pysal · jGaboardi · Apr 24, 2022 · Apr 17, 2022 · Apr 19, 2022 · Apr 21, 2022
diff --git a/notebooks/lscp.ipynb b/notebooks/lscp.ipynb
diff --git a/notebooks/mclp.ipynb b/notebooks/mclp.ipynb
diff --git a/spopt/locate/base.py b/spopt/locate/base.py
@@ -278,6 +278,37 @@ def add_facility_constraint(
                 "before setting constraints must set facility variable"
             )
 
+    @staticmethod
+    def add_predefined_facility_constraint(
+        obj: T_FacModel, model: pulp.LpProblem, predefined_fac: np.array
+    ) -> None:
+        """
+        predefined demand constraint
+
+        Parameters
+        ----------
+        obj: T_FacModel
+            bounded type of LocateSolver class
+        model: pulp.LpProblem
+            optimization model problem
+        facility_indexes: np.array
+            facility indexes zeroindexed already located
+
+        Returns
+        -------
+        None
+        """
+        if hasattr(obj, "fac_vars"):
+            fac_vars = getattr(obj, "fac_vars")
+            for ind in range(len(predefined_fac)):
+                if predefined_fac[ind]:
+                    fac_vars[ind].setInitialValue(1)
+                    fac_vars[ind].fixValue()
+        else:
+            raise AttributeError(
+                "before predefined facility must set facility variable"
+            )
+
     @staticmethod
     def add_maximal_coverage_constraint(
         obj: T_FacModel, model, ni, range_facility, range_client
@@ -306,6 +337,7 @@ def add_maximal_coverage_constraint(
         if hasattr(obj, "fac_vars") and hasattr(obj, "cli_vars"):
             fac_vars = getattr(obj, "fac_vars")
             dem_vars = getattr(obj, "cli_vars")
+
             for i in range_client:
                 model += (
                     pulp.lpSum([ni[i][j] * fac_vars[j] for j in range_facility])

diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py
@@ -44,7 +44,7 @@ def __add_obj(self) -> None:
 
     @classmethod
     def from_cost_matrix(
-        cls, cost_matrix: np.array, max_coverage: float, name: str = "LSCP"
+        cls, cost_matrix: np.array, max_coverage: float, predefined_facilities_arr: np.array = None, name: str = "LSCP"
     ):
         """
         Create a LSCP object based on cost matrix.
@@ -119,6 +119,9 @@ def from_cost_matrix(
         lscp.aij = np.zeros(cost_matrix.shape)
         lscp.aij[cost_matrix <= max_coverage] = 1
 
+        if predefined_facilities_arr is not None:
+            FacilityModelBuilder.add_predefined_facility_constraint(lscp, lscp.problem, predefined_facilities_arr)
+
         lscp.__add_obj()
         FacilityModelBuilder.add_set_covering_constraint(
             lscp, lscp.problem, lscp.aij, r_fac, r_cli
@@ -134,6 +137,7 @@ def from_geodataframe(
         demand_col: str,
         facility_col: str,
         max_coverage: float,
+        predefined_facility_col: str = None,
         distance_metric: str = "euclidean",
         name: str = "LSCP",
     ):
@@ -205,6 +209,10 @@ def from_geodataframe(
 
         """
 
+        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]
 
@@ -235,7 +243,7 @@ def from_geodataframe(
 
         distances = cdist(dem_data, fac_data, distance_metric)
 
-        return cls.from_cost_matrix(distances, max_coverage, name)
+        return cls.from_cost_matrix(distances, max_coverage, predefined_facilities_arr, name)
 
     def facility_client_array(self) -> None:
         """
@@ -316,6 +324,7 @@ def from_cost_matrix(
         weights: np.array,
         max_coverage: float,
         p_facilities: int,
+        predefined_facilities_arr: np.array = None,
         name: str = "MCLP",
     ):
         """
@@ -331,6 +340,8 @@ def from_cost_matrix(
             maximum acceptable service distance by problem
         p_facilities: int
             number of facilities to be located
+        predefined_facilities_arr: np.array, default=None
+            bool array defining which facilities are already defined
         name: str, default="MCLP"
             name of the problem
 
@@ -402,11 +413,17 @@ def from_cost_matrix(
         weights = np.reshape(weights, (cost_matrix.shape[0], 1))
 
         mclp.__add_obj(weights, r_cli)
+
+        if predefined_facilities_arr is not None:
+            FacilityModelBuilder.add_predefined_facility_constraint(mclp, mclp.problem, predefined_facilities_arr)
+
         FacilityModelBuilder.add_maximal_coverage_constraint(
             mclp, mclp.problem, mclp.aij, r_fac, r_cli
         )
+
         FacilityModelBuilder.add_facility_constraint(mclp, mclp.problem, p_facilities)
 
+
         return mclp
 
     @classmethod
@@ -419,6 +436,7 @@ def from_geodataframe(
         weights_cols: str,
         max_coverage: float,
         p_facilities: int,
+        predefined_facility_col: str = None,
         distance_metric: str = "euclidean",
         name: str = "MCLP",
     ):
@@ -442,6 +460,8 @@ def from_geodataframe(
             maximum acceptable service distance by problem
         p_facilities: int
             number of facilities to be located
+        predefined_facility_col: np.array, default=None
+            column name representing facilities are already defined
         distance_metric: str, default="euclidean"
             metrics supported by :method: `scipy.spatial.distance.cdist` used for the distance calculations
         name: str, default="MCLP"
@@ -505,6 +525,11 @@ def from_geodataframe(
         >>> mclp_from_geodataframe.facility_client_array()
         >>> mclp_from_geodataframe.fac2cli
         """
+
+        predefined_facilities_arr = None
+        if predefined_facility_col is not None:
+            predefined_facilities_arr = gdf_fac[predefined_facility_col].to_numpy()
+
         service_load = gdf_demand[weights_cols].to_numpy()
         dem = gdf_demand[demand_col]
         fac = gdf_fac[facility_col]
@@ -537,7 +562,7 @@ def from_geodataframe(
         distances = cdist(dem_data, fac_data, distance_metric)
 
         return cls.from_cost_matrix(
-            distances, service_load, max_coverage, p_facilities, name
+            distances, service_load, max_coverage, p_facilities, predefined_facilities_arr, name
         )
 
     def facility_client_array(self) -> None:

diff --git a/spopt/tests/data/lscp_preselected_loc_geodataframe_fac2cli.pkl b/spopt/tests/data/lscp_preselected_loc_geodataframe_fac2cli.pkl
diff --git a/spopt/tests/data/mclp_preselected_loc_geodataframe_fac2cli.pkl b/spopt/tests/data/mclp_preselected_loc_geodataframe_fac2cli.pkl