This page lists recent changes in the wfdb package (since version 4.0.0) that may be relevant if you are upgrading from an older version of the package. For the complete history of changes in the package, please refer to the development repository on GitHub.
- Bump Sphinx to 7.0.0
- Bump Sphinx to 7.0.0
- Integrate `fsspec` for reading WFDB files from the cloud
- Enables reading WFDB files from cloud URLs
- Add support for Numpy 2.0
- Fixes were added to address [changes to type promotion](https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-numpy-data-type-promotion) that led to overflow errors (e.g. #493).
- Fix UnboundLocalError in GQRS algorithm
- Fixes the GQRS algorithm to address an UnboundLocalError.
- Support write directory in `csv_to_wfdb`
- write_dir can now be specified when calling csv_to_wfdb.
- Use uv for for package management
- Moves package management from poetry to uv.
- Fix misordered arguments in `util.lines_to_file`
- Fixes misordered arguments in util.lines_to_file.
- Allow signals to be written with unique samples per frame
- Adds capability to write signal with unique samps_per_frame to wfdb.io.wrsamp.
- Allow expanded physical signal in `calc_adc_params`
- Updates calc_adc_params to allow an expanded physical signal to be passed. Previously only a non-expanded signal was allowed.
- Allow selection of channels when converting to EDF
- Fixes the wfdb-to_edf() function to support an optional channels argument.
- Migrates Ricker wavelet from SciPy to WFDB after deprecation
- The Ricker wavelet (scipy.signal.ricker) was removed in SciPy v1.15, so the original implementation was migrated to the WFDB package.
- Miscellaneous style and typing fixes
- Various fixes were made to code style and handling of data types.
- Handle more than 8 compressed signals in wrsamp
- Previously, the package did not support writing of compressed records with more than 8 channels.
- Use int64 instead of int for ann2rr
- Fixes 'np has no attribute np.int' error raised when running ann2rr.
- Remove upper bound on dependencies
- Previously, the package provided restrictive caps on version number of dependencies. These caps have been removed.
- Miscellaneous style and typing fixes
- Various fixes were made to code style and handling of data types.
- Converting a record into a DataFrame
- The new method :meth:`wfdb.Record.to_dataframe` can be used to convert signal data from a Record object into a Pandas DataFrame, which can then be manipulated using Pandas methods.
- Locating signals in a multi-segment record
- The new method :meth:`wfdb.MultiRecord.contained_ranges` can be used to search for time intervals within a record that contain a specific channel. The method :meth:`wfdb.MultiRecord.contained_combined_ranges` searches for time intervals that contain several specific channels at once.
- Writing custom annotation symbols
- The :func:`wfdb.wrann` function can now be used to write annotations with custom annotation types (
symbolstrings.) Custom annotation types must be defined using thecustom_labelsargument. - Correct rounding when converting floating-point signal data
- When calling :func:`wfdb.wrsamp` with a
p_signalargument, input values will be rounded to the nearest sample value rather than being truncated towards zero. The same applies to the :meth:`wfdb.Record.adc` method. - Writing signals in compressed format
- The :func:`wfdb.wrsamp` function, and the :meth:`wfdb.Record.wrsamp` method, now support writing compressed signal files. To write a compressed file, set the
fmtvalue to"508"(for an 8-bit channel),"516"(for a 16-bit channel), or"524"(for a 24-bit channel). - Decoding non-ASCII text in EDF files
- The :func:`wfdb.io.convert.edf.read_edf` and :func:`wfdb.io.convert.edf.rdedfann` functions now take an optional argument
encoding, which specifies the character encoding for text fields. ISO-8859-1 encoding is used by default, in contrast to older versions of the package which used UTF-8. - Bug fixes when writing signal metadata
- When calling :meth:`wfdb.Record.wrsamp`, the checksum and samples-per-frame fields in the header file will correctly match the signal data, rather than relying on attributes of the Record object.