Refactoring to make more systematic use of Mixin classes. Note this m…

…akes the Raster class larger, but it is easier to understand because the logic for the Raster and RasterLayer classes are more contained

pyspatialml/_plotting.py

@@ -1,5 +1,6 @@
 import math
 
+import rasterio
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
@@ -34,7 +35,7 @@ def discrete_cmap(N, base_cmap=None):
     return base.from_list(cmap_name, color_list, N)
 
 
-class RasterPlot(object):
+class RasterPlotMixin(object):
     def plot(
         self,
         cmap=None,
@@ -264,3 +265,124 @@ def plot(
             cbar.ax.tick_params(labelsize=legend_fontsize)
 
         return axs
+
+
+class RasterLayerPlotMixin(object):
+    def plot(
+        self,
+        cmap=None,
+        norm=None,
+        ax=None,
+        cax=None,
+        figsize=None,
+        out_shape=(500, 500),
+        categorical=None,
+        legend=False,
+        vmin=None,
+        vmax=None,
+        fig_kwds=None,
+        legend_kwds=None,
+    ):
+        """Plot a RasterLayer using matplotlib.pyplot.imshow
+
+        Parameters
+        ----------
+        cmap : str (default None)
+            The name of a colormap recognized by matplotlib.
+            Overrides the cmap attribute of the RasterLayer.
+
+        norm : matplotlib.colors.Normalize (opt)
+            A matplotlib.colors.Normalize to apply to the RasterLayer.
+            This overrides the norm attribute of the RasterLayer.
+
+        ax : matplotlib.pyplot.Artist (optional, default None)
+            axes instance on which to draw to plot.
+
+        cax : matplotlib.pyplot.Artist (optional, default None)
+            axes on which to draw the legend.
+
+        figsize : tuple of integers (optional, default None)
+            Size of the matplotlib.figure.Figure. If the ax argument is
+            given explicitly, figsize is ignored.
+
+        out_shape : tuple, default=(500, 500)
+            Number of rows, cols to read from the raster datasets for
+            plotting.
+
+        categorical : bool (optional, default False)
+            if True then the raster values will be considered to
+            represent discrete values, otherwise they are considered to
+            represent continuous values. This overrides the
+            RasterLayer 'categorical' attribute. Setting the argument
+            categorical to True is ignored if the
+            RasterLayer.categorical is already True.
+
+        legend : bool (optional, default False)
+            Whether to plot the legend.
+
+        vmin, xmax : scale (optional, default None)
+            vmin and vmax define the data range that the colormap
+            covers. By default, the colormap covers the complete value
+            range of the supplied data. vmin, vmax are ignored if the
+            norm parameter is used.
+
+        fig_kwds : dict (optional, default None)
+            Additional arguments to pass to the
+            matplotlib.pyplot.figure call when creating the figure
+            object. Ignored if ax is passed to the plot function.
+
+        legend_kwds : dict (optional, default None)
+            Keyword arguments to pass to matplotlib.pyplot.colorbar().
+
+        Returns
+        -------
+        ax : matplotlib axes instance
+        """
+
+        # some checks
+        if fig_kwds is None:
+            fig_kwds = {}
+
+        if ax is None:
+            if cax is not None:
+                raise ValueError("'ax' can not be None if 'cax' is not.")
+            fig, ax = plt.subplots(figsize=figsize, **fig_kwds)
+
+        ax.set_aspect("equal")
+
+        if norm:
+            if not isinstance(norm, mpl.colors.Normalize):
+                raise AttributeError(
+                    "norm argument should be a " "matplotlib.colors.Normalize object"
+                )
+
+        if cmap is None:
+            cmap = self.cmap
+
+        if norm is None:
+            norm = self.norm
+
+        if legend_kwds is None:
+            legend_kwds = {}
+
+        arr = self.read(masked=True, out_shape=out_shape)
+
+        if categorical is True:
+            if self.categorical is False:
+                N = np.bincount(arr)
+                cmap = discrete_cmap(N, base_cmap=cmap)
+            vmin, vmax = None, None
+
+        im = ax.imshow(
+            X=arr,
+            extent=rasterio.plot.plotting_extent(self.ds),
+            cmap=cmap,
+            norm=norm,
+            vmin=vmin,
+            vmax=vmax,
+        )
+
+        if legend is True:
+            plt.colorbar(im, cax=cax, ax=ax, **legend_kwds)
+
+        return ax