1.3.2

Adding anonymous s3 access to cdflib

1.3.1

General Updates

• Added tox to serve as the unit testing infrastructure, and changed which unit tests were run (see the github actions to see what exactly is running)
• Added tests to check future versions of numpy and astropy
• Added back in the remote data unit tests

xarray_to_cdf

• Added an ISTP check to determine is attribute and variable names are compliant

cdfwrite

• Clearer error message surrounding data type conversions to CDF data types

1.3.0

General Updates

• Added .devcontainer to support development of cdflib on github
• Renamed the master branch to "main"
• Added netcdf4 to the test dependencies
• unit tests no longer test to_unixtime and from_unixtime conversions. The loss in the decimal place that occurs from floating point arithmetic causes too many issues. It may be deprecated functionality in the future.

xarray_to_cdf

• In general, numpy types now have a 1-to-1 correspondence with CDF data types in xarray_to_cdf and cdf_to_xarray. See the documentation for more details
• Added an ISTP check in xarray_to_cdf to verify that epochs are monotonically increasing
• Added an ISTP check in xarray_to_cdf to determine if we need a LABL_PTR_1 or LABLAXIS
• Added ability to manually set the CDF data type of a variable in xarray_to_cdf
• Added checks in xarray_to_cdf to ensure all CDF_EPOCH16 variables can be cast to a complex128 data type
• Added automatic conversion of python datetime objects to CDF time variables in xarray_to_cdf, deprecating the from_datetime and datetime_to_cdftt2000 flags
• Added automatic conversion of numpy datetime64 arrays to CDF time variables in xarray_to_cdf, deprecating the datetime64_to_cdftt2000
• Automatically populates the FILLVAL attribute with the appropriate ISTP compliant fillvals
• Ignores variables attributes named "TIME_ATTRS" and "CDF_DATA_TYPE" from being written to the CDF. While these are used to modify the function's behavior, they will no longer show up in the CDF file.
• NaNs and NaTs will automatically be converted to the appropriate FILLVAL. To keep NaNs in the cdf file, use "nan_to_fillval=False"

cdf_to_xarray

• To help avoid some "lossy" conversion from CDF files, cdf_to_xarray will append 2 attributes to the variables in the object created. CDF_DATA_TYPE will hold the type of CDF data the variable was if it was not obvious. TIME_ATTRS contains a list of attributes that represeted time. These attributes enable better conversion of the xarray object back to a CDF file. These attributes are also automatically ignored when writing to the CDF file.
• fillval_to_nan will now automatically convert CDF time variables to datetime64('NaT')

1.2.6

Fixing a bug that occurs in cdf_to_xarray when DEPEND_0 has a length of 1

1.2.5

Allowing lists of numpy datetime64 objects to appear in variable attributes

1.2.4

Making it more explicit that attributes given a "None" value will be skipped.

1.2.3

Minor change to xarray_to_cdf:

• Convert FILLVAL to the same data type as the primary data when "istp=True" is specified

1.2.2

cdfwrite

• Added some support for casting numbers correctly if they have different inputs to the CDF. For example, if CDF_REAL8 is specified as the CDF type to use but the data is given as an integer, the data will be cast to a float64 before being written to the CDF.

1.2.1

• In the xarray_to_cdf function, added the datetime64_to_cdftt2000 to flag to convert np.datetime64 to cdftt2000
• in the xarray_to_cdf function, made a change to the datetime converter than was causing the data to be cast as a float, losing a little precision when using datetime64 ints
• Added unit tests for the above improvements, as well as the issue from 1.1.2 with arrays of numpy strings being read in as variable data

1.2.0

What's Changed

• Squeeze attribute data by @dstansby in #231

Full Changelog: 1.1.2...1.2.0