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()
def draw_single(src_dir, show=False):
# Targets
BlueLeft = "#011efe"
RedLeft = "#fe0000"
# Distractors
GreenRight = "#49ac15"
CianoRight = "#a8e6cf"
ID = os.path.basename(os.path.dirname(src_dir))
basepath = os.path.dirname(os.path.dirname(src_dir))
print src_dir, os.path.join(basepath, "P005/2015-05-19")
if src_dir == os.path.join(basepath, "P001/2015-05-19"):
data = [{"eps": 0.06, "min_samples": 1000}, {"eps": 0.06, "min_samples": 1000}]
elif src_dir == os.path.join(basepath, "P001/2015-05-27"):
data = [
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
]
elif src_dir == os.path.join(basepath, "P002/2015-05-19"):
data = [
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
]
elif src_dir == os.path.join(basepath, "P002/2015-05-20"):
data = [
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 500},
]
elif src_dir == os.path.join(basepath, "P003/2015-05-20"):
data = [
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 800},
]
elif src_dir == os.path.join(basepath, "P004/2015-05-20"):
data = [
{"eps": 0.06, "min_samples": 1500},
{"eps": 0.06, "min_samples": 1500},
{"eps": 0.06, "min_samples": 1500},
]
elif src_dir == os.path.join(basepath, "P005/2015-05-19"):
data = [
{"eps": 0.08, "min_samples": 500},
{"eps": 0.07, "min_samples": 500},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
]
elif src_dir == os.path.join(basepath, "P006/2015-05-25"):
data = [
{"eps": 0.065, "min_samples": 1700},
{"eps": 0.065, "min_samples": 1300},
{"eps": 0.065, "min_samples": 1200},
]
elif src_dir == os.path.join(basepath, "P007/2015-05-25"):
data = [
{"eps": 0.06, "min_samples": 1200},
{"eps": 0.06, "min_samples": 1200},
{"eps": 0.06, "min_samples": 1200},
]
elif src_dir == os.path.join(basepath, "P008/2015-05-26"):
data = [
{"eps": 0.06, "min_samples": 1200},
{"eps": 0.06, "min_samples": 1200},
{"eps": 0.06, "min_samples": 1200},
]
elif src_dir == os.path.join(basepath, "P009/2015-05-26"):
data = [
{"eps": 0.06, "min_samples": 1700},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
{"eps": 0.06, "min_samples": 1000},
]
elif src_dir == os.path.join(basepath, "P010/2015-05-26"):
data = [
{"eps": 0.06, "min_samples": 600},
{"eps": 0.06, "min_samples": 600},
{"eps": 0.06, "min_samples": 600},
{"eps": 0.06, "min_samples": 600},
]
paths = sorted(glob(os.path.join(src_dir, "0*")))
for i, path in enumerate(paths):
data_folder = os.path.join(src_dir, path)
beha_events_path = os.path.join(data_folder, "behavioral_events.txt")
gaze_events_path = os.path.join(data_folder, "gaze_coordenates_on_screen.txt")
gaze_data = np.genfromtxt(
gaze_events_path,
delimiter="\t",
missing_values=["NA"],
filling_values=None,
names=True,
autostrip=True,
dtype=None,
)
data[i]["beha_data"] = np.genfromtxt(
beha_events_path,
delimiter="\t",
missing_values=["NA"],
filling_values=None,
names=True,
autostrip=True,
dtype=None,
)
# DBSCAN expects data with shape (-1,2), we need to transpose ours first
data[i]["src_xy"] = np.array([gaze_data["x_norm"], gaze_data["y_norm"]]).T
data[i]["dbsc"] = categorize_points(data[i]["src_xy"], data[i]["eps"], data[i]["min_samples"])
data[i]["points_categorized"] = plot_dbscan(data[i]["src_xy"], data[i]["dbsc"])
data[i]["masks"] = categorize_masks(data[i]["src_xy"], data[i]["dbsc"])
data[i]["src_timestamps"] = gaze_data["time"]
data[i]["time_categorized"] = categorize_timestamps(data[i]["src_timestamps"], data[i]["dbsc"])
x_label = "Ciclo"
y_label = "dir. < Taxa (gaze/s) > esq."
title = "Particip. " + ID + ": Taxa de movim. oculares durante cada cor"
n_plots = len(data)
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)
# look rate at left
# look rate at right
for i, d in enumerate(data):
turnover_count = 0
turnover = [a for a, b in zip(d["masks"]["cluster_0"], d["masks"]["cluster_1"]) if a or b]
for c, n in zip(turnover, turnover[1:]):
if c != n:
turnover_count += 1
print "\n", "turnover_count:", turnover_count
left_right_xy = []
left_right_timestamps = []
for time, points in zip(d["time_categorized"].iteritems(), d["points_categorized"].iteritems()):
_, xy = points
time_key, timestamps = time
if len(timestamps) > 0 and "cluster" in time_key:
left_right_timestamps.append(timestamps)
left_right_xy.append(xy)
if np.mean(left_right_xy[0][0]) > np.mean(left_right_xy[1][0]):
left_right_xy = [left_right_xy[1], left_right_xy[0]]
left_right_timestamps = [left_right_timestamps[1], left_right_timestamps[0]]
# all stimuli, left and right
# plot_temporal_perfil(axarr[i],all_stimuli(data[i]['beha_data']), left_right_timestamps,"positions")
# v+v, v+c, a+v, a+c
plot_temporal_perfil(
axarr[i], color_pair(data[i]["beha_data"], 0), left_right_timestamps[0], "pair", c1=RedLeft, nsize=0
)
plot_temporal_perfil(
axarr[i],
color_pair(data[i]["beha_data"], 0),
left_right_timestamps[1],
"pair",
c1=GreenRight,
nsize=0,
doreversed=True,
)
plot_temporal_perfil(
axarr[i], color_pair(data[i]["beha_data"], 1), left_right_timestamps[0], "pair", c1=RedLeft, nsize=1
)
plot_temporal_perfil(
axarr[i],
color_pair(data[i]["beha_data"], 1),
left_right_timestamps[1],
"pair",
c1=CianoRight,
nsize=1,
doreversed=True,
)
plot_temporal_perfil(
axarr[i], color_pair(data[i]["beha_data"], 2), left_right_timestamps[0], "pair", c1=BlueLeft, nsize=2
)
plot_temporal_perfil(
axarr[i],
color_pair(data[i]["beha_data"], 2),
left_right_timestamps[1],
"pair",
c1=CianoRight,
nsize=2,
doreversed=True,
)
plot_temporal_perfil(
axarr[i], color_pair(data[i]["beha_data"], 3), left_right_timestamps[0], "pair", c1=BlueLeft, nsize=3
)
plot_temporal_perfil(
axarr[i],
color_pair(data[i]["beha_data"], 3),
left_right_timestamps[1],
"pair",
c1=GreenRight,
nsize=3,
doreversed=True,
)
# all stimuli (red blue), left
# plot_temporal_perfil(axarr[i],stimuli_onset(data[i]['beha_data']), left_right_timestamps[0],'colors', c1=RedLeft, c2=BlueLeft)
# all stimuli (red blue), right
# plot_temporal_perfil(axarr[i],stimuli_onset(data[i]['beha_data']), left_right_timestamps[1],"colors",c1=RedRight,c2=BlueRight, doreversed=True)
ticks = [30, 20, 10, 0, 10, 20, 30]
axarr[0].set_yticklabels(labels=ticks)
# ([x for x in range(-30,31)],)
plt.ylim(ymin=-30)
plt.ylim(ymax=30)
# figure.subplots_adjust(wspace=0.1,left=0.05, right=.98,bottom=0.1,top=0.92)
axarr[0].set_ylabel(y_label)
figure.tight_layout()
if show:
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
def draw_single(src_dir, show=False):
# Targets
BlueLeft = '#011efe'
RedLeft = '#fe0000'
# Distractors
GreenRight = '#49ac15'
CianoRight = '#a8e6cf'
ID = os.path.basename(os.path.dirname(src_dir))
basepath = os.path.dirname(os.path.dirname(src_dir))
print src_dir, os.path.join(basepath, 'P005/2015-05-19')
if src_dir == os.path.join(basepath, 'P001/2015-05-19'):
data = [{
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}]
elif src_dir == os.path.join(basepath, 'P001/2015-05-27'):
data = [{
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}]
elif src_dir == os.path.join(basepath, 'P002/2015-05-19'):
data = [{
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}]
elif src_dir == os.path.join(basepath, 'P002/2015-05-20'):
data = [{
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 500
}]
elif src_dir == os.path.join(basepath, 'P003/2015-05-20'):
data = [{
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 800
}]
elif src_dir == os.path.join(basepath, 'P004/2015-05-20'):
data = [{
'eps': 0.06,
'min_samples': 1500
}, {
'eps': 0.06,
'min_samples': 1500
}, {
'eps': 0.06,
'min_samples': 1500
}]
elif src_dir == os.path.join(basepath, 'P005/2015-05-19'):
data = [{
'eps': 0.08,
'min_samples': 500
}, {
'eps': 0.07,
'min_samples': 500
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}]
elif src_dir == os.path.join(basepath, 'P006/2015-05-25'):
data = [{
'eps': 0.065,
'min_samples': 1700
}, {
'eps': 0.065,
'min_samples': 1300
}, {
'eps': 0.065,
'min_samples': 1200
}]
elif src_dir == os.path.join(basepath, 'P007/2015-05-25'):
data = [{
'eps': 0.06,
'min_samples': 1200
}, {
'eps': 0.06,
'min_samples': 1200
}, {
'eps': 0.06,
'min_samples': 1200
}]
elif src_dir == os.path.join(basepath, 'P008/2015-05-26'):
data = [{
'eps': 0.06,
'min_samples': 1200
}, {
'eps': 0.06,
'min_samples': 1200
}, {
'eps': 0.06,
'min_samples': 1200
}]
elif src_dir == os.path.join(basepath, 'P009/2015-05-26'):
data = [{
'eps': 0.06,
'min_samples': 1700
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}, {
'eps': 0.06,
'min_samples': 1000
}]
elif src_dir == os.path.join(basepath, 'P010/2015-05-26'):
data = [{
'eps': 0.06,
'min_samples': 600
}, {
'eps': 0.06,
'min_samples': 600
}, {
'eps': 0.06,
'min_samples': 600
}, {
'eps': 0.06,
'min_samples': 600
}]
paths = sorted(glob(os.path.join(src_dir, '0*')))
for i, path in enumerate(paths):
data_folder = os.path.join(src_dir, path)
beha_events_path = os.path.join(data_folder, "behavioral_events.txt")
gaze_events_path = os.path.join(data_folder,
'gaze_coordenates_on_screen.txt')
gaze_data = np.genfromtxt(gaze_events_path,
delimiter='\t',
missing_values=['NA'],
filling_values=None,
names=True,
autostrip=True,
dtype=None)
data[i]['beha_data'] = np.genfromtxt(beha_events_path,
delimiter="\t",
missing_values=["NA"],
filling_values=None,
names=True,
autostrip=True,
dtype=None)
# DBSCAN expects data with shape (-1,2), we need to transpose ours first
data[i]['src_xy'] = np.array(
[gaze_data['x_norm'], gaze_data['y_norm']]).T
data[i]['dbsc'] = categorize_points(data[i]['src_xy'], data[i]['eps'],
data[i]['min_samples'])
data[i]['points_categorized'] = plot_dbscan(data[i]['src_xy'],
data[i]['dbsc'])
data[i]['masks'] = categorize_masks(data[i]['src_xy'], data[i]['dbsc'])
data[i]['src_timestamps'] = gaze_data['time']
data[i]['time_categorized'] = categorize_timestamps(
data[i]['src_timestamps'], data[i]['dbsc'])
x_label = 'Ciclo'
y_label = 'dir. < Taxa (gaze/s) > esq.'
title = 'Particip. ' + ID + ': Taxa de movim. oculares durante cada cor'
n_plots = len(data)
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)
# look rate at left
# look rate at right
for i, d in enumerate(data):
turnover_count = 0
turnover = [
a for a, b in zip(d['masks']['cluster_0'], d['masks']['cluster_1'])
if a or b
]
for c, n in zip(turnover, turnover[1:]):
if c != n:
turnover_count += 1
print '\n', 'turnover_count:', turnover_count
left_right_xy = []
left_right_timestamps = []
for time, points in zip(d['time_categorized'].iteritems(),
d['points_categorized'].iteritems()):
_, xy = points
time_key, timestamps = time
if len(timestamps) > 0 and 'cluster' in time_key:
left_right_timestamps.append(timestamps)
left_right_xy.append(xy)
if np.mean(left_right_xy[0][0]) > np.mean(left_right_xy[1][0]):
left_right_xy = [left_right_xy[1], left_right_xy[0]]
left_right_timestamps = [
left_right_timestamps[1], left_right_timestamps[0]
]
# all stimuli, left and right
#plot_temporal_perfil(axarr[i],all_stimuli(data[i]['beha_data']), left_right_timestamps,"positions")
# v+v, v+c, a+v, a+c
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 0),
left_right_timestamps[0],
'pair',
c1=RedLeft,
nsize=0)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 0),
left_right_timestamps[1],
'pair',
c1=GreenRight,
nsize=0,
doreversed=True)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 1),
left_right_timestamps[0],
'pair',
c1=RedLeft,
nsize=1)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 1),
left_right_timestamps[1],
'pair',
c1=CianoRight,
nsize=1,
doreversed=True)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 2),
left_right_timestamps[0],
'pair',
c1=BlueLeft,
nsize=2)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 2),
left_right_timestamps[1],
'pair',
c1=CianoRight,
nsize=2,
doreversed=True)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 3),
left_right_timestamps[0],
'pair',
c1=BlueLeft,
nsize=3)
plot_temporal_perfil(axarr[i],
color_pair(data[i]['beha_data'], 3),
left_right_timestamps[1],
'pair',
c1=GreenRight,
nsize=3,
doreversed=True)
# all stimuli (red blue), left
# plot_temporal_perfil(axarr[i],stimuli_onset(data[i]['beha_data']), left_right_timestamps[0],'colors', c1=RedLeft, c2=BlueLeft)
# all stimuli (red blue), right
# plot_temporal_perfil(axarr[i],stimuli_onset(data[i]['beha_data']), left_right_timestamps[1],"colors",c1=RedRight,c2=BlueRight, doreversed=True)
ticks = [30, 20, 10, 0, 10, 20, 30]
axarr[0].set_yticklabels(labels=ticks)
#([x for x in range(-30,31)],)
plt.ylim(ymin=-30)
plt.ylim(ymax=30)
# figure.subplots_adjust(wspace=0.1,left=0.05, right=.98,bottom=0.1,top=0.92)
axarr[0].set_ylabel(y_label)
figure.tight_layout()
if show:
plt.show()
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
