104 lines
4.6 KiB
Python
104 lines
4.6 KiB
Python
import numpy as np
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import os
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from config import conf
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from featureExtraction import gaze_analysis as ga
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import threading
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import getopt
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import sys
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from config import names as gs
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def compute_sliding_window_features(participant, ws, gazeAnalysis_instance):
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"""
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calls the gazeAnalysis instance it was given, calls it to get features and saves those to file
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"""
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window_features, window_times = gazeAnalysis_instance.get_window_features(ws, conf.get_step_size(ws))
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np.save(conf.get_window_features_file(participant, ws), window_features)
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np.save(conf.get_window_times_file(participant, ws), window_times)
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if __name__ == "__main__":
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for p in xrange(0,conf.n_participants):
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threads = [] # one thread per time window will be used and collected in this list
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# create data folder, plus one subfolder for participant p
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if not os.path.exists(conf.get_feature_folder(p)):
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os.makedirs(conf.get_feature_folder(p))
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# make sure all relevant raw data files exist in the right folder
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gaze_file = conf.get_data_folder(p) + '/gaze_positions.csv'
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pupil_diameter_file = conf.get_data_folder(p) + '/pupil_diameter.csv'
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events_file = conf.get_data_folder(p) + '/events.csv'
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assert os.path.exists(gaze_file) and os.path.exists(pupil_diameter_file) and os.path.exists(events_file)
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# load relevant data
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gaze = np.genfromtxt(gaze_file, delimiter=',', skip_header=1)
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pupil_diameter = np.genfromtxt(pupil_diameter_file, delimiter=',', skip_header=1)
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events = np.genfromtxt(events_file, delimiter=',', skip_header=1, dtype=str)
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# create instance of gazeAnalysis class that will be used for feature extraction
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# this already does some initial computation that will be useful for all window sizes:
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extractor = ga.gazeAnalysis(gaze, conf.fixation_radius_threshold, conf.fixation_duration_threshold,
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conf.saccade_min_velocity, conf.max_saccade_duration,
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pupil_diameter=pupil_diameter, event_strings=events)
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# compute sliding window features by creating one thread per window size
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for window_size in conf.all_window_sizes:
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if not os.path.exists(conf.get_window_features_file(p, window_size)):
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thread = threading.Thread(target=compute_sliding_window_features, args=(p, window_size, extractor))
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thread.start()
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threads.append(thread)
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for t in threads:
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t.join()
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print 'finished all features for participant', p
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# Merge the features from all participants into three files per window_size:
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# merged_features includes all features
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# merged_traits contains the ground truth personality score ranges
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# merged_ids contains the participant number and context (way, shop, half of the recording)
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# load ground truth from info folder:
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binned_personality = np.genfromtxt(conf.binned_personality_file, delimiter=',', skip_header=1)
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trait_labels = np.loadtxt(conf.binned_personality_file, delimiter=',', dtype=str)[0,:]
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annotation = np.genfromtxt(conf.annotation_path, delimiter=',', skip_header=1)
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for window_size in conf.all_window_sizes:
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print 'merging window size', window_size
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windowfeats_subtask_all = []
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windowfeats_subtask_ids = []
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windowfeats_subtask_all_y = []
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for p in xrange(0, conf.n_participants):
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featfilename = conf.get_window_features_file(p, window_size)
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timesfilename = conf.get_window_times_file(p, window_size)
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if os.path.exists(featfilename) and os.path.exists(timesfilename):
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data = np.load(featfilename).tolist()
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windowfeats_subtask_all.extend(data)
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windowfeats_subtask_all_y.extend([binned_personality[p, 1:]] * len(data))
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times = np.load(timesfilename)[:, 2:]
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ann = annotation[p,1:]
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ids_annotation = np.zeros((len(data), 3), dtype=int) # person, way/shop, half
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ids_annotation[:,0] = p
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ids_annotation[(times[:,1] < ann[0]),1] = conf.time_window_annotation_wayI
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ids_annotation[(times[:,0] > ann[0]) & (times[:,1] < ann[1]),1] = conf.time_window_annotation_shop
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ids_annotation[(times[:,0] > ann[1]),1] = conf.time_window_annotation_wayII
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ids_annotation[:(len(data)/2), 2] = conf.time_window_annotation_halfI
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ids_annotation[(len(data)/2):, 2] = conf.time_window_annotation_halfII
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windowfeats_subtask_ids.extend(ids_annotation.tolist())
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else:
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print 'did not find ', featfilename
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sys.exit(1)
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ids = np.array(windowfeats_subtask_ids)
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x = np.array(windowfeats_subtask_all, dtype=float)
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y = np.array(windowfeats_subtask_all_y)
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f1, f2, f3 = conf.get_merged_feature_files(window_size)
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np.savetxt(f1, x, delimiter=',', header=','.join(gs.full_long_label_list), comments='')
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np.savetxt(f2, y, delimiter=',', header=','.join(trait_labels), comments='')
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np.savetxt(f3, ids, delimiter=',', header='Participant ID', comments='')
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