BENCH: Add benchmark for small array coercions

Note that since the benchmarks are so fast, the actual results
are diluted behind one more python function call.

1 files changed, 57 insertions, 0 deletions

diff --git a/benchmarks/benchmarks/bench_array_coercion.py b/benchmarks/benchmarks/bench_array_coercion.py
new file mode 100644
index 000000000..2bae4c002
--- /dev/null
+++ b/benchmarks/benchmarks/bench_array_coercion.py
@@ -0,0 +1,57 @@
+from __future__ import absolute_import, division, print_function
+
+from .common import Benchmark
+
+import numpy as np
+
+
+class ArrayCoercionSmall(Benchmark):
+    # More detailed benchmarks for array coercion,
+    # some basic benchmarks are in `bench_core.py`.
+    params = [[range(3), [1], 1, np.array([5], dtype=np.int64), np.int64(5)]]
+    param_names = ['array_like']
+    int64 = np.dtype(np.int64)
+
+    def time_array_invalid_kwarg(self, array_like):
+        try:
+            np.array(array_like, ndmin="not-integer")
+        except TypeError:
+            pass
+
+    def time_array(self, array_like):
+        np.array(array_like)
+
+    def time_array_dtype_not_kwargs(self, array_like):
+        np.array(array_like, self.int64)
+
+    def time_array_no_copy(self, array_like):
+        np.array(array_like, copy=False)
+
+    def time_array_subok(self, array_like):
+        np.array(array_like, subok=True)
+
+    def time_array_all_kwargs(self, array_like):
+        np.array(array_like, dtype=self.int64, copy=False, order="F",
+                 subok=False, ndmin=2)
+
+    def time_asarray(self, array_like):
+        np.asarray(array_like)
+
+    def time_asarray_dtype(self, array_like):
+        np.array(array_like, dtype=self.int64)
+
+    def time_asarray_dtype(self, array_like):
+        np.array(array_like, dtype=self.int64, order="F")
+
+    def time_asanyarray(self, array_like):
+        np.asarray(array_like)
+
+    def time_asanyarray_dtype(self, array_like):
+        np.array(array_like, dtype=self.int64)
+
+    def time_asanyarray_dtype(self, array_like):
+        np.array(array_like, dtype=self.int64, order="F")
+
+    def time_ascontiguousarray(self, array_like):
+        np.ascontiguousarray(array_like)
+