Skip to main content

Compartmental modelling Python package

Project description

comod

Github release PyPI

license MIT

Build Status Documentation Status

Compartmental modelling Python package

Preview

alt tag alt tag

Check the docs to see more.

Features

  • Define a model with simple rules as strings or with custom functions.
  • Pre-defined models like SIR, SIS, SEIR, ...
  • Community-extensions of models.
  • Solve numerically for fixed or time-dependent parameters.
  • Best-fit to existing data, posibly using time windows.
  • Create compartment graphs.
  • Export LaTeX.
  • Export to Wolfram Language (Mathematica).

Installation

Assuming you have a Python3 distribution with pip, the latest pypi release can be installed with:

pip3 install comod

To install the optional dependencies you can run

pip3 install 'comod[extras]'

Mind the quotes.

Developer information

Instalation

To install a development version, cd to the directory with this file and:

pip3 install -e '.[test]'

As an alternative, a virtualenv might be used to install the package:

# Prepare a clean virtualenv and activate it
virtualenv -p /usr/bin/python3.6 venv
source venv/bin/activate
# Install the package
pip3 install -e '.[test]'

Documentation

To generate the documentation, the docs extra dependencies must be installed.

To generate an html documentation with sphinx run:

make docs

To generate a PDF documentation using LaTeX:

make pdf

Test

To run the unitary tests:

make test

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

comod-0.2.1.tar.gz (15.2 kB view details)

Uploaded Source

Built Distribution

comod-0.2.1-py2.py3-none-any.whl (15.0 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file comod-0.2.1.tar.gz.

File metadata

  • Download URL: comod-0.2.1.tar.gz
  • Upload date:
  • Size: 15.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.9.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for comod-0.2.1.tar.gz
Algorithm Hash digest
SHA256 5f2a596d5a36b09f021b2383f038d5aeb7155b304ad1e10181214ce56b1a2a8b
MD5 dc5378cdc2ec944d57ec21f667f30db7
BLAKE2b-256 2ce6009c81ea2df6b29b946e186122c41671ef0f81640053803d2c0512a61e1b

See more details on using hashes here.

File details

Details for the file comod-0.2.1-py2.py3-none-any.whl.

File metadata

  • Download URL: comod-0.2.1-py2.py3-none-any.whl
  • Upload date:
  • Size: 15.0 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.9.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for comod-0.2.1-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 f008a3ca3394b7d05faf8e04f4a4b946b5b5282b7323a40c9d74c5d9b603e5d1
MD5 31966e2aa1f91389d2bcf79957c1c5c2
BLAKE2b-256 555de2b905aebad4d89d287318d487bf4e1efcd35efb097906d1a776939888ba

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