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Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio

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efish 2018-11-29 17:16:23 +01:00
commit 258cd80a61
12 changed files with 338 additions and 100 deletions
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6
code/base_chirps.py
View File

@ -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')

18
code/base_eod.py
View File

@ -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
plt.xlabel('time [ms]', fontsize = 16)#Achsentitel
plt.ylabel('amplitude[mv]', fontsize = 16)#Achsentitel
plt.xticks(fontsize = 14)
plt.yticks(fontsize = 14)
inch_factor = 2.54
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.plot(zeit[0:1000], eod[0:1000], color = 'darkblue')
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()

48
code/base_spikes.py
View File

@ -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.xlabel('duration ISI[ms]', fontsize = 16)
plt.ylabel('number of ISI', fontsize = 16)
plt.xticks(fontsize = 14)
plt.yticks(fontsize = 14)
plt.title('A.lepto ISI Histogramm', fontsize = 24)
plt.xlabel('duration ISI[ms]', fontsize = 22)
plt.ylabel('number of ISI', fontsize = 22)
plt.tick_params(axis='both', which='major', labelsize = 22)
ax.spines['right'].set_visible(False)
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()
plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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.ylabel('Mean firing rate of the cell', fontsize = 16)
plt.tick_params(axis='both', which='major', labelsize = 14)
plt.xlabel('Range of frequency differences [Hz]', fontsize = 22)
plt.ylabel('Mean firing rate of the cell', fontsize = 22)
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.xlabel('Cell', fontsize = 16)
plt.ylabel('Adaptation size [Hz]', fontsize = 16)
plt.tick_params(axis='both', which='major', labelsize = 14)
plt.title('Adaptation of cell firing rate during a trial', fontsize = 24)
plt.xlabel('Cell', fontsize = 22)
plt.ylabel('Adaptation size [Hz]', fontsize = 22)
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()

51
code/func_chirp.py
View File

@ -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:
axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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))*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))*np.mean(eods), color = 'green', s= 22)
if idx <= 1:
ax.plot(zeit, eods, color= 'darkblue')
ax.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))*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))*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))*np.mean(eods), color = 'green', s= 22)
continue
#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)
plt.title('Change of std depending on the phase where the chirp occured')
plt.xlabel('Phase')
plt.ylabel('Standard deviation of the amplitude modulation')
norm = np.asarray(dct_phase[i]) *2*math.pi
plt.scatter(norm, chirp_mods[i], label = i, s = 22)
plt.title('Change of std depending on the phase where the chirp occured', fontsize = 24)
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)

13
code/func_spike.py
View File

@ -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.xlabel('# of trials', fontsize = 16)
plt.ylabel('Instant firing rate of the cell', fontsize = 16)
plt.tick_params(axis='both', which='major', labelsize = 14)
plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 24)
plt.xlabel('# of trials', fontsize = 22)
plt.ylabel('Instant firing rate of the cell', fontsize = 22)
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)

14
code/order_eff.py
View File

@ -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()

4
code/plot_eodform_spikehist.py
View File

@ -9,8 +9,8 @@ from IPython import embed
inch_factor = 2.54
sampling_rate = 40000
data_dir = '../data'
dataset = '2018-11-09-ad-invivo-1'
#dataset = '2018-11-13-aa-invivo-1'
#dataset = '2018-11-09-ad-invivo-1'
dataset = '2018-11-14-ad-invivo-1'
# read eod and time of baseline
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))

1
code/plot_spikesduringbaselineactivity.py
View File

@ -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)

61
code/response_beat.py
View File

@ -8,29 +8,33 @@ from IPython import embed
# define data path and important parameters
data_dir = "../data"
sampling_rate = 40 #kHz
cut_window = 40
cut_window = 100
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
window = 1
'''
# norm: -150, 150, 300 aa, #ac, aj??
data = ["2018-11-13-aa-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
#"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
data = ["2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
'''
# norm: -50
data = ["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 = ["2018-11-14-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"]
'''
data = ["2018-11-14-ad-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-am-invivo-1"]
#data = ["2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ak-invivo-1"]
#data = ["2018-11-09-ad-invivo-1", "2018-11-14-af-invivo-1"]
#data = ["2018-11-20-ad-invivo-1", "2018-11-13-ad-invivo-1"]
#data = ["2018-11-09-ad-invivo-1"]
rates = {}
for dataset in data:
rates[dataset] = {}
print(dataset)
# read baseline spikes
base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
@ -66,21 +70,48 @@ for dataset in data:
# also save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx) * 1
smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
train = smoothed_data[window:beat_window+window]
norm_train = train*1000/spikerate
rep_rates.append(np.std(norm_train))#/spikerate)
#train = smoothed_data[window:beat_window+window]
#norm_train = train*1000/spikerate
#df_rate = np.std(norm_train)
#rates[dataset][df] = [df_rate]
rep_rates.append(np.std(smoothed_data))#/spikerate)
'''
if df in rates[dataset].keys():
rates[dataset][df].append(np.std(norm_train))
else:
rates[dataset][df] = [np.std(norm_train)]
'''
break
#break
df_rate = np.mean(rep_rates)
#df_rate = rep_rates
rates[dataset][df] = df_rate
#embed()
#exit()
'''
if df in rates.keys():
rates[df].append(df_rate)
rates[dataset][df].append(df_rate)
else:
rates[df] = [df_rate]
rates[dataset][df] = [df_rate]
'''
colors = ['royalblue', 'red', 'green', 'violet', 'orange', 'black', 'gray']
fig, ax = plt.subplots()
for i, cell in enumerate(rates.keys()):
for j, df in enumerate(sorted(rates[cell].keys())):
ax.plot(df, rates[cell][df], 'o', color=colors[i])
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
fig.tight_layout()
plt.show()
'''
fig, ax = plt.subplots()
for i, k in enumerate(sorted(rates.keys())):
ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o')
for i, cell in enumerate(rates.keys()):
for j, df in enumerate(sorted(rates[cell].keys())):
ax.plot(np.ones(len(rates[cell][df]))*df, rates[cell][df], 'o', color=colors[i])
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
fig.tight_layout()
plt.show()
'''

56
code/spikes_analysis.py
View File

@ -8,7 +8,8 @@ from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-13-ah-invivo-1"
dataset = "2018-11-13-al-invivo-1"
#dataset = "2018-11-09-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",
@ -90,14 +91,14 @@ for deltaf in df_map.keys():
# make dictionaries for csi and beat
csi_trains = {}
#csi_trains = {}
csi_rates = {}
beat = {}
#beat = {}
# for plotting and calculating iterate over delta f and phases
for df in df_phase_time.keys():
csi_trains[df] = []
csi_rates[df] = []
beat[df] = []
#csi_trains[df] = {}
csi_rates[df] = {}
#beat[df] = []
beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
beat_window = 0
# beat window is at most 20 ms long, multiples of beat_duration
@ -123,9 +124,12 @@ for df in df_phase_time.keys():
std_chirp = np.std(np.mean(train_chirp, axis=0))
std_beat = np.std(np.mean(train_beat, axis=0))
beat[df].append(std_beat)
#beat[df].append(std_beat)
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
csi_rates[df][phase] = np.mean(csi_spikerate)
'''
rcs = []
rbs = []
for i, train in enumerate(train_chirp):
@ -145,9 +149,7 @@ for df in df_phase_time.keys():
# add the csi to the dictionaries with the correct df and phase
csi_trains[df].append(csi_train)
csi_rates[df].append(np.mean(csi_spikerate))
'''
# plot
plot_trials = df_phase_time[df][phase]
plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
@ -170,37 +172,39 @@ for df in df_phase_time.keys():
plt.show()
'''
colors = ['k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'firebrick']
sizes = [12, 12, 12,
12, 12, 12,
12, 12, 12,
12, 12, 18]
upper_limit = np.max(sorted(csi_rates.keys()))+30
lower_limit = np.min(sorted(csi_rates.keys()))-30
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_rates.keys())):
ax.scatter(np.ones(len(csi_rates[k]))*k, csi_rates[k], s=20)
#ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
#ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_rates.keys()))
for i, df in enumerate(sorted(csi_rates.keys())):
for j, phase in enumerate(sorted(csi_rates[df].keys())):
ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
fig.tight_layout()
plt.show()
'''
fig, ax = plt.subplots()
for i, k in enumerate(sorted(csi_trains.keys())):
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
#ax.set_xticklabels(sorted(csi_trains.keys()))
for i, k in enumerate(sorted(beat.keys())):
ax.plot(np.ones(len(beat[k]))*k, beat[k], 'o')
fig.tight_layout()
plt.show()
'''
'''
fig, ax = plt.subplots()
for i, k in enumerate(sorted(beat.keys())):
ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
#ax.set_xticklabels(sorted(csi_trains.keys()))
for i, k in enumerate(sorted(csi_trains.keys())):
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
fig.tight_layout()
plt.show()
'''

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code/spikes_beat.py Normal file
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import matplotlib.pyplot as plt
import numpy as np
from read_chirp_data import *
from read_baseline_data import *
from utility import *
from IPython import embed
# define data path and important parameters
data_dir = "../data"
sampling_rate = 40 #kHz
cut_window = 100
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
window = 1
#dataset = "2018-11-13-ad-invivo-1"
#dataset = "2018-11-13-aj-invivo-1"
#dataset = "2018-11-13-ak-invivo-1" #al
#dataset = "2018-11-14-ad-invivo-1"
dataset = "2018-11-20-af-invivo-1"
base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
base_spikes = base_spikes[1000:2000]
spikerate = len(base_spikes) / base_spikes[-1]
print(spikerate)
# read spikes during chirp stimulation
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
rates = {}
# iterate over df
for deltaf in df_map.keys():
rates[deltaf] = {}
beat_duration = int(abs(1 / deltaf) * 1000)
beat_window = 0
while beat_window + beat_duration <= cut_window/2:
beat_window = beat_window + beat_duration
for x, repetition in enumerate(df_map[deltaf]):
for phase in spikes[repetition]:
# get spikes some ms before the chirp first chirp
spikes_to_cut = np.asarray(spikes[repetition][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)]
spikes_idx = np.round(spikes_cut * sampling_rate)
# also save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx) * 1
smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
#train = smoothed_data[window*sampling_rate:beat_window*sampling_rate+window*sampling_rate]
modulation = np.std(smoothed_data)
rates[deltaf][x] = modulation
break
fig, ax = plt.subplots()
for i, df in enumerate(sorted(rates.keys())):
for j, rep in enumerate(rates[df].keys()):
if j == 15:
farbe = 'royalblue'
gro = 18
else:
farbe = 'k'
gro = 12
ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
fig.tight_layout()
plt.show()

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import matplotlib.pyplot as plt
import numpy as np
from read_chirp_data import *
from utility import *
from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-13-al-invivo-1"
# parameters for binning, smoothing and plotting
cut_window = 20
chirp_duration = 14 #ms
neuronal_delay = 5 #ms
chirp_start = int((-chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
chirp_end = int((chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
number_bins = 12
window = 1 #ms
time_axis = np.arange(-cut_window*2, cut_window*2, 1/sampling_rate) #steps
spike_bins = np.arange(-cut_window*2, cut_window*2) #ms
colors = ['k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'k',
'k', 'k', 'firebrick']
sizes = [12, 12, 12,
12, 12, 12,
12, 12, 12,
12, 12, 18]
# differentiate between phases
phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins)
cut_range = np.arange(-cut_window*2*sampling_rate, cut_window*2*sampling_rate, 1)
df_phase_binary = {}
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
for deltaf in df_map.keys():
df_phase_binary[deltaf] = {}
for rep in df_map[deltaf]:
chirp_size = int(rep[-1].strip('Hz'))
if chirp_size == 150:
continue
for phase in spikes[rep]:
for idx in np.arange(number_bins):
# check the phase
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
# get spikes between 40 ms before and after the chirp
spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
spikes_idx = np.round(spikes_cut*sampling_rate)
# save as binary, 0 no spike, 1 spike
binary_spikes = np.isin(cut_range, spikes_idx)*1
# add the spikes to the dictionary with the correct df and phase
if idx in df_phase_binary[deltaf].keys():
df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
else:
df_phase_binary[deltaf][idx] = binary_spikes
csi_rates = {}
for df in df_phase_binary.keys():
csi_rates[df] = {}
beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
beat_window = 0
# beat window is at most 20 ms long, multiples of beat_duration
while beat_window+beat_duration <= cut_window*sampling_rate:
beat_window = beat_window+beat_duration
for phase in df_phase_binary[df].keys():
# csi calculation
trials_binary = df_phase_binary[df][phase]
train_chirp = []
train_beat = []
for i, trial in enumerate(trials_binary):
smoothed_trial = smooth(trial, window, 1/sampling_rate)
train_chirp.append(smoothed_trial[chirp_start:chirp_end])
train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
std_chirp = np.std(np.mean(train_chirp, axis=0))
std_beat = np.std(np.mean(train_beat, axis=0))
csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
csi_rates[df][phase] = np.mean(csi_spikerate)
upper_limit = np.max(sorted(csi_rates.keys()))+30
lower_limit = np.min(sorted(csi_rates.keys()))-30
fig, ax = plt.subplots()
ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
for i, df in enumerate(sorted(csi_rates.keys())):
for j, phase in enumerate(sorted(csi_rates[df].keys())):
ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
fig.tight_layout()
plt.show()