Source code for segregation.local.local_multi_entropy

"""Multigroup dissimilarity index"""

__author__ = "Renan X. Cortes <renanc@ucr.edu>, Sergio J. Rey <sergio.rey@ucr.edu> and Elijah Knaap <elijah.knaap@ucr.edu>"

import numpy as np

from .._base import MultiGroupIndex, SpatialImplicitIndex

np.seterr(divide="ignore", invalid="ignore")


def _multi_local_entropy(data, groups):
    """
    Calculation of Local Entropy index for each unit

    Parameters
    ----------

    data   : a pandas DataFrame of n rows
    
    groups : list of strings.
             The variables names in data of the groups of interest of the analysis.

    Returns
    -------

    statistics : np.array(n)
                 Local Entropy values for each unit
                
    core_data  : a pandas DataFrame
                 A pandas DataFrame that contains the columns used to perform the estimate.

    Notes
    -----
    Based on Eq. 6 of pg. 139 (individual unit case) of Reardon, Sean F., and David O’Sullivan. "Measures of spatial segregation." Sociological methodology 34.1 (2004): 121-162.
    
    Reference: :cite:`reardon2004measures`.

    """

    core_data = data[groups]

    df = np.array(core_data)

    K = df.shape[1]

    ti = df.sum(axis=1)
    pik = df / ti[:, None]

    multi_LE = -np.nansum((pik * np.log(pik)) / np.log(K), axis=1)

    return multi_LE, core_data, groups


class MultiLocalEntropy(MultiGroupIndex, SpatialImplicitIndex):
    """Multigroup Local Entropy Index.

    Parameters
    ----------
    data : pandas.DataFrame or geopandas.GeoDataFrame, required
        dataframe or geodataframe if spatial index holding data for location of interest
    groups : list, required
        list of columns on dataframe holding population totals for each group
    w : libpysal.weights.KernelW, optional
        lipysal spatial kernel weights object used to define an egohood
    network : pandana.Network
        pandana Network object representing the study area
    distance : int
        Maximum distance (in units of geodataframe CRS) to consider the extent of the egohood
    decay : str
        type of decay function to apply. Options include
    precompute : bool
        Whether to precompute the pandana Network object

    Attributes
    ----------
    statistic : float
        Multigroup Dissimilarity Index value
    core_data : a pandas DataFrame
        DataFrame that contains the columns used to perform the estimate.

    Notes
    -----
    Based on Eq. 6 of pg. 139 (individual unit case) of Reardon, Sean F., and David O’Sullivan. "Measures of spatial segregation." Sociological methodology 34.1 (2004): 121-162.
    
    Reference: :cite:`reardon2004measures`.
    """

    def __init__(
        self,
        data,
        groups,
        w=None,
        network=None,
        distance=None,
        decay=None,
        precompute=None,
        function='triangular'
    ):
        """Init."""

        MultiGroupIndex.__init__(self, data, groups)
        if any([w, network, distance]):
            SpatialImplicitIndex.__init__(self, w, network, distance, decay, function, precompute)
        aux = _multi_local_entropy(self.data, self.groups)

        self.statistics = aux[0]
        self.data = aux[1]
        self.groups = aux[2]
        self._function = _multi_local_entropy