# scipy.special.gdtrc

scipy.special.gdtrc(a, b, x, out=None) = <ufunc 'gdtrc'>

  Gamma distribution survival function.

  Integral from x to infinity of the gamma probability density function,

  \[F = \int_x^\infty \frac{a^b}{\Gamma(b)} t^{b-1} e^{-at}\,dt,\]

  where \(\Gamma\) is the gamma function.

  Parameters:
    a array_like

      The rate parameter of the gamma distribution, sometimes denoted \(\beta\) (float). It is also the reciprocal of the scale parameter \(\theta\).

    b array_like

      The shape parameter of the gamma distribution, sometimes denoted \(\alpha\) (float).

    x array_like

      The quantile (lower limit of integration; float).

    out ndarray, optional

      Optional output array for the function values

  Returns:
    F scalar or ndarray

      The survival function of the gamma distribution with parameters a and b evaluated at x.

  See also

  `gdtr`

    Gamma distribution cumulative distribution function

  `scipy.stats.gamma`

    Gamma distribution

  `gdtrix`

  Notes

  The evaluation is carried out using the relation to the incomplete gamma integral (regularized gamma function).

  Wrapper for the Cephes [1] routine `gdtrc`. Calling `gdtrc` directly can improve performance compared to the `sf` method of `scipy.stats.gamma` (see last example below).

  References

  [1]

  Cephes Mathematical Functions Library, http://www.netlib.org/cephes/

  Examples

  Compute the function for `a=1` and `b=2` at `x=5`.

      >>> import numpy as np
      >>> from scipy.special import gdtrc
      >>> import matplotlib.pyplot as plt
      >>> gdtrc(1., 2., 5.)
      0.04042768199451279

  Compute the function for `a=1`, `b=2` at several points by providing a NumPy array for x.

      >>> xvalues = np.array([1., 2., 3., 4])
      >>> gdtrc(1., 1., xvalues)
      array([0.36787944, 0.13533528, 0.04978707, 0.01831564])

  `gdtrc` can evaluate different parameter sets by providing arrays with broadcasting compatible shapes for a, b and x. Here we compute the function for three different a at four positions x and `b=3`, resulting in a 3x4 array.

      >>> a = np.array([[0.5], [1.5], [2.5]])
      >>> x = np.array([1., 2., 3., 4])
      >>> a.shape, x.shape
      ((3, 1), (4,))

      >>> gdtrc(a, 3., x)
      array([[0.98561232, 0.9196986 , 0.80884683, 0.67667642],
             [0.80884683, 0.42319008, 0.17357807, 0.0619688 ],
             [0.54381312, 0.12465202, 0.02025672, 0.0027694 ]])

  Plot the function for four different parameter sets.

      >>> a_parameters = [0.3, 1, 2, 6]
      >>> b_parameters = [2, 10, 15, 20]
      >>> linestyles = ['solid', 'dashed', 'dotted', 'dashdot']
      >>> parameters_list = list(zip(a_parameters, b_parameters, linestyles))
      >>> x = np.linspace(0, 30, 1000)
      >>> fig, ax = plt.subplots()
      >>> for parameter_set in parameters_list:
      ...     a, b, style = parameter_set
      ...     gdtrc_vals = gdtrc(a, b, x)
      ...     ax.plot(x, gdtrc_vals, label=f"$a= {a},\, b={b}$", ls=style)
      >>> ax.legend()
      >>> ax.set_xlabel("$x$")
      >>> ax.set_title("Gamma distribution survival function")
      >>> plt.show()

  The gamma distribution is also available as `scipy.stats.gamma`. Using `gdtrc` directly can be much faster than calling the `sf` method of `scipy.stats.gamma`, especially for small arrays or individual values. To get the same results one must use the following parametrization: `stats.gamma(b, scale=1/a).sf(x)=gdtrc(a, b, x)`.

      >>> from scipy.stats import gamma
      >>> a = 2
      >>> b = 3
      >>> x = 1.
      >>> gdtrc_result = gdtrc(a, b, x)  # this will often be faster than below
      >>> gamma_dist_result = gamma(b, scale=1/a).sf(x)
      >>> gdtrc_result == gamma_dist_result  # test that results are equal
      True