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scipy.signal.

# ellipord

scipy.signal.ellipord(wp, ws, gpass, gstop, analog=False, fs=None)

  Elliptic (Cauer) filter order selection.

  Return the order of the lowest order digital or analog elliptic filter that loses no more than gpass dB in the passband and has at least gstop dB attenuation in the stopband.

  Parameters:
    wp, ws float

      Passband and stopband edge frequencies.

      For digital filters, these are in the same units as fs. By default, fs is 2 half-cycles/sample, so these are normalized from 0 to 1, where 1 is the Nyquist frequency. (wp and ws are thus in half-cycles / sample.) For example:

      > - Lowpass: wp = 0.2, ws = 0.3

      > - Highpass: wp = 0.3, ws = 0.2

      > - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6]

      > - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5]

      For analog filters, wp and ws are angular frequencies (e.g., rad/s).

    gpass float

      The maximum loss in the passband (dB).

    gstop float

      The minimum attenuation in the stopband (dB).

    analog bool, optional

      When True, return an analog filter, otherwise a digital filter is returned.

    fs float, optional

      The sampling frequency of the digital system.

      Added in version 1.2.0.

  Returns:
    ord int

      The lowest order for an Elliptic (Cauer) filter that meets specs.

    wn ndarray or float

      The Chebyshev natural frequency (the “3dB frequency”) for use with `ellip` to give filter results. If fs is specified, this is in the same units, and fs must also be passed to `ellip`.

  See also

  `ellip`

    Filter design using order and critical points

  `buttord`

    Find order and critical points from passband and stopband spec

  `cheb1ord`, `cheb2ord`
  `iirfilter`

    General filter design using order and critical frequencies

  `iirdesign`

    General filter design using passband and stopband spec

  Notes

  Array API Standard Support

  `ellipord` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ⛔
  JAX | ⚠️ no JIT | ⛔
  Dask | ⚠️ computes graph | n/a

  > See Support for the array API standard for more information.

  Examples

  Design an analog highpass filter such that the passband is within 3 dB above 30 rad/s, while rejecting -60 dB at 10 rad/s. Plot its frequency response, showing the passband and stopband constraints in gray.

      >>> from scipy import signal
      >>> import matplotlib.pyplot as plt
      >>> import numpy as np

      >>> N, Wn = signal.ellipord(30, 10, 3, 60, True)
      >>> b, a = signal.ellip(N, 3, 60, Wn, 'high', True)
      >>> w, h = signal.freqs(b, a, np.logspace(0, 3, 500))
      >>> plt.semilogx(w, 20 * np.log10(abs(h)))
      >>> plt.title('Elliptical highpass filter fit to constraints')
      >>> plt.xlabel('Frequency [rad/s]')
      >>> plt.ylabel('Amplitude [dB]')
      >>> plt.grid(which='both', axis='both')
      >>> plt.fill([.1, 10,  10,  .1], [1e4, 1e4, -60, -60], '0.9', lw=0) # stop
      >>> plt.fill([30, 30, 1e9, 1e9], [-99,  -3,  -3, -99], '0.9', lw=0) # pass
      >>> plt.axis([1, 300, -80, 3])
      >>> plt.show()