Metadata-Version: 2.1
Name: fipt
Version: 0.1.0
Summary: A python module to analyze fast impedance tortuosity measurements.
Home-page: https://github.com/deniz195/fipt-analysis
Author: Deniz Bozyigit
Author-email: deniz195@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: lmfit
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: dev
Requires-Dist: pandas ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

# FIPT: Fast impedance tortuosity 

FIPT is a measurement technique that allows a fast determination of the ionic resistance for battery electrodes. The technique is based on the work by J. Landesfeind (Journal of The Electrochemical Society, 163 (7) A1373-A1387 (2016) A1373) and then optimized for execution speed by D. Bozyigit at Battrion.

The measurement setup can be built based on https://github.com/deniz195/fipt 

Once data has been aquired, this repository provides the data analysis and model fitting. 

## Quick analysis
If you want to quickly analyze your impedance data:

Install:
```bash
pip install fipt
```

Put your impedance data in a file `my_fipt_data_1234.csv` in the following format: 
CSV
1st column: frequency in [Hz]
2nd column: Z' in [Ohm]
3rd column: Z'' in [Ohm]
(You can also use the file `test_data_001.csv` in `examples` folder)

```bash
python -m fipt ./my_fipt_data_1234.csv
```

## Examples 
To see how to analyze fipt data, refer to the jupyter notebook `demo_fipt.ipynb` in the examples folder.

## Quick installation
To install the fipt-analysis, simply:

```bash
pip install fipt
```

## Requirements
Required packages are `numpy`, `scipy` for data and statistical models and `lmfit` to perform the model fitting.

It is recommended (but not necessary) to install matplotlib installed, so that fipt-analysis will be able to create plots of the fitting.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)


