Efficient way to convert a structured numpy array into a 3d array.

This CL uses a more efficient way to convert the numpy structured array removing the need to copy the array into memory as a list. Using `.view()` doesn't seem to change the data buffer and should be more memory efficient.

closes #9

PiperOrigin-RevId: 587594128

xee/ext.py

@@ -464,10 +464,15 @@ def image_to_array(
         pixels_getter, params, catch=ee.ee_exception.EEException
     )
 
-    # TODO(#9): Find a way to make this more efficient. This is needed because
-    # `raw` is a structured array of all the same dtype (i.e. number of images).
-    arr = np.array(raw.tolist(), dtype=dtype)
-    data = arr.T
+    x_size = kwargs['grid']['dimensions']['width']
+    y_size = kwargs['grid']['dimensions']['height']
+
+    # This is needed because `raw` is a structured array of all the same dtype
+    # (i.e. number of images).
+    arr = raw.view(dtype).reshape(-1, x_size, y_size,)
+    arr = np.moveaxis(arr, 0, -1)
+
+    data = arr
     current_mask_value = np.array(self.mask_value, dtype=data.dtype)
     # Sets EE nodata masked value to NaNs.
     data = np.where(data == current_mask_value, np.nan, data)

xee/ext_integration_test.py

@@ -85,6 +85,7 @@ def setUp(self):
     )
 
   def test_creates_lat_long_array(self):
+    init_ee_for_tests()
     arr = xee.EarthEngineBackendArray('longitude', self.lnglat_store)
     self.assertEqual((1, 360, 180), arr.shape)