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)¶