los

code/base_eod.py

@@ -8,14 +8,16 @@ from IPython import embed #Funktionen importieren
 data_dir = "../data"
 dataset = "2018-11-09-aa-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", "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")
+
+
 time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
 zeit = np.asarray(time)
 
 
 plt.plot(zeit[0:1000], eod[0:1000])
-plt.title('A.lepto EOD')#Plottitelk
+plt.title('A.lepto EOD', fontsize = 18)#Plottitelk
-plt.xlabel('time [ms]', fontsize = 12)#Achsentitel
+plt.xlabel('time [ms]', fontsize = 16)#Achsentitel
-plt.ylabel('amplitude[mv]', fontsize = 12)#Achsentitel
+plt.ylabel('amplitude[mv]', fontsize = 16)#Achsentitel
-plt.xticks(fontsize = 12)
+plt.xticks(fontsize = 14)
-plt.yticks(fontsize = 12)
+plt.yticks(fontsize = 14)
 plt.show()

code/base_spikes.py

@@ -11,7 +11,7 @@ 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"
+dataset = "2018-11-13-ad-invivo-1"
 
 
 #for dataset in data_base:
@@ -25,12 +25,12 @@ mu = np.mean(spike_iv)
 sigma = np.std(spike_iv)
 cv = sigma/mu
 
-plt.title('A.lepto ISI Histogramm', fontsize = 14)
+plt.title('A.lepto ISI Histogramm', fontsize = 18)
-plt.xlabel('duration ISI[ms]', fontsize = 12)
+plt.xlabel('duration ISI[ms]', fontsize = 16)
-plt.ylabel('number of ISI', fontsize = 12)
+plt.ylabel('number of ISI', fontsize = 16)
 
-plt.xticks(fontsize = 12)
+plt.xticks(fontsize = 14)
-plt.yticks(fontsize = 12)
+plt.yticks(fontsize = 14)
 plt.show()
 
 
@@ -56,11 +56,12 @@ plt.show()
 
 plt.figure()
 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, color = 'green')
-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', fontsize = 18)
 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]', fontsize = 16)
-plt.ylabel('Mean firing rate of the cell')
+plt.ylabel('Mean firing rate of the cell', fontsize = 16)
+plt.tick_params(axis='both', which='major', labelsize = 14)
 plt.show()
 
 
@@ -71,9 +72,10 @@ plt.show()
 adapt = adaptation_df(sort_df, dct_rate)
 plt.figure()
 plt.boxplot(adapt)
-plt.title('Adaptation of cell firing rate during a trial')
+plt.title('Adaptation of cell firing rate during a trial', fontsize = 18)
-plt.xlabel('Cell')
+plt.xlabel('Cell', fontsize = 16)
-plt.ylabel('Adaptation size [Hz]')
+plt.ylabel('Adaptation size [Hz]', fontsize = 16)
+plt.tick_params(axis='both', which='major', labelsize = 14)
 plt.show()	
 
 

code/func_spike.py

@@ -50,9 +50,10 @@ def plot_df_spikes(sort_df, dct_rate):
 		plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
 
 	plt.legend()
-	plt.title('Firing rate of the cell for all trials, sorted by df')
+	plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 18)
-	plt.xlabel('# of trials')
+	plt.xlabel('# of trials', fontsize = 16)
-	plt.ylabel('Instant firing rate of the cell')
+	plt.ylabel('Instant firing rate of the cell', fontsize = 16)
+	plt.tick_params(axis='both', which='major', labelsize = 14)
 	return(ls_mean)
 
 

code/order_eff.py

@@ -0,0 +1,44 @@
+from read_chirp_data import *
+from func_spike import *
+import matplotlib.pyplot as plt
+import numpy as np
+from IPython import embed #Funktionen importieren
+
+
+
+
+data_dir = "../data"
+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") 
+
+
+
+data_rate_dict = {}
+for dataset in data_chirps:
+	
+	data_rate_dict[dataset] = []
+	chirp_spikes = read_chirp_spikes(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))
+	df_map = map_keys(chirp_spikes)
+
+
+	for i in df_map.keys():
+		freq = list(df_map[i])
+		k = freq[0]
+		phase = list(chirp_spikes[k].keys())[0]
+
+		spikes = chirp_spikes[k][phase]
+		rate = len(spikes)/ 1.2
+		data_rate_dict[dataset].append(rate)		
+
+
+for dataset in data_rate_dict:
+	plt.plot(data_rate_dict[dataset])
+plt.title('Test for sequence effects', fontsize = 20)
+plt.xlabel('Number of stimulus presentations', fontsize = 18)
+plt.ylabel('Firing rates of cells', fontsize = 18)
+plt.tick_params(axis='both', which='major', labelsize = 16)
+
+plt.show()
+
+

code/vector_phase.py

@@ -1,22 +0,0 @@
-from read_baseline_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
-
-
-#Zeitpunkte einer EOD über Zero-crossings finden, die in einer Steigung liegen
-data_dir = "../data"
-dataset = "2018-11-09-ad-invivo-1"
-time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
-spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
-print(len(spike_times))
-
-eod_times = zero_crossing(eod,time)
-eod_durations = np.diff(eod_times)
-print(len(spike_times))
-print(len(eod_durations))
-
-	
-#vs = vector_strength(spike_times, eod_durations)