The Kagami library (Kaga)
Project description
Kagami Library
The Kagami library is a Python package to accelerate the development of novel computational biology algorithms. It is currently under rapid growth. Although the APIs are aimed to remain consistent within a major version, compatible between releases are not guaranteed. Please note that there is no plan to include documents anytime soon.
The Kagami library is distributed under the GNU Lesser General Public License v3.0.
Dependencies
- Python >= 3.7.5
- numpy >= 1.17.4
- requests >= 2.22.0
- tables >= 3.6.1
For RWrapper:
- rpy2 >= 3.2.4
- R >= 3.6.1
For pytest, test coverage, and profiling:
- pytest >= 5.3.2
- pytest-cov >= 2.8.1
- pytest-profiling >= 1.7.0
Lower versions may work but have not been tested.
Installation
Using pip:
pip install kagami
Using Docker:
docker pull envomics/kagami
Testing
python -c "import kagami; kagami.test()"
Changelog
version 3.0
- Migrate to Python 3.7
- Add disk base for the Table
- Add chunk mapping
- Add numpy style indexing parameters for CoreTypes
- Add attribute-like access to table index
- Add dataframe-like assignment to table values
- Add handy snippets and R-like functions
- Clean None and na usage in map functions
- Fix R wrapper init libraries multiple loading bug
- Fix R wrapper library loading warning suppression
- Update unit tests
Version 2.2
- Add fixRepeat for NamedIndex and Table
- Improve Table repr
- Major refactor for package structure
Version 2.1
- Add Dockerfile
- Add setup script
- Add level properties for factor type
Version 2.0
- Add Factor CoreType
- Add NamedIndex CoreType
- Add StructuredArray CoreType
- Add Table CoreType
- Add HDF5 portals for StructuredArray and Table
- Add RData portal for Table
- Add Metadata class
- Add functional programming support
- Add BinWrapper
- Add license
- Update unit tests
Citation
If you use Kagami, DarkFusion or MOCA in a publication, we would appreciate citations: (coming soon)
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