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analysis

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Ramona 2018-11-15 10:57:49 +01:00
parent c3c95e8c98
commit b2c3a99a3b
2 changed files with 25 additions and 19 deletions
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44
code/analysis_rs.py
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@ -2,21 +2,22 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from read_baseline_data import * from read_baseline_data import *
from IPython import embed from IPython import embed
from NixFrame import *
inch_factor = 2.54 inch_factor = 2.54
data_dir = '../data' data_dir = '../data'
dataset = '2018-11-09-ad-invivo-1' dataset = '2018-11-09-ad-invivo-1'
time, eod = read_baseline_eod(os.path.join(data_dir, dataset)) time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor)) #fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
ax = fig.add_subplot(111) #ax = fig.add_subplot(111)
ax.plot(time[:1000], eod[:1000]) #ax.plot(time[:1000], eod[:1000])
ax.set_xlabel('time [ms]', fontsize=12) #ax.set_xlabel('time [ms]', fontsize=12)
ax.set_ylabel('voltage [mV]', fontsize=12) #ax.set_ylabel('voltage [mV]', fontsize=12)
plt.xticks(fontsize = 8) #plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8) #plt.yticks(fontsize = 8)
fig.tight_layout() #fig.tight_layout()
plt.savefig('eod.pdf') #plt.savefig('eod.pdf')
#interspikeintervalhistogram, windowsize = 1 ms #interspikeintervalhistogram, windowsize = 1 ms
#plt.hist #plt.hist
@ -26,14 +27,14 @@ plt.savefig('eod.pdf')
spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
interspikeintervals = np.diff(spikes) interspikeintervals = np.diff(spikes)
fig = plt.figure() #fig = plt.figure()
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001)) #plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
plt.show() #plt.show()
mu = np.mean(interspikeintervals) mu = np.mean(interspikeintervals)
sigma = np.std(interspikeintervals) sigma = np.std(interspikeintervals)
cv = sigma/mu cv = sigma/mu
print(cv) #print(cv)
# calculate zero crossings of the eod # calculate zero crossings of the eod
# plot mean of eod circles # plot mean of eod circles
@ -47,10 +48,15 @@ eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
sampling_rate = 40000.0 sampling_rate = 40000.0
eod_idx = eod_times*sampling_rate eod_idx = eod_times*sampling_rate
fig = plt.figure() #fig = plt.figure()
for i, idx in enumerate(eod_idx): eod_cuts = [];
#embed() #for i, idx in enumerate(eod_idx)-1:
#exit() #eod_cuts.append(eod[int(idx):int(eod_idx[i+1])])
plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])]) #time_cut = time[int(idx):int(eod_idx[i+1])]
plt.show() #plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
#plt.show()
data = NixToFrame(data_dir)
embed()
exit()

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code/eod_sim_rs.py
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