Use broadcast_like for 2d plot coordinates (#5099)

* Use broadcast_like for 2d plot coordinates

Use broadcast_like if either `x` or `y` inputs are 2d to ensure that
both have dimensions in the same order as the DataArray being plotted.
Convert to numpy arrays after possibly using broadcast_like. Simplifies
code, and fixes #5097 (bug when dimensions have the same size).

* Update whats-new

* Test for issue 5097

* Fix typo in doc/whats-new.rst

Co-authored-by: Mathias Hauser <[email protected]>

* Update doc/whats-new.rst

Co-authored-by: Mathias Hauser <[email protected]>

Author: johnomotani

doc/whats-new.rst

@@ -91,6 +91,8 @@ Deprecations
 
 Bug fixes
 ~~~~~~~~~
+- Fix 2d plot failure for certain combinations of dimensions when `x` is 1d and `y` is
+  2d (:issue:`5097`, :pull:`5099`). By `John Omotani <https://github.com/johnomotani>`_.
 - Ensure standard calendar times encoded with large values (i.e. greater than approximately 292 years), can be decoded correctly without silently overflowing (:pull:`5050`).  This was a regression in xarray 0.17.0.  By `Zeb Nicholls <https://github.com/znicholls>`_.
 - Added support for `numpy.bool_` attributes in roundtrips using `h5netcdf` engine with `invalid_netcdf=True` [which casts `bool`s to `numpy.bool_`] (:issue:`4981`, :pull:`4986`).
   By `Victor Negîrneac <https://github.com/caenrigen>`_.

xarray/plot/plot.py

@@ -671,28 +671,21 @@ def newplotfunc(
             darray=darray, x=x, y=y, imshow=imshow_rgb, rgb=rgb
         )
 
-        # better to pass the ndarrays directly to plotting functions
-        xval = darray[xlab].values
-        yval = darray[ylab].values
-
-        # check if we need to broadcast one dimension
-        if xval.ndim < yval.ndim:
-            dims = darray[ylab].dims
-            if xval.shape[0] == yval.shape[0]:
-                xval = np.broadcast_to(xval[:, np.newaxis], yval.shape)
-            else:
-                xval = np.broadcast_to(xval[np.newaxis, :], yval.shape)
-
-        elif yval.ndim < xval.ndim:
-            dims = darray[xlab].dims
-            if yval.shape[0] == xval.shape[0]:
-                yval = np.broadcast_to(yval[:, np.newaxis], xval.shape)
-            else:
-                yval = np.broadcast_to(yval[np.newaxis, :], xval.shape)
-        elif xval.ndim == 2:
-            dims = darray[xlab].dims
+        xval = darray[xlab]
+        yval = darray[ylab]
+
+        if xval.ndim > 1 or yval.ndim > 1:
+            # Passing 2d coordinate values, need to ensure they are transposed the same
+            # way as darray
+            xval = xval.broadcast_like(darray)
+            yval = yval.broadcast_like(darray)
+            dims = darray.dims
         else:
-            dims = (darray[ylab].dims[0], darray[xlab].dims[0])
+            dims = (yval.dims[0], xval.dims[0])
+
+        # better to pass the ndarrays directly to plotting functions
+        xval = xval.values
+        yval = yval.values
 
         # May need to transpose for correct x, y labels
         # xlab may be the name of a coord, we have to check for dim names

xarray/tests/test_plot.py

@@ -368,6 +368,34 @@ def test2d_1d_2d_coordinates_contourf(self):
         a.plot.contourf(x="time", y="depth")
         a.plot.contourf(x="depth", y="time")
 
+    def test2d_1d_2d_coordinates_pcolormesh(self):
+        # Test with equal coordinates to catch bug from #5097
+        sz = 10
+        y2d, x2d = np.meshgrid(np.arange(sz), np.arange(sz))
+        a = DataArray(
+            easy_array((sz, sz)),
+            dims=["x", "y"],
+            coords={"x2d": (["x", "y"], x2d), "y2d": (["x", "y"], y2d)},
+        )
+
+        for x, y in [
+            ("x", "y"),
+            ("y", "x"),
+            ("x2d", "y"),
+            ("y", "x2d"),
+            ("x", "y2d"),
+            ("y2d", "x"),
+            ("x2d", "y2d"),
+            ("y2d", "x2d"),
+        ]:
+            p = a.plot.pcolormesh(x=x, y=y)
+            v = p.get_paths()[0].vertices
+
+            # Check all vertices are different, except last vertex which should be the
+            # same as the first
+            _, unique_counts = np.unique(v[:-1], axis=0, return_counts=True)
+            assert np.all(unique_counts == 1)
+
     def test_contourf_cmap_set(self):
         a = DataArray(easy_array((4, 4)), dims=["z", "time"])