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efish 2018-11-22 16:58:09 +01:00
parent fd142297da
commit bc64396553
2 changed files with 38 additions and 58 deletions
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56
code/base_chirps.py
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@ -1,6 +1,6 @@
from read_chirp_data import * from read_chirp_data import *
from func_chirp import *
from utility import * from utility import *
#import nix_helpers as nh
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from IPython import embed from IPython import embed
@ -18,45 +18,13 @@ eod = read_chirp_eod(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))
df_map = map_keys(eod) df_map = map_keys(eod)
chirp_eod_plot(df_map, eod, times)
plt.close()
#die äußere Schleife geht für alle Keys durch und somit durch alle dfs #ACHTUNG: df für beide Plots anpassen!
#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz ab #momentan per Hand durch alle Frequenzen
for i in df_map.keys(): freq = list(df_map[-100])
freq = list(df_map[i])
fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
for idx, k in enumerate(freq):
ct = times[k]
e1 = eod[k]
zeit = e1[0]
eods = e1[1]
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)
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)
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)
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)
fig.suptitle('EOD for chirps', fontsize = 16)
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]')
#for i in df_map.keys():
freq = list(df_map[-50])
ls_mod = [] ls_mod = []
ls_beat = [] ls_beat = []
for k in freq: for k in freq:
@ -78,11 +46,11 @@ 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.show() plt.close()
#Chirps einer Phase zuordnen - zusammen plotten? #Chirps einer Phase zuordnen - zusammen plotten
dct_phase = {} dct_phase = {}
chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
@ -98,8 +66,8 @@ for i in sort_df:
for k in freq: for k in freq:
for phase in chirp_spikes[k]: for phase in chirp_spikes[k]:
dct_phase[i].append(phase[1]) dct_phase[i].append(phase[1])
#for idx in np.arange(num_bin):
#if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
print(dct_phase)
plt.figure()
plt.scatter(dct_phase[-100], ls_mod)
plt.title('Change of std depending on the phase where the chirp occured')
plt.show()

38
code/base_spikes.py
View File

@ -9,20 +9,19 @@ from IPython import embed #Funktionen imposrtieren
data_dir = "../data" data_dir = "../data"
dataset = "2018-11-13-ad-invivo-1" 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") #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_times = read_baseline_spikes(os.path.join(data_dir, dataset))
#inst_frequency = 1. / np.diff(spike_times) spike_iv = np.diff(spike_times)
spike_rate = 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_rate,x) plt.hist(spike_iv,x)
mu = np.mean(spike_rate) mu = np.mean(iv)
sigma = np.std(spike_rate) sigma = np.std(iv)
cv = sigma/mu cv = sigma/mu
plt.title('A.lepto ISI Histogramm', fontsize = 14) plt.title('A.lepto ISI Histogramm', fontsize = 14)
@ -45,20 +44,28 @@ sort_df = sorted(df_map.keys())
plt.figure() plt.figure()
dct_rate = {} dct_rate = {}
overall_r = {}
for i in sort_df: for i in sort_df:
freq = list(df_map[i]) freq = list(df_map[i])
dct_rate[i] = [] dct_rate[i] = []
overall_r[i] = []
for k in freq: for k in freq:
for phase in chirp_spikes[k]: for phase in chirp_spikes[k]:
spikes = chirp_spikes[k][phase] spikes = chirp_spikes[k][phase]
rate = len(spikes)/ 1.2 rate = len(spikes)/ 1.2
dct_rate[i].append(rate) dct_rate[i].append(rate)
#overall_r[i].extend(rate) #kann man nicht erweitern!
ls_mean = []
for h in sort_df: 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.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
#plt.vlines(10, ymin = 190, ymax = 310) #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.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')
plt.xlabel('# of trials') plt.xlabel('# of trials')
@ -66,18 +73,23 @@ plt.ylabel('Instant firing rate of the cell')
plt.show() plt.show()
#mittlere Feuerrate einer Frequenz auf Frequenz
#mittlere Feuerrate einer Frequenz auf Frequenz:
plt.figure() plt.figure()
ls_mean = []
for d in sort_df:
mean = np.mean(dct_rate[d])
ls_mean.append(mean)
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.mean(int(overall_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(len(sort_df)), (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() 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?