test_faddeeva_w

tests/beignet/special/test__erfc.py

@@ -59,7 +59,7 @@ def _strategy(function):
 
 
 @hypothesis.given(_strategy())
-def test_erf(data):
+def test_erfc(data):
     input, output = data
 
     torch.testing.assert_close(beignet.special.erfc(input), output)

tests/beignet/special/test__faddeeva_w.py

@@ -0,0 +1,65 @@
+import beignet.special
+import hypothesis
+import hypothesis.strategies
+import scipy
+import torch
+
+
+@hypothesis.strategies.composite
+def _strategy(function):
+    x, y = torch.meshgrid(
+        torch.linspace(
+            function(
+                hypothesis.strategies.floats(
+                    min_value=-10,
+                    max_value=-10,
+                ),
+            ),
+            function(
+                hypothesis.strategies.floats(
+                    min_value=10,
+                    max_value=10,
+                ),
+            ),
+            steps=function(
+                hypothesis.strategies.integers(
+                    min_value=128,
+                    max_value=512,
+                ),
+            ),
+            dtype=torch.float64,
+        ),
+        torch.linspace(
+            function(
+                hypothesis.strategies.floats(
+                    min_value=-10,
+                    max_value=-10,
+                ),
+            ),
+            function(
+                hypothesis.strategies.floats(
+                    min_value=10,
+                    max_value=10,
+                ),
+            ),
+            function(
+                hypothesis.strategies.integers(
+                    min_value=128,
+                    max_value=512,
+                ),
+            ),
+            dtype=torch.float64,
+        ),
+        indexing="xy",
+    )
+
+    input = x + 1.0j * y
+
+    return input, scipy.special.wofz(input)
+
+
+@hypothesis.given(_strategy())
+def test_faddeeva_w(data):
+    input, output = data
+
+    torch.testing.assert_close(beignet.special.faddeeva_w(input), output)