update benchmark results using latest numpy

pydata · Jan 10, 2019 · 825c116 · 825c116
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diff --git a/README.rst b/README.rst
@@ -31,42 +31,42 @@ Bottleneck comes with a benchmark suite::
 
     >>> bn.bench()
     Bottleneck performance benchmark
-        Bottleneck 1.3.0.dev0; Numpy 1.12.1
+        Bottleneck 1.3.0; Numpy 1.15.4
         Speed is NumPy time divided by Bottleneck time
         NaN means approx one-fifth NaNs; float64 used
 
                   no NaN     no NaN      NaN       no NaN      NaN
                    (100,)  (1000,1000)(1000,1000)(1000,1000)(1000,1000)
                    axis=0     axis=0     axis=0     axis=1     axis=1
-    nansum         67.3        0.3        0.7        2.5        2.4
-    nanmean       194.8        1.9        2.1        3.4        3.1
-    nanstd        241.5        1.6        2.1        2.7        2.6
-    nanvar        229.7        1.7        2.1        2.7        2.5
-    nanmin         34.1        0.7        1.1        0.8        2.6
-    nanmax         45.6        0.7        2.7        1.0        3.7
-    median        111.0        1.3        5.6        1.0        4.8
-    nanmedian     108.8        5.9        6.7        5.6        6.7
-    ss             16.3        1.1        1.2        1.6        1.6
-    nanargmin      89.2        2.9        5.1        2.2        5.6
-    nanargmax     107.4        3.0        5.4        2.2        5.8
-    anynan         19.4        0.3       35.0        0.5       29.9
-    allnan         39.9      146.6      128.3      115.8       75.6
-    rankdata       55.0        2.6        2.3        2.9        2.8
-    nanrankdata    59.8        2.8        2.2        3.2        2.5
-    partition       4.4        1.2        1.6        1.0        1.4
-    argpartition    3.5        1.1        1.4        1.1        1.6
-    replace        17.7        1.4        1.4        1.3        1.4
-    push         3440.0        7.8        9.5       20.0       15.5
-    move_sum     4743.0       75.7      156.1      195.4      211.1
-    move_mean    8760.9      116.2      167.4      252.1      258.8
-    move_std     8979.9       96.1      196.3      144.0      256.3
-    move_var    11216.8      127.3      243.9      225.9      321.4
-    move_min     2245.3       20.6       36.7       23.2       42.1
-    move_max     2223.7       20.5       37.2       24.1       42.4
-    move_argmin  3664.0       48.2       73.3       40.2       83.9
-    move_argmax  3916.9       42.0       75.4       41.5       81.2
-    move_median  2023.3      166.8      173.7      153.8      154.3
-    move_rank    1208.5        1.9        1.9        2.5        2.8
+    nansum         74.3        1.7        2.0        2.3        2.4
+    nanmean       223.8        2.3        2.4        3.3        2.9
+    nanstd        295.4        2.0        2.2        2.6        2.6
+    nanvar        273.5        1.9        2.1        2.6        2.5
+    nanmin         55.7        0.7        2.1        1.0        3.3
+    nanmax         49.7        0.7        1.9        0.8        2.6
+    median        160.4        1.3        6.1        1.1        6.2
+    nanmedian     164.5        7.5        8.6        7.4        8.6
+    ss             29.7        1.1        1.1        1.6        1.6
+    nanargmin      87.9        3.0        5.3        2.3        5.9
+    nanargmax      94.1        3.2        5.4        2.3        5.9
+    anynan         21.0        0.3       40.8        0.5       33.7
+    allnan         37.5      142.9      121.3      114.5       97.4
+    rankdata       68.3        2.4        2.4        2.8        2.8
+    nanrankdata    70.6        2.6        2.5        3.1        3.0
+    partition       4.0        1.2        1.6        1.0        1.4
+    argpartition    3.6        1.1        1.4        1.1        1.6
+    replace        14.1        1.6        1.6        1.6        1.6
+    push         2947.5        8.4        9.2       20.6       15.7
+    move_sum     4561.5       75.7      153.6      189.1      203.5
+    move_mean   11003.3      116.2      184.7      224.1      245.9
+    move_std    10748.8       95.7      196.4      135.0      249.4
+    move_var    13999.7      126.6      240.1      198.8      316.4
+    move_min     2259.7       20.1       38.6       25.0       43.8
+    move_max     2189.3       20.4       38.9       25.2       43.9
+    move_argmin  4389.4       41.7       76.3       42.0       85.1
+    move_argmax  4464.5       42.5       76.0       42.4       83.6
+    move_median  2969.5      163.4      155.6      154.2      155.0
+    move_rank    1395.6        1.9        2.0        2.4        2.5
 
 You can also run a detailed benchmark for a single function using, for
 example, the command::
@@ -129,4 +129,4 @@ After you have installed Bottleneck, run the suite of unit tests::
     <snip>
     Ran 169 tests in 57.205s
     OK
-    <nose.result.TextTestResult run=169 errors=0 failures=0>
+    <nose.result.TextTestResult run=169 errors=0 failures=0>
diff --git a/doc/source/conf.py b/doc/source/conf.py
@@ -39,7 +39,7 @@
 
 # General information about the project.
 project = u'Bottleneck'
-copyright = u'2010-2017 Keith Goodman'
+copyright = u'2010-2019 Keith Goodman'
 
 # Grab version from bottleneck/version.py
 ver_file = os.path.join('..', '..', 'bottleneck', 'version.py')