BENCH: add correlate/convolve benchmarks.

Related to numpygh-5978, which makes significant changes to these functions.

Author: rgommers

benchmarks/benchmarks/bench_core.py

@@ -2,78 +2,78 @@
 
 from .common import Benchmark
 
-import numpy
+import numpy as np
 
 
 class Core(Benchmark):
     def setup(self):
         self.l100 = range(100)
         self.l50 = range(50)
-        self.l = [numpy.arange(1000), numpy.arange(1000)]
-        self.l10x10 = numpy.ones((10, 10))
+        self.l = [np.arange(1000), np.arange(1000)]
+        self.l10x10 = np.ones((10, 10))
 
     def time_array_1(self):
-        numpy.array(1)
+        np.array(1)
 
     def time_array_empty(self):
-        numpy.array([])
+        np.array([])
 
     def time_array_l1(self):
-        numpy.array([1])
+        np.array([1])
 
     def time_array_l100(self):
-        numpy.array(self.l100)
+        np.array(self.l100)
 
     def time_array_l(self):
-        numpy.array(self.l)
+        np.array(self.l)
 
     def time_vstack_l(self):
-        numpy.vstack(self.l)
+        np.vstack(self.l)
 
     def time_hstack_l(self):
-        numpy.hstack(self.l)
+        np.hstack(self.l)
 
     def time_dstack_l(self):
-        numpy.dstack(self.l)
+        np.dstack(self.l)
 
     def time_arange_100(self):
-        numpy.arange(100)
+        np.arange(100)
 
     def time_zeros_100(self):
-        numpy.zeros(100)
+        np.zeros(100)
 
     def time_ones_100(self):
-        numpy.ones(100)
+        np.ones(100)
 
     def time_empty_100(self):
-        numpy.empty(100)
+        np.empty(100)
 
     def time_eye_100(self):
-        numpy.eye(100)
+        np.eye(100)
 
     def time_identity_100(self):
-        numpy.identity(100)
+        np.identity(100)
 
     def time_eye_3000(self):
-        numpy.eye(3000)
+        np.eye(3000)
 
     def time_identity_3000(self):
-        numpy.identity(3000)
+        np.identity(3000)
 
     def time_diag_l100(self):
-        numpy.diag(self.l100)
+        np.diag(self.l100)
 
     def time_diagflat_l100(self):
-        numpy.diagflat(self.l100)
+        np.diagflat(self.l100)
 
     def time_diagflat_l50_l50(self):
-        numpy.diagflat([self.l50, self.l50])
+        np.diagflat([self.l50, self.l50])
 
     def time_triu_l10x10(self):
-        numpy.triu(self.l10x10)
+        np.triu(self.l10x10)
 
     def time_tril_l10x10(self):
-        numpy.tril(self.l10x10)
+        np.tril(self.l10x10)
 
 
 class MA(Benchmark):
@@ -82,10 +82,27 @@ def setup(self):
         self.t100 = ([True] * 100)
 
     def time_masked_array(self):
-        numpy.ma.masked_array()
+        np.ma.masked_array()
 
     def time_masked_array_l100(self):
-        numpy.ma.masked_array(self.l100)
+        np.ma.masked_array(self.l100)
 
     def time_masked_array_l100_t100(self):
-        numpy.ma.masked_array(self.l100, self.t100)
+        np.ma.masked_array(self.l100, self.t100)
+
+
+class CorrConv(Benchmark):
+    params = [[50, 1000, 1e5],
+              [10, 100, 1000, 1e4],
+              ['valid', 'same', 'full']]
+    param_names = ['size1', 'size2', 'mode']
+
+    def setup(self, size1, size2, mode):
+        self.x1 = np.linspace(0, 1, num=size1)
+        self.x2 = np.cos(np.linspace(0, 2*np.pi, num=size2))
+
+    def time_correlate(self, size1, size2, mode):
+        np.correlate(self.x1, self.x2, mode=mode)
+
+    def time_convolve(self, size1, size2, mode):
+        np.convolve(self.x1, self.x2, mode=mode)