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43 lines
1.9 KiB
43 lines
1.9 KiB
2 years ago
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import numpy as np
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import pandas as pd
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data_folder = "../data/"
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stim_file = "Stiminfo_PVCre_2021_0012_s06_e14.csv"
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# Stimulus DataFrame
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stim = pd.read_csv(data_folder+stim_file)
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num_trial = len(stim)
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stim_val = {}
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trial_stim_id = {}
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stim_val['pair'], trial_stim_id['pair'] = np.unique(stim[['grat_orientation', 'grat_phase']], return_inverse=True, axis=0) # 50 trials per orientation-phase pair
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stim_val['orientation'], trial_stim_id['orientation'] = np.unique(stim['grat_orientation'], return_inverse=True) # 1000 trials per orientation
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stim_val['phase'], trial_stim_id['phase'] = np.unique(stim['grat_phase'], return_inverse=True) # 1000 trials per phase
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key_list = ['pair', 'orientation', 'phase']
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stim_id_trial = {}
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num_stim = {}
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for key in key_list:
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stim_id_trial[key] = [np.where(trial_stim_id[key] == i)[0] for i in range(len(stim_val[key]))]
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num_stim[key] = len(stim_val[key])
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# stim_id_trial[i] returns an array of trial indices where the stimulus has the value of the ith stimulus.
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def pair_id(i):
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return (int(i//num_stim['orientation']), i%num_stim['orientation'])
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trial_pair_id = np.array([pair_id(i) for i in trial_stim_id['pair']], dtype=object)
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pair_val = stim_val['pair'].reshape(num_stim['orientation'], num_stim['phase'],2)
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# for each (orientation_id = i, phase_id = j) find the trial indices
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pair_trial_id = np.ndarray((num_stim['orientation'], num_stim['phase']), dtype=object)
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for i in range(num_stim['pair']):
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pair_trial_id[pair_id(i)] = stim_id_trial['pair'][i]
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# s.close()
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stim_data = {'stim_val':stim_val, 'trial_stim_id':trial_stim_id, 'key_list':key_list, 'num_trial':num_trial,
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'trial_pair_id':trial_pair_id, 'pair_val':pair_val, 'pair_trial_id':pair_trial_id, 'stim_id_trial':stim_id_trial, 'num_stim':num_stim}
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import pickle
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file = open('stim_data.pkl', 'wb')
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pickle.dump(stim_data, file)
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file.close()
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