIoiqIomw3hcJKKexd0laE6R5VZxpTy84nTJiEUCqGqqsp7m6YrBRU/+sJSPWQ8LhVmvpVfsXA6AF/9uXfkwQACCjxtJBsCbh3OSjLHxZmnOrnDV96dstdKxb3Js4eMTarS6BT0eqfksXxUWPlZxrj/8IP1mOXbO5UR9lRQCQjGm/Jc8E2fzhdSVaHiwbhFnmvGQ0r1lddEOgsE9y/TPAd7ApTE9Ex34y1cPbkwXAQAuKdpEaZMmQIg1umBqhmVqKFDhwIAhgyxqu8777wDwPdaloopry/gq0GccptchnWDihO/52e2Y/YI9enTJ2qfwZhs1l0qU1zn22+/BeCr9CQ4fiOIdKMgwXEVH3/8MYDomO6868/AjjvuiHePvghArF0kAPRYbsvEe5mcGpEwLtMp78y8F4lEYnqbWN4ePXog4hI5pdb4yl22S8rmJWcrsd/1LrOKbnGar/4livfndUmUqE320sg6RIK9FjIOntfy73//O7oq119/PQDbm/VBRa6row1RvZ3fVGWhoqICu7o6wRj3cBwfd8a4L1++PKYng/U/mIAL8K8x5ydyowFiY9VZf6SDmEzmxvrC+zrv56zbHMPCNrdmzRpvn1StuQzX4T2D42qkT728Z/B8sKdB9hqQsDu/HKP2wSqDwYMHI82dm4aV1i9+9ferAAAHfvQiysvLUVNTk/Cc02ue541qf3B5+XsrXXT4mXVG2XLplA/uiqIoiqIoirK5EAqHPVORlpZrD53ywX3gXf8CAFQ5BZrCs5yuXbs2ZhR6cXGx3YaLr+YIbL6tMgaXJErvfvz7TwEAHt/7JAC+kh1MZJTp3syZ1ImxhqlODednTqmGROKH/yEgsLvPASVaxNFS1WMvRLVQ3BnbPvQfFwDwFQwZo0ilPd4oeKmeUV2haiBjgqlssDeDyzF+mZntZCxyMM5PegpL32+lea6s/AkA8I+c4QD8OhdU3p955hkA/nVgXRg+3K5DRWr69OkAfO9/XgteI6nMAb6yzuu14447AvAdXjhlzxjrJa+39DtmXWLdC9ZJzpNx89w398Hjk04pUlHkdlimjz76yNsX67p0rli9ejV2ePAa7LDDDpi0+/H2GOK028bVzhPftdMPDjsCgN8GTl7yI4JQcT95ZBEwMhcTps+NiUcPh8N46vMlyM7Oxq8H9/HWzexjy5+9zqqbuSVWea9cacvQk/engPIqexukos6p9MCWY1JIPA9w6RueyK+6K8IeKv5uSbefSMQfFOe5yoSjf3eC/1dXV8f0msiYbjnGhfWB9Yyfg6qwbAfB+HfAV9TlumyrnM/fabkdtvd4sN7w90Kq99LxRvYosseY+2opRp4ucJFIJKqtVC23Svuqn6x7VUFgG9wXzynLxN5o3h95Lw1e50SuN9y2xrZ3HTrlg7uiKIqiKIqibDaEknSVCXVBV5lEUKn2ppFITGwlVQTGvfENd8GCBVGf+UZMRUjGuXJ6yvtPRn2eMvZUrzyZEarw0cpHrNJul6+88jQAQOHfH2v2OP2eBRMzTyrvMv6fyvvAW84B4KssjC2WsYmJ/JeD3xGplMlMm8FY5+BnXgsqooxFli4fgK+eyH1LdV9pnssropV31iXp9x7kf//7HwDg66+/BuDXBenowmvBOhSMEWXcOb3U5bgH1gEZC0s1lj1krFtSaY83BoN1mooUVTtOE2X1lM4X3N73338ftVywfFKl53iN5cuXo+9NZ+Laa6/F7905BmLbcqPLC7Hjg08DAA75ybrUzD75cFsmxi87tS8zzfZEXH6IzQY74a1vYnoOjDF4eVGtd32O6mHV98xe6+y0h40bzuphFcoepbYMM46xsfnjXrrbK6+MWef5puIoY+Dl9ZME53MbsmdEAb755hsAfjuRmUhTU1O9AW/Sxz2YOZW/OY2NjTH3UNmbxc+yfcj2HcxazevJbTB2m+2Z7ZYOMFTHuR73yfU45ozOUFTF42UUpcLOffD3RTracJ/cBnsQeTxU3Nmz1tjYGNXb7fnjp/qKe7A8FUvtPW22G9s2pL7eOy4uJ8eGcMrzImPegdieAp5j3nNYR5RNyEayg2xfoI2iKIqiKIqiKBuFTqm48403JBRrqWS/fpRVjI773yMxKgIVPzpjyIyMjDEj8m1XKmzkpPef9BSJx/b6LQCg2q0jFXYZ257tpueumxO1zeszhgEAbqyxvtPXpA+LOSdU1v9evyDmuyC33npr1PExZpIxxFIRkA4xwbhTmcGN38mR89wXlTSea86nqsL1qXzEy5InVV3pGKK0DirvyXDQQQcBAO644w4Asb0zsjeKCmpQ2eP1Y72jek9knC3rAOsU6wKXk7GywVhTqpIcQ0F1X+YPoLLL45Ftm4rdp59+CsB3tgjWS3ns11xzDRLxQJzejJg2vc6W/a2tdwUA5A2wSqPMukwv6VC6bb9XHLU3AOCR9/2YeCqMnqtGYS9b/m42jjiju2uPPdYBAAqW2uvZzeV+qKys9M6v9PxONI6FyCyoclxMUFXnspz3t7/9DYqFzjpPPPEEAN+rXI5JSpbU1NSYa8drw3Yjx7iwHbPtxct+K+sJ2zvv+XL8C/fBe0gwUyzgu0Ylk0WXarzsheM2ZRw9e2/528cysswyo2wisrOzY3ovggTdZBJ54cueKk6D9zNeB9kjRY/7ruy+tLmgg1MVRVEURWkVfCjgS15qJDZU5pkDTgcAnDTj8Y1cus5JMCyVhMSLT7flNqxt5TL7Yn/anHd1wKiyQeiUD+7m71Y1Dv/uEgCxSjvdXOigkpWV5b2dyrdoKkTMsijfuhNleOPbO7cXT1Vk/Dv3xbdq3jTprc41b77qqrj7otJObq61n4OxsvFUvHhc5fZB5UZ680q/ZtmjEDxOrsNzIecTKp5UUXiOpX9uoqx5QWVIZvW7f/vDAAD5LpvdwbXR50rpeHi9gu4lgK8SMf5ZOkoAsfWKnvDsAeM6/EzFTcapSoUrnk841Xo6VHDfdMGR9VSO0aDyyPl77rln1PJBH3fGvdcEMpS2hpK6aAcV9sKlldrtMduyr7ivAwCkZjsHJ3eszOKYmZkZ02PA6cOf2XN1+jDn7tTdumBk9bDbyCi0yl6+i9GtrKyMuT9wyvsfty2V+ET3z3jzpRONEgtzEDB+u73nKjs722szsldZ9nLxmvPey15Ofgb8dsh2KntZeW/nteaYF34uKSmJWo71hJ+pqsdDZlDlNqm4cywO98njkj2HMqOs9HGX5OXlxVXcGxoaYp4j+JsmewXkeC62o3hjQ+R1Yp1QNgM2Uox7p3xwVxRFURTFhxahYZmAKZDYL9PNS01gOay0jJf4UDx8mWr7klK50iWA2qilUroSnfLB3cvumeky/FXaG1WGU9jpm17daKeMNQdsNlUgdmQ2374Z95ZIfZCxaFK5B2Lj+AiXPf6dR6O2zTf81pKsyh4PqpEypl366soYvKCyIP2vZQwh50t/aRnfKGPbuQ9uJ6jc3rn1IQCAPHeN+2c6J4AUHWe9sZBKLpUp1imZ5TQYfysVOdYFKu8yc7FU92UsOz+zLgXVvx9/tHHeMssuFbZEPuGsfzJrsFw+uC9mjZ02bVrcbbYE2zJdfdh7eEGZHe/yn37bAwAO/GZm1HoLLz3FLp9Hxd3G8f925xEAgOe/Wx3XM90ua2OVM7q5TJEF9rql59nl2Ma+PPtmAMAOD14Tc9/jlNdRqrPSNUOOfwj21nHb1113XewJUgD4ccyPP25DXJgtVI4tSJbU1NSY3pJEvuX8DWB74LUO9nLJe75sM9KljfWHSjoVd/Zm9erVK6pM7ImLB8vFfa9atSrqexkDz7LIdiHHUSVyRAru18QJpQmWW07lb12i8xbsUeF14nfsSdTY9s2IcDhJxV1j3BVFURRFgR97HXbhg5HUoOIejpoqySGTH8bD1NhB4FVrqlpYUlHaR6d8cGfMWrcMW/y0bKfMOsWdWUPrmtzba6DRTdn/NADAqR9OBpA4PptvvjKmU7qtyOWA2Jg4mcFRqvebIqZTlkFmx5NZ5mSsYfB/qbBzXRnnKnsgojyI4SsJ3B4VkvT0dEwYMhYA0DvdbrOb6/6lKpif2r6YMaX1UOHidaeyzc/8XjrFAL56xGvNNiN9n1n/qOYn8uvnOArGmgPA4sWLo9aRYyiIzAQpnR+kmibdNwC//e+www5xy5cs8fzzAeBXy2fHnT/k9ie9/2+66SZg9WpkZ2fjwkKrTFZVVXkx/XIsQjjfnutIXgEAIDXXnuPMQnv8+anRmZ8bGxtjYm+lwk543ZijgVOZH+Piiy+Oe1xK88ycaXteODarpTjsRNTW1sZcU3n/JvK3QvaiBP9P5LLC+fJ3k22PvVzMos17yogRtgdJur0FYXnmz7fjnFjPpYtUojIkKmuiHghijImruEcikYSOW3LciVTiZU8j4F9jLss6cOqpfu4YZdMSikRiBi0nWq49dMoHd0VRFEVRArgueukqw5BSAMjwDBw0yL01BF1lmlwyQzRFhwGZOvsiX1PatrAlRUmWTvng/sMPPwAARt98IwAg5bI/AwCyauzh1DW52GmnGMWzcnp875MAADcWfxo1XyrsUpmWb+vyjRqIzcBIZDwuP59wwgktHXKHw31OnToVQKzaIqdyVHzwO6lcyMyTMkaQ54qqG7MBMlaa201JScGdRfvaZVwsO1XAQqew57pel6/PPbF1J0BplitSbcx5vLwA8rpSUZfKFesKfcSD67I3RbYzGcMu/fq5PmPhqcwxQ2kw3lbGi9JVQvbw8LNU2mX2T9ZbmYU5eC7kNjYm8WLDz9gLuOuuuwD4aiZ7HF763sYT/yrfXou0PHsdU10PZlqWGz+yPnoMQzx4LqWzB68Tzxn3fVUCBy0lOe655x4AwM032/EH++67b5u2k5KSEnPfbql3SyrvQW91Os3wOnMbrBeyt0uOoWLvEOsPcy8w3wNdptiWAT8unjHfbKccJ8Nt8p7CMkg3GZkNmGVuKUdIovj2SCTijZmT2Vp5zjmfx8vfRDlOKLifjz76CIBfB5TNiHA4ufh1jXFXFEVRFAUAQtJVJirG3c5j+NOjux4NADht5gsbs4idGpMg4N3w5bUu/qB3pQugdpCJufrqqwEATz/9NABggFOIGuucx3djdExavHbGnsKb++4OALh+1ecAYtUE+TadKKNoUG3k/9JbWip4m0O2T5aBahzLKBV46SQAxKqhEnkO5fgBKiPc9q1FNo69MNU/l32cot7Ddfsylj3bOV+k59lrzzqhdAwtZeAF/PotswJKpT04hoNqnqz7VN7kNgj9oOkU8cknnwCI7REKquCsX9z/tttuCyCQQdTVQ/YYSM9l2RvA72WvG+C3l45u0xeGiwD47eDPVfNavQ0ZR3799dcD8B20Hl1tz3lGt50AAHvn2V6MtBx7bjNL/LE5cpyKHIvAWPY1a9YAACZMmNDq8irJwwy9d9xxB8aMzGxh6cQUFBTE3MelEs9rLDOoBnu52H7ZXrksFWWZj0E6kXEfVNb5mfWJPWzMFgrEtluZdZXbluO3WBaWlZ85doX3t969e6Ol/NIyIzCPnfc7TqVbjFyP+2TvQfCaMHa/uazMStegUz64K4qiKIric/+cagwbNgx7RKwNqh/j7v/MZ6REu8qojW7rMU4QMMLqkgMO44XmKl2DUDji5VNoabn20Kkf3BnXOiTbxZkLpT1nXWwWQyrtzEzI6a19dgMAVDt5/roVn0Stl+woeSBxBkapDMR7S9/YyHhd6btMVUUqI0Cs004i5Kh8Khz05H14t98A8FX1nun+uWQse6GbR8cLek3PPO5YAMC4ZkugdASMlWad4XWUrhRU2qXbTHAdxpeyfknFLRg3G5zPjJEHH3wwAOCzzz6L2me83h9um0qc7AGS9Ve2S6nck+DYDR4PHa86CjpkMQt0R3DjjTc2v8AZZwAARgK48847kX3Tv20ZAr0JF1xwQYeVR1EUpSO49dZb8eKLL+LHH39EZmYm9tprL9x2220YOXJkwnW+++47XHfddfjiiy+wePFi3HnnnZu921WnfnBXFEVRlK7OJZdcAgC499578XPGcADAwak2rCQY457iQq6ya+xLKBX3u4YfCAC4YvH7MTaPfLGVL+i0YA3CUA++QDOREpGJoqTwJa2A+/btG7VPvhgHX6IZnsPycFAqtyFFAW5DCko8boZ7MXxUJmgiTfV+6FBDQwOQ4sStcPT5Ch6fTEAlk6NJe9W5c+d62+A1VhIzY8YMnH/++dhtt93Q0NCAq6++Gr/4xS/w/fffx7UlBqzoM3ToUBx33HH44x//2L4ChJIcnBrSwamovOpaAEDWzTcB8AePcBqurItZh0o7G1lFAz/bxva3/nsCAGqa7Dau+fmDDVL2rkwipb1HIEV3YY5zAnBKe0aBnVZd4xw0nNuAomyJtCc7ckdRed05MfkuFEVRNjfeeOONqM+TJk1Cr1698MUXX2C//faLu85uu+2G3XazERdXXnnlBi9jR9CpH9z5BvrOO+9stH2yC5+DZWTKZsB/u5a2j5zPz//3f/+3EUrcPCzDm2++CSA2tTyPgapFMOxBJtxhKAKXlUoNQ4aCA4vai6oQGw9eZ5nIhwNG+/XrB8C/7gyFCqY9pxrG+iUHiskkXGxvMukLlak99tgDAPDhhx9GlQnw6x1Vu0QWrzI0RiZKk8cfLxyH83hf2FJotwKlbFSCIUw/nf8egOgY91T3f05FdIw7B/3/dZC1lbzg26kxKjDbqEyiFfzt43dclionB2dKC0mGX/E+QBtEqugypK5Xr14AgO23397b5+zZNjmZDMOT1qzcF9s7yyR/r2S7z8jI8IS+IAzNra2tRV1dHUJpbmB9lm8vyfPAcxW0Oo5XRvZY8HsNSWsfsvdkQ7OxYtx1ZIqiKIqiKIqyxdDU1ISLL74Ye++9d9SL3pZAp1bcyXfffQcA2OG6GwAA6TfEJiIhebBv+pF6l5rZzeegVYbOREL2+zQXKnPboH0A+IPFjnnzQQC+ghBMf07VQKYopuJHJXJzgmXi4D+WmYP+eJzBAWpUTahU8LipYEj1hefoqf2syk/bRxkiw/AYAMjq7tQTFyrDkJmZ7pofeOCBbT1kpZXI9OS8nhwkTvVIJlFiApTgd1TFWIeopCeyFiVUy6hcsUxMyMKEP8Flt95667jHIcuUKJGKHFROguEjPA4qPIqyqfl49yMAANsve9Kbxxh3ustkO3eZ6hRb96udinzvDnbds2Y+j7y8PAB+HaeyHS8hF9sc2wzjzrkNadzA+4C0muRy0rqVNonBQeC8D3Ffsh1zmywv1WyZJEomXwwq9MFEs00uBJcx7qWlpTaRVbY9xqwedrtlZWXeccl4emm1yWPgckuXLoXSPs4//3zMnj0bH3ywEcOcw+Ekfdw1xl1RFEVRFEVRcMEFF2Dq1Kl47733MGDAgE1dnA5ni3hw/8Mf/gAAePTRRwEAg53ynnnTDTHLhtyrc7jSKgORWhd/7inuJuoz7SH5uc4p8P/55dkAYhV4wH9Tl4krqFSceOKJrTzCDQ/L9OKLLwLwYwqpMsr4QMBX0hOleadaz3WpbOSlUGmPTq5EpZ0qOwBk9bAqCRX3z39hB7T+wVnWKRuP8847D4CfalteX/baMNZdxsQDvkqdKHadyHhyLicVO84PWjMSxt5SjZeql1TtWbelm0Yiu9Og2wSTo2hMqrK58OWXXwIAds7w2wbj3dNc0sIc1/NME4a6JnqR+71MUolmu49nwUrlmG2LqjanRI7/4m8Jt8neavbi9u/fP2r7JSUl3rbYvrkMt7169eqofbO9yjLJgddcn2Vav3593Bh30+j3ShcUFCCcZe83uf3s1IRC3rZljDvvMTIJFI+b1+7UU0+N2a+SGGMMLrzwQrz00kuYPn06hgwZsnELEE7SVUYVd0VRFEVRFKUrc/7552Py5Ml45ZVXkJub64VW5efney9qp556Kvr3749bb70VgBWRvv/+e+//ZcuWYdasWcjJycFWW23Vqv2HIhEvEVdLy7WHLerB/Qynwt555512xlHHoFu3bhgx6dGE62Q7KT1S7ZR3l+jEt4t0se4u9p0xgJ4S75SKlw+xCnxQ06MKzzf4ZcuWtfHINh4sI99UpdoYTIzD76h2cko1gQosVZTn9x8PwE/fzhj3gkyrQlBpp8pu/7fz3h59EADgj6q0bzbwRkjViPVBehEHFTnWBelnzGVYh9hmOF8q79KpSS4P+OM1pJNFIuVdOioR2Qbiqfvz5s2LmacomxImTON0l112QfpfrgcApGTadpJd6WLc3e9anesNZfbPJ8bYHs5LFs7w2oF0GAv+JlARZ0y7HN/E3lnZboPqNuC3Wfb88l5Ch6jgODHO47ZZPi4j2zPvPXI8DcvIsnBaXl7u/f4DQNg9M1BxT0tLQ3p6OhahP1JTU5HT1x57KC/PKzfvMTxeOV6A+/r2228B+NdMaR33338/AGDcuHFR8ydOnIjTTjsNALBkyZKoXuDly5djl1128T5PmDABEyZMwNixYzF9+vQNXeQ2sUU9uCuKoiiKoihdj0QGA0Hkw3hRUVFS6yVFOJLk4FRV3GMIeg//7W9/w/Y5sclDvFj3SLSaFqmJVt7DzncmEoqOdednKhUyBh4AXnQq/C9fugtAbJzf5ogc5U91MV7Fln65VBWoqlLBeHqcVdoZy95NTDMK093UbocqOwCkF1jFRf2kNx8uvPBCAH6sO1UkKlxFRUVR8+PFiMtYdRlnyvrHdWWmQdZLurhIVQ2A183JfXHKcknlnN9LJwjZo8T6/tNPP3nramy7srnC9O1PP/00cOY5GDhwIFIuuRyA7zKT437vOGaLirvrWMYdQ8YC8GPhL/vpf55iHcyGSoWcbYcx20T2ytHpRbZv6VjGtseY9+BvKefJ3jrp0851OJ/7kmq/zP7avXt3LI8zzKXRucoUFBREqfgp/W2uiok7HQoAOHXONO94uA/eY2RuE14rRWmOLfLBXVEURVEURVE2Gqq4dww2he2VeOyxxwAA2z//RNzlqMCHXCxbHpV3N+o+4inr8RV4xrxHRbk7deKNoy8GsHmkL2+Js846C4BTZ+CrFVQngkoH5zGekUoHVQSqJRnunOa7mHa6ymTkulH+wqOdKjsADP/Xsx10ZEpHQ+Wd3HzzzQB8lxnWlaBjDOsE6wp7cmRWU+njLN0YqO5zTAZVs2DcKrPlUUHjvuW2iCyL7GXielTNgoq7omzuzJw5E4BViPPpLuN6o7Pc71xdk/NhNwlcL5wyP2G4HXN03bKPomLcE2UlTtTbRecnKta8d3DKbcvY+GAvnhwHw7hxqv9U5GWeEd6XZG6IoAPMnSMOBgCMKoh1TmOMe1Htz0AtsLrb1mhsbERef+tys31eundOeA/iPqUCz2vz29/+NmY/iiLZ4h/cFUVRFEVRFGVDEgqHEUrC6jGZZZqjyzy4jx9v46zf7NMHANDj3n9EfR+OhON/rnSZVhnbHqOsh6M+U5EHAiq8ezP/fagIQOdQ3qlgUo2QcYTBeVQ6qIJKT+4cp7Bzmudi29PzrPKRUcjsqDb2UFX2zsk111wDAPj73/8OABg1ahSAaBU8kf+6VOCpsFGpW7VqFQDfv5mqGpU3Lkc1LYjMlMrP3AbVLyp00ulGjk355JNPAAAXXXRRvNOgKJsld9xxBwDglltuAQ45FPvuuy9SLr0CAJCSYet8Tr2dNiYYq+f9Djrl/eb+ewEArlth2wTbL8c5UYFn+6aSzl7Z/Px8AH67Ze8t26Ac6xKvN4zzuAzbLZVzblPeazg+RnrPB5V3P5O6fw740EXF3dTZ9desWYOGhgZ072XzWPQc2c07Bulgw+P76quvAPjXRlGSocs8uCuKoiiKoijKBiGUZIx7SGPcW8XcuXMBACkXXwUAKLjLmvCHInLYOOP2XIy3i3lHLd0rWop1B2JU+cbomNrNGXqePv/88wDiKx1U5Rm/J1XTZw+0nusDXTxltuvFSM+LdpGh8v7WDvsDAEZ29MEoG5UrrrAqHhNcBFNO9+xpHRfYW0OohlH9WrBgAQBfFaciJxV1KntUzbl9wFfepBMN1S6q+rNmzQLg+74PHz48an1mYPz8888BqPOD0rm5+uqrAQCPPPIIWpdeJjFybEp5eXnUVGZKZS8W22ZBQQEAXzWn84tcT8alB+fJbVM5l71yjCun4s7fL94fuJ7sOW6JNWvWxIybAezvpIz553MIr4WitIYu9+CuKIqiKAow75yzsN1226HpXBv21eTEpZzK+uZW86BwddugfQD4dpIAcM6XL3VgSTcNmREmYoz1gzQcQF9b4+bYl41IdxuO232kFRGeH3MUAODITzv/+VCaJzxsN4RdaFazy7kX2rbS5R7cpRPGY0uXAvDf+KkAMBaPqnL37t3t1MXBrjlR+jZHx7oH591Y03ndJ37zG5s5b+rUqQCildJEmSipku4x8QYAfqwh1612y69Ytw6AP/5gu44uvLJJueqqq2Lm3XTTTQD8OsEpoaJOtwnGwFI1ozomfaKptjGLIhAbq05kRtdBgwYB8LMW/vjjjwB85Y29AKqOKVsSZ555JgBg8uTJ6NvCsm0hFArF+LUT/kbITMpBR5cgbIPStSY4j/cEmVeEy3Ib3Cfn87ed8L4RL/9Ec3ie9QnStaxfvx6LFi0C4J97RWkLXe7BXVEURVEUnxWXX4SioiI0/u4PAIAmZw/pJ2JyD7Ep0QO9mwsVfWCXowAAp308ZUMVe4NDpT2ouDOs1nCgfR0Vd/vEntLDvgblFdnpjgVzN0ZRlS5El39wp9qbLP/4h3Wjyb3J3uCkEghsmTGwRxxxBADgrrvu8uYxlpDKBWMHL7/88o1bOKXTcN1110V9pgLPukSlXcaZUlVjzCzbG3vEGJ/ax7lGAbFjLqQvu1TUuC+b+0FRugYnnXQSAOD+++/H8A2w/W7dusXkTmB7ZXvmOBL2ytLhKZFjTNDdTHq7cx22Z+6Dveicz948us5wveYyPjdHXl5eTKbYIF9//TXOPffcVm1TUeLR5R/cFUVRFEUBfrrgTIwYMQKp59sB5n6Ai0tIKHwipeLOz3ZZ+5B+z9a/AODHv18+752OL3gHc8dIW2YaK0QCD/HSOtpT3N3T1LTFlTDGYI9BvQAAPbe1CZmWbsgCK10KfXBvJV1dTd4SexOUTQ8VOapnVNilCibjWQkV+6DrjHST4LqJMi2q0q50ZagGX3vttdh3A+4nLS3NG1PGdr/OjXfilG1T5nPg90HFnfN69eoVtR/GtMt1GNNOVZ/zpatMa8nJyfHKTVesIKq2Kx2FPrgriqIoiuLx/nGHYdttt0W362xoaIZnZRxtcdys4i7i3iNNdvrXQfa14E8Lpm2g0refVFd2xranxthF+w48aIjvwJPSqz8A4L977ou//OUvHV9IpcuiD+6KomwyqIpTDZdORVSwOF/6OHM9erAHs6TKjKlSWeM+GF+rKAq8h8xLLrkEh2zA/axfv95ri1TgZXw5Y8Y5ZQblYM8a53F8DNs9p4xlp5LO+RyTxW3R+Yb3lNZSX1/vxdPPnDkTR/9yWwDQh3alw9EHd0VRFEVRYnjzV+Nw6KGHIu28P9kZlUxMKBMNRSvw9n9Ezatzijtj3x/a+UgAfuz7Bd9O7djCtwPpJhOMa/dcZejjXl+HeFz9xve44447NmQxlS5KuOVFNi7Lli3D8ccfj4KCAuTl5eHXv/513HgxRVE6f3u59tprce2116KhoQENDQ2oqqpCVVUV6uvrUV9f732urq5GdXU1mpqa0NTUhIyMDGRkZKBHjx5Rf+Fw2PuLRCJRf8HvwuEwysvLUV5ejnXr1nlxsIqiKIqyObNZKe4VFRXYf//9UVZWhquvvhqpqam48847MXbsWMyaNSsmUYKidGW0vSiKsqGgWnzeeecBB++BsWPHAgAGDx6MNcedDQCI1LkkZ05Fj1YCo1X4ardMJBR2n6Nj3+/bwVoOU8s/58uXvBfqlStXAgBOOeWUhOWdMsX6xTNsjuE3MhyP4SwcvNqvXz8ANtTlH1sdCADokWZD9Ki4h+LEuBt3PHSVmTdvHmbMmGGP5b77EpZTUdrLZvXgft999+Gnn37CZ599ht122w0AcOihh2L77bfH7bffjltuuWUTl1BRNh+2pPZCR5dbb70VgO/PThj7yvjUoqIiAMDQoUPjLg/4P8yMZZcx70uWLInat6IoiqJs7oSMzErSDNOmTcMBBxyAF198EUcffXTUd5MnT8bJJ5+Mjz76CHvuuWebCjNmzBgAwGeffRY1/5BDDsH8+fMxb968Nm1XUTYF1dXV2GWXXQAAX331lWdZuHbtWmy33XYYMmQI3n//fW8AZmvZEtsLH9zlQ3ayD+7BXgauIx/cOUht1qxZAJpX8RRFiebaa68FAOy4447IusQOvKx1ynt1ox/7zpj2mqboGHcq7XUx85uiPjP2fZ8nbm5VG33yyScB+BaxPXpYH3UmWpLJnDg4NRQKeZ7zPdOtptkvw04H9vAd7bsNLwQALDr99/jmm28A6ADUrk55eTny8/NRVlbm1bOOXF7Sqhj3cePGYeDAgXjqqadivnvqqacwbNgw7LnnnqitrUVJSUlSf6SpqQnffPMNRo8eHbPtMWPGYP78+d4ocEXpDGRmZuKxxx7DvHnz8Oc//9mbf/7556OsrAyTJk1CJBLR9qIoiqIoSlK0KlQmFArhlFNOwR133IGysjLPZmn16tV46623vIeTp59+GqeffnpS26Tgv3btWtTW1qJv374xy3De8uXLMXLkyNYUWVE2KbvvvjuuuOIK3HbbbTj66KOxcuVKTJkyBXfddRdGjBgBQNtLkKuuuirq88033wwgVoHnMcoELcHELJwnrSX5QrNixYoOLbuidAWi1OXjjsNNN93kfdzpvme8/70Y9lD8qR/zjv9v7/5Dqrr/OI6/rG+3az9nmJoQlWxu2UZJu1lRWQSmg8KVrT+Ka7E5vrKY2S/6Q62w35QkVt4/yqTpKCgoKFY4RxbFGhVSsc0i8o9oho7UnJZe9fuH33PM7lLvzR+dfD7gYp5z7ud8TvA5vXufz+d9/v+zbYy/nom/4UzTDWeamYF3tZZ32j8jO5+fny9JCghoy5AbmXbjHmHcU1paWpQdESdJGvWftm2vV5X5oCBXly5dMs+RkZEhh6Tly5d32hegN3g9x93pdGr37t06ffq0vv76a0nSqVOn5Ha7zQGzaNEiFRUVedWuUad16NChHvuMf5yNYwAr2bZtm86fP6/ExETV1dUpOjpa33//vbmf8QIAALrD68D9k08+kcPhUGFhoRm4FxYWaubMmfrwww8ltWXD/i0T2BljPlpni8yMYwArsdlsysvLk8PhkN1u1/Hjx83sj8R46UxaWlqH340FtyNGjJDU/gTC+Ps0XtQktVeRMDJrRqbtjz/+kCRt2rSpt7oNDBgZGRnmn//75Ikk6dNPP5UkhYeHq2bVug7HG8/OPKrLvCEzb/zMafKuzO3q1asltVd4MdbDGHPeX70HJ908Y66JMarO3L9/X7WS7t27p+vHj8vlcnl1fqC3+FRVxul0KiUlRY8fP9bLly/166+/6tChQ+b+hoYG1dTUdKutkJAQSdKYMWM0dOjQf318bWwzyjYBVmM8Zn3x4oUePHigSZMmmfsYLwAAoDu8qipjqKqqUmhoqHbu3KmGhgbt2LFDT548Mf8nm5+f7/WcXUlyOBzy8/PzqJIRExOjhw8f6uHDh952Feh3d+7ckcPh0MqVK1VaWqqqqirdvXvXXCPCeOm+ffv2SZJiY2MlSc3NbdUsjCcPr04dMjLuxtShx48fS2ormQmg7yQnJ0tqH4tGttsYv9nZ2X3Wl5SUFEnta16Me6rxpDI3N7fP+oL3Q19XlfEp4x4YGKi4uDgVFBToxYsXio2NNYN2ybc5u5KUkJCgLVu26ObNm2a1jLKyMv3yyy/auHGjL10F+lVTU5NWr16t0NBQZWdn69GjR3I4HEpNTVVeXp4kxgsAAOgenzLuknTmzBklJCRIaluc+tVXX711Z54/f67IyEg9f/5cGzdu1JAhQ5SVlaXm5maVlpZq7Nixb30OoC9t3bpVmZmZKi4u1oIFCyRJO3fuVFpami5cuKAvvvjC57YH4ngxMnMxMW31lo0FuMZtzKjRLrVXk6mvr5fUXu9+3bp1fdJXAMD7752u4/6qxYsXKyAgQKNHj9aSJUt8baaDkSNH6vLly5o3b5527Nih9PR0TZ06VSUlJe9lEIL32+3bt7Vr1y6tXbvWDNqltjd1OhwOJSUlma/09gXjBQCAgcXnjLvb7VZoaKgWL16sY8eO9XS/AOCNfv/9d0meVXVereNuzHE35vobTwgBAOgplsm4nz17VpWVlXI6nb42AQAAAKCbvF6ceuPGDd25c0eZmZmKjIxUdHR0b/QLAN4oIiJCkrR58+YO2199gGhUrMjKyuq7jgEA0Iu8zrjn5uYqOTlZQUFBOnHiRG/0CQAAAMBrfJ7jDgAAAAxklpnjDgAAAKDvELgDAAAAFkDgDgAAAFgAgTsAAABgAQTuAAAAgAUQuAMA8I5paWmRy+XStGnTNGLECAUHBysuLk7Xr1/v764B6EcE7gAAvGM2bdqk5ORkffbZZ8rKytKGDRt0//59RUdH67fffuvv7gHoJ16/ORUAAPQet9ut3NxcJSQk6IcffjC3L1++XGFhYSosLNSMGTP6sYcA+gsZdwAAOlFeXi4/P783fnpaU1OTGhoaFBwc3GF7UFCQBg0aJH9//x4/JwBrIOMOAEAnxo4d2yHzLbUF16mpqbLZbJKk+vp61dfXd9nW4MGDFRAQ0Okx/v7+ioqKUn5+vmbNmqW5c+equrpamZmZCggI0Lfffuv7xQCwNAJ3AAA6MXz4cK1atarDtu+++051dXUqKiqSJO3bt0/bt2/vsq0JEyaovLy8y+MKCgq0YsWKDucNCwvTtWvXFBYW5t0FAHhvELgDAOCFEydO6MiRIzpw4IAWLFggSXI6nZozZ06X3+3uNJeRI0dqypQpmjVrlhYuXKiKigrt2bNH8fHxunr1qgIDA9/qGgBYk19ra2trf3cCAAArKC0t1ezZsxUfH68ff/zxrdqqqalRQ0OD+bvNZtOYMWPkdrsVGRmp+fPnKycnx9z/4MEDTZkyRampqdq7d+9bnRtAz6itrdXo0aNVU1OjUaNG9fjxr2NxKgAA3fDs2TMtW7ZM4eHhOnr0aId9dXV1qqio6PJTWVlpficlJUXjxo0zP0uXLpUkXblyRffu3dOSJUs6nOOjjz7S5MmTde3atd6/WGAAOXz4sCZOnCi73a6oqKh3uuQqU2UAAOhCS0uLVq5cqerqav38888aNmxYh/379+/3eo775s2bO8xhNxatPn36VJLU3Nzs8f2mpia53W5fLwPAa06dOqX169fL5XIpKipKBw8e1KJFi1RWVqagoKD+7p4HAncAALqwfft2Xbp0ST/99JMmTZrksd+XOe4RERGKiIjwOCY8PFySdPLkScXGxprbb9++rbKyMqrKAD0oKytLSUlJWrNmjSTJ5XLpwoULysvL05YtW/q5d56Y4w4AQCfu3r2rqVOnat68efrmm2889r9ecaYnxMTEqKioSF9++aViYmL0119/KScnR42Njbp165Y+/vjjHj8nMNA0NjZq2LBhOn36tOLj483tiYmJqq6u1rlz57pso6/nuJNxBwCgE3///bdaW1tVUlKikpISj/29EbifO3dO+/fv18mTJ3Xx4kXZbDbNnTtXmZmZBO1AD6mqqlJzc7PHy86Cg4P1559/etVWbW1tjx73JgTuAAB0Yv78+errh9P+/v5KT09Xenp6n54XgHdsNptCQkI0fvz4bn8nJCTEfHmbtwjcAQAAMOAEBgZq8ODB5oJww9OnTxUSEtKtNux2ux49eqTGxsZun9dms8lut3vVVwOBOwAAAAYcm82m6dOnq7i42Jzj3tLSouLiYq1du7bb7djtdp8DcW8RuAMAAGBAWr9+vRITE/X5559rxowZOnjwoP755x+zysy7hsAdAAAAA9KKFStUWVmpjIwMVVRUaNq0abp48aLHgtV3BeUgAQAAAAsY1N8dAAAAANA1AncAAADAAgjcAQAAAAsgcAcAAAAsgMAdAAAAsAACdwAAAMACCNwBAAAACyBwBwAAACyAwB0AAACwAAJ3AAAAwAII3AEAAAALIHAHAAAALIDAHQAAALAAAncAAADAAgjcAQAAAAsgcAcAAAAsgMAdAAAAsID/AQRae2nxGeJIAAAAAElFTkSuQmCC", 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", 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", 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ccMEF5qabbjLTp0833377rbnssstMZmammTlz5gYsefvokgf33/zmN8YYY1599VVv3gUXXGCMSXzg5N+FF15ojDHmwgsv9Obxh7a5udlsvfXWCevw5vDAAw8kLefxxx+fsM7OO+9sjDHmk08+SVBNAJhddtnFGGPMCy+84M3717/+ZYwx5mc/+1mbx/7KK68YY4zZaaed2lw21QPP1KlTjTHGnHPOOQnr7LjjjqalpcVUVlaa7Oxsbz4f0IJqHP922223pPtJ9ZednW3q6upCN3mel0suucQsWLDAU5IAmKOPPtoYY8zdd98d2k5HH9yDLwf840tN8OErLy/P1NTUmObmZjN69OiEdc4//3xjjDFvvPFGQj3ZUA/up59+ujHGmKlTpyZ8l5GR4amBqfZ31113Jd3uujy4n3XWWcYYY958882E5WOxmHd91seDe2e1ydb+cnJyTGNjo/nss8/ada54HM3NzWarrbYKfReJRLwer+uuuy5h3dtuu80YE+6lWtfjIrJXC/BfUGVv2Lo+uPfv398YY8z8+fNTLsNtpyJV+26rjRx++OHGGGO++uqrNtcfNmyYufrqq01LS4uZNm1aqDelrb8TTjjBGGMfxKPRaJvLL1y40BhjzI9+9KOE73iv+/bbb0Pz22qnfHD/4osvkn7//PPPG2MSxQX+sbczOHj4yy+/NMaYhJ7qVH/GpL7frUtdXdd7Gv94H0rWG5rsTx/claOOOsp7kSXHHXec9xKbLtttt11IpNrY6JIETLQkM4HY38MPPxwA8NxzzyVd5/333weApLFxs2bNwrfffpswnzGZBx54YMJ38XgcL7/8csJ8luOFF15IGps8a9YsVFVVhcoxY8YMAMANN9yAo446qlUPYi77r3/9C2PGjGm3r31GRgb22WcfAMDjjz+e8P2cOXMwe/ZsFBYWYpdddkn4/o033kiYx3M3ePDgtMrQ0NCA6dOnY8SIEV6cO2O7p06diqlTp2LPPff04tyD33Um6R7L7rvvjry8PMycORPz5s1LWOfRRx8FAOy///5dZkHJOjp58uSE75qbm/HMM8+0uv5LL73UaWXh2JOnn3464buWlhY8++yznbavIJ3ZJsmQIUPw29/+FnfccQf+85//YMKECbj33nvR2NiIrbbaap3KuXDhQnz33XehecYYz/4zWb384YcfAITrZUeOq7GxMenYl/a25bZgrDhjsVvjmWeewcSJExP+Vq5cuU77TvY7ESQ4jmTx4sW45ppr8Prrr+PAAw9ETU1N2vs57LDDAAAPPvgg4vF4q8sOGzYMm2++OVavXo0333wz4fspU6agrKwMW221FQYOHJjwfVvtdMqUKQnzIpEIDj30UNTU1OC///1v0vXk7+PgwYOx3XbboaysDE899VSr+0yHdamrHb2nrV27FgDQv3//dS630rPYb7/98NZbb3n3wS+++AIffPABjjzyyLS3EY/HUVVVhT59+qyvYnaY9eLj3hb9+vUD4DdMAN7gqY8++iitdYOk8svmoLghQ4YkfLd69Wo0NjYmzGc5brjhBtxwww0pyxEcaDZx4kQcfvjh+OUvf4kpU6agrq4On376KV5//XU89NBDWLVqlbfsLbfcggMOOAAHH3wwpk6d6g3ueeWVVzBx4kRUVlam3CcA9O3bF9nZ2VizZg1qa2tTHvcuu+ySdJDm0qVLE+bR77i1Fw7J1KlTcdBBB2Hs2LF4+OGHMXbsWFRUVGDGjBmYOnUqTj/9dK8R8cH93XffTXv76ZDsWKqqqgCEj4XXn/VBUlFRgfLychQXF6N3796hermhYBnbqsupWLx4caeVhQ99qdyMOnNfQTqzTQLARRddlDBYszNYtmxZ0vlsR8m+T9bG1vW4AOvIlOwhM1n97wgcxMvttsb//d//dWrugmS/E0Ho456VlYXRo0dj9913x09+8hNcfvnluO6669LeDwd2zp8/v81l22qn/K53794YOnRo6N4PtN12kn3fr18/FBYWAgCamppaXZ/njMfEF8aOsi51taP3NP4WFhcXp19QpUdz6aWXorKyEttssw1isRhaWlrw97//Haecckra27j11ltRXV2NE088sV37rq+vT/r7lYqsrKyUhgVt0SUP7rvuuisA4KuvvvLmcST+008/3apa8s0333RKGerr65POZznef//9tG7kgH1DO+mkk3DjjTfimGOOwSGHHIK9994bBx10EC699FL8+Mc/xrRp0wDYH8BDDjkE+++/P376059i7NixOOSQQ3D44Yfjsssuw4EHHojvv/++Q8eWSqFiWTuDqVOn4qqrrsLYsWPxyCOP4MADD8QHH3yAeDzuKetjx47Fp59+il122QVfffUV1qxZ0yn7Jp11LEDr5ywZyZwjupJU9bktNqbj6Mw2uffee+P2229HeXk5zjnnHEydOhUrV670bqzLli1L+kKfDm3Vu3Tr5bocV3v30VEqKioAwHtw3JAk+50IIl8UTjzxRDzxxBO46qqr8Prrr2P69OkbpJyS1u4lbbXTZN+znlRVVbXZ20VXnc6mI3V1XeFLY3l5+QbZn9L9eeqpp/D4449j0qRJ2H777TFr1ixceOGFGDJkSFrWopMmTcK1116LF198sV3OVPX19eibW4BatKS9zqBBg7BgwYJ1enjf4A/uRUVFnsdusKt36dKl2GabbXDjjTdi5syZ7dqmtCWU85cvX572tqjivvDCC54ncLrMmjULs2bNwrXXXovCwkJcc801uPjii3HnnXdi7733Di374Ycf4sMPPwRguwLvvPNOnHzyyfj73/+e0p4QAEpLS9HQ0ID+/fsjLy8vqepOdSSVMtgZTJs2DQ0NDRg7dix23nln9OnTx3tgX7RoERYuXIixY8fik08+QSwW6/QwmfbA65+qnhQVFaF3796ora0NhQQ0NjaioKAg6Tqt2a+tCytWrGi1jKnmtwUfVNtzHCxLqmPs7GNvi3Vpk8ceeywA4K9//SseeeSR0Hc5OTkJ9n5dQUfuNRuK1atXA8AG7zaORqP4xS9+AQAp7XAlTz31FA455BD89re/xfjx43HooYemtR57lrbYYos2l23rXhL8rrPuvyUlJairq0M8Hk87oy+PadSoUZ1ShnWpqx29p/Xu3RsAOl3wUTZdLrnkElx66aU46aSTAAA77rgjFi1ahPHjx7f54D558mScffbZePrpp73wuXRpbGxELVpwCoYiKw2X9UbE8fjKZWhsbFynB/cNLrfddtttKCgowPTp0/Hxxx978xkvyB/c9rDLLrtgyy23TJjPi/fBBx+kva2OlCNIVVUVLrvsMsTjceywww6tLrtmzRpcc801ANDmss3Nzd554/EF2X777bHzzjujqqpqvSZLqa+v9+LcTz/9dADhH1jGuf/kJz/xPqcLHzbbG/+fihkzZqC2tha777570npy6qmnArAvU0G1bMWKFejXr1/Sh5ZUDZtlz8ho3zsxY1STdc/FYrGQ/3174I+nzCUA2B/G3XbbLWE+XyiT7TMajXr+z+myrueErEub5I9+snCqE044IWlPQ0fL2V46616TDuvaptasWYMVK1Zg2LBhCbkZ1ieXX345Nt98cyxdurRdYyquueYa1NXV4ZBDDkma+yMZ//vf/wAAZ511VptjXJYsWYJFixZhwIABOOSQQxK+/8lPfoI+ffrgu+++SwiTWVdaWlowdepU9OrVK+2XkRUrVuCrr75C79690/bBb2xsTFn316WudvSextwi6zvpl7LpUFtbm3Bvj8VibfZQPvHEEzjjjDPwxBNP4Kijjlrn/eciitxIGn8dfPTeYA/uI0eO9N5oqqurcdZZZ4W+//e//41Vq1bhz3/+M84555yEG2gsFsPhhx+O7bffPmHbsVgMd911V+iHZbfddsP555+PeDyOe++9N+1yTp8+HW+88QYOOOAA3H333Um7iHfaaadQZr5TTz01abmOPPJIRKPRUKzwb3/7W08RD8IH3HSypN51110A7I/UyJEjvfkFBQW4++67EY1G8e9//xsNDQ1tbqsj8GH8N7/5DcrLy/H555+HvsvOzvYe6tsT305Va/To0Z1SztraWjz00EOIxWL417/+hby8PO+7rbbaCldccQUA4J///GdoPZaZ35NLLrkk6YDnjpT96aefRklJCQ4++OCEZCbXXnvtOivuCxcuxKJFi7DTTjvhZz/7mTc/Ly8P999/f0IiIpaltLTUG7cR5Iorrmi3ildSUoLGxkZsscUW6xSasy5tkoOTzjrrrNDDyLbbboubbrop6X46u961xboc17rSkWN7//33kZGR4YWurE8GDhyIf/7zn/jb3/6G5uZmnHHGGW3GdQdZuXIl7rvvPgC2tyUdnnvuOcybNw877rgjbr755oSH1+222y50n+X99/bbbw+NuRo4cCBuueUWAMA//vGPtMucDn//+9/R0tKCCRMmJE2Wlp+fjzPOOCOk3t14441eOXfcccfQ8tnZ2Qniw/LlyzFw4MCk94R1qasdvadxoGtnj41SNl1++tOf4u9//zteeeUVLFy4EM8//zxuv/320AvnZZddFqqPkyZNwmmnnYbbbrsNe++9N1auXImVK1d6YYIbJenaz7THDpLJGx5++GHz/PPPmy+//NK0tLQYY2xynKDHcfBv77339izVmIzlscceM//73/88f+KgBznt21566SWzaNEis3z5cjN58mTz2muvmYaGBmNMcms2Y1r3q+3fv79nLbV27Vrz9ttvm8cee8y8/PLLXpKfO+64I8Gq67vvvjPPPfecefzxx81HH31kWlpaTHNzs/nFL37hLfv5558bY4yZO3euefrpp80TTzzhzautrTX77beft2w6CZhqamrMyy+/bJ588knPM/ejjz5KmYBJWrOle06S/R166KHeNX/55ZdD3wU9r6WdW1tluuiii4wxNmHPpEmTzAMPPGDGjx/vfd+a5WKqcxZMwLRy5Urz5JNPmilTpnjJsJJZjm233XampqbGGGPMzJkzzdNPP22++eYbU1NTY+6++25jTKKV2bHHHmuMsYlZnnrqKfPAAw8k2JGm+vvZz37mJT2aNm2aefzxx83cuXNNQ0OD+fe//510f+nYT55xxhnGGGOamprMW2+9ZV588UWzYsUKM2/ePK/utpaA6cMPPzSPP/64mT17dihxymWXXZZ2XXnxxReNMdZy7+GHHzYPPPBAyJ+5s9tknz59zPLly40x1spw8uTJ5o033jANDQ3mySefTGmPyAQzn3zyiXnooYfMAw88YH7605+22R7buhap7EPbe1xtnatU+2mrTbX2d9pppxljUnujr6uPO5OUTZw40Tz77LNm5syZXp1bvny5Ofzww9dpfwMHDvTarfQvT/W3/fbbe/Vl2bJl5plnnjHPPvusmT17tjEmMQETbX3LysrMs88+a5577jkvAdNzzz2XMgFTqjLTDlLaeAb/fvvb33rnZ/bs2eaZZ54xTzzxhJk2bZqXSK5Xr16hdehf3dTUZN577z3z+OOPm7fffjuUgEkuO3/+fPPoo4+aBx54wPzf//1fh+rqutzTAJj8/HxTW1ub8rcj2Z/aQSqVlZXmggsuMMOHD/cSMP31r3/1ngeNsW0smAMo1TMR76XpUFFRYdtoZLj5Q3REm3+/jQw3ALx7RntZLw/upLGx0ZSUlJjZs2ebCRMmmJ///Odt+uQOHDjQ3HjjjWbOnDmmurraVFdXm++++848//zz5rTTTgv58wZ9lwcPHmweeeQRs2rVKlNXV2c+//zzlDdBY9p+SM3Ozjbnn3+++eCDD0xZWZmpr683ixYtMu+8847505/+ZIYOHeote+CBB5q77rrLzJw506xZs8bU1taa77//3kyaNCnhJeXoo482Dz74oJkzZ45Zu3atqa6uNt988425//77E7zo23pQOPXUU80HH3xgKisrTW1trZkzZ4657LLLkiZKWR8P7rm5uaa+vt4YY8yf/vSnlD+wzH6ZbplisZi57rrrzHfffec1uGDZ1uXBHbB+7ldeeaWZO3euqaurMxUVFea9994zJ510Uspj3Hvvvc3bb79tqqurTXl5uXnllVfMTjvtlPIBCbA5CbgPr6GleU4POOAA89Zbb5mqqipTXl5u3nzzTbPPPvt02Dd+3Lhx3oP3ihUrzP3332/69OnTar0YM2aMefvtt72yvP7662bPPff0Eqj95je/Sfu4+vfvbx5++GGzfPly74c86Jfe2W0SgBk6dKh57LHHzJIlS0xtba358ssvzZ///GcTjUZTPrhvscUW5rnnnjNr1qwxzc3NoXO+Ph7c1+W4WjtXqfbTVptq7S8nJ8eUlZWZuXPnJv1+XR/cSXNzs1m7dq35+uuvzaRJk8yvf/3rUA6KddkfM4Y++eST7aqjN998s/nmm29MbW2tKSsrM1988YUZP358QlbpWCxm/vCHP5gZM2Z4v1PTp0835557btLfuM54cAesn/qECRPMggULTH19vVm7dq2ZM2eOefDBB81PfvKTpOv89Kc/Na+99popKSkx9fX1ZvHixeall14yP//5z0PL5eXlmX/+859m0aJFXrZTWdfbW1eB9t/TAPvbZowxF110UdrXTx/cla5iQz+4R4xJz05j5syZ2H333dNZdIMxZswYTJ06FRMnTkx70I6iKB3jtddew49//GPsvffeXebcoWxYbr/9dlx00UXYfffd220eoCjt5fXXX8cBBxyA4cOHp23PO2PGjKRjdhRlfVNZWYlevXrh3OhwZEfaDgdtMHHcG1+MiooKFBUVtXt/G48XnKIoGw1DhgxJsMOKRCK48MIL8eMf/xjz5s3Th/YexPjx470B94qyPtl1111xxBFH4LbbbuuSnBqKsrHTJT7uiqJs3Bx44IF47LHH8Pnnn2PRokXIzs7GDjvsgJEjR6KmpgZnn312VxdR2YCsWbMGt9xyC6655hrssMMOmDt3blcXSdlEueqqq7Bq1SrcfPPNXV0URWkXsUgEsTSyr8fQsQztqrgripLAjBkz8Mgjj6C4uBiHH344jjjiCMRiMTzyyCPYc88922Wxqmwa/O1vf0MsFtOHdmW9cuyxx2LQoEGtJmJU0mPixImIRCLeX0ZGBoYOHYrTTz99veZ5UdYv3Vpxf/fdd9v03VUUpf18//33CZatiqIoSvfjuuuuw8iRI1FfX4+PP/4YEydOxAcffIC5c+euUwIgJTmxiP1rc7kO7qdbP7griqIoiqIoqTnyyCOxxx57AADOPvts9OvXDzfddBNeeumlpAmylI0bDZVRFEVRFEXpITCB4Pz587u4JJsWjHFP568jqOKuKIqiKIrSQ1i4cCEAoHfv3l1bkE0MDZVRFEVRFEVROkRFRQVKSkpQX1+PTz75BNdeey2ys7Nx9NFHd3XRlHVAH9wVRVEURVE2UQ477LDQ5xEjRuCxxx7DZptt1kUl2jTZUHaQaT+49+vXDzk5Oaivr+/QDhVFURRFUTqLnJwc9OvXr6uLsdHyr3/9C1tvvTUqKirw0EMP4b333kN2dnZXF0tZR9J+cB8+fDjmzZuHkpKS9VkeRVEURdnkeemll3Dttdfi0UcfxXbbbdfVxenW9OvXD8OHD+/qYmy07LXXXp6rzM9//nMccMABOPnkkzFv3jwUFBR0cek2HSJIz/Gloybm7QqVGT58uDYORVEURekgs2fPBgBss8022G233bq4NEpPIRaLYfz48Tj44INx991349JLL+3qIintRO0gFUVRFEVReghjx47FXnvthTvvvFPDnzsRtYNUFEVRlE2chx56CK+//nrC/AsuuACFhYVdUCKlJ3DJJZfghBNOwMSJE/G73/2uq4ujtAN9cFcURVGULuLee+9NOv/000/XB3dlvXHcccdhiy22wK233opzzjkHsVhH3cWVDeXjHjHGmA5uQ1EURVEUJS0efvhhAEDfvn0BALm5uaHv+VhSU1MDADjmmGPS3vaLL74IAMjPzwcARERYQl1dHQCgtLQUADBu3Lh2lV1RJJWVlejVqxeuzh2FnEjbEej1Jo5r635ARUUFioqK2r0/VdwVRVEURVEUpQNYxT0dH/eOoYq7oiiKoiidzpNPPgkAGDRoEAB43uHRaDQ0pSoej8dD6/Mzp7NmzQIAnHvuud4yDDXaZZddkm6b8DMfeeS2GxoaAAArV64EAPzyl79s17EqPRcq7n/PH4WcSNuP5fWmBX+tUcVdURRFURQlJdMPPRkAsOMO/QEAm+03CgDQf9etAQCfDTmwawqmKO1AH9wVRVEURekwd911FwA/dn3kyJEAgKysrNByHAjJOPTMzEwAvhpOGONeWVkJANh8880BANdcc423zF577RVal9vklMhY92QUFBR4uWomTZoEwI+F/8Mf/tDm+krPJl2rx1gHUzDpg7uiKIqiKD2GqLP+iGbZR6BIVg4AYKfoaiAH+Ky+uKuKpihtog/uiqIoiqK0yrPPPgsAGDBgAABfJQ/GpQ8ePDi0DlVuTltaWkLrNDc3A7BKNwBkZNhHEiYFkjHwjJHn8sF5XIbrcFs5OTmhfaVDJBLxegl4TB999JH3PffR1NQEAFi9ejUA4Pjjj097H8qmRzRNO8iOZj7VB3dFURRFUdrN8mPOAgB8vNZaLO71xM1dWZy0iWbaUJ1YZlhxj+a5gYIN6W9rv34G6NcfL3y1plPLqCip6PIH94kTJ+KMM87Ap59+ij322KOri6NsYrB+kVgshoEDB+JHP/oR/v73v2Po0KFdWDpFUZSNk2eeeQYA0KtXLwDwYr+pNidL2EP3mOXLlwPwPdOJjGGnCk61nNusra0FkKi8UwUPxqtzHpfhOjKOnmp/OtTV1Xm9AkOGDAHgK/vBbdteBxuHP3z4cLz55psAgIqKCgDAL37xi7T3qXR/NMZdUTqR6667DiNHjkR9fT0+/vhjTJw4ER988AHmzp3rdaUqiqIoqSmeeAsAYNUX9sG8osk+DP/y46cBJD6ob2zwoSqW5RT3HBdmk2sf7j8ti6V9DDvESoEYEK+2D+471S0DsgHTWA8UA1PKNeutsn7QB3elR3DkkUd6PTpnn302+vXrh5tuugkvvfQSTjzxxC4unaIoysbBu+++C8BXz6XanQzGk3PKZfkQzHh4fk81m8tRzaYCT091qZIn83uXbjFcR24jqJinorm52SszyyaPDQDy8vKA+tJWtzVo0CDvXI4ZM6bNfSvdn1iaMe4dTcCkD+5Kj+TAAw/ETTfdhPnz53d1URRFUTZKdiudAQCoXbgYAFBS3wgAaKqxgzIrm+PJV9xIyYnap6qMHPvok5nP2HanjqdxONtHS4B6IF5VZleprQIAmAYbomMa7XS3io/t53gLap7+GPkn/KVzDkLp8eiDu9IjWbhwIQCgd+/eXVsQRVGUjQC6pjB0MDc3F2hdVPagmt3YaB/sGRdPH3YiFXnefxkzzvh0urVQLZeqemue7FyH26CKn47iDvhqPo+BZfPWb0prMwl89NFH2G+//dZtZaVboIq7onQiFRUVKCkpQX19PT755BNce+21yM7OxtFHH93VRVMURdmo2C2jBGgCmqP2EcO4h1lOm+rsw3BFU/dS3HPdU1VWgQ3dySrMAwBE852bTFXy9XbNqwZaqgEA8Qr7NhOvLrfTGpscikp7vNE+2fNcxVvsdNu1L6Nszsvo/dvxnXU4ykaGDk5VlE7ksMMOC30eMWIEHnvsMWy22WZdVCJFURRFUZT2oQ/uSo/gX//6F7beemtUVFTgoYcewnvvvZd216miKMqmyosvvggAGDhwIABrnxhpsXaGiIY79VsabRhKc71V3KtdjDsHn9J6keEpHNjKKb/v168fAD/8hOEpHFBK20iGxPAzQ22CVpScl2qdhoYG3DhyLADgp5tZZZ1Ke6zAxrZ/lzUMZWVliEaBwkI7j+E8+fn5iK9d4J+DMptsiYp7U40dzNri4v/jTfbYqbRLVt16AQBg4P/9I+n3SvclhjRDZUzby7SGPrgrPYK99trLc5X5+c9/jgMOOAAnn3wy5s2bF8rCpyiKoiiKsrGiD+5KjyMWi2H8+PE4+OCDcffdd+PSSy/t6iIpiqJ0CRQuqIo3NzcDTkGPJEmyBPjKe51TlamcU+2mCs7BphzwOmDAAAC+Yh5UxQFg7dq1APyBpUTaQAYHp3Iey8/PnK5duxZFGfa7nN62nFlFLra9sBgAMHjw4IQBsjk5ORhZ8Q1QswbNTmUHgOa1NkNqQ7mNeW+qcbHtHFDrFHdi3DmKxMKJ7pdcdTYAYNh1D0LZNIimGeMeTWOZVtfv0NqK0k0ZO3Ys9tprL9x5553ejVpRFEVRFGVjZqNR3B966CG8/vrrCfMvuOACL+ZMUTqTSy65BCeccAImTpyI3/3ud11dHEVRlA3GlClTALhkQvDj0gEgkuuSLWU45T3ausbHbUjlnSo4FXh+ptJOVXzVqlUAgOpqp2I79ZoqONeXMfCAn+RJJnEK2kL2z7bbyetry5nT11o9Rov6ettnwqnm5mb0WjUXaAGaSlfabZSs8vZXu7ocANBYZa0um53i3tJo92virTvt8FxKBV7p/qRtB9kxwX3jeXC/9957k84//fTT9cFdWS8cd9xx2GKLLXDrrbfinHPOCQ14UhRFURRF2djo8gf3008/HaeffnpXF0PZRGmtfkWjUXz//fcbtkCKoihdyAcffADAd3ahQh2MK/+kNIL8/Hxs4xT3aJZzc8my4kbUqcWM56UDS3thmGJlpfVCp+JOZZ1iCpX62trahG2Ullpfdar+VPGpuGdnZ2Ngti1//kDrapPXv9huv/cAbzvcR1ZWFlqc0t5csgIAULNirbdc3RqbMbWx0palud7Ftru4/3hL2DIk6uRVKuz8zHP5zVk/BwBs858XEo5N6V6k7eOuMe6KoiiKoiiKsunT5Yq7oiiKoijrF44hKy4uBuAr1I2NjaEplefs7GxEcqxCnZlnXWFiOVZZz8y3SnyvzLBPO4kIRZHqdzA2HQCqqqpC+6ZaTr93Gb4oY+YB3xVH5uVoaWnBq0f+FgBw5Kje9pgHWB/3jD79AQC1Q3aCKxgikQhyl30BAGgodUr7yrVuWuptt3a17R1oqLS9As0ui2xLU0u4rFTavZj2sNLOaSRq588+6ScAgJ0mvwqle9LjYtwVRVEURVEUpTuyoUJl9MFdURRFUTZxqEwzy2lubi4AoFcv67DCWHeq5YWFhUB5etumUi4Vcqmwy/ncF6cso1TsqbSz7MGkeXIdliWoyqfCOKVdUboT+uCuKIqiKEoC0VwbshJ1YTUMmckusiEzfbI2bieu4Xn2Qb9wiD2O/EHW/pGDUpvE8i0uRKZxjU24VLPMJluqWlrmLVOz2tpA+qEyHJTqEkHFwqExcjBq1IUXeSEzmc7yMlcfx7o70UgkreRKHU3ApDVFURRFUTZR7r77bgDAdtttB8CPBWd8OWPdqVxTiae63RGofkuFnZ9ZFu6Tqj/VcrrVcPmamprQ8kF4HNwH4+bbQpapq7n77rtx/vnnd3UxlI0YfXBXFEVRFCWB2pH7IBKJIGPpfABAdm+bUyWvn31IHpJTAQC4unhHAMD1VV91QSkTuX/34wAAhw+wSnvRZnZQav7gPgCAWN9BSddrKbMKe60blFq9ohwAULm00lumZrW1gayptXp9Y9w+8NMFkgMPPatMN/g0Wyjumblhi82Men0c6+5EYhFvsHGry6niriiKoihKMgYMsGEhVKtTqdlUv+noQtqjRNM9RpLqQYXzGWcvM6pyKv3bpYsN4PvQU3lvLxtLrDuvl6KkQh/cFUVRFGUT5dit/MzjkawcAC34qLR9senRwmIAQE5fq1wXDrbb7DPIKtEjG+wD+/8V7uCtc2vV3HUtcocZ4WLbew+x4T8FQ21se2a/gQCAFYWjUFRU5C2fs+ATAEB9mYttX2HtH6tXVLlpjbfsimobglPZbF8qqLgTKu5U2nNdzHums4vk51yn2Gfm2MewDDftP/42vDP+NgDAwZ9/0s4jV7qSaCyCaBqKu8a4K4qiKIoS4qmnngIQfnAnxhjU1dUB8NVrOsLImO+mpiZkJ2yhc+A+qdSXl5cDSIx1J8y0ysyqwXk8DmZhTWe/itId0Qd3RVEURdlEMXW+Woxmq/LuX2zdYaL5RQDi+Gxt60nUY72sYp3dz8aI57lY8aLNrCI9ssw+SNc5Z5Wu4rEDTwUAjHUx+L02tyE4+YOdm0yK2PZ4hVXY69aUA/CTLFUtt+duWbU/0HVlvX2pqHAKeqLiHo5tz3USfEGGU9pjdvkc932BC47PddttDsS6v7HN7gCAw7+Z0dphKwGWLVuGv/zlL3jttddQW1uLLbfcEhMmTMAee+yRdPnnnnsO9957L2bNmoWGhgZsv/32uOaaa3DEEUe0f+exqJdwq1UiHRsIrQ/uiqIoirKJ4YeCVKe1rHRqoQpeWVmJ4nXYf2se6ZxP5xoq6OwFYGy7XJ/LMZ49OE9mTm2NzMzMjSamXek8ysrKsP/+++Pggw/Ga6+9hv79++O7775D7969U67z3nvv4Uc/+hFuuOEGFBcXY8KECfjpT3+KTz75BLvuuusGLH366IO7oiiKomyimHp/YKdptGElkSZhlZhd3Oo2FhduiYqKCmzXdzAAIH9QCQCg1+ZWmW6qsdvb4ht/cOq/Bu0GADh/1efrXvh2wtj2/oPtANvCocUAgLwhdsBnzJVf0kLFvdS65NSW2BeIygp7vqiyA8By9//5c6Z4Lw0M0+Eg2+LiYm/+pb13AQBUu5h4X3m303qn2Oc7ZT63xVdjcwP7VdrmpptuwrBhwzBhwgRv3siRI1td58477wx9vuGGG/Diiy/i5ZdfbveDeyQa8fz7W10OHXtpTEPTVxRFURSlOxGNRpO6rySjb9++3vLxeBzxeBzGmJCjDFXwjsBtk8zMTGRmZiIvLw95eXleGThf/hUVFaGoqAhZWVneH+e1x3c+uOy6HlddXR2amprQ1NSUcFw8d51xzpT0eemll7DHHnvghBNOwIABA7DrrrvigQceaNc24vE4qqqq0KdPn3bvPxqLpP3XEVRxVxRFUZRNjAPqvgYAxKP+QMxIprOAdLHucA+WvZdbVbxis91b3Wa0d38AQO5g685SUG7DcJpqrDLdVOcrxFsstOr13QOtarnQuajcXDF7XQ6nVZ4/5HQAwD59bLgPfdsLhtryMrb923gfDBkyxFsvb7ktS311OQCgocweT325PZ6SRtuDEFTcL/z6dS95VTrcUDozZHN55YA9AQD5MbrS2OtT58XC+y9LjXHVVtvDDz/8gHvvvRcXX3wxLr/8cnz66af44x//iKysLIwbNy6tbdx6662orq7GiSeeuJ5Lu+7og3sX8PzzzwMACgvtaP/KUy4AAOS4hAxMJ21uHw8AWLvWJoNoT0WiowDfGqm8UAGQo/l5Izr22GPbfTyK0p2YPHkyAN81g21AelCzrQy79V8AfLs2L2GK+7zvO++u5xIrSnrcdddd3v9nDW//+s3NzZ4azd8Mxr43NjYCdR0vY7LYd/5O0VWGbVGq6MyGGvSab4/P/LqoqJKMjIyEeHp64vNc8dyxbHTJKS0tXad93nXXXfjDH/6wTuv2JOLxOPbYYw/ccMMNAIBdd90Vc+fOxX333ZfWg/ukSZNw7bXX4sUXX1wnP/1INL3BqZEOZunVB3dFURRF2cRgbLsJPEhEXIw7MtwDcTz8sto3w2Y+rRy8c9Jtzs8dicbGRozuax9AC4dZwae53sZ6NwcUd8NY7SVhe8a/FNtt31I5p13H0xoj88Ox7UWb0U3GZUrtnfwhLO6U9sZyW8aGcusiU1dmz9Ma509/7epPUVFR0Sll/eOXr6K62ir78Xgcj+93EgA/9j3oUtPo1HfV3dNj8ODB2G677ULztt12Wzz77LNtrjt58mScffbZePrpp3HYYYetryJ2CvrgvgF4pO+2Sec3RMO2USTKBA03Xg8AGOySSCye/ToAYPj1D7W5z+O2t12E1dP+BwBY8OpnAIBZUxcBAEY8e2/6B6Ao3ZQJfbYJfQ5atUVgk6VkiO8yXZc1lfW4U9ZbnP0b55MZP7U3+cx8q7plF1mrvaxCa0nHNPHZxbYd9z3/lo4fmKIkobOcUpqbmz0PdarL/fvb35R4PA6s7Xi4S1B5p7I+dOhQAL4XO+dLt5lg7H574siDbjLtUeozMzO9AaeAPwiVsDxy21Ta16xZA8DvUWAPN5X6tlAHnPTYf//9MW/evNC8b7/9Fptvvnmr6z3xxBM488wzMXnyZBx11FHrvP9049ejHRycqg/u6xGGq2xI3nnnHQDAgW308rCrkTcE3pQ++ugjAPAUAd5oNuZ4L0VJxhNPPNHVRVCULqOltjZhHrvxo1n2gTje3BRegEp8/9a3HetvH7AzauwDaEGtVahb6ny3mrhT3A0V5GV2WT7X/LV4JwDA38vX7SXgi5P+5P2/a2/7slzsfNsLhvYDEMiU2n9HFBQUJGwjXlUGAGhwsfoNlbbnoMbF49OrfX3y8zf/g+zsbM+DPqS4u//buByK46KLLsJ+++2HG264ASeeeCKmT5+O+++/H/fff7+3zGWXXYZly5bhkUceAWDDY8aNG4d//OMf2HvvvbFy5UoANuxJvqBtLOiDeyczub/fTcNG1yLe7GPi7Zk3sohT3jmlshfLZEpkq3zUPH0TACBry528bWTuemRom/GqcgBAfalVLupK7U2cg20UZVPg6pwtAPhtSqYbTzW/MZ7Y25Ul+qOjfPBoSa7M0fYr4nrIYm4DUddeY7lWTYvl2Glmvn24qH3+Dm8bTGzDQX+yHSuKoijpseeee+L555/HZZddhuuuuw4jR47EnXfeiVNOOcVbZsWKFVi8eLH3+f7770dzczPOO+88nHfeed78cePGYeLEie3afyS2Yewg9cF9E+HRRx8FAD++q6m81eVzcuxDhOzS46BVDuJh4oK33noLAHDooYd2XqEVRVGU9QKdXkwgmylfMqOeGCQUd+dA02fFLADJXWZisRgqB+8MYwyKaq2KnuNi5+ONgRh3F8JiZGbR5Vbd5kv1tX1szPsVa2Z6v0vSTKG+vt5b/92fnAMA2LrADzOh0l403P5u5Qs3mZycnIRwk3g87glcTTU2FKex2p4Peq7X8OXdmFB4Dn83ZWiMDL/hYFrp987QH66fm5uL0z6c5H2udb0lLx9+NgBV3NvD0UcfjaOPPjrl9/JhfOrUqeu3QOsBfXDvIC8P2QEAUO8aulTXgdQKO6cZmfZmmelcZTI4dbG1GfnhmNlonrW6evDjhd42ZTdgvNIOHqoXCSU42EZRujO/i4wIfc4UinpbUyY/SRYdy3YZdw88ce/BJxzbzrEosUxO2W6z3NT2kFFpj+W59ptf6G+jsBiA35bPTTPfR/OXNiSupdR262Yf9Kv0VlQURVHWC1ZxT8NVJukvT/rog3s3JmhfFxo4lAYccERFQFp/8TNTS9O6csqUKd42WnurVRQlfdiWaecnFT0OFASs4nnm/ltt4BIq3QHezwGgsT4xxp1Es2x9ijdZhTzT/W5kRDkg246B6l32HQBgZd5wTzVmr2x2djbWDt8bGRkZKGywinhus7//FrfteEv4N8kLBXUx73RTuWeo9TevaKK/uW0DfNnu5V6Qt3CDwAcN8cWqXl5su/0dzOofzpQqB7MWLJkBAGhwPQZNVfZcNboMsFTax6/9PCG5EuD3UPOcsP3yd5XniLD9cj3+nlJZ5/aD7RwAht9iLSCD11VR9MFdURRFURRFUTqAusps5Lw2apfQZzkIzpJ8UJvsto86JYGhMVlOUcgqsCP8s4qcrZyzk2P3OuAnc5Bv+C1l1n6Kg1NrSm2ozO+/exuA/+bPN3yuT3sqKhRUBLh8nuvuB4C7774bAHD++ecnPU5F6Sgcu8GeHypbu9x7KQC/XnI+lavvLv4HgGBoTDhExg+S8ZU4htu0mOT2rDEX0sZBqJx6oW0uJEaGtnkhbgXFoWn4f3+wFOAr7TweHufvD7PxwM1rltmyuunae/8CAKhZYe8Jw657EErP4+KLL/b+f/XVVwEA27+f6G7GGPeWeqvkMg6e8+O5VnFvqbDTRWuM18b420B1eeDAgSh2seSm0Y9FL5CONQ4vtMyZL2Stst7pRVW2LIwvb3K9TpmuLfRxy/caaNtT71G9vW0WDbcuMnSTyXCuNyXFWyIjIyP0mGSMQbzG/i42VYUzvza7DKl1DJMLqO1U1wFg1apVoXkysSHdSOjeJm0tOZ+/r7y/EW43eD2VjZ9IJOL1KLW6XLxjD+7q668oiqIoiqIo3QBV3NPkgwMOAAA01dg3Y6pwnhrnMtCFFfewCi8TvHgKO5V1N80ucolcnMKe29e+vceKZRY4X3H3lMjoagBA5Zq1AICa1VZR4KBUGUNHqKLweyoBVAgYc1tTU+OtQxXwwQetukc1nmrBGWeckXRfipIKKux0kaASJRUpKlysl8HxHgAw8mZr6zX/z/8KzY9FjJvaz1lRv1eMSju/iwqbRyqEGTm2nTLhEgefsmcsq8iqabKHzLN+dFMAuPetsIe1PC4q76mU9roVVvmrXmZ72GpX2nY//YhDAAA1q217PfjzT6D0LJjEiGpyENbtlszw7wEH1kWyrb95JMfW6b0GFwOI4NVv13r3edbV0tJSlML+Rm3vlG4AMO43UbqncxA321N2kVXuc0sYZ27bOm1Y2f64XP5A276KR/bztlk43Pq1Zw60+0+WKZVtqrGxEXkutr3ZU9qt2t9cZ8vsWTm3tHj3ltJS//eWv4v8rePvJZV0rsPfS/6usj3z95YKvVTuee2U7kU0FvXaVqvLmY5p5qq4K4qiKIqiKEo3QBX3FDz00EMAgJ2fnwQA3oADPzmSs5NrcUq1Wy8aGEEfd2/tUcayx8Kx7Bm5YcUhp3eum1pFI7evtX3M7V9s9+lUhLeWW6U76BnLN/mW0hUAgNrVVjGhDWRZUzg+kQoAP3PKN38qBVQOmKJ5xYoV3j4HDbJxjVtttVVomywXvd8XLVoEADjzzDOhKMl4+OGHAfhKFl0UqEBxynrO3h7pnyx9lEeM/x0AYPFl99ntujaZm2QAkewZk2NPMvNFj1hROJY9u9g6RbDdyh4yKu33v/t1guLG4+F8trvzD7f+kFJpr11q2yGV9uplVg2sXGqVuqrltv2WlVtF8e5eo73jXOuSsDEr5G3NCxLOhdL9YfZrOqYE8TOounu2+93i/FiOrUdR5y4Tz7f1q6amNsHpqDaQoXXZkC0B2LFS/ePhXrCCaDihIBOUZRXZfeW47KeMM29x9dRX5u3yee73sGAz3908d+gQu6yLtV+UvRkKCgpCse3RaBSFK2wPV2NNuDeC/u3cd1Bx570oeJxsn7xf8feSqjwzkffrZ3sF2GPI9s32LnsUec04VboXaSdgMhrjriiKoiiKoiibPKq4CxZddjoAYH/nxlLv1LaWRiZjccqeyAbXEpPJWnxkrCwVPCrt2b1c7N4AGw2YN6iPm1qFLqOf9aLlSPnMFX52OnLoZlaNaJhtE7LUrSm30zKrKNAbVyrsUtGUsXtUEpYvX27L6pwEAGCLLVy6eacacspt9e3ryu+UGaqq48aNSyi/0jP5z3/+A8Cvb1SiZL2Urg1eD5NTvmQGw+D/kUjqdBdZAQcA/p9Ntwsx9iSrIKy0M4Y9x41BodKe0ceqbFTaOb136lcJaqWMzSd/OGI3ez5WWbeZZk9pt+27eqlV2isXl9jp0qrQdEW1VQlXNdjzubLev2dsf4915Lnqd79LflKUTYKzz7ZZN7/47zPePP5+sQeZarbnKpPF3mCO3yi361XZuv2L7e1v0LNflSS0RSBcn9cM2BFZWVno5ZT2SIZtR3lZYfclr6eq0v7WMO6cmVfpdMOxJF57GzzQ2xd/Gz+q7YURI0YghsRxImvWrEF+tTse+rfX2t87Ku3NTeEY9+AxBcfZUH3n7yPzn9BNhr+jCxbY3ixmIOfvZ0VFRWj9YPw9APzmN7+B0v1QxV1RFEVRFEVRFA9V3B30Qc7Mt2/O9LeNe9nfwgo7FYumOueHnpOoxDMenm9gmblUDqjcMbbdxbS72L38wVapzhniRsgPHAYAmFltyxaLJcYserGvK22MXV2JjZGrczGu9KWlSsI3fBlzR+WT6gJj9rj8kCFDvH1SNeSUbgNUKLgtqhGbbbYZAOD+3tsAAH5T9k3CcSibNo888ggA3zVGKuzSv5z1TmYubCu2PUhQBfTdZFybDKiF9HhnLHuWaKe5vZMr7XkDigEkUdpdvO1db8xCZmYmIpFIQr4FybmH7AAgoLSvWgIAqF5ilfaqxdZFpmqpHcNSscgqdxUuC+USdz9aXGvb76i7/oxoNIr+8NU8nvN77rkntO/f//73rZZNUUg0Gk3a5li/+btSV1fn/GY2DPF4PKFcK1fatrN27VqMyE62Vmqi0WjSNit/P6moc35Bgb1HsNeZPdbz588H4OdLSXV/U7onG8pVRh/cFUVRFGUThfaKgG+xSHyTBRdWQptjDiDNsQ+YkRwr4ERcmMvxuw4HALz+7do297+y92iUl5dj24GZoW1EXZKnzF72JTTOZH9MChUPD5hlqAxtVYOWjxmDbHmwpK7VssSrygH4iZc8O8g6Jl5yISvx1EKAonQ1Pf7Bvf4N60HOOLt4I0e025udjFn3YwMZr54ZWi74thURcYQyM2pCjKxT7rIHupj2ISMBAHNhlbt43N7QqIafO3Y7b18N33wGwM+cSDcZZqHjjUgqBVQ4pYsH1XLO79PHxt1TPQ+uS+9a6RFPVYHbmnvsuQCAnbey2/ruvBMBAP12srHyvX87Hsqmx8SJE73/pWsM6xAzC8qYb36Wfu3SlYXb43wJ59IxRmZUBYCcXLZPWxa6XHjTPs7laYBV16TSntHXtlsq7Xe/8XlKlV0eD9tyi1PYqbhTYad7TOVi+6BEpb0khdI++u4/e+eUsBwyyyyhAh+MWT733HMTyq50D+69914AwB7rcR95eXnebwOQOM5p6dKlAFy7LFyPBQnAfTOOvKTEjgOR7SHdbfGYgsfJfXCbsudPtq1hw2yvOWPjWSY55oW/mbx22v66GWnGuKODMe49/sFdURRFUTZVGiobvP+Np6w7y9N6F5omBq1Scc/IsbElmXnldj2nlkeyc9wWi9Iux4yqHBtOWdQH0WgUW+e7h/oi+0Bs6u1DbWajG5zKgeeZQqkvtC/Od7/3LQBgp512AhbVpHxpB4D9etUDqEdTiTWdoNLe5CVgEjaQKrgr60A0EvHsv9tariP0uAf3p59+GoD/9nxgke12M07FzhGKn+cIwxtZnlX2GAPf0tScdHkg6Ilr36IzcuhbK3yfnYJHpS7mRsh/GbEKXrMrG1XF8vJyOx9V3r7qltuMqXSTqXex7TUt4RuRdOHwyi1ueoyDLSqyN2aOlg+qFlQJOFKeKoNU8z84+FcAgF22sed62P62J6H/Ltb/PXuL7QH41+aEE06A0v2h0h70JE4Vky6V9rYULOmOxHqZKlaUIggVdsazF2T49Z4+0VTYGdNOtxgZ057Zz7paeO3WKe7/+u8MLzZWOi7J4/rdgVsDCMS0r7TT6mVWkaPiTqWdrjFU2hc6hd1T2v/5f965kOMFiHSUku4gwWujyl/3hXk21if9+vXz4rkBvx1SaZcKPBVn/q50BKrY/B2S42UY297aA31bLF++3BubFTxOwjFgsi2xTJzP3+6hQ+1v+9q1tj2zrXF82MCBvlOOoqSixz24K4qiKEpPoSkQ4y5DP2f++kTsuOOOKLj+egCJNpENFJ2cPWRejn2ZbXGK++iMTKAMWNZ723aX6/vs4fZBPqcfGhsbsedQFxbnFHcPvqRn2zDN95c1YM2aNe3aF2PbaQPZWBWOp09U3FVyV9pPJBb1xom0ulxcB6emxeuvvw7Af6MtLLRqd0Zhfmi5iHtjzs20NypPLRduM4yBN0LNigTe7mNZmaFtcHANMy3KQTZU7r5usb0BTS4emGokpz8b5OLQ5y/09sUY2NoSe0Oif3uduFEzPl5mOZWqm3SMkUoC4Me7y2yP3Oa7e/0UALDX7laJHLq/jWXvt6uN583acicAwJy4zYA3cKC9qfJa/fjHP4bS/aA3O9W1oJqeShFnvZOx6zKmXbrNyPoa9FoO7osKO6f57gElp9C3mZDuMTl9XY9Yf9sjlj/YtstUSvs9b8xEJBJBNBr1FDSpfjc2NuLXuw729kmlvWmFc4+h0r6E7jHlANJT2mVPF5G9G/L7tsYZAMB999nMs9yH+kxvXLC3cvBgv24xa2di1g+frKysDinShO2PvcGp8hMQukrxd5gx3+lQX1/vqenBbbDnl1m+O4OWlhbvmDiWCwBy3AsMezV4r5P3Jzn2hlPGsnP5ESNGAPBVfa7/wQcfePtk1nLtkVZ6zIO7oiiKomzqtFx8CYBAUqG6wIuzE3JoW7zN3Q/DAGjIdTaOMVoY013GhrlRfOI0my+JLrnSQMwBAPyQN2qdyz19bdQ98Gd7D7R8KLcDTes9o4N02bfQJjhqWVUOwHeTaapxg+Sr7YtvS6NLsuQemJtUcFfWgWgs4rWhVpeLa4x7q7zzzjsAfCVCuqp8VW/fcHP7W/V3eKb1FvcG4eRY5Syr2L1RO6VAxraTYIx7zA3s8QbV5Nu4vmhBsZ0W2mnZkF0B+I4b2e7mJGP3doN9425aYrOxVS1Z7e2rZqWNmaObTH1duOuvrboklU6qF9y3VOABPy6Pit2DW40FAOzRxyrxex42AgAwaO/RAIBeu+wCAFg28qDQ8cYi9ibNuMf9rQCPlq/fQ0upVVayDjix9QNQupwHH7QOTVTTpBoO+PVJwh9q2bskY9y5TanQS9UYsG5Gi/96PwBgYLat14xpL3KhAIxrBwJKO/MqeLHtKTKiBmLaWT7prBRUt3+5rVXu6RwDAE2rbP4FtmX2nEmlvWy5feCge8wyNx39z/9L2Fcq1xgZwy49t+U5DW5DuuDcf789r6q8dy0PPfQQAGDrrbfu4pJYqE7LHjMiY755z0/VC0RnGCrcXC/Yzrksl+E6beVMSIdoNJpUcedvl1TIOZ/3QPYAsoxU2vlCwuOROVCS9YLwGYbX/Mwzz+zYwSndlk3+wV1RFEVRNnXMJTaJoBev7dRkfgaAJunj7r7LbBIvnBRyMsMGCwz/jAjFnezWy39Z/MJ03UDLA/obAM1oWWMHj7ZUWa/4JhfbLt1kmoTQpSjrQiRNO8iIKu6JvPDCCwCAnwwy2D8X+CpnpPfGyzdk6a7CN/hVA3cGAAzO+wEAEHVx6KbOdrlx4IyJOyWBU3cDi2QElIAMl7zCJZqI5tm37LL+NsZbxp7yLZvKAb8ftHQ6AKB5xUIAQPUim4WN6hwA1KyyNyTp3877dCyJmhaE54HuMeySlEpIXl4ebh99OABfxdzWxQn/aA+rCAzazY7CH7SXHbCUva11Eua5zXSqC9WXUc22J4Hxvg2lfvxiQ4m98a798H27zb/clbT8Stfx8MMPA/BVJiJ7cYLzeO2pwMtxEhKptEtlO1mMezQaRabnJuPalrPqkllRgYBfu3CRye6XfCzKP175JKGcMgNxJBLBqTvY9ekY07R6ubc8Y9rbUtqXuweMlW669V2XJLg4yd4J2dalIk/1TyqTQaU+1ZgCbuPf//43AP/+qSrghoXOKq1lDu4qjDEJdVDWp6DjVLLlWM9kHQ3mEyFhlb/jcfssJ4+hpqbGm892wHse6z/vP1Te5X1LTuVvvVTm2fMN+LH7vOZKz2WTfHBXFEVRlJ6EjGnn55om/0VMuqZQ0MmN+y+8QMAG2YWUxbKqQvM9xd19ZsCZJ2gB2LnADcx0IaGf1ybaKXY2u+ZVA3lRxMvsi7CXKbXC+rc3VtqHb9/HPewqQ6HrrOlPr/eyKpse6irTCVDB3a6vf1OKZVq1bH6sdY/bZfkjAADx3HBsLd/qB9csCi1PN5rv4308j1bGr2U12dvagGyr2LXlYDuk2sawN6+x8a9NVNqXWCW6coFVqKnOAUDNantDqq+ySl+9uEEzU+RNww+w23Q3qL+t/jS075u3OASAr0wyJrh/tq94HNrfxuP1H2aPpP/2Nva37/bWn73XDrZHYdX2RwIIjKgXxzl8hc32Wu+uE73oa9eUecvUl9obbn2Z7VFY/87EyqaEzJDK+szY9uxeAVcZkcmYU09pd9O73/g8rX2fvYf1bG5e7drxavaU+Q4aUmmvcgp75Qo7XdUQVtpH3fXnlM48Ss+BPR3bbmt7NTMyMlp1j9lYkOMs+Bspfc9lrxGVdznuKzivLSeb9iLLRBUd8BV/9qylcpEiLBsV+bIy+xsn1XMeL+Ppgz0L3hg4p8KzDvz2t79dl8NTujGb1IP7Aw88AADYYw+X5HnxopTLsqFJmyZ5Y+ENIt2bQmFhoWcNxYbPgSicym7NVF3bG5L10dUqB7QmS/DSXniNzznnnA6WTuksWN8l8kcYaNsWLVXSIDmfU64vfyiBzhmclgw5wD24f5loqbOJRCIJxy73KUOWZBIcWeZU5zbZsrJLX94nJ0yYAAA444wz1uHolHUh8pfL0AzfGUUq7QybBHzXFD+Ekp9d+3H2opEoM6hSWQ/7u0fEb5VxbjVZzb5IZhpcWKkLL93FmTPQ8GFGdd46HK3P6Qdt5/0fr6kEUIl4Wbk9norS0JSx7Y2VbuqUdsa2M/ZfY9yVjhCNIU1XmY7tZ5N6cCcDX7GOB/UuVjU7kNCBN5FBbrq6/w7rtI+S4i0B+D+KwdjadaX/CqvkNVFpd44Tqbydqc4Bfmx7hbtZS/92Ko+xSPiG+4/N9gHgK5LbFtqHgSKnTPbLcS8fg/1uzt6jigEAxVta/bvPNpsDALK32hEAsGLonnZf4vj6LLUKe9Nim6q6cqHLErnUqo41K8tCxwIAdWX2/4ZKe6Md8qGLcdcHdyUNfMXdxbjn2vqcVWCVsaCrDBV3TqMuqzLHudz91py09vnbfW3PU8ua5Ep7cGyKbMtVntJuH8KotJe4hzKNblUURdk4iUQj3ktvW8t1hE3qwX3LLe3DNFbNTHudVElLOF8OgiMyxTHVL8BPfEHkAJRNHZmsqSt7EpT1BxMtUW2VbSjZy2yqHi5ZR6QSL1XuVGrx+kQq+MHjZV0P3gfWB8aYhHLIXgdO2Y0vewt5DnndpNVjMpL1aAS3xSmvuSrv65egvXGcHuROPW5wnyne1AREHCrKvk2wSwzmhBtPiXfZVhnT7k+T1wMmIowHbJKzm6zY4hk6uKylVNx3zckHmoGIS3YYycoBsuFlSrWONTEXN18HxONAPmCaG3Fg78FoLlnh799t2yrvQLy63J6LcvsiXF8WzpgqY9vleQkSTJ5G2HsuexsZ2sKBrNIOkmEu/CztMPmsIG0mAf8ew3IEk20pPYtN6sF9wITbAAA1LjNpc7374ar3G1wOw1icS0wf19AZv1rSZ/SGKSyAYqdAcyBNU5mN8a5f7RToFbabr4aK+wp786l2qlxQmS51XZyVrltU3oByvRtuWIHkfCrsxblWicwfYM9h4RB7Ayka3sfbVtFIe8Mo3Mom28gaZXstVvZOfu56zbWZUGt+mAcAqJhvlciKBc67msflnHHqyv0eEvYg8Lj4YzQj176kXVX3fdJ9KgrgK+4cs5HhepAynasMsxgDQFaRjSvN7GV/TJnZ+IFPUofcBTl7X9sevGyorsesZsVaN7XtuXpFpbdO9Sp7H6pZbev+KvfwtcbFtq91D2G73HtpWmVQFEVRuoZoNBrK5ZNyuRYdnOopf/t0wrb4Bs1BJlS3OJUqVTIVnfHzMh50Ux9UxuOlMtBTehh6KhwsJQeOyesfrPecRyVK9myx3UnbSC4vB7XJ5ddH705xcTGAxAFmwX2xza8vkrUlWR6eC05lMqtUyV3k+ILgvmQiHRnLL+9tGvO+YejTxwop0WgUdU0u4ZYIk6xx8nldwLud3yUo7vGw4k5izuwg4mVUDWdWJVTcTaDXhup7hnNwycx3iQidPbKX5DDbTZ19MhV3Wix7lsvcR7NT8gM9eqbeZUitDivrVNx9/3Z7PJ7HfV24d6JF/GYH/w/2dMked9Z7aQvJdsP5qRIach+8j0h1PTiP22IdUHoem8SDu6IoiqIoiqJ0FWknYEpjmdbYJB7c+/a1XdpMQuS9Tde67Gg1fugFM6bluO+ya11MnIuR26xXOQAg4pIlzc8YAsC3Z0qVcr01ilZ8ASAQh+d5y9qu8+Zy25Veu9rOr13JLvVyd1zsTnfTUqt0shsdCKoryUNl5CC9fFdx+jiXgNxiF1s30L7xF21mwwUKh/e3n0f48XS5I7ew+9z9WABAhVMFIk5dGOisMhvm2cG2ld/awahrv7bz135nj49JZkrcAFQOwAseFx0ReHzjPnoCALBq1SooXQPT3RPZBviZ8ZzB2Gmp/koFXvZSpRr8LRMBUY3i8l/+0YbNDeNgVFf/M/k536nTTgUE/LCZWGFvexzOfzo7uyp0XNwn3VtO2tXaPrasWmKnLoFYzUoXIuO1Z5uKne0Z8ENkVruYW7aBNW5w6k7//mvovARhORjLLtU8maxK9k5w/VRjFILLEKkUyl5ELi8TbCmdA5OdbbHFFsCllyIOoKXRnesmqsfh34Kgq0xbvw8top7x+aJfpa1jMsadbjJxKtaN/vVudiGqmfk27ru51ra1WI4LV2U21hxnyZokiWFoX0J5jwfck7gv/ubTr50uMlTeG9xxNIoYd9oj87wUFhZ6ceqs60E7yNJS26aZsJDIeyHr/7JlNmyOVtHy/iUTQcpxYoAf9y6TvLFOjBs3DkrPYJN4cFcURVEURVGUriLtBExpLNMa3frB/aGHHgIAjLzLJiKoc56zzfVWnWtyI+Mbq/04sSYvY1pd6HOuS72c4dTwaL5V3Efm2YGikUqnzKVQA7z4uwb/rZyj6Zvr7Vs/R7w3V1rlrY7JhUrdZ6e416y2ZeHANc/qscoeR5lTVsJKSvKMeKmUdto85vWzakj+ANuj0MslVSoYZgfrFm9h1cSszbfy9lW945GhQ6dKMKD8O3s831qlveJLq7SXfrkQAFDyjVUp1iyxx73EDcRbVmev04kfTg5tt7W45aFDbblYBzTV+vpn4sSJAMJxl0Di9ZFpu4PfUy3iNqhiySQnUiXmVC5P1SnVmBPZDjg4NSOXg1R9xZ3/s7ftuXnldhvCxUWOe+Hg8hY3uLxudVloWrva1nf2mFUHFPeSKqm02/Ozz+PjQ/sMerPzHPK8yrhyqbzLc8j57MXgtltzlWmrZzGVBzw/a7KYzoHjLaLRKFrcPd8ID3J6tUvlHfB7Lo984R+YP38+AOtkMvu8m90SLnabPV5OYM9y9TPqbHllV3+cvz+N/r4Y405zCP7mSqU9luXcxzJdT1EbDzVxp+q3BBxsuA8aUrCXnYp7fYWdT8WdzwZ04PEzytrt9e/f39t2tXs2YPsBUt932Pa4LOv/5ptby2Sq+GxzvJ/RTaa13BNSnWebZJ1Qeg7d+sFdURRFURRFUbqaSDSakJws1XIdoVs/uI8aZe3XGlxMO1UAvk1n5LhY93o/PpYxbc2eCsA4eJdO2L2hZxW5eFc3ytsbAU/FXYx4Z/ydCSZ7cpnjGkXWtobyKje1b/I1q+znulL7PRX2ypJwLLtU2uukBUCAXFc8qTj2ynQx7b3t8bSptA+z8exU2SORCKi1UGXoXWptHhu/nw3AV9rXzF4AAFg9xyqRC12SmYXOuvJHb1i1fB+hTlB1oKIQVDo4j4ot64Cy/njssccA+MpTKlKpTkHkNeU1pwLFz9KNIVXsKEnlmEJh0JtmORcbGV8L3+2C04aGilAZZBz9L7YptmVZvtAuX2J7lGrXOKV9TTmAwBgVNwZnrVP9gMSYdm+silPi6AsddK3huZJZnaWjhXTekS4/qbK9BpX3VBlSUynryZyEgttU5b1jJMuNEBdx61JpD/5OnPr+YygvLwfg1+c+ffpg7BM3YsCAAXjqUPZc2uvlKe5MGON6R4vdUkao/iawL2ZyzXROLpn5tq1RWWcbjGaFlXY+1EhbPR6n72Dj1zFfcQ//rnO8mxfb7j43umcE/xyFz2FJSYnnsU51vbLSt3Gl+xyXoZLOXkZ572NbpBPM6tX2N1H+1nF9thfGxAf3KTOtd0byR6V70a0f3BVFURRFURSlq4nG0vRx78kx7sxeVlUjYm7dSWEmOarswXned3UyDs++qWdVWdUt5lQBTx3IDJ8yL2Nckri7ZhFH749st59rS1zCIecSQ6WdyZSosFNxl0p7UNNiNaAywikVdyagyXVuGjlOcc/rZ5W8vEFWCSgcPhAAkDnYJlfPGLpF6HiTedE3LfgSAFA5z8a4M6adSvsPTmn/yqkeR//3AVtmEcMuHSvoShJUG6lQUIlgHVDWH1SapOOLRDolyFhswFeH+J3M+inzJKSan8r/PZnKb5d3blDMyhgLK+9AoFctJ9+VtaTV46U7VLzSKu313pgVO2V7rnU9aTWu/pc1+W2IbZsJxvb6z1V22Ro/Dh7wY2CTlUf6tsueAZ5z6T8vvfIJVf4gMn5exsNLt61UcF90JvrNb37T6vJKmMrKSuzykh0LxBpiRHy2zAJ61YqPAQArV1q3I/bm0KWEGGNwwv/+g4yMDDw51jqU8DfHj3l30/pwj5cf4+7Xi6x6513ukp3xNzfmnMzY+xVzv6lU3kmqcAL5mwv4v7vN4jffV9gb3dS1Bfd9nRgfwDFiFRUVWLzYJlIbMsQ6ywUdXlI5M8neLemeRXju+RvHz9wHr1VFRYW3juzV4j0i2BOgdDFpDk5FBx/cNRe9oiiKoiiKonQDuqXift999wEABl9zF4DEbG+gGtQYzigH+KPeOY8xeS3us4yRaysOT779c33AH9lOhb3BjcivKwsr7NXVyT2cK5rCca+XLpgKAJ4bQPBNm7F2L/zorNCp8F1lnBJfYBUBxrjnDbDr5Q+yXvjZA61fe8bgkbYM/bcNHWdQaSv++k1bvgW2PGXOp33NV1appNI+p8Ie/wlTHwGQWi2l+kAVnSpjUMVjrKAcWc868bvf/Q5K50DHHiq1vB7SzUS6zJBkLiW81lKdp5qUSjFP5ZTC/AoyFn6v/1yFzMxMLPvddW774ZuEVN6BQAbHrOTHyfr50y1tm2labMdy+GNW7LS+zDlZlNW7qVXa1ybJU8A2fsRzdyQ9D/wcPMfSC1+698heDelUIWNieXzcTlDdl2NKGGcrr61UGGX2XBkr35qDjZLIvffeC2DdMvRSmWW+E/5ubLbZZgDgKcuDB9t7v3rvW392xrZXVdl2HWxHvA6cx2WlAi/vWxwnNHz4cAD+PcXLReOuFfcZbKscmyDHkbAMrCPnnnvuOh610lEi0TTtIHvy4FRFURRFUfwwD05lkqX2cPz//oPs7Gw8edCpABLtIT1cyEl+S1gIA4AW91LKEBkmP4tmMlQmPI04kUkmeZIwLMcEjo8hOpx6JhRu3wyR4SBVhgDVx2WoTKu7VpSNgm754J7K6zTVjaqlwVcQcloSY7SBxJHqbPD0e46l8Jj1vGobwzcpIOAV624WdU6Bqyu3UypvMmMoY2CvXfkJAGD58uUAfKWE6lXQ25kZ3HjjkW4AedkubrzIvuF7Me4DbJbI3EH97Hr9rZtM1Wa7uRMT9qmNRqMoXjzdHtdSq7SXf2szRpbMs0r7ksW2nPOq7HH/dvYUAIlKDlUIKnycUoUgVDMAX5GQ7iOt+d8q7eO5554D4KurMiY6wbnFnXuptpLgdZcOLby2VHQ5n3HW0h+c68s6kyxTZywWS/hBNiKeO9SenVPU83NWhMogs7qa+prQ1HOwcO5RbO+cVrmHCMaxB/Mv7P3I9QD8tiyzzkrFDvDPWSoHHiJ9n2UvFT+n6jEJLivdL6TqL0mVWVpeJ/aUAdpb1hqs5+vi2c3xQMOG2XFLdDZZs8bmH2B8NT+z97Ynu5VEIhEMGGAd1qiSB+s62xp7qaTbEs8d1ykrs05TPNdcn9eCnxnbzvWC2VrZlnhfYhuT90Cl61A7SEVRFEVRAAAHfPYaAKA2k2FeYWWaL6ecNnWCesxt+INUky9HlT+3xRdZsuvty15GTlgFjwnFncdBpb2thxovPDUgjxsxOJb7kgYVNU4UqxGWmXzB10dfpTvQLR/cfc9mdxeJM1uo+967gbFh+3ebJvdl1DXkiHezEDfBRsa8O2eMzLCiy246KvSeW02Nr1JwJDtj2ytrkrvFMKb9L0s/svt0b+98a6fSzrdvKm6MeQMSYx+lm0xmfji2PbevVVVy3DTW22aKq9jiAHu8wuu5z9pvvW03LP3eLjt/mT2e7617TOn3VlX41sXsX7piemgbRPpIS2QcbNBhQ3rZlpaWhpZVOg7VIdbDYMwz4NdLqsBSdZIxmMFrw3WkQiV7TmQbIPxeujewXgR7XlpaWhJ/mL1u9tQ/0dymzCzKqXHjQrzMyM49ynemCjtYVAulPZjJktvkvmTW2WQ+9jLLohwnIP3Z5WciM8DKcxksh3TFkOWUPvqy9yWV64zGU7fOgw8+CKBj7ln83WBdYzw1fz+YJfTbb+09XrrN9ERqa2u9dsHzwbhzwG8PcqyNvF+xTbI3Y+uttw6tx2shM6mynQR70eQYI9nm+AzAOnP22We397CVDhKJRRFJo/c/EuvY80q3fHBXFEVRlJ6ElzTMKdXRVKKT6QSp3fHLtyegf//+uHvHo+2+m5O/8LYYN4g5EK6a6/7PcgIVFXiKZVTeExT3NmLcqa4nVdybwrHuTUJZl0q7tINUlO5At3xw95Q+5w/r+bSz0YouwyDynmDEQBcq7YB9c463uLenuuak63kDcOgPG1iOMa6VInbdV9rtslev/gyAH2sqlbJtttnG7su9YfOtfvfdd/f2xe/+546PLjKcZhdluamLce9rY+Kz+9kYu1jfsKOAjLFtXrHA21fdkqUAgPL5Nva+9Luw0n7xik/tORI/INKDW34v4TEFY3ClYwbPkYyrVtrP888/D8CP6ZT1UMabcz7rihx7IZ1EgutKZyEZj03aiqFuLQ43EokkZJHk2BW6QAW9oJkFmXGmMk7Vi3GP57t13fdinAvvId7DgxgsGAxh+NnPfma/c3WcvR0LFy4EACxdujThPFB95fWRTjuyp4Bthaqg7CGR1yAYKxtU34PngNdPOj9JxVC2U0lwX3fffTcA4Pzzz0+6bE+EMe0dub9xXV4bXrOBA23ODukqI9tmTyQSiXjubTvuuCMAv/0AfrvgvXLQoEEAfGWd545tkeeW8NzLdiPXC/a08X+2Kelkw/uwjvfqOiJp+rin5fXeCj23ZSqKoihKNyGWKxT3TCrvbgCxeDf61dSHO23f58+Zgv79++PaQXu7OWHl3Uv+FCgEX1AzaZTQEk7mlBVtDpU7KnoOZHbJuDCWCFq8yuRT7HXgi3uT+Owr7uFQuj99+2ZoQKiibIx0ywd3qhBUkZmBLYNKl1PBTRLJXXbTcV2J3w3XknR+i/CIlxnagNRKO6eHPf8PAL5f9r777gsA2G677QD4b+FSmU72Ru3Fq0aotLu4N+Emk11sVQM/tt2OnF9YZGPv+gpHlz6r5gAAGlYt8Y9rgXXdKPuhHACwbLWNQV9QE1Y/pTrXFkY42CSLhZcxzpxq9riOwxhO6Q8u3UdSeXFLZV66lwS/43XkPqRriVTUZRuQCn2yWPB4PO7ZvbGL3LOJq3c9N4G8C8bt6/eHW0elLxsKQ9v04k1XzQwfdwccAqSjS+/e1uWJitzee9sHpSVL/PY3e/ZsAP71ko4jVN54jrgcFXi6hkiP9mROMDzvMhZdesfLWHjp/iRJ1tumrhiJ8Fqtyxge6TZElZjKMWO3pVMNrw1jprlcT3GZYV3efPPNAST2LgVhPee54bmiGs4pe8l4rrmczI9ApB98cFuy513GuK+L17/SOUSj0bSed9J9JkpFt3xwVxRFUZSewF6L3wUA1Oa4UAgR607xKSbCkZ469EwAwGkfTuq0sly+5AOsWrUK/9njeDfHiWWe4u2Xgeo2LYnllI8usqfAP47kLyu+X70/L57iu0bh086pjHHvDAceRdFQmSQwBnLf/74AAGjpZ98svdhSMTBFxq8H4YCedC2ppAuFtJ/ivusDg3dSdctRBWT2tFWrVgEA3n77bQDAjBkzAABjx44F4Ge4kzGoUl0EgIIMOy/fHUdOb3uzp5sMlfbsPsX2+J3izrd5GZfcvMrGP1YvWentq3Kx9WuvWGRV7vlOab9uzWehcyRj2VPFvMv4V6n8BY9T9jZQTaIzgsbJtp9XX30VgB+vKc97qusjHWFSKXLBWFkZL87vZPxmqphowuV4/ZP1QjU3N2PX+y5DU1MTqv9ie7fo9MJpS71vX+f5szu3mD59Ng9tz6uvzKxKd5csVwb5IOU+Z7q4evnAEkSec9nrxHsAAIwcabMaL1hgx518+qkdU7Jihe0Jo1pPhVD2Wsh4Wk5TeeEnOwfJxp8Et5GqrcvPwfk89rvushmx//CHP6Cn8uyzzwIA+vXr1+FtSR9y5gXh7w/rC9sSl6M6TIWe8dv0Ed8UicfjXo+VVMWDSrbMM8EeX/4Oyd5mtiP6tvN7Xgu2UbZNTltTZuU9Q7p8sQ4df/zxSdbuGdx444247LLLcMEFF+DOO+9MusyXX36Jq666CjNmzMCiRYtwxx134MILL9yg5Wwv3erBXVEURVF6Epn5uW5qX8ToLpOR45ID8oVRJN1bn5z/5avo27cvrnYx741uYHdw36mUdpmFVfYUpEvQPUdaQMvPUnGXivwFX73W4fAFZePi008/xb///W/stNNOrS5XW1uLUaNG4YQTTsBFF13UoX2q4p4EL+ZusPNRbmFMO10dwm4O0mu9NVKdSG8fctvCVSYdUqVT3mOPPQD4sasczf7kk08C8N/u6QHLihj0271x84MAACOdX3sfdzP3fNv72WWZKTXW146CnxsdYj875YBxsP1WzgIANKy0invV4lXevsoXVQAAFrvMqH9cMg1AokoqVbVULjKpPKJllswg0jOc50Lj+9qP9HmWP2DSB5wKFJeTmTx5vZLFR0ufdqm8S8VdKs9yeSpVyXyUuWyvXr2wzLXTmlqXzbjCZWyt9GNLW6ptLHi8xo2X6B3ejqei5bsY8Twbt5pVaOtcJqf5VrnPcm2xwKn77A3jFPDPnYzRT9XbAfjnn5kwqZx+/vnnAKyCBPjqH8+VVAF5rmQWyCAy7ly6xbAs/CzVP1k3SGvHpzkZErNjdgReI14Lxs0zoypVfanuS39/3ltZtmBG6+5OLBZLcGWR955gHDrrN33Y5Xg0niuZBZz1vqTE9lrzHMqs6FLRBxJ7zmTuB9mDvSn3jLRFdXU1TjnlFDzwwAO4/vrrW112zz33xJ577gkAuPTSSzdE8TpMt3pwVxRFUZSeRMS9IGbk2RfCDKe8Z+a6UCenvOd67jLrX3Enly96L/RifvMWh3j/N3qJEZMr7Klj21snWYx7qu+kp72MZb/gq9fUPnET5LzzzsNRRx2Fww47rM0H984kEommZVYQifQgxZ0UjbCeqPEmeq07f2mnvBvxWdpIBZdJjF2Ph6ZU85tF6mTGxMe95cMx70DqwTnro0OOCvuwB/8GAPjCZcGT8ayX9rdKAGPbU9GyxmZFrV5mlYGqpWXed+WLrTogXWQUZWOlwjk7cdrf5VhoKK/2luH/mZWlrW5raeEWAIDBvWzG4Jy+dvnccqvYN1VZZY4ZVHu5e4dvPec/KNy19WEAgIsXvNv+g1IURVFCTJ48GTNnzvTG/myKdKsH900tIYTs4mKiBw4yY9ccu8QYSjN37lwAwBFHHJGwTS4jk7OkCiGRXbHrYvklE/XIbcvBjO0dpBrswpdJgKj2yONV0oeWZtI+LlU3faqU20SGfQR9keXgVBl2I+sKkaEYcsCYrGuAXxf2228/TEt6JOnBsjJFPNvl4FQrrAOLFi0C4IcZMfSHYQ1BUrUrhtxtsYV9sXjzzTdD5WfXObctBxeTYPuUbVBecxkyI21auQ95nZPFE8u60ZMHmgeTaTE0K6vIvhhmFdrrl5lvwzCouGczPNKJSvRPZ7iGvN7SBjSZHWhwOdYBbkcOagaAS75/C4B/DVnXWlpacP2wA9xS4ft/ukJWOmah//fd/xKOQ4YKpQr9Ifwt4X0x2XmRx85zI9sBtyWtjbmctN5NJzkh2xzvQ9wHj09aJvcklixZggsuuABvvvlm0hDb9Y3GuLdC0RbOYSHOeHNOwyp6i8uKGFTVU6nxcn6zc5tgZsWmGhv/2+J8nxsq7WcODKqP1ScWtMytk6L77r5dfw4A+M2M59I7cMHLh4zz/h+eZxvp8hTL/ra43Ja3r83COifisuSJ5QaX2vjYhtVU3K2TQIWLaweAJU5B/NPSjjwOKcqG40/fv4Xc3Fw8vvUYAECda5t1Zf5LRYNTzHMrrII+cI3NYbCi7/ZJt8lxInSjyXf3BnrDe7keRJ9+rMKPDWa4ANtypXOl+uOXr7bvABVFUXo4M2bMwOrVq7Hbbrt581paWvDee+/h7rvvRkNDw3oNjdIH9yRsqmqqTDYzYsQIAMCcOfbBwUuuJAYHJkNWyrZSxvMN30vCUbos7XJL1Zvlp1LBqUwQI5UbuT1JMuWAy8oegk21jnQ2tIAE/EGprDtU6HgupUpEqKZxOVnX5PaC+yKpbAVlnWIZpPWkrEtBpWqHHXYA0PEBy7RpXb16dagMe/xsnw5ttzVoEVtaal8ievXq5X3HJE1EnhsmeznxxBMBAO+//z4Af9A7rwvPC68J1w9eR6koykHEsveFZWGbl703vM7Jrpec15MHqQbv+dHsYgD+oOnMfCuicFB0VoGdT+U9qyocmsnzKZOcpUrsJ6+ltBkkyXpKmeyL9YPbys/PxxVLPvCWkzaKcqC0HNQp60ZFhS8ksW1EIhG0tLR4xyd/I2SPotwHYZm4fLJea5mcjsgkdfLcJwx0F3aRqXqUg9vmPA6MZXuXv+ktLS146tAz8SyA+8zCpMe6qXHooYd6z03kjDPOwDbbbIO//OUvm8x4Bn3KURRFURRFUbo1hYWFnmBD8vPz0bdvX2/+aaedhqFDh2L8+PEA7AvRV1995f2/bNkyzJo1CwUFBdhyyy3btf9oLIpoGmp6Osu0Rrd8cM8ctlV4hhxg6kJo0NwU/hycx/Aal2zFNLs35Qb72evudlOGyjTV2K71rCIbX0Y7OaodnAIBf13XLZ9V7WIFnRKSG7NleGzP4wD4admPff3fdj0ROy7f3ofk+Ur0MOcwQNNGaeGXM8AmfsjoP9R+b8JqiveWnmJQatUKfxDf4tqw2kllnTHAVAmpEDCGkPZjVN5pP5bKPzeZAi9VXBm/qKRHUOFmrKSsM1LJlbGtqRQ4qXgFVSlpEZkq1l2qrlTNuJ6M/U4WO82kRUFlkINTq11brCv1Ld5qV5cDAHIH2Hoa622Tji1cuAjJuP5pO6D0imP3AwBkuzIWubA7dodyIHss090PAm0pq9TeT+gIsqbBHt9zh54BAPj5m/8B4LcpwFcbqbxTeZPnguduzBgbHkT7yHfeeQeAnwyG7ZHtOFg32K7Y3qikyzEJsqdLXntZpmRqrWzvrcX5buqEFPfCYvt/rVWzs13Yo+cuk+96Q3LtOvmuLrFOsUeF10iqv6lsfKVtKO8TcsxEsrEw8lryt4HI+HN5rWWPjtxusuSDqVRruRz3ycRUqaxKU/UAA3674PgdORZEjvEg8rdc3v9kT0Ww14ltkO02Go3iibE2xI7jGfxnC3sMfC5QfBYvXhyqP8uXL8euu+7qfb711ltx6623YsyYMZg6dWoXlLBt9KoqiqIoiqIomxzy4Vt+HjFiRKeJBJFoJD07yA4mSetWD+58e/0i06YiZ4wtp4yvC6qvfAOmKsU33gHl3wEIKO5uGq9zXrmc7z7Ha2n1Vu2mVNxdspUiOz+nl6+m1ZXZN+TsIluGrBL7dl7kFPjemfYCVzZbpaDaDUz75OfnAQCop8f/cqrdjnvjpkI2vMiP0SvazMa5MQ6P54DxbxkDrdr2cZX9fvBgO5+qSf9VX9jjKbUqY+1Kq/BVO3Vwaa0fS/7bb6xbhVQ2GBtIZBpnKgVlZVbFpxI0ZIhNAiXVCKncB88Bj0uqI62pJIof2x5MJiLjxaXLhFSDUiVLkglCkilXUjkncp9Smee2Ro0aFfqe6jO3G0xKJuMZjTGocImYypzy3rvEH5xau9rW38dWWiUzsmpRaN9U0Vj32NYembEUkUgEv97NurlEqJZmOYUzk71xthcrq8DvKWOSpsLV9noU1dp99cq05fzfkefY43TlPe7NB71zwhT0bF9MuCSddrg8x84cd5zt4XvxxRdD5473yOD14royUVSyBDGArxDKZF7cRyoFMtm8ntyWgyryx2WZKCoqwjZ55QAQcJmxv3tZTnHnlK4ynL56zB+97R710l3e/7J9yHso2znrmVTN2dZY74Lb5PS77+zv7MqV9neFiW6km4p8cOI9R8blJ6sTqZR17kM6jrF+SVcW2gcOGmQHnW+11Vbe97dseai3faluy4ywbWWuld7yqRIz2m1yGva+5z62LeQ1j4bKxs8bIouu4rOhBqdqjl9FURRFURRF6QZ0K8U9WQp1wFd6qb4FfaOpxlMl4xv+1y1W/V2z1qoJNCdZ7VS3X+25td2ns3rjiH7GGWZVlQMAct18Jm8JJnTJKasKzcvt61LLO8W90CWB6Vdp1YVGl7Clicmc3PHOc0oBFbMB42+3xza6j7evwiFW/TilxiocxVvYWPasYXaZ9xusmta/v1VrpNOGH9tuz0f1Cnt8jG1fWe+fcyoa0iGEapx0t+D1WLx4sS3/AJv8ideRse9U5Km2sAeFsbeAr/TJeH8ZM60kRyqiQWRMe7I4SyDRRUY6wqRyUAjuQ25LzpeexNttt13o8/fffx9antc/qL4lcyX5y+IPYIzB49vYDI8DS/17xUc72lj1qKhbdK343X5W7Z8wY1nS435w2g+IRCI4+6Bt7THluGRnrq15CmnhGm+d7CLb+5TT26r8OattXe9FBT7DaitlrqfgrZ/8xn22x3ns6//27n88ZwMHDgyVW55bzv/lL38JAHjmmWfsNl1PWNC1RjpzSDVWblvWGRl3LOOqg9dLjm/oyW2Z9zz+flVXVyPaqxgAEK10v2V0lSmy02zXA5uX7TKpOtvezMAlKigo8M4zFXP5u8pry/m8P7O3k+NL+BuSTAVnfWEcOXtPqWoz1wB/G/jbJl2kZP2T3vPBc8Xfdnn/kY41CxcutOfH/ZbQbYll5HkJ9kTmBJTrAtcmOfV7OOznHKHE+4q8XT8qVPDWBipyfAxDK7hsLCvF+BmOq3PfRzM3DReV7oIq7oqiKIqiKIqieHQrxV2+jVM1pFLAGDyZCQ1IVIK4LFWqJUuWAPDVqg9WUmG2KoMx1gHlwGE2cVG82q7vKfG97eccNx8AGsvDanxelVUGGsrs50bGyVfb42qqcX6zjWGlcNjU/wEAMj+Zaku0o1Wsc/oWesvkD7KKesFQW87MoSPc1CobwzNsuXlueM4GrfzcloGx7SsY227V7pIqq3CsakhULxkjSGVdKuy8TtwnlRpmiaTLBa8FlUCplAZHgHNZ6Sst46yV5PDcBseBSHVLOn8Qmd1PxrTLeFS5/eAyqRwtWC+pju2yyy4AfOXx889tfWXdk37hweNiXeG6wX2vdW1sTaBe7zPJxnxPP/VYAMApZd8AAHr1tb1XzcttWc/YfSQA4MHpi7112VuUn5+P1+ZzrEcx6uvrccwom8ApWlAMAMgu9uPwc/r2clPb7nLdfSTXKfD5Lga/j5v2drHvJa78bx19LgA/cdMv3nwQy5eH07BxTIw851RQjz/+eADA448/HjpfwXPG8yrrSLLsmcF9yTqUKstucFnpu90Tke4iTU1N+LAkiszMTOzh6hF7cDKd8p7pxktwWuCck3ID6l5RUVHC/Zjw2vCaSpchLs+2J7N/An6vN+sL19l+e9sG2CaZ4ZvqNnvQfvaznwHwlXp5PlhHpk+f7n3HuHmZRVv2LLz00ksAwr0YgD+2g2XkeosWLcIrP/sDAH88GuAr7X2cus3v8p3LW5a4Dlme648b80JVPJOqeVhFDyrwHOjoO1RFk87nOlE3nkYur2wYIpFoeoNTI6q4K4qiKIqiKMomT7dS3M8880wAwBtvvAHAf5uXb+cHb27fqK1Xu8GMCl/VoUrPN3cqAzKDqPQhpjr1Q9Qq2rkDNw/te8u+K+x6LvYdAGJ97f/ZTpXnd1Ta6Q3f7Lzh6R0fb3TqAsvA7Gq5ziM2zyomOX19xSS7X1+3TzsiPmPgcADAwiyrFmYLX9mhdbaHoUm4yNSusWWsXmXLuMr5Sl/0zX9D5wnwzyG3KTPSSY9enlu6FPDcUwGRThRUfoI9KFTzGddLNYWfWUeU5CTLWJlK/U6VR4DXTSqivE4yBj44JkX6f8s6RFV/jz32CG2L3uO8/lK5TRZzzQx6VOSCx0OFuiTQu9XHjT058KVXAABVo2ysd9S5w7Ad5rjlz97DtrHXV0Y85V+ew6ysLLxTZo/vR1TenS83AGQU2/bHtpxL5X2ljSuuWWV7vnJ6W/WzgAq8K2uJ13Ngz/GUI6wLzU/feNBz9Nhpp53sNlwZpesPr9uBBx4IAJg5c6ZXPvaiSb9priOvg3Qq4T5ZZ+RYhGDdSDWm4vbb7Zieiy++GD0F5rggwXMTdb97kTx7bTLz7bWgW1GmU3ZlrDVgHbxkDHsqOMZI9tLxM9tisLeFce+cch9s13QQ4/2abZTbphI/evRoAInj2fg5OI5NKu0ytwC3yX3w+5133hmA/xwhx+xEIpEElxYA6OWU8n5OOS9wvWN5/ex1YG8Zx62wh41jEfj7nel8+GM5TpF302hWIFN4hss8nOFyZvD+GXXtLJo8hj0S09j2riASiyGaxrnv6PVRxV1RFEVRFEVRugHdSnEnHBV+xq5WXTY5TkXPcO4sC51rg8uYuhMAlNtZfHMdzjfZgS4T4KgRAID3S+w6wdhLwI+/lqoj397nZ1glAb2H+HGgBXZb2xibz5Tx8JnOmYae8XSu8bzkG8Nx2l68Wqbz2aZbRaHv/hDtZRX3KYvtOdh2kI2D51FIVZUuMk2r7LTGxbbXOKW92il6jAVO5hzAbfFccB88V3QjIIxdlbHtXI8qCs99MkWI3zGOV14PpXVkFtQgVKxkRlQZyyrrEntMeG2kA0TwOvI7TrlPKru77bYbAL9uTJs2DUBq1yDp7BKE67z99tsAfGWtsbER+z18HRYvXow11/zHW55KWvYK2x4ZixqNhVV09oTluCzMRwwcBqABGQNsptb//eDnMwj2cLyxsMaVN8+rr8eNtip8zLXfrD62507Gvuf1s+ejZrVzqFpVE5oWubEo9H9/x7nPlDS24CUAp7z3qOfMkypTJtN7f/bZZwnfSY9vWRfk9SRUPWUdSpZ3IVW5epKf+5VXXgkA+OlPfwogeabQjpCZmZnQfmWuBH7PNsheZbZzuX4wa650cKFDUXDcRHAbTEP/9ddfAwDmzZsHwPdSZ48N98F2s9deeyUcm+zpY4w+t8kybLutdX3iPUdmHpaZwDcFrrzySvztb3/r6mJs8mwoV5lu+eCuKIqiKD2R6TUFyM3Nxbb5VnTxQjBc6AUHRcpETABwRX8bgva31Z9usPJ2Z5j4KHgOadGa7+w3Cwba81/gkhoWDC626/S3Ly25A+znLPcSw1A5DlaPOgGufPDOaVmgUuCifWUwbAjwX8A4ffnll9vcptK96JYP7lRvmpYvABDIftrgYsZdrLhpcWpXUNVhvDizGubYxsd4wX1dVrpotsvM6RrVQpHhTcbGB9UiGUM6P2aVuKwCuw0qAiNbrBLvKe5OwTNNTkV0PQaMY5OK+9vLfGU+vsYeI1VFKhXecbvyDV41CwDQ5BT3GhdLW+di22tLrOJOpZ1+0cluKPJcUGWhYkunDX5PJUM6VXA7jHuU6lIw5pVe01LNbU15VXxa+2Gg8hbMqhpcR3pzSzWMSMU9mTsIrzEVOcahMy77iy9sJt9UGVVljDTdLIKxwfyOYy5Yd/hDl5ubi9ybzvfq4crrrPq+6sJfhcp6+Dtvhc+Hu69wLEpus6uL9fa8HTLQqoX/W+Hfd+Q9gefmtcUNrox2nYNHubEqvai8uzEoTnnP6WuPIbe3bbeMo6Xyzgys+e5Bgw4Yz449Dc+6sly1ZkaoLMEsnUD4HFJtDbqbAEiI6U/lFiSVeTnGIpmSLL/rLLW5O5AqZ4LMMJqTkwOUdu4+Zey7HLNCWBaZeRTwf3+YTZXr8hrKNsnfDPbC0lP9ww8/BACMGTMGQGLvXvA8pcoVwG3IfcixWDKzKr8PZmHeEBhjEup68H4te184jkA+g/DeInvHlPWPKu6KoiiKoiQlysGpeTmhKe0HczIS0973nFegzoFCe8hS04XQcTAqlfai4faFt2CofWnJ28xaMMf6W4MIGkes7LWV99KgbFpEomnaQXZQjOiWD+7Dh1s3h4YVLv611irtnktLvRv9XueUsCQxkvQ75Yhub2S+63ZkVjp2aw0rXBb6zG6u7yMDU5ZT+pjLN+H5EdvAYwW2QbMxy5hOqlxUFT957xN7/IG4bq67995722NvCcfqDy79EgDQvNoeR/1Kq+TVrbYKHpX2WpdJkpkaL/jqNQDJlVrp/y399aXqLxVdKh0yYyOXo6sBjxvwlZzNN7euPlQbVq1alVA+JZHWYmalii29uGUMvFRsOfV6xETeheA6dBjad999AQAfffQRAD+fApU1qr+s66xTS5cuBZAYzxpUyagWs9xsIzJrMPnEeboPvfkxAMCwXNt2KnqFf2TjLrNxvMmeJ/bw5bt6neHGrhzS27XvvvYH/M0FVd42OP6jpKQktO1hRxwBABjtYt6bi1x+hiLnPtPHTrOLC93Uyq7ZReUAAs4ibqxKrmvPwYe3GwfacInL1/juMYB/r0iWN4FqJK8L64Ds6ZL3AlkXUqn8wXmybvakGHf5G0HkOJJoNNphxV3e02VvCe+1nMprJsckJEPGz0uHGulsxPbNesfYd7rRMDyEvw1AYqw6xz9xH7zXSCekVO5YMjvw+kCeazm+QH4OwjrAOsHjYhvj76+8HyubDt3ywV1RFEVRejKRXPvgFnUvohkuxp2JfjJynJ1ujR9SGXyBU9qGMe45gfPm9WjQBnKADQ/ylPYRVljM3MwO9i4fvldCQq0gfStseFDzSpvQrdmFsQJAw+rVAID6UhtG2lTrLDhdmF4kkHgpC1aAzEdYgNxhl0JcM8t/0VHWHxoq0wqMna793L59N1a6TG6eP7p9k26qc57BTX5joVpGpwje3Bh/zix0rPjZxWvd1Cp5mb1sI6Wjy8gCq4B5Snx+EeBEpPnGKgBSSZK+5/Ktm6RSPKkc0g8dADbbbLPQMlyHMe0tzq+9abVVIzyl3Yttd/7o7iZf0RRWHXjDSRbLL+PMuSwVTyrsUkXitqmyrnS9AFQQeJxDhw711uE8WS7WCaV1WMeCSmdbCifrqXQMSeYMArQeo8zrdMABBwDwczKwjlAdY32WDkX8nnHqVKxZhmBOB5Z7xYoVofJTmeO2OP/gp24BALz3y0vsfKEofvzrYwAAh71hMxkzwzEV9yaXjyHP3Ydyal3+BucidejAQQAieHVxozeeQyqS7H2a63oOmjLtQ8DOI2xZeZ+JFYQHJvKhjQ8VmZy6h7is5f554cPbTQOsg8/far8F4Gc05vkC/PZFVVPG1Up4PDL2XcYfJ1NtU2VbTWfA3qbCrbfeCsDvgeJ5o5oq73+dgVTDZeZRtjWZkZe9LkEXKq5D1ZfbZFtjm0wVdy192/nbsGzZstD3wfrH+poqi6/cJpG+7TzHVPvl/W5dMcZ4++qq8RqsV8qmQbd8cFcURVGUnkw0Jz80zXBGCxSjvJe2gFocU8G9XfB8Bc8hw9FyPcXduccMcRbMQ0YCAFYN3DnBWhcABlZZU42mRTYMqHy+HZC/9mv74rx23mpv2dLvrGCxsjyccK3OCZAsHwehMylUH8/xxtaNA0+9Ov2DVtaZSDSSnuLewZ6vbv3gXru6HADQUG5jShsqG0LTxmr7Bt1cH8jO1+hiMF2Nj2W5LJ3uZpftKryf/YzKe4GbutjS3mtDn+lKE833s5lu7ub5XZr2u8VZNl5Pxn3KEfjJMrkBwH777QcAeOaZZ7x9cd6WDba7rWW57Y1orrDTphIbA16zwmVkdG4yVNo9xd1llKxuDseqUrUI+ttLZYPKDG9WVEmlWs9tMMaQ22HcOuOUk8XBUo2nAii94pXWOfHEEwEA999/vzdPXkcZd8p6Kbt7pQsF67PcHscuAH52zldffRWAf6232WYbAIm9LqxTjOeU9ZHqOWNfWQYgcYwFy73adT9z7ASPg9s66tX7AACvH3VuaP0Rdz0JAKh0ajYV9ybXS+VnQg6PuckbYNXuDJfH4ce9B+DHB20BAIj1tj/2N75ox+tQ7ZN5Il5eXM6jQllZGX69s/Wi5v3GH6Boz1ssx/bG0Ys++EMRWxbuNr88d2sAwLDbLwLgjxcA/HbGng9eFxk/K9Va3gNknUgVTxz8LlX96kkwhpv1nj00PJ+8Lp2B9NqXY1xkXDq/55TqOpDoJpTKIYw9B+xpk9viPSM4vinZ9pLN42fWWZ5L7oPHmcyhBvDPcUdzgxhjEtT+9Rk3r/QctBYpiqIoSjdjSa4NoxqUa2OkPR93p7TzpS0zIO5pjHv7YLhc8LzRJz+72A0O7V8MAMhw7jHzC7dOSDgFAAPXzAEA1H9j7VhXf/YNAGDFZ1Zp/36uDfv7eK3vy37Rt28CALZE8jBHwH8xocjBF5OamhrUAjjCDXhX1j/qKtMKfEOuK3Ux1M4Rpd5l+2yotBW3vsq+MVM9BvwuJsIGyQQLedn2lOT0pvJe4z67jIW9ywEEFXg7zSpyo/4LfceKzEL7HX3Xo055H5JjVfFIlvNCpj97jVOznW873MUdzA0a5/NeZ4//z4dt7+3LrLTZDptqXFbWalvOxnKr1Hg+7S62vWaVXc47d+Jc1ceT+y4HFU3CZahQUG2j2sAbCpfjZ6qLjGGnisQbVDI/Xaoo6lXbMYLKD8+zdFOQ7jE85/zh4JTzpbc+HWGosgN+MhDGug8ePDi0LvdJ5Y0/gFTP6fM8atSoUFlZl4IKF7chx5YQqse77747AL9uUb3/8Sv3AgBe+cnvAAAtbAPuFtLPi3Gnu4zzr3c9fU3O7aqxyt5D8txYnJyqcq8Mcfc/23Ksty3D3Hpb5xcssN3q7IngMU+cuRwZGRn49V5bAfDvJUX01s+159HLV5Hp/1CwtxGL/QyvALDgotsBAKPvucybx+sk6wjHEsg6Iz2lpcpJOD94TVK5lvQkVxmyeLH9jdh6a9sbItVunqPm5mYMWoftG2MSXGGkUxDbsRzjwjKwDgR7RLgNtlc5Lkver7kt9pzynkHnOPb+fPXVV6HtBXvTpIsKMwTz3sFzyX0MGDAgVAZuUx7nuvb0BLPTskzJQmbSIdjuWT6OM2Gb44O7zErM41Y2Pbrlg7uiKIqiKP5LG62NaXUczUz0cZcDrpX0yEriKuM5t/QpBuD7tUsGldqXjrrZHwMAln9olfeF7ywEAExdVA4AuGjpx6itrcVB8AcCqyDVvYhEY4hE2x7UnM4yrdEtH9y9eDinsHNKD3I6o9CLnA4pAFDnsh5SeJcJFgqcl3NRrd1Gbxf7ndvbTr1Mhb2detbbqlZZwo3GznMqPG26OHU32Ei2U9wz3OeMsOIe4Zu/aLzGZWpEcyBzqvOOZlwtHXYayu3beEOZ651Y7RT4UvZSWCWHmVIr3Dm7eJ51+5A3juBnqcKncsmhOkKlnU4BVDZkhjrGN1JZCMbVp1LxUzkHKMkJdrdSDUqlbEpXJK7LuhCMcQV8RSvZWAx+xyyG9N+ni4yMaWXdYSws98k6w/kyFhhIzLpKqOrtsYf1M2f9nTlzZmgbLONxb00AADx9yBkAgEbXG8Vpncta2rtGKO7uc16l68J2ijtdZwAg17XXbGZPdg402zkP+NlOTQsq1mwXxhg89uki1NXV4TeH7GSP1T3E5bt7Cbtkg12zdNYybtqytCp0PLN+ewMA4JCnb01Qvamysjxsm5zK9inH50iC86WbiVfeHqi4K4qipKJbPrgriqIoyqYKQ6RGjBiBg4dkAIjjw5KoJ5YwdKa+vt5X3BkWlcMwKRfSFnw5cv8aYxLCmqRVpwzzkOFQJJgMidtgaIzcB7fBF27CUDe+LEtRZ8strSc6rR+DL3MMeWPYHdfhvjnonIIRxQOWgUJRa8mPgm489MmnMBdzydI+LIli1KhRyEL4XDZ8+7kt+8c2CeL8N2y435RFVqS4euUnMMYgHo97ogFffINQtOC2KWrIwcR8WWYdUjYg0Zgf6tzWch2gWz+4+y4ybjBGbVhpX9MQVpEBoMapTD9/zTpHMDPbvIv/AQDIdy20V2YstC3Gsxa7fXHf2U7tzy6qc1PfLzm7yA0Wyg93ZVJxjzn7Lt5w2cXZlp0Qky+0NAXccpyXNLPG0tmCKl9dmS0flXa6yNS44+B5kWMAlE2DC2PWooxjF/adeG1XFqdbcvL7NqPqYweeCsBvK3XZsdDn/i7mvZEKfI1tm3nuM9sm4Hu/5zgVPq/aPgQx++ovt7IRzBn9bCz7Xa98krRs9789O5T34OCBNkaY/X/B7NH837i60OLucQOd13t1c7f+WVAURekaolFvbGKby3WAbnmHZndtR1LuULHoiVZj6cBwAiZ5SpaKm+eQA9iofEjrSK4jk7gwxILb4Xw+gEhLOcBXSWR4RjDxjtI2wVAZqdxwKkOgeF3YZmRiLdYZhsg89dRToeWDy7A+cZvcJ+uADMWgIkfLUGkVyPWDbg4c2MZjZdr0nXfeGYBfZ6ZPnw7Ar7/77LMPgMTwjo0hZKOwsNArl0xyU1RkrSG/+uorHLxn8njb9pCZmekdM88l6wTbZqpBh7x+MgmXVHeThd5JxbMnpmy/4QYbrvTMM8/ghQr/2iazOI14wpATgjI5INlZcyaJcY/H4wlhUPJayYRGMmyNy7EOAInXl1PWVbZnOXhThsBJdZn3Darlwfs/yyXDJrmu3KY0XJD3u2SJwDIy/fslB3ozLDZaZBX3nEhOKPFY32/fBgCsnmN92pd8aN1j3lhiVfVrVk1HS0sL4vG4VzZZhmQJylLFvfN3lKo965Cy6dEtH9wVRVEURVEUZWMhEot5YxPbWq4jdMsHd9o6FXld0laRoZUhB6MyROb87972VAFaJDEujxZw2//zTwCAHXfcEQAw+eDT3TajoW33cdPebt+FbnBndpFToIp89SEz3yoTWQUMkQlntEtwAYiFB5LJkBnTwi5ul2q+JZC8xIXKxF34TGO1s+hz3fMM7eFA3jo3lYNSOXiXaiQVkoULF9pjCNgI7rDDDqHySRtHmbjHK6tTDHjuqbJKKzGqKsF4P/4vFXdNxNQ6/V04B+vxJ6ddAQC4K74QDz/8MIBExY3INOVyYDBjS3fbbTcAwGuvvQbAV7g5ABXwB5/2728HX7IOUMVLpepRdaXySAV+0SKrYtE+jr0/gD/YlHVlxIgRAICyMmuJynsB7w177rln6HiTxboCwHmzXw6dj4m7H2vPiwuVYThS72YXQ+vuFU3e4FVfQea9i4PK2Y5z3dQbtNpovz9tbxvy9Ninizx1ju2H5yIrKwv//nwVysrKcOkhNrFVXtxX6XjfaGZ4XZ3raXE2swybe+/4C+15eWy8ty57NGSPRzDmGvDrhjyXMnFTsBeD22QPCY+vJyruhPWccdrSojUajXrmBnADkqOMdfdi3P3tUX0PqrYy8RLrk7TllEnReO2CirscpMxtcB15b5HLcR/s6ZVJkmSvbLB8/E3nZ/YS8V4j7SyJvK8Fex5bkvTIM8adIbDRwmJb9pbc0ADrxkVWaV/9+UIAwMwfygEAF/0wFVlZWWhpaUlQz3n8VM2D7UPenzllneC2WGeUTZdu+eCuKIqiKIqiKBsNOjg1NV68pVOKmoTSRdWYClhJSUlCwhcqRBzF/u233wLwFaRfvm0t4Bgf98jeJ4S2yWmRUzB7NTi1otJXh5hhLctN+aZO5d1TRERa8qiTSFIp7p6dW9xXA5h+vUUkhWkSFnVMtMReiUpXfiqx5861qeipuFOFkAopkBgbKZXJVIksqIjw3FMx4LXhPqmiB1UJqiGcx2WCadqVRP5a+z0A4JYCO8iRytufMqyCS5X1gEevT1DaZJwqzz0TZzHhyTvvvAPATxpDVSwYl7t06VIAwMCBAwEkpieXahn3VVxcDCAxAZiMgQ3WFVpMfv/996F12faZzImZBaX6J2N9ZcZCfn/m5y9i5cqVeOGI3wAI3n+SD1rlfQsIJm9y9VoMMvcUeFq/uumpu9u2M/GTBQnXhZ8LCgo8b2nPQhZAfoP9vtkNjKXaz165gVW0iLXHPfVXlwIAxj5xY0KbluMiiHQiYZnYk5IskZuMcU+17Z7EsmXLAABbbWXbrXR+yc7ORiTDnh/PVpg9t+53JBSf7dT364cdAAD445evJvR+cMreLbbJZPH1QNjOl9ebbZ/3crY5fp/KHpT7pvLMekRfczk2JrhtqtTs6ZPHI2EZuD6nrJvB8TKRQLeFN4bAjSn4YE0EQ4YMQU6m3WafHz4AAKz4xvaArZy1GgBw6HtPIhqNoqyszDsu2bvBcyztboPL8NhlW2S7YZ1RNl265YO7oiiKoiiKomw0RKNpKu490FWG6hwTiDBxCJ0MOeV7dm1trfc2TVcKKn70haV6yHhcKsx8K//TD1MBADeOHBvaZ50bYV7TYqf5gWRPBU5Fy3dKVqLS7hLaZDLhEhX3NuwgGesesG70rN2c4k5Vj0pevVDWK73xAC2h+bNnz7ZldcoIeyqoBATjTamE8U2frjFSVaHiwbhFnmvGQ0r1lddEOgsE9y/TPAd7ApTU9BZOE77Hsz2PH/zaxr4f9PgNCbGfVKJGjRoFABg50qr1b731FgDfa1kqpry+gK8GccptchnWDSpO/J6f2Y7ZIzRo0KDQPvk94NddKlNcZ84cm7mQKj0Jjt8IIt0oSHBcxbRp0zDwmjOQmZmJxX+9H0BioqYmV497l/pjNjzF3bVXtl+OVaH1K8e1MFbduOnpLub9jtdnJpS3X79+eOSjb5Gfn4/jth3sfW/qnfWksIxtEFa3/ZydLsfB5OfnJyRdYxuXsbqyl0bWIRKMeZZx8LyWN998M3oqV199NQC/N4ttKtjbObu+ENXV1didCdIyw78zQbWY7Z5nvaSkxPuO9Z9qMOE15vxUbjRAYqw67/HSQUwmc2N9kWPP2PPGMSxsc6Wlpd4+qVpzGa7DewbH1cjeCnnP4LllT0N2djaSuSN7PRrOPz8SiYTOTfMae29bO8/+js0ose3rsPr6hN4jnnOOYeB5o9ofXF7+3koXHX5mnVE2Xbrlg7uiKIqiKIqibCxEotFQlurWlusI3fLBfcQ9NnlSbRvLkbq6Oi8ubOXKlQD8+GqOwObbKmNwiUzvfv6cKQCAu3c8GkBAeWfse4b/is6Ye6rZWU7Byo2F1c5MobQHFZJkUGkPusrERa9Do4j3rxflrBYKfJNbj3GCMj5ZjlwHEtUzqitUDWRMMJUN9mZwOcYvM7OdjEUOxvlJT2Hp+620ztll3wAAHuy9TWg+3RNajD2/sVjMuw6sC4yzpSI1depUAH7mQV4LXiOpzAG+ss7rtdNOOwHwHV44Zc8YlTVeb+l3zLrEuhesk5wn4+a5b+6DxyedUqSiyO2wTB999JG3L9b1WCyGkTee69XdoUOHYtY5fwOQOD4G8Ntp70Yq6fbz1IN/DMBvA6cs/QZB8qIcF2OnF/1kDwDAbVOmJ2S4jEajeGFemXc8Px9uz1l2jW3rueW2J6LBTWudQtjHJWnrkxXOpBk8R1RSOZUe2HJMCknmAS59w1P5VfdE2EPF361gzxJg650f4x4eMxULxbg79dx9V1tb69XrYEw3kDjGhb8BrOv8HFSFg+0ACMe/A76iLtdlW+V8/k7L7bC9J4P1hr8XUr2XjjeyR5E9xtxXPB737ovB3m2puMdisVAvRb2Lx1/7ve0dOO7TF1BeXo54PO7ti+eUZWJvNO+PvJcGr3My15vgcWtse8+hWz64K4qiKIqiKMpGQyRNV5lID3SVkVCgllN2RtTV1SXEZDLujW+4P/zwQ+gz34ipCMk411NF+nOmDW8MOL1Q7c51b+hUOeSU5Vx9yWkAgM1ue7TV4/XVUX9eXHwn42sTegZc2egm8pMX/2nLKmITU/kvB7/zyiCUMplpMxjrHPzMa0FFlLHI0uUD8NUTue8XjvwdAOBNd/Fva14AJTVSeW/MCNepd0/6CwDgHrPQW+d///sfAOCLL76w67i6IB1deL1Yh4I+/Iw7p5e6HPfAOiBjYanGsoeMdUsq7cnGYLBOU5Giasdpqqye0vmC2/vqq69CywXLJ1X6FStWYOB1Z3vLXnnllbgsa5S3nmyv8RW27u/2qM06e+jsTwEAc085ypZJ5HrIZTvI8LMUy54D2YMQ622zT8arywEAOX3L3NT2pOT2tmXIdzkp6Jj1v2MvAAAcOPlm75ryfFNxlDHw8vpJgvO5Ddkzovhjj9hOZCbSzMxMT2mPihh3jqUCkvz2BO6jsjeLn2X7kO07mLWa15PbYOw22zPbLXt2qY5zPe6T63HMGZ2hqIonyyhKhZ374O+LdLThPrkN9iDyeKi4l5aWJo1xJ5FMvwciEolgQKV9hli+wvbILV5qy9Onqck7Lu5Tjg3hlOdFxrwDiT0FPMds16wjSheygewgOxZooyiKoiiKoijKBqFbKu6MAaeSQPUgkxnenCqVG/P9aKWKQMWPzhgyIyNjzIh82+Xb+2kfTgLgv/VOcgo8ADTGbXmocqdS3NlDwLfw00q/Du379kLri31xlfWapxd3UA2gcke/7lSMH2+zIJZc/m8AwNEv3w3AjyGWioB0iAnGncoMbvxOjpznuaSSxnPN+VRVuD6Vj2RZ8qSq+8yYXwMABrrMoNfWz2/1+JUwVN7T4bDDDgMA3H777QASe2ekb7LM8gj414+xulTviYyzZR1gnWJd4HIyVjYUa+pUSY6hoLov8wdQ2eXxSPWcit0nn3wCwHe2CNZLeexXXHEFUjG+8YeEeTfmbxWesdqqgm/tZLO55g+wx+s7hbi257JkMt72/w7bHgAw4fOV3qaoMFKZfOJT2xv1y9EuG2dvqxBmF5cDAHJ657ipPb6iMquu98r03Xk89T7FOBYis6DKcTFBVZ3Lct6NN94IxUJnnUcftb2x9CqXvuntJejNzmvDdiPHuLAds+0ly37Ldsjv2N55z5fjX7gP3kNkpli6RqWTRZdqvOyF4zZlHD17b3kOWEaWOd0en/z8/IQe4CD1ATeZVF74sqeK0+D9jNdB9kjR474nuy9tLOjgVEVRFEVR0oMhLXyp8xL5+WElme5fikZK61AQCxpBRER4GmHoWc0ya7H5bbV9sA7bAChKx+mWD+7N118PAIhdZONwmRkut5kx5famVJBhG9gLPzrLW/dn/33AbsO9AVMhYpZF+dadKsMb39751ss37lM/nOSpCl78uxfHGlbYOcKfn8dfdlnSfVFpJ5dUfwcA+HOmHyt7c1OiipeMy9w+HnXHK715ZRZU6V0dVBa4Ds+FnE+oeFJF4TmW/rmpsuYFVb2H9j4RANDb+ecPct74vNbK+ofXS7qXUCVi/LN0lAAS6xU94dkDxnX4mYqbjFOVClcyn3Cq9XSo4L7pgiPrqRyjQeWR8/fdd9/Q8kEfd8a9c532Qq90SdQ5u9Bxioo745gzcmwbycyzSl4037atM/e3Cv7jny1OyAzpncMi29sYLSwGAGT3tuc+q9Cet6x8G6Nb5PbJNhaNRr3zz/sfty2V+FT3z2TzpRONkghzEDB+u6PnKj8/32szsldZ9nLxmvP+zdh2fgb8dsg6JntZeW/nteaYF36mrzyXYz3hZ6rqyZAZVLlNKu4ci8N98rhkzyHXT1dxLyoqalVxb2lpSfDCl70CcjwX21GysSHyOrFOKBsBGyjGvVs+uCuKoiiKEsA9DMiXvKAdpB9Gah8kH9jjeADA6dMmb7Bidie8ZI4B0wkmQ4uIh694lRUbqlbYF95zvvmfF8aiKJ1Jt3xw55s0M5FymssMfzGncGeEHRsA4PnDzwEA/HLqwwAS4/gY95ZKfZCxaFK5B/y35V+5fTwxdpxdx63rv5u7TG7r2GuZrsqeDKqRMqZd+urKGLygsiD9r2UMIedTRZDx8nLcgXSy4XYKCgpw97ZHAPBj2RlvW+RUwF6ZqrhvKGTsN5Up1imZ5TQYfysVOdYFKu8yc7FU92UsOz+zLgXVv2++sfH7MssuFbZUPuGsfzJrsFw+uC9mjWWGy/bCtkzHGYYynL18LgDg1RE7AwAO/vyT0Ho/XHQyAKB+rVUJYwU2Xj2eZ5XJ7OzspJ7pADDxA3t+fj3aHoentBe5sSW97PJZ+a4d1thzPP2MawAAO97zF+/88zpKdVa6ZsjxD8HeOl7Dq666KskZUgA/jvmRRx4B4GcLlWML2ks8Hk8YpyHhbwDbA691sJdL3vNlm5Eubaw/VNKpuLM3a8CAAaEysScuGSwX97169erQ9zIGnmWR7UKOo2qLnJyckMNUEHlOOZW/danOW7BHhdeY37EnUWPbNyKi0TQVd41xVxRFURQFfgw2p1TeAf+FkMp7jsa6t0ryV5jkmHo7CLxmlQ0P6rseyqMoQDd9cGfMGuObswqcOuy81Aucwt6UxO+cPHOwVcEvWfheaL70qpXOKdJtRS4HhH2rAd95JlUsYWsqwvpC9iDI7Hgyy5yMNQz+LxV2rivjXGVca9CDGPDPB7dXWFiIf29nlXZea2ZxpNLO+FuqgsqGgwoXrzuVbX7m99IpBvDVI15rthnp+8z6RzU/lQrGcRSMNQeARYsWhdaRYyiIzAQpnR+kmibdNwC//e+4445Jy5cuFU22jOMbw+NafrLwi2SLY9Qdk7z/r7vuOuDLWuTn5+O8wnIAwAk7W0eql74pTRiL4OW2yLdKJOPjswqdY0++bZcZufb85Ag3rIyMjASFnfC6MUcDpzI/xoUXXpjiTCit8emn1t+fY7Pkb057Cfr/y/s3kb8Vshcl+H+qmG/Ol7+bbHvs5WIWbd5Ttt7a1mPp9haE5Zk/3zqLsX5LF6lUZUhV1rYwxiRV3GOxWErHLTnuRCrxsqcR8K8xl2UdOO2009Iqp7L+icRiXj6FtpbrCN3ywV1RFEVRFB/GXEurUIaSAkC2EzpyXVKt3HWN0+xhBMU/05Jch4832AfrurJ1G6CuKOnSLR/cv/7a+pznX/ZXAEDu1dcC8BWiwhYq7anTntHR5faRYwAA9U6l/9tq+xbLt22pTMu3dflGDSRmYCQyHpeff/nLX7Z1yJ0O9zllyhQAiWq5nMpR8cHvpHIhM0/KkfE8V1TdmA1w/Ah7LYoCDjFU2hnTTjeZ/DwXp1hky/PJSccCAI5I8/iV1rku18Zsr6z3YyyZRVVeVyrqUqFiXaGPeHBdxpvKdiZj2Fm3ZO8NY+GpzK1YsQJAON5WxovSVUL28PCzVNpl9k/WW5mFOXgu5DbaC8/xhTHr+HJnS/oZgFPFht95550AfDVT+tVH86zSHs211zEj1/Wg5TjFnWOIYpHwNNBzwXMpnT14nXjOuM5lKRy0lPS46667AADXO4e1Aw88sEPby83N9e7bbfVuSeU96L5CpxleZ26D9UL2dskxVHR8Yv1h7gXme6DLFNsy4MfFM+ab7ZTjZLhN3lNYBukmI7MBpztuIFV8eywW88bMyWytvKdwPo+Xv4lynFBwPx999BEAvw4oGxHRaHrx6xrjriiKoig9HOnj7ixDgzHufAnLqbEPp7T4nLindZcZN/2ZDVPWbkZrIqBHs7PDrWlqY0Flk0XtIFNz+eWXAwCeeOIJAMBIp7QzSULcKe5FSbq0pHc6p1lunb8P2guAr8B7+1xkY+FTZRQNqo38X3pLSwWP6kRXwjJQjWMZpQIvnQSARDVUIpUZOX6AygiVdqrp/bP9asl5jG3PKXROAE5pzy6yn1knlM7hqjqbgff3kREpl2H9llkBpdIedEagmifrPpU3uQ1CP2g6RXz88ccAEnuEgio46xf3v9122wHw6yPrIXsMZO4G2RvA72WvG+C3l85q07z/0GWGPU8XVH6bcp1UyDjyq6++GoDvoPVvp2ry3B/rPOFj9IjPdWOJvEzPfhZlORaBseylpdbd5tZbb213eZX0YYbe22+/HfvsNqDD2wsq2bIHlddYZlAN9nKxDrG9clkqyjKXgHQi4z6orPMz6xN72II2i7Ldyqyr3LYcv8WysKz8zLEr7Rk3IDMC89h5v+NUusXI9bhP9h4E7zGM3W8tK7PSM+iWD+6KoiiKovj8a+ZqbLHFFjiIL5ae4u6LShxoTKVdTm8cORYAcOmCqRugxN0TxribeNjqkp8b42mo88omSSQaS/D3T7VcR+jWD+6Max1dYN+kTTL7GABFlX6cWMy5NvBWRhWp2mVdZaOjAs/PN25+EIBEe6i/Ln7fbjfwZpwqA6NUBpK9pW9oZLyu9F2mqiKVESDRaScV0heYCsctWx4KIFFp7xfo2u3tfNtzezvFvyg7NP3giKMBAOGclkpnEbxyjJVmneF1lFn+qLRLt5ngOowvZf2SipvMWsj5zBj5ox/9CAAwffr00D6T9f5w21TiZA+QrL+yXUrlngTHbvB46HjVUe4Tse68P3UG1157bVrL9QZwxx13YN95LwLw75Wcjhs3rtPKpCiK0lHGjx+P5557Dt988w1yc3Ox33774aabbsLo0aNTrvPll1/iqquuwowZM7Bo0SLccccdG73bVbd+cFcURVGUns7FF18MALj77rvxOOxg/6NjywCEXWW8GHensOfHwop7XUD84gsrX2zlCzotWIMw1IMv0EykROSATxl2FrQCBoDBgweH9skX4+BLNMNzWB4OSuU2pCjAbUhBiWIVw7369OnjCXyxZOJU3A+da25u9lRUht8Gt8/jkwmoZHI0aa/67bd+aByvsZKad999F+eddx723HNPNDc34/LLL8fhhx+Or776KqktMWBFn1GjRuGEE07ARRdd1LECRNIcnBrRwako+8NfAADFd45vc9moGzgSc3ZYXoy7U5F448qKxkOfqbxzkAo/jx9uR/Mn08POnfViu46jp9CW0k6VHQDy+lklkwo7lffyC50rxRfJ/a0VZVOA95m/1n7fZWWYdtQxtsft5/ZzHwDnn39+l5VHURQlGa+//nro88SJEzFgwADMmDEDBx10UNJ19txzT+y5554AgEsvvXS9l7Ez6NYP7nwDfeutt7q4JMmRto986+bnX//6111TsAAsw3//+18AianleQxULYJhDzLhDkMRuKxUaoIJazoLVSE2HJ59oEjkwwGjQ4YMAeBfdyYWC6Y9pxrG+iUHiskkXAyZkUlfqEzts88+AIAPP/wwVCbAr3dU7VJZvMrQGJY/VWhMsnAczuMguk2FDitQygYl+EK1+IozAYRdZbyBxi5hXW6jS1roFHe+KN42aiwA4I/z3vTaGtuoTKJFZTv4HZelysnBmdJCkoO5eR+gDSJVdBlSN2CAHYC7ww47ePucO3cugMQwPGnNyn2xvbNM8vcq2O6zkmSWNVTSA8mUGhsbEcmy+2NvRl1dnXceeK54nKnsY9ljwe/1BbljBHtPNgQbKsa9Y3q9oiiKoiiKomxExONxXHjhhdh///1DL3qbAt1acSdffvklAGDHi21CpqLb/55y2aiLjYnUOGuzOpfwxY0Uz4wwVIYDscKDVv1QGhlCg9BnAHhwz1+E9v3LtycA8JXIjQmWiYP/qFZy0B+VkqDdHVUTKhVUS6hgSPWFijsTLNHikSEzDJFheAzgh8bk9GbIjN02r/mhhx66roespAEHSQLAPffcA8C/vryeHCRO9UgmUWIClOB3VMVYh6ikp7IWJVTLqFxRRaeNHRP+BJfdZpttQtuQiaKkAi+Rg8oJyxA8Dio8ncU9gfOvKO1h6tZjAQB7rXjOm+cl1aK7TFU4tr0xI/y7dsfWdhD4hfPe8Oo4lW0q1kHY5thmGHdeVGSTfEnjBt4HpNUkl5PWrbRJDA4C532I+5LtmNtkealmyyRRXC5o1yxto4OYJns+ysrKkJGRgYhLYMawzoqKCu+4ZDy9tNrkMXC5pUuXJu5QaRfnnXce5s6diw8++GDD7TQaTdPHXWPcFUVRFEVRFAXnn38+pkyZgvfeew+bbbZZVxen09kkHtz/+Mc/AgAeeughAMDmbuBissGq0Rizyznl3SnrmS61O+0iY06Bj0XCsX+xiGnjs78vqcY/ecgZAMIq5sbCSSedBAB47jmrzjCmkCqjjA8EfCU9VZp3qvVcd+LuxwIA+mdTabdTKu9U1zkFfKWd86Yd+HMAwB/PPLO9h6h0kN///vcA/FTb8vqy14ax7jImHvBV6lSx60TGk3M5qdhxftCakTD2lmq8VL2kas+6Ld00UtmdBpORMTmKxqQqGwszZ84EAOyTFXCVETHuWXm2zvdyv1VsIdJZ+a5tDgcAnDv3Va/dJ7NgpXLMtkVVm1Mix3/xt4TbZKw3e3GHDh0a2n5JSYm3LbZvLsNtr3GJxbhvtldZpmDPWXD9hoYGr+c9mjTWvcUrc3FxMaJ59n5TMNAq+5FIxNu2jHGPB+LjAb8Xg8fNa3faaacl7FdJjTEGf/jDH/D8889j6tSpGDly5IYtQDRNVxlV3BVFURRFUZSezHnnnYdJkybhxRdfRGFhoRda1atXL+9F7bTTTsPQoUMxfrwVdhsbG/HVV195/y9btgyzZs1CQUEBttxyy3btPxKLIdJGuCeX6wib1IP7mU6FveOOO+yMQ45Cnz59sOPzj3vLRESwGhX4RjeNOuU9y422Z+w7YwClwu7HvjMWPrFcMv79dy6N/MaovC9bZr1/+aYq1cZgYhx+R7WTU6oJVGAn7HUCAF9pp8Ley8W25xYzjj0czw4AeX2tevHaDmMAABep0r7RwBshVSPWB+lFHFTkqKhJP2MuwzpE1YvzpfIunZrk8oA/XkM6WaRS3qWjEpFtIJm6//33XWfXqCjJYMI0TnfddVf0vvcmAL6fe1aBrft5Tfz9cjHqmckVwXt3+AkAYNz0ZzxXseBvgjeOycW0y/FN7J2V7ZbLcZtss+z55b2EDlHBcWKcx21zbA2Xke2Z9x45noZlZFkaGhqQ654Lgs48Hu4+kpWVhezsbETz7TEXDLbKe25RkVdu3mN4vHK8APc5Z84cAP41U9rHvffeCwAYO3ZsaP6ECRNw+umnAwAWL14c6gVevnw5dt11V+/zrbfeiltvvRVjxozB1KlT13eR14lN6sFdURRFURRF6XmkMhgIIh/GR4wYkdZ6aRGNpTk4VRX3BILewzfeeCN2z89MWCZVrLv32U1jjH13Sjtj3/2ETeFY+GDmOanOk41ZeZej/KkuJqvY0i+XqgJVVSoYue5cFshsfQXOS1jEtgdj3LOLbYyj+klvPPzhD38A4Me6U0WiwjVixIjQ/GQx4jJWXcaZsv5xXZlpkPWSLi5SVQPgdXNyX5yyXFI55/fSCUL2KLG+f/fdd966GtuubKwwffsTTzyB5cf8GsOGDUP2DVcD8JX3jBzn595kp/wZk7HuzCD62N62F7Xe/ZadPm0yAF8hZ9thzDaRvXJ0epHtWzqWse0x5j0YM895srdO+rRzHc7nvqTaH8z+yt/5SCDGPUK1Nu4752RnZ2Nl9naIxWIoHGo9wyfu+GMAwNnfTfWOh/vgPYa/kcuXLwfgXytFaY1N8sFdURRFURRFUTYYqrh3DjaF7aV4+OGHAQC7v/EUgPAbNOD7u8vPXC7qKe/2eyrrUZfDiuq6Py4/ufdrEKnEbwycffbZAKw6A/hqBdWJoNLBeYxnpNJBFeGJg2xWVsa298oMx7bn9LKqC31vGeNOlR0ARt72WCcdmdLZUHkn119/PQDfZYZ1JegYQwWNdYU9OTKrqfRxlm4MVPc5JoOqWTBuldnyqKBx33JbRJZF9jJxPapmQcVdUTZ2Pv30UwBWIc7PCbvLxF2Me9z1IBfUuFh3rw2EY949f/Nmu/wj+1pXsou/+x+AxKzEqXq76PxEBZ73Dk6pVMvY+GAvnhwHw7hxqv9U5GWeEd6XZG6I7Oxs/HN7G8u/baFdNlmMO11lCgsLQz2IBZv1d+vaY2pubvbuQdynVOB5bX71q18l7EdRJJv8g7uiKIqiKIqirE8i0agfStXGch2hxzy4jxs3DgDw30GDAABDH/ln6HvpNiNj3aPCdSbmXGcY4+7jXxCpwksF/s6WBe08ig0HFUyqETKOMDiPSgdVUKoKjA/0Y9vt55xCKu1ZoWlWoY09HHXHpE4/HmX9c8UVVwAAbr75ZgDAbrvtBiCsgqfyX5cKPBU2KlmrV68G4Ps3U1Wj8hYPODxIZKZUfuY2qH5RoZNON9KD+uOPPwYAXHDBBclOg6JslNx+++0AgBtuuAHYYwwOPPBA5Iy/BgCQUW/bYab7XTNeDhL5G8YeZripGwsWsd/fPfpH3v4Y//77L17y2jeVdPbK9urVC4Dfbtl7yzYox7ok6w3jPC7DdksVn9uU9xqOj5He85mZmcjxMqfz9z8Q4x5jjLs95pqaGm9cV3NzM7Ya1BcAMHhkL2+edLDh8X3++ecA/GujKOnQYx7cFUVRFEVRFGW9EEkzxj2iMe7t4ttvvwUAZJx5MQBg0MN3JF0u7a4Mz8I20cDdf0mPhpa5tn5+etvuQuh5+swzzwBIrnRQlWf8nlRN6SZDL1wq7/QOzsyn4m7V1de3ORAAsFVnH4yyQfnzn/8MAF6Ci2DK6f79bfwne2sI1TCqXz/88AMAXxWnIicVdSpdVM25fcBX3qQTDdUuqvqzZs0C4Pu+b7XVVqH1mYHxs88+A6DOD0r35vLLLwcA/Oc//8FOG2B/ZWVlCZlS2YvFtllcXAzAV83p/CLXk3HpwXly21TQZa8c48qpuPP3i/cH6XDVFqtXr/Z+C+W4GcD+TsqYfz6H8FooSnvocQ/uiqIoiqIAs48/Gdtvvz0yr7gUgD84Ne7MF/I8P0gXIir8IT2bZGHaYJe123pyrA1T5SPtz1+7r1OPYX1A0YkhM8kTMLUkzgMQ62vDcftuZQfHv7jXzwEAP/vk+U4upbKxEd1iT0RdaFary1VWdmg/Pe7BXTphPLx0KQD/jZ8KAGPx+Cbdt6+NW2McbOT3l4Q3nEx5d+363Kp5nVDyruEXv/gFAGDKlCkAwkppqkyUVElH338VgEAmTbdupVt+cXk5AH/8wTadXnqlK7nssssS5l133XUA/DrBKaGiTrcJxsBSNaM6Jn2iqbYxiyKQGKtOZEbX4cOHA/CzFn7zzTcAfOWNvQCqjimbEmeddRYAYNKkSRi1Affbt29f7zdCZlLmVKrebINs58GeX+lEJfOKcFlug/vkfP62k6ALVjrU1dV5zwkApAGPR1VVFRYuXAjAP/eKsi70uAd3RVEURVF8fjjzNxgxYgQyL/sLAMAIZT3PTWnKQKTi7hsy+POyonYe1fgJe9nkTSe+M7Gzit9p/MeVbQjtMjk4NdN/GufgVOMe8A8akgUgC28usCE4sd4DAABFI23o3Q5FC9d7uZWeRY9/cKfamy633HILAKDwwnMAJCqBwKYZA3v00UcDAO68805vHmMJqVwwdvCSS0RvhKI4rrrqqtBnKvCsS1TaqZbJ+NVK18XI9kali/Gpg5xrFJA45kL6ssuMrtyXzf2gKD2Dk08+GQBw7733YucNuN9evXp57ZnjSNijy57tVI4xQXcz6e3OddieuQ/2onM+e/PoOiPdp9pLUVGRuy81Jv3+iy++wLnnntuhfSgKoA/uiqIoiqIA+OKkk7H11lsj++qrAfi2kKlgHHtiIkJfcWeiQX7Hzw/vcRwA3zby1Pe7PtkeY9oZ4+7bQSY+1Bt37Kax3s2x4sN7lfkwxmC3oXag/IBt+wEAlq+3Uis9DX1wbyc9XU3eFHsTlK6HCjvVMyrsVMGku4QcV0HFPug6I90kuG6qTIuqtCs9GarBV155JY7oojKUu3FPbJsynwO/DyrunDdgwIDQthjTLteR49U4P7jNdaGgoMArN12xgqjarnQW+uCuKIqiKIrHfw8+GNtttx2G/etfSb9nYsIYExIy1j2QkJDJmlIp7kxeSGX+yYNOBQA0OdH+vNkvd8qxpMPfh1sr4t4ulp0Wxlmeq0wgxp3WyO5lwjS70BhhPJPjwvZePeAg/O1vf1s/BVd6JPrgrihKl0FVnGq4dCqigsX50seZ69GDPRinKmNWpbLGfTC+VlEUeA+ZF198MYZ1YTkikYg3loVTZlAO9qxxHsfHsN1zylh29upxPsdkJcu2vC40NTV58fSffvopjjnQnj19aFc6G31wVxRFURQlgef22gtHHnkkcq66pvUFPeXdf1mm53tmJLniLmPg5fTfO1lDBCrw4z56ogNHkpx7dv4ZgGBse1hpz8h0A1+TDFyl5z2aXTIoscgV7y/B7bff3tlFVpRUjqNdx7Jly3DiiSeiuLgYRUVFOOaYY5LGiymK0v3by5VXXokrr7wSzc3NaG5uRm1tLWpra9HU1ISmpibvc11dHerq6hCPxxGPx5GTk4OcnBz069cv9BeNRr2/WCwW+gt+F41GUVlZicrKSpSXl3txsIqiKIqyMbNRKe7V1dU4+OCDUVFRgcsvvxyZmZm44447MGbMGMyaNSshUYKi9GS0vSiKsr6gWvz73/8e2HVnjBkzBgCw+eabA+deEFo2QvcVp7wDvsIuY9n9aTgGvq3ppP1/BQCgxXxwePp9ZiEmT54MwA+bY1JFGY5XWVmJSWNOAwBkurLQRUZmTKV/O2P67f/0cXcx7k02jO/7Jd/j3XffBQDcc889UJT1xUb14H7PPffgu+++w/Tp07HnnnsCAI488kjssMMOuO2223DDDTd0cQkVZeNhU2ovdHQZP348AIQzEcKPaWd86ogRIwAAo0aNSro84LtKMJZdxrwvXrw4tG9FURRF2diJGJmVpBXeeecdHHLIIXjuuedw7LHHhr6bNGkSTjnlFHz00UfYd99916kwe+21FwBg+vTpoflHHHEE5s+fj++//36dtqsoXUFdXR123XVXAMDnn3/uWRauXbsW22+/PUaOHIn333/fG4DZXjbF9sIHd/mQne6De7CXgevIB3cOUps1axYA4NRTT+3UY1CUTZkrr7wSALDTTjuh/423AQCandLeVOcr7vEm2+4aXLbVVEp6fZqKe4uRn/0y8bu74gvx2GPWD5732379+uGJsTbRIu8qjGHntCDDftMny96L+7lpn752G8Wb9/L21We0tZ38/MDjMXv2bAA6ALWnU1lZiV69eqGiosJL6NWZy0vaFeM+duxYDBs2DI8//njCd48//ji22GIL7LvvvmhoaEBJSUlafyQej2P27NnYY489Era91157Yf78+d4ocEXpDuTm5uLhhx/G999/j7/+9a/e/PPOOw8VFRWYOHEiYrGYthdFURRFUdKiXaEykUgEp556Km6//XZUVFR4Nktr1qzBG2+84T2cPPHEEzjjjDPS2iYF/7Vr16KhoQGDBw9OWIbzli9fjtGjR7enyIrSpey9997485//jJtuugnHHnssVq1ahcmTJ+POO+/E1ltvDUDbS5DLLrss9Pn6668HkKjA8xhlgpb/b+/+Q6q6/ziOv8zvbtdWC8XUhNiSZmWNknWzorKIr1lQWGn90dBia3xlkbNV+Ee6mf1aNCn6oX+USeVIaLBGUeEcc6OoqJCKbSZRf0QZOqZl6vSq3z/sHLveUu/NXyefD7joPefcz3mf4Nzefn68z8sPUTG2dSwtafxB8/jx4x6NHRgMXHqXExK0bds28+2swp/M35t92+4795rveuVPo/e7q55325C2n5kN914ZnzGClp+fL0ny9/fXyuI8BQQE6MS0ZS/O6TqnveOTUjvWb2/dtksXL140z5GRkaFxkhISEl4ZA9CbPJ7jnpiYqF27dun06dP69NNPJUmFhYVyOp3mDbNw4UIVFRV51K5Rp3Xo0KFu+4z/nI1jACv55ptvdPbsWSUlJam2tlbR0dHasGGDuZ/7BQAAdIfHifuECRPkcDhUUFBgJu4FBQWaMWOGxo0bJ6mtN+xVPYGdMeajdbbIzDgGsBKbzaa8vDw5HA7Z7XYdO3bMrHQgcb90ZuvWrS7vjQW3w4cPl9Q+AmH8exoPapLaq0gYvfVGb/yff/4pSdq8eXNvhQ0MGhkZGebv/3v0SJI0efJkSVJ4eLj+s8F1FG3IiznvRp1386mrL3rUjbG1jj3yac/LPYprzZo1ktorvISFhem/Z3MUGBgoSS7fwVL7mhjjIUp3797VI0l37tyRjh1Tbm6uR+cHeotXVWUSExOVkpKihw8f6t9//9WVK1d08OBBc399fb1qamq61VbIi8cCBwQEaOjQoa8cvja2hYaGehMu0O+MYdaGhgaVl5dr7Nix5j7uFwAA0B0eVZUxVFVVKTQ0VDt27FB9fb22b9+uR48emX/J5ufnezxnV5IcDod8fHzcqmTExMTo3r17unfv1XPagIHs1q1bcjgcWr16tUpLS1VVVaXbt2+ba0S4X7pvz549kqTY2FhJUnNzW++dMfLw8tQho8fdmDr08OFDSW0lMwH0neTkZEnt96LR223cv/v37++zWFJS2mrQG2tejO9UY6QyJyenz2LB26Gvq8p41eMeGBioRYsW6eTJk2poaFBsbKyZtEvezdmVpPj4eKWlpen69etmtYyysjL98ssv2rRpkzehAv2qqalJa9asUWhoqPbv36/79+/L4XAoNTVVeXl5krhfAABA93jV4y5JP/zwg+Lj4yW1LU5duXLlGwfz7NkzRUZG6tmzZ9q0aZPeeecdZWdnq7m5WaWlpRo1atQbnwPoS19//bWysrJUXFys+fPnS5J27NihrVu36ty5c1q8eLHXbQ/G+8XomYuJiZHUvgDX+BozarRL7dVk6urqJLXXu//yyy/7JFYAwNtvQNdxf9mSJUvk7++vkSNHaunSpd4242LEiBH69ddfNXfuXG3fvl3p6emaMmWKSkpK3sokBG+3mzdvaufOnVq/fr2ZtEttT+p0OBxat26dqqurvW6f+wUAgMHF6x53p9Op0NBQLVmyREePHu3puADgtf744w9J7lV1Xq7jbsxxN+b6GyOEAAD0FMv0uP/444+qrKxUYmKit00AAAAA6CaPF6devXpVt27dUlZWliIjIxUdHd0bcQHAa0VEREiStmzZ4rL95QFEo2JFdnZ23wUGAEAv8rjHPScnR8nJyQoKCtLx48d7IyYAAAAAHXg9xx0AAAAYzCwzxx0AAABA3yFxBwAAACyAxB0AAACwABJ3AAAAwAJI3AEAAAALIHEHAGCAaWlpUW5urqZOnarhw4crODhYixYt0uXLl/s7NAD9iMQdAIABZvPmzUpOTtZHH32k7OxsffXVV7p7966io6N17dq1/g4PQD/x+MmpAACg9zidTuXk5Cg+Pl4nTpwwtyckJCgsLEwFBQWaPn16P0YIoL/Q4w4AQCcePHggHx+f1756WlNTk+rr6xUcHOyyPSgoSEOGDJGfn1+PnxOANdDjDgBAJ0aNGuXS8y21Jdepqamy2WySpLq6OtXV1XXZlq+vr/z9/Ts9xs/PT1FRUcrPz9fMmTM1Z84cVVdXKysrS/7+/vr888+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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from nimare.correct import FDRCorrector\n", - "\n", - "corr = FDRCorrector(method=\"indep\", alpha=0.05)\n", - "cres = corr.transform(contrast_result)\n", - "\n", - "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia with drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia without drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression with drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression without drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - ")" + "from nimare.correct import FDRCorrector\n\ncorr = FDRCorrector(method=\"indep\", alpha=0.05)\ncres = corr.transform(contrast_result)\n\n# generate FDR corrected z-score maps for group-wise spatial homogeneity test\nplot_stat_map(\n cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia with drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia without drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression with drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression without drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "After FDR correction (via BH procedure), areas with stronger spatial intensity\n", - "are more stringent, (the number of voxels with significant p-values is reduced).\n", - "\n" + "After FDR correction (via BH procedure), areas with stronger spatial intensity\nare more stringent, (the number of voxels with significant p-values is reduced).\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for group comparisons among any two groups\n", - "In the most basic scenario of group comparison test, contrast matrix `t_con_groups`\n", - "can be generated by `create_contrast` function, with `contrast_name` specified as\n", - "\"group1-group2\".\n", - "\n" + "## GLH testing for group comparisons among any two groups\nIn the most basic scenario of group comparison test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with `contrast_name` specified as\n\"group1-group2\".\n\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "t_con_groups = inference.create_contrast(\n", - " [\n", - " \"SchizophreniaYes-SchizophreniaNo\",\n", - " \"SchizophreniaNo-DepressionNo\",\n", - " \"DepressionYes-DepressionNo\",\n", - " ],\n", - " source=\"groups\",\n", - ")\n", - "contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)\n", - "\n", - "# generate z-statistics maps for each group\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Drug Treatment Effect for Schizophrenia\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Untreated Schizophrenia vs. Untreated Depression\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Drug Treatment Effect for Depression\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - ")" + "t_con_groups = inference.create_contrast(\n [\n \"SchizophreniaYes-SchizophreniaNo\",\n \"SchizophreniaNo-DepressionNo\",\n \"DepressionYes-DepressionNo\",\n ],\n source=\"groups\",\n)\ncontrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate z-statistics maps for each group\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Drug Treatment Effect for Schizophrenia\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Untreated Schizophrenia vs. Untreated Depression\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Drug Treatment Effect for Depression\",\n threshold=scipy.stats.norm.isf(0.4),\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to group comparison\n", - "test of spatial intensity for any two groups. The null hypothesis assumes\n", - "spatial intensity estimations of two groups are equal at voxel level,\n", - "$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\n", - "within brain mask, $j$ is the index of voxel. Areas with significant p-values\n", - "(significant difference in spatial intensity estimation between two groups)\n", - "are highlighted (under significance level $0.05$).\n", - "\n" + "Four figures (displayed as z-statistics map) correspond to group comparison\ntest of spatial intensity for any two groups. The null hypothesis assumes\nspatial intensity estimations of two groups are equal at voxel level,\n$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\nwithin brain mask, $j$ is the index of voxel. Areas with significant p-values\n(significant difference in spatial intensity estimation between two groups)\nare highlighted (under significance level $0.05$).\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing with contrast matrix specified\n", - "CBMR supports more flexible GLH test by specifying a contrast matrix.\n", - "For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\n", - "represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\n", - "function. Multiple independent GLH tests can be conducted simultaneously by\n", - "including multiple contrast vectors/matrices in `t_con_group`.\n", - "\n", - "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\n", - "when it's represented as one of elements in `t_con_group` (datatype: list).\n", - "Only if all of null hypotheses are rejected at voxel level, p-values are significant.\n", - "For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\n", - "the equality of spatial intensity estimation among all of four groups (finding the\n", - "consistent activation regions). Note that only $n-1$ contrast vectors are necessary\n", - "for testing the equality of $n$ groups.\n", - "\n" + "## GLH testing with contrast matrix specified\nCBMR supports more flexible GLH test by specifying a contrast matrix.\nFor example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\nrepresented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\nfunction. Multiple independent GLH tests can be conducted simultaneously by\nincluding multiple contrast vectors/matrices in `t_con_group`.\n\nCBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\nwhen it's represented as one of elements in `t_con_group` (datatype: list).\nOnly if all of null hypotheses are rejected at voxel level, p-values are significant.\nFor example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\nthe equality of spatial intensity estimation among all of four groups (finding the\nconsistent activation regions). Note that only $n-1$ contrast vectors are necessary\nfor testing the equality of $n$ groups.\n\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "contrast_result = inference.transform(\n", - " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", - ")\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_GLH_groups_0\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"GLH_groups_0\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - ")\n", - "print(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" + "contrast_result = inference.transform(\n t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n)\nplot_stat_map(\n contrast_result.get_map(\"z_GLH_groups_0\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"GLH_groups_0\",\n threshold=scipy.stats.norm.isf(0.4),\n)\nprint(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for study-level moderators\n", - "CBMR framework can estimate global study-level moderator effects,\n", - "and allows inference on the existence of m.\n", - "\n" + "## GLH testing for study-level moderators\nCBMR framework can estimate global study-level moderator effects,\nand allows inference on the existence of m.\n\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " standardized_sample_sizes standardized_avg_age type2 type3 \\\n", - "0 0.000018 -0.003071 -0.190215 -0.186201 \n", - "\n", - " type4 type5 \n", - "0 -0.185405 -0.184005 \n", - "P-values of moderator effects `sample_sizes` is p\n", - "0 0.998586\n", - "P-value of moderator effects `avg_age` is p\n", - "0 0.755084\n" - ] - } - ], + "outputs": [], "source": [ - "contrast_name = results.estimator.moderators\n", - "t_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\n", - "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", - "print(contrast_result.tables[\"moderators_regression_coef\"])\n", - "print(\n", - " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", - " contrast_result.tables[\"p_standardized_sample_sizes\"]\n", - " )\n", - ")\n", - "print(\n", - " \"P-value of moderator effects `avg_age` is {}\".format(contrast_result.tables[\"p_standardized_avg_age\"])\n", - ")" + "contrast_name = results.estimator.moderators\nt_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\ncontrast_result = inference.transform(t_con_moderators=t_con_moderators)\nprint(contrast_result.tables[\"moderators_regression_coef\"])\nprint(\n \"P-values of moderator effects `sample_sizes` is {}\".format(\n contrast_result.tables[\"p_standardized_sample_sizes\"]\n )\n)\nprint(\n \"P-value of moderator effects `avg_age` is {}\".format(\n contrast_result.tables[\"p_standardized_avg_age\"]\n )\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This table shows the regression coefficients of study-level moderators, here,\n", - "`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\n", - "Moderator effects of both `sample_size` and `avg_age` are not significant under\n", - "significance level $0.05$. With reference to spatial intensity estimation of\n", - "a chosen subtype, spatial intensity estimations of the other $4$ subtypes of\n", - "schizophrenia are moderatored globally.\n", - "\n" + "This table shows the regression coefficients of study-level moderators, here,\n`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\nModerator effects of both `sample_size` and `avg_age` are not significant under\nsignificance level $0.05$. With reference to spatial intensity estimation of\na chosen subtype, spatial intensity estimations of the other $4$ subtypes of\nschizophrenia are moderatored globally.\n\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p\n", - "0 0.823866\n" - ] - } - ], + "outputs": [], "source": [ - "t_con_moderators = inference.create_contrast(\n", - " [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n", - ")\n", - "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", - "print(\n", - " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", - " contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", - " )\n", - ")" + "t_con_moderators = inference.create_contrast(\n [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n)\ncontrast_result = inference.transform(t_con_moderators=t_con_moderators)\nprint(\n \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n )\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "CBMR also allows flexible contrasts between study-level covariates.\n", - "For example, we can write `contrast_name` (an input to `create_contrast`\n", - "function) as `standardized_sample_sizes-standardized_avg_age` when exploring\n", - "if the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n", - "\n" + "CBMR also allows flexible contrasts between study-level covariates.\nFor example, we can write `contrast_name` (an input to `create_contrast`\nfunction) as `standardized_sample_sizes-standardized_avg_age` when exploring\nif the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n\n" ] } ], @@ -808,4 +230,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 4638854c9..36f43ecd0 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -6,11 +6,21 @@ Coordinate-based meta-regression algorithms =========================================== -A tour of CBMR algorithms in NiMARE +A tour of Coordinate-based meta-regression (CBMR) algorithms in NiMARE + +CBMR is a generative framework to approximate smooth activation intensity function +and investigate the effect of study-level moderators (e.g., year of pubilication, +sample size, subtype of stimuli). CBMR considers three stochastic models (Poisson, +Negative Binomial (NB) and Clustered NB) for modeling the random variation in foci, +and allows flexible statistical inference for either spatial homogeneity tests or +group comparison tests. It is a computationally efficient approach with +good statistical interpretability to model the locations of activation foci. This tutorial is intended to provide a brief description and example of the CBMR -algorithm implemented in NiMARE. For a more detailed introduction to the elements -of a coordinate-based meta-regression, see other stuff. +algorithm implemented in NiMARE. + +For a more detailed introduction to the elements of a coordinate-based meta-regression, +see other stuff. """ import numpy as np import scipy @@ -23,6 +33,11 @@ ############################################################################### # Load Dataset # ----------------------------------------------------------------------------- +# Here, we're going to simulate a dataset (using `nimare.generate.create_coordinate_dataset`) +# that includes 100 studies, each with 10 reported foci and sample size varying between +# 20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression) +# and drug status (Yes or No). We also add two continuous study-level moderators (sample size and +# average age) and a categorical study-level moderator (schizophrenia subtype). # data simulation ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000) @@ -50,24 +65,19 @@ ############################################################################### # Estimation of group-specific spatial intensity functions # ----------------------------------------------------------------------------- -# Unlike kernel-based CBMR methods (e.g. ALE, MKDA and SDM), CBMR provides a -# generative regression model that estimates a smooth intensity function and -# can have study-level moderators. It's developed with a spatial model to -# induce a smooth response and model the entire image jointly, and fitted with -# different variants of statistical distributions (Poisson, Negative Binomial -# (NB) or Clustered NB model) to find the most accurate but parsimonious model. -# -# CBMR framework can generate estimation of group-specific spatial internsity +# CBMR can generate estimation of group-specific spatial internsity # functions for multiple groups simultaneously, with different group-specific # spatial regression coefficients. # -# CBMR framework can also consider the effects of study-level moderators +# CBMR can also consider the effects of study-level moderators # (e.g. sample size, year of publication) by estimating regression coefficients -# of moderators (shared by all groups). Note that moderators can only have global -# effects instead of localized effects within CBMR framework. In the scenario -# that there're multiple subgroups within a group, while one or more of them don't -# have enough number of studies to be inferred as a separate group, CBMR can -# interpret them as categorical study-level moderators. +# of moderators (shared by all groups). +# +# Note that study-level moderators can only have global effects instead of localized +# effects within CBMR framework. In the scenario that there're multiple subgroups +# within a group, while one or more of them don't have enough number of studies to be +# inferred as a separate group, CBMR can interpret them as categorical study-level moderators. + from nimare.meta.cbmr import CBMREstimator dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform(dset) @@ -79,12 +89,12 @@ "standardized_avg_age", "schizophrenia_subtype:reference=type1", ], - spline_spacing=100, # a reasonable choice is 10, 100 is for speed + spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e3, - device="cpu", + tol=1e3, # a reasonable choice is 1e-1 or 1e-2, 1e3 is for speed + device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=dset) plot_stat_map( From e7bc4c17446ccec600e79960ee7f9513f20324ec Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 15:54:06 +0100 Subject: [PATCH 158/177] remove the standardize_field function as it's replicated in the StandardizeField class --- nimare/tests/utils.py | 30 ------------------------------ setup.cfg | 2 +- 2 files changed, 1 insertion(+), 31 deletions(-) diff --git a/nimare/tests/utils.py b/nimare/tests/utils.py index a0b2bc71c..0afadcb22 100644 --- a/nimare/tests/utils.py +++ b/nimare/tests/utils.py @@ -123,33 +123,3 @@ def _transform_res(meta, meta_res, corr): if isinstance(corr_expectation, type(pytest.raises(ValueError))): pytest.xfail("this meta-analysis & corrector combo fails") return cres - - -def standardize_field(dataset, metadata): - """Document This.""" - # moderators = dataset.annotations[metadata] - categorical_metadata, numerical_metadata = [], [] - for metadata_name in metadata: - if np.array_equal( - dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(str) - ): - categorical_metadata.append(metadata_name) - elif np.array_equal( - dataset.annotations[metadata_name], dataset.annotations[metadata_name].astype(float) - ): - numerical_metadata.append(metadata_name) - if len(categorical_metadata) > 0: - LGR.warning(f"Categorical metadata {categorical_metadata} can't be standardized.") - if len(numerical_metadata) == 0: - raise ValueError("No numerical metadata found.") - - moderators = dataset.annotations[numerical_metadata] - standardize_moderators = moderators - np.mean(moderators, axis=0) - standardize_moderators /= np.std(standardize_moderators, axis=0) - if isinstance(metadata, str): - column_name = "standardized_" + metadata - elif isinstance(metadata, list): - column_name = ["standardized_" + moderator for moderator in numerical_metadata] - dataset.annotations[column_name] = standardize_moderators - - return dataset diff --git a/setup.cfg b/setup.cfg index 3a00c3d8e..1b90ae62e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -40,7 +40,7 @@ classifiers = python_requires = >= 3.6 install_requires = cognitiveatlas # nimare.annotate.cogat - functorch~=0.2; python_version<"3.7" # for cbmr models + functorch~=0.2 fuzzywuzzy # nimare.annotate indexed_gzip>=1.4.0 # working with gzipped niftis joblib # parallelization From ab751a5c0ab620d4a11d739dbf215881f13faa8a Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 15:59:15 +0100 Subject: [PATCH 159/177] use pass instead of return in the abstract methods --- nimare/dataset.py | 1 - nimare/meta/cbmr.py | 2 +- nimare/meta/models.py | 6 +++--- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/nimare/dataset.py b/nimare/dataset.py index 8f157e08a..3c93d542f 100755 --- a/nimare/dataset.py +++ b/nimare/dataset.py @@ -127,7 +127,6 @@ def __repr__(self): experiments in the Dataset represented as well. """ # Get default parameter values for the object - signature = inspect.signature(self.__init__) defaults = { k: v.default diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 5d41a0c5e..46d8634cd 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -1,4 +1,4 @@ -"""Coordinate-based meta-regression (CBMR) framework for estimation and statistcial inference.""" +"""Coordinate Based Meta Regression Methods.""" import logging import re from functools import wraps diff --git a/nimare/meta/models.py b/nimare/meta/models.py index b179618cb..85c097ed5 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -83,7 +83,7 @@ def _log_likelihood_single_group(self, **kwargs): torch.Tensor Value of the log-likelihood of a single group. """ - return + pass @abc.abstractmethod def _log_likelihood_mult_group(self, **kwargs): @@ -94,7 +94,7 @@ def _log_likelihood_mult_group(self, **kwargs): torch.Tensor Value of total log-likelihood of all groups in the dataset. """ - return + pass @abc.abstractmethod def forward(self, **kwargs): @@ -105,7 +105,7 @@ def forward(self, **kwargs): torch.Tensor Value of the log-likelihood of a single group. """ - return + pass def init_spatial_weights(self): """Initialize spatial regression coefficients. From a95e116c9a5c23af876ecfba005a378185d00bfd Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 16:22:06 +0100 Subject: [PATCH 160/177] added a test for StandardizeField class. --- nimare/tests/test_meta_cbmr.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 4f7fa1047..e4871891b 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -199,3 +199,16 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): foci_per_study_tensor, prev_loss, ) + +def test_StandardizeField(testdata_cbmr_simulated): + """Unit test for StandardizeField.""" + dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform( + testdata_cbmr_simulated + ) + assert isinstance(dset, nimare.dataset.Dataset) + assert "standardized_sample_sizes" in dset.annotations + assert "standardized_avg_age" in dset.annotations + assert dset.annotations["standardized_sample_sizes"].mean() == pytest.approx(0.0, abs=1e-3) + assert dset.annotations["standardized_sample_sizes"].std() == pytest.approx(1.0, abs=1e-3) + assert dset.annotations["standardized_avg_age"].mean() == pytest.approx(0.0, abs=1e-3) + assert dset.annotations["standardized_avg_age"].std() == pytest.approx(1.0, abs=1e-3) \ No newline at end of file From 4705c47be1eed9a60aa01f9ec7f90d41d9f7dbc7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 18:58:52 +0100 Subject: [PATCH 161/177] edit example file of cbmr methods. --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 790 ++++++++++++++++++- examples/02_meta-analyses/10_plot_cbmr.py | 53 +- nimare/tests/test_meta_cbmr.py | 1 + 3 files changed, 799 insertions(+), 45 deletions(-) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb index de31f2102..81f378fb4 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ b/examples/02_meta-analyses/10_plot_cbmr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -15,197 +15,909 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n\n# Coordinate-based meta-regression algorithms\n\nA tour of Coordinate-based meta-regression (CBMR) algorithms in NiMARE\n\nCBMR is a generative framework to approximate smooth activation intensity function\nand investigate the effect of study-level moderators (e.g., year of pubilication,\nsample size, subtype of stimuli). CBMR considers three stochastic models (Poisson,\nNegative Binomial (NB) and Clustered NB) for modeling the random variation in foci,\nand allows flexible statistical inference for either spatial homogeneity tests or\ngroup comparison tests. It is a computationally efficient approach with\ngood statistical interpretability to model the locations of activation foci.\n\nThis tutorial is intended to provide a brief description and example of the CBMR\nalgorithm implemented in NiMARE.\n\nFor a more detailed introduction to the elements of a coordinate-based meta-regression, \nsee other stuff.\n" + "\n", + "\n", + "# Coordinate-based meta-regression algorithms\n", + "\n", + "A tour of Coordinate-based meta-regression (CBMR) algorithms in NiMARE\n", + "\n", + "CBMR is a generative framework to approximate smooth activation intensity function\n", + "and investigate the effect of study-level moderators (e.g., year of pubilication,\n", + "sample size, subtype of stimuli). CBMR considers three stochastic models (Poisson,\n", + "Negative Binomial (NB) and Clustered NB) for modeling the random variation in foci,\n", + "and allows flexible statistical inference for either spatial homogeneity tests or\n", + "group comparison tests. It is a computationally efficient approach with\n", + "good statistical interpretability to model the locations of activation foci.\n", + "\n", + "This tutorial is intended to provide a brief description and example of the CBMR\n", + "algorithm implemented in NiMARE.\n", + "\n", + "For a more detailed introduction to the elements of a coordinate-based meta-regression,\n", + "see the [online course](https://www.coursera.org/lecture/functional-mri-2/module-3-meta-analysis-Vd4zz)\n", + "or a [brief overview](https://libguides.princeton.edu/neuroimaging_meta).\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import numpy as np\nimport scipy\nfrom nilearn.plotting import plot_stat_map\n\nfrom nimare.generate import create_coordinate_dataset\nfrom nimare.meta import models\nfrom nimare.transforms import StandardizeField" + "import numpy as np\n", + "import scipy\n", + "from nilearn.plotting import plot_stat_map\n", + "\n", + "from nimare.generate import create_coordinate_dataset\n", + "from nimare.meta import models\n", + "from nimare.transforms import StandardizeField" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load Dataset\nHere, we're going to simulate a dataset (using `nimare.generate.create_coordinate_dataset`)\nthat includes 100 studies, each with 10 reported foci and sample size varying between\n20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression)\nand drug status (Yes or No). We also add two continuous study-level moderators (sample size and \naverage age) and a categorical study-level moderator (schizophrenia subtype).\n\n" + "## Load Dataset\n", + "Here, we're going to simulate a dataset \n", + "(using [nimare.generate.create_coordinate_dataset](https://nimare.readthedocs.io/en/latest/generated/nimare.generate.create_coordinate_dataset.html))\n", + "that includes 100 studies, each with 10 reported foci and sample size varying between\n", + "20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression)\n", + "and drug status (Yes or No). We also add two continuous study-level moderators (sample size and \n", + "average age) and a categorical study-level moderator (schizophrenia subtype).\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# data simulation\nground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n# set up group columns: diagnosis & drug_status\nn_rows = dset.annotations.shape[0]\ndset.annotations[\"diagnosis\"] = [\n \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n]\ndset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\ndset.annotations[\"drug_status\"] = (\n dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column\n# set up continuous moderators: sample sizes & avg_age\ndset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\ndset.annotations[\"avg_age\"] = np.arange(n_rows)\n# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n# as groups)\ndset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n n_rows / 5\n)\ndset.annotations[\"schizophrenia_subtype\"] = (\n dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n) # random shuffle drug_status column" + "# data simulation\n", + "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", + "# set up group columns: diagnosis & drug_status\n", + "n_rows = dset.annotations.shape[0]\n", + "dset.annotations[\"diagnosis\"] = [\n", + " \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n", + "]\n", + "dset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\n", + "dset.annotations[\"drug_status\"] = (\n", + " dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n", + ") # random shuffle drug_status column\n", + "# set up continuous moderators: sample sizes & avg_age\n", + "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\n", + "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", + "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n", + "# as groups)\n", + "dset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n", + " n_rows / 5\n", + ")\n", + "dset.annotations[\"schizophrenia_subtype\"] = (\n", + " dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n", + ") # random shuffle drug_status column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Estimation of group-specific spatial intensity functions\nCBMR can generate estimation of group-specific spatial internsity\nfunctions for multiple groups simultaneously, with different group-specific\nspatial regression coefficients.\n\nCBMR can also consider the effects of study-level moderators\n(e.g. sample size, year of publication) by estimating regression coefficients\nof moderators (shared by all groups).\n\nNote that study-level moderators can only have global effects instead of localized\neffects within CBMR framework. In the scenario that there're multiple subgroups\nwithin a group, while one or more of them don't have enough number of studies to be\ninferred as a separate group, CBMR can interpret them as categorical study-level moderators.\n\n" + "## Estimation of group-specific spatial intensity functions\n", + "CBMR can generate estimation of group-specific spatial internsity\n", + "functions for multiple groups simultaneously, with different group-specific\n", + "spatial regression coefficients.\n", + "\n", + "CBMR can also consider the effects of study-level moderators\n", + "(e.g. sample size, year of publication) by estimating regression coefficients\n", + "of moderators (shared by all groups).\n", + "\n", + "Note that study-level moderators can only have global effects instead of localized\n", + "effects within CBMR framework. In the scenario that there're multiple subgroups\n", + "within a group (e.g., indexed as subgroup-1 to subgroup-n, but one or more of them\n", + "don't have enough number of studies to be inferred as a separate group). Using\n", + "categorical encoding, CBMR can interpret the subgroups as categorical moderators\n", + "for each study (either 0 or 1), and estimate the global activation intensity \n", + "associated with each subgroup (comparing to the average).\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" + ] + } + ], "source": [ - "from nimare.meta.cbmr import CBMREstimator\n\ndset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n\ncbmr = CBMREstimator(\n group_categories=[\"diagnosis\", \"drug_status\"],\n moderators=[\n \"standardized_sample_sizes\",\n \"standardized_avg_age\",\n \"schizophrenia_subtype:reference=type1\",\n ],\n spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed\n model=models.PoissonEstimator,\n penalty=False,\n lr=1e-1,\n tol=1e3, # a reasonable choice is 1e-1 or 1e-2, 1e3 is for speed\n device=\"cpu\", # \"cuda\" if you have GPU\n)\nresults = cbmr.fit(dataset=dset)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia with drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia without drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-DepressionYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression with drug treatment\",\n threshold=1e-4,\n)\nplot_stat_map(\n results.get_map(\"spatialIntensity_group-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression without drug treatment\",\n threshold=1e-4,\n)" + "from nimare.meta.cbmr import CBMREstimator\n", + "\n", + "dset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n", + "\n", + "cbmr = CBMREstimator(\n", + " group_categories=[\"diagnosis\", \"drug_status\"],\n", + " moderators=[\n", + " \"standardized_sample_sizes\",\n", + " \"standardized_avg_age\",\n", + " \"schizophrenia_subtype:reference=type1\",\n", + " ],\n", + " spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1e-1,\n", + " tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed\n", + " device=\"cpu\", # \"cuda\" if you have GPU\n", + ")\n", + "results = cbmr.fit(dataset=dset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures correspond to group-specific spatial intensity map of four groups\n(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\nAreas with stronger spatial intensity are highlighted.\n\n" + "Now that we have fitted the model, we can plot the spatial intensity maps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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Z2dkhz0x169ZFSkqK6z4pKSlh0/MzP3kWlMzMTGRkZET9l5mZWeCyisWPeyTatm0LANi8ebOzze/PfYd45513cPToUc99f/zxxyKpg9dBYz2++eYb7NixI6q8cnJycMcdd+Dpp5/GjTfeiCuvvBKJiYm4/PLL8dBDD6F3797O293hw4dx5ZVX4g9/+AOuv/569OjRA1deeSWuvvpqPPzww+jWrRu2b99eqLa5qYh2XYuCxYsX49FHH0WPHj0wc+ZMdOvWDUuXLkVOTk7QQ/3q1avRpk0bbN68GQcOHCiSsklRtaUwFHTwsZ+dDhTlWEhMTMSECROQlpaGwYMHY/HixUhJSXFeDH799VfXF+loiHS+o+0PBWlXfstQFEVRygeZmZk4u/JZOIbsqPdJSEjAzp078+VIg5T4g3u1atXQq1cvAAiacv7ll1/QokULPP3001i7dm2+8pRuCeX2vXv3Rp0XVdz58+djwoQJ+arH+vXrsX79ejzxxBOoWrUqHn/8cYwcORITJ04M8Qjy7bff4ttvvwWQ6+Fi4sSJ+OMf/4h//vOfuP322z3LOHjwIE6cOIFzzjkHVapUcVXdqSh6KZRFwfLly3HixAn06NEDrVu3Rq1atZwH9t27d2PXrl3o0aMHVq5cibi4uCI3kykq6D0lUh/KL3xQPeuss1x/P++88zzr4vZbuO3FRUHGwk033QQA+L//+z/MnDkz6LdKlSoFuTstLQozxhVFUZSip3bt2oiLi8P+/fuDtu/fv9/zvpGQkBA2PT/3798fZMa4f/9+V1PignLy5EkcQzbuQgPER2HIchI5mJ3yK06ePFmgB/cSl/1eeOEFnHXWWVi1ahVWrFjhbF+4cCGAwI0/P7Rp0wYXXHBByPY77rgDAELc34WjMPWwOXz4MB5++GHk5OTg4osvDpv2wIEDePzxxwEgYtqsrCznuLF9Nq1atULr1q1x+PDhEHd6RUlmZqZj5z5w4EAAwS9itHO/9tprne/Rwofe/Nr/FwSunXBzNRoXFxfkdz4/8CFc+vAHgJo1a6Jdu3Yh2/ki51am3+/HzTffnK868DhWqFCw9/OCjIWaNWsCcDdjuvXWW11nGgpbz/xSVGM8GkqyLyuKosQq8fHxaN++PZKTk51tOTk5SE5O9nTL3Llz56D0QO71nembNGmChISEoDQZGRlYuXJlRFfPBaEy/Kjsi+KvkI/eJfbg3qRJE8ydOxf33nsvjhw5gnvuuSfo99dffx379+/HqFGjMHjwYGcBK4mLi8PVV1+NVq1aheQdFxeHSZMmBfkub9euHYYNG4acnBxMnjw56nquWrUKX375Jbp27YpXXnkFVatWDUlz6aWXOrMGQG4gJLd6XXPNNfD7/UE2y3/+858dRdyGD7jRREmdNGkSAODxxx9HkyZNnO1nnXUWXnnlFfj9frz++us4ceJExLwKAx/GhwwZgrS0NKxbty7ot4oVKzoP9fmxb+cMSbQ+7QvDO++8g9TUVFxxxRUhgXeeeOKJAivuu3btwu7du3HppZfihhtucLZXqVIFb7zxRkggItbl4MGDuPrqq0NmXUaPHo2mTZvmqw6pqak4efIkzj///AKZ5hRkLHCR5j333BP0IN6yZUs888wzruWU5PkGCtauglLSbVMURYlVRo4ciTfffBMzZszAli1bMHToUBw9ehSDBg0CAPTv3x8PP/ywk3748OFYsGABXnjhBfz44494/PHH8d1332HYsGEAch2hjBgxAk8++SQ++ugjbNy4Ef3790f9+vXRt29fJ589e/Zg/fr12LNnD7Kzsx3rCa6ROu2I1v1MftxBTp8+3UyfPt3MmDHDfPDBB+aHH34w2dnZxhhjtm7dGuRr2f5LTEx0XLvt3r3bfPrpp2bWrFnmP//5j+Mn2fZBTjdyH330kdm9e7fZu3evmTt3rvn888/NiRMnjDHuLuKMMUGuDOXfOeec47hjO3TokPnqq6/MrFmzzMcff2x2795tjMn1/8z0H3zwgTHGmG3btpn333/fzJ492yxbtsxkZ2ebrKwsc8sttzhp161bZ4wxZtOmTeadd94xb7/9trPt2LFjpkuXLlG586NP7KNHj5qPP/7YzJs3z/F1umzZMlO5cuWg9F6uF6M9Jm5/V111lXPOP/74Y1cXfsYYs3nzZtf9ver0wAMPGGOM2bdvn5kzZ4558803zfjx453f6YqxUaNGnq4Do3VtCcDccMMN5tSpU8YYY5YvX25mz55tNm3aZE6cOGFef/11Y4y3O0i3OvBv0KBBxhhjTp06ZZKTk82HH35o9u3bZ7Zu3er0Gdn2m266yanLt99+a2bPnm02bNhgMjMznXP+8MMPR922Dz/80BhjzMaNG82MGTPMm2++GeSvvqjHQq1atczevXuNMcbs2LHDzJ0713z55ZfmxIkTZt68eZ7uEdevX2+MyXXROG3aNPPmm2+a66+/PqpzGu5c0M+7PH/5bVekY+VVTqS+7PWn7iAVRSmPTJo0yTRs2NDEx8ebTp06mRUrVji/de/e3bnWkn//+9+mefPmJj4+3rRq1cpxuU1ycnLMmDFjTN26dU3FihXNVVddZbZu3RqURrrxlfeWSKSnpxsA5s++huZ+f+OIf3/2NTQATHp6eoGOUbE8uJOTJ0+a1NRUs2HDBjN9+nTTt29f4/f7w+ZRt25d8/TTT5uNGzeaI0eOmCNHjpht27aZDz74wPTv39+ceeaZTlrb/3O9evXMzJkzzf79+83x48fNunXrPE+GMZEfUitWrGiGDRtmli5dan7//XeTmZlpdu/ebRYtWmT+/ve/mwYNGjhpu3XrZiZNmmTWrl1rDhw4YI4dO2a2b99u5syZE/KS0qdPHzN16lSzceNGc+jQIXPkyBHz448/mjfeeCPEF32kB5a7777bLF261GRkZJhjx46ZjRs3mocffthUqlQpJG1xPLhXrlzZZGZmGmOM+fvf/x7yOx/SXnvtNdf9veoUFxdnxo4da7Zt2+a8gNl1K+oHdwCma9euJjk52Rw+fNikpaWZhQsXmssuu8zzgSyaB3c+0PHBe9++feaNN94wtWrVCns+unfvbr766iunLgsWLDAdO3Z0ApcNGTIk6nadc845ZsaMGWbv3r3OC4HtL72oxwIA06BBAzNr1izz3//+1xw7dsz88MMPZtSoUcbv93s+uJ9//vnm/fffNwcOHDBZWVlBx7w4HtwL0q5wx8qrnEh92etPH9wVRVFig5J+cPcZE8YFicXatWvRvn37aJKWGN27d8fixYuRlJTkTKUoSlnl888/R+/evaMKA63ENmvWrHFdB6EoiqKcXmRkZKB69eoY6m+Iir7IZqknTA4m5+xBenp6gdwjnz4+6RRFQf369VGnTp2gbbTT6927N7Zu3aoP7YqiKIpSTikVP+6KorjTrVs3zJo1C+vWrcPu3btRsWJFXHzxxWjSpAmOHj2Ke++9t7SrqCiKoiiKIM7nQ5xwrOKaDpHThEMVd0U5jVizZg1mzpyJGjVq4Oqrr0avXr0QFxeHmTNnomPHjvlybaooyulLUlISfD6fExVcUYoa9jH+VahQAQ0aNMDAgQOLNc6LUrzEtOK+ZMmSELeRihLLbN++PcRVqqIoiqIUlLFjx6JJkybIzMzEihUrkJSUhKVLl2LTpk0FCgCkuBPny/2LmK6Q5cT0g7uiKIqiKIrizTXXXIMOHToAAO69917Url0bzzzzDD766CPX4IPK6Y2ayiiKoiiKopQTunXrBgDYsWNHKdekbEEb92j+CoMq7oqiKIqiKOWEXbt2AQBq1qxZuhUpY6ipjKIoiqIoilIo0tPTkZqaiszMTKxcuRJPPPEEKlasiD59+pR21ZQCoA/uiqIoiqIoZZSePXsGfW/cuDFmzZqFc889t5RqVDYpKXeQUT+4165dG5UqVUJmZmahClQURVG8qVSpEmrXrl3a1VAUpYzw6quvonnz5khPT8e0adPw9ddfo2LFiqVdLaWARP3g3rBhQ2zduhWpqanFWR9FUZRyTe3atdGwYcPSroaiKGWETp06OV5l+vbti65du+KPf/wjtm7dirPOOquUa1d28CE6jy+FdWKeL1OZhg0b6g1FURRFURQlBomLi8P48eNxxRVX4JVXXsFDDz1U2lVS8om6g1QURVEURSkn9OjRA506dcLEiRPV/LkIUXeQiqIoilLGmTZtGhYsWBCyffjw4ahatWop1EgpDzz44IO49dZbkZSUhPvuu6+0q6PkA31wVxRFUZRSYvLkya7bBw4cqA/uSrFx88034/zzz8fzzz+PwYMHIy6usN7FlZLy4+4zxphC5qEoiqIoihIVM2bMAACcffbZAIDKlSsH/c7HkqNHjwIAbrzxxqjz/vDDDwEAZ555JgDAJ8wSjh8/DgA4ePAgAGDAgAH5qruiSDIyMlC9enU8VrkpKvkiW6Bnmhw8cfxnpKeno1q1avkuTxV3RVEURVEURSkEuYp7NH7cC4cq7oqiKIqiFDnz5s0DACQkJACA4zvc7/cHfVIVz8nJCdqf3/m5fv16AMDQoUOdNDQ1atOmjWvehN/5yCPzPnHiBAAgJSUFAHD77bfnq61K+YWK+z/PbIpKvsiP5ZkmG/93tOCKu3qVURRFURRFUZQYQE1lFEVRFEUpNJMmTQIQsF1v0qQJACA+Pj4oHRdC0g79jDPOABBQwwlt3DMyMgAAjRo1AgA8/vjjTppOnToF7cs8+Umo6p86dSoo7+zs7KA6MFbNnDlzAARs4e+///6wbVeUaF09xhUyBJMq7oqiKIqiKIoSA6jiriiKoihKWN577z0AQJ06dQAEFGrbLr1evXpB+1Dl5ifVbe6TlZUFADjrrLMAABUq5D6SMCiQtIGnjTzT29uYhvswr0qVKgWVRa8yVN4JZwGYD2cJ2KZly5Y5aVkG8/jtt98AAP369YNSfvFH6Q6ysIq5Ku6KoiiKoiiKEgOUuuKelJSEQYMGYfXq1ejQoUNpV0cpY7B/kbi4ONStWxf/8z//g3/+859o0KBBKdZOURTl9OTdd98FAFSvXh1AwPabajMVaqroQMB7zN69ewEE1G0ibdipglPlZp7Hjh0DEKq8UwW3fbNzG9NwH2lHz3qyTH4S/s46c1agfv36AALKvp23tItfuHAhACA9PR0AcMstt0ApP5SUjXupP7grSkkwduxYNGnSBJmZmVixYgWSkpKwdOlSbNq0yZlKVRRFURRFOZ3RB3elXHDNNdc4Mzr33nsvateujWeeeQYfffQRbrvttlKunaIoyunBkiVLAATUc6l2U2XmJ9VxIGBXzrRUr5mWv1PNZjqq2VTB6VPdVvMBd3/vMjIq95F5sAyWSfWf7ZM28EzHOvMTAKpUqQIgYOPOT6r7jATLY9m9e3coZZ+4KG3cCxuASW3clXJJt27dAAA7duwo5ZooiqIoiqJEhyruSrlk165dAICaNWuWbkUURVFOA+g1haaDVI2pJsuoplSqbdvvkydPAgjYxdNXOpGKPK+/tBmnfTrLpFouVXX53Yb7MA8q6awny6QizzozHdvJNrBudjtlVFbuwzScYaB6z2PbpUsXz3orsU9JKe764K6UC9LT05GamorMzEysXLkSTzzxBCpWrIg+ffqUdtUURVEURYlxdHGqohQhPXv2DPreuHFjzJo1C+eee24p1UhRFEVRFCV/6IO7Ui549dVX0bx5c6Snp2PatGn4+uuvg6Y+FUVRyiMffvghAKBu3boAAgssq1atCgA4fPgwgFBTEkKzEHtfpqVJCT/5e+3atQEETEuYJ81XuHCUJjH8TlMbmq/Y27z2YZ40/aEpEAMrpaamAgiYzLDdNOdhne12EtZbBohiHmz3kSNHAASO9Y033hiSlxL7xCFKUxkTOU049MFdKRd06tTJ8SrTt29fdO3aFX/84x+xdevWoCh8iqIoiqIopyv64K6UO+Li4jB+/HhcccUVeOWVV/DQQw+VdpUURVFKBQoX0i0iFeuzzz4bQLDbRyCgQNsLNak8UwXnYlOq3HXq1AEQUMylKn7o0CEAgYWlMl+pcNvbWA9+5yfzpOLupbzLBbL8XS6otfOW0E0k2yNnHlQkKtv4o7Rx90eRJuz+hdpbUWKUHj16oFOnTpg4caJzoVYURVEURTmdOW0U92nTpmHBggUh24cPH+7YiylKUfLggw/i1ltvRVJSEu67777Sro6iKEqJ8cknnwAIqMRUhwntsqlQ16hRA0B4V4y08WYaKs1UrfmdSjuV6/379weVScWdKjj3lzbwQMDlogziJN1CsoyGDRu65s2AU9KWn2XZdvUSpuG+bId0NcnjwmOvXs3KFlG7gyyc4H76PLhPnjzZdfvAgQP1wV0pFm6++Wacf/75eP755zF48OCwF2ZFURRFUZTSxmfsV1dFURRFUcosS5cuBRBQmqVCTdt1elOhXTq/UzUOp7xHgo8dDNC0fft2AEBGRgaAgLJOMYVKPe3sf/31VyevBg0aAAjMHFApZ3uoxFerVg0AcMEFF7i2pzDtkO357bffgr57zSDw2Hft2rXAdVBKn4yMDFSvXh0zal+IKv7IAuCxnGwMSN2K9PR0p1/mB7VxVxRFURRFUZQY4LQxlVEURVEUpXjgGjLaqlOhph02P6luU6mmNxUvpd32KkNkGqrfcoKfPuJZNtVyquHSfFHazAMBTy0yLgfLlO1jmSxD+n+XZboZJbh5twECx4p1of09ZzH4Oz85g8Bz07t375CylNih3Nm4K4qiKIqiKEosEhelO8ho0oRDH9wVRVEUpYxDZZrqL73FVK9eHUCo5xM6haC67WULbvs0j0attrdLFZ919FL1WXfbH7rch/WR/te9IqvKsrzqRgXfDem/nr7vZdn8neo/bd/Vv7uSH/TBXVEURVEURVEKgd/niyq4UmEDMOmDu6IoiqKUUV555RUAwEUXXQQgYH9NW2/aulP1pRJPdbswXlekL3SpdrMuLJOqv5daTi8tTG/DdrAM6UOdeUpbeFkn1rkg7oHl+gB+p607/bvTtp1lsa48V8OGDct32Ur5QR/cFUVRFEVRFKUQ+OJ88Pkjv+gW5mUY0Ad3RVEURSmz0A871WovNZsqMb2tEKlEh/Mq42UH7vWgwu20s5dl8ZMKtVuZhPbiVN7ZPqaN5H/eyxOOG7Zdv11vr2PDukm/7lTauZ3nSlHCoQ/uiqIoiqIoilII/HE++KNQ3NXGXVEURVGUIP79738DAOrXrw8goLQzKintrqkK06Zb2nxTHZaqN+3MqWzbeUQL01PdTktLAxBql04yMzOD2mBvYzsYfVXmQf/1BbFdt+sIBJRyHkNCtV+uD5DtlMf+nHPOCaozz91tt91WoLoqZRuNnKooiqIoiqIUmFdffRWNGzdGpUqVkJiYiFWrVoVN/84776BFixaoVKkSLrnkEnz22WdBvxtj8Oijj6JevXqoXLkyevbsiW3btgWlOXToEO666y5Uq1YNNWrUwD333OMsAAaAxx9/HD6fL+TPNgdLSkoK+b1SpUoFOwhxfvii+ENc4R69VXFXFEVRlDJGtWrVAIT6bZdeVbhdemqhOkwFOz09HUDAvpv50Ge5nYdU7yXczrrJWQAve3qm4yyAvU22S6bNr7cczjhIlRwADh48GFQGlXMq5lT3uZ1ly3NCeLxYBtPFCvPmzcPIkSMxZcoUJCYmYuLEiejVqxe2bt3qare/bNky3HnnnRg/fjz69OmDOXPmoG/fvli7di0uvvhiAMCzzz6Ll19+GTNmzECTJk0wZswY9OrVC5s3b3YerO+66y7s27cPCxcuxKlTpzBo0CAMGTIEc+bMAQD84x//wH333RdU9lVXXYWOHTsGbatWrRq2bt3qfC/s4tHiRhV3RVEURVEUpUBMmDABgwcPxqBBg3DRRRdhypQpqFKlCqZNm+aa/qWXXkLv3r3x4IMPomXLlhg3bhzatWvnuMM0xmDixIkYPXo0brzxRlx66aWYOXMm9u7di/nz5wMAtmzZggULFmDq1KlITExE165dMWnSJMydOxd79+4FkOviNCEhwfnbv38/Nm/ejHvuuSeoPj6fLyhd3bp1C3QcfH5frmeZSH9R2MGHQxV3RVEURSljUO3lJ80DqExT9ZXppO91wu1UsPmdSrxbnlK5lEo609M2nDbuVKClMk0l2i7TS8WmUs52SPtzWSfpqYb7UUW3y6QyzjJkntI7DvPm7IQ8llTupYIfC5w8eRJr1qzBww8/7Gzz+/3o2bMnli9f7rrP8uXLMXLkyKBtvXr1ch7Kd+7ciZSUFPTs2dP5vXr16khMTMTy5ctxxx13YPny5ahRowY6dOjgpOnZsyf8fj9WrlyJm266KaTcqVOnonnz5ujWrVvQ9iNHjqBRo0bIyclBu3bt8NRTT6FVq1b5Phb+OB/8cVEsTkXhHtxjp3coiqIoiqIopw2pqanIzs4OUanr1q2LlJQU131SUlLCpudnpDTSDKdChQqoVauWa7mZmZmYPXt2iNp+4YUXYtq0afjwww8xa9Ys5OTkoEuXLvjll18iNb3UUMW9FPjggw8AAFWrVgUAXNk4N2KdoUKQlbtiffG+3O+HDh0CkL8V5lyVXqtWLQChaopc5c4oem5vqYpSlpg7dy6AUBtW6beZY6X/FW1yN5gc18+48xOLsbaKEj2TJk1y/j///PMBBFRdqtn8znsCI6ZSDZaqOe2z6UmFn8T2/OKl0svfpRLP+xTr6KVks2x7cSHz9FLSea9jGRKpjnv9brdT2tPTsw6PFY+dVO1pG88FlCyTdee5YXr7fN5///2u9VOi44MPPsDhw4cxYMCAoO2dO3dG586dne9dunRBy5Yt8frrr2PcuHH5KsPn98MXxWyJT4yT/KKKu6IoiqIoipJvateujbi4OOzfvz9o+/79+5GQkOC6D+3NvdLzM1Ka3377Lej3rKwsHDp0yLXcqVOnok+fPhHt18844wy0bdsW27dvD5uuNFHFvQTI2rMx9588he6Gto2DvhsqeHkqhD9PIbjivDwF8LwGAIDsLV8DAOJaXu5d1sZkAMDNF9bI25IjPhH83Z/n07Zurp1g1g+LglL58n4PV6ainK6c2v9z7j8cYzk56Hd5u8B3C0cFEYo6x6eXVWL2z6uD0jMfIyMv5uR+17GkFBe2ki1nWWmXTTtqqaAzHc0PqDBTXaavcalM22VKv+syWqm0n5e27g0a5N7r6MmG26W3GdsGXKrWVL2pXksbeOmnnt+lSi6VfHqKAQKRXom06ZdK+4EDBwAEZhQ4w02lXir4XmsETkfi4+PRvn17JCcno2/fvgByz0lycjKGDRvmuk/nzp2RnJyMESNGONsWLlzoKN9NmjRBQkICkpOT0aZNGwC5fWLlypUYOnSok0daWhrWrFmD9u3bAwC++uor5OTkIDExeBZ0586dWLRoET766KOI7cnOzsbGjRtx7bXX5ucwACg5G3d9cC9GaK5y82UtS7kmilL+ePvtt3HLlWrGoiiKUpyMHDkSAwYMQIcOHdCpUydMnDgRR48exaBBgwAA/fv3R4MGDTB+/HgAwPDhw9G9e3e88MILuO666zB37lx89913eOONNwDkvrCMGDECTz75JJo1a+a4g6xfv77zctCyZUv07t0bgwcPxpQpU3Dq1CkMGzYMd9xxhxN0jEybNg316tXDNddcE1L3sWPH4rLLLsMFF1yAtLQ0PPfcc9i9ezfuvffeYjxihUMf3IuYU7/+6Px/c5dLcv9xFHW+0Qcrej6p8DEDquGOOph7unK2rwgpl+qeLz5/gQN8Ioqc4Rt+Xl35mb1rnZUmd1uFRq3zVZailDpU6jikXJR3p+8LjPhdjlsnHcUUoZb5TO5Ycxu/0eK/4LIC76soilIc3H777Thw4AAeffRRpKSkoE2bNliwYIFjlrJnz56gWZIuXbpgzpw5GD16NB555BE0a9YM8+fPd3y4A8CoUaNw9OhRDBkyBGlpaejatSsWLFgQFBxp9uzZGDZsGK666ir4/X7069cPL7/8clDdcnJykJSUhIEDB7pGzf39998xePBgpKSkoGbNmmjfvj2WLVuGiy66KN/Hge4eI6YrpOLuM3I1iVIo7Af3kKl3EuHB3StdSL52UjktHyXRPrjbDzP64K6cbsyaNQtA8MK5EydO4N6be+V+kWMrJ3RshYwrj8WogfThf49m3OYXfXBXwkE/2ECuIgkE3CDyVk8zlGPHjgEI2BPTXIMPWzIgE/EyNbH/lw9I3E7TEWmewsWoNG+R5ju///47gMDiTpqaAAEnD1xcW7NmzaC8aY5CkxfWTZrt0MxHPhJJt5Jubfd6jKKJD221aaZEryc8NzTnYX48N1u2bHHy8jI7UUqfjIwMVK9eHR9f0h5nurwcSI5mZ+P6jWuQnp5eoGBbqrgXkhAb2qAH3OB/nBs300gbd2dH8UAvtrteIvyRO4usX1BenA2QaqPLgzv/50vKGQ1aRFe2opQWYsyFbAdgEGyj7qW8B9IH5+E1XkPS5wdRh6zd37v+XqHhJQXJXVEURSkichX3KLzKhKw5zB/64K4oSszx1ltvAQgoeFTqMjMznTQ6maiUN6SrRiCg4lI5pupLpZoKtFxYyrEl92N6KvTh3EF6qdvMU5ZJlZzqOMczx7fc394m00i3loR1YfvkIl55vNzcRHJfHhOm5TGRMw5sJ/fjsaeyzjLk8XA7n4qiD+6KoiiKoiiKUgjUq0yMQNMS5zTY0ySOCzqmFdt9uW/lPiOmVqRJTIQp+HzhNf3vZhIDF5t3ILDAL4IpgaIUFirrVNNksCSpCtrqmDEGk2bPBwDcf1ff4IydRarWWKK5WD5NZpzy5IaCjI8Cjs9Te3/K3UDFMuGC/JetxDx2GPnPPvsMQEAF5hgiDGIkFWqOJdrCp6enB22nQi1t4u1tRKrdVLOpJHvZwhNp8x5OcWca7sNFjDJPmV7a8suATPykug6E2qzLYE90F8ljLN1acjsVd3lumK99PpXTH5/PB58/isWpOYV7cNcnL0VRFEVRFEWJAVRxj5Lp06cDCCgKfFNm6GKGl+6VGFgkFqLCO0p5sNrnrcRHUPyiUd69XNvJwA4yXRhVfcGqHwAAO3bsABAImc0V8lQL6MNVUaKFCru0bZWKlJfNrBtBiptcpGorgjI8u89jUblnQUWgg0Qz4wWEjE/O/L3x70+cJOqFonxDxVwq7lSFZZAfXrePHj0a9J3KNLfzOs8xSE8vQCB4E8twc79nb2cZ9Pwikeq3rKu9TV4TvPLyUvu9vMnw026nDGbF5wEq6dyHx4y269KbjjwObAPPnRJb+OP88EexONUvrSzyW06h9lYURVEURVEUpURQxd2DadOmAQAaNWoEAGjbti2AUH+027ZtAwDs27cPAJA0fx+aNGkCILBynPZtl55bM7iQaJV4L8IliKT+CdtDmX7DL7l+c6nS0G/uzp07nTT0/9usWTMAoX5wk5OTAQC7d+8GAPzpT38KXyel3DJjxgwAASVL+nGWihvHnwxP7qW2vTA9N4rx3wfdlrvBzT2kDM6Uh/Fw3eUroO4RMtsVlKk/uC5iO5X1qe9+FuL9Qh6Dl156CUBA1VMFvnzB2WBel6XCzDHE3zn2ONZSU1MBAGlpaQBCbca5H9VmIDBuqaBLjyzcl/cV/s682ZelP3iZz6FDh5z/69WrF5SG+0jbdqrcrKP08y7LYF2Y3m4nf+Mxo7JOVb5GjRoAgNq1awe1l2VKb1j85DnjpxJbRB2AKeLDXXhUcVcURVEURVGUGEAVdwGVP9qsc3U435T5SVWL6TZv3uzksXfvXgBA/fr1AQTs3rYeyH1j59s48zonLuB7GkCwZxogjC17FO9dnjbuudtTs4LVBqor0t6RbbK9BrDt0p6ReTGSHZUZHtsBAwZErrdSLvjXv/4FINDfqERJpd1LTZMKXaToho4XqHDrQ6THGTmG6PUpnHJuE272K8LM1+R5n+QlC2yvUKEC/H6/q39pINBuea1iVE2Ox/vuuy+6+isxyb333gsAeOONNwAElGU5dniP4xhklFLet+g1Rtq6uynbcq2J7Itcu0KvLPydZfOewe0sQ65lsRV36RNe7sP6HThwAEDASw638z5N1d9LebfX2VB957GgZxoeSyrxnKFmNFfeP1kH7i/t74cMGQIl9lDFXVEURVEURVEUB1Xc83jvvfcAAOeeey6AwBs03+JlRDS+cfNNmXZ2QECdpr0blQ6qClQfmPfvyPXKQgWAb+u0c5P+aVs3Oiek/su35NqRc8U/7epZNj2/sF2sNwO1yUhwLIttYHqqE3b9+cmyZaQ9lsljy2Pdr1+/kHYoZZuZM2cCCChvUmH38hAhVbBobduBYBXw2Tdmwe/348F77sjdYCvcUoV38/Uu9wmHVNPD7O/mFQYItdO18TomMhKmtO3lMX/ttdeC9v/LX/7iWZYSu/C8S9tu3sN+/fVXAAGPMA0bNgxKx35GBV6q5TbSYw3vZZzJlfcf9kXmyfuOVN5lX2ddbby8yqSkpAAIqPTyvsXjIO3TeQ9184wjZxKoqHM777dsB58J6ImN92Wv9imxiXqVURRFURRFURTFodwr7gsWLAAANGjQIGg737L5ZszvfAun+kBbNTv6Wq1atQCE2otL/7fSFo/bpTImf/96w3YAwI8//uiU2aJFi6A0UtFgnrTjkz58pRcP6cOXbbLbyX15LKhgEDnTwHT85LHv3bs3lLJLUlKS87/0GsM+JKM2EukxRUZv5BiSaqIbdp93tXV38zSTu2O45nnjocxPff8Lpy7SPz1hu2T7wxYnPHHI8SjzlCofFXhb5R86dGjEcpXTk8mTJwd997qv0PPJeeedByC0f8i+JxVp3huA0HVOv/zyC4DQccl7IWeVuR892UhVXOZv+3GXijjL5r2ZebK+rAvrwGuSjM1Cj3LM324ny2CecuZPji0eW5bBOkkPPbxn8tzp+IsxorRxj+wyMDzl/sFdURRFURRFUQqD3+eD3x/5odwfrWMDD8rdg/s777wDIPD2TF/kMqKZXJEuFWravPNNmW/eQGBlOd+6qXAQWYZUE6X6LVVzKvm2EsJtrJeXoi69cMg6EZZZrVq1oDbZ7ZT2/6yvLFva20vfvfT3ThvEW2+9FUrsQ6Xd9knsZZPu5Y3CS8GSXp7Yx8LZitq/PfvGLJxxxhkYOdClr0UbqTiCrTtV/X+993nQ7Jrf7w9pv5eiLu2Mw6X1ulZ5HTsvTz12/qr8xS68txHakTMqJ/sBZ5ulD3bpc5xjlL/Tfpv23EBgHFJplwo8FWfeV3gPkfcO2qVzTRV/Z3oq2PY2uV6GecjxINd+8Pok14jQLp1rs+x2EtrFy7Ek28Vjy2PNex3LpPpPDz6KEo5y9+CuKIqiKIqiKEWJL84PXxSLU305hVteWm4e3GlPzTdaRjWV0dO8IrVJBZD70eabXjKAwJs/36KJtEGVypmMJsfvVDqkv1pbNec2mVb+zjxllFOpukkbQze7Wbadx8LLHlfOAsiZBc5+UK1R2/fYhr7Zqa7ZfdFLEZdqsbTHliqxnBGT/TtcWfbvz0+b5+Ttqr4HVTL8xXbKvz916mN7dzrrrLNCZr5k1Eo5K0fcFHc5tmVaOZsmx6P8PdI6AwCYMmVKUBnqZ/r0gjPJtncz2q7z/PJ6vWXLFgChs1byk/dEef1m33a7J3Dm16uPEt4veR+mzbeEEbtZFvejmm7nwXpyHwnHAdPTh7pXOraBbeLaLCAwW8xZDV7r5PVJrr3xitbauHFjAAFVn/svXbrUKZPR2XVGWik3D+6KoiiKoiiKUhz443zwR7E41Z+jNu5hWbRoEYCAEiEVc2kjKxV3aXtHpLJmv+V7qdTSx60X0n6eapz0/85IcEBAXeGbPOsly/ZCKp2sg1QGbXWFZXjZy0slTx5zqTJKe3qeuyuuuCJs3ZXTg6lTpwIIqGJSDQe8lWWOMzljJG3cmaeXPbe9BsP2PGEjxx/zfnHGuwCAEQNv82yjzZS5H4XkIb1gyLEhry9es3BevwPeaqaMiClnHKQNu7weyWNq5yFVSEbjVOW9dJk2bRoAoHnz5p5peM54vabyznuFjKjKWSt+p7os96NtOH8HAuq0nDEj0uab13yvWSB6hmEZ3M8e57Ke3Efe8+RYkmvJvMaHm+JOTzRSIed2XgPlseSxo+rPOsgYKG7PCHyG4Tn/05/+FJJGKR+U+Qd3RVEURVEURSlOfFG6g/Sp4h7K/Pnznf9pO8Y3Xr4hS+8qUhWWijvxUtBse3a+bUtvKlSS3bw32GVTOeDvfGvnJ1VLW+mQMwdUR/hdKpUSbmcdqVbK9HY7pUoo08rV+/JTqnnMj7aHjEZnn8++ffu61l8pPWbMmAEgeJ0HEDqLY2+THpPk+geJ7L9S2XazcfeaJfMaC+yHE5P+HZSnXFMiZwdsZARiqWJLDx1yhssr/oJdV3kMpZeqSLOE0juIlx9s+385xpnH66+/DiBwnVEVsGShdxVpvw0E+iA/mUbeX+T9SKrH7B/MW86o2bbisi96zW7xd9vjlFs6OU6IHU+ESJXfbbbKLtPLcxyRbbDbyX3kvZ7XHx47r2uOnCWQdZHrC4DArL7tUUcpn5TJB3dFURRFURRFKSnUq4yiKIqiKCFwpqNly5YAArNCtuIuZ6GoRNNW+7///S+AgDosZ53lbDQ/6UGFajD3t/f1Wsck1X3OKEm/53LWSHpUs/OVHtW81mwwHcuUdZLIOtntpOIvo6LLGW7CuvFc/P777wBC1XPWlefInllg+Tzu7AN//vOfXeuvlF3K1IP7m2++CQDo0KFDyG8cCBxY0sWVHOxyyjqSCzb7gskLm7yY8lOayMiLlJxu54Dld+ku0t7GNJzW48Bne+XiODm1yToyb07Pud0YIpk3eJkasCyvizXPFctm6GkgcI4HDx7sWqZS8rC/S9zMzSK5RfMKGiS381MurLPxcnEqgzV5BSiS7ZDY6bwWmXIq3c2tow3Hm9eCUbf6SFMXWSbxcnErp+29joedxsu8gtes6dOnAwAGDRrk2k5FUZSyij8OUXqVKVw5ZerBXVEURVEURVFKGp/fB58/isWpUaQJR5l6cL/gggsABCthVJxlMCTitVBNTq9JZIhjql9AwDUjkQtQvKBqxZDUVDJlKGeGWbYVd25jGGouwKH6xvbT/VYk95DMx3aBBQS30yscvXSDKVV9L1d+3E8GgrGnKHmOldKHgZbYP+UYsvsn8Zrhkiq3VOLlQjEvtdgNzjbxk9cEuUDWawGmdIVI3AKgsd5yoZ+Xu0cig6+Fm4GQY1fOOvCTs2+y3nJmz6t9Xm11y4ufbIcq78WLdG8sr7VAwBED7wG8n0gXjHJhNJGODog0W7FNT7zul7Ifsw/z3siy2GflAlJ+0mHBunXrnLzbtm0b1E557+ZxYDvZR5lemth4BSyz28mZZznbyGPFGW/pDpJ14Hd5Lng8pJtJuz2shx1sSylflKkHd0VRFEVRFEUpafx+P/xRLE71Z+viVEf5u+SSSwC4u06T6p9Um2R6GZCJn3I/NxWd6rZU8KTKJtU3KstSLZfBHJjOVle4jYteWH++wbMMudDIy5aW26kguLVBHgOp/sgFSFJVJF4u/tzqxhkAnvN77rkHSunAPicVOHn+3foM+4JUx7zcsjK97FNewb1s5Bgm3FfWV84YSdd0su5AYMxLNVsqboS/S3eYxEsVt5H1kWNbBrPyCu7iFYDGPhZeLvbkdUFt3kuGWrVqAQgdP/a5Yz9g3+R4leNUBg+T90rmI8eHVM8B70BK5JxzzgEQuI5zHPMexzp4uTNmP7RnXrlNjmf5yWNFl8esC9XxQ4cOhW2D3U7Zdh4b6RZS1s0roKEM6BhuNoN5sQ8o5Y8y8eCuKIqiKIqiKKVF1AGYokgTjjLx4E57bKksAYE3eaoNUh2OZLvJt1sqBF4h18PhFYxCqlh8u5bBV/hWL1UI2/a7Ro0aQWm4r3S35RbQxa1uXvb49n5eQSXYLmnn52WHLM+FV372/zznSsnDcPfESy2mPafb+ZP241JRlyqXVAFl32D/dlPFOJ6kfalUmmUZnK2SY51l2t5bpEpPu3MZ/IZ1YJ04hqWKLwPPhFPcWYZU87y86cgyvNYo2GmIl1or08tjrxQNDHZ2/vnnAwicU9pE27OWcs2QHDP83LBhA4CAglu3bt2g/eX4Zn5cV2X3AdaD55224FS3CT2G8R4h+w1he+x7HQB89913zv8yb2mTL9Vvfuc9nfdOfh44cCCobm51YNup3hN5rHgcfv31VwChqr5XIEh5PQFCjy3HPfvEgAEDoJQPysSDu6IoiqIoiqKUFlEHYIoiTThi+sF92rRpAAK27W6+kvmW7OWr2cveWip9TB+NVxZp2yvzlNvdQsMDoX6aqQC6hYFmWmlrKxWzSH6ivWxrw80sSCVPesWRNsJe6wq8zpFdNtvZoEEDAIE+oKHWi5+kpCQAoQFMZN+QYbvt3+Vskhyf0g5X2m3L9FLRtvuWVJJZphxX0j6beVK5k+PSzWZe2o/L8cU8pR2u9HAjvU8QW92XdvHSrlwq7/IYSltm6V3DjUgzi14+4Pldg8UUDVSFZf8Kd+5kP5djiPcVxsuIZJct+5vdV9mnqA5TDefY471B2oizLMI68h7iFefAzkuOQd4LpQIvjwPHJu/tUsHnmjO7jl7XHR4TGSuCx5YqvrQE4DkI91wh1Xm2k31CKT/E9IO7oiiKoiiKopQ2Pr8fvijMp6NJE46YfnBv2rQpgFBf6rbqI21npX0ff5d22MyLNnqR/LrbyrWXz2kv+DvfnKVqxbfx3377zTV/exvbQR+vMooiy4hUp0g+be3fpC2tVNBpz0jVRa4fkDaYUlWxlQ5uY17sA0rxMWvWLAAB5ckLL9XJRp5T9hH2U6meydkcIm2n3TymyPK9wqxL1Y+/e6nkbnbnVM4iRVBl+6S9PevNfNg+tzgUzEtGdZYeLaTnnUgzgW7+3L0ipHop615+6pmnKu+FQ67DYF+Q3lmAQDwROfMl7adp2y77puw3VIuZzi1iMlVrfqampgbVi3blXv1Ero8hrCNtxN38m9epUyeoLJmHnBWSx4P3V95v2QZeBzhbYLedaXhseKzltYfnh+1gWfJex/05Xtheu0xZf7d4GUrZJqYf3BVFURRFURSltPHHRenHvTzbuFMN5xs31WRbMeJbqvS84OU/WW6Xb7fEy3+x/ZtUteUbv1Qb+JaekJAQ1A6pqFFRsKOYylXpVOh4jKSqFs4PvVs7vRQSIFSdl8dOHnOpAMnZDH5SMbHVRraDSgTbpxQfVJoieWKS9rZuY4zqkOwL3NcriqnXmgsvO277N9k/Zb+U9uZyfUskz1N2m71modhPvdYH8Djwdyp4hCqgW32k33Y5MyBnFeW4k2Na2gQDoWPYK4pspJk8lkXPREOGDAmbXgmGY5HXRuntzE195f2Eduec1eF3ImdcvOJxyFkiexaa///www8AAl5XqEx7qd5eHsVYNuOTcFzYM27cJqOPeuUp+72caUhPTwcA7NmzBwBQv379kHZ6eWaSsxRe67pkNFfpFSglJSWoLnY95QyIPROglDJRLk5FIR/cC7e3oiiKoiiKoiglQkwq7lOmTAEAJCYmAghVeWzFiG/fVKlpb00FnkhPGF6+m+Wbs5sSLaMKSnVbvulLFdHLMwVXu/MN21YXmQfTSF/OXmVHUk/l/rbSJpVMmUbaK0qlXaqlTEd1UiongLfqwz5x3333ubZHyT/02EMVj+dDnnepIhM3TxdePqVlZF+Jl6cUKo5utvDSJzLhLJzXDIJUsKUPdjcvUHJ2wWsMy+iT8pMKpVwDYB9jORMnx5Wc1ZDtl6os68R8bHVfrinhsZPnNpJaG+46okRm8uTJAAKzjzwPvK/JdVJA4F7H6yljX/D+ce655wIIKMtcFyX7jexvcibU7l8sk31I+jmXM21u8ReAQB/lfTpc3BQ5xrzWUBGpkst4Kawzy2ab7DrKtjOtzFtet7hOqGHDhgACx5Lnhio6y7THalpaGoDQeznrwD4ydOjQkGOklAw+f5TuIAu5OFUVd0VRFEVRFEWJAWJScZdKAN+wpV0o4K0OUKmQHhqIVPbc1F+7bBsvP+XSD6tUofh2LRWCvXv3BtWd+9keBKgSUE2hTSDt84j0h+tlm+qlptvt9bL7l/7mZbRIwmPM9PyU3gDs2RHp2cDNp71SON5//30AAVXPS0UmcjxKz0v2eZceWnhupacX6d9cKvKyz7hF6pR9XK6h8ELWQXqmkn3PhmNSqtpStZQelqR3CTlm7DrzmHl54JFletn4Sv/2bnjVzy1KtY2XQirPE2fKAJ0tCwf7ORV19g/2Sdqt29E92We4Hui8884DEPBswgihtK/md9qjS09r0nub2+wYt9WsWRNA6FowGVk4kv9/r3Vg4bxHRVpLRrzqwLzppYYqud3XWSbzkN6WZLRW3o95rLk/zwW/07ad+9nnk/XidUneb73aqZQcJeUOUhV3RVEURVEURYkBYlJx59vowYMHAQT81br5lZU2pFQq+Eml2itCaDSRQyVeKlMkTy6so7TjpoouI73R5g0IzChwX76V0+adZXqpjbJOXtFdo3mrZ9nSV7VX3l514Xm2Z1KkL1v2AbWZLTqoDlFFsm2egYCaJNUz6fnFTZnmPlKhkjMn/F0q19LnOstiv3CLZio903h5m/CaAZOzc8QeC9L3O/OQtvheEVGlBxupatrXFBllUa4TkP7Z5Xcir43yWNr18IrnIP1OS0VerrWRY17OwinBTJ06FUBoPBEvn+xuPvh532Bfoz017x+8R/z0008AQr3NEPbhcOeU+3I8sD7ss3INmeyzck0E28l8md6uo4wmK8e9/C7XmbBOPD7yWsKyaHdu5yHHt7xesb6czWjevHnQfjwXMpKq9BIHhK4x8ooUyz5z7733QilZfHF++KKY/ffFFe55RRV3RVEURVEURYkBYlJxl2/8VLm43c0DQyQbaC977UiqnJsfd7lNqoxSHeabtFzdzrJatGgRtB/f6tu3bx/STulJw0vtlyoDkTMTUqW02+kVITba2YtIPuSlPbDddlmvSHbLSmQ++OADAAGbTtkPvTwSyZkV6enCbWxIz0JSFSORbKjDRQ30irUg8+TvnNlhf5N2qlJls2ci6Cubnjrq1q0LINQe1auOLJOzHbt27QIA/PLLLyF1lrEZ5HocOVPAsUJVUM6QyHNgzyTIWUw5huXaH6kYynEqsct65ZVXAADDhg1zTVseoZos7yHS05H04mPD33hueM7YR6VXGa8o4awL7bCl0mvvs2XLFgBAkyZNgtKGi39ib5d29cyXfs1ZV7td0oONVKS94jl4rf3YsWMHAOCSSy4BEBg/QGBc8FrJ8U9lnfWVkcwJj70cN3I/tzVl7APSkw37gq73Kj18Ufpxj8rXexhUcVcURVEURVGUGCAmZUq++XPlOt9S3Wyn5Zu9l62l13cvGzyvyIH2PlJx5hsx7bI3b94MANi6dSsAoHPnzgCAiy66CEDgLVyqEm5v1HKbVM+o/LHM5cuXAwAuvPDCoDJpcyfb5dYmeSxkHfK7PsDL3719bKWNMz81elzhoQ2n9A8uVeFIY8ArKqL9m7QvlV5LpKIux4BU6N1swaUHE6nO02sE+7xUpGXkVRlvwG2WR6rz0mNLpAijvKZRkWOsiv/+979Omg0bNgAI9ZktPY6wLkxHBZ5eQ6SPdjdPMGyHtEWXvuOlLbz0/iRxU4bVK0YoPFc8l1R65RoRuV4BCJ2J4b7s57Tdtn2/A4FzQyWd6eRsJ/ORa2AAoFGjRgCCo3vbeUTyaiZ9ycvZ6/PPPz+kndJ23Ss6M/HyDsX0bIOcXbJhP2e7eKyohvOTs2Q81nItgJzZkv7g7bzkzLuc+bBnQJSSxe/3R/W8k581k27E5IO7oiiKoiiKopwulJSpTEw9uNMGkjZn0n+rVO3s/yN5MPHCy0OMVBXd1CKphkibfEZP279/PwDgq6++AgCsWbMGANCjRw8AAbtZqaK7qYtSeaGN7OLFiwGE2giyDjJCnVtEWPldtl0qdl6+4IlX5EqvfOx2EfYBekZQO9n889lnnwEI2Gt6Rf0kUlmXCpDEVqalIi1V7Ug20YTpvKKj2mlYL9rAtm3bFkDo7JJXn5e/E7d0su9GmukjkexweQ0AAnbDO3fuBACsXr0aALBv3z4AAbWeCqGctZD2tHLG0s0XPpGzLXJGwct22eu7vZ1tnzRpEgDg/vvvR3nlvffeAxDwmCb9/nthq8ecaZFrqxgXhNd+9hcZMZjqMJV12m9z9pazQ/Y5pHLMerPvsf5y3Mr2SJVcXi+oJtuexqTCLD0eyajGsg9L5ZozVlIVt8uRcSY44yu9uEnvP/Tbzt95LlgH6Y8/3PmW1wzp5Yt9qF+/fp55lDSvvvoqnnvuOaSkpKB169aYNGkSOnXq5Jn+nXfewZgxY7Br1y40a9YMzzzzDK699lrnd2MMHnvsMbz55ptIS0vDH/7wB0yePBnNmjVz0hw6dAj3338/Pv74Y/j9fvTr1w8vvfSSc3537drlXFNtli9fjssuu6wIW190qI27oiiKoiiKUmzMmzcPI0eOxGOPPYa1a9eidevW6NWrF3777TfX9MuWLcOdd96Je+65B+vWrUPfvn3Rt29fbNq0yUnz7LPP4uWXX8aUKVOwcuVKnHnmmejVq1eQ04277roLP/zwAxYuXIhPPvkEX3/9NYYMGRJS3n/+8x/s27fP+bOdf0QLFfdo/gpDTCnu0uZOqlgyEicQeLOXSlckRUji5V3G7Y3Yy3+0m9cGAOjQoQOAgO0qV7PPmzcPQODtnj5gL730UgDBvmypljIP+uSV6hptA5kHYZ3Y4b2UNnu7l6oo94nkv97LR7Sb9w4ivSvwWKh9X/6Rfp69PCzJOANMJyN58ny52UdL+1Mvz0uRvDdJ7wtufpSZlkp7ly5dgtJK5U2qY1Ltk3Wxy/KKZirHBustvTdJBTLcTCGPPyNhUjldt24dAOCHH34AEFD/pA0w85aRmqU9st0eIq9pUkmV6p88LiRc+zQmQ6g3Irlmwmv9kD0LLdcw8FzQbp4RVamO85NI+3JeW1k35mePbzlOZb/mPjIWhOyL8pojxx7rYKeVfUpu53WOZUg7eumVRZZp26Gz3py1k+vReKxk3AbWJTU1Neh4ULFnnaWibx8jGWfCywe+fYxOByZMmIDBgwdj0KBBAHKjJn/66aeYNm0aHnrooZD0L730Enr37o0HH3wQADBu3DgsXLgQr7zyCqZMmQJjDCZOnIjRo0fjxhtvBADMnDkTdevWxfz583HHHXdgy5YtWLBgAVavXu08Z02aNAnXXnstnn/++aDI8meffbbjHeh0RxV3RVEURVEUpVg4efIk1qxZg549ezrb/H4/evbs6TjKkCxfvjwoPQD06tXLSb9z506kpKQEpalevToSExOdNMuXL0eNGjWch3YA6NmzJ/x+P1auXBmU9w033IA6deqga9eu+OijjwrUTp/PD58/ij+fuoNUFEVRFEVRTkNSU1ORnZ3trKcgdevWdeICSFJSUsKm52ekNPQeRipUqIBatWo5ac466yy88MILeOedd/Dpp5+ia9eu6Nu3b4Ef3kuCmDKVkdPMXqGL7SnfSItSIy2MlMgpvHAhu+X0sFy8J6e4uOiWi8w4Ncf9aAZDG69evXo5eX3xxRdBZcrAFZy6YxmyDl51lOnsNvF/GRBL7hMp6Eakc2GfT7k4WE53aiCm/MOFXjKIV6SFlNLEhMjpcU4j2/vIqX+vAC1EmmLIBWNuiz/ZF2giI6ef5acXrCtDxEvXbUDotUcu+JSLzuR1g/WmmRHNeWjW4JZWHiuqSjSHW7hwYVD92X7m7eUOzx6fcgzKcy5NZqSbVpYhz3M4E0OWX54XmstgWjSpoDmbdMEb7rpHcw15vqUbUK97H9OxD8jrvj1+eO5YXztoERAYrxwHHEvyvuoVUMrtXuFlginHh1ysLk1/COvA66LbcZFt57GR40AGQpSudaXr3WiCE7IdPHYsg8dcukxWwlO7dm2MHDnS+d6xY0fs3bsXzz33HG644YZ85aUBmBRFURRFUZSYpnbt2oiLi3M86JH9+/d72pUnJCSETc/PSGnk4tesrCwcOnQorD17YmIitm/fHkXLgtHFqS54vYXzbZVqlf2m6bUwUqrdUsmjukaFg8oBP6WiZC/a9FKyWAbdbLEMudikcePGAICNGzcG5S0XB7otXJELzFgH5indbck6STWVuLnalEEiWAcqFfyUAWKkckO8lE835cBtgSCginu00AUkELogWQYYkioR4VhgOq8+Yy/Qslf72/vIvGWfYh2kCzfZl+xxfvHFFwOIfsGyVPM488XFnrwBsA62UsfpWLpZ5UI/ls0ALKwnx76c7eAic34yWJsdzp1u+Ig8NizrtttuAwB88803AAKL3nleWDep4trnUSqKchGxvF7ImQM5eyOvXfb5ktvK8yJVec3n4nuOObp6pOoq1XMg1NWqvIZ7BfaT51K6GSRu6reXC0qpvPOaIBerSteMRPYNt0XocjZI3iPkjKJcOEq4UJTp5aw14B3USS4ellYBcrs8N14zynbe3MaFsRzvcmbgdBo/8fHxaN++PZKTk9G3b18AuW1MTk72nFHr3LkzkpOTMWLECGfbwoULnUCVTZo0QUJCApKTk9GmTRsAuedu5cqVGDp0qJNHWloa1qxZ43iJ+eqrr5CTk+MEt3Nj/fr1zrX8dESfchRFURRFUZRiY+TIkRgwYAA6dOiATp06YeLEiTh69KjjZaZ///5o0KABxo8fDwAYPnw4unfvjhdeeAHXXXcd5s6di++++w5vvPEGgNwXlREjRuDJJ59Es2bN0KRJE4wZMwb169d3Xg5atmyJ3r17Y/DgwZgyZQpOnTqFYcOG4Y477nA8ysyYMQPx8fFOfI/3338f06ZNw9SpU/PdRn+cH/4o1PRo0oQjJh/c+TbKN2bpxslNufWyWWdaqmlUwqRtKgMX8S1XBqewy/RyZSXfzqWdHNMxSIMM3CTf3m3FQLpvlHWQgR+kmiLf/L0Cx9htoOpA1ZDHjiohFQIqk3Q/xmNHVTLSubGRbZeuzpTosBVuLztTqeRK21YvBc4rMJedRrqDlDbQXkFSuJ+0/XaznWbQIq/xJ8cMy6JHAk6Veq1jsfscVToGPKNaw0AgvG6w30pF/vfffw/KU9qGc0wBgWsRlXcZSEoqbt27dwcQcB+5aNEiAIFrAscjx7HdN1gf1ptKulyTIGe6vIKyebnJtPchkVz0lmWk4i5neHnOOA44Q2PPaMk8vNaIebnxlW5DeZ2Qaybc1sLIc8l7A5Ez3PJcyxkdmW+44INea1fkmOIx83JVGm7tC8cFnw/kWhB5voi8l8vrn5ypsFVzjkGOW6+ZlEhrdkqL22+/HQcOHMCjjz6KlJQUtGnTBgsWLHAWl+7ZsyfovHbp0gVz5szB6NGj8cgjj6BZs2aYP3++M5MKAKNGjcLRo0cxZMgQpKWloWvXrliwYEGQC+nZs2dj2LBhuOqqq5wATC+//HJQ3caNG4fdu3ejQoUKaNGiBebNm4dbbrmlmI9IwYnJB3dFURRFURQldhg2bJinaQyju9vceuutuPXWWz3z8/l8GDt2LMaOHeuZplatWpgzZ47n7wMGDMCAAQO8K50PfH4ffBGiGzNdYYipB3f5Ji3fxqlK2UoY34CpSsk3XoYclgEUqA5LdZHKGpUOGfLYrhff+ryUJKomLFuGnOfvtBvkG7dUW4CAmkZlg8eA9m/SCwS3UzVxe8MHAm/zrKPdlnDHAAgN40ylgOoi1SFOWclzI5V7+xjIdkXrIaS8Q9t22zOKtBeXsytSDfIKliQDhLgpQFI5J7JMqcwzr6ZNmwb9TvWZ+dpBySIFEZM2sbxxbNu2Lagu/J0qGvuebfMq683xx0BojRo1AhDo6zzW7M8cS1S9OTakfa59TBiCnuOLAZekpx2m5zqXm2++GQDw4YcfBpXBa6R9vrgv28Nj4BYgxq6nDObFMrwUSLdt5XksSxWZ/ZrHn9daHmf2n3A20V7XdlmmnFljP5OqOevEfmfnyU+OJbre69ixY1BdOA6k4s66R6MmeynrXp532L+kV5bVq1cDCCx65GyZ9NoCBI4J79mE9+YGDRoE1UU+s3jN9sk1IvasppzVYhqee44x9o3yPH5KC/UqoyiKoiiKoiiKQ0wp7m4h1IHAGybVN9tvNG3QqZLxDZaKOtVsvq3S1p02qNLHq/RwQsXDTaWSPl29FE0qZHxz5ps9bb/YHipmF1xwAYBgG3f6cKZdLj1IMA++6bMM6WnDa3W89Npiz3JIDyFsp/Ruwfrv2bMHQMADB48TzwUVeZbNc0MVEgicD6meSptpxR2piNpIm3avWRjpRUZ6hPHyoGCXIfOS26VP4osuuijou3TVxfNvj0MvrwrSZp95/vzzzwBCVTF6dOG1RI5vG9kOHuedO3cGld2wYcOgMqSXDappbl405HHn9U9eN1hvWSduv/322wEA7777LoDATJjttUZ65ogUu0H2GWl3LO2q7fMl1zeU57F89OhRXN0p15b32x92Osour99UhXmNlLOdgPeME48zFXN5X5Xe23h9lrNDvIe4KbvsL9I7ElVtxhqQ9zbpRUr2PzfvObw/8P4qrz/cl/enXbt2AQjcS3ivZB15XLw8VwGBMcJjwuPPY8WZNTk7yTqwDO7H716xTOx9efx5f2Uf4LGW3t2UkkMVd0VRFEVRFEVRHGJKcZdv41Sz+DZLGzypkgOhSpC0Bf/vf/8LIKBWyTz49i6Ve77tunlGkfWVeUoPC1ScmY5v8zLAgFv75DZ+p5Ih2yXtk6U6I/1ou/lSp40gj4lU2GW7qRTs3r0bQKhdPpVAL//3dlrpV1raWSvu8Nja9ppS3ZL9kkjf/9Km3c3Xv52/ncbLo4VUpuifl2r3unXrAAT6nvQXbreLfYX7es0E0F+7jHFARVEq62y3PeY4dqW/al6jqMRt3bo1qGyOTyKjXLrZkssZA3keuG6H0O5WHnOW1a9fPwC53hdkG6R9r+wjbtEz7bJkH/KKsmundbPrL29kZWXBl5Xb77q2zL1Owpd77FbvSAnxMMJrr93/2W+l5xZ5PSY8Nzyn0ssQ00vf8fZ54qw368F9WrVqBSAwJhkFnEozZ9AYqVLajssZ1VWrVjm/0W5eRtGWMwsMYc/+zTK4toN15H68T/FY27EU5Ewv0/B5QMZ/keND2qV7eaexbdxZBscMzw9VezluwkV1V4oHn88f3eJUnyruiqIoiqIoilLmiSnF/U9/+hMA4MsvvwQQ6sOW2EqYXInNN2Hp/UF6cpF+iOXbrlvkP4n0VSvt3YhUPFkWfUFfeOGFAEKjLdq+SmUERu7DPGS9vXyns47Sr7YbbDvzlBHppNLDY8sV+Tz2VCWkJwrWxT6fVCakbSC/s48o7rj120h+zr08pkhFlOdJ2sDb/V36/5Z9iAoT12wwL/oe5/mX/dLN5pqRh6nIebWH3mSkjaz0pEJo38p1MEBgLMpjyDzZTzmGN2/eDCCglFI55djxUuCAUH/UMsoi96FHj0svvTSojtLWmeetW7duAIC1a9c6ZbF+0t8095HnQc7csUweS7kWwe4bXmsqJkyYACA3gEt5oXbt2vBlcyYl75jmqXQdm9axvmdi/6l45zjb9wQvryJeEcglVI/lLB2/u3ka4ywVP1kG+y9tv3m95hhl3lTief+S90p+t9exSaVdxhZgniyDv7du3RpA4DlCrh2RY9l+zpBxI6SnKh47OQMn86RHHi91PNxMvjw/xK0vKCWDLy4OfnEN9EpXGFRxVxRFURRFUZQYIKYUd8JV4VSn+BZLO24bqRRJe1C+hdPemm+vUmWjfZvcz807gvTdKveJpHpLJYReZLZs2RKUj51OqtfcR+bp5jcZCLWPk0poOH/Lsj48VrTrlWVI23buRxWFx95NEeJvtOOVx1YJj7SPtqFqJCOiSltW2ZfY53hupAcI+zzyN36yTCq77dq1AxDoG4xi6uU1yM2zC+E+X331FYCAssZ96OXIK0/px532u/zd9hnPtntFepT2xbxW8VpGFV8q7LQntmcOvfxvy3ZzPNGjDT3zeEXK5DXju+++C/lNXtNkX5Dnk8gZPNn/3CJOe5VdHhgzZgwA4Prrr496n5ycHNd7iddaEzl+ZawE/s4xSKWZ49wr+jYQuiaK/Voqz8yDUTB5b+MaEHrNoWrMMnid79SpU0h75UwfZ6GZJ+vQsmVLAIFrjow8LCOBs012O+U44HceK+4rvbrJtSEk3D1PIu/J0ne+nA1gnxo3blzEvJXCUVJeZWLywV1RFEVRyjK+7LyXUpN3m/bxwTrYdKZefA6QfdL5nuavWqL1VBSlZInJB3epiPGTfoilj3L7Ny8VnG/2fEvl2zlVfRnhTdrG22qRtCHlm7CXqk0VzsvGmJ9yVT+VNLtdTCPt2+SxItKWVqquXh5G3I6F9FdPu13+TiVD2hAzH9o9SqXItuHjeZRqbjjlVQkQTtGh8mZHVbX3kb65pRpGpOLu5h2E55iKHO3QaZf9/fffA/COqCptpKmG23bn0uMD+w77PMednAmTHlH4O9dghPN24uVNRV4TeGw4O8WxTNVbeq2yYzbImQ2ZtyxTqvlERqPkebWPIRVEtpllSpt+L29BXjN4XnV2+y3cOpuyRlF40vG61nvNXEgVWN6X5PiWs0H2LAvvP7Td5r4ycrdcM8ZZWPpU//bbbwEA3bt3D2oL78v2cfKKFcA8ZBlyLZaMrCp9rXNNlu0rn+XzWUOq8jLeiNxPHtNIY9huH9OwbPkMIte+lGfvTCWNKu6KoiiKUk7xZeWZ//nzzMTi8gL4mbwHcD8ftPO+5ynu1U1a0PeMCgE3hoqiFB8+f5TuIAspRsTkgzujDtJ+jG+WfCOm/1UgoGjRnk2q81Ip4lu4VNqptlFpkiqVG9KPuXwTJlT0WKZ8++bbPJWzlStXBu1n75uYmAjA21bfyy5dKgOsM1VyN6VW2vdL//pS9ZeKLo+djNjIdFQbqaYCASWnUaNGAALHSPq6V9wJt75Cqtiyb8jZGKnYSm8nMo6BvQ89DHXu3BkAsGzZMgCBeApU1qj+ypmxX375BUCoPattd061WEYndZuRs+vL/stIitJ+m4q97S9dxknguJN28oTrP1JTU4O2UxWUipw91mUZ/I37cBzxGMu8vBRsNzt92uoyD54X9gE50yWvBbIveKn89javdQLlAa97RGGQ9xMvj2hytoTXWn7Kc+a1XspG2s9LDzXSsxHHN/sdbd/pjYZjkvcGINRWneOSZXAcSE9IXt6xZHRgembjp42cjWREWCJnCuV+8vogz1W4dV4ci2yXvH7J67FSdojJB3dFURRFKcvkHM99GfXF55kl8Qcq7XlfGczF8H2Hz/x536tnB0w8VH1XlOJDTWXCQNtpvo3yzVhGNQUCSiwVLqplfDuVnmj4Fs7fqc5JBUm+CbupitL2TioekVQ5L8WTyiFt7wDg3HPPDUoj3+hlGXIFupciJlfqu9nySztzpqXiSYVdqkjMmyprSkoKgNDIsQ0aNHD24TZZL/YJJTzy/NvbiDxP7Kde3ky8oma62SjzPHXt2hVAICYD+wjVMfZn6aGIv3McU7GWXh3sejMyKutPZY55cTvHOvsW+xq9z8j22LM8nDXi9YT1l/ETOM44OygVSebDmQMZE8Eu1/ZlDQAtWrQAEGyjDnh7a2GZMqIxjxcQGF+8tkq7WolXRGap8rqptpHWB5QHnn/+eQCBGaiixk3Jl2q4vDe4zTAB7tE9uY9cD8KxxvHgZXct/Znz3vDrr78G/W73P/ZXryi+Xj7Spd92jk2q/XItj52vjEpLODMgbdxZlte4kc8IbjEN5DiWcWFYf9le9iml7BCTD+6KoiiKUpZZdTj34a/Tmbkvo76cvIe4+Dw3yHm27SZPgedjnZfyDgA1cnJNTNTzjKIUPT6/LzrF3R/ZzCwcMf3gLj1T0O7NfjOmXRrTUpH76aefAAQUdun5RfonplJI9YEqg5tdJt945RuxVNqlyi1X4HtFcuvSpQsA4N1333XK5DapBFCxk0p6tHWSvn5tm3mpbMhjQ5VUqvXSNpf50G6daqObHSyVDCqA0le8Ep7bbrsNAPDGG2842+R5lHansh97eaFg35H5cXwCgeicn332GYDAuaZaLGdd2Kdozyn7I9VzaY8OhK6xYL1/++03AIG1E2wH86JqxjLYT6VfZxumoTLIa5GMxMyy5VjhMWcZMk4ElXj7f3ntWbNmDYDANa9p06YAAjbKtv0/EBg7S5YsARCI5sr1AkBgnHHmg+dF2s9KtZbtkn3Cy57Y/s2rf5UnvCJvFgfS175c4yLt0vk7P6muA6HehLw8hPG+xJk2mRevGfb6Jrf83LbxO/ssjyXLYDvdPNQAgT7L9rrFTWG/letLpBclqX7L9SZEppeWAXa75Mwn2ycj2drjWClbxPSDu6IoiqKUZXKO5rnJzfvuaHXxwd+N+D1EebcyofL+uy/4ZU5RlIKjXmXCIKOV8S2ftp22KkyFnWmpINFumvZxVMrkynN+J15v2PZbeySfxfJ3aTcvlQC2gfalVPHst3luo82v3Ed6xJDt8PK/LFfFu6mNUn2g2ibVA6bjd6qLPBc8NzxO0p8uEFBR1Fdt4bCVH2mHzd+kGsxjLuMLyFke9hWOR6rsAPDxxx8DCMxgUR3mvtKLE8cC1XP6eaaazLqyL9ljgnnI2SbCsd2+fXsAgb5F9Z7YXqrs9tlKn7Q/pSouowPLWSfpeadx48ZB2+nfnTMRdpv5KWchWDavbYwcSU88PC6sk/QcZdvI8zzJPiKvq3K2UNZJ2gLLGT/7f2n/Xp68yhCuq2jevHmQqUtRY/db6SmI/UGuceG5Yx+wlWjmwfEq12XJ6zXz4uwP+x49x7FvcjZI2p0DoV5UGCGY1w4eS5ZRp06doDowT9lOtouzAnYfluNY5iHv8TwuXutNiFxPYN/XmLdci0PFXT4Xsd1K2SMmH9wVRVEUpTyQcyzP9aE/b9Fo3vYQ5d2JrJonaOVJ7saW3IXdu9q8K0rR4fPHwef3dhFupysMMfngLu2t+ZbK77aHEaq4fGummkYVl3lx9fqFF14IIDSSqnzD5tu39Axj7yPf6KXHBenphSobVQZpU2x7zLDbDYQq7XyTl7ZyXjbs0vaddZZKttvMAvP08pLDY8m68FizDGl7S/tGKgv2DIqXiu/lOUBxx7aTlOs1JNKWWvYN28YVCChabmsx+Bv9ldNDCr2wSJtW9h2OX5bJPsPtVNfs6IZeESSp6nXo0AFAoP+uXbs2KA/W8dprrwUQ6IdUumzf6lS3f/zxx6DfvMaR7K9ynFKpp5pmq30cF3KMU9XkNY/t4XaeJ14juJ22/dJHOxB6feC+8vrHTzk+5focib1dejMh5VFxVxRF8SImH9wVRVEUpaxCE6nGjRsj51juiyRVOsOX7rzvjnqXp7iH2Lz7A7d54+HMwu/3h7jqlGYe0hyK2MGQZCBDKRAxD75wE76o8mVZijoXXHABgMALsv0yR5M3mt1xH5bNF1MKRhQPWAcKRV4mrXwJt1+e+XIsTWt5rOSxlMfBy5yWooF09QqELnylqCEXE7Oe7ENKCeKPc8ZlxHSFQB/cFUVRFEVRFKUw+P25f9GkKwQx+eDO6Vq+7XIKmG/zdkhzvgHLhRvSxRP34Zs003MKmAoCp5P5RswFL/wdCH375tQ834T5Vu31Vk7kwjXpjsteoEPFQrrbYh48NnKRmXzzp/rAujPIk1sobtaHpkk8H9KUSS4M5rHmeWM+3M66S5dyQEAlkeYZ0oxICY9tKiOVGxnQQ44BuWiL55f9nCYy//73v4PS22mku1KWyT4gTTHYv+kyVC6q5v62+zyanLGtdPPYunVrAIE+s2rVKgCB/nvZZZcBCDXvkK5TbRMumvrwk4toqRDKxZxEjkuaFdGMh+4jbZearJcMcsNASlzIx2PLhfccp1Q1+btcbOzWZh5L9gmOTa9Fhzx/MmiVVBzdTO+k4lkeQ7Y/9dRTAHL7w2dogGrVqqHb8dw+hQq5fcVP1Y7HmIsq85R35Nm6O5+5P+Z+clOez+mq2Xm27qgS4p6Y51aarTGdfe+T55ef7KteizelCZxUl3ndoFpuX/9lgCSpQMs85b1PXu9k3d3aKe/VrIMMPCYXxnsFY2TdZB3cApR5OWLgfZTPF+xDStkjJh/cFUVRFEVRFOV0wRcXB5+LAOKWrjDE5IM7VW7arvHt2819GFU0vhFTKaKyRxdw0uaOb8xSEWMZfPumXd2mTZucffkG37ZtWwABtU0uQLMVOyDURZZcwCbdX9pv417h52UQGelCjp9Utbg4kMeNddy1a1fQ/gBw8cUXB5Ul3TjKwD2ynTz2PBfSlRjPq23vx/+l4q6BmPLH3Xff7fw/Y8YMAKHqKJFhyuXCYI6Bdu3aAQA+//xzAAGFmwtQgUD/YlAgOf68VD32T6rKVODpqpHu4+yF6Vycyb5CV4t0l0h3aRzLHTt2DGqvVH6J24JTjheqXVzkzmPDgG/2sbCRC7p5nNwCvHEbryMcPzwWHEdcsF63bl0AgWPu5UbSbRGovQAXCMxoyBkPppOu+eSxlC5w7TKZpwyGVx4Vd8J+fvbZZyMnPc/WvWLuGDF5yrsv79NUyFtUnB/7WXqgyft6+MjhEBfC7CcyKBrPna1Ey0XK0g2xvLbIdCyDM73SNbKclbXrR1t7fucsEfu9dBJB5HVN3n9ZB3vmV96LWW8vpZ3XM+lqV6rn8jpijw95fZYz+8yLfUYpu8Tkg7uiKIqiKIqinDbo4lRv+CbNt3KqbG5hgplWBnyhQkR7TypiXuoakb/zjZhqHhBQy6jsySBO8i3czZ7N3i7dSBI3F2tSRZOBXrxs6KSKKGcJpEJqtyOSMim3s0weeyoGPDdy/YCtSkgXmUyj4Z0LjuzjUmmTdqo89gycxYAnixYtApAXNAYBVcxei8EgQFSBZXhyqZaxLAYYkwHApA2s3Vdob759+/agfTn2aYfeq1cvAKHqn7T1lcfJVg9pi06Vnypx165dAQCdO3cGEJiNkMGh5Fi23VradbPbLGempHtO2vZSpZTtke2QLhztNstjIK9NUsWUnkhYJ17z3AK5SZtir7zLE1yf0KxZM+RwFqpSXsCs+DzlPSvvWGblud7kjIqhLXdAtXV8u3t4l0lPT3eu65zdYt+0xzEQapcOBM43x75XwD8v96Asm/dM9iMGJJJrY+y8OWY40+c1C03k2jF+sm/a62WA4PEv11RJG3eZjrMBUiWXsxvMR7q7tdPItSly3LDPKGWXmHxwVxRFURRFUZTTBr8/SsW9HHqVoTrHN2PactJriVsAEb5N0ysFFT96faB6SBtUKszyDZrqD9+g3d7qqSpQeac/Vamcs55S7WZd2U62y6suNjINlUDWRb6tSy8QfHtnGzhTQSXAVuNYPt/0WU+pqvDYcIaEx5qzAVJ95TmRngXs8mWYZ3smQMkftHefO3cugFBPB3Imq2nTpgCAJk2aAACSk5MBBHwtS8WU5xcIqEH8ZJ5Mw75BxYm/8zvHBpWshISEoDJtm2z2XfZ17rNx40YAAZWeSCWaSG8UxF5XsXz5cgChNt0sk2OD9eWaEXn9kNcAGV4eCCiBbJecbWIebB/VS6ajiifX7Ugl3609Muga95W2unKWxm021M7X/l96/nr22WdRXnnssccA5M5mfdewBypUqID2v60EAJiTuceWyruPn45XlPwHrsrMzAy6fwKh9yt5XbfPobRVZ/+RHsRkMDf2F17XeT1nn+UaFo45BlIEAqo103AfXjN47/Py4ibHGmca5KyBPf6ljbs8NkSu/ZD3bF5zuF6Px41j3E4v77fSiw6/s88oZZeYfHBXFEVRFEVRlNMFn98PXxRqejRpwhGTD+5Uw/mWSwWJNm62AiBXoaekpAAI2FdzBTbfVmmDS7zCu8vIZm5eH1gvKgDyzV76wZazArTV49s37fykUm9voyJNZY9KH9Xubdu2BR0P1pvHSdooSm88trIm1TOqK3KFPWH7eP6YjvbLjGwnbZFtOz/pU1j6/VYKzh133AEAmDdvHoDAeWBfaNasGYCAIrV48WIAAR/jPBdSjbKVKirrPF+XXnopgICHF35yDFBZ4/mW/o7Zl+RaDnubtJtn2SyD7ZOeUqSiyHxYp2XLljllSV/oHOMcd3I8UlHkOhgZcdHLvzMQql7zU9qjS+8Ttl2w3R6Z3s3+WM42SEWdn9IHtlyTQtzqJP2Ge/mrLo9whuq8885DzvHcfuw/M3ccgjbuOXkzlDxucXley6JQ3un73T7PciZG3nf43VaF5Tiw7d+BgKIu9+VY5Xbep2U+HO9uyPuuVO+lxxs5o8ixybLkbJjdTq9jQbxiQLAsHlPWidcJXh95LbVnEL283jBvtW0vP8Tkg7uiKIqiKIqinDb4ovQq4yuHXmWk1wsq0lRwbXtQqU5xH9q98Q33559/DvrON2IqQtLO1ctfug2VSWmvyzrxDZmqv1TMqNJRfaBiyDo9/vjjTlkrV64MSsNP5vHDDz8ElcH2UGWgbbG0TfTyv2z/RqRSJiNt2rbO9neeC9aZ5096+QAC6oks2y3qo1Iwbr/9dtft//nPfwAA33//PYBAX5AeXXgu2Ifs2SnanVNpluse5OyU9ITCscK+JZV2tzUY7NMcb1Tt+OkV1dNrTQkjk9prL6RaLNdrcLZszJgxQXkyUuott9yCcNh23jI2g5zhkDMHUsWnOijb7eUFykbOOPJ4yxkDng8vTzbE3s485MyIAmzYsAFA7jjJysyLSnsi9xz4quRew02e4u702nzYuC9e/1NItG2OZ69+wrFn3295PpkHbbfZV1kGZ8elf3OWyf245oyeodzWe0n7eJbB+4v0aMMymQfv02wP79ecWZOe1oDQdSbyWpGdnY3a/uOoeU5eFHd+Rk3uuWtQ2V67FbqOa8VPvzrjmn1EKUVKyB1k4QxtFEVRFEVRFEUpEWJScSfS7lW+rQOhvlmZhoofPWPIiIy0MSPSLk4qbDZSuZLqE/OmnT2VJSoBf/zjH4Pyo3LQunVrl6OQS2Jioudvdp7jx493rYP0QyvVOzfvEdKGVkZ+JSyLShqPNbdTVeH+VD7couRJVVd6DFGKj549ewIAJkyYACB0dkbORkllFwicP/Y7qvdE+k5mH2CfYl9gOmkra9uaUh3mGgqq+zJ+AMcf2yPHNq8hnNWiZwu7X8q2jx49GtEQSWkno0aNcv5//vnnAQTGJI8/6yOvXTJehLQrDmfbLu1pZcRTr3UsREZBleti3HzGc9vTTz8dUp/yCmdc3nrrLbQthvwrVarknBuOG7nGhf2EY88t+q3sJxzvvObL2SEZRdyOFAsEZoyjiaJLNV7OwjFPaUfP2Vve+1hH6WnNLbIw8+KxkDPAp06dAoKXlBQLJ0+edGbzyrP3pdMFXZyqKIqiKEoQH1dsgfr166Nz2rrcDXkmMuCDZU62+45u+HTSvaioBcstq/3QLk2WPAJCFYTLW54HtDwPc5NXFlmeyulPTD64822XChLtZt28ykgVR75FUyFilEX51u0V4Y11YH5uqiKRkc2kIsn6Dx8+PGy7i4KHH34YQK5yY9eB7ZT+muWMgt1OqfjJ7YSKJ1UUHmPpZccrap6t6smoflJNUYofni/pjUSu4ZAeJYDQfkWf8JwB4z78TsVN2qlKhcvNTziVZ64RYdn0guPl+UF6kOJ2Rj8lth932r1zn+LkH//4BwDgueeeA+AdIVXOGMhjKL3uyJkz+zeZhp+8/kl7ey/bX5mvjZwRUEJhDAK5XqiwxMfHh8wqy1kunnNeeznLye9AYByyj8lZVl7b5b2b3xmThenYT/idqrobMoIq8+Q9gmtxWCbbJWcOuT/7Nttkt5Npuc3v99MsvVRgv1BKmRKycY/JB3dFURRFKc8YqbR7oap6sdKgcg6QcziwIZLCLn73ycXgBQicpZQvYvLBXdqOywiNth2c9FDCN2XpH5lv37R781IfvMq2bTulHR+RXlL4u7RJLQlYplTUvI6TnDUAQv1fSxtCbpfecqR9o7RtZxnMx1ZuuY0eBJhHOE8YStEilVyON/YpGeXUtgWXihz7ApV3GblYqvvSlp3f2Q9sVezHH38EEBpllwqbl59w9j8ZNVimt8ti1NhFixa55lkcPPjggwCAyZMnA/D2tOPlx11GYiS2pxeea6/rnowGLdVZuf5IzjbaM2XM+9FHH43c+HIK7ZhnzpwJVC66fHNycjw9phEZWVd6oQFCr/lyzEgvbew/VNKpuHM2q06dOkF14kycG6wXy2bUcCJt4FkXOS7kOiq2yR4XwXFOjqG0Ufv20wS/P0rFvXAv0/oqriiKoigxxnvH62H1OYkwOdm56jv/wmB8PhifL+8Bww/j8zvBl5T8ccl5Z6OW3zKNMzm5fzniL2+7Lyfb9Q85Wbl/2Sdz//g9Jwu+7JNh/+YsWoMK57UqvYOglAoxqbjTZo2KF/2A843Y9kwhlWSqg9IXrUzP36VNp/S2ItMBoVFVpS2pVO9Lw6ZT1kFGx5NR5qStof2/VNi5r5xZkDMQTCfVfeZHhcRWRGgzyXPO+tEuUSk5qHDxvFPZ5nf+Lj3FAAE1nueaY0b6feb5pZrv5a+f6yhoaw4Au3fvDtpHrqEgMvoh603kbI70vgEExv8ll1ziWr/iZOjQoQCAsWPHAggcb9ry81OuRZAzXvy0Zw+lT3seQxlhWar2PG8cp/yU8TFGjBhRgBYrq1evBhBYm1VYcnJyPK/fRN4r5CyK/b/sD4Tb5X1TrvdiFG1eU5o3bw4g/Ow067Njxw4Agf4tvUh51cGrrm4zEadLVN/Vq1ejf//+pV0NJQ9fXBx8UcSUiSZNOGLywV1RFEVRFHir7FEo6d/vPhDiwlOJDh9t0b1s0vO2OzbsMr3zu/t2t7yZ1+I9x/DBBx8UvPJKTBOTD+5btmwBAHTo0AFAQCGiqmMrZnxD59s238L5Xdq3SYVdKtPybV36sAZCIzASaY/L716RKosTlvnJJ58ACFXL5SfbZPvJlsqM9EgjZycIjxWPPaNmcjaE+XI/e80Cz7H0YsE+cdNNN0V5BJSCIs+rly9j9hX6Ebf35WyKHGfShl366+f+tIWnMscIpba9rbSzpVcJOcPD71Jplzbi7GsyCrN9LGQeJYmXbfjEiRMBBNRM6a+e49DNF360yqJU6zkDxvPEY8ay6d1KKRiTJk0CADz55JPo0imhwPlUqFAh5LodaXZLKu9ua8p4npkH+4Wc7ZJrqDg7xP7D2AuM90AvUxzLQMAunt6jOE65ToZ5sl+zDtKbjIwGzDqzTfbxKI11aTbLli1z+oByGpFnghZVukIQkw/uiqIoiqIAz69KQbdu3dAFuaJGyOI4W3nn/2rXXvTkSCVdKO05eWanXsp73syJsV+Wuc35VI8zpzXqDtKbRx55BADw9ttvAwgoSVLRBkLtVuUbv5f/cvkp00uvGLbayP+lb2mp4J0O0T5ZBx5D1lEq8NKTABCqhkrkMZTrB6iMMG9+Stt/+3xKbz/0PsA+oZQc7N88Jzx/Umm313BQqZJ9n+dT5kG4toGeIlasWAEgdEbIVsHZv1j+RRddBCDQv9gPOWMgYzfI2QD+LmfdgMB4OR3GtETakT/22GMAQiNH8tMtVoMcw0SuReCM2MGDBwEEorwqxQMj9E6YMAFdul2Q7/2rVKkSsl6LyHui9ELEcWNfn9mHOF6Zlgq6VywB6SWKyjq/sz9xho3RQoHQcSujrjJvuX6LdWFd+Z1rV3h9o7c6+/i4rdspSaKNzKyUTfS1W1EURVFinEnfbMdney2hyecP/cubyl+3OxU/p53yzkwpMD5jctV2epmhV5m8P9trDHKygFMngVMnYfh3MtP5y8k8ipzMozCZx2Ayj+Hlhd+jwiVXlXYTFQ98/rio/wpDTCruhHat9PUq/YMDoR5eZHRHaVvn9SYd7Sp5wDsCo1QG7HqWFtJeV3qY4PGQyggQ6mnHC+kXmAoHffJKjzXS0499nOSMB/uAUvzQVprng+dRehqh0i69zdj78Fyzf0nFzbabtbdT/fqf//kfAMCqVauCynSb/WHeVOKkeiz7rxyXUrkn9toNtocer05nnnjiiajTvvjiiwBCx+SwYcOKtE6KosQ2r776Kp577jmkpKSgdevWmDRpEjp16uSZ/p133sGYMWOwa9cuNGvWDM888wyuvfZa53djDB577DG8+eabSEtLwx/+8AdMnjwZzZo1c9IcOnQI999/Pz7++GP4/X7069cPL730khNLZPHixXjxxRexatUqZGRkoFmzZnjwwQdx1113OXkkJSVh0KBBQXWrWLFiiUTBLigx/eCuKIqiKOWdkSNHAgBeeeUVTM4zJ7nv+m6e6WvWrBmygFSahMlAgvIFnS5YbSiIMU+aMhLpwUYKX9IVcL169YLK5Iux/RJN8xzWh4tSmYcUBZiHFJTYbpp70XyU5qG2mW1uWcHiQoh3Gcd2PSv4d37S8UVWbj7mZN6DYlZAODCncn97Y20Khg0bhpHtr8PpyLx58zBy5EhMmTIFiYmJmDhxInr16oWtW7c6wqrNsmXLcOedd2L8+PHo06cP5syZg759+2Lt2rW4+OKLAeQGlXr55ZcxY8YMNGnSBGPGjEGvXr2wefNm55zfdddd2LdvHxYuXIhTp05h0KBBGDJkCObMmeOUc+mll+L//b//h7p16+KTTz5B//79Ub16dfTp08epT7Vq1bB161bneyQx0hNflItTC7nGRE1lFEVRFEVRlAIxYcIEDB48GIMGDcJFF12EKVOmoEqVKpg2bZpr+pdeegm9e/fGgw8+iJYtW2LcuHFo164dXnnlFQC5avvEiRMxevRo3Hjjjbj00ksxc+ZM7N27F/PnzweQ60luwYIFmDp1KhITE9G1a1dMmjQJc+fOddZAPPLIIxg3bhy6dOmC888/H8OHD0fv3r3x/vvvB9XH5/MhISHB+ePM7ulKTCvuVBmSk5MBBN6obfMYvuFz+pvfpRsq7kPXhHyjk29enMLnYhkZshkIqAfS7aNUNv73f/83v00ucliHL774AkBoaHnpPtM2e5ABd2iKwLRSqeHUEwcVjyXTcWGfDN1uKyPSXIF9QCl+eJ5lIB8uGK1fvz6AwPmkKZTtUpBqGM+jXCgmg3Cxj8igL+wjl112GQDg22+/DaoTEOg3VO28XLxK0xgZKE22380ch9t4XSgrPPDAA6VdBSUf2CZMWXs2AsiNluqQp/RVqhQwffK6R3KM8ZPbZRAt+97H35iWpnBclC5dSPKaz+sATRykMwnmQ/WWqiwAbNq0CUCoGZ50zcqy2E7pKtpr3DMfu52514I8xV16evHyy07lndefPIXdUdxPZAZtt/8/nU3TTp48iTVr1gS5ePX7/ejZsyeWL1/uus/y5ctD7t29evVyHsp37tyJlJQU9OzZ0/m9evXqSExMxPLly3HHHXdg+fLlqFGjhuMWHAB69uwJv9+PlStXerqGTk9PR8uWLYO2HTlyBI0aNUJOTg7atWuHp556Cq1a5T8ibbT264W1cVfFXVEURVEURck3qampyM7ODlGp69at6/jWl6SkpIRNz89IaaQZToUKFVCrVi3Pcv/9739j9erVQTbtF154IaZNm4YPP/wQs2bNQk5ODrp06YJffvklUtNLjZhW3MkPP/wAIBBu3A74QqRiJ23xqMZRFebbtwzQRCWBaiLztRcyUDVgGTIMNPc9nWCdOFBYZx5LttN2dycVc7abCoZUX3iM5AJEnhMqJXI/G/7Gc37VVbrCvqSQ4cl5PrlAmMqUDOTDhd/2bzzXsg94uRYlVMuo0LFODMjCgD922hYtWri2Q9ZJun4lclE5sRdssh20j1WU0mbO4nUAgDt7XuZs+/G3o6hWrRqAnBAnCbxf8ZrP8Z2bPtDHqWy7BSFiXhwztDtnHtJxA68D0tUk00nXrXwgsxeBs54sS45j5sn6cuZM2vjL4ItSobfvRydOnMDZ1aN8fBJ+3XOosHso7SbzqLNrpavvja4MJSKLFi3CoEGD8Oabbwap6Z07d0bnzp2d7126dEHLli3x+uuvY9y4cfkrxO+P0o+72rgriqIoiqIoJUzt2rURFxcXJJoAuSIKfelLEhISwqbnZ6Q0NNMkWVlZOHToUEi5S5YswfXXX48XX3wR/fv3D9ueM844A23btsX27dvDpitNyoTi/re//Q0AnIUQjRo1cn6T9rh8i+ZbuXR3KFeWS5s7Cd/CbTVOlkE1gUrFHXfcke82FjesExdt8LhI+3PbHpht9zo2VG5kyGhp18xPKjo85m427rt37wYQOOdKyfGXv/wFQCDcujy/nLWhrbu0iQcC59TLdp1Ie3Kmk4odt9uuGQltUqnGSy8SUrVn35beNLw8DNizcTt27ABwetuiKuWLtWvXAgDuvLqLs+3o0aPOTJjXWiK55kMq0Rz3bi5YqX4zT6raMvChXP/FewDzpPrPewHXnjH/1NRUJy+Ob6Zh3gcOHAgqW3qHieR+mHXiWi77uGRnZ6NJ9eCHQxkpVUZIpYcYx2tM3qdU2nOOBtaRxQLx8fFo3749kpOT0bdvXwC5fSk5Odnzeti5c2ckJycHBYdbuHCho3w3adIECQkJSE5ORps2bQDkBvFauXIlhg4d6uSRlpaGNWvWoH379gCAr776Cjk5OUhMTHTyXbx4Mfr06YNnnnkGQ4YMidie7OxsbNy4Mcg1ZdT4o/QqU0jFvUw8uCuKoiiKoiglz8iRIzFgwAB06NABnTp1wsSJE3H06FHHlrx///5o0KABxo8fDwAYPnw4unfvjhdeeAHXXXcd5s6di++++w5vvPEGgFyxZMSIEXjyySfRrFkzxx1k/fr1nZeDli1bonfv3hg8eDCmTJmCU6dOYdiwYbjjjjsc8WjRokXo06cPhg8fjn79+jmmVvHx8Y67z7Fjx+Kyyy7DBRdcgLS0NDz33HPYvXs37r03/2ZKvrg4+CKYezJdYShTD+5/+tOfAASChgABX6xUzWjnJsN7UzXgmz4/+ZZN228qe/xkvjJgjA3z+PXXXwvYspKDdWzSpAkAb6869m/ymFC5oQJLFcXLppBKCNUUDi6qqbYvYPVycfrA8ylnnXg+3YKTsS8wjbRtZx/imOF2qbxLT00yPRAYs9KThZfyLj0qETkG3NT903laVSmfMGAaP9u2bQsgoCBzHFCB53iW13HpdUV6GLPvCdIuXq5v4n1XjlupbssZcV5L6CHKXifGbcyb9WMaOZ557ZHraVhHOROckZERlL9dRlikbXtO3iwhlXZh255zPFdxf2O3L0iJjgVuv/12HDhwAI8++ihSUlLQpk0bLFiwwLkG79mzJ2jmtUuXLpgzZw5Gjx6NRx55BM2aNcP8+fODvAWNGjUKR48exZAhQ5CWloauXbtiwYIFQedh9uzZGDZsGK666ionANPLL7/s/D5jxgwcO3YM48ePd14aAKB79+5YvHgxgNx+MnjwYKSkpKBmzZpo3749li1bhosuuqi4DlehKVMP7oqiKIqiKErJMmzYME/TGD4k29x666249dZbPfPz+XwYO3Ysxo4d65mmVq1aTrAlN5KSkpCUlOT5O5Ar9Npib6Hwx0W5OFUV9xBsVfbpp58GEFDf+LbGt26qC1TdqAhK3+Pczv35KdMBoV4opCeN0xm5yp/Hx83jhvSXK48hj4k8Rpz1YHqpaFJ14cKUhx56qHCNUoqU+++/H0DA1p2qGRWuxo0bB213sxGXturSzpT9j/syHVUb9kuuRZGqGgBccMEFQWVJG16pnPN35iUjRfKT/X3btm3OvmrbrpyuUL19++23AQDnnXde0O9UlmWkUSrSHIMce/Tewt9tbytUyDl27Jgqdl68//JeIMe39FjGsUebd/teym1ytk76aec+3M6ypNovPc4xPol9vZDe5qKCM32O8h7sVebDY3Vw5513YkT+c1bKGWXywV1RFEVRFEVRSgxV3IsGqrUzZswAEHjblh5OpKpAhZnbqRZzP2nDZysA0jsF3+ALstihpGEdqc5QreBxsdvJbTwWbLf0hS+9EkSyheZ3VdpPb6i8kyeffBJAwMsM+4rtMUb6juY4k1FNpR9n6fmC6j7XZHAc2narXN/C8cey3bwVudVFzjJxPypztuKuKKc7q1evBhBQzOX1mONE9n95faYyz3upbePuFZXYa7aLefFewGsHP5m3tI23Z/HkOhh6b6P6T0VexhnhdUnGhpDedqTqH8gjMMsehPQmw+sflfY87zLSm8zq1dtw5513uuepKBZl/sFdURRFURRFUYoTn98PXxSuHqNJE45y8+A+YMAAAMAXX3wBIDRCG9+6pTosVXMqAFQKqDbbEUUJt7lFAD3dYZ15XKQdob2NSgdVUOmT28tPrlRVuZ3nSoktRo8eDQB49tlnAQDt2rUDEKyCe/lflwq8XEPCQBv030xVjWqY9IBhIyOl8jvz4JimQic93ci1KStWrACQ69JMUWKFCRMmAACeeuopAEC3bt2Cfmd/l3FH5HonKu1yjRMQGL9c58R9ZRwVzspWr14dQGDc8n7KMSjXurjNhsmZA7aDyjnzlNcaro+Rvuel8s722iq/MQapOZVx/PhxnFcl7/qSE3zfc6DSLr3K5NVj4taTeOSRRzChn/vuiiIpNw/uiqIoiqIoilIs+KK0cfepjXu++OmnnwDA8dHpFS1Obpe+bKnShVMAuO/AgQOLthElAOv87rvvAnBvJ1V56fNe+s2WESoJ0/GT56ZXr15F2BKlpBk1ahQAOH5zzz33XOe3c845B0BgtoZQDaP69fPPPwMIqH4cf1JRp7LHvsb8gdA1EyyDah6VwvXr1wMIeJ5q1qxZ0P6MwPjdd98BQMz5WFYUm0ceeQQA8K9//QsA0KpVKwABdZvjg+q4tH3ndirZ/AQC9036PuenjJRKtV56qpHxVuR+0i7d3ibzljbqrBvXqFBxZ/ukhznp8cq+f9ntO69xbRQGng9FiZbCGdooiqIoiqIorpic7FwzmaxTuX852UBONuakxKPydUNLu3pKUeLzAT5/FH+hLpLzVYxxc9BdjqC3GbnSXtqn05cr7WCJVJHtffv06VP0FS4lPvnkEwChSikQ6p2DKunBgwcBBGwFuS/Tp6WlAVCb9vIEg2mwT0h/yFTUpbcJ6fmCCjvXVbDP0a4eAJo2bQogtH9KH/JU1Ddu3Bj0O5U2zgKoMqaURRjAhvEXOAbZ7+X6LWk7Tu9NQGD2lIq09MZGOF4561WzZs2gvOWMt4ynsm7dOicvRoSVUdGlUs57Oa8ZzFPe0+WMHNtp27gzmndGRga6tsz1i+/Ls11Hdp4P+rxPczz3+JljeRFrj+Yem4o97oZSNsjIyED16tXx+/pFqFY19BkpJP3hI6jZ5gqkp6cHzVhFiyruiqIoiqIohcD4fDBhlFSTnQ2TnY339vr1oV0pFOVecc8vzz33HICAIiiVQKBs28BOnDjR+Z+2hOxCtB188MEHS7xeSmxCBZ59ieodVTD2LdqvSrtU6bHp6quvdv6n4ibXUhCOXXqsoa27xg9QyiOTJ08GADRv3hxAaCwTjlH53fY0RmVdRtyWsROkDTz346ysVME53qmSc6wCQJs2bQAEFHLpBYrqPmcOqKhLG325Nk1GPre9pXHb8ePH0aFJ3rqa7DwPOFm51ySpuOccTgMAxF92E5SyBRX3Q98viVpxr9W6uyruiqIoiqIoJQrtluV3kxMIxgTgX+tS9KFdKRLKnVeZwlLe1eSyPJuglB5U5KQvaamCyciqhCqb7XVGepPgvl6RFlVpV8ozQ4fmLpQcM2YMgIDnNa4VkZ5gOH5sJZrjVNqZy3HNNWX8neud+Mn0Mp4Df7dVfm6rU6dOUHuozst95Ho1bpdeZdgW6VUHCNji2/WIBI+vUoaRL3Hh0hUCVdwVRVEURVGKgSfmfoUKra4o7WooZQhV3BVFKTWkHSm9xVBho/LG7dKPM/ejD3ZbFZMen6SyxjLoVUZRFGDcuHEAgJEjRwIAatfO9VPOcUO1mWPRXmciY3rQWwz3lXEXuJ0KvLQvZ3785HoUe2aN27juTEY/Z3RW6WWGa7KYF73S8JpC7zMs27adl96wwsHjqZQDfL7oXD0W0h2kPrgriqIoiqIUAJNn9uDL+1y0NQWff/45AGDChAmlVi+l7HLamcr8+uuvuO2221CjRg1Uq1YNN954oxNFUVGUYGJ9vIwZMwZjxoxBVlYWsrKycOzYMRw7dgynTp3CqVOnnO/Hjx/H8ePHkZOTg5ycHFSqVAmVKlVC7dq1g/78fr/zFxcXF/Rn/+b3+5GRkYGMjAykpaU5drCKoiiKUiD8/uj/CsFppbgfOXIEV1yR65T+kUcewRlnnIEXX3wR3bt3x/r1651FJYqi6HhRFKX4oFr8l7/8BQDQvXt3AECjRo2C0tHsBQiYz8hAhlwISjOUlJQUAN5Bjmgywxfq/fv3AwDuvtvb//ncuXMBBMzmaH4jzfFkcKj69esHlcnF6jQB4nZ7QTy3AcDaI0ewe/duAMCSJUsAAK+99ppnPRWlsJxWD+6vvfYatm3bhlWrVqFjx44AgGuuuQYXX3wxXnjhBTz11FOlXENFOX0oS+OFHl3Gjx8PINQ/O2+UfCBglEd6vJDpgcCNmTdcafO+Z8+eoLIVRVEUpaAYn98xnYqUrjDkKwDTokWLcOWVV+L999/HTTcF+yOdM2cO7rrrLixbtgydO3cuUGU6deoEAFi1alXQ9l69emHHjh3Yvn17gfJVlNLg+PHjTjjudevWOYubDh06hFatWqFJkyb45ptvQsKBR0tZHC98cJcP2dE+uNuzDFIp475cpMYgLuFUPEVRgqG7yEsvvRQAggLI1KtXD0BgwSfHGpV4Pm7IxebcTjU8NTUVQGBhaH7G6KxZswAEFpNyca1U9XndZV3ldl4/WNd9+/Y5ZbCeGzZsAKALUMs7DMB0cMuqqAMwnd2yU8kEYOrRowfOO+88zJ49O+S32bNn4/zzz0fnzp1x4sQJpKamRvVHcnJysGHDBnTo0CEk706dOmHHjh3OKnBFiQUqV66MGTNmYPv27fi///s/Z/tf//pXpKenIykpCXFxcTpeFEVRFEWJinyZyvh8Ptx9992YMGEC0tPTHTdLBw4cwJdffuk8nLz99tsYNGhQVHnyTfvQoUM4ceKE88Zuw2179+7FhRdemJ8qK0qpkpiYiFGjRuGZZ57BTTfdhP3792Pu3LmYOHGiE1pcx0uAhx9+OOj7k08+CSBUgWcbZYAWOyAKt0nXknyhsRU0RVGiQ6rLY8eOdf7v1asXgMA4lMq6DH4m7c+ZjmN04MCB+a4f1fmkpCQAAZeULIt14zWF1wdZR15rqfqvXLnSKePRRx8FANx66635rp9ShimhAEz5tnHv378/xo8fj3fffRf33HMPAGDevHnIyspyBkyvXr2wcOHCfOXLweHmH5U3Z6ZRlFji8ccfxyeffIIBAwbgyJEj6N69O/72t785v+t4URRFURQlGvL94N6iRQt07NgRs2fPdh7cZ8+ejcsuuwwXXHABgFw1zE0JDAft0cItMrMDIChKrBAfH49p06ahY8eOqFSpEqZPn+6oP4COl3CMHj066DsX3J51Vq4dIVUxHk/bwwVVPCprVNq2bNkCAHjwwQeLq9qKUm6g+gwA9913HwDg4osvBgBnVpF2vLR5Jxy/NAOkK1t6sikMVOvp4YXrYWjz7hNBcGjTTvv1n376CQCwadMmAMCUKVMKXSeljHO6Ku5Aruo+fPhw/PLLLzhx4gRWrFiBV155xfn9+PHjSE9PjyqvhIQEAECtWrVQsWJF1+lrbqPbJkWJNb744gsAuQ/V27ZtQ5MmTZzfdLwoiqIoihIN+fIqQ1JTU1G/fn3885//xPHjx/Hkk09i7969zptsUlJSvm12AaBjx47w+XwhXjKuvvpq7NixAzt27MhvVRWl1NmwYQM6duyIu+66C+vXr0dqaio2btzorBHR8RI9zz77LACgd+/eAELDrtumQ1TcaTr0yy+/AMh1makoSskxdOhQAIGxSLWb4/ell14qsboMHz4cQKgtO2cqJ0+eXGJ1UcoG9CqT+tM6VKtaNXL6w4dRu3nbAnuVKZDiXrt2bVxzzTWYNWsWMjMz0bt3b+ehHSiYzS4A3HLLLXjooYfw3XffOd4ytm7diq+++gr/+Mc/ClJVRSlVTp06hYEDB6J+/fp46aWXsHPnTnTs2BEPPPAApk2bBkDHi6IoiqIo0VEgxR0A3nvvPdxyyy0Achen3nbbbYWuzOHDh9G2bVscPnwY//jHP3DGGWdgwoQJyM7Oxvr163HOOecUugxFKUkee+wxjBs3DsnJybjiiisAAP/85z8xevRofPrpp7j22msLnHd5HC9U5q6++moAgQW4vIzZNrT0FnHs2DEAAX/3I0aMKJG6KoqiKGUfR3Hf9n30inuz1iXjx93m+uuvR82aNVG9enXccMMNBc0miKpVq2Lx4sW4/PLL8eSTT2LMmDFo3bo1lixZUiYfQpSyzdq1a/HUU09h2LBhzkM7kBups2PHjhg8eLAT0rsg6HhRFEVRlPJFgRX3rKws1K9fH9dffz3+9a9/FXW9FEVRPNm8eTOAUK86th932rjT1p8zhIqiKIpSVDiK+/YN0SvuF1xasjbuADB//nwcOHAA/fv3L2gWiqIoiqIoihLznHFOI5wRxYP4GZUyClVOvh/cV65ciQ0bNmDcuHFo27YtunfvXqgKKIqi5JeLLroIADBq1Kig7fYEIj1WTJgwoeQqpiiKoijFSL5t3CdPnoyhQ4eiTp06mDlzZnHUSVEURVEURVEUQYFt3BVFURRFURSlPEMb92ht1vObXlK4uKuKoiiKoiiKopQI+uCuKIqiKIqiKDGAPrgriqIoiqIoSgygD+6KoiiKoiiKEgPog7uiKIqiKIqixAD64K4oiqIopxk5OTmYMmUK2rRpg7POOgt169bFNddcg2XLlpV21RRFKUX0wV1RFEVRTjMefPBBDB06FJdccgkmTJiAv//97/jpp5/QvXt3rFq1qrSrpyhKKZHvyKmKoiiKohQfWVlZmDx5Mm655Ra89dZbzvZbb70VTZs2xezZs9GpU6dSrKGiKKWFKu6KoiiKEoZdu3bB5/N5/hU1p06dwvHjx1G3bt2g7XXq1IHf70flypWLvExFUWIDVdwVRVEUJQznnHNOkPIN5D5cP/DAA4iPjwcAHDt2DMeOHYuYV1xcHGrWrBk2TeXKlZGYmIikpCR07twZ3bp1Q1paGsaNG4eaNWtiyJAhBW+MoigxjT64K4qiKEoYzjzzTNx9991B2/7617/iyJEjWLhwIQDg2WefxRNPPBExr0aNGmHXrl0R082aNQu33357ULlNmzbFt99+i6ZNm+avAYqilBn0wV1RFEVR8sHMmTPx2muv4YUXXsAVV1wBAOjfvz+6du0acd9ozVyqVq2KVq1aoXPnzrjqqquQkpKCp59+Gn379sU333yD2rVrF6oNiqLEJj5jjCntSiiKoihKLLB+/Xp06dIFffv2xZw5cwqVV3p6Oo4fP+58j4+PR61atZCVlYW2bduiR48emDRpkvP7tm3b0KpVKzzwwAN45plnClW2oihFQ0ZGBqpXr4709HRUq1atyNNLdHGqoiiKokTB77//jn79+qF58+aYOnVq0G9HjhxBSkpKxL8DBw44+wwfPhz16tVz/m6++WYAwNdff41NmzbhhhtuCCqjWbNmaNmyJb799tvib6yilCNeffVVNG7cGJUqVUJiYuJp7XJVTWUURVEUJQI5OTm46667kJaWhv/85z+oUqVK0O/PP/98vm3cR40aFWTDzkWr+/fvBwBkZ2eH7H/q1ClkZWUVtBmKogjmzZuHkSNHYsqUKUhMTMTEiRPRq1cvbN26FXXq1Cnt6oWgD+6KoiiKEoEnnngCX3zxBT7//HM0adIk5PeC2LhfdNFFuOiii0LSNG/eHAAwd+5c9O7d29m+du1abN26Vb3KKEoRMmHCBAwePBiDBg0CAEyZMgWffvoppk2bhoceeqiUaxeK2rgriqIoShg2btyI1q1b4/LLL8e9994b8rv0OFMUXH311Vi4cCFuuukmXH311di3bx8mTZqEkydPYs2aNbjwwguLvExFKW+cPHkSVapUwbvvvou+ffs62wcMGIC0tDR8+OGHEfMoaRt3VdwVRVEUJQwHDx6EMQZLlizBkiVLQn4vjgf3Dz/8EM8//zzmzp2LBQsWID4+Ht26dcO4ceP0oV1RiojU1FRkZ2eHBDurW7cufvzxx3zllZGRUaTpvNAHd0VRFEUJQ48ePVDSk9OVK1fGmDFjMGbMmBItV1GU/BEfH4+EhAScd955Ue+TkJDgBG/LL/rgriiKoiiKopQ7ateujbi4OGdBONm/fz8SEhKiyqNSpUrYuXMnTp48GXW58fHxqFSpUr7qSvTBXVEURVEURSl3xMfHo3379khOTnZs3HNycpCcnIxhw4ZFnU+lSpUK/CCeX/TBXVEURVEURSmXjBw5EgMGDECHDh3QqVMnTJw4EUePHnW8zJxu6IO7oiiKoiiKUi65/fbbceDAATz66KNISUlBmzZtsGDBgpAFq6cL6g5SURRFURRFUWIAf2lXQFEURVEURVGUyOiDu6IoiqIoiqLEAPrgriiKoiiKoigxgD64K4qiKIqiKEoMoA/uiqIoiqIoihID6IO7oiiKoiiKosQA+uCuKIqiKIqiKDGAPrgriqIoiqIoSgygD+6KoiiKoiiKEgPog7uiKIqiKIqixAD64K4oiqIoiqIoMYA+uCuKoiiKoihKDKAP7oqiKIqiKIoSA+iDu6IoiqIoiqLEAPrgriiKoiiKoigxgD64K4qiKIqiKEoMoA/uiqIoiqIoihID/H+P7bO+XSfmdQAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Schizophrenia with drug treatment\",\n", + " threshold=1e-4,\n", + " vmax=1e-3,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Schizophrenia without drug treatment\",\n", + " threshold=1e-4,\n", + " vmax=1e-3,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-DepressionYes\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Depression with drug treatment\",\n", + " threshold=1e-4,\n", + " vmax=1e-3,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-DepressionNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Depression without drug treatment\",\n", + " threshold=1e-4,\n", + " vmax=1e-3,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\nIn the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with group names specified.\n\n" + "Four figures correspond to group-specific spatial intensity map of four groups\n", + "(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\n", + "Areas with stronger spatial intensity are highlighted.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\n", + "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\n", + "can be generated by `create_contrast` function, with group names specified.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", + "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", + "INFO:nimare.meta.cbmr:type2 = index_2\n", + "INFO:nimare.meta.cbmr:type3 = index_3\n", + "INFO:nimare.meta.cbmr:type4 = index_4\n", + "INFO:nimare.meta.cbmr:type5 = index_5\n" + ] + } + ], + "source": [ + "from nimare.meta.cbmr import CBMRInference\n", + "\n", + "inference = CBMRInference(device=\"cuda\")\n", + "inference.fit(result=results)\n", + "t_con_groups = inference.create_contrast(\n", + " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n", + ")\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "from nimare.meta.cbmr import CBMRInference\n\ninference = CBMRInference(device=\"cuda\")\ninference.fit(result=results)\nt_con_groups = inference.create_contrast(\n [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n)\ncontrast_result = inference.transform(t_con_groups=t_con_groups)\n\n# generate z-score maps for group-wise spatial homogeneity test\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"SchizophreniaYes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"SchizophreniaNo\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionYes\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"DepressionYes\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"DepressionNo\",\n threshold=scipy.stats.norm.isf(0.05),\n)" + "Now that we have done spatial homogeneity tests, we can plot the z-score maps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate z-score maps for group-wise spatial homogeneity test\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"SchizophreniaYes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"SchizophreniaNo\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-DepressionYes\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"DepressionYes\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-DepressionNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"DepressionNo\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to homogeneity test of\ngroup-specific spatial intensity for four groups. The null hypothesis assumes\nhomogeneous spatial intensity over the whole brain,\n$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\nthe number of voxels within brain mask, $j$ is the index of voxel. Areas with\nsignificant p-values are highlighted (under significance level $0.05$).\n\n" + "Four figures (displayed as z-statistics map) correspond to homogeneity test of\n", + "group-specific spatial intensity for four groups. The null hypothesis assumes\n", + "homogeneous spatial intensity over the whole brain,\n", + "$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\n", + "the number of voxels within brain mask, $j$ is the index of voxel. Areas with\n", + "significant p-values are highlighted (under significance level $0.05$).\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\nThe default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n\n" + "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\n", + "The default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:nimare.results:Map 'p_group-SchizophreniaYes' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'z_group-SchizophreniaYes' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'p_group-SchizophreniaNo' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'z_group-SchizophreniaNo' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'p_group-DepressionYes' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'z_group-DepressionYes' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'p_group-DepressionNo' should be 1D, not 2D. Squeezing.\n", + "WARNING:nimare.results:Map 'z_group-DepressionNo' should be 1D, not 2D. Squeezing.\n" + ] + } + ], "source": [ - "from nimare.correct import FDRCorrector\n\ncorr = FDRCorrector(method=\"indep\", alpha=0.05)\ncres = corr.transform(contrast_result)\n\n# generate FDR corrected z-score maps for group-wise spatial homogeneity test\nplot_stat_map(\n cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia with drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Schizophrenia without drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression with drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)\n\nplot_stat_map(\n cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Depression without drug treatment (FDR corrected)\",\n threshold=scipy.stats.norm.isf(0.05),\n)" + "from nimare.correct import FDRCorrector\n", + "\n", + "corr = FDRCorrector(method=\"indep\", alpha=0.05)\n", + "cres = corr.transform(contrast_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "After FDR correction (via BH procedure), areas with stronger spatial intensity\nare more stringent, (the number of voxels with significant p-values is reduced).\n\n" + "Now that we have applied the FDR correction methods,\n", + "we can plot the FDR corrected z-score maps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Schizophrenia with drug treatment (FDR corrected)\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Schizophrenia without drug treatment (FDR corrected)\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Depression with drug treatment (FDR corrected)\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Depression without drug treatment (FDR corrected)\",\n", + " threshold=scipy.stats.norm.isf(0.05),\n", + " vmax=30,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After FDR correction (via BH procedure), areas with stronger spatial intensity\n", + "are more stringent, (the number of voxels with significant p-values is reduced).\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for group comparisons among any two groups\nIn the most basic scenario of group comparison test, contrast matrix `t_con_groups`\ncan be generated by `create_contrast` function, with `contrast_name` specified as\n\"group1-group2\".\n\n" + "## GLH testing for group comparisons among any two groups\n", + "In the most basic scenario of group comparison test, contrast matrix `t_con_groups`\n", + "can be generated by `create_contrast` function, with `contrast_name` specified as\n", + "\"group1-group2\".\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "t_con_groups = inference.create_contrast(\n [\n \"SchizophreniaYes-SchizophreniaNo\",\n \"SchizophreniaNo-DepressionNo\",\n \"DepressionYes-DepressionNo\",\n ],\n source=\"groups\",\n)\ncontrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)\n\n# generate z-statistics maps for each group\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Drug Treatment Effect for Schizophrenia\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Untreated Schizophrenia vs. Untreated Depression\",\n threshold=scipy.stats.norm.isf(0.4),\n)\n\nplot_stat_map(\n contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"Drug Treatment Effect for Depression\",\n threshold=scipy.stats.norm.isf(0.4),\n)" + "t_con_groups = inference.create_contrast(\n", + " [\n", + " \"SchizophreniaYes-SchizophreniaNo\",\n", + " \"SchizophreniaNo-DepressionNo\",\n", + " \"DepressionYes-DepressionNo\",\n", + " ],\n", + " source=\"groups\",\n", + ")\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Four figures (displayed as z-statistics map) correspond to group comparison\ntest of spatial intensity for any two groups. The null hypothesis assumes\nspatial intensity estimations of two groups are equal at voxel level,\n$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\nwithin brain mask, $j$ is the index of voxel. Areas with significant p-values\n(significant difference in spatial intensity estimation between two groups)\nare highlighted (under significance level $0.05$).\n\n" + "Now that we have done group comparison tests,\n", + "we can plot the z-score maps indicating difference in spatial intensity between two groups.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate z-statistics maps for each group\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Drug Treatment Effect for Schizophrenia\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + " vmax=2,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Untreated Schizophrenia vs. Untreated Depression\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + " vmax=2,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Drug Treatment Effect for Depression\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + " vmax=2,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing with contrast matrix specified\nCBMR supports more flexible GLH test by specifying a contrast matrix.\nFor example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\nrepresented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\nfunction. Multiple independent GLH tests can be conducted simultaneously by\nincluding multiple contrast vectors/matrices in `t_con_group`.\n\nCBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\nwhen it's represented as one of elements in `t_con_group` (datatype: list).\nOnly if all of null hypotheses are rejected at voxel level, p-values are significant.\nFor example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\nthe equality of spatial intensity estimation among all of four groups (finding the\nconsistent activation regions). Note that only $n-1$ contrast vectors are necessary\nfor testing the equality of $n$ groups.\n\n" + "Four figures (displayed as z-statistics map) correspond to group comparison\n", + "test of spatial intensity for any two groups. The null hypothesis assumes\n", + "spatial intensity estimations of two groups are equal at voxel level,\n", + "$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\n", + "within brain mask, $j$ is the index of voxel. Areas with significant p-values\n", + "(significant difference in spatial intensity estimation between two groups)\n", + "are highlighted (under significance level $0.05$).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GLH testing with contrast matrix specified\n", + "CBMR supports more flexible GLH test by specifying a contrast matrix.\n", + "For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\n", + "represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\n", + "function. Multiple independent GLH tests can be conducted simultaneously by\n", + "including multiple contrast vectors/matrices in `t_con_group`.\n", + "\n", + "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\n", + "when it's represented as one of elements in `t_con_group` (datatype: list).\n", + "Only if all of null hypotheses are rejected at voxel level, p-values are significant.\n", + "For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\n", + "the equality of spatial intensity estimation among all of four groups (finding the\n", + "consistent activation regions). Note that only $n-1$ contrast vectors are necessary\n", + "for testing the equality of $n$ groups.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "contrast_result = inference.transform(\n t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n)\nplot_stat_map(\n contrast_result.get_map(\"z_GLH_groups_0\"),\n cut_coords=[0, 0, -8],\n draw_cross=False,\n cmap=\"RdBu_r\",\n title=\"GLH_groups_0\",\n threshold=scipy.stats.norm.isf(0.4),\n)\nprint(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" + "contrast_result = inference.transform(\n", + " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## GLH testing for study-level moderators\nCBMR framework can estimate global study-level moderator effects,\nand allows inference on the existence of m.\n\n" + "Now that we have done group comparison tests with the specified contrast matrix,\n", + "we can plot the z-score maps indicating consistency in activation regions among\n", + "all four groups.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "contrast_name = results.estimator.moderators\nt_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\ncontrast_result = inference.transform(t_con_moderators=t_con_moderators)\nprint(contrast_result.tables[\"moderators_regression_coef\"])\nprint(\n \"P-values of moderator effects `sample_sizes` is {}\".format(\n contrast_result.tables[\"p_standardized_sample_sizes\"]\n )\n)\nprint(\n \"P-value of moderator effects `avg_age` is {}\".format(\n contrast_result.tables[\"p_standardized_avg_age\"]\n )\n)" + "plot_stat_map(\n", + " contrast_result.get_map(\"z_GLH_groups_0\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"GLH_groups_0\",\n", + " threshold=scipy.stats.norm.isf(0.4),\n", + ")\n", + "print(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This table shows the regression coefficients of study-level moderators, here,\n`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\nModerator effects of both `sample_size` and `avg_age` are not significant under\nsignificance level $0.05$. With reference to spatial intensity estimation of\na chosen subtype, spatial intensity estimations of the other $4$ subtypes of\nschizophrenia are moderatored globally.\n\n" + "## GLH testing for study-level moderators\n", + "CBMR framework can estimate global study-level moderator effects,\n", + "and allows inference on the existence of m.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " standardized_sample_sizes standardized_avg_age type2 type3 \\\n", + "0 -0.000769 0.005946 0.107031 0.08795 \n", + "\n", + " type4 type5 \n", + "0 0.105989 0.090762 \n", + "P-values of moderator effects `sample_sizes` is p\n", + "0 0.939472\n", + "P-value of moderator effects `avg_age` is p\n", + "0 0.557174\n" + ] + } + ], + "source": [ + "contrast_name = results.estimator.moderators\n", + "t_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\n", + "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", + "print(contrast_result.tables[\"moderators_regression_coef\"])\n", + "print(\n", + " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", + " contrast_result.tables[\"p_standardized_sample_sizes\"]\n", + " )\n", + ")\n", + "print(\n", + " \"P-value of moderator effects `avg_age` is {}\".format(\n", + " contrast_result.tables[\"p_standardized_avg_age\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table shows the regression coefficients of study-level moderators, here,\n", + "`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\n", + "Moderator effects of both `sample_size` and `avg_age` are not significant under\n", + "significance level $0.05$. With reference to spatial intensity estimation of\n", + "a chosen subtype, spatial intensity estimations of the other $4$ subtypes of\n", + "schizophrenia are moderatored globally.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p\n", + "0 0.639232\n" + ] + } + ], "source": [ - "t_con_moderators = inference.create_contrast(\n [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n)\ncontrast_result = inference.transform(t_con_moderators=t_con_moderators)\nprint(\n \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n )\n)" + "t_con_moderators = inference.create_contrast(\n", + " [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n", + ")\n", + "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", + "print(\n", + " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", + " contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", + " )\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "CBMR also allows flexible contrasts between study-level covariates.\nFor example, we can write `contrast_name` (an input to `create_contrast`\nfunction) as `standardized_sample_sizes-standardized_avg_age` when exploring\nif the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n\n" + "CBMR also allows flexible contrasts between study-level covariates.\n", + "For example, we can write `contrast_name` (an input to `create_contrast`\n", + "function) as `standardized_sample_sizes-standardized_avg_age` when exploring\n", + "if the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n", + "\n" ] } ], @@ -230,4 +942,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/10_plot_cbmr.py index 36f43ecd0..7f09cd8f3 100644 --- a/examples/02_meta-analyses/10_plot_cbmr.py +++ b/examples/02_meta-analyses/10_plot_cbmr.py @@ -19,8 +19,9 @@ This tutorial is intended to provide a brief description and example of the CBMR algorithm implemented in NiMARE. -For a more detailed introduction to the elements of a coordinate-based meta-regression, -see other stuff. +For a more detailed introduction to the elements of a coordinate-based meta-regression, +see the [online course](https://www.coursera.org/lecture/functional-mri-2/module-3-meta-analysis-Vd4zz) +or a [brief overview](https://libguides.princeton.edu/neuroimaging_meta). """ import numpy as np import scipy @@ -33,7 +34,8 @@ ############################################################################### # Load Dataset # ----------------------------------------------------------------------------- -# Here, we're going to simulate a dataset (using `nimare.generate.create_coordinate_dataset`) +# Here, we're going to simulate a dataset +# (using [nimare.generate.create_coordinate_dataset](https://nimare.readthedocs.io/en/latest/generated/nimare.generate.create_coordinate_dataset.html)) # that includes 100 studies, each with 10 reported foci and sample size varying between # 20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression) # and drug status (Yes or No). We also add two continuous study-level moderators (sample size and @@ -75,8 +77,11 @@ # # Note that study-level moderators can only have global effects instead of localized # effects within CBMR framework. In the scenario that there're multiple subgroups -# within a group, while one or more of them don't have enough number of studies to be -# inferred as a separate group, CBMR can interpret them as categorical study-level moderators. +# within a group (e.g., indexed as subgroup-1 to subgroup-n, but one or more of them +# don't have enough number of studies to be inferred as a separate group). Using +# categorical encoding, CBMR can interpret the subgroups as categorical moderators +# for each study (either 0 or 1), and estimate the global activation intensity +# associated with each subgroup (comparing to the average). from nimare.meta.cbmr import CBMREstimator @@ -93,10 +98,14 @@ model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e3, # a reasonable choice is 1e-1 or 1e-2, 1e3 is for speed + tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=dset) + +############################################################################### +# Now that we have fitted the model, we can plot the spatial intensity maps. + plot_stat_map( results.get_map("spatialIntensity_group-SchizophreniaYes"), cut_coords=[0, 0, -8], @@ -104,6 +113,7 @@ cmap="RdBu_r", title="Schizophrenia with drug treatment", threshold=1e-4, + vmax=1e-3, ) plot_stat_map( results.get_map("spatialIntensity_group-SchizophreniaNo"), @@ -112,6 +122,7 @@ cmap="RdBu_r", title="Schizophrenia without drug treatment", threshold=1e-4, + vmax=1e-3, ) plot_stat_map( results.get_map("spatialIntensity_group-DepressionYes"), @@ -120,6 +131,7 @@ cmap="RdBu_r", title="Depression with drug treatment", threshold=1e-4, + vmax=1e-3, ) plot_stat_map( results.get_map("spatialIntensity_group-DepressionNo"), @@ -128,6 +140,7 @@ cmap="RdBu_r", title="Depression without drug treatment", threshold=1e-4, + vmax=1e-3, ) ############################################################################### @@ -149,6 +162,9 @@ ) contrast_result = inference.transform(t_con_groups=t_con_groups) +############################################################################### +# Now that we have done spatial homogeneity tests, we can plot the z-score maps. + # generate z-score maps for group-wise spatial homogeneity test plot_stat_map( contrast_result.get_map("z_group-SchizophreniaYes"), @@ -157,6 +173,7 @@ cmap="RdBu_r", title="SchizophreniaYes", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -166,6 +183,7 @@ cmap="RdBu_r", title="SchizophreniaNo", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -175,6 +193,7 @@ cmap="RdBu_r", title="DepressionYes", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -184,6 +203,7 @@ cmap="RdBu_r", title="DepressionNo", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) ############################################################################### @@ -203,6 +223,10 @@ corr = FDRCorrector(method="indep", alpha=0.05) cres = corr.transform(contrast_result) +############################################################################### +# Now that we have applied the FDR correction methods, +# we can plot the FDR corrected z-score maps. + # generate FDR corrected z-score maps for group-wise spatial homogeneity test plot_stat_map( cres.get_map("z_group-SchizophreniaYes_corr-FDR_method-indep"), @@ -211,6 +235,7 @@ cmap="RdBu_r", title="Schizophrenia with drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -220,6 +245,7 @@ cmap="RdBu_r", title="Schizophrenia without drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -229,6 +255,7 @@ cmap="RdBu_r", title="Depression with drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) plot_stat_map( @@ -238,6 +265,7 @@ cmap="RdBu_r", title="Depression without drug treatment (FDR corrected)", threshold=scipy.stats.norm.isf(0.05), + vmax=30, ) ############################################################################### @@ -260,6 +288,10 @@ ) contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False) +############################################################################### +# Now that we have done group comparison tests, +# we can plot the z-score maps indicating difference in spatial intensity between two groups. + # generate z-statistics maps for each group plot_stat_map( contrast_result.get_map("z_group-SchizophreniaYes-SchizophreniaNo"), @@ -268,6 +300,7 @@ cmap="RdBu_r", title="Drug Treatment Effect for Schizophrenia", threshold=scipy.stats.norm.isf(0.4), + vmax=2, ) plot_stat_map( @@ -277,6 +310,7 @@ cmap="RdBu_r", title="Untreated Schizophrenia vs. Untreated Depression", threshold=scipy.stats.norm.isf(0.4), + vmax=2, ) plot_stat_map( @@ -286,6 +320,7 @@ cmap="RdBu_r", title="Drug Treatment Effect for Depression", threshold=scipy.stats.norm.isf(0.4), + vmax=2, ) ############################################################################### # Four figures (displayed as z-statistics map) correspond to group comparison @@ -317,6 +352,12 @@ contrast_result = inference.transform( t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False ) + +############################################################################### +# Now that we have done group comparison tests with the specified contrast matrix, +# we can plot the z-score maps indicating consistency in activation regions among +# all four groups. + plot_stat_map( contrast_result.get_map("z_GLH_groups_0"), cut_coords=[0, 0, -8], diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index e4871891b..70ae8dd71 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -200,6 +200,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): prev_loss, ) + def test_StandardizeField(testdata_cbmr_simulated): """Unit test for StandardizeField.""" dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform( From d48cd4167d4d60eecf3a195cd9d06ae2810a98e7 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 19:03:44 +0100 Subject: [PATCH 162/177] fix linter error. --- nimare/tests/test_meta_cbmr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 70ae8dd71..98d79a823 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -212,4 +212,5 @@ def test_StandardizeField(testdata_cbmr_simulated): assert dset.annotations["standardized_sample_sizes"].mean() == pytest.approx(0.0, abs=1e-3) assert dset.annotations["standardized_sample_sizes"].std() == pytest.approx(1.0, abs=1e-3) assert dset.annotations["standardized_avg_age"].mean() == pytest.approx(0.0, abs=1e-3) - assert dset.annotations["standardized_avg_age"].std() == pytest.approx(1.0, abs=1e-3) \ No newline at end of file + assert dset.annotations["standardized_avg_age"].std() == pytest.approx(1.0, abs=1e-3) + \ No newline at end of file From 677e6a56f59d54772718a469f7c48abde2ca2973 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 19:07:21 +0100 Subject: [PATCH 163/177] fix a linter error. --- nimare/tests/test_meta_cbmr.py | 1 - 1 file changed, 1 deletion(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 98d79a823..cf9d5a273 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -213,4 +213,3 @@ def test_StandardizeField(testdata_cbmr_simulated): assert dset.annotations["standardized_sample_sizes"].std() == pytest.approx(1.0, abs=1e-3) assert dset.annotations["standardized_avg_age"].mean() == pytest.approx(0.0, abs=1e-3) assert dset.annotations["standardized_avg_age"].std() == pytest.approx(1.0, abs=1e-3) - \ No newline at end of file From d3487f5b3109961fabe0ce6f1013fd6e96449899 Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Sat, 29 Apr 2023 19:11:32 +0100 Subject: [PATCH 164/177] fix a linter error --- nimare/tests/test_meta_cbmr.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index cf9d5a273..3f9fef95d 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -203,9 +203,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): def test_StandardizeField(testdata_cbmr_simulated): """Unit test for StandardizeField.""" - dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform( - testdata_cbmr_simulated - ) + dset = StandardizeField(fields=["sample_sizes", "avg_age"]).transform(testdata_cbmr_simulated) assert isinstance(dset, nimare.dataset.Dataset) assert "standardized_sample_sizes" in dset.annotations assert "standardized_avg_age" in dset.annotations From 93cff60734db1522df228123af6bf0712e377dd1 Mon Sep 17 00:00:00 2001 From: James Kent Date: Wed, 3 May 2023 09:53:29 -0500 Subject: [PATCH 165/177] fix names of notebooks --- examples/02_meta-analyses/10_plot_cbmr.ipynb | 945 ------------------ .../{10_plot_cbmr.py => 11_plot_cbmr.py} | 0 2 files changed, 945 deletions(-) delete mode 100644 examples/02_meta-analyses/10_plot_cbmr.ipynb rename examples/02_meta-analyses/{10_plot_cbmr.py => 11_plot_cbmr.py} (100%) diff --git a/examples/02_meta-analyses/10_plot_cbmr.ipynb b/examples/02_meta-analyses/10_plot_cbmr.ipynb deleted file mode 100644 index 81f378fb4..000000000 --- a/examples/02_meta-analyses/10_plot_cbmr.ipynb +++ /dev/null @@ -1,945 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Coordinate-based meta-regression algorithms\n", - "\n", - "A tour of Coordinate-based meta-regression (CBMR) algorithms in NiMARE\n", - "\n", - "CBMR is a generative framework to approximate smooth activation intensity function\n", - "and investigate the effect of study-level moderators (e.g., year of pubilication,\n", - "sample size, subtype of stimuli). CBMR considers three stochastic models (Poisson,\n", - "Negative Binomial (NB) and Clustered NB) for modeling the random variation in foci,\n", - "and allows flexible statistical inference for either spatial homogeneity tests or\n", - "group comparison tests. It is a computationally efficient approach with\n", - "good statistical interpretability to model the locations of activation foci.\n", - "\n", - "This tutorial is intended to provide a brief description and example of the CBMR\n", - "algorithm implemented in NiMARE.\n", - "\n", - "For a more detailed introduction to the elements of a coordinate-based meta-regression,\n", - "see the [online course](https://www.coursera.org/lecture/functional-mri-2/module-3-meta-analysis-Vd4zz)\n", - "or a [brief overview](https://libguides.princeton.edu/neuroimaging_meta).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy\n", - "from nilearn.plotting import plot_stat_map\n", - "\n", - "from nimare.generate import create_coordinate_dataset\n", - "from nimare.meta import models\n", - "from nimare.transforms import StandardizeField" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Dataset\n", - "Here, we're going to simulate a dataset \n", - "(using [nimare.generate.create_coordinate_dataset](https://nimare.readthedocs.io/en/latest/generated/nimare.generate.create_coordinate_dataset.html))\n", - "that includes 100 studies, each with 10 reported foci and sample size varying between\n", - "20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression)\n", - "and drug status (Yes or No). We also add two continuous study-level moderators (sample size and \n", - "average age) and a categorical study-level moderator (schizophrenia subtype).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# data simulation\n", - "ground_truth_foci, dset = create_coordinate_dataset(foci=10, sample_size=(20, 40), n_studies=1000)\n", - "# set up group columns: diagnosis & drug_status\n", - "n_rows = dset.annotations.shape[0]\n", - "dset.annotations[\"diagnosis\"] = [\n", - " \"schizophrenia\" if i % 2 == 0 else \"depression\" for i in range(n_rows)\n", - "]\n", - "dset.annotations[\"drug_status\"] = [\"Yes\" if i % 2 == 0 else \"No\" for i in range(n_rows)]\n", - "dset.annotations[\"drug_status\"] = (\n", - " dset.annotations[\"drug_status\"].sample(frac=1).reset_index(drop=True)\n", - ") # random shuffle drug_status column\n", - "# set up continuous moderators: sample sizes & avg_age\n", - "dset.annotations[\"sample_sizes\"] = [dset.metadata.sample_sizes[i][0] for i in range(n_rows)]\n", - "dset.annotations[\"avg_age\"] = np.arange(n_rows)\n", - "# set up categorical moderators: schizophrenia_subtype (as not enough data to be interpreted\n", - "# as groups)\n", - "dset.annotations[\"schizophrenia_subtype\"] = [\"type1\", \"type2\", \"type3\", \"type4\", \"type5\"] * int(\n", - " n_rows / 5\n", - ")\n", - "dset.annotations[\"schizophrenia_subtype\"] = (\n", - " dset.annotations[\"schizophrenia_subtype\"].sample(frac=1).reset_index(drop=True)\n", - ") # random shuffle drug_status column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estimation of group-specific spatial intensity functions\n", - "CBMR can generate estimation of group-specific spatial internsity\n", - "functions for multiple groups simultaneously, with different group-specific\n", - "spatial regression coefficients.\n", - "\n", - "CBMR can also consider the effects of study-level moderators\n", - "(e.g. sample size, year of publication) by estimating regression coefficients\n", - "of moderators (shared by all groups).\n", - "\n", - "Note that study-level moderators can only have global effects instead of localized\n", - "effects within CBMR framework. In the scenario that there're multiple subgroups\n", - "within a group (e.g., indexed as subgroup-1 to subgroup-n, but one or more of them\n", - "don't have enough number of studies to be inferred as a separate group). Using\n", - "categorical encoding, CBMR can interpret the subgroups as categorical moderators\n", - "for each study (either 0 or 1), and estimate the global activation intensity \n", - "associated with each subgroup (comparing to the average).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.diagnostics:0/10000 coordinates fall outside of the mask. Removing them.\n" - ] - } - ], - "source": [ - "from nimare.meta.cbmr import CBMREstimator\n", - "\n", - "dset = StandardizeField(fields=[\"sample_sizes\", \"avg_age\"]).transform(dset)\n", - "\n", - "cbmr = CBMREstimator(\n", - " group_categories=[\"diagnosis\", \"drug_status\"],\n", - " moderators=[\n", - " \"standardized_sample_sizes\",\n", - " \"standardized_avg_age\",\n", - " \"schizophrenia_subtype:reference=type1\",\n", - " ],\n", - " spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed\n", - " model=models.PoissonEstimator,\n", - " penalty=False,\n", - " lr=1e-1,\n", - " tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed\n", - " device=\"cpu\", # \"cuda\" if you have GPU\n", - ")\n", - "results = cbmr.fit(dataset=dset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fitted the model, we can plot the spatial intensity maps.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/well/nichols/users/pra123/anaconda3/envs/torch/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", - " anat_img = load_mni152_template()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-SchizophreniaYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia with drug treatment\",\n", - " threshold=1e-4,\n", - " vmax=1e-3,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia without drug treatment\",\n", - " threshold=1e-4,\n", - " vmax=1e-3,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-DepressionYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression with drug treatment\",\n", - " threshold=1e-4,\n", - " vmax=1e-3,\n", - ")\n", - "plot_stat_map(\n", - " results.get_map(\"spatialIntensity_group-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression without drug treatment\",\n", - " threshold=1e-4,\n", - " vmax=1e-3,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures correspond to group-specific spatial intensity map of four groups\n", - "(\"schizophreniaYes\", \"schizophreniaNo\", \"depressionYes\", \"depressionNo\").\n", - "Areas with stronger spatial intensity are highlighted.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generalized Linear Hypothesis (GLH) testing for spatial homogeneity\n", - "In the most basic scenario of spatial homogeneity test, contrast matrix `t_con_groups`\n", - "can be generated by `create_contrast` function, with group names specified.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nimare.meta.cbmr:standardized_sample_sizes = index_0\n", - "INFO:nimare.meta.cbmr:standardized_avg_age = index_1\n", - "INFO:nimare.meta.cbmr:type2 = index_2\n", - "INFO:nimare.meta.cbmr:type3 = index_3\n", - "INFO:nimare.meta.cbmr:type4 = index_4\n", - "INFO:nimare.meta.cbmr:type5 = index_5\n" - ] - } - ], - "source": [ - "from nimare.meta.cbmr import CBMRInference\n", - "\n", - "inference = CBMRInference(device=\"cuda\")\n", - "inference.fit(result=results)\n", - "t_con_groups = inference.create_contrast(\n", - " [\"SchizophreniaYes\", \"SchizophreniaNo\", \"DepressionYes\", \"DepressionNo\"], source=\"groups\"\n", - ")\n", - "contrast_result = inference.transform(t_con_groups=t_con_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have done spatial homogeneity tests, we can plot the z-score maps.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate z-score maps for group-wise spatial homogeneity test\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaYes\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"SchizophreniaNo\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionYes\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"DepressionYes\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"DepressionNo\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures (displayed as z-statistics map) correspond to homogeneity test of\n", - "group-specific spatial intensity for four groups. The null hypothesis assumes\n", - "homogeneous spatial intensity over the whole brain,\n", - "$H_0: \\mu_j = \\mu_0 = sum(n_{\\text{foci}})/N$, $j=1, \\cdots, N$, where $N$ is\n", - "the number of voxels within brain mask, $j$ is the index of voxel. Areas with\n", - "significant p-values are highlighted (under significance level $0.05$).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform fasle discovery rate (FDR) correction on spatial homogeneity test\n", - "The default FDR correction method is \"indep\", using Benjamini-Hochberg(BH) procedure.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:nimare.results:Map 'p_group-SchizophreniaYes' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'z_group-SchizophreniaYes' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'p_group-SchizophreniaNo' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'z_group-SchizophreniaNo' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'p_group-DepressionYes' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'z_group-DepressionYes' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'p_group-DepressionNo' should be 1D, not 2D. Squeezing.\n", - "WARNING:nimare.results:Map 'z_group-DepressionNo' should be 1D, not 2D. Squeezing.\n" - ] - } - ], - "source": [ - "from nimare.correct import FDRCorrector\n", - "\n", - "corr = FDRCorrector(method=\"indep\", alpha=0.05)\n", - "cres = corr.transform(contrast_result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have applied the FDR correction methods,\n", - "we can plot the FDR corrected z-score maps.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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pWbOmO54virYqyL+jjz46cDthfzVr1jS7du3yPWx4XG677TazYsUKV9ECYE4//XRjjDHPPvusbz1lfXG3Pw74x4+adDs6Dhw40BhjzKxZsxKmZWRkuGpg2Iv78OHDA9dbmhf3K6+80hhjzIcffpgwfywWc4/L3nhx31N1IdlfVlaWyc/PN19//XWJjhX3o7Cw0Bx88MG+aZFIxG1peuCBBxKWffLJJ40x/tah0u4Xka1JAMy///3vhO2kcy2H/TVu3NgYY8zy5ctD5+G6wwirV6le3Pv27WuMMeb7779PuXzLli3NfffdZ4qKiszcuXN9rSmp/i644AJjTPxFPBqNppx/5cqVxhhj/vSnPyVM4z3mp59+8o1PVU/54v7tt98GTp88ebIxxpg///nPgdPZymh3Hv7uu++MMSahhTjsz5jwF/fSXKulvafxj/ehp59+Oq3y64u7Utlo0KCBeemll8zWrVtNjRo1XPHSmHi0BxBvOd5XqZAETLRGM058HwD07dsXAPDWW28FLvPpp58CQGCM3sKFC/HTTz8ljGdsaK9evRKmFRcX45133kkYz3K8/fbbvvLZ29q2bZuvHPPnzwcAPPzww/jzn/+c1AuZ8/7nP/9B7969S+xrn5GRgeOOOw4AMG7cuITpixcvxqJFi1C3bl0cddRRCdM/+OCDhHE8dgceeGBaZdi9eze++uortGnTxo1z79OnDwBg1qxZmDVrFrp16+bGudvT9iR7Yl94bUyYMCFhWmFhISZOnJh0+alTp6a1nXRgn48333wzYVpRUREmTZq0x7ZlsyfrAmnevDn+9re/4amnnsLLL7+MUaNGYcSIEcjPz8fBBx9cqnKuXLkSy5Yt840zxmDVqlUAgq+HX375BYD/eijLfuXn5wf2OSnpdZcKxoozFjsZEydOxOjRoxP+1q9fX6ptB92fbVauXOnGYq9evRr3338/pk+fjl69emHHjh1pb+ePf/wjAOCll15CcXFx0nlbtmyJ1q1b4/fff8eHH36YMH3atGnYsmULDj74YDRt2jRheqp6Om3atIRxkUgEp5xyCnbs2IH3338/cDn5XDrwwAPRuXNnbNmyBW+88UbSbaZDaa7Vst7TNm/eDABo3LhxqcutKPsiRUVFmDBhAnbs2IGePXti/vz5KCgocO9FAHDIIYegVatWmDt3bgWWNDl7xcc9FY0aNQLg3SAAuJ245syZk9ayNnxwS9g5r3nz5gnTfv/9d+Tn5yeMZzkefvhhPPzww6HlsDu8jR49Gn379sVFF12EadOmYdeuXZg3bx6mT5+OkSNHYsOGDe68jz/+OE444QScdNJJmDVrltvJ6N1338Xo0aORm5sbuk0AOOCAA1CzZk1s3LgRO3fuDN3vo446KrCT5m+//ZYwjr7LyT44JLNmzcKJJ56IPn36YMyYMejTpw9ycnIwf/58zJo1CwMHDsTxxx+PGTNmuC/us2fPTnv96RC0L9u2bQOQ/r7w2kh1DYWxevXqtLaTDnzpC3MR2pPbstmTdQEAbrnlloTOmnuCNWvWBI7n9Rs0PejaLu1+AXEnpKCXzJJed6lgJ16uNxn//Oc/Q6/f0hB0f7ahj3tmZiY6deqEY445BqeddhruuusuPPDAA2lvhx07ly9fnnLeVPWU0xo0aIAWLVr47rlA6roTNL1Ro0aoW7cuAKCgoCDp8jxm3Cd+MJaV0lyrZb2n8Rm0//77p19QRdmHWbx4MXr27Im8vDzst99+mDx5Mjp37oyFCxciMzMz4Vpv2rRpiYWPvLy8wOdoGJmZmaHGCamokBf3rl27AgC+//57dxwdAd58882kqs2PP/64R8qQl5cXOJ7l+PTTT9N6oABxxfLiiy/GI488grPOOgsnn3wyevTogRNPPBF33HEH+vfv7369bdu2DSeffDL+8Ic/4IwzzkCfPn1w8skno2/fvrjzzjvRq1cv/Pzzz2XatzCljGXdE8yaNQv33nsv+vTpg1deeQW9evXCZ599huLiYldZ79OnD+bNm4ejjjoK33//PTZu3LhHtk321L6UhbDrKBVBDhgVxZ6sCz169MCwYcOwdetWXH311Zg1axbWr1/v3tDWrFkT+CGdDqnOd7rXQ2n2q6TbKCs5OTkA4L44lidB92cb+aFw4YUX4rXXXsO9996L6dOn46uvviqXckqS3fdS1dOg6bxOtm3blrK1i646e5qyXKulhR+NW7duLZftKcreplOnTli4cCFycnIwceJEDBgwYI8KiXl5eTig1n7YiaK0l2nWrBlWrFhRqpf3cn9xr1evnuv1azc5//bbbzjkkEPwyCOPYMGCBSVap7QllOPXrl2b9rqo4r799tuuN3G6LFy4EAsXLsTgwYNRt25d3H///bj11lvx9NNPo0ePHr55P//8c3z++ecA4k2STz/9NC655BI89NBDuOiii0K3sWnTJuzevRuNGzdG7dq1A1V3qjRhCuWeYO7cudi9ezf69OmDI488Eg0bNnRf2FetWoWVK1eiT58++PLLLxGLxfZ4mMyeYt26dQBSX0MlhS+q++23X+D0IBs5liXMYi6Z9dzeoDR14ZxzzgEA/Otf/8Irr7zim5aVlZVg71cRlKWOlxe///47gLhVYnkSjUZx/vnnA0CoDa3kjTfewMknn4y//e1vGDp0KE455ZS0lmPLUvv27VPOy3t4svrIaXvqvpednY1du3ahuLg47Yy+3Kd27drtkTKU5lot6z2tQYMGALDHhRZFqSgyMzPRoUMHAMAxxxyDefPm4ZlnnsFFF12E/Px8bN261ae6b9iwoUTPqvz8fOxEES5FC2Sm4bKej2KMW78G+fn5pXpxL3fZ78knn8R+++2Hr776Cl988YU7nnGLfPCXhKOOOso9KTYXX3wxAOCzzz5Le11lKYfNtm3bcOedd6K4uBiHHXZY0nk3btyI+++/HwBSzltYWOgeN+6fTZcuXXDkkUdi27ZtezVpS15enhvnPnDgQAD+Bz3j3E877TT3d7rwpbek8f+lgTGqF154YcK0WCzm850vCXx4Sg9/IP5gPProoxPG80MuaJvRaNT1f04XHseMjNJ9n5emLvChHxTGdMEFFwS2NJS1nCVlT9XxdCjttbxx40asW7cOLVu2TMiJsDe566670Lp1a/z2228l6lNx//33Y9euXTj55JMDc24E8dFHHwEArrzySjeuPoxff/0Vq1atQpMmTXDyyScnTD/ttNPQsGFDLFu2LCFMprQUFRVh1qxZqF+/ftofI+vWrcP333+PBg0apO2Dn5+fH3rtl+ZaLes9jTk99nbSL0WpKIqLi7F7924cc8wxqFGjBmbMmOFOW7p0KVavXp32fcymFqKoFUnjr4yv3uX24t62bVtMmDABV111FbZv344rr7zSN/2///0vNmzYgNtvvx1XX311wo08Fouhb9++6NKlS8K6Y7EYhg8f7nvAHX300bjhhhtQXFyMESNGpF3Or776Ch988AFOOOEEPPvss4FN1UcccYQvQ+Bll10WWK5TTz0V0WjUF7P8t7/9zVXEbfiCm06W1OHDhwOIPyzbtm3rjt9vv/3w7LPPIhqN4r///S92796dcl1lgS/j11xzDbZu3YpvvvnGN61mzZruS31JmqWornXq1GmPlTWMN998E9nZ2TjppJMSkpkMHjy41Ir7ypUrsWrVKhxxxBE488wz3fG1a9fGCy+8kJCIiGXZtGmT21/C5u677y6xipednY38/Hy0b9++VKE5pakL7KR55ZVX+l5GDj30UDz66KOB2ynP8w2Ubr9KS1n27dNPP0VGRoYburI3adq0Kf79739jyJAhKCwsxKBBg1LGddusX78ezz//PIB4a0s6vPXWW1i6dCkOP/xwPPbYYwkvr507d/bd33jfGzZsmK+vU9OmTfH4448DAJ555pm0y5wODz30EIqKijBq1KjAZGl16tTBoEGDfKrZI4884pbz8MMP981fs2ZNX0c4IH6NNG3aNPCeUJprtaz3NHZ03dN9khSlIrjzzjvxySefYOXKlVi8eDHuvPNOzJo1C5deeinq16+PK6+8ErfeeitmzpyJ+fPnY9CgQejZs6drArJPkq79TEnsIJlEYsyYMWby5Mnmu+++M0VFRcaYeHIc22vZ/uvRo4dr7cakMGPHjjUfffSR65Nse5DTRm7q1Klm1apVZu3atWbChAnmvffeM7t37zbGBFvEGZPcN7dx48auxdXmzZvNxx9/bMaOHWveeecdN8nPU089lWAZtmzZMvPWW2+ZcePGmTlz5piioiJTWFhozj//fHfeb775xhhjzJIlS8ybb75pXnvtNXfczp07zfHHH+/Om04Cph07dph33nnHvP76665375w5c0ITMEmLuHSPSdDfKaec4p7zd955xzfN9t6WtnKpynTLLbcYY+KJg8aPH29efPFFM3ToUHc6Ld6CbO3SsUCUf2eeeaab9Gju3Llm3LhxZsmSJWb37t3mv//9rzEm3A4ymbXeoEGDjDHGFBQUmBkzZpgpU6aYdevWmaVLl7rXTLIETJ9//rkZN26cWbRokS9xyp133pn2vk2ZMsUYE7fcGzNmjHnxxRd9/sx7ui40bNjQrF271hgTtzKcMGGC+eCDD8zu3bvN66+/HmqPyAQzX375pRk5cqR58cUXzRlnnJHWOU12LujzLs9fSfcr1bEK206qaznZ3+WXX26MCfdGL62P+5tvvmlGjRplRo8ebSZNmmQWLFjgXnNr1641ffv2LdX2mjZtanbs2GGMMQn+5WF/Xbp0ca+XNWvWmIkTJ5pJkyaZRYsWGWMSEzDRTnfLli1m0qRJ5q233nITML311luhCZjCykw7SGnjaf/97W9/c4/PokWLzMSJE81rr71m5s6da3bt2mWMMaZ+/fq+ZZ555hm37n/yySdm3Lhx5uOPP/YlYJLzLl++3Lz66qvmxRdfNP/85z/LdK2W5p4GwNSpU8fs3Lkz9J4d9Kd2kMq+zBVXXGFat25tMjMzTePGjc0pp5xiPvjgA3c6EzA1aNDA1K5d25xzzjlm3bp1JdpGTk5O/F4RaWVujLZJ+fe3SCsDwL13lZS98uJO8vPzTXZ2tlm0aJEZNWqUOfvss1P69TZt2tQ88sgjZvHixWb79u1m+/btZtmyZWby5Mnm8ssv9/kE2/7PBx54oHnllVfMhg0bzK5du8w333wTejM2JvVLas2aNc0NN9xgPvvsM7NlyxaTl5dnVq1aZWbOnGn+8Y9/mBYtWrjz9urVywwfPtwsWLDAbNy40ezcudP8/PPPZvz48QkfKaeffrp56aWXzOLFi83mzZvN9u3bzY8//mheeOGFBC/6VC8sl112mfnss89Mbm6u2blzp1m8eLG58847AxO27I0X91q1apm8vDxjjDH/+Mc/Qh/0zMKZbplisZh54IEHzLJly9wPMLtse/rFHYA54YQTzIwZM8y2bdvM1q1bzYcffmiOO+640BeydF7cgfgLHV+8161bZ1544QXTsGHDpOejd+/e5uOPP3bLMn36dNOtWzc3cdk111yT9n41btzYjBkzxqxdu9Z9kNt+6Xu6LgAwLVq0MGPHjjW//vqr2blzp/nuu+/M7bffbqLRaOiLe/v27c1bb71lNm7caAoLC33HfG+8uJdmv5Idq7DtpLqWk/1lZWWZLVu2mCVLlgROL+2LOyksLDSbN282P/zwgxk/frz561//6sv9UJrtMWPo66+/XqJr9LHHHjM//vij2blzp9myZYv59ttvzdChQxOyOcdiMXPjjTea+fPnu8+Hr776ylx33XWBz5Y98eIOxP3UR40aZVasWGHy8vLM5s2bzeLFi81LL71kTjvttMBlzjjjDPPee++Z7Oxsk5eXZ1avXm2mTp1qzj77bN98tWvXNv/+97/NqlWr3Gyn8lov6bUKlPyeBsSfKcYYc8stt6R9/vTFXanulPeLe8SYJF3xLRYsWIBjjjkmnVnLjd69e2PWrFkYPXp02p2HFKWy8t5776F///7o0aNHhTl3KOXLsGHDcMstt+CYY44pcad9RSkp06dPxwknnIBWrVqF2oFK5s+fH9hnR1GqC7m5uahfvz6ui7ZCzUjqsNTdphgjilcjJycH9erVK/H29h1POkVR0Lx5czf5DolEIrj55pvRv39/LF26VF/aqxFDhw51O7oryt6ka9eu6NevH5588sm0X9oVRSl/KsTHXVGUYHr16oWxY8fim2++wapVq1CzZk0cdthhaNu2LXbs2IGrrrqqoouolCMbN27E448/jvvvvx+HHXYYlixZUtFFUqoo9957LzZs2IDHHnusoouiKJWSWCSCWAqHLACIIfU8yVDFXVH2IebPn49XXnkF+++/P/r27Yt+/fohFovhlVdeQbdu3UpkbapUDYYMGYJYLKYv7cpe5ZxzzkGzZs2SJkBUKhejR49GJBJx/zIyMtCiRQsMHDhwr+Z5UfYulVpxnz17dkr/X0WpTPz8888JVqmKoiiKUloeeOABtG3bFnl5efjiiy8wevRofPbZZ1iyZEmpEgApwcQi8b+U85VxO5X6xV1RFEVRFEUJ59RTT8Wxxx4LALjqqqvQqFEjPProo5g6dWpgoi5l30ZDZRRFURRFUaoJvXr1AgAsX768gktStWCMezp/ZUEVd0VRFEVRlGrCypUrAQANGjSo2IJUMTRURlEURVEURSkTOTk5yM7ORl5eHr788ksMHjwYNWvWxOmnn17RRVNKgb64K4qiKIqiVFH++Mc/+n63adMGY8eOxUEHHVRBJaqalJcdZNov7o0aNUJWVhby8vLKtEFFURRFUSo/WVlZaNSoUUUXQ0nBf/7zH3Ts2BE5OTkYOXIkPvnkE9SsWbOii6WUkrRf3Fu1aoWlS5ciOzt7b5ZHURRFUao8U6dOxeDBg/Hqq6+ic+fOFV2cUtGoUSO0atWqoouhpKB79+6uq8zZZ5+NE044AZdccgmWLl2K/fbbr4JLV3WIID3Hl7KamJcoVKZVq1ZaSRVFURSljCxatAgAcMghh+Doo4+u4NIo1YVYLIahQ4fipJNOwrPPPos77rijoouklBC1g1QURVEURakm9OnTB927d8fTTz+t4c97ELWDVBRFUZQqzsiRIzF9+vSE8TfddBPq1q1bASVSqgO33XYbLrjgAowePRrXXnttRRdHKQH64q4oiqIoFcSIESMCxw8cOFBf3JW9xrnnnov27dvjiSeewNVXX41YrKzu4kp5+bhHjDGmjOtQFEVRFEVJizFjxgAADjjgAABArVq1fNP5WrJjxw4AwFlnnZX2uqdMmQIAqFOnDgAgIsISdu3aBQDYtGkTAGDAgAElKruiSHJzc1G/fn3cV6sdsiKpI9DzTDEG7/oFOTk5qFevXom3p4q7oiiKoiiKopSBuOKejo972VDFXVEURVGUPc7rr78OAGjWrBkAuN7h0WjUN6QqXlxc7FuevzlcuHAhAOC6665z52Go0VFHHRW4bsLffOWR6969ezcAYP369QCAiy66qET7qlRfqLg/VKcdsiKpX8vzTBH+taP0iru6yiiKoiiKoihKJUBDZRRFURRFKTPDhw8H4MWut23bFgCQmZnpm48dIRmHXqNGDQCeGk4Y456bmwsAaN26NQDg/vvvd+fp3r27b1muk0NCVb+goMC37qKiIl8ZmKtm/PjxALxY+BtvvDHpvitKulaPsTKmYFLFXVEURVEURVEqAaq4K4qiKIqSlEmTJgEAmjRpAsBTqO249AMPPNC3DFVuDqluc5nCwkIAwH777QcAyMiIv5IwKZCMgWeMPOe3x3EeLsN1ZWVl+bZFVxkq74StAFwPWwm4T3PmzHHn5Ta4jt9//x0AcN5550GpvkTTtIMsq2KuiruiKIqiKIqiVAIqXHEfPXo0Bg0ahHnz5uHYY4+t6OIoVQxeXyQWi6Fp06b405/+hIceeggtWrSowNIpiqLsm0ycOBEAUL9+fQBe7DfVZirUVNEBzz1m7dq1ADx1m8gYdqrgVLm5zp07dwJIVN6pgtve7BzHebiMjKNnOblNDgmns8xsFWjevDkAT9m31y3j4j/88EMAQE5ODgDg/PPPh1J9KK8Y9wp/cVeU8uCBBx5A27ZtkZeXhy+++AKjR4/GZ599hiVLlrhNqYqiKIqiKPsy+uKuVAtOPfVUt0XnqquuQqNGjfDoo49i6tSpuPDCCyu4dIqiKPsGs2fPBuCp51LtpsrMIdVxwIsr57xUrzkvp1PN5nxUs6mC01PdVvOBYL93mRmVy8h1cBvcJtV/7p+Mged8LDOHAFC7dm0AXow7h1T3mQmWx7J3795Qqj6xNGPcy5qASWPclWpJr169AADLly+v4JIoiqIoiqKkhyruSrVk5cqVAIAGDRpUbEEURVH2AeiawtBBqsZUk2VWUyrVdux3fn4+AC8unl7pRCryvP8yZpzx6dwm1XKpqsvfNlyG66CSznJym1TkWWbOx/3kPrBs9n7KrKxchvOwhYHqPY/t8ccfH1pupfJTXoq7vrgr1YKcnBxkZ2cjLy8PX375JQYPHoyaNWvi9NNPr+iiKYqiKIpSydHOqYqyB/njH//o+92mTRuMHTsWBx10UAWVSFEURVEUpWToi7tSLfjPf/6Djh07IicnByNHjsQnn3zia/pUFEWpjkyZMgUA0LRpUwBeB8u6desCALZt2wYgMZSEMCzEXpbzMqSEQ05v1KgRAC+0hOtk+Ao7jjIkhr8ZasPwFXtc2DJcJ0N/GArExErZ2dkAvJAZ7jfDeVhmez8Jyy0TRHEd3O/t27cD8I71WWedlbAupfITQ5qhMib1PMnQF3elWtC9e3fXVebss8/GCSecgEsuuQRLly71ZeFTFEVRFEXZV9EXd6XaEYvFMHToUJx00kl49tlncccdd1R0kRRFUSoEChfSFpGK9QEHHADAb/sIeAq03VGTyjNVcHY2pcrdpEkTAJ5iLlXxzZs3A/A6lsr1SoXbHsdy8DeHXCcV9zDlXXaQ5XTZodZet4Q2kdwf2fKgIlHVJppmjHs0jXmSLl+mpRWlktKnTx90794dTz/9tHujVhRFURRF2ZfZZxT3kSNHYvr06Qnjb7rpJjdeTFH2JLfddhsuuOACjB49Gtdee21FF0dRFKXcmDZtGgBPJaY6TBiXTYV6//33B5DcipEx3pyHSjNVa/6m0k7lesOGDb5tUnGnCs7lZQw84FkuyiRO0haS22jVqlXguplwSsbyc1t2XL2E83BZ7oe0muRx4bFXV7OqRdp2kGUT3PedF/cRI0YEjh84cKC+uCt7hXPPPRft27fHE088gauvvjrpjVlRFEVRFKWiiRj701VRFEVRlCrLZ599BsBTmqVCzdh1uqkwLp2/qRonU95TwdcOJmj6+eefAQC5ubkAPGWdYgqVesbZr1mzxl1XixYtAHgtB1TKuT9U4uvVqwcA6NChQ+D+lGU/5P78/vvvvt9hLQg89ieccEKpy6BUPLm5uahfvz7GNOqE2tHUAuDO4iIMyF6KnJwc97osCRrjriiKoiiKoiiVgH0mVEZRFEVRlL0D+5AxVp0KNeOwOaS6TaWabiphSrvtKkPkPFS/ZQM/PeK5barlVMNl+KKMmQc8pxaZl4PblPvHbXIb0v9dbjMoKCHI3QbwjhXLwvh7tmJwOodsQeC56d+/f8K2lMpDtYtxVxRFURRFUZTKSCxNO8h05kmGvrgriqIoShWHyjTVX7rF1K9fH0Ci8wlNIahuh8WC257m6ajV9nip4rOMYao+y277octlWB7pvx6WWVVuK6xsVPCDkP719L6X2+Z0qv+MfVd/d6Uk6Iu7oiiKoiiKopSBaCSSVnKlsiZg0hd3RVEURamiPPvsswCAzp07A/DirxnrzVh3qr5U4qlul8V1RXqhS7WbZeE2qfqHqeV0aeH8NtwPbkN6qHOdMhZelollLo09sOwfwN+Mdae/O2PbuS2WlefqhhtuKPG2leqDvrgriqIoiqIoShmIxCKIRFN/6JblYxjQF3dFURRFqbLQh51qdZiaTZWYbitEKtHJXGXC4sDDXlQ4nnH2clscUqEO2iZhvDiVd+4f503lPx/mhBOEHddvlzvs2LBs0tedSjvH81wpSjL0xV1RFEVRFEVRykA0FkE0DcVdY9wVRVEURfHxxhtvAACaN28OwFPamZWUcddUhRnTLWO+qQ5L1Ztx5lS27XWkC+enur1161YAiXHpJC8vz7cP9jjuB7OvynXQv740set2GQFPKecxJFT7Zf8AuZ/y2Ddu3NhXZp67Cy+8sFRlVao2mjlVURRFURRFqXIMHToU3bp1Q926ddGkSROcffbZWLp0qTt98+bNuPHGG9GpUyfUqlULrVq1wv/93/8hJyen5BuLRRFJ4w+xsr16q+KuKIqiKFWMevXqAUj0bZeuKhwvnVqoDlPB5osM47u5HnqW2+uQ6r2E41k22QoQFk/P+dgKYI+T+yXnLalbDlscpEoOAJs2bfJtg8o5FXOq+xzPbctzQni8uA3Op5Sd2bNn4/rrr0e3bt1QWFiIu+66C3379sX333+POnXqYO3atVi7di2eeOIJdO7cGatWrcK1116LtWvXYuLEiRVd/ED0xV1RFEVRFEWpckyfPt33e/To0WjSpAnmz5+PE088EYcddhgmTZrkTm/fvj0eeughXHbZZSgsLEwI10pGJBpBJJaGqww0xl1RFEVRFAuqvRzSLYbKNFVfOZ/0XiccTwWbv+2QArlOqWpLJZ3zMzacMe5UoKUyTSXa3maYik2lnPsh489lmaRTDZejim5vk8o4tyHXKd1xuG62TshjSeVeKvjKnofnsWHDhknnqVevXole2gGnc2oaL+7RMr6469WhKIqiKIqiVGmKi4tx88034w9/+AMOO+ywwHmys7MxZMgQXHPNNeVcuvRRxb0CmDx5MgCgbt26AIAT8CsAwOyO9443+fHhnLqHA4h3ngBK1sOcvdL5VSnVFNnLnVn0zjnnnBLvj6JUJiZMmAAgMYZV+jazrpyRuyQ+P72lixxF0hk2+3/D93KJFSU9hg/3rsX27dsD8FRdqtn8zWcCM6ZSDZaqOeOz6aTCIbFVyTCVXk6XSjyfUyxjmJLNbdte81xnmJLOZ12YwirV8bDp9n7KeHo66/BY8dhJ1Z6x8cygym2y7Dw3nN8+nzfeeGNg+ZT0uf7667FkyRJ89tlngdNzc3Px5z//GZ07d8b9999f4vVHolFE0mgtiYh6UlL0xV1RFEVRFEWpstxwww2YNm0aPvnkExx00EEJ07dt24b+/fujbt26mDx5csKH4L6EvriXA4XffgDAU9L/3LjA+b3WGTq/C/N9w54bvwAAFDvTc0fdAwCoN2hI6La2vvQvAEB/2g3FE7IlfAVGnOmRGnGlIJIRv0h3TnkGABCtGVcOIplZAICaJ1+e1r4qyr7E7lljAQAmL+5BXbx7F86s5fyOAMWOBzQAFObF611xQaFvWJgfH1Jhp/JuHFVwzeB4kyqV+Kiw+orSxcIZrwq9srewlWzZysq4bMZRSwWd8zF7JxVmqsv0GpfKtL1NGRMss5XK+HkZ696iRQsAnpMNx0u3GTsGXKrWVL2pXssYeOlTz99SJZdKPp1iAC/TK5Ex/VJp37hxIwCvRYEt3FTqpYIf1kdAKTnGGNx4442YPHkyZs2ahbZt2ybMk5ubi379+qFmzZqYOnUqsrKySrWt8opx1xf3vQjDVc7ttH/FFkRRqiGvvfYazj2wokuhKIqiVBTXX389xo8fjylTpqBu3bpYv349gPjHV61atZCbm4u+ffti586dGDt2LHJzc90Px8aNG5c6YdfeRF/c9zDFP3/h/n9ul7hCQaWdCp1U1k2BEzNYWOAbUmkvKvArfptH/L+U5TBFwQq7VAMBf7xiJBrzlZXfhflzPD/TaJ24gpFxZN+U5VCUvUnR0s8BWHUsL97EZHbn4byWMVdRZx1z53N+U2UHgCLnf9a34nwq7/56SFgfJcVONWadk0r8uoevd+fNqBVX3GJZ8WGGM4w5DhiRrPgwWise05vV96rgA6EoiqIkMGLECABAnz59fONHjRqFgQMHYsGCBfjyyy8BAB06dPDNs2LFCrRp0ybtbUViagepKIqiKEopYIgHFUOGbzCEhOEnDPuQITRhSqNcnx3OITunyt/sdCrDU/ibIQoyzCfPCmkLg+tgqAw7sDIcJcyaUu5H2D7Y4Tlhy8hleSylzSOPvSyzDB1Syo48J5I+ffqknGdfQ1/cy0jhqm8BAJEiR7mznSkYC+gOiwLHm8IC31Aq7cVCcSf274hQ0jlNjucSHCunhxKNJfzP1oVoh+PSW4eilJLC336I/+PUM7e+EaduyVYtiLrF3zKeHfDqm6u8O/VQ1r+Eekhv6rB+JG4dy3B+e8sXOao+ly2uEZ8nJu8VDrs/eQ2Ap8BHsuLDjMNPgaIoilJxxBX3NFxlENxamy764q4oiqIoVQBp1Qh4Ki7Vbqq/tCOmgi47lrJjpVyO87NDZTI7SM4r1W2uU26TijQVZyrtbCWQy9vj5DzS1pKwLNw/qe7L4xVkE8lleUw4L4+JbK3gfnI5HvudO3f6tiGPR9D5VBR9cVcURVEURVGUMqCuMvs4hb9+V/KFZDIKkfBFEtb5LWi8DI0Ja65hBzk2zUeoDFCV4HjG2Dk2kbSNtOcx0X2vt7VS+cnfuNr9nyExCbc54w8/M25oiT/ExO2U6oTOFIvwl8DOqQxVc35LG8jikHqJEDtIt8gxv40kAKBGyW7B7DzOcDXauBb9Ms/ZhtPJtfWRJVqvUjW49dZb3f//97//AfBUYCrShDHgUqGmekxnDaaI53gq1Fxv06ZN3XWG2RoSqtlhcfQyDp1l5vzJFHfOw2UYLy/XKednfLmcThWcQ6rrALBhwwbfOJnsif0GeIylrSXHU3GX54brtc+nsu8TiUQQiabRObW4bC/uaQY4K4qiKIqiKIpSkajiniYFG36J/1PkdCYrzUoSOq85vfNDZqdqbmTCiwBFT47zFPX4MFYjw/dbJl6isu4mXspwpjsJmNz5rHmKHXWvYM2PAIA3Zy8A4KkFgwYNCtkzRUlk95a4ihWJWNdyJLm2YEI7fOf7pxeKDt/5/iEQ3hk1lQ1kqs5IkbBWLnuatGsVijrro9sCxnrp1GMTdW7lrJPO/YoKfGajxEyBStWGirlU3KkKS0cX3rd37Njh+01lmuNrO1alVJk3bdrkbpPJm7iNVM403AadXyRS/ZZltcfJOPqwdYWp/WEOOBza+ymTWVE5p5LOZXjMGLsu3XTkceA+8NwplYtoLBra4uqbz5RNM1fFXVEURVEURVEqAaq4hzBy5EgAwGVnOkmGIn7VDMVR33h3uvUVb0TcuGupKOPJqWY76mA00x/7HhWJXIJIjF0X5aFSFxW/qazXdBS8TP9Q2s4BgMmo6R/WiM97fv+TAQDfr45nJpsxYwYAYNWqVQCAK664IrT8SvVmzJgxAOJK1hXnn17yFSTEugsFnjaRIl7dbs1yxxUlTrPHE6m0sw5GResWf8cyHV/tLE9ddBMuucN4nXLroayX4rdXF53fNfy/R4x901X1brjhBijVh+3btwPw1F6pMNPhhNOpIlP1zc7OBgBs3boVQGLMOJej2gx4ajYVdOnIwmWp/nM61y193qUrDdm8ebP7/4EHHuibh8vI2Haq3Cwjf4eVlWXh/PZ+chqPGZV1qvL7778/AKBRo0a+/eU2eew5nkOeMw6VykXaCZiMxrgriqIoiqIoSpVHFXcBlb9Lz/lzfAQVPKGsG+drPhIyHrDiUsMSMYXhqOLRjHzfcq5ewuV9SZGouAe7Tri/a6SImZUKn5NynQpe/H+/0g5n2k9rs52ixMtywAEHxCc7ygyP7YABA5Lvv1JtePnllwF4MZ6FhYWBce1ufXN+R5ipUHoshyRiolrO+HUmPrIdXsISLJEwhZ2tXNHM+HUuFXbvtzMMUtyduGHWN7Z0RWvXi4+vU9f3GzWdOOMa8f4msi6OePV1RKNRRKNRN3b32Wefjc/i1Mdrr702cD+VqsFVV10FAHjhhRcAJGYQpWosM6du2bIFgBevTdcYGesepGwXCac0mXWUvux0ZeF0bpvPDI6XWVq5fltxl57wYZldN27cCMBzyeH45s2bA/BU/zDl3XZ+ofrOY0FnGh5LKvErVqwAADRo0ACA19+AZeDyMv7+mmuugVL5UMVdURRFURRFURQXVdwdJk2aBAA46KC4+8K3y+Jx2Uce3BoAEHF8o40byx4/dG4/dOPEtQasO4KsgLGwYt6pjjsOEYV+pd1VBaVSH7XdKfzx866iLn8LX/aIUNyR6fjwOm4UdKVg7Gx8V+PzfvdrXMGgRy3dBmSmPaoRPLY81ueddx6U6sUrr7wCwFPeGOvpc42wFHe3vjm/WQ8lXh0JbtVKUOZLgHQJkAq7dG6KOvUho1am89uvtNeo4/lGey1bjsLu9ilxlPc69XxDT2F3hpnx+V6Y8LZ7DKPRKGKxmKtAytheHvPnnnvOt19///vf0zgaSmWD513GdlM1XrNmDQDPEaZVq1a++XhdUYGXarmNdKyh8sw4eT4LuCyvRa6TqrZU3qUKzrLahLnKrF8f73NFlZ71Qsboy/h0PteCnHFkSwIVdY7fb7/9fPuxdu1aAMDy5csBeK0fYfunVE7UVUZRFEVRFEVRFJdqr7hPnz4dANCiRQvfeH5lr9oYj0Vr06iebzq/6SPit60WRsS4KGPvHJXb5MdVR0MvZifLY0Rmf0wVE48AtxjpJpMhFHbX/9kpk6Osu0o7y+j+9hT3r5b8BACoWzced0slhgoGoapA5Z3zcchj379//5T7p1ReRo8e7f5PBY7qH+NMZdbGtJEZUxOmJ3eGsZGe6gm5EcR4qbBTgZdOMfzN/iNRy6HJ7VNCxd1R1qm4R2rFlbviGk5fk0xHsXRavV547S1vXcKJQ9ZHjucxliofFXg7Zvm6666DUjkZMWKE77dscaHaS+eTli1bAki8PqSCLRXphg0butOkC8xvv/0GIDGDKltn6Z7C5ehkI1VxuX7bx10q4tw248i5TpaXZWEZeE+i8s4ytW7d2rd+ez+5Da6TxyhMQeex5TZYJunQw2cmz53Wv0pGmjHuKGOMe7V/cVcURVEURVGUshCNRBCNpn4pj0b0xb1EvPnmmwC8r+dmzZoBSMxoJnukr82Nq+MH1ncyi7ouMlH/7yIryyLV9+JC/5CuMVS9hQOGjNMNU9wjPleZYBcZULFwMiuahCHn88e0c/7/ffIFAH+veKoMVAnYU96OsQUS1VTpl8te/PR7ZwziBRdcELi/SuWCSrvtSSyVKWIr7T5nphSZUyUmpM4wB0Kiim71E3HmoXJuxDKMbZduMVTUuVyN2lTWw3Ii1Pa2KWLbqbizhatYxrK/PjVBOXfXJe5hMqMkkbHL0h3EPjeq/FVe+GwjjCNnVk5eB2xtlh7s0nOc1xunM36b8dyAd6+n0i4VeCrO9erFr3M+Q+Szg3HpdHzhdM5PBdseJ/vLcB2yPsi+H7w/SScbxqWzb5a9n4Rx8bIuyf3iseWx5rOO26T6TwcfRUlGtXtxVxRFURRFUZQ9SSQWTRCHAucrLlv30mrz4s54an7RMj5bZk8Ly9TGL+Pft8e/nN34PvYOpzIYtQ4pFfYi+so68eWuKhifHslwlD06u9A5wxlGhTrpKpK2Gin95FkO97ejLMb8irsb0+5MX7z8VwCeIkAlIChulg4BPIZUZKSaSkVezkcVgq0fPKYa+165oTc71TVbGQ5zT7DV4hfHvemqZ3+/9NzA+U0JY+HZ059LuTfXgsR5WELGsLvTSxnLHqGaLn4DAbHszIDKWHYntt2OZQ/rByCVRSLro5yeTj+D559/3rcN9Znet2BLMrOIAl7sOs8v79c//PADAO9chg35TJT372TPBLrHpOqrQlcpPocZ8y3Ztm2bb1tcjmq6vQ6Wk8tIWA84Pz3Uw+bjPnCf2DcL8FqL2arBe53sFyCdfMKytbZp0waAp+pz+c8++8zd5rp16wBoi7RSjV7cFUVRFEVRFGVvEI1FEE2jc2q0WGPckzJz5kwAnhIh/Vc5lJ6t/BKWsXeEKsam3fHpGzbEFYNDW3uKB4oZX+4o70JJRxF79zuKO5UxI9V+oVIKdR0IUOGprMuWAGeY47QCuPKiM+T+U72g8hnUy5/Ki1TwwmLc5THnugnjHpl9jufupJNOgrLv89JLLwHwVDEZUwoknnPCekaVK113GSMcmNKF6rqxVHVXaWfMN69jusiIzKee4h6vK6k82SMiGyoAwFmGyjrzJfD3qDen+MoEhB8bmRFTtm7IGHbpuS1j5O11SBWS2ThVea9YRo4cCQDo2LFj6Dw8Z7xfU3mn6iszqtJLnb+pLsvlGBvO6YCnTof1w5Ax37znh7UC0RmG2+ByfKYElZPLSLcZ6brEdflySCCxfgQp7nSikQo5x/MeKI8ljx1Vf5ZB5kCR7xuA9w7Dc37FFVckzKNUD6r8i7uiKIqiKIqi7E0iadpBRlRxT+Ttt992/2fsGL94+YXML9swVVgq7kQqA/wq5xfzqo05bny47KV/dMd4WVwFPeZX4D3lPbl6aIKcNqSy7vxev51+2XTJiSueNWsGqxFUu+mTK/eX+wl4+y4deIjsvS+HUs3j+hh7SCcb+3yeffbZifuuVChjxowB4KlMhOfZVr7kuacCL/s/7C1kbLvdkSgqbocJfu0Jse2O0h6msKdwjAHsTKhU3uO/x01+N/47oG7JYyidsFK1Ekp3kDAfbPt/WQ6u47///S8A7/6pKmD5QncVGb8NePdwDjkPlWl5TqXjE9VjXh9ct2xRs2PF5bUor0F5PdmOU0Hz8TqT1yjVfxup8kuFnUgXmaCWpqB9sPeTy/BY8Pqnws5jx/nChmHnQvYvALzYfdtRR6meVMkXd0VRFEVRFEUpL9RVpgry21a/h610e6hXgwq8f7yE8exvTPvAHde+fXsAnhLBbdixgIpS5UmRZdiNW2frjz1ReryzxYAKuxvjXjalnSo74Pmzvz59dvx3irqvKIDX0nHooYcC8Fo3bcWd46gCU4lmrPavv8YdxKgOy1Zn2RrNIR1UqAZzeXvZsH5MUt1nq7T0PZetRiy79Gy3x8n4cKmkcz5uU5ZJIstk7yefs2zNYEs0j5FcJ8vGc7FlyxYAieo5y8pzZLcscPs87rwG/va3vwWWX6m6VKkX9xdffBEAcOyxxyZMY0VgxZIWV7Ky88Yik09IuJx9w+SNTd5MOZQP5zCLvFTwJmCvgzcSNuux4nN/Zedb2bTJMnLdbJ4LejCkCm+QHVrlsQ27WfNccdtMPQ145/jqq68O3KZS/vB6lwSFm6WyRQtLGlTVYX0L6zBqw2MjQ11Yr2TIUpjFrWy2DwsPtOcJC6/gfXLUqFEAgEGDBiXZW0VRlKpHNIY0XWXKtp0q9eJe2dm0K/4CzIcgX8L5pc0XpDBnDkVRgonIeO8kzZmcxsyoCUp7ZlbwsIRZUAFg1FvvAdCWMUVRlMpOJBpBJJpG59Q05klGlXpx79ChAwB/JxMqznanShupOhHZvCaRKY6pfgFe4gsiO6CEwRdypqSWL+rcJtMs24o7xzENNTvgUH3j/jOERnbYkXA9tgUW4N9P2RmOyMQcUtUPS83O5WQiGLuJkudYqXiYaInXp6xD9vVJwlq4ZMcw+zqryvCa57GSqndQC4Ssu9LOj0N++MsWMRnGIK0egwgLJ5Dnk/uhyvveRdoby3st4Bkx8BnA54m0YGRrrHw+8T4sO4LKsBU79CTseSmvY17DfDZyW7xmZQdSDmlY8M0337jr7tq1q28/5bObx4H7yWuU88sQm7CEZfZ+suVZtjbyWLHFW9pBsgz8Lc8Fj4e0mbT3h+Wwk20p1Ysq9eKuKEoVh45Lwr/dpIhtJ1TT2TkoSHl3Y9tTKO0Jse01/cp7mNI+9p2Pql0okKIoSlUnGo26OUKSzleknVNd5e/www8HEGydJtU/qTbJ+WVCJg7lckEqOtVtqTDLh7VUrKksS7VcJnPgfLa6wnHs9MLy8wue25AdjcJiaTmeCkLQPshjINUf2QFJqookKFFPWNnYAsBzfuWVV0KpGHjNSQVOnv+ga4bXglTH5HVZFcnMzHTvI9IOk4Sp4jayw5us2zKZVVhyl7AENEBqiz15X9CY9/KhYcOGABLrj33ueB3wecT6Kuspz53szCrXI217pXoOhCdSIo0bNwbg3cf5bOAzjmXgc0d29uR1aLe8chznlfsnWyNoecyyUB2njXPYPtj7Kfedx0baQsqyhSU05DZ4rpK1ZnBdvAaU6keVeHFXFEVRFEVRlIoi7QRMacyTjCrx4s547KB06fySp9og1eFUsZv8uqVCIONI01EGw5JRSBWLX9f8KudvftVLFcKO/d5///1983BZabfF32EKuyyzxF4uLKkE90vG+YWFB8hzEbY++3+ec6X8Ybp7EqYWM54z6PxxKBX4sHoZ2cM2ibKzqg1DZNg5FRk1fEM3JIahMzXjdS7qhMjAGW8ymFSJv+PHo27dum4dZx2WLRAy8UwyxZ1xwVLNky1ZsjWDy4f1UbDnIWFqrZxfJthS9gxMdkb7X55TxkTbrZayz5C8n3K4aNEiAJ6C27RpU9/ysn5zfexXZV8DLAfPO2PBqW4TOobxGSGvG8L9sZ91APD111+7/8t1y5h8qX7zN5/pfHZyuHHjRl/ZgsrAfad6T+Sx4nFYs2YNgERVPywRpOwnBiQeW9Z7XhMDBgyAUj2oEi/uiqIoiqIoilJRpJ2AKY15klGpX9xHjhwJwIttl6nAAe8rWX7JB80LJMazSSUsHVcWGdsr1ynHB6WGBxJ9mhmvHpQGmvMyRi7MYz2VT3RYbG2ylgWp5ElXHJm+OqxfQdg5srfN/WzRogUA7xrQVOt7n9GjRwNITGAirw2ZttueLluTZP2UcbgJGNEZNWFY7B8K2HEoaCqnuUmaajhuLRnOfjjKuqe0+5V3ZDruHLH4/MZV6uPT33z3Q1flk7G88p5gpzoH/N7sMi5expVL5V3GvstYZumuEUSqlsUwD3j+1mQxewaqwvL+nezcyeeOrHN8rjBfRqq4bHm92dcqrymqw1TDWd/5bJAx4tIOVVogh+U5sNcl+3DwWSgVeHkcqFzz2S4VfPY5s8vIZWQLPo8J5+W2eGyp4stIAJ6DZO8VUp3nfvKaUKoPlfrFXVEURVEURVEqmkg0mjQM056vLFTqF/d27doBSPRSt1UfGTsr4/s4XcZhc12M0Uvl624r11KlDoujl8vyy1mqVvwa//333wPXb4/jftDjVWZR5DZSlSmVp609TcbSSgWd8YxUXWT/ARmDKVUVW+ngOK6L14Cy9xg7diwAT3kKI0x1spHnlNcIr1Opnrn2jyZYQU8XNk0a2apjNVnyZhrNdK7HDP/QjXEPsYUszvDHsjPGfcqMT5GTk+PbP+nnzONCBY7HMCgPBY+VzOosHS1YR6QrSFhLYJCfe1iG1DBlPSy3A9epynvZkI4vvBakOwvg5RORLV8yfpqx7fLalNcN1WLOF5Qxmao1h9nZ2b5yscUp7DqR/WMIy8gY8SB/8yZNmvi2JdchW4Xk8eDzlc9b7gPVdbYW2PvOeXhseKzlvY/nh/vBbclnHZdnfeH+2tuU5Q/Kl6FUbSr1i7uiKIqiKIqiVDTRWJo+7tU5xp1qOL+4qSbbihG/UqXzQph/shwvv25JmH+xPU2q2vKLX6oN/Epv1qyZbz+kokZFwc4uKXulU6HjMZKqWjIf+qD9DFNIgER1Xh47ecylAiRbMzikYmKrjdwPKhHcP2XvQaUplROTjLcNqmNUh+S1wGWT+ZbHJ/hj2E1RUcjv5AmZZDw7YHUYkgo7Y92ptDu/Od3EnPnd2Pb48N1P58Vny8hwr9Ow/gE8DpxOBY9QBQQSj7/0bWf9kf10ZH+dsL4lMiYYSKzDMqY67J4n4bboTHTNNdcknV/xw7rIe6N0OwtSX/k8Ydw5W3X4m8gWl7B8HLKVyG6F5v/fffcdAM91hcp0mOod5ijGbTM/CeuF7VbEcTL7aNg65XUvWxrYOrZ69WoAQPPmzRP2M8yZSbZShPXrktlcpSvQ+vXrfWWxyylbQOyWAKWCSbNzKsr44l51s5woiqIoiqIoShWiUiruzz//PACgR48eABJVHlsx4tc3VWrGW1OBJ9IJQ36dh305BynRMqugVLfll75UquWQ62Fvd35h23F0XAfnkV7OYdtOpZ7K5W2lTSrtch4ZryiVdul6wfmoTkrlBAhXfXhNXHvttYH7o5QcOvZQxeP5kOddqsgkyOkizFPazuw78MKz3fkj+Y7y7Cjt9HOXrjIJLjOE04tSx8hHWG+j/iFErLsb2+64zBRHHWUyw6+8R6PRhDoss0/KIRVK2QfAPsayJU7WK9mqIZ0qpCrLMnE9trov+5SwBVOe21RqbbL7iJKaESNGAPBaH3ke+FyT/aQA71nH+ylzX/D5cdBBBwHwlGX2i5LXjbzeZEuofX1xm7yGpM+5bGmTrUOE1yif08nypsg6FtaHikiVXOZLYZm5be6TXUa575xXrls68rCfUKtWrQB4x5Lnhio6t2nX1a1btwJIfJazDLxGrrvuuoRjpJQPkWiadpBl7JyqiruiKIqiKIqiVAIqpeIulQB+Ycu4UCBcHaBSIR0aiFSDg9Rfe9s2YT7l0odVqlD8upYKwdq1a31l53K2gwBVAqopjAlkfB6Rfrhhsalharq9v2Fx/9Jvnucg7Bhzfg6lG4DdOiKdDYI87ZWy8dZbbwHwVL2wliAi66N0XrLPu/QS57m1M/z6sqQKVxnGsJsE33b/eBMS4y6VDp86QoWdirvr4+6PdQ+NbXeGb7z3ccL1KFVL6bAk3SVknbGPKY+ZrANhymJYjK/M2BxEWPmCslTbhCmkst8LW8oAbS1LBu+NVNR5ffC+zLh1O7snrxn2B2rZsiUAz9mEGUIZX83fjEeXTmvSvS3INYrjGjRoACCxLxjLJB3gwlyKwvqB2deVLEeqvmQkrAxcN11qqJLb1zq3yXVItyWZrZXPYx5rLs9zwd+Mbedy9vlkuRhRIJ+3YfuplB/lZQepiruiKIqiKIqiVAIqpeLOr9FNmzYB8Pxqg3xlZQwplQoOqVSHZQhNJ3OoJExlSuXkwjLKOG6q6DLTG2PeAK9Fgcvyq5wx79xmmA+9LFNYdtd0vuq5belVHbbusLLwPNstKdLLlteAxszuOagOUUWyY54BT02S6pl0hEnwZLeWkQqVr+XE9mzntWL8yrrrIiOVd4FJcV1ELGU81Lc9w6+0p4pt37Bhg3sMqKTR/SMsIyqPqexrE6QwyiyLsp+A9GeXv4m8N7LM9n2U5QjL5yB9p6UiL/vayDovW+EUPy+99BKAxHwiYZ7sQR78fG7wWmM8NZ8ffEb89NNPABLdZojM+ht0Trksn0MsD69Z2YdMXrOyTwT3k+vl/HYZZTZZ2dIkf8t+JiwTjw+vf07nthh3bq9D1m/Zf4TlZWtGx44dfcvxXMhMqtIlDkjsYxSWKZbXzFVXXQWlfInEor5nSvh8ZXtfUcVdURRFURRFUSoBlVJxl1/8VKA5PsiBIVUMdFi8dip/2SAfdzlOqoxSHeaXtOzdzm0dcsghvuX4VX/MMcck7Ce/0LmOMLVfqgxEtkywzHK/7f/DnDNStV6k8pCX8cD2vstyhbUkKOkzefJkAF5Mp7wOwxyJZMuKdLoIqhvSWcinitmKu4xxl+4xrmuMjHlP3jLkxrZHrfuCjHEPyZhqOJ+IbZ/68Wc46KCD8Ic//AFNmzYFkBiPmlAOEWfO1o6VK1cCAH777TcA/nuGzM0g++Owjsj+O1QFZQuJPAd2q5psxZR1WPb9kYqhrKcSe1vPPvssAOCGG24InLc6QjVZPkN4Hlj3pIuPDafx3PCc8RqVrjJhWcJZFsZhS6XXXuaHH34AALRt29Y3b7L8J/Z4GVfP9dLXnGW190s62EhFWj53wlqV+Xv58uUAgMMPPxyAV38Ar17wXkmvfCrrLK/MZE547GW9kcsF9SnjNSCdbHgtaH+viiOSpo97Wl7vSVDFXVEURVEURVEqAZVSpuSXP3uu8ys1KHZaftmHxVqG/Q6LwQvLHGgvIxVnfhEzLvv7778HACxduhQA0LNnTwBA586dAXhf4VKVCPqiDnOxYFmo/HGbc+fOBQB06tTJt03G3Mn9CtoneSxkGUraPyDM794+ttyG9OjV7HFlhzGc0h9cqsKp6kBYVkR7mowv9bmWWIq76zAjM6YWOGUpdGJKQ5X4FH0y7GuT5WVcaYK7jF9hl7Htxx13XEK+B+nYkirDKO9pVOSYq+LXX39151m0aBGARM9s6TjCsnA+KvB0DZEe7UFOMNwPGYsuveNlLLx0f5IEKcPqipEIzxXPJZVe2UdE9lcAEltiuCyVY8Zu297vgHduqKRzPtnayfXIPjAA0Lp1awD+7N72OlK5mkkvedl63b59+4T9lLHrYdmZSVAfHHt+7oNsXbLhdc794rGiGs4hW8l4rGVfANmyJf3g7XXJlnfZ8mG3gCjli527I9V8ZaFSvrgriqIoiqIoyr5CeYXKVKoXd8ZAMuZM+rdKr2H7/1QOJmGEOcRIVTFILZJqiIzJZ/a0DRs2AAA+/vhjAMD8+fMBAH369AHgZbiTKrpdtjDlhTGys2bNApAYI8gyyAx1QRlh5W+571KxC/OCJ7IVhIStx94vwmuAzggaJ1ty/ve//wHw4jXDMvcSqaxLBUhix8pKRZrTfPGbATHuEUdBLy50FOwU/u3puslErBj3SEiMO2Pbwf2gm4wzXL/dU9XltZuqpY+kisPlPQDw4oZXrFgBAJg3bx4AYN26dQA8tZ4KIc+L9PzmsZctlrbKF9aKFtT/xF5HWF2Xv+3x3Pfhw4cDAG688UZUVyZNmgTAc0yTvv9h2OoxW1pk3yrmBeG9n9cL76Wcj+owlXXGb7P1lq1D9jmkcsxy89pj+VmWILcke7p0K5KtALbTmFSYpeMR1ynrVphyzRYrqYrb2+Ex4PXOFl/p4ibdf+jbzuk8FyyD9ONPdr7lPUO6fPEaOu+880LXUR345JNP8Pjjj2P+/PlYt24dJk+ejLPPPtudPnDgQIwZM8a3TL9+/TB9+vRyLmn6VKoXd0VRFEVRFEVJhx07duDII4/EFVdcgXPPPTdwnv79+2PUqFHub2nbmy6quAcgY+6kiiUzcQLel71UulIpQpIwd5mgL2LpGysVD6liH3vssQC82FX2Zn/99dcBeF/39IA94ogjAPi9bKmWch305JXqGmMDuQ7CMjEONkxps8eHqYpymVT+9WEe0TJrrY10V+Cx0Pi+kiN9nsMclmSeAc4nM3nyfAXFR8v40yDnpYjPx92ZT7jGJGZOLaWbjK24h2RKjYhYdpkxdffuHaHZTGXd4H5K9yapQCZrKeTxZyZMKqfffPMNAOC7774D4Kl/MgaY65aZmmU8sr0/RN7TpJIq1T95XEiy/dOcDIluRLLPRFj/IbsVWvZh4Llg3DwzqlId55DI+HLeW1k2rs+u37LFRV7XXEbmgpDXorznyBYolsGeV15Tcjzvc9yGjKOXrixym3YcOsvNPmGyPxqPlXwBZFmys7N9x4OKPcssFX37GMlM62Ee+PYxqs6ceuqpOPXUU5POU7NmTdcZqDKgrjKKoiiKoihKtWTWrFlo0qQJOnXqhOuuu841ECkpkUgUkWgaf5FqpLgrilJ1+ctZjiqy28tQSPXdOLHtdJOB4ybj/g5xkzHsfyFbxvg7yFVGZEql8s5MqYjFh6s2e3G2iqIoSuWjf//+OPfcc9G2bVssX74cd911F0499VTMnTt3n/XEr1Qv7rKZOSx1sd3km6pTaqqOkRLZhJcsZbdsHpad92QTFzvdspMZm+a4HMNglixZAiDegYK8//77vm3KxBVsuuM2ZBnCyijns/eJ/8uEWHKZVEk3Up0L+3zKzsGyuVMTMZUcdvSSSbxSdaSUCU6IbB5nM7K9jGz6L6s9VkVgqzLy3iM7fMpOZ/K+wWPNMCN2CGRYQ9C8sl4x5I7hcB9++CEA71iz6ZzrDrPDs+unrIPynMuQGWnTym3I85wsxJDbr84dzWUyLYZUMJxNWvAmu+8xXEOeb2kDGvbs43y8BuR9334m8NyxvHbSIsB7DrEe8Bknn6thCaWCnhVhIZiyfvBa5TGVoT+EZeB9Mei4yH3nsZH1QCZClNa60no3neSE3A8eO26Dx1xaJivJufjii93/Dz/8cBxxxBFo3749Zs2ahVNOOaVE69IETIqiVCsixUVx95iiQu+vmH/Fzl8RUFwEE/ZXVJTUUSYSi8X/ogF/GZlxtT2jBpBRw/1torF4ttRoBhDNgIllul7uiqIoStWhXbt2aNSoEX7++ecSL8sX93T+ykKlkifDvsL5tUq1yv7SDOsYKdVu2ZFn69atADyFg8oBh1JRsptUwpQsboM2W9yG7GzSpk0bAMDixYt965adA4M6rsgOZiwD1ynttmSZpJpKgqw2ZZIIloFKBYcyQYxUbkhYApYg5YDzyhYCVdzTgxaQQGKHZJlGXapEhHWB84VdM3YHLW6LhNkKVga++OIL9/8mTZoA8GxW2dGPqh4TsPC6Zd2XrR3sZM4hVX07nTtt+AjPD9fBbV144YUAgE8//RSA1+md54VlkyqufR6lomh3IgYS7xey5UC23sh7l31fluOqcydVec9n53vWOVo9UnWV6jmQaLUq7+Fhif3kuZQ2gyRI/Q6zoJTKO+8JsrOqtGYk8tqw7/vyepE2xZxXtiiGOYewoyjnl63WQHhSJ9l5WEYFyPHy3IS1KNvr5jh2jGV9ly0D1bn+lIXffvsNmzZtcu/l+yL6lqMoiqIoiqJUObZv3+5Tz1esWIGFCxeiYcOGaNiwIQYPHozzzjsPzZo1w/Lly3H77bejQ4cOvlDkdInGooimoaanM08yKuWLO79G+cUsbZyClNuwGFrO+/vvvwPwlDAZm8rERfzKlckp7G2GWVnJr3MZJ8f5mKRBJm6SX++2YiA7UcgyyMQPUk2RX/5hiWPsfaDqQNWQx44qIRUCxhDSfozHjqpkqnNjI/ddWp0p6WEr3GFxplLJlbGtYQpcWGIuex5pB5mfn+8lW7KVQNE51bV7dDqnhtlAGvE7zAaSHU/jOxZsAznklXd967rr/64F4F3vgKfSMeEZ1ZqDDz4YgHff4HUrFfktW7YASLRP5Hmy4+l5L6LyznUTqbj17t0bgGcfOXPmTADePYH1kfXYvjZYHpabSrq03pMtXWFJ2cJsMu1lSCqL3qpMLBbDcUccAgCY992yhBZenjP2gWALjd2iJVX7sD5iYTa+0jaU9wnZZyKoL4w8l3ZdsdctFWoiW3TkepMlH5SqtZyP2+QxC7MqDWsBBrx6wfcD2RdEni8in+Wyr49sqbBVc9ZB1tuwlpRk5a6OfP311zjppJPc37feeisAYMCAARgxYgQWLVqEMWPGYOvWrWjevDn69u2LIUOGlNrLvTyolC/uiqIoiqIoipKMPn36JBUBaOyxJ4hEI4kOZiHzlYVK9eIuv6Tl1zhVKVt95RcwVSn5xcuUwzKBAtVhqS5SWaPSIVMe2+WiOhWmJFE14bZlynlOZ9wgvwCl2gJ4ahqVDR4Dxr9JFwiOp2oS9IUPeF/zLKO9L8mOAZCYxplKAdVFqkPNmzcHkHhupHJvHwO5X2Eqi+KHse12MhHZP0K2rkg1SMY5cz6ZICRIAeI8ga4qTLxUbE3jfGyVoi2km5Ap2AbSXW8qG8iMGta88Wv9walf+faL+0EV7YXxEwF4iY/seaWrBhOhtW7dGoB3rfNY83pmXaLqzboh43PjhyR+TJiCnvWLCZeo4stkSeznwuyBU6ZM8W2D90j7fHFZ7g+PQVCCGLucMpkXtxGmQAaNq851ubi4GJGC+Hnt3vGg0q0kyCua43xDyxUmEgFqxLBxR4Fbz3mdSdWccfe87oBElX/ZsmUAgPXr1wMAunXrBiDRTUW+WPGek46aHKashznv8PqSrizz5s0DADcRD1vLpGsL4B0TPrMJn80tWrTwlUW+s8gW8rA+InarpmzV4jy8d7CO8TlcnetPRaGuMoqiKIqiKIqiuFQqxT0ohTrgfWEy9tP2jWYMOhVafsFSUaeaza9VxrozBlV6vEqHEypLQSqV9HQNUzSpkPHLmV/2TZs29e0PFbMOHToA8Me408OZnTDoIMF18Euf27B7yNtlISy7dG2xWzmkQwj3U7pbsPyrV68G4Dlw8DjxXFCR57Z5bqhCAt75kPH+MmZaCUYqojYypj2sFUa6yEhHmDAHBXsbcl2RSAQRKu3GUoqKpbJe7P8dApV2KhsRxu0ztj3DH8cOAI98/COARFWMji5/u+Q8AMDoSdMABDvhyP3jcV6xYgUA75hRrZd9Nlj3qaYFuWjI4877n7xvsNyyTBx/0UUXAQAmToy3ILAlzHatkc4cqXI3yGtGxh3LuGr7vindTqpzXd6xYwei+c5zzAjlVP4OQ6rrcBR1e1w0SIEHmtSMAohfu/sfdEB8fEtPWQ9U833E79PtmxwbXq4UGHFtfb9ybaB7Dp8PfL7K+w+vXT6fVq5cGS+h8yzhs5ItvGxJkC2R9rXKOsJ6yzrIZxlb1mTrJMvAbXA5/g7LZWIvy2c4n698TlL9l+5uSvmhiruiKIqiKIqiKC6VSnHnlyS/Qqlm8WuWMXhSJQcSlSAZC/7rr78C8NQquQ5+vUvlnl+7Qc4osrxyndJhgYoz5+PX/IYNG3zLBe2fHMffVOnlfsn4ZBlzJ73Zg7zUGSPIYyIVdrnfVApWrVoFIDEun0pgmP+9Pa/0lZZx1kowPLZ2vKZUP+V1SaT3v4xpD/L6t9dvz2Mrr1dfekF84u54XYpYiqJxY9eLfL///X1cTbqhnX//pJJBpd2NZXedY5xhhhdDeucZ8fjbR9+dDwC4469nxLeZ5XiwO2o9lS27zrHuSr9q3qOoxC1dutS3/6yfRGa5DIolly4/8jyw3w5h3K1Uu7mt886LtySMGzcuYR9kfK+8RoKyZ9rbktdQWJZde96guP7qRmFhISL5Tvx0kNsSEJpoLCLu5bZyHYlm8B9nGpV33kej/mWkOi6fc2mq5+lgxLrcUjvjuxx0gH+b9vyi3Et/3eBea1Szp06dCsC7vlmP2bejS5cuALxrlM8p1hM7l4Js6eU8fB+Q+V9k/ZBx6WHuNHaMO7fBOkNFnaq9rDfJsrore4dIJJpe59Qy1htV3BVFURRFURSlElCpFPcrrrgCAPDBBx8ASPSwJbYSJnti80tYuj9IJxfpQyy/doMy/0mkV62MdyNS8eS26AXdqVMnAInZFhkHa4/j1zaX4TpkucO801lGljlsPsDbd65TZqSTHr08tuyRz2NPVUI6UbAs9vmkMiFjA/mb14gSTNB1G+bnHJZHgOdNKqI8TzIG3r7epf+3McZTEt34dat+iJj2J75Y41vPS2vj185VTfwtLa7qmBDT7h/6XGWccXdeEk+8YWKO44WjRr4w9g0AXnwr+8EAXl2Ux5CKGa9T1uHvv/8egNdKRcWedSdMgYsfEr8ftcyyyGXo6HHEEUf4yihdf3jeevXqBQBYsGCBuy2WT/pNcxl5f5Atd9wmrxmWMSgzZFifimHDhgHw/JerA40aNYLZFu9zYErYv8OFrUvRWOi4qJOzwFXYE5T34Bh4lyTKoYxRdxcJeWbKuROWT6b+i/IdcuD+zjxeC8PfL78YT704xq0fRx55JADvPUL2HZF12X7P4HUv+8NwHVTeZQucXCcdecLU8WQt+dyGrC+sc/b7gVI+RGIxRMU9MGy+sqCKu6IoiqIoiqJUAiqV4k7YK5zqFL9iGcdtI5UiGQ9KJYjx1vx6lTHdjG+TywW5I0jvVrlMKtVbqvh0kfnhhx9867Hnk+o1l5HrDPJNBhLj46SansxvWZaHx4pxvXIbMrady1Fl5LEPas3gNMbxymOrJEfGR9tQNZIZUVl3wrJe8prjueF5DsqKyGkcxmIxL4aXSmKAq8yjM35IWJe9DVdZ5wRXaa/hH9aQse7evkYyHYXKUemMm2U1/puuVdwmVXR738MyPcpsk7xX8V5GFV8q7OxHYrccSlVbnkuqd6xPdLTp3LmzbxuEZeM94+uvv06YJu9p8lrwnU8L2YInr7+gjNNh264O3HPPPQCAM844A8DO5DMrJaZOnTo49NBDAXitSTLzsMwEzmvbroOyHvA3VXkuK13dZN8QkuyZJ5HPZOmdL1sDeE0NGTIk5bqVslFerjKV8sVdURRFUaoyRTlxe0JphWqkRapEfLTCCpVxP2Br1gqcJyI/aGUn1ZCQGdmxFEgMfSkpocsn6ZyaUF5hd3nNRWeK6fFXoDU5noW0ouzrVMoXdxmDxiF9iKVHuT0tTAVnPBi/UqkiUtWXGd5kbLytFskYUn4Jh6naVOHCYow5ZCweVTgqafZ+cR4Z3yaPFZGxtFJ1DXMYCToW0q+ecbucTiVDOlVwPYxTl6qlHcPH8yjV3CBPbSWRZIoOlVo7q6q9jPTm5jUm1SOpuAe5g/AcN2jQwFPYA/ypH3nHn8VUqrBubKjIiOrG9YoXEarqcujsCABgyOgpgWVlH4xkbidhbirynsBjw9Yp1mWq3tK1ys7ZIFs25LrlNqWaL/eLx5Ln1Y7dp4LIfeY2ZUx/kFuQvZw8b2FlDpqWrJ9NVaM6O+lUJLFYzL2PSa919smyM4PzPPFdQ6ryMt+IXE7eM1PVYSCxpZ7blu8gsu+LXlPlhyruiqIoilJNKXYUd1PodLymNarsrErCPloty1PXBjU/zzctUtPpuJzhny47s9qhZYDXgbSs9nbxlaUIiUqjY2xCp1phf4mII/DE/OOb1coAUOjMH3XGZ2BtbmKiOkUJIxJN0w6yjGJEpXxxZ9ZBxo/xy5JfxPRfBTxFi/FsUp2XShGVLam0U22j0iRVqiCkj7n8EiZUnrlN+fXNr3kqZ19++aVvOXvZHj16AAiP1Q+LS5fKAMtMlTxIqZXx/dJfX6r+UtHlsZMZGzkf1UbGFgNeL/zWrVsD8I6R9LpXgknWv0Kq2PLakK0xUrGVbicyj4G9DB2GevbsmfDAtl0n/t+fj4mXYUf8WijOi1+PT81bD8C7zp/9Jb7eGztmckPxdQn3GPdFhQq85SoDx0XmX1dfHN9mzbjiNuSZ//r2n61Utl+6zJPAesd9l8eb/T+ys7N949n/Qypydl2X2+A0LsN6xGMs1xWmYAfF6TNWl+tgSySvAdnSJe8F8loIU/ntcWH9BKoDYc8IpWLIyMhwndk4tJGtke3bt/dNly2Fcjl5f5DP/mT9vFgXeT9gHZPZ3LU1uupRKV/cFUVRFKUqU7g5HqJRVOB8SBcxxr3Y95uw+T1aI/5YjznDqCWguDHsDBVzlPZIoRN+yg9dZzyo9jvLueq/VPejiQJWKsu7sARSLili+JNuKyHWXSjvRQVifLAS36Rmhn8+eJ3WN+7QZH+KHw2VSQJjp/k1SjVIZjUFPCWWChfVMn6dSicaqleczjgyqSDJL+EgVZFqklSSpO95mCoXpnhSXWfsHQAcdNBBvnnkF73chuyBHqaISU/uoFh+GWfOeRkfS4VdqkhcN7PWrl8fV1Fl5tgWLVq4y3CcLBevCSU58vzb44g8T7xOw9xMwrJmBsUo8zydcMIJAOI5GQae3ddZMLXKyBeFW7rGnSCGLcj2lWHkGk9BZ7nXrVvnlv+O/nHvZje2PdOLHadfu/vwjgYr1UGtPGw14v2E9VHmT2A9Y+sgWze4Tq6H8bUyJ4K9Xd7LyCGHHALAH6MOhLu1cJsyozGPF+DVL95bZVytJCwjs4xtlzkC7Glh6mR14IknngAAzJkzp4JLoqTi119/TVCzmzdvDiAxxp31KKzeyHeEoJwGsh7LvDC8t0hPeF5TStWhUr64K4qiKEpVZvHB8URg7T59DQBQ7CrvwUo0VWdXcc90hllenHaN2o4JQ5YTxuao2lF+fGf6nWvcUDKq3yLm3USd5aIBZSpOL8mMkcq6EAIee+dL94VWfujGZ/eLTFJI2G+//XDDad3jP0JcctzY+CKhzFPdj1mvSs4H/TEHt/T9nrt4adL9VKo+kWgkPcU9WjbPpUr94u5zpoAXE21XXMalcV6qwD/99BMAT2GXzi/Sn5hKIVUuqvpBcZm8ucgvYqm0S5Wb88uvbumic/zxxwMAJk6c6G6T4zgvh1TspJKebpm4Tc5vx8zLG6Q8NmzlkGq9jM3lehi3TrUxKA6WSgYVQOkVryTnwgsvBAC88MIL7jh5HmVGTXkdS2cEWVfk+lg/AS875//+9z8A8XMdSaK0ey8Iwt7NGd56dFxdpvLOMgCJfSyKiorw0LvxrKD3/OVPznoSH8jMmPrBnLifOVV06etsw2t5zZp4Zlfei2Qm5t9//x1AYl3hMec2ZJ4IKvH2//LeM3/+fADePa9du3YAvH4htu884NWd2bNnA/CyubJfC+DVM/YV4jUh42el4xD3S14T0uHCvlakyiivr+pEWObN6oj9bA1SrOU4/uY1y2O5t9m9e3eCi5JUv2V/EyLnl5EBQHBrKeDtH5dhnbLrsVK1qNQv7oqiKIpSlcnbFO+cz5h2xrxLojLGPSv+8VSjTi13nuJ8R1Sq4wgxxf4EQhGKLIxxF2o4dUKptAfFujMWHyHhIQmuOO54/zb/0S8e3vbk+98Gz5+Eq4+PdxYt3rXDVxZZXjdGnrHuzkd8ghJvjxNqfJ+jOvqWNbG4QLVpl9oxVhfUVSYJMlsZVSnGdtqqMBV2zksFiXHTjOekUkZFSaqORDo4BMWipfIsltNl3DxVFhkrzvhSqnj21zzH0aVCLiMdMeR+hPkvy17xQWoj94PrpNom1QPOx99UF3kueG54nKSfLuCpKOpVWzZs5UfGYXOaVIN5zGV+AdnKw2uF9ZEqOwC88847ALwWrCCnBh/Cho6d49iJjs3sLItdJ+iuIFubnJ2MLx/z+sMYNxlNhu842C5V9v7ZSp+MP6UqLrMDy1Yn6bzTpk0b33j6u7P/h10uDmWrGLfNe9vSpfEm/N9++w2Ad1xYJukcZcfI8zzJa0TeV2VroSyTjAWWLX72/zL+vTo6rKxevRoA0LFjxwouyb6DMSYhhwSQ6KLCDMHx52jFXzvFxcXuvTSsvwmRjlX2c411kM901jkq7vK9iNeQUvWolC/uiqIoilIdyN/mdO531HIq2cXCVSZMcS+2FHpTNzj0pkbgWN/KfT8T/Nw5PmiZdD+8pE+9Oz6+/D//2CV0Ucb9PznzRwDAac0jAHbC8HtRlN8I5d1Vz8MUeTvERcbFU52n0u6q+PHfTWpmAshzBYGc4pRHW6mkRKIx79pJMV9ZqJQv7jLeml+p/G07jFDFpbPJpk3xpBZUcbkuOpt06tQJQGImVamUUUGSzjD2MjLuUzouSKcXqmxUwGRMMVUuYrtKSKWdX/IyVi4shl3GvrPMUskOalngOsNccngsWRYea25Dxt4yJprKgt2CEqbiy1hCJTl2nKTsryGRsdTy2qDiRZo0aQIguC8Gp7Vs2RLdD3PUxHx/LKaxVSk3pt2fkt17UYj/vrlnPLfDk5952YSDsgXfe1n/+PKO0s7mbMB7APPBy2vstNNOA+Bdh1S6bG91qts//vijb1pYPZLXq6ynVOqpptmKNeuFrOPsz8J73oIFC3zjeZ54j+D4Aw6IO/RIj3Yg8f7AZeX9j0NZP2X/HIk9XjpdkeqouCuKooRRKV/cFUVRFKWqwhCpNm3aoGBH/IOrKN8JC8ynMi0siR2nihq1nOlFiR880vGCv13P9wzGeDvCDkUT17/dUaiLRGx7ORLoquOo9P/ofTCAAphUFuvcD+enG+MuOsMH+tSLaV6mWqm887eT7NCZb3+G+UUz4k0UjoiwOd+ziZVWrwASOr5SEJMJ8/ixzGtIKUeiscA8A4HzlQF9cVcUZd9AWLTF//Wnb3c7rrnN8P7ft/3pMADA4x8u8a36rgtPBuAp7G5su23z5oz776uvA/CymCqKoihKSqLR8M7Ycr4yUClf3Nlcy46jbAJmEzabkgGv2Vd23GBTNr9SuQybmTk/m4DZrMzmZH4R01KN0wGvqZfbZmcvfgnzq1omI5FNxLLjGsvML2y7gw6b1llulofr4LGRncxkR1mGqbDsTPIUlIqb5WFoEs+HDGWSHYN5rHneuB6OZ9mlpRzghTzJ8AwZRqQkxw6VkcqNTOgh6wCvGZlYi9c5Q2TeeOMN3/z2PHs7DXedOnXckDNpn5aK4447DkBieIe0TrVDuJhwicPly5cD8EJoZGdOIuslw4qOPfZYAJ59pG2pyXKxDrDOM5ESbR15vtjxnvWUHyScLjsbB+0zjyWvCdZNrkN2Euf5lUmrZGf3oNA72Ym/OqZsf/jhhwHEr4ecMweiXr16aPRcPJEOlfTiovRsMm2VvUhkVWX8O51qom6mVOfDViZXo797WFR8kJJYwpcU17kmLHMqsaeHlDMcvyTvKu8hsfD2flGdTx0X74SOsmM9pxf4O8Zz/AEZmTig6X4wsUwc0txJMukIDZ8tWBJYZ2z4HOX7Ba8hpepRKV/cFUVRFEVRFGVfIRKLeWFXKeYrC5XyxZ0qNztVUXkKsg+jCkxVkUoR1V3aC1I9JFSfpCLGbVBpop3dkiVe0zwVy65duwLw1DbZAc1W7ADvS1oqn0TaX9qKYFj6ea5D2j/KdVDVysmJewbzuLGMK1eu9C0PAIcddphvW9LGUSbukfvJY89zwXPDc8Xzasf78X+puGsippJx2WWXuf+PGTMGQKI6Sng+ed5kx2DWgaOPPhoA8N577wHw0n9v2LDBXRevLyYFAgAjXBp8oTJUq1ylz4GKmvCKpup1/cnxazOS6bRK1bDiSuGpXD47SGcc91cqvySowynrC9UudnLnsWHCN/tY2MgO3TxOQQneOI73EdYf2layHrHDOhNI8ZiH2UgGdQK1O+ACXoujtJHlfNKaTx5LaYFrb5PrlMnwqqPiTtiSesABByQo7cYZ0l2GrjLFBY5FcSx+fmOZ3v23SCjsRY5TTUxkZWVGVfe3s3xCTHuC2hwQ5pZuPK/rKuOsszgWPN6ZPbC9QV7D8j4hSFDm0yirkS0IMtZddqinAw+PlXNPoqWtO51KfIbXkk6L2l5HHBwf4dyvvv7xl4SWfd6PeM0oVZdK+eKuKIqiKIqiKPsM2jk1HMZbMv6asZtBaYI5r0z4QoWI8Z5UxMLUNSKnUzFi4hTAU8uYCEUmceIyVPllcha5LWkjSYIs1qSKJhO9cCi3KVVE2UpAtY4qub0fqZRJOZ7b5LGnYsBzI/sP2MqmtMjkPJreufTIa1wmwOH5kzHVTJzFhCczZ84E4CWNYauU3ReDSYCoAtu4NpBR67ZknFYmOjE4o101jkinBxFX6nVGdYaM4bW2Ne+7Zb5y87qWSrC3SU95Yyw6LRipEp9wwgkAgJ49ewLwWiNkcihZl6ncE7vVSrpKyPPC37RZpSIv90fuh7RwtPdZHgN5b5KtaqzTXI5l4j0vKJGbjHEPW3d1gv0TDj74YC9WXcQ4R4VTTDKo2gc5zoiVArDdVqK+IeuX7DzOemova+Rzii1qxl+GiHG2ybIWOAndhAIf5BnvPmFSWIe6CnuIEm+c2PdA55oQ3P3kCKHIu8eEx46/ecxqCgU+07tf8n9pX3tsh3hrJu9n839a5dYbXjNK1aVSvrgriqIoiqIoyj5DNJqm4l4NXWWo+lK1YSwnXUuCEohQYW/dujUAT/Gj6wPVQ8agUmGWShjVHyZGCYotp8pE5Z1+qlI5Zzml2s2ycj+5X2FlsZHzUAlkWaSThHSBoOrFfWBLBdU9W43j9vmlz3LKGFoeG7aQ8FizNYDnguvhOZG+tfb2ZZpnuyVAKRmMd58wYQKARIcQ2ZLVrl07AEDbtm0BADNmzAAAdOjQAUCiYmqr61SD1qxZgzfXrEFBQQEuPf2U+EQqcQGKO00solS3hCIm1T0jvJSpTDFmlArWs2Mm4JhjjgHgd4aykY5UxO5XMXfu3HgxRUw3W65YN5o1awbA6zMi7x/yHsBjaLsmMc6cdVi2NnEddPBhix/no+ot++1IJT9of2TSNS4rnS5kK01Qa6i9Xvt/6fz12GOPobpy3333AYi3ZuXcdCcyMjJQ+7HBAACpLUedmHYq89KzPdm0CNXgEIVdxmvTMSVBabdac9y6F/Fvy4jfEaeOu3d51nlnm1ERby/LBMD1mXfV+EKuSmZh9Svt7jpl1tawWHl7P1I41yTE+Ie0CroKu3Mso7W87LZuPDyHNeN12RQ6rngZ8XvWse3i91gTy0TPLu2R2eigpGVTKjeV8sVdURRFURRFUfYVItGo+xGcar6yUClf3KmGU7GmgsS4UFvFkd7g69evB+DFV7MHNlUexuCSsPTuMrNZkOsDy0WlS6po0gdbtgrQlYMK29q1a337HZS2noo0lT0qfVS7ly0LjuPlcaJaLuOTZc91IFE9ozpHhU/GBHP/eP443/777w8A+PXXX33r5fy24w+PFcvFcldn54k9xcUXXwwAeP31eAIingdeCwcfHHc2oF/4rFmzAHge4zwX0v3IVqqpvvN8HXHEEZ4iF6NTiaV2Gaf1hj/deR3Fj2qdcKRx1T4q70Jp5/jWrVu7+yedUmQfDu7Ppk2bAABz5sxxiym90FnHWe9kfWQLEfvBsH7JbbO+2XVNqtccynh06RIkWxS4P3L+oL4zsrVBKuoccryMgZeKfFCZWA6574rXUtWyZUtkZDnHie4yTgZVZk6Num4yzvmv4T3mY8LHPZbp1C8q8EJB94Z+hd1Vh6kAu6pyEsU9EvyykuAOw/rPYbEjn0edFl7pox4f6QyjvnW6943CfP98vJ6l0k7lXirvlrpOld7tL5Airp54rRqinwCPOePZ87wWNFdpz4q/A1CN5+9IDaelvCg+3+ufzPE5hilVk0r54q4oiqIoiqIo+wyRNF1lItXQVUa6XlCRpoJrx4NKdYrLMOabCuAvv/zi+03FiIqQjHMN80u3oTJJdweWgWWiikLVXypmVOnYSkDlnmW6//773W19+eWXvnk45Dq+++473za4P1QAGXcu/dvD/JftaUQqZTLTpnQS4W+eC5aZ54/H1FbuqVTKbZc0O6YSzkUXXRQ4/qOPPgIAfPvttwC8a4Gx1DzvPBe8huzWKfaZoNK8ceNGoG382qPSHol5dctV40KcKBKgwk7VmCq+cJV5dvR4HHvssWjcuHFoVs+wPiXMTGr3vZBqseyvwdaye+65x7dOZko9//zzk+6WHectczPIFg7ZciBVfKr7cr/DXKBsZIsjrwHZYsB7XZiTDbHHcx28D2grmseiRYsAxO/TsUzn+Scyp3ox7s51kOW0zGZ5fb6ijsIe5f3dUd45r+ct7leBGVvN367SHgtuyQISM4u6rWEhynskQWlndm+nLK467vQVs16Qoo56XbxbxNNz3RzBdUYZL++sQyrt/M3nfoHlhe943xs3Tl4M+Y4gnHvo/kPl3TsX4hzU9PqYRKms5zstWs4w6gw9JT5eTl4nSgVRTnaQZQu0URRFURRFURSlXKiUijthnDOVPg6ZURXwVCkqQJyHih+dMaiOU/1mvC6RsZpSYbORypVUn7huxtlTWWIs9yWXXOJbH5XpI488MuAoxOnRo0foNHudQ4cODSwDj6XMqCodYuy4UxlDKzO/Em6LShqPNcfTyYfLU6mV/RPseWRMsd3Kouwd/vjHPwIAhg0bBiCxdUa2RkllF/DOH6+7b7/9Fi+vWIG2bdvi5G5HAPDHuDObqilybSKCC+c60gh1TyiBDz79HA4++GA0btw4oV8I90fWbd5D2KpFNxn7upT7fvfddweXU5BKaSe33367+/8TTzwR30WnTvL4szzy3iXzRcgcD8li26WXusx4GtaPhcgsqLJfTJBnPMc98sgjCeWprrDF5dVXX0XjFPMq1Zfq7MC0L6CdUxVFURRF8fFN/4vRvHlzNB/7jG+87PzIEJkMK1SG/2fU8ofRuEmAEuwH2Tmytm/oJQTyJwZiaBqQ2EE80RZSfCgyREuGzMhOqm5HU+v1pSg+zhWeov4OrAwxSUjA5CRckr7aMkSG4TEAUJQX31aRM604IXTGWVaEzsiXtVhmcLhSRh3vYzZjtxMa43RKjboCQXwbEWdbry1ci7/+9a9QqgeV8sWdShEVJMbNBrnKSBWHQxknetBBcd9TqsFUBoNUKLsMXF+QqkhkRkCpSLL8N910U9L93hPceeedAOLKjV0G7qf0a5YtCvZ+SsVPjid0jWFLCI+xdNnhtqh8BrnncB7ZQiLLoOw9eL6kG4nsw8G+HTbyuqIn/JYtW9yHbATeiwbjWeVDPyLdZNwJfi94rvPfL73i3h/ogiOvU9lHg/HcHM/sp8T2cWfcO5fZm/zzn/8EADz++OMAwjOkyhYDDnnepI+7bDmzp8l5OOT9T8bby35IkqDxskVASWTx4sUAgjMPK9UXXhdKBVNOMe6V8sVdURRFUaozdqdTwFN0Y6LTY406XmfHjDpOOFjtLGea6HQq7QbdoaO0ZziWhU7iH2T4lXdbBZ+7eKn7kUyhhSGNDN/iRz4tVplM8cADD4xv1/nAq1WrFnp17eLsuLOtIk+ZNoXOh2KUoZ3+ZEfuMZIWo0KBl92yqZr7OqdShafynl/g+81OqkUFyT9AacnJTqpFzvm0lyt2zl0N0dGV+xFN04pSqVpUyhd3GTsuMzTacZPSoYQqk/RHZnwoXUtkTDt/h23bju2ULg9EuqRwuoxJLQ+4TamohR0n2WoAIMH/mstQKed46ZbDbcl+BzLmluuxlVuOY+ZUriOZE4ayZ5FKLusbrymeL063Y8H5oJbXQrt27fDtslUJmYuP7tgmvqCIbU84265jRXx9839Yjh9//BGAl6WU1xlbdMJ8wnn9yazBcn6+jABe1tiZM2cGrnNvcNtttwEARowYAcA7zqxHsm+N9HGXmY+J7fRS7Dp7BN/3ZDZomR9C9j+SrY12SxnXfe+996be+WoKY5hfeeUVtKnYoqQkFosl1Bnp0sbrh+5mfHFna1aTJk0ABPclUzw0tn0fIRpNU3HXGHdFURRFqVZ80fNMtGrVCq2mvADAU9gZ4+7ZQXofbFTfXevBhMQ+tX2/OSwWSrup4aj4zvi5i5cCSB4mV1Zmz1/sC0Xre0J39/8IVfhCZzoVdw6d+fi65H4G0PaRdtGMiXcmmwDFnTHthXlxYaJolz/mXSrv0h6StpBFNfwtI+62ilJ/pPDV8PVNdXD55ZennF+pWlTKF3fGNVPxog84lSL6ugOJSjLVQek1LefndBnTKd1W5HxAYlZVGUsq1fuKiOmUZeBQKmFSUaNaaf8vFXYuK1sWZAsE55PqPtdHFcZWCulMw3PO8jF+WSk/qETzvLMVhL853a6PhGo8zzXrDPtBcPpX3/8MwFPzpYInVWXGmgPAqlWrAHjXoexDQTidZWG5iWzN4Tbtlwhu//DDD0/Y173NddddBwB44IEHAHjHm2EKHMq+CLLFi0O79VB62vMYygzLUrVnvWQ95VDmx7j55ptLscfKvHnzAHh9syoa6aokrwfC8fK5Kft7MYs27ykdO3YEUDGt0/sy8+bN0xf3fYhILIZIGjll0pknGZXyxV1RFEVRFC/Wncot3UqCXGVqOLHtsf3iooirrNeOi2AR/q4T/11cw0m05A79yvu3y1a5Ykp589mCJb7fRUVF6HOsYynrxLpHQ5T3iEi05CZ3chxrkr1YuQq6o7xTaS/cEf+QL8xzPojz6S4THMYZy3TCUmtl+NZrx7NTnSfsxzCv5dGYPHlyaBmVqk2lfHH/4YcfAADHHnssAE8hoqpjK2b8QucXPVUm/pZxn1Jhl8q0VAykhzWQmIGRcF0yTjQsU+XehNucNm0agES1XA65Tzxu9jSpnkiVTmZN5LHisWfWVraGcL1czu6zwHMsXSx4TZxzzjlpHgGltMjzSoVXqmy8VtgRzV6WrSmynnEoHYVk6w1j4anMMUOp7RMuswQzw6ts4eFvqbRLNZ/XmszCbB8LuY7yJCw2/OmnnwbgqZnSr571UB57ILwfgESq9WwB43niMeO26W6llI7hw4cDAB588EG0r8ByGGMC+5TxPLMO8rqQrV2yDxVbh3j9bNy4EYCXrZmZwFmXAS8ufv369e6Le1Vnzpw57jWg7ENEo+nFr2uMu6IoiqJUT16t0wG9evVCx8/fAOC5lFBpt11lojQloFtM7fgHdEQo7wlKe2Z8uS1FNeIv2wVF7kfzvsQzL7/qfmA3btwYl50RTxrn2pk687mx7jSSoC+645tuMhzBKpb4gkWfdhn/TqW9YBd/O6Kfo7jLGPfiAv96MooS/d65fbYAMC4eSAw/VPYB1A4ynLvuugsA8NprrwHwlCSpaAOJcavyiz/Mv1wO5fzSFcNWG/k/43ZlTCmn7wvZPlkGHkOWUSrwPH52C4VUQyXyGMr+A7xpct0cyth/+3xKtx+6D/CaUMoPXt88Jzx/Umm3+3CwBUxe+zyfch2EzfG///47AOCLL74AkNgiZKvgvL64/c6dOwPwri9eh2wxkLkbZGsAp8tWN8CrL/tCnZbIOPL77rsPgOegJetfUK4GWYeJzMXAFjHa+zHLq7J3YIbeYcOGoWM5bK+goMC9Plhv7PszryHWV85LBT0slwDrO68jKuv8zeuJHwtr1651tynrLfvJcB1VkXQzMytVk0r54q4oiqIoisdrWe3Rvn179Fo7B4DlKmN16IzSRaaOjHGP/0ZNx69dKO2bCzMqZWKsj+YtQd26ddHjkNYAPDeZqGMv62aELXBi25kp1sm0Sq/1wiShDW6suxPTTqW9cBdj4IPDzWL5joAlYuCjMa/TOxV3tqJMqH0wbr311tCyKBVLJBpLyB0QNl9ZqNQv7oxrpder9AcHEh1eZHZHGVsX5IABpN9LHgjPwCiVAbucFYWM15UOEzweUhkBEp12wpAevFRjtmzZ4luerSHS6cc+TrLFg9eAsvdhrDTPB8+jdBqh0i7dZuxleK55fUnFzY6btcczV8Of/vQnAMBXX33l22ZQ6w/XTSVOqsfy+pX1Uir3xO67wf2h49W+zODBg9Oe96mnngKQWCdvuOGGPVomRVGUstKmTRvXTczm73//O/7zn/8kjB89ejQGDRrkG1ezZs1yyYBdFir1i7uiKIqiVHeowj777LMYj5YAgEG14x/MVJUBzy1GxrjD+V0slPbf8/gxbBI+0Pfff/+EclAQ4wc0QxmJbTUKJApf0gqYGVS5TX4Y2x/RDM9heZi8jevIzMzEN7/EBZ6u7eLrcz/eazlx5xSXHJcZ19/dUd6jmbYKHqyWUjlnTDuVdirxRijr8nc05ggJVlx9tEZcwJja8QTccMMNUK09OfPmzfN1qF+yZAn+9Kc/4YILLghdpl69eli6dKn7O5UQmZRImp1TI9o5VVEURVEURanG8KONPPLII2jfvj169+4dukwkEql0/SEq9Ys7VYYZM2YA8L7C7fAYfuGz+Zu/pQ0Vl6E1Ib/i5dcXm/DZWUambAY89UDaPnI8f//1r38t6S7vcViG999/H4CnbMiOoVRC7LAHmXCHoQicVyZtYfMTOxbxWHI+duyTqdvtUBsZrqDxfuUHz7NM5MMOo82bNwfgnU+GQtkKCG+sPI88x7Je8hriNcJ6yum8Ro477jgAwOeff+4rE+BdN1TtwixeZWiMTJQm9z8oHIfjeF+oKtxyyy0VXQSlBNghTDunPAPAU9kBKzMqlfda8QynpoYT2+4o7T+t3+rWMQ5ZR2USLfvZx2mcl6Fw7JTO6azXvOfzPsCMq9JMguthWOxhhx3mbnPJkrifuwzDk9as27dvx+dLlqOgoAB9uh4SPw6Mda/tDOnjzuGu+DptL3zXNz/Tn6mWijndY1w3mYLgjKimWBhfOMtHa1hht45jjYamlZz8/HyMHTsWt956a1IVffv27WjdujWKi4tx9NFH4+GHH0aXLl1Ktc3yinEvm16vKIqiKIqiKPsQb7/9NrZu3YqBAweGztOpUyeMHDkSU6ZMwdixY1FcXIzjjz8ev/32W/kVtBRUasWdfPfddwC8dON2whciFTsZi0c1jqowv9BkgiYqCVQTuV67MwNVA25DpoHmsvsSLBM7/7HMPJbcT9vuTirm3G+qpVJ94TGSHRB5TqiUyOVsOI3n/JRTTinF3iqlQaYn5/lkB2EqXDKRDzt+29N4ruU1EGYtSqiOU6FjmZiQhQl/7HkPOeSQwP2QZZLWr0R2Kid2h03uB+0QFaWimZgTr3cXH2D5uFNpr+1X2osz4/Xxx3Vb3Xs+63e9evFleI1T2a5tudUQ1jnWGcadcx3SuIH3AdZBmexMWreuX78egL8TOMvJbcl6zHWyvBkZGVi8eiNq1aqFDo0cVx1HeY/Wdu4Fjp97tI7jLpPvPd8znAypGSJjrae8B+uhnp+7Ewsfc+Lso45NcqbTIpjntfQd+tb7getSUvPyyy/j1FNPdVuCg+jZsyd69uzp/j7++ONx6KGH4r///S+GDBlS8o1Go2n6uGuMu6IoiqIoiqJg1apV+Oijj/DWW2+VaLkaNWqga9eu+Pnnn/dSyfYMVeLF/f/+7/8AACNHjgQAtG7d2p0m43GpGPOrXNodUgmgciZj7iRUhW01Tm6DagKViosvvrjE+7i3YZl4ofO4yPhzOx6Y+x52bKjccFmqJjKumUMqOjzmQTHutHriOVfKj7///e8AvHTr8vyy1YYKh4yJB7xzGha7TmQ8OeeTih3H29aMhLG3VOOlLalU7Xltu9kUQ+wiid0at3z5cgAai6rsOyxYsAAA8Jcuf3LH0U3GZDjJ7jL9GVJr1tyd0OeD9YND1vsgC1aq36xbVNRl4kPZ/4vPAK6TrdV8FrDvGdefnZ3trov1m/Nw3Rs3bvRtm/XVLpPrV18cL0fEGbJlwnWVqe0p7pl1nZbjvPg9rIajyhc5v10fdyeTalF+8P2jOMF9xmn9rxHs+66kz6hRo9CkSRP8+c9/LtFyRUVFWLx4MU477bTSbTiapquMKu6KoiiKoihKdae4uBijRo3CgAEDEgSdyy+/HC1atMDQoUMBAA888ACOO+44dOjQAVu3bsXjjz+OVatW4aqrrirVtiOxWKhdqJyvLFSpF/crrrgCgJc0BAAaNmwIwFPNGOcm03tTNeCXPof8emfsNy8EDrlemTDGhutYs2ZNKfes/GAZ27ZtCyDcVceeJo8JFUsqsFRRqHTIfgVUQqimMI6RaqrtBawuF/sOPJ+y1YnnMyg5Ga8FziNj23kNsc5wvFTepVOTnB/w6qx0sghT3qWjEpF1IEjd39ebVpXqBxOmcdi1a1eccIDIjOoo779t3o4aNWqgVq1aCfdx6RImHcbsZ4KMi5f9m/jclfWW83GdskWc9xI6RNn9xDiO62b5OI+sz7z3xGIxbNi2G9FoFE1qObH+RY7i7vQBiDruMq6/O7x490xHYfeU9nxnPJX2ImedTkw7XWTkeCrvzvj5F12Am2++GUrp+Oijj7B69Wr3fdBm9erVvhbgLVu24Oqrr8b69evRoEEDHHPMMZgzZw46d+5cnkUuMVXqxV1RFEVRFEWpnvTt2zfUaGDWrFm+30899ZRP6C0z0VianVNVcU/AVmUfeeQRAJ76xq9yfnVTXaDqRkVQeo9zPJfnUM4HJLpQSCeNfRnZy5/HJ6giSL9ceQx5TOQxYqsH55eKJlUXOoTccccdZdspZY9y4403AvBi3amaUeFq06aNb3xQjLiMVbcdWgDv+uOynI9qCa9L9kWRqhoAdOjQwbctDlkuqZxzOtclM0VyyOt92bJl7rIa267sq1C9fe211/DG2rVo2bIluneK59xYtGKte13bmUYBTxVnHWTdo3sLp9vuX1TIWXfsnCr2uvj85bNA1m/pWMYyMubdfpZynGyt47q5P1yG47mt3bt349fdQG5uLrocFD8upii+DukyA3gqfKajwtNrvSjfybZaRN92v6IuM6VGhK/7un/ehL/85S/oA0VJTpV8cVcURVEURVGUckMV9z0D1doxY8YA8L62pcOJVBWoMHM81WIuJ2P4bAVAulNQdShth4fyhGV87bXXAHhqBY+LvZ8cx2PB/ZZe+NKVIFUsNH+r0r5vQ+WdPPjggwA8lxleK7ZjDM89rxXWM5nVVPo4S4chqvvsk8F6aMe4s38L6x+3HeRWFFQW2crE5dgiZCvuirKvM2/ePABxxXzG19+hfv36ALx6wXoir395f6Yyz2epHeMelpU4rLWL6+KzgPcODrluGRtvt+LJfjB0b6P6T0Ve5hnhfcnODWEy4uWg247rMlN3f+9AOkq7cZarWeA4ZDFjarF/KGGG1MJdzrFyYt/nzZuHv/zlL4HLKIpNlX9xVxRFURRFUZS9SSQaRSQNq8d05klGtXlxHzBgAADg/ffjmchkhjZ+dUt1WKrmVACoFFBttjOKEo4LygC6r8My87jIOEJ7HJUOqqDSk1vGL0sVRqozPFdK5eLuu+8GADz22GMAgKOPPhqAXwUP81+XCrzsQ/L7778D8PybqapReZMOGDYyUyp/cx2s01TopNON7JvyxRdfAABuuummoMOgKPskw4YNAwA8/PDDAIBevXr5pvN6l3lHZH8nKu2yjxPg1V/2c+KyMo8KW2Wp+rPe8nnKOij7ugS1hsmWXO4HVXyuU95r2D/G9p5f8stvqFGjBg5p4eR+oMtMLa8PTZSKe7HznHOGPApSaedLWjTmz5CaXyO+npl/6oe77roLJ0FR0qPavLgriqIoiqIoyl4hkmaMe0Rj3EvETz/9BACuT6dU3IkcL71sqdIlUwC47MCBA/fsTpQDLPPEiRMBBO8nVXnpeS99s2WGSsL5OOS56dev3x7cE6W8uf322wHATXJx0EEHudMaN24MwGutIVTDqH798ssvADzVj/VPKupU9nitcf1AYp8JboNqHpXChQsXAvCcpw4++GDf8szA+PXXXwOAeiwrlZq77roLAPDyyy8DALp06QLAU7dZP6iOy9h3jqeSzSHgPTdzc3N9Q5kplWq9dKqR+VbkcjIu3R4n1y37r7Fs7KNCxZ3753OYcxT38oDnQ1HSpdq9uCuKoiiKooQxd8ky1KtXD11aNABghcUAiNYp8o8r9v/OcjqpMkTGHcY4jH9ILDz7L7jyyiv35m4o5U0kAkTSiF8PsEgu0WZMmFN9NYFuM/zil6oCVeUDDoj7uzIOlkgV2V729NNP3/MFriCmTZsGIFEpBRLdOaiSbtq0CYAXa8hlOf/WrVsBaEx7deKBBx4A4F0THBIq6tJtQjpfUGFnvwpec4yrB4B27doBSLw+pYc8FfXFixf7plOxYyuAKmNKVWT8+PEAvPwLrIO87mX/LRk7TvcmwGs9pdIu3dgI6ytbvRo0aOBbt2zxlvlUvvnmG3ddXbt2BZCYFV229PJZznsG1ymf6XaLXOfm+8fXsXuHOz26O67YF22Lr6c4J/6cK+JwazzT9+6tTovFphzfbw67vDINStUgNzcX9evXx5aFM1GvbuI7UsL827ajwVEnIScnx9dilS6quCuKoiiKokgy4h8BptjrnFpMi8jajuLudFaFEAj8kgQQdRT3r447E5dccsmeL6tSbaj2L+4lVXsff/xxAJ4iKJVAoGrGwLL14Omnn3bHMZaQKgtjB2+77bbyLZxSabj33nt9v6nA81pivaJaJuNXqeSxvlFFY3xqs2bN3HXLPheycVFmdOW2NH+AUp3gS+SIESMAAB07dgSQmEOBdVS6t1A9t8dRxZZZsmU2YtZntnqxVZbLhznG2O5mYRleWZ+5DbYccDwdbah4yr5pQRmf9wT60l51MZEoTBqhMunMk4xq/+KuKIqiKIoiWbVpGwoLC9G+cV1vpGMRGcl0lPf99k+6jixHaX8zswOuu+66vVJOpXqhL+4lpLqryVWxNUGpeKjISS9pqYLJzKqEaqDtOiPdJLhsWKZFVdqV6gxfKu+55x4AnvMa+4pIJxjWHztvB+upjDOX9Zp9yjid/Z045PwynwOn24o7xzVp0sS3P4xhl8vI/moc73OVsfbFbk0vC/rSXg2IRNPsnKqKu6IoiqIoyl7BxKwkgjUY9x7/YInUdFxk+JsJ3JzZh36djSFDhpRPQZVqgb64K4pSYVAVpxJHtxgqbFTeOF76OHM5erDbLk/S8Ukqa9wG42sVRYH7knnrrbcCABo1inuas97Q+YV10c4MLnN60C2Gy8q8CxxPBV7Gl3N9HDKDst2yxnHsHyOznzOWXbrMsE8W18V4fN5T6D4T33bTwGOVDvrSXo2IRNKzeixj/wl9cVcURVEURQlh3nfL3FChw9scCAAwNRynGeMo7lnx4ey1hXjvvfcAAMOGDSvnkirVgbIF2uwF1qxZgwsvvBD7778/6tWrh7POOsvNoqgoip/KXl/uuece3HPPPSgsLERhYSF27tyJnTt3oqCgAAUFBe7vXbt2YdeuXSguLkZxcTGysrKQlZWFRo0a+f6i0aj7F4vFfH/2tGg0itzcXOTm5mLr1q1uHKyiKIqilIpoNP2/MrBPKe7bt2/HSSfFTenvuusu1KhRA0899RR69+6NhQsXup1KFEXR+qIoyt6DavHf//53AEDv3r0BAK1bt/bNx7AXwAufkYkM2RGUYSjr168H4HU+pSUjQ2QYMsMP6g0bNgAALrvsstDyTpgwAYAXNsfwGxmOJ5NDNW/e3LdNdlZnCBDHM7Tui+9+RmZmJo5uH18Osfh6J81didmzZwMAnnvuudByKkpZ2ade3J977jksW7YMX331Fbp16wYAOPXUU3HYYYfhySefxMMPP1zBJVSUfYeqVF/o6DJ06FAA/rhZwHt48oWAWR7peCHnB7wHMx+4MuZ99erVvm0riqIoSmkpLx/3iJFZSZIwc+ZMnHzyyXjrrbdwzjnn+KaNHz8el156KebMmYOePXuWqjDdu3cHAHz11Ve+8f369cPy5cvx888/l2q9ilIR7Nq1y03H/c0337gxkps3b0aXLl3Qtm1bfPrppwnpwNOlKtYXvrjLl+x0X9ztVgaplHFZdlJbuHAhgOQqnqIofmgXecQRRwCAL2X7gQfG47/Z4VMmUuPrhuxszvFUw7OzswF4nVJLUkfHjh0LwLOvZOdaqerzvsuyyvG8f7Cs69atc7fBci5atAiAdkCt7uTm5qJ+/frY9MNXqFd3v9Tzb9uOAw7tjpycHF/9SZcSvfb36dMHLVu2xLhx4xKmjRs3Du3bt0fPnj2xe/duZGdnp/VHiouLsWjRIhx77LEJ6+7evTuWL1/u9gJXlMpArVq1MGbMGPz888/417/+5Y6//vrrkZOTg9GjRyMWi2l9URRFURQlLUoUKhOJRHDZZZdh2LBhyMnJcW2WNm7ciA8++MB9OXnttdcwaNCgtNbJL+3Nmzdj9+7d7he7DcetXbsWnTp1KkmRFaVC6dGjB26//XY8+uijOOecc7BhwwZMmDABTz/9tJtaXOuLx5133un7/eCDDwJIVOC5jzJBi52YheOktSQ/aGwFTVGU9JDq8gMPPOD+369fPwBePZTKukx+JuPPOR/r6MCBA0tcPqrzo0ePBuBZUnJbLBvvKbw/yDLyXkvV/8svv3S3ce+99wIALrjgghKXT6nC7KsJmC6//HIMHToUEydOxJVXXgkAeP3111FYWOhWmH79+uHDDz8s0XpZOegRa8OH857KYKYo5cn999+PadOmYcCAAdi+fTt69+6N//u//3Ona31RFEVRFCUdSvzifsghh6Bbt24YN26c++I+btw4HHfccejQoQOAuBoWpAQmg/FoyTqZcR5FqUxkZmZi5MiR6NatG7KysjBq1ChX/QG0viTj7rvv9v1mh9v99ovHEVIV4/G0HS6o4lFZo9L2ww8/AABuu+22vVVsRak2UH0GgGuvvRYAcNhhhwGA26rIOF7GvBPWX4YB0sqWTjZlgWo9HV7YH4Yx7xGRBIcx7Yxf/+mnnwAAS5YsAQA8//zzZS6TUsXZVxV3IK6633TTTfjtt9+we/dufPHFF3j22Wfd6bt27UJOTk5a62rWrBkAoGHDhqhZs2Zg8zXH0bZJUSob77//PoD4S/WyZcvQtm1bd5rWF0VRFEVR0qFErjIkOzsbzZs3x0MPPYRdu3bhwQcfxNq1a90v2dGjR5c4ZhcAunXrhkgkkuCS0bdvXyxfvhzLly8vaVEVpcJZtGgRunXrhksvvRQLFy5EdnY2Fi9e7PYR0fqSPo899hgAoH///gAS067boUNU3Bk69NtvvwGIW2YqilJ+XHfddQC8uki1m/X3mWeeKbey3HTTTQASY9nZUjlixIhyK4tSNaCrTPZP36Be3bqp59+2DY06di21q0ypFPdGjRrh1FNPxdixY5GXl4f+/fu7L+1A6WJ2AeD888/HHXfcga+//tp1y1i6dCk+/vhj/POf/yxNURWlQikoKMDAgQPRvHlzPPPMM1ixYgW6deuGW265BSNHjgSg9UVRFEVRlPQoleIOAJMmTcL5558PIN459cILLyxzYbZt24auXbti27Zt+Oc//4kaNWpg2LBhKCoqwsKFC9G4ceMyb0NRypP77rsPQ4YMwYwZM3DSSScBAB566CHcfffdePfdd3HaaaeVet3Vsb5Qmevbty8ArwMub2N2DC3dInbu3AnA87u/+eaby6WsiqIoStXHVdyXfZu+4n7wkeXj425zxhlnoEGDBqhfvz7OPPPM0q7GR926dTFr1iyceOKJePDBB3HPPffgyCOPxOzZs6vkS4hStVmwYAEefvhh3HDDDe5LOxDP1NmtWzdcffXVbkrv0qD1RVEURVGqF6VW3AsLC9G8eXOcccYZePnll/d0uRRFUUL5/vvvASS66tg+7oxxZ6w/WwgVRVEUZU/hKu4/L0pfce9wRPnGuAPA22+/jY0bN+Lyyy8v7SoURVEURVEUpfKzr9pBfvnll1i0aBGGDBmCrl27onfv3mUqgKIoSknp3LkzAOD222/3jbcbEOlYMWzYsPIrmKIoiqLsRUr82j9ixAhcd911aNKkCV555ZW9USZFURRFURRFqTSYSDTtv7JQ6hh3RVEURVEURanOMMZ94y/fpx3j3rhd5/KPcVcURVEURVEUBfHY9ejej3Ev29KKoiiKoiiKopQLqrgriqIoiqIoSlkoJ1cZVdwVRVEURVEUpRKgiruiKIqiKIqilAVV3BVFURSlelJcXIznn38eRx11FPbbbz80bdoUp556KubMmVPRRVMUpQLRF3dFURRF2ce47bbbcN111+Hwww/HsGHD8I9//AM//fQTevfuja+++qqii6coioSKezp/ZUBDZRRFURRlH6KwsBAjRozA+eefj1dffdUdf8EFF6Bdu3YYN24cunfvXoElVBRFkln/AGSm4cueGcks03ZUcVcURVGUJKxcuRKRSCT0b09TUFCAXbt2oWnTpr7xTZo0QTQaRa1atfb4NhVFqRyo4q4oiqIoSWjcuLFP+QbiL9e33HILMjPj6tnOnTuxc+fOlOuKxWJo0KBB0nlq1aqFHj16YPTo0ejZsyd69eqFrVu3YsiQIWjQoAGuueaa0u+MoiiVGn1xVxRFUZQk1KlTB5dddplv3PXXX4/t27fjww8/BAA89thjGDx4cMp1tW7dGitXrkw539ixY3HRRRf5ttuuXTt8/vnnaNeuXcl2QFGUKoO+uCuKoihKCXjllVfw3HPP4cknn8RJJ50EALj88stxwgknpFw23TCXunXrokuXLujZsydOOeUUrF+/Ho888gjOPvtsfPrpp2jUqFGZ9kFRlMpJxBhjKroQiqIoilIZWLhwIY4//nicffbZGD9+fJnWlZOTg127drm/MzMz0bBhQxQWFqJr167o06cPhg8f7k5ftmwZunTpgltuuQWPPvpombatKMqeITc3F/Xr10dOTg7qpdE5taTzS7RzqqIoiqKkwZYtW3DeeeehY8eOeOmll3zTtm/fjvXr16f827hxo7vMTTfdhAMPPND9O/fccwEAn3zyCZYsWYIzzzzTt42DDz4Yhx56KD7//PO9v7OKUo34z3/+gzZt2iArKws9evTYpy1XNVRGURRFUVJQXFyMSy+9FFu3bsVHH32E2rVr+6Y/8cQTJY5xv/32230x7Oy0umHDBgBAUVFRwvIFBQUoLCws7W4oiiJ4/fXXceutt+L5559Hjx498PTTT6Nfv35YunQpmjRpUtHFS0Bf3BVFURQlBYMHD8b777+P9957D23btk2YXpoY986dO6Nz584J83Ts2BEAMGHCBPTv398dv2DBAixdulRdZRRlDzJs2DBcffXVGDRoEADg+eefx7vvvouRI0fijjvuqODSJaIx7oqiKIqShMWLF+PII4/EiSeeiKuuuiphunSc2RP07dsXH374Ic455xz07dsX69atw/Dhw5Gfn4/58+ejU6dOe3ybilLdyM/PR+3atTFx4kScffbZ7vgBAwZg69atmDJlSsp1lHeMuyruiqIoipKETZs2wRiD2bNnY/bs2QnT98aL+5QpU/DEE09gwoQJmD59OjIzM9GrVy8MGTJEX9oVZQ+RnZ2NoqKihGRnTZs2xY8//liideXm5u7R+cLQF3dFURRFSUKfPn1Q3o3TtWrVwj333IN77rmnXLerKErJyMzMRLNmzdCyZcu0l2nWrJmbvK2k6Iu7oiiKoiiKUu1o1KgRYrGY2yGcbNiwAc2aNUtrHVlZWVixYgXy8/PT3m5mZiaysrJKVFaiL+6KoiiKoihKtSMzMxPHHHMMZsyY4ca4FxcXY8aMGbjhhhvSXk9WVlapX8RLir64K4qiKIqiKNWSW2+9FQMGDMCxxx6L7t274+mnn8aOHTtcl5l9DX1xVxRFURRFUaolF110ETZu3Ih7770X69evx1FHHYXp06cndFjdV1A7SEVRFEVRFEWpBEQrugCKoiiKoiiKoqRGX9wVRVEURVEUpRKgL+6KoiiKoiiKUgnQF3dFURRFURRFqQToi7uiKIqiKIqiVAL0xV1RFEVRFEVRKgH64q4oiqIoiqIolQB9cVcURVEURVGUSoC+uCuKoiiKoihKJUBf3BVFURRFURSlEqAv7oqiKIqiKIpSCdAXd0VRFEVRFEWpBOiLu6IoiqIoiqJUAvTFXVEURVEURVEqAfririiKoiiKoiiVAH1xVxRFURRFUZRKgL64K4qiKIqiKEol4P8DNUg1mX3lxLQAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate FDR corrected z-score maps for group-wise spatial homogeneity test\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-SchizophreniaYes_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia with drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-SchizophreniaNo_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Schizophrenia without drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-DepressionYes_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression with drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " cres.get_map(\"z_group-DepressionNo_corr-FDR_method-indep\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Depression without drug treatment (FDR corrected)\",\n", - " threshold=scipy.stats.norm.isf(0.05),\n", - " vmax=30,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After FDR correction (via BH procedure), areas with stronger spatial intensity\n", - "are more stringent, (the number of voxels with significant p-values is reduced).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GLH testing for group comparisons among any two groups\n", - "In the most basic scenario of group comparison test, contrast matrix `t_con_groups`\n", - "can be generated by `create_contrast` function, with `contrast_name` specified as\n", - "\"group1-group2\".\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "t_con_groups = inference.create_contrast(\n", - " [\n", - " \"SchizophreniaYes-SchizophreniaNo\",\n", - " \"SchizophreniaNo-DepressionNo\",\n", - " \"DepressionYes-DepressionNo\",\n", - " ],\n", - " source=\"groups\",\n", - ")\n", - "contrast_result = inference.transform(t_con_groups=t_con_groups, t_con_moderators=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have done group comparison tests,\n", - "we can plot the z-score maps indicating difference in spatial intensity between two groups.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate z-statistics maps for each group\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaYes-SchizophreniaNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Drug Treatment Effect for Schizophrenia\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - " vmax=2,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-SchizophreniaNo-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Untreated Schizophrenia vs. Untreated Depression\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - " vmax=2,\n", - ")\n", - "\n", - "plot_stat_map(\n", - " contrast_result.get_map(\"z_group-DepressionYes-DepressionNo\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"Drug Treatment Effect for Depression\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - " vmax=2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Four figures (displayed as z-statistics map) correspond to group comparison\n", - "test of spatial intensity for any two groups. The null hypothesis assumes\n", - "spatial intensity estimations of two groups are equal at voxel level,\n", - "$H_0: \\mu_{1j}=\\mu_{2j}$, $j=1, \\cdots, N$, where $N$ is the number of voxels\n", - "within brain mask, $j$ is the index of voxel. Areas with significant p-values\n", - "(significant difference in spatial intensity estimation between two groups)\n", - "are highlighted (under significance level $0.05$).\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GLH testing with contrast matrix specified\n", - "CBMR supports more flexible GLH test by specifying a contrast matrix.\n", - "For example, group comparison test `2xgroup_0-1xgroup_1-1xgroup_2` can be\n", - "represented as `t_con_group=[2, -1, -1, 0]`, as an input in `compute_contrast`\n", - "function. Multiple independent GLH tests can be conducted simultaneously by\n", - "including multiple contrast vectors/matrices in `t_con_group`.\n", - "\n", - "CBMR also allows simultaneous GLH tests (consisting of multiple contrast vectors)\n", - "when it's represented as one of elements in `t_con_group` (datatype: list).\n", - "Only if all of null hypotheses are rejected at voxel level, p-values are significant.\n", - "For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing\n", - "the equality of spatial intensity estimation among all of four groups (finding the\n", - "consistent activation regions). Note that only $n-1$ contrast vectors are necessary\n", - "for testing the equality of $n$ groups.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "contrast_result = inference.transform(\n", - " t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have done group comparison tests with the specified contrast matrix,\n", - "we can plot the z-score maps indicating consistency in activation regions among\n", - "all four groups.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The contrast matrix of GLH_0 is [[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]\n" - ] - }, - { - "data": { - "image/png": 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UFqT/apyvjttpUQ/uzXGQZkOwuV4IjDHGGFO/HHLIIZlenVNOOQXdunXDtddei7/+9a+xCRpN48aDU40xxhhjWgj77rsvAGDBggUNXJLmBWPc8/mrCy1KcTfGGGOMacksXLgQANC5c+eGLUgzw6EyxhhjjDGmTqxcuRJLly5FWVkZXnvtNVx++eVo3bo1fvCDHzR00cwm4Ad3Y4wxxphmykEHHZT1vX///rj77ruxzTbbNFCJmie2gzTGGGOMMXXilltuweDBg7Fy5UpMnz4dzz//PFq3bt3QxTKbSN4P7t26dUNJSQnKyso2Z3mMqZGSkpK8/fiNMcaYlsyoUaMyrjLjxo3DPvvsgx//+MeYP38+ttpqqwYuXfOhAPk5vtTVly/vB/e+ffti/vz5WLp0aR03aUzd6NatG/r27dvQxTDGGGOaFIWFhZg0aRL2339/3HzzzbjgggsaukimltQqVKZv375+YDLGGGOMaaLst99+GDVqFKZMmYJzzjnHSZjqCce4G2OMMc2c6dOn48knn8yZfvbZZ6N9+/YNUCLTEjjvvPNw7LHHYubMmTjttNMaujimFvjB3RhjjGkgpk6dGjv9pJNO8oO72WwcddRRGDhwIG644QaceuqpKCysq7u42VI+7gVVVVVVdVyHMcYYY0xe3HHHHQCArl27AgDatGmT9TsfS9asWQMAOOKII/Je92OPPQYAaNeuHQCgQEIX1q1bBwBYtmwZAGD8+PG1KrsxyqpVq9CxY0dc2mY7lBTUPDy1rKoSl6/7N1auXIkOHTrUentW3I0xxhhjjKkDacU9nxj3umHF3RhjjDH1zn333QcA6NWrFwBkvMNTqVTWJ1XxysrKrOX5nZ9z584FAJx++umZeRhqtPvuu8eum/A7H3l03evXrwcAlJaWAgCOP/74Wu2rablQcb+63XYoKaj5sbysqgL/u2bTFfd8LCeNMcYYY4wxDYxDZYwxxhhTZ2666SYAYez6gAEDAADFxcVZ83EgJOPQi4qKAIRqOGGM+6pVqwAA/fr1AwBcdtllmXlGjRqVtSzXyU9CVb+8vDxr3RUVFVlloOX1n//8ZwBhLPyZZ55Z7b4bs6XsIK24G2OMMcYY0wSw4m6MMcaYannooYcAAD169AAQKtTRuPStt946axmq3Pykus1lNm7cCADYaqutAACtWqUfScrKygDkxsAzRp7zR6dxHi7DdTG5ELdFVxkq74S9AFwPewm4Ty+//HJmXm6D6/jqq68AAEcffTRMyyWVpx1kXRVzK+7GGGOMMcY0ARpccZ85cyYmTJiAN954AyNGjGjo4phmBusXKSwsRM+ePfH9738fV199Nfr06dOApTPGmMbJgw8+CADo2LEjgDD2m2ozFWqq6EDoHvPFF18ACNVtojHsVMGpcnOda9euBZCrvFMFj3qzcxrn4TIaR89ycpv8JPydZWavQO/evQGEyn503RoX//TTTwMAVq5cCQA45phjYFoOWyrGvcEf3I3ZElxxxRUYMGAAysrK8Oqrr2LmzJl48cUX8e6772a6Uo0xxhhjGjN+cDctgkMOOSTTo3PKKaegW7duuPbaa/HXv/4Vxx13XAOXzhhjGgfPPfccgFA9V7WbKjM/qY4DYVw556V6zXn5O9Vszkc1myo4PdWjaj4Q7/eumVG5jK6D2+A2qf5z/zQGnvOxzPwEgLZt2wIIY9z5SXWfmWB5LEePHg3T/CnMM8a9rgmYHONuWiT77rsvAGDBggUNXBJjjDHGmPyw4m5aJAsXLgQAdO7cuWELYowxjQC6pjB0kKox1WTNakqlOhr7vWHDBgBhXDy90okq8rz+Mmac8encJtVyVdX1exQuw3VQSWc5uU0q8iwz5+N+ch9Ytuh+alZWLsN52MNA9Z7Hdu+9904st2n6bCnF3Q/upkWwcuVKLF26FGVlZXjttddw+eWXo3Xr1vjBD37Q0EUzxhhjTBPHg1ONqUcOOuigrO/9+/fH3XffjW222aaBSmSMMcYYUzv84G5aBLfccgsGDx6MlStXYvr06Xj++eezuj6NMaYl8thjjwEAevbsCSAcYNm+fXsAwLfffgsgN5SEMCwkuiznZUgJP/l7t27dAIShJVwnw1c4cJQhMfzOUBuGr0SnJS3DdTL0h6FATKy0dOlSAGHIDPeb4Twsc3Q/CcutCaK4Du736tWrAYTH+ogjjshZl2n6FCLPUJmqmuepDj+4mxbBqFGjMq4y48aNwz777IMf//jHmD9/flYWPmOMMcaYxoof3E2Lo7CwEJMmTcL++++Pm2++GRdccEFDF8kYYxoEChdqi0jFumvXrgCybR+BUIGODtSk8kwVnINNqXL36NEDQKiYqyq+fPlyAOHAUl2vKtzRaSwHv/OT66TinqS86wBZ/q4DaqPrVmgTyf3RngeLRM2bVJ4x7qk85ql2+TotbUwTZb/99sOoUaMwZcqUzIXaGGOMMaYx02gU9+nTp+PJJ5/MmX722Wdn4sWMqU/OO+88HHvssZg5cyZOO+20hi6OMcZsMR5//HEAoUpMdZgwLpsKdadOnQBUb8XIGG/OQ6WZqjW/U2mncr1kyZKsbVJxpwrO5TUGHggtFzWJk9pCcht9+/aNXTcTTmksP7cVjatXOA+X5X6o1SSPC4+9Xc2aF3nbQdZNcG88D+5Tp06NnX7SSSf5wd1sFo466igMHDgQN9xwA0499dRqL8zGGGOMMQ1NQVX01dUYY4wxzZYXX3wRQKg0q0LN2HW6qTAund+pGlenvNcEHzuYoOmTTz4BAKxatQpAqKxTTKFSzzj7zz//PLOuPn36AAh7DqiUc3+oxHfo0AEAMGjQoNj9qct+6P589dVXWd+TehB47PfZZ59NLoNpeFatWoWOHTvijm47oG2qZgFwbWUFxi+dj5UrV2bqZW1wjLsxxhhjjDFNgEYTKmOMMcaYzQPHkDFWnQo147D5SXWbSjXdVJKU9qirDNF5qH5rBz894rltquVUwzV8UWPmgdCpRfNycJu6f9wmt6H+77rNuKCEOHcbIDxWLAvj79mLwd/5yR4EnpuxY8fmbMs0HVpcjLsxxhhjjDFNkcI87SDzmac6/OBujDHGNHOoTFP9pVtMx44dAeQ6n9AUgup2Uix41NM8H7U6Ol1VfJYxSdVn2aN+6LoMy6P+60mZVXVbSWWjgh+H+tfT+163zd+p/jP23f7upjb4wd0YY4wxxpg6kCooyCu5Ul0TMPnB3RhjjGmm3HzzzQCAIUOGAAjjrxnrzVh3qr5U4qlu18V1Rb3QVe1mWbhNqv5JajldWjh/FO4Ht6Ee6lynxsJrmVjmTbEH1vEB/M5Yd/q7M7ad22JZea7OOOOMWm/btBz84G6MMcYYY0wdKCgsQEGq5hfdurwMA35wN8YYY5ot9GGnWp2kZlMlptsKUSW6OleZpDjwpAcVTmecvW6Ln1So47ZJGC9O5Z37x3lr8p9PcsKJIxrXHy130rFh2dTXnUo7p/NcGVMdfnA3xhhjjDGmDqQKC5DKQ3F3jLsxxhhjsrj//vsBAL179wYQKu3MSsq4a6rCjOnWmG+qw6p6M86cynZ0HfnC+alur1ixAkBuXDopKyvL2ofoNO4Hs6/qOuhfvymx69EyAqFSzmNIqPbr+ADdTz323bt3zyozz91xxx23SWU1zRtnTjXGGGOMMY2eW265Bf3790dJSQn22GMPvP7664nz7rfffigoKMj5+6//+q/MPCeddFLO75ucCKswhYI8/lBYt0dvK+7GGGNMM6NDhw4Acn3b1VWF09WpheowFeyVK1cCCOO7uR56lkfXoeq9wuksm/YCJMXTcz72AkSn6X7pvLV1y2GPg6rkALBs2bKsbVA5p2JOdZ/TuW09J4THi9vgfCab++67DxMnTsS0adOwxx57YMqUKRgzZgzmz58fOz7g4YcfzvSOAOnzNnToUBx77LFZ840dOxYzZszIfNe61Niw4m6MMcYYYxo1kydPxqmnnooJEyZgyJAhmDZtGtq2bYvp06fHzt+lSxf06tUr8/f000+jbdu2OQ/urVu3zpqvc+fOm1S+glRB2lmmpr884uCrw4q7McYY08yg2stPusVQTaTqq/Op9zrhdCrY/E4lPm6dqmqrks75GRvOGHcq0KpMU4mObjNJxaZSzv3Q+HMtkzrVcDmq6NFtUhnnNnSd6o7DdbN3Qo8llXtV8E3Ihg0b8NZbb+HCCy/MTEulUjjooIPwyiuv5LWO22+/HT/84Q9znJNmz56NHj16oHPnzjjggANw1VVXZfUk5UuqsACpwjwGp6JuD+6uHcYYY4wxptGydOlSVFRUoGfPnlnTe/bsidLS0hqXf/311/Huu+/ilFNOyZo+duxY3HnnnZg1axauvfZaPPfcczjkkEOqtQNtaKy4NwCPPPIIAKB9+/YAckecq/KxfPlyALUbYc5R6V26dIldp26TWfSOPPLIWu+PMU2Je++9F0BuDKteqJOyPrItjR8/fvMX1phacNNNN2X+HzhwIIBQ1aWaze+sx8yYSoVRVXPGZzNWOBozDGQ7vySp9Pq7KvG8T7GMSUo2tx1VTLnOJCWd9zpuQ1F1POn36H5qDDSddXiseOxUtWdsPDOocpssO88N54+ezzPPPDO2fCY/br/9duy6664YNWpU1vQf/vCHmf933XVX7Lbbbhg4cCBmz56NAw88sFbbKEilUJBHb0mBtJPaYsXdGGOMMcY0Wrp164bCwkIsWbIka/qSJUvQq1evapdds2YN7r33Xvz0pz+tcTvbbbcdunXrhk8++aRO5d2cWHE3xhhjmgFRJVt7WRmXzThqVdA5H905qDBTXabXuCrT0W2q77pmK03qxaLi3KdPHwChkw2nq9tMNAZcVWuq3lSvNQZefeq1J43TVcmnUwwQZnolGtOvSvvXX38NIOxRYA83lXpV8JPGCLRkiouLMXz4cMyaNQvjxo0DkD73s2bNwhlnnFHtsg888ADWr1+PE044ocbtfPbZZ1i2bBm23nrrWpdxS8W4+8F9M8JwFTZ4dkluu+22AHIvEHoBIuzie/bZZwEA+++/f+I2Oc+gQYOy1k20m5QXBpbx5ZdfBhB25fFC40QQpqnxl7/8BUCYoEUfGvSTaMiM/k6mTp2a+V9v/j/72c/qVHZjjDHZTJw4EePHj8eIESMwatQoTJkyBWvWrMGECRMAACeeeCL69OmDSZMmZS13++23Y9y4cTkDTlevXo3LL78cRx99NHr16oUFCxbg/PPPx6BBgzBmzJgttl+1xQ/uxhhjjDGmUXP88cfj66+/xiWXXILS0lLsvvvuePLJJzMDVhcvXpzjyDN//ny8+OKL+Oc//5mzvsLCQsybNw933HEHVqxYgd69e+Pggw/GlVdeuUle7rR7rHE+K+6Nj1mzZgEABgwYACBU46jkafegdodpdyO7Mtnld9dddwEIVXEgVPOHDBkCIFT+oumoo9sk2qWnA3noZ8p9qu1gDWO2BHfffTeA7IFzDAlQBZ3tK6l7O0lx18FucXDe3//+91nbSBocrt31p59+evU7akye8FqvdY29rgw/YdiHhtAk1fOkuhudlvRd74HaBktKSrKms72w16w6uA6GynAAK++BSdaUuh9J+xB9IExaRpflsVSbRx57LbM+G5hczjjjjMTQmNmzZ+dM22GHHRJ7Tdu0aYOnnnqqPou3RfCDuzHGGGOMMXUgrbjn4SqDeAejfPGDez3x+OOPZ/7XwT18e+Ybvto+UhHQ73xLpMLBATscJBRNCKEDh6jAs7uHb/I6EInf1fqL36nO0Loyup8/+MEPajgqxmwe2OvEniLW06gyp0qZpmFPUtwJ101UsYuqYtpzpaq99mhFU7ZHy0L7N1X0or1wXIfj6I2iVo1Abo8v1V+1I9aeXq3LXI7z895SnR0k51V1m+vUbbIdsG2xPbO9xPWKaU+CDirV0AmWhfun6r4erzibSC6rvXo8Jtpbwf3kcjz2a9euzdpGUm+7MVH84G6MMcYYY0wdsKtME+Hmm28GEMaWA8npnFXl5nyqeGgMoRIXe1hTPKKWiW/+uk1V/6kIcH7uS3Tfa7JiMmZTobJONU2TJakqGFXHkhIsJbWJmpS2pPYa3ZbGw+s61M4uye5N7fOi6j/Lx/bHcpx22mmx6zIth4kTJ2b+//vf/w4gVIG1l4cx4KpQs36xh5c9u9pTzPVGM1km2RoS7fnV+5a2B5aZ81enuHMeLsN4eV2nzs9eZv1d2zDVdQAZL3FO07ErHDfAY6y2lpzO+6ueG643ej5N46egoAAFqTwGp1bW7cHdCZiMMcYYY4xpAlhxz5MZM2YACBUFVaLXrFmTmZfx5Xy7piJGtVpj6vi7xrcRjUvX+NnoNFX1owp5ddtgmfg794/7QBUiup/c99tuuy1rW1QL6K1qTL5QYdfYVlWkkmJm41AlXWNbVS3Xdamapop9deg8XFavAUn7Vd02NK6eCjxxT1jLhoq5Ku5aB1nHeN3mNV5dZjhde5CXLVuW2SbHd2lbUTid21D3M6Lqt5Y1Ok3bTtK6ktT+JAccfkb3U5NZ8X5JJZ3L8Jjxvqrja/Q4cB947kzTIlWYQiqPwampqrpp5lbcjTHGGGOMaQJYcU9g+vTpAIB+/foBAIYNGwYgfGOmyvXxxx8DAL788svMsoyt48hxvnUzzo0KiMa7qgLCt3r1vo1zwdDfuAxVFsbxcRn1suanqi5cD1M0R/ezV69eAIDtt98+a53cBr3fFy1aBAA4+eSTYUwcd9xxB4CwzmsvkypubH81ZUHNB9ZxXYfG51aXYVVVei1nUnvT+Tg9qc3HLZtU/t/97ncAQlXPCnzLgnk+dBwT0brJtse2tnTpUgBh9myNGdfeWSBst1TQk8aJ8L7E37lurffqSkOWL1+e+Z+p6bVnTHur2G7USS2prCwL54/uJ3/jMeP9kqo8M5F369Yta3+5TXXD4ifPWTRHi2k65J2Aqcox7sYYY4wxxjR7rLgLVP4GDhwIIBwdrkoZVS3O9/7772fW8cUXXwAAevfuDSCMe+PbufrfJvnMalwvicuqlpRpTUfYJ2Vy5KfG7lFJ4D5FXQO47xrPyHV17do1az95bMePHx9bVtPyuP322wGE9Y1KlNbLJDVNFbqoKp6U3VDXpeNDtB6rUqmxr3EkucfouJakdVTnLJUUH0+0x4Df7ULTsjjllFMAAH/84x8B5GYQZd3TzKnffPMNgPC+RdcYjXWPU7aTsg6zLnLsCl1Z+Du3zXuG5jDR8SdRxV094ZMyu3799dcAQpccTud9mvfIJOU9ej+m+s5jwR5tHkveRz/99FMAYQZy3j9ZBi6v8ffO0dA0seJujDHGGGOMyWDFPeChhx4CAGyzzTYAwjdovsVrRjS+cfNNmXF2QKhOM96NSgdVBXVwIepxmxQ3W52Pu8b1qZOGxrprzB3LSHWB+8D5qU5Ey6+uOZppj9vkseWxPvroo3P2wzRv7rzzTgCh8qYKe5JDhKpgtYlt13akceRJ7hJJKjmJeqsnucDo9CSXDZKPUw1JOibqM6+xvSz3rbfemrX8L37xi7y3bZoOPO8a28172Oeffw4gdITp27dv1nysZ1TgVS2Poo41VJ4ZJ6/3H9ZFrpP3HVXeta6zrFGSXGVKS0sBhCq93rd4HDQ+nb3YcW1W759U1DmdznLcDz4TLFiwAEBudvSk3jPTtLCrjDHGGGOMMSZDi1fcn3zySQBAnz59sqZrJlF+51s41QfGqkWzr3Xp0gVAqDJQeVb/W43FUw92dc7Q2PeoOqej9FXR4Do11l1Vfs0Sx+ncp+h+clkeC1UktaeB8/GTx37s2LEwzZeZM2dm/lfXGM1equq4OqZo9ka2IVUT49A6z/qqar+i3stxSmPSPEnl0f1J8nvX/a+O6jK7xq1TVT4q8NGynH766TVu1zROpk6dmvU96b5C55Ntt90WQG790LqnijTvDUDu+JDPPvsMQG474L2Q7ilcjk42SblN1Pc8Oo1w27w3c50sL8vCMvCaROWdZaKjHNcf3U9ug+tMypxMeGy5DZZJr0W8Z/Lcuf01MfKMcUcdY9xb/IO7McYYY4wxdSFVUIBUquaH8lQtQiLjaHEP7g888ACA8O2ZXuRJiplO53d1hom6unBkOd+6o7GwcdtQ9U3Vb1XNqeRHlRBOY7mSFPUkhU8VEW6zQ4cOWfsU3U+N/09y0uAy6pdL9Z9+74xBPPbYY2GaPlTao57ESTHpSW4USQqWuiOxjlUXK6q/aQyrqvmq6ieNTYkrvzotae+a7n+Soh7nIJM0b9K1KunYJTn1RNdv5a/pwnsbYRw5s3KyHrC3WT3YdfwT6zh/Z/w247mBsE1RaVcFnooz7yva68VtMi6dY6p0nAkV7Og0HS/DdST1tHE6r086RoRx6RybFd1Pwrh4bUu6Xzy2PNa813GbVP/p4GNMdbS4B3djjDHGGGPqk4LCFAryGJxaUFm34aUt5sGd8dR8o2VWU82elpSpLSmrImO+6ZIBhG/+fIsmGoOqypnGqfO7+kbzbT6qmqsvtCqA/J3r1CynqrppjGFc3Cz3XV06dL+0F0B7Ftj7QbXGse9NG3qzU12L1sUkRVzV4iQVXONutb5GvZZrcmpQlU+VdaLXiDi0/bDts05rz5dmrdReOd12dF+SvN9VWSTaHvX3msYZAMC0adOytmGf6cYFe5Kj7maMXef55fX6gw8+AJDbs6SfrO96/WbdjrsnsOe3uhwHQHi/5H2YMd8KM3ZzW1yOanp0HSwnl1HYDjSjedJ83AfuE8dmAWFvMXs1eK3T65OOvUnK1tq/f38AoarP5V988cXMNpm13D3SpsU8uBtjjDHGGLM5SBUWIJXH4NRUpWPcq+XZZ58FECoRqphrjKwq7qrKEVXWom/5SSp1kqKnaPw81TiNsWUmOCBUV/gmz3LptpNQ1ZFlUGUwqq5wG0nx8qrk6TFXlVHj6Xnu9t9//2rLbhoHt912G4BQFVM1HEhWltnOtMdIY9y5zqR47ugYjKjzRJSkTMXaRpIyAsfFqSd5vSe5xej+JDlMxfm/J6mZmhFTexw0hl2vR3pM4/aZ62Y2TivvDcv06dMBAIMHD06ch+eM12sq77xXaEZVdS2juqzLMTacvwOhOq09ZkRjvnnNT+oFojMMt8Hlou1cy8lltD1rW9KxZEntI05xpxONKuSczmugHkseO6r+LIPmQIl7RuAzDM/5ySefnDOPaRk0+wd3Y4wxxhhjNicFedpBFlhxz+XRRx/N/M/YMb7x8g1Z3VVUFVbFnSQpaNF4dr5tq5sKleQ494botqkc8He+tfOTqmVU6dCeA6ojGmNbk1c1y0i1UueP7qeqhDqvjt7XT1XzuD7GHjIbXfR8jhs3Lrb8puG44447AGSP8wBye3Gi09QxScc/KFp/VdmOi3FP6iVLagtJbi3aDrV3IIpmIFYVWx06tIcrKf9CtKx6DNWlqqZeQnUHSfLBjv6vbZzr+MMf/gAgvM5YBdyy0F1F47eBsA7yk/Po/UXvR6oes35w3dqjFo0VrymPgdanqONU3HxJ2Y2j+USIqvxJ2YrVRSaupyluH6L7yWX0Xs9rBI9d0jVHewm0LDq+AAh79aOOOqZl0iwf3I0xxhhjjNlS2FXGGGOMMTmwp2OnnXYCEPY4RRV37YWiEs1Y7f/85z8AQnVYe521N5qfdFChGszlo8smjWNSdZ89Sup7rr1G6qgWXa86qiWN2eB83KaWSdEyRfeTir9mRdcebsKy8Vx88803AHLVc5aV5yjas8Dt87izDvz85z+PLb9pvjSrB/c//elPAIARI0bk/MaGwIalFlfa2LXLuiYLtugFkxc2vZjyU7vk9SKl3e1ssPyudpHRaZyH3Xps+NxfHRynXZssI9fN7rm4G0NN4Q06oFWPbdLFmueK22bqaSA8x6eeemrsNs2Wh/VdiQs3q8kWLSlpkE7npw6si5JkcarJmpISFOl+KNH5kgaZsis9ztYxCttb0oDRuPJoqItukyRZ3Gq3fdLxiM6TFF7Ba9aMGTMAABMmTIjdT2OMaa6kCpGnq0zdttOsHtyNMcYYY4zZ0hSkClCQymNwah7zVEezenAfNGgQgGwljIqzJkMiSQPVqktvDuRayEWTs9CakegAlCSoWjElNZVMTeXMNMtRxZ3TmIaaA3CovnH/ab9Vkz0k1xO1wAKy9zMpHb3aYKqqn2Tlx+U0EUy0i5Ln2DQ8TLTE+qltKFo/SVIPl6rcqsTrQLEktTgO9jbxk9cEHSCbNABTrRBJXAI0llsH+iXZPRId+FpdD4S2Xe114Cd737Tc2rOXtH9J+xq3Ln5yP6y8b17U3livtUBoxMB7AO8nasGoA6OJGh0QDVuJhp4k3S+1HrMO897IbbHO6gBSftKw4O23386se9iwYVn7qfduHgfuJ+so59cQm6SEZdH9ZM+z9jbyWLHHW+0gWQZ+13PB46E2k9H9YTmiybZMy6JZPbgbY4wxxhizpUmlUkjlMTg1VeHBqRnlb9dddwUQb52m6p+qTTq/JmTipy4Xp6JT3VYFT1U2Vd+oLKtarskcOF9UXeE0Dnph+fkGz23oQKOkWFpOp4IQtw96DFT90QFIqiqSJIu/uLKxB4Dn/Kc//SlMw8A6pwqcnv+4OsO6oOpYki0r59c6lZTcK4q2YcJltbzaY6TWdFp2IGzzqmar4kb4u9phkiRVPIqWR9u2JrNKSu6SlIAmeiySLPb0uuCY9y1Dly5dAOS2n+i5Yz1g3WR71XaqycP0Xsn1aPuIS1yWlEiJdO/eHUB4HWc75j2OZUiyM2Y9jPa8cpq2Z/3ksaLlMctCdXz58uXV7kN0P3XfeWzUFlLLlpTQUBM6VtebwXWxDpiWR7N4cDfGGGOMMaahyDsBUx7zVEezeHBnPLYqS0D4Jk+1QdXhmmI3+XZLhSAp5Xp1JCWjUBWLb9eafIVv9apCRGO/O3XqlDUPl1W7rbiELnFlS4rHjy6XlFSC+6VxfklxyHouktYX/Z/n3Gx5mO6eJKnFjOeMO38aP66KuqpcqgJq3WD9jlPF2J40vlSVZt0Ge6u0rXObUfcWVekZd67Jb1gGloltWFV8TTxTneLObaial+Smo9tIGqMQnYckqbU6vx57Uz8w2dnAgQMBhOeUMdHRXksdM6Rthp/z5s0DECq4PXv2zFpe2zfXx3FV0TrAcvC8Mxac6jahYxjvEVpvCPcneq8DgDfffDPzv65bY/JV/eZ33tN57+Tn119/nVW2uDJw36neEz1WPA6ff/45gFxVPykRpF5PgNxjy3bPOjF+/HiYlkGzeHA3xhhjjDGmocg7AVMe81RHk35wnz59OoAwtj3OK5lvyUlezUnx1qr0cf58XFk0tlfXqdPjUsMDuT7NVADj0kBzXo21VcWsJp/opNja6noWVMlTVxyNEU4aV5B0jqLb5n726dMHQFgHnGp98zNz5kwAuQlMtG5o2u7o79qbpO1T43A1blvnV0U7WrdUSeY2tV1pfDbXSeVO22VczLzGj2v74jo1DlcdbtR9gkTVfY2L17hyVd71GGoss7prxFFTz2KSBzy/O1lM/UBVWOtXdedO67m2Id5XmC+jprhsrW/Ruso6RXWYajjbHu8NGiPObRGWkfeQpDwH0XVpG+S9UBV4PQ5sm7y3q4LPMWfRMiZdd3hMNFcEjy1VfI0E4Dmo7rlC1XnuJ+uEaTk06Qd3Y4wxxhhjGpqCVAoFeYRP5zNPdTTpB/ftttsOQK6XelT10dhZje/j7xqHzXUxRq8mX/eocp3kOZ0Ef+ebs6pWfBv/6quvYtcfncb9oMerZlHkNmoqU02ettHfNJZWFXTGM1J10fEDGoOpqkpU6eA0rot1wGw+7r77bgCh8pREkupEfrhjOh60qhp1EAAKoqpTKv3/tJf/nTWPxk7HOabo9pPSrKvqx9+TVPK4uHMqZzVlUGWb0Hh7lpvr4f7F5aHgujSrszpaqPNOTT2BcX7uSRlSk5T1JJ96rtPKe93QcRisC+rOAoT5RLTnS+OnGduudVPrDdVizheXMZmqNT+XLl2aVS7GlSfVEx0fQ1hGxojH+Zv36NEja1u6Du0V0uPB+yvvt9wHXgfYWxDdd87DY8Njrdcenh/uB7el9zouz/bC/Y1uU8sfly/DNG+a9IO7McYYY4wxDU2qME8f95Yc4041nG/cVJOjihHfUtV5Ick/Wafr2y1J8i+O/qaqtr7xq9rAt/RevXpl7YcqalQUollMdVQ6FToeI1XVqvOhj9vPJIUEyFXn9djpMVcFSHsz+EnFJKo2cj+oRHD/zOaDSlNNTkwab/uTndPqUlV5Wm2qXJdWpVBZveJelYr4Qgf//3x4kCUw+F5QlK5DN856D0Bu7w2QnLlX66XGm+v4lpqcp6L7nNQLxXqaND6AbYK/U8EjVAHjyqO+7dozoL2K2u60TWtMMJDbhpOyyNbUk8dt0ZnoZz/7WbXzm2zYFnltVLezOPWV9xPGnbNXh9+J9rgk5ePQXqJoLzT/f++9dLuk6wqV6STVO8lRjNtmfhK2i2iPG6dp9tGkdWq9156GlStXAgAWL14MAOjdu3fOfiY5M2kvRdK4Ls3mqq5ApaWlWWWJllN7QKI9AaaByXNwKur44F63pY0xxhhjjDFbhCapuE+bNg0AsMceewDIVXmiihHfvqlSM96aCjxRJ4wk72Z9c45TojWroKrb+qavSnWSMwVHu/MNO6ouch2cR72ck7Zdk3qqy0eVNlUydR6NV1SlXV0vOB/VSVVOgGTVh3XitNNOi90fU3vo2EMVj+dDzzvPyc/3GpCevj5wraDCvjEYZ7IxUJtZ71W1jXFUqGIbaBX0LlHlDtZ5zve2T/8eKPB3/WtJZln1RCbshUvqQVAFWz3Y41ygtKcuqQ1r9kn9pEKprhRRpV574rRd8XyxTLr/qsqyTFxPVN3XMSU8dqq416TWVncdMTUzdepUAGHvI88D72s6TgoI73W8njL3Be8f22yzDYBQWea4KK03Wt+0JzRav7hN1iH1Odde17j8C0BYR3mfri5viraxpDFURFVyzZfCMnPb3KdoGXXfOa+uW3v1OE6ob9++AMJjyXNDFZ3bjLbVFStWAMi9l7MMrCOnn356zjEyW4aCVJ52kHUcnGrF3RhjjDHGmCZAk1TcVQngG7bGhQLJ6gCVCnVoIKoGx6m/0W1HSfIpVx9WVaH4dq0KwRdffJFVdi4XdRCgSkA1hTGBjM8j6oebFJuapKZH9zcp7l/95jVbJOEx5vz8VDeAaO+IOhvEedqbuvHwww8DCFW9JBWZ/HyPtHpUFSjsVRvS9ZIKe1V5vOKuse6ZVhOJcUdGcQ/izTlWg99bFWWt6793TTtpzHi7NKeO6xiKJDTmXZ2ptO5FYZtUVVtVS3VYUncJbTPRMrM9aDmSlMWkGF/1t48jqXxxWaqjJCmkOr6APWWAe8uqg9dGKuqsH6yTjFuPZvdkneF4oG233RZA6GzCDKGMr+Z3xqOr05q6t2l9i07r3LkzgNyxYJpZuCb//6RxYNW5R9U0lowklYHrpksNVfJoXec2uQ51W9Jsrbwf81hzeZ4LfmdsO5eLnk+Wi9clvd8m7afZcmwpO0gr7sYYY4wxxjQBmqTizrfRZcuWAQj9auN8ZTWGlEoFP6lUJ2UIzSdzqJKkMtXk5MIyahw3VXTN9MaYNyDsUeCyfCtnzDu3maQ2apmSsrvm81bPbatXddK6k8rC8xztSVEvW9YBx8zWH1SHqCJFY56BUE366fB0b07V+sCjvGxN1veM4h4o8JlYd8aOV8TXpWiMYKiwB17/Qax7RmlvHZQto+KnPycMSztp3PXO0kw900yqNTlZaGZRzYkQbQvq/c51UEmj+0dSRlR1sFFVM3pN0SyLrPtJ/uz6nei1kWWOXkdZjqR8Duo7rYq8jrXRNq+9cCab2267DUBuPpEkT/Y4D37eN1jXGE/N+wfvER999BGAXLcZwjpc3TnlsmwPLA/rrI4h0zqrYyK4n1wv54+WUbPJak+TftdxJiwTjw/rP3/nthh3Hl2Htm/taWN52ZsxePDgrOV4LjSTqrrEAbljjJIyxbLOnHLKKTBbloLCVOxYrdz56va8YsXdGGOMMcaYJkCTVNz1jZ8qF6fHOTDUFAOdFK9dkyoX5+Ou01iuJA9kvknr6HZua8cdd8xajm/1w4cPz9lPddJIUvtVZSDaM6EqZXQ/kzLE5tt7UZOHvMYDR/ddy1VT3LKpmUceeQRAGNOp9ZB1iUp7pca06/fgs3JDcM7KAgU+4ypTs+KeKgpcJ4oCD/LiQNEqLgnWFahOrYN1sc5F6qS2AW1//J09O6xvGqeqKlu0J4Je2XTq6NmzJ4DceNScfZU4c/Z2LFy4EADw2Wef5ZRZczPoeBztKWBboSqocbmq1kZ7ErQXU9uwjv1RxVDbqRLd1s033wwAOOOMM2LnbYlQTdZ7iDodqYtPFP7Gc8NzxjqqrjJJWcJZFsZhq9IbXeaDDz4AAAwYMCBr3uryn0Sna1w910tfc5Y1ul/qYKOKdFI+h6SxHwsWLAAA7LrrrgDC9gOE7YLXSrZ/Kussr2YyJzz22m50ubgxZawD6mTDuuDxXg1HQZ4+7nl5vVeDFXdjjDHGGGOaAE1SpuSbP0eu8y01LnZa3+yTYi2TvifF4CVlDowuo4oz34gZl/3+++8DAObPnw8A2GuvvQAAQ4YMARC+hasqEfdGrdNUPaPyx22+8sorAIAddtgha5uMudP9itsnPRZahtqOD0jyu48eW25DPXqdPa7uMIZT/cF5/E/aLR0Lm1HaJaZdY9zL1wTTywNPYyruFfkr7oXFgRpM5b0k8H9m3GoQ4841Zepa4Exzwi7p+jx9zpc58ad0jWCdV0VaM69qvoG4Xh5V59WxpaYMo7ymUZFjror//Oc/mXnmzZsHINczWx1HWBbORwWeriHq0R7nBMP90Fh09Y7XWHh1f1LilGG7YuTCc8VzSaVXXUx0vAKQ2xPDZVnPGbsd9X4HwnNDJZ3zaW8n16NjYACgX79+ALKze0fXUZOrmXrJa+/1wIEDc/ZTY9fVMz5prJXeyzk/90F7l6KwnnO/eKyohvOTvWQ81joWQHu21A8+ui7tedeej2gPiNmypFKpvJ53ajNmMo4m+eBujDHGGGNMY2FLhco0qQd3xkAy5kz9W9VrOPp/TQ4mSSQ5xKgCHacWqRqiMfnMnrZkSTrb4zPPPAMAeOuttwAA++23H4AwblZV9GjZkpQXxsjOnj0bQG6MIMugGeriMsLqd913VeySvOBJUubKpPVE94uwDtAZwXGytefvf/87gDBeM3rcJwztkZkvE7seKOuh8h5k06QaGCjrGcV9AxX3wHGlFjHuhRmlPa0iU73nsq2CTy5RlXGdCRxsysN4dcbADhs2DEBu71JSndffSdx8Wndr6ukjNcXh8hoAhHHDn376KQDgjTfeAAB8+eWXAEK1ngqh+tBrPK32WEZVvqRetLjxJ9F1JLV1/R6dzn2/6aabAABnnnkmWioPPfQQgNAxTX3/k4iqx+xp0bFVzAvCaz/ri2YMpjpMZZ3x2+y9Ze9Q9BxSOWa5WfdYfpYlzi0p+ru6FWkvQNRpTBVmdTzSrMZah1W5Zo+VquLR7fAYsL6zx1dd3NT9h77t/J3ngmVQP/7qzrdeM9Qjn3Xo6KOPTlxHc+WWW27B9ddfj9LSUgwdOhQ33XQTRo0aFTvvzJkzMWHChKxprVu3zhn/15hwjLsxxhhjjGny3HfffZg4cSIuvfRSzJkzB0OHDsWYMWPw1VdfJS7ToUMHfPnll5m/RYsWbdK2qbjn81cXmpTirjF3qmJpJk4gfLNXpasmRUhJcpeJeyNW31hVPFSpGzFiBIAwdpWj2e+77z4A4ds9PWB32203ANletlRLuQ568qq6xthAroOwTHzLTFLaotOTVEVdpib/+iSP6Dj3DqLuCjwWju+rPerznEql8Is90/GdGQ921Ky0U2EPP9PrrQgUeKrlFYECXyXtgNnkYhX3YNlWJdk+6KS4OF1XMt7xG4N2X5muOx07dsTee+8NIFQQVXlTdUzVPlXBozHzSdlMtW2wHap7kyqQ1fUUUgFkJkwqp2+//TYA4L333gMQqn8aA8x1a6ZmjUeO7g/Ra5oqqar+6XEh1e2fczLkuhHpmImk8UPRXmgdw8Bzwbh5ZlSlOs5PovHlvLaybFxftIdGe1y0XnMZ1j11UdH7tZZBx2xF59U6pdN5neM2NI5eXVl0m9E4dJabvXY6Ho3HSvM2sCxLly7NOh5U7FlmVfSjx0jzTCR54EePUUti8uTJOPXUUzMq+rRp0/DEE09g+vTpuOCCC2KXKSgoyDgDNQWsuBtjjDHGmCbNhg0b8NZbb+Gggw7KTEulUjjooIMyhhxxrF69Gv369cO2226LI444IiN+1JaCghQKUnn8FbQgxd0Ys3k5Y+90/HTGMSaiuGeU9rXp2Fkq6htVaV+bPZ0x7xnlPeMqIz0thYFCFlHcKwMXGY2Lz6jzHGcRzFdIn3c7lBhjTIti6dKlqKioyIzbID179sSHH34Yu8wOO+yA6dOnY7fddsPKlStxww03YO+998Z7772XNbaoMdGkHty1mzkpdXG0y7emQak1DYxUtAuvupTd2j2sdnTaxcVBtxxkxq45LscwmHfffRcAMGbMmMy6nnrqqaxtauIKdt1xG1qGpDLqfNF94v+aEEuXqSnpRk3nIno+dXCwdnc6EVPt4UCv5hyisPfee+d0P+tnEqyfTBGv1m1A7rVHB3zqoDO9brCuMyyM4TwMa4ibV9sVQ+4YDvf0009nlZ/7z3Un2eFF26e2QQ0r0pAZtWnlNnRwZXUhhtx+Sx5orsm0GFLBcDa14K3uusdwDT3fagOadO/jfKwDet2Pth+eO5Y3mrQICO9DbAdsS3pfTUooFXevSArB1PbBuspjqqE/hGXgdTHuuOi+89hoO9BEiJpwTa1380lOyP3gseM2eMzVMtlUz1577ZWx4gbS94qddtoJf/jDH3DllVfWal12lTHGbDF+OLg9MLh9qLQH8etU2dPTAs/iQGkv/zY7tn3DqjVZ36mwb1gT3KQ2BGM8yuNfEHgxK0iFN+WiYBl1oNkYxL6n6PMexMCnKpvvy4cxxphkunXrhsLCwoxTH1myZEneMexFRUUYNmwYPvnkk1pv3w/uMSS9hfNtlWpV9E0zaWCkqt06kIfqGhUOKgf8VEUpOmgzScniNmizxW3oYJP+/fsDAN55552sdfOTZYwbuKIDzFgGrlPttrRM/K5lj7Pa1CQRLAOVCn5qghhVbkiS8hmnHMQNEASsuOcLLSAB1vH2DVeYLUA+g5ZVzWPPF+Md6UrAeh9V6pjMiTarHOjH7TIBC+st2772dnCQOT+ZrC2azp02fITl5Tq4reOOOw4A8MILLwAIB72zLbNsquJG274qimxfOhhVB+TrtViVxrjeN53WnHuAakKv+Rx8z/sPrR6puqp6DuQmENNreFJiPz2XajNI4tTvJAtKVd7ZC6CDVdWakWjdiF73kwaZ6z2Cv2siNYUDRTm/9loDyUmddPCwRgXodD03ST3K0XVzGgfGsr1rz0BLbD/FxcUYPnw4Zs2ahXHjxgFIH8tZs2bl3XNXUVGBd955B4ceeuhmLGnd8FOOMcYYY4xp8kycOBHjx4/HiBEjMGrUKEyZMgVr1qzJuMyceOKJ6NOnDyZNmgQAuOKKK7Dnnnti0KBBWLFiBa6//nosWrQIp5xySq23nSpMZY3Rqm6+utAkH9z5Nso3ZrVxilNuk2LWOS/VNCphGpvKxEV8y9XkFNFtJllZ6du5xslxPiZp0MRN+vYeVQzUvlHLoIkfVE3RN/+kxDHRfaDqQNWQx44qIRUCKpO0H+OxoypZ07mJovuuVmcmP3jcDu1VBXTpnhgiw+lAboiMhsZs0NCZ1UGK9DLaQGaHylQGg1NTwaBUDjQtLA7rclWlDmANYqSDUJnKwB4yKZkTIuqitj9tM1So6D7ArtKkcSzROkeVjgnPqLxvv/32AMLrBuutKvLffPNN1jo1NpxtCgivRVTeNZGUKm6jR48GENpHPvvsswDCawLbI9txNKkSy8NyU0lX6z3t6UpKypZkkxldhtRk0ducUcVde3h5zjgGgj000aQxuo6kMWJJNr5qG0p1X8dMxI0j03PJewPRHm4919qjo+utLvlg0tgVbVM8ZklWpdWNfWG74POBjgXR80X0Xq42n9pTEVXN2QbZbpN6Umoas9PcOf744/H111/jkksuQWlpKXbffXc8+eSTmQGrixcvzqo/33zzDU499VSUlpaic+fOGD58OF5++WUMGTKkoXahRprkg7sxxhhjjDHKGWeckRgawyzy5MYbb8SNN95YL9stSBVkBKia5qsLTerBXd+k9W2cqlRUCeMbMFUpfeNlymFNoEB1WOP7qKxR6dCUx9FyUZ1KUpKomnDbmnKevzNukG/cqrYAoZpGZYPHgPFv6gLB6VRN4t7wgfBtnmWM7kt1xwDITeNMpYDqItWh3r17A8g9N6rcR4+B7le+DiEtHca2H9orUHxqUNqpsgO5SvuGVYHCvpaDU9PzblgTpDlfHahIgeK+cR0TLwWJTQKVPCWDUlu1yb0sZX4rCdaRpLATXhtSYa+V9lhpTCwv6B9//HFmmejvVNFY96Ixr1p32f6YCK1fv3RCK9Z1Xk9Yn9mWqHqzbWh8LhC2YaagZ/tiwiWWS5MlcZzLUUcdBQB47LHHsrbBa2S0DXFZ7g+PQVyCmGg5o8m8ottIUiDjprXktqwqMmPcefx5reVxZv2pLiY66dqu21TXGNYzVc1ZJta76Dr5ybZUWloKABg5cmRWWdgOVHFn2fNRk5OU9STnHdYvdWV54403ACAziJG9ZeraAoTHhPdswntznz59ssqizyxJvX06RiQ6rkB7tTgPzz3bGOtGS24/DcWWGpzqBEzGGGOMMcY0AZqU4s63co055RsmYz+pQAFhDDpVMr7BUlGnms23Vca6MwZVPV7V4YSKR5xKpZ6uOhKdn1TI+ObMN3vGZHF/qJgNGjQIQHaMOz2cGZdLBwmug2/63Ia6bCSNjlfXlmgvRzSeMrqf6m7B8i9evBhA6MDB48RzQUWe2+a5oQoJhOdD1VOuqyXHxebDQV3SdUsTLFWuX5f1XePZgdyYdlXa169Kr5ux7bSBDGPcAyVsg7gtFTK2PVdHYPw7f6PtY1WCmlRAhb1V0NaKsv3Ho/+rqvnvf/87vU1RxejowmuJtu+s7Us7Yhv59NNPs7bdt2/frG2oywbVtDgXDfXh5vVPrxsst5aJ048//ngAwIMPPggg7AmLutaoM0dNuRu0507jjjWuOnrdVLeTltyWec1jnaOyy+s3VWFeI7W3E8hVdTV2nYq53lfVvY3XZ+0d4j0kTtllfVF3JKrazDWg9zYup/WD0+Pcc3iseH/VGH4uy/vTwoULAYT3Et4rWUYeF3Vqi+4n2wiPCY8/jxV71tSFiWXgNrgcvyflMokuy+PP+yvrAI+1uruZLYcVd2OMMcYYY0yGJqW469s41Sy+zTIGT1VyIFcJ0ljw//znPwBCtUrXwbd3Ve75thvnjKLl1XWqwwIVZ87Ht3lNJhC3fzqN36lk6H7xmKn7DFFv9jgvdcYI8piowq77TaVg0aJFAHLj8qkEJvnfR+dVX2luKynza0tnw6uPAAgV9czn+uAziHGvCM7pRqrqa8JeFXWPSVLa+Z1Ke3kQ215ZHngTSwKmVkWBKlsRxLoXhipTxQb+lq2+PtBrKABgPNIuLoVBIia0Ksr6nN+6X1q1LC9P9FKmX7vmOKCiqMo661i0zbHtql81r1FU4ubPn5+1bbZPolku42LJtceA5dFxO4Rxt6p2c1tHH300AOCee+7J2QeN79X44bjsmdFtqWqu18m4nBtxcf0tDY1L1/hldRjhtTcah816q84tej0mPDc8p+oyxPnVOz56ntjrzXJwmZ133hlAeP1mFnAqzewpPvzwwwHkxo5rj+rrr7+e+Y1x85pFW3sW/vrXvwLI7cXg2A6WkcvxPsVjHc2loD29nIfPA5r/RduHxqUnudNEY9y5DbYZnh/WCW031WV1N5uHgoJUfoNTC6y4G2OMMcYY0+xpUor7ySefDAD45z//CSDXw5ZElTAdic03YXV/UCcX9SHWt924zH+KetVqvBvReD5ui17QO+ywA4DcbIuMg41O49s2l+E6tNxJ3uksI8ucNB8Q7jvXqRnpVOnhseWIfB57qhLqRMGyRM8nlQmNDeR31hGTpvyNtMqEjYEqS6W9PPt7JXuvyjbIZ6jgcVpGec+4x2THtHN6RnEPPjcEbjIUzymsM+adIzYYCw8AyBaw8cjgPQEAqaCe39cuPd7jlJKg56t14C1dnG4b77zzTkaRS8qLQDcZjZFVJxXC+FaOgwHCtqgxqlwn6ynb8Pvvvw8gVEqpnLLtJClwQK4ftWZZ5DJ09Nhtt92yyqixzmxf++67LwBgzpw5mW2xfOo3zWX0+qA9d9wmjyXLGJcZMsnZavLkyQDSiVVaCtG6BeQeGyq7PA88ztF7QpKrSFIGcoXb4DZ5Dvk9zmmMvVT85DZYfxn7zes1s4Nz3VTief/SeyW/R8exqdKuuQW4Tm6Dvw8dmu6143OEjh3Rthx9zmC91/Ewen60B07XSUeeJHW8up58PT8kri6YLUNBYSFScg1Mmq8uWHE3xhhjjDGmCdCkFHfCUeFUp/gWyzjuKKoUaTwo38IZb823V1XZGN+my8W5I6h3qy5Tk+qtSghdZD744IOs9UTnU/Way+g643yTgdz4OFXTq/Nb1vLwWDGuV7ehse1cjioKj32cIsTfGMerx9ZkU1Ue9HpsDOK0VXkPPunWUhGo6hWB0l6xLozvZtw7/dgzn1TWE5T2dRVU2uMVdzWTSUVi3Bnv/uRe+6XLKypiRh0LFPaCkrTSd/8nazJ145lnngEQKmtchi5HSR7S6uPO+F3+ThUdCOtlUqZHjS/mtYrXMqr4qrAznjjac5jkv01UlaSjDTMBJmXK5DXjzTffzPlNr2l6nWDb1rJpD57G58dlnE7adkvg4osvBgAcdthhAJLvFXrfibuXJC2j9wLNUsrfeX2m0sx7S1L2bSB3TBTrtSrPXMcuu+wCILy3cQwIXXOoGnMbvM6PGjUqZ385D+sYe6G5TpZhp512AhD2JmnmYc0Ezn2K7qe2A37nseKy6uqmY0NIdfc8Re/J6p2vvQGsU1deeWWN6zZ1w64yxhhjjDHGmAxNUnFXRYyf9CFWj/Lob0kqON/s+ZbKt3Oq+prhTWPjo2qRxpDyTThJ1aYKp4qTvknrqH4qadH94jwa36bHimgsLbfF/dWyRtFjoX71jNvl71QyNIaY62HcoypF0Rg+nkeNn4/z1DYAqFay3vNYVgZK0YbAn5uK+4bgszz7M3tath97+Jk9PYxpz1balULWzYxne6jazh47Nv1b8F1V2Ixa2C7IjFySbretWq3PcXxg3WGdZ7vTnjB1ROHvHINRndtJkpuKXhNYX9k7xbZM1Vtdq6I5G9jOtPdQ2w2nq5pPNBsl22U0vpoKorqbaEy/OvZwelIPXlKZ436rbpxNc0Prlmbu5rHR+1Xc8dTzndRzoSqw3pdUHdbeoGgvC+8/jN3mspq5W8eMsReWnuovvfQSAGD06NFZ+8L7cvQ4JeUK4Dp0GzoWSzOrqtc6x2RFvfK5fT5rqCqv+UZ0OT2mNbXh6P5xHm5bn0F07EtLdmfa0mwpxb1JPrgbY4wxxhjTWChI5WkHWUcxokk+uDPrIOPH+GbJN2L6rwKhosV4NlXnVSniW7gq7VTbqHSoShWH+pjrmzChosdt6ts33+apnL322mtZy0WX3WOPPQAkx+onxaWrMsAyUyWPU9w1zlL99VX1p9qiSo1mbOR8VBsZWwyESk6/fv0AhMdIve5NmqpAWecn9Dvnq6is9jP9f1X2b1S4AmW9siJe0UuCSntRoLS3KmmV9QkAh7w2GwCw1dZBtsZe6V6cu9ukMy+ynv/uX+k61LNn6L5CtVizk8b1yAFhO2P9ZSZFjd+mYh/1S9c8CWx3GidPOP5j6dKlWdOpCqoiF23rug3+xmXYjujipOtKUrDj4vQZq8t1UPHkdVF7uvRaoGOLklT+6LSkcQItgaR7BI+7nqO4/BokKQ4+yRFNe0t4reWnnrOk8VJRNH5eHWrU2Yj3DNY7xr7TjYZtkvcGIDdWne2S22A7UCckdckhmh2Yzmz8jMJjxnUwIyzRcQW6nF4f9N5f3Tgv1gnul16/1H3KNB+a5IO7McYYY4wxjQWHylQDY6f5Nso3Y81qCoRKLBUuqmV8O1UnGr6F83eqc6og6ZtwVH3Q2PYkxaMmVU7VcFUOGXsHANtss03WPPpGr9vQEehJipiO1I+L5dc4c85LxZMKu6pIXDez1paWlgLIzRzbp0+fzDKcpuVinTDZFKSC+rAFtpWSixEVdZrE6GdxKltpL24XxJ52COO5SzqnFbjWnbYKvqfb+Fm9AqX90+z6GvVaZvtiZlTWeSpzVOI4nW2ddYt1je4zGnMd7eVhrxGvJ2yPmj9BM2CqIsn1ML5WcyJEtxv1sgaAHXfcEUCuB3iSWwu3qRmNebyAsH3x2qpxtUpSRmZVeeNU25rGB7QEbrjhBgDAyy+/DCC33uj1j/AYRf3A9Rqf1HOharguF9fDBMRn9+QyOh6EbY3tISnuWv3MeW/4/PPPs36P1j/W16Qsvkke6erbzmNMtZ/HQXvZo8uoms2eAY1x57aS2o0+I8TlNNB2rHlhWH7dX9Yp03xokg/uxhhjjDHGNBYKUgX5Ke6pmsPMqqNJP7jz7ZQxnYx7i74ZMy6N81KR++ijjwCECrs6v6g/MRVdqg9UGeLiMvnGq2/EqrSryq0j8JMyue29994AgAcffDCzTU5TJYAKjaou+ZZJvX6jMZWqbOixYS+HqvUam8v1MG6damNcHCyVDCqA6hVv4sko78Env/MiU9MnELq9ZH6jAl0U9OIEhuyF5cH5CtxlCuUa1aoocHGg0r5Vuj5QXecnECrtrTulz3dJl7Q6nOqQVs1/dUDa7/mGZ9KxsKy/QO4YC9bDr776CkA4doLtjko8VTPWedZT9XWOwnmoDPJapJmYuW1tK2xv3IbmiaASH/1frz1vvfUWgPCat9122wEIY5SjvvNA2Haee+45AGE2V45rAcJ2xrFCbH8aP6tqLfeLxyrJ4SJ6DVGVMd8Mn80RzbzJHhoeT54Xon7iQHidVdeyJOWW55L1XtVizX7LT6rr0XUnKcyczvsSe2N1XbynR8c3xa0vbhq/s87yWHIb3M84hxogPMbc37i8KTzOOr5EXZRU/daeEqLza2RAdL90XB33TzPZRtuxaV406Qd3Y4wxxhhjGhq7ylSDqgt8y2dsZ1QVpsLOealUMG6a8XFUynTkOb+TpDfs6Ft7TZ7F+rvGzasSwH1gfClVvOjbPKfRpUKXUUcM3Y8k/2UdFR+nNqr6QLVN1QPOx+9UF3kueG7UMSGqFFJFsVdtnrQK2kLgIlPADH0bg/EVRYHiW5Q+noXF6fpQWJT9CQAVRdmuLxn/9vJAZRKj9opAWafbDGPgqcwXSUx7227putqma3i+2/ZIK29tenRKL9sp3fNV2DGtjj+/Ol1nWL+jbYLqXVKML9v28OHDAYR1i84VJOpSBeTnmU1VXLMDa68T2xOn9+/fP2s6/d05/gMI2yw/tVeM2+a1jZkjP/vsMwDhcWGZ1DkqGiPPnkaN1dfrqvYWapk0Flh7/KL/a/x7S3KVIRxXMXjwYAC5arfWdyrX0esz52EPEu8FSVm01SmI8/Hc6zZZB6JKNNfB3i4dl6XXa66LvT+se3SOY91kb5DGnQO5LirMEMz7KI8lt9GjR4+sMnCdup/cLx7baB3Wdqzr0Hs8j0vSeBOi4wmi9zWuW8fiUHHX5yLut2l+NMkHd2OMMcYYYxoLBanCTBhqTfPVhSb54K7x1nxL5feowwhVXL41U02jist1cfT6DjvsACA3M52+YfPtW51hosvoG706LqjTC9USqgycX5V3EnWVUKWdb/IaK5cUw66x7yyzKtlxPQtcZ5JLDo8ly8JjzW1o7C3jG6ksRHtQklT8JOeAlk7xnkcCADa8HIyHaBUo7K3Tx61yY5DxryQYsxFkR20VqORRb3ZmTi0KPqvEtz1VmJ0BtVVJ4BIS+LyHsfCBChco7fyk0t42UNcBoE339P/FXdNKHJX215FWESsr0+1aY4GB5JheqnojRowAENbfOXPmZK2DnuqHHnpoer+DekilK+qtTnX7ww8/zPotqR1pfdV2SqWealpU7VPllMtS1eQ1j/vD6VQmeY3gdMb2q0c7kHt94LJ6/eOntk8dn6NEp6ubCWmJirsxxiTRJB/cjTHGmOYKQ6QYOsWXKb6s8cWQL2NJyYSA8EWUAooKKxoOqRae3LaGQ5FoMiRNZKjb4DoopBG+qPJlWUWdQYMGAQhfkKMvcwx546BsLsNt88WUghFDZVgGCkVJIa08ttGXZ74ca2itnid9GdVjreG0PFdq9QrkDnzl+dTBxCwn65DZgqQK03/5zFcH/OBuTAugoHUwVkPUywK6PwTfi6rLnKrL8mYUxK6nMkp7EA8tijyVdsbIl3QMFHd6tHcNnEsClR0AWndL33QLO6edUe75d/qmFIT+GmOMMY2DVCr9l898daBJPrizu5Zvu1Qd+DYfTWnON2AduKEWT1yGb9Kcn13AVBDYncw3Yg544e9A7ts3u+b5Jsy36qS3cqID13SAUnSADhULtdviOnhsdJCZvvlTfWDZmeQpLhU3y8PQJJ4PDWXSgcE81qoWcTrLrpZyQKiSRC3IgNwwItM8oWWoDqpmnWP7BMKQMx2kN3ToUABhnXn99dcBhPV3zz33BJAb3qHWqdEQLiZc4ueCBQsAhAqhDuYk2i5p8cowHtpHsl1Gy6VJbphIiQP52I448J7tlKomf+fxUZu5aHl5LNkW2TaTBh3yeqFJq1RxjAu9U8WzJaZsv+aaawCE9YHnNsniNM4yU0MZNQxSw6D0XGlCI26b12DOF7336fnlJ+tq0uBN7gfVct0v3kOplkev/5ogSRVoXafe+zSBkZY9bj/1Xq29GUnJr5KSMbJsWoa4BGVJRgy8j/L5gnXIND+a5IO7McYYY4wxjYWCwkIUxAggcfPVhSb54E6Vm7FrfPuOsw+jisY3YipFVHdpAacxd3xjVkWM2+DbN+Pq3n333cyyfIMfNmwYgFBt0wFoUcUOyLXI0gFsan8ZfRvXN3oti9o/6jqoajHhBY8by7hw4cKs5QFgl112ydqW2jhqAirdTx57ngu1EuN5jcb78X9V3J2IqXqKhv8XAKD8jb9mTS8IbCJpF5lqm/4sRs2kJEkTbR4rNsQPJixqE1hMBgNhi9unzztDZEq6plVFDkQFwsGoD6SdDLHDDul52MNEq0bax0UHpnNwJusK44Vpl0i7NLblkSNHpssZ1F9VfjNlihlwyvZCtYuD3Hl9YMK3JUuW5BwXIDfumO0wLsEbp/E6wvbDY8F2xAHrPXv2BBAmYkqykYwbBBodgAuEPRra46Ex19o7oQpjXA8e16nJ8Fqi4k5Yz3mv04Rd+hk9njyOammsiq0mXlILYdYTrkeNAaJKtA5SVhtiVap1Pm6DPb1qjay9stHyMdae39lLxHofZxkbXTfLqPdfliHa86v3YpY7SWnn8wXruEYCEL2ORM9n0jnXdbHOmOZLk3xwN8YYY4wxptHgwanJ8E2ab+VU2eLSBHNezkMFjAoR4z2piCWpa0R/5xsx1TwgVMuo7KnioW/hSQkxNAZPf4+zWFMVTRO9JMXQqYqovQRULaiSR/ejJmVSp3ObPPZUDHhudPxAVJVQi0zO4/TO+VE08nAAQPlbTwAIB6VW8UISfNJnNqq8F6jCXpStoFeUBSqqDGzl/LScLGoX9H51SCtZJV3SKromVwKAt0vSbhKdOqXVY00ApjGw0brCePNPPvkka1m2fcahjxkzBkCu+qexvnotiKqHjEWnyk8Vc5999gEA7LXXXgCAf/zjHwByk0NpW47aWkbLFt1n7ZnivvM7Y3upUur+6H6ohWN0n/UY6LVJVUx1ImGZ4hIF6X6xPEnrbklwfML2228PIHdclI4xiMLzznqiMdKsY9r7wU/G1bNuJsXXR+18eb5ZrqSEf0n2oNw275msR0xIxLJGFXeN7eZYlqReaKJjx/jJuhkdLwNkt38dU6Ux7jofewNUJdfeDa6HxzQaCaDnXNsi1806Y5ovTfLB3RhjjDHGmEZDKpWn4t4CXWWo+vLNmLGcdC2JSyDCt2m6UjDuk64Pffr0ARDGoFJh1jdoqj98g457q6eqQOWdfqqqnLOcqnazrNxP7ldSWaLoPFQCWRZ9W1cXCL69cx/YU0ElIKrGcft802c5VVXhsWEPCY81ewN4LrgenpM4xwRuX9M8R3sCTM0w5p0wQRMvJ6zNhZGLUKo43auRotLOTyrtmcRM2XGbKY55aBOosG2zFfdUh7TCnmrfCQDwYcchmd6qwqCNU4Fi26CS1atXr/SyQd2JxmSz7rKuc5l33nkHQKjSE1WiibpRkOi4ildeeQVAbkw3t8m2wfJyzIheP/QaoOnlgVAJ5H5pbxPXwf2jesn5qOLpuB1V8uP2R51KuKzG6qqbSFxvaHS90f/V+eu6665DS+XSSy8FADz77LMAcscj6HmJ3vt0PIImIdT7h8ZfE71fJbnRALmx6qw/6iDGZVQ15nW9Y8eOGNn6GwDlqCrfAPRvDWwsB7AaVRtlzEPbyGeqEMDn6R7DtkBB6xIAZfi/pUWJLm7a1tjToL0G0favMe56bIiO/Ug65hzDwOPGcxedX++36qLD76wzpvnSJB/cjTHGGGOMaSwUpFKZ/CY1zVcXmuSDO9VwvuVSSWCMW1QB0FHopaWlAML4ao7A5tvqZ599lrUtVabjnE50mxrDTqVL3+z5uypM7BVgrB7fvhnnp0p9dBoVaSp7VPqodn/88cdZx4Pl5nHSGEV144kqa6qeUV3REfaE+8fzx/k6deoEIMxsp7HI0Tg/9RRmuVuy80R9ULz3MQCADa8+AiCi5EWcjyrpGtQqUNCDmPWNorgrVOg5f6pdh+AzXTdTW3UCADyxpDBd/1YuzrQBqoM83+p3zLqkYzmi09RlgnWF22BdV6cUVRS5Hpbp5ZdfzmxLvdDZxtnutD2yh4g9C5pxMcnfGchVr/mp8ejqPhGNC47uj84fF3+svQ2qqPNTPbB1TAqJK5P6hif5VbdEGLfM+5a6/WiMNBBeGzkv66LGcvN8a0y39sTofYffo6qwtoNo/DsQKuq6bFFREX46vDeAclStLwMqv0XlyqBOBQp71Xp+D9pCZUz9yIzVCep+kHjugNYlOGDnjigoaQtgDZ5a1jrHTYZtk2XW3rDofiYdC5KUA4Lb4jHlNYrnhr3Peu6iy+rYD67bse0thyb54G6MMcYYY0yjoSBPV5mCFugqo/6yVAqo4EbjQVWd4jKM+eYb7r///e+s73wjpiKkca5JfulRGCvHN3iWgWXiGzJVf1XMqNKxl4CKIct02WWXZbb12muvZc3DT67jvffey9oG94cqA+PONTYxyX85+htRpYzl5LHksSX8znPBMvP8qcsHECqVuu24rI+m9hTveSSA0O+9qlVkvEjwf9WGwKu7OF2Pi9sETkUZT/igbVDBDZZLtUm307TqFSrvs5YVo0uXLujTJ7d3Sp1Q2FZYt1RpjxuDwTrN9kaVnp9JWT2TxpQwM2l07IWqxTpeg71lF198cdY6mRnzmGOOQXVE47w1N4PG22rPgar46gWuzlJxWTiJ9jjyeGuPAc9HkpMNiU7nOrRnxADz5s0DEF6nNROp9nZGYU80r8v81Guo9u7ofFpPuM3o/Zbnk+tg7DbrKtsty7R27VocuW0h0LUbKtcEGcXLgp7o9YE6HVxvqLizZ69iQ8RpiW2AZRXnqxRdX4Jrz/fbtAPWL0GqbQegDfBqZa/M/vB+zZ41dVoDcseZ6LUi6Vhq/hQ9JzwuGvMO5PZecd1s16wjpgHZQnaQdQu0McYYY4wxxmwRmqTiTjTulZ98WwVy4/k4D2NLBwwYACBUx6noMcaM6NuuKmxRVLlS9YnrZrwilSXGcv/4xz/OWh+V6aFDh8YchTR77LFH4m/RdU6aNCm2DOpDq+pdnHuExtBq5lfCbVFJ47HmdDr5cHkqH3FZ8jiPxlKqr66pG/R7j8L4dwQx7oXFgT80XR60LQTzFfAzmH/26g5pVasc+Ne//pW1iMbZsg6wTrEucD6NlY3GmlKV5BgKxpFq/gC2P6pb2rZ5DWGvFt1kovVSe+Iuuugi5ENNSjs5//zzM//fcMMNAMI2yfbC8ui1S/NFaI6H6mLbNZ5WPb+TxrEQzYKq42LiPOM57Te/+U1OeVoq7HG56667AITjn3RMUrT+J+Xu4HnXc8f52G74XcdDse3FZb/VesLrNa/52juU3kbDj2Vo06ZNpozqtBaXWZjHkMdCey/0WHIdSV74+qzAz+j55HnQHin25rVk96XGggenGmOMMaZZsTu+BEqAynVrgK5A5dogFGZd+oG0siz9ydCY8jVBgrFgEHzcYHi1oCVJVrRMApdqlx4su2u7VcA3SIfOAPjP1sPrvJ/GbC6a5IM733b5lkq/5jhXGVVx9C2aChGzLOpbd1KGN5aB64tuM8nTWOPF+TbN8p999tnV7nd9cOGFFwIIlRv1n1VfYO1RiO6nKn46nVDxZE8Ij7G67CRlzYsqQ5rVjz0kWgZT/zD+ncrOufvvCABpn+UYCorS5+/F1elxFFjP9lKVOY/bbbcdgLAHjPGl/M4xGpp5URWuOJ9wKs8cI8I63blz53T5Epwf1EGK05n9lER93Bn3zmU2J7/61a8AANdffz2A5Ayp2mOgx1Bdd7TnLPqbzsNPXv803j4p9lfXG0V7BEwuzEHAXlg9VtHjqueC513PP9uM9iprLxfPOa+97OXkdyBsh9yG9rK2adMGyDZka5SsWbMmU7e5T9H95LHjtCRvdX2O4D0t6djzXHE9cWNDdN2sE6YRsIVi3Jvkg7sxxhhjmg69S98GEFHUOcg9UNo5KLUiCO2i0h4q7uuzvkcVdw5UrVKhSZLFtWpHxT0wYWiXfvlo3Sn98ksFfnBQxlTb9kAX4J/Ls0NcjGlImuSDu8aD8Q2UKkQ0blIdSvimqyOzqSrQtSRJfUjadjS2U+P4iL5V83eNSd0ScJuqqCUdJ+01AHL9r7kMVQVOV8UnO74xN7ad2+B6ol7AnEY/YI3fNJsf1u8/vLYYALD99tsDCOtUJq55LdtW2BuiihzrApV3zVzM+sj5NZad31kPoqrYhx9+CCA3yy57dJJ8wln/NGuwzh/d1qBBgwCEGS63BOeddx4AYOrUqQCSnXaSfNw1EyOJqnw810nXPc0Greqsjj/S3sZoTxnXfckll9S88y0U9nbdeeedAIC+ffsCCO8/URcSHY+lrjD81N6SuHFbQG5mXZ7raC+XXvObixd/q1atstqF5jlJuv/oMeWn3uuSjlu0R4Xnib+xJ9Gx7Y2IVCpPxd0x7sYYY4xpROzRZiVQuRJV69cB6yK2jrVU2svXBImH+H1tdsw7AJSvC4S1iuABuSIQmgqDl5XiwqxPjXVfv4LKe/qzuP2K9PwdOgEA9mvfCQjeT1JbdQLKl+Odor61PibG1AdN8sGdcc1UvOgDzrfWqDOFKslU49WLVufn7xrTqW4rOh+Qm1VVY0lVvW+ImE4tg2bHY5lVUYsqOhqLrsq79ixoD4R6EFNJ4ProMR9VChkzyXPO8nF8g9lyUG3ieaeyze/8XZ1igFA94rlmm1HfZ55fVfMVjqNgrDkALFq0KGsZHUNBNPuhOj+omqaOEUDY/nfdddfY8m1OTj/9dADAFVdcASA83ozl5yfLqM4VqopHew/V015jb1VhJzxvbKf81PwY55xzzibssXnjjTcAhGOztCcLyO0VSeqB0XOa5Dqj9wrtRYn+r/WhOVJRUZFzTIg+X6jiruNOVInXnkYgPMecl3XgxBNPrLd9MnWjoLAQBXnklMlnnupokg/uxhhjjGlcfL8nH2C/SSvtAKo2BuGMkkgp87082y2Gn4xpV6WdSvyG1WGIzsYyJmUKXibKs0N0aL+XUdzbpNfRqiR4Ae2QVtrXf5MWH1p3DkSjTmuDz1AYSrVP/z+04xpg/kdItU+/GC/fZkR1h8aYeqNJPrh/8MEHAIARI9INhW+tVHWiihmVOqpp6o+qcZ+qsKsyzelJb9RAbgZGosoHvx9//PF57Xd9wm0+/vjjAHLVFv3UUfHR37TXQVU6HRnPY8Vjz2yA7A3herlcdMwCz7EqFawTRx55ZJ5HwGwqel6TvIxZV+gjHl2WvSnazjSGXeNxuTxj4f/zn/8ACDOURuNtNV60W7duAHJ7ePhdlXZVKFnXNAtz9FjoOrYkSbHhU6ZMARDG7KtfPdthnBd+vjHKqtazB4zniceM26a7ldk0brrpJgDAVVddBQDYd999AYQ9kkBYbznOi+eGPdXq0MTrdk29W6oyO8NtLjx2HDPHa6C6M3E62wvbIsd1xfV+vfzyywDCOmAaEalUfvHrjnE3xhhjTEOTZQ1LMStQ3Pm9Sr7THSYTnx44xFSUyycV+SCenSo7AJSvSf/GWPfKcsa6B9sMYt4LGPNeROU9/QhU3C6twBe1S7/4tAli3os7BEkAO4UGCSVd07+VrE2r86k16c/OQcz+N9uPjj02pgVgO8hkfv3rXwMA/vKXvwAIlSRVtIHcuFWNb03yL0+KXUvKKBpVG/m/ekurgtcYsn2yDDyGLKMq8OokAOSqoYoeQx0/QAWT69YR+nHnU91+6KXNOmG2HKzfmhVQlfboGA6qeVr3eT51HYRK4ldffQUAePXVVwHk9gjF+Vhz+0OGDAEQ1i/WQ/YYqOey9gbwd+11A8L20hjatKJx5JdeeimAUA3U9heXq0HbMNGxCOwRW7ZsGYAwy6vZPDBD7+TJkwEAAwcOzPzG+so2l+WlHpmu47WI3hPVhYjtJjsb7lq0dKqqqnKypPNT3WI0kzCvqXSMiZ6TBQsWAMg/K7NpvjTJB3djjDHGNA72aBMMPl4fhlZVVVZkf2rYVTA9VMWzbSirZHqlOMYwnj39f/CSHqjwG9cFA67Lue2qrHWkCoOQ2KL0Z1GgvBe1SwsBZd+kFfjWHdMvIm06hy/kdKDZsCr9W0nXtKjQOvB+77zx/wAAywYfANOyKEgVoiAPNT2feaqjST+4M661R48eAHL9wYFchxf+xumaTTHOAQNIHiUfp74lZWBURU/fthsCjddVhwkeD/VoB3KddpJQX2Cqp8yOqY416vQTPU7a48E6YDY/jJXm+eB5VFcKKu3qNhNdhuea9Ut92TVuVnM1fP/73wcAvP7661nbjOv94brpWKPqsdZfbZeq3JPo2A3uDx2vGjOXX3553vPeeOONAHLb5BlnnFGvZTLGmHy45ZZbcP3116O0tBRDhw7FTTfdhFGjRsXO+6c//Ql33nkn3n33XQDA8OHDcc0112TNf9JJJ+GOO+7IWm7MmDF48sknN99O1JEm/eBujDHGtHQmTpwIALj55psz0wYPHgwgOUSG3/myqyFhmkhQX9BpwQoAWJ9W3LNjd7NfgGmBlwk+Deal+l1QmJL5s6enCpMH9GksO5X28kCB31AZTA82TrW+sCwwXViTLmtRoLQXt0u/7BevSH8v+yYUAEs6B642geK+4du0GNEuiMFvE8Twd9n4FACgdND+mWPMUFMNu+N0tVf96KOPMtvlOW7J3HfffZg4cSKmTZuGPfbYA1OmTMGYMWMwf/78jIAbZfbs2fjRj36EvffeGyUlJbj22mtx8MEH47333suYYgDA2LFjMWPGjMx3NS3Jm4I8B6cW1G1wavM3WzXGGGOMMU2ayZMn49RTT8WECRMwZMgQTJs2DW3btsX06dNj57/nnnvwi1/8Arvvvjt23HFH3HbbbaisrMSsWbOy5mvdujV69eqV+WMERmOlSSvufAPlSaBiEA2P4eApdn9r2mB2m3MZvoVxoJZ2o7MLn4OvuE3a2wGRlO9i+6jKxn//93/XdpfrHZbhqafS6oCmllf7zGjYgybcYSgC51WlhiFDX3zxBYDwWHI+DuzT1O3RUBsNV7AKseVQ+zjWDQ4Y7d27N4DwfDIUKmop2L17dwDheeQ51nbJOsQ6oklfWEf23HNPAMBLL72UVSYgrDdbb711VrnV4lVDYzRRmu5/XDgOp/G60Fw499xzG7oIphZEQ5ieeeaZrN+otKtladI9UlVgTtckWu3bt8cX7XcBAPT+6l/hiqm+t0q3cca6F/D7xnTbThW1yv4sDowfyoIw1GD6Rlo1B84wUQU+SY2nwk7FnZ/lGdk/sHkOvgWrRpvAlabNt+l9LlkdKrD0j9+wmr7zQSIz+s8HLjhbBfu7dep5AMDibffKsTrWc6GJtBySFrJhwwa89dZbWVayqVQKBx10EF555ZW81rF27VqUl5dnzDjI7Nmz0aNHD3Tu3BkHHHAArrrqqszzTG3YUjHuVtyNMcYYY0yjZenSpaioqMiMcyI9e/bMuPDUxP/7f/8PvXv3xkEHHZSZNnbsWNx5552YNWsWrr32Wjz33HM45JBD8s5h0RA0acWdvPfeewDCdOPRhC9EFTu+2WryIKrCVN001omqA9VErjea/pzJEzRFMbfBZRsTLBMbBcvMY8n9jNrdqWLO/aZaymV4zHiMdAAizwljLnW5KPyN5/zAAw/chL01mwLrL88vzycHCFM90kQ+0W5H/sZzrXUgyVqUUBWkcsUyMbkSE/5E591xxx1j90PLpNavRAeVk+iATe4H7RCNaWg+++wzAMCgQYMAhO1Ve0TVsIHXfM7PGHnWcQ4wp4If5Z3CbQCke2P3ah20G/7InlPGzgeKNL8XFqfbYauSdPno506f98JgeqvyXB/3wuLszKgFgZ97ofgmZGLcq7Jj3pXyqnSZAnMabLUyvL+3K09yrMleGXsB2gY9DH1bvQkAWLz1iByrTR5r3nd57kz98Zvf/Ab33nsvZs+endXT8cMf/jDz/6677orddtsNAwcOxOzZs2v/fJFK5enj7hh3Y4wxxhjTTOnWrRsKCwuzxBkgLdb06tWr2mVvuOEG/OY3v8E///lP7LbbbtXOu91226Fbt2745JNP6lzmzUWzUNzPOussAMgMUOjXr1/mN43HpWLMN121O6S6RuWMKnJSKnOqwlE1TrdBRY9KRfQNr7HAMj388MMAwuOi8efReGDue9KxoXLDZammalwzP6no8JjHxbgvWrQIQHjOzZbjF7/4BYAw1baeX/baMNZdY+KB8Jwmxa4TjSfnfGq1yulRa0bCcSdU49WWVFV71m1100iyO432xjE5imNSTWNhzpw5AMJxW9pjljSWSMd8cDl+st3HWbBSOS4qKsKblT3RqVMnDFo1H0BEeed6g09ut1B83amsVzHDqijwRe3C9kwfd34WBa4xzKBaLK4yvBSEynu2Wr6Bq06lS70uoqYXrsse+0JlnWp/UZv09bBVu/R9sVXbILFZSfqe2r/NBwCAz7vvlrn38dmAvR48dyeeeCJMmuLiYgwfPhyzZs3CuHHjACAz0LS66+51112Hq6++Gk899RRGjBhR43Y+++wzLFu2LDM2qja0Gvp9tAp6qKqdb9WqWq87ihV3Y4wxxhjTqJk4cSL+9Kc/4Y477sAHH3yA008/HWvWrMGECRMApF90ooNXr732Wlx88cWYPn06+vfvj9LSUpSWlmbEptWrV+O8887Dq6++ioULF2LWrFk44ogjMGjQIIwZM6ZB9jEfmoXiTk4++WQAYdIQAJnRw1TNdGS9JlDiGy8/qT4w9pvKHj+5Xk0YE4Xr+Pzzzzdxz7YcLOOAAQMAJLvqRH/TY0IFhQoslRn1DVYPW6o1HGhCNXX58uWZbdrlovHA86m9TupFHFXkWBfUz5jzsA6xzXC6Ku/q1KTzA2Gb5TKsu0nKuzoqEW0Dcep+Y+5WNS0TJkzj57BhwwCEbmBsB1Tg2Z71Oq4x8eowFr0naFz8+vXr8V7r/mjfvj36ffN+bDkLpLeNI0c0YyoVbCrw0Uyr9GXnvOH3dNsvycyb3fYzqn/QH0DlvTDYV3WbiYPbpLsM3WaKAp93Zlot7rAiPf+36WOzbqt1GaWd19B33nkHQHjOTDbHH388vv76a1xyySUoLS3F7rvvjieffDJzrV+8eHFWD+/UqVOxYcMGHHPMMVnrufTSS3HZZZehsLAQ8+bNwx133IEVK1agd+/eOPjgg3HllVduupf7FqBZPbgbY4wxxpjmyRlnnJEYGjN79uys7wsXLqx2XW3atMlYYTclmuWDe1SV/c1vfgMgVN+otPNtiuoCVTcqguo9zulcnp86H5DrQqFOGo0ZlpFl5vGJc9zgvDwWegx1pDy/s9eD86uiSSWHg1AuuOCCuu2UqVfOPPNMAGGsO9Vu9rD0798/a3pcjLjGqkcdWoCw/nFZzTTIesmxKKqSA6GbBrelMbyqnPN3rkszRar38scff5xZ1rHtprFyzjnnAAD+8pe/AAC23XbbrN+p9nJMCBV19l6xDbLtMWMqf4+6f3FsFNtONKfKu622RYcOHdB3+XtZ2w9V72yKEuz4MjHvEcW9hCp8EMtelWAXkwoypBYHjjDq766R+MVBjDs/AaBVUdA7WBKMwwm+p4qy3UQyPQZBTH5FWfo4pdalVfZBq9PXj9fWd8nkNuG5MqY6HONujDHGGGNME6BZKu5RqNbecccdAEK1Vx1OVFWgwszpVIu5XDSGD8hW09WdgqrDKaecUo97tnlgGanOUH3hcYnuJ6fxWHC/1QtfXQlqioXmdyvtjRsq7+Sqq64CELrMsK5EHWPUO5rtTLOasu3wd3W+oLrPMRlsh9EYd45vYfvjtuPciuLKor1MXI49QlHF3ZjGzhtvvAEgVMz1esx2ovVfr89U5nkvjca4J2UljvZ2LWg/GG3atEHvpe8i2ED6I1hHxtAlcJnJxLzHxLYrlfJbQRCcTscX+r3TfaY4iIVXpZ7LFaSylweAojZBD1yguBe1S5eweKvirM/CYunRC8rGjLFV69P3yW+//TZzbn70ox8l7psxxIq7McYYY4wxTYBmr7iT8ePHA0BmIAJVBI17VXVYVXOqC1QdqDZHM4oSTovLANrYYZl5XOitHo0L5jQqMVRB1ZNb45dVhVF1hufKNC0uuugiAGnfXAD4zne+AyBbBU/yX1cFXseQfPXVVwDSaa+B0PeYaqE6YETRTKn8znWwTTM+V51udGzKq6++CgA4++yz4w6DMY2SyZMnAwCuueYaAMC+++6b9Tvru+Yd0fFOVNp1jBMQtl+Oc+KymkelrKwM3xT2QceOHQGk7xE9FqfbVY7yHnwWVaO0K6nCwGO+KNtjnSo5s65mVHCJiafiHnq0h9eQViWBC5Uo78XtAsW9QzCGq33wGbjhcF2Eyvvbb7+dOTfG5IMVd2OMMcYYY5oALUZxJx999BEAYMiQIQCSs8XpdPWypUoXFw+rmRhPOumk+t2JLQDL/OCDDwKI30+q8up5r77ZmqGScD5+8tw05sQHpmbOP/98AMCkSZMAANtss03mt+7duwMIe2sI1TyOj/j3v/8NIFT92P5UUaeyx7rG9QO5Yya4DSrqVArnzp0LIHSe2n777bOW//rrrwEAb775JgA7P5imza9//WsAwO233w4A2HnnnQGE6jjbB9VxjX3ndPY2d4hkiuR9c1WQGZKfvDeow5o61bREeD6MyZcW9+BujDHGmMZF6TajUFVVha0/Tw/U1JCZQpm/ukGqDEtJFQWmEsVB6GdJMHg2GIzKRE3hcsGg1GD56ganFpakRYRWwSe/F7VrE3wGNslByExhEGJUkEqv6+4F6/HTn/40cR+MSaKgKs6guwVBtxkdaa/x6V27dgUQxsESVZGjy/7gBz+o/wI3EI8//jiAXKUUyHXnoEq6bNkyAKF7DJfl/CtWrADgmPaWxBVXXAEgrBOanY6KOseQ8Hd1vqDCznEVrHOMqweA7bbbDkBu/VQPeSrqzFrI39lTxF4AK2OmOfLnP/8ZQJh/gW2Q9V7Hb7FNUmmnexMQ9p5SaVc3NsL2yl6vzp07Z9bd+4t0z1ZVWZBTJfA9rypLf1asDjK2frs2s77yNWXBtCAuP/henvkMHtzXNdyDe1H79P0v1TZ9bWtzmHM/NBdWrVqFjh07YuXKlVk9UPU1v2LF3RhjjDGNgi96j0BlZSW2KZ0DIFl5jwbNFcjAzwKaTBRlP2QXlQVWjByUKi/1XK6gMHt5PpwDQKo4e1phSWv5nv1AnwqU9lRJWvR75Nsu+PGPf5x8AIypgRavuNeW66+/HkCoCKoSCDTvGNgpU6Zk/mecIqsQ/a3PO++8LV4u0zShAs+6RPWOPVusW4yNZaZUtjd1bDr44IMz/1Mx17EUhG2XjjWMdXf+ANMSmTp1KgBg8ODBAHJzmbCN6veo0xiVdc24rbkTNAaey7FXtl27duj39b/S66hBeQeA8rWB4r4qPW/5mvT1oSJ4UN8YfFY0ggd3K+3NDyvuxhhjjGnRLOo+FAAyD/A1xbxHYfIjhsrwYbqyPAiVSYiP1wf2wuKirO9A5IE84QEerYKkjsWBDWTr9AvOjE8rcPrpp1dTamPyww/utaSlq8nNuTfBNBxU5NRLWh2aNLMqofoXdZ1Rj3cuy23wk/G2VtpNS4YPlRdffDGA0HmNY0XUCYbtJ5q3g+2UsetJ7Zpjyvg7xzvxk/NHPeKbOn5oN/WFH9yNMcYY0yhZ1H0o1qxZgyGVabtgVd6BUH2nYr4xUMiplFNpDxMuSYgMlXZR3EPlPRIOlKCsF7QKLKOLsqdfNWs+rrzyytrssjHV4gd3Y0yDQVWcShzdYqiSU3njdKp7qsDTgz3q8qSOT4y75Se3QVcZYwwyD5kTJ04EAHTr1g1A2G7oKsO2GB1nojk96BbDZTXvAqdTgedyOnYsOoasqeGHdlPf+MHdGGOMMY2agjZixRz5n/p5KvBIL6J/e/DJQaiquKvSnnGjCWLbM6p6q0gIHhX2IvktmPf59d3wj3/8AwAwefLk2u6mMTWSa0LewHz++ec47rjj0KlTJ3To0AFHHHFEJouiMSabpt5eLr74Ylx88cXYuHEjNm7ciLVr12Lt2rUoLy9HeXl55vu6deuwbt06VFZWorKyEiUlJSgpKUG3bt2y/lKpVOavsLAw6y/6WyqVwqpVq7Bq1SqsWLEiE1trjDHGNGYaleK+evVq7L///li5ciV+/etfo6ioCDfeeCNGjx6NuXPnZpIgGWPcXowxmw+qxb/4xS8AAKNHjwYA9OvXL2s+hr0AYfiMJjKkvStD20pLSwGEg09piccQGYbM8IV6yZIlWATghz/8IcrfeiKnrFQgq4LQukoOoA1U8KrKdLkKZVA7aP8YKPUZhT34XtA6O349ax5+FqV/e/jf6/Dcc88BAG699dacMhpTXzSqB/dbb70VH3/8MV5//XWMHDkSAHDIIYdgl112wW9/+1tcc801DVxCYxoPzam90NFl0qRJAHL92Xkj5wMBszzS8ULnB8KsjYxl15j3xYsXZ23bGGOMaezUKgHTs88+iwMOOAAPP/wwjjzyyKzf/vznP+MnP/kJXn75Zey1116bVJhRo0YBAF5//fWs6WPGjMGCBQvwySefbNJ6jWkI1q1bh2HDhgEA3n777Yyd2vLly7HzzjtjwIABeOGFF3LSgedLc2wvfHDXh+x8H9yjvQxcRh/caUHHhEsnnHBCve6DMc0Z2kXutttuAJCVQGbrrbcGENqysq1Riefjhg4253QmZFq6dCmAcFBqXBuNKu9V69PzFe99DO6++24AoX1lt27dMGr1++kZKyuyV0JlPShn5rvGtgffAeDZlW0z5Zw3bx4AD0Bt6WzpBEy1inHfb7/9sO222+Kee+7J+e2ee+7BwIEDsddee2H9+vVYunRpXn+ksrIS8+bNw4gRI3LWPWrUKCxYsCCTmdOYpkCbNm1wxx134JNPPsH//u//Zqb/8pe/xMqVKzFz5kwUFha6vRhjjDEmL2oVKlNQUIATTjgBkydPxsqVK9GxY0cAaTu1f/7zn5mHk7/85S+YMGFCXuvkm/by5cuxfv36zBt7FE774osvsMMOO9SmyMY0KHvssQfOP/98XHvttTjyyCOxZMkS3HvvvZgyZUomtbjbS8iFF16Y9f2qq64CkKvAcx8ZI0tVPZp+ndPUWpIvNF9++WW9lt2YloCqy1dccUXm/zFjxgAI26Eq65r8TO1gOR/b6EknnZRYjqLh/xU7ner8zJkzAaQtKV9pswO6dOmCnZbPy56ZMe5U1vk9iFv/uN2gTFmeeuqpzGKXXHIJAODYY49NLJ8xm4tax7ifeOKJmDRpEh588EH89Kc/BQDcd9992LhxY6bBjBkzBk8//XSt1ssuMXrERuHNuSl7uZqWy2WXXYbHH38c48ePx+rVqzF69GicddZZmd/dXowxxhiTD7V+cN9xxx0xcuRI3HPPPZkH93vuuQd77rknBg0aBCCthsUpgdXBeLTqBplxHmOaEsXFxZg+fTpGjhyJkpISzJgxI6M0AW4v1XHRRRdlfeeA26222gpA2APB4xl1uKCKR7WeKuAHH3wAADjvvPM2V7GNaTFQfQaA0047DQCwyy67AECmV5FxvIx5J2y/DAOklS2dbOoC1Xo6vGy33XYoRY9MQqnoNRgIx8Qwfv2jjz4CsBjvvvsuAGDatGl1LpMx9cEmucqceOKJOPvss/HZZ59h/fr1ePXVV3HzzTdnfl+3bh1WrlyZ17p69eoFAOjSpQtat24d233Nab17996U4hrT4LCbtaysDB9//DEGDBiQ+c3txRhjjDH5UCtXGbJ06VL07t0bV199NdatW4errroKX3zxReZNdubMmbWO2QWAkSNHoqCgIMcl4+CDD8aCBQuwYMGC2hbVmAZn3rx5GDlyJH7yk59g7ty5WLp0Kd55553MGBG3l/y57rrrAABjx44FkJt2PRo6RMWdoUOfffYZgLRlpjFmy3H66acDCNsi1W6239/97ndbrCxnn302gDDOntdU9lROnTp1i5XFNA+2tKvMJinu3bp1wyGHHIK7774bZWVlGDt2bOahHdi0mF0AOOaYY3DBBRfgzTffzLhlzJ8/H8888wx+9atfbUpRjWlQysvLcdJJJ6F379743e9+h08//RQjR47Eueeei+nTpwNwezHGGGNMfmyS4g4ADz30EI455hgA6cGpxx13XJ0L8+2332LYsGH49ttv8atf/QpFRUWYPHkyKioqMHfuXHTv3r3O2zBmS3LppZfiyiuvxKxZs7D//vsDAK6++mpcdNFFeOKJJ3DooYdu8rpbYnuhMnfwwQcDCAfg8jIWjaGlm8zatWsBhH7355xzzhYpqzHGmOZPo/Zxj3LYYYehc+fO6NixIw4//PBNXU0W7du3x+zZs/G9730PV111FS6++GIMHToUzz33XLN8CDHNmzlz5uCaa67BGWeckXloB9KZOkeOHIlTTz01k9J7U3B7McYYY1oWm6y4b9y4Eb1798Zhhx2G22+/vb7LZYwxibz/fjoTorrqRH3cGePOWH/2EBpjjDH1RZNR3B999FF8/fXXOPHEEzd1FcYYY4wxxpg8qfXg1Ndeew3z5s3DlVdeiWHDhmH06NGbo1zGGJPIkCFDAADnn39+1vRoByIdKyZPnrzlCmaMMcZsRmqtuE+dOhWnn346evTogTvvvHNzlMkYY4wxxhgjbHKMuzHGGGOMMS2ZJhPjbowxxhhjjNly+MHdGGOMMcaYJoAf3I0xxhhjjGkC+MHdGGOMMcaYJoAf3I0xxhhjjGkC+MHdGGOMaWRUVlZi2rRp2H333bHVVluhZ8+eOOSQQ/Dyyy83dNGMMQ2IH9yNMcaYRsZ5552H008/HbvuuismT56M//mf/8FHH32E0aNH4/XXX2/o4hljGohaZ041xhhjzOZj48aNmDp1Ko455hjcddddmenHHnsstttuO9xzzz0YNWpUA5bQGNNQWHE3xhhjqmHhwoUoKChI/KtvysvLsW7dOvTs2TNreo8ePZBKpdCmTZt636Yxpmlgxd0YY4yphu7du2cp30D64frcc89FcXExAGDt2rVYu3ZtjesqLCxE586dq52nTZs22GOPPTBz5kzstdde2HfffbFixQpceeWV6Ny5M372s59t+s4YY5o0fnA3xhhjqqFdu3Y44YQTsqb98pe/xOrVq/H0008DAK677jpcfvnlNa6rX79+WLhwYY3z3X333Tj++OOztrvddtvhpZdewnbbbVe7HTDGNBv84G6MMcbUgjvvvBO33norfvvb32L//fcHAJx44onYZ599alw23zCX9u3bY+edd8Zee+2FAw88EKWlpfjNb36DcePG4YUXXkC3bt3qtA/GmKZJQVVVVVVDF8IYY4xpCsydOxd77703xo0bhz//+c91WtfKlSuxbt26zPfi4mJ06dIFGzduxLBhw7Dffvvhpptuyvz+8ccfY+edd8a5556La6+9tk7bNsbUD6tWrULHjh2xcuVKdOjQod7nVzw41RhjjMmDb775BkcffTQGDx6M2267Leu31atXo7S0tMa/r7/+OrPM2Wefja233jrzd9RRRwEAnn/+ebz77rs4/PDDs7ax/fbbY6eddsJLL720+XfWmBbELbfcgv79+6OkpAR77LFHo7ZcdaiMMcYYUwOVlZX4yU9+ghUrVuD//u//0LZt26zfb7jhhlrHuJ9//vlZMewctLpkyRIAQEVFRc7y5eXl2Lhx46buhjFGuO+++zBx4kRMmzYNe+yxB6ZMmYIxY8Zg/vz56NGjR0MXLwc/uBtjjDE1cPnll+Opp57CP/7xDwwYMCDn902JcR8yZAiGDBmSM8/gwYMBAPfeey/Gjh2bmT5nzhzMnz/frjLG1COTJ0/GqaeeigkTJgAApk2bhieeeALTp0/HBRdc0MCly8Ux7sYYY0w1vPPOOxg6dCi+973v4ZRTTsn5XR1n6oODDz4YTz/9NI488kgcfPDB+PLLL3HTTTdhw4YNeOutt7DDDjvU+zaNaWls2LABbdu2xYMPPohx48Zlpo8fPx4rVqzAY489VuM6tnSMuxV3Y4wxphqWLVuGqqoqPPfcc3juuedyft8cD+6PPfYYbrjhBtx777148sknUVxcjH333RdXXnmlH9qNqSeWLl2KioqKnGRnPXv2xIcfflirda1atape50vCD+7GGGNMNey3337Y0p3Tbdq0wcUXX4yLL754i27XGFM7iouL0atXL2y77bZ5L9OrV69M8rba4gd3Y4wxxhjT4ujWrRsKCwszA8LJkiVL0KtXr7zWUVJSgk8//RQbNmzIe7vFxcUoKSmpVVmJH9yNMcYYY0yLo7i4GMOHD8esWbMyMe6VlZWYNWsWzjjjjLzXU1JSsskP4rXFD+7GGGOMMaZFMnHiRIwfPx4jRozAqFGjMGXKFKxZsybjMtPY8IO7McYYY4xpkRx//PH4+uuvcckll6C0tBS77747nnzyyZwBq40F20EaY4wxxhjTBEg1dAGMMcYYY4wxNeMHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCeAHd2OMMcYYY5oAfnA3xhhjjDGmCfD/AaKrtEk6uKdGAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_stat_map(\n", - " contrast_result.get_map(\"z_GLH_groups_0\"),\n", - " cut_coords=[0, 0, -8],\n", - " draw_cross=False,\n", - " cmap=\"RdBu_r\",\n", - " title=\"GLH_groups_0\",\n", - " threshold=scipy.stats.norm.isf(0.4),\n", - ")\n", - "print(\"The contrast matrix of GLH_0 is {}\".format(contrast_result.metadata[\"GLH_groups_0\"]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GLH testing for study-level moderators\n", - "CBMR framework can estimate global study-level moderator effects,\n", - "and allows inference on the existence of m.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " standardized_sample_sizes standardized_avg_age type2 type3 \\\n", - "0 -0.000769 0.005946 0.107031 0.08795 \n", - "\n", - " type4 type5 \n", - "0 0.105989 0.090762 \n", - "P-values of moderator effects `sample_sizes` is p\n", - "0 0.939472\n", - "P-value of moderator effects `avg_age` is p\n", - "0 0.557174\n" - ] - } - ], - "source": [ - "contrast_name = results.estimator.moderators\n", - "t_con_moderators = inference.create_contrast(contrast_name, source=\"moderators\")\n", - "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", - "print(contrast_result.tables[\"moderators_regression_coef\"])\n", - "print(\n", - " \"P-values of moderator effects `sample_sizes` is {}\".format(\n", - " contrast_result.tables[\"p_standardized_sample_sizes\"]\n", - " )\n", - ")\n", - "print(\n", - " \"P-value of moderator effects `avg_age` is {}\".format(\n", - " contrast_result.tables[\"p_standardized_avg_age\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This table shows the regression coefficients of study-level moderators, here,\n", - "`sample_sizes` and `avg_age` are standardized in the preprocessing steps.\n", - "Moderator effects of both `sample_size` and `avg_age` are not significant under\n", - "significance level $0.05$. With reference to spatial intensity estimation of\n", - "a chosen subtype, spatial intensity estimations of the other $4$ subtypes of\n", - "schizophrenia are moderatored globally.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "P-values of difference in two moderator effectors (`sample_size-avg_age`) is p\n", - "0 0.639232\n" - ] - } - ], - "source": [ - "t_con_moderators = inference.create_contrast(\n", - " [\"standardized_sample_sizes-standardized_avg_age\"], source=\"moderators\"\n", - ")\n", - "contrast_result = inference.transform(t_con_moderators=t_con_moderators)\n", - "print(\n", - " \"P-values of difference in two moderator effectors (`sample_size-avg_age`) is {}\".format(\n", - " contrast_result.tables[\"p_standardized_sample_sizes-standardized_avg_age\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CBMR also allows flexible contrasts between study-level covariates.\n", - "For example, we can write `contrast_name` (an input to `create_contrast`\n", - "function) as `standardized_sample_sizes-standardized_avg_age` when exploring\n", - "if the moderator effects of `sample_sizes` and `avg_age` are equivalent.\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/02_meta-analyses/10_plot_cbmr.py b/examples/02_meta-analyses/11_plot_cbmr.py similarity index 100% rename from examples/02_meta-analyses/10_plot_cbmr.py rename to examples/02_meta-analyses/11_plot_cbmr.py From 4bbe51e971142baa699c37e0fe584125bd89c447 Mon Sep 17 00:00:00 2001 From: James Kent Date: Wed, 3 May 2023 10:03:14 -0500 Subject: [PATCH 166/177] remove functorch (it was absorbed into torch) --- setup.cfg | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 9b96c8e1f..426b14eb4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -39,7 +39,6 @@ classifiers = python_requires = >= 3.8 install_requires = cognitiveatlas # nimare.annotate.cogat - functorch~=0.2 fuzzywuzzy # nimare.annotate joblib # parallelization matplotlib>=3.3 # this is for nilearn, which doesn't include it in its reqs From b36ef7d4cade2bdb8735f69c747f065c0e23f24c Mon Sep 17 00:00:00 2001 From: Yifan Yu Date: Thu, 18 May 2023 11:59:53 +0100 Subject: [PATCH 167/177] fix math mode and links in cbmr example. --- examples/02_meta-analyses/11_plot_cbmr.py | 38 ++++++++++++----------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/examples/02_meta-analyses/11_plot_cbmr.py b/examples/02_meta-analyses/11_plot_cbmr.py index 7f09cd8f3..206da1c99 100644 --- a/examples/02_meta-analyses/11_plot_cbmr.py +++ b/examples/02_meta-analyses/11_plot_cbmr.py @@ -20,8 +20,9 @@ algorithm implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-regression, -see the [online course](https://www.coursera.org/lecture/functional-mri-2/module-3-meta-analysis-Vd4zz) -or a [brief overview](https://libguides.princeton.edu/neuroimaging_meta). +see the +`online course `_ +or a `brief overview `_. """ import numpy as np import scipy @@ -34,11 +35,12 @@ ############################################################################### # Load Dataset # ----------------------------------------------------------------------------- -# Here, we're going to simulate a dataset -# (using [nimare.generate.create_coordinate_dataset](https://nimare.readthedocs.io/en/latest/generated/nimare.generate.create_coordinate_dataset.html)) +# Here, we're going to simulate a dataset +# (using `nimare.generate.create_coordinate_dataset +# `_ # that includes 100 studies, each with 10 reported foci and sample size varying between # 20 and 40. We separate them into four groups according to diagnosis (schizophrenia or depression) -# and drug status (Yes or No). We also add two continuous study-level moderators (sample size and +# and drug status (Yes or No). We also add two continuous study-level moderators (sample size and # average age) and a categorical study-level moderator (schizophrenia subtype). # data simulation @@ -80,7 +82,7 @@ # within a group (e.g., indexed as subgroup-1 to subgroup-n, but one or more of them # don't have enough number of studies to be inferred as a separate group). Using # categorical encoding, CBMR can interpret the subgroups as categorical moderators -# for each study (either 0 or 1), and estimate the global activation intensity +# for each study (either 0 or 1), and estimate the global activation intensity # associated with each subgroup (comparing to the average). from nimare.meta.cbmr import CBMREstimator @@ -98,8 +100,8 @@ model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed - device="cpu", # "cuda" if you have GPU + tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed + device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=dset) @@ -210,9 +212,9 @@ # Four figures (displayed as z-statistics map) correspond to homogeneity test of # group-specific spatial intensity for four groups. The null hypothesis assumes # homogeneous spatial intensity over the whole brain, -# $H_0: \mu_j = \mu_0 = sum(n_{\text{foci}})/N$, $j=1, \cdots, N$, where $N$ is -# the number of voxels within brain mask, $j$ is the index of voxel. Areas with -# significant p-values are highlighted (under significance level $0.05$). +# :math:`H_0: \mu_j = \mu_0 = sum(n_{\text{foci}})/N`, :math:`j=1, \cdots, N`, where +# :math:`N` is the number of voxels within brain mask, :math:`j` is the index of voxel. +# Areas with significant p-values are highlighted (under significance level :math:`0.05`). ############################################################################### # Perform fasle discovery rate (FDR) correction on spatial homogeneity test @@ -326,10 +328,10 @@ # Four figures (displayed as z-statistics map) correspond to group comparison # test of spatial intensity for any two groups. The null hypothesis assumes # spatial intensity estimations of two groups are equal at voxel level, -# $H_0: \mu_{1j}=\mu_{2j}$, $j=1, \cdots, N$, where $N$ is the number of voxels -# within brain mask, $j$ is the index of voxel. Areas with significant p-values +# :math:`H_0: \mu_{1j}=\mu_{2j}`, :math:`j=1, \cdots, N`, where :math:`N` is the number +# of voxels within brain mask, :math:`j` is the index of voxel. Areas with significant p-values # (significant difference in spatial intensity estimation between two groups) -# are highlighted (under significance level $0.05$). +# are highlighted (under significance level :math:`0.05`). ############################################################################### @@ -346,8 +348,8 @@ # Only if all of null hypotheses are rejected at voxel level, p-values are significant. # For example, `t_con_group=[[1,-1,0,0], [1,0,-1,0], [0,0,1,-1]]` is used for testing # the equality of spatial intensity estimation among all of four groups (finding the -# consistent activation regions). Note that only $n-1$ contrast vectors are necessary -# for testing the equality of $n$ groups. +# consistent activation regions). Note that only :math:`n-1` contrast vectors are necessary +# for testing the equality of :math:`n` groups. contrast_result = inference.transform( t_con_groups=[[[1, -1, 0, 0], [1, 0, -1, 0], [0, 0, 1, -1]]], t_con_moderators=False @@ -392,8 +394,8 @@ # This table shows the regression coefficients of study-level moderators, here, # `sample_sizes` and `avg_age` are standardized in the preprocessing steps. # Moderator effects of both `sample_size` and `avg_age` are not significant under -# significance level $0.05$. With reference to spatial intensity estimation of -# a chosen subtype, spatial intensity estimations of the other $4$ subtypes of +# significance level :math:`0.05`. With reference to spatial intensity estimation of +# a chosen subtype, spatial intensity estimations of the other :math:`4` subtypes of # schizophrenia are moderatored globally. t_con_moderators = inference.create_contrast( From c442bdaba0e314ab51039c10577f144f4502831a Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Thu, 20 Jul 2023 16:25:31 -0400 Subject: [PATCH 168/177] add a test with none moderator variable for CBMRInference --- nimare/tests/test_meta_cbmr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 3f9fef95d..34cc68c60 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -38,7 +38,7 @@ def cbmr_result(testdata_cbmr_simulated, model): cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], + moderators=None, spline_spacing=200, model=model, penalty=False, @@ -56,7 +56,7 @@ def cbmr_result(testdata_cbmr_simulated, model): @pytest.fixture(scope="session") def inference_results(testdata_cbmr_simulated, cbmr_result): """Test inference results for CBMR estimator.""" - inference = CBMRInference(device="cuda") + inference = CBMRInference(device="cpu") inference.fit(cbmr_result) t_con_groups = inference.create_contrast( [ From 20e890888a3fa7d65ed66f2fb71b2b7af8450068 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Thu, 20 Jul 2023 16:55:33 -0400 Subject: [PATCH 169/177] add a separate test for moderators=None --- nimare/tests/test_meta_cbmr.py | 36 ++++++++++++++++++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 34cc68c60..53913ea5b 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -38,8 +38,8 @@ def cbmr_result(testdata_cbmr_simulated, model): cbmr = CBMREstimator( group_categories=["diagnosis", "drug_status"], - moderators=None, - spline_spacing=200, + moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], + spline_spacing=100, model=model, penalty=False, lr=1e-2, @@ -121,7 +121,39 @@ def test_firth_penalty(testdata_cbmr_simulated): ) res = cbmr.fit(dataset=dset) assert isinstance(res, nimare.results.MetaResult) + + +def test_moderators_none(testdata_cbmr_simulated): + """Unit test for Firth penalty.""" + dset = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( + testdata_cbmr_simulated + ) + cbmr = CBMREstimator( + group_categories=["diagnosis", "drug_status"], + moderators=None, + spline_spacing=100, + model=models.PoissonEstimator, + penalty=False, + lr=1e-2, + tol=1e7, + device="cpu", + ) + res = cbmr.fit(dataset=dset) + assert isinstance(res, nimare.results.MetaResult) + inference = CBMRInference(device="cpu") + inference.fit(res) + t_con_groups = inference.create_contrast( + [ + "DepressionYes", + ], + source="groups", + ) + inference_results = inference.transform( + t_con_groups=t_con_groups + ) + + assert isinstance(inference_results, nimare.results.MetaResult) def test_CBMREstimator_update(testdata_cbmr_simulated): """Unit test for CBMR estimator update function.""" From 3730e4b1a90db5c2fdbbdf234307d3ebb479e196 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 26 Aug 2023 22:07:55 +0100 Subject: [PATCH 170/177] Replace functorch.hessian by torch.func.hessian to remove pytorch warning message when running cbmr. --- nimare/meta/models.py | 9 ++++----- setup.cfg | 2 +- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 85c097ed5..6aa35c474 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -3,7 +3,6 @@ import copy import logging -import functorch import numpy as np import pandas as pd import torch @@ -389,7 +388,7 @@ def nll_spatial_coef(group_spatial_coef): **ll_single_group_kwargs, ) - f_spatial_coef = functorch.hessian(nll_spatial_coef)(group_spatial_coef) + f_spatial_coef = torch.func.hessian(nll_spatial_coef)(group_spatial_coef) f_spatial_coef = f_spatial_coef.reshape((self.spatial_coef_dim, self.spatial_coef_dim)) cov_spatial_coef = np.linalg.inv(f_spatial_coef.detach().numpy()) var_spatial_coef = np.diag(cov_spatial_coef) @@ -423,7 +422,7 @@ def nll_moderators_coef(moderators_coef): **ll_single_group_kwargs, ) - f_moderators_coef = functorch.hessian(nll_moderators_coef)(moderators_coef) + f_moderators_coef = torch.func.hessian(nll_moderators_coef)(moderators_coef) f_moderators_coef = f_moderators_coef.reshape( (self.moderators_coef_dim, self.moderators_coef_dim) ) @@ -560,7 +559,7 @@ def nll_spatial_coef(spatial_coef): **ll_mult_group_kwargs, ) - h = functorch.hessian(nll_spatial_coef)(spatial_coef) + h = torch.func.hessian(nll_spatial_coef)(spatial_coef) h = h.view(n_involved_groups * self.spatial_coef_dim, -1) return h.detach().cpu().numpy() @@ -632,7 +631,7 @@ def nll_moderator_coef(moderator_coef): **ll_mult_group_kwargs, ) - h = functorch.hessian(nll_moderator_coef)(moderator_coef) + h = torch.func.hessian(nll_moderator_coef)(moderator_coef) h = h.view(self.moderators_coef_dim, self.moderators_coef_dim) return h.detach().cpu().numpy() diff --git a/setup.cfg b/setup.cfg index ffd73a8e8..85fb92e3a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -57,7 +57,7 @@ install_requires = scipy>=1.6.0 sparse>=0.13.0 # for kernel transformers statsmodels!=0.13.2 # this version doesn't install properly - torch # for cbmr models + torch>=2.0 # for cbmr models tqdm # progress bars throughout package packages = find: include_package_data = False From 668311e58438fdda349dba285e3f3c4643daab88 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 26 Aug 2023 22:41:41 +0100 Subject: [PATCH 171/177] solve lint error --- nimare/tests/test_meta_cbmr.py | 32 -------------------------------- 1 file changed, 32 deletions(-) diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 602ed3d1b..6315f4f72 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -121,39 +121,7 @@ def test_firth_penalty(testdata_cbmr_simulated): ) res = cbmr.fit(dataset=dset) assert isinstance(res, nimare.results.MetaResult) - - -def test_moderators_none(testdata_cbmr_simulated): - """Unit test for Firth penalty.""" - dset = StandardizeField(fields=["sample_sizes", "avg_age", "schizophrenia_subtype"]).transform( - testdata_cbmr_simulated - ) - cbmr = CBMREstimator( - group_categories=["diagnosis", "drug_status"], - moderators=None, - spline_spacing=100, - model=models.PoissonEstimator, - penalty=False, - lr=1e-2, - tol=1e7, - device="cpu", - ) - res = cbmr.fit(dataset=dset) - assert isinstance(res, nimare.results.MetaResult) - inference = CBMRInference(device="cpu") - inference.fit(res) - t_con_groups = inference.create_contrast( - [ - "DepressionYes", - ], - source="groups", - ) - inference_results = inference.transform( - t_con_groups=t_con_groups - ) - - assert isinstance(inference_results, nimare.results.MetaResult) def test_moderators_none(testdata_cbmr_simulated): """Unit test for Firth penalty.""" From 9e344843acf76855e9ac3e51754e7d7ea6d055c0 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 2 Sep 2023 18:43:31 +0100 Subject: [PATCH 172/177] fix a bug with L-BFGS algorithm and speed up the execution time --- nimare/meta/cbmr.py | 6 +-- nimare/meta/models.py | 68 ++++++++-------------------------- nimare/tests/test_meta_cbmr.py | 17 +++++---- 3 files changed, 27 insertions(+), 64 deletions(-) diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 2dc8b5eee..5fd3ae992 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -110,10 +110,10 @@ def __init__( spline_spacing=10, model=models.PoissonEstimator, penalty=False, - n_iter=1000, - lr=1e-2, + n_iter=2000, + lr=1, lr_decay=0.999, - tol=1e-2, + tol=1e-9, device="cpu", **kwargs, ): diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 6aa35c474..327cec387 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -1,6 +1,5 @@ """CBMR Models.""" import abc -import copy import logging import numpy as np @@ -44,10 +43,10 @@ def __init__( spatial_coef_dim=None, moderators_coef_dim=None, penalty=False, - lr=0.1, + lr=1, lr_decay=0.999, - n_iter=1000, - tol=1e-2, + n_iter=2000, + tol=1e-9, device="cpu", ): super().__init__() @@ -186,47 +185,17 @@ def closure(): loss.backward() return loss - loss = optimizer.step(closure) + optimizer.step(closure) scheduler.step() + # recalculate the loss function + loss = self(coef_spline_bases, moderators, foci_per_voxel, foci_per_study) + if torch.isnan(loss): raise ValueError( f"""The current learing rate {str(self.lr)} or choice of model gives rise to NaN log-likelihood, please try Poisson model or adjust learning rate to a smaller value.""" ) - # reset the L-BFGS params if NaN appears in coefficient of regression - if any( - [ - torch.any(torch.isnan(self.spatial_coef_linears[group].weight)) - for group in self.groups - ] - ): - if self.iter == 1: # NaN occurs in the first iteration - raise ValueError( - """The current learing rate {str(self.lr)} gives rise to NaN values, adjust - to a smaller value.""" - ) - spatial_coef_linears, overdispersion = dict(), dict() - for group in self.groups: - group_spatial_linear = torch.nn.Linear( - self.spatial_coef_dim, 1, bias=False - ).double() - group_spatial_linear.weight = torch.nn.Parameter( - self.last_state["spatial_coef_linears." + group + ".weight"] - ) - spatial_coef_linears[group] = group_spatial_linear - - if hasattr(self, "overdispersion"): - group_overdispersion = torch.nn.Parameter( - self.last_state["overdispersion." + group] - ) - overdispersion[group] = group_overdispersion - self.spatial_coef_linears = torch.nn.ModuleDict(spatial_coef_linears) - if hasattr(self, "overdispersion"): - self.overdispersion = torch.nn.ParameterDict(overdispersion) - LGR.debug("Reset L-BFGS optimizer......") - else: - self.last_state = copy.deepcopy(self.state_dict()) return loss @@ -246,7 +215,8 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc Dictionary of group-wise number of foci per study. """ torch.manual_seed(100) - optimizer = torch.optim.LBFGS(self.parameters(), self.lr) + optimizer = torch.optim.LBFGS(params=self.parameters(), lr=self.lr, max_iter=self.n_iter, + tolerance_change=self.tol, line_search_fn='strong_wolfe') # load dataset info to torch.tensor coef_spline_bases = torch.tensor( coef_spline_bases, dtype=torch.float64, device=self.device @@ -274,20 +244,12 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc if self.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - for i in range(self.n_iter): - loss = self._update( - optimizer, - coef_spline_bases, - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss, - ) - loss_diff = loss - prev_loss - LGR.debug(f"Iter {self.iter:04d}: log-likelihood {loss:.4f}") - if torch.abs(loss_diff) < self.tol: - break - prev_loss = loss + loss = self._update(optimizer, + coef_spline_bases, + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss) return diff --git a/nimare/tests/test_meta_cbmr.py b/nimare/tests/test_meta_cbmr.py index 6315f4f72..d77c8f69b 100644 --- a/nimare/tests/test_meta_cbmr.py +++ b/nimare/tests/test_meta_cbmr.py @@ -42,8 +42,9 @@ def cbmr_result(testdata_cbmr_simulated, model): spline_spacing=100, model=model, penalty=False, - lr=1e-2, - tol=1e7, + n_iter=1000, + lr=1, + tol=1e4, device="cpu", ) res = cbmr.fit(dataset=dset) @@ -113,10 +114,10 @@ def test_firth_penalty(testdata_cbmr_simulated): group_categories=["diagnosis", "drug_status"], moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], spline_spacing=100, - model=models.ClusteredNegativeBinomialEstimator, + model=models.PoissonEstimator, penalty=True, - lr=1e-1, - tol=1e7, + lr=1, + tol=1e4, device="cpu", ) res = cbmr.fit(dataset=dset) @@ -134,8 +135,8 @@ def test_moderators_none(testdata_cbmr_simulated): spline_spacing=100, model=models.PoissonEstimator, penalty=False, - lr=1e-2, - tol=1e7, + lr=1, + tol=1e4, device="cpu", ) res = cbmr.fit(dataset=dset) @@ -162,7 +163,7 @@ def test_CBMREstimator_update(testdata_cbmr_simulated): cbmr = CBMREstimator( moderators=["standardized_sample_sizes", "standardized_avg_age", "schizophrenia_subtype"], model=models.PoissonEstimator, - lr=1e-4, + lr=1, ) cbmr._collect_inputs(testdata_cbmr_simulated, drop_invalid=True) From c68866e89a915b170042711b8d0c0c78c40b3b62 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 2 Sep 2023 20:54:03 +0100 Subject: [PATCH 173/177] fix lint code error --- nimare/meta/models.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 327cec387..5470d51d1 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -244,12 +244,8 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc if self.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - loss = self._update(optimizer, - coef_spline_bases, - moderators_by_group_tensor, - foci_per_voxel_tensor, - foci_per_study_tensor, - prev_loss) + self._update(optimizer, coef_spline_bases, moderators_by_group_tensor, + foci_per_voxel_tensor, foci_per_study_tensor, prev_loss) return From 2531c19cd7c58aed3662969b15b401ed496a0209 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 2 Sep 2023 21:05:30 +0100 Subject: [PATCH 174/177] fix a lint error --- nimare/meta/models.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/nimare/meta/models.py b/nimare/meta/models.py index 5470d51d1..f2a8df30d 100644 --- a/nimare/meta/models.py +++ b/nimare/meta/models.py @@ -215,8 +215,13 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc Dictionary of group-wise number of foci per study. """ torch.manual_seed(100) - optimizer = torch.optim.LBFGS(params=self.parameters(), lr=self.lr, max_iter=self.n_iter, - tolerance_change=self.tol, line_search_fn='strong_wolfe') + optimizer = torch.optim.LBFGS( + params=self.parameters(), + lr=self.lr, + max_iter=self.n_iter, + tolerance_change=self.tol, + line_search_fn="strong_wolfe", + ) # load dataset info to torch.tensor coef_spline_bases = torch.tensor( coef_spline_bases, dtype=torch.float64, device=self.device @@ -244,8 +249,14 @@ def _optimizer(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foc if self.iter == 0: prev_loss = torch.tensor(float("inf")) # initialization loss difference - self._update(optimizer, coef_spline_bases, moderators_by_group_tensor, - foci_per_voxel_tensor, foci_per_study_tensor, prev_loss) + self._update( + optimizer, + coef_spline_bases, + moderators_by_group_tensor, + foci_per_voxel_tensor, + foci_per_study_tensor, + prev_loss, + ) return From ff5bdc3e79687eecc5f30fe9739e0791b1251a52 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sat, 9 Sep 2023 19:51:55 +0100 Subject: [PATCH 175/177] add a notebook for comparing cbmr and cbma --- .../12_plot_compare_cbmr_and_cbma.py | 226 ++++++++++++++++++ 1 file changed, 226 insertions(+) create mode 100644 examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py diff --git a/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py new file mode 100644 index 000000000..bf2ecf9aa --- /dev/null +++ b/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py @@ -0,0 +1,226 @@ +""" + +.. _metas_cbmr_vs_cbma: + +================================================================ +Compare coordinate-based meta-regression and meta-analysis methods +================================================================ + +A comparison between coordinate-based meta-regression (CBMR) and +coordinate-based meta-analysis (CBMA) in NiMARE + +CBMR is a generative framework to approximate smooth activation intensity function and investigate +the effect of study-level moderators (e.g., year of pubilication, sample size, subtype of stimuli). +It allows flexible statistical inference for either spatial homogeneity tests or group comparison +tests. Additionally, it's a computationally efficient approach with good statistical +interpretability to model the locations of activation foci. + +This tutorial is intended to provide an intuitive comparison of CBMA and MKDA results on +neurosynth dataset. + +For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral +`_ +and `documatation `_. + +""" +import os + +from nimare.extract import download_abstracts, fetch_neurosynth +from nimare.io import convert_neurosynth_to_dataset +from nimare.meta import models +from nilearn.plotting import plot_stat_map + +############################################################################### +# Download the Neurosynth Dataset +# ----------------------------------------------------------------------------- +# Neurosynth is a large-scale functional magnetic resonance imaing (fMRI) database. +# There are currently 507891 activations reported in 14371 studies in the Neurosynth +# database, with interactive, downloadable meta-analyses of 1334 terms. There is also +# a `platform `_ designed for automated synthesis of fMRI data. + +out_dir = os.path.abspath("../example_data/") +os.makedirs(out_dir, exist_ok=True) + +files = fetch_neurosynth( + data_dir=out_dir, + version="7", + overwrite=False, + source="abstract", + vocab="terms", +) +# Note that the files are saved to a new folder within "out_dir" named "neurosynth". +neurosynth_db = files[0] + +neurosynth_dset = convert_neurosynth_to_dataset( + coordinates_file=neurosynth_db["coordinates"], + metadata_file=neurosynth_db["metadata"], + annotations_files=neurosynth_db["features"], +) +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset.pkl.gz")) + +neurosynth_dset = download_abstracts(neurosynth_dset, "example@example.edu") +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset_with_abstracts.pkl.gz")) + +############################################################################### +# For term-based meta-analyses, we split the whole Neurosynth dataset into two subsets, +# one including all studies in the Neurosynth database whose abstracts include the term +# at least once, the other including all the remaining studies. Here, we will conduct +# meta-analyses based on the term "pain", and explore the spatial convergence between +# pain studies and other fMRI studies. + +# extract study_id for pain dataset and non-pain dataset +all_study_id = list(neurosynth_dset.annotations["id"]) +pain_study_id = neurosynth_dset.get_studies_by_label(labels=["terms_abstract_tfidf__pain"]) +non_pain_study_id = list(set(all_study_id) - set(pain_study_id)) # 13855 studies +# add an additional column for group +neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(pain_study_id), "group"] = "pain" +neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(non_pain_study_id), "group"] = "non_pain" + +############################################################################### +# Estimation of group-specific spatial intensity functions +# ----------------------------------------------------------------------------- +# Now we are going to run CBMR framework on the Neurosynth Dataset and estimate +# spatial intensity functions for both pain studies and non-pain fMRI studies. + +from nimare.meta.cbmr import CBMREstimator +cbmr = CBMREstimator( + group_categories="group", + moderators=None, + spline_spacing=10, # a reasonable choice is 10 or 5, 100 is for speed + model=models.PoissonEstimator, + penalty=False, + lr=1e-1, + tol=1e-2, # a reasonable choice is 1e-2, 1e3 is for speed + device="cpu", # "cuda" if you have GPU +) +results = cbmr.fit(dataset=neurosynth_dset) + +############################################################################### +# Now that we have fitted the model, we can plot the spatial intensity maps. + +plot_stat_map( + results.get_map("spatialIntensity_group-Pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Pain studies", + threshold=3e-4, + vmax=1e-3, +) +plot_stat_map( + results.get_map("spatialIntensity_group-Non_pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Non-pain fMRI studies", + threshold=3e-4, + vmax=1e-3, +) + +############################################################################### +# These two figures correspond to group-specific spatial intensity map of pain group +# and non-pain group. Areas with stronger spatial intensity are highlighted. + +############################################################################### +# Group-wise tests for spatial homogeneity +# ----------------------------------------------------------------------------- +# For group-wise spatial homogeneity test, we generate contrast matrix *t_con_groups* +# by specifying the group names in *create_contrast* function, and generate group-wise +# p-value and z-score maps for spatial homogeneity tests. +from nimare.meta.cbmr import CBMRInference + +inference = CBMRInference(device="cpu") +inference.fit(result=results) +t_con_groups = inference.create_contrast( + ["Pain", "Non_pain"], source="groups" +) +contrast_result = inference.transform(t_con_groups=t_con_groups) + +############################################################################### + +# generate z-score maps for group-wise spatial homogeneity test. +plot_stat_map( + contrast_result.get_map("z_group-Pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Z-score map for spatial homogeneity test on pain studies", + threshold=20, + vmax=30, +) + +plot_stat_map( + contrast_result.get_map("z_group-Non_pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Z-score map for spatial homogeneity test on non-pain fMRI studies", + threshold=20, + vmax=30, +) + +############################################################################### +# Group comparison test between pain studies and non-pain fMRI studies +# ----------------------------------------------------------------------------- +# CBMR framework also allows flexible statistical inference for group comparison +# between any two or more groups. For example, it's straightforward to generate +# contrast matrix *t_con_groups* by specifying *contrast_name* as "group1-group2". + +inference = CBMRInference(device="cpu") +inference.fit(result=results) +t_con_groups = inference.create_contrast( + ["Pain-Non_pain"], source="groups" +) +contrast_result = inference.transform(t_con_groups=t_con_groups) + +############################################################################### + +# generate z-statistics maps for each group +plot_stat_map( + contrast_result.get_map("z_group-Pain-Non_pain"), + cut_coords=[0, 0, 0], + draw_cross=False, + cmap="RdBu_r", + title="Spatial convergence between pain studies and Non-pain fMRI studies", + threshold=6, + vmax=20, +) + +############################################################################### +# This figure (displayed as z-statistics map) shows CBMR group comparison test +# of spatial intensity between pain studies and non-pain studies in Neurosynth. +# The null hypothesis assumes spatial intensity estimations of two groups are equal +# at voxel level, $H_0: \mu_{1j}=\mu_{2j}, j=1,\cdots,N$, where $N$ is number of +# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules +# (siginificant difference in spatial intensity estimation between two groups) are +# highlighted. We found that estimated activation level are significantly different +# in ... between pain group and non-pain group. + +############################################################################### +# Run MKDA on Neurosynth dataset +# ----------------------------------------------------------------------------- +# For the purpose of justifying the validity of CBMR framework, we compare the estimated +# spatial covergence of activation regions between pain studies and non-pain fMRI studies +# with MKDA. + +from nimare.meta.cbma.mkda import MKDAChi2 + +pain_dset = neurosynth_dset.slice(ids=pain_study_id) +non_pain_dset = neurosynth_dset.slice(ids=pain_study_id) + +meta = MKDAChi2() +results = meta.fit(pain_dset, non_pain_dset) + +plot_stat_map( + results.get_map("z_desc-consistency"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="MKDA Chi-square analysis between pain studies and non-pain studies", + threshold=5, +) + +############################################################################### +# This figure (displayed as z-statistics map) shows MKDA spatial covergence of +# activation between pain studies and non-pain fMRI studies. We found the results are +# very consistent with CBMR approach, with higher specificity but lower sensitivity. From 2f4a33f643d5307f349493abb120f3a3bbbb690b Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Sun, 10 Sep 2023 15:29:14 +0100 Subject: [PATCH 176/177] fix documentation error in notebook --- .../12_plot_compare_cbmr_and_cbma.py | 12 ++++++------ nimare/meta/cbmr.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py index bf2ecf9aa..37e4e9c26 100644 --- a/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py +++ b/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py @@ -19,8 +19,8 @@ neurosynth dataset. For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral -`_ -and `documatation `_. +`_ and +`documatation `_. """ import os @@ -69,12 +69,12 @@ # pain studies and other fMRI studies. # extract study_id for pain dataset and non-pain dataset -all_study_id = list(neurosynth_dset.annotations["id"]) +all_study_id = neurosynth_dset.annotations["id"] pain_study_id = neurosynth_dset.get_studies_by_label(labels=["terms_abstract_tfidf__pain"]) -non_pain_study_id = list(set(all_study_id) - set(pain_study_id)) # 13855 studies +non_pain_study_id = list(set(list(all_study_id)) - set(pain_study_id)) # 13855 studies # add an additional column for group -neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(pain_study_id), "group"] = "pain" -neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(non_pain_study_id), "group"] = "non_pain" +neurosynth_dset.annotations.loc[all_study_id.isin(pain_study_id), "group"] = "pain" +neurosynth_dset.annotations.loc[all_study_id.isin(non_pain_study_id), "group"] = "non_pain" ############################################################################### # Estimation of group-specific spatial intensity functions diff --git a/nimare/meta/cbmr.py b/nimare/meta/cbmr.py index 5fd3ae992..303873394 100644 --- a/nimare/meta/cbmr.py +++ b/nimare/meta/cbmr.py @@ -359,7 +359,7 @@ def _preprocess_input(self, dataset): n_group_study = len(group_study_id) group_foci_per_study = np.array( [(group_coordinates["study_id"] == i).sum() for i in group_study_id] - ) + ) # try groupby group_foci_per_study = group_foci_per_study.reshape((n_group_study, 1)) foci_per_voxel[group] = group_foci_per_voxel From 1df77899d29bd8e63d37b18666c37f87b01b7e80 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 12 Sep 2023 20:51:33 +0100 Subject: [PATCH 177/177] try not to run this notebook due to memory consideration --- ..._plot_compare_cbmr_and_cbma.py => 12_compare_cbmr_and_cbma.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/02_meta-analyses/{12_plot_compare_cbmr_and_cbma.py => 12_compare_cbmr_and_cbma.py} (100%) diff --git a/examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py similarity index 100% rename from examples/02_meta-analyses/12_plot_compare_cbmr_and_cbma.py rename to examples/02_meta-analyses/12_compare_cbmr_and_cbma.py