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https://github.com/KarrLab/de_sim

Python-based object-oriented discrete-event simulation tool for complex, data-driven modeling
https://github.com/KarrLab/de_sim

computational-modelling data-driven-model discrete-event-simulation object-oriented-programming python simulation

Last synced: 5 days ago
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Python-based object-oriented discrete-event simulation tool for complex, data-driven modeling

Host: GitHub
URL: https://github.com/KarrLab/de_sim
Owner: KarrLab
License: mit
Created: 2019-01-20T16:22:01.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2020-12-17T14:35:16.000Z (almost 4 years ago)
Last Synced: 2024-11-03T12:35:06.084Z (11 days ago)
Topics: computational-modelling, data-driven-model, discrete-event-simulation, object-oriented-programming, python, simulation
Language: Python
Homepage:
Size: 7.81 MB
Stars: 32
Watchers: 9
Forks: 6
Open Issues: 16
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

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# *DE-Sim*: a Python-based object-oriented discrete-event simulator for modeling complex systems

*DE-Sim* is an open-source, Python-based object-oriented discrete-event simulation (DES) tool that makes it easy to use large, heterogeneous datasets and high-level data science tools such as [NumPy](https://numpy.org/), [Scipy](https://scipy.org/scipylib/index.html), [pandas](https://pandas.pydata.org/), and [SQLAlchemy](https://www.sqlalchemy.org/) to build and simulate complex computational models. Similar to [Simula](http://www.simula67.info/), *DE-Sim* models are implemented by defining logical process objects which read the values of a set of variables and schedule events to modify their values at discrete instants in time.

To help users build and simulate complex, data-driven models, *DE-Sim* provides the following features:

* **High-level, object-oriented modeling:** *DE-Sim* makes it easy for users to use object-oriented Python programming to build models. This makes it easy to use large, heterogeneous datasets and high-level data science packages such as NumPy, pandas, SciPy, and SQLAlchemy to build complex models.

* **Stop conditions:** DE-Sim makes it easy to terminate simulations when specific criteria are reached. Researchers can specify stop conditions as functions that return true when a simulation should conclude.

* **Results checkpointing:** DE-Sim makes it easy to record the results of simulations by using a configurable checkpointing module.

* **Reproducible simulations:** To help researchers debug simulations, repeated executions of the same simulation with the same configuration and same random number generator seed produce the same results.

* **Space-time visualizations:** DE-Sim generates space-time visualizations of simulation trajectories. These diagrams can help researchers understand and debug simulations.

## Projects that use *DE-Sim*

*DE-Sim* has been used to develop [WC-Sim](https://github.com/KarrLab/wc_sim), a multi-algorithmic simulator for [whole-cell models](https://www.wholecell.org).

## Examples

* [Minimal simulation](de_sim/examples/minimal_simulation.py): a minimal example of a simulation

* [Random walk](de_sim/examples/random_walk.py): a random one-dimensional walk which increments or decrements a variable with equal probability at each event

* [Parallel hold (PHOLD)](de_sim/examples/phold.py): model developed by Richard Fujimoto for benchmarking parallel DES simulators

* [Epidemic](https://github.com/KarrLab/de_sim/blob/master/de_sim/examples/sirs.py): an SIR model of an epidemic of an infectious disease

## Tutorial

Please see [sandbox.karrlab.org](https://sandbox.karrlab.org/tree/de_sim) for interactive tutorials on creating and executing models with *DE-Sim*.

## Template for models and simulations

[`de_sim/examples/minimal_simulation.py`](de_sim/examples/minimal_simulation.py) contains a template for implementing and simulating a model with *DE-Sim*.

## Installation

1. Install dependencies

    

    * Python >= 3.7

    * pip >= 19

2. Install this package using one of these methods

    * Install the latest release from PyPI

      ```

      pip install de_sim

      ```

    * Install a Docker image with the latest release from DockerHub

      ```

      docker pull karrlab/de_sim

      ```

    * Install the latest version from GitHub

      ```

      pip install git+https://github.com/KarrLab/de_sim.git#egg=de_sim

      ```

## API documentation

Please see the [API documentation](https://docs.karrlab.org/de_sim/source/de_sim.html).

## Performance

Please see the [*DE-Sim* article](joss_paper/paper.md) for information about the performance of *DE-Sim*.

## Strengths and weaknesses compared to other DES tools

Please see the [*DE-Sim* article](joss_paper/paper.md) for a comparison of *DE-Sim* with other DES tools.

## License

The package is released under the [MIT license](LICENSE).

## Citing *DE-Sim*

Please use the following reference to cite *DE-Sim*:

Arthur P. Goldberg & Jonathan Karr. (2020). [DE-Sim: an object-oriented, discrete-event simulation tool for data-intensive modeling of complex systems in Python. Journal of Open Source Software, 5(55), 2685.](https://doi.org/10.21105/joss.02685)

## Contributing to *DE-Sim*

We enthusiastically welcome contributions to *DE-Sim*! Please see the [guide to contributing](CONTRIBUTING.md) and the [developer's code of conduct](CODE_OF_CONDUCT.md).

## Development team

This package was developed by the [Karr Lab](https://www.karrlab.org) at the Icahn School of Medicine at Mount Sinai in New York, USA by the following individuals:

* [Arthur Goldberg](https://www.mountsinai.org/profiles/arthur-p-goldberg)

* [Jonathan Karr](https://www.karrlab.org)

## Acknowledgements

This work was supported by National Science Foundation award 1649014, National Institutes of Health award R35GM119771, and the Icahn Institute for Data Science and Genomic Technology.

## Questions and comments

Please submit questions and issues to [GitHub](https://github.com/KarrLab/de_sim/issues) or contact the [Karr Lab](mailto:[email protected]).