"""
Tools for working with distributions
"""
import numpy as np
from hyperspy.misc.borrowed.astroML.bayesian_blocks import bayesian_blocks
from scipy.special import gammaln
from scipy import optimize


def scotts_bin_width(data, return_bins=False):
    r"""Return the optimal histogram bin width using Scott's rule:

    Parameters
    ----------
    data : array-like, ndim=1
        observed (one-dimensional) data
    return_bins : bool (optional)
        if True, then return the bin edges

    Returns
    -------
    width : float
        optimal bin width using Scott's rule
    bins : ndarray
        bin edges: returned if `return_bins` is True

    Notes
    -----
    The optimal bin width is

    .. math::
        \Delta_b = \frac{3.5\sigma}{n^{1/3}}

    where :math:`\sigma` is the standard deviation of the data, and
    :math:`n` is the number of data points.

    See Also
    --------
    knuth_bin_width
    freedman_bin_width
    astroML.plotting.hist
    """
    data = np.asarray(data)
    if data.ndim != 1:
        raise ValueError("data should be one-dimensional")

    n = data.size
    sigma = np.std(data)

    dx = 3.5 * sigma * 1. / (n ** (1. / 3))

    if return_bins:
        Nbins = np.ceil((data.max() - data.min()) * 1. / dx)
        Nbins = max(1, Nbins)
        bins = data.min() + dx * np.arange(Nbins + 1)
        return dx, bins
    else:
        return dx


def freedman_bin_width(data, return_bins=False):
    r"""Return the optimal histogram bin width using the Freedman-Diaconis rule

    Parameters
    ----------
    data : array-like, ndim=1
        observed (one-dimensional) data
    return_bins : bool (optional)
        if True, then return the bin edges

    Returns
    -------
    width : float
        optimal bin width using Scott's rule
    bins : ndarray
        bin edges: returned if `return_bins` is True

    Notes
    -----
    The optimal bin width is

    .. math::
        \Delta_b = \frac{2(q_{75} - q_{25})}{n^{1/3}}

    where :math:`q_{N}` is the :math:`N` percent quartile of the data, and
    :math:`n` is the number of data points.

    See Also
    --------
    knuth_bin_width
    scotts_bin_width
    astroML.plotting.hist
    """
    data = np.asarray(data)
    if data.ndim != 1:
        raise ValueError("data should be one-dimensional")

    n = data.size
    if n < 4:
        raise ValueError("data should have more than three entries")

    dsorted = np.sort(data)
    v25 = dsorted[n / 4 - 1]
    v75 = dsorted[(3 * n) / 4 - 1]

    dx = 2 * (v75 - v25) * 1. / (n ** (1. / 3))

    if return_bins:
        Nbins = np.ceil((dsorted[-1] - dsorted[0]) * 1. / dx)
        Nbins = max(1, Nbins)
        bins = dsorted[0] + dx * np.arange(Nbins + 1)
        return dx, bins
    else:
        return dx


class KnuthF(object):

    r"""Class which implements the function minimized by knuth_bin_width

    Parameters
    ----------
    data : array-like, one dimension
        data to be histogrammed

    Notes
    -----
    the function F is given by

    .. math::
        F(M|x,I) = n\log(M) + \log\Gamma(\frac{M}{2})
        - M\log\Gamma(\frac{1}{2})
        - \log\Gamma(\frac{2n+M}{2})
        + \sum_{k=1}^M \log\Gamma(n_k + \frac{1}{2})

    where :math:`\Gamma` is the Gamma function, :math:`n` is the number of
    data points, :math:`n_k` is the number of measurements in bin :math:`k`.

    See Also
    --------
    knuth_bin_width
    astroML.plotting.hist
    """

    def __init__(self, data):
        self.data = np.array(data, copy=True)
        if self.data.ndim != 1:
            raise ValueError("data should be 1-dimensional")
        self.data.sort()
        self.n = self.data.size

    def bins(self, M):
        """Return the bin edges given a width dx"""
        return np.linspace(self.data[0], self.data[-1], int(M) + 1)

    def __call__(self, M):
        return self.eval(M)

    def eval(self, M):
        """Evaluate the Knuth function

        Parameters
        ----------
        dx : float
            Width of bins

        Returns
        -------
        F : float
            evaluation of the negative Knuth likelihood function:
            smaller values indicate a better fit.
        """
        M = int(M)
        bins = self.bins(M)
        nk, bins = np.histogram(self.data, bins)

        return -(self.n * np.log(M)
                 + gammaln(0.5 * M)
                 - M * gammaln(0.5)
                 - gammaln(self.n + 0.5 * M)
                 + np.sum(gammaln(nk + 0.5)))


def knuth_bin_width(data, return_bins=False):
    r"""Return the optimal histogram bin width using Knuth's rule [1]_

    Parameters
    ----------
    data : array-like, ndim=1
        observed (one-dimensional) data
    return_bins : bool (optional)
        if True, then return the bin edges

    Returns
    -------
    dx : float
        optimal bin width. Bins are measured starting at the first data point.
    bins : ndarray
        bin edges: returned if `return_bins` is True

    Notes
    -----
    The optimal number of bins is the value M which maximizes the function

    .. math::
        F(M|x,I) = n\log(M) + \log\Gamma(\frac{M}{2})
        - M\log\Gamma(\frac{1}{2})
        - \log\Gamma(\frac{2n+M}{2})
        + \sum_{k=1}^M \log\Gamma(n_k + \frac{1}{2})

    where :math:`\Gamma` is the Gamma function, :math:`n` is the number of
    data points, :math:`n_k` is the number of measurements in bin :math:`k`.

    References
    ----------
    .. [1] Knuth, K.H. "Optimal Data-Based Binning for Histograms".
           arXiv:0605197, 2006

    See Also
    --------
    KnuthF
    freedman_bin_width
    scotts_bin_width
    """
    knuthF = KnuthF(data)
    dx0, bins0 = freedman_bin_width(data, True)
    M0 = len(bins0) - 1
    M = optimize.fmin(knuthF, len(bins0))[0]
    bins = knuthF.bins(M)
    dx = bins[1] - bins[0]

    if return_bins:
        return dx, bins
    else:
        return dx


def histogram(a, bins=10, range=None, **kwargs):
    """Enhanced histogram

    This is a histogram function that enables the use of more sophisticated
    algorithms for determining bins.  Aside from the `bins` argument allowing
    a string specified how bins are computed, the parameters are the same
    as numpy.histogram().

    Parameters
    ----------
    a : array_like
        array of data to be histogrammed

    bins : int or list or str (optional)
        If bins is a string, then it must be one of:
        'blocks' : use bayesian blocks for dynamic bin widths
        'knuth' : use Knuth's rule to determine bins
        'scotts' : use Scott's rule to determine bins
        'freedman' : use the Freedman-diaconis rule to determine bins

    range : tuple or None (optional)
        the minimum and maximum range for the histogram.  If not specified,
        it will be (x.min(), x.max())

    other keyword arguments are described in numpy.hist().

    Returns
    -------
    hist : array
        The values of the histogram. See `normed` and `weights` for a
        description of the possible semantics.
    bin_edges : array of dtype float
        Return the bin edges ``(length(hist)+1)``.

    See Also
    --------
    numpy.histogram
    astroML.plotting.hist
    """
    a = np.asarray(a)

    # if range is specified, we need to truncate the data for
    # the bin-finding routines
    if (range is not None and (bins in ['blocks', 'knuth',
                                        'scotts', 'freedman'])):
        a = a[(a >= range[0]) & (a <= range[1])]

    if bins == 'blocks':
        bins = bayesian_blocks(a)
    elif bins == 'knuth':
        da, bins = knuth_bin_width(a, True)
    elif bins == 'scotts':
        da, bins = scotts_bin_width(a, True)
    elif bins == 'freedman':
        da, bins = freedman_bin_width(a, True)
    elif isinstance(bins, str):
        raise ValueError("unrecognized bin code: '%s'" % bins)

    return np.histogram(a, bins, range, **kwargs)
