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

buh

Browse Source
This commit is contained in:
efish 2018-11-28 15:07:59 +01:00
commit b9573c6563
5 changed files with 67 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
code/plot_eodform_spikehist.py
View File

@ -21,7 +21,7 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
interspikeintervals = np.diff(spikes)
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')
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
plt.xlabel("time [ms]", fontsize = 22)
plt.xticks(fontsize = 18)
plt.ylabel("number of \n interspikeintervals", fontsize = 22)
@ -83,10 +83,10 @@ plt.yticks(fontsize=18)
ax1.spines['top'].set_visible(False)
ax2 = ax1.twinx()
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
ax2.plot(time_axis, mu_eod, color='black', lw=2)
ax2.set_ylabel('voltage [mV]', fontsize=22)
ax2.tick_params(axis='y', labelcolor='darkblue')
ax2.tick_params(axis='y', labelcolor='navy')
ax2.spines['top'].set_visible(False)
plt.yticks(fontsize=18)

16
code/repetition_firingrate.py
View File

@ -8,7 +8,7 @@ from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
data_dir = "../data"
dataset = "2018-11-13-aj-invivo-1"
dataset = "2018-11-14-al-invivo-1"
inch_factor = 2.54
# parameters for binning, smoothing and plotting
cut_window = 60
@ -23,8 +23,8 @@ spike_bins = np.arange(-cut_window, cut_window+1) #ms
# read data from files
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
eod = read_chirp_eod(os.path.join(data_dir, dataset))
chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
#eod = read_chirp_eod(os.path.join(data_dir, dataset))
#chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
# make a delta f map for the quite more complicated keys
df_map = map_keys(spikes)
@ -42,12 +42,16 @@ for deltaf in df_map.keys():
df_phase_time[deltaf] = {}
df_phase_binary[deltaf] = {}
for rep in df_map[deltaf]:
chirp_size = int(rep[-1].strip('Hz'))
# print(chirp_size)
if chirp_size == 150:
continue
for phase in spikes[rep]:
for idx in np.arange(num_bin):
# check the phase
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
# get spikes between 50 ms befor and after the chirp
# get spikes between 60 ms before and after the chirp
spikes_to_cut = np.asarray(spikes[rep][phase])
spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
spikes_idx = np.round(spikes_cut*sampling_rate)
@ -98,5 +102,5 @@ for df in df_phase_time.keys():
fig.tight_layout()
#plt.show()
#exit()
namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
plt.savefig(namefigure)
namefigure = '../figures/%s_%i_%i_firingrate.png' %(dataset, df, index_phase)
plt.savefig(namefigure)

60
code/response_beat.py
View File

@ -1,30 +1,72 @@
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
from read_chirp_data import *
from utility import *
from IPython import embed
# define sampling rate and data path
sampling_rate = 40 #kHz
# define data path and important parameters
data_dir = "../data"
cut_window = 20
sampling_rate = 40 #kHz
cut_window = 40
cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
window = 1
data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
'''
# norm: -150, 150, 300
data = ["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"]
# 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"]
rates = {}
for dataset in data:
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
df_map = map_keys(spikes)
print(dataset)
for df in df_map.keys():
beat_duration = 1/df
'''
if df == 50:
pass
else:
continue
'''
print(df)
rep_rates = []
beat_duration = int(abs(1 / df) * 1000)
beat_window = 0
while beat_window + beat_duration <= cut_window:
while beat_window + beat_duration <= cut_window/2:
beat_window = beat_window + beat_duration
for rep in df_map[df]:
for phase in spikes[rep]:
response = spikes[rep][phase]
# get spikes 40 ms before the chirp first chirp
spikes_to_cut = np.asarray(spikes[rep][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:beat_window+window]
rep_rates.append(np.std(train))
break
#cut = response[response[]]
df_rate = np.mean(rep_rates)
if df in rates.keys():
rates[df].append(df_rate)
else:
rates[df] = [df_rate]
fig, ax = plt.subplots()
for i, k in enumerate(sorted(rates.keys())):
ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o')
#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
fig.tight_layout()
plt.show()

2
code/spikes_analysis.py
View File

@ -71,7 +71,7 @@ for dataset in data:
# check the phase
if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
# get spikes between 50 ms before and after the chirp
# 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)

4
code/stimulus_chirp.py
View File

@ -40,8 +40,8 @@ ax1 = fig.add_subplot(211)
plt.yticks(fontsize=18)
ax2 = fig.add_subplot(212, sharex=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
ax1.set_ylabel("field [mV]", fontsize = 22)