def organize(src_directory, dst_directory, gaze_file_filter):
"""
organize does not delete nor overrides any source data
it will copy and convert data from source
it will rename at destination
it DOES overrides any previous copies in destination
it must be run after pupil surface data have been exported
"""
print('doing:', src_directory)
# destination
dst_filenames = [
'info.yml', 'session_configuration.ini', 'session_events.txt',
'gaze_coordenates_' + gaze_file_filter.replace('*', '') + '.txt'
]
dst_files = [os.path.join(dst_directory, f) for f in dst_filenames]
# source
src_files = get_source_files(src_directory,
gaze_file_filter=gaze_file_filter)
# loading
beha_file = load_fpe_timestamps(src_files[2], converted=False)
gaze_file = load_gaze_data(src_files[3], converted=False)
start_time = gaze_file['gaze_timestamp'][0]
# copying
copy_file(src_files[0], dst_files[0])
copy_file(src_files[1], dst_files[1])
#conversion
convert_beha(beha_file, dst_files[2], start_time=start_time)
convert_gaze(gaze_file, dst_files[3], start_time=start_time)
# feedback
print('done:', src_directory)
def analyse(i, factor='donut_slice', inspect=False, data_files=None):
print('\nRunning analysis for session:')
print('\t', PATHS_DESTIN[i])
print('\t', PATHS_SOURCE[i])
parameters = p[i]
if not data_files:
data_files = get_data_files(
PATHS_DESTIN[i], gaze_file_filter=parameters['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
ini_file = load_ini_data(data_files[1])
time_file = load_fpe_timestamps(data_files[2])
all_gaze_data = load_gaze_data(data_files[3])
title = ' - '.join([
'%02d' % i,
'%02d' % info_file['feature_degree'], info_file['group'],
info_file['nickname'],
str(factor)
])
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'],
time_file['event'])
ini_data = zip(ini_file['trial'], ini_file['contingency'],
ini_file['feature'])
trials = get_events_per_trial(ini_data, time_data)
responses = get_responses(time_file)
button_proportion = analyse_button(trials, responses, title, inspect)
features = ini_file['feature']
trial_intervals = get_trial_intervals(trials, uncategorized=True)
gaze_rate_per_trial, gaze_rate_mirror = get_gaze_rate_per_trial(
trial_intervals,
all_gaze_data,
factor=factor,
inspect=inspect,
min_block_size=parameters['min_block_size'],
do_correction=parameters['do_correction'],
do_remove_outside_screen=parameters['do_remove_outside_screen'],
do_remove_outside_session_time=parameters[
'do_remove_outside_session_time'],
do_manual_correction=parameters['do_manual_correction'],
do_confidence_threshold=parameters['confidence_threshold'])
gaze_proportion = get_relative_gaze_rate(ini_file['feature'],
gaze_rate_per_trial,
gaze_rate_mirror)
# draw.rates([gaze_proportion, []],
# title= title,
# save= not inspect,
# y_label='Looking proportion',
# single= True,
# first_label= 'feature fp',
# y_limit= [-0.1, 1.1],
# )
return gaze_proportion, button_proportion, latency(trials)
def analyse_intra_subject(i,
factor='donut_slice',
inspect=False,
data_files=None):
print('\nRunning analysis for session:')
print('\t', PATHS_DESTIN[i])
print('\t', PATHS_SOURCE[i])
parameters = p[i]
if not data_files:
data_files = get_data_files(PATHS_DESTIN[i],
gaze_file_filter=p[i]['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
fp_ini_file, fn_ini_file = load_ini_data_intrasubject(data_files[1])
time_file = load_fpe_timestamps(data_files[2])
responses = get_responses(time_file)
title = ' - '.join(
['%02d' % i, info_file['nickname'], 'square (FP)',
str(factor)])
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'],
time_file['event'])
fp_ini_data = zip(fp_ini_file['trial'], fp_ini_file['contingency'],
fp_ini_file['feature'])
fp_trials = get_events_per_trial(fp_ini_data, time_data)
fp_button_proportion = analyse_button(fp_trials, responses, title)
title = ' - '.join(
['%02d' % i, info_file['nickname'], 'X (FN)',
str(factor)])
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'],
time_file['event'])
fn_ini_data = zip(fn_ini_file['trial'], fn_ini_file['contingency'],
fn_ini_file['feature'])
fn_trials = get_events_per_trial(fn_ini_data, time_data)
fn_button_proportion = analyse_button(fn_trials, responses, title)
all_gaze_data = load_gaze_data(data_files[3])
all_trial_intervals = get_trial_intervals({
**fp_trials,
**fn_trials
},
uncategorized=True)
gaze_rate_per_trial, gaze_rate_mirror = get_gaze_rate_per_trial(
all_trial_intervals,
all_gaze_data,
factor=factor,
inspect=False,
min_block_size=parameters['min_block_size'],
do_correction=parameters['do_correction'],
do_remove_outside_screen=parameters['do_remove_outside_screen'],
do_remove_outside_session_time=parameters[
'do_remove_outside_session_time'],
do_manual_correction=parameters['do_manual_correction'],
do_confidence_threshold=parameters['confidence_threshold'])
fp_gaze_rate = get_relative_gaze_rate(
fp_ini_file['feature'], gaze_rate_per_trial[fp_ini_file['trial']],
gaze_rate_mirror[fp_ini_file['trial']])
fn_gaze_rate = get_relative_gaze_rate(
fn_ini_file['feature'], gaze_rate_per_trial[fn_ini_file['trial']],
gaze_rate_mirror[fn_ini_file['trial']])
title = ' - '.join(
['%02d' % i, info_file['nickname'], 'square(FP)_X(FN)',
str(factor)])
draw.rates(
[fp_gaze_rate, fn_gaze_rate],
title,
save=not inspect,
y_label='Proporção média de olhar estímulo distintivo',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo',
y_limit=[-0.1, 1.1],
)
title = ' - '.join([
'%02d' % i, info_file['nickname'], 'square(FP)_X(FN)',
str(factor), 'button'
])
draw.rates(
[fp_button_proportion, fn_button_proportion],
title,
save=not inspect,
y_label='Proporção média de R durante o S+',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo',
y_limit=[-0.1, 1.1],
)
latencies = (latency(fp_trials), latency(fn_trials))
button = (fp_button_proportion, fn_button_proportion)
gaze = (fp_gaze_rate, fn_gaze_rate)
return button, gaze, latencies
def analyse_intrasubject(i, inspect=False, data_files=None):
parameters = PARAMETERS[i]
print('\nRunning analysis for session:')
print('\t', PATHS_DESTIN[i])
print('\t', PATHS_SOURCE[i])
if not data_files:
data_files = get_data_files(PATHS_DESTIN[i],
gaze_file_filter=parameters['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
fp_ini_file, fn_ini_file = load_ini_data_intrasubject(data_files[1])
time_file = load_fpe_timestamps(data_files[2])
all_gaze_data = load_gaze_data(data_files[3])
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'], time_file['event'])
fp_ini_data = zip(fp_ini_file['trial'], fp_ini_file['contingency'], fp_ini_file['feature'])
fp_trials = get_events_per_trial(fp_ini_data, time_data)
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'], time_file['event'])
fn_ini_data = zip(fn_ini_file['trial'], fn_ini_file['contingency'], fn_ini_file['feature'])
fn_trials = get_events_per_trial(fn_ini_data, time_data)
fp_positive_intervals, fp_negative_intervals = get_trial_intervals(fp_trials)
fn_positive_intervals, fn_negative_intervals = get_trial_intervals(fn_trials)
title = ' - '.join(('%02d'%i, str(info_file['feature_degree']), info_file['nickname'], 'FP'))
titlea = title + '_fp_positive'
titleb = title + '_fp_negative'
title = ' - '.join(('%02d'%i, str(info_file['feature_degree']), info_file['nickname'], 'FN'))
titlec = title + '_fn_positive'
titled = title + '_fn_negative'
all_gaze_data = load_gaze_data(data_files[3])
all_trial_intervals = get_trial_intervals({**fp_trials, **fn_trials}, uncategorized=True)
all_gaze_data = clean_gaze_data(all_gaze_data,
do_remove_outside_session_time=all_trial_intervals,
do_correction=parameters['do_correction'],
do_remove_outside_screen=parameters['do_remove_outside_screen'],
do_manual_correction=parameters['do_manual_correction'],
do_confidence_threshold=parameters['confidence_threshold'],
min_block_size=parameters['min_block_size'],
inspect=False)
mask = remove_outside_session_time(all_gaze_data['time'], fp_positive_intervals)
fp_positive_gaze_data = all_gaze_data[mask]
fp_pdispersion = dispersion(fp_positive_gaze_data, fp_positive_intervals, titlea)
mask = remove_outside_session_time(all_gaze_data['time'], fp_negative_intervals)
fp_negative_gaze_data = all_gaze_data[mask]
fp_ndispersion = dispersion(fp_negative_gaze_data, fp_negative_intervals, titleb)
mask = remove_outside_session_time(all_gaze_data['time'], fn_positive_intervals)
fn_positive_gaze_data = all_gaze_data[mask]
fn_pdispersion = dispersion(fn_positive_gaze_data, fn_positive_intervals, titlec)
mask = remove_outside_session_time(all_gaze_data['time'], fn_negative_intervals)
fn_negative_gaze_data = all_gaze_data[mask]
fn_ndispersion = dispersion(fn_negative_gaze_data, fn_negative_intervals, titled)
# title = ' - '.join(('%02d'%i, str(info_file['feature_degree']), info_file['nickname'], 'distinctive'))
# draw.rates(
# [fp_pdispersion, fn_pdispersion],
# title= title+'_pdispersion',
# save= not inspect,
# y_label='Graus do ângulo de visão (RMS)',
# single=False,
# first_label='AP',
# second_label='AN',
# y_limit= [1., 15.]
# )
# title = ' - '.join(('%02d'%i, str(info_file['feature_degree']), info_file['nickname'], 'common'))
# draw.rates(
# [fp_ndispersion, fn_ndispersion],
# title= title+'_ndispersion',
# save= not inspect,
# y_label='Graus do ângulo de visão (RMS)',
# single=False,
# first_label='AP',
# second_label='AN',
# y_limit= [1., 15.]
# )
# title = ' - '.join(('heat', '%02d'%i, str(info_file['feature_degree']), info_file['nickname'], 'distinctive-common'))
# imgs, y_max = custom_heatmap([fp_positive_gaze_data,fn_negative_gaze_data,
# fp_negative_gaze_data,fn_positive_gaze_data])
# scale_path = heatmap_scale(size=368., title= title, y_max= y_max)
# plot_path = draw.images_four(
# imgs,
# title=title,
# save= not inspect)
# draw.join_images(plot_path, scale_path)
gaze = (fp_positive_gaze_data, fp_negative_gaze_data, fn_positive_gaze_data, fn_negative_gaze_data)
disp = (fp_pdispersion, fp_ndispersion, fn_pdispersion, fn_ndispersion)
return gaze, disp
def analyse(i, inspect=False, data_files=None):
parameters = PARAMETERS[i]
print('\nRunning analysis for session:')
print('\t', PATHS_DESTIN[i])
print('\t', PATHS_SOURCE[i])
if not data_files:
data_files = get_data_files(PATHS_DESTIN[i],
gaze_file_filter=parameters['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
ini_file = load_ini_data(data_files[1])
time_file = load_fpe_timestamps(data_files[2])
all_gaze_data = load_gaze_data(data_files[3])
title = ' - '.join(('%02d'%i, str(info_file['feature_degree']), info_file['nickname'], info_file['group']))
time_data = zip(time_file['time'], time_file['bloc'], time_file['trial'], time_file['event'])
ini_data = zip(ini_file['trial'], ini_file['contingency'], ini_file['feature'])
trials = get_events_per_trial(ini_data, time_data)
positive_intervals, negative_intervals = get_trial_intervals(trials)
# trial_intervals = get_trial_intervals(trials, uncategorized=True)
titlea = title + '_positive'
titleb = title + '_negative'
positive_gaze_data = clean_gaze_data(all_gaze_data,
do_remove_outside_session_time=positive_intervals,
do_correction=parameters['do_correction'],
do_remove_outside_screen=parameters['do_remove_outside_screen'],
do_manual_correction=parameters['do_manual_correction'],
do_confidence_threshold=parameters['confidence_threshold'],
min_block_size=parameters['min_block_size'],
inspect=False)
pdispersion = dispersion(positive_gaze_data, positive_intervals, titlea)
negative_gaze_data = clean_gaze_data(all_gaze_data,
do_remove_outside_session_time=negative_intervals,
do_correction=parameters['do_correction'],
do_remove_outside_screen=parameters['do_remove_outside_screen'],
do_manual_correction=parameters['do_manual_correction'],
do_confidence_threshold=parameters['confidence_threshold'],
min_block_size=parameters['min_block_size'],
inspect=False)
ndispersion = dispersion(negative_gaze_data, negative_intervals, titleb)
# draw.rates(
# [pdispersion, ndispersion],
# title= title+'_dispersion',
# save= not inspect,
# y_label='2d dispersion (RMS) in deggres of visual angle',
# single=False,
# first_label='S+',
# second_label='S-',
# y_limit= [1., 15.]
# )
# title = ' - '.join(('heat', '%02d'%i, str(info_file['feature_degree']), info_file['group'], info_file['nickname'], 'S+ S-'))
# imgs, y_max = custom_heatmap([positive_gaze_data, negative_gaze_data])
# scale_path = heatmap_scale(size=371., title= title, y_max= y_max)
# plot_path = draw.images(
# imgs,
# (sw, sh),
# save=not inspect,
# title=title)
# draw.join_images(plot_path, scale_path)
return (positive_gaze_data, negative_gaze_data), (pdispersion, ndispersion)