issues

Fixes #360

Note: several tests for netCDF4 were previously defined twice, by accident.

Fixed a regression in v0.4 where saving to netCDF could fail with the error ValueError: could not automatically determine time units.

Fixes #276

Exmaple usage:

``` In [8]: ds = xray.Dataset({'x': range(3), 'y': ('x', list('abc'))})

In [9]: ds Out[9]: <xray.Dataset> Dimensions: (x: 3) Coordinates: * x (x) int64 0 1 2 Data variables: y (x) |S1 'a' 'b' 'c'

In [10]: ds.swap_dims({'x': 'y'}) Out[10]: <xray.Dataset> Dimensions: (y: 3) Coordinates: * y (y) |S1 'a' 'b' 'c' x (y) int64 0 1 2 Data variables: empty ```

This is a slightly more verbose API than strictly necessary, because the new dimension names must be along existing dimensions (e.g., we could spell this ds.set_dims(['y'])). But I still think it's a good idea, for two reasons: 1. It's more explicit. Users control know which dimensions are being swapped. 2. It opens up the possibility of specifying new dimensions with dictionary like syntax, e.g., ds.swap_dims('x': ('y', list('abc')))

CC @aykuznetsova

Previously conflicting _FillValue and missing_value only raised an AssertionError, now it's more informative.

Fixes #344

These warnings are typically spurious on xray objects.

Note that this does result in a small performance penalty for these functions (e.g., a few percent). This can be avoided by install bottleneck.

CC @jhammon

Fixes #341

xref #360

In this change: - Fix tests that relied on implicit conversion to int64 (Python's int on windows is int32). - Be more careful about always closing files, even in tests.

Not addressed (yet): - Issues with scipy.io.netcdf_file (#341)

Fixes #354

resample lets you resample a dataset or array along a time axis to a coarser resolution. The syntax is the same as pandas, except you need to supply the time dimension explicitly:

``` In [1]: time = pd.date_range('2000-01-01', freq='6H', periods=10)

In [2]: array = xray.DataArray(np.arange(10), [('time', time)])

In [3]: array.resample('1D', dim='time') Out[3]: <xray.DataArray (time: 3)> array([ 1.5, 5.5, 8.5]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ```

You can specify how to do the resampling with the how argument and other options such as closed and label let you control labeling:

In [4]: array.resample('1D', dim='time', how='sum', label='right') Out[4]: <xray.DataArray (time: 3)> array([ 6, 22, 17]) Coordinates: * time (time) datetime64[ns] 2000-01-02 2000-01-03 2000-01-04

first and last methods on groupby objects let you take the first or last examples from each group along the grouped axis:

In [5]: array.groupby('time.day').first() Out[5]: <xray.DataArray (day: 3)> array([0, 4, 8]) Coordinates: * day (day) int64 1 2 3