v0.3.1 (22 October, 2014)¶
This is mostly a bug-fix release to make xray compatible with the latest release of pandas (v0.15).
We added several features to better support working with missing values and exporting xray objects to pandas. We also reorganized the internal API for serializing and deserializing datasets, but this change should be almost entirely transparent to users.
Other than breaking the experimental DataStore API, there should be no backwards incompatible changes.
- Added count() and dropna() methods, copied from pandas, for working with missing values (GH247, GH58).
- Added DataArray.to_pandas for converting a data array into the pandas object with the same dimensionality (1D to Series, 2D to DataFrame, etc.) (GH255).
- Support for reading gzipped netCDF3 files (GH239).
- Reduced memory usage when writing netCDF files (GH251).
- ‘missing_value’ is now supported as an alias for the ‘_FillValue’ attribute on netCDF variables (GH245).
- Trivial indexes, equivalent to range(n) where n is the length of the dimension, are no longer written to disk (GH245).
- Compatibility fixes for pandas v0.15 (GH262).
- Fixes for display and indexing of NaT (not-a-time) (GH238, GH240)
- Fix slicing by label was an argument is a data array (GH250).
- Test data is now shipped with the source distribution (GH253).
- Ensure order does not matter when doing arithemtic with scalar data arrays (GH254).
- Order of dimensions preserved with DataArray.to_dataframe (GH260).
v0.3.0 (21 September 2014)¶
- Revamped coordinates: “coordinates” now refer to all arrays that are not used to index a dimension. Coordinates are intended to allow for keeping track of arrays of metadata that describe the grid on which the points in “variable” arrays lie. They are preserved (when unambiguous) even though mathematical operations.
- Dataset math Dataset objects now support all arithmetic operations directly. Dataset-array operations map across all dataset variables; dataset-dataset operations act on each pair of variables with the same name.
- GroupBy math: This provides a convenient shortcut for normalizing by the average value of a group.
- The dataset __repr__ method has been entirely overhauled; dataset objects now show their values when printed.
- You can now index a dataset with a list of variables to return a new dataset: ds[['foo', 'bar']].
Backwards incompatible changes¶
- Dataset.__eq__ and Dataset.__ne__ are now element-wise operations instead of comparing all values to obtain a single boolean. Use the method equals() instead.
v0.2.0 (14 August 2014)¶
This is major release that includes some new features and quite a few bug fixes. Here are the highlights:
- There is now a direct constructor for DataArray objects, which makes it possible to create a DataArray without using a Dataset. This is highlighted in the refreshed tutorial.
- You can perform aggregation operations like mean directly on Dataset objects, thanks to Joe Hamman. These aggregation methods also worked on grouped datasets.
- xray now works on Python 2.6, thanks to Anna Kuznetsova.
- A number of methods and attributes were given more sensible (usually shorter) names: labeled -> sel, indexed -> isel, select -> select_vars, unselect -> drop_vars, dimensions -> dims, coordinates -> coords, attributes -> attrs.
- New load_data() and close() methods for datasets facilitate lower level of control of data loaded from disk.
v0.1.1 (20 May 2014)¶
xray 0.1.1 is a bug-fix release that includes changes that should be almost entirely backwards compatible with v0.1:
- Python 3 support (GH53)
- Required numpy version relaxed to 1.7 (GH129)
- Return numpy.datetime64 arrays for non-standard calendars (GH126)
- Support for opening datasets associated with NetCDF4 groups (GH127)
- Bug-fixes for concatenating datetime arrays (GH134)
Special thanks to new contributors Thomas Kluyver, Joe Hamman and Alistair Miles.
v0.1 (2 May 2014)¶