Skip to main content
`Ctrl`+`K`

# scipy.signal.sweep_poly

scipy.signal.sweep_poly(t, poly, phi=0)

  Frequency-swept cosine generator, with a time-dependent frequency.

  This function generates a sinusoidal function whose instantaneous frequency varies with time. The frequency at time t is given by the polynomial poly.

  Parameters:
    t ndarray

      Times at which to evaluate the waveform.

    poly 1-D array_like or instance of numpy.poly1d

      The desired frequency expressed as a polynomial. If poly is a list or ndarray of length n, then the elements of poly are the coefficients of the polynomial, and the instantaneous frequency is

      > `f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]`

      If poly is an instance of numpy.poly1d, then the instantaneous frequency is

      > `f(t) = poly(t)`

    phi float, optional

      Phase offset, in degrees, Default: 0.

  Returns:
    sweep_poly ndarray

      A numpy array containing the signal evaluated at t with the requested time-varying frequency. More precisely, the function returns `cos(phase + (pi/180)*phi)`, where phase is the integral (from 0 to t) of `2 * pi * f(t)`; `f(t)` is defined above.

  See also

  `chirp`

  Notes

  New in version 0.8.0.

  If poly is a list or ndarray of length n, then the elements of poly are the coefficients of the polynomial, and the instantaneous frequency is:

  > `f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]`

  If poly is an instance of `numpy.poly1d`, then the instantaneous frequency is:

  > `f(t) = poly(t)`

  Finally, the output s is:

  > `cos(phase + (pi/180)*phi)`

  where phase is the integral from 0 to t of `2 * pi * f(t)`, `f(t)` as defined above.

  Examples

  Compute the waveform with instantaneous frequency:

      f(t) = 0.025*t**3 - 0.36*t**2 + 1.25*t + 2

  over the interval 0 <= t <= 10.

      >>> import numpy as np
      >>> from scipy.signal import sweep_poly
      >>> p = np.poly1d([0.025, -0.36, 1.25, 2.0])
      >>> t = np.linspace(0, 10, 5001)
      >>> w = sweep_poly(t, p)

  Plot it:

      >>> import matplotlib.pyplot as plt
      >>> plt.subplot(2, 1, 1)
      >>> plt.plot(t, w)
      >>> plt.title("Sweep Poly\nwith frequency " +
      ...           "$f(t) = 0.025t^3 - 0.36t^2 + 1.25t + 2$")
      >>> plt.subplot(2, 1, 2)
      >>> plt.plot(t, p(t), 'r', label='f(t)')
      >>> plt.legend()
      >>> plt.xlabel('t')
      >>> plt.tight_layout()
      >>> plt.show()