Toolbox for compartment-based dynamic systems with costing and optimization
Atomica is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
For detailed documentation, visit https://atomica.tools/docs
Atomica is available for Python 3 only. Because we develop using Python 3.7, it is possible that dictionary order is relevant (although we endeavour to use ordered dictionaries via
Sciris in places where order matters). Therefore, we only officially support Python 3.7, as this is the first Python release that guarantees ordering of all dictionaries.
Atomica is distributed via PyPI, and the PyPI version corresponds to
master branch of this repository. To install via PyPI, it is only necessary to run
pip install atomica
matplotlib will automatically take place via
pip because they are dependencies of Atomica. However, in practice these packages may require system-level setup so it is usually easiest to install them separately beforehand. We recommend using Anaconda, which facilitates getting the binaries and dependencies like QT installed in a platform-agnostic manner. We also recommend working within an Anaconda environment.
You may also wish to install
mkl first, before installing
numpy etc. to improve performance. So for example:
conda install mkl conda install numpy scipy matplotlib
If you want to install a different branch of Atomica, or plan to make changes to the Atomica source code, you will need to install Atomica via Git rather than via PyPI. This can be performed using
git clone https://github.com/atomicateam/atomica.git cd atomica python setup.py develop
Atomica includes a suite of tests, some of which get automatically run and others that are used manually. The automated test suite can be executed with
pytest, and can be run from within an isolated environment using
tox. To use the tests, you will need to follow the steps above to perform a 'Git installation' because the tests are not included in the PyPI distribution. After installation, you can run individual test scripts from the
tests directory with commands like:
Note that many of the tests open
matplotlib figures as part of the test. If the test script is run on a machine without a display available, the error
_tkinter.TclError: couldn't connect to display "localhost:0.0"
will be raised. In that case, simply set the
matplotlib backend to
agg which allows the calls to succeed with a display present. For example, run
export MPLBACKEND=agg python tests/testworkflow.py
To run the automated suite, install the test dependencies using
pip install -r requirements.txt
which will install the additional development dependencies. Then, to run the automated suite, from the root directory (the one containing
To run the tests in an isolated virtual environment, from the root directory, run
If you don't have
tox, install it using
pip install tox. The default configuration expects Python 3.6 and Python 3.7 to be on your system - to test only against a specific version, pass the python version as an argument to
tox -e py37
to test Python 3.7 only.
Installation fails due to missing
python setup.py develop in a new environment,
numpy must be installed prior to
scipy. In some cases,
numpy may fail due to missing compiler options. In that case, you may wish to install
numpy via Anaconda
(by installing Python through Anaconda, and using
conda install numpy scipy matplotlib). In general, our experience
has been that it is easier to set up the C binaries for
numpy and the QT dependencies for
matplotlib via Anaconda
rather than doing this via the system, which involves different steps on every platform.
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