From 53e62e9def9992485392a14b78455f8c76c1c7b0 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:05:41 -0700 Subject: [PATCH 01/36] Add combine bins challenge file --- bin/challenge.py | 6 ++- bin/challenge_comb.py | 115 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 119 insertions(+), 2 deletions(-) create mode 100644 bin/challenge_comb.py diff --git a/bin/challenge.py b/bin/challenge.py index 5ec60273..e322c7d6 100755 --- a/bin/challenge.py +++ b/bin/challenge.py @@ -7,6 +7,7 @@ ## get root dir, one dir above this script root_dir=os.path.join(os.path.split(sys.argv[0])[0],"..") +print(root_dir) sys.path.append(root_dir) import tomo_challenge as tc @@ -66,7 +67,7 @@ def main(config_yaml): training_data, training_z, validation_data, validation_z, config['metrics'], metrics_fn) - output_file.write (f"{classifier_name} {run} {settings} {scores} \n") + output_file.write(f"{classifier_name} {run} {settings} {scores} \n") @@ -93,6 +94,7 @@ def run_one(classifier_name, bands, settings, train_data, train_z, valid_data, print ("Training...") C.train(train_data,train_z) + #perfect = C.train(train_data,train_z) print ("Applying...") results = C.apply(valid_data) @@ -102,7 +104,7 @@ def run_one(classifier_name, bands, settings, train_data, train_z, valid_data, print ("Making some pretty plots...") name = str(classifier.__name__) - tc.metrics.plot_distributions(valid_z, results, f"plots/{name}_{settings}_{bands}.png") + tc.metrics.plot_distributions(valid_z, results, f"/global/cscratch1/sd/abault/tomo_challenge/plots/{name}_{settings}_{bands}.png") return scores diff --git a/bin/challenge_comb.py b/bin/challenge_comb.py new file mode 100644 index 00000000..b887e8e4 --- /dev/null +++ b/bin/challenge_comb.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python +import sys +import os +import click +import yaml +import jinja2 + +## get root dir, one dir above this script +root_dir=os.path.join(os.path.split(sys.argv[0])[0],"..") +print(root_dir) +sys.path.append(root_dir) +import tomo_challenge as tc + +@click.command() +@click.argument('config_yaml', type=str) +def main(config_yaml): + with open(config_yaml, 'r') as fp: + config_str = jinja2.Template(fp.read()).render() + config = yaml.load(config_str, Loader=yaml.Loader) + + # Get the classes associated with each + for name in config['run']: + try: + config['cls'] = tc.Tomographer._find_subclass(name) + except KeyError: + raise ValueError(f"Tomographer {name} is not defined") + + # Decide if anyone needs the colors calculating and/or errors loading + anyone_wants_colors = False + anyone_wants_errors = False + for run in config['run'].values(): + for version in run.values(): + if version.get('errors'): + anyone_wants_errors = True + if version.get('colors'): + anyone_wants_colors = True + + + bands = config['bands'] + + training_data = tc.load_data( + config['training_file'], + bands, + errors=anyone_wants_errors, + colors=anyone_wants_colors + ) + + validation_data = tc.load_data( + config['validation_file'], + bands, + errors=anyone_wants_errors, + colors=anyone_wants_colors + ) + + training_z = tc.load_redshift(config['training_file']) + validation_z = tc.load_redshift(config['validation_file']) + + newbins = config['newbins'] + + if config['metrics_impl'] == 'jax-cosmo': + metrics_fn = tc.jc_compute_scores + else: + metrics_fn = tc.compute_scores + + with open(config['output_file'],'w') as output_file: + for classifier_name, runs in config['run'].items(): + for run, settings in runs.items(): + scores = run_one(classifier_name, bands, settings, + training_data, training_z, validation_data, validation_z, + config['metrics'], metrics_fn, newbins) + + output_file.write(f"{classifier_name} {run} {settings} {scores} \n") + + + +def run_one(classifier_name, bands, settings, train_data, train_z, valid_data, + valid_z, metrics, metrics_fn, newbins): + classifier = tc.Tomographer._find_subclass(classifier_name) + + if classifier.wants_arrays: + errors = settings.get('errors') + colors = settings.get('colors') + train_data = tc.dict_to_array(train_data, bands, errors=errors, colors=colors) + valid_data = tc.dict_to_array(valid_data, bands, errors=errors, colors=colors) + + print ("Executing: ", classifier_name, bands, settings) + + ## first check if options are valid + print (settings, classifier.valid_options) + for key in settings.keys(): + if key not in classifier.valid_options and key not in ['errors', 'colors']: + raise ValueError(f"Key {key} is not recognized by classifier {classifier_name}") + + print ("Initializing classifier...") + C=classifier(bands, settings, newbins) + + print ("Training...") + C.train(train_data,train_z) + #perfect = C.train(train_data,train_z) + + print ("Applying...") + results = C.apply(valid_data) + + print ("Getting metric...") + scores = metrics_fn(results, valid_z, metrics=metrics) + + print ("Making some pretty plots...") + name = str(classifier.__name__) + tc.metrics.plot_distributions(valid_z, results, f"/global/cscratch1/sd/abault/tomo_challenge/plots/{name}_{settings}_{bands}_{newbins}.png") + + return scores + + +if __name__=="__main__": + main() From 7b4344b5414ff796e0b70d1596976eefe2a34011 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:10:50 -0700 Subject: [PATCH 02/36] Add results from combining bins --- ...ors': True, 'errors': True}_riz_[0, 2].png | Bin 0 -> 17581 bytes ...ors': True, 'errors': True}_riz_[0, 3].png | Bin 0 -> 23439 bytes rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out | 87 +++++++++++++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh | 13 ++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.txt | 1 + .../rf_uniform/riz_rf_u_5bins0_1.yaml | 20 +++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out | 123 ++++++++++++++++++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh | 13 ++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.txt | 1 + .../rf_uniform/riz_rf_u_5bins0_2.yaml | 20 +++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out | 123 ++++++++++++++++++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh | 13 ++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.txt | 1 + .../rf_uniform/riz_rf_u_5bins0_3.yaml | 20 +++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out | 19 +++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh | 13 ++ .../rf_uniform/riz_rf_u_5bins0_4.yaml | 20 +++ 17 files changed, 487 insertions(+) create mode 100644 rf_comb_bins/rf_uniform/RF_Uniform_CombineBins_{'bins': 5, 'colors': True, 'errors': True}_riz_[0, 2].png create mode 100644 rf_comb_bins/rf_uniform/RF_Uniform_CombineBins_{'bins': 5, 'colors': True, 'errors': True}_riz_[0, 3].png create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.txt create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.txt create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.txt create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh create mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml diff --git a/rf_comb_bins/rf_uniform/RF_Uniform_CombineBins_{'bins': 5, 'colors': True, 'errors': True}_riz_[0, 2].png b/rf_comb_bins/rf_uniform/RF_Uniform_CombineBins_{'bins': 5, 'colors': True, 'errors': True}_riz_[0, 2].png new file mode 100644 index 0000000000000000000000000000000000000000..1a22d2b7e62bc819551f5e12cb6b9a16588b2a99 GIT binary patch literal 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RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpcv91grmu/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3607270253935149) +params--bias_1 ~ delta(2.5813978122461982) +params--bias_2 ~ delta(1.7476661701541452) +params--bias_3 ~ delta(2.2043998086715852) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmptcvyvon0/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3607270253935149) +params--bias_1 ~ delta(2.5813978122461982) +params--bias_2 ~ delta(1.7476661701541452) +params--bias_3 ~ delta(2.2043998086715852) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmphre8qzz7/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh new file mode 100644 index 00000000..8fce28f5 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_u_5bins0_1 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.txt b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.txt new file mode 100644 index 00000000..64ff2957 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.txt @@ -0,0 +1 @@ +RF_Uniform_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 350.62629992935354, 'FOM_ww': 69.62333557208532, 'SNR_gg': 961.3423758223748, 'FOM_gg': 998.7919662764982, 'SNR_3x2': 970.9692811096805, 'FOM_3x2': 2878.907835879231} diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml new file mode 100644 index 00000000..b0158486 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.txt +newbins: [0,1] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Uniform_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out new file mode 100644 index 00000000..95f3432e --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out @@ -0,0 +1,123 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Normal_CombineBins +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +4 +(304,) +(4, 304) +20788198.45604642 +13638173 +3954220.3104969305 +2671150 +949766.5599671043 +366555 +1235231.8060845833 +552676 +biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpyxpim019/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +20788198.45604642 +13638173 +3954220.3104969305 +2671150 +949766.5599671043 +366555 +1235231.8060845833 +552676 +biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.5242656370502428) +params--bias_1 ~ delta(1.4803437884420307) +params--bias_2 ~ delta(2.591061532286026) +params--bias_3 ~ delta(2.235001711824981) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpxlun9xrw/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +20788198.45604642 +13638173 +3954220.3104969305 +2671150 +949766.5599671043 +366555 +1235231.8060845833 +552676 +biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.5242656370502428) +params--bias_1 ~ delta(1.4803437884420307) +params--bias_2 ~ delta(2.591061532286026) +params--bias_3 ~ delta(2.235001711824981) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpeielvm2f/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh new file mode 100644 index 00000000..c37e185d --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_u_5bins0_2 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.txt b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.txt new file mode 100644 index 00000000..8272b6cd --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.txt @@ -0,0 +1 @@ +RF_Uniform_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 332.91946164294785, 'FOM_ww': 1573.8141960234443, 'SNR_gg': 969.7202847355114, 'FOM_gg': 3157.055747538249, 'SNR_3x2': 984.1608880916402, 'FOM_3x2': 28180.732187357935} diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml new file mode 100644 index 00000000..d2a04161 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.txt +newbins: [0,2] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Uniform_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out new file mode 100644 index 00000000..30c6ea9d --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out @@ -0,0 +1,123 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Normal_CombineBins +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +4 +(304,) +(4, 304) +10488700.542079326 +7558622 +4114672.935744032 +2780348 +11369287.471830672 +6520914 +954756.1829410096 +368670 +biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpmx_ziuuz/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +10488700.542079326 +7558622 +4114672.935744032 +2780348 +11369287.471830672 +6520914 +954756.1829410096 +368670 +biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.387647185172023) +params--bias_1 ~ delta(1.4799129230384225) +params--bias_2 ~ delta(1.7435113347347737) +params--bias_3 ~ delta(2.5897311496487636) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp2poqiwrl/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +10488700.542079326 +7558622 +4114672.935744032 +2780348 +11369287.471830672 +6520914 +954756.1829410096 +368670 +biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.387647185172023) +params--bias_1 ~ delta(1.4799129230384225) +params--bias_2 ~ delta(1.7435113347347737) +params--bias_3 ~ delta(2.5897311496487636) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp8h1r92p_/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh new file mode 100644 index 00000000..fde7052e --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_u_5bins0_3 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.txt b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.txt new file mode 100644 index 00000000..235c0384 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.txt @@ -0,0 +1 @@ +RF_Uniform_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 344.4664127650933, 'FOM_ww': 707.9343834362847, 'SNR_gg': 1111.5530740704078, 'FOM_gg': 1722.9278602544168, 'SNR_3x2': 1115.220976362594, 'FOM_3x2': 36901.359762732245} diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml new file mode 100644 index 00000000..9c168f25 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.txt +newbins: [0,3] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Uniform_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out new file mode 100644 index 00000000..298d5ec3 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out @@ -0,0 +1,19 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +5 diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh new file mode 100644 index 00000000..a436c071 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_u_5bins0_4 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml new file mode 100644 index 00000000..6ec1b906 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.txt +newbins: [0,4] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Uniform_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True From 53533670a702a6ec02bd2ec729df08e67e6fbd48 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:13:32 -0700 Subject: [PATCH 03/36] Add results from uniform bins --- ...0, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 30234 bytes ...2, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 12699 bytes ...4, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 23233 bytes ...6, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 25532 bytes ...8, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 27730 bytes rf_uniform/firecrown/riz_10bins.err | 2 + rf_uniform/firecrown/riz_10bins.out | 99 ++++++++++++++++++ rf_uniform/firecrown/riz_10bins.txt | 1 + rf_uniform/firecrown/riz_2bins.err | 2 + rf_uniform/firecrown/riz_2bins.out | 78 ++++++++++++++ rf_uniform/firecrown/riz_2bins.txt | 1 + rf_uniform/firecrown/riz_4bins.err | 2 + rf_uniform/firecrown/riz_4bins.out | 82 +++++++++++++++ rf_uniform/firecrown/riz_4bins.txt | 1 + rf_uniform/firecrown/riz_6bins.err | 2 + rf_uniform/firecrown/riz_6bins.out | 87 +++++++++++++++ rf_uniform/firecrown/riz_6bins.txt | 1 + rf_uniform/firecrown/riz_8bins.err | 2 + rf_uniform/firecrown/riz_8bins.out | 95 +++++++++++++++++ rf_uniform/firecrown/riz_8bins.txt | 1 + ...0, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 30192 bytes ...2, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 12690 bytes ...4, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 23368 bytes ...6, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 25701 bytes ...8, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 27826 bytes rf_uniform/jax/u_10bins.err | 6 ++ rf_uniform/jax/u_10bins.out | 20 ++++ rf_uniform/jax/u_10bins.txt | 1 + rf_uniform/jax/u_2bins.err | 6 ++ rf_uniform/jax/u_2bins.out | 19 ++++ rf_uniform/jax/u_2bins.txt | 1 + rf_uniform/jax/u_4bins.err | 6 ++ rf_uniform/jax/u_4bins.out | 19 ++++ rf_uniform/jax/u_4bins.txt | 1 + rf_uniform/jax/u_6bins.err | 6 ++ rf_uniform/jax/u_6bins.out | 20 ++++ rf_uniform/jax/u_6bins.txt | 1 + rf_uniform/jax/u_8bins.err | 6 ++ rf_uniform/jax/u_8bins.out | 20 ++++ rf_uniform/jax/u_8bins.txt | 1 + rf_uniform/rf_uniform_10bins.sh | 13 +++ rf_uniform/rf_uniform_2bins.sh | 13 +++ rf_uniform/rf_uniform_4bins.sh | 13 +++ rf_uniform/rf_uniform_6bins.sh | 13 +++ rf_uniform/rf_uniform_8bins.sh | 13 +++ rf_uniform/riz_10bins.yaml | 19 ++++ rf_uniform/riz_2bins.yaml | 19 ++++ rf_uniform/riz_4bins.yaml | 19 ++++ rf_uniform/riz_6bins.yaml | 19 ++++ rf_uniform/riz_8bins.yaml | 19 ++++ 50 files changed, 749 insertions(+) create mode 100644 rf_uniform/firecrown/RandomForestUniform_{'bins': 10, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/firecrown/RandomForestUniform_{'bins': 2, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/firecrown/RandomForestUniform_{'bins': 4, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/firecrown/RandomForestUniform_{'bins': 6, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/firecrown/RandomForestUniform_{'bins': 8, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/firecrown/riz_10bins.err create mode 100644 rf_uniform/firecrown/riz_10bins.out create mode 100644 rf_uniform/firecrown/riz_10bins.txt create mode 100644 rf_uniform/firecrown/riz_2bins.err create mode 100644 rf_uniform/firecrown/riz_2bins.out create mode 100644 rf_uniform/firecrown/riz_2bins.txt create mode 100644 rf_uniform/firecrown/riz_4bins.err create mode 100644 rf_uniform/firecrown/riz_4bins.out create mode 100644 rf_uniform/firecrown/riz_4bins.txt create mode 100644 rf_uniform/firecrown/riz_6bins.err create mode 100644 rf_uniform/firecrown/riz_6bins.out create mode 100644 rf_uniform/firecrown/riz_6bins.txt create mode 100644 rf_uniform/firecrown/riz_8bins.err create mode 100644 rf_uniform/firecrown/riz_8bins.out create mode 100644 rf_uniform/firecrown/riz_8bins.txt create mode 100644 rf_uniform/jax/RandomForestUniform_{'bins': 10, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/jax/RandomForestUniform_{'bins': 2, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/jax/RandomForestUniform_{'bins': 4, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/jax/RandomForestUniform_{'bins': 6, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/jax/RandomForestUniform_{'bins': 8, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_uniform/jax/u_10bins.err create mode 100644 rf_uniform/jax/u_10bins.out create mode 100644 rf_uniform/jax/u_10bins.txt create mode 100644 rf_uniform/jax/u_2bins.err create mode 100644 rf_uniform/jax/u_2bins.out create mode 100644 rf_uniform/jax/u_2bins.txt create mode 100644 rf_uniform/jax/u_4bins.err create mode 100644 rf_uniform/jax/u_4bins.out create 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zvFWsLDGmOBH-UOZ5H}Z{l+mYEfgs!B=*%iP|buC%>IAE fZ2uZ@oWJ@OM$i@O+`xt&sl!)=^^^ literal 0 HcmV?d00001 diff --git a/rf_uniform/firecrown/riz_10bins.err b/rf_uniform/firecrown/riz_10bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_uniform/firecrown/riz_10bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_10bins.out b/rf_uniform/firecrown/riz_10bins.out new file mode 100644 index 00000000..7d3fd907 --- /dev/null +++ b/rf_uniform/firecrown/riz_10bins.out @@ -0,0 +1,99 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier RandomForest_log_comb_bins_0_1 +Found classifier RandomForest_comb_bins_0_1 +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RF_Uniform_comb_bins_0_1 +Found classifier RF_Normal_comb_bins_0_1 +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +Executing: RandomForestUniform riz {'bins': 10, 'colors': True, 'errors': True} +{'bins': 10, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.26931938 1.4427957 1.65394431 + 2.05448922 2.08940756 2.15840691 2.9422926 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp_wyq1su4/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3129771819046112) +params--bias_1 ~ delta(1.4802418423775587) +params--bias_2 ~ delta(1.688167164042944) +params--bias_3 ~ delta(1.9143202136126234) +params--bias_4 ~ delta(1.9135796211211558) +params--bias_5 ~ delta(2.17790148913288) +params--bias_6 ~ delta(2.43662933258849) +params--bias_7 ~ delta(2.429499096047649) +params--bias_8 ~ delta(2.6129443035996056) +params--bias_9 ~ delta(2.574852039673552) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp8kqkuv5j/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3129771819046112) +params--bias_1 ~ delta(1.4802418423775587) +params--bias_2 ~ delta(1.688167164042944) +params--bias_3 ~ delta(1.9143202136126234) +params--bias_4 ~ delta(1.9135796211211558) +params--bias_5 ~ delta(2.17790148913288) +params--bias_6 ~ delta(2.43662933258849) +params--bias_7 ~ delta(2.429499096047649) +params--bias_8 ~ delta(2.6129443035996056) +params--bias_9 ~ delta(2.574852039673552) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpb92kwb5q/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_10bins.txt b/rf_uniform/firecrown/riz_10bins.txt new file mode 100644 index 00000000..914bd7cb --- /dev/null +++ b/rf_uniform/firecrown/riz_10bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_10 {'bins': 10, 'colors': True, 'errors': True} {'SNR_ww': 354.9197295938117, 'FOM_ww': 152.37715773462213, 'SNR_gg': 1308.84532609303, 'FOM_gg': 21171.68195209693, 'SNR_3x2': 1311.9719252996663, 'FOM_3x2': 47065.21003389874} diff --git a/rf_uniform/firecrown/riz_2bins.err b/rf_uniform/firecrown/riz_2bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_uniform/firecrown/riz_2bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_2bins.out b/rf_uniform/firecrown/riz_2bins.out new file mode 100644 index 00000000..83333e30 --- /dev/null +++ b/rf_uniform/firecrown/riz_2bins.out @@ -0,0 +1,78 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +Executing: RandomForestUniform riz {'bins': 2, 'colors': True, 'errors': True} +{'bins': 2, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpk4ugda01/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.5419310820732355) +params--bias_1 ~ delta(2.6139281837984605) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpi_huawso/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.5419310820732355) +params--bias_1 ~ delta(2.6139281837984605) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp9urj5559/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_2bins.txt b/rf_uniform/firecrown/riz_2bins.txt new file mode 100644 index 00000000..d840ddca --- /dev/null +++ b/rf_uniform/firecrown/riz_2bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_2 {'bins': 2, 'colors': True, 'errors': True} {'SNR_ww': 327.4681067505708, 'FOM_ww': 105.1466965449306, 'SNR_gg': 678.5458935974033, 'FOM_gg': 908.8160809482351, 'SNR_3x2': 701.5622646511446, 'FOM_3x2': 4534.786413137936} diff --git a/rf_uniform/firecrown/riz_4bins.err b/rf_uniform/firecrown/riz_4bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_uniform/firecrown/riz_4bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_4bins.out b/rf_uniform/firecrown/riz_4bins.out new file mode 100644 index 00000000..bb59c6ec --- /dev/null +++ b/rf_uniform/firecrown/riz_4bins.out @@ -0,0 +1,82 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +Executing: RandomForestUniform riz {'bins': 4, 'colors': True, 'errors': True} +{'bins': 4, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpod5u669r/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3069241571401855) +params--bias_1 ~ delta(1.4794681602751933) +params--bias_2 ~ delta(1.783300810771281) +params--bias_3 ~ delta(2.606220681084049) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp31actg_6/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3069241571401855) +params--bias_1 ~ delta(1.4794681602751933) +params--bias_2 ~ delta(1.783300810771281) +params--bias_3 ~ delta(2.606220681084049) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp14pmm2e6/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_4bins.txt b/rf_uniform/firecrown/riz_4bins.txt new file mode 100644 index 00000000..152c09dc --- /dev/null +++ b/rf_uniform/firecrown/riz_4bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_4 {'bins': 4, 'colors': True, 'errors': True} {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065} diff --git a/rf_uniform/firecrown/riz_6bins.err b/rf_uniform/firecrown/riz_6bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_uniform/firecrown/riz_6bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_6bins.out b/rf_uniform/firecrown/riz_6bins.out new file mode 100644 index 00000000..60d37562 --- /dev/null +++ b/rf_uniform/firecrown/riz_6bins.out @@ -0,0 +1,87 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +Executing: RandomForestUniform riz {'bins': 6, 'colors': True, 'errors': True} +{'bins': 6, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 2.15840691 + 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp07cua5ps/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.311222505636419) +params--bias_1 ~ delta(1.4798622016556051) +params--bias_2 ~ delta(1.7446521401354267) +params--bias_3 ~ delta(2.2055391179955746) +params--bias_4 ~ delta(2.4374906738379183) +params--bias_5 ~ delta(2.6163694037048817) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp_opurs95/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.311222505636419) +params--bias_1 ~ delta(1.4798622016556051) +params--bias_2 ~ delta(1.7446521401354267) +params--bias_3 ~ delta(2.2055391179955746) +params--bias_4 ~ delta(2.4374906738379183) +params--bias_5 ~ delta(2.6163694037048817) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp_kntxq3s/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_6bins.txt b/rf_uniform/firecrown/riz_6bins.txt new file mode 100644 index 00000000..3aba50ff --- /dev/null +++ b/rf_uniform/firecrown/riz_6bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_6 {'bins': 6, 'colors': True, 'errors': True} {'SNR_ww': 353.4995684607554, 'FOM_ww': 148.5449428914542, 'SNR_gg': 1155.0212895482066, 'FOM_gg': 2450.1410700563783, 'SNR_3x2': 1158.502391443923, 'FOM_3x2': 13404.867225041602} diff --git a/rf_uniform/firecrown/riz_8bins.err b/rf_uniform/firecrown/riz_8bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_uniform/firecrown/riz_8bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_8bins.out b/rf_uniform/firecrown/riz_8bins.out new file mode 100644 index 00000000..e513db27 --- /dev/null +++ b/rf_uniform/firecrown/riz_8bins.out @@ -0,0 +1,95 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +Executing: RandomForestUniform riz {'bins': 8, 'colors': True, 'errors': True} +{'bins': 8, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.26931938 1.65394431 2.08940756 + 2.15840691 2.9422926 3.03609133] +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpg684ef0g/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.311643344163664) +params--bias_1 ~ delta(1.480475703762752) +params--bias_2 ~ delta(1.6870486938697402) +params--bias_3 ~ delta(1.9111074288323902) +params--bias_4 ~ delta(2.2003309928037784) +params--bias_5 ~ delta(2.4331942419305235) +params--bias_6 ~ delta(2.620459106603769) +params--bias_7 ~ delta(2.6361862117129045) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp_lgodn5s/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.311643344163664) +params--bias_1 ~ delta(1.480475703762752) +params--bias_2 ~ delta(1.6870486938697402) +params--bias_3 ~ delta(1.9111074288323902) +params--bias_4 ~ delta(2.2003309928037784) +params--bias_5 ~ delta(2.4331942419305235) +params--bias_6 ~ delta(2.620459106603769) +params--bias_7 ~ delta(2.6361862117129045) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmphtnjtt9v/chain.txt +**************************** +Calculating derivatives using 20 total models +WARNING: The inverse covariance matrix produced by your pipeline + is not symmetric. This probably indicates a mistake somewhere. + If you are only using cosmosis-standard-library likelihoods please + open an issue about this on the cosmosis site. +Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_8bins.txt b/rf_uniform/firecrown/riz_8bins.txt new file mode 100644 index 00000000..5446933e --- /dev/null +++ b/rf_uniform/firecrown/riz_8bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_8 {'bins': 8, 'colors': True, 'errors': True} {'SNR_ww': 354.9124627287553, 'FOM_ww': 46.28382955361007, 'SNR_gg': 1250.552493913522, 'FOM_gg': 2881.0801502048794, 'SNR_3x2': 1253.8090733033507, 'FOM_3x2': 21691.232888150553} diff --git a/rf_uniform/jax/RandomForestUniform_{'bins': 10, 'colors': True, 'errors': True}_riz.png b/rf_uniform/jax/RandomForestUniform_{'bins': 10, 'colors': True, 'errors': True}_riz.png new file mode 100644 index 0000000000000000000000000000000000000000..95f821b5837cfe63db2fb0d0fd5fc5aedd553a33 GIT binary patch literal 30192 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00000000..38ef0f89 --- /dev/null +++ b/rf_uniform/jax/u_10bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_10bins.out b/rf_uniform/jax/u_10bins.out new file mode 100644 index 00000000..8ded743b --- /dev/null +++ b/rf_uniform/jax/u_10bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForestUniform riz {'bins': 10, 'colors': True, 'errors': True} +{'bins': 10, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.26931938 1.4427957 1.65394431 + 2.05448922 2.08940756 2.15840691 2.9422926 3.03609133] +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_uniform/jax/u_10bins.txt b/rf_uniform/jax/u_10bins.txt new file mode 100644 index 00000000..d02d3789 --- /dev/null +++ b/rf_uniform/jax/u_10bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_10 {'bins': 10, 'colors': True, 'errors': True} {'SNR_ww': 354.3929748535156, 'FOM_ww': 37.14365768432617, 'FOM_DETF_ww': 0.8497052192687988, 'SNR_gg': 1302.3973388671875, 'FOM_gg': 1204.456298828125, 'FOM_DETF_gg': 9.355816841125488, 'SNR_3x2': 1305.5096435546875, 'FOM_3x2': 5697.7705078125, 'FOM_DETF_3x2': 57.619598388671875} diff --git a/rf_uniform/jax/u_2bins.err b/rf_uniform/jax/u_2bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_uniform/jax/u_2bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_2bins.out b/rf_uniform/jax/u_2bins.out new file mode 100644 index 00000000..9bf3b624 --- /dev/null +++ b/rf_uniform/jax/u_2bins.out @@ -0,0 +1,19 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForestUniform riz {'bins': 2, 'colors': True, 'errors': True} +{'bins': 2, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_uniform/jax/u_2bins.txt b/rf_uniform/jax/u_2bins.txt new file mode 100644 index 00000000..485b36a3 --- /dev/null +++ b/rf_uniform/jax/u_2bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_2 {'bins': 2, 'colors': True, 'errors': True} {'SNR_ww': 327.1508483886719, 'FOM_ww': 3.3171546459198, 'FOM_DETF_ww': 0.092340387403965, 'SNR_gg': 677.09228515625, 'FOM_gg': 32.099971771240234, 'FOM_DETF_gg': 0.1580391675233841, 'SNR_3x2': 700.0398559570312, 'FOM_3x2': 852.3770751953125, 'FOM_DETF_3x2': 12.79428482055664} diff --git a/rf_uniform/jax/u_4bins.err b/rf_uniform/jax/u_4bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_uniform/jax/u_4bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_4bins.out b/rf_uniform/jax/u_4bins.out new file mode 100644 index 00000000..68e78bb7 --- /dev/null +++ b/rf_uniform/jax/u_4bins.out @@ -0,0 +1,19 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForestUniform riz {'bins': 4, 'colors': True, 'errors': True} +{'bins': 4, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 2.08940756 3.03609133] +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_uniform/jax/u_4bins.txt b/rf_uniform/jax/u_4bins.txt new file mode 100644 index 00000000..002a3fca --- /dev/null +++ b/rf_uniform/jax/u_4bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_4 {'bins': 4, 'colors': True, 'errors': True} {'SNR_ww': 351.2440185546875, 'FOM_ww': 23.247020721435547, 'FOM_DETF_ww': 0.6437975764274597, 'SNR_gg': 1106.9310302734375, 'FOM_gg': 527.278076171875, 'FOM_DETF_gg': 5.282368183135986, 'SNR_3x2': 1110.4364013671875, 'FOM_3x2': 2280.726806640625, 'FOM_DETF_3x2': 43.6702995300293} diff --git a/rf_uniform/jax/u_6bins.err b/rf_uniform/jax/u_6bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_uniform/jax/u_6bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_6bins.out b/rf_uniform/jax/u_6bins.out new file mode 100644 index 00000000..3945af90 --- /dev/null +++ b/rf_uniform/jax/u_6bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForestUniform riz {'bins': 6, 'colors': True, 'errors': True} +{'bins': 6, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 2.15840691 + 3.03609133] +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_uniform/jax/u_6bins.txt b/rf_uniform/jax/u_6bins.txt new file mode 100644 index 00000000..393170ac --- /dev/null +++ b/rf_uniform/jax/u_6bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_6 {'bins': 6, 'colors': True, 'errors': True} {'SNR_ww': 352.9674072265625, 'FOM_ww': 30.201784133911133, 'FOM_DETF_ww': 0.7597047090530396, 'SNR_gg': 1151.7760009765625, 'FOM_gg': 819.4025268554688, 'FOM_DETF_gg': 7.07086181640625, 'SNR_3x2': 1155.22509765625, 'FOM_3x2': 3679.62744140625, 'FOM_DETF_3x2': 49.80576705932617} diff --git a/rf_uniform/jax/u_8bins.err b/rf_uniform/jax/u_8bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_uniform/jax/u_8bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_8bins.out b/rf_uniform/jax/u_8bins.out new file mode 100644 index 00000000..6482e8ca --- /dev/null +++ b/rf_uniform/jax/u_8bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForestUniform riz {'bins': 8, 'colors': True, 'errors': True} +{'bins': 8, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +[0.02450252 0.68055436 0.858418 1.26931938 1.65394431 2.08940756 + 2.15840691 2.9422926 3.03609133] +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_uniform/jax/u_8bins.txt b/rf_uniform/jax/u_8bins.txt new file mode 100644 index 00000000..3c9dbde6 --- /dev/null +++ b/rf_uniform/jax/u_8bins.txt @@ -0,0 +1 @@ +RandomForestUniform run_8 {'bins': 8, 'colors': True, 'errors': True} {'SNR_ww': 354.352294921875, 'FOM_ww': 36.73133850097656, 'FOM_DETF_ww': 0.8435292840003967, 'SNR_gg': 1244.733642578125, 'FOM_gg': 990.042724609375, 'FOM_DETF_gg': 8.24472427368164, 'SNR_3x2': 1247.9957275390625, 'FOM_3x2': 4521.0576171875, 'FOM_DETF_3x2': 55.020790100097656} diff --git a/rf_uniform/rf_uniform_10bins.sh b/rf_uniform/rf_uniform_10bins.sh new file mode 100644 index 00000000..ead46ae7 --- /dev/null +++ b/rf_uniform/rf_uniform_10bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:40:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=u_10bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_10bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_2bins.sh b/rf_uniform/rf_uniform_2bins.sh new file mode 100644 index 00000000..62778cb5 --- /dev/null +++ b/rf_uniform/rf_uniform_2bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:15:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=u_2bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_2bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_4bins.sh b/rf_uniform/rf_uniform_4bins.sh new file mode 100644 index 00000000..d0bcbd40 --- /dev/null +++ b/rf_uniform/rf_uniform_4bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=u_4bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_4bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_6bins.sh b/rf_uniform/rf_uniform_6bins.sh new file mode 100644 index 00000000..456143b9 --- /dev/null +++ b/rf_uniform/rf_uniform_6bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:25:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_u_6bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_6bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_8bins.sh b/rf_uniform/rf_uniform_8bins.sh new file mode 100644 index 00000000..2e80f40c --- /dev/null +++ b/rf_uniform/rf_uniform_8bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:30:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=u_8bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_8bins.yaml \ No newline at end of file diff --git a/rf_uniform/riz_10bins.yaml b/rf_uniform/riz_10bins.yaml new file mode 100644 index 00000000..796896d6 --- /dev/null +++ b/rf_uniform/riz_10bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForestUniform: + run_10: + # This setting is sent to the classifier + bins: 10 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_uniform/riz_2bins.yaml b/rf_uniform/riz_2bins.yaml new file mode 100644 index 00000000..a106658c --- /dev/null +++ b/rf_uniform/riz_2bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForestUniform: + run_2: + # This setting is sent to the classifier + bins: 2 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_uniform/riz_4bins.yaml b/rf_uniform/riz_4bins.yaml new file mode 100644 index 00000000..fe76757d --- /dev/null +++ b/rf_uniform/riz_4bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForestUniform: + run_4: + # This setting is sent to the classifier + bins: 4 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_uniform/riz_6bins.yaml b/rf_uniform/riz_6bins.yaml new file mode 100644 index 00000000..57651cb2 --- /dev/null +++ b/rf_uniform/riz_6bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForestUniform: + run_6: + # This setting is sent to the classifier + bins: 6 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_uniform/riz_8bins.yaml b/rf_uniform/riz_8bins.yaml new file mode 100644 index 00000000..cc436286 --- /dev/null +++ b/rf_uniform/riz_8bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForestUniform: + run_8: + # This setting is sent to the classifier + bins: 8 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True From e6abf4b5dbccf0bfd88bbe51b932ba7794e2403e Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:16:16 -0700 Subject: [PATCH 04/36] add log combined bins results --- ...ors': True, 'errors': True}_riz_[0, 1].png | Bin 0 -> 18130 bytes rf_comb_bins/rf_log/riz_rf_log_5bins0_1.err | 2 ++ rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh | 13 ++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml | 20 ++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err | 2 ++ rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml | 20 ++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err | 2 ++ rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml | 20 ++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err | 29 ++++++++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh | 13 ++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml | 20 ++++++++++++ 11 files changed, 141 insertions(+) create mode 100644 rf_comb_bins/rf_log/RF_Log_CombineBins_{'bins': 5, 'colors': True, 'errors': True}_riz_[0, 1].png create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.err create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err create mode 100644 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/dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_log_5bins0_1 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml new file mode 100644 index 00000000..73a41aec --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.txt +newbins: [0,1] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Log_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml new file mode 100644 index 00000000..321664cf --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.txt +newbins: [0,2] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Log_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml new file mode 100644 index 00000000..23337867 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.txt +newbins: [0,3] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Log_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err new file mode 100644 index 00000000..fc69186d --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err @@ -0,0 +1,29 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py:153: RuntimeWarning: invalid value encountered in double_scalars + (bz*nz_bin).sum()/(nz_bin).sum() for nz_bin in n_of_z] +Traceback (most recent call last): + File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 115, in + main() + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 829, in __call__ + return self.main(*args, **kwargs) + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 782, in main + rv = self.invoke(ctx) + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 1066, in invoke + return ctx.invoke(self.callback, **ctx.params) + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 610, in invoke + return callback(*args, **kwargs) + File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 70, in main + config['metrics'], metrics_fn, newbins) + File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 105, in run_one + scores = metrics_fn(results, valid_z, metrics=metrics) + File "/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py", line 57, in compute_scores + tomo_bin, z, what) + File "/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py", line 195, in compute_mean_covariance + C_sig[ci, cj] = ccl.angular_cl(cosmo, Ti, Tj, ell) + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/pyccl/cls.py", line 98, in angular_cl + check(status, cosmo=cosmo_in) + File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/pyccl/pyutils.py", line 48, in check + raise CCLError("Error %s: %s" % (error_types[status], msg)) +pyccl.errors.CCLError: Error CCL_ERROR_INTEG: ccl_cls.c: ccl_angular_cls_limber(); integration error + diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh new file mode 100644 index 00000000..bea31b40 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_log_5bins0_4 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml new file mode 100644 index 00000000..246a92e1 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml @@ -0,0 +1,20 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.txt +newbins: [0,4] +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: firecrown + +run: + # This is a class name which will be looked up + RF_Log_CombineBins: + run_5: + # This setting is sent to the classifier + bins: 5 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True From 6536683110b2c09ab520ba5642698688b4752cca Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:17:08 -0700 Subject: [PATCH 05/36] Add files via upload --- riz_rf_4bins.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 riz_rf_4bins.txt diff --git a/riz_rf_4bins.txt b/riz_rf_4bins.txt new file mode 100644 index 00000000..679b4fc7 --- /dev/null +++ b/riz_rf_4bins.txt @@ -0,0 +1 @@ +RandomForest run_4 {'bins': 4, 'colors': True, 'errors': True} {'SNR_ww': 354.6609820268124, 'FOM_ww': 75.86045148833578, 'SNR_gg': 1225.4257263396669, 'FOM_gg': 1567.6121506994662, 'SNR_3x2': 1227.6735428278516, 'FOM_3x2': 11719.728722321575} From ef2809bdbd1d65374db5783627dfa854c1f1ff73 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:18:56 -0700 Subject: [PATCH 06/36] Add log bins results --- ...0, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 24795 bytes ...2, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 12962 bytes ...4, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 17748 bytes ...6, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 26214 bytes ...8, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 22321 bytes rf_log/firecrown/riz_10bins.err | 2 + rf_log/firecrown/riz_10bins.out | 98 ++++++++++++++++++ rf_log/firecrown/riz_10bins.txt | 1 + rf_log/firecrown/riz_2bins.err | 2 + rf_log/firecrown/riz_2bins.out | 81 +++++++++++++++ rf_log/firecrown/riz_2bins.txt | 1 + rf_log/firecrown/riz_4bins.err | 2 + rf_log/firecrown/riz_4bins.out | 86 +++++++++++++++ rf_log/firecrown/riz_4bins.txt | 1 + rf_log/firecrown/riz_6bins.err | 2 + rf_log/firecrown/riz_6bins.out | 90 ++++++++++++++++ rf_log/firecrown/riz_6bins.txt | 1 + rf_log/firecrown/riz_8bins.err | 2 + rf_log/firecrown/riz_8bins.out | 94 +++++++++++++++++ rf_log/firecrown/riz_8bins.txt | 1 + ...2, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 12903 bytes ...4, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 17859 bytes ...6, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 26282 bytes ...8, 'colors': True, 'errors': True}_riz.png | Bin 0 -> 22767 bytes rf_log/jax/log_2bins.err | 6 ++ rf_log/jax/log_2bins.out | 20 ++++ rf_log/jax/log_2bins.txt | 1 + rf_log/jax/log_4bins.err | 6 ++ rf_log/jax/log_4bins.out | 20 ++++ rf_log/jax/log_4bins.txt | 1 + rf_log/jax/log_6bins.err | 6 ++ rf_log/jax/log_6bins.out | 20 ++++ rf_log/jax/log_6bins.txt | 1 + rf_log/jax/log_8bins.err | 6 ++ rf_log/jax/log_8bins.out | 20 ++++ rf_log/jax/log_8bins.txt | 1 + rf_log/rf_log_10bins.sh | 13 +++ rf_log/rf_log_2bins.sh | 13 +++ rf_log/rf_log_4bins.sh | 13 +++ rf_log/rf_log_6bins.sh | 13 +++ rf_log/rf_log_8bins.sh | 13 +++ rf_log/riz_10bins.yaml | 19 ++++ rf_log/riz_2bins.yaml | 19 ++++ rf_log/riz_4bins.yaml | 19 ++++ rf_log/riz_6bins.yaml | 19 ++++ rf_log/riz_8bins.yaml | 19 ++++ 46 files changed, 732 insertions(+) create mode 100644 rf_log/firecrown/RandomForest_log_{'bins': 10, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_log/firecrown/RandomForest_log_{'bins': 2, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_log/firecrown/RandomForest_log_{'bins': 4, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_log/firecrown/RandomForest_log_{'bins': 6, 'colors': True, 'errors': True}_riz.png create mode 100644 rf_log/firecrown/RandomForest_log_{'bins': 8, 'colors': True, 'errors': True}_riz.png create mode 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zHMN%?K77!u2@eg8tFES->&dR)-y9W6`+3f^XeedpnFWzrfx$6|quM~!<7K{vm8$97 z{pN*?yu6d(K!}Ko6T*b?EhxAFnIa~Jry5p@`L%SlRG+?lc~+-4-R<2yG&D4)DT{&K z$RL6p-0iF*Z&JFs$yqdv74IIsu3mr-4@GGL z6YWiGEd7drhK1^WI`eG}V^!PdCzp5k3z~`sQXa9&hRAUm!P9g;7~FUTPy6$Wh@j4g zs|X4Xz6h2n)AN%Fuvh82M$xp28*HPKkd>u?2hNazyZZ=k82Tlw>t^Ge+{^dXJ>3vg zL#OUXRbBThf`@x{^58@wFX6dJ4e)_ushy1AlZt{rj-2nw)n$W!d;~}A`AKud(VpIl z4%0}NgYBt%u#4ap?9ewhH?LZsSaG&&$kTR!X=GBY4V${|o`X1*R=MpxB~?}bFlJRE ztPUGEjMty*Mfy=PcyXZlHV6bc9fc=P!mfyz!MDVaw;?0tU#_)SfmuifPqjD+`PBcj zJC)DpCIPns80r=N`LcA~wRN%Dz?l-4prA<}{{G^?Rm)<)Dd!&NmC38qJv;aR1s=N~ z?6kqRHah;b*Xmrab$tvSvAfIeb|vn9b!|S-<-0*Ah2-5m@7^n=3OqBXT>GbRQ?6EA ziBpa7rb(bu+@vuXy^vJ_7QvS!)Eao+ /tmp/tmpxv1_s87x/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1230673520631358) +params--bias_1 ~ delta(1.1860558567792236) +params--bias_2 ~ delta(1.195801073258373) +params--bias_3 ~ delta(1.1891119117180495) +params--bias_4 ~ delta(1.2063992094916378) +params--bias_5 ~ delta(1.2268082360867738) +params--bias_6 ~ delta(1.284071282386922) +params--bias_7 ~ delta(1.3991101109924111) +params--bias_8 ~ delta(1.5083271037932677) +params--bias_9 ~ delta(1.8610866508424537) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpze52pg_b/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1230673520631358) +params--bias_1 ~ delta(1.1860558567792236) +params--bias_2 ~ delta(1.195801073258373) +params--bias_3 ~ delta(1.1891119117180495) +params--bias_4 ~ delta(1.2063992094916378) +params--bias_5 ~ delta(1.2268082360867738) +params--bias_6 ~ delta(1.284071282386922) +params--bias_7 ~ delta(1.3991101109924111) +params--bias_8 ~ delta(1.5083271037932677) +params--bias_9 ~ delta(1.8610866508424537) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpw7x3ru8w/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_log/firecrown/riz_10bins.txt b/rf_log/firecrown/riz_10bins.txt new file mode 100644 index 00000000..41323916 --- /dev/null +++ b/rf_log/firecrown/riz_10bins.txt @@ -0,0 +1 @@ +RandomForest_log run_10 {'bins': 10, 'colors': True, 'errors': True} {'SNR_ww': 353.9796471119985, 'FOM_ww': 26.366472753947036, 'SNR_gg': 1500.0369417052223, 'FOM_gg': 181648.05441643775, 'SNR_3x2': 1501.7979111080479, 'FOM_3x2': 205723.43115529948} diff --git a/rf_log/firecrown/riz_2bins.err b/rf_log/firecrown/riz_2bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_log/firecrown/riz_2bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_2bins.out b/rf_log/firecrown/riz_2bins.out new file mode 100644 index 00000000..cf803af4 --- /dev/null +++ b/rf_log/firecrown/riz_2bins.out @@ -0,0 +1,81 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForest +Found classifier Random +Found classifier RandomForestNewBinning +file successfully created +first loop successfully entered +second loop successfully entered +Executing: RandomForest_log riz {'bins': 2, 'colors': True, 'errors': True} +{'bins': 2, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpj_wxylxl/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1823465400324054) +params--bias_1 ~ delta(1.5994619605861637) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpr02dfndf/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1823465400324054) +params--bias_1 ~ delta(1.5994619605861637) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpzr0mrtc5/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_log/firecrown/riz_2bins.txt b/rf_log/firecrown/riz_2bins.txt new file mode 100644 index 00000000..ee010203 --- /dev/null +++ b/rf_log/firecrown/riz_2bins.txt @@ -0,0 +1 @@ +RandomForest_log run_2 {'bins': 2, 'colors': True, 'errors': True} {'SNR_ww': 332.1252628616624, 'FOM_ww': 14.983596087283678, 'SNR_gg': 879.7755517457832, 'FOM_gg': 6800.843125725617, 'SNR_3x2': 885.1510794851065, 'FOM_3x2': 69314.82169237411} diff --git a/rf_log/firecrown/riz_4bins.err b/rf_log/firecrown/riz_4bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_log/firecrown/riz_4bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_4bins.out b/rf_log/firecrown/riz_4bins.out new file mode 100644 index 00000000..e7168fad --- /dev/null +++ b/rf_log/firecrown/riz_4bins.out @@ -0,0 +1,86 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +file successfully created +first loop successfully entered +second loop successfully entered +Executing: RandomForest_log riz {'bins': 4, 'colors': True, 'errors': True} +{'bins': 4, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmps010x8ar/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1501039624489349) +params--bias_1 ~ delta(1.2125371578497253) +params--bias_2 ~ delta(1.2973669314394134) +params--bias_3 ~ delta(1.6922830217318134) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpt4dul75j/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1501039624489349) +params--bias_1 ~ delta(1.2125371578497253) +params--bias_2 ~ delta(1.2973669314394134) +params--bias_3 ~ delta(1.6922830217318134) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp8twbx9vp/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_log/firecrown/riz_4bins.txt b/rf_log/firecrown/riz_4bins.txt new file mode 100644 index 00000000..cd89a196 --- /dev/null +++ b/rf_log/firecrown/riz_4bins.txt @@ -0,0 +1 @@ +RandomForest_log run_4 {'bins': 4, 'colors': True, 'errors': True} {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823} diff --git a/rf_log/firecrown/riz_6bins.err b/rf_log/firecrown/riz_6bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_log/firecrown/riz_6bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_6bins.out b/rf_log/firecrown/riz_6bins.out new file mode 100644 index 00000000..6e2d8223 --- /dev/null +++ b/rf_log/firecrown/riz_6bins.out @@ -0,0 +1,90 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +file successfully created +first loop successfully entered +second loop successfully entered +Executing: RandomForest_log riz {'bins': 6, 'colors': True, 'errors': True} +{'bins': 6, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp0omxbfyw/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1318921984696868) +params--bias_1 ~ delta(1.2002875299227955) +params--bias_2 ~ delta(1.2071750132422534) +params--bias_3 ~ delta(1.2512898807957415) +params--bias_4 ~ delta(1.4097344198654462) +params--bias_5 ~ delta(1.7674882543368329) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpuhbr2059/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1318921984696868) +params--bias_1 ~ delta(1.2002875299227955) +params--bias_2 ~ delta(1.2071750132422534) +params--bias_3 ~ delta(1.2512898807957415) +params--bias_4 ~ delta(1.4097344198654462) +params--bias_5 ~ delta(1.7674882543368329) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp6bzvd12p/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_log/firecrown/riz_6bins.txt b/rf_log/firecrown/riz_6bins.txt new file mode 100644 index 00000000..2be35ef9 --- /dev/null +++ b/rf_log/firecrown/riz_6bins.txt @@ -0,0 +1 @@ +RandomForest_log run_6 {'bins': 6, 'colors': True, 'errors': True} {'SNR_ww': 349.54961947303974, 'FOM_ww': 74.88239992499268, 'SNR_gg': 1283.0924805979128, 'FOM_gg': 2748.246880322987, 'SNR_3x2': 1285.0762826516666, 'FOM_3x2': 25527.319489134214} diff --git a/rf_log/firecrown/riz_8bins.err b/rf_log/firecrown/riz_8bins.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_log/firecrown/riz_8bins.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_8bins.out b/rf_log/firecrown/riz_8bins.out new file mode 100644 index 00000000..0e30e7ba --- /dev/null +++ b/rf_log/firecrown/riz_8bins.out @@ -0,0 +1,94 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RandomForest +Found classifier Random +file successfully created +first loop successfully entered +second loop successfully entered +Executing: RandomForest_log riz {'bins': 8, 'colors': True, 'errors': True} +{'bins': 8, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpvo3k8p_f/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1283607319843982) +params--bias_1 ~ delta(1.1917699159422768) +params--bias_2 ~ delta(1.1913858725422282) +params--bias_3 ~ delta(1.2075610181991745) +params--bias_4 ~ delta(1.2333877957410335) +params--bias_5 ~ delta(1.3351428777871712) +params--bias_6 ~ delta(1.463420009715028) +params--bias_7 ~ delta(1.8185301775278142) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp427g9z20/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1283607319843982) +params--bias_1 ~ delta(1.1917699159422768) +params--bias_2 ~ delta(1.1913858725422282) +params--bias_3 ~ delta(1.2075610181991745) +params--bias_4 ~ delta(1.2333877957410335) +params--bias_5 ~ delta(1.3351428777871712) +params--bias_6 ~ delta(1.463420009715028) +params--bias_7 ~ delta(1.8185301775278142) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp8zmkzgp2/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_log/firecrown/riz_8bins.txt b/rf_log/firecrown/riz_8bins.txt new file mode 100644 index 00000000..7871e4f7 --- /dev/null +++ b/rf_log/firecrown/riz_8bins.txt @@ -0,0 +1 @@ +RandomForest_log run_8 {'bins': 8, 'colors': True, 'errors': True} {'SNR_ww': 352.1677239587554, 'FOM_ww': 46.08137841821119, 'SNR_gg': 1407.4491566491292, 'FOM_gg': 11509.47849600147, 'SNR_3x2': 1409.2785646359137, 'FOM_3x2': 76079.64260621843} diff --git a/rf_log/jax/RandomForest_log_{'bins': 2, 'colors': True, 'errors': True}_riz.png b/rf_log/jax/RandomForest_log_{'bins': 2, 'colors': True, 'errors': True}_riz.png new file mode 100644 index 0000000000000000000000000000000000000000..52d907b6ba113960fd9a5a6d60337e0d36dc2cf1 GIT binary patch literal 12903 zcmd^m2UJvPx8*HBEfp0-L{Wk$pkyTq62ySy3=#`OKr%>@j0mElU?WjNp=63EIU|U) 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Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_2bins.out b/rf_log/jax/log_2bins.out new file mode 100644 index 00000000..4dbb7d76 --- /dev/null +++ b/rf_log/jax/log_2bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForest_log riz {'bins': 2, 'colors': True, 'errors': True} +{'bins': 2, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_log/jax/log_2bins.txt b/rf_log/jax/log_2bins.txt new file mode 100644 index 00000000..db748bf5 --- /dev/null +++ b/rf_log/jax/log_2bins.txt @@ -0,0 +1 @@ +RandomForest_log run_2 {'bins': 2, 'colors': True, 'errors': True} {'SNR_ww': 331.62371826171875, 'FOM_ww': 0.4617920517921448, 'FOM_DETF_ww': 0.013744479045271873, 'SNR_gg': 878.622314453125, 'FOM_gg': 53.327491760253906, 'FOM_DETF_gg': 0.786997377872467, 'SNR_3x2': 883.9085693359375, 'FOM_3x2': 255.66043090820312, 'FOM_DETF_3x2': 23.005399703979492} diff --git a/rf_log/jax/log_4bins.err b/rf_log/jax/log_4bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_log/jax/log_4bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_4bins.out b/rf_log/jax/log_4bins.out new file mode 100644 index 00000000..a3ca2a94 --- /dev/null +++ b/rf_log/jax/log_4bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForest_log riz {'bins': 4, 'colors': True, 'errors': True} +{'bins': 4, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_log/jax/log_4bins.txt b/rf_log/jax/log_4bins.txt new file mode 100644 index 00000000..193387e6 --- /dev/null +++ b/rf_log/jax/log_4bins.txt @@ -0,0 +1 @@ +RandomForest_log run_4 {'bins': 4, 'colors': True, 'errors': True} {'SNR_ww': 343.78753662109375, 'FOM_ww': 2.5369784832000732, 'FOM_DETF_ww': 0.18284554779529572, 'SNR_gg': 1126.6553955078125, 'FOM_gg': 460.51934814453125, 'FOM_DETF_gg': 7.035050868988037, 'SNR_3x2': 1128.9976806640625, 'FOM_3x2': 1246.9041748046875, 'FOM_DETF_3x2': 83.07261657714844} diff --git a/rf_log/jax/log_6bins.err b/rf_log/jax/log_6bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_log/jax/log_6bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_6bins.out b/rf_log/jax/log_6bins.out new file mode 100644 index 00000000..662dff2f --- /dev/null +++ b/rf_log/jax/log_6bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForest_log riz {'bins': 6, 'colors': True, 'errors': True} +{'bins': 6, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_log/jax/log_6bins.txt b/rf_log/jax/log_6bins.txt new file mode 100644 index 00000000..3ab0e0a0 --- /dev/null +++ b/rf_log/jax/log_6bins.txt @@ -0,0 +1 @@ +RandomForest_log run_6 {'bins': 6, 'colors': True, 'errors': True} {'SNR_ww': 348.9933776855469, 'FOM_ww': 12.718839645385742, 'FOM_DETF_ww': 0.5421679019927979, 'SNR_gg': 1277.877685546875, 'FOM_gg': 823.6866455078125, 'FOM_DETF_gg': 11.198760986328125, 'SNR_3x2': 1279.864990234375, 'FOM_3x2': 2050.5126953125, 'FOM_DETF_3x2': 102.53814697265625} diff --git a/rf_log/jax/log_8bins.err b/rf_log/jax/log_8bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_log/jax/log_8bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_8bins.out b/rf_log/jax/log_8bins.out new file mode 100644 index 00000000..72a73e64 --- /dev/null +++ b/rf_log/jax/log_8bins.out @@ -0,0 +1,20 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForest_log riz {'bins': 8, 'colors': True, 'errors': True} +{'bins': 8, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_log/jax/log_8bins.txt b/rf_log/jax/log_8bins.txt new file mode 100644 index 00000000..451fea14 --- /dev/null +++ b/rf_log/jax/log_8bins.txt @@ -0,0 +1 @@ +RandomForest_log run_8 {'bins': 8, 'colors': True, 'errors': True} {'SNR_ww': 351.5631408691406, 'FOM_ww': 20.807268142700195, 'FOM_DETF_ww': 0.6687530279159546, 'SNR_gg': 1400.4603271484375, 'FOM_gg': 1209.8232421875, 'FOM_DETF_gg': 14.164243698120117, 'SNR_3x2': 1402.2911376953125, 'FOM_3x2': 3289.179931640625, 'FOM_DETF_3x2': 114.87232208251953} diff --git a/rf_log/rf_log_10bins.sh b/rf_log/rf_log_10bins.sh new file mode 100644 index 00000000..9c6305be --- /dev/null +++ b/rf_log/rf_log_10bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:50:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=log_10bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_10bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_2bins.sh b/rf_log/rf_log_2bins.sh new file mode 100644 index 00000000..e8473c83 --- /dev/null +++ b/rf_log/rf_log_2bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:30:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=log_2bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_2bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_4bins.sh b/rf_log/rf_log_4bins.sh new file mode 100644 index 00000000..e89394e8 --- /dev/null +++ b/rf_log/rf_log_4bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:30:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=log_4bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_4bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_6bins.sh b/rf_log/rf_log_6bins.sh new file mode 100644 index 00000000..e9ffe739 --- /dev/null +++ b/rf_log/rf_log_6bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:30:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=log_6bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_6bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_8bins.sh b/rf_log/rf_log_8bins.sh new file mode 100644 index 00000000..d61b9132 --- /dev/null +++ b/rf_log/rf_log_8bins.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:40:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=log_8bins +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_8bins.yaml \ No newline at end of file diff --git a/rf_log/riz_10bins.yaml b/rf_log/riz_10bins.yaml new file mode 100644 index 00000000..83df49d8 --- /dev/null +++ b/rf_log/riz_10bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForest_log: + run_10: + # This setting is sent to the classifier + bins: 10 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_log/riz_2bins.yaml b/rf_log/riz_2bins.yaml new file mode 100644 index 00000000..fc7b4cf7 --- /dev/null +++ b/rf_log/riz_2bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForest_log: + run_2: + # This setting is sent to the classifier + bins: 2 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_log/riz_4bins.yaml b/rf_log/riz_4bins.yaml new file mode 100644 index 00000000..c96efe85 --- /dev/null +++ b/rf_log/riz_4bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForest_log: + run_4: + # This setting is sent to the classifier + bins: 4 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_log/riz_6bins.yaml b/rf_log/riz_6bins.yaml new file mode 100644 index 00000000..17b3a3e9 --- /dev/null +++ b/rf_log/riz_6bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForest_log: + run_6: + # This setting is sent to the classifier + bins: 6 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/rf_log/riz_8bins.yaml b/rf_log/riz_8bins.yaml new file mode 100644 index 00000000..e2e9b44e --- /dev/null +++ b/rf_log/riz_8bins.yaml @@ -0,0 +1,19 @@ +metrics: all +bands: riz +training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 +validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 +output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + RandomForest_log: + run_8: + # This setting is sent to the classifier + bins: 8 + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True From 0079231442b8950bd96bfbd06b460e6314e65844 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:22:23 -0700 Subject: [PATCH 07/36] Add legend to plots --- tomo_challenge/metrics.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tomo_challenge/metrics.py b/tomo_challenge/metrics.py index d129377a..eec578ad 100644 --- a/tomo_challenge/metrics.py +++ b/tomo_challenge/metrics.py @@ -142,9 +142,9 @@ def compute_mean_covariance(tomo_bin, z, what): # work out the number density per steradian steradian_to_arcmin2 = (180*60/np.pi)**2 n_eff_total = n_eff_total_arcmin2 * steradian_to_arcmin2 - + nbin = int(tomo_bin.max()) + 1 - + z_mid, n_of_z = get_n_of_z(tomo_bin, z) bz = 1/ccl.growth_factor(cosmo, 1/(1+z_mid)) bofz = (z_mid, bz) @@ -233,13 +233,14 @@ def plot_distributions(z, tomo_bin, filename, nominal_edges=None): nbin = int(tomo_bin.max()) + 1 for i in range(nbin): w = np.where(tomo_bin == i) - plt.hist(z[w], bins=50, histtype='step') + plt.hist(z[w], bins=50, histtype='step', label = f'bin {i}') # Plot verticals at nominal edges, if given if nominal_edges is not None: for x in nominal_edges: plt.axvline(x, color='k', linestyle=':') - + + plt.legend() plt.savefig(filename) plt.close() From 4beb9c8571e8e234285baa6ac5d12ba6a406d0b1 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:24:03 -0700 Subject: [PATCH 08/36] Add log bin, uniform bin, and combine bin classifiers --- .../classifiers/rf_log_comb_bins.py | 127 +++++++++++++++++ .../classifiers/rf_log_percentile.py | 116 ++++++++++++++++ tomo_challenge/classifiers/rf_uniform.py | 119 ++++++++++++++++ .../classifiers/rf_uniform_comb_bins.py | 128 ++++++++++++++++++ 4 files changed, 490 insertions(+) create mode 100644 tomo_challenge/classifiers/rf_log_comb_bins.py create mode 100644 tomo_challenge/classifiers/rf_log_percentile.py create mode 100644 tomo_challenge/classifiers/rf_uniform.py create mode 100644 tomo_challenge/classifiers/rf_uniform_comb_bins.py diff --git a/tomo_challenge/classifiers/rf_log_comb_bins.py b/tomo_challenge/classifiers/rf_log_comb_bins.py new file mode 100644 index 00000000..a76b499a --- /dev/null +++ b/tomo_challenge/classifiers/rf_log_comb_bins.py @@ -0,0 +1,127 @@ +""" +This is an example tomographic bin generator using a random forest for logarithmic bins. + +Every classifier module needs to: + - have construction of the type + __init__ (self, bands, options) (see examples below) + - implement two functions: + train (self, training_data,training_z) + apply (self, data). + - define valid_options class varible. + +See Classifier Documentation below. +""" + +from .base import Tomographer +import numpy as np +from sklearn.ensemble import RandomForestClassifier + + +class RF_Log_CombineBins(Tomographer): + """ Random Forest Classifier """ + + # valid parameter -- see below + valid_options = ['bins'] + # this settings means arrays will be sent to train and apply instead + # of dictionaries + wants_arrays = True + + def __init__ (self, bands, options, newbins): + """Constructor + + Parameters: + ----------- + bands: str + string containg valid bands, like 'riz' or 'griz' + options: dict + options come through here. Valid keys are listed as valid_options + class variable. + + Note: + ----- + Valiad options are: + 'bins' - number of tomographic bins + + """ + self.bands = bands + self.opt = options + self.newbins = newbins + + def train (self, training_data, training_z): + """Trains the classifier + + Parameters: + ----------- + training_data: numpy array, size Ngalaxes x Nbands + training data, each row is a galaxy, each column is a band as per + band defined above + training_z: numpy array, size Ngalaxies + true redshift for the training sample + + """ + + n_bin = self.opt['bins'] + newbins = self.newbins + + print("Finding bins for training data") + # Now put the training data into redshift bins. + # Use zero so that the one object with minimum + # z in the whole survey will be in the lowest bin + training_bin = np.zeros(training_z.size) + + # Find the edges that split the redshifts into n_z bins of + # equal number counts in each +# p = np.linspace(0, 100, n_bin + 1) + p = np.logspace(0, 2, n_bin+1) + print('the bins are in logspace') + z_edges = np.percentile(training_z, p) + + # Now find all the objects in each of these bins + for i in range(n_bin): + z_low = z_edges[i] + z_high = z_edges[i + 1] + training_bin[(training_z > z_low) & (training_z < z_high)] = i + + #combine bins + newbins.sort() + training_bin[training_bin == newbins[1]] = newbins[0] + + #rename/move bins + if newbins[1] != np.max(training_bin): + training_bin[training_bin == np.max(training_bin)] = newbins[1] + + # for speed, cut down to 5% of original size + cut_percent = 0.05 + print('cut percent:', cut_percent) + cut = np.random.uniform(0, 1, training_z.size) < cut_percent + training_bin = training_bin[cut] + training_data = training_data[cut] + + # Can be replaced with any classifier + classifier = RandomForestClassifier() + + print("Fitting classifier") + # Lots of data, so this will take some time + classifier.fit(training_data, training_bin) + + self.classifier = classifier + self.z_edges = z_edges + + + def apply (self, data): + """Applies training to the data. + + Parameters: + ----------- + Data: numpy array, size Ngalaxes x Nbands + testing data, each row is a galaxy, each column is a band as per + band defined above + + Returns: + tomographic_selections: numpy array, int, size Ngalaxies + tomographic selection for galaxies return as bin number for + each galaxy. + """ + tomo_bin = self.classifier.predict(data) + return tomo_bin + diff --git a/tomo_challenge/classifiers/rf_log_percentile.py b/tomo_challenge/classifiers/rf_log_percentile.py new file mode 100644 index 00000000..f3fb009b --- /dev/null +++ b/tomo_challenge/classifiers/rf_log_percentile.py @@ -0,0 +1,116 @@ +""" +This is an example tomographic bin generator using a random forest for logarithmic bins. + +Every classifier module needs to: + - have construction of the type + __init__ (self, bands, options) (see examples below) + - implement two functions: + train (self, training_data,training_z) + apply (self, data). + - define valid_options class varible. + +See Classifier Documentation below. +""" + +from .base import Tomographer +import numpy as np +from sklearn.ensemble import RandomForestClassifier + + +class RandomForest_log(Tomographer): + """ Random Forest Classifier """ + + # valid parameter -- see below + valid_options = ['bins'] + # this settings means arrays will be sent to train and apply instead + # of dictionaries + wants_arrays = True + + def __init__ (self, bands, options): + """Constructor + + Parameters: + ----------- + bands: str + string containg valid bands, like 'riz' or 'griz' + options: dict + options come through here. Valid keys are listed as valid_options + class variable. + + Note: + ----- + Valiad options are: + 'bins' - number of tomographic bins + + """ + self.bands = bands + self.opt = options + + def train (self, training_data, training_z): + """Trains the classifier + + Parameters: + ----------- + training_data: numpy array, size Ngalaxes x Nbands + training data, each row is a galaxy, each column is a band as per + band defined above + training_z: numpy array, size Ngalaxies + true redshift for the training sample + + """ + + n_bin = self.opt['bins'] + print("Finding bins for training data") + # Now put the training data into redshift bins. + # Use zero so that the one object with minimum + # z in the whole survey will be in the lowest bin + training_bin = np.zeros(training_z.size) + + # Find the edges that split the redshifts into n_z bins of + # equal number counts in each +# p = np.linspace(0, 100, n_bin + 1) + p = np.logspace(0, 2, n_bin+1) + print('the bins are in logspace') + z_edges = np.percentile(training_z, p) + + # Now find all the objects in each of these bins + for i in range(n_bin): + z_low = z_edges[i] + z_high = z_edges[i + 1] + training_bin[(training_z > z_low) & (training_z < z_high)] = i + + # for speed, cut down to 5% of original size + cut_percent = 0.05 + print('cut percent:', cut_percent) + cut = np.random.uniform(0, 1, training_z.size) < cut_percent + training_bin = training_bin[cut] + training_data = training_data[cut] + + # Can be replaced with any classifier + classifier = RandomForestClassifier() + + print("Fitting classifier") + # Lots of data, so this will take some time + classifier.fit(training_data, training_bin) + + self.classifier = classifier + self.z_edges = z_edges + + + def apply (self, data): + """Applies training to the data. + + Parameters: + ----------- + Data: numpy array, size Ngalaxes x Nbands + testing data, each row is a galaxy, each column is a band as per + band defined above + + Returns: + tomographic_selections: numpy array, int, size Ngalaxies + tomographic selection for galaxies return as bin number for + each galaxy. + """ + tomo_bin = self.classifier.predict(data) + return tomo_bin + diff --git a/tomo_challenge/classifiers/rf_uniform.py b/tomo_challenge/classifiers/rf_uniform.py new file mode 100644 index 00000000..92fff67c --- /dev/null +++ b/tomo_challenge/classifiers/rf_uniform.py @@ -0,0 +1,119 @@ +""" +This is an example tomographic bin generator using a random forest. + +Every classifier module needs to: + - have construction of the type + __init__ (self, bands, options) (see examples below) + - implement two functions: + train (self, training_data,training_z) + apply (self, data). + - define valid_options class varible. + +See Classifier Documentation below. +""" + +from .base import Tomographer +import numpy as np +from sklearn.ensemble import RandomForestClassifier + + +class RandomForestUniform(Tomographer): + """ Random Forest Classifier """ + + # valid parameter -- see below + valid_options = ['bins'] + # this settings means arrays will be sent to train and apply instead + # of dictionaries + wants_arrays = True + + def __init__ (self, bands, options): + """Constructor + + Parameters: + ----------- + bands: str + string containg valid bands, like 'riz' or 'griz' + options: dict + options come through here. Valid keys are listed as valid_options + class variable. + + Note: + ----- + Valiad options are: + 'bins' - number of tomographic bins + + """ + self.bands = bands + self.opt = options + + def train (self, training_data, training_z): + """Trains the classifier + + Parameters: + ----------- + training_data: numpy array, size Ngalaxes x Nbands + training data, each row is a galaxy, each column is a band as per + band defined above + training_z: numpy array, size Ngalaxies + true redshift for the training sample + + """ + + n_bin = self.opt['bins'] + print("Finding bins for training data") + # Now put the training data into redshift bins. + # Use zero so that the one object with minimum + # z in the whole survey will be in the lowest bin + training_bin = np.zeros(training_z.size) + + # Find the edges that split the redshifts into n_z bins of + # equal number counts in each + ####USE NP.RANDOM.UNIFORM TO GET BIN EDGES + #p = np.linspace(0, 100, n_bin + 1) + gen = np.random.RandomState(123) + #z_edges = np.percentile(training_z, p) + z_edges = gen.uniform(0, 3, n_bin - 1) + z_edges = np.insert(z_edges, 0, np.min(training_z)) + z_edges = np.insert(z_edges, 0, np.max(training_z)) + z_edges.sort() + print(z_edges) + + # Now find all the objects in each of these bins + for i in range(n_bin): + z_low = z_edges[i] + z_high = z_edges[i + 1] + training_bin[(training_z > z_low) & (training_z < z_high)] = i + + # for speed, cut down to 5% of original size + cut = np.random.uniform(0, 1, training_z.size) < 0.05 + training_bin = training_bin[cut] + training_data = training_data[cut] + + # Can be replaced with any classifier + classifier = RandomForestClassifier() + + print("Fitting classifier") + # Lots of data, so this will take some time + classifier.fit(training_data, training_bin) + + self.classifier = classifier + self.z_edges = z_edges + + + def apply (self, data): + """Applies training to the data. + + Parameters: + ----------- + Data: numpy array, size Ngalaxes x Nbands + testing data, each row is a galaxy, each column is a band as per + band defined above + + Returns: + tomographic_selections: numpy array, int, size Ngalaxies + tomographic selection for galaxies return as bin number for + each galaxy. + """ + tomo_bin = self.classifier.predict(data) + return tomo_bin + diff --git a/tomo_challenge/classifiers/rf_uniform_comb_bins.py b/tomo_challenge/classifiers/rf_uniform_comb_bins.py new file mode 100644 index 00000000..105f1ecd --- /dev/null +++ b/tomo_challenge/classifiers/rf_uniform_comb_bins.py @@ -0,0 +1,128 @@ +""" +This is an example tomographic bin generator using a random forest. + +Every classifier module needs to: + - have construction of the type + __init__ (self, bands, options) (see examples below) + - implement two functions: + train (self, training_data,training_z) + apply (self, data). + - define valid_options class varible. + +See Classifier Documentation below. +""" + +from .base import Tomographer +import numpy as np +from sklearn.ensemble import RandomForestClassifier + + +class RF_Uniform_CombineBins(Tomographer): + """ Random Forest Classifier """ + + # valid parameter -- see below + valid_options = ['bins'] + # this settings means arrays will be sent to train and apply instead + # of dictionaries + wants_arrays = True + + def __init__ (self, bands, options, newbins): + """Constructor + + Parameters: + ----------- + bands: str + string containg valid bands, like 'riz' or 'griz' + options: dict + options come through here. Valid keys are listed as valid_options + class variable. + + Note: + ----- + Valiad options are: + 'bins' - number of tomographic bins + + """ + self.bands = bands + self.opt = options + self.newbins = newbins + + def train (self, training_data, training_z): + """Trains the classifier + + Parameters: + ----------- + training_data: numpy array, size Ngalaxes x Nbands + training data, each row is a galaxy, each column is a band as per + band defined above + training_z: numpy array, size Ngalaxies + true redshift for the training sample + + """ + + n_bin = self.opt['bins'] + newbins = self.newbins + + print("Finding bins for training data") + # Now put the training data into redshift bins. + # Use zero so that the one object with minimum + # z in the whole survey will be in the lowest bin + training_bin = np.zeros(training_z.size) + + # Find the edges that split the redshifts into n_z bins of + # equal number counts in each + ####USE NP.RANDOM.UNIFORM TO GET BIN EDGES + #p = np.linspace(0, 100, n_bin + 1) + gen = np.random.RandomState(123) + #z_edges = np.percentile(training_z, p) + z_edges = gen.uniform(0, 3, n_bin - 1) + z_edges = np.insert(z_edges, 0, np.min(training_z)) + z_edges = np.insert(z_edges, 0, np.max(training_z)) + z_edges.sort() + + # Now find all the objects in each of these bins + for i in range(n_bin): + z_low = z_edges[i] + z_high = z_edges[i + 1] + training_bin[(training_z > z_low) & (training_z < z_high)] = i + + #combine bins + training_bin[training_bin == newbins[1]] = newbins[0] + + #rename/move bins + if newbins[1] != np.max(training_bin): + training_bin[training_bin == np.max(training_bin)] = newbins[1] + + # for speed, cut down to 5% of original size + cut = np.random.uniform(0, 1, training_z.size) < 0.05 + training_bin = training_bin[cut] + training_data = training_data[cut] + + # Can be replaced with any classifier + classifier = RandomForestClassifier() + + print("Fitting classifier") + # Lots of data, so this will take some time + classifier.fit(training_data, training_bin) + + self.classifier = classifier + self.z_edges = z_edges + + + def apply (self, data): + """Applies training to the data. + + Parameters: + ----------- + Data: numpy array, size Ngalaxes x Nbands + testing data, each row is a galaxy, each column is a band as per + band defined above + + Returns: + tomographic_selections: numpy array, int, size Ngalaxies + tomographic selection for galaxies return as bin number for + each galaxy. + """ + tomo_bin = self.classifier.predict(data) + return tomo_bin + From 3044a782f1b0d2a446626575eaaa38f923e0c5ef Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:26:29 -0700 Subject: [PATCH 09/36] Add random plots --- ... 2, 'colors'_ True, 'errors'_ False}_riz.png | Bin 0 -> 13560 bytes ... 4, 'colors'_ True, 'errors'_ False}_riz.png | Bin 0 -> 20511 bytes ..._ 4, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 23318 bytes 3 files 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z)YO#3*-Alru*K06Vw`yoJ3ssxOcp`Nt*(N7Eb38V*4iU~z{F@W_v?GD?R+1r3}cA$ z-McHzTHSVPU$Yn92D1b;Tm~oe&CrmxnWbeI;IpdA$dH4`#+OA$r9Z<)~K3x2cfZF`Eg4R`i-j)?Fe`Fa^EGl+|kSyQdT0?idUOm$kA*yy=jhU_Y=->ew)eWdwMj*69>5Gkq r;waJGHr|F;z2P0-_y4!Nafn?|Q-I<*3~jmX?ml+3<)@A3Z!C>cqR literal 0 HcmV?d00001 From 7f6bc4c280c438ca0748bbf7ce5a7b8e6fa1b618 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:30:50 -0700 Subject: [PATCH 11/36] Add files via upload --- rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out | 92 +++++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_1.txt | 1 + rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out | 124 ++++++++++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh | 13 ++ rf_comb_bins/rf_log/riz_rf_log_5bins0_2.txt | 1 + rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out | 124 ++++++++++++++++++++ rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh | 13 ++ rf_comb_bins/rf_log/riz_rf_log_5bins0_3.txt | 1 + 8 files changed, 369 insertions(+) create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.txt create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_2.txt create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh create mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_3.txt diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out new file mode 100644 index 00000000..b9b11830 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out @@ -0,0 +1,92 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Normal_CombineBins +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Log_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpg7kdsum8/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1884258020603244) +params--bias_1 ~ delta(1.7318281282478987) +params--bias_2 ~ delta(1.2284861515879035) +params--bias_3 ~ delta(1.3620825506058292) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpybi295wo/chain.txt +**************************** +Calculating derivatives using 20 total models +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.1884258020603244) +params--bias_1 ~ delta(1.7318281282478987) +params--bias_2 ~ delta(1.2284861515879035) +params--bias_3 ~ delta(1.3620825506058292) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmphrgb4k7e/chain.txt +**************************** +Calculating derivatives using 20 total models +WARNING: The inverse covariance matrix produced by your pipeline + is not symmetric. This probably indicates a mistake somewhere. + If you are only using cosmosis-standard-library likelihoods please + open an issue about this on the cosmosis site. +Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.txt b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.txt new file mode 100644 index 00000000..82dda41f --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.txt @@ -0,0 +1 @@ +RF_Log_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 347.1550052435396, 'FOM_ww': 43.281384668444375, 'SNR_gg': 1178.1020177366966, 'FOM_gg': 1937.6331074516759, 'SNR_3x2': 1180.154312947983, 'FOM_3x2': 8170.834082030528} diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out new file mode 100644 index 00000000..722b62aa --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out @@ -0,0 +1,124 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Normal_CombineBins +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Log_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +4 +(304,) +(4, 304) +2908203.3878848907 +2390598 +462445.53912042466 +382317 +18103916.630815372 +10452442 +5452851.574774352 +4003197 +biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpguev67td/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +2908203.3878848907 +2390598 +462445.53912042466 +382317 +18103916.630815372 +10452442 +5452851.574774352 +4003197 +biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.2165171174262217) +params--bias_1 ~ delta(1.2095866496138667) +params--bias_2 ~ delta(1.7320274660041521) +params--bias_3 ~ delta(1.3621242159140188) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp7hlh1tzf/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +2908203.3878848907 +2390598 +462445.53912042466 +382317 +18103916.630815372 +10452442 +5452851.574774352 +4003197 +biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.2165171174262217) +params--bias_1 ~ delta(1.2095866496138667) +params--bias_2 ~ delta(1.7320274660041521) +params--bias_3 ~ delta(1.3621242159140188) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmp5hc4lzdd/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh new file mode 100644 index 00000000..cbc61ba0 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_log_5bins0_2 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/rf_comb_bins/riz_rf_log_5bins0_2.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.txt b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.txt new file mode 100644 index 00000000..44def0c0 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.txt @@ -0,0 +1 @@ +RF_Log_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 347.11524596040493, 'FOM_ww': 12.496906081438635, 'SNR_gg': 1130.6537186403334, 'FOM_gg': 1725.6973362282138, 'SNR_3x2': 1132.7475032766674, 'FOM_3x2': 4340.897488251889} diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out new file mode 100644 index 00000000..268ee555 --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out @@ -0,0 +1,124 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestNormal +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Normal_CombineBins +Found classifier RandomForestCombineBins +Found classifier RF_Uniform_CombineBins +Found classifier Random +Found classifier RandomForest_comb_test +Executing: RF_Log_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} +{'bins': 5, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +4 +(304,) +(4, 304) +6249319.62865952 +4660882 +536666.3042368268 +446018 +2100382.2024054904 +1711346 +18041048.9972932 +10410308 +biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpdjkk27ig/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +6249319.62865952 +4660882 +536666.3042368268 +446018 +2100382.2024054904 +1711346 +18041048.9972932 +10410308 +biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3408019402034894) +params--bias_1 ~ delta(1.2032391164411007) +params--bias_2 ~ delta(1.2273276137061064) +params--bias_3 ~ delta(1.732998581530268) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpv54za3kd/chain.txt +**************************** +Calculating derivatives using 20 total models +4 +(304,) +(4, 304) +6249319.62865952 +4660882 +536666.3042368268 +446018 +2100382.2024054904 +1711346 +18041048.9972932 +10410308 +biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] +Running in serial mode (one process). + +Parameter Priors +---------------- +params--omega_k ~ delta(0.0) +params--omega_c ~ U(0.25, 0.32) +params--omega_b ~ U(0.04, 0.05) +params--h ~ U(0.5, 0.9) +params--n_s ~ U(0.9, 1.02) +params--sigma8 ~ U(0.74, 0.94) +params--w0 ~ delta(-1.0) +params--wa ~ delta(0.0) +params--bias_0 ~ delta(1.3408019402034894) +params--bias_1 ~ delta(1.2032391164411007) +params--bias_2 ~ delta(1.2273276137061064) +params--bias_3 ~ delta(1.732998581530268) + + +**************************** +* Running sampler 1/1: fisher +* Saving output -> /tmp/tmpbl2byvzj/chain.txt +**************************** +Calculating derivatives using 20 total models +Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh new file mode 100644 index 00000000..7dba95cc --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +#SBATCH -p regular +#SBATCH -N 1 +#SBATCH -t 00:20:00 +#SBATCH -L SCRATCH +#SBATCH --job-name=riz_rf_log_5bins0_3 +#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.out +#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.err +#SBATCH --constraint=haswell + +source activate tomo +python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.txt b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.txt new file mode 100644 index 00000000..65fe966e --- /dev/null +++ b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.txt @@ -0,0 +1 @@ +RF_Log_CombineBins run_5 {'bins': 5, 'colors': True, 'errors': True} {'SNR_ww': 346.29772734556116, 'FOM_ww': 20.391566310838982, 'SNR_gg': 1132.9429122353106, 'FOM_gg': 1108.2282006433647, 'SNR_3x2': 1135.5513182455525, 'FOM_3x2': 3247.449479310309} From 7fad9c1d665535869b220b82ca41f63414b10f3d Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:32:07 -0700 Subject: [PATCH 12/36] Add files via upload --- ...olors'_ True, 'errors'_ True}_riz_[0, 1].png | Bin 0 -> 16135 bytes rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err | 2 ++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err | 2 ++ rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err | 2 ++ 4 files changed, 6 insertions(+) create mode 100644 rf_comb_bins/rf_uniform/RF_Uniform_CombineBins_{'bins'_ 5, 'colors'_ 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z_*=$vbdsjk8^R>P=NVBmOzz?q^BUKwUKDAo1Un?bPOOja)X;YuIRpwweEsk1d)@dq zr;!j7?exsd<FlL3!a()p>8dgdssl<3Ie;YyVNDyL|ad7_xGGq(5Y{koRXpwC4|id zIxxxrX3EmK+R3RG7tN!Vjo;exoPuNWfC4ox-`&~lm?jKZJ598F=q|ELLNC-TgFK1% zXLkQI*_j;&gKoCnQ@O1hG(n8+VVf#jY_M!bH@0*DYQe%jGo9_tRYJIc0rOyB(iki( zFvjge_o-@v%WA~AG!s@xr~CRyMA8kXJ28=}Ls8kVURrW_vv596xsc*4KM!0Cy(2#V zp1vMilobps!(`HF8A#`n_1Q)Ja(8Wr18Ik~JUGGZt#-$*gf=;3Yqp>JgZ?I6{?FYf f{}-Gwy1S2$qv(9Z;8qp95F&R~`AU|I;r;&uvc9LU literal 0 HcmV?d00001 diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err new file mode 100644 index 00000000..95f10515 --- /dev/null +++ b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err @@ -0,0 +1,2 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") From 395edcd658cc82a179dee2493797654b4585d84b Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:32:33 -0700 Subject: [PATCH 13/36] Delete riz_rf_log_5bins0_4.err --- rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err | 29 --------------------- 1 file changed, 29 deletions(-) delete mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err deleted file mode 100644 index fc69186d..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.err +++ /dev/null @@ -1,29 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py:153: RuntimeWarning: invalid value encountered in double_scalars - (bz*nz_bin).sum()/(nz_bin).sum() for nz_bin in n_of_z] -Traceback (most recent call last): - File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 115, in - main() - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 829, in __call__ - return self.main(*args, **kwargs) - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 782, in main - rv = self.invoke(ctx) - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 1066, in invoke - return ctx.invoke(self.callback, **ctx.params) - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/click/core.py", line 610, in invoke - return callback(*args, **kwargs) - File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 70, in main - config['metrics'], metrics_fn, newbins) - File "/global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py", line 105, in run_one - scores = metrics_fn(results, valid_z, metrics=metrics) - File "/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py", line 57, in compute_scores - tomo_bin, z, what) - File "/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/metrics.py", line 195, in compute_mean_covariance - C_sig[ci, cj] = ccl.angular_cl(cosmo, Ti, Tj, ell) - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/pyccl/cls.py", line 98, in angular_cl - check(status, cosmo=cosmo_in) - File "/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/pyccl/pyutils.py", line 48, in check - raise CCLError("Error %s: %s" % (error_types[status], msg)) -pyccl.errors.CCLError: Error CCL_ERROR_INTEG: ccl_cls.c: ccl_angular_cls_limber(); integration error - From 08ed843c29c3e5548139cdbf2b30f301630f11e8 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:32:42 -0700 Subject: [PATCH 14/36] Delete riz_rf_log_5bins0_4.sh --- rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh deleted file mode 100644 index bea31b40..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_log_5bins0_4 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml \ No newline at end of file From a21f656760c0f2b0a44d30779f8723e6fcccf0ab Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:32:57 -0700 Subject: [PATCH 15/36] Delete riz_rf_log_5bins0_4.yaml --- rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml deleted file mode 100644 index 246a92e1..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_4.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_4.txt -newbins: [0,4] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Log_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True From 57a36e736840c6e0de11cc42fc3f6f1e67a91d66 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:33:09 -0700 Subject: [PATCH 16/36] Delete riz_rf_u_5bins0_4.out --- rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out | 19 ------------------- 1 file changed, 19 deletions(-) delete mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out deleted file mode 100644 index 298d5ec3..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.out +++ /dev/null @@ -1,19 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -5 From 58714cea87e8ad78dea6e9d762e2405093ad3255 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:33:16 -0700 Subject: [PATCH 17/36] Delete riz_rf_u_5bins0_4.sh --- rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh deleted file mode 100644 index a436c071..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_u_5bins0_4 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml \ No newline at end of file From 440e835689b2cfb70470aff6d54c9327c43bb9b3 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:33:23 -0700 Subject: [PATCH 18/36] Delete riz_rf_u_5bins0_4.yaml --- .../rf_uniform/riz_rf_u_5bins0_4.yaml | 20 ------------------- 1 file changed, 20 deletions(-) delete mode 100644 rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml deleted file mode 100644 index 6ec1b906..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_4.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_4.txt -newbins: [0,4] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Uniform_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True From 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U(0q*v{~7@0cIdb>2Oo6#KYp^&tN;K2 literal 0 HcmV?d00001 diff --git a/rf_log/jax/log_10bins.err b/rf_log/jax/log_10bins.err new file mode 100644 index 00000000..38ef0f89 --- /dev/null +++ b/rf_log/jax/log_10bins.err @@ -0,0 +1,6 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 + warnings.warn("Setting inf (undetected) bands to mag=30") +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. + warnings.warn('No GPU/TPU found, falling back to CPU.') +/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. + warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_10bins.out b/rf_log/jax/log_10bins.out new file mode 100644 index 00000000..a85808fb --- /dev/null +++ b/rf_log/jax/log_10bins.out @@ -0,0 +1,19 @@ +/global/cscratch1/sd/abault/tomo_challenge/bin/.. +Found classifier RandomForest_log +Found classifier IBandOnly +Found classifier RandomForestUniform +Found classifier RF_Log_CombineBins +Found classifier RandomForest +Found classifier RF_Uniform_CombineBins +Found classifier Random +Executing: RandomForest_log riz {'bins': 10, 'colors': True, 'errors': True} +{'bins': 10, 'colors': True, 'errors': True} ['bins'] +Initializing classifier... +Training... +Finding bins for training data +the bins are in logspace +cut percent: 0.05 +Fitting classifier +Applying... +Getting metric... +Making some pretty plots... diff --git a/rf_log/jax/log_10bins.txt b/rf_log/jax/log_10bins.txt new file mode 100644 index 00000000..c2ddca9e --- /dev/null +++ b/rf_log/jax/log_10bins.txt @@ -0,0 +1 @@ +RandomForest_log run_10 {'bins': 10, 'colors': True, 'errors': True} {'SNR_ww': 353.3896484375, 'FOM_ww': 25.69645118713379, 'FOM_DETF_ww': 0.7485368847846985, 'SNR_gg': 1490.1297607421875, 'FOM_gg': 1509.4334716796875, 'FOM_DETF_gg': 16.625934600830078, 'SNR_3x2': 1491.8892822265625, 'FOM_3x2': 4315.92431640625, 'FOM_DETF_3x2': 124.80682373046875} From dbbe1427aa158e5f2c0e053a40ced81c79eb9e25 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 31 Aug 2020 16:45:07 -0700 Subject: [PATCH 20/36] Add .ipynb with plots showing results --- log_uniform_and_combined_bins.ipynb | 397 ++++++++++++++++++++++++++++ 1 file changed, 397 insertions(+) create mode 100644 log_uniform_and_combined_bins.ipynb diff --git a/log_uniform_and_combined_bins.ipynb b/log_uniform_and_combined_bins.ipynb new file mode 100644 index 00000000..68be4388 --- /dev/null +++ b/log_uniform_and_combined_bins.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Log and Uniform Bins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The log bins were created using a percentile distribution based on np.logspace (instead of linspace). The uniform bins were created by pulling random numbers from a uniform distribution between 0 and 3. The random forest classifier from sklearn was used each time, and the number of bins used was 2, 4, 6, 8, 10." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "bins = [2,4,6,8,10]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#these scores were calculated using firecrown\n", + "#each dictionary corresponds to the scores for a different number of bins, in the order: 2, 4, 6, 8, 10\n", + "log_scores_f = [{'SNR_ww': 332.1252628616624, 'FOM_ww': 14.983596087283678, 'SNR_gg': 879.7755517457832, 'FOM_gg': 6800.843125725617, 'SNR_3x2': 885.1510794851065, 'FOM_3x2': 69314.82169237411},\n", + " {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823},\n", + " {'SNR_ww': 349.54961947303974, 'FOM_ww': 74.88239992499268, 'SNR_gg': 1283.0924805979128, 'FOM_gg': 2748.246880322987, 'SNR_3x2': 1285.0762826516666, 'FOM_3x2': 25527.319489134214},\n", + " {'SNR_ww': 352.1677239587554, 'FOM_ww': 46.08137841821119, 'SNR_gg': 1407.4491566491292, 'FOM_gg': 11509.47849600147, 'SNR_3x2': 1409.2785646359137, 'FOM_3x2': 76079.64260621843},\n", + " {'SNR_ww': 353.9796471119985, 'FOM_ww': 26.366472753947036, 'SNR_gg': 1500.0369417052223, 'FOM_gg': 181648.05441643775, 'SNR_3x2': 1501.7979111080479, 'FOM_3x2': 205723.43115529948}]\n", + "\n", + "uniform_scores_f = [{'SNR_ww': 327.4681067505708, 'FOM_ww': 105.1466965449306, 'SNR_gg': 678.5458935974033, 'FOM_gg': 908.8160809482351, 'SNR_3x2': 701.5622646511446, 'FOM_3x2': 4534.786413137936},\n", + " {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065},\n", + " {'SNR_ww': 353.4995684607554, 'FOM_ww': 148.5449428914542, 'SNR_gg': 1155.0212895482066, 'FOM_gg': 2450.1410700563783, 'SNR_3x2': 1158.502391443923, 'FOM_3x2': 13404.867225041602},\n", + " {'SNR_ww': 354.9124627287553, 'FOM_ww': 46.28382955361007, 'SNR_gg': 1250.552493913522, 'FOM_gg': 2881.0801502048794, 'SNR_3x2': 1253.8090733033507, 'FOM_3x2': 21691.232888150553},\n", + " {'SNR_ww': 354.9197295938117, 'FOM_ww': 152.37715773462213, 'SNR_gg': 1308.84532609303, 'FOM_gg': 21171.68195209693, 'SNR_3x2': 1311.9719252996663, 'FOM_3x2': 47065.21003389874}]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#these scores were calculated using jax\n", + "log_scores_j = [{'SNR_ww': 331.62371826171875, 'FOM_ww': 0.4617920517921448, 'FOM_DETF_ww': 0.013744479045271873, 'SNR_gg': 878.622314453125, 'FOM_gg': 53.327491760253906, 'FOM_DETF_gg': 0.786997377872467, 'SNR_3x2': 883.9085693359375, 'FOM_3x2': 255.66043090820312, 'FOM_DETF_3x2': 23.005399703979492},\n", + " {'SNR_ww': 343.78753662109375, 'FOM_ww': 2.5369784832000732, 'FOM_DETF_ww': 0.18284554779529572, 'SNR_gg': 1126.6553955078125, 'FOM_gg': 460.51934814453125, 'FOM_DETF_gg': 7.035050868988037, 'SNR_3x2': 1128.9976806640625, 'FOM_3x2': 1246.9041748046875, 'FOM_DETF_3x2': 83.07261657714844}, \n", + " {'SNR_ww': 348.9933776855469, 'FOM_ww': 12.718839645385742, 'FOM_DETF_ww': 0.5421679019927979, 'SNR_gg': 1277.877685546875, 'FOM_gg': 823.6866455078125, 'FOM_DETF_gg': 11.198760986328125, 'SNR_3x2': 1279.864990234375, 'FOM_3x2': 2050.5126953125, 'FOM_DETF_3x2': 102.53814697265625},\n", + " {'SNR_ww': 351.5631408691406, 'FOM_ww': 20.807268142700195, 'FOM_DETF_ww': 0.6687530279159546, 'SNR_gg': 1400.4603271484375, 'FOM_gg': 1209.8232421875, 'FOM_DETF_gg': 14.164243698120117, 'SNR_3x2': 1402.2911376953125, 'FOM_3x2': 3289.179931640625, 'FOM_DETF_3x2': 114.87232208251953},\n", + " {'SNR_ww': 353.3896484375, 'FOM_ww': 25.69645118713379, 'FOM_DETF_ww': 0.7485368847846985, 'SNR_gg': 1490.1297607421875, 'FOM_gg': 1509.4334716796875, 'FOM_DETF_gg': 16.625934600830078, 'SNR_3x2': 1491.8892822265625, 'FOM_3x2': 4315.92431640625, 'FOM_DETF_3x2': 124.80682373046875}]\n", + "\n", + "uniform_scores_j = [{'SNR_ww': 327.1508483886719, 'FOM_ww': 3.3171546459198, 'FOM_DETF_ww': 0.092340387403965, 'SNR_gg': 677.09228515625, 'FOM_gg': 32.099971771240234, 'FOM_DETF_gg': 0.1580391675233841, 'SNR_3x2': 700.0398559570312, 'FOM_3x2': 852.3770751953125, 'FOM_DETF_3x2': 12.79428482055664},\n", + " {'SNR_ww': 351.2440185546875, 'FOM_ww': 23.247020721435547, 'FOM_DETF_ww': 0.6437975764274597, 'SNR_gg': 1106.9310302734375, 'FOM_gg': 527.278076171875, 'FOM_DETF_gg': 5.282368183135986, 'SNR_3x2': 1110.4364013671875, 'FOM_3x2': 2280.726806640625, 'FOM_DETF_3x2': 43.6702995300293},\n", + " {'SNR_ww': 352.9674072265625, 'FOM_ww': 30.201784133911133, 'FOM_DETF_ww': 0.7597047090530396, 'SNR_gg': 1151.7760009765625, 'FOM_gg': 819.4025268554688, 'FOM_DETF_gg': 7.07086181640625, 'SNR_3x2': 1155.22509765625, 'FOM_3x2': 3679.62744140625, 'FOM_DETF_3x2': 49.80576705932617},\n", + " {'SNR_ww': 354.352294921875, 'FOM_ww': 36.73133850097656, 'FOM_DETF_ww': 0.8435292840003967, 'SNR_gg': 1244.733642578125, 'FOM_gg': 990.042724609375, 'FOM_DETF_gg': 8.24472427368164, 'SNR_3x2': 1247.9957275390625, 'FOM_3x2': 4521.0576171875, 'FOM_DETF_3x2': 55.020790100097656},\n", + " {'SNR_ww': 354.3929748535156, 'FOM_ww': 37.14365768432617, 'FOM_DETF_ww': 0.8497052192687988, 'SNR_gg': 1302.3973388671875, 'FOM_gg': 1204.456298828125, 'FOM_DETF_gg': 9.355816841125488, 'SNR_3x2': 1305.5096435546875, 'FOM_3x2': 5697.7705078125, 'FOM_DETF_3x2': 57.619598388671875}]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_metrics(bins, scores, title, nrows, ncols, sep_figs = True):\n", + " \n", + " assert len(bins) == len(scores), \"The number of bins must be equal to the number of scores (i.e len(bins) == len(scores))\"\n", + " \n", + " if sep_figs == False:\n", + " plt.figure(figsize = (10,7))\n", + " plt.plot(bins, [scores[0]['SNR_ww'], scores[1]['SNR_ww'], scores[2]['SNR_ww'], scores[3]['SNR_ww'], scores[4]['SNR_ww']], 'o', label = 'SNR_ww')\n", + " plt.plot(bins, [scores[0]['SNR_gg'], scores[1]['SNR_gg'], scores[2]['SNR_gg'], scores[3]['SNR_gg'], scores[4]['SNR_gg']], 'o', label = 'SNR_gg')\n", + " plt.plot(bins, [scores[0]['SNR_3x2'], scores[1]['SNR_3x2'], scores[2]['SNR_3x2'], scores[3]['SNR_3x2'], scores[4]['SNR_3x2']], 'o', label = 'SNR_3x2')\n", + " plt.plot(bins, [scores[0]['FOM_ww'], scores[1]['FOM_ww'], scores[2]['FOM_ww'], scores[3]['FOM_ww'], scores[4]['FOM_ww']], 'o', label = 'FOM_ww')\n", + " plt.plot(bins, [scores[0]['FOM_gg'], scores[1]['FOM_gg'], scores[2]['FOM_gg'], scores[3]['FOM_gg'], scores[4]['FOM_gg']], 'o', label = 'FOM_gg')\n", + " plt.plot(bins, [scores[0]['FOM_3x2'], scores[1]['FOM_3x2'], scores[2]['FOM_3x2'], scores[3]['FOM_3x2'], scores[4]['FOM_3x2']], 'o', label = 'FOM_3x2')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_ww'], scores[1]['FOM_DETF_ww'], scores[2]['FOM_DETF_ww'], scores[3]['FOM_DETF_ww'], scores[4]['FOM_DETF_ww']], 'o', label = 'FOM_DETF_ww')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_gg'], scores[1]['FOM_DETF_gg'], scores[2]['FOM_DETF_gg'], scores[3]['FOM_DETF_gg'], scores[4]['FOM_DETF_gg']], 'o', label = 'FOM_DETF_gg')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_3x2'], scores[1]['FOM_DETF_3x2'], scores[2]['FOM_DETF_3x2'], scores[3]['FOM_DETF_3x2'], scores[4]['FOM_DETF_3x2']], 'o', label = 'FOM_DETF_3x2')\n", + " plt.xlabel('Number of Bins', fontsize = 14)\n", + " plt.ylabel('Scores', fontsize = 14)\n", + " plt.title(title, fontsize = 16)\n", + " plt.legend()\n", + " \n", + " if sep_figs == True:\n", + " \n", + " metrics = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2', 'FOM_DETF_ww', 'FOM_DETF_gg', 'FOM_DETF_3x2']\n", + "# fig, axes = plt.subplots(nrows = nrows, ncols = ncols, figsize = (15,10))\n", + " \n", + " fig = plt.figure(figsize = (ncols*5, nrows*5))\n", + "# fig = plt.figure(figsize = (15,15))\n", + "# plt.xlabel('Number of Bins')\n", + "# plt.axes(frameon=False)\n", + "# plt.xticks([])\n", + "# plt.yticks([])\n", + " plt.axis(\"off\")\n", + " fig.suptitle(title, fontsize = 16)\n", + "# fig.tight_layout()\n", + " fig.subplots_adjust(top = .95)\n", + "# fig.text(0.5, 0.38, 'Number of Bins', fontsize = 14, ha = 'center')\n", + "# fig.text(0.07, 0.5, 'Score', fontsize = 14, va = 'center', rotation = 'vertical')\n", + " \n", + " numplots = nrows*ncols\n", + " \n", + " for i in range(numplots):\n", + " ax = fig.add_subplot(nrows,ncols,i+1)\n", + " ax.plot(bins,[scores[0][metrics[i]], scores[1][metrics[i]], scores[2][metrics[i]], scores[3][metrics[i]], scores[4][metrics[i]]], 'o', label = metrics[i])\n", + " ax.set_xlabel('Number of Bins')\n", + " ax.set_ylabel('Score')\n", + " ax.legend()\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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8X6dTdIV8huLM04czc203i/8UxYBMH6P43r9CMcDSX2XmD2vq9WcdPdGf39+A6eHvpyefSVfruDeKe/7NLZcZFGdQ/yQzO3ZZvp4i2bu+Zv6nImIxxRnIetfrSRpCYgAPSEqSBlBEfIAieZmRmb9qdnuk4cTfn6QqMNmTpCEgIv6Y4sbIt1Oc6Xo7xdm3pRT3AzNYSw3i709SVdmNU5KGhjUU1ySdSDF8/kqKswpz3NGUGs7fn6RK8syeJEmSJFWQt16QJEmSpAoy2ZMkSZKkCjLZkyRJkqQKMtmTJEmSpAoy2ZMkSZKkCmrpWy/ssMMOOXHixGY3Q9IAu+uuu57KzDHNbkd/GJ+k6jE2SRqKuopNLZ3sTZw4kQULFjS7GZIGWET8ttlt6C/jk1Q9xiZJQ1FXsclunJIkSZJUQSZ7kiRJklRBJnuSJEmSVEEtfc1ePevXr2fZsmW8+OKLzW5Ky9hyyy0ZP348o0aNanZTpEozPnXPeCQNPmNT3xmzNNRVLtlbtmwZ22yzDRMnTiQimt2cIS8zefrpp1m2bBm77LJLs5sjVZrxqWvGI6k5jE19Y8xSK6hcN84XX3yR7bff3mDVQxHB9ttv79E8aRAYn7pmPJKaw9jUN8YstYKGJXsRsWVE3BER90TEkoj4Sll+WkQsj4hF5eOwsnxiRKytKf/3fqx7oDZjWPD9kgaPv7eu+f5IzeFvr2983zTUNbIb5zrgoMxcExGjgJsj4vJy2ncy85t15nkkM6c0sE3SkHLRwuWceeVSnly9lrGj2zhp5mRmTR3X7GYNmKpvn6TWZXySNBQNdGxqWLKXmQmsKV+OKh/ZqPX1lcFezXLRwuXMuWAxa9dvAGD56rXMuWAxQCW+g1XfvsFgfJIaw/jUP8YmqTEaEZsaes1eRIyIiEXASuDqzLy9nPTpiLg3Is6OiO1qZtklIhZGxI0RMb2TZR4XEQsiYsGqVav61b72N3T56rUkm97QixYu79dyAebOncuee+7JPvvsw5QpU7j99ts58MADmTZt2sY6CxYs4MADDwTghhtu4PWvfz1Tp05l99135x/+4R/63QYNbWdeuXTjj7nd2vUbOPPKpU1q0cCq+vY1WqPik7FJMj71RyvvO1188cUblz1t2jRuvvnmLusvWrSId77znRvb9POf/7zf2yh1pRGxqaHJXmZuKLtljgf2i4i9gH8D3gJMAVYA3yqrrwB2zsypwN8BP4mIbessc15mTsvMaWPGjOlX+xoV7G+99VYuvfRS7r77bu69916uueYaJkyYAMDKlSu5/PLL6843ffp0Fi5cyMKFC7n00ku55ZZb+tUODW1Prl7bq/JWU/Xta7RGxCdjk1QwPvVdK+87vfvd7+aee+5h0aJFnH322Xzyk5/ssk1bbbUV5513HkuWLOGKK67gs5/9LKtXr+77RkrdaERsGpTRODNzNXADcGhm/q5MAl8Bvg/sV9ZZl5lPl8/vAh4BdmtkuxoV7FesWMEOO+zAFltsAcAOO+zA2LFjATjppJP4+te/3uX8bW1tTJkyheXLOz9Ktvfee7N69Woyk+23357zzjsPgI9+9KNcc801HHbYYdx7770ATJ06la9+9asAnHLKKfzgBz/o1/ZpYIwd3dar8lbT7O0rew6sjIj7asq+VvYqWBQRV0XE2JppcyLi4YhYGhEza8rfHhGLy2n/HIN0NX4j4tNgxKZVq1ZxyCGHsO+++/KpT32KN7/5zTz11FMAfO1rX2P33XfnkEMO4UMf+hDf/Ga9S7elxmt2fGplrbzvtPXWW28cUOX555/f+PzOO+9kn3324cUXX+T5559nzz335L777mO33XZj0qRJAIwdO5Ydd9yR/vYqk7rSiNjUyNE4x0TE6PJ5G3Aw8GBE7FRT7Sjgvpr6I8rnuwKTgN80qn3QuGD/nve8hyeeeILddtuNv/7rv+bGG2/cOO2d73wnW2yxBddff32n8z/77LM89NBDzJgxo9M673rXu7jllltYsmQJu+66K7/61a8AuO2229h///2ZMWMGv/rVr3juuecYOXLkxiNdN998M9On1+0hq0F20szJtI0asVlZ26gRnDRzcpNaNLCGwPadAxzaoezMzNyn7HFwKXAqQETsAcwG9iznOas9HlH0RjiOIiZNqrPMhmhEfBqM2PSVr3yFgw46iLvvvpujjjqKxx9/HCi6Xp1//vksXLiQCy64gAULFvR5O6T+anZ8auWDUa287wRw4YUXsvvuu3P44Ydz9tlnA/COd7yDI444gi996Ut8/vOf5yMf+Qh77bXXZvPdcccdvPTSS7zlLW/px1ZKXWtEbGrkmb2dgOsj4l7gTopr9i4F/qkMTPcCfwp8rqw/A7g3Iu4BfgEcn5nPNLB9DQv2W2+9NXfddRfz5s1jzJgx/MVf/AXnnHPOxulf+tKX6h6h+tWvfsU+++zDm970Jt773vfypje9qdN1TJ8+nZtuuombbrqJE044gcWLF7N8+XLe8IY3sPXWW2+cfvPNN3P44YezZs0aXnjhBR577DEmT65GMtHqZk0dx+lH78240W0EMG50G6cfvXdlLnJv9vZl5k3AMx3Knqt5+To2DRp1JPCzsofBo8DDFF3PdwK2zcxby0GnzgNmNb71jYlPgxGbbr75ZmbPng3AoYceynbbbbex/Mgjj6StrY1tttmG973vfX3eDqm/mh2faOGDUa287wRw1FFH8eCDD3LRRRdxyimnbCw/9dRTufrqq1mwYAGf//znN5tnxYoVfPSjH+VHP/oRr3lN5W5RrSGkEbGpkaNx3gtMrVP+0U7qnw+c36j21NP+xjViRKkRI0Zw4IEHcuCBB7L33ntz7rnnbpx20EEHccopp3DbbbdtNs/06dO59NJL+fWvf80BBxzAUUcdxZQp9e9EMWPGDL73ve/x+OOPM3fuXC688EJ+8YtfbDxr9453vIMFCxaw6667csghh/DUU0/x/e9/n7e//e393jYNnFlTx1UmuatnKG5fRMwFjgF+T3HACWAcUPuDXFaWrS+fdyzvbNnHUex4sfPOO/ernY2KT42OTUVO3PNyqVmaGZ8y86aImNihrNuDUcCjEdF+MOoxyoNRABHRfjCq/sVtA6SV951qzZgxg0ceeYSnnnqKHXbYgWeeeYY1a9awfv16XnzxRV73utcB8Nxzz3H44Yfz9a9/nf3337/f2yh1Z6Bj07A/PDFr6jhuOfkgHj3jcG45+aABeXOXLl3KQw89tPH1okWLePOb37xZnS9+8Yv80z/9U935d9ttN+bMmcM3vvGNTtcxYcIEnnrqKR566CF23XVXDjjgAL75zW9uTPZe+9rXMmHCBObPn8/+++/P9OnTN5suDVeZ+cXMnAD8GPh0WVyv61N2Ud7ZsgdsACkY+Pg0GLHpgAMOYP78+QBcddVVPPvssxvL/+u//osXX3yRNWvWcNlll/VrW6Qqioi5EfEE8GHKM3sUB5ieqKnWftBpHD08GDWQI5lD6+47PfzwwxsPPN1999289NJLbL/99gAcd9xxfO1rX+PDH/4wX/jCFwB46aWXOOqoozjmmGP4wAc+0K/tk5pl2Cd7jbBmzRqOPfZY9thjD/bZZx/uv/9+TjvttM3qHHbYYXS1M3j88cdz00038eijj3Za54//+I/ZbbdiDJvp06ezfPlyDjjggI3Tp0+fzhvf+Ea22morpk+fzrJly0z2pE1+Ary/fL4MmFAzbTzwZFk+vk55SxqM2PTlL3+Zq666in333ZfLL7+cnXbaiW222WbjNTFve9vbOProo5k2bRqvf/3rB3LzpJbXqINRA30gqhEGIz6df/757LXXXkyZMoUTTzyRn//850QE5513HiNHjuQv//IvOfnkk7nzzju57rrrmD9/PjfddBPnnHMOU6ZMYcqUKSxatGggN1tquGjlrjXTpk3Ljhf5P/DAA7z1rW9tUotal++bhpKIuCszp3Vfs9vlTAQuzcy9yteTMvOh8vnfAH+SmX8eEXtSJH/7AWOBa4FJmbkhIu4E/ga4Hfgl8C+Z+cvu1j1c49O6desYMWIEI0eO5NZbb+WEE07YuHO0Zs0att56a1544QVmzJjBvHnz2HfffV+1jOHwPqk1DVRsKpc1kZr41GHam4HLMnOviJgDkJmnl9OuBE4DHgOuz8zdy/IPAQdm5qe6Wu9wjU2N5PunZusqNjXsmj1JaqaI+ClwILBDRCwDvgwcFhGTgVeA3wLHA2TmkoiYD9wPvAycmJntN5I6gWIwhTaKa2Eaej1Mq3v88cf54Ac/yCuvvMJrX/tavv/972+cdtxxx3H//ffz4osvcuyxx9ZN9KThqvZgFHAE8GD5/BKKew9/m+Jg1CTgjvJg1B8iYn+Kg1HHAP8y2O2WNLSZ7A1xP/rRj/jud7+7Wdm73vUuvve97zWpRVJryMwP1Sn+YRf15wJz65QvAF515H246yo2LVy4sO48P/nJTwajadKQ58GoxnLfSdqkksleZm68UWar+/jHP87HP/7xhq5jKHflvWjh8oaM+CU1S1XiU6Ni01COR9JAGYoHo6oSm2Bw9p3aGbM01FVugJYtt9ySp59+2h9fD2UmTz/9NFtuuWWzm/IqFy1czpwLFrN89VoSWL56LXMuWMxFC5c3u2lSnxifujaU45FUZcamvjFmqRVU7sze+PHjWbZsGQMxtPBwseWWWzJ+/PjuKw6yM69cytr1GzYrW7t+A2deudSze2pJxqfuDdV4JFWZsanvjFka6iqX7I0aNYpddtml2c3QAHhy9dpelUtDnfFJ0lBkbJKqq3LdOFUdY0e39apckiRJ0iYmexqyTpo5mbZRIzYraxs1gpNmTm5SiyRJkqTWUblunKqO9uvyHI1TkiRJ6j2TPQ1ps6aOM7mTJEmS+sBunJIkSZJUQSZ7kiRJklRBduOUJEmvctHC5V4zLWnIMTb1jsmeJEnazEULlzPngsWsXb8BgOWr1zLngsUA7lRJahpjU+/ZjVOSJG3mzCuXbtyZard2/QbOvHJpk1okScamvjDZkyRJm3ly9dpelUvSYDA29Z7JniRJ2szY0W29KpekwWBs6j2TvRZ30cLlvOuM69jl5Mt41xnXcdHC5c1ukiSpxZ00czJto0ZsVtY2agQnzZzcpBZJkrGpLxygpYV5kaokqRHa/4c44p2kocTY1Hsmey2sq4tU/dJLkvpj1tRx/i+RNOQYm3rHbpwtzItUJUmSJHXGZK+FeZGqJEmSpM6Y7LUwL1KVJEmS1Bmv2WthXqQqSZIkqTMmey3Oi1QlSZIk1WM3TkmSJEmqIJM9SZIkSaogkz1JkiRJqiCTPUmSJEmqIJM9SZIkSaogkz1JkiRJqiCTPUmVFBFnR8TKiLivpuzMiHgwIu6NiAsjYnTNtDkR8XBELI2ImTXlb4+IxeW0f46IGOxtkSRJ6guTPUlVdQ5waIeyq4G9MnMf4NfAHICI2AOYDexZznNWRIwo5/k34DhgUvnouExJkqQhqWHJXkRsGRF3RMQ9EbEkIr5Slp8WEcsjYlH5OKxmnrpH1iWptzLzJuCZDmVXZebL5cvbgPHl8yOBn2Xmusx8FHgY2C8idgK2zcxbMzOB84BZg7MFkiRJ/dPIM3vrgIMy823AFODQiNi/nPadzJxSPn4J3R5Zl6SB9r+By8vn44AnaqYtK8vGlc87ltcVEcdFxIKIWLBq1aoBbq6kqrCbuaTB0rBkLwtrypejykd2MUvdI+uNap+k4Ssivgi8DPy4vahOteyivK7MnJeZ0zJz2pgxY/rfUElVdQ52M5c0CBp6zV5EjIiIRcBK4OrMvL2c9OnyyNXZEbFdWdbZkfWOy/TIuaQ+i4hjgfcCHy67ZkIRbybUVBsPPFmWj69TLkl9ZjdzSYOlocleZm7IzCkUAWu/iNiL4ijUWyi6dq4AvlVW79ERdI+cS+qriDgU+AJwRGa+UDPpEmB2RGwREbtQHCG/IzNXAH+IiP3L7lHHABcPesMlDTcD2s3cA+XS8DUoo3Fm5mrgBuDQzPxdmQS+AnyfTV01OzuyLkm9FhE/BW4FJkfEsoj4BPCvwDbA1eUAUf8OkJlLgPnA/cAVwImZuaFc1AnADyiOpj/Cph0wSRpwjY0R0J0AACAASURBVOhm7oFyafga2agFR8QYYH1mro6INuBg4BsRsVN5tBzgKKD94uRLgJ9ExLeBsZRH1hvVPknVlpkfqlP8wy7qzwXm1ilfAOw1gE2TpLpqupm/227mkgZCw5I9YCfg3PIi4tcA8zPz0oj4j4iYQnH06THgU1AcWY+I9iPrL7P5kXVJkqTKqulm/id1upm/6mB4Zm6IiD+UI53fTtHN/F8Gu92ShraGJXuZeS8wtU75R7uYp+6RdUmSpKoou5kfCOwQEcuAL1OMvrkFRTdzgNsy8/huDoafQDGyZxtFF3O7mUvaTCPP7EmSJKkDu5lLGiyDMkCLJEmSJGlwmexJkiRJUgWZ7EmSJElSBZnsSZIkSVIFmexJkiRJUgWZ7EmSJElSBXnrBUmS+uCihcs588qlPLl6LWNHt3HSzMnMmjqu2c2SJGkjkz1JknrpooXLmXPBYtauL+5tvXz1WuZcsBjAhE+SNGSY7EmS1EtnXrl0Y6LXbu36DZx55VKTPUlNZa8D1TLZkySpl55cvbZX5ZI0GOx1oI4coEWSpF4aO7qtV+WSNBi66nWg4clkT5KkXjpp5mTaRo3YrKxt1AhOmjm5SS2SJHsd6NVM9iRJ6qVZU8dx+tF7M250GwGMG93G6UfvbTcpSU1lrwN15DV7kiT1wayp40zuJA0pJ82cvNk1e2Cvg+HOZE+SJEmqgPYDUI7GqXYme5IkSVJF2OtAtbxmT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIm6pLkhriooXLOfPKpTy5ei1jR7dx0szJ3uhXkqRBZLInSRpwFy1czpwLFrN2/QYAlq9ey5wLFgOY8EmSNEhM9iRJA+7MK5duTPTarV2/gTOvXGqyJ6mp7HWg4cRr9iRVUkScHRErI+K+mrIPRMSSiHglIqZ1qD8nIh6OiKURMbOm/O0Rsbic9s8REYO5Ha3qydVre1UuSYOhvdfB8tVrSTb1Orho4fJmN01qCJM9SVV1DnBoh7L7gKOBm2oLI2IPYDawZznPWRExopz8b8BxwKTy0XGZqmPs6LZelUvSYOiq14FURSZ7kiopM28CnulQ9kBm1vuPfiTws8xcl5mPAg8D+0XETsC2mXlrZiZwHjCr0W2vgpNmTqZt1IjNytpGjeCkmZOb1CJJsteBhh+TPUmCccATNa+XlWXjyucdy+uKiOMiYkFELFi1alVDGtoqZk0dx+lH78240W0EMG50G6cfvbfXxUhqKnsdaLgx2ZMkqHcdXnZRXldmzsvMaZk5bcyYMQPWuFY1a+o4bjn5IB4943BuOfkgEz2p5DXFzWOvAw03JnuSVJyxm1DzejzwZFk+vk65JPXHOXhNcVPY60DDjbdekCS4BPhJRHwbGEux03RHZm6IiD9ExP7A7cAxwL80sZ2SKiAzb4qIiR3KHgCoc3Ju4zXFwKMR0X5N8WOU1xSX87VfU3x5QxtfAbOmjjO507DhmT1JlRQRPwVuBSZHxLKI+EREHBURy4B3ApdFxJUAmbkEmA/cD1wBnJiZ7cO1nQD8gGLQlkdwR0rS4Or3NcVeTywNXw07sxcRW1J0RdiiXM8vMvPLNdP/ATgTGJOZT5VHuB4A2kfKuy0zj29U+yRVW2Z+qJNJF3ZSfy4wt075AmCvAWyaJPVGv68pzsx5wDyAadOmdXrdsaTqaWQ3znXAQZm5JiJGATdHxOWZeVtETAAOAR7vMM8jmTmlgW2SJElqJV5TLKnPGtaNMwtrypejykf70aTvAJ+ni1HtJEmSxCXA7IjYIiJ2YdM1xSuAP0TE/uUonMcAFzezoZKGnoZesxcRIyJiEbASuDozb4+II4DlmXlPnVl2iYiFEXFjREzvZJn2O5ckSS3La4olDZaGjsZZBqMpETEauDAi9gG+CLynTvUVwM6Z+XREvB24KCL2zMznOizTfueSJKlleU2xpMEyKKNxZuZq4AaK4YN3Ae4phwweD9wdEW/KzHWZ+XRZ/y6KI1S7DUb7JEmSJKlqGpbsRcSY8oweEdEGHAwszMwdM3NiZk6kuLh438z8n7L+iLL+rhR90n/TqPZJkiRJUpU1shvnTsC5ZQL3GmB+Zl7aRf0ZwFcj4mVgA3B8Zj7TwPZJkiRJUmU1LNnLzHuBqd3UmVjz/Hzg/Ea1R5IkSZKGk0G5Zk+SJEmSNLhM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgnqc7EVEW0RMbmRjJKkzxiBJQ5GxSdJQ1qNkLyLeBywCrihfT4mISxrZMElqZwySNBQZmyQNdT09s3casB+wGiAzFwETG9MkSXqV0zAGSRp6TsPYJGkI62my93Jm/r6hLZGkzhmDJA1FxiZJQ9rIHta7LyL+EhgREZOAzwD/3bhmSdJmjEGShiJjk6Qhradn9v4G2BNYB/wE+D3w2UY1SpI66HUMioizI2JlRNxXU/aGiLg6Ih4q/25XM21ORDwcEUsjYmZN+dsjYnE57Z8jIgZ86yS1KvePJA1p3Z7Zi4gRwCWZeTDwxcY3SZI26UcMOgf4V+C8mrKTgWsz84yIOLl8/YWI2AOYTbHTNha4JiJ2y8wNwL8BxwG3Ab8EDgUu799WSWp17h9JagXdntkrd3ZeiIjXD0J7JGkzfY1BmXkT8EyH4iOBc8vn5wKzasp/lpnrMvNR4GFgv4jYCdg2M2/NzKRIHGchadhz/0hSK+jpNXsvAosj4mrg+fbCzPxMQ1olSZsbqBj0xsxcUc67IiJ2LMvHUZy5a7esLFtfPu9YXldEHEdxFpCdd965l02T1ILcP5I0pPU02busfEhSMzQ6BtW7Di+7KK8rM+cB8wCmTZvWaT1JleH+kaQhrUfJXmaeGxGvBXYri5Zm5vrGNUuSNhnAGPS7iNipPKu3E7CyLF8GTKipNx54siwfX6dcktw/kjTk9Wg0zog4EHgI+B5wFvDriJjRwHZJ0kYDGIMuAY4tnx8LXFxTPjsitoiIXYBJwB1ll88/RMT+5Sicx9TMI2mY62tscrRgSYOlp7de+Bbwnsz8k8ycAcwEvtO4ZknSZnodgyLip8CtwOSIWBYRnwDOAA6JiIeAQ8rXZOYSYD5wP3AFcGI5+ALACcAPKAZteQRH4pS0SV/3j86hGNm3VvtowZOAa8vXdBgt+FDgrHIkUNg0WvCk8tFxmZKGuZ5eszcqM5e2v8jMX0fEqAa1SZI66nUMyswPdTLp3Z3UnwvMrVO+ANirF22VNHz0af8oM2+KiIkdio8EDiyfnwvcAHyBmtGCgUcjon204McoRwsGiIj20YI9ICVpo54mewsi4ofAf5SvPwzc1ZgmSdKrGIMkDUUDGZsaNlqwIwVLw1dPu3GeACwBPgP8LUVXp+Mb1ShJ6sAYJGkoGozY1O/RgjNzXmZOy8xpY8aMGdDGSRraenpmbyTw3cz8NkDZV3yLhrVKkjZnDJI0FA1kbHK0YEkDrqdn9q4F2mpetwHXDHxzJKkuY5CkoWggY5OjBUsacD1N9rbMzDXtL8rnW3U1Q0RsGRF3RMQ9EbEkIr7SYfo/RERGxA41ZXWHFpY07PU6BknSIOhTbHK0YEmDpafdOJ+PiH0z826AiJgGrO1mnnXAQZm5phyZ6uaIuDwzb4uICRSB7PH2yh2GFh4LXBMRu9UENEnDV19ikCQ1Wp9ik6MFSxosPU32Pgv8Z0Q8SXHx71jgL7qaITMTaD/aNap8tF84/B3g82ze3aDu0MIUR74kDW+9jkGSNAiMTZKGtC67cUbEOyLiTZl5J7A78HPgZYpuBI92t/CIGBERiyguMr46M2+PiCOA5Zl5T4fq44Anal7XHUI4Io6LiAURsWDVqlXdNUFSC+tvDJKkRjA2SWoV3V2z9/+Al8rn7wT+Efge8Cwwr7uFZ+aGzJxCMULUfhGxD/BF4NQ61Xs0hLDDB0vDSr9ikCQ1iLFJUkvorhvniMx8pnz+F8C8zDwfOL88Y9cjmbk6Im6g6Kq5C3BPMXAU44G7I2I/Oh9aWNLwNSAxSJIGmLFJUkvo7szeiIhoTwjfDVxXM63LRDEixkTE6PJ5G3AwsDAzd8zMiZk5kSLB2zcz/4dOhhbu9RZJqpI+xyBJaiBjk6SW0F1A+ilwY0Q8RTG61K8AIuKPgN93M+9OwLnlDUZfA8zPzEs7q5yZSyKifWjhl9l8aGFJw1N/YpAkNYqxSVJL6DLZy8y5EXEtReJ2VTnCJhTJ2990M++9wNRu6kzsuD7qDC0saXjqTwySpEYxNklqFd12NcjM2+qU/boxzZGkzRmDJA1FxiZJraC7a/YkSZIkSS3IZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE+SJEmSKshkT5IkSZIqyGRPkiRJkirIZE/SsBMRfxsR90XEkoj4bFn2hoi4OiIeKv9uV1N/TkQ8HBFLI2Jm81ouSZLUcyZ7koaViNgL+CtgP+BtwHsjYhJwMnBtZk4Cri1fExF7ALOBPYFDgbMiYkQz2i5JktQbJnuShpu3Ardl5guZ+TJwI3AUcCRwblnnXGBW+fxI4GeZuS4zHwUepkgUJUmShjSTPUnDzX3AjIjYPiK2Ag4DJgBvzMwVAOXfHcv644AnauZfVpa9SkQcFxELImLBqlWrGrYBkiRJPWGyJ2lYycwHgG8AVwNXAPcAL3cxS9RbTCfLnpeZ0zJz2pgxY/rdVkmSpP4w2ZM07GTmDzNz38ycATwDPAT8LiJ2Aij/riyrL6M489duPPDkYLZXkiSpL0z2JA07EbFj+Xdn4Gjgp8AlwLFllWOBi8vnlwCzI2KLiNgFmATcMbgtliRJ6j2TPUnD0fkRcT/wX8CJmfkscAZwSEQ8BBxSviYzlwDzgfspun2emJkbmtNsSVXnrWEkDaSRzW6AJA22zJxep+xp4N2d1J8LzG10uyQNbx1uDfMScEVEXFaWXZuZZ0TEyRS3hvlCh1vDjAWuiYjdPCAlqZ1n9iRJkoYGbw0jaUCZ7EmSJA0NDbk1jLeFkYYvkz1JkqQhoFG3hvG2MNLwZbInSZI0RHhrGEkDyWRPkiRpiPDWMJIGkqNxSpIkDR3nR8T2wHrKW8NExBnA/Ij4BPA48AEobg0TEe23hnkZbw0jqQOTPUmSpCHCW8NIGkh245QkSZKkCjLZkyRJkqQKMtmTJEmSpApqWLIXEVtGxB0RcU9ELImIr5TlX4uIeyNiUURcFRFjy/KJEbG2LF8UEf/eqLZJkiRJUtU1coCWdcBBmbkmIkYBN0fE5cCZmXkKQER8BjgVOL6c55HMnNLANkmSJEnSsNCwZC8zE1hTvhxVPjIzn6up9jogG9UGSZIkSRquGnrNXkSMiIhFwErg6sy8vSyfGxFPAB+mOLPXbpeIWBgRN0bEq4YeLuc9LiIWRMSCVatWNbL5kiRJktSyGprsZeaGslvmeGC/iNirLP9iZk4Afgx8uqy+Atg5M6cCfwf8JCK2rbPMeZk5LTOnjRkzppHNlyRJkqSWNSijcWbmauAG4NAOk34CvL+ss668aSiZeRfwCLDbYLRPkiRJkqqmkaNxjomI0eXzNuBg4MGImFRT7QjgwZr6I8rnuwKTgN80qn2SJEmSVGWNHI1zJ+DcMoF7DTA/My+NiPMjYjLwCvBbNo3EOQP4akS8DGwAjs/MZxrYPkmSJEmqrEaOxnkvMLVO+fs7qX8+cH6j2iNJkiRJw8mgXLMnSZIkSRpcJnuSJEmSVEEme5IkSZJUQSZ7kiRJklRBJnuSJEmSVEEme5IkSZJUQSZ7kiRJklRBJnuSJEmSVEEme5IkSZJUQSZ7kiRJklRBJnuSJEmSVEEme5KGnYj4XEQsiYj7IuKnEbFlRLwhIq6OiIfKv9vV1J8TEQ9HxNKImNnMtkuSJPWUyZ6kYSUixgGfAaZl5l7ACGA2cDJwbWZOAq4tXxMRe5TT9wQOBc6KiBHNaLskSVJvmOxJGo5GAm0RMRLYCngSOBI4t5x+LjCrfH4k8LPMXJeZjwIPA/sNcnslSZJ6zWRP0rCSmcuBbwKPAyuA32fmVcAbM3NFWWcFsGM5yzjgiZpFLCvLXiUijouIBRGxYNWqVY3aBEmSpB4x2ZM0rJTX4h0J7AKMBV4XER/papY6ZVmvYmbOy8xpmTltzJgx/W+sJElSP5jsSRpuDgYezcxVmbkeuAD4X8DvImIngPLvyrL+MmBCzfzjKbp9SpIkDWkme5KGm8eB/SNiq4gI4N3AA8AlwLFlnWOBi8vnlwCzI2KLiNgFmATcMchtliRJ6rWRzW6AJA2mzLw9In4B3A28DCwE5gFbA/Mj4hMUCeEHyvpLImI+cH9Z/8TM3NCUxkuSJPWCyZ6kYSczvwx8uUPxOoqzfPXqzwXmNrpdkhQRnwM+SXFt8GLg4xSjBv8cmAg8BnwwM58t688BPgFsAD6TmVcOfqslDVV245QkSRoCvA+opIFmsidJkjR0eB9QSQPGZE+SJGkIaNR9QL0HqDR8mexJkiQNAY26D6j3AJWGL5M9SZKkocH7gEoaUCZ7kiRJQ4P3AZU0oLz1giRJ0hDgfUAlDTSTPUmSpCHC+4BKGkh245QkSZKkCjLZkyRJkqQKMtmTJEmSpAoy2ZMkSZKkCjLZkyRJkqQKMtmTJEmSpAoy2ZMkSZKkCjLZkyRJkqQKaliyFxFbRsQdEXFPRCyJiK+U5V+LiHsjYlFEXBURY2vmmRMRD0fE0oiY2ai2SZIkSVLVNfLM3jrgoMx8GzAFODQi9gfOzMx9MnMKcClwKkBE7AHMBvYEDgXOiogRDWyfJEmSJFVWw5K9LKwpX44qH5mZz9VUex2Q5fMjgZ9l5rrMfBR4GNivUe2TJEmSpCpr6DV7ETEiIhYBK4GrM/P2snxuRDwBfJjyzB4wDniiZvZlZVnHZR4XEQsiYsGqVasa2XxJkiRJalkNTfYyc0PZXXM8sF9E7FWWfzEzJwA/Bj5dVo96i6izzHmZOS0zp40ZM6ZRTZckSZKkljYoo3Fm5mrgBopr8Wr9BHh/+XwZMKFm2njgyYY3TpIkSZIqqJGjcY6JiNHl8zbgYODBiJhUU+0I4MHy+SXA7IjYIiJ2ASYBdzSqfZIkSZJUZSMbuOydgHPLETVfA8zPzEsj4vyImAy8AvwWOB4gM5dExHzgfuBl4MTM3NDA9kmSJElSZTUs2cvMe4GpdcrfX6d6+7S5wNxGtUmSJEmShotBuWZPkiRJkjS4TPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iQNKxExOSIW1Tyei4jPRsQbIuLqiHio/LtdzTxzIuLhiFgaETOb2X5JkqSeMtmTNKxk5tLMnJKZU4C3Ay8AFwInA9dm5iTg2vI1EbEHMBvYEzgUOKu8f6gkSdKQZrInaTh7N/BIZv4WOBI4tyw/F5hVPj8S+FlmrsvMR4GHgf0GvaWSJEm9ZLInaTibDfy0fP7GzFwBUP7dsSwfBzxRM8+ysuxVIuK4iFgQEQtWrVrVoCZLkiT1jMmepGEpIl4LHAH8Z3dV65RlvYqZOS8zp2XmtDFjxvS3iZIkSf1isidpuPoz4O7M/F35+ncRsRNA+XdlWb4MmFAz33jgyUFrpSRJUh+Z7Ekarj7Epi6cAJcAx5bPjwUurimfHRFbRMQuwCTgjkFrpSRJUh+NbHYDJGmwRcRWwCHAp2qKzwDmR8QngMeBDwBk5pKImA/cD7wMnJiZGwa5yZIkSb1msidp2MnMF4DtO5Q9TTE6Z736c4G5g9A0SZKkATMskr2LFi7nzCuX8uTqtYwd3cZJMycza2rdwfQkadAYmyTViojJwM9rinYFTgXOK8snAo8BH8zMZ8t55gCfADYAn8nMK/vbDmOTVB2Vv2bvooXLmXPBYpavXksCy1evZc4Fi7lo4fJmN03SMGZsktRRZi7NzCmZOQV4O/ACcCFwMnBtZk4Cri1fExF7UNxCZk/gUOCsiBjRnzYYm6RqqXyyd+aVS1m7fvPLa9au38CZVy5tUoskydgkqVvvBh7JzN8CRwLnluXnArPK50cCP8vMdZn5KPAwsF9/Vmpskqql8snek6vX9qpckgaDsUlSN2azacTgN2bmCoDy745l+TjgiZp5lpVlm4mI4yJiQUQsWLVqVZcrNTZJ1VL5ZG/s6LZelUvSYDA2SepMRLwWOAL4z+6q1inLVxVkzsvMaZk5bcyYMV0u0NgkVUvlk72TZk6mbdTm3dfbRo3gpJmTm9QiSTI2SerSnwF3Z+bvyte/i4idAMq/K8vyZcCEmvnGA0/2Z8XGJqlaKp/szZo6jtOP3ptxo9sIYNzoNk4/em9HlZLUVMYmSV34EJu6cAJcAhxbPj8WuLimfHZEbBERuwCTgDv6s2Jjk1Qtw+LWC7OmjjNISRpyjE2SOoqIrYBDgE/VFJ8BzI+ITwCPAx8AyMwlETEfuB94GTgxMzfQT8YmqTqGRbInSZLUCjLzBWD7DmVPU4zOWa/+XGDuIDRNUguqfDdOSZIkSRqOTPYkSZIkqYJM9iRJkiSpgkz2JEmSJKmCTPYkSZIkqYJM9iRJkiSpgiIzm92GPouIVcBvezHLDsBTDWpOs1V528Dta3W93b43Z+aYRjVmMPQyPvn5tza3r7X1ZvuMTdXi9rU2t2+TTmNTSyd7vRURCzJzWrPb0QhV3jZw+1pd1bevv6r+/rh9rc3tG76q/t64fa3N7esZu3FKkiRJUgWZ7EmSJElSBQ23ZG9esxvQQFXeNnD7Wl3Vt6+/qv7+uH2tze0bvqr+3rh9rc3t64Fhdc2eJEmSJA0Xw+3MniRJkiQNCyZ7kiRJklRBlU/2ImJCRFwfEQ9ExJKI+Ntmt6kRImJERCyMiEub3ZaBFhGjI+IXEfFg+Tm+s9ltGkgR8bnyu3lfRPw0IrZsdpv6IyLOjoiVEXFfTdkbIuLqiHio/LtdM9s4FBibWp+xqbUYm3rO+NT6jE+tpZHxqfLJHvAy8PeZ+VZgf+DEiNijyW1qhL8FHmh2Ixrku8AVmbk78DYqtJ0RMQ74DDAtM/cCRgCzm9uqfjsHOLRD2cnAtZk5Cbi2fD3cGZtan7GptZyDsamnjE+tz/jUWs6hQfGp8sleZq7IzLvL53+g+LKPa26rBlZEjAcOB37Q7LYMtIjYFpgB/BAgM1/KzNXNbdWAGwm0RcRIYCvgySa3p18y8ybgmQ7FRwLnls/PBWYNaqOGIGNTazM2tR5jU88Zn1qb8an1NDI+VT7ZqxURE4GpwO3NbcmA+7/A54FXmt2QBtgVWAX8qOxq8YOIeF2zGzVQMnM58E3gcWAF8PvMvKq5rWqIN2bmCih2IoAdm9yeIcXY1JKMTdVgbOqG8aklGZ+qYUDi07BJ9iJia+B84LOZ+Vyz2zNQIuK9wMrMvKvZbWmQkcC+wL9l5v/P3r2HyVVWib//rpNE0lzDJTC5OYkSggQUpA3x5wTQCMmMIwk+cghegJ8cIxxQvDESPQf5OU9GGGRwcBTNSAS8gBlAYBREJqDA+QGhIWgSIBLEgU4iRCCImAQS1/mjdsdKp9PppLu6qnZ/P89TT+1ae7+73t2drK5V+93vPgJ4hRINsynGX88AxgEjgd0i4kP17ZX6k7mpaZmbVHrmp6ZlftJmA6LYi4ghVJLV9zPzxnr3p4+9AzghIn4LXAe8KyK+V98u9al2oD0zO75RvJ5KAiuLdwNPZeaazHwNuBH4H3XuUy08GxEjAIrn5+rcn4Zgbmpq5qZyMDdtg/mpqZmfyqFP8lPpi72ICCpjlh/LzH+pd3/6WmbOyczRmTmWysWpd2Zmab7dyMzfAc9ExIQiNBV4tI5d6mtPA5MjYtfi3+pUSnQRdZVbgNOK5dOAm+vYl4Zgbmpu5qbSMDd1wfzU3MxPpdEn+Wlwn3Wncb0D+DCwJCIeKWKfz8xb69gn7ZiPA9+PiNcBvwH+Z53702cy84GIuB54mMrsZ4uBefXtVe9ExLXAscB+EdEOfBG4CFgQEWdQSdIn1a+HDcPc1PzMTU3E3LRDzE/Nz/zURGqZnyIz+6qfkiRJkqQGUfphnJIkSZI0EFnsSZIkSVIJWexJkiRJUglZ7EmSJElSCVnsSZIkSVIJWexpKxGREXFp1evPRsSFfbTvqyLi/X2xr+28z0kR8VhE3NUpPjYi1kXEIxHxy4j43x33oYmI1oi4vNZ9k7RzzE2SGpX5SY3KYk9d2QC8LyL2q3dHqkXEoB3Y/Azg/87Md3ax7snMPDwz3wJcDXweIDPbMvMTfdBVSbVhbpLUqMxPakgWe+rKRio3p/xU5xWdv12KiD8Wz8dGxC8iYkFE/DoiLoqID0bEoohYEhFvrNrNuyPinmK7vy/aD4qISyLiwYj4VUR8rGq/d0XED4AlXfTnlGL/SyPi4iJ2AfA3wDcj4pLtHOuewItV7/XjYvnCiJgfET+PiN9ExCeK+G4R8ZPim62lEXFyz36kkvqAucncJDUq85P5qSENrncH1LC+DvwqIv55B9q8BXgT8ALwG+DbmTkpIs4FPg58sthuLHAM8Ebgrog4EDgVeCkz3xYRuwD/X0T8rNh+EnBoZj5V/WYRMRK4GDiSStL5WUTMzMwvRcS7gM9mZlsX/XxjRDwC7AHsChy1jeM5GHhnsd3yiLgCmA6sysz3FH3Yawd+PpJ6z9xkbpIalfnJ/NRwPLOn7AIM1wAAIABJREFULmXmH4BrgB05Nf9gZq7OzA3Ak0BHwllCJUl1WJCZf87MJ6gktoOB44FTi0TyALAvML7YflHnZFV4G/DzzFyTmRuB7wNH96CfHUMR3kglic7bxnY/ycwNmfl74DnggOJY3h0RF0fElMx8qQfvJ6mPmJsAc5PUkMxPgPmp4VjsqTtfpTJ+e7eq2EaKfzcREcDrqtZtqFr+c9XrP7PlWeTs9D4JBPDxIpEcnpnjMrMj4b2yjf5FTw+kG7ew7SRXfTybgMGZ+Wsq34YtAb5cDHuQ1L/MTX9hbpIai/npL8xPDcBiT9uUmS8AC6gkrQ6/pfIfFmAGMGQndn1SRPwfxVj0NwDLgduBsyJiCEBEHBQRu3W3EyrfYh0TEftF5QLkU4Bf7GBf/obKN2k9Ugx/+FNmfg/4CvDWHXw/Sb1kbtqauUlqDOanrZmf6str9rQ9lwLnVL3+d+DmiFgELGTb3xx1ZzmVxHIAcGZmro+Ib1MZrvBw8a3XGmBmdzvJzNURMQe4i8o3Vbdm5s09eP+OcecBvAr8XzvQ98OASyLiz8BrwFk70FZS3zE3bcncJDUO89OWzE91FJmdzwpLkiRJkpqdwzglSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphCz2JEmSJKmELPYkSZIkqYQs9iRJkiSphAbXuwO9sd9+++XYsWPr3Q1Jfeyhhx76fWYOr3c/esP8JJWPuUlSI+ouNzV1sTd27Fja2trq3Q1JfSwi/rvefegt85NUPuYmSY2ou9zkME5JkiRJKiGLPUmSJEkqIYs9SZIkSSqhpr5mryuvvfYa7e3trF+/vt5daTpDhw5l9OjRDBkypN5dkUrJ/NRz5iOp/5ibes+cpUZVumKvvb2dPfbYg7FjxxIR9e5O08hMnn/+edrb2xk3bly9uyOVkvmpZ8xHUv8yN/WOOUuNrHTDONevX8++++5rstpBEcG+++7rt3pSDZmfesZ8JPUvc1PvmLPUyEpX7AEmq53kz02qPf+f9Yw/J6l/+X+ud/z5qVGVbhinpMZx0+KVXHL7clatXcfIYS2cN20CM48YVe9uSZL5SVJD6uvcNOCLPZO9VBs3LV7JnBuXsO61TQCsXLuOOTcuAfD/WA+Zn6TaMD/1jrlJqo1a5KZSDuPsqY4f6Mq160j+8gO9afHKXu130KBBHH744Zsfv/3tbwG49957mTRpEgcffDAHH3ww8+bN29zmwgsvJCJYsWLF5thll11GRNDW1tar/kj1cMntyzcnqw7rXtvEJbcvr1OPmov5Saod89POK0Numj59Om95y1uYOHEiZ555Jps2bdrmtgDnnXceBx98MG9+85s58cQTWbt2ba+OVdqWWuSmAV3s1SrZt7S08Mgjj2x+jB07lt/97nd84AMf4Jvf/CaPP/449957L9/61rf4yU9+srndYYcdxnXXXbf59fXXX88hhxzSq75I9bJq7bodimtL5iepdsxPO68MuWnBggX88pe/ZOnSpaxZs4b/+I//6Hb74447jqVLl/KrX/2Kgw46iC9/+cu9OlZpW2qRmwZ0sdefyf7rX/86p59+Om9961sB2G+//fjnf/5nLrroos3bzJw5k5tvvhmA3/zmN+y1114MHz58m/tcsGABn/70pwH413/9V97whjcA8OSTT/I3f/M3LFq0iPe9730A3HzzzbS0tPDqq6+yfv36zdtKtTJyWMsOxbWlZs9PAFdeeSUHHXQQxx57LB/96Ec555xzgEqOmjx5Mm9729u44IIL2H333fv8mKTumJ92Xhly05577gnAxo0befXVVzdPrjJjxgyuueYaAL71rW/xwQ9+EIDjjz+ewYMrVz5NnjyZ9vb2PjxK6S9qkZsGdLFXq2S/bt26zcMQTjzxRACWLVvGkUceucV2ra2tLFu2bPPrPffckzFjxrB06VKuvfZaTj755G7f5+ijj+aee+4B4J577mHfffdl5cqV3HvvvUyZMoW3vvWtLF68ePP6Qw89lAcffJAHHniAo446qlfHKG3PedMm0DJk0BaxliGDOG/ahDr1qLk0e35atWoV//iP/8j999/PHXfcweOPP7553bnnnsu5557Lgw8+yMiRI3t1PNLOMD/tvGbPTR2mTZvG/vvvzx577MH73/9+AObNm8eXvvQl7rnnHi699FK+9rWvbdVu/vz5/O3f/u3OHqbUrVrkpgFd7NUq2VcPRfjRj34EVG642dW0vJ1js2bN4rrrruOmm27anOy25a/+6q/44x//yMsvv8wzzzzDBz7wAe6++27uuecepkyZwuDBgznwwAN57LHHWLRoEZ/+9Ke3WC/V0swjRvHl9x3GqGEtBDBqWAtfft9hXsTfQ82enxYtWsQxxxzDPvvsw5AhQzjppJM2r7vvvvs2v/7ABz7Qq+ORdob5aec1e27qcPvtt7N69Wo2bNjAnXfeCcABBxzAl770Jd75zndy6aWXss8++2zRZu7cuQwePHjzGT+pr9UiNw3o2Tg7fnD9MaPUxIkTaWtr44QTTtgce+ihh7YaV/7e976X8847j9bW1s3DDLrz9re/ne985ztMmDCBKVOmMH/+fO677z4uvfRSAKZMmcJtt93GkCFDePe7383pp5/Opk2b+MpXvtK3Byh1YeYRo/zwtJOaPT9lZp/3U+pL5qed0+y5qdrQoUM54YQTuPnmmznuuOMAWLJkCfvuuy+rVq3aYturr76aH//4xyxcuNB76qmm+jo3DehiD/ov2Z999tkcddRRvO997+Pwww/n+eef53Of+xwXXHDBFtu1tLRw8cUXc9BBB/Vov0cffTQXXHABF1xwAUcccQR33XUXLS0t7LXXXpvXn3rqqZx66qkMHz6c559/nt/97ndMnDixz49RUt9q5vw0adIkPvWpT/Hiiy+yxx57cMMNN3DYYYcBlWtebrjhBk4++eQtJlaQ1ByaOTd1jIgaMWIEGzdu5NZbb9082mnRokXcdtttLF68mGOOOYbjjz+ecePG8dOf/pSLL76YX/ziF+y66641OVapVgZ8sddfRowYwfe+9z0++tGP8vLLL5OZfPKTn+S9733vVtvOmjWrx/udMmUKzzzzDEcffTSDBg1izJgxHHzwwZvXH3XUUTz77LMcffTRALz5zW9m//33b5pvpbyXj1R7tchPo0aN4vOf/zxHHXUUI0eO5JBDDtn8JdRXv/pVPvShD3HppZfynve8Z3NckqrVIje98sornHDCCWzYsIFNmzbxrne9izPPPJMNGzbw0Y9+lO985zuMHDmSSy+9lI985CPceeednHPOOWzYsGHz2b/JkyfzzW9+s0+PVaqVaOahNq2trdn5PiqPPfYYb3rTm+rUo+bXSD+/zjeWhMp1AV5XUX4R8VBmtta7H71hfqp8g7777ruzceNGTjzxRD7ykY9w4okn8qc//YmWlhYiguuuu45rr71282x61Qbaz0uNz9yk7vhzVL10l5s8s6eG1d29fCz2pMZ34YUX8l//9V+sX7+e448/npkzZwKVa27OOeccMpNhw4Yxf/78OvdUkqRysthrEkcddRQbNmzYIvbd73538zUwZeRNb6XmsK38tK2JoKZMmcIvf/nL/uiapAFsIH52kjorZbG3ral6m9kDDzxQ8/dotCG9I4e1sLKLws6b3qqZmZ96ptHykVR25qbeMWepUZXuPntDhw7l+eef9z/dDspMnn/+eYYOHVrvrmzmTW/VExExJiLuiojHImJZRJxbxPeJiDsi4oniee+qNnMiYkVELI+IaVXxIyNiSbHu8ig++UTELhHxwyL+QESM3Zm+mp96phHzkVRm5qbeMWepkZXuzN7o0aNpb29nzZo19e5K0xk6dCijR4+udzc26897+aipbQQ+k5kPR8QewEMRcQdwOrAwMy+KiPOB84HPRcQhwCxgIjAS+K+IOCgzNwFXALOB+4FbgenAbcAZwIuZeWBEzAIuBk7e0Y6an3qu0fKRVGbmpt4zZ6lRla7YGzJkCOPGjat3N9RHvOmtticzVwOri+WXI+IxYBQwAzi22Oxq4OfA54r4dZm5AXgqIlYAkyLit8CemXkfQERcA8ykUuzNAC4s9nU98G8REbmDX4ObnyQ1InOTVF6lG8YpaeAqhlceATwAHFAUgh0F4f7FZqOAZ6qatRexUcVy5/gWbTJzI/ASsG8X7z87Itoios1vyCVJUr1Z7EkqhYjYHbgB+GRm/qG7TbuIZTfx7tpsGcicl5mtmdk6fPjw7XVZkiSppiz2JDW9iBhCpdD7fmbeWISfjYgRxfoRwHNFvB0YU9V8NLCqiI/uIr5Fm4gYDOwFvND3RyJJktR3LPYkNbVixswrgccy81+qVt0CnFYsnwbcXBWfVcywOQ4YDywqhnq+HBGTi32e2qlNx77eD9y5o9frSZIk9bfSTdAiacB5B/BhYElEPFLEPg9cBCyIiDOAp4GTADJzWUQsAB6lMpPn2cVMnABnAVcBLVQmZrmtiF8JfLeYzOUFKrN5SpIkNTSLPUlNLTPvpetr6gCmbqPNXGBuF/E24NAu4uspikVJkqRm4TBOSZIkSSohiz1JkiRJKiGLPUmSJEkqIYs9SZIkSSohiz1JkiRJKiGLPUmSJEkqoZoVexExPyKei4ilXaz7bERkROxXFZsTESsiYnlETKtVvyRJkiRpIKjlmb2rgOmdgxExBjiOyk2OO2KHULlJ8cSizTciYlAN+yZJkiRJpVazYi8z7wZe6GLVZcA/AFkVmwFcl5kbMvMpYAUwqVZ9kyRJkqSy69dr9iLiBGBlZv6y06pRwDNVr9uLWFf7mB0RbRHRtmbNmhr1VJIkSZKaW78VexGxK/AF4IKuVncRyy5iZOa8zGzNzNbhw4f3ZRclSZIkqTQG9+N7vREYB/wyIgBGAw9HxCQqZ/LGVG07GljVj32TJEmSpFLptzN7mbkkM/fPzLGZOZZKgffWzPwdcAswKyJ2iYhxwHhgUX/1TZIkSZLKppa3XrgWuA+YEBHtEXHGtrbNzGXAAuBR4KfA2Zm5qVZ9kyRJkqSyq9kwzsw8ZTvrx3Z6PReYW6v+SJIkSdJA0q+zcUqSJEmS+ofFniRJkiSVkMWeJEmSJJWQxZ4kSVKNRMSgiFgcET8uXu8TEXdExBPF895V286JiBURsTwiplXFj4yIJcW6y6O4h1Uxi/kPi/gDETG2v49PUmOz2JMkSaqdc4HHql6fDyzMzPHAwuI1EXEIMAuYCEwHvhERg4o2VwCzqdyaanyxHuAM4MXMPBC4DLi4tociqdlY7EmSJNVARIwG3gN8uyo8A7i6WL4amFkVvy4zN2TmU8AKYFJEjAD2zMz7MjOBazq16djX9cDUjrN+kgQWe5IkSbXyVeAfgD9XxQ7IzNUAxfP+RXwU8EzVdu1FbFSx3Dm+RZvM3Ai8BOzbuRMRMTsi2iKibc2aNb09JklNxGJPUlOLiPkR8VxELK2K/TAiHikev42IR4r42IhYV7Xum1VtvCZGUp+JiL8HnsvMh3rapItYdhPvrs2Wgcx5mdmama3Dhw/vYXcklUHNbqouSf3kKuDfqAxtAiAzT+5YjohLqXzb3eHJzDy8i/10XBNzP3ArlWtibqPqmpiImEXlmpiTu2gvSdXeAZwQEX8HDAX2jIjvAc9GxIjMXF0M0Xyu2L4dGFPVfjSwqoiP7iJe3aY9IgYDewEv1OqAJDUfz+xJamqZeTfb+HBTnJ37P4Fru9uH18RI6muZOSczR2fmWCoTr9yZmR8CbgFOKzY7Dbi5WL4FmFWMJhhHZSKWRcVQz5cjYnKRe07t1KZjX+8v3mOrM3uSBi6LPUllNgV4NjOfqIqNK6ZB/0VETClivb4mBrwuRlKPXAQcFxFPAMcVr8nMZcAC4FHgp8DZmbmpaHMWlUleVgBPUhl1AHAlsG9ErAA+TTGzpyR1cBinpDI7hS3P6q0GXp+Zz0fEkcBNETGRPrgmBirXxQDzAFpbW/12XRIAmflz4OfF8vPA1G1sNxeY20W8DTi0i/h64KQ+7KqkkrHYk1RKxfUr7wOO7Ihl5gZgQ7H8UEQ8CRyE18RIkqQSchinpLJ6N/B4Zm4enhkRwztuUhwRb6ByTcxvvCZGkiSVkcWepKYWEdcC9wETIqI9Is4oVs1i64lZjgZ+FRG/pDLZypmZ2XGWzmtiJElSqTiMU1JTy8xTthE/vYvYDcAN29jea2IkSVKpeGZPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKqGbFXkTMj4jnImJpVeySiHg8In4VET+KiGFV6+ZExIqIWB4R02rVL0nlso1cc2FErIyIR4rH31Wt6zLXRMSREbGkWHd5REQR3yUifljEH4iIsf15fJIkSTurlmf2rgKmd4rdARyamW8Gfg3MAYiIQ4BZwMSizTciYlAN+yapPK5i61wDcFlmHl48boXt5porgNnA+OLRsc8zgBcz80DgMuDiWh2IJElSX6pZsZeZdwMvdIr9LDM3Fi/vB0YXyzOA6zJzQ2Y+BawAJtWqb5LKo6tc040uc01EjAD2zMz7MjOBa4CZVW2uLpavB6Z2nPWTJElqZPW8Zu8jwG3F8ijgmap17UVsKxExOyLaIqJtzZo1Ne6ipCZ2TjFkfH5E7F3EtpVrRhXLneNbtCm+rHoJ2LerNzQ/SZKkRlKXYi8ivgBsBL7fEepis+yqbWbOy8zWzGwdPnx4rbooqbldAbwROBxYDVxaxLeVa7rLQeYnSZLUlPq92IuI04C/Bz5YDJeCyrfoY6o2Gw2s6u++SSqHzHw2Mzdl5p+Bf+cvw8K3lWva+cuw8ur4Fm0iYjCwFz0fNipJklQ3/VrsRcR04HPACZn5p6pVtwCzilnvxlGZHGFRf/ZNUnkU1+B1OBHomKmzy1yTmauBlyNicnE93qnAzVVtTiuW3w/cWfVFlSRJUsMaXKsdR8S1wLHAfhHRDnyRyuybuwB3FPMb3J+ZZ2bmsohYADxKZXjn2Zm5qVZ9k1Qe28g1x0bE4VSGW/4W+BjAdnLNWVRm9myhcj1xxzXFVwLfjYgVVM7ozar9UUmSJPVezYq9zDyli/CV3Ww/F5hbq/5IKqe+yjWZ2QYc2kV8PXBSb/ooSZJUD/WcjVOSJEmSVCMWe5IkSZJUQhZ7kiRJklRCFnuSJEmSVEIWe5IkSZJUQhZ7kiRJklRCFnuSJEmSVEIWe5IkSZJUQhZ7kiRJklRCFnuSJEmSVEIWe5IkSZJUQhZ7kiRJklRCFnuSJEmSVEIWe5IkSZJUQhZ7kiRJklRCFnuSJEl9LCKGRsSiiPhlRCyLiP9VxPeJiDsi4oniee+qNnMiYkVELI+IaVXxIyNiSbHu8oiIIr5LRPywiD8QEWP7+zglNTaLPUmSpL63AXhXZr4FOByYHhGTgfOBhZk5HlhYvCYiDgFmAROB6cA3ImJQsa8rgNnA+OIxvYifAbyYmQcClwEX98eBSWoeFnuSJEl9LCv+WLwcUjwSmAFcXcSvBmYWyzOA6zJzQ2Y+BawAJkXECGDPzLwvMxO4plObjn1dD0ztOOsnSWCxJ0mSVBMRMSgiHgGeA+7IzAeAAzJzNUDxvH+x+Sjgmarm7UVsVLHcOb5Fm8zcCLwE7NtFP2ZHRFtEtK1Zs6avDk9SE7DYkyRJqoHM3JSZhwOjqZylO7Sbzbs6I5fdxLtr07kf8zKzNTNbhw8fvr1uSyoRiz1JkqQaysy1wM+pXGv3bDE0k+L5uWKzdmBMVbPRwKoiPrqL+BZtImIwsBfwQk0OQlJTstiT1NQiYn5EPBcRS6til0TE4xHxq4j4UUQMK+JjI2JdRDxSPL5Z1cbZ7iT1mYgYXpV7WoB3A48DtwCnFZudBtxcLN8CzCpyzjgqE7EsKoZ6vhwRk4u8dGqnNh37ej9wZ3FdnyQBFnuSmt9V/GVmug53AIdm5puBXwNzqtY9mZmHF48zq+LOdiepL40A7oqIXwEPUrlm78fARcBxEfEEcFzxmsxcBiwAHgV+CpydmZuKfZ0FfJvKpC1PArcV8SuBfSNiBfBpipk9JanD4Hp3QJJ6IzPv7ny2LTN/VvXyfirfeG9T9Wx3xeuO2e5uozLb3YXFptcD/xYR4bfnkrqTmb8Cjugi/jwwdRtt5gJzu4i3AVtd75eZ64GTet1ZSaXlmT1JZfcR/vItOMC4iFgcEb+IiClFrNez3YEz3kmSpMYyIM7s3bR4JZfcvpxVa9cxclgL502bwMwjRm2/oaSmFhFfADYC3y9Cq4HXZ+bzEXEkcFNETKQPZruDyox3wDyA1tZWz/xJkqS6Kn2xd9Pilcy5cQnrXqsMe1+5dh1zblwCYMEnlVhEnAb8PTC1Y8hlZm4ANhTLD0XEk8BB9Gy2u3Znu5MkSc2k9MM4L7l9+eZCr8O61zZxye3L69QjSbUWEdOBzwEnZOafquLDI2JQsfwGKhOx/MbZ7iRJUhmV/szeqrXrdiguqblExLXAscB+EdEOfJHK7Ju7AHcUd1C4v5h582jgSxGxEdgEnJmZHWfpzqIys2cLlWv8qme7+24x290LwKx+OCxJkqReK32xN3JYCyu7KOxGDmupQ28k9bXMPKWL8JXb2PYG4IZtrHO2O0mSVCqlH8Z53rQJtAwZtEWsZcggzps2oU49kiRJkqTa6/GZvYhooTKLXVNd7NYxCYuzcUqNb926dTz99NP17oYkbWXdunVQGR4uSU2jR8VeRLwX+ArwOir3qDoc+FJmnlDLzvWVmUeMsriTGtx//ud/8tnPfpZXX30VgGbLM5LKqyM/UZm91/wkqWn0dBjnhcAkYC1AZj4CjK1NlyQNRBdeeCGLFi1i2LBhgHlGUuPoyE9UJnYyP0lqGj0t9jZm5ks17YmkAW3w4MHstdde9e6GJG3F/CSpWfX0mr2lEfEBYFBEjAc+Afzv2nVL0kBz6KGH8oMf/IBNmzYB7BIRX8M8I6kBdOQnAD8HSWomPT2z93FgIrAB+AHwEvDJWnVK0sDzta99jWXLlrHLLrsAvAHzjKQG0ZGfgMTPQZKayHaLvYgYBNySmV/IzLcVj/+nuPdUd+3mR8RzEbG0KrZPRNwREU8Uz3tXrZsTESsiYnlETOvVUUlqKps2beKEE05g7ty5PPjggwCP9STPSFKtVecnKrmpR5+DJKkRbLfYy8xNwJ8iYkcHq18FTO8UOx9YmJnjgYXFayLiEGAWlbOH04FvFEWmpAFg0KBB7Lrrrrz0kpcGS2os5idJzayn1+ytB5ZExB3AKx3BzPzEthpk5t0RMbZTeAZwbLF8NfBz4HNF/LrM3AA8FRErqMz+eV8P+yepyQ0dOpTDDjuM4447DmBMRFwO3ecZSeoPHfkJ+OuO3ATmJ0mNr6fF3k+KR28dkJmrATJzdUTsX8RHAfdXbddexLYSEbOB2QCvf/3r+6BLkhrBe97zHt7znvcAMH/+/D8BD9W3R5JU0ZGfTj/99D9ibpLURHpU7GXm1RHxOoqbiQLLM/O1PuxHdPW22+jLPGAeQGtra5fbSGo+p512Gq+++iq//vWvoTKC4Ad9nGckaad05KfTTz/9FSrFXl9/DpKkmujRbJwRcSzwBPB14BvAryPi6J14v2cjYkSxzxHAc0W8HRhTtd1oYNVO7F9Sk/r5z3/O+PHjOfvsswH+mp3PM5LUpzryE5Xc1JvPQZLUr3p664VLgeMz85jMPBqYBly2E+93C3BasXwacHNVfFZE7BIR44DxwKKd2L+kJvWZz3yGn/3sZ/ziF78AWM7O5xlJ6lMd+YnKGb3efA6SpH7V02v2hmTm8o4XmfnriBjSXYOIuJbKZCz7RUQ78EXgImBBRJwBPA2cVOxvWUQsAB4FNgJnF7OAShogXnvtNSZMmLD5dU/yjCT1B/OTpGbV02KvLSKuBL5bvP4g27lAOTNP2caqqdvYfi4wt4f9kVQyra2tnHHGGXz4wx8G2CMi/h0nQpDUADryE5XcdCw9+BwkSY2gp8M4zwKWAZ8AzqVyBu7MWnVK0sBzxRVXMHHiRC6//HKA/THPSGoQHfmJSm7yc5CkphGZ25/QMiJ2A9Z3DK0sbni+S2b+qcb961Zra2u2tbXVswuS+sgrr7zC0KFDGTRoEBHxEHAUDZBndpb5SSqPjvw0ePDghzKztVE+B+0Mc5NUPhHxUGa2drWup2f2FgItVa9bgP/qbcckqcPUqVNZt25ddcg8I6khmJ8kNaueFntDM/OPHS+K5V1r0yVJA9H69evZfffdN782z0hqFOYnSc2qp8XeKxHx1o4XEdEKrOtme0naIbvtthsPP/zw5tfmGUmNwvwkqVn1tNj7JPAfEXFPRNwNXAecU7tuSRpovvrVr3LSSScxZcoUgAn0MM9ExPyIeC4illbF9omIOyLiieJ576p1cyJiRUQsj4hpVfEjI2JJse7yiIgivktE/LCIPxARY/vuqCU1g478BEzwc5CkZtJtsRcRb4uIv8rMB4GDgR9SuQ/eT4Gn+qF/kkruwQcf5He/+x1ve9vbePzxxzn55JMBkp7nmauA6Z1i5wMLM3M8lWuOzweIiEOAWcDEos03iokWAK4AZgPji0fHPs/C8/IOAAAauElEQVQAXszMA6ncRPninTlOSc2nc34CXsDPQZKayPbO7H0LeLVYfjvweeDrwIvAvBr2S9IA8bGPfYzXve51ANx333380z/9E8Bz9DDPZObdVD6AVZsBXF0sXw3MrIpfl5kbMvMpYAUwKSJGAHtm5n1ZmaL4mk5tOvZ1PTC146yfpHLrnJ+AEfg5SFIT2V6xNygzOz5EnQzMy8wbMvP/BQ6sbdckDQSbNm1in332AeCHP/whs2fPBljbyzxzQGauBiie9y/io4BnqrZrL2KjiuXO8S3aZOZG4CVg367eNCJmR0RbRLStWbNmJ7suqVF0zk/A7/0cJKmZbLfYi4jBxfJU4M6qdYO72F6SdsimTZvYuHEjAAsXLuRd73pX9eq+zjNdnZHLbuLdtdk6mDkvM1szs3X48OE72UVJjaJzfgL+ULXaz0GSGt72EtW1wC8i4vdUZp26ByAiDqTy7bYk9copp5zCMcccw3777UdLS0vHBC29zTPPRsSIzFxdDNF8roi3A2OqthsNrCrio7uIV7dpL7782outh41KKqHO+Qn4I/g5SFLz6LbYy8y5EbGQyhj1nxXXskDljODHa905SeX3hS98galTp7J69WqOP/54qi6H602euQU4DbioeL65Kv6DiPgXYCSViVgWZeamiHg5IiYDDwCnAl/rtK/7gPcDd1blQkkl1jk/Vd1rz89BkprCdocgZOb9XcR+XZvuSBqIJk+evFWsp3kmIq4FjgX2i4h24ItUirwFEXEG8DRwUrHPZRGxAHiUyox6Z2fmpmJXZ1GZ2bMFuK14AFwJfDciVlA5ozdrx49QUrPqTX6SpHpzvLmkppaZp2xj1dRtbD8XmNtFvA04tIv4eopiUZIkqZn09KbqkiRJkqQmYrEnSZIkSSVksSdJkiRJJeQ1e5IkSZKawk2LV3LJ7ctZtXYdI4e1cN60Ccw8YlS9u9WwLPYkSZIkNbybFq9kzo1LWPdaZSLtlWvXMefGJQAWfNvgME5JkiRJDe+S25dvLvQ6rHttE5fcvrxOPWp8FnuSJEmSGt6qtet2KC6LPUmSJElNYOSwlh2Ky2JPkiRJUhM4b9oEWoYM2iLWMmQQ502bUKceNT4naJEkSZLU8DomYXE2zp6z2JMkSZLUFGYeMcribgc4jFOSJEmSSshiT5IkqY9FxJiIuCsiHouIZRFxbhHfJyLuiIgniue9q9rMiYgVEbE8IqZVxY+MiCXFussjIor4LhHxwyL+QESM7e/jlNTYLPYkSZL63kbgM5n5JmAycHZEHAKcDyzMzPHAwuI1xbpZwERgOvCNiOiYieIKYDYwvnhML+JnAC9m5oHAZcDF/XFgkpqHxZ4kSVIfy8zVmflwsfwy8BgwCpgBXF1sdjUws1ieAVyXmRsy8ylgBTApIkYAe2bmfZmZwDWd2nTs63pgasdZP0kCiz1JkqSaKoZXHgE8AByQmauhUhAC+xebjQKeqWrWXsRGFcud41u0ycyNwEvAvl28/+yIaIuItjVr1vTNQUlqChZ7kiRJNRIRuwM3AJ/MzD90t2kXsewm3l2bLQOZ8zKzNTNbhw8fvr0uSyoRiz1JkqQaiIghVAq972fmjUX42WJoJsXzc0W8HRhT1Xw0sKqIj+4ivkWbiBgM7AW80PdHIqlZWexJkiT1seLauSuBxzLzX6pW3QKcViyfBtxcFZ9VzLA5jspELIuKoZ4vR8TkYp+ndmrTsa/3A3cW1/VJEuBN1SVJkmrhHcCHgSUR8UgR+zxwEbAgIs4AngZOAsjMZRGxAHiUykyeZ2fmpqLdWcBVQAtwW/GASjH53YhYQeWM3qxaH5Sk5mKxJ0mS1Mcy8166vqYOYOo22swF5nYRbwMO7SK+nqJYlKSuOIxTkiRJkkrIYk9SKUXEhIh4pOrxh4j4ZERcGBErq+J/V9VmTkSsiIjlETGtKn5kRCwp1l3ufawkSVIzsNiTVEqZuTwzD8/Mw4EjgT8BPypWX9axLjNvBYiIQ6hc7zIRmA58IyIGFdtfAcymMmHC+GK9JElSQ6tLsRcRn4qIZRGxNCKujYihEbFPRNwREU8Uz3vXo2+SSmkq8GRm/nc328wArsvMDZn5FLACmFRMjb5nZt5XzHJ3DTCz9l2WJEnqnX4v9iJiFPAJoDUzDwUGUfk2/XxgYWaOBxYWryWpL8wCrq16fU5E/Coi5ld9sTQKeKZqm/YiNqpY7hzfSkTMjoi2iGhbs2ZN3/VekiRpJ9RrGOdgoKW4AeiuVG4OOgO4ulh/NX5zLqkPRMTrgBOA/yhCVwBvBA4HVgOXdmzaRfPsJr51MHNeZrZmZuvw4cN71W9JkqTe6vdiLzNXAl+hcm+Z1cBLmfkz4IDixqEUz/t31d5vziXtoL8FHs7MZwEy89nM3JSZfwb+HZhUbNcOjKlqN5rKF1HtxXLnuCRJUkOrxzDOvamcxRsHjAR2i4gP9bS935xL2kGnUDWEs7gGr8OJwNJi+RZgVkTsEhHjqEzEsqj48unliJhczMJ5KnBz/3RdkiRp59XjpurvBp7KzDUAEXEj8D+AZyNiRGauLj6MPVeHvkkqkYjYFTgO+FhV+J8j4nAqQzF/27EuM5dFxALgUWAjcHZmbiranAVcBbQAtxUPSZKkhlaPYu9pYHLxIWwdlVny2oBXgNOAi4pnvzmX1CuZ+Sdg306xD3ez/VxgbhfxNuDQPu+gJElSDfV7sZeZD0TE9cDDVL49XwzMA3YHFkTEGVQKwpP6u2+SJEmSVBb1OLNHZn4R+GKn8AYqZ/kkSZIkSb1Ur1svSJIkSZJqyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkrIYk+SJEmSSshiT5IkSZJKyGJPkiRJkkpocL07IEmSJKlv3LR4JZfcvpxVa9cxclgL502bwMwjRtW7W6oTiz1JkiSpBG5avJI5Ny5h3WubAFi5dh1zblwCYME3QDmMU1JpRcRvI2JJRDwSEW1FbJ+IuCMiniie967afk5ErIiI5RExrSp+ZLGfFRFxeUREPY5HkqTuXHL78s2FXod1r23iktuX16lHqjeLPUll987MPDwzW4vX5wMLM3M8sLB4TUQcAswCJgLTgW9ExKCizRXAbGB88Zjej/2XJKlHVq1dt0NxlZ/FnqSBZgZwdbF8NTCzKn5dZm7IzKeAFcCkiBgB7JmZ92VmAtdUtZEkqWGMHNayQ3GVn8WepDJL4GcR8VBEzC5iB2TmaoDief8iPgp4pqptexEbVSx3jm8lImZHRFtEtK1Zs6YPD0OSpO07b9oEWoYM2iLWMmQQ502bUKceqd6coEVSmb0jM1dFxP7AHRHxeDfbdnUdXnYT3zqYOQ+YB9Da2trlNpIk1UrHJCzOxqkOFnuSSiszVxXPz0XEj4BJwLMRMSIzVxdDNJ8rNm8HxlQ1Hw2sKuKju4hLktRwZh4xyuJOmzmMU1IpRcRuEbFHxzJwPLAUuAU4rdjsNODmYvkWYFZE7BIR46hMxLKoGOr5ckRMLmbhPLWqjSRJUsPyzJ6ksjoA+FFxl4TBwA8y86cR8SCwICLOAJ4GTgLIzGURsQB4FNgInJ2ZHfNXnwVcBbQAtxUPSZKkhmaxJ6mUMvM3wFu6iD8PTN1Gm7nA3C7ibcChfd1HSZKkWnIYpyRJkiSVkMWeJEmSJJWQxZ4kSZIklZDFniRJkiSVkMWeJEmSJJWQxZ4kSZIklZDFniRJkiSVkMWeJEmSJJWQxZ4kSZIklVBdir2IGBYR10fE4xHxWES8PSL2iYg7IuKJ4nnvevRNkiSptyJifkQ8FxFLq2Lb/KwTEXMiYkVELI+IaVXxIyNiSbHu8oiIIr5LRPywiD8QEWP78/gkNYd6ndn7V+CnmXkw8BbgMeB8YGFmjgcWFq8lSZKa0VXA9E6xLj/rRMQhwCxgYtHmGxExqGhzBTAbGF88OvZ5BvBiZh4IXAZcXLMjkdS0+r3Yi4g9gaOBKwEy89XMXAvMAK4uNrsamNnffZMkSeoLmXk38EKn8LY+68wArsvMDZn5FLACmBQRI4A9M/O+zEzgmk5tOvZ1PTC146yfJHWox5m9NwBrgO9ExOKI+HZE7AYckJmrAYrn/btqHBGzI6ItItrWrFnTf72WJEnqnW191hkFPFO1XXsRG1Usd45v0SYzNwIvAft29aZ+dpIGrnoUe4OBtwJXZOYRwCvswJDNzJyXma2Z2Tp8+PBa9VGSJKm/dHVGLruJd9dm66CfnaQBqx7FXjvQnpkPFK+vp1L8PVsMV6B4fq4OfZMkSaqVbX3WaQfGVG03GlhVxEd3Ed+iTUQMBvZi62Gjkga4fi/2MvN3wDMRMaEITQUeBW4BTitipwE393ffJEmSamhbn3VuAWYVM2yOozIRy6JiqOfLETG5uB7v1E5tOvb1fuDO4ro+SdpscJ3e9+PA9yPidcBvgP9JpfBcEBFnAE8DJ9Wpb5IkSb0SEdcCxwL7RUQ78EXgIrr4rJOZyyJiAZUvvzcCZ2fmpmJXZ1GZ2bMFuK14QGWiu+9GxAoqZ/Rm9cNhSWoydSn2MvMRoLWLVVP7uy+SJGlrNy1eySW3L2fV2nWMHNbCedMmMPOIUdtvKAAy85RtrOrys05mzgXmdhFvAw7tIr4evxiXtB31OrOnPuIfY0lSX7tp8Urm3LiEda9VTi6tXLuOOTcuAfBvjCQ1kXrdVF19oOOP8cq160j+8sf4psUr6901SVITu+T25ZsLvQ7rXtvEJbcvr1OPJEk7w2KvifnHWJJUC6vWrtuhuCSpMVnsNTH/GEvbFhFjIuKuiHgsIpZFxLlF/MKIWBkRjxSPv6tqMyciVkTE8oiYVhU/MiKWFOsuL2bFk0pr5LCWHYpLkhqTxV4T84+x1K2NwGcy803AZODsiDikWHdZZh5ePG4FKNbNAiYC04FvRMSgYvsrgNlUpkMfX6zXAHfT4pW846I7GXf+T3jHRXeWagj9edMm0DJk0BaxliGDOG/ahG20kCQ1Iou9JuYfY2nbMnN1Zj5cLL8MPAZ0N7PEDOC6zNyQmU8BK4BJxY2P98zM+4p7WF0DzKxx99Xgyn7N9MwjRvHl9x3GqGEtBDBqWAtfft9hTs4iSU3G2TibWMcfXWfjlLoXEWOBI4AHgHcA50TEqUAblbN/L1IpBO+vatZexF4rljvHu3qf2VTOAPL617++T49BjaW7a6bLkoNnHjGqNMciSQOVxV6T84+x1L2I2B24AfhkZv4hIq4A/hHI4vlS4CNAV9fhZTfxrYOZ84B5AK2trV1uo3LwmmlJUjNwGKek0oqIIVQKve9n5o0AmflsZm7KzD8D/w5MKjZvB8ZUNR8NrCrio7uIawDzmmlJUjOw2JNUSsWMmVcCj2Xmv1TFR1RtdiKwtFi+BZgVEbtExDgqE7EsyszVwMsRMbnY56nAzf1yEGpYXjMtSWoGDuOUVFbvAD4MLImIR4rY54FTIuJwKkMxfwt8DCAzl0XEAuBRKjN5np2ZHRdlnQVcBbQAtxUPDWBeMy1JagYWe5JKKTPvpevr7W7tps1cYG4X8Tbg0L7rncrAa6YlSY3OYZySJEmSVEIWe5IkSZJUQg7jlCTVxE2LV3pNmyRJdWSxJ0nqczctXsmcG5dsvvH4yrXrmHPjEgALPkmS+onDOCVJfe6S25dvLvQ6rHttE5fcvrxOPZIkaeCx2JMk9blVa9ftUFySJPU9iz1JUp8bOaxlh+KSJKnvWexJkvrcedMm0DJk0BaxliGDOG/ahDr1SJKkgccJWiRJfa5jEhZn45TUaJwpWAOJxZ4kqSZmHjHKD1CSGoozBWugcRinJEmSBgRnCtZA45k9qY4cSjKw+fuX1IjKnJucKVgDjcWeVCcOJRnY/P1LakRlz00jh7WwsovCzpmCVVYO45TqxKEkA5u/f0mNqOy5yZmCNdB4Zk+qE4eSDGz+/iU1orLnJmcK1kBjsSfViUNJBjZ//5Ia0UDITc4UrIHEYZxSnTiUZGDz9y+pEZmbpHLxzJ5UJw4lGdj8/UtqROYmqVws9qQ6cijJwObvX1IjMjdJ5eEwTkmSJEkqIYs9SZIkSSohiz1JkiRJKiGLPUnqgYiYHhHLI2JFRJxf7/5IkiRtj8WeJG1HRAwCvg78LXAIcEpEHFLfXkmSJHXPYk+Stm8SsCIzf5OZrwLXATPq3CdJkqRuWexJ0vaNAp6pet1exLYQEbMjoi0i2tasWdNvnZMkSepKU99n76GHHvp9RPz3DjTZD/h9rfpTZ2U+NvD4mt2OHt9f16ojOym6iOVWgcx5wDyAiFizA/nJ339z8/ia244cX6Plph22g5+d/N03N4+vufVJbmrqYi8zh+/I9hHRlpmttepPPZX52MDja3YlOL52YEzV69HAqu4a7Eh+KsHPp1seX3Pz+MrF3PQXHl9z8/h6xmGckrR9DwLjI2JcRLwOmAXcUuc+SZIkdaupz+xJUn/IzI0RcQ5wOzAImJ+Zy+rcLUmSpG4NtGJvXr07UENlPjbw+Jpd0x9fZt4K3Fqj3Tf9z2c7PL7m5vENXGX/2Xh8zc3j64HI3GqOAUmSJElSk/OaPUmSJEkqIYs9SZIkSSqh0hd7ETEmIu6KiMciYllEnFvvPtVCRAyKiMUR8eN696WvRcSwiLg+Ih4vfo9vr3ef+lJEfKr4t7k0Iq6NiKH17lNvRMT8iHguIpZWxfaJiDsi4oniee969rERmJuan7mpuZibes781PzMT82llvmp9MUesBH4TGa+CZgMnB0Rh9S5T7VwLvBYvTtRI/8K/DQzDwbeQomOMyJGAZ8AWjPzUCozPc6qb6967SpgeqfY+cDCzBwPLCxeD3TmpuZnbmouV2Fu6inzU/MzPzWXq6hRfip9sZeZqzPz4WL5ZSr/2EfVt1d9KyJGA+8Bvl3vvvS1iNgTOBq4EiAzX83MtfXtVZ8bDLRExGBgV7Zzs+5Gl5l3Ay90Cs8Ari6WrwZm9munGpC5qbmZm5qPuannzE/NzfzUfGqZn0pf7FWLiLHAEcAD9e1Jn/sq8A/An+vdkRp4A7AG+E4x1OLbEbFbvTvVVzJzJfAV4GlgNfBSZv6svr2qiQMyczVUPkQA+9e5Pw3F3NSUzE3lYG7aDvNTUzI/lUOf5KcBU+xFxO7ADcAnM/MP9e5PX4mIvweey8yH6t2XGhkMvBW4IjOPAF6hRMNsivHXM4BxwEhgt/+/vbsLkas+4zj+/WmiYKheGBCLF2mjVUFR6kuL2voWpNDSijSItFVEoVqMbcEbvbDiTU1TSykUixXRYqoEESIqVNAo0oJRbHSlEotR2uBbxKJoa2qax4tztk42u5sdM3HOHL+fm5k5c+Z/njPM/uD5n//sJPn+eKvSp8lsmlhmk3rPfJpY5pP+7zPR7CVZTBNWa6vqvnHXM2KnA99O8gpwD3BOkrvGW9JIbQW2VtX0jOK9NAHWFyuAl6tqW1V9CNwHnDbmmvaFN5IcDtDevjnmejrBbJpoZlM/mE1zMJ8mmvnUDyPJp943e0lCs2b5har61bjrGbWquraqjqiqZTRfTn20qnozu1FVrwP/THJ0u+lc4G9jLGnU/gF8NclB7Wf1XHr0JeoB9wOXtPcvAdaPsZZOMJsmm9nUG2bTLMynyWY+9cZI8mnRyMrprtOBHwBTSTa1266rqofGWJOGswpYm+QAYAtw6ZjrGZmqejLJvcAzNP/97K/AreOtau8kuRs4C1iaZCvwM+AmYF2Sy2hCeuX4KuwMs2nymU0TxGwaivk0+cynCbIv8ylVNao6JUmSJEkd0ftlnJIkSZL0WWSzJ0mSJEk9ZLMnSZIkST1ksydJkiRJPWSzJ0mSJEk9ZLOn3SSpJDcPPL4myQ0jGvuOJN8dxVh7OM7KJC8k2TBj+7Ik/0myKcmzSf4y/Ts0SU5O8pt9XZukT8ZsktRV5pO6ymZPs9kOXJBk6bgLGZRk/yF2vwz4UVWdPctzL1XViVV1AnAncB1AVT1dVVePoFRJ+4bZJKmrzCd1ks2eZrOD5scpfzrziZmzS0nea2/PSvJ4knVJXkxyU5LvJdmYZCrJ8oFhViR5ot3vW+3r90+yJslTSZ5L8sOBcTck+SMwNUs9F7XjP59kdbvteuAM4HdJ1uzhXA8G/jVwrAfa+zckuT3JY0m2JLm63b4kyYPtzNbzSS5c2FsqaQTMJrNJ6irzyXzqpEXjLkCd9VvguSS/GOI1JwDHAm8DW4DbqurUJD8GVgE/afdbBpwJLAc2JDkSuBh4p6pOSXIg8OckD7f7nwocV1UvDx4syeeB1cBJNKHzcJLzq+rGJOcA11TV07PUuTzJJuBzwEHAV+Y4n2OAs9v9Nie5BfgG8GpVfbOt4ZAh3h9Je89sMpukrjKfzKfO8cqeZlVV7wJ/AIa5NP9UVb1WVduBl4DpwJmiCalp66pqZ1X9nSbYjgHOAy5ug+RJ4FDgqHb/jTPDqnUK8FhVbauqHcBa4OsLqHN6KcJymhC9dY79Hqyq7VX1FvAmcFh7LiuSrE7ytap6ZwHHkzQiZhNgNkmdZD4B5lPn2OxpPr+mWb+9ZGDbDtrPTZIABww8t33g/s6BxzvZ9SpyzThOAQFWtUFyYlV9oaqmA+/9OerLQk9kHvczd8gNns//gEVV9SLNbNgU8PN22YOkT5fZ9DGzSeoW8+lj5lMH2OxpTlX1NrCOJrSmvULzBwvwHWDxJxh6ZZL92rXoXwQ2A38CrkyyGCDJl5IsmW8QmlmsM5MsTfMF5IuAx4es5QyambQFaZc//Luq7gJ+CXx5yONJ2ktm0+7MJqkbzKfdmU/j5Xf2tCc3A1cNPP49sD7JRuAR5p45ms9mmmA5DLiiqj5IchvNcoVn2lmvbcD58w1SVa8luRbYQDNT9VBVrV/A8afXnQf4L3D5ELUfD6xJshP4ELhyiNdKGh2zaVdmk9Qd5tOuzKcxStXMq8KSJEmSpEnnMk5JkiRJ6iGbPUmSJEnqIZs9SZIkSeohmz1JkiRJ6iGbPUmSJEnqIZs9SZIkSeohmz1JkiRJ6qGPALrpRyD+wsGKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_metrics(bins, uniform_scores_f, 'Uniform Binning Scores for All Metrics Using Firecrown', 2, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combining log and uniform bins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I decided to use 5 bins here, but turn it into 4 bins by combining 2 bins. I'm comparing to the uncombined 4 bins scores for each log and uniform bins. This is using firecrown results only." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", + "comb_5bins_log = [{'SNR_ww': 347.1550052435396, 'FOM_ww': 43.281384668444375, 'SNR_gg': 1178.1020177366966, 'FOM_gg': 1937.6331074516759, 'SNR_3x2': 1180.154312947983, 'FOM_3x2': 8170.834082030528},\n", + " {'SNR_ww': 347.11524596040493, 'FOM_ww': 12.496906081438635, 'SNR_gg': 1130.6537186403334, 'FOM_gg': 1725.6973362282138, 'SNR_3x2': 1132.7475032766674, 'FOM_3x2': 4340.897488251889},\n", + " {'SNR_ww': 346.29772734556116, 'FOM_ww': 20.391566310838982, 'SNR_gg': 1132.9429122353106, 'FOM_gg': 1108.2282006433647, 'SNR_3x2': 1135.5513182455525, 'FOM_3x2': 3247.449479310309}]\n", + "\n", + "#uncombined score\n", + "log_4bins = {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", + "comb_5bins_uniform = [{'SNR_ww': 350.62629992935354, 'FOM_ww': 69.62333557208532, 'SNR_gg': 961.3423758223748, 'FOM_gg': 998.7919662764982, 'SNR_3x2': 970.9692811096805, 'FOM_3x2': 2878.907835879231},\n", + " {'SNR_ww': 332.91946164294785, 'FOM_ww': 1573.8141960234443, 'SNR_gg': 969.7202847355114, 'FOM_gg': 3157.055747538249, 'SNR_3x2': 984.1608880916402, 'FOM_3x2': 28180.732187357935},\n", + " {'SNR_ww': 344.4664127650933, 'FOM_ww': 707.9343834362847, 'SNR_gg': 1111.5530740704078, 'FOM_gg': 1722.9278602544168, 'SNR_3x2': 1115.220976362594, 'FOM_3x2': 36901.359762732245}]\n", + "\n", + "#uncobined score\n", + "uniform_4bins = {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_combine_metrics(scores, uncomb_scores, title):\n", + "\n", + " assert len(scores[0]) == 6\n", + " assert len(scores) == 3\n", + " \n", + " x = [1, 2, 3, 4, 5, 6]\n", + " xlabels = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2']\n", + " color = ['b', 'g', 'orange', 'r', 'y', 'c']\n", + " \n", + " #plot the combined bins metrics\n", + " plt.figure(figsize = (10,7)) \n", + " for i in range(len(scores)):\n", + " plt.plot(x, [scores[i]['SNR_ww'], scores[i]['SNR_gg'], scores[i]['SNR_3x2'], scores[i]['FOM_ww'], scores[i]['FOM_gg'], scores[i]['FOM_3x2']], 'o', c = color[i], label = f'0-{i+1}')\n", + "\n", + " #plot the uncombined 4 bins metrics\n", + " plt.plot(x, [uncomb_scores['SNR_ww'], uncomb_scores['SNR_gg'], uncomb_scores['SNR_3x2'], uncomb_scores['FOM_ww'], uncomb_scores['FOM_gg'], uncomb_scores['FOM_3x2']], 'ko', label = '4bins uncombined')\n", + " \n", + " plt.xlabel('Metrics')\n", + " plt.ylabel('Score')\n", + " plt.xticks(x, xlabels)\n", + " plt.legend()\n", + " plt.title(title)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_combine_metrics(comb_5bins_log, log_4bins, 'Scores for 5 bins; log; combine bin 0 with other bins')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UaYa4QershPZ2WLgQMov79vZ1D3Jz585l0qRJ7LTTTmyyySbMmDGDK664Yo359t9/f7bZZpt1W5kkSao8Q9wgzZwJy5b1rC1bVtTXxYMPPsj48eNXPW9ububBBx9ct4VKkqQNliFukBYtGlx9oDJzjZpXTkqSpL4Y4gZpwoTB1QequbmZxYsXr3q+ZMkSxo0bx7Rp05g2bRrnnXfeuq1AkiStHws64b9b4JIRxf2C9XBy/KtgiBukjg5oaupZa2oq6utir7324r777mPBggW8/PLLzJ49myOOOIJ58+Yxb948TjrppHVbgSRJWncLOmFuOyxbCGRxP7d9WIKcIW6Q2tpg1iyYOBEiivtZs4r6uhg1ahTf/OY3Ofjgg5k8eTIf/OAHmTJlyhrzfehDH+Itb3kL9957L83NzXznO99ZtxVLkqSBu20mrFzt5PiVy4r6EIvezsXakLW2tmZXV1eP2t13383kyZOHqUcabu5/SdKAXTIC6C07BfzVK+t9dRFxS2a29jbNkThJkqSBaurjJPi+6nVkiJMkSRqoN3fAyNVOjh/ZVNSHmCFOkiRpoHZsg71nQdNEIIr7vWcV9SE2asjXKEmSVGU7tg1LaFudI3GSJEkVZIiTJEmqIENcA7nmmmt44xvfyKRJkzjjjDPWmL548WLe/va3M3nyZKZMmcJZZ501DL2UJEmNwBD3KnTO76TlzBZGnD6CljNb6Jy/7p/SvHLlSk455RSuvvpq7rrrLi699FLuuuuuHvOMGjWKr371q9x9993cdNNN/Pu///sa80iSpI2DIW6QOud30v6jdhY+vZAkWfj0Qtp/1L7OQW7u3LlMmjSJnXbaiU022YQZM2ZwxRVX9Jhn++23Z4899gBgyy23ZPLkyTz44IPrtF5JklRNhrhBmnntTJYt7/l1G8uWL2Pmtev2dRsPPvgg48ePX/W8ubm534D2wAMP8Lvf/Y599tlnndYrSZKqyRA3SIueXjSo+kD19vVnEdHrvM899xzvf//7OfPMM9lqq63Wab2SJKmaDHGDNGFM71+r0Vd9oJqbm1m8ePGq50uWLGHcuHFMmzaNadOmcd555wGwfPly3v/+99PW1sYRRxyxTuuUJEnVZYgbpI7pHTSN7vl1G02jm+iYvm5ft7HXXntx3333sWDBAl5++WVmz57NEUccwbx585g3bx4nnXQSmckJJ5zA5MmT+bu/+7t1Wp8kSaq2uoW4iNgsIuZGxG0RcWdEnF7WT4uIByNiXnl7d02bz0TE/RFxb0QcXFPfMyLml9POjvI4Y0RsGhGXlfWbI6KlXtvTrW23NmYdOouJYyYSBBPHTGTWobNo223dPrl51KhRfPOb3+Tggw9m8uTJfPCDH2TKlCk95vn1r3/NxRdfzJw5c1aN0P3kJz9Zp/VKkqRqit7OxVovCy6C1msy87mIGA3cAHwSOAR4LjO/str8uwCXAnsDrwd+DrwhM1dGxNyy7U3AT4CzM/PqiPgbYGpmnhQRM4D3ZeZR/fWrtbU1u7q6etTuvvtuJk+evB62WlXk/pckNaqIuCUzW3ubVreRuCw8Vz4dXd76S4yHAbMz86XMXADcD+wdEdsDW2XmjVkkzouAw2vaXFg+/j4wvXuUTpIkaUNW13PiImJkRMwDHgV+lpk3l5M+FhG3R8T5EbF1WdsBWFzTfElZ26F8vHq9R5vMXAE8DWxbl42RJElqIHUNcZm5MjOnAc0Uo2q7AucCfw5MAx4GvlrO3tsIWvZT769NDxHRHhFdEdG1dOnSQW6FJElS4xmSq1Mz8yngOuCQzHykDHevAN+iOAcOihG28TXNmoGHynpzL/UebSJiFDAGeKKX9c/KzNbMbB07dux62y5JkqThUs+rU8dGxGvLx5sDBwH3lOe4dXsfcEf5+EpgRnnF6Y7AzsDczHwYeDYi9i3PdzsGuKKmzbHl4yOBOVmvKzUkSZIayKg6Lnt74MKIGEkRFi/PzKsi4uKImEZx2PMB4K8BMvPOiLgcuAtYAZySmSvLZZ0MXABsDlxd3gC+A1wcEfdTjMDNqOP2SJIkNYy6hbjMvB3YvZf60f206QDW+NTczOwCdu2l/iLwgXXraeO45ppr+OQnP8nKlSs58cQT+fSnP91j+osvvsj+++/PSy+9xIoVKzjyyCM5/fTTh6m3kiRpOPmNDa/Ggk747xa4ZERxv6BznRe5cuVKTjnlFK6++mruuusuLr30Uu66664e82y66abMmTOH2267jXnz5nHNNddw0003rfO6JUlS9RjiBmtBJ8xth2ULgSzu57avc5CbO3cukyZNYqeddmKTTTZhxowZXHHFFT3miQi22GILoPgO1eXLl+PH4kmStHEyxA3WbTNh5bKetZXLivo6ePDBBxk//k8X5zY3N/Pggw+uMd/KlSuZNm0a48aN453vfCf77LPPOq1XkiRVkyFusJYtGlx9gHq7qLa3UbaRI0cyb948lixZwty5c7njjjvWmEeSJG34DHGD1TRhcPUBam5uZvHiP31hxZIlSxg3btyqL7o/77zzesz/2te+lgMPPJBrrrlmndYrSZKqyRA3WG/ugJFNPWsjm4r6Othrr7247777WLBgAS+//DKzZ8/miCOOYN68ecybN4+TTjqJpUuX8tRTTwHwwgsv8POf/5w3velN67ReSZJUTfX8nLgN045txf1tM4tDqE0TigDXXX+VRo0axTe/+U0OPvhgVq5cyfHHH8+UKVN6zPPwww9z7LHHsnLlSl555RU++MEP8t73vned1itJkqopNrYvOGhtbc2urq4etbvvvpvJkycPU4803Nz/kqRGFRG3ZGZrb9M8nCpJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDXANZuXIlu+++e4/PfjvwwANZ/SNRAK688krOOOOMoexeXW2xxRa91j//+c/z85//fL2so6/XUpKkKjLEvQqdnZ20tLQwYsQIWlpa6OzsXC/LPeusswb8eWV/+Zd/yac//en1st5G9sUvfpGDDjpouLshSVLDMcQNUmdnJ+3t7SxcuJDMZOHChbS3t69zkFuyZAk//vGPOfHEE9eY9r3vfY/99tuPXXfdlblz5wJwwQUX8LGPfQyA4447jk984hPst99+7LTTTnz/+98Him942H///Zk2bRq77rorv/rVr9ZYdktLC4899hgAXV1dHHjggQCcdtppHH/88Rx44IHstNNOnH322avaXHTRRUydOpU3v/nNHH300QAsXLiQ6dOnM3XqVKZPn86iRYtW9e3kk0/m7W9/OzvttBO//OUvOf7445k8eTLHHXdcj7586lOfYo899mD69OksXbp0Vfvu7WlpaeELX/gCe+yxB7vtthv33HMPAM8//zzHH388e+21F7vvvjtXXHEFUHw12YwZM5g6dSpHHXUUL7zwwiD3iiRJjcsQN0gzZ85k2bJlPWrLli1j5syZ67TcU089lS996UuMGLHmLnn++ef5zW9+wznnnMPxxx/fa/uHH36YG264gauuumrVCN0ll1zCwQcfzLx587jtttuYNm3aoPp0zz338D//8z/MnTuX008/neXLl3PnnXfS0dHBnDlzuO222zjrrLMA+NjHPsYxxxzD7bffTltbG5/4xCdWLefJJ59kzpw5fP3rX+fQQw/lb//2b7nzzjuZP38+8+bNW7WNe+yxB7feeisHHHAAp59+eq992m677bj11ls5+eST+cpXvgJAR0cH73jHO/jtb3/LL37xC/7v//2/PP/885x77rk0NTVx++23M3PmTG655ZZBbb8kSY3MEDdI3SNMA60PxFVXXcW4cePYc889e53+oQ99CID999+fZ555hqeeemqNeQ4//HBGjBjBLrvswiOPPALAXnvtxXe/+11OO+005s+fz5Zbbjmofr3nPe9h0003ZbvttmPcuHE88sgjzJkzhyOPPJLtttsOgG222QaAG2+8kb/6q78C4Oijj+aGG25YtZxDDz2UiGC33Xbjda97HbvtthsjRoxgypQpPPDAAwCMGDGCo446CoAPf/jDPdrXOuKIIwDYc889V7X96U9/yhlnnMG0adM48MADefHFF1m0aBHXX389H/7whwGYOnUqU6dOHdT2S5LUyAxxgzRhwoRB1Qfi17/+NVdeeSUtLS3MmDGDOXPmrAofABHRY/7VnwNsuummqx53fx/u/vvvz/XXX88OO+zA0UcfzUUXXbRGu1GjRvHKK68A8OKLL/a5zJEjR7JixQoys9f1r652nu7ljBgxoscyR4wYwYoVK9bavrc+dfcHiu39wQ9+wLx585g3bx6LFi1adW7hQPoqSVIVGeIGqaOjg6amph61pqYmOjo6XvUy//Vf/5UlS5bwwAMPMHv2bN7xjnfwve99b9X0yy67DIAbbriBMWPGMGbMmAEtd+HChYwbN46PfvSjnHDCCdx6661rzNPS0rLqMOMPfvCDtS5z+vTpXH755Tz++OMAPPHEEwDst99+zJ49GyjOG3zrW986oD52e+WVV1ad+3bJJZcMqv3BBx/MN77xjVXh9Xe/+x1QhNjucxXvuOMObr/99kH1SZKkRjZquDtQNW1tbUBxbtyiRYuYMGECHR0dq+r1sPXWW7PffvvxzDPPcP755w+43XXXXceXv/xlRo8ezRZbbNHrSNwXvvAFTjjhBP7f//t/7LPPPmtd5pQpU5g5cyYHHHAAI0eOZPfdd+eCCy7g7LPP5vjjj+fLX/4yY8eO5bvf/e6gtvE1r3kNd955J3vuuSdjxoxZFVwH4nOf+xynnnoqU6dOJTNpaWnhqquu4uSTT+YjH/kIU6dOZdq0aey9996D6pMkSY0sukcvNhatra25+meF3X333QP+aA9teNz/kqRGFRG3ZGZrb9M8nCpJklRBhjhJkqQKMsSVNrbDyiq43yVJVWWIAzbbbDMef/xx/6BvZDKTxx9/nM0222y4uyJJ0qB5dSrQ3NzMkiVLVn3VkzYem222Gc3NzcPdDUmSBs0QB4wePZodd9xxuLshSZI0YB5OlSRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqqC6hbiI2Cwi5kbEbRFxZ0ScXta3iYifRcR95f3WNW0+ExH3R8S9EXFwTX3PiJhfTjs7IqKsbxoRl5X1myOipV7bI0mS1EjqORL3EvCOzHwzMA04JCL2BT4NXJuZOwPXls+JiF2AGcAU4BDgnIgYWS7rXKAd2Lm8HVLWTwCezMxJwNeBf6vj9kiSJDWMuoW4LDxXPh1d3hI4DLiwrF8IHF4+PgyYnZkvZeYC4H5g74jYHtgqM2/MzAQuWq1N97K+D0zvHqWTJEnakNX1nLiIGBkR84BHgZ9l5s3A6zLzYYDyflw5+w7A4prmS8raDuXj1es92mTmCuBpYNte+tEeEV0R0bV06dL1tXmSJEnDpq4hLjNXZuY0oJliVG3XfmbvbQQt+6n312b1fszKzNbMbB07duzaui1JktTwhuTq1Mx8CriO4ly2R8pDpJT3j5azLQHG1zRrBh4q68291Hu0iYhRwBjgibpshCRJUgOp59WpYyPiteXjzYGDgHuAK4Fjy9mOBa4oH18JzCivON2R4gKGueUh12cjYt/yfLdjVmvTvawjgTnleXOSJEkbtFF1XPb2wIXlFaYjgMsz86qIuBG4PCJOABYBHwDIzDsj4nLgLmAFcEpmriyXdTJwAbA5cHV5A/gOcHFE3E8xAjejjtsjSZLUMGJjG7hqbW3Nrq6u4e6GJEnSWkXELZnZ2ts0v7FBkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJg9A5v5OWM1sYcfoIWs5soXN+57D0o57fnSpJkrRB6ZzfSfuP2lm2fBkAC59eSPuP2gFo261tSPviSJwkSdIAzbx25qoA123Z8mXMvHbmkPfFECdJkjRAi55eNKh6PRniJEmSBmjCmAmDqteTIU6SJGmAOqZ30DS6qUetaXQTHdM7hrwvhjhJkqQBatutjVmHzmLimIkEwcQxE5l16Kwhv6gBIDJzyJ+aLeYAACAASURBVFc6nFpbW7Orq2u4uyFJkrRWEXFLZrb2Ns2ROEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqoLqFuIgYHxG/iIi7I+LOiPhkWT8tIh6MiHnl7d01bT4TEfdHxL0RcXBNfc+ImF9OOzsioqxvGhGXlfWbI6KlXtsjSZLUSOo5ErcC+FRmTgb2BU6JiF3KaV/PzGnl7ScA5bQZwBTgEOCciBhZzn8u0A7sXN4OKesnAE9m5iTg68C/1XF7JEmSGkbdQlxmPpyZt5aPnwXuBnbop8lhwOzMfCkzFwD3A3tHxPbAVpl5Y2YmcBFweE2bC8vH3wemd4/SSZIkbciG5Jy48jDn7sDNZeljEXF7RJwfEVuXtR2AxTXNlpS1HcrHq9d7tMnMFcDTwLa9rL89Iroiomvp0qXrZZskSZKGU91DXERsAfwAODUzn6E4NPrnwDTgYeCr3bP20jz7qffXpmchc1ZmtmZm69ixYwe5BZIkSY2nriEuIkZTBLjOzPwvgMx8JDNXZuYrwLeAvcvZlwDja5o3Aw+V9eZe6j3aRMQoYAzwRH22RpIkqXHU8+rUAL4D3J2ZX6upb18z2/uAO8rHVwIzyitOd6S4gGFuZj4MPBsR+5bLPAa4oqbNseXjI4E55XlzkiRJG7RRdVz2XwBHA/MjYl5Z+yzwoYiYRnHY8wHgrwEy886IuBy4i+LK1lMyc2XZ7mTgAmBz4OryBkVIvDgi7qcYgZtRx+2RJElqGLGxDVy1trZmV1fXcHdDkiRprSLilsxs7W2a39ggSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaqguoW4iBgfEb+IiLsj4s6I+GRZ3yYifhYR95X3W9e0+UxE3B8R90bEwTX1PSNifjnt7IiIsr5pRFxW1m+OiJZ6bY8kSVIjqedI3ArgU5k5GdgXOCUidgE+DVybmTsD15bPKafNAKYAhwDnRMTIclnnAu3AzuXtkLJ+AvBkZk4Cvg78Wx23R5IkqWHULcRl5sOZeWv5+FngbmAH4DDgwnK2C4HDy8eHAbMz86XMXADcD+wdEdsDW2XmjZmZwEWrtele1veB6d2jdJIkSRuyITknrjzMuTtwM/C6zHwYiqAHjCtn2wFYXNNsSVnboXy8er1Hm8xcATwNbNvL+tsjoisiupYuXbp+NkqSJGkY1T3ERcQWwA+AUzPzmf5m7aWW/dT7a9OzkDkrM1szs3Xs2LFr67IkSVLDq2uIi4jRFAGuMzP/qyw/Uh4ipbx/tKwvAcbXNG8GHirrzb3Ue7SJiFHAGOCJ9b8lkiRJjWXAIS4iNo+INw5i/gC+A9ydmV+rmXQlcGz5+Fjgipr6jPKK0x0pLmCYWx5yfTYi9i2XecxqbbqXdSQwpzxvTpIkaYM2oBAXEYcC84BryufTIuLKtTT7C+Bo4B0RMa+8vRs4A3hnRNwHvLN8TmbeCVwO3FWu55TMXFku62Tg2xQXO/wvcHVZ/w6wbUTcD/wd5ZWukiRJG7oYyMBVRNwCvAO4LjN3L2u3Z+bUOvdvvWttbc2urq7h7oYkSdJaRcQtmdna27SBHk5dkZlPr8c+SZIkaR2MGuB8d0TEXwEjI2Jn4BPAb+rXLUmSJPVnoCNxH6f4JoWXgEsoPo/t1Hp1SpIkSf1b60hc+dVXV2bmQcDM+ndJkiRJa7PWkbjyCtFlETFmCPojSZKkARjoOXEvAvMj4mfA893FzPxEXXolSZKkfg00xP24vEmSJKkBDCjEZeaFEbEJ8IaydG9mLq9ftyRJktSfAYW4iDgQuBB4gOJL58dHxLGZeX39uiZJkqS+DPRw6leB/5OZ9wJExBuAS4E969UxSZIk9W2gnxM3ujvAAWTm74HR9emSJEmS1magI3FdEfEd4OLyeRtwS326JEmSpLUZaIg7GTiF4uu2ArgeOKdenZIkSVL/BhriRgFnZebXYNW3OGxat15JkiSpXwM9J+5aYPOa55sDP1//3ZEkSdJADDTEbZaZz3U/KR831adLkiRJWpuBhrjnI2KP7icR0Qq8UJ8uSZIkaW0Gek7cqcB/RsRDQAKvB46qW68kSZLUr35H4iJir4j4s8z8LfAm4DJgBXANsGAI+idJkqRerO1w6n8AL5eP3wJ8Fvh34ElgVh37JUmSpH6s7XDqyMx8onx8FDArM38A/CAi5tW3a5IkSerL2kbiRkZEd9CbDsypmTbQ8+kkSZK0nq0tiF0K/DIiHqO4GvVXABExCXi6zn2TJElSH/oNcZnZERHXAtsDP83MLCeNAD5e785JkiSpd2s9JJqZN/VS+319uiNJkqSBGOiH/UqSJKmBGOIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVVDdQlxEnB8Rj0bEHTW10yLiwYiYV97eXTPtMxFxf0TcGxEH19T3jIj55bSzIyLK+qYRcVlZvzkiWuq1LZIkSY2mniNxFwCH9FL/emZOK28/AYiIXYAZwJSyzTkRMbKc/1ygHdi5vHUv8wTgycycBHwd+Ld6bYgkSVKjqVuIy8zrgScGOPthwOzMfCkzFwD3A3tHxPbAVpl5Y2YmcBFweE2bC8vH3wemd4/SSZIkbeiG45y4j0XE7eXh1q3L2g7A4pp5lpS1HcrHq9d7tMnMFcDTwLa9rTAi2iOiKyK6li5duv62RJIkaZgMdYg7F/hzYBrwMPDVst7bCFr2U++vzZrFzFmZ2ZqZrWPHjh1cjyVJkhrQkIa4zHwkM1dm5ivAt4C9y0lLgPE1szYDD5X15l7qPdpExChgDAM/fCtJklRpQxriynPcur0P6L5y9UpgRnnF6Y4UFzDMzcyHgWcjYt/yfLdjgCtq2hxbPj4SmFOeNydJkrTBG1WvBUfEpcCBwHYRsQT4AnBgREyjOOz5APDXAJl5Z0RcDtwFrABOycyV5aJOprjSdXPg6vIG8B3g4oi4n2IEbka9tkWSJKnRxMY2eNXa2ppdXV3D3Q1JkqS1iohbMrO1t2l+Y4MkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYLqFuIi4vyIeDQi7qipbRMRP4uI+8r7rWumfSYi7o+IeyPi4Jr6nhExv5x2dkREWd80Ii4r6zdHREu9tkWSJKnR1HMk7gLgkNVqnwauzcydgWvL50TELsAMYErZ5pyIGFm2ORdoB3Yub93LPAF4MjMnAV8H/q1uWyJJktRg6hbiMvN64InVyocBF5aPLwQOr6nPzsyXMnMBcD+wd0RsD2yVmTdmZgIXrdame1nfB6Z3j9JJkiRt6Ib6nLjXZebDAOX9uLK+A7C4Zr4lZW2H8vHq9R5tMnMF8DSwbW8rjYj2iOiKiK6lS5eup02RJEkaPo1yYUNvI2jZT72/NmsWM2dlZmtmto4dO/ZVdlGSJKlxDHWIe6Q8REp5/2hZXwKMr5mvGXiorDf3Uu/RJiJGAWNY8/CtJEnSBmmoQ9yVwLHl42OBK2rqM8orTnekuIBhbnnI9dmI2Lc83+2Y1dp0L+tIYE553pwkSdIGb1S9FhwRlwIHAttFxBLgC8AZwOURcQKwCPgAQGbeGRGXA3cBK4BTMnNluaiTKa503Ry4urwBfAe4OCLupxiBm1GvbZEkSWo0sbENXrW2tmZXV9dwd0OSJGmtIuKWzGztbVqjXNggSZKkQTDESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFDUuIi4gHImJ+RMyLiK6ytk1E/Cwi7ivvt66Z/zMRcX9E3BsRB9fU9yyXc39EnB0RMRzbI0mSNh6dnZ20tLQwYsQIWlpa6OzsHJZ+DOdI3Nszc1pmtpbPPw1cm5k7A9eWz4mIXYAZwBTgEOCciBhZtjkXaAd2Lm+HDGH/JUnSRqazs5P29nYWLlxIZrJw4ULa29uHJcg10uHUw4ALy8cXAofX1Gdn5kuZuQC4H9g7IrYHtsrMGzMzgYtq2kiSJK13M2fOZNmyZT1qy5YtY+bMmUPel+EKcQn8NCJuiYj2sva6zHwYoLwfV9Z3ABbXtF1S1nYoH69eX0NEtEdEV0R0LV26dD1uhiRJ2pgsWrRoUPV6Gq4Q9xeZuQfwLuCUiNi/n3l7O88t+6mvWcyclZmtmdk6duzYwfdWkiQJmDBhwqDq9TQsIS4zHyrvHwV+COwNPFIeIqW8f7ScfQkwvqZ5M/BQWW/upS5JklQXHR0dNDU19ag1NTXR0dEx5H0Z8hAXEa+JiC27HwP/B7gDuBI4tpztWOCK8vGVwIyI2DQidqS4gGFuecj12YjYt7wq9ZiaNpIkSetdW1sbs2bNYuLEiUQEEydOZNasWbS1tQ15X6K4JmAIVxixE8XoG8Ao4JLM7IiIbYHLgQnAIuADmflE2WYmcDywAjg1M68u663ABcDmwNXAx3MtG9Ta2ppdXV3rfbskSZLWt4i4peaTPHpOG+oQN9wMcZIkqSr6C3GN9BEjkiRJGiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkySpQXXO76TlzBZGnD6CljNb6JzfOdxdUgMZNdwdkCRJa+qc30n7j9pZtnwZAAufXkj7j9oBaNtt6L+nU43HkThJkhrQzGtnrgpw3ZYtX8bMa2cOU4/UaAxxkiQ1oEVPLxpUXUOnsxNaWmDEiOK+c5iOchviJElqQBPGTBhUXUOjsxPa22HhQsgs7tvbhyfIGeIkSWpAHdM7aBrd1KPWNLqJjukdw9QjAcycCct6HuVm2bKiPtQMcZIkNaC23dqYdegsJo6ZSBBMHDORWYfO8qKGYbaoj6PZfdXryatTJUlqUG27tRnaGsyECcUh1N7qQ82ROEmSpAHq6ICmnke5aWoq6kPNECdJkjRAbW0waxZMnAgRxf2sWUV9qHk4VZIkaRDa2oYntK3OkThJkqQKMsRJkiRVkCFOkiSpggxxkiQ1qgWd8N8tcMmI4n7BMH2/kxqSIU6SBEDn/E5azmxhxOkjaDmzhc75BoZhtaCTzq99hJaPLmREW9Ly0YV0fu0jBjmtYoiTJNE5v5P2H7Wz8OmFJMnCpxfS/qN2g9ww6jz7k7TPWs7CxyCBhY9B+6zldJ79yeHumhpEZOZw92FItba2ZldX13B3Q5IaSsuZLSx8es2PoZ84ZiIPnPrA0HdItIwNFj62Zn3idvDA0o3rb/fGLCJuyczW3qY5EidJYtHTvX/xY1911d+ixwdX18bHECdJYsKY3r/4sa+66m/C9tsOqq6NjyFOG7zOM/+GlnGjGBFBy7hRdJ75N8PdpY2e+6TxdEzvoGl0zy+EbBrdRMf0YfhCSAHQ8aWzaNp8kx61ps03oeNLZw1Tj9RoDHHaoHWe+Te0/+O5LFy6sjgxeOlK2v/xXEPDMHKfNKa23dqYdegsJo6ZSBBMHDORWYfOom23BvhuoY1UW1sbxx53PiNHTgSCkSMncuxx59PWCN/3pIbghQ3aoI3fbiRLHn9ljXrztiNY/NjKYeiR3CfSwHR2Qns7LFv2p1pT0/B92bqGhxc2aKP1YC9hob+66s990sD8YNmGMnNmzwAHxfOZM4enP2o8hjht0F7fx/m/fdVVf+6TBrWgE+a2w7KFQBb3c9sNcsNoUR8XBvdV18bHEKcN2sR3vIbVzgtm802KuoaH+6RB3TYTVq427LNyWVHXsJjQx4XBfdW18THEaYP25rf/B9PeN4IdtoUAdtgWpr1vBG9++38Md9c2Wu6TBrWsj+Gdvuqqu46O4hy4Wk1NRV0CQ9x69dETD6J5u2BEBM3bBR898aDh7tJG75yT25h2wEX88SMTyS8Ef/zIRKYdcBHnnOxZwcPFfdKgmvoY3umrrrpraysuYpg4ESKKey9qUC2vTl1PPnriQXRefC0vvPyn2uabQNvR0/nWt3++3tcnSetV9zlxtYdURzbB3rNgR1ODNFy8OnUIXP3fPQMcwAsvF3VJang7tnHDilkseXIir7wSLHlyIjesMMBJjWzUcHdgXUXEIcBZwEjg25l5xnD046E+vsuur7okNZLOTvjIR2D58j/VRo+G7+LhO6lRVXokLiJGAv8OvAvYBfhQROwyHH3xYxMkVdknP9nJ8uXtQPkRIyxk+fJ2PvlJP2JEalSVDnHA3sD9mfmHzHwZmA0cNhwdedfh03v92IR3HT59OLojSYPy+OMzgdU+YoRlZV1SI6p6iNsBWFzzfElZ6yEi2iOiKyK6li5dWpeOfOvbP6ft6Ok9PjbBixokVUdfHyXiR4xIjarq58RFL7U1LrfNzFnALCiuTq1XZ7717Z/Dt+u1dEmqn223ncDjjy/stS6pMVV9JG4JML7meTPw0DD1RZIq66yzOthkk56fLLvJJk2cdZafLCs1qqqHuN8CO0fEjhGxCTADuHKY+yRJldPW1sb5589i4sSJRAQTJ07k/PNn0ealqVLDqvyH/UbEu4EzKT5i5PzM7Pffxnp92K8kSdL61t+H/Vb9nDgy8yfAT4a7H5IkSUOp6odTJUmSNkqGOEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAoyxEmSJFVQZOZw92FIRcRSYGGdV7Md8Fid16HBc780HvdJY3K/NB73SWMaiv0yMTPH9jZhowtxQyEiujKzdbj7oZ7cL43HfdKY3C+Nx33SmIZ7v3g4VZIkqYIMcZIkSRVkiKuPWcPdAfXK/dJ43CeNyf3SeNwnjWlY94vnxEmSJFWQI3GSJEkVZIiTJEmqIEOcJElSBRniShExMyLujIjbI2JeROwTEddFRFfNPK0RcV35+MCIeDoifhcR90TEV4at8xs4903jqPe+iIjDapbdFRFvXcv80yLixpo+HbVeNrRCImJl+Xp131rK+lsjYm75ut8TEe01bU6LiIyISTW1vy1rfhbZeuK+aRxDuS8i4pqIuK38vXReRIxcS9++XK779oj4YUS8dqDbZYgDIuItwHuBPTJzKnAQsLicPC4i3tVH019l5u7A7sB7I+Iv6t/bjYv7pnEM0b64FnhzZk4Djge+vZZuLQOOycwpwCHAmYP5BbiBeCEzp9XcHoiIPwMuAU7KzDcBbwX+OiLeU9NuPjCj5vmRwF1D1+2NgvumcQzlvvhgZr4Z2BUYC3xgLfP/DNi1/L36e+AzA90oQ1xhe+CxzHwJIDMfy8yHymlfBv6pv8aZ+QIwD9ihr3kiYn5EvDYKj0fEMWX94og4KCJ+EhFTy9rvIuLz5eN/jogT13kLq2so9s3YiPhZRNwaEf8REQsjYrty2ufK/5B+FhGXRsTfr5etqqa674vMfC7/dMn8a4AEiIi9yv9SN4uI15T/4e6amb/PzPvKtg8Bj1L80tzYnQJckJm3QrGvgH8APl0zz38DhwFExE7A08DSvhYYER+MiK+Vjz8ZEX8oH/95RNwQEXtHxH+VtcMi4oWI2KTcZ3+owzZW1XrfN+V8J0TE76MYGf9WRHyzrP95RNwUEb+NiC9GxHN12Kaqqsu+yMxnyoejgE340++xK2r+9v91RHSW8/80M1eUbW4Cmge6AYa4wk+B8eUPwDkRcUDNtBuBlyLi7X01joitgZ2B6/tZx6+BvwCmAH8A3lbW96XYadcDb4uIrYAV5bxQ/Gfwq8Fv0gZjKPbNF4A5mbkH8ENgQtm2FXg/xQjSEcDGfihjKPYFEfG+iLgH+DHFaByZ+VvgSuBfgC8B38vMO1ZrtzfFL8z/HfSWVdvmNYeIfljWpgC3rDZfV1nv9gywOCJ2BT4EXLaW9VzPn35vvQ14PCJ24E+/o26l+Fnpnn4HsBewD3DzoLdqwzAk+yYiXg98juLvyTuBN9VMPgs4KzP3Ah7qpfnGYqh+TgCIiP+h+KfyWeD7Zbkd+HxEvA34FPDxXpoeD1w9kHWAIQ4o/vsH9qR4gZcCl0XEcTWz/Au9jzK8LSJuB/4IXJWZf+xnNb8C9i9v5wK7lb8AnyjX3z39rRR/vLaIiCagJTPvXZftq7Ih2jdvBWaX67sGeLKmfkVmvpCZzwI/Wpdtqboh2hdk5g/LQxuHA/9cM+mLFH+gWimC3CoRsT1wMfCRzHxlMNu1Aag9TPS+shaU//2vZvXabIpDRYdT/APTp3K/bRERWwLjKQ5D7U8R2H5VjiTcHxGTgb2Br9VOf1VbVn1Dsm8oXu9fZuYTmbkc+M+aaW+peX7JoHq/YRmqfVEsIPNgiqMXmwLvKGuPAJ8HfgF8KjOfqG0T8f/bu9MQvao7juPfn44acXwRIwgKUjcKYnTU6qum1pWq2LyImLjrixZLrFUUrNjiroi+0GqpRIl71YoIcSmNEtOMVtMIMVGpiljcQZFW3DXh54tzrnMz40xmkplxnnl+n1dz77nrHJ5z/2e59+hiSiPOvaO6IxLEfcf2etvLbV8CnE1pgWnSlgEzKLWctv7ahz0b+I2kvhFO0dRi5wDLKQ/B4xko3FZRHk5z6rargV8xtJbQdSYhbzTG9V1rEvKifa4VwB5N1zawA9ALbF/PA0BtvX4M+IPt5zbtzqadlxnacnwgQ8fyPAKcCrzV6gIaybPAmcCrlLJrDiVIeKam9wNHA98AT1IqQj9lI62vXWYi8iZl1aaZqN8JALa/pPQgzG2tng18BOzc3lbS6ZQxxye3hpRsVII4QNKPJe3VWtUHvDlos6sofeVD2H4NuAa4cLhz2H4b2BHYy/YbwNPABdQgzvbXlEHiJ1C6V/vb6d1qMvKGkhcn1PMdBcxsrT+ujunpBY4dZv+uMBl5IWlPSap/H0DpHv2oJi+idBndC1xbt9maUjO+y/aDQ4/Ytf4MnNEEzJJmUf5nG7Rg1nGKF1LybTRWUMqlpqJ5KPCV7Y9b6ecCz9r+EJhF6dp7ebPuZnqZiLz5N3CIpJmSemhVrijPk2Z5wZA9u9u454Wk3tozQM2LY4BX6vLBlErO/sAFknar639Rj/9L25+P5QZ6xrLxNNYL3KTyVts64HVKl1HTj43txyWNNJjxFmqm2P7vMNusBJpXjfspD7SnW+n9wOG2P5fUTxnc2NVBHJOTN5cB96l8nuKfwPvAJ7ZXSVoCrKEEK89TBrV2q8nIi3nAaZK+Ab4A5tt2HQy8zvZfVV7X/5ekwyi12Z8Bs1pdu2fYfmHzbrWz2X5f0inArbX7U8ANtocMCbB9/xgO3U/pSl1he72kt6kPqGolsBMDLW9rgQ/G0rIw3U1E3th+V9LVlP//e5SWpKasOhe4R9L5lBbrbi7DNjBBv5PtgCWStqE875cBt9TlWylDPt6r+bG4lmM3U7pdn6h12OdsnzWak2Xu1Oh69ce13vY6lc9o/MXlExdI6rX9aR2fuAL4dfMmU0TEVNEqq3oordOLbT9cy64vamVoAXCi7bkjHy06RVriIsrbqH+TtAXwNWUsYmORpL0pY7DuTAAXEVPUpZKOoJRVSymfxoAyxuvmOkzh/9Q3vmN6SEvcOJN0JvC7Qaufsb3wh7ieGJC8mTqSF1ObpJWU7p22U22/+ENcTwxI3kwdUyEvEsRFREREdKC8nRoRERHRgRLERURERHSgBHER0fUkWdLdreUeSR9KenQj+/VJOmaE9J9I+tN4XmtERCNBXEQEfAbsI2nbunwk8O4o9uujfMxzCEk9tp+3fc44XWNExAYSxEVEFH9nYFaOE4H7mgRJ20laLGmVpNWS5tbZIi4H5tdJtedLulTSIklLgbsk/bxpzatfcr9d0ouS1kqaJ2lLSXdIeqmuP2+ybzoiOleCuIiI4n5ggaQZwL6Ur983LgaW2T6IMtXUdcBWlMmsH6iTaj9Qtz0QmGv7pEHH/yPwse3ZdS7ZZZSWvF1s72N7NnD7RN1cREw/CeIiIgDba4EfUVrhHh+UfBTwe0kvAMspwUUiMwAAAQZJREFUH1TddZhDLalzLQ52BGWuxuZ8/wPeAHaXdFOdP3HUk2tHRCSIi4gYsAS4nlZXaiVgXm1x67O9q+3/DHOMz4ZZL2CDD3PWQG4/SmC4ELhtUy88IrpPgriIiAGLgcu/54vr/wB+W6cuQtL+df0nwPajPPZS4OxmQdJMSTsCW9h+iNLdesDmXHxEdJcEcRERle13bN/4PUlXUMbArZX0Ul0GeArYu3mxYSOHvxKYWV9iWEMZW7cLsLx2094BXDQe9xER3SHTbkVERER0oLTERURERHSgBHERERERHShBXEREREQHShAXERER0YESxEVERER0oARxERERER0oQVxEREREB/oWM771isJY9pkAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_combine_metrics(comb_5bins_uniform, uniform_4bins, 'Scores for 5 bins; uniform; combine bin 0 with other bins')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tomo-conda [conda env:.conda-tomo]", + "language": "python", + "name": "conda-env-.conda-tomo-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e465d0a59717c1d6130842c19d849fef2f08ed7c Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:10:45 -0700 Subject: [PATCH 21/36] Delete rf_log_comb_bins.py merged all methods into one file --- .../classifiers/rf_log_comb_bins.py | 127 ------------------ 1 file changed, 127 deletions(-) delete mode 100644 tomo_challenge/classifiers/rf_log_comb_bins.py diff --git a/tomo_challenge/classifiers/rf_log_comb_bins.py b/tomo_challenge/classifiers/rf_log_comb_bins.py deleted file mode 100644 index a76b499a..00000000 --- a/tomo_challenge/classifiers/rf_log_comb_bins.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -This is an example tomographic bin generator using a random forest for logarithmic bins. - -Every classifier module needs to: - - have construction of the type - __init__ (self, bands, options) (see examples below) - - implement two functions: - train (self, training_data,training_z) - apply (self, data). - - define valid_options class varible. - -See Classifier Documentation below. -""" - -from .base import Tomographer -import numpy as np -from sklearn.ensemble import RandomForestClassifier - - -class RF_Log_CombineBins(Tomographer): - """ Random Forest Classifier """ - - # valid parameter -- see below - valid_options = ['bins'] - # this settings means arrays will be sent to train and apply instead - # of dictionaries - wants_arrays = True - - def __init__ (self, bands, options, newbins): - """Constructor - - Parameters: - ----------- - bands: str - string containg valid bands, like 'riz' or 'griz' - options: dict - options come through here. Valid keys are listed as valid_options - class variable. - - Note: - ----- - Valiad options are: - 'bins' - number of tomographic bins - - """ - self.bands = bands - self.opt = options - self.newbins = newbins - - def train (self, training_data, training_z): - """Trains the classifier - - Parameters: - ----------- - training_data: numpy array, size Ngalaxes x Nbands - training data, each row is a galaxy, each column is a band as per - band defined above - training_z: numpy array, size Ngalaxies - true redshift for the training sample - - """ - - n_bin = self.opt['bins'] - newbins = self.newbins - - print("Finding bins for training data") - # Now put the training data into redshift bins. - # Use zero so that the one object with minimum - # z in the whole survey will be in the lowest bin - training_bin = np.zeros(training_z.size) - - # Find the edges that split the redshifts into n_z bins of - # equal number counts in each -# p = np.linspace(0, 100, n_bin + 1) - p = np.logspace(0, 2, n_bin+1) - print('the bins are in logspace') - z_edges = np.percentile(training_z, p) - - # Now find all the objects in each of these bins - for i in range(n_bin): - z_low = z_edges[i] - z_high = z_edges[i + 1] - training_bin[(training_z > z_low) & (training_z < z_high)] = i - - #combine bins - newbins.sort() - training_bin[training_bin == newbins[1]] = newbins[0] - - #rename/move bins - if newbins[1] != np.max(training_bin): - training_bin[training_bin == np.max(training_bin)] = newbins[1] - - # for speed, cut down to 5% of original size - cut_percent = 0.05 - print('cut percent:', cut_percent) - cut = np.random.uniform(0, 1, training_z.size) < cut_percent - training_bin = training_bin[cut] - training_data = training_data[cut] - - # Can be replaced with any classifier - classifier = RandomForestClassifier() - - print("Fitting classifier") - # Lots of data, so this will take some time - classifier.fit(training_data, training_bin) - - self.classifier = classifier - self.z_edges = z_edges - - - def apply (self, data): - """Applies training to the data. - - Parameters: - ----------- - Data: numpy array, size Ngalaxes x Nbands - testing data, each row is a galaxy, each column is a band as per - band defined above - - Returns: - tomographic_selections: numpy array, int, size Ngalaxies - tomographic selection for galaxies return as bin number for - each galaxy. - """ - tomo_bin = self.classifier.predict(data) - return tomo_bin - From cc42d0f46b60d0fe62b9a5247952d137f8790d4b Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:10:56 -0700 Subject: [PATCH 22/36] Delete rf_log_percentile.py merged all methods into one file --- .../classifiers/rf_log_percentile.py | 116 ------------------ 1 file changed, 116 deletions(-) delete mode 100644 tomo_challenge/classifiers/rf_log_percentile.py diff --git a/tomo_challenge/classifiers/rf_log_percentile.py b/tomo_challenge/classifiers/rf_log_percentile.py deleted file mode 100644 index f3fb009b..00000000 --- a/tomo_challenge/classifiers/rf_log_percentile.py +++ /dev/null @@ -1,116 +0,0 @@ -""" -This is an example tomographic bin generator using a random forest for logarithmic bins. - -Every classifier module needs to: - - have construction of the type - __init__ (self, bands, options) (see examples below) - - implement two functions: - train (self, training_data,training_z) - apply (self, data). - - define valid_options class varible. - -See Classifier Documentation below. -""" - -from .base import Tomographer -import numpy as np -from sklearn.ensemble import RandomForestClassifier - - -class RandomForest_log(Tomographer): - """ Random Forest Classifier """ - - # valid parameter -- see below - valid_options = ['bins'] - # this settings means arrays will be sent to train and apply instead - # of dictionaries - wants_arrays = True - - def __init__ (self, bands, options): - """Constructor - - Parameters: - ----------- - bands: str - string containg valid bands, like 'riz' or 'griz' - options: dict - options come through here. Valid keys are listed as valid_options - class variable. - - Note: - ----- - Valiad options are: - 'bins' - number of tomographic bins - - """ - self.bands = bands - self.opt = options - - def train (self, training_data, training_z): - """Trains the classifier - - Parameters: - ----------- - training_data: numpy array, size Ngalaxes x Nbands - training data, each row is a galaxy, each column is a band as per - band defined above - training_z: numpy array, size Ngalaxies - true redshift for the training sample - - """ - - n_bin = self.opt['bins'] - print("Finding bins for training data") - # Now put the training data into redshift bins. - # Use zero so that the one object with minimum - # z in the whole survey will be in the lowest bin - training_bin = np.zeros(training_z.size) - - # Find the edges that split the redshifts into n_z bins of - # equal number counts in each -# p = np.linspace(0, 100, n_bin + 1) - p = np.logspace(0, 2, n_bin+1) - print('the bins are in logspace') - z_edges = np.percentile(training_z, p) - - # Now find all the objects in each of these bins - for i in range(n_bin): - z_low = z_edges[i] - z_high = z_edges[i + 1] - training_bin[(training_z > z_low) & (training_z < z_high)] = i - - # for speed, cut down to 5% of original size - cut_percent = 0.05 - print('cut percent:', cut_percent) - cut = np.random.uniform(0, 1, training_z.size) < cut_percent - training_bin = training_bin[cut] - training_data = training_data[cut] - - # Can be replaced with any classifier - classifier = RandomForestClassifier() - - print("Fitting classifier") - # Lots of data, so this will take some time - classifier.fit(training_data, training_bin) - - self.classifier = classifier - self.z_edges = z_edges - - - def apply (self, data): - """Applies training to the data. - - Parameters: - ----------- - Data: numpy array, size Ngalaxes x Nbands - testing data, each row is a galaxy, each column is a band as per - band defined above - - Returns: - tomographic_selections: numpy array, int, size Ngalaxies - tomographic selection for galaxies return as bin number for - each galaxy. - """ - tomo_bin = self.classifier.predict(data) - return tomo_bin - From f2551e73bc18d5d9a5c275aa20cc73856d563399 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:11:12 -0700 Subject: [PATCH 23/36] Delete rf_uniform.py merged all methods into one file --- tomo_challenge/classifiers/rf_uniform.py | 119 ----------------------- 1 file changed, 119 deletions(-) delete mode 100644 tomo_challenge/classifiers/rf_uniform.py diff --git a/tomo_challenge/classifiers/rf_uniform.py b/tomo_challenge/classifiers/rf_uniform.py deleted file mode 100644 index 92fff67c..00000000 --- a/tomo_challenge/classifiers/rf_uniform.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -This is an example tomographic bin generator using a random forest. - -Every classifier module needs to: - - have construction of the type - __init__ (self, bands, options) (see examples below) - - implement two functions: - train (self, training_data,training_z) - apply (self, data). - - define valid_options class varible. - -See Classifier Documentation below. -""" - -from .base import Tomographer -import numpy as np -from sklearn.ensemble import RandomForestClassifier - - -class RandomForestUniform(Tomographer): - """ Random Forest Classifier """ - - # valid parameter -- see below - valid_options = ['bins'] - # this settings means arrays will be sent to train and apply instead - # of dictionaries - wants_arrays = True - - def __init__ (self, bands, options): - """Constructor - - Parameters: - ----------- - bands: str - string containg valid bands, like 'riz' or 'griz' - options: dict - options come through here. Valid keys are listed as valid_options - class variable. - - Note: - ----- - Valiad options are: - 'bins' - number of tomographic bins - - """ - self.bands = bands - self.opt = options - - def train (self, training_data, training_z): - """Trains the classifier - - Parameters: - ----------- - training_data: numpy array, size Ngalaxes x Nbands - training data, each row is a galaxy, each column is a band as per - band defined above - training_z: numpy array, size Ngalaxies - true redshift for the training sample - - """ - - n_bin = self.opt['bins'] - print("Finding bins for training data") - # Now put the training data into redshift bins. - # Use zero so that the one object with minimum - # z in the whole survey will be in the lowest bin - training_bin = np.zeros(training_z.size) - - # Find the edges that split the redshifts into n_z bins of - # equal number counts in each - ####USE NP.RANDOM.UNIFORM TO GET BIN EDGES - #p = np.linspace(0, 100, n_bin + 1) - gen = np.random.RandomState(123) - #z_edges = np.percentile(training_z, p) - z_edges = gen.uniform(0, 3, n_bin - 1) - z_edges = np.insert(z_edges, 0, np.min(training_z)) - z_edges = np.insert(z_edges, 0, np.max(training_z)) - z_edges.sort() - print(z_edges) - - # Now find all the objects in each of these bins - for i in range(n_bin): - z_low = z_edges[i] - z_high = z_edges[i + 1] - training_bin[(training_z > z_low) & (training_z < z_high)] = i - - # for speed, cut down to 5% of original size - cut = np.random.uniform(0, 1, training_z.size) < 0.05 - training_bin = training_bin[cut] - training_data = training_data[cut] - - # Can be replaced with any classifier - classifier = RandomForestClassifier() - - print("Fitting classifier") - # Lots of data, so this will take some time - classifier.fit(training_data, training_bin) - - self.classifier = classifier - self.z_edges = z_edges - - - def apply (self, data): - """Applies training to the data. - - Parameters: - ----------- - Data: numpy array, size Ngalaxes x Nbands - testing data, each row is a galaxy, each column is a band as per - band defined above - - Returns: - tomographic_selections: numpy array, int, size Ngalaxies - tomographic selection for galaxies return as bin number for - each galaxy. - """ - tomo_bin = self.classifier.predict(data) - return tomo_bin - From 19794f5e0bd966c1daa58298a386ca27f366f7fa Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:11:22 -0700 Subject: [PATCH 24/36] Delete rf_uniform_comb_bins.py merged all methods into one file --- .../classifiers/rf_uniform_comb_bins.py | 128 ------------------ 1 file changed, 128 deletions(-) delete mode 100644 tomo_challenge/classifiers/rf_uniform_comb_bins.py diff --git a/tomo_challenge/classifiers/rf_uniform_comb_bins.py b/tomo_challenge/classifiers/rf_uniform_comb_bins.py deleted file mode 100644 index 105f1ecd..00000000 --- a/tomo_challenge/classifiers/rf_uniform_comb_bins.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -This is an example tomographic bin generator using a random forest. - -Every classifier module needs to: - - have construction of the type - __init__ (self, bands, options) (see examples below) - - implement two functions: - train (self, training_data,training_z) - apply (self, data). - - define valid_options class varible. - -See Classifier Documentation below. -""" - -from .base import Tomographer -import numpy as np -from sklearn.ensemble import RandomForestClassifier - - -class RF_Uniform_CombineBins(Tomographer): - """ Random Forest Classifier """ - - # valid parameter -- see below - valid_options = ['bins'] - # this settings means arrays will be sent to train and apply instead - # of dictionaries - wants_arrays = True - - def __init__ (self, bands, options, newbins): - """Constructor - - Parameters: - ----------- - bands: str - string containg valid bands, like 'riz' or 'griz' - options: dict - options come through here. Valid keys are listed as valid_options - class variable. - - Note: - ----- - Valiad options are: - 'bins' - number of tomographic bins - - """ - self.bands = bands - self.opt = options - self.newbins = newbins - - def train (self, training_data, training_z): - """Trains the classifier - - Parameters: - ----------- - training_data: numpy array, size Ngalaxes x Nbands - training data, each row is a galaxy, each column is a band as per - band defined above - training_z: numpy array, size Ngalaxies - true redshift for the training sample - - """ - - n_bin = self.opt['bins'] - newbins = self.newbins - - print("Finding bins for training data") - # Now put the training data into redshift bins. - # Use zero so that the one object with minimum - # z in the whole survey will be in the lowest bin - training_bin = np.zeros(training_z.size) - - # Find the edges that split the redshifts into n_z bins of - # equal number counts in each - ####USE NP.RANDOM.UNIFORM TO GET BIN EDGES - #p = np.linspace(0, 100, n_bin + 1) - gen = np.random.RandomState(123) - #z_edges = np.percentile(training_z, p) - z_edges = gen.uniform(0, 3, n_bin - 1) - z_edges = np.insert(z_edges, 0, np.min(training_z)) - z_edges = np.insert(z_edges, 0, np.max(training_z)) - z_edges.sort() - - # Now find all the objects in each of these bins - for i in range(n_bin): - z_low = z_edges[i] - z_high = z_edges[i + 1] - training_bin[(training_z > z_low) & (training_z < z_high)] = i - - #combine bins - training_bin[training_bin == newbins[1]] = newbins[0] - - #rename/move bins - if newbins[1] != np.max(training_bin): - training_bin[training_bin == np.max(training_bin)] = newbins[1] - - # for speed, cut down to 5% of original size - cut = np.random.uniform(0, 1, training_z.size) < 0.05 - training_bin = training_bin[cut] - training_data = training_data[cut] - - # Can be replaced with any classifier - classifier = RandomForestClassifier() - - print("Fitting classifier") - # Lots of data, so this will take some time - classifier.fit(training_data, training_bin) - - self.classifier = classifier - self.z_edges = z_edges - - - def apply (self, data): - """Applies training to the data. - - Parameters: - ----------- - Data: numpy array, size Ngalaxes x Nbands - testing data, each row is a galaxy, each column is a band as per - band defined above - - Returns: - tomographic_selections: numpy array, int, size Ngalaxies - tomographic selection for galaxies return as bin number for - each galaxy. - """ - tomo_bin = self.classifier.predict(data) - return tomo_bin - From be83a598045b46c71648e036f2696fafca07d0fa Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:12:45 -0700 Subject: [PATCH 25/36] Add all methods to one file File has options for log, random (previously called uniform) and combining bins, as well as setting a seed. --- tomo_challenge/classifiers/myclassifier.py | 168 +++++++++++++++++++++ 1 file changed, 168 insertions(+) create mode 100644 tomo_challenge/classifiers/myclassifier.py diff --git a/tomo_challenge/classifiers/myclassifier.py b/tomo_challenge/classifiers/myclassifier.py new file mode 100644 index 00000000..338fb7fb --- /dev/null +++ b/tomo_challenge/classifiers/myclassifier.py @@ -0,0 +1,168 @@ +""" +This is an example tomographic bin generator using a random forest. + +Every classifier module needs to: + - have construction of the type + __init__ (self, bands, options) (see examples below) + - implement two functions: + train (self, training_data,training_z) + apply (self, data). + - define valid_options class varible. + +See Classifier Documentation below. +""" + +from .base import Tomographer +import numpy as np +from sklearn.ensemble import RandomForestClassifier + + +class funbins(Tomographer): + """ Random Forest Classifier """ + + # valid parameter -- see below + valid_options = ['bins', 'seed', 'method', 'combinebins'] + # this settings means arrays will be sent to train and apply instead + # of dictionaries + wants_arrays = True + + def __init__ (self, bands, options): + """Constructor + + Parameters: + ----------- + bands: str + string containg valid bands, like 'riz' or 'griz' + options: dict + options come through here. Valid keys are listed as valid_options + class variable. + + Note: + ----- + Valiad options are: + 'bins' - number of tomographic bins + 'seed' - the seed to use for RandomState + 'method' - what method to use for binning: 'log', 'random', 'linear' + 'combinebins' - what 2 bins to combine as a list + + """ + self.bands = bands + self.opt = options + + def train (self, training_data, training_z): + """Trains the classifier + + Parameters: + ----------- + training_data: numpy array, size Ngalaxes x Nbands + training data, each row is a galaxy, each column is a band as per + band defined above + training_z: numpy array, size Ngalaxies + true redshift for the training sample + + """ + #reading in elements from yaml + n_bin = self.opt['bins'] + + if self.opt['seed'] is None: + seed = 123 + print('The default seed is 123') + else: + seed = self.opt['seed'] + print(f'The seed is {seed}') + + if self.opt['method'] is None: + method = 'log' + print('The default method for binning has been set to log.') + else: + method = self.opt['method'] + assert method in ['log', 'random', 'linear'], 'The method must be log, random, or linear' + print(f'The method has been set to {method}') + + if self.opt['combinebins'] is None: + combine = None + elif self.opt['combinebins'] is not None: + combine = self.opt['combinebins'] + assert len(combine == 2), "You can only combine 2 bins at one time right now!" + print(f'You are combining bins {combine[0]} and {combine[1]}') + + #set up a reproducible random state + gen = np.random.RandomState(seed) + + #find bins + print("Finding bins for training data") + + training_bin = np.zeros(training_z.size) + + if method == 'log': + #creating percentile binning in logspace + p = np.logspace(0, 2, n_bin + 1) + z_edges = np.percentile(training_z, p) + + if method == 'random': + #creating a random binning by pulling from a uniform distribution + z_edges = gen.uniform(0, 3, n_bin - 1) + z_edges = np.insert(z_edges, 0, np.min(training_z)) + z_edges = np.insert(z_edges, 0, np.max(training_z)) + z_edges.sort() + + if method == 'linear': + #the given random forest linear binning + p = np.linspace(0, 100, n_bin +1) + z_edges = np.percentile(training_z, p) + + #can add stuff here about david's method if it can be generalized to n bins + #n_bin = 8 + #training_bin = np.load('dc2-labels.npy')#[:len(training_z)] + #print(len(training_bin)) + + # Now find all the objects in each of these bins + for i in range(n_bin): + z_low = z_edges[i] + z_high = z_edges[i + 1] + training_bin[(training_z > z_low) & (training_z < z_high)] = i + + if combine: + max_ = np.max(training_bin) + if combine[1] == max_: + #combine the bins, combining the higher/max bin into the lower bin is also moving because its max bin + training_bin[training_bin == combine[1]] = combine[0] + if combine[1] != max_: + #combine the bins + training_bin[training_bin == combine[1]] = combine[0] + #move the max bin into the empty spot + training_bin[training_bin == max_] = combine[1] + + # for speed, cut down to 5% of original size + cut = gen.uniform(0, 1, training_z.size) < 0.05 + training_bin = training_bin[cut] + training_data = training_data[cut] + + # Can be replaced with any classifier + classifier = RandomForestClassifier() + + print("Fitting classifier") + # Lots of data, so this will take some time + classifier.fit(training_data, training_bin) + + self.classifier = classifier + #self.z_edges = z_edges + + + def apply (self, data): + """Applies training to the data. + + Parameters: + ----------- + Data: numpy array, size Ngalaxes x Nbands + testing data, each row is a galaxy, each column is a band as per + band defined above + + Returns: + tomographic_selections: numpy array, int, size Ngalaxies + tomographic selection for galaxies return as bin number for + each galaxy. + """ + tomo_bin = self.classifier.predict(data) + return tomo_bin + From b498451f383d3655633bbc1ccbfdef05f5e5ff3a Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:13:24 -0700 Subject: [PATCH 26/36] Delete challenge_comb.py no longer needed. adding all methods to one file eliminated the need to change this file. --- bin/challenge_comb.py | 115 ------------------------------------------ 1 file changed, 115 deletions(-) delete mode 100644 bin/challenge_comb.py diff --git a/bin/challenge_comb.py b/bin/challenge_comb.py deleted file mode 100644 index b887e8e4..00000000 --- a/bin/challenge_comb.py +++ /dev/null @@ -1,115 +0,0 @@ -#!/usr/bin/env python -import sys -import os -import click -import yaml -import jinja2 - -## get root dir, one dir above this script -root_dir=os.path.join(os.path.split(sys.argv[0])[0],"..") -print(root_dir) -sys.path.append(root_dir) -import tomo_challenge as tc - -@click.command() -@click.argument('config_yaml', type=str) -def main(config_yaml): - with open(config_yaml, 'r') as fp: - config_str = jinja2.Template(fp.read()).render() - config = yaml.load(config_str, Loader=yaml.Loader) - - # Get the classes associated with each - for name in config['run']: - try: - config['cls'] = tc.Tomographer._find_subclass(name) - except KeyError: - raise ValueError(f"Tomographer {name} is not defined") - - # Decide if anyone needs the colors calculating and/or errors loading - anyone_wants_colors = False - anyone_wants_errors = False - for run in config['run'].values(): - for version in run.values(): - if version.get('errors'): - anyone_wants_errors = True - if version.get('colors'): - anyone_wants_colors = True - - - bands = config['bands'] - - training_data = tc.load_data( - config['training_file'], - bands, - errors=anyone_wants_errors, - colors=anyone_wants_colors - ) - - validation_data = tc.load_data( - config['validation_file'], - bands, - errors=anyone_wants_errors, - colors=anyone_wants_colors - ) - - training_z = tc.load_redshift(config['training_file']) - validation_z = tc.load_redshift(config['validation_file']) - - newbins = config['newbins'] - - if config['metrics_impl'] == 'jax-cosmo': - metrics_fn = tc.jc_compute_scores - else: - metrics_fn = tc.compute_scores - - with open(config['output_file'],'w') as output_file: - for classifier_name, runs in config['run'].items(): - for run, settings in runs.items(): - scores = run_one(classifier_name, bands, settings, - training_data, training_z, validation_data, validation_z, - config['metrics'], metrics_fn, newbins) - - output_file.write(f"{classifier_name} {run} {settings} {scores} \n") - - - -def run_one(classifier_name, bands, settings, train_data, train_z, valid_data, - valid_z, metrics, metrics_fn, newbins): - classifier = tc.Tomographer._find_subclass(classifier_name) - - if classifier.wants_arrays: - errors = settings.get('errors') - colors = settings.get('colors') - train_data = tc.dict_to_array(train_data, bands, errors=errors, colors=colors) - valid_data = tc.dict_to_array(valid_data, bands, errors=errors, colors=colors) - - print ("Executing: ", classifier_name, bands, settings) - - ## first check if options are valid - print (settings, classifier.valid_options) - for key in settings.keys(): - if key not in classifier.valid_options and key not in ['errors', 'colors']: - raise ValueError(f"Key {key} is not recognized by classifier {classifier_name}") - - print ("Initializing classifier...") - C=classifier(bands, settings, newbins) - - print ("Training...") - C.train(train_data,train_z) - #perfect = C.train(train_data,train_z) - - print ("Applying...") - results = C.apply(valid_data) - - print ("Getting metric...") - scores = metrics_fn(results, valid_z, metrics=metrics) - - print ("Making some pretty plots...") - name = str(classifier.__name__) - tc.metrics.plot_distributions(valid_z, results, f"/global/cscratch1/sd/abault/tomo_challenge/plots/{name}_{settings}_{bands}_{newbins}.png") - - return scores - - -if __name__=="__main__": - main() From 8a9d8a1281a40799e86006140f14fad44af503e7 Mon Sep 17 00:00:00 2001 From: Abby Bault Date: Mon, 7 Sep 2020 14:33:34 -0700 Subject: [PATCH 27/36] Remove unnecessary files removes unnecessary files and update others to include new updates to code --- .DS_Store | Bin 0 -> 6148 bytes log_random_and_combined_bins.ipynb | 443 ++++++++++++++++++ log_uniform_and_combined_bins.ipynb | 397 ---------------- results/.DS_Store | Bin 0 -> 6148 bytes results/comb_bins/.DS_Store | Bin 0 -> 6148 bytes results/comb_bins/log/.DS_Store | Bin 0 -> 6148 bytes ...ors': True, 'errors': True}_riz_[0, 1].png | Bin ...ors'_ True, 'errors'_ True}_riz_[0, 2].png | Bin ...ors'_ True, 'errors'_ True}_riz_[0, 3].png | Bin .../comb_bins/log}/riz_rf_log_5bins0_1.txt | 0 .../comb_bins/log}/riz_rf_log_5bins0_2.txt | 0 .../comb_bins/log}/riz_rf_log_5bins0_3.txt | 0 results/comb_bins/random/.DS_Store | Bin 0 -> 6148 bytes ...ors': True, 'errors': True}_riz_[0, 2].png | Bin ...ors': True, 'errors': True}_riz_[0, 3].png | Bin ...ors'_ True, 'errors'_ True}_riz_[0, 1].png | Bin 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{rf_log => results/log}/jax/log_10bins.txt | 0 {rf_log => results/log}/jax/log_2bins.txt | 0 {rf_log => results/log}/jax/log_4bins.txt | 0 {rf_log => results/log}/jax/log_6bins.txt | 0 {rf_log => results/log}/jax/log_8bins.txt | 0 results/random/.DS_Store | Bin 0 -> 6148 bytes results/random/firecrown/.DS_Store | Bin 0 -> 6148 bytes ...0, 'colors': True, 'errors': True}_riz.png | Bin ...2, 'colors': True, 'errors': True}_riz.png | Bin ...4, 'colors': True, 'errors': True}_riz.png | Bin ...6, 'colors': True, 'errors': True}_riz.png | Bin ...8, 'colors': True, 'errors': True}_riz.png | Bin .../random}/firecrown/riz_10bins.txt | 0 .../random}/firecrown/riz_2bins.txt | 0 .../random}/firecrown/riz_4bins.txt | 0 .../random}/firecrown/riz_6bins.txt | 0 .../random}/firecrown/riz_8bins.txt | 0 results/random/jax/.DS_Store | Bin 0 -> 6148 bytes ...0, 'colors': True, 'errors': True}_riz.png | Bin ...2, 'colors': True, 'errors': True}_riz.png | Bin ...4, 'colors': True, 'errors': True}_riz.png | Bin ...6, 'colors': True, 'errors': True}_riz.png | Bin ...8, 'colors': True, 'errors': True}_riz.png | Bin .../random}/jax/u_10bins.txt | 0 .../random}/jax/u_2bins.txt | 0 .../random}/jax/u_4bins.txt | 0 .../random}/jax/u_6bins.txt | 0 .../random}/jax/u_8bins.txt | 0 riz_rf_4bins.txt => results/riz_rf_4bins.txt | 0 rf_comb_bins/rf_log/riz_rf_log_5bins0_1.err | 2 - rf_comb_bins/rf_log/riz_rf_log_5bins0_1.out | 92 ---- rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh | 13 - rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml | 20 - rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err | 2 - rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out | 124 ----- rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh | 13 - rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml | 20 - rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err | 2 - rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out | 124 ----- rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh | 13 - rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml | 20 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err | 2 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out | 87 ---- rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh | 13 - .../rf_uniform/riz_rf_u_5bins0_1.yaml | 20 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err | 2 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out | 123 ----- rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh | 13 - .../rf_uniform/riz_rf_u_5bins0_2.yaml | 20 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err | 2 - rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out | 123 ----- rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh | 13 - .../rf_uniform/riz_rf_u_5bins0_3.yaml | 20 - rf_log/firecrown/riz_10bins.err | 2 - rf_log/firecrown/riz_10bins.out | 98 ---- rf_log/firecrown/riz_2bins.err | 2 - rf_log/firecrown/riz_2bins.out | 81 ---- rf_log/firecrown/riz_4bins.err | 2 - rf_log/firecrown/riz_4bins.out | 86 ---- rf_log/firecrown/riz_6bins.err | 2 - rf_log/firecrown/riz_6bins.out | 90 ---- rf_log/firecrown/riz_8bins.err | 2 - rf_log/firecrown/riz_8bins.out | 94 ---- rf_log/jax/log_10bins.err | 6 - rf_log/jax/log_10bins.out | 19 - rf_log/jax/log_2bins.err | 6 - rf_log/jax/log_2bins.out | 20 - rf_log/jax/log_4bins.err | 6 - rf_log/jax/log_4bins.out | 20 - rf_log/jax/log_6bins.err | 6 - rf_log/jax/log_6bins.out | 20 - rf_log/jax/log_8bins.err | 6 - rf_log/jax/log_8bins.out | 20 - rf_log/rf_log_10bins.sh | 13 - rf_log/rf_log_2bins.sh | 13 - rf_log/rf_log_4bins.sh | 13 - rf_log/rf_log_6bins.sh | 13 - rf_log/rf_log_8bins.sh | 13 - rf_log/riz_10bins.yaml | 19 - rf_log/riz_2bins.yaml | 19 - rf_log/riz_4bins.yaml | 19 - rf_log/riz_6bins.yaml | 19 - rf_log/riz_8bins.yaml | 19 - rf_uniform/firecrown/riz_10bins.err | 2 - rf_uniform/firecrown/riz_10bins.out | 99 ---- rf_uniform/firecrown/riz_2bins.err | 2 - rf_uniform/firecrown/riz_2bins.out | 78 --- rf_uniform/firecrown/riz_4bins.err | 2 - rf_uniform/firecrown/riz_4bins.out | 82 ---- rf_uniform/firecrown/riz_6bins.err | 2 - rf_uniform/firecrown/riz_6bins.out | 87 ---- rf_uniform/firecrown/riz_8bins.err | 2 - rf_uniform/firecrown/riz_8bins.out | 95 ---- rf_uniform/jax/u_10bins.err | 6 - rf_uniform/jax/u_10bins.out | 20 - rf_uniform/jax/u_2bins.err | 6 - rf_uniform/jax/u_2bins.out | 19 - rf_uniform/jax/u_4bins.err | 6 - rf_uniform/jax/u_4bins.out | 19 - rf_uniform/jax/u_6bins.err | 6 - rf_uniform/jax/u_6bins.out | 20 - rf_uniform/jax/u_8bins.err | 6 - rf_uniform/jax/u_8bins.out | 20 - rf_uniform/rf_uniform_10bins.sh | 13 - rf_uniform/rf_uniform_2bins.sh | 13 - rf_uniform/rf_uniform_4bins.sh | 13 - rf_uniform/rf_uniform_6bins.sh | 13 - rf_uniform/rf_uniform_8bins.sh | 13 - rf_uniform/riz_10bins.yaml | 19 - rf_uniform/riz_2bins.yaml | 19 - rf_uniform/riz_4bins.yaml | 19 - rf_uniform/riz_6bins.yaml | 19 - rf_uniform/riz_8bins.yaml | 19 - 150 files changed, 443 insertions(+), 2767 deletions(-) create mode 100644 .DS_Store create mode 100644 log_random_and_combined_bins.ipynb delete mode 100644 log_uniform_and_combined_bins.ipynb create mode 100644 results/.DS_Store create mode 100644 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b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..6fb4ed926e13656f11aa3d0afde313a9c7edd2c7 GIT binary patch literal 6148 zcmeHK&59a95Uy6U+ZsYV1lD6;a`T`VA`2Usxb7jhg$cHuW`;HV07MQnD68dtg{ zIqNw>WO|MWQb_!1GV-IXXmb3I45)W^3}fg)0uxw!f6~X1dw&##Nm{G@V7YwZ(a|yI zC0=?``s5De)J?r~lC-?>2i3du(t&U7w^Bwc+w|DmF7skD@P7Z?}hJ1V< z#(|u+fp<*CIbd`IKAZASgNdnJdXWyp5KP`2BCq>11^Z_nn#uXnF^MFsy5H5&YF zwQF}AM>6i?3d>!rJsO$B05L!e?2`d|%<#wioRsDw28e-!U_hM@0#%`7F*B%-4s3J@ zfLK7c7PM6@p>l*p$6{s>M^KnaMKr0*mKe;WqhHuK$6{vCqyw|X2eT_PTcI$$I?gY2 zI55W`wZs51u*pE)bj#}gzxZ?gznMfmVt^PpC" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_onemetric(jaxbins, 'FOM_DETF_3x2', random_scores_j, 'random', log_scores_j, 'log')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_metrics(bins, scores, title, nrows, ncols, metric = None, sep_figs = True):\n", + " \n", + " assert len(bins) == len(scores), \"The number of bins must be equal to the number of scores (i.e len(bins) == len(scores))\"\n", + " \n", + " if sep_figs == False:\n", + " plt.figure(figsize = (10,7))\n", + " plt.plot(bins, [scores[0]['SNR_ww'], scores[1]['SNR_ww'], scores[2]['SNR_ww'], scores[3]['SNR_ww'], scores[4]['SNR_ww'], scores[5]['SNR_ww'], scores[6]['SNR_ww'], scores[7]['SNR_ww'], scores[8]['SNR_ww']], 'o', label = 'SNR_ww')\n", + " plt.plot(bins, [scores[0]['SNR_gg'], scores[1]['SNR_gg'], scores[2]['SNR_gg'], scores[3]['SNR_gg'], scores[4]['SNR_gg'], scores[5]['SNR_gg'], scores[6]['SNR_gg'], scores[7]['SNR_gg'], scores[8]['SNR_gg']], 'o', label = 'SNR_gg')\n", + " plt.plot(bins, [scores[0]['SNR_3x2'], scores[1]['SNR_3x2'], scores[2]['SNR_3x2'], scores[3]['SNR_3x2'], scores[4]['SNR_3x2'], scores[5]['SNR_3x2'], scores[6]['SNR_3x2'], scores[7]['SNR_3x2'], scores[8]['SNR_3x2']], 'o', label = 'SNR_3x2')\n", + " plt.plot(bins, [scores[0]['FOM_ww'], scores[1]['FOM_ww'], scores[2]['FOM_ww'], scores[3]['FOM_ww'], scores[4]['FOM_ww'], scores[5]['FOM_ww'], scores[6]['FOM_ww'], scores[7]['FOM_ww'], scores[8]['FOM_ww']], 'o', label = 'FOM_ww')\n", + " plt.plot(bins, [scores[0]['FOM_gg'], scores[1]['FOM_gg'], scores[2]['FOM_gg'], scores[3]['FOM_gg'], scores[4]['FOM_gg'], scores[5]['FOM_gg'], scores[6]['FOM_gg'], scores[7]['FOM_gg'], scores[8]['FOM_gg']], 'o', label = 'FOM_gg')\n", + " plt.plot(bins, [scores[0]['FOM_3x2'], scores[1]['FOM_3x2'], scores[2]['FOM_3x2'], scores[3]['FOM_3x2'], scores[4]['FOM_3x2'], scores[5]['FOM_3x2'], scores[6]['FOM_3x2'], scores[7]['FOM_3x2'], scores[8]['FOM_3x2']], 'o', label = 'FOM_3x2')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_ww'], scores[1]['FOM_DETF_ww'], scores[2]['FOM_DETF_ww'], scores[3]['FOM_DETF_ww'], scores[4]['FOM_DETF_ww'], scores[5]['FOM_DETF_ww'], scores[6]['FOM_DETF_ww'], scores[7]['FOM_DETF_ww'], scores[8]['FOM_DETF_ww']], 'o', label = 'FOM_DETF_ww')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_gg'], scores[1]['FOM_DETF_gg'], scores[2]['FOM_DETF_gg'], scores[3]['FOM_DETF_gg'], scores[4]['FOM_DETF_gg'], scores[5]['FOM_DETF_gg'], scores[6]['FOM_DETF_gg'], scores[7]['FOM_DETF_gg'], scores[8]['FOM_DETF_gg']], 'o', label = 'FOM_DETF_gg')\n", + " plt.plot(bins, [scores[0]['FOM_DETF_3x2'], scores[1]['FOM_DETF_3x2'], scores[2]['FOM_DETF_3x2'], scores[3]['FOM_DETF_3x2'], scores[4]['FOM_DETF_3x2'], scores[5]['FOM_DETF_3x2'], scores[6]['FOM_DETF_3x2'], scores[7]['FOM_DETF_3x2'], scores[8]['FOM_DETF_3x2']], 'o', label = 'FOM_DETF_3x2')\n", + " plt.xlabel('Number of Bins', fontsize = 14)\n", + " plt.ylabel('Scores', fontsize = 14)\n", + " plt.title(title, fontsize = 16)\n", + " plt.legend()\n", + " \n", + " if sep_figs == True:\n", + " \n", + " metrics = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2', 'FOM_DETF_ww', 'FOM_DETF_gg', 'FOM_DETF_3x2']\n", + "# fig, axes = plt.subplots(nrows = nrows, ncols = ncols, figsize = (15,10))\n", + " \n", + " fig = plt.figure(figsize = (ncols*5, nrows*5))\n", + "# fig = plt.figure(figsize = (15,15))\n", + "# plt.xlabel('Number of Bins')\n", + "# plt.axes(frameon=False)\n", + "# plt.xticks([])\n", + "# plt.yticks([])\n", + " plt.axis(\"off\")\n", + " fig.suptitle(title, fontsize = 16)\n", + "# fig.tight_layout()\n", + " fig.subplots_adjust(top = .95)\n", + "# fig.text(0.5, 0.38, 'Number of Bins', fontsize = 14, ha = 'center')\n", + "# fig.text(0.07, 0.5, 'Score', fontsize = 14, va = 'center', rotation = 'vertical')\n", + " \n", + " numplots = nrows*ncols\n", + " \n", + " for i in range(numplots):\n", + " ax = fig.add_subplot(nrows,ncols,i+1)\n", + " ax.plot(bins,[scores[0][metrics[i]], scores[1][metrics[i]], scores[2][metrics[i]], scores[3][metrics[i]], scores[4][metrics[i]], scores[5][metrics[i]], scores[6][metrics[i]], scores[7][metrics[i]], scores[8][metrics[i]], scores[9][metrics[i]]], 'o', label = metrics[i])\n", + " ax.set_xlabel('Number of Bins')\n", + " ax.set_ylabel('Score')\n", + " ax.set_xticks(bins)\n", + " ax.set_xticklabels([str(i) for i in bins])\n", + " ax.legend()\n", + " \n", + " plt.savefig('fig.png')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_metrics(jaxbins, log_scores_j, 'Log Binning Scores for All Metrics Using Jax', 3, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_metrics(bins, log_scores_f, 'Log Binning Scores for All Metrics Using Firecrown', 2, 3, sep_figs = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_metrics(bins, uniform_scores_f, 'Uniform Binning Scores for All Metrics Using Firecrown', 2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combining log and uniform bins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I decided to use 5 bins here, but turn it into 4 bins by combining 2 bins. I'm comparing to the uncombined 4 bins scores for each log and uniform bins. This is using firecrown results only." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", + "comb_5bins_log = [{'SNR_ww': 347.1550052435396, 'FOM_ww': 43.281384668444375, 'SNR_gg': 1178.1020177366966, 'FOM_gg': 1937.6331074516759, 'SNR_3x2': 1180.154312947983, 'FOM_3x2': 8170.834082030528},\n", + " {'SNR_ww': 347.11524596040493, 'FOM_ww': 12.496906081438635, 'SNR_gg': 1130.6537186403334, 'FOM_gg': 1725.6973362282138, 'SNR_3x2': 1132.7475032766674, 'FOM_3x2': 4340.897488251889},\n", + " {'SNR_ww': 346.29772734556116, 'FOM_ww': 20.391566310838982, 'SNR_gg': 1132.9429122353106, 'FOM_gg': 1108.2282006433647, 'SNR_3x2': 1135.5513182455525, 'FOM_3x2': 3247.449479310309}]\n", + "\n", + "#uncombined score\n", + "log_4bins = {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", + "comb_5bins_random = [{'SNR_ww': 350.62629992935354, 'FOM_ww': 69.62333557208532, 'SNR_gg': 961.3423758223748, 'FOM_gg': 998.7919662764982, 'SNR_3x2': 970.9692811096805, 'FOM_3x2': 2878.907835879231},\n", + " {'SNR_ww': 332.91946164294785, 'FOM_ww': 1573.8141960234443, 'SNR_gg': 969.7202847355114, 'FOM_gg': 3157.055747538249, 'SNR_3x2': 984.1608880916402, 'FOM_3x2': 28180.732187357935},\n", + " {'SNR_ww': 344.4664127650933, 'FOM_ww': 707.9343834362847, 'SNR_gg': 1111.5530740704078, 'FOM_gg': 1722.9278602544168, 'SNR_3x2': 1115.220976362594, 'FOM_3x2': 36901.359762732245}]\n", + "\n", + "#uncobined score\n", + "random_4bins = {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_combine_metrics(scores, uncomb_scores, title):\n", + "\n", + " assert len(scores[0]) == 6\n", + " assert len(scores) == 3\n", + " \n", + " x = [1, 2, 3, 4, 5, 6]\n", + " xlabels = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2']\n", + " color = ['b', 'g', 'orange', 'r', 'y', 'c']\n", + " \n", + " #plot the combined bins metrics\n", + " plt.figure(figsize = (10,7)) \n", + " for i in range(len(scores)):\n", + " plt.plot(x, [scores[i]['SNR_ww'], scores[i]['SNR_gg'], scores[i]['SNR_3x2'], scores[i]['FOM_ww'], scores[i]['FOM_gg'], scores[i]['FOM_3x2']], 'o', c = color[i], label = f'0-{i+1}')\n", + "\n", + " #plot the uncombined 4 bins metrics\n", + " plt.plot(x, [uncomb_scores['SNR_ww'], uncomb_scores['SNR_gg'], uncomb_scores['SNR_3x2'], uncomb_scores['FOM_ww'], uncomb_scores['FOM_gg'], uncomb_scores['FOM_3x2']], 'ko', label = '4bins uncombined')\n", + " \n", + " plt.xlabel('Metrics')\n", + " plt.ylabel('Score')\n", + " plt.xticks(x, xlabels)\n", + " plt.legend()\n", + " plt.title(title)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_combine_metrics(comb_5bins_log, log_4bins, 'Scores for 5 bins; log; combine bin 0 with other bins')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9LFq0iBdeeIE5c+Zw2GGHMX/+fObPn8/xxx+/biuQJEnrblEHzGuH5YuBLO7ntY9IkDPEDVFbG8yeDZMnQ0RxP3t2UV8XY8aM4eyzz+bAAw9kypQpfOhDH2Lq1KlrTPeRj3yEt7zlLdxzzz00Nzfz7W9/e91WLEmSBu+2mbCq18nxq5YX9WEWfZ2LtSFrbW3Nzs7OHrW77rqLKVOmjFCLNNLc/5KkQbt4FNBXdgr46IvrfXURcUtmtvY1zp44SZKkwWrq5yT4/up1ZIiTJEkarDfOgtG9To4f3VTUh5khTpIkabB2bIN9ZkPTZCCK+31mF/VhNmbY1yhJklRlO7aNSGjrzZ44SZKkCjLESZIkVZAhroFcffXVvO51r2PnnXfm9NNPX2P80qVLeec738mUKVOYOnUqZ5555gi0UpIkNQJD3MvQsaCDljNaGHXaKFrOaKFjwbp/S/OqVas48cQTueqqq1i4cCGXXHIJCxcu7DHNmDFj+MpXvsJdd93FTTfdxH//93+vMY0kSdo4GOKGqGNBB+0/amfxE4tJksVPLKb9R+3rHOTmzZvHzjvvzE477cQmm2zCjBkzuPzyy3tMs91227HnnnsCsOWWWzJlyhQeeOCBdVqvJEmqJkPcEM28dibLV/T8uY3lK5Yz89p1+7mNBx54gIkTJ65+3NzcPGBAu//++/nd737Hm970pnVaryRJqiZD3BAteWLJkOqD1dfPn0VEn9M+/fTTfOADH+CMM85gq622Wqf1SpKkajLEDdGkcX3/rEZ/9cFqbm5m6dKlqx93dXUxYcIEpk2bxrRp0/j6178OwIoVK/jABz5AW1sbhx122DqtU5IkVZchbohmTZ9F09ieP7fRNLaJWdPX7ec29t57b+69914WLVrECy+8wJw5czjssMOYP38+8+fP5/jjjyczOfbYY5kyZQp///d/v07rkyRJ1Va3EBcRm0XEvIi4LSLujIjTyvqpEfFARMwvb++tmeezEXFfRNwTEQfW1PeKiAXluLOiPM4YEZtGxKVl/eaIaKnX9nRr262N2QfPZvK4yQTB5HGTmX3wbNp2W7dvbh4zZgxnn302Bx54IFOmTOFDH/oQU6dO7THNr3/9ay666CLmzp27uofuJz/5yTqtV5IkVVP0dS7WellwEbRekZlPR8RY4AbgJOAg4OnM/HKv6d8AXALsA2wP/Bx4bWauioh55bw3AT8BzsrMqyLib4DdM/P4iJgBvD8zPzxQu1pbW7Ozs7NH7a677mLKlCnrYatVRe5/SVKjiohbMrO1r3F164nLwtPlw7HlbaDEeAgwJzOfz8xFwH3APhGxHbBVZt6YReK8EDi0Zp4LyuHvAdO7e+kkSZI2ZHU9Jy4iRkfEfOBPwDWZeXM56lMRcXtEnBcRryprOwBLa2bvKms7lMO96z3mycyVwBPANnXZGEmSpAZS1xCXmasycxrQTNGrtitwLvAaYBrwEPCVcvK+etBygPpA8/QQEe0R0RkRncuWLRviVkiSJDWeYbk6NTP/DFwHHJSZD5fh7kXgmxTnwEHRwzaxZrZm4MGy3txHvcc8ETEGGAc81sf6Z2dma2a2jh8/fr1tlyRJ0kip59Wp4yPileXw5sABwN3lOW7d3g/cUQ5fAcworzjdEdgFmJeZDwFPRcSby/PdjgQur5nnqHL4cGBu1utKDUmSpAYypo7L3g64ICJGU4TFyzLzyoi4KCKmURz2vB/4JEBm3hkRlwELgZXAiZm5qlzWCcD5wObAVeUN4NvARRFxH0UP3Iw6bo8kSVLDqFuIy8zbgT36qB8xwDyzgDW+NTczO4Fd+6g/B3xw3VraOK6++mpOOukkVq1axXHHHccpp5zSY/xzzz3Hfvvtx/PPP8/KlSs5/PDDOe2000aotZIkaST5iw0vx6IO+GELXDyquF/Usc6LXLVqFSeeeCJXXXUVCxcu5JJLLmHhwoU9ptl0002ZO3cut912G/Pnz+fqq6/mpptuWud1S5Kk6jHEDdWiDpjXDssXA1ncz2tf5yA3b948dt55Z3baaSc22WQTZsyYweWXX95jmohgiy22AIrfUF2xYgV+LZ4kSRsnQ9xQ3TYTVi3vWVu1vKivgwceeICJE1+6OLe5uZkHHnhgjelWrVrFtGnTmDBhAu9+97t505vetE7rlSRJ1WSIG6rlS4ZWH6S+Lqrtq5dt9OjRzJ8/n66uLubNm8cdd9yxxjSSJGnDZ4gbqqZJQ6sPUnNzM0uXvvSDFV1dXUyYMGH1D91//etf7zH9K1/5Svbff3+uvvrqdVqvJEmqJkPcUL1xFoxu6lkb3VTU18Hee+/Nvffey6JFi3jhhReYM2cOhx12GPPnz2f+/Pkcf/zxLFu2jD//+c8APPvss/z85z/n9a9//TqtV5IkVVM9vyduw7RjW3F/28ziEGrTpCLAdddfpjFjxnD22Wdz4IEHsmrVKo455himTp3aY5qHHnqIo446ilWrVvHiiy/yoQ99iPe9733rtF5JklRNsbH9wEFra2t2dnb2qN11111MmTJlhFqkkeb+lyQ1qoi4JTNb+xrn4VRJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4hrIqlWr2GOPPXp899v+++9P769EAbjiiis4/fTTh7N5dbXFFlv0Wf+Xf/kXfv7zn6+XdfT3XEqSVEWGuJeho6ODlpYWRo0aRUtLCx0dHetluWeeeeagv6/sr//6rznllFPWy3ob2b/+679ywAEHjHQzJElqOIa4Iero6KC9vZ3FixeTmSxevJj29vZ1DnJdXV38+Mc/5rjjjltj3He/+1323Xdfdt11V+bNmwfA+eefz6c+9SkAjj76aD796U+z7777stNOO/G9730PKH7hYb/99mPatGnsuuuu/OpXv1pj2S0tLTzyyCMAdHZ2sv/++wNw6qmncswxx7D//vuz0047cdZZZ62e58ILL2T33XfnjW98I0cccQQAixcvZvr06ey+++5Mnz6dJUuWrG7bCSecwDvf+U522mknfvnLX3LMMccwZcoUjj766B5t+cxnPsOee+7J9OnTWbZs2er5u7enpaWFz3/+8+y5557stttu3H333QA888wzHHPMMey9997sscceXH755UDx02QzZsxg991358Mf/jDPPvvsEPeKJEmNyxA3RDNnzmT58uU9asuXL2fmzJnrtNyTTz6ZL37xi4wateYueeaZZ/jNb37DOeecwzHHHNPn/A899BA33HADV1555eoeuosvvpgDDzyQ+fPnc9tttzFt2rQhtenuu+/mpz/9KfPmzeO0005jxYoV3HnnncyaNYu5c+dy2223ceaZZwLwqU99iiOPPJLbb7+dtrY2Pv3pT69ezuOPP87cuXP52te+xsEHH8zf/d3fceedd7JgwQLmz5+/ehv33HNPbr31Vt7xjndw2mmn9dmmbbfdlltvvZUTTjiBL3/5ywDMmjWLd73rXfz2t7/lF7/4Bf/n//wfnnnmGc4991yampq4/fbbmTlzJrfccsuQtl+SpEZmiBui7h6mwdYH48orr2TChAnstddefY7/yEc+AsB+++3Hk08+yZ///Oc1pjn00EMZNWoUb3jDG3j44YcB2HvvvfnOd77DqaeeyoIFC9hyyy2H1K6/+qu/YtNNN2XbbbdlwoQJPPzww8ydO5fDDz+cbbfdFoCtt94agBtvvJGPfvSjABxxxBHccMMNq5dz8MEHExHstttuvPrVr2a33XZj1KhRTJ06lfvvvx+AUaNG8eEPfxiAj33sYz3mr3XYYYcBsNdee62e92c/+xmnn34606ZNY//99+e5555jyZIlXH/99XzsYx8DYPfdd2f33Xcf0vZLktTIDHFDNGnSpCHVB+PXv/41V1xxBS0tLcyYMYO5c+euDh8AEdFj+t6PATbddNPVw92/h7vffvtx/fXXs8MOO3DEEUdw4YUXrjHfmDFjePHFFwF47rnn+l3m6NGjWblyJZnZ5/p7q52mezmjRo3qscxRo0axcuXKtc7fV5u62wPF9n7/+99n/vz5zJ8/nyVLlqw+t3AwbZUkqYoMcUM0a9YsmpqaetSampqYNWvWy17mF77wBbq6urj//vuZM2cO73rXu/jud7+7evyll14KwA033MC4ceMYN27coJa7ePFiJkyYwCc+8QmOPfZYbr311jWmaWlpWX2Y8fvf//5alzl9+nQuu+wyHn30UQAee+wxAPbdd1/mzJkDFOcNvu1tbxtUG7u9+OKLq899u/jii4c0/4EHHsh//dd/rQ6vv/vd74AixHafq3jHHXdw++23D6lNkiQ1sjEj3YCqaWtrA4pz45YsWcKkSZOYNWvW6no9vOpVr2LfffflySef5Lzzzhv0fNdddx1f+tKXGDt2LFtssUWfPXGf//znOfbYY/mP//gP3vSmN611mVOnTmXmzJm84x3vYPTo0eyxxx6cf/75nHXWWRxzzDF86UtfYvz48XznO98Z0ja+4hWv4M4772SvvfZi3Lhxq4PrYHzuc5/j5JNPZvfddyczaWlp4corr+SEE07g4x//OLvvvjvTpk1jn332GVKbJElqZNHde7GxaG1tzd7fFXbXXXcN+qs9tOFx/0uSGlVE3JKZrX2N83CqJElSBRniJEmSKsgQV9rYDiur4H6XJFWVIQ7YbLPNePTRR/1A38hkJo8++iibbbbZSDdFkqQh8+pUoLm5ma6urtU/9aSNx2abbUZzc/NIN0OSpCEzxAFjx45lxx13HOlmSJIkDZqHUyVJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkiqobiEuIjaLiHkRcVtE3BkRp5X1rSPimoi4t7x/Vc08n42I+yLinog4sKa+V0QsKMedFRFR1jeNiEvL+s0R0VKv7ZEkSWok9eyJex54V2a+EZgGHBQRbwZOAa7NzF2Aa8vHRMQbgBnAVOAg4JyIGF0u61ygHdilvB1U1o8FHs/MnYGvAf9Zx+2RJElqGHULcVl4unw4trwlcAhwQVm/ADi0HD4EmJOZz2fmIuA+YJ+I2A7YKjNvzMwELuw1T/eyvgdM7+6lkyRJ2pDV9Zy4iBgdEfOBPwHXZObNwKsz8yGA8n5COfkOwNKa2bvK2g7lcO96j3kycyXwBLBNH+1oj4jOiOhctmzZ+to8SZKkEVPXEJeZqzJzGtBM0au26wCT99WDlgPUB5qndztmZ2ZrZraOHz9+bc2WJElqeMNydWpm/hm4juJctofLQ6SU938qJ+sCJtbM1gw8WNab+6j3mCcixgDjgMfqshGSJEkNpJ5Xp46PiFeWw5sDBwB3A1cAR5WTHQVcXg5fAcworzjdkeIChnnlIdenIuLN5fluR/aap3tZhwNzy/PmJEmSNmhj6rjs7YALyitMRwGXZeaVEXEjcFlEHAssAT4IkJl3RsRlwEJgJXBiZq4ql3UCcD6wOXBVeQP4NnBRRNxH0QM3o47bI0mS1DBiY+u4am1tzc7OzpFuhiRJ0lpFxC2Z2drXOH+xQZIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSUPQsaCDljNaGHXaKFrOaKFjQceItKOev50qSZK0QelY0EH7j9pZvmI5AIufWEz7j9oBaNutbVjbYk+cJEnSIM28dubqANdt+YrlzLx25rC3xRAnSZI0SEueWDKkej0Z4iRJkgZp0rhJQ6rXkyFOkiRpkGZNn0XT2KYetaaxTcyaPmvY22KIkyRJGqS23dqYffBsJo+bTBBMHjeZ2QfPHvaLGgAiM4d9pSOptbU1Ozs7R7oZkiRJaxURt2Rma1/j7ImTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAoyxEmSJFWQIU6SJKmCDHGSJEkVZIiTJEmqIEOcJElSBRniJEmSKsgQJ0mSVEGGOEmSpAqqW4iLiIkR8YuIuCsi7oyIk8r6qRHxQETML2/vrZnnsxFxX0TcEyCngIIAACAASURBVBEH1tT3iogF5bizIiLK+qYRcWlZvzkiWuq1PZIkSY2knj1xK4HPZOYU4M3AiRHxhnLc1zJzWnn7CUA5bgYwFTgIOCciRpfTnwu0A7uUt4PK+rHA45m5M/A14D/ruD2SJEkNo24hLjMfysxby+GngLuAHQaY5RBgTmY+n5mLgPuAfSJiO2CrzLwxMxO4EDi0Zp4LyuHvAdO7e+kkSZI2ZMNyTlx5mHMP4Oay9KmIuD0izouIV5W1HYClNbN1lbUdyuHe9R7zZOZK4Algmz7W3x4RnRHRuWzZsvWyTZIkSSOp7iEuIrYAvg+cnJlPUhwafQ0wDXgI+Er3pH3MngPUB5qnZyFzdma2Zmbr+PHjh7gFkiRJjaeuIS4ixlIEuI7M/F+AzHw4M1dl5ovAN4F9ysm7gIk1szcDD5b15j7qPeaJiDHAOOCx+myNJElS46jn1akBfBu4KzO/WlPfrmay9wN3lMNXADPKK053pLiAYV5mPgQ8FRFvLpd5JHB5zTxHlcOHA3PL8+YkSZI2aGPquOy3AkcACyJifln7J+AjETGN4rDn/cAnATLzzoi4DFhIcWXriZm5qpzvBOB8YHPgqvIGRUi8KCLuo+iBm1HH7ZEkSWoYsbF1XLW2tmZnZ+dIN0OSJGmtIuKWzGzta5y/2CBJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqqC6hbiImBgRv4iIuyLizog4qaxvHRHXRMS95f2raub5bETcFxH3RMSBNfW9ImJBOe6siIiyvmlEXFrWb46IlnptjyRJUiOpZ0/cSuAzmTkFeDNwYkS8ATgFuDYzdwGuLR9TjpsBTAUOAs6JiNHlss4F2oFdyttBZf1Y4PHM3Bn4GvCfddweSZKkhlG3EJeZD2XmreXwU8BdwA7AIcAF5WQXAIeWw4cAczLz+cxcBNwH7BMR2wFbZeaNmZnAhb3m6V7W94Dp3b10kiRJG7JhOSeuPMy5B3Az8OrMfAiKoAdMKCfbAVhaM1tXWduhHO5d7zFPZq4EngC26WP97RHRGRGdy5YtWz8bJUmSNILqHuIiYgvg+8DJmfnkQJP2UcsB6gPN07OQOTszWzOzdfz48WtrsiRJUsOra4iLiLEUAa4jM/+3LD9cHiKlvP9TWe8CJtbM3gw8WNab+6j3mCcixgDjgMfW/5ZIkiQ1lkGHuIjYPCJeN4TpA/g2cFdmfrVm1BXAUeXwUcDlNfUZ5RWnO1JcwDCvPOT6VES8uVzmkb3m6V7W4cDc8rw5SZKkDdqgQlxEHAzMB64uH0+LiCvWMttbgSOAd0XE/PL2XuB04N0RcS/w7vIxmXkncBmwsFzPiZm5qlzWCcC3KC52+H/AVWX928A2EXEf8PeUV7pKkiRt6GIwHVcRcQvwLuC6zNyjrN2embvXuX3rXWtra3Z2do50MyRJktYqIm7JzNa+xg32cOrKzHxiPbZJkiRJ62DMIKe7IyI+CoyOiF2ATwO/qV+zJEmSNJDB9sT9LcUvKTwPXEzxfWwn16tRkiRJGthae+LKn766IjMPAGbWv0mSJElam7X2xJVXiC6PiHHD0B5JkiQNwmDPiXsOWBAR1wDPdBcz89N1aZUkSZIGNNgQ9+PyJkmSpAYwqBCXmRdExCbAa8vSPZm5on7NkiRJ0kAGFeIiYn/gAuB+ih+dnxgRR2Xm9fVrmiRJkvoz2MOpXwH+MjPvAYiI1wKXAHvVq2GSJEnq32C/J25sd4ADyMzfA2Pr0yRJkiStzWB74joj4tvAReXjNuCW+jRJkiRJazPYEHcCcCLFz20FcD1wTr0aJUmSpIENNsSNAc7MzK/C6l9x2LRurZIkSdKABntO3LXA5jWPNwd+vv6bI0mSpMEYbIjbLDOf7n5QDjfVp0mSJElam8GGuGciYs/uBxHRCjxbnyZJkiRpbQZ7TtzJwP9ExINAAtsDH65bqyRJkjSgAXviImLviPiLzPwt8HrgUmAlcDWwaBjaJ0mSpD6s7XDqN4AXyuG3AP8E/DfwODC7ju2SJEnSANZ2OHV0Zj5WDn8YmJ2Z3we+HxHz69s0SZIk9WdtPXGjI6I76E0H5taMG+z5dJIkSVrP1hbELgF+GRGPUFyN+iuAiNgZeKLObZMkSVI/BgxxmTkrIq4FtgN+lplZjhoF/G29GydJkqS+rfWQaGbe1Eft9/VpjiRJkgZjsF/2K0mSpAZiiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQXULcRFxXkT8KSLuqKmdGhEPRMT88vbemnGfjYj7IuKeiDiwpr5XRCwox50VEVHWN42IS8v6zRHRUq9tkSRJajT17Ik7Hzioj/rXMnNaefsJQES8AZgBTC3nOSciRpfTnwu0A7uUt+5lHgs8npk7A18D/rNeGyJJktRo6hbiMvN64LFBTn4IMCczn8/MRcB9wD4RsR2wVWbemJkJXAgcWjPPBeXw94Dp3b10kiRJG7qROCfuUxFxe3m49VVlbQdgac00XWVth3K4d73HPJm5EngC2KavFUZEe0R0RkTnsmXL1t+WSJIkjZDhDnHnAq8BpgEPAV8p6331oOUA9YHmWbOYOTszWzOzdfz48UNrsSRJUgMa1hCXmQ9n5qrMfBH4JrBPOaoLmFgzaTPwYFlv7qPeY56IGAOMY/CHbyVJkiptWENceY5bt/cD3VeuXgHMKK843ZHiAoZ5mfkQ8FREvLk83+1I4PKaeY4qhw8H5pbnzUmSJG3wxtRrwRFxCbA/sG1EdAGfB/aPiGkUhz3vBz4JkJl3RsRlwEJgJXBiZq4qF3UCxZWumwNXlTeAbwMXRcR9FD1wM+q1LZIkSY0mNrbOq9bW1uzs7BzpZkiSJK1VRNySma19jfMXGyRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFVS3EBcR50XEnyLijpra1hFxTUTcW96/qmbcZyPivoi4JyIOrKnvFRELynFnRUSU9U0j4tKyfnNEtNRrWyRJkhpNPXvizgcO6lU7Bbg2M3cBri0fExFvAGYAU8t5zomI0eU85wLtwC7lrXuZxwKPZ+bOwNeA/6zblkiSJDWYuoW4zLweeKxX+RDggnL4AuDQmvqczHw+MxcB9wH7RMR2wFaZeWNmJnBhr3m6l/U9YHp3L50kSdKGbrjPiXt1Zj4EUN5PKOs7AEtrpusqazuUw73rPebJzJXAE8A2fa00ItojojMiOpctW7aeNkWSJGnkNMqFDX31oOUA9YHmWbOYOTszWzOzdfz48S+ziZIkSY1juEPcw+UhUsr7P5X1LmBizXTNwINlvbmPeo95ImIMMI41D99KkiRtkIY7xF0BHFUOHwVcXlOfUV5xuiPFBQzzykOuT0XEm8vz3Y7sNU/3sg4H5pbnzUmSJG3wxtRrwRFxCbA/sG1EdAGfB04HLouIY4ElwAcBMvPOiLgMWAisBE7MzFXlok6guNJ1c+Cq8gbwbeCiiLiPogduRr22RZIkqdHExtZ51dramp2dnSPdDEmSpLWKiFsys7WvcY1yYYMkSZKGwBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpIkqYIMcZIkSRU0IiEuIu6PiAURMT8iOsva1hFxTUTcW96/qmb6z0bEfRFxT0QcWFPfq1zOfRFxVkTESGyPJEnaeHR0dNDS0sKoUaNoaWmho6NjRNoxkj1x78zMaZnZWj4+Bbg2M3cBri0fExFvAGYAU4GDgHMiYnQ5z7lAO7BLeTtoGNsvSZI2Mh0dHbS3t7N48WIyk8WLF9Pe3j4iQa6RDqceAlxQDl8AHFpTn5OZz2fmIuA+YJ+I2A7YKjNvzMwELqyZR5Ikab2bOXMmy5cv71Fbvnw5M2fOHPa2jFSIS+BnEXFLRLSXtVdn5kMA5f2Esr4DsLRm3q6ytkM53Lu+hohoj4jOiOhctmzZetwMSZK0MVmyZMmQ6vU0UiHurZm5J/Ae4MSI2G+Aafs6zy0HqK9ZzJydma2Z2Tp+/Piht1aSJAmYNGnSkOr1NCIhLjMfLO//BPwA2Ad4uDxESnn/p3LyLmBizezNwINlvbmPuiRJUl3MmjWLpqamHrWmpiZmzZo17G0Z9hAXEa+IiC27h4G/BO4ArgCOKic7Cri8HL4CmBERm0bEjhQXMMwrD7k+FRFvLq9KPbJmHkmSpPWura2N2bNnM3nyZCKCyZMnM3v2bNra2oa9LVFcEzCMK4zYiaL3DWAMcHFmzoqIbYDLgEnAEuCDmflYOc9M4BhgJXByZl5V1luB84HNgauAv821bFBra2t2dnau9+2SJEla3yLilppv8ug5brhD3EgzxEmSpKoYKMQ10leMSJIkaZAMcZIkSRVkiJMkSaogQ5wkSVIFGeIkSZIqyBAnSZJUQYY4SZKkCjLESZIkVZAhTpKkBtWxoIOWM1oYddooWs5ooWNBx0g3SQ1kzEg3QJIkraljQQftP2pn+YrlACx+YjHtP2oHoG234f+dTjUee+IkSWpAM6+duTrAdVu+Yjkzr505Qi1SozHESZLUgJY8sWRIdQ2fjg5oaYFRo4r7jhE6ym2IkySpAU0aN2lIdQ2Pjg5ob4fFiyGzuG9vH5kgZ4iTJKkBzZo+i6axTT1qTWObmDV91gi1SAAzZ8Lynke5Wb68qA83Q5wkSQ2obbc2Zh88m8njJhMEk8dNZvbBs72oYYQt6edodn/1evLqVEmSGlTbbm2GtgYzaVJxCLWv+nCzJ06SJGmQZs2Cpp5HuWlqKurDzRAnSZI0SG1tMHs2TJ4MEcX97NlFfbh5OFWSJGkI2tpGJrT1Zk+cJElSBRniJEmSKsgQJ0mSVEGGOEmSGtWiDvhhC1w8qrhfNEK/76SGZIiTJAHQsaCDljNaGHXaKFrOaKFjgYFhRC3qoOOrH6flE4sZ1Za0fGIxHV/9uEFOqxniJEl0LOig/UftLH5iMUmy+InFtP+o3SA3gjrOOon22StY/AgksPgRaJ+9go6zThrppqlBRGaOdBuGVWtra3Z2do50MySpobSc0cLiJ9b8GvrJ4yZz/8n3D3+DRMv4YPEja9Ynbwv3L9u4Prs3ZhFxS2a29jXOnjhJEkue6PuHH/urq/6WPDq0ujY+hjhJEpPG9f3Dj/3VVX+TtttmSHVtfAxx2uB1nPE3tEwYw6gIWiaMoeOMvxnpJm303CeNZ9b0WTSN7fmDkE1jm5g1fQR+EFIAzPrimTRtvkmPWtPmmzDri2eOUIvUaAxx2qB1nPE3tP/juSxetqo4MXjZKtr/8VxDwwhynzSmtt3amH3wbCaPm0wQTB43mdkHz6Zttwb4baGNVFtbG0cdfR6jR08GgtGjJ3PU0efR1gi/96SG4IUN2qBN3HY0XY++uEa9eZtRLH1k1Qi0SO4TaXA6OqC9HZYvf6nW1DRyP7aukeGFDdpoPdBHWBiorvpznzQwv1i2ocyc2TPAQfF45syRaY8ajyFOG7Tt+zn/t7+66s990qAWdcC8dli+GMjifl67QW4ELennwuD+6tr4GOK0QZv8rlfQ67xgNt+kqGtkuE8a1G0zYVWvbp9Vy4u6RsSkfi4M7q+ujY8hThu0N77zG0x7/yh22AYC2GEbmPb+Ubzxnd8Y6aZttNwnDWp5P907/dVVd7NmFefA1WpqKuoSGOLWq08cdwDN2wajImjeNvjEcQeMdJM2euec0Ma0d1zIHz8+mfx88MePT2baOy7knBM8K3ikuE8aVFM/3Tv91VV3bW3FRQyTJ0NEce9FDarl1anrySeOO4COi67l2Rdeqm2+CbQdMZ1vfuvn6319krRedZ8TV3tIdXQT7DMbdjQ1SCPFq1OHwVU/7BngAJ59oahLUsPbsY0bVs6m6/HJvPhi0PX4ZG5YaYCTGtmYkW7AuoqIg4AzgdHAtzLz9JFox4P9/JZdf3VJaiQdHfDxj8OKFS/Vxo6F7+DhO6lRVbonLiJGA/8NvAd4A/CRiHjDSLTFr02QVGUnndTBihXtQPkVIyxmxYp2TjrJrxiRGlWlQxywD3BfZv4hM18A5gCHjERD3nPo9D6/NuE9h04fieZI0pA8+uhMoNdXjLC8rEtqRFUPcTsAS2sed5W1HiKiPSI6I6Jz2bJldWnIN7/1c9qOmN7jaxO8qEFSdfT3VSJ+xYjUqKp+Tlz0UVvjctvMnA3MhuLq1Ho15pvf+jl8q15Ll6T62WabSTz66OI+65IaU9V74rqAiTWPm4EHR6gtklRZZ545i0026fnNspts0sSZZ/rNslKjqnqI+y2wS0TsGBGbADOAK0a4TZJUOW1tbZx33mwmT55MRDB58mTOO282bV6aKjWsyn/Zb0S8FziD4itGzsvMAf9trNeX/UqSJK1vA33Zb9XPiSMzfwL8ZKTbIUmSNJyqfjhVkiRpo2SIkyRJqiBDnCRJUgUZ4iRJkirIECdJklRBhjhJkqQKMsRJkiRVkCFOkiSpggxxkiRJFWSIkyRJqiBDnCRJUgVFZo50G4ZVRCwDFtd5NdsCj9R5HRo690vjcZ80JvdL43GfNKbh2C+TM3N8XyM2uhA3HCKiMzNbR7od6sn90njcJ43J/dJ43CeNaaT3i4dTJUmSKsgQJ0mSVEGGuPqYPdINUJ/cL43HfdKY3C+Nx33SmEZ0v3hOnCRJUgXZEydJklRBhjhJkqQKMsRJkiRVkCGuFBEzI+LOiLg9IuZHxJsi4rqI6KyZpjUiriuH94+IJyLidxFxd0R8ecQav4Fz3zSOeu+LiDikZtmdEfG2tUw/LSJurGnTh9fLhlZIRKwqn6/uW0tZf1tEzCuf97sjor1mnlMjIiNi55ra35U1v4tsPXHfNI7h3BcRcXVE3Fa+L309IkavpW1fKtd9e0T8ICJeOdjtMsQBEfEW4H3Anpm5O3AAsLQcPSEi3tPPrL/KzD2APYD3RcRb69/ajYv7pnEM0764FnhjZk4DjgG+tZZmLQeOzMypwEHAGUN5A9xAPJuZ02pu90fEXwAXA8dn5uuBtwGfjIi/qplvATCj5vHhwMLha/ZGwX3TOIZzX3woM98I7AqMBz64lumvAXYt31d/D3x2sBtliCtsBzySmc8DZOYjmflgOe5LwD8PNHNmPgvMB3bob5qIWBARr4zCoxFxZFm/KCIOiIifRMTuZe13EfEv5fC/RcRx67yF1TUc+2Z8RFwTEbdGxDciYnFEbFuO+1z5H9I1EXFJRPzDetmqaqr7vsjMp/OlS+ZfASRAROxd/pe6WUS8ovwPd9fM/H1m3lvO+yDwJ4o3zY3dicD5mXkrFPsK+L/AKTXT/BA4BCAidgKeAJb1t8CI+FBEfLUcPiki/lAOvyYiboiIfSLif8vaIRHxbERsUu6zP9RhG6tqve+bcrpjI+L3UfSMfzMizi7rr4mImyLitxHxrxHxdB22qarqsi8y88lycAywCS+9j11e89n/yYjoKKf/WWauLOe5CWge7AYY4go/AyaWL4BzIuIdNeNuBJ6PiHf2N3NEvArYBbh+gHX8GngrMBX4A/D2sv5mip12PfD2iNgKWFlOC8V/Br8a+iZtMIZj33wemJuZewI/ACaV87YCH6DoQToM2NgPZQzHviAi3h8RdwM/puiNIzN/C1wB/DvwReC7mXlHr/n2oXjD/H9D3rJq27zmENEPytpU4JZe03WW9W5PAksjYlfgI8Cla1nP9bz0vvV24NGI2IGX3qNupXitdI+/A9gbeBNw85C3asMwLPsmIrYHPkfxefJu4PU1o88EzszMvYEH+5h9YzFcrxMAIuKnFP9UPgV8ryy3A/8SEW8HPgP8bR+zHgNcNZh1gCEOKP77B/aieIKXAZdGxNE1k/w7ffcyvD0ibgf+CFyZmX8cYDW/AvYrb+cCu5VvgI+V6+8e/zaKD68tIqIJaMnMe9Zl+6psmPbN24A55fquBh6vqV+emc9m5lPAj9ZlW6pumPYFmfmD8tDGocC/1Yz6V4oPqFaKILdaRGwHXAR8PDNfHMp2bQBqDxO9v6wF5X//vfSuzaE4VHQoxT8w/Sr32xYRsSUwkeIw1H4Uge1XZU/CfRExBdgH+Grt+Je1ZdU3LPuG4vn+ZWY+lpkrgP+pGfeWmscXD6n1G5bh2hfFAjIPpDh6sSnwrrL2MPAvwC+Az2TmY7XzRMRMik6cjkFtEYa41TJzVWZel5mfBz5F0QPTPW4usBnFfzm1flUew94NOCEipg2wiu7/Yt8OXEfxIXg4L725/Zbiw+nt5bS/Az7Bmv8lbHSGYd/EEOsbrWHYF7Xruh54TfehbWBrYAtgy3I9AJS91z8G/jkzb3p5W7bBuZM1e473Ys1zeX4EHAEsqTkENJAbgY8D91C8d72dIiT8uhz/K+A9wArg5xT/CL2NtfS+bmTqsW98r3p56vU6ASAzn6M4gnBITXk34FFg+9ppI+IoinOO22pOKVkrQxwQEa+LiF1qStOAxb0mm0VxrHwNmfl74AvAP/a3jsxcCmwL7JKZf+D/t3dvIVZVcRzHvz+bbmgPYtCDIBU9iZZ2e+xqEkb5IHiJCnuJwC5GQkUEZhFEPVQaiYaaXdQiAgkjAzPHLqaQt6iHqIdSIYkSU8mUXw9rHeZ4GR1zHOfM+X2eZu+1zzp7n8Xs/d/rCuuBWdQgzvZBSifxyZTm1c7m9HbVF2VDKYvJ9fvGA0Ob9t9Z+/QMAe7o5vNtoS/KQtIVklT/vprSPPpHTV5AaTJ6F3ixHnMe5c14qe0Pjs2xbb0OTG8EzJKGUX6zI2owaz/FJyjl1hPrKPelxovmzcA/tvc0pc8Evra9GxhGadr7/rSuZmA5E2XzLXCjpKGSOmh6uaI8TxrbU4/5ZHvr9bKQNKS2DFDLYgLwY92+nvKSMxaYJemyuv/2mv9dtvefygV0nMrBA9gQYK7KqLZDwE+UJqNGOza2V0k6UWfG+dRCsf1LN8dsABpDjTspD7T1TemdwK2290vqpHRubOsgjr4pm2eBZSrTU3wB7AL22t4oaSWwhRKsbKJ0am1XfVEWk4D7JP0LHACm2HbtDHzI9nsqw/W/knQL5W32BmBYU9PudNubT+9SW5vtXZLuARbW5k8Br9g+pkuA7eWnkHUnpSl1ne3Dkn6lPqCqDcAldNW8bQV+P5WahYHuTJSN7R2SXqD8/jspNUmNe9VM4B1Jj1NqrNv5HnaEM/R/MhhYKel8yvN+DTC/bi+kdPnYWctjUb2PzaM0u35W32G/sf1gT74sa6dG26v/XIdtH1KZRuMNlykukDTE9t+1f+I64IHGSKaIiP6i6V7VQamdXmT7o3rvOlBfhqYC02xPPHFu0SpSExdRRqO+L2kQcJDSF7FhgaSRlD5YbyWAi4h+arakcZR71WrK1BhQ+njNq90U/qKO+I6BITVxvUzS/cCjR+3+0vaMs3E+0SVl03+kLPo3SRsozTvN7rW97WycT3RJ2fQf/aEsEsRFREREtKCMTo2IiIhoQQniIiIiIlpQgriIaHuSLOntpu0OSbslfXySz42RNOEE6ddKeq03zzUioiFBXEQE7ANGSbqwbt8G7OjB58ZQJvM8hqQO25tsP9JL5xgRcYQEcRERxSd0rcoxDVjWSJA0WNIiSRslfSdpYl0tYg4wpS6qPUXSbEkLJK0Glkq6qVGbV2dyXyxpm6StkiZJOkfSEknb6/7H+vqiI6J1JYiLiCiWA1MlXQBcSZn9vuFpYI3t6yhLTb0EnEtZzHpFXVR7RT32GmCi7buPyv8ZYI/t0XUt2TWUmrzhtkfZHg0sPlMXFxEDT4K4iAjA9lbgUkot3KqjkscDT0raDKylTKg6opusVta1Fo82jrJWY+P7/gR+Bi6XNLeun9jjxbUjIhLERUR0WQm8TFNTaiVgUq1xG2N7hO0fusljXzf7BRwxMWcN5K6iBIYzgDf/74lHRPtJEBcR0WURMOc4M65/Cjxcly5C0ti6fy9wUQ/zXg081NiQNFTSxcAg2x9SmluvPp2Tj4j2kiAuIqKy/ZvtV4+T9BylD9xWSdvrNsDnwMjGwIaTZP88MLQOYthC6Vs3HFhbm2mXAE/1xnVERHvIHVno0gAAAERJREFUslsRERERLSg1cREREREtKEFcRERERAtKEBcRERHRghLERURERLSgBHERERERLShBXEREREQLShAXERER0YL+A7wrL+HOWON6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_combine_metrics(comb_5bins_random, random_4bins, 'Scores for 5 bins; random; combine bin 0 with other bins')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tomo-conda [conda env:.conda-tomo]", + "language": "python", + "name": "conda-env-.conda-tomo-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/log_uniform_and_combined_bins.ipynb b/log_uniform_and_combined_bins.ipynb deleted file mode 100644 index 68be4388..00000000 --- a/log_uniform_and_combined_bins.ipynb +++ /dev/null @@ -1,397 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "source": [ - "%pylab inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Log and Uniform Bins" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The log bins were created using a percentile distribution based on np.logspace (instead of linspace). The uniform bins were created by pulling random numbers from a uniform distribution between 0 and 3. The random forest classifier from sklearn was used each time, and the number of bins used was 2, 4, 6, 8, 10." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "bins = [2,4,6,8,10]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#these scores were calculated using firecrown\n", - "#each dictionary corresponds to the scores for a different number of bins, in the order: 2, 4, 6, 8, 10\n", - "log_scores_f = [{'SNR_ww': 332.1252628616624, 'FOM_ww': 14.983596087283678, 'SNR_gg': 879.7755517457832, 'FOM_gg': 6800.843125725617, 'SNR_3x2': 885.1510794851065, 'FOM_3x2': 69314.82169237411},\n", - " {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823},\n", - " {'SNR_ww': 349.54961947303974, 'FOM_ww': 74.88239992499268, 'SNR_gg': 1283.0924805979128, 'FOM_gg': 2748.246880322987, 'SNR_3x2': 1285.0762826516666, 'FOM_3x2': 25527.319489134214},\n", - " {'SNR_ww': 352.1677239587554, 'FOM_ww': 46.08137841821119, 'SNR_gg': 1407.4491566491292, 'FOM_gg': 11509.47849600147, 'SNR_3x2': 1409.2785646359137, 'FOM_3x2': 76079.64260621843},\n", - " {'SNR_ww': 353.9796471119985, 'FOM_ww': 26.366472753947036, 'SNR_gg': 1500.0369417052223, 'FOM_gg': 181648.05441643775, 'SNR_3x2': 1501.7979111080479, 'FOM_3x2': 205723.43115529948}]\n", - "\n", - "uniform_scores_f = [{'SNR_ww': 327.4681067505708, 'FOM_ww': 105.1466965449306, 'SNR_gg': 678.5458935974033, 'FOM_gg': 908.8160809482351, 'SNR_3x2': 701.5622646511446, 'FOM_3x2': 4534.786413137936},\n", - " {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065},\n", - " {'SNR_ww': 353.4995684607554, 'FOM_ww': 148.5449428914542, 'SNR_gg': 1155.0212895482066, 'FOM_gg': 2450.1410700563783, 'SNR_3x2': 1158.502391443923, 'FOM_3x2': 13404.867225041602},\n", - " {'SNR_ww': 354.9124627287553, 'FOM_ww': 46.28382955361007, 'SNR_gg': 1250.552493913522, 'FOM_gg': 2881.0801502048794, 'SNR_3x2': 1253.8090733033507, 'FOM_3x2': 21691.232888150553},\n", - " {'SNR_ww': 354.9197295938117, 'FOM_ww': 152.37715773462213, 'SNR_gg': 1308.84532609303, 'FOM_gg': 21171.68195209693, 'SNR_3x2': 1311.9719252996663, 'FOM_3x2': 47065.21003389874}]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#these scores were calculated using jax\n", - "log_scores_j = [{'SNR_ww': 331.62371826171875, 'FOM_ww': 0.4617920517921448, 'FOM_DETF_ww': 0.013744479045271873, 'SNR_gg': 878.622314453125, 'FOM_gg': 53.327491760253906, 'FOM_DETF_gg': 0.786997377872467, 'SNR_3x2': 883.9085693359375, 'FOM_3x2': 255.66043090820312, 'FOM_DETF_3x2': 23.005399703979492},\n", - " {'SNR_ww': 343.78753662109375, 'FOM_ww': 2.5369784832000732, 'FOM_DETF_ww': 0.18284554779529572, 'SNR_gg': 1126.6553955078125, 'FOM_gg': 460.51934814453125, 'FOM_DETF_gg': 7.035050868988037, 'SNR_3x2': 1128.9976806640625, 'FOM_3x2': 1246.9041748046875, 'FOM_DETF_3x2': 83.07261657714844}, \n", - " {'SNR_ww': 348.9933776855469, 'FOM_ww': 12.718839645385742, 'FOM_DETF_ww': 0.5421679019927979, 'SNR_gg': 1277.877685546875, 'FOM_gg': 823.6866455078125, 'FOM_DETF_gg': 11.198760986328125, 'SNR_3x2': 1279.864990234375, 'FOM_3x2': 2050.5126953125, 'FOM_DETF_3x2': 102.53814697265625},\n", - " {'SNR_ww': 351.5631408691406, 'FOM_ww': 20.807268142700195, 'FOM_DETF_ww': 0.6687530279159546, 'SNR_gg': 1400.4603271484375, 'FOM_gg': 1209.8232421875, 'FOM_DETF_gg': 14.164243698120117, 'SNR_3x2': 1402.2911376953125, 'FOM_3x2': 3289.179931640625, 'FOM_DETF_3x2': 114.87232208251953},\n", - " {'SNR_ww': 353.3896484375, 'FOM_ww': 25.69645118713379, 'FOM_DETF_ww': 0.7485368847846985, 'SNR_gg': 1490.1297607421875, 'FOM_gg': 1509.4334716796875, 'FOM_DETF_gg': 16.625934600830078, 'SNR_3x2': 1491.8892822265625, 'FOM_3x2': 4315.92431640625, 'FOM_DETF_3x2': 124.80682373046875}]\n", - "\n", - "uniform_scores_j = [{'SNR_ww': 327.1508483886719, 'FOM_ww': 3.3171546459198, 'FOM_DETF_ww': 0.092340387403965, 'SNR_gg': 677.09228515625, 'FOM_gg': 32.099971771240234, 'FOM_DETF_gg': 0.1580391675233841, 'SNR_3x2': 700.0398559570312, 'FOM_3x2': 852.3770751953125, 'FOM_DETF_3x2': 12.79428482055664},\n", - " {'SNR_ww': 351.2440185546875, 'FOM_ww': 23.247020721435547, 'FOM_DETF_ww': 0.6437975764274597, 'SNR_gg': 1106.9310302734375, 'FOM_gg': 527.278076171875, 'FOM_DETF_gg': 5.282368183135986, 'SNR_3x2': 1110.4364013671875, 'FOM_3x2': 2280.726806640625, 'FOM_DETF_3x2': 43.6702995300293},\n", - " {'SNR_ww': 352.9674072265625, 'FOM_ww': 30.201784133911133, 'FOM_DETF_ww': 0.7597047090530396, 'SNR_gg': 1151.7760009765625, 'FOM_gg': 819.4025268554688, 'FOM_DETF_gg': 7.07086181640625, 'SNR_3x2': 1155.22509765625, 'FOM_3x2': 3679.62744140625, 'FOM_DETF_3x2': 49.80576705932617},\n", - " {'SNR_ww': 354.352294921875, 'FOM_ww': 36.73133850097656, 'FOM_DETF_ww': 0.8435292840003967, 'SNR_gg': 1244.733642578125, 'FOM_gg': 990.042724609375, 'FOM_DETF_gg': 8.24472427368164, 'SNR_3x2': 1247.9957275390625, 'FOM_3x2': 4521.0576171875, 'FOM_DETF_3x2': 55.020790100097656},\n", - " {'SNR_ww': 354.3929748535156, 'FOM_ww': 37.14365768432617, 'FOM_DETF_ww': 0.8497052192687988, 'SNR_gg': 1302.3973388671875, 'FOM_gg': 1204.456298828125, 'FOM_DETF_gg': 9.355816841125488, 'SNR_3x2': 1305.5096435546875, 'FOM_3x2': 5697.7705078125, 'FOM_DETF_3x2': 57.619598388671875}]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_metrics(bins, scores, title, nrows, ncols, sep_figs = True):\n", - " \n", - " assert len(bins) == len(scores), \"The number of bins must be equal to the number of scores (i.e len(bins) == len(scores))\"\n", - " \n", - " if sep_figs == False:\n", - " plt.figure(figsize = (10,7))\n", - " plt.plot(bins, [scores[0]['SNR_ww'], scores[1]['SNR_ww'], scores[2]['SNR_ww'], scores[3]['SNR_ww'], scores[4]['SNR_ww']], 'o', label = 'SNR_ww')\n", - " plt.plot(bins, [scores[0]['SNR_gg'], scores[1]['SNR_gg'], scores[2]['SNR_gg'], scores[3]['SNR_gg'], scores[4]['SNR_gg']], 'o', label = 'SNR_gg')\n", - " plt.plot(bins, [scores[0]['SNR_3x2'], scores[1]['SNR_3x2'], scores[2]['SNR_3x2'], scores[3]['SNR_3x2'], scores[4]['SNR_3x2']], 'o', label = 'SNR_3x2')\n", - " plt.plot(bins, [scores[0]['FOM_ww'], scores[1]['FOM_ww'], scores[2]['FOM_ww'], scores[3]['FOM_ww'], scores[4]['FOM_ww']], 'o', label = 'FOM_ww')\n", - " plt.plot(bins, [scores[0]['FOM_gg'], scores[1]['FOM_gg'], scores[2]['FOM_gg'], scores[3]['FOM_gg'], scores[4]['FOM_gg']], 'o', label = 'FOM_gg')\n", - " plt.plot(bins, [scores[0]['FOM_3x2'], scores[1]['FOM_3x2'], scores[2]['FOM_3x2'], scores[3]['FOM_3x2'], scores[4]['FOM_3x2']], 'o', label = 'FOM_3x2')\n", - " plt.plot(bins, [scores[0]['FOM_DETF_ww'], scores[1]['FOM_DETF_ww'], scores[2]['FOM_DETF_ww'], scores[3]['FOM_DETF_ww'], scores[4]['FOM_DETF_ww']], 'o', label = 'FOM_DETF_ww')\n", - " plt.plot(bins, [scores[0]['FOM_DETF_gg'], scores[1]['FOM_DETF_gg'], scores[2]['FOM_DETF_gg'], scores[3]['FOM_DETF_gg'], scores[4]['FOM_DETF_gg']], 'o', label = 'FOM_DETF_gg')\n", - " plt.plot(bins, [scores[0]['FOM_DETF_3x2'], scores[1]['FOM_DETF_3x2'], scores[2]['FOM_DETF_3x2'], scores[3]['FOM_DETF_3x2'], scores[4]['FOM_DETF_3x2']], 'o', label = 'FOM_DETF_3x2')\n", - " plt.xlabel('Number of Bins', fontsize = 14)\n", - " plt.ylabel('Scores', fontsize = 14)\n", - " plt.title(title, fontsize = 16)\n", - " plt.legend()\n", - " \n", - " if sep_figs == True:\n", - " \n", - " metrics = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2', 'FOM_DETF_ww', 'FOM_DETF_gg', 'FOM_DETF_3x2']\n", - "# fig, axes = plt.subplots(nrows = nrows, ncols = ncols, figsize = (15,10))\n", - " \n", - " fig = plt.figure(figsize = (ncols*5, nrows*5))\n", - "# fig = plt.figure(figsize = (15,15))\n", - "# plt.xlabel('Number of Bins')\n", - "# plt.axes(frameon=False)\n", - "# plt.xticks([])\n", - "# plt.yticks([])\n", - " plt.axis(\"off\")\n", - " fig.suptitle(title, fontsize = 16)\n", - "# fig.tight_layout()\n", - " fig.subplots_adjust(top = .95)\n", - "# fig.text(0.5, 0.38, 'Number of Bins', fontsize = 14, ha = 'center')\n", - "# fig.text(0.07, 0.5, 'Score', fontsize = 14, va = 'center', rotation = 'vertical')\n", - " \n", - " numplots = nrows*ncols\n", - " \n", - " for i in range(numplots):\n", - " ax = fig.add_subplot(nrows,ncols,i+1)\n", - " ax.plot(bins,[scores[0][metrics[i]], scores[1][metrics[i]], scores[2][metrics[i]], scores[3][metrics[i]], scores[4][metrics[i]]], 'o', label = metrics[i])\n", - " ax.set_xlabel('Number of Bins')\n", - " ax.set_ylabel('Score')\n", - " ax.legend()\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_metrics(bins, uniform_scores_f, 'Uniform Binning Scores for All Metrics Using Firecrown', 2, 3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Combining log and uniform bins" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I decided to use 5 bins here, but turn it into 4 bins by combining 2 bins. I'm comparing to the uncombined 4 bins scores for each log and uniform bins. This is using firecrown results only." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", - "comb_5bins_log = [{'SNR_ww': 347.1550052435396, 'FOM_ww': 43.281384668444375, 'SNR_gg': 1178.1020177366966, 'FOM_gg': 1937.6331074516759, 'SNR_3x2': 1180.154312947983, 'FOM_3x2': 8170.834082030528},\n", - " {'SNR_ww': 347.11524596040493, 'FOM_ww': 12.496906081438635, 'SNR_gg': 1130.6537186403334, 'FOM_gg': 1725.6973362282138, 'SNR_3x2': 1132.7475032766674, 'FOM_3x2': 4340.897488251889},\n", - " {'SNR_ww': 346.29772734556116, 'FOM_ww': 20.391566310838982, 'SNR_gg': 1132.9429122353106, 'FOM_gg': 1108.2282006433647, 'SNR_3x2': 1135.5513182455525, 'FOM_3x2': 3247.449479310309}]\n", - "\n", - "#uncombined score\n", - "log_4bins = {'SNR_ww': 344.3523864717793, 'FOM_ww': 7.472714237157305, 'SNR_gg': 1129.9924402851957, 'FOM_gg': 2217.7143967350557, 'SNR_3x2': 1132.3499261021177, 'FOM_3x2': 17438.170598255823}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "#in order combine bins 0-1, 0-2, 0-3, 0-4\n", - "comb_5bins_uniform = [{'SNR_ww': 350.62629992935354, 'FOM_ww': 69.62333557208532, 'SNR_gg': 961.3423758223748, 'FOM_gg': 998.7919662764982, 'SNR_3x2': 970.9692811096805, 'FOM_3x2': 2878.907835879231},\n", - " {'SNR_ww': 332.91946164294785, 'FOM_ww': 1573.8141960234443, 'SNR_gg': 969.7202847355114, 'FOM_gg': 3157.055747538249, 'SNR_3x2': 984.1608880916402, 'FOM_3x2': 28180.732187357935},\n", - " {'SNR_ww': 344.4664127650933, 'FOM_ww': 707.9343834362847, 'SNR_gg': 1111.5530740704078, 'FOM_gg': 1722.9278602544168, 'SNR_3x2': 1115.220976362594, 'FOM_3x2': 36901.359762732245}]\n", - "\n", - "#uncobined score\n", - "uniform_4bins = {'SNR_ww': 351.79965915406984, 'FOM_ww': 55.91220189211659, 'SNR_gg': 1109.8455489042597, 'FOM_gg': 1629.7915293979788, 'SNR_3x2': 1113.3628584854432, 'FOM_3x2': 5025.4907299214065}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_combine_metrics(scores, uncomb_scores, title):\n", - "\n", - " assert len(scores[0]) == 6\n", - " assert len(scores) == 3\n", - " \n", - " x = [1, 2, 3, 4, 5, 6]\n", - " xlabels = ['SNR_ww', 'SNR_gg', 'SNR_3x2', 'FOM_ww', 'FOM_gg', 'FOM_3x2']\n", - " color = ['b', 'g', 'orange', 'r', 'y', 'c']\n", - " \n", - " #plot the combined bins metrics\n", - " plt.figure(figsize = (10,7)) \n", - " for i in range(len(scores)):\n", - " plt.plot(x, [scores[i]['SNR_ww'], scores[i]['SNR_gg'], scores[i]['SNR_3x2'], scores[i]['FOM_ww'], scores[i]['FOM_gg'], scores[i]['FOM_3x2']], 'o', c = color[i], label = f'0-{i+1}')\n", - "\n", - " #plot the uncombined 4 bins metrics\n", - " plt.plot(x, [uncomb_scores['SNR_ww'], uncomb_scores['SNR_gg'], uncomb_scores['SNR_3x2'], uncomb_scores['FOM_ww'], uncomb_scores['FOM_gg'], uncomb_scores['FOM_3x2']], 'ko', label = '4bins uncombined')\n", - " \n", - " plt.xlabel('Metrics')\n", - " plt.ylabel('Score')\n", - " plt.xticks(x, xlabels)\n", - " plt.legend()\n", - " plt.title(title)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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-Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1884258020603244) -params--bias_1 ~ delta(1.7318281282478987) -params--bias_2 ~ delta(1.2284861515879035) -params--bias_3 ~ delta(1.3620825506058292) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpybi295wo/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1884258020603244) -params--bias_1 ~ delta(1.7318281282478987) -params--bias_2 ~ delta(1.2284861515879035) -params--bias_3 ~ delta(1.3620825506058292) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmphrgb4k7e/chain.txt -**************************** -Calculating derivatives using 20 total models -WARNING: The inverse covariance matrix produced by your pipeline - is not symmetric. This probably indicates a mistake somewhere. - If you are only using cosmosis-standard-library likelihoods please - open an issue about this on the cosmosis site. -Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh deleted file mode 100644 index 10d83fec..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_log_5bins0_1 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml deleted file mode 100644 index 73a41aec..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_1.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_1.txt -newbins: [0,1] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Log_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out deleted file mode 100644 index 722b62aa..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.out +++ /dev/null @@ -1,124 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Normal_CombineBins -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Found classifier RandomForest_comb_test -Executing: RF_Log_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -4 -(304,) -(4, 304) -2908203.3878848907 -2390598 -462445.53912042466 -382317 -18103916.630815372 -10452442 -5452851.574774352 -4003197 -biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpguev67td/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -2908203.3878848907 -2390598 -462445.53912042466 -382317 -18103916.630815372 -10452442 -5452851.574774352 -4003197 -biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.2165171174262217) -params--bias_1 ~ delta(1.2095866496138667) -params--bias_2 ~ delta(1.7320274660041521) -params--bias_3 ~ delta(1.3621242159140188) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp7hlh1tzf/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -2908203.3878848907 -2390598 -462445.53912042466 -382317 -18103916.630815372 -10452442 -5452851.574774352 -4003197 -biases= [1.2165171174262217, 1.2095866496138667, 1.7320274660041521, 1.3621242159140188] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.2165171174262217) -params--bias_1 ~ delta(1.2095866496138667) -params--bias_2 ~ delta(1.7320274660041521) -params--bias_3 ~ delta(1.3621242159140188) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp5hc4lzdd/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh deleted file mode 100644 index cbc61ba0..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_log_5bins0_2 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/rf_comb_bins/riz_rf_log_5bins0_2.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml deleted file mode 100644 index 321664cf..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_2.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_2.txt -newbins: [0,2] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Log_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out deleted file mode 100644 index 268ee555..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.out +++ /dev/null @@ -1,124 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Normal_CombineBins -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Found classifier RandomForest_comb_test -Executing: RF_Log_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -4 -(304,) -(4, 304) -6249319.62865952 -4660882 -536666.3042368268 -446018 -2100382.2024054904 -1711346 -18041048.9972932 -10410308 -biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpdjkk27ig/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -6249319.62865952 -4660882 -536666.3042368268 -446018 -2100382.2024054904 -1711346 -18041048.9972932 -10410308 -biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3408019402034894) -params--bias_1 ~ delta(1.2032391164411007) -params--bias_2 ~ delta(1.2273276137061064) -params--bias_3 ~ delta(1.732998581530268) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpv54za3kd/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -6249319.62865952 -4660882 -536666.3042368268 -446018 -2100382.2024054904 -1711346 -18041048.9972932 -10410308 -biases= [1.3408019402034894, 1.2032391164411007, 1.2273276137061064, 1.732998581530268] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3408019402034894) -params--bias_1 ~ delta(1.2032391164411007) -params--bias_2 ~ delta(1.2273276137061064) -params--bias_3 ~ delta(1.732998581530268) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpbl2byvzj/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh deleted file mode 100644 index 7dba95cc..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_log_5bins0_3 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml b/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml deleted file mode 100644 index 23337867..00000000 --- a/rf_comb_bins/rf_log/riz_rf_log_5bins0_3.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_log_5bins0_3.txt -newbins: [0,3] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Log_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out deleted file mode 100644 index 9d22628e..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.out +++ /dev/null @@ -1,87 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Normal_CombineBins -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Found classifier RandomForest_comb_test -Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpcv91grmu/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3607270253935149) -params--bias_1 ~ delta(2.5813978122461982) -params--bias_2 ~ delta(1.7476661701541452) -params--bias_3 ~ delta(2.2043998086715852) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmptcvyvon0/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3607270253935149) -params--bias_1 ~ delta(2.5813978122461982) -params--bias_2 ~ delta(1.7476661701541452) -params--bias_3 ~ delta(2.2043998086715852) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmphre8qzz7/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh deleted file mode 100644 index 8fce28f5..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_u_5bins0_1 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml deleted file mode 100644 index b0158486..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_1.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_1.txt -newbins: [0,1] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Uniform_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out deleted file mode 100644 index 95f3432e..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.out +++ /dev/null @@ -1,123 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Normal_CombineBins -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Found classifier RandomForest_comb_test -Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -4 -(304,) -(4, 304) -20788198.45604642 -13638173 -3954220.3104969305 -2671150 -949766.5599671043 -366555 -1235231.8060845833 -552676 -biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpyxpim019/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -20788198.45604642 -13638173 -3954220.3104969305 -2671150 -949766.5599671043 -366555 -1235231.8060845833 -552676 -biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.5242656370502428) -params--bias_1 ~ delta(1.4803437884420307) -params--bias_2 ~ delta(2.591061532286026) -params--bias_3 ~ delta(2.235001711824981) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpxlun9xrw/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -20788198.45604642 -13638173 -3954220.3104969305 -2671150 -949766.5599671043 -366555 -1235231.8060845833 -552676 -biases= [1.5242656370502428, 1.4803437884420307, 2.591061532286026, 2.235001711824981] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.5242656370502428) -params--bias_1 ~ delta(1.4803437884420307) -params--bias_2 ~ delta(2.591061532286026) -params--bias_3 ~ delta(2.235001711824981) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpeielvm2f/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh deleted file mode 100644 index c37e185d..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_u_5bins0_2 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml deleted file mode 100644 index d2a04161..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_2.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_2.txt -newbins: [0,2] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Uniform_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out deleted file mode 100644 index 30c6ea9d..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.out +++ /dev/null @@ -1,123 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Normal_CombineBins -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Found classifier RandomForest_comb_test -Executing: RF_Uniform_CombineBins riz {'bins': 5, 'colors': True, 'errors': True} -{'bins': 5, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -4 -(304,) -(4, 304) -10488700.542079326 -7558622 -4114672.935744032 -2780348 -11369287.471830672 -6520914 -954756.1829410096 -368670 -biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpmx_ziuuz/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -10488700.542079326 -7558622 -4114672.935744032 -2780348 -11369287.471830672 -6520914 -954756.1829410096 -368670 -biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.387647185172023) -params--bias_1 ~ delta(1.4799129230384225) -params--bias_2 ~ delta(1.7435113347347737) -params--bias_3 ~ delta(2.5897311496487636) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp2poqiwrl/chain.txt -**************************** -Calculating derivatives using 20 total models -4 -(304,) -(4, 304) -10488700.542079326 -7558622 -4114672.935744032 -2780348 -11369287.471830672 -6520914 -954756.1829410096 -368670 -biases= [1.387647185172023, 1.4799129230384225, 1.7435113347347737, 2.5897311496487636] -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.387647185172023) -params--bias_1 ~ delta(1.4799129230384225) -params--bias_2 ~ delta(1.7435113347347737) -params--bias_3 ~ delta(2.5897311496487636) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp8h1r92p_/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh deleted file mode 100644 index fde7052e..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_rf_u_5bins0_3 -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge_comb.py /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml \ No newline at end of file diff --git a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml b/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml deleted file mode 100644 index 9c168f25..00000000 --- a/rf_comb_bins/rf_uniform/riz_rf_u_5bins0_3.yaml +++ /dev/null @@ -1,20 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_comb_bins/riz_rf_u_5bins0_3.txt -newbins: [0,3] -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: firecrown - -run: - # This is a class name which will be looked up - RF_Uniform_CombineBins: - run_5: - # This setting is sent to the classifier - bins: 5 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_log/firecrown/riz_10bins.err b/rf_log/firecrown/riz_10bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_log/firecrown/riz_10bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_10bins.out b/rf_log/firecrown/riz_10bins.out deleted file mode 100644 index 9aae1c3f..00000000 --- a/rf_log/firecrown/riz_10bins.out +++ /dev/null @@ -1,98 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -file successfully created -first loop successfully entered -second loop successfully entered -Executing: RandomForest_log riz {'bins': 10, 'colors': True, 'errors': True} -{'bins': 10, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpxv1_s87x/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1230673520631358) -params--bias_1 ~ delta(1.1860558567792236) -params--bias_2 ~ delta(1.195801073258373) -params--bias_3 ~ delta(1.1891119117180495) -params--bias_4 ~ delta(1.2063992094916378) -params--bias_5 ~ delta(1.2268082360867738) -params--bias_6 ~ delta(1.284071282386922) -params--bias_7 ~ delta(1.3991101109924111) -params--bias_8 ~ delta(1.5083271037932677) -params--bias_9 ~ delta(1.8610866508424537) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpze52pg_b/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1230673520631358) -params--bias_1 ~ delta(1.1860558567792236) -params--bias_2 ~ delta(1.195801073258373) -params--bias_3 ~ delta(1.1891119117180495) -params--bias_4 ~ delta(1.2063992094916378) -params--bias_5 ~ delta(1.2268082360867738) -params--bias_6 ~ delta(1.284071282386922) -params--bias_7 ~ delta(1.3991101109924111) -params--bias_8 ~ delta(1.5083271037932677) -params--bias_9 ~ delta(1.8610866508424537) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpw7x3ru8w/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_log/firecrown/riz_2bins.err b/rf_log/firecrown/riz_2bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_log/firecrown/riz_2bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_2bins.out b/rf_log/firecrown/riz_2bins.out deleted file mode 100644 index cf803af4..00000000 --- a/rf_log/firecrown/riz_2bins.out +++ /dev/null @@ -1,81 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForest -Found classifier Random -Found classifier RandomForestNewBinning -file successfully created -first loop successfully entered -second loop successfully entered -Executing: RandomForest_log riz {'bins': 2, 'colors': True, 'errors': True} -{'bins': 2, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpj_wxylxl/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1823465400324054) -params--bias_1 ~ delta(1.5994619605861637) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpr02dfndf/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1823465400324054) -params--bias_1 ~ delta(1.5994619605861637) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpzr0mrtc5/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_log/firecrown/riz_4bins.err b/rf_log/firecrown/riz_4bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_log/firecrown/riz_4bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_4bins.out b/rf_log/firecrown/riz_4bins.out deleted file mode 100644 index e7168fad..00000000 --- a/rf_log/firecrown/riz_4bins.out +++ /dev/null @@ -1,86 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -file successfully created -first loop successfully entered -second loop successfully entered -Executing: RandomForest_log riz {'bins': 4, 'colors': True, 'errors': True} -{'bins': 4, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmps010x8ar/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1501039624489349) -params--bias_1 ~ delta(1.2125371578497253) -params--bias_2 ~ delta(1.2973669314394134) -params--bias_3 ~ delta(1.6922830217318134) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpt4dul75j/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1501039624489349) -params--bias_1 ~ delta(1.2125371578497253) -params--bias_2 ~ delta(1.2973669314394134) -params--bias_3 ~ delta(1.6922830217318134) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp8twbx9vp/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_log/firecrown/riz_6bins.err b/rf_log/firecrown/riz_6bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_log/firecrown/riz_6bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_6bins.out b/rf_log/firecrown/riz_6bins.out deleted file mode 100644 index 6e2d8223..00000000 --- a/rf_log/firecrown/riz_6bins.out +++ /dev/null @@ -1,90 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -file successfully created -first loop successfully entered -second loop successfully entered -Executing: RandomForest_log riz {'bins': 6, 'colors': True, 'errors': True} -{'bins': 6, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp0omxbfyw/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1318921984696868) -params--bias_1 ~ delta(1.2002875299227955) -params--bias_2 ~ delta(1.2071750132422534) -params--bias_3 ~ delta(1.2512898807957415) -params--bias_4 ~ delta(1.4097344198654462) -params--bias_5 ~ delta(1.7674882543368329) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpuhbr2059/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1318921984696868) -params--bias_1 ~ delta(1.2002875299227955) -params--bias_2 ~ delta(1.2071750132422534) -params--bias_3 ~ delta(1.2512898807957415) -params--bias_4 ~ delta(1.4097344198654462) -params--bias_5 ~ delta(1.7674882543368329) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp6bzvd12p/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_log/firecrown/riz_8bins.err b/rf_log/firecrown/riz_8bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_log/firecrown/riz_8bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_log/firecrown/riz_8bins.out b/rf_log/firecrown/riz_8bins.out deleted file mode 100644 index 0e30e7ba..00000000 --- a/rf_log/firecrown/riz_8bins.out +++ /dev/null @@ -1,94 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -file successfully created -first loop successfully entered -second loop successfully entered -Executing: RandomForest_log riz {'bins': 8, 'colors': True, 'errors': True} -{'bins': 8, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpvo3k8p_f/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1283607319843982) -params--bias_1 ~ delta(1.1917699159422768) -params--bias_2 ~ delta(1.1913858725422282) -params--bias_3 ~ delta(1.2075610181991745) -params--bias_4 ~ delta(1.2333877957410335) -params--bias_5 ~ delta(1.3351428777871712) -params--bias_6 ~ delta(1.463420009715028) -params--bias_7 ~ delta(1.8185301775278142) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp427g9z20/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.1283607319843982) -params--bias_1 ~ delta(1.1917699159422768) -params--bias_2 ~ delta(1.1913858725422282) -params--bias_3 ~ delta(1.2075610181991745) -params--bias_4 ~ delta(1.2333877957410335) -params--bias_5 ~ delta(1.3351428777871712) -params--bias_6 ~ delta(1.463420009715028) -params--bias_7 ~ delta(1.8185301775278142) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp8zmkzgp2/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_log/jax/log_10bins.err b/rf_log/jax/log_10bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_log/jax/log_10bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_10bins.out b/rf_log/jax/log_10bins.out deleted file mode 100644 index a85808fb..00000000 --- a/rf_log/jax/log_10bins.out +++ /dev/null @@ -1,19 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForest_log riz {'bins': 10, 'colors': True, 'errors': True} -{'bins': 10, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_log/jax/log_2bins.err b/rf_log/jax/log_2bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_log/jax/log_2bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_2bins.out b/rf_log/jax/log_2bins.out deleted file mode 100644 index 4dbb7d76..00000000 --- a/rf_log/jax/log_2bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForest_log riz {'bins': 2, 'colors': True, 'errors': True} -{'bins': 2, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_log/jax/log_4bins.err b/rf_log/jax/log_4bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_log/jax/log_4bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_4bins.out b/rf_log/jax/log_4bins.out deleted file mode 100644 index a3ca2a94..00000000 --- a/rf_log/jax/log_4bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForest_log riz {'bins': 4, 'colors': True, 'errors': True} -{'bins': 4, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_log/jax/log_6bins.err b/rf_log/jax/log_6bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_log/jax/log_6bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_6bins.out b/rf_log/jax/log_6bins.out deleted file mode 100644 index 662dff2f..00000000 --- a/rf_log/jax/log_6bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForest_log riz {'bins': 6, 'colors': True, 'errors': True} -{'bins': 6, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_log/jax/log_8bins.err b/rf_log/jax/log_8bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_log/jax/log_8bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_log/jax/log_8bins.out b/rf_log/jax/log_8bins.out deleted file mode 100644 index 72a73e64..00000000 --- a/rf_log/jax/log_8bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForest_log riz {'bins': 8, 'colors': True, 'errors': True} -{'bins': 8, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -the bins are in logspace -cut percent: 0.05 -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_log/rf_log_10bins.sh b/rf_log/rf_log_10bins.sh deleted file mode 100644 index 9c6305be..00000000 --- a/rf_log/rf_log_10bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:50:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=log_10bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_10bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_2bins.sh b/rf_log/rf_log_2bins.sh deleted file mode 100644 index e8473c83..00000000 --- a/rf_log/rf_log_2bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:30:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=log_2bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_2bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_4bins.sh b/rf_log/rf_log_4bins.sh deleted file mode 100644 index e89394e8..00000000 --- a/rf_log/rf_log_4bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:30:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=log_4bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_4bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_6bins.sh b/rf_log/rf_log_6bins.sh deleted file mode 100644 index e9ffe739..00000000 --- a/rf_log/rf_log_6bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:30:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=log_6bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_6bins.yaml \ No newline at end of file diff --git a/rf_log/rf_log_8bins.sh b/rf_log/rf_log_8bins.sh deleted file mode 100644 index d61b9132..00000000 --- a/rf_log/rf_log_8bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:40:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=log_8bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_log/riz_8bins.yaml \ No newline at end of file diff --git a/rf_log/riz_10bins.yaml b/rf_log/riz_10bins.yaml deleted file mode 100644 index 83df49d8..00000000 --- a/rf_log/riz_10bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_10bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForest_log: - run_10: - # This setting is sent to the classifier - bins: 10 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_log/riz_2bins.yaml b/rf_log/riz_2bins.yaml deleted file mode 100644 index fc7b4cf7..00000000 --- a/rf_log/riz_2bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_2bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForest_log: - run_2: - # This setting is sent to the classifier - bins: 2 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_log/riz_4bins.yaml b/rf_log/riz_4bins.yaml deleted file mode 100644 index c96efe85..00000000 --- a/rf_log/riz_4bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_4bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForest_log: - run_4: - # This setting is sent to the classifier - bins: 4 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_log/riz_6bins.yaml b/rf_log/riz_6bins.yaml deleted file mode 100644 index 17b3a3e9..00000000 --- a/rf_log/riz_6bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_6bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForest_log: - run_6: - # This setting is sent to the classifier - bins: 6 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_log/riz_8bins.yaml b/rf_log/riz_8bins.yaml deleted file mode 100644 index e2e9b44e..00000000 --- a/rf_log/riz_8bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_log/log_8bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForest_log: - run_8: - # This setting is sent to the classifier - bins: 8 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_uniform/firecrown/riz_10bins.err b/rf_uniform/firecrown/riz_10bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_uniform/firecrown/riz_10bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_10bins.out b/rf_uniform/firecrown/riz_10bins.out deleted file mode 100644 index 7d3fd907..00000000 --- a/rf_uniform/firecrown/riz_10bins.out +++ /dev/null @@ -1,99 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier RandomForest_log_comb_bins_0_1 -Found classifier RandomForest_comb_bins_0_1 -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RF_Uniform_comb_bins_0_1 -Found classifier RF_Normal_comb_bins_0_1 -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -Executing: RandomForestUniform riz {'bins': 10, 'colors': True, 'errors': True} -{'bins': 10, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.26931938 1.4427957 1.65394431 - 2.05448922 2.08940756 2.15840691 2.9422926 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp_wyq1su4/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3129771819046112) -params--bias_1 ~ delta(1.4802418423775587) -params--bias_2 ~ delta(1.688167164042944) -params--bias_3 ~ delta(1.9143202136126234) -params--bias_4 ~ delta(1.9135796211211558) -params--bias_5 ~ delta(2.17790148913288) -params--bias_6 ~ delta(2.43662933258849) -params--bias_7 ~ delta(2.429499096047649) -params--bias_8 ~ delta(2.6129443035996056) -params--bias_9 ~ delta(2.574852039673552) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp8kqkuv5j/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3129771819046112) -params--bias_1 ~ delta(1.4802418423775587) -params--bias_2 ~ delta(1.688167164042944) -params--bias_3 ~ delta(1.9143202136126234) -params--bias_4 ~ delta(1.9135796211211558) -params--bias_5 ~ delta(2.17790148913288) -params--bias_6 ~ delta(2.43662933258849) -params--bias_7 ~ delta(2.429499096047649) -params--bias_8 ~ delta(2.6129443035996056) -params--bias_9 ~ delta(2.574852039673552) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpb92kwb5q/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_2bins.err b/rf_uniform/firecrown/riz_2bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_uniform/firecrown/riz_2bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_2bins.out b/rf_uniform/firecrown/riz_2bins.out deleted file mode 100644 index 83333e30..00000000 --- a/rf_uniform/firecrown/riz_2bins.out +++ /dev/null @@ -1,78 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -Executing: RandomForestUniform riz {'bins': 2, 'colors': True, 'errors': True} -{'bins': 2, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpk4ugda01/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.5419310820732355) -params--bias_1 ~ delta(2.6139281837984605) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpi_huawso/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.5419310820732355) -params--bias_1 ~ delta(2.6139281837984605) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp9urj5559/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_4bins.err b/rf_uniform/firecrown/riz_4bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_uniform/firecrown/riz_4bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_4bins.out b/rf_uniform/firecrown/riz_4bins.out deleted file mode 100644 index bb59c6ec..00000000 --- a/rf_uniform/firecrown/riz_4bins.out +++ /dev/null @@ -1,82 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -Executing: RandomForestUniform riz {'bins': 4, 'colors': True, 'errors': True} -{'bins': 4, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpod5u669r/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3069241571401855) -params--bias_1 ~ delta(1.4794681602751933) -params--bias_2 ~ delta(1.783300810771281) -params--bias_3 ~ delta(2.606220681084049) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp31actg_6/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.3069241571401855) -params--bias_1 ~ delta(1.4794681602751933) -params--bias_2 ~ delta(1.783300810771281) -params--bias_3 ~ delta(2.606220681084049) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp14pmm2e6/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_6bins.err b/rf_uniform/firecrown/riz_6bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_uniform/firecrown/riz_6bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_6bins.out b/rf_uniform/firecrown/riz_6bins.out deleted file mode 100644 index 60d37562..00000000 --- a/rf_uniform/firecrown/riz_6bins.out +++ /dev/null @@ -1,87 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -Executing: RandomForestUniform riz {'bins': 6, 'colors': True, 'errors': True} -{'bins': 6, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 2.15840691 - 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp07cua5ps/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.311222505636419) -params--bias_1 ~ delta(1.4798622016556051) -params--bias_2 ~ delta(1.7446521401354267) -params--bias_3 ~ delta(2.2055391179955746) -params--bias_4 ~ delta(2.4374906738379183) -params--bias_5 ~ delta(2.6163694037048817) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp_opurs95/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.311222505636419) -params--bias_1 ~ delta(1.4798622016556051) -params--bias_2 ~ delta(1.7446521401354267) -params--bias_3 ~ delta(2.2055391179955746) -params--bias_4 ~ delta(2.4374906738379183) -params--bias_5 ~ delta(2.6163694037048817) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp_kntxq3s/chain.txt -**************************** -Calculating derivatives using 20 total models -Making some pretty plots... diff --git a/rf_uniform/firecrown/riz_8bins.err b/rf_uniform/firecrown/riz_8bins.err deleted file mode 100644 index 95f10515..00000000 --- a/rf_uniform/firecrown/riz_8bins.err +++ /dev/null @@ -1,2 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") diff --git a/rf_uniform/firecrown/riz_8bins.out b/rf_uniform/firecrown/riz_8bins.out deleted file mode 100644 index e513db27..00000000 --- a/rf_uniform/firecrown/riz_8bins.out +++ /dev/null @@ -1,95 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestNormal -Found classifier RandomForestUniform -Found classifier RandomForest -Found classifier Random -Executing: RandomForestUniform riz {'bins': 8, 'colors': True, 'errors': True} -{'bins': 8, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.26931938 1.65394431 2.08940756 - 2.15840691 2.9422926 3.03609133] -Fitting classifier -Applying... -Getting metric... -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmpg684ef0g/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.311643344163664) -params--bias_1 ~ delta(1.480475703762752) -params--bias_2 ~ delta(1.6870486938697402) -params--bias_3 ~ delta(1.9111074288323902) -params--bias_4 ~ delta(2.2003309928037784) -params--bias_5 ~ delta(2.4331942419305235) -params--bias_6 ~ delta(2.620459106603769) -params--bias_7 ~ delta(2.6361862117129045) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmp_lgodn5s/chain.txt -**************************** -Calculating derivatives using 20 total models -Running in serial mode (one process). - -Parameter Priors ----------------- -params--omega_k ~ delta(0.0) -params--omega_c ~ U(0.25, 0.32) -params--omega_b ~ U(0.04, 0.05) -params--h ~ U(0.5, 0.9) -params--n_s ~ U(0.9, 1.02) -params--sigma8 ~ U(0.74, 0.94) -params--w0 ~ delta(-1.0) -params--wa ~ delta(0.0) -params--bias_0 ~ delta(1.311643344163664) -params--bias_1 ~ delta(1.480475703762752) -params--bias_2 ~ delta(1.6870486938697402) -params--bias_3 ~ delta(1.9111074288323902) -params--bias_4 ~ delta(2.2003309928037784) -params--bias_5 ~ delta(2.4331942419305235) -params--bias_6 ~ delta(2.620459106603769) -params--bias_7 ~ delta(2.6361862117129045) - - -**************************** -* Running sampler 1/1: fisher -* Saving output -> /tmp/tmphtnjtt9v/chain.txt -**************************** -Calculating derivatives using 20 total models -WARNING: The inverse covariance matrix produced by your pipeline - is not symmetric. This probably indicates a mistake somewhere. - If you are only using cosmosis-standard-library likelihoods please - open an issue about this on the cosmosis site. -Making some pretty plots... diff --git a/rf_uniform/jax/u_10bins.err b/rf_uniform/jax/u_10bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_uniform/jax/u_10bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_10bins.out b/rf_uniform/jax/u_10bins.out deleted file mode 100644 index 8ded743b..00000000 --- a/rf_uniform/jax/u_10bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForestUniform riz {'bins': 10, 'colors': True, 'errors': True} -{'bins': 10, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.26931938 1.4427957 1.65394431 - 2.05448922 2.08940756 2.15840691 2.9422926 3.03609133] -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_uniform/jax/u_2bins.err b/rf_uniform/jax/u_2bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_uniform/jax/u_2bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_2bins.out b/rf_uniform/jax/u_2bins.out deleted file mode 100644 index 9bf3b624..00000000 --- a/rf_uniform/jax/u_2bins.out +++ /dev/null @@ -1,19 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForestUniform riz {'bins': 2, 'colors': True, 'errors': True} -{'bins': 2, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_uniform/jax/u_4bins.err b/rf_uniform/jax/u_4bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_uniform/jax/u_4bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_4bins.out b/rf_uniform/jax/u_4bins.out deleted file mode 100644 index 68e78bb7..00000000 --- a/rf_uniform/jax/u_4bins.out +++ /dev/null @@ -1,19 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForestUniform riz {'bins': 4, 'colors': True, 'errors': True} -{'bins': 4, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 2.08940756 3.03609133] -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_uniform/jax/u_6bins.err b/rf_uniform/jax/u_6bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_uniform/jax/u_6bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_6bins.out b/rf_uniform/jax/u_6bins.out deleted file mode 100644 index 3945af90..00000000 --- a/rf_uniform/jax/u_6bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForestUniform riz {'bins': 6, 'colors': True, 'errors': True} -{'bins': 6, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.65394431 2.08940756 2.15840691 - 3.03609133] -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_uniform/jax/u_8bins.err b/rf_uniform/jax/u_8bins.err deleted file mode 100644 index 38ef0f89..00000000 --- a/rf_uniform/jax/u_8bins.err +++ /dev/null @@ -1,6 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/../tomo_challenge/data.py:89: UserWarning: Setting inf (undetected) bands to mag=30 - warnings.warn("Setting inf (undetected) bands to mag=30") -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lib/xla_bridge.py:130: UserWarning: No GPU/TPU found, falling back to CPU. - warnings.warn('No GPU/TPU found, falling back to CPU.') -/global/homes/a/abault/.conda/envs/tomo/lib/python3.7/site-packages/jax/lax/lax.py:5905: UserWarning: Explicitly requested dtype requested in astype is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more. - warnings.warn(msg.format(dtype, fun_name , truncated_dtype)) diff --git a/rf_uniform/jax/u_8bins.out b/rf_uniform/jax/u_8bins.out deleted file mode 100644 index 6482e8ca..00000000 --- a/rf_uniform/jax/u_8bins.out +++ /dev/null @@ -1,20 +0,0 @@ -/global/cscratch1/sd/abault/tomo_challenge/bin/.. -Found classifier RandomForest_log -Found classifier IBandOnly -Found classifier RandomForestUniform -Found classifier RF_Log_CombineBins -Found classifier RandomForest -Found classifier RandomForestCombineBins -Found classifier RF_Uniform_CombineBins -Found classifier Random -Executing: RandomForestUniform riz {'bins': 8, 'colors': True, 'errors': True} -{'bins': 8, 'colors': True, 'errors': True} ['bins'] -Initializing classifier... -Training... -Finding bins for training data -[0.02450252 0.68055436 0.858418 1.26931938 1.65394431 2.08940756 - 2.15840691 2.9422926 3.03609133] -Fitting classifier -Applying... -Getting metric... -Making some pretty plots... diff --git a/rf_uniform/rf_uniform_10bins.sh b/rf_uniform/rf_uniform_10bins.sh deleted file mode 100644 index ead46ae7..00000000 --- a/rf_uniform/rf_uniform_10bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:40:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=u_10bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_10bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_2bins.sh b/rf_uniform/rf_uniform_2bins.sh deleted file mode 100644 index 62778cb5..00000000 --- a/rf_uniform/rf_uniform_2bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:15:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=u_2bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_2bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_4bins.sh b/rf_uniform/rf_uniform_4bins.sh deleted file mode 100644 index d0bcbd40..00000000 --- a/rf_uniform/rf_uniform_4bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:20:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=u_4bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_4bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_6bins.sh b/rf_uniform/rf_uniform_6bins.sh deleted file mode 100644 index 456143b9..00000000 --- a/rf_uniform/rf_uniform_6bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:25:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=riz_u_6bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_6bins.yaml \ No newline at end of file diff --git a/rf_uniform/rf_uniform_8bins.sh b/rf_uniform/rf_uniform_8bins.sh deleted file mode 100644 index 2e80f40c..00000000 --- a/rf_uniform/rf_uniform_8bins.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -#SBATCH -p regular -#SBATCH -N 1 -#SBATCH -t 00:30:00 -#SBATCH -L SCRATCH -#SBATCH --job-name=u_8bins -#SBATCH --output=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.out -#SBATCH --error=/global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.err -#SBATCH --constraint=haswell - -source activate tomo -python /global/cscratch1/sd/abault/tomo_challenge/bin/challenge.py /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/riz_8bins.yaml \ No newline at end of file diff --git a/rf_uniform/riz_10bins.yaml b/rf_uniform/riz_10bins.yaml deleted file mode 100644 index 796896d6..00000000 --- a/rf_uniform/riz_10bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_10bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForestUniform: - run_10: - # This setting is sent to the classifier - bins: 10 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_uniform/riz_2bins.yaml b/rf_uniform/riz_2bins.yaml deleted file mode 100644 index a106658c..00000000 --- a/rf_uniform/riz_2bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_2bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForestUniform: - run_2: - # This setting is sent to the classifier - bins: 2 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_uniform/riz_4bins.yaml b/rf_uniform/riz_4bins.yaml deleted file mode 100644 index fe76757d..00000000 --- a/rf_uniform/riz_4bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_4bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForestUniform: - run_4: - # This setting is sent to the classifier - bins: 4 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_uniform/riz_6bins.yaml b/rf_uniform/riz_6bins.yaml deleted file mode 100644 index 57651cb2..00000000 --- a/rf_uniform/riz_6bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_6bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForestUniform: - run_6: - # This setting is sent to the classifier - bins: 6 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True diff --git a/rf_uniform/riz_8bins.yaml b/rf_uniform/riz_8bins.yaml deleted file mode 100644 index cc436286..00000000 --- a/rf_uniform/riz_8bins.yaml +++ /dev/null @@ -1,19 +0,0 @@ -metrics: all -bands: riz -training_file: /global/cscratch1/sd/abault/tomo_challenge/data/training.hdf5 -validation_file: /global/cscratch1/sd/abault/tomo_challenge/data/validation.hdf5 -output_file: /global/cscratch1/sd/abault/tomo_challenge/rf_uniform/u_8bins.txt -# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" -metrics_impl: jax-cosmo - -run: - # This is a class name which will be looked up - RandomForestUniform: - run_8: - # This setting is sent to the classifier - bins: 8 - # These special settings decide whether the - # color and error colums are passed to the classifier - # as well as the magnitudes - colors: True - errors: True From 5dc82f57e2ec25cd8244fc376bb706976e349b48 Mon Sep 17 00:00:00 2001 From: Abby Bault Date: Mon, 7 Sep 2020 14:45:16 -0700 Subject: [PATCH 28/36] add example yaml files --- .DS_Store | Bin 6148 -> 6148 bytes example/funbins_example.yaml | 21 +++++++++++++++++++++ example/funbins_example_combine.yaml | 22 ++++++++++++++++++++++ 3 files changed, 43 insertions(+) create mode 100644 example/funbins_example.yaml create mode 100644 example/funbins_example_combine.yaml diff --git a/.DS_Store b/.DS_Store index 6fb4ed926e13656f11aa3d0afde313a9c7edd2c7..d9f7f813c17ec4c678cc4ef5aca410bebc221279 100644 GIT binary patch delta 125 zcmZoMXfc=|#>B!ku~2NHo+3X70|Nsi1A_nqLjgk$L+Zx3wTz25Ff$2*#Mv2A87deO z8FC?VljWFhNur391sCPz%-GDs^o?mVI|n}p(D2O*nZGkn<`;3~0GiDNlxEl* IA+m-U0I540QUCw| delta 77 zcmZoMXfc=|#>B)qu~2NHo+3XR0|Nsi1A_nqLk>d;L-EGAwTz5xn*~|FF>P#MW!}ur d!OsCyyjhUrJM(0I5l0T7a*%qK%@HDNm;ruW56J)k diff --git a/example/funbins_example.yaml b/example/funbins_example.yaml new file mode 100644 index 00000000..be4d37de --- /dev/null +++ b/example/funbins_example.yaml @@ -0,0 +1,21 @@ +metrics: all +bands: riz +training_file: data/training.hdf5 +validation_file: data/mini_validation.hdf5 +output_file: tomo_challenge/funbins_example_4bins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + funbins: + run_4: + # This setting is sent to the classifier + bins: 4 + seed: 123 + method: 'log' + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True diff --git a/example/funbins_example_combine.yaml b/example/funbins_example_combine.yaml new file mode 100644 index 00000000..56fc9d94 --- /dev/null +++ b/example/funbins_example_combine.yaml @@ -0,0 +1,22 @@ +metrics: all +bands: riz +training_file: /data/training.hdf5 +validation_file: /data/validation.hdf5 +output_file: tomo_challenge/funbins_example_combinebins.txt +# Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" +metrics_impl: jax-cosmo + +run: + # This is a class name which will be looked up + funbins: + run_4: + # This setting is sent to the classifier + bins: 4 + seed: 123 + method: 'log' + combinebins: [1,2] + # These special settings decide whether the + # color and error colums are passed to the classifier + # as well as the magnitudes + colors: True + errors: True From cac930a105884f663b921b25f69a64ffd2d7265e Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Mon, 7 Sep 2020 14:57:58 -0700 Subject: [PATCH 29/36] Update challenge.py fix the error of plots not going to the right folder --- bin/challenge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/challenge.py b/bin/challenge.py index e322c7d6..1e1d5628 100755 --- a/bin/challenge.py +++ b/bin/challenge.py @@ -104,7 +104,7 @@ def run_one(classifier_name, bands, settings, train_data, train_z, valid_data, print ("Making some pretty plots...") name = str(classifier.__name__) - tc.metrics.plot_distributions(valid_z, results, f"/global/cscratch1/sd/abault/tomo_challenge/plots/{name}_{settings}_{bands}.png") + tc.metrics.plot_distributions(valid_z, results, f"plots/{name}_{settings}_{bands}.png") return scores From de595c046b9ac537363f16d2661afc1697d561e2 Mon Sep 17 00:00:00 2001 From: Abby Bault <47161792+theabbybault@users.noreply.github.com> Date: Thu, 10 Sep 2020 11:55:22 -0700 Subject: [PATCH 30/36] Update example yaml files --- example/funbins_example.yaml | 5 +++-- example/funbins_example_combine.yaml | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/example/funbins_example.yaml b/example/funbins_example.yaml index be4d37de..d66295b5 100644 --- a/example/funbins_example.yaml +++ b/example/funbins_example.yaml @@ -1,8 +1,8 @@ metrics: all bands: riz training_file: data/training.hdf5 -validation_file: data/mini_validation.hdf5 -output_file: tomo_challenge/funbins_example_4bins.txt +validation_file: data/validation.hdf5 +output_file: funbins_example_4bins.txt # Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" metrics_impl: jax-cosmo @@ -14,6 +14,7 @@ run: bins: 4 seed: 123 method: 'log' + combinebins: # These special settings decide whether the # color and error colums are passed to the classifier # as well as the magnitudes diff --git a/example/funbins_example_combine.yaml b/example/funbins_example_combine.yaml index 56fc9d94..8699d19b 100644 --- a/example/funbins_example_combine.yaml +++ b/example/funbins_example_combine.yaml @@ -2,7 +2,7 @@ metrics: all bands: riz training_file: /data/training.hdf5 validation_file: /data/validation.hdf5 -output_file: tomo_challenge/funbins_example_combinebins.txt +output_file: funbins_example_combinebins.txt # Backend implementing the metrics, either: "firecrown" (default), "jax-cosmo" metrics_impl: jax-cosmo From 6f06dc60f6ef82d4b39374ad92a988ed63e6d038 Mon Sep 17 00:00:00 2001 From: Abby Bault Date: Mon, 14 Sep 2020 11:14:33 -0700 Subject: [PATCH 31/36] Update classifier with another binning method new binning method is from David Kirkby. Calculates the bin edges so they are equally spaced in comoving distance (I've called it 'chi'). --- .DS_Store | Bin 6148 -> 6148 bytes results/.DS_Store | Bin 6148 -> 6148 bytes results/random/.DS_Store | Bin 6148 -> 6148 bytes tomo_challenge/.DS_Store | Bin 0 -> 6148 bytes tomo_challenge/classifiers/myclassifier.py | 21 +++++++++++++++++++-- 5 files changed, 19 insertions(+), 2 deletions(-) create mode 100644 tomo_challenge/.DS_Store diff --git a/.DS_Store b/.DS_Store index d9f7f813c17ec4c678cc4ef5aca410bebc221279..7dd7cbe3dae1324bbcefc658ae319c869ccb3acb 100644 GIT binary patch delta 110 zcmZoMXfc@J&&ahgU^g=(*Jd7;WsJ;*24<5tuy{=V%eqFIkD-JipCK2B;~A0}G8hsW ia)2lNikPa09 diff --git a/results/.DS_Store b/results/.DS_Store index 821197423b36a07a3f323dcf3d7af9b24c1cdfe7..fc2c86551b4c47e4390bc7321cfe41c5316396f8 100644 GIT binary patch delta 16 XcmZoMXffEZmW|oaz-;pdwh%!8FZ2a8 delta 16 XcmZoMXffEZmTmGTR?p4f*!TniINb&< diff --git a/results/random/.DS_Store b/results/random/.DS_Store index 56cba989ef878495f0f1a48f3585f939dab2e744..8431f7d68f3380672c43fced718cea489f3d3761 100644 GIT binary patch delta 16 XcmZoMXffEZiIv&Vz-;qYR(~M?FewE% delta 16 XcmZoMXffEZiFNWu7SGMUSviFOIRgeS diff --git a/tomo_challenge/.DS_Store b/tomo_challenge/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..2385de36b5ffe9920a155da85546a111c255c3d6 GIT binary patch literal 6148 zcmeHK!AiqG5S^`6w=H51f*$wct%sJ1Jqe-KgEt|f2bDIlRRb}RCbej-`~rW+ zncZ!r)Sd*9G81Os?Ci`g`x17t03e!!ZVjLe01}ljSHR{6p>fhV$ypDf&~qdZzzu2- z`g76j_>T78%w(>M;Nw&$M(y(D|TH2_1`> zL49;!L%)wSo+l(ho9+^X%A#X2Gl(N7!lWXaRAH|e!la{L**M2yX3(UA&@1CSc4c92 zC_=A}ex<`fI0m_82AF|a23AbBO!fb0@%?``iF?cdGq6+)h%piJD_(wp~zzs9-rwn`noBdSp literal 0 HcmV?d00001 diff --git a/tomo_challenge/classifiers/myclassifier.py b/tomo_challenge/classifiers/myclassifier.py index 338fb7fb..fffe5d92 100644 --- a/tomo_challenge/classifiers/myclassifier.py +++ b/tomo_challenge/classifiers/myclassifier.py @@ -15,6 +15,8 @@ from .base import Tomographer import numpy as np from sklearn.ensemble import RandomForestClassifier +import jax_cosmo.parameters +import jax_cosmo.background class funbins(Tomographer): @@ -42,7 +44,7 @@ class variable. Valiad options are: 'bins' - number of tomographic bins 'seed' - the seed to use for RandomState - 'method' - what method to use for binning: 'log', 'random', 'linear' + 'method' - what method to use for binning: 'log', 'random', 'linear', 'chi' 'combinebins' - what 2 bins to combine as a list """ @@ -76,7 +78,7 @@ def train (self, training_data, training_z): print('The default method for binning has been set to log.') else: method = self.opt['method'] - assert method in ['log', 'random', 'linear'], 'The method must be log, random, or linear' + assert method in ['log', 'random', 'linear', 'chi'], 'The method must be log, random, linear, or chi' print(f'The method has been set to {method}') if self.opt['combinebins'] is None: @@ -116,6 +118,21 @@ def train (self, training_data, training_z): #training_bin = np.load('dc2-labels.npy')#[:len(training_z)] #print(len(training_bin)) + if method == 'chi': + z = np.asarray(training_z) + # Tabulate comoving distance over a grid spanning the full range of input redshifts. + zgrid = np.linspace(0, z.max(), 1000) + agrid = 1 / (1 + zgrid) + model = jax_cosmo.parameters.Planck15() + chi_grid = jax_cosmo.background.radial_comoving_distance(model, agrid) + # Compute bin edges that are equally spaced in chi. + chi_edges = np.linspace(0, chi_grid[-1], n_bin + 1) + z_edges = np.empty(n_bin + 1) + z_edges[0] = 0. + z_edges[-1] = z.max() + z_edges[1:-1] = np.interp(chi_edges[1:-1], chi_grid, zgrid) + + # Now find all the objects in each of these bins for i in range(n_bin): z_low = z_edges[i] From 3e7c2cc94b1d6970ec155ba5721c9a9422dc861a Mon Sep 17 00:00:00 2001 From: Abby Bault Date: Mon, 14 Sep 2020 11:15:10 -0700 Subject: [PATCH 32/36] Add results for bins 12-20 --- ...123, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 32491 bytes ...123, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 34189 bytes ...123, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 36196 bytes ...123, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 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results for bins 12-20 --- results/.DS_Store | Bin 6148 -> 6148 bytes results/log/.DS_Store | Bin 6148 -> 6148 bytes ...2, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 31814 bytes ...4, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 33675 bytes ...6, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 35895 bytes ...8, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 36292 bytes ...0, 'colors'_ True, 'errors'_ True}_riz.png | Bin 0 -> 38725 bytes results/log/jax/log_12bins.txt | 1 + results/log/jax/log_14bins.txt | 1 + results/log/jax/log_16bins.txt | 1 + results/log/jax/log_18bins.txt | 1 + results/log/jax/log_20bins.txt | 1 + results/random/.DS_Store | Bin 6148 -> 8196 bytes 13 files changed, 5 insertions(+) create mode 100644 results/log/jax/RandomForest_log_{'bins'_ 12, 'colors'_ True, 'errors'_ True}_riz.png create mode 100644 results/log/jax/RandomForest_log_{'bins'_ 14, 'colors'_ True, 'errors'_ True}_riz.png create mode 100644 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" ] @@ -141,7 +160,8 @@ } ], "source": [ - "plot_onemetric(jaxbins, 'FOM_DETF_3x2', random_scores_j, 'random', log_scores_j, 'log')" + "#plot all 3 log, random, and comoving distance bins scores on the same plot\n", + "plot_onemetric(jaxbins, 'FOM_DETF_3x2', chi_scores, 'chi', log_scores_j, 'log', random_scores_j, 'random')" ] }, { @@ -199,7 +219,7 @@ " ax.set_xticklabels([str(i) for i in bins])\n", " ax.legend()\n", " \n", - " plt.savefig('fig.png')\n" + " #plt.savefig('fig.png')\n" ] }, { @@ -221,7 +241,7 @@ } ], "source": [ - "plot_metrics(jaxbins, random_scores_j, 'Random Binning Scores for all Metrics Using Jax', 3, 3, sep_figs = True)" + "plot_metrics(jaxbins, random_scores_j, 'Random Binning Scores for all Metrics Using Jax', 3, 3)" ] }, { @@ -252,25 +272,32 @@ "metadata": {}, "outputs": [], "source": [ - "plot_metrics(bins, log_scores_f, 'Log Binning Scores for All Metrics Using Firecrown', 2, 3, sep_figs = True)" + "#this was just a placeholder while waiting for jobs to run\n", + "empty_dict = {'SNR_ww': 0, 'FOM_ww': 0, 'FOM_DETF_ww': 0, 'SNR_gg': 0, 'FOM_gg': 0, 'FOM_DETF_gg': 0, 'SNR_3x2': 0, 'FOM_3x2': 0, 'FOM_DETF_3x2': 0}\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "plot_metrics(bins, uniform_scores_f, 'Uniform Binning Scores for All Metrics Using Firecrown', 2, 3)" + "plot_metrics(jaxbins, chi_scores, 'Chi Binning Scores for All Metrics Using Jax', 3, 3)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -287,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ From b6009e8656b92feac361290604e86726d1cbee5b Mon Sep 17 00:00:00 2001 From: Abby Bault Date: Mon, 14 Sep 2020 12:39:43 -0700 Subject: [PATCH 36/36] Update example .yaml file --- .DS_Store | Bin 6148 -> 6148 bytes example/.DS_Store | Bin 0 -> 6148 bytes example/funbins_example.yaml | 13 +++++++++---- example/funbins_example_combine.yaml | 22 ---------------------- 4 files changed, 9 insertions(+), 26 deletions(-) create mode 100644 example/.DS_Store delete mode 100644 example/funbins_example_combine.yaml diff --git a/.DS_Store b/.DS_Store index 7dd7cbe3dae1324bbcefc658ae319c869ccb3acb..3257b5fc4353748b766d1a458d5ecbda76a01e84 100644 GIT binary patch delta 16 XcmZoMXffEZmSu7%yXWR_EPTQMHR}b? delta 16 XcmZoMXffEZmWA2a&~ozzmJne8Fa-rT diff --git a/example/.DS_Store b/example/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0

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zD&!1Ba!a6EV7&P`bq^4ylUQYJ{a)caM4|ngP^kb3xlFEuyY$i&mvu0hRO8){rkxHpTm8U@#kZRJYh$<2_d>`LjdK&CP0@!18&X=9AK^f&l(%BAbxg z&nz?dn-S0bxbF|F1XyZFEKN{_q!z8?O_Vl8<+Q JS3N7b Date: Mon, 14 Sep 2020 11:53:41 -0700 Subject: [PATCH 35/36] Update jupyter notebook with chi results --- ...mbined_bins.ipynb => funbins_results.ipynb | 73 +++++++++++++------ 1 file changed, 50 insertions(+), 23 deletions(-) rename log_random_and_combined_bins.ipynb => funbins_results.ipynb (68%) diff --git a/log_random_and_combined_bins.ipynb b/funbins_results.ipynb similarity index 68% rename from log_random_and_combined_bins.ipynb rename to funbins_results.ipynb index d658a10a..b606b0bd 100644 --- a/log_random_and_combined_bins.ipynb +++ b/funbins_results.ipynb @@ -37,7 +37,7 @@ "metadata": {}, "outputs": [], "source": [ - "bins = [2,4,6,8,10]\n", + "firecrownbins = [2,4,6,8,10]\n", "jaxbins = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]" ] }, @@ -65,11 +65,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#these scores were calculated using jax\n", + "#each dictionary corresponds to the scores for a different number of bins, in order: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20\n", "log_scores_j = [{'SNR_ww': 331.62371826171875, 'FOM_ww': 0.4617920517921448, 'FOM_DETF_ww': 0.013744479045271873, 'SNR_gg': 878.622314453125, 'FOM_gg': 53.327491760253906, 'FOM_DETF_gg': 0.786997377872467, 'SNR_3x2': 883.9085693359375, 'FOM_3x2': 255.66043090820312, 'FOM_DETF_3x2': 23.005399703979492},\n", " {'SNR_ww': 343.78753662109375, 'FOM_ww': 2.5369784832000732, 'FOM_DETF_ww': 0.18284554779529572, 'SNR_gg': 1126.6553955078125, 'FOM_gg': 460.51934814453125, 'FOM_DETF_gg': 7.035050868988037, 'SNR_3x2': 1128.9976806640625, 'FOM_3x2': 1246.9041748046875, 'FOM_DETF_3x2': 83.07261657714844}, \n", " {'SNR_ww': 348.9933776855469, 'FOM_ww': 12.718839645385742, 'FOM_DETF_ww': 0.5421679019927979, 'SNR_gg': 1277.877685546875, 'FOM_gg': 823.6866455078125, 'FOM_DETF_gg': 11.198760986328125, 'SNR_3x2': 1279.864990234375, 'FOM_3x2': 2050.5126953125, 'FOM_DETF_3x2': 102.53814697265625},\n", @@ -90,16 +91,28 @@ " {'SNR_ww': 355.1941223144531, 'FOM_ww': 39.79475402832031, 'FOM_DETF_ww': 0.8728305697441101, 'SNR_gg': 1584.9996337890625, 'FOM_gg': 1644.683349609375, 'FOM_DETF_gg': 12.5249605178833, 'SNR_3x2': 1587.56787109375, 'FOM_3x2': 7788.44873046875, 'FOM_DETF_3x2': 64.36880493164062},\n", " {'SNR_ww': 355.55487060546875, 'FOM_ww': 39.971771240234375, 'FOM_DETF_ww': 0.8794472813606262, 'SNR_gg': 1690.378173828125, 'FOM_gg': 3778.079345703125, 'FOM_DETF_gg': 27.599533081054688, 'SNR_3x2': 1692.511474609375, 'FOM_3x2': 17064.439453125, 'FOM_DETF_3x2': 134.9441680908203},\n", " {'SNR_ww': 357.0149841308594, 'FOM_ww': 43.642425537109375, 'FOM_DETF_ww': 0.9857875108718872, 'SNR_gg': 1796.6209716796875, 'FOM_gg': 4112.5537109375, 'FOM_DETF_gg': 31.20852279663086, 'SNR_3x2': 1798.3226318359375, 'FOM_3x2': 17760.564453125, 'FOM_DETF_3x2': 152.83560180664062},\n", - " {'SNR_ww': 357.0483093261719, 'FOM_ww': 43.82171630859375, 'FOM_DETF_ww': 0.9936894178390503, 'SNR_gg': 1842.779052734375, 'FOM_gg': 4300.279296875, 'FOM_DETF_gg': 32.844966888427734, 'SNR_3x2': 1844.4127197265625, 'FOM_3x2': 18573.9375, 'FOM_DETF_3x2': 160.4752197265625}]\n" + " {'SNR_ww': 357.0483093261719, 'FOM_ww': 43.82171630859375, 'FOM_DETF_ww': 0.9936894178390503, 'SNR_gg': 1842.779052734375, 'FOM_gg': 4300.279296875, 'FOM_DETF_gg': 32.844966888427734, 'SNR_3x2': 1844.4127197265625, 'FOM_3x2': 18573.9375, 'FOM_DETF_3x2': 160.4752197265625}]\n", + "\n", + "chi_scores = [{'SNR_ww': 346.5560302734375, 'FOM_ww': 9.114718437194824, 'FOM_DETF_ww': 0.2503356337547302, 'SNR_gg': 883.410888671875, 'FOM_gg': 39.0669059753418, 'FOM_DETF_gg': 0.4050174355506897, 'SNR_3x2': 896.4786376953125, 'FOM_3x2': 843.8729248046875, 'FOM_DETF_3x2': 24.316349029541016},\n", + " {'SNR_ww': 354.8981628417969, 'FOM_ww': 32.0916748046875, 'FOM_DETF_ww': 0.8403875231742859, 'SNR_gg': 1135.8251953125, 'FOM_gg': 856.9429931640625, 'FOM_DETF_gg': 8.652419090270996, 'SNR_3x2': 1138.489013671875, 'FOM_3x2': 2470.149169921875, 'FOM_DETF_3x2': 52.71646499633789},\n", + " {'SNR_ww': 355.50146484375, 'FOM_ww': 37.755615234375, 'FOM_DETF_ww': 0.926099956035614, 'SNR_gg': 1291.5467529296875, 'FOM_gg': 1620.9384765625, 'FOM_DETF_gg': 15.487205505371094, 'SNR_3x2': 1293.889404296875, 'FOM_3x2': 5691.78515625, 'FOM_DETF_3x2': 107.30359649658203},\n", + " {'SNR_ww': 357.10772705078125, 'FOM_ww': 41.865394592285156, 'FOM_DETF_ww': 0.9800273776054382, 'SNR_gg': 1454.014892578125, 'FOM_gg': 2702.856689453125, 'FOM_DETF_gg': 23.363046646118164, 'SNR_3x2': 1455.990234375, 'FOM_3x2': 9601.0830078125, 'FOM_DETF_3x2': 137.81097412109375},\n", + " {'SNR_ww': 357.3131103515625, 'FOM_ww': 42.954627990722656, 'FOM_DETF_ww': 0.998559296131134, 'SNR_gg': 1558.8424072265625, 'FOM_gg': 3393.94921875, 'FOM_DETF_gg': 28.310165405273438, 'SNR_3x2': 1560.711669921875, 'FOM_3x2': 11873.3955078125, 'FOM_DETF_3x2': 151.22581481933594},\n", + " {'SNR_ww': 357.59197998046875, 'FOM_ww': 43.62521743774414, 'FOM_DETF_ww': 1.0060865879058838, 'SNR_gg': 1667.6494140625, 'FOM_gg': 3912.66796875, 'FOM_DETF_gg': 32.30958557128906, 'SNR_3x2': 1669.431396484375, 'FOM_3x2': 13472.0498046875, 'FOM_DETF_3x2': 162.13380432128906},\n", + " {'SNR_ww': 357.6208801269531, 'FOM_ww': 44.28642654418945, 'FOM_DETF_ww': 1.013672113418579, 'SNR_gg': 1766.71826171875, 'FOM_gg': 4394.3798828125, 'FOM_DETF_gg': 36.23318862915039, 'SNR_3x2': 1768.39990234375, 'FOM_3x2': 14904.86328125, 'FOM_DETF_3x2': 171.53282165527344},\n", + " {'SNR_ww': 357.7373962402344, 'FOM_ww': 44.8723258972168, 'FOM_DETF_ww': 1.0189826488494873, 'SNR_gg': 1846.935546875, 'FOM_gg': 4927.30517578125, 'FOM_DETF_gg': 39.980995178222656, 'SNR_3x2': 1848.527099609375, 'FOM_3x2': 16619.26953125, 'FOM_DETF_3x2': 179.15760803222656},\n", + " {'SNR_ww': 357.7864685058594, 'FOM_ww': 45.14503860473633, 'FOM_DETF_ww': 1.02281653881073, 'SNR_gg': 1935.201171875, 'FOM_gg': 5388.544921875, 'FOM_DETF_gg': 43.69058609008789, 'SNR_3x2': 1936.7386474609375, 'FOM_3x2': 17930.822265625, 'FOM_DETF_3x2': 187.78726196289062},\n", + " {'SNR_ww': 357.82049560546875, 'FOM_ww': 45.32192611694336, 'FOM_DETF_ww': 1.0239624977111816, 'SNR_gg': 1993.5626220703125, 'FOM_gg': 5813.80712890625, 'FOM_DETF_gg': 46.539649963378906, 'SNR_3x2': 1995.0289306640625, 'FOM_3x2': 19282.109375, 'FOM_DETF_3x2': 193.2664337158203}]\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "def plot_onemetric(bins, metric, scores1, bintype1, scores2 = None, bintype2 = None):\n", + "def plot_onemetric(bins, metric, scores1, bintype1, scores2 = None, bintype2 = None, scores3 = None, bintype3 = None):\n", " \n", " y1 = []\n", " for score in scores1:\n", @@ -112,14 +125,20 @@ " y2 = []\n", " for score in scores2:\n", " y2.append(score[metric])\n", - " plt.plot(bins,y2,'o', label = bintype2)\n", + " plt.plot(bins, y2, 'o', label = bintype2)\n", + " \n", + " if scores3 is not None:\n", + " y3 = []\n", + " for score in scores3:\n", + " y3.append(score[metric])\n", + " plt.plot(bins, y3, 'o', label = bintype3)\n", " \n", " plt.legend()\n", " plt.xticks(bins, [str(i) for i in bins])\n", " plt.title(f'{metric} scores')\n", " plt.xlabel('Number of Bins')\n", " plt.ylabel('Score')\n", - " plt.savefig('fig2')" + " #plt.savefig('fig2')" ] }, { @@ -129,7 +148,7 @@ "outputs": [ { "data": { - "image/png": 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OOYb169fzxBNP8Nd//ddND2OSJG3N4itXbRbGAHo2bmLxlasMZKPMQFaI88YkSWPd2vU9w2rXyLVkIMtMIqJ0GWPaeB6qliQ1x/Sp7bzi4R/xicmdTI8HWJu7ctaTC7jx+a8vXVrLablAtsMOO/Dggw+yyy67GMoGkZk8+OCD7LDDDqVLkSSNYV884A5m33g+7fEEADPiAf6u7XxuOWAW8NqitbWalgtkM2bMoLu7m3Xr1pUuZUzbYYcdmDGj+UtDSJLGj1f++v9CHcZ6tccTVTsfKFNUi2q5QNbW1sbee+9dugxJksa/MbT4dqub2DeGlSRJgxtDi2+3OgOZJEka2BhafLvVGcgkSdLAxtDi262u5eaQSZKkUTRGFt9udfaQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkjdSKTjhnNpwxtXpc0Vm6Io1T3odMkqSRWNEJ3zsVNvZU2xtWV9vgfbs0bPaQSZI0EsvOfCaM9drYU7VLw2QgkyRpJDZ0D69d2gIDmSRJIzFlxvDapS0wkEmSNBLzT4O29s3b2tqrdmmYDGSSJI3EnAVw7LkwZSYQ1eOx5zqhXyPiVZaSJI3UnAUGMI0Ke8gkSZIKM5BJkiQVZiCTJEkqzEAmSZJUWMMCWUR8NSLuj4hbBtj3FxGREbFrn7ZFEXFnRKyKiKMbVZckqYlc61Eakkb2kF0IvLF/Y0TMBF4P3NOn7QDgncCB9Wu+HBGTGlibJKnRetd63LAayGfWejSUSc/SsECWmVcDvxtg1znAJ4Ds03Yc8K3MfDwz7wLuBA5pVG2SpCZwrUdpyJo6hywi3gKsycxf9Nu1F7C6z3Z33SZJGq9c61EasqYFsoh4LvBpYKA1JWKAthygjYg4JSK6IqJr3bp1o1miJGk0udajNGTN7CF7CbA38IuIuBuYAdwUEXtQ9YjN7HPsDGDtQG+SmedlZkdmdkybNq3BJUuSRsy1HqUha1ogy8yVmblbZs7KzFlUIWxeZv4WuBx4Z0RsHxF7A/sA1zerNklSA7jWozRkDVvLMiIuAY4Cdo2IbuD0zLxgoGMz89aI6ARuA54EPpyZmxpVmySpSVzrURqShgWyzDxxK/tn9dv+LPDZRtUjSZI0VnmnfkmSpMIMZJIkSYUZyCRJkgpr2BwySZJa3dLla1h85SrWru9h+tR2Fh69H8fP9b7mGj4DmSRJI7B0+RoWLVlJz8bqpgBr1vewaMlKAEOZhs0hS0mSRmDxlaueDmO9ejZuYvGVqwpVpPHMQCZJ0gisXd8zrHZpSwxkkiSNwPSp7cNql7bEQCZJJa3ohHNmwxlTq8cVnaUr0hAtPHo/2tsmbdbW3jaJhUfvV6gijWdO6pekUlZ0wvdOhY31ENeG1dU2uNzQONA7cd+rLDUaIjNL1zBiHR0d2dXVVboMSRqZc2ZXIay/KTPh47c0vx5JDRURN2Zmx0D7HLKUpFI2dA+vXVLLMpBJUilTZgyvXVLLMpBJUinzT4O2flfktbVX7ZImFAOZJJUyZwEce241Z4yoHo891wn90gTkVZaSVNKcBQYwSfaQSZIklWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUzS2LWiE86ZDWdMrR5XdJauSJIawsXFJY1NKzrhe6fCxp5qe8PqahtcjFtSy7GHTNLYtOzMZ8JYr409VbsktRgDmaSxaUP38NolaRwzkEkam6bMGF67JI1jBjJJY9P806CtffO2tvaqXZJajIFM0tg0ZwEcey5MmQlE9XjsuU7ol9SSvMpS0tg1Z4EBTNKEYA+ZJElSYQYySZKkwgxkkiRJhTUskEXEVyPi/oi4pU/b4oj4ZUSsiIjvRsTUPvsWRcSdEbEqIo5uVF2SJEljTSN7yC4E3tiv7UfA7MycA/wKWAQQEQcA7wQOrF/z5YiY1MDaJEmSxoyGBbLMvBr4Xb+2H2bmk/XmtUDvHR6PA76VmY9n5l3AncAhjapNkiRpLCk5h+xPgCvq53sBq/vs667bniUiTomIrojoWrduXYNLlCRJarwigSwiPg08CVzc2zTAYTnQazPzvMzsyMyOadOmNapESZKkpmn6jWEj4iTgGGB+ZvaGrm5gZp/DZgBrm12bJElSCU3tIYuINwJ/CbwlM//QZ9flwDsjYvuI2BvYB7i+mbVJkiSV0rAesoi4BDgK2DUiuoHTqa6q3B74UUQAXJuZf5qZt0ZEJ3Ab1VDmhzNzU6NqkyRJGkvimVHD8aejoyO7urpKlyFJkrRVEXFjZnYMtM879UuSJBVmIJMkSSrMQCZJklSYgUwaz1Z0wjmz4Yyp1eOKztIVSZJGoOn3IZM0SlZ0wvdOhY091faG1dU2wJwF5eqSJA2bPWTSeLXszGfCWK+NPVW7JGlcMZBJ49WG7uG1S5LGLAOZNF5NmTG8dknSmGUgk8ar+adBW/vmbW3tVbskaVwxkEnj1ZwFcOy5MGUmENXjsec6oV+SxiGvspTGszkLDGCS1ALsIZMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZGpdKzrhnNlwxtTqcUVn6YokSRrQ5NIFSA2xohO+dyps7Km2N6yutgHmLChXlyRJA7CHTK1p2ZnPhLFeG3uqdkmSxhgDmVrThu7htUuSVJCBTK1pyozhtUuSVJCBTK1p/mnQ1r55W1t71S5J0hjTsEAWEV+NiPsj4pY+bS+IiB9FxB3148599i2KiDsjYlVEHN2oujRBzFkAx54LU2YCUT0ee64T+iVJY1JkZmPeOOII4BHg65k5u247C/hdZn4uIj4J7JyZfxkRBwCXAIcA04GrgH0zc9OWPqOjoyO7uroaUr8kSdJoiogbM7NjoH0N6yHLzKuB3/VrPg64qH5+EXB8n/ZvZebjmXkXcCdVOJMkSWp5zZ5Dtntm3gtQP+5Wt+8FrO5zXHfd9iwRcUpEdEVE17p16xparCRJUjOMlUn9MUDbgGOpmXleZnZkZse0adMaXJYkSVLjNTuQ3RcRewLUj/fX7d3AzD7HzQDWNrk2SZKkIpodyC4HTqqfnwRc1qf9nRGxfUTsDewDXN/k2iRJkopo2FqWEXEJcBSwa0R0A6cDnwM6I+L9wD3ACQCZeWtEdAK3AU8CH97aFZaSJEmtomGBLDNPHGTX/EGO/yzw2UbVI0mSNFaNlUn9kiRJE5aBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKG3Igi4j2iNivkcVIkiRNREMKZBFxLHAz8IN6++URcXkjC5MkSZoohtpDdgZwCLAeIDNvBmY1piRJkqSJZaiB7MnM3NDQSiRJkiaoyUM87paI+B/ApIjYBzgV+M/GlSVJkjRxDLWH7CPAgcDjwDeBDcDHGlWUJEnSRLLVHrKImARcnpmvAz7d+JIkSZImlq32kGXmJuAPETGlCfVIkiRNOEOdQ/YYsDIifgQ82tuYmac2pCpJkqQJZKiB7F/rP5IkSRplQwpkmXlRRDwH2LduWpWZGxtXliRJ0sQxpEAWEUcBFwF3AwHMjIiTMvPqxpUmSZI0MQx1yPLzwBsycxVAROwLXAK8olGFSZIkTRRDvQ9ZW28YA8jMXwFtjSlJkiRpYhlqD1lXRFwA/HO9/S7gxsaUJEmSNLEMNZB9EPgw1ZJJAVwNfLlRRUmSJE0kQw1kk4EvZeYX4Om792/fsKokSZImkKHOIVsGtPfZbgeuGv1y1FQrOuGc2XDG1OpxRWfpiiRJmpCG2kO2Q2Y+0ruRmY9ExHMbVJOaYUUnfO9U2NhTbW9YXW0DzFlQri5JkiagofaQPRoR83o3IqID6GlMSWqKZWc+E8Z6beyp2iVJUlMNtYfsY8C/RMRaIIHpwDsaVpUab0P38NolSVLDbLGHLCJeGRF7ZOYNwMuAS4EngR8AdzWhPjXKlBnDa5ckSQ2ztSHL/wc8UT//I+BTwD8ADwHnNbAuNdr806CtffO2tvaqXZIkNdXWhiwnZebv6ufvAM7LzO8A34mImxtbmhqqd+L+sjOrYcopM6ow5oR+SZKabquBLCImZ+aTwHzglGG8dlAR8XHgZKr5aCuB9wHPpRoSnUW1iPmCzHxopJ+hIZizwAAmSdIYsLUhy0uAn0bEZVRXVf4MICJeCmwYyQdGxF5Ud/zvyMzZwCTgncAngWWZuQ/Vfc8+OZL3lyRJGm+2GMgy87PAnwMXAodnZvZ53Ue24XMnA+0RMZmqZ2wtcBxwUb3/IuD4bXh/SZKkcWOrw46Zee0Abb8a6Qdm5pqIOBu4h6rX7YeZ+cOI2D0z762PuTcidhvo9RFxCvXQ6Qtf+MKRliFJkjRmDPXGsKMmInam6g3bm+p+ZjtGxLuH+vrMPC8zOzKzY9q0aY0qU5IkqWmaHsiA1wF3Zea6zNwILAH+GLgvIvYEqB/vL1CbJElS05UIZPcAh0bEcyMiqK7evB24HDipPuYk4LICtUmSJDXdiG9dMVKZeV1EfBu4iequ/8upbjK7E9AZEe+nCm0nNLs2SZKkEpoeyAAy83Tg9H7Nj1P1lkmSJE0oJYYsJUmS1IeBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMImly5AktS6li5fw+IrV7F2fQ/Tp7az8Oj9OH7uXqXLksYcA5kkqSGWLl/DoiUr6dm4CYA163tYtGQlgKFM6schS0lSQyy+ctXTYaxXz8ZNLL5yVaGKpLHLHjJJKqiVh/TWru8ZVrs0kdlDJkmF9A7prVnfQ/LMkN7S5WtKlzYqpk9tH1a7NJEZyCSpkFYf0lt49H60t03arK29bRILj96vUEXS2OWQpSQV0upDer1Dr606JCuNJgOZJBUyfWo7awYIX600pHf83L0MYNIQOGQpSYU4pCeplz1kklSIQ3qSehUJZBExFTgfmA0k8CfAKuBSYBZwN7AgMx8qUZ8kNYtDepKg3JDll4AfZObLgIOB24FPAssycx9gWb0tSZLU8poeyCLi+cARwAUAmflEZq4HjgMuqg+7CDi+2bVJkiSVUKKH7MXAOuBrEbE8Is6PiB2B3TPzXoD6cbeBXhwRp0REV0R0rVu3rnlVS5IkNUiJQDYZmAd8JTPnAo8yjOHJzDwvMzsys2PatGmNqlGSJKlpSgSybqA7M6+rt79NFdDui4g9AerH+wvUJkmS1HRNv8oyM38bEasjYr/MXAXMB26r/5wEfK5+vKzZtUkaW1p54W1J6qvUfcg+AlwcEc8B/gt4H1VvXWdEvB+4BzihUG2SxoDehbd713rsXXgbMJRJajlFAllm3gx0DLBrfrNrkTQ2bWnhbQOZpFbj0kmSxqRWX3hbkvoykEkakwZbYLuVFt6WpF4GMkljkgtvS5pIXFxcGsda+SpEF96WNJEYyKRxaiJchejC25ImCocspXFqS1chSpLGFwOZNE55FaIktQ4DmTROeRWiJLUOA5k0TnkVoiS1Dif1S+OUVyFKUuswkEnjmFchSlJrcMhSkiSpMAOZJElSYQYySZKkwgxkkiRJhTmpXy2rldd5lCS1FgOZWtJEWOdRktQ6HLJUS3KdR0nSeGIgU0tynUdJ0nhiIFNLcp1HSdJ4YiBTS3KdR0nSeOKkfrUk13mUJI0nBjK1LNd5lCSNFw5ZSpIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMK87cUEtus3zokAAA2gSURBVHT5Gu/TJUnSGGAgm6CWLl/DoiUrn16Ae836HhYtWQlgKJMkqckcspygFl+56ukw1qtn4yYWX7mqUEWSJE1cBrIJau36nmG1S5KkxjGQTVDTp7YPq12SJDWOgWyCWnj0frS3Tdqsrb1tEguP3q9QRZIkTVxO6p+geifue5WlJEnlGcgmsOPn7mUAkyRpDHDIUpIkqbBigSwiJkXE8oj4fr39goj4UUTcUT/uXKo2SZKkZirZQ/ZR4PY+258ElmXmPsCyeluSJKnlFQlkETEDeDNwfp/m44CL6ucXAcc3uy5JkqQSSvWQfRH4BPBUn7bdM/NegPpxt4FeGBGnRERXRHStW7eu8ZVKkiQ1WNMDWUQcA9yfmTeO5PWZeV5mdmRmx7Rp00a5OkmSpOYrcduLw4C3RMSbgB2A50fEN4D7ImLPzLw3IvYE7i9QmyRJUtM1vYcsMxdl5ozMnAW8E/j3zHw3cDlwUn3YScBlza5NkiSphLF0H7LPAa+PiDuA19fbkiRJLa/onfoz8yfAT+rnDwLzS9YjSZJUwljqIZMkSZqQDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAPZlqzohHNmwxlTq8cVnaUrkiRJLWhy6QLGrBWd8L1TYWNPtb1hdbUNMGdBubokSVLLsYdsMMvOfCaM9drYU7VLkiSNIgPZYDZ0D69dkiRphAxkg5kyY3jtkiRJI2QgG8z806CtffO2tvaqXZIkaRQZyAYzZwEcey5MmQlE9XjsuU7olyRJo67pV1lGxEzg68AewFPAeZn5pYh4AXApMAu4G1iQmQ81u77NzFlgAJMkSQ1XoofsSeDPM3N/4FDgwxFxAPBJYFlm7gMsq7clSZJaXtMDWWbem5k31c9/D9wO7AUcB1xUH3YRcHyza5MkSSqh6ByyiJgFzAWuA3bPzHuhCm3AboO85pSI6IqIrnXr1jWrVEmSpIYpFsgiYifgO8DHMvPhob4uM8/LzI7M7Jg2bVrjCpQkSWqSIoEsItqowtjFmbmkbr4vIvas9+8J3F+iNkmSpGZreiCLiAAuAG7PzC/02XU5cFL9/CTgsmbXJkmSVEKJxcUPA/4nsDIibq7bPgV8DuiMiPcD9wAnFKhNkiSp6ZoeyDLzGiAG2T2/mbVIkiSNBd6pX5IkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklRYiTv1jxtLl69h8ZWrWLu+h+lT21l49H4cP3ev0mVJkqQWYyAbxNLla1i0ZCU9GzcBsGZ9D4uWrAQwlEmSpFHlkOUgFl+56ukw1qtn4yYWX7mqUEWSJKlVGcgGsXZ9z7DaJUmSRspANojpU9uH1S5JkjRSBrJBLDx6P9rbJm3W1t42iYVH71eoIkmS1Kqc1D+I3on7XmUpSZIazUC2BcfP3csAJkmSGs4hS0mSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVFplZuoYRi4h1wG+a8FG7Ag804XNK8fzGv1Y/x1Y/P2j9c/T8xr9WP8dmnN+LMnPaQDvGdSBrlojoysyO0nU0iuc3/rX6Obb6+UHrn6PnN/61+jmWPj+HLCVJkgozkEmSJBVmIBua80oX0GCe3/jX6ufY6ucHrX+Ont/41+rnWPT8nEMmSZJUmD1kkiRJhRnIJEmSCjOQDSIiZkbEjyPi9oi4NSI+WrqmRoiISRGxPCK+X7qWRoiIqRHx7Yj4Zf13+UelaxpNEfHx+t/nLRFxSUTsULqmbRURX42I+yPilj5tL4iIH0XEHfXjziVr3BaDnN/i+t/oioj4bkRMLVnjthroHPvs+4uIyIjYtURto2Gw84uIj0TEqvq/ybNK1betBvk3+vKIuDYibo6Irog4pGSN22Kw3++lv2cMZIN7EvjzzNwfOBT4cEQcULimRvgocHvpIhroS8APMvNlwMG00LlGxF7AqUBHZs4GJgHvLFvVqLgQeGO/tk8CyzJzH2BZvT1eXcizz+9HwOzMnAP8CljU7KJG2YU8+xyJiJnA64F7ml3QKLuQfucXEa8BjgPmZOaBwNkF6hotF/Lsv7+zgM9k5suB0+rt8Wqw3+9Fv2cMZIPIzHsz86b6+e+pfpHvVbaq0RURM4A3A+eXrqURIuL5wBHABQCZ+URmri9b1aibDLRHxGTgucDawvVss8y8Gvhdv+bjgIvq5xcBxze1qFE00Pll5g8z88l681pgRtMLG0WD/B0CnAN8AhjXV5MNcn4fBD6XmY/Xx9zf9MJGySDnl8Dz6+dTGMffNVv4/V70e8ZANgQRMQuYC1xXtpJR90WqL8enShfSIC8G1gFfq4dlz4+IHUsXNVoycw3V/4XfA9wLbMjMH5atqmF2z8x7ofoyBXYrXE8j/QlwRekiRltEvAVYk5m/KF1Lg+wLvDoirouIn0bEK0sXNMo+BiyOiNVU3zvjvRcXeNbv96LfMwayrYiInYDvAB/LzIdL1zNaIuIY4P7MvLF0LQ00GZgHfCUz5wKPMr6HujZTz284DtgbmA7sGBHvLluVtkVEfJpqOOXi0rWMpoh4LvBpqqGuVjUZ2JlqCGwh0BkRUbakUfVB4OOZORP4OPXIw3g21n6/G8i2ICLaqP6yLs7MJaXrGWWHAW+JiLuBbwGvjYhvlC1p1HUD3ZnZ27P5baqA1ipeB9yVmesycyOwBPjjwjU1yn0RsSdA/Thuh4MGExEnAccA78rWu0HkS6j+x+EX9XfODOCmiNijaFWjqxtYkpXrqUYexu2FCwM4ieo7BuBfgHE7qR8G/f1e9HvGQDaI+v9sLgBuz8wvlK5ntGXmosyckZmzqCaC/3tmtlTvSmb+FlgdEfvVTfOB2wqWNNruAQ6NiOfW/17n00IXLfRzOdUvBOrHywrWMuoi4o3AXwJvycw/lK5ntGXmyszcLTNn1d853cC8+r/RVrEUeC1AROwLPAd4oGhFo2stcGT9/LXAHQVr2SZb+P1e9HvGO/UPIiIOB34GrOSZOVafysx/K1dVY0TEUcBfZOYxpWsZbRHxcqqLFp4D/Bfwvsx8qGxVoyciPgO8g2qYazlwcu+k4vEqIi4BjqLqXbgPOJ3ql10n8EKqIHpCZg40aXzMG+T8FgHbAw/Wh12bmX9apMBRMNA5ZuYFffbfTXV18LgMLIP8Hf4z8FXg5cATVN+p/16qxm0xyPmtorpqfTLwGPCh8TrlZbDf71TzyIp9zxjIJEmSCnPIUpIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEkqKiIyIj7fZ/svIuKMUXrvCyPiv4/Ge23lc06IiNsj4sf92mdFRE9E3BwRv4iI/+y9L15EdETEuY2uTdL4YCCTVNrjwNsiYkzd1TwiJg3j8PdT3ZfpNQPs+3VmvjwzD6ZasPhTAJnZlZmnjkKpklqAgUxSaU8C51Gtj7eZ/j1cEfFI/XhUvYBzZ0T8KiI+FxHviojrI2JlRLykz9u8LiJ+Vh93TP36SRGxOCJuiIgVEfGBPu/744j4JtVNI/vXc2L9/rdExN/VbacBhwP/GBGLt3Kuzwce6vNZ36+fnxERX42In0TEf0XEqXX7jhHxr3Xv2i0R8Y6h/UgljTeTSxcgScA/ACsi4qxhvOZgYH/gd1SrMJyfmYdExEeBjwAfq4+bRbXky0uAH0fES4H3ABsy85URsT3wHxHxw/r4Q4DZmXlX3w+LiOnA3wGvoApVP4yI4zPzzIh4LdWd2bsGqPMlEXEz8DzgucCrBjmflwGvqY9bFRFfAd4IrM3MN9c1TBnGz0fSOGIPmaTiMvNh4OvAcIbwbsjMe+ulon4N9AaqlVQhrFdnZj6VmXdQBbeXAW8A3lMHpeuAXYB96uOv7x/Gaq8EflIv5v4kcDFwxBDq7B2yfAlVSDxvkOP+NTMfr5cTuh/YvT6X10XE30XEqzNzwxA+T9I4ZCCTNFZ8kWou1o592p6k/p6qFwR+Tp99fdfsfKrP9lNs3vvff324BAL4SB2UXp6Ze2dmb6B7dJD6YqgnsgWXM3iI63s+m4DJmfkrqh65lcDf1sOjklqQgUzSmFAv4ttJFcp63U0VSACOA9pG8NYnRMR29byyF1Mtknwl8MGIaAOIiH0jYsctvQlVT9qREbFrPeH/ROCnw6zlcKrevCGph0n/kJnfAM4G5g3z8ySNE84hkzSWfB74sz7b/wRcFhHXA8sYvPdqS1ZRBafdgT/NzMci4nyqYc2b6p63dcDxW3qTzLw3IhYBP6bqLfu3zLxsCJ/fO4csgCeAk4dR+0HA4oh4CtgIfHAYr5U0jkRm/958SZIkNZNDlpIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJh/x9YIO0mD/+X+QAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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