# scipy.stats.exponweib

scipy.stats.exponweib = <scipy.stats._continuous_distns.exponweib_gen object>

  An exponentiated Weibull continuous random variable.

  As an instance of the `rv_continuous` class, `exponweib` object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

  See also

  `weibull_min`, `numpy.random.Generator.weibull`

  Notes

  The probability density function for `exponweib` is:

  \[f(x, a, c) = a c [1-\exp(-x^c)]^{a-1} \exp(-x^c) x^{c-1}\]

  and its cumulative distribution function is:

  \[F(x, a, c) = [1-\exp(-x^c)]^a\]

  for \(x > 0\), \(a > 0\), \(c > 0\).

  `exponweib` takes \(a\) and \(c\) as shape parameters:

  - \(a\) is the exponentiation parameter, with the special case \(a=1\) corresponding to the (non-exponentiated) Weibull distribution `weibull_min`.

  - \(c\) is the shape parameter of the non-exponentiated Weibull law.

  The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the `loc` and `scale` parameters. Specifically, `exponweib.pdf(x, a, c, loc, scale)` is identically equivalent to `exponweib.pdf(y, a, c) / scale` with `y = (x - loc) / scale`. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

  References

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

  Examples

      >>> from scipy.stats import exponweib
      >>> import matplotlib.pyplot as plt
      >>> fig, ax = plt.subplots(1, 1)

  Calculate the first four moments:

      >>> a, c = 2.89, 1.95
      >>> mean, var, skew, kurt = exponweib.stats(a, c, moments='mvsk')

  Display the probability density function (`pdf`):

      >>> x = np.linspace(exponweib.ppf(0.01, a, c),
      ...                 exponweib.ppf(0.99, a, c), 100)
      >>> ax.plot(x, exponweib.pdf(x, a, c),
      ...        'r-', lw=5, alpha=0.6, label='exponweib pdf')

  Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

  Freeze the distribution and display the frozen `pdf`:

      >>> rv = exponweib(a, c)
      >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

  Check accuracy of `cdf` and `ppf`:

      >>> vals = exponweib.ppf([0.001, 0.5, 0.999], a, c)
      >>> np.allclose([0.001, 0.5, 0.999], exponweib.cdf(vals, a, c))
      True

  Generate random numbers:

      >>> r = exponweib.rvs(a, c, size=1000)

  And compare the histogram:

      >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
      >>> ax.legend(loc='best', frameon=False)
      >>> plt.show()

  Methods

  rvs(a, c, loc=0, scale=1, size=1, random_state=None) | Random variates.
  pdf(x, a, c, loc=0, scale=1) | Probability density function.
  logpdf(x, a, c, loc=0, scale=1) | Log of the probability density function.
  cdf(x, a, c, loc=0, scale=1) | Cumulative distribution function.
  logcdf(x, a, c, loc=0, scale=1) | Log of the cumulative distribution function.
  sf(x, a, c, loc=0, scale=1) | Survival function (also defined as `1 - cdf`, but sf is sometimes more accurate).
  logsf(x, a, c, loc=0, scale=1) | Log of the survival function.
  ppf(q, a, c, loc=0, scale=1) | Percent point function (inverse of `cdf` — percentiles).
  isf(q, a, c, loc=0, scale=1) | Inverse survival function (inverse of `sf`).
  moment(n, a, c, loc=0, scale=1) | Non-central moment of order n
  stats(a, c, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  entropy(a, c, loc=0, scale=1) | (Differential) entropy of the RV.
  fit(data) | Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
  expect(func, args=(a, c), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) | Expected value of a function (of one argument) with respect to the distribution.
  median(a, c, loc=0, scale=1) | Median of the distribution.
  mean(a, c, loc=0, scale=1) | Mean of the distribution.
  var(a, c, loc=0, scale=1) | Variance of the distribution.
  std(a, c, loc=0, scale=1) | Standard deviation of the distribution.
  interval(alpha, a, c, loc=0, scale=1) | Endpoints of the range that contains fraction alpha [0, 1] of the distribution

scipy.stats.exponnorm scipy.stats.exponpow