python3-openturns 1.5-7build2 / usr / lib / python3 / dist-packages / openturns / stattests.py

# This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.12
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.




"""
Statistical tests.
"""


from sys import version_info
if version_info >= (2,6,0):
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        from os.path import dirname
        import imp
        fp = None
        try:
            fp, pathname, description = imp.find_module('_stattests', [dirname(__file__)])
        except ImportError:
            import _stattests
            return _stattests
        if fp is not None:
            try:
                _mod = imp.load_module('_stattests', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _stattests = swig_import_helper()
    del swig_import_helper
else:
    import _stattests
del version_info
try:
    _swig_property = property
except NameError:
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def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
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            return
    method = class_type.__swig_setmethods__.get(name,None)
    if method: return method(self,value)
    if (not static):
        self.__dict__[name] = value
    else:
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def _swig_setattr(self,class_type,name,value):
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def _swig_getattr(self,class_type,name):
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def _swig_repr(self):
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    except: strthis = ""
    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)

try:
    _object = object
    _newclass = 1
except AttributeError:
    class _object : pass
    _newclass = 0


class SwigPyIterator(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, SwigPyIterator, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, SwigPyIterator, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
    __repr__ = _swig_repr
    __swig_destroy__ = _stattests.delete_SwigPyIterator
    __del__ = lambda self : None;
    def value(self): return _stattests.SwigPyIterator_value(self)
    def incr(self, n=1): return _stattests.SwigPyIterator_incr(self, n)
    def decr(self, n=1): return _stattests.SwigPyIterator_decr(self, n)
    def distance(self, *args): return _stattests.SwigPyIterator_distance(self, *args)
    def equal(self, *args): return _stattests.SwigPyIterator_equal(self, *args)
    def copy(self): return _stattests.SwigPyIterator_copy(self)
    def next(self): return _stattests.SwigPyIterator_next(self)
    def __next__(self): return _stattests.SwigPyIterator___next__(self)
    def previous(self): return _stattests.SwigPyIterator_previous(self)
    def advance(self, *args): return _stattests.SwigPyIterator_advance(self, *args)
    def __eq__(self, *args): return _stattests.SwigPyIterator___eq__(self, *args)
    def __ne__(self, *args): return _stattests.SwigPyIterator___ne__(self, *args)
    def __iadd__(self, *args): return _stattests.SwigPyIterator___iadd__(self, *args)
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    def __add__(self, *args): return _stattests.SwigPyIterator___add__(self, *args)
    def __sub__(self, *args): return _stattests.SwigPyIterator___sub__(self, *args)
    def __iter__(self): return self
SwigPyIterator_swigregister = _stattests.SwigPyIterator_swigregister
SwigPyIterator_swigregister(SwigPyIterator)

GCC_VERSION = _stattests.GCC_VERSION
class TestFailed:
    """TestFailed is used to raise an uniform exception in tests."""

    __type = "TestFailed"

    def __init__(self, reason=""):
        self.reason = reason

    def type(self):
        return TestFailed.__type

    def what(self):
        return self.reason

    def __str__(self):
        return TestFailed.__type + ": " + self.reason

    def __lshift__(self, ch):
        self.reason += ch
        return self

import openturns.base
import openturns.common
import openturns.wrapper
import openturns.typ
import openturns.statistics
import openturns.graph
import openturns.func
import openturns.geom
import openturns.diff
import openturns.optim
import openturns.solver
import openturns.algo
import openturns.experiment
import openturns.model_copula
class VisualTest(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, VisualTest, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, VisualTest, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    __swig_getmethods__["DrawEmpiricalCDF"] = lambda x: _stattests.VisualTest_DrawEmpiricalCDF
    if _newclass:DrawEmpiricalCDF = staticmethod(_stattests.VisualTest_DrawEmpiricalCDF)
    __swig_getmethods__["DrawHistogram"] = lambda x: _stattests.VisualTest_DrawHistogram
    if _newclass:DrawHistogram = staticmethod(_stattests.VisualTest_DrawHistogram)
    def DrawQQplot(*args):
        """
        Draw a QQ-plot as an OpenTURNS :class:`~openturns.Graph`.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample`
            Tested sample.
        tested_quantity : :class:`~openturns.Distribution` or :class:`~openturns.NumericalSample`
            Tested model or sample.
        n_points : int, if `tested_quantity` is a :class:`~openturns.NumericalSample`
            The number of points that is used for interpolating the empirical CDF of
            the two samples (with possibly different sizes).

            It will default to `DistributionImplementation-DefaultPointNumber` from
            the :class:`~openturns.ResourceMap`.

        Notes
        -----
        The QQ-plot is a visual fitting test for univariate distributions. It
        opposes the sample quantiles to those of the tested quantity (either a
        distribution or another sample) by plotting the following points could:

        .. math::

            \\left(x^{(i)},
                  \\bullet\\left[\\widehat{F}\\left(x^{(i)}\\right)\\right]
            \\right), \\quad i = 1, \\ldots, m

        where :math:`\\widehat{F}` denotes the empirical CDF of the (first) tested
        sample and :math:`\\bullet` denotes either the quantile function of the tested
        distribution or the empirical quantile function of the second tested sample.

        If the given sample fits to the tested distribution or sample, then the points
        should be close to be aligned (up to the uncertainty associated with the
        estimation of the empirical probabilities) with the **first bissector**  whose
        equation reads:

        .. math::

            y = x, \\quad x \\in \\Rset

        Examples
        --------
        >>> import openturns as ot
        >>> from openturns.viewer import View

        Generate a random sample from a Normal distribution:

        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Weibull(2., .5)
        >>> sample = distribution.getSample(30)
        >>> sample.setDescription(['Sample'])

        Draw a QQ-plot against a given (inferred) distribution:

        >>> tested_distribution = ot.WeibullFactory().build(sample)
        >>> QQ_plot = ot.VisualTest_DrawQQplot(sample, tested_distribution)
        >>> View(QQ_plot).show()

        Draw a two-sample QQ-plot:

        >>> another_sample = distribution.getSample(50)
        >>> another_sample.setDescription(['Another sample'])
        >>> QQ_plot = ot.VisualTest_DrawQQplot(sample, another_sample)
        >>> View(QQ_plot).show()
        """
        return _stattests.VisualTest_DrawQQplot(*args)

    if _newclass:DrawQQplot = staticmethod(DrawQQplot)
    __swig_getmethods__["DrawQQplot"] = lambda x: DrawQQplot
    def DrawHenryLine(*args):
        """
        Draw an Henry plot as an OpenTURNS :class:`~openturns.Graph`.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested (univariate) sample.
        normal_distribution : :class:`~openturns.Normal`, optional
            Tested (univariate) normal distribution.

            If not given, this will build a :class:`~openturns.Normal` distribution
            from the given sample using the :class:`~openturns.NormalFactory`.

        Notes
        -----
        The Henry plot is a visual fitting test for the normal distribution. It
        opposes the sample quantiles to those of the standard normal distribution
        (the one with zero mean and unit variance) by plotting the following points
        could:

        .. math::

            \\left(x^{(i)},
                  \\Phi^{-1}\\left[\\widehat{F}\\left(x^{(i)}\\right)\\right]
            \\right), \\quad i = 1, \\ldots, m

        where :math:`\\widehat{F}` denotes the empirical CDF of the sample and
        :math:`\\Phi^{-1}` denotes the quantile function of the standard normal
        distribution.

        If the given sample fits to the tested normal distribution (with mean
        :math:`\\mu` and standard deviation :math:`\\sigma`), then the points should be
        close to be aligned (up to the uncertainty associated with the estimation
        of the empirical probabilities) on the **Henry line** whose equation reads:

        .. math::

            y = \\frac{x - \\mu}{\\sigma}, \\quad x \\in \\Rset

        The Henry plot is a special case of the more general QQ-plot.

        See Also
        --------
        VisualTest_DrawQQplot, FittingTest_Kolmogorov

        Examples
        --------
        >>> import openturns as ot
        >>> from openturns.viewer import View

        Generate a random sample from a Normal distribution:

        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Normal(2., .5)
        >>> sample = distribution.getSample(30)

        Draw an Henry plot against a given (wrong) Normal distribution:

        >>> henry_graph = ot.VisualTest_DrawHenryLine(sample, distribution)
        >>> henry_graph.setTitle('Henry plot against given %s' % ot.Normal(3., 1.))
        >>> View(henry_graph).show()

        Draw an Henry plot against an inferred Normal distribution:

        >>> henry_graph = ot.VisualTest_DrawHenryLine(sample)
        >>> henry_graph.setTitle('Henry plot against inferred Normal distribution')
        >>> View(henry_graph).show()
        """
        return _stattests.VisualTest_DrawHenryLine(*args)

    if _newclass:DrawHenryLine = staticmethod(DrawHenryLine)
    __swig_getmethods__["DrawHenryLine"] = lambda x: DrawHenryLine
    __swig_getmethods__["DrawClouds"] = lambda x: _stattests.VisualTest_DrawClouds
    if _newclass:DrawClouds = staticmethod(_stattests.VisualTest_DrawClouds)
    __swig_getmethods__["DrawLinearModel"] = lambda x: _stattests.VisualTest_DrawLinearModel
    if _newclass:DrawLinearModel = staticmethod(_stattests.VisualTest_DrawLinearModel)
    __swig_getmethods__["DrawLinearModelResidual"] = lambda x: _stattests.VisualTest_DrawLinearModelResidual
    if _newclass:DrawLinearModelResidual = staticmethod(_stattests.VisualTest_DrawLinearModelResidual)
    __swig_getmethods__["DrawCobWeb"] = lambda x: _stattests.VisualTest_DrawCobWeb
    if _newclass:DrawCobWeb = staticmethod(_stattests.VisualTest_DrawCobWeb)
    __swig_getmethods__["DrawKendallPlot"] = lambda x: _stattests.VisualTest_DrawKendallPlot
    if _newclass:DrawKendallPlot = staticmethod(_stattests.VisualTest_DrawKendallPlot)
    __swig_destroy__ = _stattests.delete_VisualTest
    __del__ = lambda self : None;
VisualTest_swigregister = _stattests.VisualTest_swigregister
VisualTest_swigregister(VisualTest)

def VisualTest_DrawEmpiricalCDF(*args):
  return _stattests.VisualTest_DrawEmpiricalCDF(*args)
VisualTest_DrawEmpiricalCDF = _stattests.VisualTest_DrawEmpiricalCDF

def VisualTest_DrawHistogram(*args):
  return _stattests.VisualTest_DrawHistogram(*args)
VisualTest_DrawHistogram = _stattests.VisualTest_DrawHistogram

def VisualTest_DrawQQplot(*args):
  """
    Draw a QQ-plot as an OpenTURNS :class:`~openturns.Graph`.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample`
        Tested sample.
    tested_quantity : :class:`~openturns.Distribution` or :class:`~openturns.NumericalSample`
        Tested model or sample.
    n_points : int, if `tested_quantity` is a :class:`~openturns.NumericalSample`
        The number of points that is used for interpolating the empirical CDF of
        the two samples (with possibly different sizes).

        It will default to `DistributionImplementation-DefaultPointNumber` from
        the :class:`~openturns.ResourceMap`.

    Notes
    -----
    The QQ-plot is a visual fitting test for univariate distributions. It
    opposes the sample quantiles to those of the tested quantity (either a
    distribution or another sample) by plotting the following points could:

    .. math::

        \\left(x^{(i)},
              \\bullet\\left[\\widehat{F}\\left(x^{(i)}\\right)\\right]
        \\right), \\quad i = 1, \\ldots, m

    where :math:`\\widehat{F}` denotes the empirical CDF of the (first) tested
    sample and :math:`\\bullet` denotes either the quantile function of the tested
    distribution or the empirical quantile function of the second tested sample.

    If the given sample fits to the tested distribution or sample, then the points
    should be close to be aligned (up to the uncertainty associated with the
    estimation of the empirical probabilities) with the **first bissector**  whose
    equation reads:

    .. math::

        y = x, \\quad x \\in \\Rset

    Examples
    --------
    >>> import openturns as ot
    >>> from openturns.viewer import View

    Generate a random sample from a Normal distribution:

    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Weibull(2., .5)
    >>> sample = distribution.getSample(30)
    >>> sample.setDescription(['Sample'])

    Draw a QQ-plot against a given (inferred) distribution:

    >>> tested_distribution = ot.WeibullFactory().build(sample)
    >>> QQ_plot = ot.VisualTest_DrawQQplot(sample, tested_distribution)
    >>> View(QQ_plot).show()

    Draw a two-sample QQ-plot:

    >>> another_sample = distribution.getSample(50)
    >>> another_sample.setDescription(['Another sample'])
    >>> QQ_plot = ot.VisualTest_DrawQQplot(sample, another_sample)
    >>> View(QQ_plot).show()
    """
  return _stattests.VisualTest_DrawQQplot(*args)

def VisualTest_DrawHenryLine(*args):
  """
    Draw an Henry plot as an OpenTURNS :class:`~openturns.Graph`.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested (univariate) sample.
    normal_distribution : :class:`~openturns.Normal`, optional
        Tested (univariate) normal distribution.

        If not given, this will build a :class:`~openturns.Normal` distribution
        from the given sample using the :class:`~openturns.NormalFactory`.

    Notes
    -----
    The Henry plot is a visual fitting test for the normal distribution. It
    opposes the sample quantiles to those of the standard normal distribution
    (the one with zero mean and unit variance) by plotting the following points
    could:

    .. math::

        \\left(x^{(i)},
              \\Phi^{-1}\\left[\\widehat{F}\\left(x^{(i)}\\right)\\right]
        \\right), \\quad i = 1, \\ldots, m

    where :math:`\\widehat{F}` denotes the empirical CDF of the sample and
    :math:`\\Phi^{-1}` denotes the quantile function of the standard normal
    distribution.

    If the given sample fits to the tested normal distribution (with mean
    :math:`\\mu` and standard deviation :math:`\\sigma`), then the points should be
    close to be aligned (up to the uncertainty associated with the estimation
    of the empirical probabilities) on the **Henry line** whose equation reads:

    .. math::

        y = \\frac{x - \\mu}{\\sigma}, \\quad x \\in \\Rset

    The Henry plot is a special case of the more general QQ-plot.

    See Also
    --------
    VisualTest_DrawQQplot, FittingTest_Kolmogorov

    Examples
    --------
    >>> import openturns as ot
    >>> from openturns.viewer import View

    Generate a random sample from a Normal distribution:

    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Normal(2., .5)
    >>> sample = distribution.getSample(30)

    Draw an Henry plot against a given (wrong) Normal distribution:

    >>> henry_graph = ot.VisualTest_DrawHenryLine(sample, distribution)
    >>> henry_graph.setTitle('Henry plot against given %s' % ot.Normal(3., 1.))
    >>> View(henry_graph).show()

    Draw an Henry plot against an inferred Normal distribution:

    >>> henry_graph = ot.VisualTest_DrawHenryLine(sample)
    >>> henry_graph.setTitle('Henry plot against inferred Normal distribution')
    >>> View(henry_graph).show()
    """
  return _stattests.VisualTest_DrawHenryLine(*args)

def VisualTest_DrawClouds(*args):
  return _stattests.VisualTest_DrawClouds(*args)
VisualTest_DrawClouds = _stattests.VisualTest_DrawClouds

def VisualTest_DrawLinearModel(*args):
  return _stattests.VisualTest_DrawLinearModel(*args)
VisualTest_DrawLinearModel = _stattests.VisualTest_DrawLinearModel

def VisualTest_DrawLinearModelResidual(*args):
  return _stattests.VisualTest_DrawLinearModelResidual(*args)
VisualTest_DrawLinearModelResidual = _stattests.VisualTest_DrawLinearModelResidual

def VisualTest_DrawCobWeb(*args):
  return _stattests.VisualTest_DrawCobWeb(*args)
VisualTest_DrawCobWeb = _stattests.VisualTest_DrawCobWeb

def VisualTest_DrawKendallPlot(*args):
  return _stattests.VisualTest_DrawKendallPlot(*args)
VisualTest_DrawKendallPlot = _stattests.VisualTest_DrawKendallPlot

class FittingTest(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, FittingTest, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, FittingTest, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    def BestModelBIC(*args):
        """
        Select the best model according to the Bayesian information criterion.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distributions.

        Returns
        -------
        best_model : :class:`~openturns.Distribution`
            The best distribution for the sample according to Bayesian information
            criterion.
            This may raise a warning if the best model does not perform well.

        See Also
        --------
        FittingTest_BIC

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Normal()
        >>> sample = distribution.getSample(30)
        >>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
        >>> best_model = ot.FittingTest_BestModelBIC(sample, tested_distributions)
        >>> print(best_model)
        Normal(mu = -0.0944924, sigma = 0.989808)
        """
        return _stattests.FittingTest_BestModelBIC(*args)

    if _newclass:BestModelBIC = staticmethod(BestModelBIC)
    __swig_getmethods__["BestModelBIC"] = lambda x: BestModelBIC
    def BestModelKolmogorov(*args):
        """
        Select the best model according to the Kolmogorov goodness-of-fit test.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distributions.

        Returns
        -------
        best_model : :class:`~openturns.Distribution`
            The best distribution for the sample according to Bayesian information
            criterion.
            This may raise a warning if the best model does not perform well.
        best_result : :class:`~openturns.TestResult`
            Best test result.

        See Also
        --------
        FittingTest_Kolmogorov

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Normal()
        >>> sample = distribution.getSample(30)
        >>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
        >>> best_model, best_result = ot.FittingTest_BestModelKolmogorov(sample, tested_distributions)
        >>> print(best_model)
        Normal(mu = -0.0944924, sigma = 0.989808)
        """
        return _stattests.FittingTest_BestModelKolmogorov(*args)

    if _newclass:BestModelKolmogorov = staticmethod(BestModelKolmogorov)
    __swig_getmethods__["BestModelKolmogorov"] = lambda x: BestModelKolmogorov
    def BestModelChiSquared(*args):
        """
        Select the best model according to the :math:`\\chi^2` goodness-of-fit test.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distributions.

        Returns
        -------
        best_model : :class:`~openturns.Distribution`
            The best distribution for the sample according to Bayesian information
            criterion.
            This may raise a warning if the best model does not perform well.
        best_result : :class:`~openturns.TestResult`
            Best test result.

        See Also
        --------
        FittingTest_ChiSquared

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Poisson()
        >>> sample = distribution.getSample(30)
        >>> tested_distributions = [ot.PoissonFactory(), ot.UserDefinedFactory()]
        >>> best_model = ot.FittingTest_BestModelBIC(sample, tested_distributions)
        >>> print(best_model)
        Poisson(lambda = 1.06667)
        """
        return _stattests.FittingTest_BestModelChiSquared(*args)

    if _newclass:BestModelChiSquared = staticmethod(BestModelChiSquared)
    __swig_getmethods__["BestModelChiSquared"] = lambda x: BestModelChiSquared
    def BIC(*args):
        """
        Compute the Bayesian information criterion.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distribution.
        n_parameters : int, :math:`0 \\leq k`, optional
            The number of parameters in the distribution that have been estimated from
            the sample.
            This parameter must not be provided if a :class:`~openturns.DistributionFactory`
            was provided as the second argument (it will internally be set to the
            number of parameters estimated by the :class:`~openturns.DistributionFactory`).
            It can be specified if  a :class:`~openturns.Distribution` was provided
            as the second argument, but if it is not, it will be set equal to 0.

        Returns
        -------
        BIC : float
            The Bayesian information criterion.

        Notes
        -----
        The Bayesian information criterion is defined as follows:

        .. math::

            {\\rm BIC} = \\frac{1}{m}
                        \\left(- 2 \\log L(\\vect{x}^{(i)}, i = 1, \\ldots, m)
                              + k \\log m\\right)

        where :math:`\\log L` denotes the log-likelihood of the sample with respect to
        the given distribution, and :math:`k` denotes the number of estimated
        parameters in the distribution.

        This is used for model selection.

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Normal()
        >>> sample = distribution.getSample(30)
        >>> ot.FittingTest_BIC(sample, distribution)
        2.7938693005063415
        >>> ot.FittingTest_BIC(sample, distribution, 2)
        3.0206157926171517
        >>> ot.FittingTest_BIC(sample, ot.NormalFactory())
        3.0108025506670955
        """
        return _stattests.FittingTest_BIC(*args)

    if _newclass:BIC = staticmethod(BIC)
    __swig_getmethods__["BIC"] = lambda x: BIC
    def Kolmogorov(*args):
        """
        Perform a Kolmogorov goodness-of-fit test for 1-d continuous distributions.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distribution.
        level : float, :math:`0 \\leq {\\rm level} \\leq 1`, optional
            This is the value such that :math:`\\alpha = 1 - {\\rm level}` is the risk of
            committing a Type I error, that is an incorrect rejection of a true
            null hypothesis.
        n_parameters : int, :math:`0 \\leq k`, optional
            The number of parameters in the distribution that have been estimated from
            the sample.
            This parameter must not be provided if a :class:`~openturns.DistributionFactory`
            was provided as the second argument (it will internally be set to the
            number of parameters estimated by the :class:`~openturns.DistributionFactory`).
            It can be specified if  a :class:`~openturns.Distribution` was provided
            as the second argument, but if it is not, it will be set equal to 0.

        Returns
        -------
        test_result : :class:`~openturns.TestResult`
            Test result.

        Raises
        ------
        TypeError : If the distribution is not continuous or if the sample is
            multivariate.

        Notes
        -----
        The present implementation of the Kolmogorov goodness-of-fit test is
        two-sided. This uses an external C implementation of the Kolmogorov cumulative
        distribution function by [Simard2010]_.

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Normal()
        >>> sample = distribution.getSample(30)
        >>> ot.FittingTest_Kolmogorov(sample, ot.NormalFactory(), .99)
        class=TestResult name=Unnamed type=KolmogorovDistribution binaryQualityMeasure=true p-value threshold=0.01 p-value=0.846896 description=[Normal(mu = -0.0944924, sigma = 0.989808) vs sample Normal]
        """
        return _stattests.FittingTest_Kolmogorov(*args)

    if _newclass:Kolmogorov = staticmethod(Kolmogorov)
    __swig_getmethods__["Kolmogorov"] = lambda x: Kolmogorov
    def ChiSquared(*args):
        """
        Perform a :math:`\\chi^2` goodness-of-fit test for 1-d discrete distributions.

        Parameters
        ----------
        sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
            Tested sample.
        model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
            Tested distribution.
        level : float, :math:`0 \\leq {\\rm level} \\leq 1`, optional
            This the value such that :math:`\\alpha = 1 - {\\rm level}` is the risk of
            committing a Type I error, that is an incorrect rejection of a true
            null hypothesis.
        n_parameters : int, :math:`0 \\leq k`, optional
            The number of parameters in the distribution that have been estimated from
            the sample.
            This parameter must not be provided if a :class:`~openturns.DistributionFactory`
            was provided as the second argument (it will internally be set to the
            number of parameters estimated by the :class:`~openturns.DistributionFactory`).
            It can be specified if  a :class:`~openturns.Distribution` was provided
            as the second argument, but if it is not, it will be set equal to 0.

        Returns
        -------
        test_result : :class:`~openturns.TestResult`
            Test result.

        Raises
        ------
        TypeError : If the distribution is not discrete or if the sample is
            multivariate.

        Notes
        -----
        This is an interface to the `chisq.test function from the
        'stats' R package <http://stat.ethz.ch/R-manual/R-patched/library/stats/html/chisq.test.html>`_.

        Examples
        --------
        >>> import openturns as ot
        >>> ot.RandomGenerator.SetSeed(0)
        >>> distribution = ot.Poisson()
        >>> sample = distribution.getSample(30)
        >>> ot.FittingTest_ChiSquared(sample, ot.PoissonFactory(), .99)
        class=TestResult name=Unnamed type=ChiSquaredPoisson binaryQualityMeasure=true p-value threshold=0.01 p-value=0.606136 description=[]
        """
        return _stattests.FittingTest_ChiSquared(*args)

    if _newclass:ChiSquared = staticmethod(ChiSquared)
    __swig_getmethods__["ChiSquared"] = lambda x: ChiSquared
    __swig_destroy__ = _stattests.delete_FittingTest
    __del__ = lambda self : None;
FittingTest_swigregister = _stattests.FittingTest_swigregister
FittingTest_swigregister(FittingTest)

def FittingTest_BestModelBIC(*args):
  """
    Select the best model according to the Bayesian information criterion.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distributions.

    Returns
    -------
    best_model : :class:`~openturns.Distribution`
        The best distribution for the sample according to Bayesian information
        criterion.
        This may raise a warning if the best model does not perform well.

    See Also
    --------
    FittingTest_BIC

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Normal()
    >>> sample = distribution.getSample(30)
    >>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
    >>> best_model = ot.FittingTest_BestModelBIC(sample, tested_distributions)
    >>> print(best_model)
    Normal(mu = -0.0944924, sigma = 0.989808)
    """
  return _stattests.FittingTest_BestModelBIC(*args)

def FittingTest_BestModelKolmogorov(*args):
  """
    Select the best model according to the Kolmogorov goodness-of-fit test.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distributions.

    Returns
    -------
    best_model : :class:`~openturns.Distribution`
        The best distribution for the sample according to Bayesian information
        criterion.
        This may raise a warning if the best model does not perform well.
    best_result : :class:`~openturns.TestResult`
        Best test result.

    See Also
    --------
    FittingTest_Kolmogorov

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Normal()
    >>> sample = distribution.getSample(30)
    >>> tested_distributions = [ot.ExponentialFactory(), ot.NormalFactory()]
    >>> best_model, best_result = ot.FittingTest_BestModelKolmogorov(sample, tested_distributions)
    >>> print(best_model)
    Normal(mu = -0.0944924, sigma = 0.989808)
    """
  return _stattests.FittingTest_BestModelKolmogorov(*args)

def FittingTest_BestModelChiSquared(*args):
  """
    Select the best model according to the :math:`\\chi^2` goodness-of-fit test.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    models : list of :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distributions.

    Returns
    -------
    best_model : :class:`~openturns.Distribution`
        The best distribution for the sample according to Bayesian information
        criterion.
        This may raise a warning if the best model does not perform well.
    best_result : :class:`~openturns.TestResult`
        Best test result.

    See Also
    --------
    FittingTest_ChiSquared

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Poisson()
    >>> sample = distribution.getSample(30)
    >>> tested_distributions = [ot.PoissonFactory(), ot.UserDefinedFactory()]
    >>> best_model = ot.FittingTest_BestModelBIC(sample, tested_distributions)
    >>> print(best_model)
    Poisson(lambda = 1.06667)
    """
  return _stattests.FittingTest_BestModelChiSquared(*args)

def FittingTest_BIC(*args):
  """
    Compute the Bayesian information criterion.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distribution.
    n_parameters : int, :math:`0 \\leq k`, optional
        The number of parameters in the distribution that have been estimated from
        the sample.
        This parameter must not be provided if a :class:`~openturns.DistributionFactory`
        was provided as the second argument (it will internally be set to the
        number of parameters estimated by the :class:`~openturns.DistributionFactory`).
        It can be specified if  a :class:`~openturns.Distribution` was provided
        as the second argument, but if it is not, it will be set equal to 0.

    Returns
    -------
    BIC : float
        The Bayesian information criterion.

    Notes
    -----
    The Bayesian information criterion is defined as follows:

    .. math::

        {\\rm BIC} = \\frac{1}{m}
                    \\left(- 2 \\log L(\\vect{x}^{(i)}, i = 1, \\ldots, m)
                          + k \\log m\\right)

    where :math:`\\log L` denotes the log-likelihood of the sample with respect to
    the given distribution, and :math:`k` denotes the number of estimated
    parameters in the distribution.

    This is used for model selection.

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Normal()
    >>> sample = distribution.getSample(30)
    >>> ot.FittingTest_BIC(sample, distribution)
    2.7938693005063415
    >>> ot.FittingTest_BIC(sample, distribution, 2)
    3.0206157926171517
    >>> ot.FittingTest_BIC(sample, ot.NormalFactory())
    3.0108025506670955
    """
  return _stattests.FittingTest_BIC(*args)

def FittingTest_Kolmogorov(*args):
  """
    Perform a Kolmogorov goodness-of-fit test for 1-d continuous distributions.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distribution.
    level : float, :math:`0 \\leq {\\rm level} \\leq 1`, optional
        This is the value such that :math:`\\alpha = 1 - {\\rm level}` is the risk of
        committing a Type I error, that is an incorrect rejection of a true
        null hypothesis.
    n_parameters : int, :math:`0 \\leq k`, optional
        The number of parameters in the distribution that have been estimated from
        the sample.
        This parameter must not be provided if a :class:`~openturns.DistributionFactory`
        was provided as the second argument (it will internally be set to the
        number of parameters estimated by the :class:`~openturns.DistributionFactory`).
        It can be specified if  a :class:`~openturns.Distribution` was provided
        as the second argument, but if it is not, it will be set equal to 0.

    Returns
    -------
    test_result : :class:`~openturns.TestResult`
        Test result.

    Raises
    ------
    TypeError : If the distribution is not continuous or if the sample is
        multivariate.

    Notes
    -----
    The present implementation of the Kolmogorov goodness-of-fit test is
    two-sided. This uses an external C implementation of the Kolmogorov cumulative
    distribution function by [Simard2010]_.

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Normal()
    >>> sample = distribution.getSample(30)
    >>> ot.FittingTest_Kolmogorov(sample, ot.NormalFactory(), .99)
    class=TestResult name=Unnamed type=KolmogorovDistribution binaryQualityMeasure=true p-value threshold=0.01 p-value=0.846896 description=[Normal(mu = -0.0944924, sigma = 0.989808) vs sample Normal]
    """
  return _stattests.FittingTest_Kolmogorov(*args)

def FittingTest_ChiSquared(*args):
  """
    Perform a :math:`\\chi^2` goodness-of-fit test for 1-d discrete distributions.

    Parameters
    ----------
    sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
        Tested sample.
    model : :class:`~openturns.Distribution` or :class:`~openturns.DistributionFactory`
        Tested distribution.
    level : float, :math:`0 \\leq {\\rm level} \\leq 1`, optional
        This the value such that :math:`\\alpha = 1 - {\\rm level}` is the risk of
        committing a Type I error, that is an incorrect rejection of a true
        null hypothesis.
    n_parameters : int, :math:`0 \\leq k`, optional
        The number of parameters in the distribution that have been estimated from
        the sample.
        This parameter must not be provided if a :class:`~openturns.DistributionFactory`
        was provided as the second argument (it will internally be set to the
        number of parameters estimated by the :class:`~openturns.DistributionFactory`).
        It can be specified if  a :class:`~openturns.Distribution` was provided
        as the second argument, but if it is not, it will be set equal to 0.

    Returns
    -------
    test_result : :class:`~openturns.TestResult`
        Test result.

    Raises
    ------
    TypeError : If the distribution is not discrete or if the sample is
        multivariate.

    Notes
    -----
    This is an interface to the `chisq.test function from the
    'stats' R package <http://stat.ethz.ch/R-manual/R-patched/library/stats/html/chisq.test.html>`_.

    Examples
    --------
    >>> import openturns as ot
    >>> ot.RandomGenerator.SetSeed(0)
    >>> distribution = ot.Poisson()
    >>> sample = distribution.getSample(30)
    >>> ot.FittingTest_ChiSquared(sample, ot.PoissonFactory(), .99)
    class=TestResult name=Unnamed type=ChiSquaredPoisson binaryQualityMeasure=true p-value threshold=0.01 p-value=0.606136 description=[]
    """
  return _stattests.FittingTest_ChiSquared(*args)

class HypothesisTest(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, HypothesisTest, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, HypothesisTest, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    __swig_getmethods__["ChiSquared"] = lambda x: _stattests.HypothesisTest_ChiSquared
    if _newclass:ChiSquared = staticmethod(_stattests.HypothesisTest_ChiSquared)
    __swig_getmethods__["Pearson"] = lambda x: _stattests.HypothesisTest_Pearson
    if _newclass:Pearson = staticmethod(_stattests.HypothesisTest_Pearson)
    __swig_getmethods__["Smirnov"] = lambda x: _stattests.HypothesisTest_Smirnov
    if _newclass:Smirnov = staticmethod(_stattests.HypothesisTest_Smirnov)
    __swig_getmethods__["Spearman"] = lambda x: _stattests.HypothesisTest_Spearman
    if _newclass:Spearman = staticmethod(_stattests.HypothesisTest_Spearman)
    __swig_getmethods__["PartialPearson"] = lambda x: _stattests.HypothesisTest_PartialPearson
    if _newclass:PartialPearson = staticmethod(_stattests.HypothesisTest_PartialPearson)
    __swig_getmethods__["PartialRegression"] = lambda x: _stattests.HypothesisTest_PartialRegression
    if _newclass:PartialRegression = staticmethod(_stattests.HypothesisTest_PartialRegression)
    __swig_getmethods__["PartialSpearman"] = lambda x: _stattests.HypothesisTest_PartialSpearman
    if _newclass:PartialSpearman = staticmethod(_stattests.HypothesisTest_PartialSpearman)
    __swig_getmethods__["FullPearson"] = lambda x: _stattests.HypothesisTest_FullPearson
    if _newclass:FullPearson = staticmethod(_stattests.HypothesisTest_FullPearson)
    __swig_getmethods__["FullRegression"] = lambda x: _stattests.HypothesisTest_FullRegression
    if _newclass:FullRegression = staticmethod(_stattests.HypothesisTest_FullRegression)
    __swig_getmethods__["FullSpearman"] = lambda x: _stattests.HypothesisTest_FullSpearman
    if _newclass:FullSpearman = staticmethod(_stattests.HypothesisTest_FullSpearman)
    __swig_destroy__ = _stattests.delete_HypothesisTest
    __del__ = lambda self : None;
HypothesisTest_swigregister = _stattests.HypothesisTest_swigregister
HypothesisTest_swigregister(HypothesisTest)

def HypothesisTest_ChiSquared(*args):
  return _stattests.HypothesisTest_ChiSquared(*args)
HypothesisTest_ChiSquared = _stattests.HypothesisTest_ChiSquared

def HypothesisTest_Pearson(*args):
  return _stattests.HypothesisTest_Pearson(*args)
HypothesisTest_Pearson = _stattests.HypothesisTest_Pearson

def HypothesisTest_Smirnov(*args):
  return _stattests.HypothesisTest_Smirnov(*args)
HypothesisTest_Smirnov = _stattests.HypothesisTest_Smirnov

def HypothesisTest_Spearman(*args):
  return _stattests.HypothesisTest_Spearman(*args)
HypothesisTest_Spearman = _stattests.HypothesisTest_Spearman

def HypothesisTest_PartialPearson(*args):
  return _stattests.HypothesisTest_PartialPearson(*args)
HypothesisTest_PartialPearson = _stattests.HypothesisTest_PartialPearson

def HypothesisTest_PartialRegression(*args):
  return _stattests.HypothesisTest_PartialRegression(*args)
HypothesisTest_PartialRegression = _stattests.HypothesisTest_PartialRegression

def HypothesisTest_PartialSpearman(*args):
  return _stattests.HypothesisTest_PartialSpearman(*args)
HypothesisTest_PartialSpearman = _stattests.HypothesisTest_PartialSpearman

def HypothesisTest_FullPearson(*args):
  return _stattests.HypothesisTest_FullPearson(*args)
HypothesisTest_FullPearson = _stattests.HypothesisTest_FullPearson

def HypothesisTest_FullRegression(*args):
  return _stattests.HypothesisTest_FullRegression(*args)
HypothesisTest_FullRegression = _stattests.HypothesisTest_FullRegression

def HypothesisTest_FullSpearman(*args):
  return _stattests.HypothesisTest_FullSpearman(*args)
HypothesisTest_FullSpearman = _stattests.HypothesisTest_FullSpearman

class LinearModelTest(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, LinearModelTest, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, LinearModelTest, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    __swig_getmethods__["LinearModelAdjustedRSquared"] = lambda x: _stattests.LinearModelTest_LinearModelAdjustedRSquared
    if _newclass:LinearModelAdjustedRSquared = staticmethod(_stattests.LinearModelTest_LinearModelAdjustedRSquared)
    __swig_getmethods__["LinearModelFisher"] = lambda x: _stattests.LinearModelTest_LinearModelFisher
    if _newclass:LinearModelFisher = staticmethod(_stattests.LinearModelTest_LinearModelFisher)
    __swig_getmethods__["LinearModelResidualMean"] = lambda x: _stattests.LinearModelTest_LinearModelResidualMean
    if _newclass:LinearModelResidualMean = staticmethod(_stattests.LinearModelTest_LinearModelResidualMean)
    __swig_getmethods__["LinearModelRSquared"] = lambda x: _stattests.LinearModelTest_LinearModelRSquared
    if _newclass:LinearModelRSquared = staticmethod(_stattests.LinearModelTest_LinearModelRSquared)
    __swig_destroy__ = _stattests.delete_LinearModelTest
    __del__ = lambda self : None;
LinearModelTest_swigregister = _stattests.LinearModelTest_swigregister
LinearModelTest_swigregister(LinearModelTest)

def LinearModelTest_LinearModelAdjustedRSquared(*args):
  return _stattests.LinearModelTest_LinearModelAdjustedRSquared(*args)
LinearModelTest_LinearModelAdjustedRSquared = _stattests.LinearModelTest_LinearModelAdjustedRSquared

def LinearModelTest_LinearModelFisher(*args):
  return _stattests.LinearModelTest_LinearModelFisher(*args)
LinearModelTest_LinearModelFisher = _stattests.LinearModelTest_LinearModelFisher

def LinearModelTest_LinearModelResidualMean(*args):
  return _stattests.LinearModelTest_LinearModelResidualMean(*args)
LinearModelTest_LinearModelResidualMean = _stattests.LinearModelTest_LinearModelResidualMean

def LinearModelTest_LinearModelRSquared(*args):
  return _stattests.LinearModelTest_LinearModelRSquared(*args)
LinearModelTest_LinearModelRSquared = _stattests.LinearModelTest_LinearModelRSquared

class NormalityTest(_object):
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, NormalityTest, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, NormalityTest, name)
    def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    __swig_getmethods__["AndersonDarlingNormal"] = lambda x: _stattests.NormalityTest_AndersonDarlingNormal
    if _newclass:AndersonDarlingNormal = staticmethod(_stattests.NormalityTest_AndersonDarlingNormal)
    __swig_getmethods__["CramerVonMisesNormal"] = lambda x: _stattests.NormalityTest_CramerVonMisesNormal
    if _newclass:CramerVonMisesNormal = staticmethod(_stattests.NormalityTest_CramerVonMisesNormal)
    __swig_destroy__ = _stattests.delete_NormalityTest
    __del__ = lambda self : None;
NormalityTest_swigregister = _stattests.NormalityTest_swigregister
NormalityTest_swigregister(NormalityTest)

def NormalityTest_AndersonDarlingNormal(*args):
  return _stattests.NormalityTest_AndersonDarlingNormal(*args)
NormalityTest_AndersonDarlingNormal = _stattests.NormalityTest_AndersonDarlingNormal

def NormalityTest_CramerVonMisesNormal(*args):
  return _stattests.NormalityTest_CramerVonMisesNormal(*args)
NormalityTest_CramerVonMisesNormal = _stattests.NormalityTest_CramerVonMisesNormal

class DickeyFullerTest(openturns.common.PersistentObject):
    __swig_setmethods__ = {}
    for _s in [openturns.common.PersistentObject]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, DickeyFullerTest, name, value)
    __swig_getmethods__ = {}
    for _s in [openturns.common.PersistentObject]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{}))
    __getattr__ = lambda self, name: _swig_getattr(self, DickeyFullerTest, name)
    __repr__ = _swig_repr
    def getClassName(self):
        """
        Accessor to the object's name.

        Returns
        -------
        class_name : str
            The object class name (`object.__class__.__name__`).
        """
        return _stattests.DickeyFullerTest_getClassName(self)

    def testUnitRootInDriftAndLinearTrendModel(self, level=0.95): return _stattests.DickeyFullerTest_testUnitRootInDriftAndLinearTrendModel(self, level)
    def testUnitRootInDriftModel(self, level=0.95): return _stattests.DickeyFullerTest_testUnitRootInDriftModel(self, level)
    def testUnitRootInAR1Model(self, level=0.95): return _stattests.DickeyFullerTest_testUnitRootInAR1Model(self, level)
    def runStrategy(self, level=0.95): return _stattests.DickeyFullerTest_runStrategy(self, level)
    def testUnitRootAndNoLinearTrendInDriftAndLinearTrendModel(self, level=0.95): return _stattests.DickeyFullerTest_testUnitRootAndNoLinearTrendInDriftAndLinearTrendModel(self, level)
    def testNoUnitRootAndNoLinearTrendInDriftAndLinearTrendModel(self, level=0.95): return _stattests.DickeyFullerTest_testNoUnitRootAndNoLinearTrendInDriftAndLinearTrendModel(self, level)
    def testUnitRootAndNoDriftInDriftModel(self, level=0.95): return _stattests.DickeyFullerTest_testUnitRootAndNoDriftInDriftModel(self, level)
    def testNoUnitRootAndNoDriftInDriftModel(self, level=0.95): return _stattests.DickeyFullerTest_testNoUnitRootAndNoDriftInDriftModel(self, level)
    def setVerbose(self, *args): return _stattests.DickeyFullerTest_setVerbose(self, *args)
    def getVerbose(self): return _stattests.DickeyFullerTest_getVerbose(self)
    def __init__(self, *args): 
        this = _stattests.new_DickeyFullerTest(*args)
        try: self.this.append(this)
        except: self.this = this
    __swig_destroy__ = _stattests.delete_DickeyFullerTest
    __del__ = lambda self : None;
DickeyFullerTest_swigregister = _stattests.DickeyFullerTest_swigregister
DickeyFullerTest_swigregister(DickeyFullerTest)

# This file is compatible with both classic and new-style classes.