Helper Script
Contents
Helper Script#
!pip install nilearn --quiet
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
Here is a collection of Global Constants used in the code:
N_SUBJECTS = Number of subjects included
intN_PARCELS = The number of regions of interest
intTR = Time resolution, in seconds
floatHEMIS = The parcels (region of interests) are matched across hemisphere with the same order
[list of strings]N_RUNS = Number of times the experiment was performed on each subject
intRUNS = Name tags for the runs
[list of strings]EXPERIMENTS = Contains task names as keys and task conditions as value
dict of dict
N_SUBJECTS = 100
N_PARCELS = 360
TR = 0.72
HEMIS = ["Right", "Left"]
RUNS = ['LR','RL']
N_RUNS = 2
EXPERIMENTS = {
'EMOTION' : {'cond':['fear','neut']},
'GAMBLING' : {'cond':['loss','win']},
'LANGUAGE' : {'cond':['math','story']},
'SOCIAL' : {'cond':['mental','rnd']}
}
HCP_DIR = r'/Users/rajdeep_ch/Documents/nma/project/hcp_task'
subjects = list(np.loadtxt(os.path.join(HCP_DIR,'subjects_list.txt'), dtype='str'))
Here is the list of 100 subject ids
subjects
['100307',
'100408',
'101915',
'102816',
'103414',
'103515',
'103818',
'105115',
'105216',
'106016',
'106319',
'110411',
'111009',
'111312',
'111514',
'111716',
'113215',
'113619',
'114924',
'115320',
'117122',
'117324',
'118730',
'118932',
'119833',
'120111',
'120212',
'122317',
'123117',
'124422',
'125525',
'126325',
'127933',
'128632',
'129028',
'130013',
'130316',
'130922',
'131924',
'133625',
'133827',
'133928',
'134324',
'135932',
'136833',
'137128',
'138231',
'138534',
'139637',
'140824',
'142828',
'143325',
'148032',
'148335',
'149337',
'149539',
'150524',
'151223',
'151526',
'151627',
'153025',
'153429',
'154431',
'156233',
'156637',
'157336',
'158035',
'158540',
'159239',
'159340',
'160123',
'161731',
'162329',
'162733',
'163129',
'163432',
'167743',
'172332',
'175439',
'176542',
'178950',
'182739',
'185139',
'188347',
'189450',
'190031',
'192439',
'192540',
'193239',
'194140',
'196144',
'196750',
'197550',
'198451',
'199150',
'199655',
'200614',
'201111',
'201414',
'205119']
task_list = list(EXPERIMENTS.keys())
Helper 1 - Loading Time Series for single subject#
def load_single_timeseries(subject, experiment, run, remove_mean=True):
"""Load timeseries data for a single subject and single run.
Args:
subject (str): subject ID to load
experiment (str): Name of experiment
run (int): (0 or 1)
remove_mean (bool): If True, subtract the parcel-wise mean (typically the mean BOLD signal is not of interest)
Returns
ts (n_parcel x n_timepoint array): Array of BOLD data values
"""
RUNS = {0: 'LR', 1: 'RL'}
bold_run = RUNS[run]
bold_path = f"{HCP_DIR}/subjects/{subject}/{experiment}/tfMRI_{experiment}_{bold_run}"
bold_file = "data.npy"
ts = np.load(f"{bold_path}/{bold_file}")
if remove_mean:
ts -= ts.mean(axis=1, keepdims=True)
return ts
Helper 2 - Loading evs#
def load_evs(subject, experiment, run):
"""Load EVs (explanatory variables) data for one task experiment.
Args:
subject (str): subject ID to load
experiment (str) : Name of experiment
run (int): 0 or 1
Returns
evs (list of lists): A list of frames associated with each condition
"""
frames_list = []
RUNS = {0: 'LR', 1: 'RL'}
TR = 0.72
task_key = f'tfMRI_{experiment}_{RUNS[run]}'
EXPERIMENTS = {
'MOTOR' : {'cond':['lf','rf','lh','rh','t','cue']},
'WM' : {'cond':['0bk_body','0bk_faces','0bk_places','0bk_tools','2bk_body','2bk_faces','2bk_places','2bk_tools']},
'EMOTION' : {'cond':['fear','neut']},
'GAMBLING' : {'cond':['loss','win']},
'LANGUAGE' : {'cond':['math','story']},
'RELATIONAL' : {'cond':['match','relation']},
'SOCIAL' : {'cond':['mental','rnd']}
}
for cond in EXPERIMENTS[experiment]['cond']:
ev_file = f"{HCP_DIR}/subjects/{subject}/{experiment}/{task_key}/EVs/{cond}.txt"
ev_array = np.loadtxt(ev_file, ndmin=2, unpack=True)
ev = dict(zip(["onset", "duration", "amplitude"], ev_array))
#print(ev)
# Determine when trial starts, rounded down
start = np.floor(ev["onset"] / TR).astype(int)
#print(start)
# Use trial duration to determine how many frames to include for trial
duration = np.ceil(ev["duration"]/TR).astype(int)
#print("Start: {} | Duration: {}".format(start,duration))
# Take the range of frames that correspond to this specific trial
frames = [s + np.arange(0, d) for s, d in zip(start, duration)]
frames_list.append(frames)
# frames_list is returning a list of time duration
# ev is returning the explanatory variables
return frames_list
Helper 3 - Average Frames#
CURRENTLY NOT WORKING#
def average_frames(timeseries_data, evs_data, experiment_name, task_cond):
"""
The function takes in the brain activity measures and behavioural measures for a particular subject in a given task condition.
The function returns the brain activity average across all trail points.
The corresponding average contains the time-average brain activity across all ROIs
Input:
timeseries_data (ndarray): Contains the timeseries data for a single subject in a given experiment
evs_data (ndarray): Contains the evs data for the single subject
experiment_name (str): Name of the experiment (EMOTION/SOCIAL)
task_condition (str): string name for task condition (fear/neut)
Returns:
average_values (ndarray): time-average activation across all ROIs
shape = (ROIs,1)
"""
idx = EXPERIMENTS[experiment_name]['cond'].index(task_cond)
print('IDX: {}'.format(idx))
print('Here is evs[idx][-1][-1]: {}'.format(evs_data[idx][-1][-1]),end="\n")
print("Length of timepoints: {}".format(timeseries_data.shape[2]))
#if evs_data[idx][-1][-1] > timeseries_data.shape[2]:
#print("Conditional works!")
#average_values = np.mean(np.concatenate([np.mean(timeseries_data[:,evs_data[idx][i]],axis=1,keepdims=True) for i in range(len(evs_data[idx])-1)], axis = -1), axis = 1)
#else:
#print("No need for conditional to work")
#average_values = np.mean(np.concatenate([np.mean(timeseries_data[:,evs_data[idx][i]],axis=1,keepdims=True) for i in range(len(evs_data[idx]))], axis = -1), axis = 1)
#return average_values
average_frames(emotion_timeseries[0],emotion_evs[0],'EMOTION','neut')
IDX: 1
Here is evs[idx][-1][-1]: 156
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 16 in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X22sZmlsZQ%3D%3D?line=0'>1</a> average_frames(emotion_timeseries[0],emotion_evs[0],'EMOTION','neut')
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 16 in average_frames(timeseries_data, evs_data, experiment_name, task_cond)
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X22sZmlsZQ%3D%3D?line=20'>21</a> print('IDX: {}'.format(idx))
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X22sZmlsZQ%3D%3D?line=22'>23</a> print('Here is evs[idx][-1][-1]: {}'.format(evs_data[idx][-1][-1]),end="\n")
---> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X22sZmlsZQ%3D%3D?line=23'>24</a> print("Length of timepoints: {}".format(timeseries_data.shape[2]))
IndexError: tuple index out of range
Function 1 - load_all_timeseries()#
Loads the timeseries for all subjects
def load_all_timeseries(subject_list, experiment_name, run_num):
"""
Reads the timeseries data of all subjects contained in the subject list for a given experiment
and given run.
Inputs:
subject_list (list): List containing subject ids
experiment_name (str): Name of task under consideration
run_num (int): Number of run task
Return:
data_array (ndarray): timeseries data of all subjects
shape of data returned : (100,360,timepoints)
"""
data_list = []
for sub_id in subject_list:
sub_data = load_single_timeseries(sub_id,experiment_name,run_num)
data_list.append(sub_data)
data_array = np.array(data_list)
return data_array
loading task data for emotion, social, gambling data for all subjects in the first run
# currently taking data for run 1 (LR)
emotion_timeseries = load_all_timeseries(subjects,'EMOTION',0)
social_timeseries = load_all_timeseries(subjects,'SOCIAL',0)
gambling_timeseries = load_all_timeseries(subjects,'GAMBLING',0)
Function 2 - load_all_evs()#
Loads the evs data for all subjects across all task conditions.
def load_all_evs(subject_list,experiment_name,run_num):
"""
Reads the evs data for all subjects in subject list across all task conditions
Inputs:
subject_list (list): List containing subject ids
experiment_name (str): Name of task under consideration
run_num (int): Number of run task
Returns:
data_array (ndarray): explanatory variables (ev) for all subjects
"""
data_list = []
for sub_id in subject_list:
sub_evs = load_evs(sub_id,experiment_name,run_num)
data_list.append(sub_evs)
data_array = np.array(data_list)
return data_array
#return data_list
# load_all_evs() returns a list of explanatory variables data
emotion_evs = load_all_evs(subjects,'EMOTION',0)
#social_evs = load_all_evs(subjects,'SOCIAL',0)
gambling_evs = load_all_evs(subjects,'GAMBLING',0)
emotion_evs = load_all_evs(subjects,'EMOTION',0)
emotion_evs.shape
(100, 2, 3, 25)
Function 3 - all_average_frames()#
The function loads in the average timeseries for all subjects for a given experiement’s task condition.
def all_average_frames(subject_list, timeseries_data, evs_data, experiment_name, task_cond):
"""
The function takes in the brain activity measures and behavioural measures for all subjects in a given task condition.
The function returns the brain activity average across all trail points.
The corresponding average contains the time-average brain activity across all ROIs
Input:
timeseries_data (ndarray): Contains the timeseries data for all subjects in a given experiment
evs_data (ndarray): Contains the evs data for all subject
experiment_name (str): Name of the experiment (EMOTION/SOCIAL)
task_condition (str): string name for task condition (fear/neut)
Returns:
average_values (ndarray): time-average activation across all ROIs
shape = (number of subjects, ROIs , 1)
"""
data_array = np.zeros((len(subject_list),360))
for sub_num in range(len(subject_list)):
sub_average = average_frames(timeseries_data[sub_num], evs_data[sub_num], experiment_name, task_cond)
data_array[sub_num] = sub_average
#np.append(data_array,sub_average)
return data_array
print("Shape of Emotion EVS: {}".format(np.array(emotion_evs).shape))
print("Shape of Social EVS: {}".format(np.array(social_evs).shape))
print("Shape of Gambling EVS: {}".format(np.array(gambling_evs).shape))
Shape of Emotion EVS: (100, 2, 3, 25)
Shape of Social EVS: (100, 2)
Shape of Gambling EVS: (100, 2, 2, 39)
bug 1 - adding conditional for load_evs() to ensure the duration_times line up timeseries_timepoints
currently average_frames() does not work.
remove the buggy conditional
def average_frames(timeseries_data, evs_data, experiment_name, task_cond):
"""
The function takes in the timeseries data and evs data for a particular subject and a given task condition
for a particular experiment
"""
idx = EXPERIMENTS[experiment_name]['cond'].index(task_cond)
#print('IDX: {}'.format(idx))
#print('Here is evs[idx][-1][-1]: {}'.format(evs_data[idx][-1][-1]),end="\n")
#print("Length of timepoints: {}".format(timeseries_data.shape[2]))
#if evs_data[idx][-1][-1] > timeseries_data.shape[2]:
#print("Conditional works!")
#average_values = np.mean(np.concatenate([np.mean(timeseries_data[:,evs_data[idx][i]],axis=1,keepdims=True) for i in range(len(evs_data[idx])-1)], axis = -1), axis = 1)
#else:
#print("No need for conditional to work")
average_values = np.mean(np.concatenate([np.mean(timeseries_data[:,evs_data[idx][i]],axis=1,keepdims=True) for i in range(len(evs_data[idx]))], axis = -1), axis = 1)
return average_values
def load_all_task_score(subject_list, experiment_name, run_num):
"""
Load task performance statistics for one task experiment.
Args:
subject_list (str): subject IDs to load
experiment_name (str) : Name of experiment
run_num (int): 0 or 1
Returns
task_scores (list of lists): A list of task performance scores for all subjects in a particular run
"""
task_scores = []
RUNS = {0: 'LR', 1: 'RL'}
#TR = 0.72
task_key = f'tfMRI_{experiment_name}_{RUNS[run_num]}'
EXPERIMENTS = {
'EMOTION' : {'cond':['fear','neut']},
'GAMBLING' : {'cond':['loss','win']},
'SOCIAL' : {'cond':['mental','rnd']}
}
for sub_id in subject_list:
#ev_file = f"{HCP_DIR}/subjects/{subject}/{experiment}/{task_key}/EVs/{cond}.txt"
task_file_loc = os.path.join(HCP_DIR,'subjects/{}/{}/{}/EVs/Stats.txt'.format(sub_id,experiment_name,task_key))
#ev_array = np.loadtxt(ev_file, ndmin=2, unpack=True)
#ev = dict(zip(["onset", "duration", "amplitude"], ev_array))
sub_task_score = get_task_data(task_file_loc)
task_scores.append(sub_task_score)
return task_scores
def get_task_data(taskfile_loc):
"""
Loads the task data from file location and returns a tuple containing column name
and data
Input:
taskfile (TextIOWrapper) - file location for performance statistics
Return:
task_score = (column name, task score)
"""
subject_all_score = []
with open(taskfile_loc,'r') as taskfile:
for line in taskfile:
data = line.strip().split(':')
string_data = data[0]
numeric_data = float(data[1])
output_data = ((string_data,numeric_data))
subject_all_score.append(output_data)
taskfile.close()
return subject_all_score
emotion_ts = load_all_task(subjects[:25],'EMOTION',0)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 36 in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X51sZmlsZQ%3D%3D?line=0'>1</a> emotion_ts = load_all_task(subjects[:25],'EMOTION',0)
NameError: name 'load_all_task' is not defined
emotion_ts
[[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 623.8),
('Median Shape RT', 660.588235294)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 670.866666667),
('Median Shape RT', 711.944444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 834.533333333),
('Median Shape RT', 864.647058824)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 661.2),
('Median Shape RT', 711.111111111)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 856.133333333),
('Median Shape RT', 784.722222222)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 759.285714286),
('Median Shape RT', 771.722222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 797.066666667),
('Median Shape RT', 917.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 717.0),
('Median Shape RT', 755.944444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.888888888889),
('Median Face RT', 700.533333333),
('Median Shape RT', 591.0625)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 723.333333333),
('Median Shape RT', 785.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 812.933333333),
('Median Shape RT', 722.882352941)],
[('Face Accuracy', 0.866666666667),
('Shape Accuracy', 1.0),
('Median Face RT', 910.461538462),
('Median Shape RT', 825.444444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 816.8),
('Median Shape RT', 698.777777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 699.933333333),
('Median Shape RT', 789.235294118)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 773.133333333),
('Median Shape RT', 738.222222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 865.866666667),
('Median Shape RT', 786.833333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 523.266666667),
('Median Shape RT', 555.0)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 951.666666667),
('Median Shape RT', 901.722222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 787.2),
('Median Shape RT', 738.722222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 897.4),
('Median Shape RT', 840.0)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 886.533333333),
('Median Shape RT', 797.888888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 943.2),
('Median Shape RT', 861.611111111)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 778.428571429),
('Median Shape RT', 777.888888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 652.466666667),
('Median Shape RT', 710.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.888888888889),
('Median Face RT', 745.6),
('Median Shape RT', 909.0625)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 872.133333333),
('Median Shape RT', 751.833333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 696.0),
('Median Shape RT', 751.666666667)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 1016.78571429),
('Median Shape RT', 1088.66666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 921.733333333),
('Median Shape RT', 719.333333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 577.8),
('Median Shape RT', 819.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 904.733333333),
('Median Shape RT', 873.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 614.533333333),
('Median Shape RT', 609.0)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 847.066666667),
('Median Shape RT', 793.764705882)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 660.266666667),
('Median Shape RT', 641.777777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 894.066666667),
('Median Shape RT', 934.111111111)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 886.666666667),
('Median Shape RT', 895.722222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 551.8),
('Median Shape RT', 668.647058824)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 612.333333333),
('Median Shape RT', 546.0)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 625.6),
('Median Shape RT', 749.722222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 895.6),
('Median Shape RT', 908.333333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 889.466666667),
('Median Shape RT', 855.823529412)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 817.266666667),
('Median Shape RT', 838.611111111)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 0.888888888889),
('Median Face RT', 864.928571429),
('Median Shape RT', 800.3125)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 1101.33333333),
('Median Shape RT', 958.833333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 769.533333333),
('Median Shape RT', 860.388888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 711.333333333),
('Median Shape RT', 728.888888889)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 816.142857143),
('Median Shape RT', 877.944444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 743.8),
('Median Shape RT', 710.235294118)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 725.466666667),
('Median Shape RT', 794.722222222)],
[('Face Accuracy', 0.8),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 860.25),
('Median Shape RT', 825.176470588)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 746.933333333),
('Median Shape RT', 700.888888889)],
[('Face Accuracy', 0.866666666667),
('Shape Accuracy', 1.0),
('Median Face RT', 913.538461538),
('Median Shape RT', 995.666666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 663.266666667),
('Median Shape RT', 741.882352941)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 795.933333333),
('Median Shape RT', 756.666666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 747.2),
('Median Shape RT', 701.388888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 573.466666667),
('Median Shape RT', 558.176470588)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 837.8),
('Median Shape RT', 893.5)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 743.428571429),
('Median Shape RT', 695.0)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 1003.57142857),
('Median Shape RT', 760.944444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 603.866666667),
('Median Shape RT', 678.277777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 681.733333333),
('Median Shape RT', 600.444444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 689.133333333),
('Median Shape RT', 630.611111111)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 747.466666667),
('Median Shape RT', 802.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 872.8),
('Median Shape RT', 917.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 1014.26666667),
('Median Shape RT', 878.588235294)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 775.2),
('Median Shape RT', 713.055555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 692.866666667),
('Median Shape RT', 793.176470588)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 982.6),
('Median Shape RT', 970.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.833333333333),
('Median Face RT', 824.6),
('Median Shape RT', 875.266666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 890.066666667),
('Median Shape RT', 1022.22222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 829.066666667),
('Median Shape RT', 798.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 642.866666667),
('Median Shape RT', 673.0)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 908.666666667),
('Median Shape RT', 932.222222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 857.0),
('Median Shape RT', 804.444444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 576.533333333),
('Median Shape RT', 594.055555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 624.533333333),
('Median Shape RT', 685.277777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 789.4),
('Median Shape RT', 824.055555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 856.333333333),
('Median Shape RT', 953.833333333)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 1012.86666667),
('Median Shape RT', 936.277777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 699.6),
('Median Shape RT', 740.411764706)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 1072.33333333),
('Median Shape RT', 779.222222222)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 849.2),
('Median Shape RT', 815.5)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 728.2),
('Median Shape RT', 716.117647059)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 732.142857143),
('Median Shape RT', 675.111111111)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 868.6),
('Median Shape RT', 797.888888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 666.733333333),
('Median Shape RT', 777.722222222)],
[('Face Accuracy', 0.866666666667),
('Shape Accuracy', 1.0),
('Median Face RT', 1074.23076923),
('Median Shape RT', 876.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 948.333333333),
('Median Shape RT', 979.555555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 840.133333333),
('Median Shape RT', 911.444444444)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 634.4),
('Median Shape RT', 694.888888889)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 822.666666667),
('Median Shape RT', 783.333333333)],
[('Face Accuracy', 0.933333333333),
('Shape Accuracy', 1.0),
('Median Face RT', 670.142857143),
('Median Shape RT', 659.777777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 749.0),
('Median Shape RT', 709.055555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 747.6),
('Median Shape RT', 729.647058824)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 738.6),
('Median Shape RT', 806.166666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 666.0),
('Median Shape RT', 668.941176471)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 835.933333333),
('Median Shape RT', 793.277777778)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 928.133333333),
('Median Shape RT', 1104.05555556)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 1.0),
('Median Face RT', 804.8),
('Median Shape RT', 806.166666667)],
[('Face Accuracy', 1.0),
('Shape Accuracy', 0.944444444444),
('Median Face RT', 741.4),
('Median Shape RT', 804.294117647)]]
social_ts = load_all_task(subjects[:5],'SOCIAL',0)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 38 in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#X53sZmlsZQ%3D%3D?line=0'>1</a> social_ts = load_all_task(subjects[:5],'SOCIAL',0)
NameError: name 'load_all_task' is not defined
social_ts[0]
[('Mean RT Mental', 479.0),
('Mean RT Random', 575.0),
('ACC Mental', 1.0),
('ACC Random', 1.0),
('Percent Mental', 0.6),
('Percent Random', 0.4),
('Percent Unsure', 0.0),
('Percent No Response', 0.0)]
task_score_col = ['Face Accuracy','Shape Accuracy', 'Median Face RT','Median Shape RT']
df1 = pd.DataFrame(columns=task_score_col)
df1.head()
| Face Accuracy | Shape Accuracy | Median Face RT | Median Shape RT |
|---|
HCP_DIR
'/Users/rajdeep_ch/Documents/nma/project/hcp_task'
taskfile_loc = os.path.join(HCP_DIR,'subjects/159340/EMOTION/tfMRI_EMOTION_LR/EVs/Stats.txt')
taskfile_loc
'/Users/rajdeep_ch/Documents/nma/project/hcp_task/subjects/159340/EMOTION/tfMRI_EMOTION_LR/EVs/Stats.txt'
taskfile = open(taskfile_loc,"r")
#print(taskfile.read())
type(taskfile)
_io.TextIOWrapper
test_list = []
with open(taskfile_loc,'r') as taskfile:
for line in taskfile:
data = line.strip().split(':')
print(data)
string_data = data[0]
numbers = float(data[1])
print(float(data[1]))
print(numbers)
test_list.append((string_data,numbers))
['Face Accuracy', ' 1.0']
1.0
1.0
['Shape Accuracy', ' 1.0']
1.0
1.0
['Median Face RT', ' 890.066666667']
890.066666667
890.066666667
['Median Shape RT', ' 1022.22222222']
1022.22222222
1022.22222222
df = pd.DataFrame(np.array(test_list).T)
df.head()
| 0 | 1 | 2 | 3 | |
|---|---|---|---|---|
| 0 | Face Accuracy | Shape Accuracy | Median Face RT | Median Shape RT |
| 1 | 1.0 | 1.0 | 890.066666667 | 1022.22222222 |
df.columns.name = None
df.columns = list(df.iloc[0,:])
df.drop(0)
| Face Accuracy | Shape Accuracy | Median Face RT | Median Shape RT | |
|---|---|---|---|---|
| 1 | 1.0 | 1.0 | 890.066666667 | 1022.22222222 |
Working with regions file#
def get_task_contrast(subject_list,experiment_name,run_num):
# load in the timeseries for the subject
# load in the evs for the subject
# use the average frames function to get the needed data
task_timeseries = load_all_timeseries(subject_list,experiment_name,run_num)
task_evs = load_all_evs(subject_list,experiment_name,run_num)
task_average = all_average_frames(subject_list,experiment_name,run_num)
regions_loc = os.path.join(HCP_DIR,'regions.npy')
regions = np.load(regions_loc).T
region_info = dict(Name=list(regions[0]), Map= list(regions[1]), Hemi = ['Right'] * int(N_PARCELS/2) + ['Left'] * int(N_PARCELS/2))
region_df = pd.DataFrame(region_info)
region_df.head()
| Name | Map | Hemi | |
|---|---|---|---|
| 0 | R_V1 | Visual1 | Right |
| 1 | R_MST | Visual2 | Right |
| 2 | R_V6 | Visual2 | Right |
| 3 | R_V2 | Visual2 | Right |
| 4 | R_V3 | Visual2 | Right |
#SOCIAL
my_exp = 'SOCIAL'
#calculate group contrast SOCIAL
group_contrast_social = 0
# for a particular subject in the list of subjects
for s in subjects:
# for a particular run of the selected subject
# the loop collects timeseries and evs data for all runs
# it calculates the constrast across the task conditions in each run and adds it to the group's data
for r in [0, 1]:
data = load_single_timeseries(subject=s, experiment=my_exp,
run=r, remove_mean=True)
evs = load_evs(subject=s, experiment=my_exp,run=r)
mental_activity = average_frames(data, evs, my_exp, 'mental')
rnd_activity = average_frames(data, evs, my_exp, 'rnd')
contrast = mental_activity - rnd_activity
group_contrast_social += contrast
group_contrast_social /= (len(subjects)*2) # remember: 2 sessions per subject
#Plot contrast bar graph SOCIAL
df = pd.DataFrame({'contrast' : group_contrast_social,
'network' : region_info['Map'],
'hemi' : region_info['Hemi']
})
# we will plot the fear foot minus neutral foot contrast so we only need one plot
#plt.figure()
#sns.barplot(y='network', x='contrast', data=df, hue='hemi')
#plt.title('group contrast of mental vs random')
#plt.show()
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short_df = df.iloc[:25,0:2]
short_df
| contrast | network | |
|---|---|---|
| 0 | 0.362457 | Visual1 |
| 1 | 6.231128 | Visual2 |
| 2 | -9.718446 | Visual2 |
| 3 | -10.336252 | Visual2 |
| 4 | 5.514508 | Visual2 |
| 5 | 10.860714 | Visual2 |
| 6 | 10.315644 | Visual2 |
| 7 | -4.458975 | Somatomotor |
| 8 | -1.319525 | Somatomotor |
| 9 | 3.766011 | Cingulo-Oper |
| 10 | 19.709574 | Language |
| 11 | 12.801832 | Default |
| 12 | -20.018026 | Visual2 |
| 13 | -3.090936 | Frontopariet |
| 14 | -7.402316 | Frontopariet |
| 15 | -2.314018 | Visual2 |
| 16 | 18.293229 | Visual2 |
| 17 | 24.647216 | Visual2 |
| 18 | 4.595847 | Visual2 |
| 19 | 20.287001 | Visual2 |
| 20 | 20.595323 | Visual2 |
| 21 | 18.707127 | Visual2 |
| 22 | -1.171799 | Visual2 |
| 23 | -5.061730 | Auditory |
| 24 | 11.030532 | Default |
gambling_average1[0][0] - gambling_average2[0][0]
8.964454038151064
Creating the task data and personality dataset#
HCP_DIR
'/Users/rajdeep_ch/Documents/nma/project/hcp_task'
personlity_file = r'/Users/rajdeep_ch/Documents/nma/project/personality_data.csv'
personality_df = pd.read_csv(personlity_file)
personality_df = personality_df.iloc[:,0:6]
emotion_ts = load_all_task_score(subjects[:5],'EMOTION',0)
social_ts = load_all_task_score(subjects,'SOCIAL',0)
gambling_ts = load_all_task_score(subjects,'GAMBLING',0)
emotion_ts = load_all_task_score(subjects,'EMOTION',0)
emotion_face_score = np.zeros(len(emotion_ts))
emotion_shape_score = np.zeros(len(emotion_ts))
# testing for accuracy == 0
emotion_acc_val1 = np.zeros(len(emotion_ts))
emotion_acc_val2 = np.zeros(len(emotion_ts))
# loop running over all participants
for i in range(len(emotion_ts)):
sub_task_vals = emotion_ts[i]
#print(sub_task_vals[0],sub_task_vals[2])
#print(sub_task_vals[1],sub_task_vals[3][1])
face_acc, face_time = sub_task_vals[0][1], sub_task_vals[2][1]
# test
emotion_acc_val1[i] = face_acc
shape_acc, shape_time = sub_task_vals[1][1], sub_task_vals[3][1]
# test
emotion_acc_val2[i] = shape_acc
#print("Score for shape: {}".format(shape_time,shape_acc),end="\n")
#print("Score for face: {}".format(face_time / face_acc))
#emotion_face_score[i] = (face_time / face_acc) / 100
# (100 / 100) does not produce needed result
# need to convert the value to percentage
# use multi-nomial LR
#emotion_shape_score[i] = (shape_time/ shape_acc) / 100
social_ts = load_all_task_score(subjects,'SOCIAL',0)
social_mental_score = np.zeros(len(social_ts))
social_random_score = np.zeros(len(social_ts))
# test arrays for checking social's accuracy == 0 ?
social_acc_val1 = np.zeros(len(social_ts))
social_acc_val2 = np.zeros(len(social_ts))
#dict_check = {}
subject_id_list_to_remove = []
for k in range(len(social_ts)):
#if social_ts[k][3][1] not in dict_check:
#dict_check[social_ts[k][3][1]] = 1
#else:
#dict_check[social_ts[k][3][1]] += 1
#print(social_ts[k][3])
if (social_ts[k][3][1]) == 0.0:
subject_id_list_to_remove.append(subjects[k])
subject_id_list_to_remove
['133827', '192439']
social_ts[0]
[('Mean RT Mental', 479.0),
('Mean RT Random', 575.0),
('ACC Mental', 1.0),
('ACC Random', 1.0),
('Percent Mental', 0.6),
('Percent Random', 0.4),
('Percent Unsure', 0.0),
('Percent No Response', 0.0)]
for i in range(len(social_ts)):
sub_social_score = social_ts[i]
#print(sub_social_score)
# mental reaction time; mental accuracy
#print(sub_social_score[0],sub_social_score[2])
# random reaction time; random accracy
#print(sub_social_score[1],sub_social_score[3],end="\n")
mental_rt, mental_acc = sub_social_score[0][1], sub_social_score[2][1]
# test for mental_acc
social_acc_val1[i] = mental_acc
random_rt, random_acc = sub_social_score[1][1], sub_social_score[3][1]
# test for random_acc
social_acc_val2[i] = random_acc
# zero division error since mental_acc == 0
social_mental_score[i] = mental_rt / mental_acc
#social_random_score[i] = random_rt / random_acc
#print('MENTAL: {}'.format(mental_rt / mental_acc))
#print("RANDOM: {}".format(random_rt / random_acc),end="\n\n")
social_acc_val2
array([1. , 1. , 1. , 1. , 1. , 0.5, 0.5, 0.5, 1. , 1. , 0.5, 1. , 1. ,
0.5, 1. , 1. , 0.5, 1. , 1. , 1. , 0.5, 1. , 1. , 1. , 0.5, 1. ,
1. , 1. , 1. , 0.5, 0.5, 1. , 0.5, 1. , 1. , 1. , 1. , 1. , 1. ,
1. , 0. , 1. , 0.5, 1. , 1. , 1. , 1. , 1. , 1. , 0.5, 1. , 0.5,
1. , 0.5, 1. , 0.5, 0.5, 1. , 0.5, 1. , 1. , 1. , 1. , 1. , 0.5,
1. , 1. , 1. , 0.5, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.5, 1. ,
1. , 1. , 1. , 0.5, 1. , 1. , 1. , 1. , 0. , 1. , 1. , 0.5, 0.5,
1. , 0.5, 1. , 1. , 1. , 1. , 1. , 1. , 0.5])
HERE
gambling_ts = load_all_task(subjects,'GAMBLING',0)
gambling_task_score = np.zeros(len(gambling_ts))
# array to test accuracy scores
gambling_acc = np.zeros(len(gambling_ts))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 84 in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y162sZmlsZQ%3D%3D?line=0'>1</a> gambling_ts = load_all_task(subjects,'GAMBLING',0)
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y162sZmlsZQ%3D%3D?line=2'>3</a> gambling_task_score = np.zeros(len(gambling_ts))
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y162sZmlsZQ%3D%3D?line=4'>5</a> # array to test accuracy scores
NameError: name 'load_all_task' is not defined
for i in range(len(gambling_ts)):
sub_gambling_score = gambling_ts[i]
#print(sub_gambling_score)
#print(sub_gambling_score[0], sub_gambling_score[2])
gamb_time = sub_gambling_score[0][1]
gamb_acc = sub_gambling_score[2][1]
gambling_acc[i] = gamb_acc
#print(gamb_acc)
np.any(gambling_acc == 0)
False
corr_df = pd.DataFrame(emotion_average)
corr_mat = corr_df.corr()
plt.figure(figsize=(10,7))
plt.pcolor(corr_mat)
plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x28c125dc0>
Classifying the personality trait data#
personality_df.head()
| Subject | NEOFAC_A | NEOFAC_O | NEOFAC_C | NEOFAC_N | NEOFAC_E | |
|---|---|---|---|---|---|---|
| 0 | 100307 | 37 | 24 | 35 | 15 | 37 |
| 1 | 100408 | 33 | 29 | 34 | 15 | 33 |
| 2 | 101915 | 35 | 30 | 45 | 8 | 31 |
| 3 | 102816 | 36 | 27 | 32 | 10 | 31 |
| 4 | 103414 | 27 | 30 | 31 | 20 | 34 |
test_personality = pd.DataFrame(personality_df)
test_personality.tail()
| Subject | NEOFAC_A | NEOFAC_O | NEOFAC_C | NEOFAC_N | NEOFAC_E | |
|---|---|---|---|---|---|---|
| 95 | 199655 | 24 | 29 | 33 | 15 | 28 |
| 96 | 200614 | 42 | 22 | 43 | 9 | 38 |
| 97 | 201111 | 36 | 35 | 42 | 31 | 23 |
| 98 | 201414 | 41 | 37 | 37 | 8 | 38 |
| 99 | 205119 | 40 | 30 | 37 | 11 | 33 |
for p in range(3):
aSr = test_personality.iloc[p,1:]
aSr
class_personality = pd.DataFrame()
class_personality.head()
script for loading the personality data and classifying the personality trait scores
score between 0 to 15 - 0 (LOW)
score between 16 to 30 - 1 (AVG)
else (score higher than 30) - 2 (HIGH)
label_list = []
for row_num in range(test_personality.shape[0]):
sub_data = test_personality.iloc[row_num,:]
for value in sub_data[1:]:
if value in range(0,16):
label_list.append(0)
elif value in range(16,31):
label_list.append(1)
else:
label_list.append(2)
label_list.insert(0,sub_data[0])
class_personality = pd.concat([class_personality,pd.Series(label_list)],axis=1)
label_list = []
class_personality.head()
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100307 | 100408 | 101915 | 102816 | 103414 | 103515 | 103818 | 105115 | 105216 | 106016 | ... | 196144 | 196750 | 197550 | 198451 | 199150 | 199655 | 200614 | 201111 | 201414 | 205119 |
| 1 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | ... | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 1 |
| 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | ... | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| 4 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | ... | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 2 | 0 | 0 |
5 rows × 100 columns
class_personality = class_personality.transpose()
class_personality.columns = personality_df.columns
class_personality.head()
| Subject | NEOFAC_A | NEOFAC_O | NEOFAC_C | NEOFAC_N | NEOFAC_E | |
|---|---|---|---|---|---|---|
| 0 | 100307 | 2 | 1 | 2 | 0 | 2 |
| 0 | 100408 | 2 | 1 | 2 | 0 | 2 |
| 0 | 101915 | 2 | 1 | 2 | 0 | 2 |
| 0 | 102816 | 2 | 1 | 2 | 0 | 2 |
| 0 | 103414 | 1 | 1 | 2 | 1 | 2 |
class_personality.iloc[:25,:]
| Subject | NEOFAC_A | NEOFAC_O | NEOFAC_C | NEOFAC_N | NEOFAC_E | |
|---|---|---|---|---|---|---|
| 0 | 100307 | 2 | 1 | 2 | 0 | 2 |
| 0 | 100408 | 2 | 1 | 2 | 0 | 2 |
| 0 | 101915 | 2 | 1 | 2 | 0 | 2 |
| 0 | 102816 | 2 | 1 | 2 | 0 | 2 |
| 0 | 103414 | 1 | 1 | 2 | 1 | 2 |
| 0 | 103515 | 1 | 1 | 2 | 1 | 1 |
| 0 | 103818 | 2 | 1 | 2 | 1 | 1 |
| 0 | 105115 | 2 | 1 | 1 | 1 | 1 |
| 0 | 105216 | 1 | 1 | 1 | 1 | 1 |
| 0 | 106016 | 2 | 1 | 2 | 0 | 1 |
| 0 | 106319 | 2 | 2 | 1 | 1 | 1 |
| 0 | 110411 | 2 | 1 | 2 | 0 | 2 |
| 0 | 111009 | 2 | 2 | 2 | 0 | 1 |
| 0 | 111312 | 2 | 1 | 2 | 1 | 1 |
| 0 | 111514 | 1 | 2 | 2 | 1 | 1 |
| 0 | 111716 | 0 | 2 | 2 | 0 | 2 |
| 0 | 113215 | 2 | 2 | 2 | 1 | 2 |
| 0 | 113619 | 2 | 1 | 2 | 0 | 2 |
| 0 | 114924 | 1 | 1 | 2 | 1 | 2 |
| 0 | 115320 | 2 | 1 | 1 | 1 | 2 |
| 0 | 117122 | 1 | 1 | 2 | 1 | 0 |
| 0 | 117324 | 2 | 1 | 2 | 0 | 1 |
| 0 | 118730 | 1 | 1 | 2 | 0 | 1 |
| 0 | 118932 | 2 | 2 | 1 | 1 | 1 |
| 0 | 119833 | 1 | 1 | 1 | 1 | 2 |
class_personality.to_csv(r'/Users/rajdeep_ch/Documents/nma/project/personality_data_classified.csv',index=False)
list(np.where(class_personality["Subject"] == '133827'))
[array([], dtype=int64)]
testing = list(class_personality['Subject'])
testing.index(133827)
40
testing.index(192439)
86
class_personality.iloc[40,:]
Subject 133827
NEOFAC_A 2
NEOFAC_O 1
NEOFAC_C 2
NEOFAC_N 0
NEOFAC_E 1
Name: 0, dtype: int64
class_personality.iloc[86,:]
Subject 192439
NEOFAC_A 2
NEOFAC_O 1
NEOFAC_C 2
NEOFAC_N 2
NEOFAC_E 2
Name: 0, dtype: int64
class_personality = class_personality.drop(40)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/Untitled-7.ipynb Cell 118 in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/Untitled-7.ipynb#Y364sZmlsZQ%3D%3D?line=0'>1</a> class_personality = class_personality.drop(40)
File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
305 if len(args) > num_allow_args:
306 warnings.warn(
307 msg.format(arguments=arguments),
308 FutureWarning,
309 stacklevel=stacklevel,
310 )
--> 311 return func(*args, **kwargs)
File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:4954, in DataFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
4806 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
4807 def drop(
4808 self,
(...)
4815 errors: str = "raise",
4816 ):
4817 """
4818 Drop specified labels from rows or columns.
4819
(...)
4952 weight 1.0 0.8
4953 """
-> 4954 return super().drop(
4955 labels=labels,
4956 axis=axis,
4957 index=index,
4958 columns=columns,
4959 level=level,
4960 inplace=inplace,
4961 errors=errors,
4962 )
File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:4267, in NDFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
4265 for axis, labels in axes.items():
4266 if labels is not None:
-> 4267 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
4269 if inplace:
4270 self._update_inplace(obj)
File ~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:4340, in NDFrame._drop_axis(self, labels, axis, level, errors, consolidate, only_slice)
4338 labels_missing = (axis.get_indexer_for(labels) == -1).any()
4339 if errors == "raise" and labels_missing:
-> 4340 raise KeyError(f"{labels} not found in axis")
4342 if is_extension_array_dtype(mask.dtype):
4343 # GH#45860
4344 mask = mask.to_numpy(dtype=bool)
KeyError: '[40] not found in axis'
help(load_all_timeseries)
Help on function load_all_timeseries in module __main__:
load_all_timeseries(subject_list, experiment_name, run_num)
Reads the timeseries data of all subjects contained in the subject list for a given experiment
and given run.
Inputs:
subject_list (list): List containing subject ids
experiment_name (str): Name of task under consideration
run_num (int): Number of run task
Return:
data_array (ndarray): timeseries data of all subjects
shape of data returned : (100,360,timepoints)
help(load_all_evs)
Help on function load_all_evs in module __main__:
load_all_evs(subject_list, experiment_name, run_num)
Reads the evs data for all subjects in subject list across all task conditions
Inputs:
subject_list (list): List containing subject ids
experiment_name (str): Name of task under consideration
run_num (int): Number of run task
Returns:
data_array (ndarray): explanatory variables (ev) for all subjects
help(all_average_frames)
Help on function all_average_frames in module __main__:
all_average_frames(subject_list, timeseries_data, evs_data, experiment_name, task_cond)
The function takes in the brain activity measures and behavioural measures for all subjects in a given task condition.
The function returns the brain activity average across all trail points.
The corresponding average contains the time-average brain activity across all ROIs
Input:
timeseries_data (ndarray): Contains the timeseries data for all subjects in a given experiment
evs_data (ndarray): Contains the evs data for all subject
experiment_name (str): Name of the experiment (EMOTION/SOCIAL)
task_condition (str): string name for task condition (fear/neut)
Returns:
average_values (ndarray): time-average activation across all ROIs
shape = (number of subjects, ROIs , 1)
help(load_all_task_score)
Help on function load_all_task_score in module __main__:
load_all_task_score(subject_list, experiment_name, run_num)
Load task performance statistics for one task experiment.
Args:
subject_list (str): subject IDs to load
experiment_name (str) : Name of experiment
run_num (int): 0 or 1
Returns
task_scores (list of lists): A list of task performance scores for all subjects in a particular run
emotion_timeseries = load_all_timeseries(subjects,'EMOTION',0)
emotion_evs = load_all_evs(subjects,'EMOTION',0)
emotion_average = all_average_frames(emotion_timeseries,emotion_evs,'EMOTION','f')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb Cell 117 in <cell line: 3>()
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y246sZmlsZQ%3D%3D?line=0'>1</a> emotion_timeseries = load_all_timeseries(subjects,'EMOTION',0)
<a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y246sZmlsZQ%3D%3D?line=1'>2</a> emotion_evs = load_all_evs(subjects,'EMOTION',0)
----> <a href='vscode-notebook-cell:/Users/rajdeep_ch/Documents/nma/tutorials/project_main.ipynb#Y246sZmlsZQ%3D%3D?line=2'>3</a> emotion_average = all_average_frames(emotion_timeseries,emotion_evs,'EMOTION','f')
TypeError: all_average_frames() missing 1 required positional argument: 'task_cond'
emotion_ts = load_all_task_score(subjects,'EMOTION',0)
emotion_face_score = np.zeros(len(emotion_ts))
emotion_shape_score = np.zeros(len(emotion_ts))
avoid_subjects = ['133827', '192439']
# loop running over all participants
for i in range(len(emotion_ts)):
sub_task_vals = emotion_ts[i]
#print(sub_task_vals[0][1],sub_task_vals[2][1])
#print(sub_task_vals[1][1],sub_task_vals[3][1])
if subjects[i] not in avoid_subjects:
face_acc, face_time = sub_task_vals[0][1], sub_task_vals[2][1]
ies_face = face_time / (100 * face_acc)
emotion_face_score[i] = ies_face
shape_acc, shape_time = sub_task_vals[1][1], sub_task_vals[3][1]
ies_shape = shape_time / (100 * shape_acc)
emotion_shape_score[i] = ies_shape
else:
emotion_face_score[i] = -1
emotion_shape_score[i] = -1
# test
## emotion_acc_val1[i] = face_acc
###shape_acc, shape_time = sub_task_vals[1][1], sub_task_vals[3][1]
# test
## emotion_acc_val2[i] = shape_acc
#print("Score for shape: {}".format(shape_time,shape_acc),end="\n")
#print("Score for face: {}".format(face_time / face_acc))
#emotion_face_score[i] = (face_time / face_acc) / 100
# (100 / 100) does not produce needed result
# need to convert the value to percentage
# use multi-nomial LR
#emotion_shape_score[i] = (shape_time/ shape_acc) / 100
emotion IES for condition 1 - emotion_face_score
emotion IES for condition 2 - emotion_shape_score
social_ts = load_all_task_score(subjects,'SOCIAL',0)
social_mental_score = np.zeros(len(social_ts))
social_random_score = np.zeros(len(social_ts))
for i in range(len(social_ts[:])):
sub_social_score = social_ts[i]
#print(sub_social_score)
# mental reaction time; mental accuracy
#print(sub_social_score[0],sub_social_score[2])
# random reaction time; random accracy
#print(sub_social_score[1],sub_social_score[3],end="\n")
if subjects[i] not in avoid_subjects:
mental_rt, mental_acc = sub_social_score[0][1], sub_social_score[2][1]
ies_mental = mental_rt / (100 * mental_acc)
print("MENTAL RT: {} | MENTAL ACC : {}| MENTAL IES: {}".format(mental_rt,mental_acc,ies_mental))
social_mental_score[i] = ies_mental
print(social_mental_score[i])
random_rt, random_acc = sub_social_score[1][1], sub_social_score[3][1]
ies_random = random_rt / (100 * random_acc)
social_random_score[i] = ies_random
else:
social_mental_score[i] = -1
social_random_score[i] = -1
# test for mental_acc
#social_acc_val1[i] = mental_acc
#random_rt, random_acc = sub_social_score[1][1], sub_social_score[3][1]
# test for random_acc
#social_acc_val2[i] = random_acc
# zero division error since mental_acc == 0
#social_mental_score[i] = mental_rt / mental_acc
#social_random_score[i] = random_rt / random_acc
#print('MENTAL: {}'.format(mental_rt / mental_acc))
#print("RANDOM: {}".format(random_rt / random_acc),end="\n\n")
MENTAL RT: 479.0 | MENTAL ACC : 1.0| MENTAL IES: 4.79
4.79
MENTAL RT: 1059.33333333 | MENTAL ACC : 1.0| MENTAL IES: 10.5933333333
10.5933333333
MENTAL RT: 1193.0 | MENTAL ACC : 1.0| MENTAL IES: 11.93
11.93
MENTAL RT: 1189.0 | MENTAL ACC : 1.0| MENTAL IES: 11.89
11.89
MENTAL RT: 738.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.386666666669999
7.386666666669999
MENTAL RT: 720.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 10.807499999994597
10.807499999994597
MENTAL RT: 914.0 | MENTAL ACC : 1.0| MENTAL IES: 9.14
9.14
MENTAL RT: 914.0 | MENTAL ACC : 1.0| MENTAL IES: 9.14
9.14
MENTAL RT: 712.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.126666666669999
7.126666666669999
MENTAL RT: 680.333333333 | MENTAL ACC : 1.0| MENTAL IES: 6.80333333333
6.80333333333
MENTAL RT: 1181.66666667 | MENTAL ACC : 1.0| MENTAL IES: 11.8166666667
11.8166666667
MENTAL RT: 862.0 | MENTAL ACC : 1.0| MENTAL IES: 8.62
8.62
MENTAL RT: 1148.66666667 | MENTAL ACC : 1.0| MENTAL IES: 11.4866666667
11.4866666667
MENTAL RT: 524.666666667 | MENTAL ACC : 1.0| MENTAL IES: 5.2466666666699995
5.2466666666699995
MENTAL RT: 776.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.76666666667
7.76666666667
MENTAL RT: 683.0 | MENTAL ACC : 1.0| MENTAL IES: 6.83
6.83
MENTAL RT: 797.0 | MENTAL ACC : 0.333333333333| MENTAL IES: 23.910000000023906
23.910000000023906
MENTAL RT: 939.666666667 | MENTAL ACC : 1.0| MENTAL IES: 9.396666666669999
9.396666666669999
MENTAL RT: 705.0 | MENTAL ACC : 0.666666666667| MENTAL IES: 10.574999999994713
10.574999999994713
MENTAL RT: 591.0 | MENTAL ACC : 1.0| MENTAL IES: 5.91
5.91
MENTAL RT: 827.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.273333333330001
8.273333333330001
MENTAL RT: 707.0 | MENTAL ACC : 1.0| MENTAL IES: 7.07
7.07
MENTAL RT: 856.666666667 | MENTAL ACC : 1.0| MENTAL IES: 8.566666666669999
8.566666666669999
MENTAL RT: 658.666666667 | MENTAL ACC : 1.0| MENTAL IES: 6.586666666669999
6.586666666669999
MENTAL RT: 865.666666667 | MENTAL ACC : 1.0| MENTAL IES: 8.656666666669999
8.656666666669999
MENTAL RT: 984.0 | MENTAL ACC : 1.0| MENTAL IES: 9.84
9.84
MENTAL RT: 849.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.49333333333
8.49333333333
MENTAL RT: 1058.0 | MENTAL ACC : 1.0| MENTAL IES: 10.58
10.58
MENTAL RT: 457.666666667 | MENTAL ACC : 1.0| MENTAL IES: 4.57666666667
4.57666666667
MENTAL RT: 833.0 | MENTAL ACC : 1.0| MENTAL IES: 8.33
8.33
MENTAL RT: 1255.0 | MENTAL ACC : 0.333333333333| MENTAL IES: 37.65000000003764
37.65000000003764
MENTAL RT: 806.0 | MENTAL ACC : 1.0| MENTAL IES: 8.06
8.06
MENTAL RT: 767.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.676666666669999
7.676666666669999
MENTAL RT: 762.333333333 | MENTAL ACC : 1.0| MENTAL IES: 7.623333333330001
7.623333333330001
MENTAL RT: 856.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.56333333333
8.56333333333
MENTAL RT: 789.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.89666666667
7.89666666667
MENTAL RT: 707.333333333 | MENTAL ACC : 1.0| MENTAL IES: 7.073333333330001
7.073333333330001
MENTAL RT: 605.0 | MENTAL ACC : 1.0| MENTAL IES: 6.05
6.05
MENTAL RT: 647.0 | MENTAL ACC : 1.0| MENTAL IES: 6.47
6.47
MENTAL RT: 797.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.97666666667
7.97666666667
MENTAL RT: 988.333333333 | MENTAL ACC : 1.0| MENTAL IES: 9.88333333333
9.88333333333
MENTAL RT: 991.0 | MENTAL ACC : 1.0| MENTAL IES: 9.91
9.91
MENTAL RT: 546.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 8.197499999995902
8.197499999995902
MENTAL RT: 966.333333333 | MENTAL ACC : 1.0| MENTAL IES: 9.66333333333
9.66333333333
MENTAL RT: 578.666666667 | MENTAL ACC : 1.0| MENTAL IES: 5.7866666666699995
5.7866666666699995
MENTAL RT: 730.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 10.957499999994521
10.957499999994521
MENTAL RT: 557.333333333 | MENTAL ACC : 1.0| MENTAL IES: 5.573333333330001
5.573333333330001
MENTAL RT: 742.0 | MENTAL ACC : 1.0| MENTAL IES: 7.42
7.42
MENTAL RT: 702.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.02666666667
7.02666666667
MENTAL RT: 912.0 | MENTAL ACC : 1.0| MENTAL IES: 9.12
9.12
MENTAL RT: 1022.0 | MENTAL ACC : 1.0| MENTAL IES: 10.22
10.22
MENTAL RT: 744.333333333 | MENTAL ACC : 1.0| MENTAL IES: 7.44333333333
7.44333333333
MENTAL RT: 724.0 | MENTAL ACC : 0.666666666667| MENTAL IES: 10.85999999999457
10.85999999999457
MENTAL RT: 1204.33333333 | MENTAL ACC : 1.0| MENTAL IES: 12.0433333333
12.0433333333
MENTAL RT: 2141.66666667 | MENTAL ACC : 1.0| MENTAL IES: 21.4166666667
21.4166666667
MENTAL RT: 1458.33333333 | MENTAL ACC : 1.0| MENTAL IES: 14.583333333299999
14.583333333299999
MENTAL RT: 759.333333333 | MENTAL ACC : 1.0| MENTAL IES: 7.59333333333
7.59333333333
MENTAL RT: 1031.33333333 | MENTAL ACC : 1.0| MENTAL IES: 10.3133333333
10.3133333333
MENTAL RT: 549.666666667 | MENTAL ACC : 1.0| MENTAL IES: 5.4966666666699995
5.4966666666699995
MENTAL RT: 647.0 | MENTAL ACC : 1.0| MENTAL IES: 6.47
6.47
MENTAL RT: 926.333333333 | MENTAL ACC : 1.0| MENTAL IES: 9.263333333330001
9.263333333330001
MENTAL RT: 841.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.41333333333
8.41333333333
MENTAL RT: 756.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 11.347499999994326
11.347499999994326
MENTAL RT: 1367.66666667 | MENTAL ACC : 1.0| MENTAL IES: 13.676666666700001
13.676666666700001
MENTAL RT: 555.333333333 | MENTAL ACC : 1.0| MENTAL IES: 5.55333333333
5.55333333333
MENTAL RT: 573.333333333 | MENTAL ACC : 1.0| MENTAL IES: 5.73333333333
5.73333333333
MENTAL RT: 1001.66666667 | MENTAL ACC : 1.0| MENTAL IES: 10.0166666667
10.0166666667
MENTAL RT: 706.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.06666666667
7.06666666667
MENTAL RT: 543.333333333 | MENTAL ACC : 1.0| MENTAL IES: 5.43333333333
5.43333333333
MENTAL RT: 571.0 | MENTAL ACC : 0.666666666667| MENTAL IES: 8.564999999995718
8.564999999995718
MENTAL RT: 662.0 | MENTAL ACC : 1.0| MENTAL IES: 6.62
6.62
MENTAL RT: 759.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.59666666667
7.59666666667
MENTAL RT: 1313.0 | MENTAL ACC : 1.0| MENTAL IES: 13.13
13.13
MENTAL RT: 694.0 | MENTAL ACC : 1.0| MENTAL IES: 6.94
6.94
MENTAL RT: 818.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.183333333330001
8.183333333330001
MENTAL RT: 1002.33333333 | MENTAL ACC : 1.0| MENTAL IES: 10.0233333333
10.0233333333
MENTAL RT: 940.666666667 | MENTAL ACC : 1.0| MENTAL IES: 9.406666666669999
9.406666666669999
MENTAL RT: 620.666666667 | MENTAL ACC : 1.0| MENTAL IES: 6.206666666669999
6.206666666669999
MENTAL RT: 1090.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 16.357499999991823
16.357499999991823
MENTAL RT: 918.666666667 | MENTAL ACC : 1.0| MENTAL IES: 9.18666666667
9.18666666667
MENTAL RT: 787.0 | MENTAL ACC : 1.0| MENTAL IES: 7.87
7.87
MENTAL RT: 819.666666667 | MENTAL ACC : 1.0| MENTAL IES: 8.19666666667
8.19666666667
MENTAL RT: 1365.0 | MENTAL ACC : 1.0| MENTAL IES: 13.65
13.65
MENTAL RT: 738.333333333 | MENTAL ACC : 1.0| MENTAL IES: 7.38333333333
7.38333333333
MENTAL RT: 754.0 | MENTAL ACC : 1.0| MENTAL IES: 7.54
7.54
MENTAL RT: 786.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.86666666667
7.86666666667
MENTAL RT: 618.333333333 | MENTAL ACC : 1.0| MENTAL IES: 6.18333333333
6.18333333333
MENTAL RT: 836.0 | MENTAL ACC : 1.0| MENTAL IES: 8.36
8.36
MENTAL RT: 1434.5 | MENTAL ACC : 0.666666666667| MENTAL IES: 21.517499999989244
21.517499999989244
MENTAL RT: 1173.0 | MENTAL ACC : 1.0| MENTAL IES: 11.73
11.73
MENTAL RT: 706.666666667 | MENTAL ACC : 1.0| MENTAL IES: 7.06666666667
7.06666666667
MENTAL RT: 802.333333333 | MENTAL ACC : 1.0| MENTAL IES: 8.023333333330001
8.023333333330001
MENTAL RT: 505.333333333 | MENTAL ACC : 1.0| MENTAL IES: 5.0533333333299995
5.0533333333299995
MENTAL RT: 665.666666667 | MENTAL ACC : 1.0| MENTAL IES: 6.65666666667
6.65666666667
MENTAL RT: 604.333333333 | MENTAL ACC : 1.0| MENTAL IES: 6.043333333330001
6.043333333330001
MENTAL RT: 1012.66666667 | MENTAL ACC : 1.0| MENTAL IES: 10.1266666667
10.1266666667
MENTAL RT: 909.0 | MENTAL ACC : 1.0| MENTAL IES: 9.09
9.09
MENTAL RT: 797.0 | MENTAL ACC : 1.0| MENTAL IES: 7.97
7.97
social_mental_score
array([ 4.79 , 10.59333333, 11.93 , 11.89 , 7.38666667,
10.8075 , 9.14 , 9.14 , 7.12666667, 6.80333333,
11.81666667, 8.62 , 11.48666667, 5.24666667, 7.76666667,
6.83 , 23.91 , 9.39666667, 10.575 , 5.91 ,
8.27333333, 7.07 , 8.56666667, 6.58666667, 8.65666667,
9.84 , 8.49333333, 10.58 , 4.57666667, 8.33 ,
37.65 , 8.06 , 7.67666667, 7.62333333, 8.56333333,
7.89666667, 7.07333333, 6.05 , 6.47 , 7.97666667,
-1. , 9.88333333, 9.91 , 8.1975 , 9.66333333,
5.78666667, 10.9575 , 5.57333333, 7.42 , 7.02666667,
9.12 , 10.22 , 7.44333333, 10.86 , 12.04333333,
21.41666667, 14.58333333, 7.59333333, 10.31333333, 5.49666667,
6.47 , 9.26333333, 8.41333333, 11.3475 , 13.67666667,
5.55333333, 5.73333333, 10.01666667, 7.06666667, 5.43333333,
8.565 , 6.62 , 7.59666667, 13.13 , 6.94 ,
8.18333333, 10.02333333, 9.40666667, 6.20666667, 16.3575 ,
9.18666667, 7.87 , 8.19666667, 13.65 , 7.38333333,
7.54 , -1. , 7.86666667, 6.18333333, 8.36 ,
21.5175 , 11.73 , 7.06666667, 8.02333333, 5.05333333,
6.65666667, 6.04333333, 10.12666667, 9.09 , 7.97 ])
gambling_ts = load_all_task_score(subjects,'GAMBLING',0)
gambling_task_score = np.zeros(len(gambling_ts))
# array to test accuracy scores
gambling_acc = np.zeros(len(gambling_ts))
for i in range(len(gambling_ts)):
sub_gambling_score = gambling_ts[i]
#print(sub_gambling_score)
#print(sub_gambling_score[0], sub_gambling_score[2])
if subjects[i] not in avoid_subjects:
gamb_time = sub_gambling_score[0][1]
gamb_acc = sub_gambling_score[2][1]
ies_gamb = gamb_time / (100 * gamb_acc)
gambling_task_score[i] = ies_gamb
else:
gambling_task_score[i] = -1
gambling_task_score
array([ 3.6090625 , 4.811875 , 5.093125 , 4.874375 , 4.7040625 ,
3.329375 , 3.678125 , 3.555 , 3.0796875 , 3.3903125 ,
8.1771875 , 3.0190625 , 3.63625 , 3.7440625 , 2.980625 ,
5.8440625 , 3.91225806, 4.42006243, 3.9453125 , 6.2940625 ,
5.06305931, 5.6909375 , 3.14 , 5.16795005, 5.880625 ,
6.27080125, 5.4365625 , 5.219375 , 2.9871875 , 4.95983351,
5.125625 , 2.6371875 , 4.2840625 , 6.8284375 , 5.77125 ,
2.46875 , 2.4490625 , 3.4141103 , 4.2884375 , 3.6946875 ,
-1. , 6.61125 , 2.85502601, 2.980625 , 3.8365625 ,
3.7728125 , 4.10375 , 3.84375 , 3.84099896, 3.721875 ,
3.68316337, 5.5071875 , 3.68375 , 3.7296875 , 4.6921875 ,
5.94125 , 5.76899063, 3.7725 , 3.7203125 , 3.2825 ,
4.596875 , 4.6284375 , 3.79338189, 4.7103125 , 4.845625 ,
3.1428125 , 3.633125 , 5.4121875 , 4.22625 , 3.4578125 ,
3.6975 , 4.195625 , 4.4125 , 4.7103125 , 4.3484375 ,
4.226875 , 4.76906667, 4.43125 , 5.1765625 , 5.125 ,
4.8859375 , 3.4203125 , 4.8871875 , 4.51629553, 3.34875 ,
4.825 , -1. , 5.075 , 5.2553125 , 4.35 ,
3.8226847 , 3.8321875 , 3.1084375 , 4.6753125 , 3.450625 ,
3.010625 , 4.2125 , 5.56375 , 3.1003125 , 5.1009375 ])
c = list(zip(subjects,emotion_face_score))
for s, fs in c[:5]:
print(s,fs)
100307 6.2379999999999995
100408 6.70866666667
101915 8.34533333333
102816 6.612
103414 8.56133333333
subject_ies_dict = {}
for index_sub in range(len(subjects)):
subject_id = subjects[index_sub]
emo1, emo2, soc1, soc2, gamb = emotion_shape_score[index_sub], emotion_face_score[index_sub], social_mental_score[index_sub], social_random_score[index_sub], gambling_task_score[index_sub]
subject_ies_dict[subject_id] = [emo1,emo2,soc1,soc2,gamb]
subject_ies_dict
{'100307': [6.994463667822115, 6.2379999999999995, 4.79, 5.75, 3.6090625],
'100408': [7.119444444440001, 6.70866666667, 10.5933333333, 12.675, 4.811875],
'101915': [9.155086505199602, 8.34533333333, 11.93, 13.99, 5.093125],
'102816': [7.1111111111100005, 6.612, 11.89, 14.195, 4.874375],
'103414': [7.84722222222, 8.56133333333, 7.386666666669999, 7.955, 4.7040625],
'103515': [7.71722222222,
8.135204081638621,
10.807499999994597,
21.38,
3.329375],
'103818': [9.175555555559999, 7.970666666670001, 9.14, 19.8, 3.678125],
'105115': [7.55944444444, 7.17, 9.14, 15.1, 3.555],
'105216': [6.649453124999169,
7.005333333329999,
7.126666666669999,
6.49,
3.0796875],
'106016': [7.855, 7.23333333333, 6.80333333333, 7.095, 3.3903125],
'106319': [7.654048442908308,
8.129333333329999,
11.8166666667,
12.22,
8.1771875],
'110411': [8.25444444444, 10.505325443788267, 8.62, 7.605, 3.0190625],
'111009': [6.98777777778, 8.168, 11.4866666667, 10.125, 3.63625],
'111312': [8.356608996547461,
6.999333333329999,
5.2466666666699995,
33.92,
3.7440625],
'111514': [7.38222222222, 7.73133333333, 7.76666666667, 8.095, 2.980625],
'111716': [7.868333333330001, 8.65866666667, 6.83, 18.53, 5.8440625],
'113215': [5.8764705882380595,
5.23266666667,
23.910000000023906,
9.3,
3.912258064516129],
'113619': [9.017222222220001,
9.51666666667,
9.396666666669999,
11.51,
4.420062434962581],
'114924': [7.38722222222,
7.872000000000001,
10.574999999994713,
6.955,
3.9453125],
'115320': [8.4, 8.974, 5.91, 7.23, 6.2940625],
'117122': [7.97888888889,
8.86533333333,
8.273333333330001,
8.44,
5.063059313218065],
'117324': [8.61611111111, 9.432, 7.07, 9.84, 5.6909375],
'118730': [7.77888888889, 8.340306122456552, 8.566666666669999, 6.88, 3.14],
'118932': [7.10555555556,
6.52466666667,
6.586666666669999,
10.725,
5.167950052025806],
'119833': [10.226953124998722, 7.456, 8.656666666669999, 15.24, 5.880625],
'120111': [7.51833333333, 8.72133333333, 9.84, 11.975, 6.270801248701936],
'120212': [7.51666666667, 6.96, 8.49333333333, 6.425, 5.4365625],
'122317': [10.8866666667, 10.894132653111035, 10.58, 8.57, 5.219375],
'123117': [7.19333333333, 9.21733333333, 4.57666666667, 4.665, 2.9871875],
'124422': [8.195, 5.778, 8.33, 7.76, 4.959833506766452],
'125525': [8.735, 9.04733333333, 37.65000000003764, 18.02, 5.125625],
'126325': [6.09, 6.14533333333, 8.06, 9.36, 2.6371875],
'127933': [8.404567474048662,
8.47066666667,
7.676666666669999,
22.64,
4.2840625],
'128632': [6.41777777778, 6.60266666667, 7.623333333330001, 12.02, 6.8284375],
'129028': [9.341111111110001,
8.940666666670001,
8.56333333333,
15.03,
5.77125],
'130013': [8.95722222222, 8.86666666667, 7.89666666667, 7.27, 2.46875],
'130316': [7.079792387551567, 5.518, 7.073333333330001, 10.445, 2.4490625],
'130922': [5.46, 6.123333333330001, 6.05, 6.195, 3.4141103017703225],
'131924': [7.4972222222200005, 6.256, 6.47, 6.235, 4.2884375],
'133625': [9.08333333333, 8.956, 7.97666666667, 6.385, 3.6946875],
'133827': [-1.0, -1.0, -1.0, -1.0, -1.0],
'133928': [8.38611111111, 8.17266666667, 9.88333333333, 4.585, 6.61125],
'134324': [9.003515624998874,
9.267091836742598,
9.91,
15.18,
2.855026014565161],
'135932': [9.58833333333, 11.0133333333, 8.197499999995902, 5.765, 2.980625],
'136833': [8.60388888889, 7.69533333333, 9.66333333333, 11.425, 3.8365625],
'137128': [7.28888888889,
7.113333333330001,
5.7866666666699995,
6.445,
3.7728125],
'138231': [8.779444444440001,
8.744387755106695,
10.957499999994521,
11.23,
4.10375],
'138534': [7.520138408311773, 7.438, 5.573333333330001, 10.04, 3.84375],
'139637': [7.947222222220001, 7.25466666667, 7.42, 6.54, 3.840998959421935],
'140824': [8.737162629759405, 10.753125, 7.02666666667, 14.4, 3.721875],
'142828': [7.00888888889, 7.46933333333, 9.12, 7.135, 3.683163371489032],
'143325': [9.95666666667, 10.540828402357485, 10.22, 29.34, 5.5071875],
'148032': [7.855224913496637, 6.63266666667, 7.44333333333, 10.12, 3.68375],
'148335': [7.56666666667,
7.959333333329999,
10.85999999999457,
14.12,
3.7296875],
'149337': [7.0138888888899995, 7.472, 12.0433333333, 12.745, 4.6921875],
'149539': [5.910103806228664, 5.73466666667, 21.4166666667, 26.76, 5.94125],
'150524': [8.935, 8.378, 14.583333333299999, 21.96, 5.768990634756128],
'151223': [6.95, 7.9653061224564174, 7.59333333333, 8.915, 3.7725],
'151526': [7.60944444444, 10.752551020396698, 10.3133333333, 10.2, 3.7203125],
'151627': [6.78277777778, 6.03866666667, 5.4966666666699995, 6.905, 3.2825],
'153025': [6.004444444440001, 6.817333333330001, 6.47, 5.43, 4.596875],
'153429': [6.30611111111,
6.8913333333299995,
9.263333333330001,
6.96,
4.6284375],
'154431': [8.025, 7.47466666667, 8.41333333333, 7.205, 3.793381893863226],
'156233': [9.175555555559999, 8.728, 11.347499999994326, 8.37, 4.7103125],
'156637': [9.302698961940848,
10.1426666667,
13.676666666700001,
24.94,
4.845625],
'157336': [7.130555555559999,
7.752000000000001,
5.55333333333,
5.56,
3.1428125],
'158035': [8.398339100347481, 6.92866666667, 5.73333333333, 5.995, 3.633125],
'158540': [9.705, 9.826, 10.0166666667, 6.005, 5.4121875],
'159239': [10.503200000008201, 8.246, 7.06666666667, 8.72, 4.22625],
'159340': [10.222222222200001, 8.90066666667, 5.43333333333, 6.82, 3.4578125],
'160123': [7.9855555555599995,
8.29066666667,
8.564999999995718,
7.28,
3.6975],
'161731': [7.125882352944529, 6.42866666667, 6.62, 6.1, 4.195625],
'162329': [9.32222222222, 9.08666666667, 7.59666666667, 5.81, 4.4125],
'162733': [8.04444444444, 8.57, 13.13, 12.23, 4.7103125],
'163129': [5.94055555556, 5.76533333333, 6.94, 6.16, 4.3484375],
'163432': [6.85277777778, 6.24533333333, 8.183333333330001, 8.515, 4.226875],
'167743': [8.24055555556, 7.894, 10.0233333333, 11.74, 4.769066666666667],
'172332': [9.53833333333, 8.56333333333, 9.406666666669999, 7.685, 4.43125],
'175439': [9.36277777778, 10.1286666667, 6.206666666669999, 6.25, 5.1765625],
'176542': [7.839653979243689, 6.996, 16.357499999991823, 11.815, 5.125],
'178950': [7.79222222222, 10.7233333333, 9.18666666667, 10.67, 4.8859375],
'182739': [8.155, 8.492, 7.87, 25.7, 3.4203125],
'185139': [7.582422145334156, 7.282, 8.19666666667, 9.025, 4.8871875],
'188347': [6.75111111111, 7.844387755106374, 13.65, 7.93, 4.516295525491612],
'189450': [7.97888888889, 8.686, 7.38333333333, 6.115, 3.34875],
'190031': [7.777222222220001, 6.66733333333, 7.54, 13.4, 4.825],
'192439': [-1.0, -1.0, -1.0, -1.0, -1.0],
'192540': [9.79555555556, 9.48333333333, 7.86666666667, 8.865, 5.075],
'193239': [9.11444444444, 8.40133333333, 6.18333333333, 13.85, 5.2553125],
'194140': [6.94888888889, 6.343999999999999, 8.36, 13.88, 4.35],
'196144': [7.833333333330001,
8.226666666669999,
21.517499999989244,
20.92,
3.8226847034322584],
'196750': [6.597777777779999, 7.180102040820422, 11.73, 5.8, 3.8321875],
'197550': [7.090555555559999, 7.49, 7.06666666667, 28.98, 3.1084375],
'198451': [7.725674740493048, 7.476, 8.023333333330001, 8.58, 4.6753125],
'199150': [8.06166666667, 7.386, 5.0533333333299995, 5.33, 3.450625],
'199655': [7.0829065744021555, 6.66, 6.65666666667, 9.225, 3.010625],
'200614': [7.93277777778, 8.35933333333, 6.043333333330001, 8.83, 4.2125],
'201111': [11.0405555556, 9.28133333333, 10.1266666667, 10.27, 5.56375],
'201414': [8.06166666667, 8.048, 9.09, 11.725, 3.1003125],
'205119': [8.516055363325183, 7.414, 7.97, 21.4, 5.1009375]}
testing_ies = pd.DataFrame(subject_ies_dict)
testing_ies = testing_ies.transpose()
testing_ies.head()
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 100307 | 6.994464 | 6.238000 | 4.790000 | 5.750 | 3.609062 |
| 100408 | 7.119444 | 6.708667 | 10.593333 | 12.675 | 4.811875 |
| 101915 | 9.155087 | 8.345333 | 11.930000 | 13.990 | 5.093125 |
| 102816 | 7.111111 | 6.612000 | 11.890000 | 14.195 | 4.874375 |
| 103414 | 7.847222 | 8.561333 | 7.386667 | 7.955 | 4.704063 |
testing_ies.reset_index(inplace=True)
testing_ies = testing_ies.rename(columns = {'index':'Subjects'})
testing_ies = testing_ies.rename(columns = {'0': 'EMOTION IES'})
testing_ies.columns = ['SUBJECTS',"EMOTION T1 IES","EMOTION T2 IES","SOCIAL T1 IES","SOCIAL T2 IES","GAMBLING IES"]
testing_ies[:50]
| SUBJECTS | EMOTION T1 IES | EMOTION T2 IES | SOCIAL T1 IES | SOCIAL T2 IES | GAMBLING IES | |
|---|---|---|---|---|---|---|
| 0 | 100307 | 6.994464 | 6.238000 | 4.790000 | 5.750 | 3.609062 |
| 1 | 100408 | 7.119444 | 6.708667 | 10.593333 | 12.675 | 4.811875 |
| 2 | 101915 | 9.155087 | 8.345333 | 11.930000 | 13.990 | 5.093125 |
| 3 | 102816 | 7.111111 | 6.612000 | 11.890000 | 14.195 | 4.874375 |
| 4 | 103414 | 7.847222 | 8.561333 | 7.386667 | 7.955 | 4.704063 |
| 5 | 103515 | 7.717222 | 8.135204 | 10.807500 | 21.380 | 3.329375 |
| 6 | 103818 | 9.175556 | 7.970667 | 9.140000 | 19.800 | 3.678125 |
| 7 | 105115 | 7.559444 | 7.170000 | 9.140000 | 15.100 | 3.555000 |
| 8 | 105216 | 6.649453 | 7.005333 | 7.126667 | 6.490 | 3.079687 |
| 9 | 106016 | 7.855000 | 7.233333 | 6.803333 | 7.095 | 3.390312 |
| 10 | 106319 | 7.654048 | 8.129333 | 11.816667 | 12.220 | 8.177188 |
| 11 | 110411 | 8.254444 | 10.505325 | 8.620000 | 7.605 | 3.019062 |
| 12 | 111009 | 6.987778 | 8.168000 | 11.486667 | 10.125 | 3.636250 |
| 13 | 111312 | 8.356609 | 6.999333 | 5.246667 | 33.920 | 3.744063 |
| 14 | 111514 | 7.382222 | 7.731333 | 7.766667 | 8.095 | 2.980625 |
| 15 | 111716 | 7.868333 | 8.658667 | 6.830000 | 18.530 | 5.844062 |
| 16 | 113215 | 5.876471 | 5.232667 | 23.910000 | 9.300 | 3.912258 |
| 17 | 113619 | 9.017222 | 9.516667 | 9.396667 | 11.510 | 4.420062 |
| 18 | 114924 | 7.387222 | 7.872000 | 10.575000 | 6.955 | 3.945312 |
| 19 | 115320 | 8.400000 | 8.974000 | 5.910000 | 7.230 | 6.294062 |
| 20 | 117122 | 7.978889 | 8.865333 | 8.273333 | 8.440 | 5.063059 |
| 21 | 117324 | 8.616111 | 9.432000 | 7.070000 | 9.840 | 5.690938 |
| 22 | 118730 | 7.778889 | 8.340306 | 8.566667 | 6.880 | 3.140000 |
| 23 | 118932 | 7.105556 | 6.524667 | 6.586667 | 10.725 | 5.167950 |
| 24 | 119833 | 10.226953 | 7.456000 | 8.656667 | 15.240 | 5.880625 |
| 25 | 120111 | 7.518333 | 8.721333 | 9.840000 | 11.975 | 6.270801 |
| 26 | 120212 | 7.516667 | 6.960000 | 8.493333 | 6.425 | 5.436562 |
| 27 | 122317 | 10.886667 | 10.894133 | 10.580000 | 8.570 | 5.219375 |
| 28 | 123117 | 7.193333 | 9.217333 | 4.576667 | 4.665 | 2.987188 |
| 29 | 124422 | 8.195000 | 5.778000 | 8.330000 | 7.760 | 4.959834 |
| 30 | 125525 | 8.735000 | 9.047333 | 37.650000 | 18.020 | 5.125625 |
| 31 | 126325 | 6.090000 | 6.145333 | 8.060000 | 9.360 | 2.637188 |
| 32 | 127933 | 8.404567 | 8.470667 | 7.676667 | 22.640 | 4.284063 |
| 33 | 128632 | 6.417778 | 6.602667 | 7.623333 | 12.020 | 6.828437 |
| 34 | 129028 | 9.341111 | 8.940667 | 8.563333 | 15.030 | 5.771250 |
| 35 | 130013 | 8.957222 | 8.866667 | 7.896667 | 7.270 | 2.468750 |
| 36 | 130316 | 7.079792 | 5.518000 | 7.073333 | 10.445 | 2.449063 |
| 37 | 130922 | 5.460000 | 6.123333 | 6.050000 | 6.195 | 3.414110 |
| 38 | 131924 | 7.497222 | 6.256000 | 6.470000 | 6.235 | 4.288437 |
| 39 | 133625 | 9.083333 | 8.956000 | 7.976667 | 6.385 | 3.694688 |
| 40 | 133827 | -1.000000 | -1.000000 | -1.000000 | -1.000 | -1.000000 |
| 41 | 133928 | 8.386111 | 8.172667 | 9.883333 | 4.585 | 6.611250 |
| 42 | 134324 | 9.003516 | 9.267092 | 9.910000 | 15.180 | 2.855026 |
| 43 | 135932 | 9.588333 | 11.013333 | 8.197500 | 5.765 | 2.980625 |
| 44 | 136833 | 8.603889 | 7.695333 | 9.663333 | 11.425 | 3.836562 |
| 45 | 137128 | 7.288889 | 7.113333 | 5.786667 | 6.445 | 3.772813 |
| 46 | 138231 | 8.779444 | 8.744388 | 10.957500 | 11.230 | 4.103750 |
| 47 | 138534 | 7.520138 | 7.438000 | 5.573333 | 10.040 | 3.843750 |
| 48 | 139637 | 7.947222 | 7.254667 | 7.420000 | 6.540 | 3.840999 |
| 49 | 140824 | 8.737163 | 10.753125 | 7.026667 | 14.400 | 3.721875 |
testing_ies.to_csv(r'/Users/rajdeep_ch/Documents/nma/project/ies_scores.csv',index=False)