96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
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 imposrtieren
|
|
|
|
|
|
data_dir = "../data"
|
|
dataset = "2018-11-13-ad-invivo-1"
|
|
#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-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-ah-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") Durchgang für alle Datensets - zwischenspeichern von Daten?
|
|
|
|
|
|
|
|
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)
|
|
|
|
mu = np.mean(iv)
|
|
sigma = np.std(iv)
|
|
cv = sigma/mu
|
|
|
|
plt.title('A.lepto ISI Histogramm', fontsize = 14)
|
|
plt.xlabel('duration ISI[ms]', fontsize = 12)
|
|
plt.ylabel('number of ISI', fontsize = 12)
|
|
|
|
plt.xticks(fontsize = 12)
|
|
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)
|
|
sort_df = sorted(df_map.keys())
|
|
|
|
plt.figure()
|
|
dct_rate = {}
|
|
overall_r = {}
|
|
for i in sort_df:
|
|
freq = list(df_map[i])
|
|
dct_rate[i] = []
|
|
overall_r[i] = []
|
|
for k in freq:
|
|
for phase in chirp_spikes[k]:
|
|
spikes = chirp_spikes[k][phase]
|
|
rate = len(spikes)/ 1.2
|
|
dct_rate[i].append(rate)
|
|
#overall_r[i].extend(rate) #kann man nicht erweitern!
|
|
|
|
ls_mean = []
|
|
for h in sort_df:
|
|
mean = np.mean(dct_rate[h])
|
|
ls_mean.append(mean)
|
|
plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
|
|
|
|
#plt.vlines(10, ymin = 190, ymax = 310)
|
|
#Anfang Spur und Endpunkt bestimmen
|
|
#relativ zur mittleren Feuerrate
|
|
#wie hoch ist die Adaption von Zellen
|
|
plt.legend()
|
|
plt.title('Firing rate of the cell for all trials, sorted by df')
|
|
plt.xlabel('# of trials')
|
|
plt.ylabel('Instant firing rate of the cell')
|
|
plt.show()
|
|
|
|
|
|
|
|
#mittlere Feuerrate einer Frequenz auf Frequenz:
|
|
|
|
plt.figure()
|
|
plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
|
|
#plt.scatter(np.arange(0,len(ls_mean),1), np.mean(int(overall_r)))
|
|
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.xlabel('Range of frequency differences [Hz]')
|
|
plt.ylabel('Mean firing rate of the cell')
|
|
plt.show()
|
|
|
|
|
|
|
|
#Boxplot
|
|
#wie viel Prozent macht die Adaption von Zellen aus?
|
|
|
|
|
|
#Reihen-Plot
|
|
#macht die zeitliche Reihenfolge der Präsentation einen Unterschied in der Zellantwort?
|