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GNN_particles_PlotFigure.py
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import os
import numpy as np
import umap
import torch
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.ticker import FormatStrFormatter
from torch_geometric.nn import MessagePassing
import torch_geometric.utils as pyg_utils
import imageio.v2 as imageio
from matplotlib import rc
from ParticleGraph.utils import set_size
from scipy.ndimage import median_filter
# os.environ["PATH"] += os.pathsep + '/usr/local/texlive/2023/bin/x86_64-linux'
# from data_loaders import *
from GNN_particles_Ntype import *
from ParticleGraph.fitting_models import *
from ParticleGraph.sparsify import *
from ParticleGraph.models.utils import *
from ParticleGraph.models.MLP import *
from ParticleGraph.utils import to_numpy, CustomColorMap
import matplotlib as mpl
from io import StringIO
import sys
from scipy.stats import pearsonr
from scipy.spatial import Voronoi, voronoi_plot_2d
from sklearn.mixture import GaussianMixture
import warnings
import seaborn as sns
# from pysr import PySRRegressor
class Interaction_Particle_extract(MessagePassing):
"""Interaction Network as proposed in this paper:
https://proceedings.neurips.cc/paper/2016/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html"""
def __init__(self, config, device, aggr_type=None, bc_dpos=None):
super(Interaction_Particle_extract, self).__init__(aggr=aggr_type) # "Add" aggregation.
config.simulation = config.simulation
config.graph_model = config.graph_model
config.training = config.training
self.device = device
self.input_size = config.graph_model.input_size
self.output_size = config.graph_model.output_size
self.hidden_dim = config.graph_model.hidden_dim
self.n_layers = config.graph_model.n_mp_layers
self.n_particles = config.simulation.n_particles
self.max_radius = config.simulation.max_radius
self.rotation_augmentation = config.training.rotation_augmentation
self.noise_level = config.training.noise_level
self.embedding_dim = config.graph_model.embedding_dim
self.n_dataset = config.training.n_runs
self.prediction = config.graph_model.prediction
self.update_type = config.graph_model.update_type
self.n_layers_update = config.graph_model.n_layers_update
self.hidden_dim_update = config.graph_model.hidden_dim_update
self.sigma = config.simulation.sigma
self.model = config.graph_model.particle_model_name
self.bc_dpos = bc_dpos
self.n_ghosts = int(config.training.n_ghosts)
self.n_particles_max = config.simulation.n_particles_max
self.lin_edge = MLP(input_size=self.input_size, output_size=self.output_size, nlayers=self.n_layers,
hidden_size=self.hidden_dim, device=self.device)
if config.simulation.has_cell_division:
self.a = nn.Parameter(
torch.tensor(np.ones((self.n_dataset, self.n_particles_max, 2)), device=self.device,
requires_grad=True, dtype=torch.float32))
else:
self.a = nn.Parameter(
torch.tensor(np.ones((self.n_dataset, int(self.n_particles) + self.n_ghosts, self.embedding_dim)),
device=self.device,
requires_grad=True, dtype=torch.float32))
if self.update_type != 'none':
self.lin_update = MLP(input_size=self.output_size + self.embedding_dim + 2, output_size=self.output_size,
nlayers=self.n_layers_update, hidden_size=self.hidden_dim_update, device=self.device)
def forward(self, data=[], data_id=[], training=[], vnorm=[], phi=[], has_field=False):
self.data_id = data_id
self.vnorm = vnorm
self.cos_phi = torch.cos(phi)
self.sin_phi = torch.sin(phi)
self.training = training
self.has_field = has_field
x, edge_index = data.x, data.edge_index
edge_index, _ = pyg_utils.remove_self_loops(edge_index)
pos = x[:, 1:3]
d_pos = x[:, 3:5]
particle_id = x[:, 0:1]
if has_field:
field = x[:, 6:7]
else:
field = torch.ones_like(x[:, 6:7])
pred = self.propagate(edge_index, pos=pos, d_pos=d_pos, particle_id=particle_id, field=field)
return pred, self.in_features, self.lin_edge_out
def message(self, pos_i, pos_j, d_pos_i, d_pos_j, particle_id_i, particle_id_j, field_j):
# squared distance
r = torch.sqrt(torch.sum(self.bc_dpos(pos_j - pos_i) ** 2, dim=1)) / self.max_radius
delta_pos = self.bc_dpos(pos_j - pos_i) / self.max_radius
dpos_x_i = d_pos_i[:, 0] / self.vnorm
dpos_y_i = d_pos_i[:, 1] / self.vnorm
dpos_x_j = d_pos_j[:, 0] / self.vnorm
dpos_y_j = d_pos_j[:, 1] / self.vnorm
if self.rotation_augmentation & (self.training == True):
new_delta_pos_x = self.cos_phi * delta_pos[:, 0] + self.sin_phi * delta_pos[:, 1]
new_delta_pos_y = -self.sin_phi * delta_pos[:, 0] + self.cos_phi * delta_pos[:, 1]
delta_pos[:, 0] = new_delta_pos_x
delta_pos[:, 1] = new_delta_pos_y
new_dpos_x_i = self.cos_phi * dpos_x_i + self.sin_phi * dpos_y_i
new_dpos_y_i = -self.sin_phi * dpos_x_i + self.cos_phi * dpos_y_i
dpos_x_i = new_dpos_x_i
dpos_y_i = new_dpos_y_i
new_dpos_x_j = self.cos_phi * dpos_x_j + self.sin_phi * dpos_y_j
new_dpos_y_j = -self.sin_phi * dpos_x_j + self.cos_phi * dpos_y_j
dpos_x_j = new_dpos_x_j
dpos_y_j = new_dpos_y_j
embedding_i = self.a[self.data_id, to_numpy(particle_id_i), :].squeeze()
embedding_j = self.a[self.data_id, to_numpy(particle_id_j), :].squeeze()
match self.model:
case 'PDE_A':
in_features = torch.cat((delta_pos, r[:, None], embedding_i), dim=-1)
case 'PDE_B' | 'PDE_B_bis' | 'PDE_Cell_B':
in_features = torch.cat((delta_pos, r[:, None], dpos_x_i[:, None], dpos_y_i[:, None], dpos_x_j[:, None],
dpos_y_j[:, None], embedding_i), dim=-1)
case 'PDE_G':
in_features = torch.cat((delta_pos, r[:, None], dpos_x_i[:, None], dpos_y_i[:, None],
dpos_x_j[:, None], dpos_y_j[:, None], embedding_j), dim=-1)
case 'PDE_GS':
in_features = torch.cat((r[:, None], embedding_j), dim=-1)
case 'PDE_E':
in_features = torch.cat(
(delta_pos, r[:, None], embedding_i, embedding_j), dim=-1)
out = self.lin_edge(in_features) * field_j
self.in_features = in_features
self.lin_edge_out = out
return out
def update(self, aggr_out):
return aggr_out # self.lin_node(aggr_out)
def psi(self, r, p):
if (len(p) == 3): # PDE_B
cohesion = p[0] * 0.5E-5 * r
separation = -p[2] * 1E-8 / r
return (cohesion + separation) * p[1] / 500 #
else: # PDE_A
return r * (p[0] * torch.exp(-r ** (2 * p[1]) / (2 * self.sigma ** 2)) - p[2] * torch.exp(
-r ** (2 * p[3]) / (2 * self.sigma ** 2)))
class model_qiqj(nn.Module):
def __init__(self, size=None, device=None):
super(model_qiqj, self).__init__()
self.device = device
self.size = size
self.qiqj = nn.Parameter(torch.randn((int(self.size), 1), device=self.device,requires_grad=True, dtype=torch.float32))
def forward(self):
x = []
for l in range(self.size):
for m in range(l,self.size,1):
x.append(self.qiqj[l] * self.qiqj[m])
return torch.stack(x)
class PDE_B_extract(MessagePassing):
"""Interaction Network as proposed in this paper:
https://proceedings.neurips.cc/paper/2016/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html"""
def __init__(self, aggr_type=None, p=None, bc_dpos=None):
super(PDE_B_extract, self).__init__(aggr=aggr_type) # "mean" aggregation.
self.p = p
self.bc_dpos = bc_dpos
self.a1 = 0.5E-5
self.a2 = 5E-4
self.a3 = 1E-8
def forward(self, data):
x, edge_index = data.x, data.edge_index
edge_index, _ = pyg_utils.remove_self_loops(edge_index)
acc = self.propagate(edge_index, x=x)
sum = self.cohesion + self.alignment + self.separation
return acc, sum, self.cohesion, self.alignment, self.separation, self.diffx, self.diffv, self.r, self.type
def message(self, x_i, x_j):
r = torch.sum(self.bc_dpos(x_j[:, 1:3] - x_i[:, 1:3]) ** 2, dim=1) # distance squared
pp = self.p[to_numpy(x_i[:, 5]), :]
cohesion = pp[:, 0:1].repeat(1, 2) * self.a1 * self.bc_dpos(x_j[:, 1:3] - x_i[:, 1:3])
alignment = pp[:, 1:2].repeat(1, 2) * self.a2 * self.bc_dpos(x_j[:, 3:5] - x_i[:, 3:5])
separation = pp[:, 2:3].repeat(1, 2) * self.a3 * self.bc_dpos(x_i[:, 1:3] - x_j[:, 1:3]) / (
r[:, None].repeat(1, 2))
self.cohesion = cohesion
self.alignment = alignment
self.separation = separation
self.r = r
self.diffx = self.bc_dpos(x_j[:, 1:3] - x_i[:, 1:3])
self.diffv = self.bc_dpos(x_j[:, 3:5] - x_i[:, 3:5])
self.type = x_i[:, 5]
return (separation + alignment + cohesion)
def psi(self, r, p):
cohesion = p[0] * self.a1 * r
separation = -p[2] * self.a3 / r
return (cohesion + separation)
class Mesh_RPS_extract(MessagePassing):
"""Interaction Network as proposed in this paper:
https://proceedings.neurips.cc/paper/2016/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html"""
def __init__(self, aggr_type=None, config=None, device=None, bc_dpos=None):
super(Mesh_RPS_extract, self).__init__(aggr=aggr_type)
config.simulation = config.simulation
config.graph_model = config.graph_model
self.device = device
self.input_size = config.graph_model.input_size
self.output_size = config.graph_model.output_size
self.hidden_size = config.graph_model.hidden_dim
self.nlayers = config.graph_model.n_mp_layers
self.embedding_dim = config.graph_model.embedding_dim
self.nparticles = config.simulation.n_particles
self.ndataset = config.training.n_runs
self.bc_dpos = bc_dpos
self.lin_phi = MLP(input_size=self.input_size, output_size=self.output_size, nlayers=self.nlayers,
hidden_size=self.hidden_size, device=self.device)
self.a = nn.Parameter(
torch.tensor(np.ones((int(self.ndataset), int(self.nparticles), self.embedding_dim)), device=self.device,
requires_grad=True, dtype=torch.float32))
def forward(self, data, data_id):
self.data_id = data_id
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
uvw = data.x[:, 6:9]
laplacian_uvw = self.propagate(edge_index, uvw=uvw, discrete_laplacian=edge_attr)
particle_id = to_numpy(x[:, 0])
embedding = self.a[self.data_id, particle_id, :]
input_phi = torch.cat((laplacian_uvw, uvw, embedding), dim=-1)
pred = self.lin_phi(input_phi)
return pred, input_phi, embedding
def message(self, uvw_j, discrete_laplacian):
return discrete_laplacian[:, None] * uvw_j
def update(self, aggr_out):
return aggr_out # self.lin_node(aggr_out)
def psi(self, r, p):
return p * r
def load_training_data(dataset_name, n_runs, log_dir, device):
x_list = []
y_list = []
print('load data ...')
time.sleep(0.5)
for run in trange(n_runs):
# check if path exists
if os.path.exists(f'graphs_data/{dataset_name}/x_list_{run}.pt'):
x = torch.load(f'graphs_data/{dataset_name}/x_list_{run}.pt', map_location=device)
y = torch.load(f'graphs_data/{dataset_name}/y_list_{run}.pt', map_location=device)
else:
x = np.load(f'graphs_data/{dataset_name}/x_list_{run}.npy')
x = torch.tensor(x, dtype=torch.float32, device=device)
y = np.load(f'graphs_data/{dataset_name}/y_list_{run}.npy')
y = torch.tensor(y, dtype=torch.float32, device=device)
x_list.append(x)
y_list.append(y)
vnorm = torch.load(os.path.join(log_dir, 'vnorm.pt'), map_location=device).squeeze()
ynorm = torch.load(os.path.join(log_dir, 'ynorm.pt'), map_location=device).squeeze()
print("vnorm:{:.2e}, ynorm:{:.2e}".format(to_numpy(vnorm), to_numpy(ynorm)))
x = []
y = []
return x_list, y_list, vnorm, ynorm
def plot_embedding_func_cluster_tracking(model, config, embedding_cluster, cmap, index_particles, indexes, type_list,
n_particle_types, n_particles, ynorm, epoch, log_dir, embedding_type, style, device):
if embedding_type == 1:
embedding = to_numpy(model.a.clone().detach())
embedding = embedding[indexes.astype(int)]
fig, ax = fig_init()
for n in range(n_particle_types):
pos = np.argwhere(type_list == n).squeeze().astype(int)
plt.scatter(embedding[pos, 0], embedding[pos, 1], s=1, alpha=0.25)
if 'latex' in style:
plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
else:
plt.xlabel(r'$a_{0}$', fontsize=68)
plt.ylabel(r'$a_{1}$', fontsize=68)
plt.xlim(config.plotting.embedding_lim)
plt.ylim(config.plotting.embedding_lim)
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/all_embedding_{epoch}.tif", dpi=170.7)
plt.close()
else:
fig, ax = fig_init()
for k in trange(0, config.simulation.n_frames - 2):
embedding = to_numpy(model.a[k * n_particles:(k + 1) * n_particles, :].clone().detach())
for n in range(n_particle_types):
plt.scatter(embedding[index_particles[n], 0], embedding[index_particles[n], 1], s=1,
color=cmap.color(n), alpha=0.025)
if 'latex' in style:
plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
else:
plt.xlabel(r'$a_{0}$', fontsize=68)
plt.ylabel(r'$a_{1}$', fontsize=68)
plt.xlim(config.plotting.embedding_lim)
plt.ylim(config.plotting.embedding_lim)
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/all_embedding_{epoch}.tif", dpi=170.7)
plt.close()
func_list, proj_interaction = analyze_edge_function_tracking(rr=[], vizualize=False, config=config,
model_MLP=model.lin_edge, model_a=model.a,
n_particles=n_particles, ynorm=ynorm,
indexes=indexes, type_list = type_list,
cmap=cmap, embedding_type = embedding_type, device=device)
fig, ax = fig_init()
proj_interaction = (proj_interaction - np.min(proj_interaction)) / (np.max(proj_interaction) - np.min(proj_interaction) + 1e-10)
if embedding_type == 1:
for n in range(n_particle_types):
pos = np.argwhere(type_list == n).squeeze().astype(int)
plt.scatter(proj_interaction[pos, 0], proj_interaction[pos, 1], s=1, alpha=0.25)
else:
for n in range(n_particle_types):
plt.scatter(proj_interaction[index_particles[n], 0],
proj_interaction[index_particles[n], 1], color=cmap.color(n), s=1, alpha=0.25)
plt.xlabel(r'UMAP 0', fontsize=68)
plt.ylabel(r'UMAP 1', fontsize=68)
plt.xlim([-0.2, 1.2])
plt.ylim([-0.2, 1.2])
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/UMAP_{epoch}.tif", dpi=170.7)
plt.close()
embedding = to_numpy(model.a.clone().detach())
if embedding_type == 1:
embedding = embedding[indexes.astype(int)]
else:
embedding = embedding[0:n_particles]
labels, n_clusters, new_labels = sparsify_cluster(config.training.cluster_method, proj_interaction, embedding,
config.training.cluster_distance_threshold, type_list,
n_particle_types, embedding_cluster)
accuracy = metrics.accuracy_score(type_list, new_labels)
fig, ax = fig_init()
for n in np.unique(labels):
pos = np.argwhere(labels == n).squeeze().astype(int)
plt.scatter(proj_interaction[pos, 0], proj_interaction[pos, 1], s=1, alpha=0.25)
return accuracy, n_clusters, new_labels
def plot_embedding_func_cluster_state(model, config,embedding_cluster, cmap, type_list, type_stack, id_list,
n_particle_types, ynorm, epoch, log_dir, style, device):
fig, ax = fig_init()
for n in range(n_particle_types):
pos = torch.argwhere(type_stack == n).squeeze()
plt.scatter(to_numpy(model.a[pos, 0]), to_numpy(model.a[pos, 1]), s=1, color=cmap.color(n), alpha=0.25)
if 'latex' in style:
plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
else:
plt.xlabel(r'$a_{0}$', fontsize=68)
plt.ylabel(r'$a_{1}$', fontsize=68)
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/embedding_{epoch}.tif", dpi=170.7)
plt.close()
fig, ax = fig_init()
func_list, true_type_list, short_model_a_list, proj_interaction = analyze_edge_function_state(rr=[], config=config,
model=model,
id_list=id_list, type_list=type_list, ynorm=ynorm,
cmap=cmap, visualize=True, device=device)
plt.savefig(f"./{log_dir}/results/function_{epoch}.tif", dpi=170.7)
plt.close()
fig, ax = fig_init()
for n in range(n_particle_types):
pos = np.argwhere(true_type_list == n).squeeze().astype(int)
if len(pos)>0:
plt.scatter(proj_interaction[pos, 0], proj_interaction[pos, 1], color=cmap.color(n), s=100, alpha=0.25, edgecolors='none')
plt.xlabel(r'UMAP 0', fontsize=68)
plt.ylabel(r'UMAP 1', fontsize=68)
plt.xlim([-0.2, 1.2])
plt.ylim([-0.2, 1.2])
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/UMAP_{epoch}.tif", dpi=170.7)
plt.close()
embedding = proj_interaction
labels, n_clusters, new_labels = sparsify_cluster_state(config.training.cluster_method, proj_interaction, embedding,
config.training.cluster_distance_threshold, true_type_list,
n_particle_types, embedding_cluster)
fig, ax = fig_init()
for n in range(n_particle_types):
pos = np.argwhere(new_labels == n).squeeze().astype(int)
if len(pos)>0:
plt.scatter(proj_interaction[pos, 0], proj_interaction[pos, 1], color=cmap.color(n), s=10, alpha=0.25)
plt.xlim([-0.2, 1.2])
plt.ylim([-0.2, 1.2])
plt.tight_layout()
plt.close()
accuracy = metrics.accuracy_score(true_type_list, new_labels)
# calculate type for all nodes
fig, ax = fig_init()
median_center_list = []
for n in range(n_clusters):
pos = np.argwhere(new_labels == n).squeeze().astype(int)
pos = np.array(pos)
if pos.size > 0:
median_center = short_model_a_list[pos, :]
plt.scatter(to_numpy(short_model_a_list[pos,0]),to_numpy(short_model_a_list[pos,1]))
median_center = torch.mean(median_center, dim=0)
plt.scatter(to_numpy(median_center[0]), to_numpy(median_center[1]), s=100, color='black')
median_center_list.append(median_center)
median_center_list = torch.stack(median_center_list)
median_center_list = median_center_list.to(dtype=torch.float32)
plt.close()
distance = torch.sum((model.a[:, None, :] - median_center_list[None, :, :]) ** 2, dim=2)
result = distance.min(dim=1)
min_index = result.indices
new_labels = to_numpy(min_index).astype(int)
accuracy = metrics.accuracy_score(to_numpy(type_stack.squeeze()), new_labels)
return accuracy, n_clusters, new_labels
def plot_embedding_func_cluster(model, config,embedding_cluster, cmap, index_particles, type_list,
n_particle_types, n_particles, ynorm, epoch, log_dir, alpha, style, device):
fig, ax = fig_init()
if config.training.do_tracking:
embedding = to_numpy(model.a[0:n_particles])
else:
embedding = get_embedding(model.a, 1)
if config.training.particle_dropout > 0:
embedding = embedding[0:n_particles]
if n_particle_types > 1000:
plt.scatter(embedding[:, 0], embedding[:, 1], c=to_numpy(x[:, 5]) / n_particles, s=10,
cmap=cc)
else:
for n in range(n_particle_types):
pos = torch.argwhere(type_list == n)
pos = to_numpy(pos)
if len(pos) > 0:
plt.scatter(embedding[pos, 0], embedding[pos, 1], color=cmap.color(n), s=10)
if 'latex' in style:
plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
else:
plt.xlabel(r'$a_{0}$', fontsize=68)
plt.ylabel(r'$a_{1}$', fontsize=68)
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/embedding_{epoch}.tif", dpi=170.7)
plt.close()
fig, ax = fig_init()
if 'PDE_N' in config.graph_model.signal_model_name:
model_MLP_ = model.lin_phi
else:
model_MLP_ = model.lin_edge
func_list, proj_interaction = analyze_edge_function(rr=[], vizualize=True, config=config, model_MLP=model_MLP_, model_a=model.a, type_list=to_numpy(type_list), n_particles=n_particles, dataset_number=1, ynorm=ynorm, cmap=cmap, device=device)
plt.close()
# trans = umap.UMAP(n_neighbors=100, n_components=2, init='spectral').fit(func_list_)
# proj_interaction = trans.transform(func_list_)
# tsne = TSNE(n_components=2, random_state=0)
# proj_interaction = tsne.fit_transform(func_list_)
fig, ax = fig_init()
proj_interaction = (proj_interaction - np.min(proj_interaction)) / (
np.max(proj_interaction) - np.min(proj_interaction) + 1e-10)
for n in range(n_particle_types):
pos = torch.argwhere(type_list == n)
pos = to_numpy(pos)
if len(pos) > 0:
plt.scatter(proj_interaction[pos, 0],
proj_interaction[pos, 1], color=cmap.color(n), s=200, alpha=0.1)
plt.xlabel(r'UMAP 0', fontsize=68)
plt.ylabel(r'UMAP 1', fontsize=68)
plt.xlim([-0.2, 1.2])
plt.ylim([-0.2, 1.2])
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/UMAP_functions_{epoch}.tif", dpi=170.7)
plt.close()
config.training.cluster_distance_threshold = 0.01
labels, n_clusters, new_labels = sparsify_cluster(config.training.cluster_method, proj_interaction, embedding,
config.training.cluster_distance_threshold, type_list,
n_particle_types, embedding_cluster)
accuracy = metrics.accuracy_score(to_numpy(type_list), new_labels)
fig, ax = fig_init()
for n in range(n_clusters):
pos = np.argwhere(labels == n)
if pos.size > 0:
plt.scatter(embedding[pos, 0], embedding[pos, 1], s=10)
if 'latex' in style:
plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
else:
plt.xlabel(r'$a_{0}$', fontsize=68)
plt.ylabel(r'$a_{1}$', fontsize=68)
plt.tight_layout()
plt.savefig(f"./{log_dir}/results/clustered_embedding_{epoch}.tif", dpi=170.7)
plt.close()
# model_a_ = model.a[1].clone().detach()
# for n in range(n_clusters):
# pos = np.argwhere(labels == n).squeeze().astype(int)
# pos = np.array(pos)
# if pos.size > 0:
# median_center = model_a_[pos, :]
# median_center = torch.median(median_center, dim=0).values
# model_a_[pos, :] = median_center
#
# embedding = to_numpy(model_a_)
# fig, ax = fig_init()
# for n in range(n_clusters):
# pos = np.argwhere(labels == n)
# if pos.size > 0:
# plt.scatter(embedding[pos, 0], embedding[pos, 1],s=10)
# plt.xlabel(r'$\ensuremath{\mathbf{a}}_{i0}$', fontsize=68)
# plt.ylabel(r'$\ensuremath{\mathbf{a}}_{i1}$', fontsize=68)
# plt.tight_layout()
# plt.savefig(f"./{log_dir}/results/UMAP_clustered_embedding_{epoch}.tif", dpi=170.7)
# plt.close()
return accuracy, n_clusters, new_labels
def plot_focused_on_cell(config, run, style, step, cell_id, device):
dataset_name = config.dataset
simulation_config = config.simulation
model_config = config.graph_model
training_config = config.training
has_adjacency_matrix = (simulation_config.connectivity_file != '')
has_mesh = (config.graph_model.mesh_model_name != '')
only_mesh = (config.graph_model.particle_model_name == '') & has_mesh
has_ghost = config.training.n_ghosts > 0
max_radius = simulation_config.max_radius
min_radius = simulation_config.min_radius
n_particle_types = simulation_config.n_particle_types
n_particles = simulation_config.n_particles
n_nodes = simulation_config.n_nodes
n_runs = training_config.n_runs
n_frames = simulation_config.n_frames
delta_t = simulation_config.delta_t
cmap = CustomColorMap(config=config) # create colormap for given model_config
dimension = simulation_config.dimension
has_siren = 'siren' in model_config.field_type
has_siren_time = 'siren_with_time' in model_config.field_type
has_field = ('PDE_ParticleField' in config.graph_model.particle_model_name)
l_dir = get_log_dir(config)
log_dir = os.path.join(l_dir, 'try_{}'.format(config_file.split('/')[-1]))
files = glob.glob(f"./{log_dir}/tmp_recons/*")
for f in files:
os.remove(f)
print('Load data ...')
x_list = torch.load(f'graphs_data/{dataset_name}/x_list_{run}.pt', map_location=device)
mass_time_series = get_time_series(x_list, cell_id, feature='mass')
vx_time_series = get_time_series(x_list, cell_id, feature='velocity_x')
vy_time_series = get_time_series(x_list, cell_id, feature='velocity_y')
v_time_series = np.sqrt(vx_time_series ** 2 + vy_time_series ** 2)
stage_time_series = get_time_series(x_list, cell_id, feature="stage")
stage_time_series_color = ["blue" if i == 0 else "orange" if i == 1 else "green" if i == 2 else "pink" for i in stage_time_series]
for it in trange(0,n_frames,step):
x = x_list[it].clone().detach()
T1 = x[:, 5:6].clone().detach()
H1 = x[:, 6:8].clone().detach()
X1 = x[:, 1:3].clone().detach()
index_particles = get_index_particles(x, n_particle_types, dimension)
pos_cell = torch.argwhere(x[:,0] == cell_id)
if len(pos_cell)>0:
if 'latex' in style:
plt.rcParams['text.usetex'] = True
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
if 'color' in style:
# # matplotlib.use("Qt5Agg")
matplotlib.rcParams['savefig.pad_inches'] = 0
fig = plt.figure(figsize=(24, 12))
ax = fig.add_subplot(1, 2, 1)
ax.xaxis.get_major_formatter()._usetex = False
ax.yaxis.get_major_formatter()._usetex = False
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
ax.yaxis.set_major_locator(plt.MaxNLocator(3))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
index_particles = []
for n in range(n_particle_types):
pos = torch.argwhere((T1.squeeze() == n) & (H1[:, 0].squeeze() == 1))
pos = to_numpy(pos[:, 0].squeeze()).astype(int)
index_particles.append(pos)
# plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
# s=marker_size, color=cmap.color(n))
size = set_size(x, index_particles[n], 10)
plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
s=size, color=cmap.color(n))
dead_cell = np.argwhere(to_numpy(H1[:, 0]) == 0)
if len(dead_cell) > 0:
plt.scatter(to_numpy(X1[dead_cell[:, 0].squeeze(), 0]), to_numpy(X1[dead_cell[:, 0].squeeze(), 1]),
s=2, color=mc, alpha=0.5)
if 'latex' in style:
plt.xlabel(r'$x$', fontsize=68)
plt.ylabel(r'$y$', fontsize=68)
plt.xticks(fontsize=48.0)
plt.yticks(fontsize=48.0)
elif 'frame' in style:
plt.xlabel(r'x_i', fontsize=13)
plt.ylabel('y', fontsize=16)
plt.xticks(fontsize=16.0)
plt.yticks(fontsize=16.0)
ax.tick_params(axis='both', which='major', pad=15)
plt.text(0, 1.05,
f'frame {it}, {int(n_particles_alive)} alive particles ({int(n_particles_dead)} dead), {edge_index.shape[1]} edges ',
ha='left', va='top', transform=ax.transAxes, fontsize=16)
plt.xticks([])
plt.yticks([])
center_x = to_numpy(x[pos_cell, 1])
center_y = to_numpy(x[pos_cell, 2])
plt.xlim([center_x - 0.1, center_x + 0.1])
plt.ylim([center_y - 0.1, center_y + 0.1])
ax = fig.add_subplot(2, 2, 2)
plt.plot(mass_time_series, color=mc, ls="--")
plt.scatter([i for i in range(it)], mass_time_series[0:it], color=stage_time_series_color[0:it], s=15)
# plt.plot(mass_time_series[0:it], color = color,linewidth=3)
plt.ylim(0, max(mass_time_series) + 50)
ax = fig.add_subplot(2, 2, 4)
plt.plot(v_time_series, color=mc, ls="--")
plt.plot(v_time_series[0:it], color = 'red',linewidth=4)
num = f"{it:06}"
plt.tight_layout()
plt.savefig(f"./{log_dir}/tmp_recons/cell_{cell_id}_frame_{num}.tif", dpi=80)
plt.close()
def plot_generated(config, run, style, step, device):
dataset_name = config.dataset
simulation_config = config.simulation
model_config = config.graph_model
training_config = config.training
has_adjacency_matrix = (simulation_config.connectivity_file != '')
has_mesh = (config.graph_model.mesh_model_name != '')
only_mesh = (config.graph_model.particle_model_name == '') & has_mesh
has_ghost = config.training.n_ghosts > 0
max_radius = simulation_config.max_radius
min_radius = simulation_config.min_radius
n_particle_types = simulation_config.n_particle_types
n_particles = simulation_config.n_particles
n_nodes = simulation_config.n_nodes
n_runs = training_config.n_runs
n_frames = simulation_config.n_frames
delta_t = simulation_config.delta_t
cmap = CustomColorMap(config=config) # create colormap for given model_config
dimension = simulation_config.dimension
has_siren = 'siren' in model_config.field_type
has_siren_time = 'siren_with_time' in model_config.field_type
has_field = ('PDE_ParticleField' in config.graph_model.particle_model_name)
l_dir = get_log_dir(config)
log_dir = os.path.join(l_dir, 'try_{}'.format(config_file.split('/')[-1]))
files = glob.glob(f"./{log_dir}/tmp_recons/*")
for f in files:
os.remove(f)
print('Load data ...')
x_list = torch.load(f'graphs_data/{dataset_name}/x_list_{run}.pt', map_location=device)
for it in trange(0,n_frames,step):
x = x_list[it].clone().detach()
T1 = x[:, 5:6].clone().detach()
H1 = x[:, 6:8].clone().detach()
X1 = x[:, 1:3].clone().detach()
if 'black' in style:
plt.style.use('dark_background')
if 'latex' in style:
plt.rcParams['text.usetex'] = True
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
if 'voronoi' in style:
# matplotlib.use("Qt5Agg")
matplotlib.rcParams['savefig.pad_inches'] = 0
vor, vertices_pos, vertices_per_cell, all_points = get_vertices(points=X1, device=device)
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(1, 1, 1)
plt.xticks([])
plt.yticks([])
index_particles = []
voronoi_plot_2d(vor, ax=ax, show_vertices=False, line_colors='white', line_width=4, line_alpha=1, point_size=0)
if 'color' in style:
for n in range(n_particle_types):
pos = torch.argwhere((T1.squeeze() == n) & (H1[:, 0].squeeze() == 1))
pos = to_numpy(pos[:, 0].squeeze()).astype(int)
index_particles.append(pos)
size = set_size(x, index_particles[n], 10) / 10
patches = []
for i in index_particles[n]:
cell = vertices_per_cell[i]
vertices = to_numpy(vertices_pos[cell, :])
patches.append(Polygon(vertices, closed=True))
pc = PatchCollection(patches, alpha=0.75, facecolors=cmap.color(n))
ax.add_collection(pc)
if 'center' in style:
plt.scatter(to_numpy(X1[index_particles[n], 0]), to_numpy(X1[index_particles[n], 1]), s=size,
color=cmap.color(n))
if 'vertices' in style:
plt.scatter(to_numpy(vertices_pos[:, 0]), to_numpy(vertices_pos[:, 1]), s=5, color='w')
plt.xlim([0.5, 0.55])
plt.ylim([0.5, 0.55])
plt.tight_layout()
plt.savefig(f"./{log_dir}/detail.tif", dpi=85.35)
plt.close()
im = imread(f"./{log_dir}/detail.tif")
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(1, 1, 1)
plt.xticks([])
plt.yticks([])
index_particles = []
voronoi_plot_2d(vor, ax=ax, show_vertices=False, line_colors='white', line_width=1, line_alpha=0.5, point_size=0)
if 'color' in style:
for n in range(n_particle_types):
pos = torch.argwhere((T1.squeeze() == n) & (H1[:, 0].squeeze() == 1))
pos = to_numpy(pos[:, 0].squeeze()).astype(int)
index_particles.append(pos)
size = set_size(x, index_particles[n], 10) / 10
patches = []
for i in index_particles[n]:
cell = vertices_per_cell[i]
vertices = to_numpy(vertices_pos[cell, :])
patches.append(Polygon(vertices, closed=True))
pc = PatchCollection(patches, alpha=0.75, facecolors=cmap.color(n))
ax.add_collection(pc)
if 'center' in style:
plt.scatter(to_numpy(X1[index_particles[n], 0]), to_numpy(X1[index_particles[n], 1]), s=size,
color=cmap.color(n))
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
ax = fig.add_subplot(3, 3, 1)
ax.imshow(im)
plt.xticks([])
plt.yticks([])
plt.tight_layout()
num = f"{it:06}"
plt.savefig(f"./{log_dir}/tmp_recons/frame_{num}.tif", dpi=85.35)
plt.close()
else:
if 'color' in style:
matplotlib.rcParams['savefig.pad_inches'] = 0
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(1, 1, 1)
ax.xaxis.get_major_formatter()._usetex = False
ax.yaxis.get_major_formatter()._usetex = False
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
ax.yaxis.set_major_locator(plt.MaxNLocator(3))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
index_particles = []
for n in range(n_particle_types):
pos = torch.argwhere((T1.squeeze() == n) & (H1[:, 0].squeeze() == 1))
pos = to_numpy(pos[:, 0].squeeze()).astype(int)
index_particles.append(pos)
# plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
# s=marker_size, color=cmap.color(n))
size = 10 # set_size(x, index_particles[n], 10) / 10
plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
s=size, color=cmap.color(n))
dead_cell = np.argwhere(to_numpy(H1[:, 0]) == 0)
if len(dead_cell) > 0:
plt.scatter(to_numpy(X1[dead_cell[:, 0].squeeze(), 0]), to_numpy(X1[dead_cell[:, 0].squeeze(), 1]),
s=2, color=mc, alpha=0.5)
if 'latex' in style:
plt.xlabel(r'$x$', fontsize=68)
plt.ylabel(r'$y$', fontsize=68)
plt.xticks(fontsize=48.0)
plt.yticks(fontsize=48.0)
elif 'frame' in style:
plt.xlabel(r'x_i', fontsize=13)
plt.ylabel('y', fontsize=16)
plt.xticks(fontsize=16.0)
plt.yticks(fontsize=16.0)
ax.tick_params(axis='both', which='major', pad=15)
plt.text(0, 1.05,
f'frame {it}, {int(n_particles_alive)} alive particles ({int(n_particles_dead)} dead), {edge_index.shape[1]} edges ',
ha='left', va='top', transform=ax.transAxes, fontsize=16)
plt.xticks([])
plt.yticks([])
plt.xlim([0,1])
plt.ylim([0,1])
plt.tight_layout()
num = f"{it:06}"
plt.savefig(f"./{log_dir}/tmp_recons/frame_{num}.tif", dpi=80)
plt.close()
elif 'bw' in style:
matplotlib.rcParams['savefig.pad_inches'] = 0
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(1, 1, 1)
ax.xaxis.get_major_formatter()._usetex = False
ax.yaxis.get_major_formatter()._usetex = False
ax.xaxis.set_major_locator(plt.MaxNLocator(3))
ax.yaxis.set_major_locator(plt.MaxNLocator(3))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
index_particles = []
for n in range(n_particle_types):
pos = torch.argwhere((T1.squeeze() == n) & (H1[:, 0].squeeze() == 1))
pos = to_numpy(pos[:, 0].squeeze()).astype(int)
index_particles.append(pos)
# plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
# s=marker_size, color=cmap.color(n))
size = 10
plt.scatter(to_numpy(x[index_particles[n], 1]), to_numpy(x[index_particles[n], 2]),
s=size, color='w')
dead_cell = np.argwhere(to_numpy(H1[:, 0]) == 0)
if len(dead_cell) > 0:
plt.scatter(to_numpy(X1[dead_cell[:, 0].squeeze(), 0]), to_numpy(X1[dead_cell[:, 0].squeeze(), 1]),
s=2, color=mc, alpha=0.5)
if 'latex' in style:
plt.xlabel(r'$x$', fontsize=68)
plt.ylabel(r'$y$', fontsize=68)
plt.xticks(fontsize=48.0)
plt.yticks(fontsize=48.0)
elif 'frame' in style:
plt.xlabel(r'x_i', fontsize=13)
plt.ylabel('y', fontsize=16)
plt.xticks(fontsize=16.0)
plt.yticks(fontsize=16.0)
ax.tick_params(axis='both', which='major', pad=15)
plt.text(0, 1.05,
f'frame {it}, {int(n_particles_alive)} alive particles ({int(n_particles_dead)} dead), {edge_index.shape[1]} edges ',
ha='left', va='top', transform=ax.transAxes, fontsize=16)
plt.xticks([])
plt.yticks([])
plt.xlim([0,1])
plt.ylim([0,1])
plt.tight_layout()
num = f"{it:06}"
plt.savefig(f"./{log_dir}/tmp_recons/frame_{num}.tif", dpi=80)
plt.close()
def plot_confusion_matrix(index, true_labels, new_labels, n_particle_types, epoch, it, fig, ax, style):
# print(f'plot confusion matrix epoch:{epoch} it: {it}')
plt.text(-0.25, 1.1, f'{index}', ha='left', va='top', transform=ax.transAxes, fontsize=12)
confusion_matrix = metrics.confusion_matrix(true_labels, new_labels) # , normalize='true')
cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=confusion_matrix)
if n_particle_types > 8:
cm_display.plot(ax=fig.gca(), cmap='Blues', include_values=False, colorbar=False)
else:
cm_display.plot(ax=fig.gca(), cmap='Blues', include_values=True, values_format='d', colorbar=False)
accuracy = metrics.accuracy_score(true_labels, new_labels)
plt.title(f'accuracy: {np.round(accuracy, 2)}', fontsize=12)
# print(f'accuracy: {np.round(accuracy,3)}')
plt.xticks(fontsize=10.0)
plt.yticks(fontsize=10.0)
plt.xlabel(r'Predicted label', fontsize=12)
plt.ylabel(r'True label', fontsize=12)
return accuracy
def plot_cell_rates(config, device, log_dir, n_particle_types, type_list, x_list, new_labels, cmap, logger, style):
n_frames = config.simulation.n_frames
delta_t = config.simulation.delta_t