ag_tallies[j, :] = trial['ag_tallies']
fd_tallies[j, :] = trial['fd_tallies']
samples = 5.0
van_tallies = van_tallies / samples
van_tallies[:, 2] = 1.0 - van_tallies[:, 0] - van_tallies[:, 1]
rand_tallies = rand_tallies / samples
rand_tallies[:, 2] = 1.0 - rand_tallies[:, 0] - rand_tallies[:, 1]
es_tallies = es_tallies / samples
es_tallies[:, 2] = 1.0 - es_tallies[:, 0] - es_tallies[:, 1]
ag_tallies = ag_tallies / samples
ag_tallies[:, 2] = 1.0 - ag_tallies[:, 0] - ag_tallies[:, 1]
fd_tallies = fd_tallies / samples
fd_tallies[:, 2] = 1.0 - fd_tallies[:, 0] - fd_tallies[:, 1]
van_means, van_sems = statistics.mean_sem(van_tallies)
rand_means, rand_sems = statistics.mean_sem(rand_tallies)
es_means, es_sems = statistics.mean_sem(es_tallies)
ag_means, ag_sems = statistics.mean_sem(ag_tallies)
fd_means, fd_sems = statistics.mean_sem(fd_tallies)
print "Van: " + str(np.mean(van_tallies, axis=0))
print "Early: " + str(np.mean(es_tallies, axis=0))
print "Rand: " + str(np.mean(rand_tallies, axis=0))
print "AG: " + str(np.mean(ag_tallies, axis=0))
print "FD: " + str(np.mean(fd_tallies, axis=0))
print np.sum(fd_means), np.sum(ag_means), np.sum(es_means), np.sum(
van_means), np.sum(rand_means)
plt.style.use('ggplot')
rec_scores = pickle.load(open(data_dir + 'multiple_trials/rec_scores.pkl', 'r')) rec_cutoffs = pickle.load(open(data_dir + 'multiple_trials/rec_cutoffs.pkl', 'r')) rec_mo_scores = pickle.load(open(data_dir + 'multiple_trials/rec_mo_scores.pkl', 'r')) rec_mo_cutoffs = pickle.load(open(data_dir + 'multiple_trials/rec_mo_cutoffs.pkl', 'r')) print "Averaging over " + str(len(all_trials)) + " trials" rec_scores = rec_scores[:] rec_cutoffs = rec_cutoffs[:] rec_mo_scores = rec_mo_scores[:] rec_mo_cutoffs = rec_mo_cutoffs[:] els = 150 data = (np.array(rec_scores) / np.array(rec_cutoffs))[-100: -50] data = np.clip(data, 0, 1) mean, sem = statistics.mean_sem(data) data_mo = (np.array(rec_mo_scores) / np.array(rec_mo_cutoffs))[-100:-50] data_mo = np.clip(data_mo, 0, 1) mean, sem = statistics.mean_sem(data_mo) datas = [data[:, :]] datas_mo = [data_mo[:, :]] labels = ['DFO'] colors = ['blue'] mean = np.mean(data[:, :els], axis=0) std = scipy.stats.sem(data[:, :els], axis=0) mean_mo = np.mean(data_mo[:, :els], axis=0) std_mo = scipy.stats.sem(data_mo[:, :els], axis=0) print mean.shape x = np.arange(mean.shape[0])
es_samples.append(trial['es_tallies'])
van_tallies.append(np.mean(van_samples, axis=0))
rand_tallies.append(np.mean(rand_samples, axis=0))
es_tallies.append(np.mean(es_samples, axis=0))
samples = 1.0
van_tallies = np.array(van_tallies) / samples
rand_tallies = np.array(rand_tallies) / samples
es_tallies = np.array(es_tallies) / samples
van_tallies[:, -1] = 1 - van_tallies[:, 0] - van_tallies[:, 1]
rand_tallies[:, -1] = 1 - rand_tallies[:, 0] - rand_tallies[:, 1]
es_tallies[:, -1] = 1 - es_tallies[:, 0] - es_tallies[:, 1]
van_means, van_sems = statistics.mean_sem(van_tallies)
rand_means, rand_sems = statistics.mean_sem(rand_tallies)
es_means, es_sems = statistics.mean_sem(es_tallies)
# van_means, van_sems = van_means[::-1], van_sems[::-1]
# rand_means, rand_sems = rand_means[::-1], rand_sems[::-1]
# es_means, es_sems = es_means[::-1], es_sems[::-1]
all_van_means.append(van_means)
all_van_sems.append(van_sems)
all_rand_means.append(rand_means)
all_rand_sems.append(rand_sems)
all_es_means.append(es_means)
all_es_sems.append(es_sems)
