REF: Use rasterio.io.MemoryFile in merge (corteva#765)

rioxarray/merge.py

@@ -8,6 +8,7 @@
 
 import numpy
 from rasterio.crs import CRS
+from rasterio.io import MemoryFile
 from rasterio.merge import merge as _rio_merge
 from xarray import DataArray, Dataset
 
@@ -53,49 +54,32 @@ def colormap(self, *args, **kwargs):
         See: https://github.com/corteva/rioxarray/issues/479
         """
         try:
-            rio_file = self.xds.rio._manager.acquire()
+            rio_file = self._xds.rio._manager.acquire()
             return rio_file.colormap(*args, **kwargs)
         except AttributeError:
             return None
 
-    def read(self, window, out_shape, *args, **kwargs) -> numpy.ma.MaskedArray:
-        # pylint: disable=unused-argument
+    def read(self, *args, **kwargs) -> numpy.ma.MaskedArray:
         """
         This method is meant to be used by the rasterio.merge.merge function.
         """
-        data_window = self._xds.rio.isel_window(window)
-        if data_window.shape != out_shape:
-            # in this section, the data is geographically the same
-            # however it is not the same dimensions as requested
-            # so need to resample to the requested shape
-            if len(out_shape) == 3:
-                _, out_height, out_width = out_shape
-            else:
-                out_height, out_width = out_shape
-            data_window = self._xds.rio.reproject(
-                self._xds.rio.crs,
+        with MemoryFile() as memfile:
+            with memfile.open(
+                driver="GTiff",
+                height=int(self._xds.rio.height),
+                width=int(self._xds.rio.width),
+                count=self.count,
+                dtype=self.dtypes[0],
+                crs=self.crs,
                 transform=self.transform,
-                shape=(out_height, out_width),
-            )
-
-        nodata = self.nodatavals[0]
-        mask = False
-        fill_value = None
-        if nodata is not None and numpy.isnan(nodata):
-            mask = numpy.isnan(data_window)
-        elif nodata is not None:
-            mask = data_window == nodata
-            fill_value = nodata
-
-        # make sure the returned shape matches
-        # the expected shape. This can be the case
-        # when the xarray dataset was squeezed to 2D beforehand
-        if len(out_shape) == 3 and len(data_window.shape) == 2:
-            data_window = data_window.values.reshape((1, out_height, out_width))
-
-        return numpy.ma.array(
-            data_window, mask=mask, fill_value=fill_value, dtype=self.dtypes[0]
-        )
+                nodata=self.nodatavals[0],
+            ) as dataset:
+                data = self._xds.values
+                if data.ndim == 2:
+                    dataset.write(data, 1)
+                else:
+                    dataset.write(data)
+                return dataset.read(*args, **kwargs)
 
 
 def merge_arrays(