d_real = disc(real_data)
d_fake = disc(fake_data)
disc_loss = F.relu(1 - d_real).mean() + F.relu(1 + d_fake).mean()
if disc_loss > 0:
disc_loss.backward(torch.ones(1))
optim_disc.step()
print(i,
disc_loss.cpu().detach().numpy(),
"{0:.2f}s".format(time() - tic))
tic = time()
if i % 100 == 0:
gen.eval()
for j in range(1):
a, b, c = gen(z_test)
a = a.cpu().detach().numpy().transpose(0, 2, 1).reshape(
-1, 3, 2).transpose(0, 2, 1).astype(np.float64)
b = b.cpu().detach().numpy().transpose(0, 2, 1).reshape(
-1, 3, 2).transpose(0, 2, 1).astype(np.float64)
c = c.cpu().detach().numpy().transpose(0, 2, 1).reshape(
-1, 3, 2).transpose(0, 2, 1).astype(np.float64)
raster([[a, b, c]])
plt.savefig("outs/test{:010d}.png".format(i))
plt.close()
gen.train()
d_fake = disc(fake_data[0], fake_data[1], fake_data[2])
d_real = disc(a, pad(b, a), pad(c, a))
disc_loss = F.relu(1 - d_real).mean() + F.relu(1 + d_fake).mean()
if disc_loss > 0:
disc_loss.backward(torch.ones(1))
optim_disc.step()
print(i,
disc_loss.cpu().detach().numpy(),
"{0:.2f}s".format(time() - tic))
tic = time()
if i % 100 == 0:
gen.eval()
for j in range(1):
a, b, c = gen.forward([1, latent_dim, num_curves], z_test)
a = a.cpu().detach().numpy().transpose(0, 2, 1).reshape(
-1, 3, 2).transpose(0, 2, 1).astype(np.float64)
b = b.cpu().detach().numpy().transpose(0, 2, 1).reshape(
-1, 3, 2).transpose(0, 2, 1).astype(np.float64)
#c = c.cpu().detach().numpy().transpose(0, 2, 1).reshape(
# -1, 3, 2).transpose(0, 2, 1).astype(np.float64)
raster([[a, b]]) #, c]])
plt.savefig("outs/test{:010d}.png".format(i))
plt.close()
gen.train()
def fr_for_units_by_led(location):
base, sess = os.path.split(location)
with open(os.path.join(location, 'spike_and_trials.pickle'), 'rb') as f:
data = pickle.load(f)
stepname, durations = get_LED_stepname(sess)
framechan, shift = get_frame_channel(sess)
LEDchan = get_LED_channel(sess)
trial_numbers = numpy.asarray(data['trial_records']['trial_number'])
step_names = numpy.asarray(data['trial_records']['step_name'])
contrasts = numpy.asarray(data['trial_records']['contrast'])
max_durations = numpy.asarray(data['trial_records']['max_duration'])
phases = numpy.asarray(data['trial_records']['phase'])
orientations = numpy.asarray(data['trial_records']['orientation'])
led_status = numpy.multiply(
numpy.asarray(data['trial_records']['led_on']),
numpy.nan_to_num(numpy.asarray(
data['trial_records']['led_intensity'])))
which_step = step_names == stepname
trial_numbers = trial_numbers[which_step]
step_names = step_names[which_step]
contrasts = contrasts[which_step]
max_durations = max_durations[which_step]
phases = phases[which_step]
orientations = orientations[which_step]
led_status = led_status[which_step]
frame_start_index = []
frame_end_index = []
for trial_number in trial_numbers:
event_for_trial = data['trial_records']['events']['ttl_events'][
trial_number]
frame_start_index.append(event_for_trial[LEDchan]['rising'][0])
frame_end_index.append(event_for_trial[LEDchan]['falling'][0])
frame_start_index = numpy.asarray(frame_start_index)
frame_end_index = numpy.asarray(frame_end_index)
unit_responses_that_session = {}
unit_responses_that_session['session'] = sess
unit_responses_that_session['subject'] = get_subject_from_session(sess)
for i, unit in enumerate(data['spike_records']['units']):
if not unit['manual_quality'] in ['good', 'mua']: continue
unit_details = {}
unit_details['uid'] = get_unit_id(sess, unit['shank_no'],
unit['cluster_id'])
unit_details['manual_quality'] = unit['manual_quality']
unit_details['unit_depth'] = get_unit_depth(sess, unit['y_loc'])
unit_details['unit_depth'] = get_unit_dist_to_LED(sess, unit['y_loc'])
spike_time = numpy.squeeze(numpy.asarray(unit['spike_time']))
spike_raster = {}
spike_raster_expanded = {} # t-100ms to t+500 ms
for trial_number in trial_numbers:
frame_start_time = frame_start_index[trial_numbers ==
trial_number][0] / 30000
frame_end_time = frame_end_index[trial_numbers ==
trial_number][0] / 30000
spike_raster[trial_number] = spike_time[numpy.bitwise_and(
spike_time > frame_start_time,
spike_time < frame_end_time)] - frame_start_time
spike_raster_expanded[trial_number] = spike_time[numpy.bitwise_and(
spike_time > (frame_start_time - 0.5), spike_time <
(frame_end_time + 1))] - frame_start_time
unit_details['spike_raster'] = spike_raster
unit_details['spike_raster_expanded'] = spike_raster_expanded
# plot by LED
LED_OFFs = led_status == 0
trial_led_off = trial_numbers[LED_OFFs]
trial_led_on = trial_numbers[numpy.bitwise_not(LED_OFFs)]
events_led_off = []
events_led_on = []
for tr in trial_led_off:
events_led_off.append(spike_raster_expanded[tr])
for tr in trial_led_on:
events_led_on.append(spike_raster_expanded[tr])
# pdb.set_trace()
# isi = numpy.diff(unit['spike_time'],axis=0)
# histc, binedge = numpy.histogram(isi,range=(0,0.1),bins=100)
# fig = plt.figure(figsize=(3,3),dpi=200,facecolor='none',edgecolor='none')
# plt.clf()
# plt.bar(binedge[0:100]*1000,histc,align='edge',edgecolor='none',color='k')
# plt.show()
fig = plt.figure(figsize=(3, 3),
dpi=200,
facecolor='none',
edgecolor='none')
plt.clf()
ax_off = plt.subplot(211)
raster(events_led_off)
ax_on = plt.subplot(212)
raster(events_led_on, color='b')
plt.show()
print('done_unit')
unit_responses_that_session[i] = unit_details
print('done session')
