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

code/plot_eodform_spikehist.py

@@ -7,31 +7,45 @@ from IPython import embed
 
 # plot and data values
 inch_factor = 2.54
+sampling_rate = 40000
 data_dir = '../data'
-#dataset = '2018-11-09-ad-invivo-1'
+dataset = '2018-11-09-ad-invivo-1'
-dataset = '2018-11-14-al-invivo-1'
+#dataset = '2018-11-13-aa-invivo-1'
 
 # read eod and time of baseline
 time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
 
+
+
+eod_norm = eod - np.mean(eod)
+
+# calculate eod times and indices by zero crossings
+threshold = 0
+shift_eod = np.roll(eod_norm, 1)
+eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
+
+eod_duration = eod_times[2]- eod_times[1] #time in s
+
+eod_duration = eod_times[2]- eod_times[1]
+
+
+
 # read spikes during baseline activity
-spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
+spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
 # calculate interpike intervals and plot them
-interspikeintervals = np.diff(spikes)
+interspikeintervals = np.diff(spikes)/eod_duration
 
 fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
-plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='royalblue')
-plt.xlabel("time [ms]", fontsize = 22)
+plt.xlabel("eod cycles", 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.show()
-plt.show()
 #plt.savefig('isis.pdf')
-exit()
 plt.savefig('isis.png')
 
 
@@ -85,10 +99,10 @@ 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='navy', 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=22)
-ax2.tick_params(axis='y', labelcolor='navy')
+ax2.tick_params(axis='y', labelcolor='royalblue')
 ax2.spines['top'].set_visible(False)
 
 plt.yticks(fontsize=18)

code/repetition_firingrate.py

@@ -54,16 +54,17 @@ for deltaf in df_map.keys():
                     # get spikes between 60 ms before 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_raster = spikes_to_cut[(spikes_to_cut > -cut_window+5) & (spikes_to_cut < cut_window-5)]
                     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_time[deltaf][idx].append(spikes_raster)
                         df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
                     else:
-                        df_phase_time[deltaf][idx] = [spikes_cut]
+                        df_phase_time[deltaf][idx] = [spikes_raster]
                         df_phase_binary[deltaf][idx] = binary_spikes
 
 
@@ -80,15 +81,13 @@ for df in df_phase_time.keys():
 
         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))
+        fig, ax = plt.subplots(2, 1, sharex=True, figsize=(18/inch_factor, 13/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[1].plot(time_axis[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='royalblue', lw = 2)
 
-
+        ax[0].set_title('df = %s Hz' %(df), fontsize = 18)
-
-        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))

code/response_beat.py

@@ -13,7 +13,7 @@ cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
 window = 1
 
 # norm: -150, 150, 300  aa, #ac, aj??
-data = ["2018-11-13-al-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
+data = ["2018-11-13-aa-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
         #"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
 
 '''
@@ -67,10 +67,10 @@ for dataset in data:
                 binary_spikes = np.isin(cut_range, spikes_idx) * 1
                 smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
                 train = smoothed_data[window:beat_window+window]
-                norm_train = train*1000#/spikerate
+                norm_train = train*1000/spikerate
                 rep_rates.append(np.std(norm_train))#/spikerate)
                 break
-        df_rate = np.median(rep_rates)/spikerate
+        df_rate = np.mean(rep_rates)
         #embed()
         #exit()
         if df in rates.keys():

code/spikes_analysis.py

@@ -8,7 +8,8 @@ from IPython import embed
 # define sampling rate and data path
 sampling_rate = 40 #kHz
 data_dir = "../data"
-#dataset = "2018-11-13-al-invivo-1"
+dataset = "2018-11-13-ah-invivo-1"
+
 '''
 data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",
     "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
@@ -18,9 +19,10 @@ data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-inviv
     "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1",
     "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
     "2018-11-20-ai-invivo-1"]
-'''
+
 data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
     "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
+'''
 
 # parameters for binning, smoothing and plotting
 cut_window = 20
@@ -53,147 +55,152 @@ df_phase_binary = {}
 #embed()
 #exit()
 
-for dataset in data:
+#for dataset in data:
-    spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
-    df_map = map_keys(spikes)
+df_map = map_keys(spikes)
-    print(dataset)
+print(dataset)
-    # iterate over delta f, repetition, phases and a single chirp
+# iterate over delta f, repetition, phases and a single chirp
-    for deltaf in df_map.keys():
+for deltaf in df_map.keys():
-        df_phase_time[deltaf] = {}
+    df_phase_time[deltaf] = {}
-        df_phase_binary[deltaf] = {}
+    df_phase_binary[deltaf] = {}
-        for rep in df_map[deltaf]:
+    for rep in df_map[deltaf]:
-            chirp_size = int(rep[-1].strip('Hz'))
+        chirp_size = int(rep[-1].strip('Hz'))
-            #print(chirp_size)
+        #print(chirp_size)
-            if chirp_size == 150:
+        if chirp_size == 150:
-                continue
+            continue
-            for phase in spikes[rep]:
+        for phase in spikes[rep]:
-                for idx in np.arange(number_bins):
+            for idx in np.arange(number_bins):
-                    # check the phase
+                # check the phase
-                    if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
+                if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
-
+
-                        # get spikes between 40 ms before and after the chirp
+                    # get spikes between 40 ms before and after the chirp
-                        spikes_to_cut = np.asarray(spikes[rep][phase])
+                    spikes_to_cut = np.asarray(spikes[rep][phase])
-                        spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
+                    spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
-                        spikes_idx = np.round(spikes_cut*sampling_rate)
+                    spikes_idx = np.round(spikes_cut*sampling_rate)
-                        # also save as binary, 0 no spike, 1 spike
+                    # also save as binary, 0 no spike, 1 spike
-                        binary_spikes = np.isin(cut_range, spikes_idx)*1
+                    binary_spikes = np.isin(cut_range, spikes_idx)*1
-
+
-                        # add the spikes to the dictionaries with the correct df and phase
+                    # add the spikes to the dictionaries with the correct df and phase
-                        if idx in df_phase_time[deltaf].keys():
+                    if idx in df_phase_time[deltaf].keys():
-                            df_phase_time[deltaf][idx].append(spikes_cut)
+                        df_phase_time[deltaf][idx].append(spikes_cut)
-                            df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
+                        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
-
-
-    # make dictionaries for csi and beat
-    csi_trains = {}
-    csi_rates = {}
-    beat = {}
-    # for plotting and calculating iterate over delta f and phases
-    for df in df_phase_time.keys():
-        csi_trains[df] = []
-        csi_rates[df] = []
-        beat[df] = []
-        beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
-        beat_window = 0
-        # beat window is at most 20 ms long, multiples of beat_duration
-        while beat_window+beat_duration <= cut_window*sampling_rate:
-            beat_window = beat_window+beat_duration
-        for phase in df_phase_time[df].keys():
-
-            # csi calculation
-            # trains for synchrony and rate
-            trials_binary = df_phase_binary[df][phase]
-
-            train_chirp = []
-            train_beat = []
-            #csi_spikerate = []
-            for i, trial in enumerate(trials_binary):
-                smoothed_trial = smooth(trial, window, 1/sampling_rate)
-                train_chirp.append(smoothed_trial[chirp_start:chirp_end])
-                train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
-                #std_chirp = np.std(smoothed_trial[chirp_start:chirp_end])
-                #std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start])
-                #csi = (std_chirp - std_beat)/(std_chirp + std_beat)
-                #csi_spikerate.append(csi)
-
-            std_chirp = np.std(np.mean(train_chirp, axis=0))
-            std_beat = np.std(np.mean(train_beat, axis=0))
-            beat[df].append(std_beat)
-            csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
-
-            rcs = []
-            rbs = []
-            for i, train in enumerate(train_chirp):
-                for j, train2 in enumerate(train_chirp):
-                    if i >= j:
-                        continue
                     else:
-                        rc, _ = ss.pearsonr(train, train2)
+                        df_phase_time[deltaf][idx] = [spikes_cut]
-                        rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
+                        df_phase_binary[deltaf][idx] = binary_spikes
-                        rcs.append(rc)
+
-                        rbs.append(rb)
+
-
+# make dictionaries for csi and beat
-            r_train_chirp = np.mean(rcs)
+csi_trains = {}
-            r_train_beat = np.mean(rbs)
+csi_rates = {}
-
+beat = {}
-            csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
+# for plotting and calculating iterate over delta f and phases
-
+for df in df_phase_time.keys():
-            # add the csi to the dictionaries with the correct df and phase
+    csi_trains[df] = []
-            csi_trains[df].append(csi_train)
+    csi_rates[df] = []
-            csi_rates[df].append(np.mean(csi_spikerate))
+    beat[df] = []
-
+    beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
-            '''
+    beat_window = 0
-            # plot
+    # beat window is at most 20 ms long, multiples of beat_duration
-            plot_trials = df_phase_time[df][phase]
+    while beat_window+beat_duration <= cut_window*sampling_rate:
-            plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
+        beat_window = beat_window+beat_duration
-    
+    for phase in df_phase_time[df].keys():
-            # calculation
+
-            #overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
+        # csi calculation
-    
+        # trains for synchrony and rate
-            smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
+        trials_binary = df_phase_binary[df][phase]
-    
+
-            fig, ax = plt.subplots(2, 1, sharex=True)
+        train_chirp = []
-            for i, trial in enumerate(plot_trials):
+        train_beat = []
-                ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
+        #csi_spikerate = []
-            ax[1].plot(time_axis, smoothed_spikes*1000)
+        for i, trial in enumerate(trials_binary):
-    
+            smoothed_trial = smooth(trial, window, 1/sampling_rate)
-            ax[0].set_title(df)
+            train_chirp.append(smoothed_trial[chirp_start:chirp_end])
-            ax[0].set_ylabel('repetition', fontsize=12)
+            train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
-    
+            #std_chirp = np.std(smoothed_trial[chirp_start:chirp_end])
-            ax[1].set_xlabel('time [ms]', fontsize=12)
+            #std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start])
-            ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
+            #csi = (std_chirp - std_beat)/(std_chirp + std_beat)
-            plt.show()
+            #csi_spikerate.append(csi)
-            '''
+
-
+        std_chirp = np.std(np.mean(train_chirp, axis=0))
-    '''
+        std_beat = np.std(np.mean(train_beat, axis=0))
-    fig, ax = plt.subplots()
+        beat[df].append(std_beat)
-    for i, k in enumerate(sorted(csi_rates.keys())):
+        csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
-        ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
+
-        #ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
+        rcs = []
-    ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+        rbs = []
-    ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
+        for i, train in enumerate(train_chirp):
-    #ax.set_xticklabels(sorted(csi_rates.keys()))
+            for j, train2 in enumerate(train_chirp):
-    fig.tight_layout()
+                if i >= j:
-    plt.show()
+                    continue
-    
+                else:
-    fig, ax = plt.subplots()
+                    rc, _ = ss.pearsonr(train, train2)
-    for i, k in enumerate(sorted(csi_trains.keys())):
+                    rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
-        ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
+                    rcs.append(rc)
-        #ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
+                    rbs.append(rb)
-    ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+
-    ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
+        r_train_chirp = np.mean(rcs)
-    #ax.set_xticklabels(sorted(csi_trains.keys()))
+        r_train_beat = np.mean(rbs)
-    fig.tight_layout()
+
-    plt.show()
+        csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
-    '''
+
-
+        # add the csi to the dictionaries with the correct df and phase
-    fig, ax = plt.subplots()
+        csi_trains[df].append(csi_train)
-    for i, k in enumerate(sorted(beat.keys())):
+        csi_rates[df].append(np.mean(csi_spikerate))
-        ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
+
-    ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+        '''
-    #ax.set_xticklabels(sorted(csi_trains.keys()))
+        # plot
-    fig.tight_layout()
+        plot_trials = df_phase_time[df][phase]
-    plt.show()
+        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)
+        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)
+
+        ax[0].set_title(df)
+        ax[0].set_ylabel('repetition', fontsize=12)
+
+        ax[1].set_xlabel('time [ms]', fontsize=12)
+        ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
+        plt.show()
+        '''
+
+upper_limit = np.max(sorted(csi_rates.keys()))+30
+lower_limit = np.min(sorted(csi_rates.keys()))-30
+
+fig, ax = plt.subplots()
+for i, k in enumerate(sorted(csi_rates.keys())):
+    ax.scatter(np.ones(len(csi_rates[k]))*k, csi_rates[k], s=20)
+    #ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
+#ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
+#ax.set_xticklabels(sorted(csi_rates.keys()))
+fig.tight_layout()
+plt.show()
+
+'''
+fig, ax = plt.subplots()
+for i, k in enumerate(sorted(csi_trains.keys())):
+    ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
+    #ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
+ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
+#ax.set_xticklabels(sorted(csi_trains.keys()))
+fig.tight_layout()
+plt.show()
+'''
+
+'''
+fig, ax = plt.subplots()
+for i, k in enumerate(sorted(beat.keys())):
+    ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
+ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+#ax.set_xticklabels(sorted(csi_trains.keys()))
+fig.tight_layout()
+plt.show()
+'''

code/stimulus_chirp.py

@@ -35,13 +35,13 @@ for k, t in enumerate(time):
     p += f * stepsize
     signal[k] = a * np.sin(6.28318530717959 * p)
 
-fig = plt.figure(figsize = (20/inch_factor, 15/inch_factor))
+fig = plt.figure(figsize = (20/inch_factor, 12/inch_factor))
 ax1 = fig.add_subplot(211)
 plt.yticks(fontsize=18)
 ax2 = fig.add_subplot(212, sharex=ax1)
 plt.setp(ax1.get_xticklabels(), visible=False)
-ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
+ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
-ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
+ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
 
 ax1.set_ylabel("field [mV]", fontsize = 22)