Merge remote-tracking branch 'upstream/master'

Author: yohai

.gitattributes

@@ -1,2 +1,3 @@
 # reduce the number of merge conflicts
 doc/whats-new.rst merge=union
+xarray/_version.py export-subst

.gitignore

@@ -38,7 +38,9 @@ nosetests.xml
 .ropeproject/
 .tags*
 .testmon*
+.tmontmp/
 .pytest_cache
+dask-worker-space/
 
 # asv environments
 .asv

.travis.yml

@@ -101,7 +101,9 @@ install:
   - python xarray/util/print_versions.py
 
 script:
-  - python -OO -c "import xarray"
+  # TODO: restore this check once the upstream pandas issue is fixed:
+  # https://github.com/pandas-dev/pandas/issues/21071 
+  # - python -OO -c "import xarray"
   - if [[ "$CONDA_ENV" == "docs" ]]; then
       conda install -c conda-forge sphinx sphinx_rtd_theme sphinx-gallery numpydoc;
       sphinx-build -n -j auto -b html -d _build/doctrees doc _build/html;

HOW_TO_RELEASE

@@ -7,21 +7,20 @@ Time required: about an hour.
  2. Look over whats-new.rst and the docs. Make sure "What's New" is complete
     (check the date!) and add a brief summary note describing the release at the
     top.
- 3. Update the version in setup.py and switch to `ISRELEASED = True`.
- 4. If you have any doubts, run the full test suite one final time!
+ 3. If you have any doubts, run the full test suite one final time!
       py.test
- 5. On the master branch, commit the release in git:
+ 4. On the master branch, commit the release in git:
       git commit -a -m 'Release v0.X.Y'
- 6. Tag the release:
+ 5. Tag the release:
       git tag -a v0.X.Y -m 'v0.X.Y'
- 7. Build source and binary wheels for pypi:
+ 6. Build source and binary wheels for pypi:
       python setup.py bdist_wheel sdist
- 8. Use twine to register and upload the release on pypi. Be careful, you can't
+ 7. Use twine to register and upload the release on pypi. Be careful, you can't
     take this back!
       twine upload dist/xarray-0.X.Y*
     You will need to be listed as a package owner at
     https://pypi.python.org/pypi/xarray for this to work.
- 9. Push your changes to master:
+ 8. Push your changes to master:
       git push upstream master
       git push upstream --tags
  9. Update the stable branch (used by ReadTheDocs) and switch back to master:
@@ -32,25 +31,22 @@ Time required: about an hour.
     It's OK to force push to 'stable' if necessary.
     We also update the stable branch with `git cherrypick` for documentation
     only fixes that apply the current released version.
-10. Revert ISRELEASED in setup.py back to False. Don't change the version
-    number: in normal development, we keep the version number in setup.py as the
-    last released version.
-11. Add a section for the next release (v.X.(Y+1)) to doc/whats-new.rst.
-12. Commit your changes and push to master again:
+10. Add a section for the next release (v.X.(Y+1)) to doc/whats-new.rst.
+11. Commit your changes and push to master again:
       git commit -a -m 'Revert to dev version'
       git push upstream master
     You're done pushing to master!
-13. Issue the release on GitHub. Click on "Draft a new release" at
+12. Issue the release on GitHub. Click on "Draft a new release" at
     https://github.com/pydata/xarray/releases and paste in the latest from
     whats-new.rst.
-14. Update the docs. Login to https://readthedocs.org/projects/xray/versions/
+13. Update the docs. Login to https://readthedocs.org/projects/xray/versions/
     and switch your new release tag (at the bottom) from "Inactive" to "Active".
     It should now build automatically.
-15. Update conda-forge. Clone https://github.com/conda-forge/xarray-feedstock
+14. Update conda-forge. Clone https://github.com/conda-forge/xarray-feedstock
     and update the version number and sha256 in meta.yaml. (On OS X, you can
     calculate sha256 with `shasum -a 256 xarray-0.X.Y.tar.gz`). Submit a pull
     request (and merge it, once CI passes).
-16. Issue the release announcement! For bug fix releases, I usually only email
+15. Issue the release announcement! For bug fix releases, I usually only email
     xarray@googlegroups.com. For major/feature releases, I will email a broader
     list (no more than once every 3-6 months):
       pydata@googlegroups.com, xarray@googlegroups.com,

MANIFEST.in

@@ -4,3 +4,5 @@ recursive-include doc *
 prune doc/_build
 prune doc/generated
 global-exclude .DS_Store
+include versioneer.py
+include xarray/_version.py

asv_bench/benchmarks/interp.py

@@ -0,0 +1,54 @@
+from __future__ import absolute_import, division, print_function
+
+import numpy as np
+import pandas as pd
+
+import xarray as xr
+
+from . import parameterized, randn, requires_dask
+
+nx = 3000
+long_nx = 30000000
+ny = 2000
+nt = 1000
+window = 20
+
+randn_xy = randn((nx, ny), frac_nan=0.1)
+randn_xt = randn((nx, nt))
+randn_t = randn((nt, ))
+randn_long = randn((long_nx, ), frac_nan=0.1)
+
+
+new_x_short = np.linspace(0.3 * nx, 0.7 * nx, 100)
+new_x_long = np.linspace(0.3 * nx, 0.7 * nx, 1000)
+new_y_long = np.linspace(0.1, 0.9, 1000)
+
+
+class Interpolation(object):
+    def setup(self, *args, **kwargs):
+        self.ds = xr.Dataset(
+            {'var1': (('x', 'y'), randn_xy),
+             'var2': (('x', 't'), randn_xt),
+             'var3': (('t', ), randn_t)},
+            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))})
+
+    @parameterized(['method', 'is_short'],
+                   (['linear', 'cubic'], [True, False]))
+    def time_interpolation(self, method, is_short):
+        new_x = new_x_short if is_short else new_x_long
+        self.ds.interp(x=new_x, method=method).load()
+
+    @parameterized(['method'],
+                   (['linear', 'nearest']))
+    def time_interpolation_2d(self, method):
+        self.ds.interp(x=new_x_long, y=new_y_long, method=method).load()
+
+
+class InterpolationDask(Interpolation):
+    def setup(self, *args, **kwargs):
+        requires_dask()
+        super(InterpolationDask, self).setup(**kwargs)
+        self.ds = self.ds.chunk({'t': 50})

asv_bench/benchmarks/rolling.py

@@ -35,7 +35,7 @@ def setup(self, *args, **kwargs):
     @parameterized(['func', 'center'],
                    (['mean', 'count'], [True, False]))
     def time_rolling(self, func, center):
-        getattr(self.ds.rolling(x=window, center=center), func)()
+        getattr(self.ds.rolling(x=window, center=center), func)().load()
 
     @parameterized(['func', 'pandas'],
                    (['mean', 'count'], [True, False]))
@@ -44,19 +44,20 @@ def time_rolling_long(self, func, pandas):
             se = self.da_long.to_series()
             getattr(se.rolling(window=window), func)()
         else:
-            getattr(self.da_long.rolling(x=window), func)()
+            getattr(self.da_long.rolling(x=window), func)().load()
 
     @parameterized(['window_', 'min_periods'],
                    ([20, 40], [5, None]))
     def time_rolling_np(self, window_, min_periods):
         self.ds.rolling(x=window_, center=False,
-                        min_periods=min_periods).reduce(getattr(np, 'nanmean'))
+                        min_periods=min_periods).reduce(
+                            getattr(np, 'nanmean')).load()
 
     @parameterized(['center', 'stride'],
                    ([True, False], [1, 200]))
     def time_rolling_construct(self, center, stride):
         self.ds.rolling(x=window, center=center).construct(
-            'window_dim', stride=stride).mean(dim='window_dim')
+            'window_dim', stride=stride).mean(dim='window_dim').load()
 
 
 class RollingDask(Rolling):

ci/requirements-py27-windows.yml

@@ -8,7 +8,6 @@ dependencies:
   - h5py
   - h5netcdf
   - matplotlib
-  - netcdf4
   - pathlib2
   - pytest
   - flake8
@@ -21,4 +20,4 @@ dependencies:
   - rasterio
   - zarr
   - pip:
-    - cftime
+    - netcdf4

ci/requirements-py36.yml

@@ -20,6 +20,7 @@ dependencies:
   - rasterio
   - bottleneck
   - zarr
+  - pseudonetcdf>=3.0.1
   - pip:
     - coveralls
     - pytest-cov