Saturate image when robust=True

Carefully, so that saturation is always consistent across
color channels and facets of big plots.

xarray/plot/plot.py

@@ -16,7 +16,7 @@
 from datetime import datetime
 
 from .utils import (_determine_cmap_params, _infer_xy_labels, get_axis,
-                    import_matplotlib_pyplot)
+                    import_matplotlib_pyplot, ROBUST_PERCENTILE)
 from .facetgrid import FacetGrid
 from xarray.core.pycompat import basestring
 
@@ -419,14 +419,32 @@ def newplotfunc(darray, x=None, y=None, figsize=None, size=None,
         # Decide on a default for the colorbar before facetgrids
         if add_colorbar is None:
             add_colorbar = plotfunc.__name__ != 'contour'
-        if plotfunc.__name__ == 'imshow' and \
-                darray.ndim == (3 + (row is not None) + (col is not None)):
+        imshow_rgb = plotfunc.__name__ == 'imshow' and \
+            darray.ndim == (3 + (row is not None) + (col is not None))
+        if imshow_rgb:
             # Don't add a colorbar when showing an image with explicit colors
             add_colorbar = False
+            # Manually stretch colors for robust cmap
+            if robust:
+                flat = darray.values.ravel(order='K')
+                flat = flat[~np.isnan(flat)]
+                if flat.size == 0:
+                    # All data will be masked, so skip percentile calculation
+                    vmin, vmax = 0, 1
+                if vmin is None:
+                    vmin = np.percentile(flat, ROBUST_PERCENTILE)
+                if vmax is None:
+                    vmax = np.percentile(flat, 100 - ROBUST_PERCENTILE)
+                darray = (darray - vmin) / (vmax - vmin)
+                robust = False
+                del flat
+            # Clip range to [0, 1] to avoid visual artefacts
+            darray.values[:] = np.clip(darray.values, 0, 1)
 
         # Handle facetgrids first
         if row or col:
             allargs = locals().copy()
+            allargs.pop('imshow_rgb')
             allargs.update(allargs.pop('kwargs'))
 
             # Need the decorated plotting function
@@ -575,6 +593,11 @@ def imshow(x, y, z, ax, **kwargs):
     While other plot methods require the DataArray to be strictly
     two-dimensional, ``imshow`` also accepts a 3D array where the third
     dimension can be interpreted as RGB or RGBA color channels.
+    In this case, ``robust=True`` will saturate the image in the
+    usual way, consistenly between all bands and facets.
+
+    This method will clip oversaturated pixels to the valid range,
+    instead of wrapping around to new colors like matplotlib.
 
     .. note::
         This function needs uniformly spaced coordinates to

xarray/plot/utils.py

@@ -10,6 +10,8 @@
 from ..core.pycompat import basestring
 from ..core.utils import is_scalar
 
+ROBUST_PERCENTILE = 2.0
+
 
 def _load_default_cmap(fname='default_colormap.csv'):
     """
@@ -160,7 +162,6 @@ def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None,
     cmap_params : dict
         Use depends on the type of the plotting function
     """
-    ROBUST_PERCENTILE = 2.0
     import matplotlib as mpl
 
     calc_data = np.ravel(plot_data[~pd.isnull(plot_data)])