280 lines
9.7 KiB
Python
Executable File
280 lines
9.7 KiB
Python
Executable File
#!/usr/bin/env python3
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from pathops import dir_must_exist
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import os
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import dill
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import numpy as np
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import pdb
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import json
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from natsort import natsorted
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import random
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random.seed(42)
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np.random.seed(42)
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import itertools
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import copy
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import logging
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from loggerops import create_logger, log_newline
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import shutil
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import os
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from pathlib import Path
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from datetime import datetime
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import re
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from copy import deepcopy
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logger = logging.getLogger(__name__)
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nowtime = datetime.now()
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from config import server, socketio, participants
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def set_trace():
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import logging
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log = logging.getLogger('werkzeug')
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log.setLevel(logging.ERROR)
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log = logging.getLogger('engineio')
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log.setLevel(logging.ERROR)
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pdb.set_trace()
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def find_participants(folder='./participant_data/'):
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'''
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Returns a tuple of (participant number, participant filepath) for every
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participant folder found in directory provided
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'''
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part_folder = [os.path.join(folder, o) for o in os.listdir(folder)
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if os.path.isdir(os.path.join(folder,o))]
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for path in part_folder:
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part_key = os.path.basename(path)
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participants[part_key] = deepcopy(Participant(participant_dir=path))
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participants[part_key].load('info')
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participants[part_key].load('parameters')
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return participants
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def gen_participant_num(participants, N = 100):
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# generate array of numbers that haven't been taken between 0-100
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# if list is empty increment until list isnt empty
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# Choose a number
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taken_nums = []
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for part_key in participants.keys():
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participant = participants[part_key]
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taken_nums.append(int(participant['info']['number']))
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inds = np.arange(N)+1
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taken_inds = np.in1d(inds, taken_nums)
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inds = inds[~taken_inds]
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return inds
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class Participant:
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def __init__(self, participant_dir=None, number=None, age=None, gender=None, handedness=None, general_notes=None, parameters={}, gen_time=datetime.now()):
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'''
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'''
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dir_must_exist(participant_dir)
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self.participant_dir = participant_dir
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self.data_paths = {}
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self.generate_folder_hierachy()
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self.parameters = parameters
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self.gen_time = gen_time
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self.data = {
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"info": {
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"number": number,
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"age": age,
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"gender": gender,
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"handedness": handedness,
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"general_notes": general_notes
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},
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"mat_test": {
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"notes": ''
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},
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"eeg_story_train": {
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"notes": ''
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},
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"eeg_mat_train": {
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"notes": ''
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},
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"eeg_test": {
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"notes": ''
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},
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"tone_test": {
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"notes": ''
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},
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"click_test": {
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"notes": ''
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},
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"pta": {
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"notes": ''
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}
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}
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self.data['parameters'] = parameters
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def generate_folder_hierachy(self):
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'''
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'''
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sub_dirs = ["mat_test", "tone_test", "pta", "click_test", "info",
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"eeg_story_train", "eeg_mat_train", "eeg_test",
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"eeg_test/stimulus", "parameters"]
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for dir_name in sub_dirs:
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dn = os.path.join(*dir_name.split('/'))
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path = os.path.join(self.participant_dir, dn)
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dir_must_exist(path)
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self.data_paths[dir_name] = path
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def __setitem__(self, key, item):
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self.data[key] = item
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def __getitem__(self, key):
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return self.data[key]
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def set_info(self, info):
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self.data['info'] = info
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def save(self, data_key):
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'''
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'''
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directory = self.data_paths[data_key]
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with open(os.path.join(directory, '{}.pkl'.format(data_key)), 'wb') as f:
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dill.dump(self.data[data_key], f)
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def load(self, data_key):
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'''
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'''
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folder = os.path.join(self.participant_dir, data_key)
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# print(f"Participant {self.data['info']['number']}: {folder}")
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with open(os.path.join(folder, "{}.pkl".format(data_key)), 'rb') as f:
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self.data[data_key].update(dill.load(f))
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def roll_independant(A, r):
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rows, column_indices = np.ogrid[:A.shape[0], :A.shape[1]]
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# Use always a negative shift, so that column_indices are valid.
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# (could also use module operation)
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r[r < 0] += A.shape[1]
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column_indices = column_indices - r[:,np.newaxis]
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result = A[rows, column_indices]
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return result
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def main():
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'''
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'''
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logger.warning("***REMEMBER THIS SCRIPTS WILL NOT OVERWRITE ANY EXISTING PARTICIPANT DATA. PLEASE DELETE THIS MANUALLY IF NEEDED!***")
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participants = find_participants()
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with open('./test_params.json') as json_file:
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general_params = json.load(json_file)
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# Generate all permutations of tests for couterbalancing
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tests = general_params['tests']
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cb_tests = list(itertools.permutations(tests))
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tone_freqs = general_params['tone_freqs']
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cb_tone_freqs = list(itertools.permutations(tone_freqs))
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# Make sure that the number of participants is a multiple of the number of
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# counterbalanced tests
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cb_lcm = np.lcm.reduce([len(cb_tests), len(cb_tone_freqs), 4])
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n_participants = cb_lcm * 3
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part_nums = gen_participant_num(participants, N=n_participants)
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n_decoder_repeats = general_params['decoder_test_SNR_repeats']
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# Get all decoder test stimuli
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listDir = "./matrix_test/short_concat_stim/out"
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stim_dirs = natsorted([x for x in os.listdir(listDir) if os.path.isdir(os.path.join(listDir, x))])
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for i in part_nums:
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participant_params = {}
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# Set the order of tests to be presented to the current participant,
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# using previous counterbalancing above
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participant_params['tests'] = list(cb_tests[(i-1) % len(cb_tests)])
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# Randomy shuffle order of stimuli to be presented in decoder testing
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sd_copy = copy.copy(stim_dirs)
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random.shuffle(sd_copy)
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participant_params['decoder_test_lists'] = sd_copy
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# Generate randomised stimulus/SNR combinations for each participant
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snrs = np.array(general_params['decoder_test_SNRs'], dtype=float)
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np.random.shuffle(snrs)
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snrs = np.repeat(snrs[np.newaxis], n_decoder_repeats, axis=0)
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snrs = roll_independant(snrs, np.array(np.arange(n_decoder_repeats)+1-n_decoder_repeats))
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participant_params['decoder_test_SNRs'] = snrs
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# Is the hearing loss simulator active for this participant?
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# Even numbers yes, odd numbers no
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hl_sim_active = ((i-1) % 2) == 0
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participant_params['hl_sim_active'] = hl_sim_active
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# What order are the decoder stories presented?
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dec_train_lists = general_params['decoder_train_lists']
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if (int((i-1)/2) % 2) == 0:
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# Play second story first for half the participants
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participant_params['decoder_train_lists'] = dec_train_lists[4:8]+dec_train_lists[0:4]
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else:
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participant_params['decoder_train_lists'] = dec_train_lists
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# What order are the decoder test stimuli presented?
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dtl_copy = copy.copy(general_params['decoder_test_lists'])
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np.random.shuffle(dtl_copy)
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participant_params['decoder_test_lists'] = dtl_copy
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# What order are the behavioural test stimuli presented?
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participant_params['behavioural_train_lists'] = np.random.choice(
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general_params['behavioural_train_lists'],
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[general_params['behavioural_train_N']], replace=False)
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participant_params['behavioural_test_lists'] = np.random.choice(
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general_params['behavioural_test_lists'],
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[general_params['behavioural_test_N']], replace=False)
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# What order are the tone SNRs presented at?
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n_tone_repeats = general_params['tone_repeats']
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tone_snrs = np.array(general_params['tone_SNRs'], dtype=float)
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np.random.shuffle(tone_snrs)
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tone_snrs = np.repeat(tone_snrs[np.newaxis], n_tone_repeats, axis=1)
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# Remove inf SNRs
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infs = np.isinf(tone_snrs)
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tone_snrs = tone_snrs[~infs]
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# Prepend inf SNRS so they are always presented first
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tone_snrs = np.concatenate([[np.inf]*infs.sum(), tone_snrs])
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participant_params['tone_SNRs'] = tone_snrs
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# What order are the tones presented at?
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# Set the order of tone frequencies to be presented to the current
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# participant, using previous counterbalancing above
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participant_params['tone_freqs'] = list(cb_tone_freqs[int(((i-1)/4)) % len(cb_tone_freqs)])
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final_params = copy.copy(general_params)
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final_params.update(participant_params)
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key = "participant_{}".format(i)
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logger.info("{:<78}".format(f"Generating: {key}"))
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participants[key] = Participant(participant_dir="./participant_data/{}".format(key),
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number=i, parameters=final_params, gen_time=nowtime)
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participants[key].save("info")
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participants[key].save("parameters")
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# Log all parameters of the current participant
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for key, val in participants[key].parameters.items():
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if type(val) is np.ndarray:
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val = val.tolist()
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trunc_str = re.sub(r'^(.{75}).*$', '\g<1>...', f"{key:<25}{val}")
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logger.info(f"{trunc_str: <78}")
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logger.info("-"*78)
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print(f"Generated {part_nums.size} new participant databases")
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if __name__ == '__main__':
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logs_dir = Path('./logs/')
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logs_dir.mkdir(exist_ok=True)
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logfile_dir = logs_dir / __file__
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logfile_dir.mkdir(exist_ok=True)
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logfile_name = nowtime.strftime("%m-%d-%Y_%H-%M-%S")+'.log'
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logger = create_logger(
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logger_streamlevel=10,
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log_filename=str(logfile_dir/logfile_name),
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logger_filelevel=10
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)
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main()
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