ENH: Speed improvements and deprecations for np.select

The idea for this (and some of the code) originally comes from
Graeme B Bell (gh-3537).
Choose is not as fast and pretty limited, so an iterative
copyto is used instead.

Closes gh-3259, gh-3537, gh-3551, and gh-3254

1 files changed, 57 insertions, 6 deletions

diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 3e102cf6a..145a7be4d 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -150,6 +150,13 @@ class TestAverage(TestCase):
 
 
 class TestSelect(TestCase):
+    choices = [np.array([1, 2, 3]),
+               np.array([4, 5, 6]),
+               np.array([7, 8, 9])]
+    conditions = [np.array([False, False, False]),
+                  np.array([False, True, False]),
+                  np.array([False, False, True])]
+
     def _select(self, cond, values, default=0):
         output = []
         for m in range(len(cond)):
@@ -157,18 +164,62 @@ class TestSelect(TestCase):
         return output
 
     def test_basic(self):
-        choices = [np.array([1, 2, 3]),
-                   np.array([4, 5, 6]),
-                   np.array([7, 8, 9])]
-        conditions = [np.array([0, 0, 0]),
-                      np.array([0, 1, 0]),
-                      np.array([0, 0, 1])]
+        choices = self.choices
+        conditions = self.conditions
         assert_array_equal(select(conditions, choices, default=15),
                            self._select(conditions, choices, default=15))
 
         assert_equal(len(choices), 3)
         assert_equal(len(conditions), 3)
 
+    def test_broadcasting(self):
+        conditions = [np.array(True), np.array([False, True, False])]
+        choices = [1, np.arange(12).reshape(4, 3)]
+        assert_array_equal(select(conditions, choices), np.ones((4, 3)))
+        # default can broadcast too:
+        assert_equal(select([True], [0], default=[0]).shape, (1,))
+
+    def test_return_dtype(self):
+        assert_equal(select(self.conditions, self.choices, 1j).dtype,
+                     np.complex_)
+        # But the conditions need to be stronger then the scalar default
+        # if it is scalar.
+        choices = [choice.astype(np.int8) for choice in self.choices]
+        assert_equal(select(self.conditions, choices).dtype, np.int8)
+
+        d = np.array([1, 2, 3, np.nan, 5, 7])
+        m = np.isnan(d)
+        assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0])
+
+    def test_deprecated_empty(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter("always")
+            assert_equal(select([], [], 3j), 3j)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, select, [], [])
+            warnings.simplefilter("error")
+            assert_raises(DeprecationWarning, select, [], [])
+
+    def test_non_bool_deprecation(self):
+        choices = self.choices
+        conditions = self.conditions[:]
+        with warnings.catch_warnings():
+            warnings.filterwarnings("always")
+            conditions[0] = conditions[0].astype(np.int_)
+            assert_warns(DeprecationWarning, select, conditions, choices)
+            conditions[0] = conditions[0].astype(np.uint8)
+            assert_warns(DeprecationWarning, select, conditions, choices)
+            warnings.filterwarnings("error")
+            assert_raises(DeprecationWarning, select, conditions, choices)
+
+    def test_many_arguments(self):
+        # This used to be limited by NPY_MAXARGS == 32
+        conditions = [np.array([False])] * 100
+        choices = [np.array([1])] * 100
+        select(conditions, choices)
+
 
 class TestInsert(TestCase):
     def test_basic(self):