1.3.3

1.3.3

Worked through a backlog of bugs

xarray_to_cdf

• Fixed errors that come up with ISTP compliance when a support_data variable has no time variance and depends on another support_data variable
• Fully remove the deprecated flags of from_unixtime, from_datetime, unixtime_to_cdftt2000, datetime_to_cdftt2000, datetime64_to_cdftt2000
• Added a check to verify that LABLAXIS/LABL_PTR variables have the same dimensions as the data they need to label

cdfwrite

• Now throw an error when np.uint64 data is given, as it is not a supported data type
• Fixed a bug that wrote a record's length incorrectly for certain string inputs, causing corrupt CDF files to be generated

cdfread

• Fixed a bug where string data was given an extra dimension once read

cdf_to_xarray

• Fixed a bug that caused all data to be squeezed, removing dimensions of size 1

epochs

• Fixed a bug that occured in CDF_TT2000 to np.datetime64 conversion code, which caused all "minutes" to be dropped from the dates if the array contained any dates from before 1972 (which includes all FILLVALs)

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