pybamm-eis
Science Score: 54.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
1 of 7 committers (14.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.6%) to scientific vocabulary
Keywords from Contributors
Repository
Basic Info
- Host: GitHub
- Owner: pybamm-team
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://pybamm-eis.readthedocs.io/
- Size: 535 KB
Statistics
- Stars: 31
- Watchers: 4
- Forks: 12
- Open Issues: 12
- Releases: 4
Metadata Files
README.md
PyBaMM EIS
PyBaMM EIS rapidly calculates the electrochemical impedance of any battery model defined using PyBaMM, see the documentation
🔋 Using PyBaMM EIS
The easiest way to use PyBaMM EIS is to compute the impedance of a model of your choice with the default parameters:
```python import pybammeis import pybamm import numpy as np
DFN with capacitance
model = pybamm.lithiumion.DFN(options={"surface form": "differential"}) eissim = pybammeis.EISSimulation(model)
calculate impedance at log-spaced frequencies
eissim.solve(np.logspace(-4, 4, 30)) eissim.nyquist_plot() ```
💻 About PyBaMM
The example simulations use the package PyBaMM (Python Battery Mathematical Modelling). PyBaMM is an open-source battery simulation package written in Python. Our mission is to accelerate battery modelling research by providing open-source tools for multi-institutional, interdisciplinary collaboration. Broadly, PyBaMM consists of (i) a framework for writing and solving systems of differential equations, (ii) a library of battery models and parameters, and (iii) specialized tools for simulating battery-specific experiments and visualizing the results. Together, these enable flexible model definitions and fast battery simulations, allowing users to explore the effect of different battery designs and modeling assumptions under a variety of operating scenarios.
🚀 Installation
In order to run the notebooks in this repository you will need to install the pybamm-eis package. We recommend installing within a virtual environment in order to not alter any python distribution files on your machine.
PyBaMM is available on GNU/Linux, MacOS and Windows. For more detailed instructions on how to install PyBaMM, see the PyBaMM documentation.
Linux/Mac OS
To install the requirements on Linux/Mac OS use the following terminal commands:
Clone the repository
bash git clone https://github.com/pybamm-team/pybamm-eisChange into the
pybamm-eisdirectorybash cd pybamm-eisCreate a virtual environment
bash virtualenv envActivate the virtual environment
bash source env/bin/activateInstall the
pybammeispackagebash pip install .
Windows
To install the requirements on Windows use the following commands:
Clone the repository
bash git clone https://github.com/pybamm-team/pybamm-eisChange into the
pybamm-eisdirectorybash cd pybamm-eisCreate a virtual environment
bash python -m virtualenv envActivate the virtual environment
bash \path\to\env\Scripts\activatewhere\path\to\envis the path to the environment created in step 3 (e.g.C:\Users\'Username'\env\Scripts\activate.bat).Install the
pybammeispackagebash pip install .
As an alternative, you can set up Windows Subsystem for Linux. This allows you to run a full Linux distribution within Windows.
Developer
To install as a developer follow the instructions above, replacing the final step with
bash
pip install -e .
This will allow you to edit the code locally.
📖 Documentation
API documentation for the pybammeis package can be built locally using Sphinx. To build the documentation, first change into the docs directory, and then execute the following command:
bash
make html
This will generate a number of html files in the docs/_build/html directory. To view the documentation, open the file docs/_build/html/index.html in a web browser.
🛠️ Contributing to PyBaMM
If you'd like to help us develop PyBaMM by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at these guidelines first.
🎉 Acknowledgments
This code was developed as part of the Oxford Mathematics Summer Project "Efficient Linear Algebra Methods to Determine Li-ion Battery Behaviour".
Student: Rishit Dhoot Supervisors: Prof Colin Please and Dr. Robert Timms
📫 Get in touch
For any questions, comments, suggestions or bug reports, please see the contact page.
📃 License
pybamm-eis is fully open source. For more information about its license, see LICENSE.
Owner
- Name: PyBaMM Team
- Login: pybamm-team
- Kind: organization
- Website: https://www.pybamm.org/
- Twitter: pybamm_
- Repositories: 24
- Profile: https://github.com/pybamm-team
Citation (CITATION.cff)
cff-version: 1.2.0
title: 'PyBaMM EIS: Efficient Linear Algebra Methods to Determine Li-ion Battery Behaviour'
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Rishit
family-names: Dhoot
- given-names: Robert
family-names: Timms
- given-names: Colin
family-names: Please
version: "0.1.5" # Update this when you release a new version
repository-code: 'https://www.github.com/pybamm-team/pybamm-eis'
GitHub Events
Total
- Create event: 9
- Issues event: 5
- Release event: 2
- Watch event: 15
- Delete event: 5
- Issue comment event: 15
- Push event: 8
- Pull request review comment event: 2
- Pull request review event: 9
- Pull request event: 15
- Fork event: 7
Last Year
- Create event: 9
- Issues event: 5
- Release event: 2
- Watch event: 15
- Delete event: 5
- Issue comment event: 15
- Push event: 8
- Pull request review comment event: 2
- Pull request review event: 9
- Pull request event: 15
- Fork event: 7
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Robert Timms | t****s@m****k | 57 |
| rish31415 | r****5@g****m | 51 |
| Eric G. Kratz | k****n | 5 |
| dependabot[bot] | 4****] | 4 |
| Noël Hallemans | 9****s | 1 |
| Brady Planden | 5****n | 1 |
| Arjun Verma | a****c@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 15
- Total pull requests: 20
- Average time to close issues: 11 days
- Average time to close pull requests: about 20 hours
- Total issue authors: 8
- Total pull request authors: 7
- Average comments per issue: 0.6
- Average comments per pull request: 0.7
- Merged pull requests: 18
- Bot issues: 0
- Bot pull requests: 7
Past Year
- Issues: 6
- Pull requests: 12
- Average time to close issues: N/A
- Average time to close pull requests: about 10 hours
- Issue authors: 5
- Pull request authors: 5
- Average comments per issue: 0.33
- Average comments per pull request: 0.75
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 7
Top Authors
Issue Authors
- rtimms (8)
- noelhallemans (4)
- Akila1993 (2)
- whileman133 (1)
- larer (1)
- keithbriggs (1)
- agriyakhetarpal (1)
- YannickNoelStephanKuhn (1)
Pull Request Authors
- rtimms (10)
- kratman (10)
- dependabot[bot] (8)
- arjxn-py (2)
- BradyPlanden (2)
- noelhallemans (1)
- YannickNoelStephanKuhn (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- matplotlib *
- pybamm @ git+https://github.com/pybamm-team/PyBaMM.git@088ca3827c831e1b93c4667b6f4353f64fb32cbf
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- pypa/gh-action-pypi-publish release/v1 composite
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