zwischendrin

code/base_chirps.py

@@ -8,7 +8,7 @@ from IPython import embed
 
 
 data_dir = "../data"
-dataset = "2018-11-13-ah-invivo-1"
+dataset = "2018-11-13-ad-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"]
 
 
@@ -41,7 +41,9 @@ for i in sort_df:
 chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
 df_map = map_keys(chirp_spikes)
 sort_df = sorted(df_map.keys())
-dct_phase = plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
+example = [-50, 200, 400]
+
+dct_phase = plot_std_chirp(example, df_map, chirp_spikes, chirp_mods)
 plt.show()
 plt.close('all')
 

code/base_eod.py

@@ -14,10 +14,16 @@ 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', fontsize = 18)#Plottitelk
+inch_factor = 2.54
-plt.xlabel('time [ms]', fontsize = 16)#Achsentitel
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
-plt.ylabel('amplitude[mv]', fontsize = 16)#Achsentitel
+
-plt.xticks(fontsize = 14)
+plt.plot(zeit[0:1000], eod[0:1000], color = 'darkblue')
-plt.yticks(fontsize = 14)
+plt.title('A.lepto EOD', fontsize = 24)#Plottitel
+plt.xlabel('time [ms]', fontsize = 22)#Achsentitel
+plt.ylabel('amplitude[mv]', fontsize = 22)#Achsentitel
+plt.tick_params(axis='both', which='major', labelsize = 22)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+fig.tight_layout()
 plt.show()

code/base_spikes.py

@@ -12,25 +12,28 @@ data_base = ("2018-11-09-ab-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-
 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 = "2018-11-13-ad-invivo-1"
-
+inch_factor = 2.54
 
 #for dataset in data_base:
 
 spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))	
 spike_iv = np.diff(spike_times)
 x = np.arange(0.001, 0.01, 0.0001)
-plt.hist(spike_iv,x)
+
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
+plt.hist(spike_iv,x, color = 'darkblue')
 
 mu = np.mean(spike_iv)
 sigma = np.std(spike_iv)
 cv = sigma/mu
 
-plt.title('A.lepto ISI Histogramm', fontsize = 18)
+plt.title('A.lepto ISI Histogramm', fontsize = 24)
-plt.xlabel('duration ISI[ms]', fontsize = 16)
+plt.xlabel('duration ISI[ms]', fontsize = 22)
-plt.ylabel('number of ISI', fontsize = 16)
+plt.ylabel('number of ISI', fontsize = 22)
-
+plt.tick_params(axis='both', which='major', labelsize = 22)
-plt.xticks(fontsize = 14)
+ax.spines['right'].set_visible(False)
-plt.yticks(fontsize = 14)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
 plt.show()
 
 
@@ -47,6 +50,7 @@ sort_df = sorted(df_map.keys())
 dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
 
 plt.figure()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 ls_mean = plot_df_spikes(sort_df, dct_rate)
 plt.show()
 
@@ -54,14 +58,17 @@ plt.show()
 
 	#mittlere Feuerrate einer Frequenz auf Frequenz:
 
-plt.figure()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
-plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
+plt.plot(np.arange(0,len(ls_mean),1),ls_mean, color = 'darkblue')
 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', fontsize = 18)
+plt.title('Mean firing rate of a cell for a range of frequency differences', fontsize = 24)
 plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
-plt.xlabel('Range of frequency differences [Hz]', fontsize = 16)
+plt.xlabel('Range of frequency differences [Hz]', fontsize = 22)
-plt.ylabel('Mean firing rate of the cell', fontsize = 16)
+plt.ylabel('Mean firing rate of the cell', fontsize = 22)
-plt.tick_params(axis='both', which='major', labelsize = 14)
+plt.tick_params(axis='both', which='major', labelsize = 18)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
 plt.show()
 
 
@@ -70,12 +77,15 @@ plt.show()
 	#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
 
 adapt = adaptation_df(sort_df, dct_rate)
-plt.figure()
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 plt.boxplot(adapt)
-plt.title('Adaptation of cell firing rate during a trial', fontsize = 18)
+plt.title('Adaptation of cell firing rate during a trial', fontsize = 24)
-plt.xlabel('Cell', fontsize = 16)
+plt.xlabel('Cell', fontsize = 22)
-plt.ylabel('Adaptation size [Hz]', fontsize = 16)
+plt.ylabel('Adaptation size [Hz]', fontsize = 22)
-plt.tick_params(axis='both', which='major', labelsize = 14)
+plt.tick_params(axis='both', which='major', labelsize = 18)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+plt.tight_layout()
 plt.show()	
 
 

code/func_chirp.py

@@ -2,8 +2,10 @@ from read_baseline_data import *
 from read_chirp_data import *
 from utility import *
 import matplotlib.pyplot as plt
+import math
 import numpy as np
 
+inch_factor = 2.54
 
 def chirp_eod_plot(df_map, eod, times):
 	#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
@@ -11,7 +13,7 @@ def chirp_eod_plot(df_map, eod, times):
 
 	for i in df_map.keys():
 		freq = list(df_map[i]) 
-		fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
+		fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 
 		for idx, k in enumerate(freq):
 			ct = times[k]
@@ -19,22 +21,28 @@ def chirp_eod_plot(df_map, eod, times):
 			zeit = e1[0]
 			eods = e1[1]
 	
-			if idx <= 3:
+			if idx <= 1:
-				axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+				ax.plot(zeit, eods, color= 'darkblue')
-				axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+				ax.scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
-			elif 4<= idx <= 7:
+			#elif 4<= idx <= 7:
-				axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			#	axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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:
+			#elif 8<= idx <= 11:
-				axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			#	axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
-				axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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)
+				continue
-				axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+				#axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+				#axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
+
+		ax.set_ylabel('Amplitude [mV]', fontsize = 22)
+		ax.set_xlabel('Time [ms]', fontsize = 22)		
+		ax.tick_params(axis='both', which='major', labelsize = 18)
+		ax.spines['right'].set_visible(False)
+		ax.spines['top'].set_visible(False)
+		fig.suptitle('EOD for chirps', fontsize = 24)
+		fig.tight_layout()
 
-	axs[0,1].set_ylabel('Amplitude [mV]')
-	axs[1,0].set_xlabel('Time [ms]')			
-	fig.suptitle('EOD for chirps', fontsize = 16)
 
 
 
@@ -69,7 +77,7 @@ def cut_chirps(freq, eod, times):
 
 def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
 		
-	plt.figure()	
+	fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 	dct_phase = {}
 	num_bin = 12
 	phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
@@ -82,10 +90,13 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
 				dct_phase[i].append(phase[1])
 	
 	for i in sort_df:
-		plt.scatter(dct_phase[i], chirp_mods[i], label = i)
+		norm = np.asarray(dct_phase[i]) *2*math.pi
-	plt.title('Change of std depending on the phase where the chirp occured')	
+		plt.scatter(norm, chirp_mods[i], label = i, s = 22)
-	plt.xlabel('Phase')
+	plt.title('Change of std depending on the phase where the chirp occured', fontsize = 24)	
-	plt.ylabel('Standard deviation of the amplitude modulation')
+	plt.xlabel('Phase', fontsize = 22)
+	plt.ylabel('Standard deviation of the amplitude modulation', fontsize = 22)
+	plt.xticks([0, math.pi/2, math.pi, math.pi*1.5, math.pi*2], ('0', '$\pi$ /2', '$\pi$', '1.5 $\pi$', '2$\pi$'))
+	plt.tick_params(axis='both', which='major', labelsize = 18)
 	plt.legend()
 	return(dct_phase)
 

code/func_spike.py

@@ -43,6 +43,8 @@ def spike_rates(sort_df, df_map, chirp_spikes):
 
 def plot_df_spikes(sort_df, dct_rate):
 #gibt die Feuerrate gegen die Frequenz aufgetragen
+	inch_factor = 2.54
+	fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 	ls_mean = []
 	for h in sort_df:	
 		mean = np.mean(dct_rate[h])
@@ -50,10 +52,13 @@ 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', fontsize = 18)
+	plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 24)
-	plt.xlabel('# of trials', fontsize = 16)
+	plt.xlabel('# of trials', fontsize = 22)
-	plt.ylabel('Instant firing rate of the cell', fontsize = 16)
+	plt.ylabel('Instant firing rate of the cell', fontsize = 22)
-	plt.tick_params(axis='both', which='major', labelsize = 14)
+	plt.tick_params(axis='both', which='major', labelsize = 18)
+	ax.spines['right'].set_visible(False)
+	ax.spines['top'].set_visible(False)
+	plt.tight_layout()
 	return(ls_mean)
 
 

code/order_eff.py

@@ -12,6 +12,7 @@ data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-a
 
 
 
+inch_factor = 2.54
 data_rate_dict = {}
 for dataset in data_chirps:
 	
@@ -31,14 +32,17 @@ for dataset in data_chirps:
 		rate = len(spikes)/ 1.2
 		data_rate_dict[dataset].append(rate)		
 
-
+fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 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.title('Test for sequence effects', fontsize = 24)
+plt.xlabel('Number of stimulus presentations', fontsize = 22)
+plt.ylabel('Firing rates of cells', fontsize = 22)
+plt.tick_params(axis='both', which='major', labelsize = 22)
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+fig.tight_layout()
 plt.show()
 
 

code/plot_spikesduringbaselineactivity.py

@@ -17,6 +17,7 @@ interspikeintervals = np.diff(spikes)*1000
 
 fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
 plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
+#Titel fehlt!!
 plt.xlabel("time [ms]", fontsize = 22)
 plt.xticks(fontsize = 18)
 plt.ylabel("Number of \n Interspikeinterval", fontsize = 22)