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