spkit.filterDC

spkit.filterDC(X, alpha=256, return_background=False, initialize_zero=True)

Filter out DC component using IIR filter

Filter out DC component - Remving drift using Recursive (IIR type) filter

\[y[n] = ((alpha-1)/alpha) * ( x[n] - x[n-1] -y[n-1])\]

where \(y[-1] = x[0]\), \(x[-1] = x[0]\) resulting \(y[0] = 0\)

Implemenatation works for single (1d array) or multi-channel (2d array)

Parameters:
Xarray, 1d, 2d, (n,) or (n,ch)

  • input signal single channel (n,) or multi-channel, channel axis should be 1 shape ~ (n,ch)

alpha: scalar, default alpha=256

  • filter coefficient, higher it is, more suppressed dc component (0 frequency component)

  • with alpha=256, dc component is suppressed by 20 dB

initialize_zero: bool, default True

  • If True, running background b will be initialize it with x[0], resulting y[0] = 0

  • if False, b = 0, resulting y[0] ~ x[0], and slowly drifting towards zeros line

  • recommended to set True

Returns:
Youtput vector,
  • shape same as input X (n,) or (n,ch)

See also

filterDC_sGolay

filter out DC using Savitzky-Golay filter

Notes

  • filterDC employes causal IIR filter to remove drift from signal, which introduces the shift (delay) in the filtered signal. It is recommonded to check out filterDC_sGolay

References

Examples

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
x = sp.create_signal_1d(n=1000, sg_polyorder=1, sg_winlen=1, seed=1)
x -= x.mean()
y = sp.filterDC(x.copy(),alpha=32)
plt.figure(figsize=(12,3))
plt.plot(x,label='x : raw signal')
plt.plot(y,label='y : filtered - dc removed')
plt.plot(x-y,label='x-y : removed component (drift)')
plt.legend()
plt.xlim([0,len(x)])
plt.show()
../../_images/spkit-filterDC-1.png