# Source code for esda.join_counts_local

    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(BaseEstimator):  # noqa: N801
        """Univariate 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_Count 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 : None/int
               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:
                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()``.

            Attributes
            ----------
            LJC             : numpy array
                              array containing the univariate
                              Local Join Count (LJC).
            p_sim           : numpy array
                              array containing the simulated
                              p-values for each unit.

            """

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

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

            Examples
            --------
            >>> import libpysal
            >>> w = libpysal.weights.lat2W(4, 4)
            >>> y = np.ones(16)
            >>> y[0:8] = 0
            >>> LJC_uni = Local_Join_Count(connectivity=w).fit(y)
            >>> LJC_uni.LJC
            >>> LJC_uni.p_sim

            Guerry data replicating 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['SELECTED'] = 0
            >>> guerry_ds.loc[(guerry_ds['Donatns'] > 10997), 'SELECTED'] = 1
            >>> w = libpysal.weights.Queen.from_dataframe(guerry_ds)
            >>> LJC_uni = Local_Join_Count(connectivity=w).fit(guerry_ds['SELECTED'])
            >>> LJC_uni.LJC
            >>> LJC_uni.p_sim
            """
            # Need to ensure that the np.array() are of
            # dtype='float' for numba
            y = np.array(y, dtype="float")

            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")

            keep_simulations = self.keep_simulations
            n_jobs = self.n_jobs
            # seed = self.seed

            self.y = y
            self.n = len(y)
            self.w = w

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

            if permutations:
                self.p_sim, self.rjoins = _crand_plus(
                    z=self.y,
                    w=self.w,
                    observed=self.LJC,
                    permutations=permutations,
                    keep=keep_simulations,
                    n_jobs=n_jobs,
                    stat_func=_ljc_uni,
                    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(y, w, drop_islands):
            # Create adjacency list. Note that remove_symmetric=False - this is
            # different from the esda.Join_Counts() function.
            if isinstance(w, weights.W):
                adj_list = w.to_adjlist(remove_symmetric=False, drop_islands=drop_islands)
                zseries = pd.Series(y, index=w.id_order)
            else:
                if drop_islands:
                    adj_list = w.adjacency.drop(w.isolates).reset_index()
                else:
                    adj_list = w.adjacency.reset_index()
                zseries = pd.Series(y, index=w.unique_ids)
            focal = zseries.loc[adj_list.focal].values
            neighbor = zseries.loc[adj_list.neighbor].values
            LJC = (focal == 1) & (neighbor == 1)
            adj_list_LJC = pd.DataFrame(
                adj_list.focal.values, LJC.astype("uint8")
            ).reset_index()
            adj_list_LJC.columns = ["LJC", "ID"]
            adj_list_LJC = adj_list_LJC.groupby(by="ID").sum()
            LJC = np.array(adj_list_LJC.LJC.values, dtype="float")
            return LJC

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

    # Note: scaling not used

    @_njit(fastmath=True)
    def _ljc_uni(i, z, permuted_ids, weights_i, scaling):  # noqa: ARG001
        # self_weight = weights_i[0]
        other_weights = weights_i[1:]
        zi, zrand = _prepare_univariate(i, z, permuted_ids, other_weights)
        return zi * (zrand @ other_weights)