45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
from read_baseline_data import *
|
|
from IPython import embed
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import thunderfish.peakdetection as pd
|
|
from IPython import embed
|
|
|
|
## beat
|
|
|
|
data_dir = '../data'
|
|
dataset = '2018-11-09-ad-invivo-1'
|
|
inch_factor = 2.54
|
|
|
|
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
|
eod_norm = eod - np.mean(eod)
|
|
eod_norm = eod_norm[10000:20000]
|
|
|
|
x = np.arange(0., len(eod_norm))
|
|
|
|
y = np.sin(time[10000:20000]*2*np.pi*600)*0.5
|
|
ampl = eod_norm + y
|
|
p, t = pd.detect_peaks(ampl, 0.1)
|
|
|
|
time_axis = np.arange(len(ampl))
|
|
|
|
|
|
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
|
plt.plot(time[10000:20000], ampl)
|
|
plt.plot(time[10000:20000][p], ampl[p], lw=2, color='k')
|
|
plt.plot(time[10000:20000][t], ampl[t], lw=2, color='k')
|
|
ax.set_xlabel("time [ms]", fontsize = 22)
|
|
plt.xticks(fontsize = 18)
|
|
ax.set_ylabel("eod amplitude [mV]", fontsize = 22)
|
|
plt.yticks(fontsize = 18)
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
fig.tight_layout()
|
|
|
|
plt.show()
|
|
#plt.savefig('beat.png')
|
|
|
|
|
|
|
|
|