Skip to main content

Latest release PyPI CI status Test coverage

BioSimulators-BoolNet

BioSimulators-compliant command-line interface to the BoolNet simulation program.

This command-line interface and Docker image enable users to use BoolNet to execute COMBINE/OMEX archives that describe one or more simulation experiments (in SED-ML format) of one or more logical models (in SBML-qual format).

A list of the algorithms and algorithm parameters supported by BoolNet is available at BioSimulators.

A simple web application and web service for using BoolNet to execute COMBINE/OMEX archives is also available at runBioSimulations.

Contents

Installation

Install Python package

pip install biosimulators-boolnet

Install Docker image

docker pull ghcr.io/biosimulators/boolnet

Usage

Local usage

usage: boolnet [-h] [-d] [-q] -i ARCHIVE [-o OUT_DIR] [-v]

BioSimulators-compliant command-line interface to the BoolNet simulation program <https://sysbio.uni-ulm.de/?Software:BoolNet>.

optional arguments:
  -h, --help            show this help message and exit
  -d, --debug           full application debug mode
  -q, --quiet           suppress all console output
  -i ARCHIVE, --archive ARCHIVE
                        Path to OMEX file which contains one or more SED-ML-
                        encoded simulation experiments
  -o OUT_DIR, --out-dir OUT_DIR
                        Directory to save outputs
  -v, --version         show program's version number and exit

Usage through Docker container

The entrypoint to the Docker image supports the same command-line interface described above.

For example, the following command could be used to use the Docker image to execute the COMBINE/OMEX archive ./modeling-study.omex and save its outputs to ./.

docker run \
  --tty \
  --rm \
  --mount type=bind,source="$(pwd)",target=/root/in,readonly \
  --mount type=bind,source="$(pwd)",target=/root/out \
  ghcr.io/biosimulators/boolnet:latest \
    -i /root/in/modeling-study.omex \
    -o /root/out

Documentation

Documentation is available at https://biosimulators.github.io/Biosimulators_BoolNet/.

License

This package is released under the MIT license.

Development team

This package was developed by the Center for Reproducible Biomedical Modeling and the Karr Lab at the Icahn School of Medicine at Mount Sinai in New York.

Questions and comments

Please contact the BioSimulators Team with any questions or comments.

Download files

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

Source Distribution

biosimulators_boolnet-0.1.7.tar.gz (11.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

biosimulators_boolnet-0.1.7-py2.py3-none-any.whl (12.4 kB view details)

Uploaded Python 2Python 3

File details

Details for the file biosimulators_boolnet-0.1.7.tar.gz.

File metadata

  • Download URL: biosimulators_boolnet-0.1.7.tar.gz
  • Upload date:
  • Size: 11.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.7.10

File hashes

Hashes for biosimulators_boolnet-0.1.7.tar.gz
Algorithm Hash digest
SHA256 16e8c10f286fdeb7efbde5329dc3bfbb3ae56e04852b2e723c283629443ee11e
MD5 3cbb59b11efa662a83752ca9520b7f61
BLAKE2b-256 31b9be6abeeb98b336d145fa1366ab650b14751a24a168ee62a4c6c12410bedc

See more details on using hashes here.

File details

Details for the file biosimulators_boolnet-0.1.7-py2.py3-none-any.whl.

File metadata

  • Download URL: biosimulators_boolnet-0.1.7-py2.py3-none-any.whl
  • Upload date:
  • Size: 12.4 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.7.10

File hashes

Hashes for biosimulators_boolnet-0.1.7-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 35a7bf3e0304b7851cf20a41380deb8cb8126e8074a3087993a6f1f85310ac91
MD5 4ea7948497d9a61f7e6b5c1334b7698b
BLAKE2b-256 0637fceaea5c6696d390d89dcf667f4b9abcab1afc120fa2112c30dfbbb1e173

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page