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-0.68.tar.gz (209.4 kB view details)

Uploaded Source

Built Distribution

copasi_basico-0.68-py3-none-any.whl (183.7 kB view details)

Uploaded Python 3

File details

Details for the file copasi_basico-0.68.tar.gz.

File metadata

  • Download URL: copasi_basico-0.68.tar.gz
  • Upload date:
  • Size: 209.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for copasi_basico-0.68.tar.gz
Algorithm Hash digest
SHA256 07e481c1b439186bf91f1876051d1a3359b26ae950ca00c9fcc9d3e27ad59f9f
MD5 5b1750757a666ccdd5be3a07b9a7974c
BLAKE2b-256 4324ab1756e11953f2a54933842e8b649e94b9e5d1c29ed3a4b53685091762af

See more details on using hashes here.

File details

Details for the file copasi_basico-0.68-py3-none-any.whl.

File metadata

  • Download URL: copasi_basico-0.68-py3-none-any.whl
  • Upload date:
  • Size: 183.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for copasi_basico-0.68-py3-none-any.whl
Algorithm Hash digest
SHA256 70b7fbd34eca3517d17dda9905c632401aef289d5d812aea01832b22c7831ff5
MD5 3679dd38b9e836cbf6163509f70011b4
BLAKE2b-256 ce6dcec32cae7a553f0297c027cf169a17b13aec2389b20937528110f039bd0a

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