kagami 3.0.12

The Kagami library (Kaga)

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)