def draw_single(src_dir, show=False):
print os.path.dirname(src_dir)
ID = os.path.basename(os.path.dirname(src_dir))
paths = sorted(glob(os.path.join(src_dir, '0*')))
# global vars
data = []
ymax = []
for path in paths:
be = load_data_from_path(path)
responses = all_responses(be)
data.append((stimuli_onset(be), responses))
ymax.append(len(responses))
ymax = np.amax(ymax)
x_label = 'Ciclos'
y_label = 'Taxa (respostas por segundo)'
title = 'Particip. ' + ID + ': taxa de resp. durante as cores Verm. e Azul.'
n_plots = len(paths)
if n_plots == 1:
figsize = (4, 4)
elif n_plots == 2:
figsize = (8, 4)
elif n_plots == 3:
figsize = (12, 4)
else:
figsize = (14, 4)
# figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0)
figure, axarr = plt.subplots(1,
n_plots,
sharey=True,
sharex=False,
figsize=figsize)
figure.suptitle(title)
figure.text(0.5, 0.02, x_label)
#figure.text(0.014, 0.5, y_label, rotation='vertical',verticalalignment='center',horizontalalignment='right')
for i, d in enumerate(data):
(onsets, responses) = d
plot_temporal_perfil(axarr[i], onsets, responses)
#plt.xlim(xmax = 300)
axarr[0].set_ylabel(y_label)
# axarr[0].legend(loc='upper right')
# axarr[1].set_xlabel(x_label)
#plt.ylim(ymax = ymax, ymin = 0)
#plt.text(-16,4,ID,fontsize=18)
#figure.subplots_adjust(wspace=0.05,left=0.05, right=.98,bottom=0.1,top=0.92)
figure.tight_layout()
# save/plot figure
if show:
plt.show()
def draw_single_proportion(src_dir, show=True):
print(os.path.dirname(src_dir))
ID = os.path.basename(os.path.dirname(src_dir))
paths = sorted(glob(os.path.join(src_dir, '0*')))
data = []
x_label = 'Successive cycles'
y_label = 'Button-pressing proportion'
title = ID
figure, axarr = plt.subplots(1,
3,
sharey=True,
sharex=True,
figsize=(9, 3))
# figure.suptitle(title);
# figure.text(0.5, 0.02, x_label)
for path, axis in zip(paths, axarr):
beha_data = load_data(os.path.join(path, "behavioral_events.txt"))
target_intervals = stimuli_onset(beha_data)
red_intervals = zip(target_intervals[0], target_intervals[1])
blue_intervals = zip(target_intervals[1], target_intervals[0][1:])
responses = all_responses(beha_data)
red_data = rate_in(red_intervals, responses)
blue_data = rate_in(blue_intervals, responses)
relative_rate = [
r / (r + b) if r + b > 0 else np.nan
for r, b in zip(red_data, blue_data)
]
axis.plot(relative_rate, color="k", label="Right")
# remove outer frame
axis.spines['top'].set_visible(False)
axis.spines['bottom'].set_visible(False)
axis.spines['left'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.set_ylim(0., 1.)
axis.set_xlim(-0.5, len(relative_rate) + 0.5)
axis.set_xticklabels([x for x in range(-1, len(relative_rate) + 1, 2)])
#remove ticks
axis.xaxis.set_ticks_position('none')
axis.yaxis.set_ticks_position('none')
axarr[0].set_ylabel(y_label)
axarr[0].spines['left'].set_visible(True)
axarr[1].set_xlabel(x_label)
figure.tight_layout()
# save/plot figure
if show:
plt.show()
def draw_single(src_dir, show=False):
print os.path.dirname(src_dir)
ID = os.path.basename(os.path.dirname(src_dir))
paths = sorted(glob(os.path.join(src_dir,'0*')))
# global vars
data = []
ymax = []
for path in paths:
be = load_data_from_path(path)
responses = all_responses(be)
data.append((stimuli_onset(be), responses))
ymax.append(len(responses))
ymax = np.amax(ymax)
x_label = 'Ciclos'
y_label = 'Taxa (respostas por segundo)'
title = 'Particip. '+ID+': taxa de resp. durante as cores Verm. e Azul.'
n_plots = len(paths)
if n_plots == 1:
figsize = (4, 4)
elif n_plots == 2:
figsize = (8, 4)
elif n_plots == 3:
figsize = (12, 4)
else:
figsize = (14, 4)
# figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0)
figure, axarr = plt.subplots(1, n_plots, sharey=True, sharex=False, figsize=figsize)
figure.suptitle(title);
figure.text(0.5, 0.02, x_label)
#figure.text(0.014, 0.5, y_label, rotation='vertical',verticalalignment='center',horizontalalignment='right')
for i, d in enumerate(data):
(onsets, responses) = d
plot_temporal_perfil(axarr[i], onsets, responses)
#plt.xlim(xmax = 300)
axarr[0].set_ylabel(y_label)
# axarr[0].legend(loc='upper right')
# axarr[1].set_xlabel(x_label)
#plt.ylim(ymax = ymax, ymin = 0)
#plt.text(-16,4,ID,fontsize=18)
#figure.subplots_adjust(wspace=0.05,left=0.05, right=.98,bottom=0.1,top=0.92)
figure.tight_layout()
# save/plot figure
if show:
plt.show()
def rate_switching(src_dir, cycles_set=slice(0,8)):
paths = sorted(glob(os.path.join(src_dir,'0*')))
data = []
for path in paths:
all_gaze_data = load_data(os.path.join(path,"gaze_coordenates_on_screen.txt"))
left_gaze_mask, right_gaze_mask = gaze_mask_left_right(all_gaze_data)
switchings = switching_timestamps(all_gaze_data, left_gaze_mask, right_gaze_mask)
beha_data = load_data(os.path.join(path,"behavioral_events.txt"))
target_intervals = stimuli_onset(beha_data)
red_intervals = zip(target_intervals[0], target_intervals[1])
blue_intervals = zip(target_intervals[1], target_intervals[0][1:])
red_data = rate_in(red_intervals, switchings)[cycles_set]
blue_data = rate_in(blue_intervals, switchings)[cycles_set]
data.append((red_data,blue_data,switchings.shape[0]))
return data
def relative_rate_blue_red(src_dir, cycles_set=slice(0,8)):
paths = sorted(glob(os.path.join(src_dir,'0*')))
data = []
for path in paths:
beha_data = load_data(os.path.join(path,"behavioral_events.txt"))
target_intervals = stimuli_onset(beha_data)
red_intervals = zip(target_intervals[0], target_intervals[1])
blue_intervals = zip(target_intervals[1], target_intervals[0][1:])
responses = all_responses(beha_data)
red_data = rate_in(red_intervals, responses)
blue_data = rate_in(blue_intervals, responses)
relative_rate_all = relative_rate(red_data, blue_data)
data.append(relative_rate_all[cycles_set])
return data
def relative_rate_left_right(src_dir, target_intervals='all_onsets'):
timestamps = 'time'
paths = sorted(glob(os.path.join(src_dir,'0*')))
data = []
for path in paths:
all_gaze_data = load_data(os.path.join(path,"gaze_coordenates_on_screen.txt"))
all_gaze_data, left_gaze_mask, right_gaze_mask = gaze_mask_left_right(all_gaze_data)
left_timestamps = all_gaze_data[left_gaze_mask][timestamps]
right_timestamps = all_gaze_data[right_gaze_mask][timestamps]
beha_data = load_data(os.path.join(path,"behavioral_events.txt"))
if 'all_onsets' in target_intervals:
l_target_intervals = all_stimuli(beha_data)
l_target_intervals = zip(l_target_intervals, l_target_intervals[1:])
left_data = rate_in(l_target_intervals, left_timestamps)
right_data = rate_in(l_target_intervals, right_timestamps)
relative_rate_all = relative_rate(left_data, right_data)
data.append(relative_rate_all)
elif 'left_right_onsets' in target_intervals:
l_target_intervals = all_stimuli(beha_data)
l_target_intervals = zip(l_target_intervals, l_target_intervals[1:])
left_data = rate_in(l_target_intervals, left_timestamps)
right_data = rate_in(l_target_intervals, right_timestamps)
relative_rate_all = relative_rate(left_data, right_data)
data.append([relative_rate_all[::2],relative_rate_all[1::2]])
elif 'red_blue_onsets' in target_intervals:
l_target_intervals = stimuli_onset(beha_data)
red_intervals = zip(l_target_intervals[0], l_target_intervals[1])
left_red_data = rate_in(red_intervals, left_timestamps)
red_intervals = zip(l_target_intervals[0], l_target_intervals[1])
right_red_data = rate_in(red_intervals, right_timestamps)
blue_intervals = zip(l_target_intervals[1], l_target_intervals[0][1:])
left_blue_data = rate_in(blue_intervals, left_timestamps)
blue_intervals = zip(l_target_intervals[1], l_target_intervals[0][1:])
right_blue_data = rate_in(blue_intervals, right_timestamps)
relative_rate_positive = relative_rate(left_red_data, right_red_data)
relative_rate_negative = relative_rate(left_blue_data, right_blue_data)
data.append((relative_rate_positive, relative_rate_negative))
return data
def draw_single(src_dir, show=True):
print(os.path.dirname(src_dir))
ID = os.path.basename(os.path.dirname(src_dir))
paths = sorted(glob(os.path.join(src_dir, '0*')))
data = []
ymax = []
for path in paths:
be = load_data(os.path.join(path, "behavioral_events.txt"))
responses = all_responses(be)
data.append((stimuli_onset(be), responses))
ymax.append(len(responses))
ymax = np.amax(ymax)
x_label = 'Ciclos'
y_label = 'Taxa (respostas por segundo)'
title = 'Particip. ' + ID + ': taxa de resp. durante as cores Verm. e Azul.'
n_plots = len(paths)
if n_plots == 1:
figsize = (4, 4)
elif n_plots == 2:
figsize = (8, 4)
elif n_plots == 3:
figsize = (12, 4)
else:
figsize = (14, 4)
figure, axarr = plt.subplots(1,
n_plots,
sharey=True,
sharex=False,
figsize=figsize)
figure.suptitle(title)
figure.text(0.5, 0.02, x_label)
for i, d in enumerate(data):
(onsets, responses) = d
temporal_perfil(axarr[i], onsets, responses)
axarr[0].set_ylabel(y_label)
figure.tight_layout()
# save/plot figure
if show:
plt.show()
def baseline_tracking_extinction_switching_correlation():
from constants import INNER_PATHS_DISCRIMINATION
from methods import get_data_path
X = []
Y = []
filenames = [os.path.join(get_data_path(), filename) for filename in INNER_PATHS_DISCRIMINATION]
for filename in filenames:
# ID = os.path.basename(os.path.dirname(filename))
timestamps = 'time'
paths = sorted(glob(os.path.join(filename,'0*')))
baseline_tracking_path = paths[0]
all_gaze_data = load_data(os.path.join(baseline_tracking_path,"gaze_coordenates_on_screen.txt"))
left_gaze_mask, right_gaze_mask = gaze_mask_left_right(all_gaze_data)
left_timestamps = all_gaze_data[left_gaze_mask][timestamps]
right_timestamps = all_gaze_data[right_gaze_mask][timestamps]
beha_data = load_data(os.path.join(baseline_tracking_path,"behavioral_events.txt"))
# left_right_onsets
l_target_intervals = all_stimuli(beha_data)
l_target_intervals = zip(l_target_intervals, l_target_intervals[1:])
left_data = rate_in(l_target_intervals, left_timestamps)
right_data = rate_in(l_target_intervals, right_timestamps)
relative_rate_all = relative_rate(left_data, right_data)
X.append(np.nanmean(relative_rate_all[::2])-np.nanmean(relative_rate_all[1::2]))
extintion_switching_path = paths[1]
all_gaze_data = load_data(os.path.join(extintion_switching_path,"gaze_coordenates_on_screen.txt"))
left_gaze_mask, right_gaze_mask = gaze_mask_left_right(all_gaze_data)
switchings = switching_timestamps(all_gaze_data, left_gaze_mask, right_gaze_mask)
beha_data = load_data(os.path.join(extintion_switching_path,"behavioral_events.txt"))
target_intervals = stimuli_onset(beha_data)
red_intervals = zip(target_intervals[0], target_intervals[1])
blue_intervals = zip(target_intervals[1], target_intervals[0][1:])
red_data = rate_in(red_intervals, switchings)
blue_data = rate_in(blue_intervals, switchings)
Y.append(np.nanmean(blue_data)-np.nanmean(red_data))
return np.array(X), np.array(Y)
figsize = (11, 4)
else:
figsize = (16, 4)
# figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0)
figure, axarr = plt.subplots(1,
n_plots,
sharey=True,
sharex=False,
figsize=figsize)
figure.suptitle(title)
figure.text(0.5, 0.02, x_label)
for i, path in enumerate(paths):
be, ge = load_npdata_from_path(path)
plot_temporal_perfil(axarr[i], stimuli_onset(be), ge['time'])
axarr[0].set_ylabel(y_label)
#axarr.legend(loc=(0.0,0.73))
# axarr[1].set_xlabel(x_label)
plt.ylim(ymin=0)
figure.subplots_adjust(wspace=0.1,
left=0.05,
right=.98,
bottom=0.1,
top=0.92)
#figure.tight_layout()
plt.show()
n_plots = len(paths) if n_plots == 1: figsize = (6, 4) elif n_plots == 2: figsize = (11, 4) else: figsize = (16, 4) # figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0) figure, axarr = plt.subplots(1, n_plots, sharey=True, sharex=False, figsize=figsize) figure.suptitle(title); figure.text(0.5, 0.02, x_label) for i, path in enumerate(paths): be, fx = load_npdata_from_path(path) plot_temporal_perfil(axarr[i], stimuli_onset(be), fx['start_time']) #axarr.set_ylabel(y_label) #axarr.legend(loc=(0.0,0.73)) # axarr[1].set_xlabel(x_label) plt.ylim(ymin = 0) figure.subplots_adjust(wspace=0.1,left=0.05, right=.98,bottom=0.1,top=0.92) # figure.tight_layout() plt.show() # print all column names # for key in be.dtype.names: # print key
figsize = (11, 4)
else:
figsize = (16, 4)
# figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0)
figure, axarr = plt.subplots(1,
n_plots,
sharey=True,
sharex=False,
figsize=figsize)
figure.suptitle(title)
figure.text(0.5, 0.02, x_label)
for i, path in enumerate(paths):
be, fx = load_npdata_from_path(path)
plot_temporal_perfil(axarr[i], stimuli_onset(be), fx['start_time'])
#axarr.set_ylabel(y_label)
#axarr.legend(loc=(0.0,0.73))
# axarr[1].set_xlabel(x_label)
plt.ylim(ymin=0)
figure.subplots_adjust(wspace=0.1,
left=0.05,
right=.98,
bottom=0.1,
top=0.92)
# figure.tight_layout()
plt.show()
n_plots = len(paths) if n_plots == 1: figsize = (6, 4) elif n_plots == 2: figsize = (11, 4) else: figsize = (16, 4) # figure.add_axes([0.1, 0.1, 0.8, 0.8], frameon = 0) figure, axarr = plt.subplots(1, n_plots, sharey=True, sharex=False, figsize=figsize) figure.suptitle(title); figure.text(0.5, 0.02, x_label) for i, path in enumerate(paths): be, ge = load_npdata_from_path(path) plot_temporal_perfil(axarr[i], stimuli_onset(be), ge['time']) axarr[0].set_ylabel(y_label) #axarr.legend(loc=(0.0,0.73)) # axarr[1].set_xlabel(x_label) plt.ylim(ymin = 0) figure.subplots_adjust(wspace=0.1,left=0.05, right=.98,bottom=0.1,top=0.92) #figure.tight_layout() plt.show() # print all column names # for key in be.dtype.names: # print key
