Updating participant generation scripts
This commit is contained in:
@@ -42,7 +42,6 @@ class DaTestThread(BaseThread):
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stimFolder='./da_stim/',
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noiseFilepath="./da_stim/noise/wav/noise/noise_norm.wav",
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noiseRMSFilepath="./da_stim/noise/rms/noise_rms.npy",
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daPeakFilepath="./da_stim/stimulus/peak/10min_da_peak.npy",
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red_coef="./calibration/out/reduction_coefficients/da_red_coef.npy",
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cal_coef="./calibration/out/calibration_coefficients/da_cal_coef.npy",
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nTrials=2, socketio=None, participant=None, srt_50=None,
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+6
-5
@@ -62,9 +62,6 @@ class EEGTestThread(BaseThread):
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self.question = []
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self.response = []
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with open('./test_params.json') as json_file:
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self.params = json.load(json_file)
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self.reduction_coef = np.load(red_coef)*np.load(cal_coef)
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# Percent speech inteligibility (estimated using behavioural measure)
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@@ -187,6 +184,8 @@ class EEGTestThread(BaseThread):
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raise KeyError("Behavioural matrix test results not available, make "
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"sure the behavioural test has been run before "
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"running this test.")
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save_dir = self.participant.data_paths['eeg_test/stimulus']
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'''
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# Estimate speech intelligibility thresholds using predicted
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# psychometric function
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s_50 *= 0.01
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@@ -194,14 +193,16 @@ class EEGTestThread(BaseThread):
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snrs = (x/(4*s_50))+srt_50
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snrs = np.append(snrs, np.inf)
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snr_map = pd.DataFrame({"speech_intel" : np.append(self.si, 0.0), "snr": snrs})
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save_dir = self.participant.data_paths['eeg_test/stimulus']
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snr_map_path = os.path.join(save_dir, "snr_map.csv")
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snr_map.to_csv(snr_map_path)
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snrs = np.repeat(snrs[np.newaxis], 4, axis=0)
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snrs = roll_independant(snrs, np.array([0,-1,-2,-3]))
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noise_file = PySndfile(self.noise_path, 'r')
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stim_dirs = [x for x in os.listdir(self.listDir) if os.path.isdir(os.path.join(self.listDir, x))]
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shuffle(stim_dirs)
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'''
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snrs = self.participant.data['parameters']['decoder_test_SNRs'] + srt_50
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stim_dirs = self.participant.data['parameters']['decoder_test_stim_dirs']
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noise_file = PySndfile(self.noise_path, 'r')
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wav_files = []
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question = []
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marker_files = []
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Executable
+217
@@ -0,0 +1,217 @@
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#!/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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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] = Participant(participant_dir=path)
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participants[part_key].load('info')
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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'][0]))
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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={}):
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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.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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"da_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", "da_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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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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print("***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_freq']
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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)])
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n_participants = cb_lcm * 6
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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_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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# What order are the decoder stories presented?
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# What order are the behavioral test stimuli presented?
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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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snrs = np.repeat(tone_snrs[np.newaxis], n_tone_repeats, axis=1)
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participant_params['tone_SNRs'] = 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[(i-1) % len(cb_tone_freqs)])
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key = "participant_{}".format(i)
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participants[key] = Participant(participant_dir="./participant_data/{}".format(key), parameters=participant_params)
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participants[key].save("info")
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breakpoint()
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if __name__ == '__main__':
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main()
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-136
@@ -1,136 +0,0 @@
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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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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] = Participant(participant_dir=path)
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participants[part_key].load('info')
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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'][0]))
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n = 0
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num_found = False
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while not num_found:
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potential_nums = np.arange(N)+n+1
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try:
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valid_nums = np.setdiff1d(potential_nums, taken_nums)
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except:
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set_trace()
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if valid_nums.size:
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num_found = True
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else:
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n += N
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return np.random.choice(valid_nums)
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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):
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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.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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"da_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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def generate_folder_hierachy(self):
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'''
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'''
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sub_dirs = ["mat_test", "da_test", "pta", "click_test", "info",
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"eeg_story_train", "eeg_mat_train", "eeg_test",
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"eeg_test/stimulus", ]
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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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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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@@ -24,7 +24,7 @@ from app import generate_matrix_stimulus
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from matrix_test.helper_modules.filesystem import globDir, organiseWavs, prepareOutDir
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from matrix_test_thread import MatTestThread
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from pathops import dir_must_exist
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from participant import Participant, find_participants, gen_participant_num
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from gen_participants import Participant, find_participants, gen_participant_num
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from config import server, socketio, participants
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@@ -30,7 +30,7 @@ from app import generate_matrix_stimulus
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from matrix_test.helper_modules.filesystem import globDir, organiseWavs, prepareOutDir
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from matrix_test_thread import MatTestThread
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from pathops import dir_must_exist
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from participant import Participant
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from gen_participants import Participant
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from config import server, socketio, participants
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from route import *
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+1
-1
@@ -26,7 +26,7 @@ from app import generate_matrix_stimulus
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from matrix_test.helper_modules.filesystem import globDir, organiseWavs, prepareOutDir
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from matrix_test_thread import MatTestThread
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from pathops import dir_must_exist
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from participant import Participant
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from gen_participants import Participant
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from matrix_test.helper_modules.signalops import play_wav
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from config import server, socketio, participants
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@@ -57,15 +57,6 @@
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<li class="nav-item active">
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<a class="nav-link" href="/home">Home<span class="sr-only">(current)</span></a>
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</li>
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<li class="nav-item dropdown">
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<a class="nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
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Participant manager
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</a>
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<div class="dropdown-menu" aria-labelledby="navbarDropdown">
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<a class="dropdown-item" href="/participant/create">Create new participant</a>
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<a class="dropdown-item" href="/participant/manage">Manage participants</a>
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</div>
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</li>
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<li class="nav-item dropdown">
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<a class="nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
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Calibration and stimulus generation
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Reference in New Issue
Block a user