make tests more robust to dot product implementation

tests/test_core.py

@@ -434,14 +434,17 @@ def test_aggregate_with_weights(ds=ds):
 
 
 	# Get pixel overlaps
-	wm = get_pixel_overlaps(gdf,pix_agg)
+	wm_for = get_pixel_overlaps(gdf,pix_agg,impl='for_loop')
+	wm_dot = get_pixel_overlaps(gdf,pix_agg,impl='dot_product')
 
 	# Get aggregate
-	agg = aggregate(ds,wm)
+	agg_for = aggregate(ds,wm_for,impl='for_loop')
+	agg_dot = aggregate(ds,wm_dot,impl='dot_product')
 
 	# Since the "test" for the input ds has [0,2] for the two 
 	# equatorial pixels, the average should just be 1.0
-	assert np.allclose([v for v in agg.agg.test.values],1.0)
+	assert np.allclose([v for v in agg_for.agg.test.values],1.0)
+	assert np.allclose([v for v in agg_dot.agg.test.values],1.0)
 
 
 
@@ -467,13 +470,16 @@ def test_aggregate_with_mismatched_grid():
 
 
 	# Get pixel overlaps
-	wm = get_pixel_overlaps(gdf,pix_agg)
+	wm_for = get_pixel_overlaps(gdf,pix_agg,impl='for_loop')
+	wm_dot = get_pixel_overlaps(gdf,pix_agg,impl='dot_product')
 
 	# Get aggregate
-	agg = aggregate(ds,wm)
+	agg_for = aggregate(ds,wm_for,impl='for_loop')
+	agg_dot = aggregate(ds,wm_dot,impl='dot_product')
 
 	# On change in rtol, see note in test_aggregate_basic
-	assert np.allclose([v for v in agg.agg.test.values],1.4999,rtol=1e-4)
+	assert np.allclose([v for v in agg_for.agg.test.values],1.4999,rtol=1e-4)
+	assert np.allclose([v for v in agg_dot.agg.test.values],1.4999,rtol=1e-4)
 
 
 # Should probably test multiple polygons just to be sure... 
@@ -501,16 +507,19 @@ def test_aggregate_with_all_nans():
 
 
 	# Get pixel overlaps
-	wm = get_pixel_overlaps(gdf,pix_agg)
+	wm_for = get_pixel_overlaps(gdf,pix_agg,impl='for_loop')
+	wm_dot = get_pixel_overlaps(gdf,pix_agg,impl='dot_product')
 
 	# Get aggregate
-	agg = aggregate(ds,wm)
+	agg_for = aggregate(ds,wm_for,impl='for_loop')
+	agg_dot = aggregate(ds,wm_dot,impl='dot_product')
 
 	# Should only return nan 
 	# (this is not a great assert - but agg.agg.test[0] comes out as [array(nan)], 
 	# which... I'm not entirely sure how to reproduce. It quaks like a single nan,
 	# but it's unclear to me how to get it to work)
-	assert np.all([np.isnan(k) for k in agg.agg.test])
+	assert np.all([np.isnan(k) for k in agg_for.agg.test])
+	assert np.all([np.isnan(k) for k in agg_dot.agg.test])
 
 def test_aggregate_with_some_nans():
 	ds = xr.Dataset({'test':(['lon','lat'],np.array([[np.nan,1],[2,np.nan]])),
@@ -530,13 +539,16 @@ def test_aggregate_with_some_nans():
 
 
 	# Get pixel overlaps
-	wm = get_pixel_overlaps(gdf,pix_agg)
+	wm_for = get_pixel_overlaps(gdf,pix_agg,impl='for_loop')
+	wm_dot = get_pixel_overlaps(gdf,pix_agg,impl='dot_product')
 
 	# Get aggregate
-	agg = aggregate(ds,wm)
+	agg_for = aggregate(ds,wm_for,impl='for_loop')
+	agg_dot = aggregate(ds,wm_dot,impl='dot_product')
 
 	# Should be 1.5; with one pixel valued 1, one pixel valued 2. 
-	assert np.allclose([agg.agg.test[0]],1.5,rtol=1e-4)
+	assert np.allclose([agg_for.agg.test[0]],1.5,rtol=1e-4)
+	assert np.allclose([agg_dot.agg.test[0]],1.5,rtol=1e-4)