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scipy.signal.

# dbode

scipy.signal.dbode(system, w=None, n=100)

  Calculate Bode magnitude and phase data of a discrete-time system.

  Parameters:
    systemdlti | tuple

      An instance of the LTI class `dlti` or a tuple describing the system. The number of elements in the tuple determine the interpretation. I.e.:

      - `system`: Instance of LTI class `dlti`. Note that derived instances, such as instances of `TransferFunction`, `ZerosPolesGain`, or `StateSpace`, are allowed as well.

      - `(num, den, dt)`: Rational polynomial as described in `TransferFunction`. The coefficients of the polynomials should be specified in descending exponent order, e.g., z² + 3z + 5 would be represented as `[1, 3, 5]`.

      - `(zeros, poles, gain, dt)`: Zeros, poles, gain form as described in `ZerosPolesGain`.

      - `(A, B, C, D, dt)`: State-space form as described in `StateSpace`.

    warray_like, optional

      Array of frequencies normalized to the Nyquist frequency being π, i.e., having unit radiant / sample. Magnitude and phase data is calculated for every value in this array. If not given, a reasonable set will be calculated.

    nint, optional

      Number of frequency points to compute if w is not given. The n frequencies are logarithmically spaced in an interval chosen to include the influence of the poles and zeros of the system.

  Returns:
    w1D ndarray

      Array of frequencies normalized to the Nyquist frequency being `np.pi/dt` with `dt` being the sampling interval of the system parameter. The unit is rad/s assuming `dt` is in seconds.

    mag1D ndarray

      Magnitude array in dB

    phase1D ndarray

      Phase array in degrees

  See also

  `dfreqresp`, `dlti`, `TransferFunction`, `ZerosPolesGain`, `StateSpace`

  Notes

  This function is a convenience wrapper around `dfreqresp` for extracting magnitude and phase from the calculated complex-valued amplitude of the frequency response.

  Added in version 0.18.0.

  Examples

  The following example shows how to create a Bode plot of a 5-th order Butterworth lowpass filter with a corner frequency of 100 Hz:

      >>> import matplotlib.pyplot as plt
      >>> import numpy as np
      >>> from scipy import signal
      ...
      >>> T = 1e-4  # sampling interval in s
      >>> f_c, o = 1e2, 5  # corner frequency in Hz (i.e., -3 dB value) and filter order
      >>> bb, aa = signal.butter(o, f_c, 'lowpass', fs=1/T)
      ...
      >>> w, mag, phase = signal.dbode((bb, aa, T))
      >>> w /= 2*np.pi  # convert unit of frequency into Hertz
      ...
      >>> fg, (ax0, ax1) = plt.subplots(2, 1, sharex='all', figsize=(5, 4),
      ...                               tight_layout=True)
      >>> ax0.set_title("Bode Plot of Butterworth Lowpass Filter " +
      ...               rf"($f_c={f_c:g}\,$Hz, order={o})")
      >>> ax0.set_ylabel(r"Magnitude in dB")
      >>> ax1.set(ylabel=r"Phase in Degrees",
      ...         xlabel="Frequency $f$ in Hertz", xlim=(w[1], w[-1]))
      >>> ax0.semilogx(w, mag, 'C0-', label=r"$20\,\log_{10}|G(f)|$")  # Magnitude plot
      >>> ax1.semilogx(w, phase, 'C1-', label=r"$\angle G(f)$")  # Phase plot
      ...
      >>> for ax_ in (ax0, ax1):
      ...     ax_.axvline(f_c, color='m', alpha=0.25, label=rf"${f_c=:g}\,$Hz")
      ...     ax_.grid(which='both', axis='x')  # plot major & minor vertical grid lines
      ...     ax_.grid(which='major', axis='y')
      ...     ax_.legend()
      >>> plt.show()