# Source code for spopt.locate.p_center

    # ruff: noqa: B009

    import warnings

    import numpy as np
    import pulp
    from geopandas import GeoDataFrame
    from scipy.spatial.distance import cdist

    from .base import BaseOutputMixin, FacilityModelBuilder, LocateSolver

    class PCenter(LocateSolver, BaseOutputMixin):
        r"""
        Implement the :math:`p`-center optimization model and solve it. The
        :math:`p`-center problem, as adapted from :cite:`daskin_2013`,
        can be formulated as:

        .. math::

           \begin{array}{lllll}
           \displaystyle \textbf{Minimize}      & \displaystyle W                                   &&                                          & (1)                                                                               \\
           \displaystyle \textbf{Subject To}    & \displaystyle \sum_{j \in J}{X_{ij} = 1}          && \forall i \in I                          & (2)                                                                               \\
                                                & \displaystyle \sum_{j \in J}{Y_j} = p             &&                                          & (3)                                                                               \\
                                                & X_{ij} \leq Y_{j}                                 && \forall i \in I \quad \forall j \in J    & (4)                                                                               \\
                                                & W \geq \displaystyle \sum_{j \in J}{d_{ij}X_{ij}} && \forall i \in I                          & (5)                                                                               \\
                                                & X_{ij} \in \{0, 1\}                               && \forall i \in I \quad \forall j \in J    & (6)                                                                               \\
                                                & Y_j \in \{0, 1\}                                  && \forall j \in J                          & (7)                                                                               \\
                                                &                                                   &&                                          &                                                                                   \\
           \displaystyle \textbf{Where}         && i                                                & =                                         & \textrm{index of demand points/areas/objects in set } I                           \\
                                                && j                                                & =                                         & \textrm{index of potential facility sites in set } J                              \\
                                                && p                                                & =                                         & \textrm{the number of facilities to be sited}                                     \\
                                                && d_{ij}                                           & =                                         & \textrm{shortest distance or travel time between locations } i \textrm{ and } j   \\
                                                && X_{ij}                                           & =                                         & \begin{cases}
                                                                                                                                                   1, \textrm{if client location } i \textrm{ is served by facility } j             \\
                                                                                                                                                   0, \textrm{otherwise}                                                            \\
                                                                                                                                                  \end{cases}                                                                       \\
                                                && Y_j                                              & =                                         & \begin{cases}
                                                                                                                                                   1, \textrm{if a facility is sited at location } j                                \\
                                                                                                                                                   0, \textrm{otherwise}                                                            \\
                                                                                                                                                  \end{cases}                                                                      \\
                                                && W                                                & =                                         & \textrm{maximum distance between any demand site and its associated facility}
           \end{array}

        Parameters
        ----------

        name : str
            The problem name.
        problem : pulp.LpProblem
            A ``pulp`` instance of an optimization model that contains
            constraints, variables, and an objective function.
        aij : numpy.array
            A cost matrix in the form of a 2D array between origins and destinations.

        Attributes
        ----------

        name : str
            The problem name.
        problem : pulp.LpProblem
            A ``pulp`` instance of an optimization model that contains
            constraints, variables, and an objective function.
        fac2cli : numpy.array
            A 2D array storing facility to client relationships where each
            row represents a facility and contains an array of client indices
            with which it is associated. An empty client array indicates
            the facility is associated with no clients.
        cli2fac : numpy.array
            The inverse of ``fac2cli`` where client to facility relationships
            are shown.
        aij : numpy.array
            A cost matrix in the form of a 2D array between origins and destinations.

        """  # noqa: E501

        def __init__(self, name: str, problem: pulp.LpProblem, aij: np.array):
            self.problem = problem
            self.name = name
            self.aij = aij

        def __add_obj(self) -> None:
            """
            Add the objective function to the model.

            Minimize W

            Returns
            -------

            None

            """
            weight = getattr(self, "weight_var")

            self.problem += weight, "objective function"

        @classmethod
        def from_cost_matrix(
            cls,
            cost_matrix: np.array,
            p_facilities: int,
            predefined_facilities_arr: np.array = None,
            name: str = "p-center",
        ):
            """
            Create a ``PCenter`` object based on a cost matrix.

            Parameters
            ----------

            cost_matrix: numpy.array
                A cost matrix in the form of a 2D array between origins and destinations.
            p_facilities : int
                The number of facilities to be located.
            predefined_facilities_arr : numpy.array (default None)
                A binary 1D array of service facilities that must appear in the
                solution. For example, consider 3 facilites ``['A', 'B', 'C']``.
                If facility ``'B'`` must be in the model solution, then the passed
                in array should be ``[0, 1, 0]``.
            name : str (default 'p-center')
                The problem name.

            Returns
            -------

            spopt.locate.p_center.PCenter

            Examples
            --------

            >>> from spopt.locate import PCenter
            >>> from spopt.locate.util import simulated_geo_points
            >>> import geopandas
            >>> import pulp
            >>> import spaghetti

            Create a regular lattice.

            >>> lattice = spaghetti.regular_lattice((0, 0, 10, 10), 9, exterior=True)
            >>> ntw = spaghetti.Network(in_data=lattice)
            >>> streets = spaghetti.element_as_gdf(ntw, arcs=True)
            >>> streets_buffered = geopandas.GeoDataFrame(
            ...     geopandas.GeoSeries(streets["geometry"].buffer(0.2).unary_union),
            ...     crs=streets.crs,
            ...     columns=["geometry"]
            ... )

            Simulate points about the lattice.

            >>> demand_points = simulated_geo_points(streets_buffered, needed=100, seed=5)
            >>> facility_points = simulated_geo_points(streets_buffered, needed=5, seed=6)

            Snap the points to the network of lattice edges.

            >>> ntw.snapobservations(demand_points, "clients", attribute=True)
            >>> clients_snapped = spaghetti.element_as_gdf(
            ...     ntw, pp_name="clients", snapped=True
            ... )
            >>> ntw.snapobservations(facility_points, "facilities", attribute=True)
            >>> facilities_snapped = spaghetti.element_as_gdf(
            ...     ntw, pp_name="facilities", snapped=True
            ... )

            Calculate the cost matrix from origins to destinations.

            >>> cost_matrix = ntw.allneighbordistances(
            ...    sourcepattern=ntw.pointpatterns["clients"],
            ...    destpattern=ntw.pointpatterns["facilities"]
            ... )

            Create and solve a ``PCenter`` instance from the cost matrix.

            >>> pcenter_from_cost_matrix = PCenter.from_cost_matrix(
            ...     cost_matrix, p_facilities=4
            ... )
            >>> pcenter_from_cost_matrix = pcenter_from_cost_matrix.solve(
            ...     pulp.PULP_CBC_CMD(msg=False)
            ... )

            Get the facility-client associations.

            >>> for fac, cli in enumerate(pcenter_from_cost_matrix.fac2cli):
            ...     print(f"facility {fac} serving {len(cli)} clients")
            facility 0 serving 15 clients
            facility 1 serving 24 clients
            facility 2 serving 33 clients
            facility 3 serving 0 clients
            facility 4 serving 28 clients

            """
            r_cli = range(cost_matrix.shape[0])
            r_fac = range(cost_matrix.shape[1])

            model = pulp.LpProblem(name, pulp.LpMinimize)

            p_center = PCenter(name, model, cost_matrix)

            FacilityModelBuilder.add_facility_integer_variable(p_center, r_fac, "y[{i}]")
            FacilityModelBuilder.add_client_assign_variable(
                p_center, r_cli, r_fac, "z[{i}_{j}]"
            )
            FacilityModelBuilder.add_weight_continuous_variable(p_center)

            if predefined_facilities_arr is not None:
                FacilityModelBuilder.add_predefined_facility_constraint(
                    p_center, predefined_facilities_arr
                )

            p_center.__add_obj()

            FacilityModelBuilder.add_facility_constraint(p_center, p_facilities)
            FacilityModelBuilder.add_assignment_constraint(p_center, r_fac, r_cli)
            FacilityModelBuilder.add_opening_constraint(p_center, r_fac, r_cli)
            FacilityModelBuilder.add_minimized_maximum_constraint(
                p_center, cost_matrix, r_fac, r_cli
            )

            return p_center

        @classmethod
        def from_geodataframe(
            cls,
            gdf_demand: GeoDataFrame,
            gdf_fac: GeoDataFrame,
            demand_col: str,
            facility_col: str,
            p_facilities: int,
            predefined_facility_col: str = None,
            distance_metric: str = "euclidean",
            name: str = "p-center",
        ):
            """

            Create an ``PCenter`` object from ``geopandas.GeoDataFrame`` objects.
            Calculate the cost matrix between demand and facility locations
            before building the problem within the ``from_cost_matrix()`` method.

            Parameters
            ----------

            gdf_demand : geopandas.GeoDataFrame
                Demand locations.
            gdf_fac : geopandas.GeoDataFrame
                Facility locations.
            demand_col : str
                Demand sites geometry column name.
            facility_col : str
                Facility candidate sites geometry column name.
            p_facilities: int
                The number of facilities to be located.
            predefined_facility_col : str (default None)
                Column name representing facilities are already defined.
                This a binary assignment per facility. For example, consider 3 facilites
                ``['A', 'B', 'C']``. If facility ``'B'`` must be in the model solution,
                then the column should be ``[0, 1, 0]``.
            distance_metric : str (default 'euclidean')
                A metric used for the distance calculations supported by
                `scipy.spatial.distance.cdist <https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html>`_.
            name : str (default 'p-center')
                The name of the problem.

            Returns
            -------

            spopt.locate.p_center.PCenter

            Examples
            --------

            >>> from spopt.locate import PCenter
            >>> from spopt.locate.util import simulated_geo_points
            >>> import geopandas
            >>> import pulp
            >>> import spaghetti

            Create a regular lattice.

            >>> lattice = spaghetti.regular_lattice((0, 0, 10, 10), 9, exterior=True)
            >>> ntw = spaghetti.Network(in_data=lattice)
            >>> streets = spaghetti.element_as_gdf(ntw, arcs=True)
            >>> streets_buffered = geopandas.GeoDataFrame(
            ...     geopandas.GeoSeries(streets["geometry"].buffer(0.2).unary_union),
            ...     crs=streets.crs,
            ...     columns=["geometry"]
            ... )

            Simulate points about the lattice.

            >>> demand_points = simulated_geo_points(streets_buffered, needed=100, seed=5)
            >>> facility_points = simulated_geo_points(streets_buffered, needed=5, seed=6)

            Snap the points to the network of lattice edges
            and extract as ``GeoDataFrame`` objects.

            >>> ntw.snapobservations(demand_points, "clients", attribute=True)
            >>> clients_snapped = spaghetti.element_as_gdf(
            ...     ntw, pp_name="clients", snapped=True
            ... )
            >>> ntw.snapobservations(facility_points, "facilities", attribute=True)
            >>> facilities_snapped = spaghetti.element_as_gdf(
            ...     ntw, pp_name="facilities", snapped=True
            ... )

            Create and solve a ``PCenter`` instance from the ``GeoDataFrame`` objects.

            >>> pcenter_from_geodataframe = PCenter.from_geodataframe(
            ...     clients_snapped,
            ...     facilities_snapped,
            ...     "geometry",
            ...     "geometry",
            ...     p_facilities=4,
            ...     distance_metric="euclidean"
            ... )
            >>> pcenter_from_geodataframe = pcenter_from_geodataframe.solve(
            ...     pulp.PULP_CBC_CMD(msg=False)
            ... )

            Get the facility-client associations.

            >>> for fac, cli in enumerate(pcenter_from_geodataframe.fac2cli):
            ...     print(f"facility {fac} serving {len(cli)} clients")
            facility 0 serving 14 clients
            facility 1 serving 26 clients
            facility 2 serving 34 clients
            facility 3 serving 0 clients
            facility 4 serving 26 clients

            """  # noqa: E501

            predefined_facilities_arr = None
            if predefined_facility_col is not None:
                predefined_facilities_arr = gdf_fac[predefined_facility_col].to_numpy()

            dem = gdf_demand[demand_col]
            fac = gdf_fac[facility_col]

            dem_type_geom = dem.geom_type.unique()
            fac_type_geom = fac.geom_type.unique()

            _msg = (
                " geodataframe contains mixed type geometries or is not a point. Be "
                "sure deriving centroid from geometries doesn't affect the results."
            )
            if len(dem_type_geom) > 1 or "Point" not in dem_type_geom:
                warnings.warn(f"Demand{_msg}", UserWarning, stacklevel=2)
                dem = dem.centroid

            if len(fac_type_geom) > 1 or "Point" not in fac_type_geom:
                warnings.warn(f"Facility{_msg}", UserWarning, stacklevel=2)
                fac = fac.centroid

            dem_data = np.array([dem.x.to_numpy(), dem.y.to_numpy()]).T
            fac_data = np.array([fac.x.to_numpy(), fac.y.to_numpy()]).T

            if gdf_demand.crs != gdf_fac.crs:
                raise ValueError(
                    "Geodataframes crs are different: "
                    f"gdf_demand-{gdf_demand.crs}, gdf_fac-{gdf_fac.crs}"
                )

            distances = cdist(dem_data, fac_data, distance_metric)

            return cls.from_cost_matrix(
                distances, p_facilities, predefined_facilities_arr, name
            )

        def facility_client_array(self) -> None:
            """

            Create a 2D array storing **facility to client relationships** where each
            row represents a facility and contains an array of client indices
            with which it is associated. An empty client array indicates
            the facility is associated with no clients.

            Returns
            -------

            None

            """
            fac_vars = getattr(self, "fac_vars")
            cli_vars = getattr(self, "cli_assgn_vars")
            len_fac_vars = len(fac_vars)

            self.fac2cli = []

            for j in range(len_fac_vars):
                array_cli = []
                if fac_vars[j].value() > 0:
                    for i in range(len(cli_vars)):
                        if cli_vars[i, j].value() > 0:
                            array_cli.append(i)

                self.fac2cli.append(array_cli)

        def solve(self, solver: pulp.LpSolver, results: bool = True):
            """
            Solve the ``PCenter`` model.

            Parameters
            ----------

            solver : pulp.LpSolver
                A solver supported by ``pulp``.
            results : bool (default True)
                If ``True`` it will create metainfo (which facilities cover
                which demand) and vice-versa, and the uncovered demand.

            Returns
            -------

            spopt.locate.p_center.PCenter

            """
            self.problem.solve(solver)
            self.check_status()

            if results:
                self.facility_client_array()
                self.client_facility_array()

            return self