11178329243

Committed 04 Oct 2024 10:28AM UTC coverage: 8.915% (-0.02%) from 8.931%

Build # 11178329243

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github

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Pablo1990

Commit Message

Improved way of space exploration parameters

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/src/pyVertexModel/analysis/analyse_simulations.py

import os

import numpy as np
import pandas as pd

from src.pyVertexModel.analysis.analyse_simulation import analyse_simulation, analyse_edge_recoil
from src.pyVertexModel.util.utils import save_variables, load_variables

folder = '/media/pablo/d7c61090-024c-469a-930c-f5ada47fb049/PabloVicenteMunuera/VertexModel/pyVertexModel/Result/'
all_files_features = []
lst = os.listdir(folder)
lst.sort(reverse=True)
for _, file in enumerate(lst):
    print(file)

    # if file is a directory
    if os.path.isdir(os.path.join(folder, file)):
        files_within_folder = os.listdir(os.path.join(folder, file))
        # Analyse the edge recoil
        if os.path.exists(os.path.join(folder, file, 'before_ablation.pkl')):
            # if the analysis file exists, load it
            # if os.path.exists(os.path.join(folder, file, 'recoil_info_apical.pkl')):
            #     # recoiling_info_df_basal = load_variables(
            #     #     os.path.join(folder, file, 'recoil_info_basal.pkl'))['recoiling_info_df_basal']
            #     recoiling_info_df_apical = load_variables(
            #         os.path.join(folder, file, 'recoil_info_apical.pkl'))['recoiling_info_df_apical']
            # else:
            file_name = os.path.join(folder, file, 'before_ablation.pkl')

            t_end = 0.5
            n_ablations = 2
            recoiling_edge_info_df_apical = analyse_edge_recoil(os.path.join(folder, file, 'before_ablation.pkl'),
                                                           type_of_ablation='recoil_edge_info_apical',
                                                           n_ablations=n_ablations, location_filter=0, t_end=t_end)
            if recoiling_edge_info_df_apical is None:
                continue

            # recoiling_info = analyse_edge_recoil(os.path.join(folder, file, 'data_step_300.pkl'),
            #                                      n_ablations=n_ablations, location_filter=2, t_end=t_end)
            # recoiling_info_df_basal = pd.DataFrame(recoiling_info)
            # recoiling_info_df_basal.to_excel(os.path.join(folder, file, 'recoil_info_basal.xlsx'))
            # save_variables({'recoiling_info_df_basal': recoiling_info_df_basal},
            #                os.path.join(folder, file, 'recoil_info_basal.pkl'))

            # Analyse the simulation
            features_per_time_df, post_wound_features, important_features, features_per_time_all_cells_df = (
                analyse_simulation(os.path.join(folder, file)))

            if important_features is not None and len(important_features) > 5:
                #important_features['recoiling_speed_apical'] = recoiling_info_df_apical[0]['initial_recoil_in_s']
                #important_features['K'] = recoiling_info_df_apical[0]['K']

                important_features['K_edge'] = np.mean([recoiling_edge_info_df_apical[i]['K'] for i in range(n_ablations) if
                                                        1 > recoiling_edge_info_df_apical[i]['K'] > 1e-5])
                important_features['recoiling_speed_edge_apical'] = np.mean([recoiling_edge_info_df_apical[i]['initial_recoil_in_s'] for i in range(n_ablations) if
                                                                             1 > recoiling_edge_info_df_apical[i]['initial_recoil_in_s'] > 1e-5])

                # important_features['recoiling_speed_basal'] = np.mean(recoiling_info_df_basal['initial_recoil_in_s'])
                # important_features['recoiling_speed_basal_std'] = np.std(recoiling_info_df_basal['initial_recoil_in_s'])

                # Sort features_per_time_df by time
                features_per_time_df_std = features_per_time_all_cells_df.groupby(by='time').std().reset_index()
                id_before_ablation = np.where(features_per_time_df_std.time < 30)[0][-1]
                important_features['std_difference_area_top'] = features_per_time_df_std.loc[id_before_ablation].Area_top - features_per_time_df_std.loc[0].Area_top

                # Count the number of cells that have an area_top lower than the average area_top
                features_per_time_df_mean = features_per_time_all_cells_df.groupby(by='time').mean().reset_index()
                count_of_smalls_cells_per_time_0 = features_per_time_all_cells_df[features_per_time_all_cells_df.Area_top < features_per_time_df_mean.loc[0].Area_top].groupby(by='time').count().reset_index()
                count_of_smalls_cells_per_time_ablation = features_per_time_all_cells_df[features_per_time_all_cells_df.Area_top < features_per_time_df_mean.loc[id_before_ablation].Area_top].groupby(by='time').count().reset_index()
                try:
                    important_features['cells_area_top_lower_than_average_diff'] = count_of_smalls_cells_per_time_ablation.loc[id_before_ablation].ID - count_of_smalls_cells_per_time_0.loc[0].ID
                except KeyError as e:
                    #print("Error cells_area_top_lower_than_average_diff: ", e)
                    important_features['cells_area_top_lower_than_average_diff'] = np.nan

                important_features['folder'] = file

                # Extract the variables from folder name
                file_splitted = file.split('_')
                variables_to_show = {'Cells', 'visc', 'lVol', 'refV0', 'kSubs', 'lt', 'ltExt', 'refA0', 'eTriAreaBarrier',
                                     'eARBarrier', 'RemStiff', 'lS1', 'lS2', 'lS3', 'pString'}
                for i in range(3, len(file_splitted), 1):
                    if file_splitted[i] in variables_to_show:
                        important_features[file_splitted[i]] = file_splitted[i + 1]

                # Transform the dictionary into a dataframe
                important_features = pd.DataFrame([important_features])
                all_files_features.append(important_features)

# Concatenate the elements of the list all_files_features
all_files_features = pd.concat(all_files_features, axis=0)

# Export to xls file
df = pd.DataFrame(all_files_features)
df.to_excel(os.path.join(folder, 'all_files_features.xlsx'))

1	import os	×
2
3	import numpy as np	×
4	import pandas as pd	×
5
6	from src.pyVertexModel.analysis.analyse_simulation import analyse_simulation, analyse_edge_recoil	×
7	from src.pyVertexModel.util.utils import save_variables, load_variables	×
8
9	folder = '/media/pablo/d7c61090-024c-469a-930c-f5ada47fb049/PabloVicenteMunuera/VertexModel/pyVertexModel/Result/'	×
10	all_files_features = []	×
11	lst = os.listdir(folder)	×
12	lst.sort(reverse=True)	×
13	for _, file in enumerate(lst):	×
14	print(file)	×
15
16	# if file is a directory
17	if os.path.isdir(os.path.join(folder, file)):	×
18	files_within_folder = os.listdir(os.path.join(folder, file))	×
19	# Analyse the edge recoil
20	if os.path.exists(os.path.join(folder, file, 'before_ablation.pkl')):	×
21	# if the analysis file exists, load it
22	# if os.path.exists(os.path.join(folder, file, 'recoil_info_apical.pkl')):
23	# # recoiling_info_df_basal = load_variables(
24	# # os.path.join(folder, file, 'recoil_info_basal.pkl'))['recoiling_info_df_basal']
25	# recoiling_info_df_apical = load_variables(
26	# os.path.join(folder, file, 'recoil_info_apical.pkl'))['recoiling_info_df_apical']
27	# else:
28	file_name = os.path.join(folder, file, 'before_ablation.pkl')	×
29
30	t_end = 0.5	×
31	n_ablations = 2	×
32	recoiling_edge_info_df_apical = analyse_edge_recoil(os.path.join(folder, file, 'before_ablation.pkl'),	×
33	type_of_ablation='recoil_edge_info_apical',
34	n_ablations=n_ablations, location_filter=0, t_end=t_end)
35	if recoiling_edge_info_df_apical is None:	×
36	continue	×
37
38	# recoiling_info = analyse_edge_recoil(os.path.join(folder, file, 'data_step_300.pkl'),
39	# n_ablations=n_ablations, location_filter=2, t_end=t_end)
40	# recoiling_info_df_basal = pd.DataFrame(recoiling_info)
41	# recoiling_info_df_basal.to_excel(os.path.join(folder, file, 'recoil_info_basal.xlsx'))
42	# save_variables({'recoiling_info_df_basal': recoiling_info_df_basal},
43	# os.path.join(folder, file, 'recoil_info_basal.pkl'))
44
45	# Analyse the simulation
46	features_per_time_df, post_wound_features, important_features, features_per_time_all_cells_df = (	×
47	analyse_simulation(os.path.join(folder, file)))
48
49	if important_features is not None and len(important_features) > 5:	×
50	#important_features['recoiling_speed_apical'] = recoiling_info_df_apical[0]['initial_recoil_in_s']
51	#important_features['K'] = recoiling_info_df_apical[0]['K']
52
53	important_features['K_edge'] = np.mean([recoiling_edge_info_df_apical[i]['K'] for i in range(n_ablations) if	×
54	1 > recoiling_edge_info_df_apical[i]['K'] > 1e-5])
UNCOV 55	important_features['recoiling_speed_edge_apical'] = np.mean([recoiling_edge_info_df_apical[i]['initial_recoil_in_s'] for i in range(n_ablations) if	×
56	1 > recoiling_edge_info_df_apical[i]['initial_recoil_in_s'] > 1e-5])
57
58	# important_features['recoiling_speed_basal'] = np.mean(recoiling_info_df_basal['initial_recoil_in_s'])
59	# important_features['recoiling_speed_basal_std'] = np.std(recoiling_info_df_basal['initial_recoil_in_s'])
60
61	# Sort features_per_time_df by time
62	features_per_time_df_std = features_per_time_all_cells_df.groupby(by='time').std().reset_index()	×
UNCOV 63	id_before_ablation = np.where(features_per_time_df_std.time < 30)[0][-1]	×
UNCOV 64	important_features['std_difference_area_top'] = features_per_time_df_std.loc[id_before_ablation].Area_top - features_per_time_df_std.loc[0].Area_top	×
65
66	# Count the number of cells that have an area_top lower than the average area_top
67	features_per_time_df_mean = features_per_time_all_cells_df.groupby(by='time').mean().reset_index()	×
68	count_of_smalls_cells_per_time_0 = features_per_time_all_cells_df[features_per_time_all_cells_df.Area_top < features_per_time_df_mean.loc[0].Area_top].groupby(by='time').count().reset_index()	×
69	count_of_smalls_cells_per_time_ablation = features_per_time_all_cells_df[features_per_time_all_cells_df.Area_top < features_per_time_df_mean.loc[id_before_ablation].Area_top].groupby(by='time').count().reset_index()	×
70	try:	×
71	important_features['cells_area_top_lower_than_average_diff'] = count_of_smalls_cells_per_time_ablation.loc[id_before_ablation].ID - count_of_smalls_cells_per_time_0.loc[0].ID	×
72	except KeyError as e:	×
73	#print("Error cells_area_top_lower_than_average_diff: ", e)
74	important_features['cells_area_top_lower_than_average_diff'] = np.nan	×
75
UNCOV 76	important_features['folder'] = file	×
77
78	# Extract the variables from folder name
UNCOV 79	file_splitted = file.split('_')	×
80	variables_to_show = {'Cells', 'visc', 'lVol', 'refV0', 'kSubs', 'lt', 'ltExt', 'refA0', 'eTriAreaBarrier',	×
81	'eARBarrier', 'RemStiff', 'lS1', 'lS2', 'lS3', 'pString'}
82	for i in range(3, len(file_splitted), 1):	×
UNCOV 83	if file_splitted[i] in variables_to_show:	×
UNCOV 84	important_features[file_splitted[i]] = file_splitted[i + 1]	×
85
86	# Transform the dictionary into a dataframe
UNCOV 87	important_features = pd.DataFrame([important_features])	×
UNCOV 88	all_files_features.append(important_features)	×
89
90	# Concatenate the elements of the list all_files_features
UNCOV 91	all_files_features = pd.concat(all_files_features, axis=0)	×
92
93	# Export to xls file
UNCOV 94	df = pd.DataFrame(all_files_features)	×
UNCOV 95	df.to_excel(os.path.join(folder, 'all_files_features.xlsx'))	×

Pablo1990 / pyVertexModel / 11178329243

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