from __future__ import division
import numpy as np
from src.data_interface import trd
suc = 0
for t in trd.trial_id_list:
v = trd.get_trial(t).IsAlert.view()
if v.min() == v.max() == 0:
suc += 1
print suc/len(trd.trial_id_list)
import numpy as np
import matplotlib.pyplot as plt
from src.utils2 import get_path
from src.data_interface import trd, L
path = get_path(__file__) + '/..'
for fname in L[2:]:
for tid in trd.trial_id_list:
v = trd.get_trial(tid).get_feature(fname).view()
plt.plot(range(len(v)), v, 'b-', alpha=0.1)
ax = plt.gca()
ax.set_xlim(0,1200)
ax.set_xlabel('Observation number')
ax.set_ylabel(fname)
ax.set_title('{0} in 500 trials overlayed'.format(fname))
plot_path = '{0}/plots/naive_{1}.'.format(path, fname)
plt.savefig(plot_path + 'png', format='png', dpi=300)
plt.savefig(plot_path + 'pdf', format='pdf')
plt.cla()
import matplotlib.pyplot as plt
import numpy as np
from src.data_interface import trd
from src.utils2 import get_path
path = get_path(__file__) + '/..'
trials = trd.trial_id_list
means = [trd.get_trial(i).get_feature('V1').view().mean() for i in trials]
plt.title('Mean pr. trial of feature V1')
plt.plot(range(len(trials)), means)
plt.gca().set_xticklabels(trials)
plt.savefig('{0}/plots/mean_pr_trial_v1.pdf'.format(path), format="pdf")
plt.cla()
from json import dump
import numpy as np
from src.data_interface import trd, L
from src.utils import get_path
sess_root = get_path(__file__) + '/..'
features_to_calculate = L[2:]
trials = list(trd.trial_id_list)
calculations = {}
for feature_name in features_to_calculate:
tmp = {"trial_results": {}}
for trial_id in trials:
unique_values = np.unique(
trd.get_trial(trial_id).get_feature(feature_name).view())
tmp["trial_results"][trial_id] = unique_values.size
tmp["max"] = max(tmp["trial_results"].values())
tmp["min"] = min(tmp["trial_results"].values())
calculations[feature_name] = tmp
f = open(sess_root + '/src/unique_values_pr_trial.json', 'w')
dump(calculations, f, indent=4)
f.close()
