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

code/base_chirps.py

@@ -1,4 +1,5 @@
 from read_chirp_data import *
+from utility import *
 #import nix_helpers as nh 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -15,26 +16,14 @@ data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-inviv
 #for dataset in data:
 eod = read_chirp_eod(os.path.join(data_dir, dataset))
 times = read_chirp_times(os.path.join(data_dir, dataset))
-
+df_map = map_keys(eod)
-
-
-df_map = {} #Keys werden nach df sortiert ausgegeben
-for k in eod.keys():
-	df = k[1]
-	ch = k[3] 
-	if df in df_map.keys():
-		df_map[df].append(k)
-	else:
-		df_map[df] = [k]
-
-print(ch) #die Chirphöhe wird ausgegeben, um zu bestimmen, ob Chirps oder Chirps large benutzt wurde
 
 
 
 #die äußere Schleife geht für alle Keys durch und somit durch alle dfs
-#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz in 4 Subplots ab
+#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz ab
-for idx in df_map.keys():
+for i in df_map.keys():
-	freq = list(df_map[idx]) 
+	freq = list(df_map[i]) 
 	fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
 
 	for idx, k in enumerate(freq):
@@ -58,18 +47,37 @@ for idx in df_map.keys():
 
 
 fig.suptitle('EOD for chirps', fontsize = 16)
-plt.show()
+axs[0,0].set_ylabel('Amplitude [mV]') 
+axs[0,1].set_xlabel('Amplitude [mV]')
+axs[1,0].set_xlabel('Time [ms]')
+axs[1,1].set_xlabel('Time [ms]')
 
 
 
-#Problem: axs hat keine label-Funktion, also müsste axes nochmal definiert werden. Momentan erscheint Schrift nur auf einem der Subplots
+#for i in df_map.keys():
+
+freq = list(df_map['-50Hz'])
+ls_mod = []
+beat_mods = []
+for k in freq: 
+	e1 = eod[k]
+	zeit = np.asarray(e1[0])
+	ampl = np.asarray(e1[1])
+
+	ct = times[k]
+	for chirp in ct:
+		time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
+		eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
+		beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
 
-#ax = plt.gca()
+		chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
-#ax.set_ylabel('Time [ms]')
+		beat_mod = np.std(beat_cut) #Std vom Bereich vor dem Chirp
-#ax.set_xlabel('Amplitude [mV]')
+		ls_mod.append(chirp_mod)
-#ax.label_outer()
+		beat_mods.append(beat_mod)
 
+#Länge des Mods ist 160, 16 Wiederholungen mal 10 Chirps pro Trial
+#Verwendung der Std für die Amplitudenmodulation?
 
 
 
-#next Step: relative Amplitudenmodulation berechnen, Max und Min der Amplitude bestimmen, EOD und Chirps zuordnen, Unterschied berechnen
+#Chirps einer Phase zuordnen - zusammen plotten?

code/base_spikes.py

@@ -1,16 +1,19 @@
 from read_baseline_data import *
+from read_chirp_data import *
+from utility import *
 #import nix_helpers as nh 
 import matplotlib.pyplot as plt
 import numpy as np
 from IPython import embed #Funktionen importieren
 
+
 data_dir = "../data"
-dataset = "2018-11-09-aa-invivo-1"
+dataset = "2018-11-09-ad-invivo-1"
 #data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-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", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
-spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
 
 
-#spike_frequency = len(spike_times) / spike_times[-1]
+
+spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
 #inst_frequency = 1. / np.diff(spike_times)
 spike_rate = np.diff(spike_times)
 
@@ -21,7 +24,6 @@ plt.hist(spike_rate,x)
 mu = np.mean(spike_rate)
 sigma = np.std(spike_rate)
 cv = sigma/mu
-print(cv)
 
 plt.title('A.lepto ISI Histogramm', fontsize = 14)
 plt.xlabel('duration ISI[ms]', fontsize = 12)
@@ -32,3 +34,24 @@ plt.yticks(fontsize = 12)
 plt.show()
 
 
+
+
+#Nyquist-Theorem Plot:
+
+chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+df_map = map_keys(chirp_spikes)
+
+
+for i in df_map.keys():
+	freq = list(df_map[i])
+	for k in freq:
+		spikes = chirp_spikes[k]
+		phase_map = map_keys(spikes)
+		for p in phase_map: 
+			spike_rate = 1./ np.diff(p)
+
+print(spike_rate)
+#
+#	plt.plot(spikes, rate)
+#	plt.show()
+

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

@@ -4,13 +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))
-times = read_chirp_times(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]
@@ -19,33 +27,55 @@ for k in spikes.keys():
     else:
         df_map[df] = [k]
 
-# make phases together, 12 phases
+# differentiate between phases
-spikes_mat = {}
+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):
-            spikes_one_chirp = spikes[rep][phase]
+                # check the phase
-            if deltaf == '-50Hz' and phase == (9, 0.54):
+                if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
-                spikes_mat[deltaf, rep, phase] = spikes_one_chirp
+
-
+                    # get spikes between 50 ms befor and after the chirp
-plot_spikes = spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)]
+                    spikes_to_cut = np.asarray(spikes[rep][phase])
-
+                    spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
-mu = 1
+                    spikes_idx = np.round(spikes_cut*sampling_rate)
-sigma = 1
+                    # also save as binary, 0 no spike, 1 spike
-time_gauss = np.arange(-4, 4, 1)
+                    binary_spikes = np.isin(cut_range, spikes_idx)*1
-gauss = gaussian(time_gauss, mu, sigma)
+
-# spikes during time vec (00010000001)?
+                    # add the spikes to the dictionaries with the correct df and phase
-smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
+                    if idx in df_phase_time[deltaf].keys():
-window = np.mean(np.diff(plot_spikes))
+                        df_phase_time[deltaf][idx].append(spikes_cut)
-time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)
+                        df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
-
+                    else:
-fig, ax = plt.subplots()
+                        df_phase_time[deltaf][idx] = [spikes_cut]
-ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k')
+                        df_phase_binary[deltaf][idx] = binary_spikes
-ax.plot(time_vec, smoothed_spikes)
-plt.show()
 
-#embed()
+# for plotting iterate over delta f and phases
-#exit()
+for df in df_phase_time.keys():
-#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
+    for phase in df_phase_time[df].keys():
-#ax.plot(hist_data[1][:-1], hist_data[0])
+        plot_trials = df_phase_time[df][phase]
+        plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
+
+        smoothed_spikes = smooth(plot_trials_binary, window)
+
+        fig, ax = plt.subplots(2, 1)
+        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)
+
+        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 [?]', fontsize=12)
+        plt.show()

code/utility.py

@@ -1,4 +1,5 @@
 import numpy as np
+from IPython import embed
 
 
 def zero_crossing(eod, time):
@@ -23,3 +24,25 @@ 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():
+		df = k[1]
+		#ch = k[3] 
+		if df in df_map.keys():
+			df_map[df].append(k)
+		else:
+			df_map[df] = [k]
+	return df_map
+	#print(ch)