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efish 2018-11-28 17:30:08 +01:00
parent 5cd62554fa
commit 23675241f0
4 changed files with 94 additions and 88 deletions
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48
code/base_chirps.py
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@ -8,45 +8,48 @@ from IPython import embed
data_dir = "../data"
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")
dataset = "2018-11-13-ah-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"]
#for dataset in data:
for dataset in data:
print(dataset)
eod = read_chirp_eod(os.path.join(data_dir, dataset))
times = read_chirp_times(os.path.join(data_dir, dataset))
df_map = map_keys(eod)
sort_df = sorted(df_map.keys())
eod = read_chirp_eod(os.path.join(data_dir, dataset))
times = read_chirp_times(os.path.join(data_dir, dataset))
df_map = map_keys(eod)
sort_df = sorted(df_map.keys())
chirp_eod_plot(df_map, eod, times)
eods = chirp_eod_plot(df_map, eod, times)
plt.show()
plt.close('all')
chirp_mods = []
beat_mods = []
for i in sort_df:
chirp_mods = {}
beat_mods = []
for i in sort_df:
chirp_mods[i] = []
freq = list(df_map[i])
ls_mod, beat_mod = cut_chirps(freq, eod, times)
chirp_mods.append(ls_mod)
chirp_mods[i].append(ls_mod)
beat_mods.append(beat_mod)
#Chirps einer Phase zuordnen - zusammen plotten
chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(chirp_spikes)
sort_df = sorted(df_map.keys())
#plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
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)
plt.show()
plt.close('all')
'''
#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
name = str(dataset.strip('invivo-1'))
name = str(dataset.replace('-invivo-1', ''))
print('saving ../results/Chirpcut/Cc_' + name + '.dat')
f = open('../results/Chirpcut/Cc_' + name + '.dat' , 'w')
f.write(str(sort_df))
f.write(str(df_map))
@ -56,3 +59,4 @@ for dataset in data:
#f.write(str(chirp_mods))
#f.write(str(beat_mods))
f.close()
'''

87
code/base_spikes.py
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@ -3,7 +3,7 @@ from read_chirp_data import *
from func_spike import *
import matplotlib.pyplot as plt
import numpy as np
from IPython import embed #Funktionen imposrtieren
from IPython import embed #Funktionen importieren
@ -11,77 +11,77 @@ 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"
'''
for dataset in data_base:
#for dataset in data_base:
print(dataset)
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)
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(spike_iv)
sigma = np.std(spike_iv)
cv = sigma/mu
mu = np.mean(spike_iv)
sigma = np.std(spike_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.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.xticks(fontsize = 12)
plt.yticks(fontsize = 12)
plt.show()
for dataset in data_chirps:
#for dataset in data_chirps:
#Nyquist-Theorem Plot:
print(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)
sort_df = sorted(df_map.keys())
dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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)
sort_df = sorted(df_map.keys())
plt.figure()
ls_mean = plot_df_spikes(sort_df, dct_rate)
dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
plt.figure()
ls_mean = plot_df_spikes(sort_df, dct_rate)
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.ones(len(ls_mean))*over_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.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.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()
#Adaption der Zellen:
#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
adapt = adaptation_df(sort_df, dct_rate)
plt.figure()
plt.boxplot(adapt)
plt.title('Adaptation of cell firing rate during a trial')
plt.xlabel('Cell')
plt.ylabel('Adaptation size [Hz]')
adapt = adaptation_df(sort_df, dct_rate)
plt.figure()
plt.boxplot(adapt)
plt.title('Adaptation of cell firing rate during a trial')
plt.xlabel('Cell')
plt.ylabel('Adaptation size [Hz]')
plt.show()
'''
#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
name = str(dataset.strip('invivo-1'))
name = str(dataset.replace('-invivo-1', ''))
f = open('../results/Nyquist/Ny_' + name + '.txt' , 'w')
f.write(str(sort_df))
f.write(str(df_map))
@ -91,3 +91,4 @@ for dataset in data_chirps:
f.write(str(over_r))
f.write(str(adapt))
f.close()
'''

36
code/func_chirp.py
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@ -21,23 +21,21 @@ def chirp_eod_plot(df_map, eod, times):
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)
axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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)
axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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)
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)
axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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[0,1].set_ylabel('Amplitude [mV]')
axs[1,0].set_xlabel('Time [ms]')
axs[1,1].set_xlabel('Time [ms]')
plt.close()
fig.suptitle('EOD for chirps', fontsize = 16)
@ -60,15 +58,17 @@ def cut_chirps(freq, eod, times):
ls_beat.extend(beat_cut)
beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
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))*beat_mod, color = 'violet')
plt.close()
#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))*beat_mod, color = 'violet')
return(ls_mod, beat_mod)
def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
plt.figure()
dct_phase = {}
num_bin = 12
@ -81,7 +81,11 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
for phase in chirp_spikes[k]:
dct_phase[i].append(phase[1])
plt.scatter(dct_phase[i], ls_mod[i])
for i in sort_df:
plt.scatter(dct_phase[i], chirp_mods[i], label = i)
plt.title('Change of std depending on the phase where the chirp occured')
plt.close()
plt.xlabel('Phase')
plt.ylabel('Standard deviation of the amplitude modulation')
plt.legend()
return(dct_phase)

3
code/vector_phase.py
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@ -18,8 +18,5 @@ eod_durations = np.diff(eod_times)
print(len(spike_times))
print(len(eod_durations))
#for st in spike_times:
#et = eod_times[eod_times < st]
#dt = st - et
#vs = vector_strength(spike_times, eod_durations)