Indexing benchmarking (pydata#1851)

* Start adding benchmarking for basic operations.

* Update indexing.py

* Benchmark for indexing.

* Make it a class.

* Add key to setup

* Use seed in randn and randint. Fix bug in dask benchmark

.gitignore

@@ -34,6 +34,9 @@ nosetests.xml
 .cache
 .ropeproject/
 
+# asv environments
+.asv
+
 # Translations
 *.mo
 

asv_bench/benchmarks/__init__.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 
-np.random.seed(10)
 _counter = itertools.count()
 
 
@@ -17,15 +16,27 @@ def requires_dask():
         raise NotImplementedError
 
 
-def randn(shape, frac_nan=None, chunks=None):
+def randn(shape, frac_nan=None, chunks=None, seed=0):
+    rng = np.random.RandomState(seed)
     if chunks is None:
-        x = np.random.standard_normal(shape)
+        x = rng.standard_normal(shape)
     else:
         import dask.array as da
-        x = da.random.standard_normal(shape, chunks=chunks)
+        rng = da.random.RandomState(seed)
+        x = rng.standard_normal(shape, chunks=chunks)
 
     if frac_nan is not None:
-        inds = random.sample(range(x.size), int(x.size * frac_nan))
+        inds = rng.choice(range(x.size), int(x.size * frac_nan))
         x.flat[inds] = np.nan
 
     return x
+
+
+def randint(low, high=None, size=None, frac_minus=None, seed=0):
+    rng = np.random.RandomState(seed)
+    x = rng.randint(low, high, size)
+    if frac_minus is not None:
+        inds = rng.choice(range(x.size), int(x.size * frac_minus))
+        x.flat[inds] = -1
+
+    return x

asv_bench/benchmarks/indexing.py

@@ -0,0 +1,128 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from . import randn, randint, requires_dask
+
+
+nx = 3000
+ny = 2000
+nt = 1000
+
+basic_indexes = {
+    '1slice': {'x': slice(0, 3)},
+    '1slice-1scalar': {'x': 0, 'y': slice(None, None, 3)},
+    '2slicess-1scalar': {'x': slice(3, -3, 3), 'y': 1, 't': slice(None, -3, 3)}
+}
+
+basic_assignment_values = {
+    '1slice': xr.DataArray(randn((3, ny), frac_nan=0.1), dims=['x', 'y']),
+    '1slice-1scalar': xr.DataArray(randn(int(ny / 3) + 1, frac_nan=0.1),
+                                   dims=['y']),
+    '2slicess-1scalar': xr.DataArray(randn(int((nx - 6) / 3), frac_nan=0.1),
+                                     dims=['x'])
+}
+
+outer_indexes = {
+    '1d': {'x': randint(0, nx, 400)},
+    '2d':  {'x': randint(0, nx, 500), 'y': randint(0, ny, 400)},
+    '2d-1scalar': {'x': randint(0, nx, 100), 'y': 1, 't': randint(0, nt, 400)}
+}
+
+outer_assignment_values = {
+    '1d': xr.DataArray(randn((400, ny), frac_nan=0.1), dims=['x', 'y']),
+    '2d': xr.DataArray(randn((500, 400), frac_nan=0.1), dims=['x', 'y']),
+    '2d-1scalar': xr.DataArray(randn(100, frac_nan=0.1), dims=['x'])
+}
+
+vectorized_indexes = {
+    '1-1d': {'x': xr.DataArray(randint(0, nx, 400), dims='a')},
+    '2-1d': {'x': xr.DataArray(randint(0, nx, 400), dims='a'),
+             'y': xr.DataArray(randint(0, ny, 400), dims='a')},
+    '3-2d': {'x': xr.DataArray(randint(0, nx, 400).reshape(4, 100),
+                               dims=['a', 'b']),
+             'y': xr.DataArray(randint(0, ny, 400).reshape(4, 100),
+                               dims=['a', 'b']),
+             't': xr.DataArray(randint(0, nt, 400).reshape(4, 100),
+                               dims=['a', 'b'])},
+}
+
+vectorized_assignment_values = {
+    '1-1d': xr.DataArray(randn((400, 2000)), dims=['a', 'y'],
+                         coords={'a': randn(400)}),
+    '2-1d': xr.DataArray(randn(400), dims=['a', ], coords={'a': randn(400)}),
+    '3-2d': xr.DataArray(randn((4, 100)), dims=['a', 'b'],
+                         coords={'a': randn(4), 'b': randn(100)})
+}
+
+
+class Base(object):
+    def setup(self, key):
+        self.ds = xr.Dataset(
+            {'var1': (('x', 'y'), randn((nx, ny), frac_nan=0.1)),
+             'var2': (('x', 't'), randn((nx, nt))),
+             'var3': (('t', ), randn(nt))},
+            coords={'x': np.arange(nx),
+                    'y': np.linspace(0, 1, ny),
+                    't': pd.date_range('1970-01-01', periods=nt, freq='D'),
+                    'x_coords': ('x', np.linspace(1.1, 2.1, nx))})
+
+
+class Indexing(Base):
+    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())]
+
+    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())]
+
+    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):
+    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())]
+
+    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())]
+
+    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):
+        requires_dask()
+        super(IndexingDask, self).setup(key)
+        self.ds = self.ds.chunk({'x': 100, 'y': 50, 't': 50})