diff --git a/analysis_graphs_new.py b/analysis_graphs_new.py
index 631d5d8..9498822 100644
--- a/analysis_graphs_new.py
+++ b/analysis_graphs_new.py
@@ -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)
# ---------------------------------------------------------------------------------------------------------------------
diff --git a/icr_analysis.py b/icr_analysis.py
index 3d061ab..940063c 100644
--- a/icr_analysis.py
+++ b/icr_analysis.py
@@ -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
diff --git a/open_nixio.py b/open_nixio.py
index 1d92f14..e213f34 100644
--- a/open_nixio.py
+++ b/open_nixio.py
@@ -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})
diff --git a/open_nixio_new.py b/open_nixio_new.py
index 6e0c328..05dc896 100644
--- a/open_nixio_new.py
+++ b/open_nixio_new.py
@@ -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
\ No newline at end of file