Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio

code/plot_eodform_spikehist.py

@@ -20,9 +20,18 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
 # calculate interpike intervals and plot them
 interspikeintervals = np.diff(spikes)
 
-fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
-plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
-plt.show()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
+plt.xlabel("time [ms]", fontsize = 22)
+plt.xticks(fontsize = 18)
+plt.ylabel("number of \n interspikeintervals", fontsize = 22)
+plt.yticks(fontsize = 18)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+fig.tight_layout()
+#plt.show()
+plt.savefig('isis.pdf')
+
 
 # calculate coefficient of variation
 mu = np.mean(interspikeintervals)
@@ -64,21 +73,23 @@ std_eod = np.nanstd(eod_cuts, axis=0)*3
 time_axis = np.arange(max_cut)/sampling_rate*1000
 
 # plot eod form and spike histogram
-fig, ax1 = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
-ax1.hist(spike_times, color='crimson')
-ax1.set_xlabel('time [ms]', fontsize=12)
-ax1.set_ylabel('number', fontsize=12)
-ax1.tick_params(axis='y', labelcolor='crimson')
-plt.yticks(fontsize=8)
+fig, ax1 = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+ax1.hist(spike_times, color='firebrick')
+ax1.set_xlabel('time [ms]', fontsize=22)
+ax1.set_ylabel('number', fontsize=22)
+ax1.tick_params(axis='y', labelcolor='firebrick')
+plt.xticks(fontsize=18)
+plt.yticks(fontsize=18)
 ax1.spines['top'].set_visible(False)
 
 ax2 = ax1.twinx()
-ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='dodgerblue', alpha=0.5)
+ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
 ax2.plot(time_axis, mu_eod, color='black', lw=2)
-ax2.set_ylabel('voltage [mV]', fontsize=12)
-ax2.tick_params(axis='y', labelcolor='dodgerblue')
+ax2.set_ylabel('voltage [mV]', fontsize=22)
+ax2.tick_params(axis='y', labelcolor='darkblue')
+ax2.spines['top'].set_visible(False)
 
-plt.xticks(fontsize=8)
-plt.yticks(fontsize=8)
+plt.yticks(fontsize=18)
 fig.tight_layout()
-plt.show()
+#plt.show()
+plt.savefig('eodform_spikehist.pdf')

code/plot_spikesduringbaselineactivity.py

@@ -15,11 +15,19 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
 # calculate interpike intervals and plot them
 interspikeintervals = np.diff(spikes)*1000
 
-fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
-plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1))
-ax.set_xlabel('time [ms]', fontsize=14)
-ax.set_ylabel('number of interspikeintervals', fontsize=14)
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
+plt.xlabel("time [ms]", fontsize = 22)
+plt.xticks(fontsize = 18)
+plt.ylabel("Number of \n Interspikeinterval", fontsize = 22)
+plt.yticks(fontsize = 18)
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 fig.tight_layout()
-plt.show()
+plt.show()
+#plt.savefig('isis.pdf')
+
+
+
+
+

code/repetition_firingrate.py

@@ -0,0 +1,102 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats as ss
+from read_chirp_data import *
+from utility import *
+from IPython import embed
+
+# define sampling rate and data path
+sampling_rate = 40 #kHz
+data_dir = "../data"
+dataset = "2018-11-09-ad-invivo-1"
+inch_factor = 2.54
+# parameters for binning, smoothing and plotting
+cut_window = 60
+chirp_size = 14 #ms
+neuronal_delay = 5 #ms
+chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
+chirp_end = int((chirp_size/2+neuronal_delay+cut_window+1)*sampling_rate)
+num_bin = 12
+window = 1 #ms
+time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
+spike_bins = np.arange(-cut_window, cut_window+1) #ms
+
+# read data from files
+spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+eod = read_chirp_eod(os.path.join(data_dir, dataset))
+chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
+
+# make a delta f map for the quite more complicated keys
+df_map = map_keys(spikes)
+
+# differentiate between phases
+phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
+cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1)
+
+# make dictionaries for spiketimes
+df_phase_time = {}
+df_phase_binary = {}
+
+# iterate over delta f, repetition, phases and a single chirp
+for deltaf in df_map.keys():
+    df_phase_time[deltaf] = {}
+    df_phase_binary[deltaf] = {}
+    for rep in df_map[deltaf]:
+        for phase in spikes[rep]:
+            for idx in np.arange(num_bin):
+                # check the phase
+                if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
+
+                    # get spikes between 50 ms befor and after the chirp
+                    spikes_to_cut = np.asarray(spikes[rep][phase])
+                    spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
+                    spikes_idx = np.round(spikes_cut*sampling_rate)
+                    # also save as binary, 0 no spike, 1 spike
+                    binary_spikes = np.isin(cut_range, spikes_idx)*1
+
+                    # add the spikes to the dictionaries with the correct df and phase
+                    if idx in df_phase_time[deltaf].keys():
+                        df_phase_time[deltaf][idx].append(spikes_cut)
+                        df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
+                    else:
+                        df_phase_time[deltaf][idx] = [spikes_cut]
+                        df_phase_binary[deltaf][idx] = binary_spikes
+
+
+# for plotting and calculating iterate over delta f and phases
+for df in df_phase_time.keys():
+    for index_phase, phase in enumerate(df_phase_time[df].keys()):
+
+        # plot
+        plot_trials = df_phase_time[df][phase]
+        plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
+
+        # calculation
+        #overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
+
+        smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
+
+        fig, ax = plt.subplots(2, 1, sharex=True, figsize=(20/inch_factor, 15/inch_factor))
+        for i, trial in enumerate(plot_trials):
+            ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
+
+        ax[1].plot(time_axis, smoothed_spikes*1000, color='royalblue', lw = 2)
+
+
+
+        ax[0].set_title('df = %s Hz' %(df))
+        ax[0].set_ylabel('repetition', fontsize=22)
+        ax[0].yaxis.set_label_coords(-0.1, 0.5)
+        ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2))
+        ax[0].set_yticklabels(np.arange(1, len(plot_trials)+1,2), fontsize=18)
+
+        ax[1].set_xlabel('time [ms]', fontsize=22)
+        ax[1].yaxis.set_label_coords(-0.1, 0.5)
+        ax[1].set_ylabel('firing rate [Hz]', fontsize=22)
+        plt.xticks(fontsize=18)
+        plt.yticks(fontsize=18)
+        fig.tight_layout()
+        #plt.show()
+        #exit()
+        namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
+        plt.savefig(namefigure)