path = get_path(__file__) + '/..'
trials = range(0,51)
ticklabels = []
for i in trials:
if i==trials[0] or i%10 == 0:
ticklabels.append(i)
else:
ticklabels.append('')
font = {'weight': 'normal', 'size': 16}
for label in L_clean:
data = [d.get_trial(i).get_feature(label).view() for i in trials]
plt.title('Boxplot of feature {0} in the trials {1}-{2}'.format(
label, trials[0], trials[-1]), font)
plt.boxplot(data)
ax = plt.gca()
ax.set_xticklabels(ticklabels)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(10)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(10)
ax.set_xlabel('Trial Id', font)
ax.set_ylabel(label, font)
plt.savefig(
'{0}/plots/boxplots/{1}-t{2}-t{3}.pdf'.format(
path, label, trials[0], trials[-1]),
format='pdf', papertype='a4')
import numpy as np
from matplotlib import pyplot as plt
from src.data_interface import d
from src.utils import get_path
path = get_path(__file__) + '/../plots/gear_idea'
V10_idx = 31
IsAlert_idx = 2
for trial_id in d.trial_id_list:
t = d.get_trial(trial_id)
v = t.view()
unique_values = t.V10.unique_values()
if unique_values in [1,4,5]:
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.set_ylim(-1, 2)
ax.plot(range(len(v)), v[:, V10_idx])
ax.plot(range(len(v)), v[:, IsAlert_idx])
ax.set_title('V10 vs IsAlert - Trial: %s' % (trial_id,))
file_name = '/t%s.png' % (trial_id,)
fig.savefig(path + '/' + file_name)
