scipy.signal.lfilter¶

scipy.signal.
lfilter
(b, a, x, axis=1, zi=None)[source]¶ Filter data along onedimension with an IIR or FIR filter.
Filter a data sequence, x, using a digital filter. This works for many fundamental data types (including Object type). The filter is a direct form II transposed implementation of the standard difference equation (see Notes).
Parameters: b : array_like
The numerator coefficient vector in a 1D sequence.
a : array_like
The denominator coefficient vector in a 1D sequence. If
a[0]
is not 1, then both a and b are normalized bya[0]
.x : array_like
An Ndimensional input array.
axis : int, optional
The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis. Default is 1.
zi : array_like, optional
Initial conditions for the filter delays. It is a vector (or array of vectors for an Ndimensional input) of length
max(len(a), len(b))  1
. If zi is None or is not given then initial rest is assumed. Seelfiltic
for more information.Returns: y : array
The output of the digital filter.
zf : array, optional
If zi is None, this is not returned, otherwise, zf holds the final filter delay values.
See also
lfiltic
 Construct initial conditions for
lfilter
. lfilter_zi
 Compute initial state (steady state of step response) for
lfilter
. filtfilt
 A forwardbackward filter, to obtain a filter with linear phase.
savgol_filter
 A SavitzkyGolay filter.
sosfilt
 Filter data using cascaded secondorder sections.
sosfiltfilt
 A forwardbackward filter using secondorder sections.
Notes
The filter function is implemented as a direct II transposed structure. This means that the filter implements:
a[0]*y[n] = b[0]*x[n] + b[1]*x[n1] + ... + b[M]*x[nM]  a[1]*y[n1]  ...  a[N]*y[nN]
where M is the degree of the numerator, N is the degree of the denominator, and n is the sample number. It is implemented using the following difference equations (assuming M = N):
a[0]*y[n] = b[0] * x[n] + d[0][n1] d[0][n] = b[1] * x[n]  a[1] * y[n] + d[1][n1] d[1][n] = b[2] * x[n]  a[2] * y[n] + d[2][n1] ... d[N2][n] = b[N1]*x[n]  a[N1]*y[n] + d[N1][n1] d[N1][n] = b[N] * x[n]  a[N] * y[n]
where d are the state variables.
The rational transfer function describing this filter in the ztransform domain is:
1 M b[0] + b[1]z + ... + b[M] z Y(z) =  X(z) 1 N a[0] + a[1]z + ... + a[N] z
Examples
Generate a noisy signal to be filtered:
>>> from scipy import signal >>> import matplotlib.pyplot as plt >>> t = np.linspace(1, 1, 201) >>> x = (np.sin(2*np.pi*0.75*t*(1t) + 2.1) + ... 0.1*np.sin(2*np.pi*1.25*t + 1) + ... 0.18*np.cos(2*np.pi*3.85*t)) >>> xn = x + np.random.randn(len(t)) * 0.08
Create an order 3 lowpass butterworth filter:
>>> b, a = signal.butter(3, 0.05)
Apply the filter to xn. Use lfilter_zi to choose the initial condition of the filter:
>>> zi = signal.lfilter_zi(b, a) >>> z, _ = signal.lfilter(b, a, xn, zi=zi*xn[0])
Apply the filter again, to have a result filtered at an order the same as filtfilt:
>>> z2, _ = signal.lfilter(b, a, z, zi=zi*z[0])
Use filtfilt to apply the filter:
>>> y = signal.filtfilt(b, a, xn)
Plot the original signal and the various filtered versions:
>>> plt.figure >>> plt.plot(t, xn, 'b', alpha=0.75) >>> plt.plot(t, z, 'r', t, z2, 'r', t, y, 'k') >>> plt.legend(('noisy signal', 'lfilter, once', 'lfilter, twice', ... 'filtfilt'), loc='best') >>> plt.grid(True) >>> plt.show()