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# scipy.special.voigt_profile

scipy.special.voigt_profile(x, sigma, gamma, out=None) = <ufunc 'voigt_profile'>

  Voigt profile.

  The Voigt profile is a convolution of a 1-D Normal distribution with standard deviation `sigma` and a 1-D Cauchy distribution with half-width at half-maximum `gamma`.

  If `sigma = 0`, PDF of Cauchy distribution is returned. Conversely, if `gamma = 0`, PDF of Normal distribution is returned. If `sigma = gamma = 0`, the return value is `Inf` for `x = 0`, and `0` for all other `x`.

  Parameters:
    xarray_like

      Real argument

    sigmaarray_like

      The standard deviation of the Normal distribution part

    gammaarray_like

      The half-width at half-maximum of the Cauchy distribution part

    outndarray, optional

      Optional output array for the function values

  Returns:
    scalar or ndarray

      The Voigt profile at the given arguments

  See also

  `wofz`

    Faddeeva function

  Notes

  It can be expressed in terms of Faddeeva function

  \[V(x; \sigma, \gamma) = \frac{Re[w(z)]}{\sigma\sqrt{2\pi}},\]
  \[z = \frac{x + i\gamma}{\sqrt{2}\sigma}\]

  where \(w(z)\) is the Faddeeva function.

  References

  [1]

  https://en.wikipedia.org/wiki/Voigt_profile

  Examples

  Calculate the function at point 2 for `sigma=1` and `gamma=1`.

      >>> from scipy.special import voigt_profile
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> voigt_profile(2, 1., 1.)
      0.09071519942627544

  Calculate the function at several points by providing a NumPy array for x.

      >>> values = np.array([-2., 0., 5])
      >>> voigt_profile(values, 1., 1.)
      array([0.0907152 , 0.20870928, 0.01388492])

  Plot the function for different parameter sets.

      >>> fig, ax = plt.subplots(figsize=(8, 8))
      >>> x = np.linspace(-10, 10, 500)
      >>> parameters_list = [(1.5, 0., "solid"), (1.3, 0.5, "dashed"),
      ...                    (0., 1.8, "dotted"), (1., 1., "dashdot")]
      >>> for params in parameters_list:
      ...     sigma, gamma, linestyle = params
      ...     voigt = voigt_profile(x, sigma, gamma)
      ...     ax.plot(x, voigt, label=rf"$\sigma={sigma},\, \gamma={gamma}$",
      ...             ls=linestyle)
      >>> ax.legend()
      >>> plt.show()

  Verify visually that the Voigt profile indeed arises as the convolution of a normal and a Cauchy distribution.

      >>> from scipy.signal import convolve
      >>> x, dx = np.linspace(-10, 10, 500, retstep=True)
      >>> def gaussian(x, sigma):
      ...     return np.exp(-0.5 * x**2/sigma**2)/(sigma * np.sqrt(2*np.pi))
      >>> def cauchy(x, gamma):
      ...     return gamma/(np.pi * (np.square(x)+gamma**2))
      >>> sigma = 2
      >>> gamma = 1
      >>> gauss_profile = gaussian(x, sigma)
      >>> cauchy_profile = cauchy(x, gamma)
      >>> convolved = dx * convolve(cauchy_profile, gauss_profile, mode="same")
      >>> voigt = voigt_profile(x, sigma, gamma)
      >>> fig, ax = plt.subplots(figsize=(8, 8))
      >>> ax.plot(x, gauss_profile, label="Gauss: $G$", c='b')
      >>> ax.plot(x, cauchy_profile, label="Cauchy: $C$", c='y', ls="dashed")
      >>> xx = 0.5*(x[1:] + x[:-1])  # midpoints
      >>> ax.plot(xx, convolved[1:], label="Convolution: $G * C$", ls='dashdot',
      ...         c='k')
      >>> ax.plot(x, voigt, label="Voigt", ls='dotted', c='r')
      >>> ax.legend()
      >>> plt.show()