ENH: add Moran_Local.plot (#355)

* static plot

* simplify

* add tests

* fix docstrings

esda/moran.py

@@ -83,7 +83,7 @@ class Moran:
     w            : W | Graph
                    original w object
     z            : array
-                   zero-mean, unit standard deviation normalized y                   
+                   zero-mean, unit standard deviation normalized y
     permutations : int
                    number of permutations
     I            : float
@@ -673,7 +673,7 @@ class Moran_Rate(Moran):  # noqa: N801
                    if adjusted is True, y is standardized rates
                    otherwise, y is raw rates
     z            : array
-                   zero-mean, unit standard deviation normalized y                   
+                   zero-mean, unit standard deviation normalized y
     w            : W | Graph
                    original w object
     permutations : int
@@ -928,7 +928,7 @@ class Moran_Local:  # noqa: N801
     w : W | Graph
         original w object
     z : array
-        zero-mean, unit standard deviation normalized y    
+        zero-mean, unit standard deviation normalized y
     permutations : int
         number of random permutations for calculation of pseudo p_values
     Is : array
@@ -1249,7 +1249,28 @@ def explore(self, gdf, crit_value=0.05, **kwargs):
         """
         gdf = gdf.copy()
         gdf["Moran Cluster"] = self.get_cluster_labels(crit_value)
-        return _explore_local_moran(self, gdf, crit_value, **kwargs)
+        return _viz_local_moran(self, gdf, crit_value, "explore", **kwargs)
+
+    def plot(self, gdf, crit_value=0.05, **kwargs):
+        """Create static map of LISA indicators
+
+        Parameters
+        ----------
+        gdf : geopandas.GeoDataFrame
+            geodataframe used to conduct the local Moran analysis
+        crit_value : float, optional
+            critical value to determine statistical significance, by default 0.05
+        kwargs : dict, optional
+            additional keyword arguments passed to the geopandas `explore` method
+
+        Returns
+        -------
+        ax
+            matplotlib axis
+        """
+        gdf = gdf.copy()
+        gdf["Moran Cluster"] = self.get_cluster_labels(crit_value)
+        return _viz_local_moran(self, gdf, crit_value, "plot", **kwargs)
 
 
 class Moran_Local_BV:  # noqa: N801
@@ -1571,7 +1592,7 @@ class Moran_Local_Rate(Moran_Local):  # noqa: N801
         if adjusted is True, y is standardized rates
         otherwise, y is raw rates
     z : array
-        zero-mean, unit standard deviation normalized y    
+        zero-mean, unit standard deviation normalized y
     w : W | Graph
         original w object
     permutations : int
@@ -1773,8 +1794,8 @@ def by_col(
             df[col] = rate_df[col]
 
 
-def _explore_local_moran(moran_local, gdf, crit_value, **kwargs):
-    """Plot local Moran values as an interactive map
+def _viz_local_moran(moran_local, gdf, crit_value, method, **kwargs):
+    """Common helper for local Moran's I vizualization
 
     Parameters
     ----------
@@ -1784,20 +1805,24 @@ def _explore_local_moran(moran_local, gdf, crit_value, **kwargs):
         geodataframe used to create the Moran_Local class
     crit_value : float, optional
         critical value for determining statistical significance, by default 0.05
+    method : str {"explore", "plot"}
+        GeoDataFrame method to be used
     kwargs : dict, optional
         additional keyword arguments are passed directly
-        to geopandas.explore, by default None
+        to the plotting method, by default None
 
     Returns
     -------
-    m
-        folium.Map
+    m | ax
+        either folium.Map or maptlotlib.Axes
     """
 
     try:
         from matplotlib import colors
-    except ImportError:
-        raise ImportError("matplotlib library must be installed to use the explore feature") from None
+    except ImportError as err:
+        raise ImportError(
+            "matplotlib library must be installed to use the vizualization feature"
+        ) from err
 
     gdf = gdf.copy()
     gdf["Moran Cluster"] = moran_local.get_cluster_labels(crit_value)
@@ -1817,8 +1842,9 @@ def _explore_local_moran(moran_local, gdf, crit_value, **kwargs):
     if "cmap" not in kwargs:
         kwargs["cmap"] = hmap
 
-    m = gdf[["Moran Cluster", "p-value", "geometry"]].explore("Moran Cluster", **kwargs)
-    return m
+    return getattr(gdf[["Moran Cluster", "p-value", "geometry"]], method)(
+        "Moran Cluster", **kwargs
+    )
 
 
 def _get_cluster_labels(moran_local, crit_value):

esda/tests/test_moran.py

@@ -236,6 +236,31 @@ def test_Moran_Local_explore(self, w):
         assert out_str.count("#fdae61") == 6
         assert out_str.count("#d3d3d3") == 280
 
+    @parametrize_sac
+    def test_Moran_Local_plot(self, w):
+        import matplotlib
+
+        matplotlib.use("Agg")
+
+        lm = moran.Moran_Local(
+            sac1.HSG_VAL.values,
+            w,
+            transformation="r",
+            permutations=99,
+            keep_simulations=True,
+            seed=SEED,
+        )
+        ax = lm.plot(sac1)
+        unique, counts = np.unique(ax.collections[0].get_facecolors(), axis=0, return_counts=True)
+        np.testing.assert_array_almost_equal(unique, np.array([
+            [0.17254902, 0.48235294, 0.71372549, 1.],
+            [0.5372549 , 0.81176471, 0.94117647, 1.],
+            [0.82745098, 0.82745098, 0.82745098, 1.],
+            [0.84313725, 0.09803922, 0.10980392, 1.],
+            [0.99215686, 0.68235294, 0.38039216, 1.]]
+        ))
+        np.testing.assert_array_equal(counts, np.array([86,3, 298,38, 3]))
+
     @parametrize_desmith
     def test_Moran_Local_parallel(self, w):
         lm = moran.Moran_Local(