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class_analysis.py
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import os
from re import M
import h5py
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
from utils import get_mouse_id, multi_bar_plot
GROUPS = [
'../data/10Hz WT/',
'../data/10Hz ephrin/',
'../data/10Hz WT sham/',
'../data/10Hz ephrin sham/'
]
class Analysis():
def __init__(self, groups):
self.groups = groups
def extract_scalars(self, base_dir, raw=False, save_to_csv=False):
files = os.listdir(base_dir)
print("[EXTRACTING SCALARS FROM", len(files), "FILES]")
if raw == True:
session_files = []
bad_sessions = ['session_20190205095213 WT3 10Hz', 'session_20190206115259 WT6 10Hz']
for session in files:
if session[:7] == 'session' and session not in bad_sessions:
result_filepath = base_dir + session + '/proc/results_00.h5'
session_files.append(result_filepath)
else:
session_files = files
session_dicts = {}
for session in session_files:
if raw == True:
session = session.split(base_dir)[1]
condition_session_title = session.split('/')[0].replace(' ', '-')
session_title = condition_session_title[:condition_session_title.index('(')-1].strip()
session_condition = condition_session_title[condition_session_title.index('(')+1:-1].strip()
else:
condition_session_title = session.split('.')[0]
session_title = session[session.index("-")+1:-3]
if session_title not in session_dicts:
session_dicts[session_title] = {}
scalar_dict = {}
with h5py.File(base_dir + '/' + session, 'r') as f:
scalars = f['scalars']
for scalar in scalars:
scalar_data = scalars[scalar]
scalar_dict[scalar] = scalar_data
scalar_df = pd.DataFrame(scalar_dict)
if save_to_csv == True:
scalar_df.to_csv('./scalar_csv/'+condition_session_title+".csv")
if session.split("-")[0] not in session_dicts[session_title]:
if raw == True:
# session_dicts[session_title] = scalar_df
session_dicts[session_title][session_condition] = scalar_df
else:
session_dicts[session_title][session.split("-")[0]] = scalar_df
# timestamps = f['timestamps']
# print(timestamps)
# break
print("[EXTRACTED", len(session_dicts), "SESSIONS WITH CONTROL, STIM AND POST]")
return session_dicts
def scalar_analysis(self, session_dict, scalar, stat, overall_stats=False, session_stats=False):
group_condtions = ['control', 'stim', 'post']
session_titles = []
stat_list = []
controls, stims, posts = [], [], []
# GET LIST OF LISTS CONTAINING MEANS
# should change to be more effiient
for session in session_dict:
session_titles.append(session)
control_summary = session_dict[session]['control'].describe()
stim_summary = session_dict[session]['stim'].describe()
post_summary = session_dict[session]['post'].describe()
controls.append(control_summary[scalar][stat])
stims.append(stim_summary[scalar][stat])
posts.append(post_summary[scalar][stat])
stat_list.append(controls)
stat_list.append(stims)
stat_list.append(posts)
## PLOT MEAN OF GROUP MEANS
if overall_stats == True:
if stat == 'mean':
mean_of_means = []
count = 0
for group in stat_list:
print("Mean for", group_condtions[count], "group:", np.mean(group))
mean_of_means.append(np.mean(group))
count += 1
plt.bar(group_condtions, mean_of_means)
# plt.plot(group_condtions, mean_of_means, marker='o')
plt.title(stat.capitalize() + " " + scalar+" per frame")
plt.xlabel("Group Condtions")
plt.ylabel(scalar)
# plt.savefig("./WT Analysis/WT "+ scalar +" overall per frame.png")
plt.show()
else:
print("[ERROR]: stat is not mean")
## PLOT EACH GROUPS MEANS
if session_stats == True:
session_labels = []
for i in range(len(session_dict)):
subject_id = session_titles[i].split('-')[1]
session_labels.append("S" + str(i+1) + " (" + subject_id + ")")
# session_labels = session_titles # full title (comment out for S1, S2, S3...)
title = stat.capitalize() + " " + scalar+" per frame"
multi_bar_plot(stat_list, session_labels, scalar, title)
return stat_list
def oneway_anova(self, data):
# convert to dict
groups_dict = {}
groups = ['control', 'stim', 'post']
for idx, means in enumerate(data):
if groups[idx] not in groups_dict:
groups_dict[groups[idx]] = means
f_value, p_value = stats.f_oneway(groups_dict['control'], groups_dict['stim'], groups_dict['post'])
return f_value, p_value
def compare_means(self, groups, scalar, stat='mean'):
comparison_dict = {}
for group in groups:
group_name = group.split('/')[-2]
if group_name not in comparison_dict:
comparison_dict[group_name] = {}
extracted_dicts = self.extract_scalars(group, save_to_csv=False, raw=True)
stat_list = self.scalar_analysis(extracted_dicts, scalar, stat)
mean_stat = [np.mean(stats_list) for stats_list in stat_list]
comparison_dict[group_name]['control'] = mean_stat[0]
comparison_dict[group_name]['stim'] = mean_stat[1]
comparison_dict[group_name]['post'] = mean_stat[2]
comparison_dict[group_name]['count'] = len(stat_list[0])
f_value, p_value = self.oneway_anova(stat_list)
comparison_dict[group_name]['p_value'] = p_value
comparison_df = pd.DataFrame(comparison_dict)
return comparison_df
def get_subject_dict(self, groups):
comparison_dict = {}
for group in groups:
mouse_dict = {}
extracted_dicts = self.extract_scalars(group, save_to_csv=False, raw=True)
for sessions in extracted_dicts:
mouse_id = get_mouse_id(sessions)
if mouse_id not in mouse_dict:
mouse_dict[mouse_id] = []
for mouse_id in mouse_dict:
for session in extracted_dicts:
if mouse_id in session:
# mouse_dict[mouse_id][session] = session
# mouse_dict[mouse_id][session] = extracted_dicts[sessions]
# print(extracted_dicts[sessions]['control'].describe())
# mouse_dict[mouse_id].append(session)
mouse_dict[mouse_id].append(extracted_dicts[session])
group_name = group.split('/')[-2]
if group_name not in comparison_dict:
comparison_dict[group_name] = mouse_dict
return comparison_dict
def compare_subjects(self, comparison_dict, scalar, stat, plot=False):
group_stat_dict = {}
for group in comparison_dict:
stat_dict = {}
for mouse in comparison_dict[group]:
if mouse not in stat_dict:
stat_dict[mouse] = []
controls, stims, posts = [], [], []
mouse_groups = comparison_dict[group][mouse]
# print(mouse)
# print(mouse_groups)
for single_mouse in mouse_groups:
# print(single_mouse)
control_summary = single_mouse['control'].describe()
stim_summary = single_mouse['stim'].describe()
post_summary = single_mouse['post'].describe()
controls.append(control_summary[scalar][stat])
stims.append(stim_summary[scalar][stat])
posts.append(post_summary[scalar][stat])
stat_dict[mouse].append(controls)
stat_dict[mouse].append(stims)
stat_dict[mouse].append(posts)
group_stat_dict[group] = stat_dict
if plot:
for subject in stat_dict:
session_labels = []
stat_list = stat_dict[subject]
for i in range(len(stat_list[0])):
session_labels.append("S" + str(i+1) + " (" + subject + ")")
title = stat.capitalize() + " " + scalar+" per frame (" + group + ")"
multi_bar_plot(stat_list, session_labels, scalar, title)
return group_stat_dict
if __name__ == "__main__":
analysis = Analysis(GROUPS)
print(analysis.groups)
extracted_dicts = analysis.extract_scalars('../data/10Hz WT/', save_to_csv=False, raw=True)
print(extracted_dicts)
stat_list = analysis.scalar_analysis(extracted_dicts, 'velocity_2d_mm', 'mean', session_stats=False, overall_stats=False)
print(stat_list)
comparison_df = analysis.compare_means(analysis.groups, 'velocity_2d_mm')
print(comparison_df)
subject_comparison = analysis.compare_subjects(analysis.get_subject_dict(GROUPS), 'velocity_2d_mm', 'mean')
print(subject_comparison)
# ./finals -- The grouped results (control, stim, post)
# ../../ -- raw results
# extracted_dicts = extract_scalars('../data/10Hz WT/', save_to_csv=False, raw=True)
# print(extracted_dicts)