Releases: IAMconsortium/pyam
Release v3.2.0
Release v3.2.0
Highlights
This release fixes an issue in the categorize() method, adds a check against infinite values in timeseries data, and simplifies the behavior of binary operations (addition, subtraction, division) with non-SI units.
Individual updates
Release v3.1.0
Release v3.1.0
Highlights
This release implements integration with the latest ixmp4 version including support
for datetime-format timeseries, versioning and database transactions.
Dependency changes
Bumped minimum version of ixmp4 to 0.13.0 for compatibility with versioning
and database transactions.
Release v3.0.0
Release v3.0.0
Highlights
- Implement reading and writing of netcdf files implemented by @LinhHo
- Remove automated sorting of timeseries data for performance improvement
- Add support Python 3.13
API changes
The internal object that holds the timeseries data is not automatically sorted any more, so the pd.DataFrame returned from IamDataFrame.data may change when upgrading to pyam v3.0. You can use the new method IamDataFrame.sort_data() to explicitly sort the timeseries data by the data dimensions.
Individual updates
- #901 Add support for Python 3.13
- #899 Add
to_netcdf()method - #896 Add
sort_data()method - #896 Sort columns of
timeseries()with mixed time domain - #893 No sorting of timeseries data on initialization or append
- #879 Add
read_netcdf()function
New Contributors
Full Changelog: v2.3.0...v3.0.0
Release v2.3.0
Release v2.3.0
Highlights
This release includes a new option to filter by variable depth.
Individual updates
- #886 Implement filtering by variable
depth - #880 Use
pd.Series.iloc[pos]for forward-compatibility - #877 Support
engineand otherpd.ExcelFilekeywords
New Contributors
- @korsbakken made their first contribution in #877
- @quant12345 made their first contribution in #880
Release v2.2.4
Release v2.2.4
Support filtering by a measurand argument with tuples of variable and units and improve the filter() documentation.
Release v2.2.3
Release v2.2.3
Patch release to set minimum requirement of ixmp4 to latest version and pin numpy to <2.0.
Release v2.2.2
Release v2.2.2
Patch release to correctly handle regexp-characters ([]{}?^) in filter() and better handling of the logging-configuration.
Release v2.2.1
Release v2.2.1
Patch release to explicitly include the requests dependency, which was dropped from ixmp4 with release 0.8.1.
Release v2.2.0
Release v2.2.0
Highlights
- Direct integration with ixmp4 platforms hosted by IIASA
- More flexible and intuitive signature for data validation and categorization
- Support for Python 3.12 and migrate to poetry & ruff
Dependency changes
- Replace pandas-datareader by wbdata to query World Bank data sources
- Bumped minimum version of pandas and numpy to fit ixmp4's requirements.
Individual updates
- #838 Support filters when reading from an ixmp4 platform
- #837 Support filters as direct keyword arguments for
categorize()similar tovalidate()signature (see #804) - #832 Improve the test-suite for the ixmp4 integration
- #827 Migrate to poetry for project management
- #830 Implement more consistent logging behavior with ixmp4
- #829 Add
pyam.iiasa.platforms()for a list of available platforms - #826 Add
read_ixmp4()function and extend integration test - #825 Add support for Python 3.12
- #824 Update ixmp4 requirement to >=0.7.1
- #821 Introduce ruff
- #818 Use context manager for reading Excel file
- #813 Fix a corner case in region-aggregation with missing data
- #797 Add
to_ixmp4()method to write to an ixmp4 platform
Release v2.1.0
Release v2.1.0
Highlights
- More flexible and intuitive options for data validation
- Improved support for region-aggregation with inconsistent data/weight index
- Fix wrong color codes for AR6 Illustrative Pathways
Individual updates
- #804 Support filters as direct keyword arguments for
validate()method - #801 Support initializing with
metadataframe in long format - #796 Raise explicit error message if no connection to IIASA manager service
- #794 Fix wrong color codes for AR6 Illustrative Pathways
- #792 Support region-aggregation with weights-index >> data-index