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https://github.com/dhhagan/opcsim
opcsim is a python package for simulating low-cost optical particle sensors
https://github.com/dhhagan/opcsim
Last synced: 3 months ago
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opcsim is a python package for simulating low-cost optical particle sensors
- Host: GitHub
- URL: https://github.com/dhhagan/opcsim
- Owner: dhhagan
- License: mit
- Created: 2016-11-03T18:27:42.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-11-26T12:31:38.000Z (about 4 years ago)
- Last Synced: 2024-06-11T16:05:09.801Z (8 months ago)
- Language: Python
- Homepage:
- Size: 27.6 MB
- Stars: 14
- Watchers: 6
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-air-quality - py-opcsim - _Python library to simulate OPCs and Nephlometers under different conditions_ (Software Libraries)
README
[](https://badge.fury.io/py/opcsim)
[](https://zenodo.org/badge/latestdoi/72774719)
[](https://github.com/dhhagan/opcsim/blob/master/LICENSE)
[](https://codecov.io/gh/dhhagan/opcsim)
# opcsim
opcsim is a Python library for simulating low-cost Optical Particle Sensors (both Optical Particle Counters and Nephelometers) and
their response to various aerosol distributions.
## Citation
The paper for this library can be found on the AMT website [here](https://amt.copernicus.org/articles/13/6343/2020/amt-13-6343-2020.html). It should be cited as:
Hagan, D.H. and Kroll, J.H.: Assessing the accuracy of low-cost optical particle sensors using a physics-based approach, **Atmos. Meas. Tech.**, 13, 6343-6355, https://doi.org/10.5194/amt-13-6343-2020, 2020.
## Documentation
Full online documentation can be found [here][1].
The docs include a [tutorial][2], an [example gallery][3], and an [API Reference][4].
In addition, documentation can be built locally for development purposes. To do so, please check out the complete details in the *contributing to opcsim* section of the documentation.
## Docker
If you are familiar with Docker, there is a Docker image available to get up and running with OPCSIM with ease. To get started
with an ephemeral container with a jupyter lab interface, navigate to your preferred working directory and execute:
```sh
$ docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v "$PWD":/home/joyvan/work dhhagan/opcsim:latest
```
Once executed, you should see the url with token in your terminal that will allow you to bring up the jupyter lab instance.
## Dependencies
Opcsim is supported for python3.6.1+.
Installation requires [scipy][5], [numpy][6], [pandas][7], [matplotlib][8],
and [seaborn][9].
## Installation
To install (or upgrade to) the latest stable release:
```sh
$ pip install opcsim [--upgrade]
```
To install the development version directly from GitHub using pip:
```sh
$ pip install git+https://github.com/dhhagan/opcsim.git
```
In addition, you can either clone the repository and install from source or download/unzip the zip file and install from source using poetry:
```sh
$ git clone https://github.com/dhhagan/opcsim.git
$ cd /opcsim
$ poetry install
```
## Testing
All tests are automagically run via GitHub actions and Travis.ci. For results of these tests, please click on the link in the above travis badge. In addition, you can run tests locally using poetry.
To run tests locally:
```sh
$ poetry run pytest tests
```
## Development
**opcsim** development takes place on GitHub. Issues and bugs can be submitted and tracked via the [GitHub Issue Tracker][10] for this repository. As of `v0.5.0`, *opcsim* uses [poetry][11] for versioning and managing dependencies and releases.
[1]: https://dhhagan.github.io/opcsim/
[2]: https://dhhagan.github.io/opcsim/tutorial.html
[3]: https://dhhagan.github.io/opcsim/examples/index.html
[4]: https://dhhagan.github.io/opcsim/api.html
[5]: https://www.scipy.org/
[6]: http://www.numpy.org/
[7]: http://pandas.pydata.org/
[8]: http://matplotlib.org/
[9]: https://seaborn.pydata.org/
[10]: https://github.com/dhhagan/opcsim/issues
[11]: https://python-poetry.org/