d"), (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()
    