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.

Also adds checks for requested vs returned data types from EE. Will try to use requested type but will warn user if conversion is not possible then use returned EE type.

Closes #9

PiperOrigin-RevId: 587594128

docs/conf.py

@@ -16,8 +16,8 @@
 
 # Print Python environment info for easier debugging on ReadTheDocs
 
-import sys
 import subprocess
+import sys
 import xee  # verify this works
 
 print('python exec:', sys.executable)

xee/ext.py

@@ -25,7 +25,7 @@
 import math
 import os
 import sys
-from typing import Any, Dict, List, Iterable, Literal, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Literal, Optional, Tuple, Union
 from urllib import parse
 import warnings
 
@@ -464,9 +464,30 @@ 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)
+    # Extract out the shape information from EE response.
+    y_size, x_size = raw.shape
+    n_bands = len(raw.dtype)
+
+    # Get a view (no copy) of the data as the returned type from EE
+    # then reshape to the correct shape based on the request.
+    # This is needed because `raw` is a structured array of all the same dtype
+    # (i.e. number of images) and this converts it to an ndarray.
+    arr = raw.view(raw.dtype[0]).reshape(
+        y_size,
+        x_size,
+        n_bands,
+    )
+
+    # try converting the data to desired dtype in place without copying
+    # if conversion is not allowed then just use the EE returned dtype
+    try:
+      arr = arr.astype(dtype, copy=False)
+    except ValueError:
+      warnings.warn(
+          f'Could convert EE results to requested dtype {dtype} '
+          f'falling back to returned dtype from EE {np.dtype(raw.dtype[0])}'
+      )
+
     data = arr.T
     current_mask_value = np.array(self.mask_value, dtype=data.dtype)
     # Sets EE nodata masked value to NaNs.

xee/ext_test.py

@@ -2,10 +2,8 @@
 
 from absl.testing import absltest
 from absl.testing import parameterized
-
 import numpy as np
 import xee
-
 from xee import ext
 
 

xee/types.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Type definitions for Earth Engine concepts (and others)."""
-from typing import Dict, List, Tuple, Union, TypedDict
+from typing import Dict, List, Tuple, TypedDict, Union
 
 TileIndex = Tuple[int, int, int]
 # x_min, y_min, x_max, y_max