Should resolve all remaining items in #183.

This makes the xray API a little more similar to pandas, which makes heavy use of name = None for objects that can but don't always have names like Series and Index.

It will be a particular useful option to have around when we add a direct constructor for DataArray objects (#115). For now, arrays will probably only end up being named None if they are the result of some mathematical operation where the name could be ambiguous.

Cleaned up a few cases where attributes was used instead of attrs in function signatures.

Fixes: #190 - [x] Switch names in xray itself - [x] Switch names in tests - [x] Switch names in documentation

If 'x' was a scalar variable in a dataset and you set a new variable with 'x' as a dimension, you could end up with a broken Dataset object.

This is why mutating state is a bad idea.

Now Dataset.getitem raises a KeyError when it can't find a variable.

This PR changes the DataArray representation so that it displays more of the information associated with a data array: - "Coordinates" are indicated by their name and the repr of the corresponding pandas.Index object (to indicate how they are used as indices). - "Linked" dataset variables are also listed. - These are other variables in the dataset associated with a DataArray which are also indexed along with the DataArray. - They accessible from the dataset attribute or by indexing the data array with a string. - Perhaps their most convenient aspect is that they enable groupby operations by name for DataArray objets. - This is an admitedly somewhat confusing (though convenient) notion that I am considering removing, but we if we don't remove them we should certainly expose their existence more clearly, given the potential benefits in expressiveness and costs in performance.

Questions to resolve: - Is "Linked dataset variables" the best name for these? - Perhaps it would be useful to show more information about these linked variables, such as their dimensions and/or shape?

Examples of the new repr are on nbviewer: http://nbviewer.ipython.org/gist/shoyer/94936e5b71613683d95a

Related: #164

Fixes #182

With this patch, it is possible to perform the following operations: - data_array.name = 'foo' - data_array.coordinates = ... - data_array.coordinates[0] = ... - data_array.coordinates['x'] = ... - dataset.coordinates['x'] = ... - dataset.rename(..., inplace=True)

It is no longer possible to set data_array.variable = ...., which was technically part of the public API but I would guess unused.

Fixes #140

With this PR, it is possible to close the data store from which a dataset was loaded via ds.close() or automatically when a dataset is used with a context manager:

python with xray.open_dataset('data.nc') as ds: ...

The ability to cleanly close files opened from disk is pretty essential -- we probably should have had this a while ago. It should not be necessary to use the low-level/unstable datastore API to get this functionality.

Implementation question: With this current implementation, calling ds.close() on (and using a context manager with) a dataset not linked to any file objects is a no-op. Should we raise an exception instead? Something like IOError('no file object to close')?

CC @ToddSmall

A first attempt at implementing Dataset reduction methods.

131

This change mainly involves an alternative import of OrderedDict, modified dict and set comprehensions, and using unittest2 for testing.

This should fix a bug @mgarvert encountered with concatenating variables with different array attributes.

In the process of fixing this issue, I encountered and fixed another bug with utils.remove_incompatible_items.

According to the toy benchmark below, this shaves off between 20% (diff-indexes) to 40% (same-indexes) of xray's overhead for array math:

import numpy as np import xray x = np.random.randn(1000, 1000) y = np.random.randn(1000, 1000) dx = xray.DataArray(x) dy = xray.DataArray(y) %timeit x + x # raw-numpy %timeit dx + dx # same-indexes %timeit dx + dy # diff-indexes

You can now do ds.groupby('time.month').mean() to apply the mean over all groups and variables in a dataset.

It is not optimized like the DataArray.groupby summary methods but it should work.

Thanks @jhamman for laying the groundwork for this!

Fixes #142 Fixes #148

All existing code should still work but issue a FutureWarning if any of the old names are used.

Full list of updates:

| Old | New | | --- | --- | | Coordinate | Index | | coordinates | indexes | | noncoordinates | nonindexes | | indexed | isel | | labeled | sel | | select | select_vars | | unselect | drop_vars |

Most of these are both Dataset and DataArray methods/properties.

Almost ready, except for failing tests on Python 3.

This recently added some complexity for a very nominal speed benefit. And it appears that it breaks joblib serialization, somehow (even though pickle works). So for now, revert it -- and consider filing a joblib bug if we can narrow it down.

Fixes #156, #157

To elaborate on the changes: 1. When an array is indexed, its encoding will be reset. This takes care of the invalid chunksize issue. More generally, this seems like the right choice because it's not clear that the right encoding will be the same after slicing an array, anyways. 2. If an array has encoding['dtype'] = np.dtype('S1') (e.g., it was originally encoded in characters), it will be stacked up to be saved as a character array, even if it's being saved to a NetCDF4 file. Previously, the array would be cast to 'S1' without stacking, which would result in silent loss of data.

Fixes #115.

Related: #116, #117.

Note: a remaining major task will be to rewrite/reorganize the docs to introduce DataArray first, entirely independently of Dataset. This will make it easier for new users to figure out how to get started with xray, since DataArray is much simpler.

This was causing an error in the following situation:

ds = xray.Dataset() ds['time'] = ('time', [np.datetime64('2001-05-01') for i in range(5)]) ds['variable'] = ('time', np.arange(5.)) ds.to_netcdf('test.nc') ds = xray.open_dataset('./test.nc') ss = ds.indexed(time=slice(0, 2)) ss.dumps()

Thanks @shoyer for the fix.