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""" This sub-module contains filters for cleaning experimental data.
"""
import numpy as np
def gauss_conv(x, sigma=10, ext_x=3):
"""Smooth data using a Gaussian convolution.
Args:
x (ndarray): noisy data
sigma (float): std. dev. of Gaussian curve, given as number of data
points
ext_x (float): Gaussian curve will extend from ext_x * sigma in each
direction
Returns:
ndarray: filtered data
"""
wind = _gauss(sigma, ext_x)
wlen = len(wind)
assert wlen <= len(x), "Window size must be smaller than data size"
assert sigma * ext_x >= 1, \
"Window size must be larger than 1. Increase ext_x."
s = np.r_[x[wlen - 1:0:-1], x, x[-2:-wlen - 1:-1]]
y_out = np.convolve(wind / wind.sum(), s, mode='valid')
y_out = y_out[wlen // 2:-wlen // 2 + 1]
return y_out
def _gauss(sigma, n_sigma=3):
"""Generate a discrete, normalized Gaussian centered on zero.
Used for filtering data.
Args:
sigma (float): standard deviation
n_sigma (float): extend x in each direction by ext_x * sigma
Returns:
ndarray: discrete Gaussian curve
"""
x_range = n_sigma * sigma
x = np.arange(-x_range, x_range + 1e-5, 1, dtype=float)
y = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * (x / sigma)**2)
return y
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