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)
# self
from methods import get_data_path
from constants import INNER_PATHS
from drawing import save_all
# from drawing.eye_movements_dbscan import draw_single as draw_gaze_dbscan # NOTE: xy must be normalized
from drawing.eye_movements_quantiles import draw_single as draw_gaze_quantiles # NOTE: xy must be normalized
from drawing.responses import draw_single as draw_responses
# matplotlib configurations
font = {'family': 'serif', 'size': 12}
matplotlib.rc('font', **font)
# assign the root data folder
data_path = get_data_path()
# save all figures inside its respective data folder
# save_all(data_path,draw_gaze_quantiles,'taxa_movimentos_oculares_B_quantis.png')
# save_all(data_path,draw_responses,'taxa_de_respostas_ao_botao_A.png')
# show single figure
ip = INNER_PATHS[1]
draw_responses(os.path.join(data_path, ip))
# draw_gaze_quantiles(os.path.join(data_path, ip))
# from helpers import delete_from_inner_paths
# from helpers import copy_to_inner_paths
# filenames = ['README.md']
# frompath = os.path.join(data_path,'README.md')
# copy_to_inner_paths(data_path, data_path, filenames)
def draw_proportions(cycles_set=slice(0, 9), show=True, output_path=None):
def get_axis_limits(ax, xscale=.45, yscale=0.05):
"""
https://stackoverflow.com/questions/24125058/add-label-to-subplot-in-matplotlib
"""
return ax.get_xlim()[1] * xscale, ax.get_ylim()[1] * yscale
filenames = [
os.path.join(get_data_path(), filename) for filename in INNER_PATHS
if filename in INNER_PATHS_DISCRIMINATION
]
data = np.array([
relative_rate_blue_red(filename, cycles_set) for filename in filenames
])
x_label = 'Successive cycles'
y_label = 'Button-pressing proportion'
figure, axarr = plt.subplots(1,
3,
sharey=True,
sharex=False,
figsize=(6, 3))
cycles_list = range(0, data.shape[2])
for condition, axis in zip(range(0, 3), axarr):
data_means = np.array(
[np.nanmean(data[:, condition, cycles]) for cycles in cycles_list])
data_min = np.array(
[np.nanmin(data[:, condition, cycles]) for cycles in cycles_list])
data_max = np.array(
[np.nanmax(data[:, condition, cycles]) for cycles in cycles_list])
axis.plot(data_means, color='k')
axis.plot((-0.5, len(data_means) - 1 + 0.5), (0.5, 0.5),
color='k',
ls='dotted',
alpha=0.3)
axis.errorbar(np.arange(len(data_means)),
data_means,
[data_means - data_min, data_max - data_means],
fmt='.k',
ecolor='k',
lw=1,
alpha=0.3)
axis.set_xlim([-0.5, len(data_means) - 1 + 0.5])
# 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)
lowx, highx = axis.get_xlim()
axis.yaxis.set_ticks(np.arange(0, 1.1, 0.25))
axis.xaxis.set_ticks([0, 8])
#remove ticks
axis.xaxis.set_ticks_position('none')
axis.yaxis.set_ticks_position('none')
size = 14
axarr[0].set_ylabel(y_label)
axarr[0].spines['left'].set_visible(True)
axarr[0].annotate('A', xy=get_axis_limits(axarr[0]), size=size)
axarr[1].set_xlabel(x_label)
axarr[1].annotate('B', xy=get_axis_limits(axarr[1]), size=size)
axarr[2].annotate('A', xy=get_axis_limits(axarr[2]), size=size)
offset = 0
x = 1
for axis in axarr:
axis.set_xticklabels([x + offset, 9 + offset])
offset = offset + 9
figure.tight_layout()
figure.subplots_adjust(wspace=0.05)
# bw = 0.26
# w = .28
# size = 14
# figure.text(bw, 0.25, 'A',size=size)
# figure.text(bw+w, 0.25, 'B',size=size)
# figure.text(bw+(w*2), 0.25, 'A',size=size)
if output_path:
plt.savefig(output_path, bbox_inches='tight')
plt.close()
else:
# save/plot figure
if show:
plt.show()
# from constants import INNER_PATHS
# from methods import get_data_path
# filenames = [os.path.join(get_data_path(), filename) for filename in INNER_PATHS]
# data = np.array([relative_rate_blue_red(filename) for filename in filenames])
# print(data.shape)
# data_means = np.array([np.mean(data[:, condition, cycles]) for condition in range(0,3) for cycles in range(0,8)])
# data_min = np.array([np.min(data[:, condition, cycles]) for condition in range(0,3) for cycles in range(0,8)])
# data_max = np.array([np.max(data[:, condition, cycles]) for condition in range(0,3) for cycles in range(0,8)])
# axis = plt.gca()
# plt.plot(data_means, color='k')
# plt.errorbar(np.arange(len(data_means)),
# data_means,
# [data_means-data_min,data_max-data_means],
# fmt='k', ecolor='gray', lw=1)
# plt.show()
if __name__ == '__main__':
from constants import INNER_PATHS
from methods import get_data_path
filenames = [
os.path.join(get_data_path(), filename) for filename in INNER_PATHS
]
for filename in filenames:
print(filename)
draw_single_proportion(filename)
draw_proportions()
# draw_proportions(output_path=os.path.join(get_data_path(),'button_press.png'))