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-0.80.tar.gz (288.0 kB view details)

Uploaded Source

Built Distribution

copasi_basico-0.80-py3-none-any.whl (264.6 kB view details)

Uploaded Python 3

File details

Details for the file copasi_basico-0.80.tar.gz.

File metadata

  • Download URL: copasi_basico-0.80.tar.gz
  • Upload date:
  • Size: 288.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for copasi_basico-0.80.tar.gz
Algorithm Hash digest
SHA256 31c47cbdd761754325aef6cf3e172007d236dd8b155613cbb12b5412d5969167
MD5 b8317dcf74c16806a815fc72df3eabfb
BLAKE2b-256 28db1cd7dc2c797792efa782fa7ba1ac081879a0ae56243b60b7e2bcdd06740f

See more details on using hashes here.

Provenance

File details

Details for the file copasi_basico-0.80-py3-none-any.whl.

File metadata

  • Download URL: copasi_basico-0.80-py3-none-any.whl
  • Upload date:
  • Size: 264.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for copasi_basico-0.80-py3-none-any.whl
Algorithm Hash digest
SHA256 762969999a14a12bbd49e5135bf0e3b4639a7cce526f25c65a334e918506e970
MD5 1805cb0fed3f677fc0c94b18c0375247
BLAKE2b-256 dbab14ec9c3857b814511a7f39acdaf00e205f210572b9dba8d947d636e0b752

See more details on using hashes here.

Provenance

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