Classize benchmarks/indexing.py

Author: fujiisoup

asv_bench/benchmarks/__init__.py

@@ -2,7 +2,6 @@
 from __future__ import division
 from __future__ import print_function
 import itertools
-import random
 
 import numpy as np
 

asv_bench/benchmarks/indexing.py

@@ -6,7 +6,7 @@
 import pandas as pd
 import xarray as xr
 
-from . import randn, randint, requires_dask
+from . import parameterized, randn, randint, requires_dask
 
 
 nx = 3000
@@ -61,7 +61,7 @@
 
 
 class Base(object):
-    def setup(self, key):
+    def setup(self, *args, **kwargs):
         self.ds = xr.Dataset(
             {'var1': (('x', 'y'), randn((nx, ny), frac_nan=0.1)),
              'var2': (('x', 't'), randn((nx, nt))),
@@ -73,53 +73,41 @@ def setup(self, key):
 
 
 class Indexing(Base):
+    @parameterized(['key'], (list(basic_indexes.keys())))
     def time_indexing_basic(self, key):
         self.ds.isel(**basic_indexes[key]).load()
 
-    time_indexing_basic.param_names = ['key']
-    time_indexing_basic.params = [list(basic_indexes.keys())]
-
+    @parameterized(['key'], (list(outer_indexes.keys())))
     def time_indexing_outer(self, key):
         self.ds.isel(**outer_indexes[key]).load()
 
-    time_indexing_outer.param_names = ['key']
-    time_indexing_outer.params = [list(outer_indexes.keys())]
-
+    @parameterized(['key'], (list(vectorized_indexes.keys())))
     def time_indexing_vectorized(self, key):
         self.ds.isel(**vectorized_indexes[key]).load()
 
-    time_indexing_vectorized.param_names = ['key']
-    time_indexing_vectorized.params = [list(vectorized_indexes.keys())]
-
 
 class Assignment(Base):
+    @parameterized(['key'], (list(basic_indexes.keys())))
     def time_assignment_basic(self, key):
         ind = basic_indexes[key]
         val = basic_assignment_values[key]
         self.ds['var1'][ind.get('x', slice(None)),
                         ind.get('y', slice(None))] = val
 
-    time_assignment_basic.param_names = ['key']
-    time_assignment_basic.params = [list(basic_indexes.keys())]
-
+    @parameterized(['key'], (list(outer_indexes.keys())))
     def time_assignment_outer(self, key):
         ind = outer_indexes[key]
         val = outer_assignment_values[key]
         self.ds['var1'][ind.get('x', slice(None)),
                         ind.get('y', slice(None))] = val
 
-    time_assignment_outer.param_names = ['key']
-    time_assignment_outer.params = [list(outer_indexes.keys())]
-
+    @parameterized(['key'], (list(vectorized_indexes.keys())))
     def time_assignment_vectorized(self, key):
         ind = vectorized_indexes[key]
         val = vectorized_assignment_values[key]
         self.ds['var1'][ind.get('x', slice(None)),
                         ind.get('y', slice(None))] = val
 
-    time_assignment_vectorized.param_names = ['key']
-    time_assignment_vectorized.params = [list(vectorized_indexes.keys())]
-
 
 class IndexingDask(Indexing):
     def setup(self, key):