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cutoff to factor for pause at end of trial applied

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This commit is contained in:
Carolin Sachgau 2018-11-22 11:59:52 +01:00
parent ccd7623421
commit 625ab29dfd
4 changed files with 62 additions and 26 deletions
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44
analysis_graphs_new.py
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@ -21,14 +21,39 @@ cell_name = sys.argv[1].split('/')[-2]
# Open Nixio File
intervals_dict = open_nixio_new(sys.argv[1])
# Kernel Density estimator: gaussian fit
t = np.arange(-sigma*4, sigma*4, 1/sampling_rate)
fxn = np.exp(-0.5*(t/sigma)**2) / np.sqrt(2*np.pi) / sigma # gaussian function
for (repro, speed, direct, pos, comb) in intervals_dict:
spike_train = intervals_dict[speed, direct, pos, comb]
avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
# for (rep, speed, direct, pos, comb) in intervals_dict:
# spike_train = intervals_dict[rep, speed, direct, pos, comb]
# avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
# p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
#
# # Graphing
# fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)
#
# # Firing Rate Graph
# firing_times = np.arange(0, len(avg_convolve_spikes))
# ax1.plot((firing_times / sampling_rate), avg_convolve_spikes)
# ax1.set_title('Firing Rate of trial ' + str((speed, direct)) + ' comb = ' + str(comb) + '\n')
# ax1.set_xlabel('Time (s)')
# ax1.set_ylabel('Firing rate (Hz)')
#
# # Fourier Graph
# ax2.semilogy(freq[freq < 200], p[freq < 200])
# ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
# # ax2.axvline(x=max_four,linestyle='--', color='green')
#
# plt.savefig(('nonavg_' + '_' + str(rep) + '_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
# + '_' + str(comb) + '_' + str(direct) + '.png'))
# plt.close(fig)
for (rep, time, speed, direct, comb) in intervals_dict:
spike_train = intervals_dict[(rep, time, speed, direct, comb)]
avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate, time)
p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
# Graphing
@ -37,17 +62,16 @@ for (repro, speed, direct, pos, comb) in intervals_dict:
# Firing Rate Graph
firing_times = np.arange(0, len(avg_convolve_spikes))
ax1.plot((firing_times / sampling_rate), avg_convolve_spikes)
ax1.set_title('Firing Rate of trial ' + str((speed, pos)) + ' comb = ' + str(comb) + '\n')
ax1.set_title('Firing Rate of trial ' + str((speed, direct)) + ' comb = ' + str(comb) + '\n')
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Firing rate (Hz)')
# Fourier Graph
ax2.semilogy(freq[freq < 200], p[freq < 200])
ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
# ax2.axvline(x=max_four,linestyle='--', color='green')
plt.savefig(('nonavg_' + str(repro) +'_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
+ '_' + str(comb) + '_' + str(direct) + '.png'))
ax2.semilogy(freq[freq < 400], p[freq < 400])
ax2.axhline(y=(mn_four + std_four), xmin=0, xmax=1, linestyle='--', color='red')
plt.tight_layout()
plt.savefig((str(rep) + '_''avg_' + '_' + str(cell_name) + '_' + str(speed) + '_' + str(comb)
+ '_' + str(direct) + '.png'))
plt.close(fig)
# ---------------------------------------------------------------------------------------------------------------------

38
icr_analysis.py
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@ -23,21 +23,31 @@ def avgNestedLists(nested_vals):
return output
def gaussian_convolve(spike_train, fxn, sampling_rate):
def gaussian_convolve(all_spike_trains, fxn, sampling_rate, time):
"""
Takes an array of spike trains of different sizes,
convolves it with a gaussian, returns the average gaussian convolve spikes
"""
all_convolve_spikes = []
trial_length = int((spike_train[-1] - spike_train[0]) * sampling_rate)
spike_train = spike_train - spike_train[0] # changing spike train to start at 0 (subtracting baseline)
trial_time = np.arange(0, (trial_length + 1), 1)
trial_bool = np.zeros(len(trial_time))
# Boolean list in length of trial length, where 1 means spike happened, 0 means no spike
spike_indx = (spike_train * sampling_rate).astype(np.int)
trial_bool[spike_indx] = 1
# trial_bool = trial_bool[30000:(len(trial_bool)-30000)]
convolve_spikes = np.asarray(
[convolve(trial_bool, fxn, mode='valid')]) # convolve gaussian with boolean spike list
all_convolve_spikes.append(convolve_spikes[0, :])
avg_convolve_spikes = avgNestedLists(all_convolve_spikes)
for spike_train in all_spike_trains:
time_cutoff = time * sampling_rate
trial_length = int((spike_train[-1] - spike_train[0]) * sampling_rate)
spike_train = spike_train - spike_train[0] # changing spike train to start at 0 (subtracting baseline)
trial_time = np.arange(0, (trial_length + 1), 1)
trial_bool = np.zeros(len(trial_time))
#Boolean list in length of trial length, where 1 means spike happened, 0 means no spike
spike_indx = (spike_train * sampling_rate).astype(np.int)
trial_bool[spike_indx] = 1
# trial_bool = trial_bool[30000:(len(trial_bool)-30000)]
convolve_spikes = np.asarray([convolve(trial_bool, fxn, mode='valid')]) # convolve gaussian with boolean spike list
all_convolve_spikes.append(convolve_spikes[0, :][:time_cutoff])
#
# cutoff = min([len(i) for i in all_convolve_spikes])
# for ix, convolved in enumerate(all_convolve_spikes):
# all_convolve_spikes[ix] = all_convolve_spikes[ix][:cutoff]
#avg_convolve_spikes = avgNestedLists(all_convolve_spikes)
avg_convolve_spikes = np.mean(all_convolve_spikes, 0)
return avg_convolve_spikes

2
open_nixio.py
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@ -33,7 +33,7 @@ def open_nixio(nix_file, avg_opt):
curr_spikes = spikes[(spikes < comb_pos[idx + 1]) & (spikes > comb_pos[idx])]
if avg_opt == 'average':
intervals_dict[(curr_speed, curr_dir, curr_comb)].append((curr_pos, curr_spikes))
intervals_dict[(curr_speed, curr_dir, curr_comb)].append(curr_spikes)
else:
intervals_dict.update({(curr_speed, curr_dir, curr_pos, curr_comb): curr_spikes})

4
open_nixio_new.py
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@ -24,6 +24,7 @@ def open_nixio_new(nix_file):
if tag.name.startswith('Baseline'):
continue
curr_comb = tag.metadata["RePro-Info"]["settings"]["object"]
travel_dist = tag.metadata["RePro-Info"]["settings"]["traveldist"]
repro_pos, = tag.position
repro_ext, = tag.extent
idx_qry = np.logical_and(repro_pos < comb_pos, comb_pos < (repro_pos + repro_ext))
@ -34,9 +35,10 @@ def open_nixio_new(nix_file):
if idx == (len(comb_pos)-1):
break
curr_speed = feature_dict['speed'][idx]
travel_time = travel_dist/curr_speed
curr_pos = comb_pos[idx]
curr_dir = feature_dict['direction'][idx]
curr_spikes = spikes[(spikes < comb_pos[idx + 1]) & (spikes > comb_pos[idx])]
intervals_dict.update({(tag.name, curr_speed, curr_dir, curr_pos, curr_comb): curr_spikes})
intervals_dict[(tag.name, travel_time, curr_speed, curr_dir, curr_comb)].append(curr_spikes)
return intervals_dict