Skip to main content

Simplified COPASI interface for python

Project description

Python package Documentation Status Quality Gate Status Binder DOI codecov DOI

BasiCO

This project hosts a simplified python interface to COPASI. While all functionality from COPASI is exposed via automatically generated SWIG wrappers, this package aims to add a layer on top of that, to hide most of the complexity away when calling COPASI functions.

COPASI Logo

Installation

The package works with python 3.7+, provided the following packages are installed:

  • python-copasi
  • pandas
  • numpy
  • matplotlib
  • PyYAML

that are freely available on pypi, they will be automatically installed when installing via setup.py.

Once done, just have the basico directory in the PYTHONPATH or sys.path.

Or you could directly install everything you need right from pypi

pip install copasi-basico

from this git repo:

pip install git+https://github.com/copasi/basico.git

Usage

The following modules are available:

  • model_io: functionality, for creating / loading / saving models.
  • model_info: functionality to getting / setting model elements from pandas dataframes
  • task_timecourse: a wrapper for running time course simulations
  • task_parameter_estimation: a wrapper for parameter estimation
  • task_optimization: a wrapper for computing optimizations with arbitrary objective functions
  • task_steadystate: a wrapper for computing steady states
  • task_scan: a wrapper for parameter scans / repeats
  • task_sensitivities: a wrapper for computing sensitivities
  • compartment_array_tools: utility for plotting and the like

Documentation is continually updated at: https://basico.readthedocs.org/.

Please use the issue tracker for bug reports and feature requests.

Run the tests

basico comes with a number of unit tests based on pytest. To run them, change to the root directory of this project and run:

python3 -m pytest

that will ensure that basico is in the python path, and the test run as expected. Some tests require more data, that is not included in the repository, such as tests for PEtab and petab select, for those, specify the environment variables to the directories where the files are for example:

PETAB_BENCHMARK_MODELS=/path/to/petab/benchmark/models
PETAB_SELECT_MODELS=/path/to/petab/select/models

for example:

PETAB_BENCHMARK_MODELS=../Benchmark-Models-PEtab/Benchmark-Models PETAB_SELECT_MODELS=../petab_select/test_cases  python3 -m pytest

Acknowledgements

This project has been possible thanks to the BMBF funded de.NBI initiative (031L0104A):

de.NBI logo

License

The packages available on this page are provided under the Artistic License 2.0, which is an OSI approved license. This license allows non-commercial and commercial use free of charge.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

copasi_basico-0.71.tar.gz (210.5 kB view details)

Uploaded Source

Built Distribution

copasi_basico-0.71-py3-none-any.whl (184.2 kB view details)

Uploaded Python 3

File details

Details for the file copasi_basico-0.71.tar.gz.

File metadata

  • Download URL: copasi_basico-0.71.tar.gz
  • Upload date:
  • Size: 210.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for copasi_basico-0.71.tar.gz
Algorithm Hash digest
SHA256 4710b4ac61670f07c39ef6d13d6531073f151891dd42965e4d599e2b1b3c3c76
MD5 d7279c1461cb9ccd8b52d855f42d9f47
BLAKE2b-256 09129211a15d850e85c5d11c6f98d2228651f9d4b0498faff409d44ac724f1be

See more details on using hashes here.

File details

Details for the file copasi_basico-0.71-py3-none-any.whl.

File metadata

  • Download URL: copasi_basico-0.71-py3-none-any.whl
  • Upload date:
  • Size: 184.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for copasi_basico-0.71-py3-none-any.whl
Algorithm Hash digest
SHA256 513375dda8d4c94af1df541fc3f14d8529f94dbdcf88b19fb22a4daeaa6add2a
MD5 f7c6676596027eedded74951a313d929
BLAKE2b-256 58b7f5c9c4b720dda4a70fd1d0829331bcc14fc114c7fdec0614cb8f4fe7bd98

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page