jgrewe/gp_neurobio
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

finished

Browse Source
This commit is contained in:
efish 2018-11-21 16:53:14 +01:00
parent da8f7237af
commit 8e6e6cfe1b
2 changed files with 49 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
code/base_chirps.py
View File

@ -58,7 +58,7 @@ axs[1,1].set_xlabel('Time [ms]')
freq = list(df_map['-50Hz']) freq = list(df_map['-50Hz'])
ls_mod = [] ls_mod = []
beat_mods = [] ls_beat = []
for k in freq: for k in freq:
e1 = eod[k] e1 = eod[k]
zeit = np.asarray(e1[0]) zeit = np.asarray(e1[0])
@ -69,14 +69,19 @@ for k in freq:
time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)] time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)] eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)] beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
beat_mod = np.std(beat_cut) #Std vom Bereich vor dem Chirp ls_mod.append(chirp_mod) #in die richtige Reihenfolge bringen?
ls_mod.append(chirp_mod) #momentan nicht nach Chirp-Platz sortiert, sondern nacheinander
beat_mods.append(beat_mod)
#erst beat_cuts auf die gleiche Länge bringen!
ls_beat.append(beat_cut)
#Länge des Mods ist 160, 16 Wiederholungen mal 10 Chirps pro Trial #beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
#Verwendung der Std für die Amplitudenmodulation? plt.figure()
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))/2, color = 'violet')
plt.show()

48
code/base_spikes.py
View File

@ -4,7 +4,7 @@ from utility import *
#import nix_helpers as nh #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 #Funktionen importieren from IPython import embed #Funktionen imposrtieren
data_dir = "../data" data_dir = "../data"
@ -40,18 +40,44 @@ plt.show()
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)
ls_rate = {}
for i in df_map.keys(): for i in df_map.keys():
freq = list(df_map[i]) freq = list(df_map[i])
ls_rate[i] = []
for k in freq: for k in freq:
spikes = chirp_spikes[k] for phase in chirp_spikes[k]:
phase_map = map_keys(spikes) spikes = chirp_spikes[k][phase]
for p in phase_map: rate = len(spikes)/ 1.2
spike_rate = 1./ np.diff(p) ls_rate[i].append(rate)
plt.figure()
sort_df = sorted(df_map.keys(),reverse = False)
print(sort_df)
for i in sort_df:
plt.plot(np.arange(0,len(ls_rate[i]),1),ls_rate[i], label = i)
plt.vlines(10, ymin = 200, ymax = 300)
plt.vlines(30, ymin = 200, ymax = 300)
plt.vlines(50, ymin = 200, ymax = 300)
plt.vlines(70, ymin = 200, ymax = 300)
plt.vlines(90, ymin = 200, ymax = 300)
plt.vlines(110, ymin = 200, ymax = 300)
plt.vlines(130, ymin = 200, ymax = 300)
plt.vlines(150, ymin = 200, ymax = 300)
plt.legend()
plt.show()
print(spike_rate)
#
# plt.plot(spikes, rate)
# plt.show()
#mittlere Feuerrate einer Frequenz auf Frequenz
plt.figure()
ls_mean = []
for i in sort_df:
mean = np.mean(ls_rate[i])
ls_mean.append(mean)
plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
plt.show()