def ANCOVA_for_attractive_saccades(dataset):
sac.calc_saccade_stats(dataset)
black_saccade_angle = []
black_angle_prior = []
checkered_saccade_angle = []
checkered_angle_prior = []
keys = dataset.keys_saccade_array
evasive_threshold = 0.836248003234
for i, key in enumerate(keys):
trajec = dataset.trajecs[key]
a = dataset.angle_of_saccade_array[i]
ap = dataset.angle_prior_array[i]
if 1:#np.abs(a-ap) < evasive_threshold:
if 'black' in trajec.post_type:
black_saccade_angle.append(a)
black_angle_prior.append(ap)
elif 'checkered' in trajec.post_type:
checkered_saccade_angle.append(a)
checkered_angle_prior.append(ap)
black_saccade_angle = np.array(black_saccade_angle)
black_angle_prior = np.array(black_angle_prior)
checkered_saccade_angle = np.array(checkered_saccade_angle)
checkered_angle_prior = np.array(checkered_angle_prior)
groups = [[black_angle_prior, black_saccade_angle], [checkered_angle_prior, checkered_saccade_angle]]
floris.ANCOVA(groups)
def t_test_for_evasive_saccades(dataset):
sac.calc_saccade_stats(dataset)
black_saccade_angle = []
checkered_saccade_angle = []
keys = dataset.keys_saccade_array
evasive_threshold = 0.836248003234
for i, key in enumerate(keys):
trajec = dataset.trajecs[key]
a = dataset.angle_of_saccade_array[i]
ap = dataset.angle_prior_array[i]
if np.abs(a-ap) > evasive_threshold:
if 'black' in trajec.post_type:
black_saccade_angle.append(dataset.angle_subtended_array[i])
elif 'checkered' in trajec.post_type:
checkered_saccade_angle.append(dataset.angle_subtended_array[i])
black_saccade_angle = np.array(black_saccade_angle)
checkered_saccade_angle = np.array(checkered_saccade_angle)
if 1:
fig = plt.figure()
ax = fig.add_subplot(111)
bins = np.linspace( np.log(5*np.pi/180.), np.log(180*np.pi/180.), 20)
bins, hists, curves = floris.histogram(ax, [np.log(black_saccade_angle), np.log(checkered_saccade_angle)], bins=bins, colors=['black', 'teal'], return_vals=True, normed_occurences=True, curve_line_alpha=0)
deg_ticks = np.array([5, 10, 30, 60, 90, 180])
deg_tick_strings = [str(d) for d in deg_ticks]
rad_ticks = deg_ticks*np.pi/180.
rad_ticks_log = np.log(rad_ticks)
dist_tick_strings = ['(21)', '(10)', '(2.7)', '(0.9)', '(0.4)', '(0)']
x_tick_strings = []
for i, d in enumerate(dist_tick_strings):
x_tick_strings.append( deg_tick_strings[i] + '\n' + dist_tick_strings[i] )
ax.set_xlim(rad_ticks_log[0], rad_ticks_log[-1])
ax.set_ylim(0,1)
fa.adjust_spines(ax,['left', 'bottom'])
ax.set_xlabel('Retinal size, deg\n(distance, cm)', labelpad=10)
ax.set_ylabel('Occurances')
ax.set_xticks(rad_ticks_log.tolist())
ax.set_xticklabels(x_tick_strings)
fig.subplots_adjust(bottom=0.25, top=0.9, right=0.9, left=0.2)
filename = 'saccade_histogram_black_vs_checkered' + '.pdf'
fig.savefig(filename, format='pdf')
print 't test: ', scipy.stats.ttest_ind(black_saccade_angle, checkered_saccade_angle)
print 'mann whitney u: ', scipy.stats.mannwhitneyu(black_saccade_angle, checkered_saccade_angle)
print 'black: ', np.mean(black_saccade_angle), 'n: ', len(black_saccade_angle)
print 'checkered: ', np.mean(checkered_saccade_angle), 'n: ', len(checkered_saccade_angle)
print 'total n - 2: ', len(black_saccade_angle) + len(checkered_saccade_angle) - 2
