ASV: asvs for normalize functions

asv_bench/benchmarks/tslibs/normalize.py

@@ -0,0 +1,32 @@
+try:
+    from pandas._libs.tslibs import normalize_i8_timestamps, is_date_array_normalized
+except ImportError:
+    from pandas._libs.tslibs.conversion import (
+        normalize_i8_timestamps,
+        is_date_array_normalized,
+    )
+
+import pandas as pd
+
+from .tslib import _sizes, _tzs
+
+
+class Normalize:
+    params = [
+        _sizes,
+        _tzs,
+    ]
+    param_names = ["size", "tz"]
+
+    def setup(self, size, tz):
+        # use an array that will have is_date_array_normalized give True,
+        #  so we do not short-circuit early.
+        dti = pd.date_range("2016-01-01", periods=10, tz=tz).repeat(size // 10)
+        self.i8data = dti.asi8
+
+    def time_normalize_i8_timestamps(self, size, tz):
+        normalize_i8_timestamps(self.i8data, tz)
+
+    def time_is_date_array_normalized(self, size, tz):
+        # TODO: cases with different levels of short-circuiting
+        is_date_array_normalized(self.i8data, tz)