powerlaw.py

#The MIT License (MIT)
#
#Copyright (c) 2013 Jeff Alstott
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
#
#The above copyright notice and this permission notice shall be included in
#all copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
#THE SOFTWARE.

# as described in https://docs.python.org/2/library/functions.html#print
from __future__ import print_function
import sys

__version__ = "1.3.4"

class Fit(object):
    """
    A fit of a data set to various probability distributions, namely power
    laws. For fits to power laws, the methods of Clauset et al. 2007 are used.
    These methods identify the portion of the tail of the distribution that
    follows a power law, beyond a value xmin. If no xmin is
    provided, the optimal one is calculated and assigned at initialization.

    Parameters
    ----------
    data : list or array
    discrete : boolean, optional
        Whether the data is discrete (integers).
    xmin : int or float, optional
        The data value beyond which distributions should be fitted. If
        None an optimal one will be calculated.
    xmax : int or float, optional
        The maximum value of the fitted distributions.
    estimate_discrete : bool, optional
        Whether to estimate the fit of a discrete power law using fast
        analytical methods, instead of calculating the fit exactly with
        slow numerical methods. Very accurate with xmin>6
    sigma_threshold : float, optional
        Upper limit on the standard error of the power law fit. Used after
        fitting, when identifying valid xmin values.
    parameter_range : dict, optional
        Dictionary of valid parameter ranges for fitting. Formatted as a
        dictionary of parameter names ('alpha' and/or 'sigma') and tuples
        of their lower and upper limits (ex. (1.5, 2.5), (None, .1)
    """

    def __init__(self, data,
                 discrete=False,
                 xmin=None, xmax=None,
                 fit_method='Likelihood',
                 estimate_discrete=True,
                 discrete_approximation='round',
                 sigma_threshold=None,
                 parameter_range=None,
                 fit_optimizer=None,
                 xmin_distance='D',
                 **kwargs):

        self.data_original = data
        # import logging
        from numpy import asarray
        self.data = asarray(self.data_original, dtype='float')

        self.discrete = discrete

        self.fit_method = fit_method
        self.estimate_discrete = estimate_discrete
        self.discrete_approximation = discrete_approximation
        self.sigma_threshold = sigma_threshold
        self.parameter_range = parameter_range

        self.given_xmin = xmin
        self.given_xmax = xmax
        self.xmin = self.given_xmin
        self.xmax = self.given_xmax

        self.xmin_distance = xmin_distance

        if 0 in self.data:
            print("Values less than or equal to 0 in data. Throwing out 0 or negative values", file=sys.stderr)
            self.data = self.data[self.data>0]

        if self.xmax:
            self.xmax = float(self.xmax)
            self.fixed_xmax = True
            n_above_max = sum(self.data>self.xmax)
            self.data = self.data[self.data<=self.xmax]
        else:
            n_above_max = 0
            self.fixed_xmax = False

        if not all(self.data[i] <= self.data[i+1] for i in range(len(self.data)-1)):
            from numpy import sort
            self.data = sort(self.data)

        self.fitting_cdf_bins, self.fitting_cdf = cdf(self.data, xmin=None, xmax=self.xmax)

        if xmin and type(xmin)!=tuple and type(xmin)!=list:
            self.fixed_xmin = True
            self.xmin = float(xmin)
            self.noise_flag = None
            pl = Power_Law(xmin=self.xmin,
                           xmax=self.xmax,
                           discrete=self.discrete,
                           fit_method=self.fit_method,
                           estimate_discrete=self.estimate_discrete,
                           data=self.data,
                           parameter_range=self.parameter_range)
            setattr(self,self.xmin_distance, getattr(pl, self.xmin_distance))
            self.alpha = pl.alpha
            self.sigma = pl.sigma
            #self.power_law = pl
        else:
            self.fixed_xmin=False
            print("Calculating best minimal value for power law fit", file=sys.stderr)
            self.find_xmin()

        self.data = self.data[self.data>=self.xmin]
        self.n = float(len(self.data))
        self.n_tail = self.n + n_above_max

        self.supported_distributions = {'power_law': Power_Law,
                                        'lognormal': Lognormal,
                                        'exponential': Exponential,
                                        'truncated_power_law': Truncated_Power_Law,
                                        'stretched_exponential': Stretched_Exponential,
                                        }
                                        #'gamma': None}

    def __getattr__(self, name):
        if name in self.supported_distributions.keys():
            #from string import capwords
            #dist = capwords(name, '_')
            #dist = globals()[dist] #Seems a hack. Might try import powerlaw; getattr(powerlaw, dist)
            dist = self.supported_distributions[name]
            if dist == Power_Law:
                parameter_range = self.parameter_range
            else:
                parameter_range = None
            setattr(self,
                    name,
                    dist(data=self.data,
                         xmin=self.xmin,
                         xmax=self.xmax,
                         discrete=self.discrete,
                         fit_method=self.fit_method,
                         estimate_discrete=self.estimate_discrete,
                         discrete_approximation=self.discrete_approximation,
                         parameter_range=parameter_range,
                         parent_Fit=self))
            return getattr(self, name)
        else:
            raise AttributeError(name)

    def find_xmin(self, xmin_distance=None):
        """
        Returns the optimal xmin beyond which the scaling regime of the power
        law fits best. The attribute self.xmin of the Fit object is also set.

        The optimal xmin beyond which the scaling regime of the power law fits
        best is identified by minimizing the Kolmogorov-Smirnov distance
        between the data and the theoretical power law fit.
        This is the method of Clauset et al. 2007.
        """
        from numpy import unique, asarray, argmin
#Much of the rest of this function was inspired by Adam Ginsburg's plfit code,
#specifically the mapping and sigma threshold behavior:
#http://code.google.com/p/agpy/source/browse/trunk/plfit/plfit.py?spec=svn359&r=357
        if not self.given_xmin:
            possible_xmins = self.data
        else:
            possible_ind = min(self.given_xmin)<=self.data
            possible_ind *= self.data<=max(self.given_xmin)
            possible_xmins = self.data[possible_ind]
        xmins, xmin_indices = unique(possible_xmins, return_index=True)
#Don't look at last xmin, as that's also the xmax, and we want to at least have TWO points to fit!
        xmins = xmins[:-1]
        xmin_indices = xmin_indices[:-1]

        if xmin_distance is None:
            xmin_distance = self.xmin_distance

        if len(xmins)<=0:
            print("Less than 2 unique data values left after xmin and xmax "
                  "options! Cannot fit. Returning nans.", file=sys.stderr)
            from numpy import nan, array
            self.xmin = nan
            self.D = nan
            self.V = nan
            self.Asquare = nan
            self.Kappa = nan
            self.alpha = nan
            self.sigma = nan
            self.n_tail = nan
            setattr(self, xmin_distance+'s', array([nan]))
            self.alphas = array([nan])
            self.sigmas = array([nan])
            self.in_ranges = array([nan])
            self.xmins = array([nan])
            self.noise_flag = True
            return self.xmin

        def fit_function(xmin):
            pl = Power_Law(xmin=xmin,
                           xmax=self.xmax,
                           discrete=self.discrete,
                           estimate_discrete=self.estimate_discrete,
                           fit_method=self.fit_method,
                           data=self.data,
                           parameter_range=self.parameter_range,
                           parent_Fit=self)
            return getattr(pl, xmin_distance), pl.alpha, pl.sigma, pl.in_range()

        fits = asarray(list(map(fit_function, xmins)))
        # logging.warning(fits.shape)
        setattr(self, xmin_distance+'s', fits[:,0])
        self.alphas = fits[:,1]
        self.sigmas = fits[:,2]
        self.in_ranges = fits[:,3].astype(bool)
        self.xmins = xmins

        good_values = self.in_ranges

        if self.sigma_threshold:
            good_values = good_values * (self.sigmas < self.sigma_threshold)

        if good_values.all():
            min_D_index = argmin(getattr(self, xmin_distance+'s'))
            self.noise_flag = False
        elif not good_values.any():
            min_D_index = argmin(getattr(self, xmin_distance+'s'))
            self.noise_flag = True
        else:
            from numpy.ma import masked_array
            masked_Ds = masked_array(getattr(self, xmin_distance+'s'), mask=-good_values)
            min_D_index = masked_Ds.argmin()
            self.noise_flag = False

        if self.noise_flag:
            print("No valid fits found.", file=sys.stderr)
        
        #Set the Fit's xmin to the optimal xmin
        self.xmin = xmins[min_D_index]
        setattr(self, xmin_distance, getattr(self, xmin_distance+'s')[min_D_index])
        self.alpha = self.alphas[min_D_index]
        self.sigma = self.sigmas[min_D_index]

        #Update the fitting CDF given the new xmin, in case other objects, like
        #Distributions, want to use it for fitting (like if they do KS fitting)
        self.fitting_cdf_bins, self.fitting_cdf = self.cdf()

        return self.xmin


    def nested_distribution_compare(self, dist1, dist2, nested=True, **kwargs):
        """
        Returns the loglikelihood ratio, and its p-value, between the two
        distribution fits, assuming the candidate distributions are nested.

        Parameters
        ----------
        dist1 : string
            Name of the first candidate distribution (ex. 'power_law')
        dist2 : string
            Name of the second candidate distribution (ex. 'exponential')
        nested : bool or None, optional
            Whether to assume the candidate distributions are nested versions
            of each other. None assumes not unless the name of one distribution
            is a substring of the other. True by default.

        Returns
        -------
        R : float
            Loglikelihood ratio of the two distributions' fit to the data. If
            greater than 0, the first distribution is preferred. If less than
            0, the second distribution is preferred.
        p : float
            Significance of R
        """
        return self.distribution_compare(dist1, dist2, nested=nested, **kwargs)

    def distribution_compare(self, dist1, dist2, nested=None, **kwargs):
        """
        Returns the loglikelihood ratio, and its p-value, between the two
        distribution fits, assuming the candidate distributions are nested.

        Parameters
        ----------
        dist1 : string
            Name of the first candidate distribution (ex. 'power_law')
        dist2 : string
            Name of the second candidate distribution (ex. 'exponential')
        nested : bool or None, optional
            Whether to assume the candidate distributions are nested versions
            of each other. None assumes not unless the name of one distribution
            is a substring of the other.

        Returns
        -------
        R : float
            Loglikelihood ratio of the two distributions' fit to the data. If
            greater than 0, the first distribution is preferred. If less than
            0, the second distribution is preferred.
        p : float
            Significance of R
        """
        if (dist1 in dist2) or (dist2 in dist1) and nested is None:
            print("Assuming nested distributions", file=sys.stderr)
            nested = True

        dist1 = getattr(self, dist1)
        dist2 = getattr(self, dist2)

        loglikelihoods1 = dist1.loglikelihoods(self.data)
        loglikelihoods2 = dist2.loglikelihoods(self.data)

        return loglikelihood_ratio(
            loglikelihoods1, loglikelihoods2,
            nested=nested,
            **kwargs)

    def loglikelihood_ratio(self, dist1, dist2, nested=None, **kwargs):
        """
        Another name for distribution_compare.
        """
        return self.distribution_compare(dist1, dist2, nested=nested, **kwargs)

    def cdf(self, original_data=False, survival=False, **kwargs):
        """
        Returns the cumulative distribution function of the data.

        Parameters
        ----------
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)
        survival : bool, optional
            Whether to return the complementary cumulative distribution
            function, 1-CDF, also known as the survival function.

        Returns
        -------
        X : array
            The sorted, unique values in the data.
        probabilities : array
            The portion of the data that is less than or equal to X.
        """
        if original_data:
            data = self.data_original
            xmin = None
            xmax = None
        else:
            data = self.data
            xmin = self.xmin
            xmax = self.xmax
        return cdf(data, xmin=xmin, xmax=xmax, survival=survival,
                   **kwargs) 
        return cdf(data, xmin=xmin, xmax=xmax, survival=survival,
                   **kwargs) 

    def ccdf(self, original_data=False, survival=True, **kwargs):
        """
        Returns the complementary cumulative distribution function of the data.

        Parameters
        ----------
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)
        survival : bool, optional
            Whether to return the complementary cumulative distribution
            function, also known as the survival function, or the cumulative
            distribution function, 1-CCDF.

        Returns
        -------
        X : array
            The sorted, unique values in the data.
        probabilities : array
            The portion of the data that is greater than or equal to X.
        """
        if original_data:
            data = self.data_original
            xmin = None
            xmax = None
        else:
            data = self.data
            xmin = self.xmin
            xmax = self.xmax
        return cdf(data, xmin=xmin, xmax=xmax, survival=survival,
                   **kwargs)

    def pdf(self, original_data=False, **kwargs):
        """
        Returns the probability density function (normalized histogram) of the
        data.

        Parameters
        ----------
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)

        Returns
        -------
        bin_edges : array
            The edges of the bins of the probability density function.
        probabilities : array
            The portion of the data that is within the bin. Length 1 less than
            bin_edges, as it corresponds to the spaces between them.
        """
        if original_data:
            data = self.data_original
            xmin = None
            xmax = None
        else:
            data = self.data
            xmin = self.xmin
            xmax = self.xmax
        edges, hist = pdf(data, xmin=xmin, xmax=xmax, **kwargs)
        return edges, hist

    def plot_cdf(self, ax=None, original_data=False, survival=False, **kwargs):
        """
        Plots the CDF to a new figure or to axis ax if provided.

        Parameters
        ----------
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)
        survival : bool, optional
            Whether to plot a CDF (False) or CCDF (True). False by default.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        if original_data:
            data = self.data_original
        else:
            data = self.data
        return plot_cdf(data, ax=ax, survival=survival, **kwargs)

    def plot_ccdf(self, ax=None, original_data=False, survival=True, **kwargs):
        """
        Plots the CCDF to a new figure or to axis ax if provided.

        Parameters
        ----------
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)
        survival : bool, optional
            Whether to plot a CDF (False) or CCDF (True). True by default.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        if original_data:
            data = self.data_original
        else:
            data = self.data
        return plot_cdf(data, ax=ax, survival=survival, **kwargs)

    def plot_pdf(self, ax=None, original_data=False,
                 linear_bins=False, **kwargs):
        """
        Plots the probability density function (PDF) or the data to a new figure
        or to axis ax if provided.

        Parameters
        ----------
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.
        original_data : bool, optional
            Whether to use all of the data initially passed to the Fit object.
            If False, uses only the data used for the fit (within xmin and
            xmax.)
        linear_bins : bool, optional
            Whether to use linearly spaced bins (True) or logarithmically
            spaced bins (False). False by default.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        if original_data:
            data = self.data_original
        else:
            data = self.data
        return plot_pdf(data, ax=ax, linear_bins=linear_bins, **kwargs)

class Distribution(object):
    """
    An abstract class for theoretical probability distributions. Can be created
    with particular parameter values, or fitted to a dataset. Fitting is
    by maximum likelihood estimation by default.

    Parameters
    ----------
    xmin : int or float, optional
        The data value beyond which distributions should be fitted. If
        None an optimal one will be calculated.
    xmax : int or float, optional
        The maximum value of the fitted distributions.
    discrete : boolean, optional
        Whether the distribution is discrete (integers).

    data : list or array, optional
        The data to which to fit the distribution. If provided, the fit will
        be created at initialization.
    fit_method : "Likelihood" or "KS", optional
        Method for fitting the distribution. "Likelihood" is maximum Likelihood
        estimation. "KS" is minimial distance estimation using The
        Kolmogorov-Smirnov test.

    parameters : tuple or list, optional
        The parameters of the distribution. Will be overridden if data is
        given or the fit method is called.
    parameter_range : dict, optional
        Dictionary of valid parameter ranges for fitting. Formatted as a
        dictionary of parameter names ('alpha' and/or 'sigma') and tuples
        of their lower and upper limits (ex. (1.5, 2.5), (None, .1)
    initial_parameters : tuple or list, optional
        Initial values for the parameter in the fitting search.

    discrete_approximation : "round", "xmax" or int, optional
        If the discrete form of the theoeretical distribution is not known,
        it can be estimated. One estimation method is "round", which sums
        the probability mass from x-.5 to x+.5 for each data point. The other
        option is to calculate the probability for each x from 1 to N and
        normalize by their sum. N can be "xmax" or an integer.

    parent_Fit : Fit object, optional
        A Fit object from which to use data, if it exists.
    """

    def __init__(self,
                 xmin=1, xmax=None,
                 discrete=False,
                 fit_method='Likelihood',
                 data=None,
                 parameters=None,
                 parameter_range=None,
                 initial_parameters=None,
                 discrete_approximation='round',
                 parent_Fit=None,
                 **kwargs):

        self.xmin = xmin
        self.xmax = xmax
        self.discrete = discrete
        self.fit_method = fit_method
        self.discrete_approximation = discrete_approximation

        self.parameter1 = None
        self.parameter2 = None
        self.parameter3 = None
        self.parameter1_name = None
        self.parameter2_name = None
        self.parameter3_name = None

        if parent_Fit:
            self.parent_Fit = parent_Fit

        if parameters is not None:
            self.parameters(parameters)

        if parameter_range:
            self.parameter_range(parameter_range)

        if initial_parameters:
            self._given_initial_parameters(initial_parameters)

        if (data is not None) and not (parameter_range and self.parent_Fit):
            self.fit(data)


    def fit(self, data=None, suppress_output=False):
        """
        Fits the parameters of the distribution to the data. Uses options set
        at initialization.
        """

        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        if self.fit_method=='Likelihood':
            def fit_function(params):
                self.parameters(params)
                return -sum(self.loglikelihoods(data))
        elif self.fit_method=='KS':
            def fit_function(params):
                self.parameters(params)
                self.KS(data)
                return self.D
        from scipy.optimize import fmin
        parameters, negative_loglikelihood, iter, funcalls, warnflag, = \
            fmin(
                lambda params: fit_function(params),
                self.initial_parameters(data),
                full_output=1,
                disp=False)
        self.parameters(parameters)
        if not self.in_range():
            self.noise_flag=True
        else:
            self.noise_flag=False
        if self.noise_flag and not suppress_output:
            print("No valid fits found.", file=sys.stderr)
        self.loglikelihood =-negative_loglikelihood
        self.KS(data)

    def KS(self, data=None):
        """
        Returns the Kolmogorov-Smirnov distance D between the distribution and
        the data. Also sets the properties D+, D-, V (the Kuiper testing
        statistic), and Kappa (1 + the average difference between the
        theoretical and empirical distributions).

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        if len(data)<2:
            print("Not enough data. Returning nan", file=sys.stderr)
            from numpy import nan
            self.D = nan
            self.D_plus = nan
            self.D_minus = nan
            self.Kappa = nan
            self.V = nan
            self.Asquare = nan
            return self.D

        if  hasattr(self, 'parent_Fit'):
            bins = self.parent_Fit.fitting_cdf_bins
            Actual_CDF = self.parent_Fit.fitting_cdf
            ind = bins>=self.xmin
            bins = bins[ind]
            Actual_CDF = Actual_CDF[ind]
            dropped_probability = Actual_CDF[0]
            Actual_CDF -= dropped_probability
            Actual_CDF /= 1-dropped_probability
        else:
            bins, Actual_CDF = cdf(data)

        Theoretical_CDF = self.cdf(bins)

        CDF_diff = Theoretical_CDF - Actual_CDF

        self.D_plus = CDF_diff.max()
        self.D_minus = -1.0*CDF_diff.min()
        from numpy import mean
        self.Kappa = 1 + mean(CDF_diff)

        self.V = self.D_plus + self.D_minus
        self.D = max(self.D_plus, self.D_minus)
        self.Asquare = sum((
                            (CDF_diff**2) / 
                            (Theoretical_CDF * (1 - Theoretical_CDF))
                            )[1:]
                           )
        return self.D

    def ccdf(self,data=None, survival=True):
        """
        The complementary cumulative distribution function (CCDF) of the
        theoretical distribution. Calculated for the values given in data
        within xmin and xmax, if present.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        survival : bool, optional
            Whether to calculate a CDF (False) or CCDF (True).
            True by default.

        Returns
        -------
        X : array
            The sorted, unique values in the data.
        probabilities : array
            The portion of the data that is less than or equal to X.
        """
        return self.cdf(data=data, survival=survival)

    def cdf(self,data=None, survival=False):
        """
        The cumulative distribution function (CDF) of the theoretical
        distribution. Calculated for the values given in data within xmin and
        xmax, if present.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        survival : bool, optional
            Whether to calculate a CDF (False) or CCDF (True).
            False by default.

        Returns
        -------
        X : array
            The sorted, unique values in the data.
        probabilities : array
            The portion of the data that is less than or equal to X.
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        n = len(data)
        from sys import float_info
        if not self.in_range():
            from numpy import tile
            return tile(10**float_info.min_10_exp, n)

        if self._cdf_xmin==1:
#If cdf_xmin is 1, it means we don't have the numerical accuracy to
            #calculate this tail. So we make everything 1, indicating
            #we're at the end of the tail. Such an xmin should be thrown
            #out by the KS test.
            from numpy import ones
            CDF = ones(n)
            return CDF

        CDF = self._cdf_base_function(data) - self._cdf_xmin
        #if self.xmax:
        #    CDF = CDF - (1 - self._cdf_base_function(self.xmax))

        norm = 1 - self._cdf_xmin
        if self.xmax:
            norm = norm - (1 - self._cdf_base_function(self.xmax))

        CDF = CDF/norm

        if survival:
            CDF = 1 - CDF

        return CDF

    @property
    def _cdf_xmin(self):
        return self._cdf_base_function(self.xmin)


    def pdf(self, data=None):
        """
        Returns the probability density function (normalized histogram) of the
        theoretical distribution for the values in data within xmin and xmax,
        if present.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.

        Returns
        -------
        probabilities : array
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        n = len(data)
        from sys import float_info
        if not self.in_range():
            from numpy import tile
            return tile(10**float_info.min_10_exp, n)

        if not self.discrete:
            f = self._pdf_base_function(data)
            C = self._pdf_continuous_normalizer
            likelihoods = f*C
        else:
            if self._pdf_discrete_normalizer:
                f = self._pdf_base_function(data)
                C = self._pdf_discrete_normalizer
                likelihoods = f*C
            elif self.discrete_approximation=='round':
                lower_data = data-.5
                upper_data = data+.5
#Temporarily expand xmin and xmax to be able to grab the extra bit of
#probability mass beyond the (integer) values of xmin and xmax
#Note this is a design decision. One could also say this extra 
#probability "off the edge" of the distribution shouldn't be included,
#and that implementation is retained below, commented out. Note, however,
#that such a cliff means values right at xmin and xmax have half the width to
#grab probability from, and thus are lower probability than they would otherwise
#be. This is particularly concerning for values at xmin, which are typically 
#the most likely and greatly influence the distribution's fit.
                self.xmin -= .5
                if self.xmax:
                    self.xmax += .5
                #Clean data for invalid values before handing to cdf, which will purge them
                #lower_data[lower_data<self.xmin] +=.5
                #if self.xmax:
                #    upper_data[upper_data>self.xmax] -=.5
                likelihoods = self.cdf(upper_data)-self.cdf(lower_data)
                self.xmin +=.5
                if self.xmax:
                    self.xmax -= .5
            else:
                if self.discrete_approximation=='xmax':
                    upper_limit = self.xmax
                else:
                    upper_limit = self.discrete_approximation
#            from mpmath import exp
                from numpy import arange
                X = arange(self.xmin, upper_limit+1)
                PDF = self._pdf_base_function(X)
                PDF = (PDF/sum(PDF)).astype(float)
                likelihoods = PDF[(data-self.xmin).astype(int)]
        likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        return likelihoods

    @property
    def _pdf_continuous_normalizer(self):
        C = 1 - self._cdf_xmin
        if self.xmax:
            C -= 1 - self._cdf_base_function(self.xmax+1)
        C = 1.0/C
        return C

    @property
    def _pdf_discrete_normalizer(self):
        return False

    def parameter_range(self, r, initial_parameters=None):
        """
        Set the limits on the range of valid parameters to be considered while
        fitting.

        Parameters
        ----------
        r : dict
            A dictionary of the parameter range. Restricted parameter 
            names are keys, and with tuples of the form (lower_bound,
            upper_bound) as values.
        initial_parameters : tuple or list, optional
            Initial parameter values to start the fitting search from.
        """
        from types import FunctionType
        if type(r)==FunctionType:
            self._in_given_parameter_range = r
        else:
            self._range_dict = r

        if initial_parameters:
            self._given_initial_parameters = initial_parameters

        if self.parent_Fit:
            self.fit(self.parent_Fit.data)

    def in_range(self):
        """
        Whether the current parameters of the distribution are within the range
        of valid parameters.
        """
        try:
            r = self._range_dict
            result = True
            for k in r.keys():
#For any attributes we've specificed, make sure we're above the lower bound
#and below the lower bound (if they exist). This must be true of all of them.
                lower_bound, upper_bound = r[k]
                if upper_bound:
                    result *= getattr(self, k) < upper_bound
                if lower_bound:
                    result *= getattr(self, k) > lower_bound
            return result
        except AttributeError:
            try:
                in_range = self._in_given_parameter_range(self)
            except AttributeError:
                in_range = self._in_standard_parameter_range()
        return bool(in_range)

    def initial_parameters(self, data):
        """
        Return previously user-provided initial parameters or, if never
        provided,  calculate new ones. Default initial parameter estimates are
        unique to each theoretical distribution.
        """
        try:
            return self._given_initial_parameters
        except AttributeError:
            return self._initial_parameters(data)

    def likelihoods(self, data):
        """
        The likelihoods of the observed data from the theoretical distribution.
        Another name for the probabilities or probability density function.
        """
        return self.pdf(data) 

    def loglikelihoods(self, data):
        """
        The logarithm of the likelihoods of the observed data from the
        theoretical distribution.
        """
        from numpy import log
        return log(self.likelihoods(data))

    def plot_ccdf(self, data=None, ax=None, survival=True, **kwargs):
        """
        Plots the complementary cumulative distribution function (CDF) of the
        theoretical distribution for the values given in data within xmin and
        xmax, if present. Plots to a new figure or to axis ax if provided.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.
        survival : bool, optional
            Whether to plot a CDF (False) or CCDF (True). True by default.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        return self.plot_cdf(data, ax=None, survival=survival, **kwargs)

    def plot_cdf(self, data=None, ax=None, survival=False, **kwargs):
        """
        Plots the cumulative distribution function (CDF) of the
        theoretical distribution for the values given in data within xmin and
        xmax, if present. Plots to a new figure or to axis ax if provided.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.
        survival : bool, optional
            Whether to plot a CDF (False) or CCDF (True). False by default.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        from numpy import unique
        bins = unique(trim_to_range(data, xmin=self.xmin, xmax=self.xmax))
        CDF = self.cdf(bins, survival=survival)
        if not ax:
            import matplotlib.pyplot as plt
            plt.plot(bins, CDF, **kwargs)
            ax = plt.gca()
        else:
            ax.plot(bins, CDF, **kwargs)
        ax.set_xscale("log")
        ax.set_yscale("log")
        return ax

    def plot_pdf(self, data=None, ax=None, **kwargs):
        """
        Plots the probability density function (PDF) of the
        theoretical distribution for the values given in data within xmin and
        xmax, if present. Plots to a new figure or to axis ax if provided.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.
        ax : matplotlib axis, optional
            The axis to which to plot. If None, a new figure is created.

        Returns
        -------
        ax : matplotlib axis
            The axis to which the plot was made.
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        from numpy import unique
        bins = unique(trim_to_range(data, xmin=self.xmin, xmax=self.xmax))
        PDF = self.pdf(bins)
        from numpy import nan
        PDF[PDF==0] = nan
        if not ax:
            import matplotlib.pyplot as plt
            plt.plot(bins, PDF, **kwargs)
            ax = plt.gca()
        else:
            ax.plot(bins, PDF, **kwargs)
        ax.set_xscale("log")
        ax.set_yscale("log")
        return ax

    def generate_random(self,n=1, estimate_discrete=None):
        """
        Generates random numbers from the theoretical probability distribution.
        If xmax is present, it is currently ignored.

        Parameters
        ----------
        n : int or float
            The number of random numbers to generate
        estimate_discrete : boolean
            For discrete distributions, whether to use a faster approximation of
            the random number generator. If None, attempts to inherit
            the estimate_discrete behavior used for fitting from the Distribution
            object or the parent Fit object, if present. Approximations only
            exist for some distributions (namely the power law). If an
            approximation does not exist an estimate_discrete setting of True
            will not be inherited.

        Returns
        -------
        r : array
            Random numbers drawn from the distribution
        """
        from numpy.random import rand
        from numpy import array
        r = rand(n)
        if not self.discrete:
            x = self._generate_random_continuous(r)
        else:
            if (estimate_discrete and not hasattr(self, '_generate_random_discrete_estimate') ):
                raise AttributeError("This distribution does not have an "
                                     "estimation of the discrete form for generating simulated "
                                     "data. Try the exact form with estimate_discrete=False.")
            if estimate_discrete is None:
                if not hasattr(self, '_generate_random_discrete_estimate'):
                    estimate_discrete = False
                elif hasattr(self, 'estimate_discrete'):
                    estimate_discrete = self.estimate_discrete
                elif hasattr('parent_Fit'):
                    estimate_discrete = self.parent_Fit.estimate_discrete
                else:
                    estimate_discrete = False
            if estimate_discrete:
                x = self._generate_random_discrete_estimate(r)
            else:
                x = array([self._double_search_discrete(R) for R in r],
                          dtype='float')
        return x

    def _double_search_discrete(self, r):
        #Find a range from x1 to x2 that our random probability fits between
        x2 = int(self.xmin)
        while self.ccdf(data=[x2]) >= (1 - r):
            x1 = x2
            x2 = 2*x1
        #Use binary search within that range to find the exact answer, up to
        #the limit of being between two integers.
        x = bisect_map(x1, x2, self.ccdf, 1-r)
        return x

class Power_Law(Distribution):

    def __init__(self, estimate_discrete=True, **kwargs):
        self.estimate_discrete = estimate_discrete
        Distribution.__init__(self, **kwargs)

    def parameters(self, params):
        self.alpha = params[0]
        self.parameter1 = self.alpha
        self.parameter1_name = 'alpha'

    @property
    def name(self):
        return "power_law"

    @property
    def sigma(self):
#Only is calculable after self.fit is started, when the number of data points is
#established
        from numpy import sqrt
        return (self.alpha - 1) / sqrt(self.n)

    def _in_standard_parameter_range(self):
        return self.alpha>1

    def fit(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        self.n = len(data)
        from numpy import log, sum
        if not self.discrete and not self.xmax:
            self.alpha = 1 + (self.n / sum(log(data/self.xmin)))
            if not self.in_range():
                Distribution.fit(self, data, suppress_output=True)
            self.KS(data)
        elif self.discrete and self.estimate_discrete and not self.xmax:
            self.alpha = 1 + (self.n / sum(log(data / (self.xmin - .5))))
            if not self.in_range():
                Distribution.fit(self, data, suppress_output=True)
            self.KS(data)
        else:
            Distribution.fit(self, data, suppress_output=True)

        if not self.in_range():
            self.noise_flag=True
        else:
            self.noise_flag=False

    def _initial_parameters(self, data):
        from numpy import log, sum
        return 1 + len(data)/sum(log(data / (self.xmin)))

    def _cdf_base_function(self, x):
        if self.discrete:
            from scipy.special import zeta
            CDF = 1 - zeta(self.alpha, x)
        else:
#Can this be reformulated to not reference xmin? Removal of the probability
#before xmin and after xmax is handled in Distribution.cdf(), so we don't
#strictly need this element. It doesn't hurt, for the moment.
            CDF = 1-(x/self.xmin)**(-self.alpha+1)
        return CDF

    def _pdf_base_function(self, x):
        return x**-self.alpha

    @property
    def _pdf_continuous_normalizer(self):
        return (self.alpha-1) * self.xmin**(self.alpha-1)

    @property
    def _pdf_discrete_normalizer(self):
        C = 1.0 - self._cdf_xmin
        if self.xmax:
            C -= 1 - self._cdf_base_function(self.xmax+1)
        C = 1.0/C
        return C

    def _generate_random_continuous(self, r):
            return self.xmin * (1 - r) ** (-1/(self.alpha - 1))
    def _generate_random_discrete_estimate(self, r):
            x = (self.xmin - 0.5) * (1 - r) ** (-1/(self.alpha - 1)) + 0.5
            from numpy import around
            return around(x)

class Exponential(Distribution):

    def parameters(self, params):
        self.Lambda = params[0]
        self.parameter1 = self.Lambda
        self.parameter1_name = 'lambda'

    @property
    def name(self):
        return "exponential"

    def _initial_parameters(self, data):
        from numpy import mean
        return 1/mean(data)

    def _in_standard_parameter_range(self):
        return self.Lambda>0

    def _cdf_base_function(self, x):
        from numpy import exp
        CDF = 1 - exp(-self.Lambda*x)
        return CDF

    def _pdf_base_function(self, x):
        from numpy import exp
        return exp(-self.Lambda * x)

    @property
    def _pdf_continuous_normalizer(self):
        from numpy import exp
        return self.Lambda * exp(self.Lambda * self.xmin)

    @property
    def _pdf_discrete_normalizer(self):
        from numpy import exp
        C = (1 - exp(-self.Lambda)) * exp(self.Lambda * self.xmin)
        if self.xmax:
            Cxmax = (1 - exp(-self.Lambda)) * exp(self.Lambda * self.xmax)
            C = 1.0/C - 1.0/Cxmax
            C = 1.0/C
        return C

    def pdf(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import exp
#        likelihoods = exp(-Lambda*data)*\
#                Lambda*exp(Lambda*xmin)
            likelihoods = self.Lambda*exp(self.Lambda*(self.xmin-data))
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        else:
            likelihoods = Distribution.pdf(self, data)
        return likelihoods

    def loglikelihoods(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import log
#        likelihoods = exp(-Lambda*data)*\
#                Lambda*exp(Lambda*xmin)
            loglikelihoods = log(self.Lambda) + (self.Lambda*(self.xmin-data))
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            loglikelihoods[loglikelihoods==0] = log(10**float_info.min_10_exp)
        else:
            loglikelihoods = Distribution.loglikelihoods(self, data)
        return loglikelihoods

    def _generate_random_continuous(self, r):
        from numpy import log
        return self.xmin - (1/self.Lambda) * log(1-r)

class Stretched_Exponential(Distribution):

    def parameters(self, params):
        self.Lambda = params[0]
        self.parameter1 = self.Lambda
        self.parameter1_name = 'lambda'
        self.beta = params[1]
        self.parameter2 = self.beta
        self.parameter2_name = 'beta'

    @property
    def name(self):
        return "stretched_exponential"

    def _initial_parameters(self, data):
        from numpy import mean
        return (1/mean(data), 1)

    def _in_standard_parameter_range(self):
        return self.Lambda>0 and self.beta>0

    def _cdf_base_function(self, x):
        from numpy import exp
        CDF = 1 - exp(-(self.Lambda*x)**self.beta)
        return CDF

    def _pdf_base_function(self, x):
        from numpy import exp
        return (((x*self.Lambda)**(self.beta-1)) *
                exp(-((self.Lambda*x)**self.beta)))

    @property
    def _pdf_continuous_normalizer(self):
        from numpy import exp
        C = self.beta*self.Lambda*exp((self.Lambda*self.xmin)**self.beta)
        return C

    @property
    def _pdf_discrete_normalizer(self):
        return False

    def pdf(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import exp
            likelihoods = ((data*self.Lambda)**(self.beta-1) *
                           self.beta * self.Lambda *
                           exp((self.Lambda*self.xmin)**self.beta -
                               (self.Lambda*data)**self.beta))
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        else:
            likelihoods = Distribution.pdf(self, data)
        return likelihoods

    def loglikelihoods(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import log
            loglikelihoods = ( 
                    log((data*self.Lambda)**(self.beta-1) *
                        self.beta * self. Lambda) + 
                    (self.Lambda*self.xmin)**self.beta - 
                        (self.Lambda*data)**self.beta)
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            from numpy import inf
            loglikelihoods[loglikelihoods==-inf] = log(10**float_info.min_10_exp)
        else:
            loglikelihoods = Distribution.loglikelihoods(self, data)
        return loglikelihoods

    def _generate_random_continuous(self, r):
        from numpy import log
#        return ( (self.xmin**self.beta) -
#            (1/self.Lambda) * log(1-r) )**(1/self.beta)
        return (1/self.Lambda)* ( (self.Lambda*self.xmin)**self.beta -
            log(1-r) )**(1/self.beta)

class Truncated_Power_Law(Distribution):

    def parameters(self, params):
        self.alpha = params[0]
        self.parameter1 = self.alpha
        self.parameter1_name = 'alpha'
        self.Lambda = params[1]
        self.parameter2 = self.Lambda
        self.parameter2_name = 'lambda'

    @property
    def name(self):
        return "truncated_power_law"

    def _initial_parameters(self, data):
        from numpy import log, sum, mean
        alpha = 1 + len(data)/sum( log( data / (self.xmin) ))
        Lambda = 1/mean(data)
        return (alpha, Lambda)

    def _in_standard_parameter_range(self):
        return self.Lambda>0 and self.alpha>1

    def _cdf_base_function(self, x):
        from mpmath import gammainc
        from numpy import vectorize
        gammainc = vectorize(gammainc)

        CDF = ( (gammainc(1-self.alpha,self.Lambda*x)).astype('float') /
                self.Lambda**(1-self.alpha)
                    )
        CDF = 1 -CDF
        return CDF

    def _pdf_base_function(self, x):
        from numpy import exp
        return x**(-self.alpha) * exp(-self.Lambda * x)

    @property
    def _pdf_continuous_normalizer(self):
        from mpmath import gammainc
        C = ( self.Lambda**(1-self.alpha) /
                float(gammainc(1-self.alpha,self.Lambda*self.xmin)))
        return C

    @property
    def _pdf_discrete_normalizer(self):
        if 0:
            return False
        from mpmath import lerchphi
        from mpmath import exp # faster /here/ than numpy.exp
        C = ( float(exp(self.xmin * self.Lambda) /
            lerchphi(exp(-self.Lambda), self.alpha, self.xmin)) )
        if self.xmax:
            Cxmax = ( float(exp(self.xmax * self.Lambda) /
                lerchphi(exp(-self.Lambda), self.alpha, self.xmax)) )
            C = 1.0/C - 1.0/Cxmax
            C = 1.0/C
        return C

    def pdf(self, data=None):
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and False:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import exp
            from mpmath import gammainc
#        likelihoods = (data**-alpha)*exp(-Lambda*data)*\
#                (Lambda**(1-alpha))/\
#                float(gammainc(1-alpha,Lambda*xmin))
            likelihoods = ( self.Lambda**(1-self.alpha) /
                    (data**self.alpha *
                            exp(self.Lambda*data) *
                            gammainc(1-self.alpha,self.Lambda*self.xmin)
                            ).astype(float)
                    )
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        else:
            likelihoods = Distribution.pdf(self, data)
        return likelihoods

    def _generate_random_continuous(self, r):
        def helper(r):
            from numpy import log
            from numpy.random import rand
            while 1:
                x = self.xmin - (1/self.Lambda) * log(1-r)
                p = ( x/self.xmin )**-self.alpha
                if rand()<p:
                    return x
                r = rand()
        from numpy import array
        return array(list(map(helper, r)))

class Lognormal(Distribution):

    def parameters(self, params):
        self.mu = params[0]
        self.parameter1 = self.mu
        self.parameter1_name = 'mu'

        self.sigma = params[1]
        self.parameter2 = self.sigma
        self.parameter2_name = 'sigma'

    @property
    def name(self):
        return "lognormal"

    def pdf(self, data=None):
        """
        Returns the probability density function (normalized histogram) of the
        theoretical distribution for the values in data within xmin and xmax,
        if present.

        Parameters
        ----------
        data : list or array, optional
            If not provided, attempts to use the data from the Fit object in
            which the Distribution object is contained.

        Returns
        -------
        probabilities : array
        """
        if data is None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        n = len(data)
        from sys import float_info
        from numpy import tile
        if not self.in_range():
            return tile(10**float_info.min_10_exp, n)

        if not self.discrete:
            f = self._pdf_base_function(data)
            C = self._pdf_continuous_normalizer
            if C > 0:
                likelihoods = f/C
            else:
                likelihoods = tile(10**float_info.min_10_exp, n)
        else:
            if self._pdf_discrete_normalizer:
                f = self._pdf_base_function(data)
                C = self._pdf_discrete_normalizer
                likelihoods = f*C
            elif self.discrete_approximation=='round':
                likelihoods = self._round_discrete_approx(data)
            else:
                if self.discrete_approximation=='xmax':
                    upper_limit = self.xmax
                else:
                    upper_limit = self.discrete_approximation
#            from mpmath import exp
                from numpy import arange
                X = arange(self.xmin, upper_limit+1)
                PDF = self._pdf_base_function(X)
                PDF = (PDF/sum(PDF)).astype(float)
                likelihoods = PDF[(data-self.xmin).astype(int)]
        likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        return likelihoods

    def _round_discrete_approx(self, data):
        """
        This function reformulates the calculation to avoid underflow errors
        with the erf function. As implemented, erf(x) quickly approaches 1
        while erfc(x) is more accurate. Since erfc(x) = 1 - erf(x),
        calculations can be written using erfc(x)
        """
        import numpy as np
        import scipy.special as ss
        """ Temporarily expand xmin and xmax to be able to grab the extra bit of
        probability mass beyond the (integer) values of xmin and xmax
        Note this is a design decision. One could also say this extra 
        probability "off the edge" of the distribution shouldn't be included,
        and that implementation is retained below, commented out. Note, however,
        that such a cliff means values right at xmin and xmax have half the width to
        grab probability from, and thus are lower probability than they would otherwise
        be. This is particularly concerning for values at xmin, which are typically 
        the most likely and greatly influence the distribution's fit.
        """
        lower_data = data-.5
        upper_data = data+.5
        self.xmin -= .5
        if self.xmax:
            self.xmax += .5


        # revised calculation written to avoid underflow errors
        arg1 = (np.log(lower_data)-self.mu) / (np.sqrt(2)*self.sigma)      
        arg2 = (np.log(upper_data)-self.mu) / (np.sqrt(2)*self.sigma)      
        likelihoods = 0.5*(ss.erfc(arg1) - ss.erfc(arg2))
        if not self.xmax:
            norm = 0.5*ss.erfc((np.log(self.xmin)-self.mu) / (np.sqrt(2)*self.sigma))
        else:
            # may still need to be fixed
            norm = - self._cdf_xmin + self._cdf_base_function(self.xmax)
        self.xmin +=.5
        if self.xmax:
            self.xmax -= .5

        return likelihoods/norm

    def _initial_parameters(self, data):
        from numpy import mean, std, log
        logdata = log(data)
        return (mean(logdata), std(logdata))

    def _in_standard_parameter_range(self):
#The standard deviation can't be negative
        return self.sigma>0

    def _cdf_base_function(self, x):
        from numpy import sqrt, log
        from scipy.special import erf
        return  0.5 + ( 0.5 * 
                erf((log(x)-self.mu) / (sqrt(2)*self.sigma)))

    def _pdf_base_function(self, x):
        from numpy import exp, log
        return ((1.0/x) *
                exp(-( (log(x) - self.mu)**2 )/(2*self.sigma**2)))

    @property
    def _pdf_continuous_normalizer(self):
        from mpmath import erfc
#        from scipy.special import erfc
        from scipy.constants import pi
        from numpy import sqrt, log
        C = (erfc((log(self.xmin) - self.mu) / (sqrt(2) * self.sigma)) /
             sqrt(2/(pi*self.sigma**2)))
        return float(C)

    @property
    def _pdf_discrete_normalizer(self):
        return False

    def _generate_random_continuous(self, r):
        from numpy import exp, sqrt, log, frompyfunc
        from mpmath import erf, erfinv
        #This is a long, complicated function broken into parts.
        #We use mpmath to maintain numerical accuracy as we run through
        #erf and erfinv, until we get to more sane numbers. Thanks to
        #Wolfram Alpha for producing the appropriate inverse of the CCDF
        #for me, which is what we need to calculate these things.
        erfinv = frompyfunc(erfinv,1,1)
        Q = erf( ( log(self.xmin) - self.mu ) / (sqrt(2)*self.sigma))
        Q = Q*r - r + 1.0
        Q = erfinv(Q).astype('float')
        return exp(self.mu + sqrt(2)*self.sigma*Q)

#    def _generate_random_continuous(self, r1, r2=None):
#        from numpy import log, sqrt, exp, sin, cos
#        from scipy.constants import pi
#        if r2==None:
#            from numpy.random import rand
#            r2 = rand(len(r1))
#            r2_provided = False
#        else:
#            r2_provided = True
#
#        rho = sqrt(-2.0 * self.sigma**2.0 * log(1-r1))
#        theta = 2.0 * pi * r2
#        x1 = exp(rho * sin(theta))
#        x2 = exp(rho * cos(theta))
#
#        if r2_provided:
#            return x1, x2
#        else:
#            return x1

def nested_loglikelihood_ratio(loglikelihoods1, loglikelihoods2, **kwargs):
    """
    Calculates a loglikelihood ratio and the p-value for testing which of two
    probability distributions is more likely to have created a set of
    observations. Assumes one of the probability distributions is a nested
    version of the other.

    Parameters
    ----------
    loglikelihoods1 : list or array
        The logarithms of the likelihoods of each observation, calculated from
        a particular probability distribution.
    loglikelihoods2 : list or array
        The logarithms of the likelihoods of each observation, calculated from
        a particular probability distribution.
    nested : bool, optional
        Whether one of the two probability distributions that generated the
        likelihoods is a nested version of the other. True by default.
    normalized_ratio : bool, optional
        Whether to return the loglikelihood ratio, R, or the normalized
        ratio R/sqrt(n*variance)

    Returns
    -------
    R : float
        The loglikelihood ratio of the two sets of likelihoods. If positive, 
        the first set of likelihoods is more likely (and so the probability
        distribution that produced them is a better fit to the data). If
        negative, the reverse is true.
    p : float
        The significance of the sign of R. If below a critical values
        (typically .05) the sign of R is taken to be significant. If above the
        critical value the sign of R is taken to be due to statistical
        fluctuations.
    """
    return loglikelihood_ratio(loglikelihoods1, loglikelihoods2,
            nested=True, **kwargs)

def loglikelihood_ratio(loglikelihoods1, loglikelihoods2,
        nested=False, normalized_ratio=False):
    """
    Calculates a loglikelihood ratio and the p-value for testing which of two
    probability distributions is more likely to have created a set of
    observations.

    Parameters
    ----------
    loglikelihoods1 : list or array
        The logarithms of the likelihoods of each observation, calculated from
        a particular probability distribution.
    loglikelihoods2 : list or array
        The logarithms of the likelihoods of each observation, calculated from
        a particular probability distribution.
    nested: bool, optional
        Whether one of the two probability distributions that generated the
        likelihoods is a nested version of the other. False by default.
    normalized_ratio : bool, optional
        Whether to return the loglikelihood ratio, R, or the normalized
        ratio R/sqrt(n*variance)

    Returns
    -------
    R : float
        The loglikelihood ratio of the two sets of likelihoods. If positive, 
        the first set of likelihoods is more likely (and so the probability
        distribution that produced them is a better fit to the data). If
        negative, the reverse is true.
    p : float
        The significance of the sign of R. If below a critical values
        (typically .05) the sign of R is taken to be significant. If above the
        critical value the sign of R is taken to be due to statistical
        fluctuations.
    """
    from numpy import sqrt
    from scipy.special import erfc

    n = float(len(loglikelihoods1))

    if n==0:
        R = 0
        p = 1
        return R, p
    from numpy import asarray
    loglikelihoods1 = asarray(loglikelihoods1)
    loglikelihoods2 = asarray(loglikelihoods2)

    #Clean for extreme values, if any
    from numpy import inf, log
    from sys import float_info
    min_val = log(10**float_info.min_10_exp)
    loglikelihoods1[loglikelihoods1==-inf] = min_val
    loglikelihoods2[loglikelihoods2==-inf] = min_val

    R = sum(loglikelihoods1-loglikelihoods2)

    from numpy import mean
    mean_diff = mean(loglikelihoods1)-mean(loglikelihoods2)
    variance = sum(
            ( (loglikelihoods1-loglikelihoods2) - mean_diff)**2
            )/n

    if nested:
        from scipy.stats import chi2
        p = 1 - chi2.cdf(abs(2*R), 1)
    else:
        p = erfc( abs(R) / sqrt(2*n*variance))

    if normalized_ratio:
        R = R/sqrt(n*variance)

    return R, p

def cdf(data, survival=False, **kwargs):
    """
    The cumulative distribution function (CDF) of the data.

    Parameters
    ----------
    data : list or array, optional
    survival : bool, optional
        Whether to calculate a CDF (False) or CCDF (True). False by default.

    Returns
    -------
    X : array
        The sorted, unique values in the data.
    probabilities : array
        The portion of the data that is less than or equal to X.
    """
    return cumulative_distribution_function(data, survival=survival, **kwargs)

def ccdf(data, survival=True, **kwargs):
    """
    The complementary cumulative distribution function (CCDF) of the data.

    Parameters
    ----------
    data : list or array, optional
    survival : bool, optional
        Whether to calculate a CDF (False) or CCDF (True). True by default.

    Returns
    -------
    X : array
        The sorted, unique values in the data.
    probabilities : array
        The portion of the data that is less than or equal to X.
    """
    return cumulative_distribution_function(data, survival=survival, **kwargs)

def cumulative_distribution_function(data,
    xmin=None, xmax=None,
    survival=False, **kwargs):
    """
    The cumulative distribution function (CDF) of the data.

    Parameters
    ----------
    data : list or array, optional
    survival : bool, optional
        Whether to calculate a CDF (False) or CCDF (True). False by default.
    xmin : int or float, optional
        The minimum data size to include. Values less than xmin are excluded.
    xmax : int or float, optional
        The maximum data size to include. Values greater than xmin are
        excluded.

    Returns
    -------
    X : array
        The sorted, unique values in the data.
    probabilities : array
        The portion of the data that is less than or equal to X.
    """

    from numpy import array
    data = array(data)
    if not data.any():
        from numpy import nan
        return array([nan]), array([nan])

    data = trim_to_range(data, xmin=xmin, xmax=xmax)

    n = float(len(data))
    from numpy import sort
    data = sort(data)
    all_unique = not( any( data[:-1]==data[1:] ) )

    if all_unique:
        from numpy import arange
        CDF = arange(n)/n
    else:
#This clever bit is a way of using searchsorted to rapidly calculate the 
#CDF of data with repeated values comes from Adam Ginsburg's plfit code,
#specifically https://github.com/keflavich/plfit/commit/453edc36e4eb35f35a34b6c792a6d8c7e848d3b5#plfit/plfit.py
        from numpy import searchsorted, unique
        CDF = searchsorted(data, data,side='left')/n
        unique_data, unique_indices = unique(data, return_index=True)
        data=unique_data
        CDF = CDF[unique_indices]

    if survival:
        CDF = 1-CDF
    return data, CDF

def is_discrete(data):
    """Checks if every element of the array is an integer."""
    from numpy import floor
    return (floor(data)==data.astype(float)).all()

def trim_to_range(data, xmin=None, xmax=None, **kwargs):
    """
    Removes elements of the data that are above xmin or below xmax (if present)
    """
    from numpy import asarray
    data = asarray(data)
    if xmin:
        data = data[data>=xmin]
    if xmax:
        data = data[data<=xmax]
    return data

def pdf(data, xmin=None, xmax=None, linear_bins=False, **kwargs):
    """
    Returns the probability density function (normalized histogram) of the
    data.

    Parameters
    ----------
    data : list or array
    xmin : float, optional
        Minimum value of the PDF. If None, uses the smallest value in the data.
    xmax : float, optional
        Maximum value of the PDF. If None, uses the largest value in the data.
    linear_bins : float, optional
        Whether to use linearly spaced bins, as opposed to logarithmically
        spaced bins (recommended for log-log plots).

    Returns
    -------
    bin_edges : array
        The edges of the bins of the probability density function.
    probabilities : array
        The portion of the data that is within the bin. Length 1 less than
        bin_edges, as it corresponds to the spaces between them.
    """
    from numpy import logspace, histogram, floor, unique
    from math import ceil, log10
    if not xmax:
        xmax = max(data)
    if not xmin:
        xmin = min(data)
    if linear_bins:
        bins = range(int(xmin), int(xmax))
    else:
        log_min_size = log10(xmin)
        log_max_size = log10(xmax)
        number_of_bins = ceil((log_max_size-log_min_size)*10)
        bins=unique(
                floor(
                    logspace(
                        log_min_size, log_max_size, num=number_of_bins)))
    hist, edges = histogram(data, bins, density=True)
    return edges, hist

def checkunique(data):
    """Quickly checks if a sorted array is all unique elements."""
    for i in range(len(data)-1):
        if data[i]==data[i+1]:
            return False
    return True

#def checksort(data):
#    """
#    Checks if the data is sorted, in O(n) time. If it isn't sorted, it then
#    sorts it in O(nlogn) time. Expectation is that the data will typically
#    be sorted. Presently slower than numpy's sort, even on large arrays, and
#    so is useless.
#    """
#
#    n = len(data)
#    from numpy import arange
#    if not all(data[i] <= data[i+1] for i in arange(n-1)):
#        from numpy import sort
#        data = sort(data)
#    return data

def plot_ccdf(data, ax=None, survival=False, **kwargs):
    return plot_cdf(data, ax=ax, survival=True, **kwargs)
    """
    Plots the complementary cumulative distribution function (CDF) of the data
    to a new figure or to axis ax if provided.

    Parameters
    ----------
    data : list or array
    ax : matplotlib axis, optional
        The axis to which to plot. If None, a new figure is created.
    survival : bool, optional
        Whether to plot a CDF (False) or CCDF (True). True by default.

    Returns
    -------
    ax : matplotlib axis
        The axis to which the plot was made.
    """

def plot_cdf(data, ax=None, survival=False, **kwargs):
    """
    Plots the cumulative distribution function (CDF) of the data to a new
    figure or to axis ax if provided.

    Parameters
    ----------
    data : list or array
    ax : matplotlib axis, optional
        The axis to which to plot. If None, a new figure is created.
    survival : bool, optional
        Whether to plot a CDF (False) or CCDF (True). False by default.

    Returns
    -------
    ax : matplotlib axis
        The axis to which the plot was made.
    """
    bins, CDF = cdf(data, survival=survival, **kwargs)
    if not ax:
        import matplotlib.pyplot as plt
        plt.plot(bins, CDF, **kwargs)
        ax = plt.gca()
    else:
        ax.plot(bins, CDF, **kwargs)
    ax.set_xscale("log")
    ax.set_yscale("log")
    return ax

def plot_pdf(data, ax=None, linear_bins=False, **kwargs):
    """
    Plots the probability density function (PDF) to a new figure or to axis ax
    if provided.

    Parameters
    ----------
    data : list or array
    ax : matplotlib axis, optional
        The axis to which to plot. If None, a new figure is created.
    linear_bins : bool, optional
        Whether to use linearly spaced bins (True) or logarithmically
        spaced bins (False). False by default.

    Returns
    -------
    ax : matplotlib axis
        The axis to which the plot was made.
    """
    edges, hist = pdf(data, linear_bins=linear_bins, **kwargs)
    bin_centers = (edges[1:]+edges[:-1])/2.0
    from numpy import nan
    hist[hist==0] = nan
    if not ax:
        import matplotlib.pyplot as plt
        plt.plot(bin_centers, hist, **kwargs)
        ax = plt.gca()
    else:
        ax.plot(bin_centers, hist, **kwargs)
    ax.set_xscale("log")
    ax.set_yscale("log")
    return ax

def bisect_map(mn, mx, function, target):
    """
    Uses binary search to find the target solution to a function, searching in
    a given ordered sequence of integer values.

    Parameters
    ----------
    seq : list or array, monotonically increasing integers
    function : a function that takes a single integer input, which monotonically
        decreases over the range of seq.
    target : the target value of the function

    Returns
    -------
    value : the input value that yields the target solution. If there is no
    exact solution in the input sequence, finds the nearest value k such that 
    function(k) <= target < function(k+1). This is similar to the behavior of
    bisect_left in the bisect package. If even the first, leftmost value of seq
    does not satisfy this condition, -1 is returned.
    """
    if function([mn]) < target or function([mx]) > target:
        return -1
    while 1:
        if mx==mn+1:
            return mn
        m = (mn + mx) / 2
        value = function([m])[0]
        if value > target:
            mn = m
        elif value < target:
            mx = m
        else:
            return m

######################
#What follows are functional programming forms of the above code, which are more
#clunky and have somewhat less functionality. However, they are here if your
#really want them.

class Distribution_Fit(object):
    def __init__(self, data, name, xmin, discrete=False, xmax=None, method='Likelihood', estimate_discrete=True):
        self.data = data
        self.discrete = discrete
        self.xmin = xmin
        self.xmax = xmax
        self.method = method
        self.name = name
        self.estimate_discrete = estimate_discrete

        return

    def __getattr__(self, name):
        param_names = {'lognormal': ('mu', 'sigma', None),
                       'exponential': ('Lambda', None, None),
                       'truncated_power_law': ('alpha', 'Lambda', None),
                       'power_law': ('alpha', None, None),
                       'negative_binomial': ('r', 'p', None),
                       'stretched_exponential': ('Lambda', 'beta', None),
                       'gamma': ('k', 'theta', None)}
        param_names = param_names[self.name]

        if name in param_names:
            if name == param_names[0]:
                setattr(self, name, self.parameter1)
            elif name == param_names[1]:
                setattr(self, name, self.parameter2)
            elif name == param_names[2]:
                setattr(self, name, self.parameter3)
            return getattr(self, name)
        elif name in ['parameters',
                      'parameter1_name',
                      'parameter1',
                      'parameter2_name',
                      'parameter2',
                      'parameter3_name',
                      'parameter3',
                      'loglikelihood']:

            self.parameters, self.loglikelihood = distribution_fit(self.data, distribution=self.name, discrete=self.discrete,
                                                                   xmin=self.xmin, xmax=self.xmax, search_method=self.method, estimate_discrete=self.estimate_discrete)
            self.parameter1 = self.parameters[0]
            if len(self.parameters) < 2:
                self.parameter2 = None
            else:
                self.parameter2 = self.parameters[1]
            if len(self.parameters) < 3:
                self.parameter3 = None
            else:
                self.parameter3 = self.parameters[2]

            self.parameter1_name = param_names[0]
            self.parameter2_name = param_names[1]
            self.parameter3_name = param_names[2]

            if name == 'parameters':
                return self.parameters
            elif name == 'parameter1_name':
                return self.parameter1_name
            elif name == 'parameter2_name':
                return self.parameter2_name
            elif name == 'parameter3_name':
                return self.parameter3_name
            elif name == 'parameter1':
                return self.parameter1
            elif name == 'parameter2':
                return self.parameter2
            elif name == 'parameter3':
                return self.parameter3
            elif name == 'loglikelihood':
                return self.loglikelihood
        if name == 'D':
            if self.name != 'power_law':
                self.D = None
            else:
                self.D = power_law_ks_distance(self.data, self.parameter1, xmin=self.xmin, xmax=self.xmax, discrete=self.discrete)
            return self.D
        if name == 'p':
            print("A p value outside of a loglihood ratio comparison to another candidate distribution is not currently supported.\n \
                    If your data set is particularly large and has any noise in it at all, using such statistical tools as the Monte Carlo method\n\
                    can lead to erroneous results anyway; the presence of the noise means the distribution will obviously not perfectly fit the\n\
                    candidate distribution, and the very large number of samples will make the Monte Carlo simulations very close to a perfect\n\
                    fit. As such, such a test will always fail, unless your candidate distribution perfectly describes all elements of the\n\
                    system, including the noise. A more helpful analysis is the comparison between multiple, specific candidate distributions\n\
                    (the loglikelihood ratio test), which tells you which is the best fit of these distributions.", file=sys.stderr)
            self.p = None
            return self.p
#
#        elif name in ['power_law_loglikelihood_ratio',
#                'power_law_p']:
#            pl_R, pl_p = distribution_compare(self.data, 'power_law', self.power_law.parameters, name, self.parameters, self.discrete, self.xmin, self.xmax)
#            self.power_law_loglikelihood_ratio = pl_R
#            self.power_law_p = pl_p
#            if name=='power_law_loglikelihood_ratio':
#                return self.power_law_loglikelihood_ratio
#            if name=='power_law_p':
#                return self.power_law_p
#        elif name in ['truncated_power_law_loglikelihood_ratio',
#                'truncated_power_law_p']:
#            tpl_R, tpl_p = distribution_compare(self.data, 'truncated_power_law', self.truncated_power_law.parameters, name, self.parameters, self.discrete, self.xmin, self.xmax)
#            self.truncated_power_law_loglikelihood_ratio = tpl_R
#            self.truncated_power_law_p = tpl_p
#            if name=='truncated_power_law_loglikelihood_ratio':
#                return self.truncated_power_law_loglikelihood_ratio
#            if name=='truncated_power_law_p':
#                return self.truncated_power_law_p
        else:
            raise AttributeError(name)


def distribution_fit(data, distribution='all', discrete=False, xmin=None, xmax=None, \
        comparison_alpha=None, search_method='Likelihood', estimate_discrete=True):
    from numpy import log

    if distribution == 'negative_binomial' and not is_discrete(data):
        print("Rounding to integer values for negative binomial fit.", file=sys.stderr)
        from numpy import around
        data = around(data)
        discrete = True

    #If we aren't given an xmin, calculate the best possible one for a power law. This can take awhile!
    if xmin is None or xmin == 'find' or type(xmin) == tuple or type(xmin) == list:
        print("Calculating best minimal value", file=sys.stderr)
        if 0 in data:
            print("Value 0 in data. Throwing out 0 values", file=sys.stderr)
            data = data[data != 0]
        xmin, D, alpha, loglikelihood, n_tail, noise_flag = find_xmin(data, discrete=discrete, xmax=xmax, search_method=search_method, estimate_discrete=estimate_discrete, xmin_range=xmin)
    else:
        alpha = None

    if distribution == 'power_law' and alpha:
        return [alpha], loglikelihood

    xmin = float(xmin)
    data = data[data >= xmin]

    if xmax:
        xmax = float(xmax)
        data = data[data <= xmax]

    #Special case where we call distribution_fit multiple times to do all comparisons
    if distribution == 'all':
        print("Analyzing all distributions", file=sys.stderr)
        print("Calculating power law fit", file=sys.stderr)
        if alpha:
            pl_parameters = [alpha]
        else:
            pl_parameters, loglikelihood = distribution_fit(data, 'power_law', discrete, xmin, xmax, search_method=search_method, estimate_discrete=estimate_discrete)
        results = {}
        results['xmin'] = xmin
        results['xmax'] = xmax
        results['discrete'] = discrete
        results['fits'] = {}
        results['fits']['power_law'] = (pl_parameters, loglikelihood)

        print("Calculating truncated power law fit", file=sys.stderr)
        tpl_parameters, loglikelihood, R, p = distribution_fit(data, 'truncated_power_law', discrete, xmin, xmax, comparison_alpha=pl_parameters[0], search_method=search_method, estimate_discrete=estimate_discrete)
        results['fits']['truncated_power_law'] = (tpl_parameters, loglikelihood)
        results['power_law_comparison'] = {}
        results['power_law_comparison']['truncated_power_law'] = (R, p)
        results['truncated_power_law_comparison'] = {}

        supported_distributions = ['exponential', 'lognormal', 'stretched_exponential', 'gamma']

        for i in supported_distributions:
            print("Calculating %s fit" % (i,), file=sys.stderr)
            parameters, loglikelihood, R, p = distribution_fit(data, i, discrete, xmin, xmax, comparison_alpha=pl_parameters[0], search_method=search_method, estimate_discrete=estimate_discrete)
            results['fits'][i] = (parameters, loglikelihood)
            results['power_law_comparison'][i] = (R, p)

            R, p = distribution_compare(data, 'truncated_power_law', tpl_parameters, i, parameters, discrete, xmin, xmax)
            results['truncated_power_law_comparison'][i] = (R, p)
        return results

    #Handle edge case where we don't have enough data
    no_data = False
    if xmax and all((data > xmax) + (data < xmin)):
        #Everything is beyond the bounds of the xmax and xmin
        no_data = True
    if all(data < xmin):
        no_data = True
    if len(data) < 2:
        no_data = True
    if no_data:
        from numpy import array
        from sys import float_info
        parameters = array([0, 0, 0])
        if search_method == 'Likelihood':
            loglikelihood = -10 ** float_info.max_10_exp
        if search_method == 'KS':
            loglikelihood = 1
        if comparison_alpha is None:
            return parameters, loglikelihood
        R = 10 ** float_info.max_10_exp
        p = 1
        return parameters, loglikelihood, R, p

    n = float(len(data))

    #Initial search parameters, estimated from the data
#    print("Calculating initial parameters for search", file=sys.stderr)
    if distribution == 'power_law' and not alpha:
        initial_parameters = [1 + n / sum(log(data / (xmin)))]
    elif distribution == 'exponential':
        from numpy import mean
        initial_parameters = [1 / mean(data)]
    elif distribution == 'stretched_exponential':
        from numpy import mean
        initial_parameters = [1 / mean(data), 1]
    elif distribution == 'truncated_power_law':
        from numpy import mean
        initial_parameters = [1 + n / sum(log(data / xmin)), 1 / mean(data)]
    elif distribution == 'lognormal':
        from numpy import mean, std
        logdata = log(data)
        initial_parameters = [mean(logdata), std(logdata)]
    elif distribution == 'negative_binomial':
        initial_parameters = [1, .5]
    elif distribution == 'gamma':
        from numpy import mean
        initial_parameters = [n / sum(log(data / xmin)), mean(data)]

    if search_method == 'Likelihood':
#        print("Searching using maximum likelihood method", file=sys.stderr)
        #If the distribution is a continuous power law without an xmax, and we're using the maximum likelihood method, we can compute the parameters and likelihood directly
        if distribution == 'power_law' and not discrete and not xmax and not alpha:
            from numpy import array, nan
            alpha = 1 + n /\
                sum(log(data / xmin))
            loglikelihood = n * log(alpha - 1.0) - n * log(xmin) - alpha * sum(log(data / xmin))
            if loglikelihood == nan:
                loglikelihood = 0
            parameters = array([alpha])
            return parameters, loglikelihood
        elif distribution == 'power_law' and discrete and not xmax and not alpha and estimate_discrete:
            from numpy import array, nan
            alpha = 1 + n /\
                sum(log(data / (xmin - .5)))
            loglikelihood = n * log(alpha - 1.0) - n * log(xmin) - alpha * sum(log(data / xmin))
            if loglikelihood == nan:
                loglikelihood = 0
            parameters = array([alpha])
            return parameters, loglikelihood

        #Otherwise, we set up a likelihood function
        likelihood_function = likelihood_function_generator(distribution, discrete=discrete, xmin=xmin, xmax=xmax)

        #Search for the best fit parameters for the target distribution, on this data
        from scipy.optimize import fmin
        parameters, negative_loglikelihood, iter, funcalls, warnflag, = \
            fmin(
                lambda p: -sum(log(likelihood_function(p, data))),
                initial_parameters, full_output=1, disp=False)
        loglikelihood = -negative_loglikelihood

        if comparison_alpha:
            R, p = distribution_compare(data, 'power_law', [comparison_alpha], distribution, parameters, discrete, xmin, xmax)
            return parameters, loglikelihood, R, p
        else:
            return parameters, loglikelihood

    elif search_method == 'KS':
        print("Not yet supported. Sorry.", file=sys.stderr)
        return
#        #Search for the best fit parameters for the target distribution, on this data
#        from scipy.optimize import fmin
#        parameters, KS, iter, funcalls, warnflag, = \
#                fmin(\
#                lambda p: -sum(log(likelihood_function(p, data))),\
#                initial_parameters, full_output=1, disp=False)
#        loglikelihood =-negative_loglikelihood
#
#        if comparison_alpha:
#            R, p = distribution_compare(data, 'power_law',[comparison_alpha], distribution, parameters, discrete, xmin, xmax)
#            return parameters, loglikelihood, R, p
#        else:
#            return parameters, loglikelihood


def distribution_compare(data, distribution1, parameters1,
                         distribution2, parameters2,
                         discrete, xmin, xmax, nested=None, **kwargs):
    no_data = False
    if xmax and all((data > xmax) + (data < xmin)):
        #Everything is beyond the bounds of the xmax and xmin
        no_data = True
    if all(data < xmin):
        no_data = True

    if no_data:
        R = 0
        p = 1
        return R, p

    likelihood_function1 = likelihood_function_generator(distribution1, discrete, xmin, xmax)
    likelihood_function2 = likelihood_function_generator(distribution2, discrete, xmin, xmax)

    likelihoods1 = likelihood_function1(parameters1, data)
    likelihoods2 = likelihood_function2(parameters2, data)

    if ((distribution1 in distribution2) or
        (distribution2 in distribution1)
            and nested is None):
        print("Assuming nested distributions", file=sys.stderr)
        nested = True

    from numpy import log
    R, p = loglikelihood_ratio(log(likelihoods1), log(likelihoods2),
                               nested=nested, **kwargs)

    return R, p


def likelihood_function_generator(distribution_name, discrete=False, xmin=1, xmax=None):

    if distribution_name == 'power_law':
        likelihood_function = lambda parameters, data:\
            power_law_likelihoods(
                data, parameters[0], xmin, xmax, discrete)

    elif distribution_name == 'exponential':
        likelihood_function = lambda parameters, data:\
            exponential_likelihoods(
                data, parameters[0], xmin, xmax, discrete)

    elif distribution_name == 'stretched_exponential':
        likelihood_function = lambda parameters, data:\
            stretched_exponential_likelihoods(
                data, parameters[0], parameters[1], xmin, xmax, discrete)

    elif distribution_name == 'truncated_power_law':
        likelihood_function = lambda parameters, data:\
            truncated_power_law_likelihoods(
                data, parameters[0], parameters[1], xmin, xmax, discrete)

    elif distribution_name == 'lognormal':
        likelihood_function = lambda parameters, data:\
            lognormal_likelihoods(
                data, parameters[0], parameters[1], xmin, xmax, discrete)

    elif distribution_name == 'negative_binomial':
        likelihood_function = lambda parameters, data:\
            negative_binomial_likelihoods(
                data, parameters[0], parameters[1], xmin, xmax)

    elif distribution_name == 'gamma':
        likelihood_function = lambda parameters, data:\
            gamma_likelihoods(
                data, parameters[0], parameters[1], xmin, xmax)

    return likelihood_function

def find_xmin(data, discrete=False, xmax=None, search_method='Likelihood', return_all=False, estimate_discrete=True, xmin_range=None):
    from numpy import sort, unique, asarray, argmin, vstack, arange, sqrt
    if 0 in data:
        print("Value 0 in data. Throwing out 0 values", file=sys.stderr)
        data = data[data != 0]
    if xmax:
        data = data[data <= xmax]
#Much of the rest of this function was inspired by Adam Ginsburg's plfit code, specifically around lines 131-143 of this version: http://code.google.com/p/agpy/source/browse/trunk/plfit/plfit.py?spec=svn359&r=357
    if not all(data[i] <= data[i + 1] for i in range(len(data) - 1)):
        data = sort(data)
    if xmin_range == 'find' or xmin_range is None:
        possible_xmins = data
    else:
        possible_xmins = data[data <= max(xmin_range)]
        possible_xmins = possible_xmins[possible_xmins >= min(xmin_range)]
    xmins, xmin_indices = unique(possible_xmins, return_index=True)
    xmins = xmins[:-1]
    if len(xmins) < 2:
        from sys import float_info
        xmin = 1
        D = 1
        alpha = 0
        loglikelihood = -10 ** float_info.max_10_exp
        n_tail = 1
        noise_flag = True
        Ds = 1
        alphas = 0
        sigmas = 1

        if not return_all:
            return xmin, D, alpha, loglikelihood, n_tail, noise_flag
        else:
            return xmin, D, alpha, loglikelihood, n_tail, noise_flag, xmins, Ds, alphas, sigmas

    xmin_indices = xmin_indices[:-1]  # Don't look at last xmin, as that's also the xmax, and we want to at least have TWO points to fit!

    if search_method == 'Likelihood':
        alpha_MLE_function = lambda xmin: distribution_fit(data, 'power_law', xmin=xmin, xmax=xmax, discrete=discrete, search_method='Likelihood', estimate_discrete=estimate_discrete)
        fits = asarray(list(map(alpha_MLE_function, xmins)))
    elif search_method == 'KS':
        alpha_KS_function = lambda xmin: distribution_fit(data, 'power_law', xmin=xmin, xmax=xmax, discrete=discrete, search_method='KS', estimate_discrete=estimate_discrete)[0]
        fits = asarray(list(map(alpha_KS_function, xmins)))

    params = fits[:, 0]
    alphas = vstack(params)[:, 0]
    loglikelihoods = fits[:, 1]

    ks_function = lambda index: power_law_ks_distance(data, alphas[index], xmins[index], xmax=xmax, discrete=discrete)
    Ds = asarray(list(map(ks_function, arange(len(xmins)))))

    sigmas = (alphas - 1) / sqrt(len(data) - xmin_indices + 1)
    good_values = sigmas < .1
    #Find the last good value (The first False, where sigma > .1):
    xmin_max = argmin(good_values)
    if good_values.all():  # If there are no fits beyond the noise threshold
        min_D_index = argmin(Ds)
        noise_flag = False
    elif xmin_max > 0:
        min_D_index = argmin(Ds[:xmin_max])
        noise_flag = False
    else:
        min_D_index = argmin(Ds)
        noise_flag = True

    xmin = xmins[min_D_index]
    D = Ds[min_D_index]
    alpha = alphas[min_D_index]
    loglikelihood = loglikelihoods[min_D_index]
    n_tail = sum(data >= xmin)

    if not return_all:
        return xmin, D, alpha, loglikelihood, n_tail, noise_flag
    else:
        return xmin, D, alpha, loglikelihood, n_tail, noise_flag, xmins, Ds, alphas, sigmas


def power_law_ks_distance(data, alpha, xmin, xmax=None, discrete=False, kuiper=False):
    from numpy import arange, sort, mean
    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]
    n = float(len(data))
    if n < 2:
        if kuiper:
            return 1, 1, 2
        return 1

    if not all(data[i] <= data[i + 1] for i in arange(n - 1)):
        data = sort(data)

    if not discrete:
        Actual_CDF = arange(n) / n
        Theoretical_CDF = 1 - (data / xmin) ** (-alpha + 1)

    if discrete:
        from scipy.special import zeta
        if xmax:
            bins, Actual_CDF = cumulative_distribution_function(data,xmin=xmin,xmax=xmax)
            Theoretical_CDF = 1 - ((zeta(alpha, bins) - zeta(alpha, xmax+1)) /\
                    (zeta(alpha, xmin)-zeta(alpha,xmax+1)))
        if not xmax:
            bins, Actual_CDF = cumulative_distribution_function(data,xmin=xmin)
            Theoretical_CDF = 1 - (zeta(alpha, bins) /\
                    zeta(alpha, xmin))

    D_plus = max(Theoretical_CDF - Actual_CDF)
    D_minus = max(Actual_CDF - Theoretical_CDF)
    Kappa = 1 + mean(Theoretical_CDF - Actual_CDF)

    if kuiper:
        return D_plus, D_minus, Kappa

    D = max(D_plus, D_minus)

    return D


def power_law_likelihoods(data, alpha, xmin, xmax=False, discrete=False):
    if alpha < 0:
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    xmin = float(xmin)
    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    if not discrete:
        likelihoods = (data ** -alpha) *\
                      ((alpha - 1) * xmin ** (alpha - 1))
    if discrete:
        if alpha < 1:
            from numpy import tile
            from sys import float_info
            return tile(10 ** float_info.min_10_exp, len(data))
        if not xmax:
            from scipy.special import zeta
            likelihoods = (data ** -alpha) /\
                zeta(alpha, xmin)
        if xmax:
            from scipy.special import zeta
            likelihoods = (data ** -alpha) /\
                          (zeta(alpha, xmin) - zeta(alpha, xmax + 1))
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def negative_binomial_likelihoods(data, r, p, xmin=0, xmax=False):

    #Better to make this correction earlier on in distribution_fit, so as to not recheck for discreteness and reround every time fmin is used.
    #if not is_discrete(data):
    #    print("Rounding to nearest integer values for negative binomial fit.", file=sys.stderr)
    #    from numpy import around
    #    data = around(data)

    xmin = float(xmin)
    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    from numpy import asarray
    from scipy.misc import comb
    pmf = lambda k: comb(k + r - 1, k) * (1 - p) ** r * p ** k
    likelihoods = asarray(list(map(pmf, data))).flatten()

    if xmin != 0 or xmax:
        xmax = max(data)
        from numpy import arange
        normalization_constant = sum(list(map(pmf, arange(xmin, xmax + 1))))
        likelihoods = likelihoods / normalization_constant

    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def exponential_likelihoods(data, Lambda, xmin, xmax=False, discrete=False):
    if Lambda < 0:
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    from numpy import exp
    if not discrete:
#        likelihoods = exp(-Lambda*data)*\
#                Lambda*exp(Lambda*xmin)
        likelihoods = Lambda * exp(Lambda * (xmin - data))  # Simplified so as not to throw a nan from infs being divided by each other
    if discrete:
        if not xmax:
            likelihoods = exp(-Lambda * data) *\
                             (1 - exp(-Lambda)) * exp(Lambda * xmin)
        if xmax:
            likelihoods = exp(-Lambda * data) * (1 - exp(-Lambda))\
                / (exp(-Lambda * xmin) - exp(-Lambda * (xmax + 1)))
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def stretched_exponential_likelihoods(data, Lambda, beta, xmin, xmax=False, discrete=False):
    if Lambda < 0:
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    from numpy import exp
    if not discrete:
#        likelihoods = (data**(beta-1) * exp(-Lambda*(data**beta)))*\
#            (beta*Lambda*exp(Lambda*(xmin**beta)))
        likelihoods = data ** (beta - 1) * beta * Lambda * exp(Lambda * (xmin ** beta - data ** beta))  # Simplified so as not to throw a nan from infs being divided by each other
    if discrete:
        if not xmax:
            xmax = max(data)
        if xmax:
            from numpy import arange
            X = arange(xmin, xmax + 1)
            PDF = X ** (beta - 1) * beta * Lambda * exp(Lambda * (xmin ** beta - X ** beta))  # Simplified so as not to throw a nan from infs being divided by each other
            PDF = PDF / sum(PDF)
            likelihoods = PDF[(data - xmin).astype(int)]
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def gamma_likelihoods(data, k, theta, xmin, xmax=False, discrete=False):
    if k <= 0 or theta <= 0:
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    from numpy import exp
    from mpmath import gammainc
#    from scipy.special import gamma, gammainc #Not NEARLY numerically accurate enough for the job
    if not discrete:
        likelihoods = (data ** (k - 1)) / (exp(data / theta) * (theta ** k) * float(gammainc(k)))
        #Calculate how much probability mass is beyond xmin, and normalize by it
        normalization_constant = 1 - float(gammainc(k, 0, xmin / theta, regularized=True))  # Mpmath's regularized option divides by gamma(k)
        likelihoods = likelihoods / normalization_constant
    if discrete:
        if not xmax:
            xmax = max(data)
        if xmax:
            from numpy import arange
            X = arange(xmin, xmax + 1)
            PDF = (X ** (k - 1)) / (exp(X / theta) * (theta ** k) * float(gammainc(k)))
            PDF = PDF / sum(PDF)
            likelihoods = PDF[(data - xmin).astype(int)]
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def truncated_power_law_likelihoods(data, alpha, Lambda, xmin, xmax=False, discrete=False):
    if alpha < 0 or Lambda < 0:
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    from numpy import exp
    if not discrete:
        from mpmath import gammainc
#        from scipy.special import gamma, gammaincc #Not NEARLY accurate enough to do the job
#        likelihoods = (data**-alpha)*exp(-Lambda*data)*\
#                (Lambda**(1-alpha))/\
#                float(gammaincc(1-alpha,Lambda*xmin))
        #Simplified so as not to throw a nan from infs being divided by each other
        likelihoods = (Lambda ** (1 - alpha)) /\
                      ((data ** alpha) * exp(Lambda * data) * gammainc(1 - alpha, Lambda * xmin)).astype(float)
    if discrete:
        if not xmax:
            xmax = max(data)
        if xmax:
            from numpy import arange
            X = arange(xmin, xmax + 1)
            PDF = (X ** -alpha) * exp(-Lambda * X)
            PDF = PDF / sum(PDF)
            likelihoods = PDF[(data - xmin).astype(int)]
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods


def lognormal_likelihoods(data, mu, sigma, xmin, xmax=False, discrete=False):
    from numpy import log
    if sigma <= 0 or mu < log(xmin):
        #The standard deviation can't be negative, and the mean of the logarithm of the distribution can't be smaller than the log of the smallest member of the distribution!
        from numpy import tile
        from sys import float_info
        return tile(10 ** float_info.min_10_exp, len(data))

    data = data[data >= xmin]
    if xmax:
        data = data[data <= xmax]

    if not discrete:
        from numpy import sqrt, exp
#        from mpmath import erfc
        from scipy.special import erfc
        from scipy.constants import pi
        likelihoods = (1.0 / data) * exp(-((log(data) - mu) ** 2) / (2 * sigma ** 2)) *\
            sqrt(2 / (pi * sigma ** 2)) / erfc((log(xmin) - mu) / (sqrt(2) * sigma))
#        likelihoods = likelihoods.astype(float)
    if discrete:
        if not xmax:
            xmax = max(data)
        if xmax:
            from numpy import arange, exp
#            from mpmath import exp
            X = arange(xmin, xmax + 1)
#            PDF_function = lambda x: (1.0/x)*exp(-( (log(x) - mu)**2 ) / 2*sigma**2)
#            PDF = asarray(list(map(PDF_function,X)))
            PDF = (1.0 / X) * exp(-((log(X) - mu) ** 2) / (2 * (sigma ** 2)))
            PDF = (PDF / sum(PDF)).astype(float)
            likelihoods = PDF[(data - xmin).astype(int)]
    from sys import float_info
    likelihoods[likelihoods == 0] = 10 ** float_info.min_10_exp
    return likelihoods