Provides methods for the analysis of PV modules using different modalities
Project description
PVInspect
This package provides methods for the analysis of solar modules using different imaging modalities. We put huge efforts into providing a clean and easy to use API and additional tooling like the wrapper classes for module images that bundle image with meta data in an expressive way. You are invited to publish you own methods using this API and hence contribute to building a consistent and open tooling that might be useful to others. If you like to integrate your methods directly, please create a merge request.
Package status
This package is in an early stage of development. Please be aware that the API might change regularily within the next months.
Installation
We recommend to use the Anaconda Python distribution and to create a separate environment using Python3.7:
conda create -n pvinspect pip python=3.7
Then you can install this package using pip
:
conda activate pvinspect
pip install pvinspect
Usage
Activate the conda environment and start your dev environment (for example Jupyter Lab):
conda activate pvinspect
jupyter lab
This package contains example notebooks that demonstate the most common use cases (to be extended soon). For more details, please refer to the API docs.
Issues
In case you encounter anything that does not work, please open an issue and provide a precise description (include your OS version, python distribution and the like) as well as a minimal code example to reproduce the issue.
Known issues
In case you install pvinspect
in a conda environment using pip
on Windows, the shapely
library, which is installed as a dependency of pvinspect
does not find geos.dll
. This can be fixed by installing shapely
using conda:
conda install shapely
This is reported as a bug to shapely: #1032
Citations
In case you use methods from this package for research purposes, please make sure to cite the the corresponding papers correctly. Please refer to the documentation for the correct references.
Acknowledgement
We greatly acknowledge the HI-ERN for providing us a demo dataset of electroluminescense images that is published along with this package.
We gratefully acknowledge funding of the Federal Ministry for Economic Affairs and Energy (BMWi: Grant No. 0324286, iPV4.0).
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