semvec/pypi-packages huggingface dataset:
arcGisFeatureCache 0.1.0a1
...
Requires Python: >=3.10


<p align="center">
   <em><h1>ArcGisFeatureCache</h1></em>
</p>

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The ArcGIS Feature Layer Caching Library is a Python library designed to cache ArcGIS feature layers locally, enabling faster data access and improved performance for applications that frequently access the same data from an ArcGIS server. By storing data locally, this library reduces server load, conserves bandwidth, and allows for offline access to ArcGIS feature layers.

---

**Documentation**: <a href="https://Hazedd.github.io/ArcGisFeatureCache/" target="_blank">https://Hazedd.github.io/Hazedd/ArcGisFeatureCache/</a>

**Source Code**: <a href="https://github.com/Hazedd/ArcGisFeatureCache" target="_blank">https://github.com/Hazedd/ArcGisFeatureCache</a>

---

## Install

```batch
pip install arcGisFeatureCache
```

## Usage

```py
from arcGisFeatureCache import get_feature_service

url = "https://xxxx.xxx/arcgis/rest/services/xxxxxx/FeatureServer"
feature_service = get_feature_service(url)

# get all features from service
feature_service.get_all_features()

# get features from one or more layers
feature_service.get_layer_features(["layer_a", "layer_b"])

```

### async
```py
from arcGisFeatureCache import ArcGisFeatureService

url = "https://xxxx.xxx/arcgis/rest/services/xxxxxx/FeatureServer"
feature_service = await ArcGisFeatureService.factory(url)

```


## Roadmap:

- [X] pr and github actions setup
- [X] docs as website
- [X] init release 0.1.0
- [ ] 100% code coverage
- [ ] query by intersect geometry (rtree)
- [ ] query by attribute
- [ ] make pickle work so we can safe and load state


## Contributing
Contributions to the ArcGIS Feature Layer Caching Library are welcome! If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on GitHub.


## License
This project is licensed under the terms of the MIT license.
flipnslide 0.0.1
A concise Python package to preprocess large scientific images for use with GPUs.
Requires Python: >=3.8

# Flip-n-Slide

Flip-n-Slide is a concise tiling and augmentation strategy to prepare large scientific images for use with GPU-enabled algorithms. `flipnslide` is a Python package that outputs PyTorch ready preprocessed datasets from a single large image.

## Documentation

The documentation for `flipnside` is available on [Read the Docs](https://flipnslide.readthedocs.io/).

## Installation and Dependencies

For now, `flipnslide` can be installed from PyPI using pip, by running:

```bash
pip install flipnslide
```
Check back later this week for instructions on installing from conda forge.

## Attribution

If you make use of this code, please cite the companion ML4RS @ ICLR paper:

    @inproceedings{flipnslide,
      author       = {Ellianna Abrahams and
                      Tasha Snow and
                      Matthew R. Siegfried and
                      Fernando Pérez},
      title        = {A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation Imagery},
      booktitle    = {Machine Learning for Remote Sensing Workshop {ML4RS} at The Twelfth International Conference 
                      on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
      publisher    = {OpenReview.net},
      year         = {2024},
      url          = {upcoming},
    }

## License

Copyright 2024 Ellianna Abrahams, Tasha Snow, Matthew R. Siegfried, Fernando Pérez, and contributors.

``flipnslide`` is free software made available under the MIT License. For details see
the [LICENSE](https://github.com/elliesch/flipnslide/blob/main/LICENSE) file.

## Contributors

See the [AUTHORS](https://github.com/elliesch/flipnslide/blob/main/AUTHORS) file for a complete list of contributors to the project.