-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathedit_trial.py
187 lines (154 loc) · 8.8 KB
/
edit_trial.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
from pathlib import Path
from scripts.data_processing import parse, plot, utils
from datetime import datetime
import numpy as np
import argparse
def select_trial(raw_path, ascii_path, config, questions, stimuli_path, data_path, subj):
subj_datapath, subj_rawpath = Path(data_path) / subj, Path(raw_path) / subj
if not subj_rawpath.exists():
raise ValueError('Participant not found')
subj_items, subj_profile = load_subj_trials(subj_rawpath, ascii_path, config, stimuli_path, subj_datapath)
trials_flags = utils.load_flags(subj_items, subj_datapath)
main_menu(subj_items, trials_flags, subj_profile, subj_datapath, stimuli_path, questions)
def list_participants(raw_path, processed_path):
participants = [dir_.name for dir_ in utils.get_dirs(raw_path)]
participants = flag_and_order_participants(raw_path, processed_path, participants)
chosen_participant = list_options(participants, prompt='Choose a participant: ')
return participants[chosen_participant]
def flag_and_order_participants(raw_path, processed_path, participants):
participants_dates = [datetime.strptime(date, '%d-%m-%Y %H:%M') for date in
[utils.load_matfile(raw_path / participant / 'metadata.mat')['fst_date'] for participant in
participants]]
for i, participant in enumerate(participants):
processed_trials_path = processed_path / participant
raw_trials_path = raw_path / participant
if processed_trials_path.exists():
processed_trials = [dir_.name for dir_ in utils.get_dirs(processed_trials_path)]
trials_flags = utils.load_flags(processed_trials, processed_trials_path)
edited_trials = [trials_flags[trial]['edited'] for trial in trials_flags]
all_edited = np.all(edited_trials) and len(edited_trials) == len(utils.get_files(raw_trials_path)) - 2
if all_edited:
participants[i] += ' \u2705'
participants = [participant for _, participant in sorted(zip(participants_dates, participants))]
return participants
def show_trial_menu(subj_items, trials_flags, subj_datapath, stimuli_path, questions, chosen_option):
chosen_item = subj_items[chosen_option]
trial_flags = trials_flags[chosen_item]
trial_path = subj_datapath / chosen_item
stimuli = utils.load_stimuli(chosen_item, stimuli_path)
trial_menu(chosen_item, trial_flags, trial_path, stimuli, questions)
updated_options = items_list(subj_items, trials_flags)
return updated_options
def main_menu(subj_items, trials_flags, subj_profile, subj_datapath, stimuli_path, questions):
options = items_list(subj_items, trials_flags)
chosen_item = print_mainmenu(subj_profile, options)
while chosen_item != len(options) - 1:
options = show_trial_menu(subj_items, trials_flags, subj_datapath, stimuli_path, questions, chosen_item)
chosen_item = print_mainmenu(subj_profile, options)
def items_list(subj_items, trials_flags):
options = [subj_items[i] + ' ' + parse_flags(trials_flags[subj_items[i]]) for i in range(len(subj_items))]
options += ['Exit']
return options
def trial_menu(item, trial_flags, trial_path, stimuli, questions):
actions = ['Questions answers', 'Words associations', 'Plot calibration', 'Edit screens', 'Flag as wrong', 'Exit']
print('\n' + item)
action = actions[list_options(actions, '')]
while action != 'Exit':
handle_action(item, action, stimuli, questions, trial_flags, trial_path)
print('\n' + item)
action = actions[list_options(actions, '')]
def print_mainmenu(subj_profile, options):
print('Participant:', subj_profile['name'][0])
chosen_option = list_options(options, 'Enter the item number to edit: ')
return chosen_option
def handle_action(item, action, stimuli, questions_file, trial_flags, trial_path):
if action == 'Questions answers':
trial_flags['wrong_answers'] = read_questions_and_answers(questions_file, item, trial_path)
elif action == 'Words associations':
read_words_associations(questions_file, item, trial_path)
elif action == 'Plot calibration':
plot.calibration(trial_path)
elif action == 'Edit screens':
trial_flags['edited'] = plot.trial(stimuli, trial_path, editable=True) or trial_flags['edited']
elif action == 'Flag as wrong':
trial_flags['iswrong'] = not trial_flags['iswrong'][0]
trial_flags['edited'] = True
elif action == 'Exit':
exit()
utils.update_flags(trial_flags, trial_path)
def list_options(options, prompt):
for i in range(len(options)):
print(f'{i + 1}. {options[i]}')
choice = input(prompt)
while not choice.isdigit() or int(choice) < 1 or int(choice) > len(options):
print('Invalid choice. Please enter a number between 1 and', len(options))
choice = input(prompt)
return int(choice) - 1
def read_words_associations(questions_file, item, trial_path):
_, _, words = utils.load_questions_and_words(questions_file, item)
answers = utils.load_answers(trial_path, filename='words.pkl')
for i in range(len(words)):
print(f'{i + 1}. {words[i]}')
answer = answers[i] if len(answers) > i else ''
print(f' {answer}')
input('Press enter to continue...')
def read_questions_and_answers(questions_file, item, trial_path):
questions, possible_answers, _ = utils.load_questions_and_words(questions_file, item)
answers = utils.load_answers(trial_path, filename='answers.pkl')
for i in range(len(questions)):
print(f'{i + 1}. {questions[i]} ({possible_answers[i]})')
answer = answers[i] if len(answers) > i else ''
print(f' {answer}')
wrong_answers = input('Number of wrong answers: ')
while not wrong_answers.isdigit() or int(wrong_answers) < 0 or int(wrong_answers) > len(questions):
print('Invalid choice. Please enter a number between 0 and', len(questions))
wrong_answers = input('Number of wrong answers: ')
return int(wrong_answers)
def load_subj_trials(subj_rawpath, ascii_path, config, stimuli_path, data_path):
subj_items = [item.name[:-4] for item in subj_rawpath.glob('*.mat')
if item.name not in ['Test.mat', 'metadata.mat']]
if data_path.exists():
subj_processeditems = [item.name for item in data_path.iterdir() if item.is_dir()]
missing_items = [item for item in subj_items if item not in subj_processeditems]
else:
missing_items = subj_items
parse.save_profile(subj_rawpath, data_path)
for rawitem in missing_items:
parse.item(subj_rawpath / f'{rawitem}.mat', subj_rawpath, ascii_path, config, stimuli_path, data_path)
subj_profile = utils.load_profile(data_path)
subj_items = utils.reorder(subj_items, subj_profile['stimuli_order'][0])
return subj_items, subj_profile
def parse_flags(flags):
trial_status = ''
if flags['edited'][0]:
if flags['iswrong'][0]:
trial_status += '\u274C '
else:
trial_status += '\u2705 '
wrong_validations = int(flags['firstval_iswrong'][0]) + int(flags['lastval_iswrong'][0])
if wrong_validations > 0:
trial_status += '\u26A0\uFE0F ' + str(wrong_validations) + ' '
if flags['wrong_answers'][0]:
trial_status += '\u2b55 ' + str(flags['wrong_answers'][0]) + ' '
return trial_status
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Edit trials from a given participant')
parser.add_argument('--raw', type=str, default='data/raw',
help='Path where participants raw data is stored in ASCII format')
parser.add_argument('--ascii_path', type=str, default='asc',
help='Path where .asc files are stored in a participants folder')
parser.add_argument('--config', type=str, default='metadata/stimuli_config.mat',
help='Config file with the stimuli information')
parser.add_argument('--questions', type=str, default='metadata/stimuli_questions.mat',
help='Questions and possible answers file for each stimuli')
parser.add_argument('--stimuli_path', type=str, default='stimuli', help='Path where the stimuli are stored')
parser.add_argument('--data', type=str, default='data/processed/trials',
help='Path where the processed data is stored in pkl format')
parser.add_argument('--subj', type=str, help='Participant\'s name')
args = parser.parse_args()
stimuli_path, data_path, raw_path = Path(args.stimuli_path), Path(args.data), Path(args.raw)
if args.subj:
select_trial(raw_path, args.ascii_path, args.config, args.questions, stimuli_path, data_path, args.subj)
else:
subj = list_participants(raw_path, data_path).split()[0]
select_trial(raw_path, args.ascii_path, args.config, args.questions, stimuli_path, data_path, subj)