# Source code for esda.join_counts_local_mv

    import numpy as np
    import pandas as pd
    from libpysal import weights
    from sklearn.base import BaseEstimator

    from esda.crand import _prepare_univariate
    from esda.crand import crand as _crand_plus
    from esda.crand import njit as _njit

    PERMUTATIONS = 999

    class Join_Counts_Local_MV(BaseEstimator):  # noqa: N801
        """Multivariate Local Join Count Statistic"""

        def __init__(
            self,
            connectivity=None,
            permutations=PERMUTATIONS,
            n_jobs=1,
            keep_simulations=True,
            seed=None,
            island_weight=0,
            drop_islands=True,
        ):
            """
            Initialize a Local_Join_Counts_MV estimator

            Parameters
            ----------
            connectivity : W | Graph
                spatial weights instance as W or Graph aligned with y
            permutations : int
                number of random permutations for calculation of pseudo p_values
            n_jobs : int
                Number of cores to be used in the conditional randomisation. If -1,
                all available cores are used.
            keep_simulations : bool (default True)
               If ``True``, the entire matrix of replications under the null is stored
               in memory and accessible; otherwise, replications are not saved
            seed : int (default None)
                Seed to ensure reproducibility of conditional randomizations.
                Must be set here, and not outside of the function, since numba
                does not correctly interpret external seeds
                nor numpy.random.RandomState instances.
            island_weight : int or float (default 0)
                value to use as a weight for the "fake" neighbor for every island.
                If ``numpy.nan``, will propagate to the final local statistic depending
                on the ``stat_func``. If ``0``, then the lag is always zero for islands.
            drop_islands : bool (default True)
                Whether or not to preserve islands as entries in the adjacency
                list. By default, observations with no neighbors do not appear
                in the adjacency list. If islands are kept, they are coded as
                self-neighbors with zero weight. See ``libpysal.weights.to_adjlist()``.
            """

            self.connectivity = connectivity
            self.permutations = permutations
            self.n_jobs = n_jobs
            self.keep_simulations = keep_simulations
            self.seed = seed
            self.island_weight = island_weight
            self.drop_islands = drop_islands

        def fit(self, variables, n_jobs=1, permutations=999):
            """
            Parameters
            ----------
            variables     : numpy.ndarray
                            array(s) containing binary (0/1) data
            Returns
            -------
            the fitted estimator.

            Notes
            -----
            Technical details and derivations can be found in :cite:`AnselinLi2019`.

            Examples
            --------
            >>> import libpysal
            >>> w = libpysal.weights.lat2W(4, 4)
            >>> x = np.ones(16)
            >>> x[0:8] = 0
            >>> z = [0,1,0,1,1,1,1,1,0,0,1,1,0,0,1,1]
            >>> y = [0,1,1,1,1,1,1,1,0,0,0,1,0,0,1,1]
            >>> LJC_MV = Local_Join_Counts_MV(connectivity=w).fit([x, y, z])
            >>> LJC_MV.LJC
            >>> LJC_MV.p_sim

            Guerry data extending GeoDa tutorial
            >>> import libpysal
            >>> import geopandas as gpd
            >>> guerry = libpysal.examples.load_example('Guerry')
            >>> guerry_ds = gpd.read_file(guerry.get_path('Guerry.shp'))
            >>> guerry_ds['infq5'] = 0
            >>> guerry_ds['donq5'] = 0
            >>> guerry_ds['suic5'] = 0
            >>> guerry_ds.loc[(guerry_ds['Infants'] > 23574), 'infq5'] = 1
            >>> guerry_ds.loc[(guerry_ds['Donatns'] > 10973), 'donq5'] = 1
            >>> guerry_ds.loc[(guerry_ds['Suicids'] > 55564), 'suic5'] = 1
            >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds)
            >>> LJC_MV = Local_Join_Counts_MV(
            ...     connectivity=w
            ... ).fit([guerry_ds['infq5'], guerry_ds['donq5'], guerry_ds['suic5']])
            >>> LJC_MV.LJC
            >>> LJC_MV.p_sim
            """

            w = self.connectivity
            # Fill the diagonal with 0s
            if isinstance(w, weights.W):
                w = weights.util.fill_diagonal(w, val=0)
                w.transform = "b"
            else:
                w = w.assign_self_weight(0).eliminate_zeros().transform("b")

            self.n = len(variables[0])
            self.w = w

            self.variables = np.array(variables, dtype="float")

            # Need to ensure that the product is an
            # np.array() of dtype='float' for numba
            self.ext = np.array(np.prod(np.vstack(variables), axis=0), dtype="float")

            self.LJC = self._statistic(variables, w, self.drop_islands)

            if permutations:
                self.p_sim, self.rjoins = _crand_plus(
                    z=self.ext,
                    w=self.w,
                    observed=self.LJC,
                    permutations=permutations,
                    keep=True,
                    n_jobs=n_jobs,
                    stat_func=_ljc_mv,
                    island_weight=self.island_weight,
                )
                # Set p-values for those with LJC of 0 to NaN
                self.p_sim[self.LJC == 0] = "NaN"

            return self

        @staticmethod
        def _statistic(variables, w, drop_islands):
            # Create adjacency list. Note that remove_symmetric=False -
            # different from the esda.Join_Counts() function.
            if isinstance(w, weights.W):
                adj_list = w.to_adjlist(remove_symmetric=False, drop_islands=drop_islands)
                # The zseries
                zseries = [pd.Series(i, index=w.id_order) for i in variables]
            else:
                if drop_islands:
                    adj_list = w.adjacency.drop(w.isolates).reset_index()
                else:
                    adj_list = w.adjacency.reset_index()
                zseries = [pd.Series(i, index=w.unique_ids) for i in variables]
            # The focal values
            focal = [zseries[i].loc[adj_list.focal].values for i in range(len(variables))]
            # The neighbor values
            neighbor = [
                zseries[i].loc[adj_list.neighbor].values for i in range(len(variables))
            ]

            # Find instances where all surrounding
            # focal and neighbor values == 1
            focal_all = np.array(np.all(np.dstack(focal) == 1, axis=2))
            neighbor_all = np.array(np.all(np.dstack(neighbor) == 1, axis=2))
            MCLC = (focal_all) & (neighbor_all)
            # Convert list of True/False to boolean array
            # and unlist (necessary for building pd.DF)
            MCLC = list(MCLC * 1)

            # Create a df that uses the adjacency list
            # focal values and the BBs counts
            adj_list_MCLC = pd.DataFrame(adj_list.focal.values, MCLC).reset_index()
            # Temporarily rename the columns
            adj_list_MCLC.columns = ["MCLC", "ID"]
            adj_list_MCLC = adj_list_MCLC.groupby(by="ID").sum()

            return np.array(adj_list_MCLC.MCLC.values, dtype="float")

    # --------------------------------------------------------------
    # Conditional Randomization Function Implementations
    # --------------------------------------------------------------

    # Note: scaling not used

    @_njit(fastmath=True)
    def _ljc_mv(i, z, permuted_ids, weights_i, scaling):  # noqa: ARG001
        other_weights = weights_i[1:]
        zi, zrand = _prepare_univariate(i, z, permuted_ids, other_weights)
        return zi * (zrand @ other_weights)