fertig

code/base_chirps.py

@@ -8,45 +8,48 @@ from IPython import embed
 
 
 data_dir = "../data"
-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", "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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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", "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") 
+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", "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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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", "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"]
 
 
+#for dataset in data:
 
-for dataset in data:
+eod = read_chirp_eod(os.path.join(data_dir, dataset))
-	print(dataset)
+times = read_chirp_times(os.path.join(data_dir, dataset))
-	eod = read_chirp_eod(os.path.join(data_dir, dataset))
+df_map = map_keys(eod)
-	times = read_chirp_times(os.path.join(data_dir, dataset))
+sort_df = sorted(df_map.keys())
-	df_map = map_keys(eod)
-	sort_df = sorted(df_map.keys())
 
 
-	chirp_eod_plot(df_map, eod, times)
+eods = chirp_eod_plot(df_map, eod, times)
+plt.show()
+plt.close('all')
 
 
-	chirp_mods =  []
+
-	beat_mods = []
+chirp_mods =  {}
-	for i in sort_df:
+beat_mods = []
+for i in sort_df:
+	chirp_mods[i] = []
 	freq = list(df_map[i])
 	ls_mod, beat_mod = cut_chirps(freq, eod, times)
-		chirp_mods.append(ls_mod)
+	chirp_mods[i].append(ls_mod)
 	beat_mods.append(beat_mod)
 
 
-
-
 	#Chirps einer Phase zuordnen - zusammen plotten
 
-	chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
-	df_map = map_keys(chirp_spikes)
+df_map = map_keys(chirp_spikes)
-	sort_df = sorted(df_map.keys())
+sort_df = sorted(df_map.keys())
-
+dct_phase = plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
-	#plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
+plt.show()
-
+plt.close('all')
-
 
 
+'''
 	#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
-	name = str(dataset.strip('invivo-1'))
+	name = str(dataset.replace('-invivo-1', ''))
+	print('saving ../results/Chirpcut/Cc_' + name + '.dat')
 	f = open('../results/Chirpcut/Cc_' + name + '.dat' , 'w') 
 	f.write(str(sort_df)) 
 	f.write(str(df_map)) 
@@ -56,3 +59,4 @@ for dataset in data:
 	#f.write(str(chirp_mods))
 	#f.write(str(beat_mods)) 
 	f.close() 
+'''

code/base_spikes.py

@@ -3,7 +3,7 @@ from read_chirp_data import *
 from func_spike import *
 import matplotlib.pyplot as plt
 import numpy as np
-from IPython import embed #Funktionen imposrtieren
+from IPython import embed #Funktionen importieren
 
 
 
@@ -11,77 +11,77 @@ data_dir = "../data"
 data_base = ("2018-11-09-ab-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", "2018-11-13-af-invivo-1", "2018-11-13-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-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-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", "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_chirps = ("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", "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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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", "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") 
 
+dataset = "2018-11-14-al-invivo-1"
 
 
-'''
+#for dataset in data_base:
-for dataset in data_base:
 
-	print(dataset)
+spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))	
-	spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))	
+spike_iv = np.diff(spike_times)
-	spike_iv = np.diff(spike_times)
+x = np.arange(0.001, 0.01, 0.0001)
-	x = np.arange(0.001, 0.01, 0.0001)
+plt.hist(spike_iv,x)
-	plt.hist(spike_iv,x)
 
-	mu = np.mean(spike_iv)
+mu = np.mean(spike_iv)
-	sigma = np.std(spike_iv)
+sigma = np.std(spike_iv)
-	cv = sigma/mu
+cv = sigma/mu
 
-	plt.title('A.lepto ISI Histogramm', fontsize = 14)
+plt.title('A.lepto ISI Histogramm', fontsize = 14)
-	plt.xlabel('duration ISI[ms]', fontsize = 12)
+plt.xlabel('duration ISI[ms]', fontsize = 12)
-	plt.ylabel('number of ISI', fontsize = 12)
+plt.ylabel('number of ISI', fontsize = 12)
 
-	plt.xticks(fontsize = 12)
+plt.xticks(fontsize = 12)
-	plt.yticks(fontsize = 12)
+plt.yticks(fontsize = 12)
-'''
+plt.show()
 
 
 
-for dataset in data_chirps:
+#for dataset in data_chirps:
 
 	#Nyquist-Theorem Plot:
-	print(dataset)
+chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
-	chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+times = read_chirp_times(os.path.join(data_dir, dataset))
-	times = read_chirp_times(os.path.join(data_dir, dataset))
+eod = read_chirp_eod(os.path.join(data_dir, dataset))
-	eod = read_chirp_eod(os.path.join(data_dir, dataset))
+df_map = map_keys(chirp_spikes)
-	df_map = map_keys(chirp_spikes)
+sort_df = sorted(df_map.keys())
-	sort_df = sorted(df_map.keys())
-
-	dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
 
-	plt.figure()
+dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
-	ls_mean = plot_df_spikes(sort_df, dct_rate)
 
+plt.figure()
+ls_mean = plot_df_spikes(sort_df, dct_rate)
+plt.show()
 
 
 
 	#mittlere Feuerrate einer Frequenz auf Frequenz:
 
-	plt.figure()
+plt.figure()
-	plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
+plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
-	plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r)
+plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r)
-	plt.title('Mean firing rate of a cell for a range of frequency differences')
+plt.title('Mean firing rate of a cell for a range of frequency differences')
-	plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
+plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
-	plt.xlabel('Range of frequency differences [Hz]')
+plt.xlabel('Range of frequency differences [Hz]')
-	plt.ylabel('Mean firing rate of the cell')
+plt.ylabel('Mean firing rate of the cell')
-
+plt.show()
 
 
 
 	#Adaption der Zellen:
 	#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
 
-	adapt = adaptation_df(sort_df, dct_rate)
+adapt = adaptation_df(sort_df, dct_rate)
-	plt.figure()
+plt.figure()
-	plt.boxplot(adapt)
+plt.boxplot(adapt)
-	plt.title('Adaptation of cell firing rate during a trial')
+plt.title('Adaptation of cell firing rate during a trial')
-	plt.xlabel('Cell')
+plt.xlabel('Cell')
-	plt.ylabel('Adaptation size [Hz]')
+plt.ylabel('Adaptation size [Hz]')
+plt.show()	
 
 
 
+'''
 
 	#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
-	name = str(dataset.strip('invivo-1'))
+	name = str(dataset.replace('-invivo-1', ''))
 	f = open('../results/Nyquist/Ny_' + name + '.txt' , 'w') 
 	f.write(str(sort_df)) 
 	f.write(str(df_map)) 
@@ -91,3 +91,4 @@ for dataset in data_chirps:
 	f.write(str(over_r)) 
 	f.write(str(adapt))
 	f.close() 
+'''

code/func_chirp.py

@@ -21,23 +21,21 @@ def chirp_eod_plot(df_map, eod, times):
 	
 			if idx <= 3:
 				axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+				axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
 			elif 4<= idx <= 7:
 				axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+				axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
 			elif 8<= idx <= 11:
 				axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+				axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
 			else: 
 				axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+				axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
 
-	fig.suptitle('EOD for chirps', fontsize = 16)
+	axs[0,1].set_ylabel('Amplitude [mV]')
-	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]')
+	fig.suptitle('EOD for chirps', fontsize = 16)
-	plt.close()
+
 
 
 
@@ -60,15 +58,17 @@ def cut_chirps(freq, eod, times):
 			ls_beat.extend(beat_cut)
 
 	beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
-	plt.figure()
+	#plt.figure()
-	plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
+	#plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
-	plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
+	#plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
-	plt.close()
 	return(ls_mod, beat_mod)
 
 
 
-def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
+
+
+def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
+		
 	plt.figure()	
 	dct_phase = {}
 	num_bin = 12
@@ -81,7 +81,11 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
 			for phase in chirp_spikes[k]:
 				dct_phase[i].append(phase[1])
 	
-		plt.scatter(dct_phase[i], ls_mod[i])
+	for i in sort_df:
+		plt.scatter(dct_phase[i], chirp_mods[i], label = i)
 	plt.title('Change of std depending on the phase where the chirp occured')	
-	plt.close()
+	plt.xlabel('Phase')
+	plt.ylabel('Standard deviation of the amplitude modulation')
+	plt.legend()
+	return(dct_phase)
 

code/vector_phase.py

@@ -18,8 +18,5 @@ eod_durations = np.diff(eod_times)
 print(len(spike_times))
 print(len(eod_durations))
 
-#for st in spike_times:
-	#et = eod_times[eod_times < st]
-	#dt = st - et
 	
 #vs = vector_strength(spike_times, eod_durations)