34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
|
import numpy as np
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
def testcurve(x, frequency=10, peak_width=2, amplitude=20, bias=0):
|
||
|
|
# want peak at n*time == frequency
|
||
|
|
nearest_peak = np.round(x / frequency, 0)
|
||
|
|
# if not peak at 0 and within peak_width
|
||
|
|
# print(x, nearest_peak)
|
||
|
|
if nearest_peak > 0 and abs((x - nearest_peak * frequency)) < peak_width:
|
||
|
|
# return sin that does one period within 2*peak_width
|
||
|
|
return amplitude * np.sin(2*np.pi * (x - nearest_peak * frequency - peak_width) / (2*peak_width)) + bias
|
||
|
|
else:
|
||
|
|
return bias
|
||
|
|
|
||
|
|
# 0 = pk - width
|
||
|
|
# 2pi = pk + width
|
||
|
|
|
||
|
|
def baseline(data):
|
||
|
|
# find the value where the most values with low gradients are closest to
|
||
|
|
gradients = np.abs(np.gradient(data))
|
||
|
|
# consider the values where the absolute gradient is in the bottom 20%
|
||
|
|
n_gradients = len(data) // 20
|
||
|
|
consider_indices = np.argsort(gradients)[:n_gradients]
|
||
|
|
# of those, only consider values where the value
|
||
|
|
consider_values = data[consider_indices]
|
||
|
|
|
||
|
|
|
||
|
|
xdata = np.arange(0, 100, 0.01)
|
||
|
|
ydata = np.vectorize(testcurve)(xdata)
|
||
|
|
|
||
|
|
|
||
|
|
plt.plot(xdata, ydata)
|
||
|
|
plt.show()
|