
def _parse_args(self, df,  loc=0, scale=1):
    return (df, ), loc, scale

def _parse_args_rvs(self, df,  loc=0, scale=1, size=None):
    return self._argcheck_rvs(df,  loc, scale, size=size)

def _parse_args_stats(self, df,  loc=0, scale=1, moments='mv'):
    return (df, ), loc, scale, moments


A chi continuous random variable.

    As an instance of the `rv_continuous` class, `chi` 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.
    
    Methods
    -------
    rvs(df, loc=0, scale=1, size=1, random_state=None)
        Random variates.
    pdf(x, df, loc=0, scale=1)
        Probability density function.
    logpdf(x, df, loc=0, scale=1)
        Log of the probability density function.
    cdf(x, df, loc=0, scale=1)
        Cumulative distribution function.
    logcdf(x, df, loc=0, scale=1)
        Log of the cumulative distribution function.
    sf(x, df, loc=0, scale=1)
        Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
    logsf(x, df, loc=0, scale=1)
        Log of the survival function.
    ppf(q, df, loc=0, scale=1)
        Percent point function (inverse of ``cdf`` --- percentiles).
    isf(q, df, loc=0, scale=1)
        Inverse survival function (inverse of ``sf``).
    moment(n, df, loc=0, scale=1)
        Non-central moment of order n
    stats(df, loc=0, scale=1, moments='mv')
        Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
    entropy(df, loc=0, scale=1)
        (Differential) entropy of the RV.
    fit(data)
        Parameter estimates for generic data.
        See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
        keyword arguments.
    expect(func, args=(df,), 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(df, loc=0, scale=1)
        Median of the distribution.
    mean(df, loc=0, scale=1)
        Mean of the distribution.
    var(df, loc=0, scale=1)
        Variance of the distribution.
    std(df, loc=0, scale=1)
        Standard deviation of the distribution.
    interval(alpha, df, loc=0, scale=1)
        Endpoints of the range that contains fraction alpha [0, 1] of the
        distribution

    Notes
    -----
    The probability density function for `chi` is:

    .. math::

        f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
                   x^{k-1} \exp \left( -x^2/2 \right)

    for :math:`x >= 0` and :math:`k > 0` (degrees of freedom, denoted ``df``
    in the implementation). :math:`\Gamma` is the gamma function
    (`scipy.special.gamma`).

    Special cases of `chi` are:

        - ``chi(1, loc, scale)`` is equivalent to `halfnorm`
        - ``chi(2, 0, scale)`` is equivalent to `rayleigh`
        - ``chi(3, 0, scale)`` is equivalent to `maxwell`

    `chi` takes ``df`` as a shape parameter.

    The probability density above is defined in the "standardized" form. To shift
    and/or scale the distribution use the ``loc`` and ``scale`` parameters.
    Specifically, ``chi.pdf(x, df, loc, scale)`` is identically
    equivalent to ``chi.pdf(y, df) / 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.

    Examples
    --------
    >>> from scipy.stats import chi
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    
    Calculate the first four moments:
    
    >>> df = 78
    >>> mean, var, skew, kurt = chi.stats(df, moments='mvsk')
    
    Display the probability density function (``pdf``):
    
    >>> x = np.linspace(chi.ppf(0.01, df),
    ...                 chi.ppf(0.99, df), 100)
    >>> ax.plot(x, chi.pdf(x, df),
    ...        'r-', lw=5, alpha=0.6, label='chi 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 = chi(df)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    
    Check accuracy of ``cdf`` and ``ppf``:
    
    >>> vals = chi.ppf([0.001, 0.5, 0.999], df)
    >>> np.allclose([0.001, 0.5, 0.999], chi.cdf(vals, df))
    True
    
    Generate random numbers:
    
    >>> r = chi.rvs(df, size=1000)
    
    And compare the histogram:
    
    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()
    

    

Planck15 instance of FlatLambdaCDM cosmology

(from Planck Collaboration 2016, A&A, 594, A13 (Paper XIII), Table 4 (TT, TE, EE + lowP + lensing + ext))