import analysis_utils as a
if __name__ == "__main__":
path_to_files = '/Volumes/Plata1/Shared/Ariel/fiorentini_data/'
file_name = sys.argv[1]
p, l, data_rec = a.get_data('%s/%s' % (path_to_files, file_name))
fix_idx = np.where(data_rec['task'] == 'fixation')
per_idx = np.where(data_rec['task'] == 'periphery')
fix_idx = np.where(data_rec['task'] == 'fixation')
c = data_rec['correct']
amp = data_rec['contrast']
th_per = a.analyze(amp[per_idx], c[per_idx])
th_fix = a.analyze(amp[fix_idx], c[fix_idx])
x, y = th_per[4], th_per[5]
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.plot(x, y, 'o')
x_for_plot = np.linspace(np.min(x), np.max(x), 100)
ax.plot(x_for_plot, a.weibull(x_for_plot, th_per[0], th_per[3]))
fig.suptitle('Periphery task:thresh=%1.2f, slope=%1.2f' %
(th_per[0], th_per[3]))
staircase = amp[per_idx]
correct_this_block = correct_this_run[block_type == i]
if i == 'A':
if p['task'] == ' Annulus ':
contrast_this_block = contrast_this_block - p[' annulus_contrast']
for n in range(trials_per_condition):
contrast_A[n+(trials_per_condition*file_idx)] *= contrast_this_block[n]
correct_A[n+(trials_per_condition*file_idx)] *= correct_this_block[n]
#print contrast_this_block, correct_this_block
block_file_stem = file_stem + '_' + labelit[idx_block]
fig_name_A = 'data/analyzed_data/%s.png'%(block_file_stem)
else:
contrast_this_block = contrast_this_block[p[' trials_per_dummy']:]
correct_this_block = correct_this_block[p[' trials_per_dummy']:]
for n in range(trials_per_condition):
contrast_B[n+(trials_per_condition*file_idx)] *= contrast_this_block[n]
correct_B[n+(trials_per_condition*file_idx)] *= correct_this_block[n]
block_file_stem = file_stem + '_' + labelit[idx_block]
fig_name_B = 'data/analyzed_data/%s.png'%(block_file_stem)
#fig_name = 'data/analyzed_data/%s.png'%(file_stem)
th,lower,upper = ana.analyze(contrast_A, correct_A, guess, flake, slope, fig_name_A)
print "Threshold estimate: %s, CI: [%s,%s]"%(th, lower, upper)
th,lower,upper = ana.analyze(contrast_B, correct_B, guess, flake, slope, fig_name_B)
print "Threshold estimate: %s, CI: [%s,%s]"%(th, lower, upper)
correct_this_run = data_rec['correct']
block_type = data_rec['block_type']
print p[' trials_per_dummy']
for n in range(trials_per_condition):
if n >= p[' trials_per_dummy']:
contrast[n+(trials_per_condition*file_idx)] *= contrast_this_run[n]
correct[n+(trials_per_condition*file_idx)] *= correct_this_run[n]
if not os.path.exists('data/analyzed_data'):
os.mkdir('data/analyzed_data')
labelit = ['annulus_on','annulus_off']
for idx_block,i in enumerate(['A','B']):
contrast_this_block = contrast_this_run[block_type == i]
correct_this_block = correct_this_run[block_type == i]
if i == 'A':
if p['task'] == ' Annulus ':
contrast_this_block = contrast_this_block - p[' annulus_contrast']
else:
print i
contrast_this_block = contrast_this_block[p[' trials_per_dummy']:]
correct_this_block = correct_this_block[p[' trials_per_dummy']:]
block_file_stem = file_stem + '_' + labelit[idx_block]
fig_name = 'data/analyzed_data/%s.png'%(block_file_stem)
th,lower,upper = ana.analyze(contrast_this_block, correct_this_block, guess, flake, slope, fig_name)
print "Threshold estimate: %s, CI: [%s,%s]"%(th, lower, upper)
import analysis_utils as a
if __name__ == "__main__":
path_to_files = "/Volumes/Plata1/Shared/Ariel/fiorentini_data/"
file_name = sys.argv[1]
p, l, data_rec = a.get_data("%s/%s" % (path_to_files, file_name))
fix_idx = np.where(data_rec["task"] == "fixation")
per_idx = np.where(data_rec["task"] == "periphery")
fix_idx = np.where(data_rec["task"] == "fixation")
c = data_rec["correct"]
amp = data_rec["contrast"]
th_per = a.analyze(amp[per_idx], c[per_idx])
th_fix = a.analyze(amp[fix_idx], c[fix_idx])
x, y = th_per[4], th_per[5]
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.plot(x, y, "o")
x_for_plot = np.linspace(np.min(x), np.max(x), 100)
ax.plot(x_for_plot, a.weibull(x_for_plot, th_per[0], th_per[3]))
fig.suptitle("Periphery task:thresh=%1.2f, slope=%1.2f" % (th_per[0], th_per[3]))
staircase = amp[per_idx]
ax = fig.add_subplot(1, 2, 2)