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# scipy.signal.lfilter

`scipy.signal.``lfilter`(b, a, x, axis=-1, zi=None)

  Filter data along one-dimension 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 1-D sequence. a : array_like The denominator coefficient vector in a 1-D sequence. If `a[0]` is not 1, then both a and b are normalized by `a[0]`. x : array_like An N-dimensional 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 N-dimensional input) of length `max(len(a), len(b)) - 1`. If zi is None or is not given then initial rest is assumed. See `lfiltic` 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 forward-backward filter, to obtain a filter with linear phase.
  `savgol_filter`
    A Savitzky-Golay filter.
  `sosfilt`
    Filter data using cascaded second-order sections.
  `sosfiltfilt`
    A forward-backward filter using second-order 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[n-1] + ... + b[M]*x[n-M]
                            - a[1]*y[n-1] - ... - a[N]*y[n-N]

  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][n-1]
        d[0][n] = b[1] * x[n] - a[1] * y[n] + d[1][n-1]
        d[1][n] = b[2] * x[n] - a[2] * y[n] + d[2][n-1]
      ...
      d[N-2][n] = b[N-1]*x[n] - a[N-1]*y[n] + d[N-1][n-1]
      d[N-1][n] = b[N] * x[n] - a[N] * y[n]

  where d are the state variables.

  The rational transfer function describing this filter in the z-transform 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*(1-t) + 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()

  (Source code)