paint
The first FAIR database for Concentrating Solar Power plants.
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.9%) to scientific vocabulary
Repository
The first FAIR database for Concentrating Solar Power plants.
Basic Info
- Host: GitHub
- Owner: ARTIST-Association
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://paint-database.org/
- Size: 85.4 MB
Statistics
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
- Releases: 2
Metadata Files
README.md
PAINT
Welcome to PAINT
This repository contains code associated with the PAINT database. The PAINT database
makes operational data of concentrating solar power plants available in accordance with the FAIR data principles, i.e.,
making them findable, accessible, interoperable, and reusable. Currently, the data encompasses calibration images,
deflectometry measurements, kinematic settings, and weather information of the concentrating solar power plant in
Jülich, Germany, with the global power plant id (GPPD) WRI1030197. Metadata for all database entries follows the
spatio-temporal asset catalog (STAC) standard.
What can this repository do for you?
This repository contains two main types of code:
1. Preprocessing: This code was used to preprocess the data and extract all metadata into the STAC format. This
preprocessing included moving and renaming files to be in the correct structure, converting coordinates to the WGS84
format, and generating all STAC files (items, collections, and catalogs). This code is found in the subpackage
paint.preprocessing and executed in the scripts located in preprocessing-scripts. This code could be useful if
you have similar data that you would also like to process and include in the PAINT database!
2. Data Access and Usage: This code enables data from the PAINT database to be easily accessed from a code-base
and applied for a specific use case. Specifically, we provide a StacClient for browsing the STAC metadata files in
the PAINT database and downloading specific files. Furthermore, we provide utilities to generate custom benchmarks
for evaluating various calibration algorithms and also a torch.Dataset for efficiently loading and using calibration
data. This code is found in the subpackage paint.data and examples of possible execution are found in the
scripts folder.
In the following, we will highlight how to use the code in more detail!
Installation
We heavily recommend installing the PAINT package in a dedicated Python3.9+ virtual environment. You can install the
latest stable version of PAINT directly from PyPI using:
bash
pip install paint-csp
Alternatively, You can install the latest developmental version of PAINT directly from the GitHub repository via:
bash
pip install git+https://github.com/ARTIST-Association/PAINT
You can also install PAINT locally. To achieve this, there are two steps you need to follow:
1. Clone the PAINT repository:
bash
git clone https://github.com/ARTIST-Association/PAINT.git
2. Install the package from the main branch:
- Install basic dependencies: pip install .
- If you want to develop paint, install an editable version with developer dependencies: pip install -e ".[dev]"
Structure
The PAINT repository is structured as shown below:
.
├── html # Code for the paint-database.org website
├── markers # Saved markers for the WRI1030197 power plant in Jülich
├── paint # Python package
│ ├── data
│ ├── preprocessing
│ └── util
├── plots # Scripts used to generate plots found in our paper
├── preprocessing-scripts # Scripts used for preprocessing and STAC generation
├── scripts # Scripts highlighting example usage of the data
└── test # Tests for the python package
├── data
├── preprocessing
└── util
Example usage:
In the scripts folder there are multiple scripts highlighting how PAINT can be used. Detailed
descriptions of these scripts are available via our Documentation.
How to contribute
Check out our contribution guidelines if you are interested in contributing to the PAINT project :fire:.
Please also carefully check our code of conduct :blue_heart:.
Acknowledgments
This work is supported by the Helmholtz AI platform grant.
Owner
- Name: ARTIST-Association
- Login: ARTIST-Association
- Kind: organization
- Repositories: 1
- Profile: https://github.com/ARTIST-Association
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: PAINT
message: >-
If you use this software, please cite it using the metadata
from this file.
type: software
authors:
- given-names: Kaleb
family-names: Phipps
email: kaleb.phipps@kit.edu
affiliation: Karlsruhe Institute of Technology (KIT)
orcid: 'https://orcid.org/0000-0002-9197-1739'
- given-names: Mathias
family-names: Kuhl
email: mathias.kuhl@dlr.de
affiliation: German Aerospace Center (DLR)
orcid: 'https://orcid.org/0000-0003-0097-7260'
- given-names: Marie
family-names: Weiel
email: marie.weiel@kit.edu
affiliation: Karlsruhe Institute of Technology (KIT)
orcid: 'https://orcid.org/0000-0001-9648-4385'
- given-names: Marlene
family-names: Busch
email: marlene.busch@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Jan
family-names: Lewen
email: jan.lewen@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Nicolas
family-names: Blumenröhr
email: nicolas.blumenroehr@kit.edu
affiliation: Karlsuhre Institute of Technology (KIT)
orcid: 'https://orcid.org/0009-0007-0235-4995'
- given-names: Daniel
family-names: Maldonado Quinto
email: daniel.maldonadoquinto@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Charlotte
family-names: Debus
email: charlotte.debus@kit.edu
affiliation: Karlsruhe Institute of Technology (KIT)
orcid: 'https://orcid.org/0000-0002-7156-2022'
- given-names: Felix
family-names: Göhring
email: felix.goehring@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Oliver
family-names: Kaufhold
email: oliver.kaufhold@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Achim
family-names: Streit
email: achim.streit@kit.edu
affiliation: Karlsruhe Institute of Technology (KIT)
orcid: 'https://orcid.org/0000-0002-5065-469X'
- given-names: Robert
family-names: Pitz-Paal
orcid: 'https://orcid.org/0000-0002-3542-3391'
email: Robert.pitz-paal@dlr.de
affiliation: German Aerospace Center (DLR)
- given-names: Markus
family-names: Götz
email: markus.goetz@kit.edu
affiliation: Karlsruhe Institute of Technology (KIT)
orcid: 'https://orcid.org/0000-0002-2233-1041'
- given-names: Max
family-names: Pargmann
email: max.pargmann@dlr.de
affiliation: German Aerospace Center (DLR)
orcid: 'https://orcid.org/0000-0002-4705-6285'
repository-code: 'https://github.com/ARTIST-Association/PAINT'
url: 'https://www.paint-database.org'
abstract: >-
The PAINT database makes operational data of concentrating
solar power plants available in accordance with the FAIR
data principles, i.e., making them findable, accessible,
interoperable, and reusable. Currently, the data
encompasses calibration images, deflectometry
measurements, kinematic settings, and weather information
of the concentrating solar power plant in Jülich, Germany,
with the global power plant id (GPPD) WRI1030197. Metadata
for all database entries follow the spatio-temporal asset
catalog (STAC) specification. PAINT closely follows open
science guidelines and makes all software publicly
available in the project's source code repository.
keywords:
- Solar Tower Power Plant
- Concentrated Solar Energy
- Database
- Heliostat Calibration
- Deflectometry
- Surface Reconstruction
- Benchmark
commit: bc6c2640b5d51e5014b3e05690536afd1399c572
license: MIT
version: 1.0.1
date-released: '2025-05-28'
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 18
- Total pull requests: 43
- Average time to close issues: about 2 months
- Average time to close pull requests: 9 days
- Total issue authors: 4
- Total pull request authors: 6
- Average comments per issue: 0.28
- Average comments per pull request: 1.56
- Merged pull requests: 32
- Bot issues: 0
- Bot pull requests: 8
Past Year
- Issues: 15
- Pull requests: 43
- Average time to close issues: 12 days
- Average time to close pull requests: 9 days
- Issue authors: 3
- Pull request authors: 6
- Average comments per issue: 0.13
- Average comments per pull request: 1.56
- Merged pull requests: 32
- Bot issues: 0
- Bot pull requests: 8
Top Authors
Issue Authors
- kalebphipps (10)
- MarleneBusch (5)
- mcw92 (2)
- Markus-Goetz (1)
Pull Request Authors
- kalebphipps (27)
- pre-commit-ci[bot] (8)
- Filos1992 (4)
- mcw92 (2)
- Markus-Goetz (1)
- mathiaskuhl (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- actions/checkout v4 composite
- actions/setup-python main composite
- codecov/codecov-action v4.0.1 composite
- marocchino/sticky-pull-request-comment v2 composite
- Flask *
- colorlog *
- deepdiff *
- h5py *
- matplotlib *
- numpy *
- opencv-python *
- openpyxl *
- pandas *
- pyproj *
- pystac *
- seaborn *
- torch *
- waitress *
- wetterdienst *
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pypa/gh-action-pypi-publish release/v1 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pypa/gh-action-pypi-publish release/v1 composite
- sphinx-autoapi *
- sphinx-rtd-theme *
- sphinxcontrib-napoleon *
- sphinxemoji *