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

code/read_chirp_data.py

@@ -1,5 +1,7 @@
 import numpy as np
 import os
+import nixio as nix
+from IPython import embed
 
 
 def read_chirp_spikes(dataset):
@@ -85,10 +87,37 @@ def read_chirp_times(dataset):
     return chirp_times
 
 
+def read_chirp_stimulus(dataset):
+    base = dataset.split(os.path.sep)[-1] + ".nix"
+    nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly)
+    b = nix_file.blocks[0]
+    data = {}
+    for t in b.tags:
+        if "Chirps" in t.name:
+            stims = []
+            index = int(t.name.split("_")[-1])
+            df = t.metadata["RePro-Info"]["settings"]["deltaf"]
+            cs = t.metadata["RePro-Info"]["settings"]["chirpsize"]
+            stim_da = t.references["GlobalEFieldStimulus"]
+            si = stim_da.dimensions[0].sampling_interval
+            for mt in b.multi_tags:
+                if mt.positions[0] >= t.position[0] and \
+                   mt.positions[0] < (t.position[0] + t.extent[0]):
+                    break
+            for i in range(len(mt.positions)):
+                start_index = int(mt.positions[i] / si)
+                end_index = int((mt.positions[i] + mt.extents[i]) / si) - 1
+                stim = stim_da[start_index:end_index]
+                time = stim_da.dimensions[0].axis(len(stim)) + mt.positions[i]
+                stims.append((time, stim))
+            data[(index, df, cs)] = stims
+    nix_file.close()
+    return data
+
 if __name__ == "__main__":
     data_dir = "../data"
-    dataset = "2018-11-09-ad-invivo-1"
+    dataset = "2018-11-20-ad-invivo-1"
-    spikes = load_chirp_spikes(os.path.join(data_dir, dataset))
+    #spikes = load_chirp_spikes(os.path.join(data_dir, dataset))
-    chirp_times = load_chirp_times(os.path.join(data_dir, dataset))
+    #chirp_times = load_chirp_times(os.path.join(data_dir, dataset))
-    chirp_eod = load_chirp_eod(os.path.join(data_dir, dataset))
+    #chirp_eod = load_chirp_eod(os.path.join(data_dir, dataset))
-
+    stim = read_chirp_stimulus(os.path.join(data_dir, dataset))

code/spikes_analysis.py

@@ -4,14 +4,21 @@ 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"
+# parameters for binning, smoothing and plotting
+num_bin = 12
+window = sampling_rate
+time_axis = np.arange(-50, 50, 1/sampling_rate)
 
+# 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 = {}
 for k in spikes.keys():
     df = k[1]
@@ -20,61 +27,55 @@ for k in spikes.keys():
     else:
         df_map[df] = [k]
 
-# make phases together, 12 phases
+# differentiate between phases
-phase_vec = np.arange(0, 1+1/12, 1/12)
+phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
 cut_range = np.arange(-50*sampling_rate, 50*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]:
-            #print(phase)
+            for idx in np.arange(num_bin):
-            for idx in range(len(phase_vec)-1):
+                # 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 > -50) & (spikes_to_cut < 50)]
                     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
 
-                    if phase_vec[idx] in df_phase_time[deltaf].keys():
+                    # add the spikes to the dictionaries with the correct df and phase
-                        df_phase_time[deltaf][phase_vec[idx]].append(spikes[rep][phase])
+                    if idx in df_phase_time[deltaf].keys():
-                        df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes))
+                        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][phase_vec[idx]] = [spikes[rep][phase]]
+                        df_phase_time[deltaf][idx] = [spikes_cut]
-                        df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes
+                        df_phase_binary[deltaf][idx] = binary_spikes
 
+# for plotting iterate over delta f and phases
+for df in df_phase_time.keys():
+    for phase in df_phase_time[df].keys():
+        plot_trials = df_phase_time[df][phase]
+        plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
 
-plot_trials = df_phase_binary['-50Hz'][0.0]
+        smoothed_spikes = smooth(plot_trials_binary, window)
-#hist_data = plt.hist(plot_trials)
-#ax.plot(hist_data[1][:-1], hist_data[0])
 
-fig, ax = plt.subplots()
+        fig, ax = plt.subplots(2, 1)
-for i, trial in enumerate(plot_trials):
+        for i, trial in enumerate(plot_trials):
-    embed()
+            ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
-    exit()
+        ax[1].plot(time_axis, smoothed_spikes)
-    trial[trial == 0] = np.nan
-    ax.scatter(np.ones(len(trial)), trial, marker='|', color='k', size=12)
-plt.show()
 
+        ax[0].set_title(df)
+        ax[0].set_ylabel('repetition', fontsize=12)
 
-#mu = 1
+        ax[1].set_xlabel('time [ms]', fontsize=12)
-#sigma = 1
+        ax[1].set_ylabel('firing rate [?]', fontsize=12)
-#time_gauss = np.arange(-4, 4, 1)
+        plt.show()
-#gauss = gaussian(time_gauss, mu, sigma)
-# spikes during time vec (00010000001)?
-
-#smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
-#window = np.mean(np.diff(plot_spikes))
-#time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)
-
-#ax.plot(time_vec, smoothed_spikes)
-
-#embed()
-#exit()
-#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
-#ax.plot(hist_data[1][:-1], hist_data[0])

code/utility.py

@@ -1,4 +1,5 @@
 import numpy as np
+from IPython import embed
 
 
 def zero_crossing(eod, time):
@@ -23,6 +24,17 @@ def gaussian(x, mu, sig):
 	y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
 	return y
 
+
+def smooth(data, window):
+	mu = 1
+	sigma = window
+	time_gauss = np.arange(-4 * sigma, 4 * sigma, 1)
+	gauss = gaussian(time_gauss, mu, sigma)
+	gauss_norm = gauss/(np.sum(gauss)/len(gauss))
+	smoothed_data = np.convolve(data, gauss_norm, 'same')
+	return smoothed_data
+
+
 def map_keys(input):
 	df_map = {} 
 	for k in input.keys():