Merge pull request #224 from ahuang11/no_compute

CHANGELOG.rst

@@ -28,6 +28,8 @@ Bug Fixes
 ~~~~~~~~~
 - :py:func:`~xskillscore.sign_test` now works for ``xr.Dataset`` inputs.
   (:issue:`198`, :pr:`199`) `Aaron Spring`_
+- Passing weights no longer triggers eager computation.
+  (:issue:`218`, :pr:`224`). `Andrew Huang`_
 
 Internal Changes
 ~~~~~~~~~~~~~~~~

xskillscore/core/np_deterministic.py

@@ -71,6 +71,11 @@ def _check_weights(weights):
     elif np.all(np.isnan(weights)):
         return None
     else:
+        if weights.min() < 0:
+            raise ValueError(
+                "Weights has a minimum below 0. Please submit a weights array "
+                "of positive numbers."
+            )
         return weights
 
 

xskillscore/core/utils.py

@@ -81,12 +81,6 @@ def _preprocess_weights(a, dim, new_dim, weights):
     if weights is None:
         return None
     else:
-        # Throw error if there are negative weights.
-        if weights.min() < 0:
-            raise ValueError(
-                "Weights has a minimum below 0. Please submit a weights array "
-                "of positive numbers."
-            )
         # Scale weights to vary from 0 to 1.
         weights = weights / weights.max()
         # Check that the weights array has the same size

xskillscore/tests/test_skipna_functionality.py

@@ -1,3 +1,4 @@
+import dask
 import numpy as np
 import pytest
 from xarray.tests import assert_allclose
@@ -34,6 +35,33 @@
 NON_WEIGHTED_METRICS = [median_absolute_error]
 
 
+class CountingScheduler:
+    """Simple dask scheduler counting the number of computes.
+    Reference: https://stackoverflow.com/questions/53289286/"""
+
+    def __init__(self, max_computes=0):
+        self.total_computes = 0
+        self.max_computes = max_computes
+
+    def __call__(self, dsk, keys, **kwargs):
+        self.total_computes += 1
+        if self.total_computes > self.max_computes:
+            raise RuntimeError(
+                "Too many computes. Total: %d > max: %d."
+                % (self.total_computes, self.max_computes)
+            )
+        return dask.get(dsk, keys, **kwargs)
+
+
+def raise_if_dask_computes(max_computes=0):
+    """
+    Return a dummy context manager so
+    that this can be used for non-dask objects
+    """
+    scheduler = CountingScheduler(max_computes)
+    return dask.config.set(scheduler=scheduler)
+
+
 def drop_nans(a, b, weights=None, dim="time"):
     """
     Masks a and b where they have pairwise nans.
@@ -51,8 +79,9 @@ def test_skipna_returns_same_value_as_dropped_pairwise_nans(a_1d_nan, b_1d_nan,
     """Tests that DataArrays with pairwise nans return the same result
     as the same two with those nans dropped."""
     a_dropped, b_dropped, _ = drop_nans(a_1d_nan, b_1d_nan)
-    res_with_nans = metric(a_1d_nan, b_1d_nan, "time", skipna=True)
-    res_dropped_nans = metric(a_dropped, b_dropped, "time")
+    with raise_if_dask_computes():
+        res_with_nans = metric(a_1d_nan, b_1d_nan, "time", skipna=True)
+        res_dropped_nans = metric(a_dropped, b_dropped, "time")
     assert_allclose(res_with_nans, res_dropped_nans)
 
 
@@ -65,20 +94,22 @@ def test_skipna_returns_same_value_as_dropped_pairwise_nans_with_weights(
     a_dropped, b_dropped, weights_time_dropped = drop_nans(
         a_1d_nan, b_1d_nan, weights_time
     )
-    res_with_nans = metric(
-        a_1d_nan, b_1d_nan, "time", skipna=True, weights=weights_time
-    )
-    res_dropped_nans = metric(
-        a_dropped, b_dropped, "time", weights=weights_time_dropped
-    )
+    with raise_if_dask_computes():
+        res_with_nans = metric(
+            a_1d_nan, b_1d_nan, "time", skipna=True, weights=weights_time
+        )
+        res_dropped_nans = metric(
+            a_dropped, b_dropped, "time", weights=weights_time_dropped
+        )
     assert_allclose(res_with_nans, res_dropped_nans)
 
 
 @pytest.mark.parametrize("metric", WEIGHTED_METRICS + NON_WEIGHTED_METRICS)
 def test_skipna_returns_nan_when_false(a_1d_nan, b_1d_nan, metric):
     """Tests that nan is returned if there's any nans in the time series
     and skipna is False."""
-    res = metric(a_1d_nan, b_1d_nan, "time", skipna=False)
+    with raise_if_dask_computes():
+        res = metric(a_1d_nan, b_1d_nan, "time", skipna=False)
     assert np.isnan(res).all()
 
 
@@ -88,10 +119,12 @@ def test_skipna_broadcast_weights_assignment_destination(
 ):
     """Tests that 'assignment destination is read-only' is not raised
     https://github.com/xarray-contrib/xskillscore/issues/79"""
-    metric(a_nan, b_nan, ["lat", "lon"], weights=weights_lonlat, skipna=True)
+    with raise_if_dask_computes():
+        metric(a_nan, b_nan, ["lat", "lon"], weights=weights_lonlat, skipna=True)
 
 
 def test_nan_skipna(a, b):
     # Randomly add some nans to a
     a = a.where(np.random.random(a.shape) < 0.5)
-    pearson_r(a, b, dim="lat", skipna=True)
+    with raise_if_dask_computes():
+        pearson_r(a, b, dim="lat", skipna=True)