def analyse_excluded(feature_degree):
positive = []
negative = []
positive_button = []
negative_button = []
positive_latency = []
negative_latency = []
for path in PATHS_DESTIN:
i = PATHS_DESTIN.index(path)
if not p[i]['excluded']:
continue
data_files = get_data_files(path,
gaze_file_filter=p[i]['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
if not ((info_file['group'] == 'positive') or
(info_file['group'] == 'negative')):
continue
if info_file['feature_degree'] == feature_degree:
looking_rate, button_rate, latencies = analyse(
i, inspect=False, data_files=data_files)
if info_file['group'] == 'positive':
positive.append(np.array(looking_rate))
positive_button.append(np.array(button_rate))
positive_latency.append(np.array(latencies))
elif info_file['group'] == 'negative':
negative.append(np.array(looking_rate))
negative_button.append(np.array(button_rate))
negative_latency.append(np.array(latencies))
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_excluded(feature_degree):
fp_positivex, fp_positivey = [], []
fp_negativex, fp_negativey = [], []
fn_positivex, fn_positivey = [], []
fn_negativex, fn_negativey = [], []
fp_pdispersion, fp_ndispersion = [], []
fn_pdispersion, fn_ndispersion = [], []
a, b = 0, 0
for path in PATHS_DESTIN:
i = PATHS_DESTIN.index(path)
parameters = PARAMETERS[i]
if not parameters['excluded']:
continue
data_files = get_data_files(path,
gaze_file_filter=parameters['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
if not ((info_file['group'] == 'positive') or (info_file['group'] == 'negative')):
continue
if info_file['feature_degree'] == feature_degree:
gaze, gaze_dispersion = analyse(i,
inspect= False,
data_files= data_files)
(positive_gaze, negative_gaze) = gaze
(pdispersion, ndispersion) = gaze_dispersion
if info_file['group'] == 'positive':
a += 1
if info_file['group'] == 'negative':
b += 1
print('positive:', a)
print('negative:', b)
def analyse_experiment_intrasubject(feature_degree=9, factor='donut_slice'):
positive_gaze = []
negative_gaze = []
positive_button = []
negative_button = []
positive_latency = []
negative_latency = []
sex = []
age = []
preference_xs = []
preference_sq = []
preference_na = []
# no_conditional_discrimination = [
# '2017_12_18_001_BRE', '2017_12_15_001_KAR', '2017_12_14_001_JUL',
# '2017_12_14_000_TAT', '2017_12_13_004_ANT',
# '2017_12_18_000_MAR', '2017_12_13_003_JOE'
# ]
for path in PATHS_DESTIN:
i = PATHS_DESTIN.index(path)
if p[i]['excluded']:
continue
data_files = get_data_files(path,
gaze_file_filter=p[i]['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
if not info_file['group'] == 'fp-square/fn-x':
continue
# if info_file['nickname'] in no_conditional_discrimination:
# continue
if info_file['feature_degree'] == feature_degree:
if cache_exists(info_file['nickname']):
looking_rate, button_rate, latencies = load_from_cache(
info_file['nickname'])
else:
button_rate, looking_rate, latencies = analyse_intra_subject(
i, factor, inspect=False, data_files=data_files)
save_to_cache(info_file['nickname'],
(looking_rate, button_rate, latencies))
print(info_file['nickname'])
(fp_button, fn_button) = button_rate
(fp_gaze, fn_gaze) = looking_rate
(fp_latency, fn_latency) = latencies
positive_button.append(np.array(fp_button))
negative_button.append(np.array(fn_button))
positive_gaze.append(np.array(fp_gaze))
negative_gaze.append(np.array(fn_gaze))
positive_latency.append(np.array(fp_latency))
negative_latency.append(np.array(fn_latency))
sex.append(info_file['sex'])
age.append(info_file['age'])
preference_xs.append(info_file['nickname'] + ' ' +
info_file['after_the_fact_preference'])
print('male: %02d' % sex.count('m'))
print('fema: %02d' % sex.count('f'))
print('age min. %.2f, avg. %.2f, max. %.2f' %
(np.min(age), np.mean(age), np.max(age)))
print(preference_xs)
# draw.all_proportions_intra(
# gaze_proportion=(positive_gaze, negative_gaze),
# button_proportion=(positive_button, negative_button)
# )
positive, negative, positive_error, negative_error = statistics(
positive_button, negative_button, [
str(feature_degree) + '_intra_button_positive_relative_rate.txt',
str(feature_degree) + '_intra_button_negative_relative_rate.txt'
], True)
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join([
'average button-pressing proportion', 'intra-subject',
'fp %i°' % feature_degree,
str(factor)
]),
save=True,
y_label='Proporção média de R durante S+',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo',
y_limit=[-0.1, 1.1])
positive, negative, positive_error, negative_error = statistics(
positive_gaze, negative_gaze, [
str(feature_degree) + '_intra_gaze_positive_relative_rate.txt',
str(feature_degree) + '_intra_gaze_negative_relative_rate.txt'
], True)
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join([
'average looking proportion', 'intra-subject',
'fp %i°' % feature_degree,
str(factor)
]),
save=True,
y_label='Proporção média de olhar ao estímulo distintivo',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo',
y_limit=[-0.1, 1.1])
positive, negative, positive_error, negative_error = statistics(
positive_latency, negative_latency, [
str(feature_degree) + '_intra_latency_positive_relative_rate.txt',
str(feature_degree) + '_intra_latency_negative_relative_rate.txt'
])
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join([
'average latency', 'intra-subject',
'fp %i°' % feature_degree,
str(factor)
]),
save=True,
y_label='Latência média (s)',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo')
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_experiment(feature_degree, factor):
positive = []
negative = []
positive_button = []
negative_button = []
positive_latency = []
negative_latency = []
positive_sex = []
negative_sex = []
positive_age = []
negative_age = []
looking_output = [
str(feature_degree) + '_looking_positive_relative_rate.txt',
str(feature_degree) + '_looking_negative_relative_rate.txt'
]
button_output = [
str(feature_degree) + '_button_positive_relative_rate.txt',
str(feature_degree) + '_button_negative_relative_rate.txt'
]
latency_output = [
str(feature_degree) + '_latency_positive_relative_rate.txt',
str(feature_degree) + '_latency_negative_relative_rate.txt'
]
for path in PATHS_DESTIN:
i = PATHS_DESTIN.index(path)
if p[i]['excluded']:
continue
data_files = get_data_files(path,
gaze_file_filter=p[i]['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
if not ((info_file['group'] == 'positive') or
(info_file['group'] == 'negative')):
continue
if info_file['feature_degree'] == feature_degree:
if cache_exists(info_file['nickname']):
gaze_proportion, button_proportion, latencies = load_from_cache(
info_file['nickname'])
else:
gaze_proportion, button_proportion, latencies = analyse(
i, factor, inspect=False, data_files=data_files)
save_to_cache(info_file['nickname'],
(gaze_proportion, button_proportion, latencies))
print(info_file['nickname'])
# draw.rates([button_proportion, gaze_proportion],
# title = ' - '.join(['%02d'%i, '%02d'%info_file['feature_degree'], info_file['group'], info_file['nickname'], str(factor)]),
# save= True,
# y_label='Proporção',
# single= False,
# first_label= 'Pressionar botão durante S+',
# second_label='Olhar estímulo distintivo',
# y_limit= [-0.1, 1.1],
# )
if info_file['group'] == 'positive':
positive_sex.append(info_file['sex'])
positive_age.append(info_file['age'])
# wrong size
if '2017_11_13_000_GAB' in info_file['nickname']:
positive.append(np.array(gaze_proportion)[:27])
positive_button.append(np.array(button_proportion)[:27])
positive_latency.append(np.array(latencies)[:27])
continue
positive.append(np.array(gaze_proportion))
positive_button.append(np.array(button_proportion))
positive_latency.append(np.array(latencies))
elif info_file['group'] == 'negative':
negative_sex.append(info_file['sex'])
negative_age.append(info_file['age'])
negative.append(np.array(gaze_proportion))
negative_button.append(np.array(button_proportion))
negative_latency.append(np.array(latencies))
print('positive male: %02d' % positive_sex.count('m'))
print('positive fema: %02d' % positive_sex.count('f'))
print('negative male: %02d' % negative_sex.count('m'))
print('negative fema: %02d' % negative_sex.count('f'))
print('positive age min. %.2f, avg. %.2f, max. %.2f' %
(np.min(positive_age), np.mean(positive_age), np.max(positive_age)))
print('negative age min. %.2f, avg. %.2f, max. %.2f' %
(np.min(negative_age), np.mean(negative_age), np.max(negative_age)))
# draw.all_proportions(
# gaze_proportion=(positive, negative),
# button_proportion=(positive_button, negative_button),
# factor=factor
# )
positive, negative, positive_error, negative_error = statistics(
positive, negative, looking_output, True)
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join([
'average looking proportion',
'fp %i°' % feature_degree,
str(factor)
]),
save=True,
y_label='Proporção média de olhar ao estímulo distintivo',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo',
y_limit=[-0.1, 1.1])
positive, negative, positive_error, negative_error = statistics(
positive_button, negative_button, button_output, True)
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join([
'average button-pressing proportion',
'fp %i°' % feature_degree,
str(factor)
]),
save=True,
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])
positive, negative, positive_error, negative_error = statistics(
positive_latency, negative_latency, latency_output)
draw.rates(data=[positive, negative],
error=[positive_error, negative_error],
title=' - '.join(
['average latency',
'fp %i°' % feature_degree,
str(factor)]),
save=True,
y_label='Latência média (s)',
single=False,
first_label='Aspecto Positivo',
second_label='Aspecto Negativo')
def analyse_experiment_intrasubject(feature_degree):
fp_positivex, fp_positivey = [], []
fp_negativex, fp_negativey = [], []
fn_positivex, fn_positivey = [], []
fn_negativex, fn_negativey = [], []
fp_pdispersion, fp_ndispersion = [], []
fn_pdispersion, fn_ndispersion = [], []
for path in PATHS_DESTIN:
i = PATHS_DESTIN.index(path)
parameters = PARAMETERS[i]
if parameters['excluded']:
continue
data_files = get_data_files(path,
gaze_file_filter=parameters['gaze_file_filter'])
info_file = load_yaml_data(data_files[0])
if not info_file['group'] == 'fp-square/fn-x':
continue
if info_file['feature_degree'] == feature_degree:
if cache_exists_i(info_file['nickname']):
gaze, gaze_dispersion = load_from_cache_i(info_file['nickname'])
else:
gaze, gaze_dispersion = analyse_intrasubject(i, inspect= False,data_files= data_files)
save_to_cache_i(info_file['nickname'],(gaze, gaze_dispersion))
(fp_posgaze, fp_neggaze, fn_posgaze, fn_neggaze) = gaze
(fp_pdisp, fp_ndisp, fn_pdisp, fn_ndisp) = gaze_dispersion
fp_positivex.append(fp_posgaze['x_norm'])
fp_positivey.append(fp_posgaze['y_norm'])
fp_negativex.append(fp_neggaze['x_norm'])
fp_negativey.append(fp_neggaze['y_norm'])
fp_pdispersion.append(fp_pdisp)
fp_ndispersion.append(fp_ndisp)
fn_positivex.append(fn_posgaze['x_norm'])
fn_positivey.append(fn_posgaze['y_norm'])
fn_negativex.append(fn_neggaze['x_norm'])
fn_negativey.append(fn_neggaze['y_norm'])
fn_pdispersion.append(fn_pdisp)
fn_ndispersion.append(fn_ndisp)
fp_positivex = np.hstack(fp_positivex)
fp_positivey = np.hstack(fp_positivey)
fp_negativex = np.hstack(fp_negativex)
fp_negativey = np.hstack(fp_negativey)
fn_positivex = np.hstack(fn_positivex)
fn_positivey = np.hstack(fn_positivey)
fn_negativex = np.hstack(fn_negativex)
fn_negativey = np.hstack(fn_negativey)
dt = {'names':['x_norm', 'y_norm'], 'formats':[np.float64, np.float64]}
fp_negative = np.zeros(fp_negativex.shape[0], dtype=dt)
fp_negative['x_norm'] = fp_negativex
fp_negative['y_norm'] = fp_negativey
fp_positive = np.zeros(fp_positivex.shape[0], dtype=dt)
fp_positive['x_norm'] = fp_positivex
fp_positive['y_norm'] = fp_positivey
fn_negative = np.zeros(fn_negativex.shape[0], dtype=dt)
fn_negative['x_norm'] = fn_negativex
fn_negative['y_norm'] = fn_negativey
fn_positive = np.zeros(fn_positivex.shape[0], dtype=dt)
fn_positive['x_norm'] = fn_positivex
fn_positive['y_norm'] = fn_positivey
# np.savetxt('positive%i.txt'%feature_degree, positive)
# np.savetxt('negative%i.txt'%feature_degree, negative)
d1, d2, e1, e2 = statistics(fp_pdispersion, fp_ndispersion)
d3, d4, e3, e4 = statistics(fn_pdispersion, fn_ndispersion)
labels = [
'Média do desvio padrão do ângulo de visão (RMS)',
'Tentativas',
'AP',
'AN',
'distintivo',
'comum'
]
draw.rates_double(
(d1, d4, d2, d3),
(e1, e4, e2, e3),
title= 'comparing FP and FN looking dispersion - '+str(feature_degree)+'_intra',
labels= labels,
y_limit=[6., 16.]
)
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)
