faninsar
A fancy InSAR time series library, in a Pythonic, fast, and flexible way.
Science Score: 77.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (21.1%) to scientific vocabulary
Keywords
Repository
A fancy InSAR time series library, in a Pythonic, fast, and flexible way.
Basic Info
- Host: GitHub
- Owner: Fanchengyan
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://faninsar.readthedocs.io
- Size: 17.5 MB
Statistics
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
FanInSAR is a Fancy Interferometric Synthetic Aperture Radar (InSAR) time series analysis library written in Python. It aims to provide a foundational library for the development of InSAR algorithms, facilitating efficient processing of InSAR time series data by offering a Pythonic, fast, and flexible approach.
Why FanInSAR?
Most existing community InSAR software adopts a workflow-oriented approach. While this lowers the entry barrier for new users, it often compromises flexibility and extensibility. For algorithm researchers, integrating new methods into these rigid workflows can be challenging, highlighting the need for a more adaptable framework for InSAR time series analysis.
FanInSAR is designed to bridge this gap. It serves as a foundational library for InSAR time series processing, offering a flexible and extensible framework tailored for algorithm researchers and developers. FanInSAR is not a complete end-to-end InSAR processing system; rather, it provides building blocks for creating custom workflows. Its high-level API abstracts the complexity of the processing pipeline and hides low-level implementation details, allowing users to focus on developing and testing new algorithms. For researchers aiming to rapidly prototype and deploy their own InSAR methods, FanInSAR offers a fast and efficient starting point.
Highlight Features
- Pythonic: FanInSAR is written in Python and provides a user-friendly API. For example, a series of well-known InSAR datasets are provided in the form of Python classes; loading data from
HyP3orLiCSARproducts is as simple as providing the corresponding home directory. Sampling values from an interferometric dataset is as easy as calling thequery()method by passing the spatial (Points, BoundingBox, Polygons) and temporal (Pairs) queries. The warping process during sampling (such as reprojecting and resampling) is automatically handled by the library. - Fast: The core computation in FanInSAR is implemented using
PyTorch, a high-performance deep learning library. This allows for efficient processing on both CPU and GPU, enabling faster execution. - Flexible: FanInSAR is designed to be flexible, allowing for customization and extension. Users can easily inherit classes or customize the processing pipeline for their specific needs.
Installation
FanInSAR is a Python package, and requires Python >= 3.8. You can install the latest release of FanInSAR using pip from the PyPI:
bash
pip install FanInSAR
or from GitHub:
bash
pip install git+https://github.com/Fanchengyan/FanInSAR.git
Documentation
The detailed documentation is available at: https://faninsar.readthedocs.io/en/latest/
:warning: Note
FanInSAR is under active development and is currently in the alpha stage. Its API may change in the future until it reaches a stable version.
Citation
Fan, C., & Liu, L. (2024). FanInSAR: A Fancy InSAR time series library, in a Pythonic, fast, and flexible way (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.11398347
bib
@software{fan_2024_11398347,
author = {Fan, Chengyan and
Liu, Lin},
title = {{FanInSAR: A Fancy InSAR time series library, in a
Pythonic, fast, and flexible way}},
month = may,
year = 2024,
publisher = {Zenodo},
version = {0.0.1},
doi = {10.5281/zenodo.11398347},
url = {https://doi.org/10.5281/zenodo.11398347}
}
Owner
- Name: Fanchengyan
- Login: Fanchengyan
- Kind: user
- Repositories: 12
- Profile: https://github.com/Fanchengyan
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Fan" given-names: "Chengyan" - family-names: "Liu" given-names: "Lin" title: "FanInSAR: A Fancy InSAR time series library, in a Pythonic, fast, and flexible way" version: 0.0.1 doi: 10.5281/zenodo.11398347 date-released: 2024-5-31 url: "https://doi.org/10.5281/zenodo.11398347"
GitHub Events
Total
- Watch event: 5
- Push event: 10
- Pull request event: 2
- Fork event: 1
- Create event: 2
Last Year
- Watch event: 5
- Push event: 10
- Pull request event: 2
- Fork event: 1
- Create event: 2
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| fanchengyan | f****4@l****n | 279 |
| GitButler | g****r@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 0
- Total pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: about 10 hours
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: about 10 hours
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- Fanchengyan (2)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- Jinja2 <3.1
- myst-parser *
- recommonmark *
- sphinx ==3.5.3
- sphinx_rtd_theme *
- sphinxcontrib-video *
- data_downloader *
- matplotlib *
- numpy *
- pandas *
- rasterio *
- rioxarray *
- rtree *
- tqdm *
- xarray *