Merge pull request #422 from jGaboardi/standard_locate_model_names

`locate` models – standardized default names
pysal · Dec 10, 2023 · e505d5d · e505d5d
2 parents 8800d2f + 9a4f3fe
commit e505d5d
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Showing 3 changed files with 17 additions and 17 deletions.
diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py
@@ -127,7 +127,7 @@ def from_cost_matrix(
         predefined_facilities_arr: np.array = None,
         demand_quantity_arr: np.array = None,
         facility_capacity_arr: np.array = None,
-        name: str = "LSCP",
+        name: str = "lscp",
     ):
         """
         Create an ``LSCP`` object based on a cost matrix. A capacitated
@@ -151,7 +151,7 @@ def from_cost_matrix(
             Amount of demand at each client location.
         facility_capacity_arr : numpy.array (default None)
             Capacity for service at each facility location.
-        name : str, (default 'LSCP')
+        name : str, (default 'lscp')
             The name of the problem.
 
         Returns
@@ -295,7 +295,7 @@ def from_geodataframe(
         demand_quantity_col: str = None,
         facility_capacity_col: str = None,
         distance_metric: str = "euclidean",
-        name: str = "LSCP",
+        name: str = "lscp",
     ):
         """
         Create an ``LSCP`` object from ``geopandas.GeoDataFrame`` objects.
@@ -330,7 +330,7 @@ def from_geodataframe(
         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 'LSCP')
+        name : str (default 'lscp')
             The name of the problem.
 
         Returns
@@ -615,7 +615,7 @@ def from_cost_matrix(
         service_radius: float,
         solver: pulp.LpSolver,
         predefined_facilities_arr: np.array = None,
-        name: str = "LSCP-B",
+        name: str = "lscp-b",
     ):
         """
         Create an ``LSCPB`` object based on a cost matrix.
@@ -634,7 +634,7 @@ def from_cost_matrix(
             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 'LSCP-B')
+        name : str (default 'lscp-b')
             The problem name.
 
         Returns
@@ -755,7 +755,7 @@ def from_geodataframe(
         solver: pulp.LpSolver,
         predefined_facility_col: str = None,
         distance_metric: str = "euclidean",
-        name: str = "LSCP-B",
+        name: str = "lscp-b",
     ):
         """
 
@@ -786,7 +786,7 @@ def from_geodataframe(
         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 'LSCP')
+        name : str (default 'lscp-b')
             The name of the problem.
 
         Returns
@@ -1052,7 +1052,7 @@ def from_cost_matrix(
         service_radius: float,
         p_facilities: int,
         predefined_facilities_arr: np.array = None,
-        name: str = "MCLP",
+        name: str = "mclp",
     ):
         """
         Create a ``MCLP`` object based on cost matrix.
@@ -1073,7 +1073,7 @@ def from_cost_matrix(
             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 'MCLP')
+        name : str (default 'mclp')
             The problem name.
 
         Returns
@@ -1196,7 +1196,7 @@ def from_geodataframe(
         p_facilities: int,
         predefined_facility_col: str = None,
         distance_metric: str = "euclidean",
-        name: str = "MCLP",
+        name: str = "mclp",
     ):
         """
 
@@ -1229,7 +1229,7 @@ def from_geodataframe(
         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 'MCLP')
+        name : str (default 'mclp')
             The name of the problem.
 
         Returns

diff --git a/spopt/locate/p_dispersion.py b/spopt/locate/p_dispersion.py
@@ -81,7 +81,7 @@ def from_cost_matrix(
         cost_matrix: np.array,
         p_facilities: int,
         predefined_facilities_arr: np.array = None,
-        name: str = "P-Dispersion",
+        name: str = "p-dispersion",
     ):
         """
         Create a ``PDispersion`` object based on a cost matrix.
@@ -98,7 +98,7 @@ def from_cost_matrix(
             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-Dispersion')
+        name : str (default 'p-dispersion')
             The name of the problem.
 
         Returns
@@ -199,7 +199,7 @@ def from_geodataframe(
         p_facilities: int,
         predefined_facility_col: str = None,
         distance_metric: str = "euclidean",
-        name: str = "P-Dispersion",
+        name: str = "p-dispersion",
     ):
         """
         Create a ``PDispersion`` object based on a geodataframe. Calculate the
@@ -222,7 +222,7 @@ def from_geodataframe(
         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-Dispersion')
+        name : str (default 'p-dispersion')
             The name of the problem.
 
         Returns

diff --git a/spopt/locate/p_median.py b/spopt/locate/p_median.py
@@ -639,7 +639,7 @@ def __init__(
         p_facilities: int,
         capacities: np.array = None,
         distance_metric: str = "euclidean",
-        name="k-nearest p median",
+        name="k-nearest-p-median",
     ):
         self.ai_sum = weights_sum
         self.clients = clients