# Virtual TIFF
A Parser for creating Virtual Zarr stores from TIFF files using [VirtualiZarr 2.0](https://virtualizarr.readthedocs.io/en/stable/index.html) and [async-tiff](https://developmentseed.org/async-tiff/latest/).
## Background
First, some thoughts on why we should virtualize GeoTIFFs and/or COGS:
1. Provide faster access to non-cloud-optimized GeoTIFFS that contain some form of internal tiling without any data duplication [see notebook #1](demos/01_faster_loading_3.0.ipynb).
2. Provide fully async I/O for both GeoTIFFs and COGs using Zarr-Python
3. Allow loading a stack of GeoTIFFS/COGS into a data cube while minimizing the number of GET requests relative to using stackstac/xstac, thereby decreasing cost and increasing performance
4. Provide users access to a lazily loaded DataTree providing both the data and the overviews, allowing scientists to use the overviews not only for tile-based visualization but also quickly iterating on analytics
5. Include etags in the virtualized datasets to support reproducibility
6. A motivation that's less clear to me, but maybe possible, is using the virtualization layer to access COGs with disparate CRSs as a single dataset (https://github.com/zarr-developers/geozarr-spec/issues/53)
## Getting started
1. Clone the repository: `git clone https://github.com/virtual-zarr/virtual-tiff.git`.
2. Pull baseline image data from dvc remote `pixi run -e test download-test-images` WARNING: This will download ~1.4GB of TIFFs for testing to your machine.
3. Run the test suite using `pixi run -e test run-tests` WARNING: Some tests will fail due to incomplete status of the implementation.
4. Start a shell if needed in the development environment using `pixi run -e test zsh`.
## License
`virtual-tiff` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
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"description": "# Virtual TIFF\n\nA Parser for creating Virtual Zarr stores from TIFF files using [VirtualiZarr 2.0](https://virtualizarr.readthedocs.io/en/stable/index.html) and [async-tiff](https://developmentseed.org/async-tiff/latest/).\n\n## Background\n\nFirst, some thoughts on why we should virtualize GeoTIFFs and/or COGS:\n\n1. Provide faster access to non-cloud-optimized GeoTIFFS that contain some form of internal tiling without any data duplication [see notebook #1](demos/01_faster_loading_3.0.ipynb).\n2. Provide fully async I/O for both GeoTIFFs and COGs using Zarr-Python\n3. Allow loading a stack of GeoTIFFS/COGS into a data cube while minimizing the number of GET requests relative to using stackstac/xstac, thereby decreasing cost and increasing performance\n4. Provide users access to a lazily loaded DataTree providing both the data and the overviews, allowing scientists to use the overviews not only for tile-based visualization but also quickly iterating on analytics\n5. Include etags in the virtualized datasets to support reproducibility\n6. A motivation that's less clear to me, but maybe possible, is using the virtualization layer to access COGs with disparate CRSs as a single dataset (https://github.com/zarr-developers/geozarr-spec/issues/53)\n\n## Getting started\n\n1. Clone the repository: `git clone https://github.com/virtual-zarr/virtual-tiff.git`.\n2. Pull baseline image data from dvc remote `pixi run -e test download-test-images` WARNING: This will download ~1.4GB of TIFFs for testing to your machine.\n3. Run the test suite using `pixi run -e test run-tests` WARNING: Some tests will fail due to incomplete status of the implementation.\n4. Start a shell if needed in the development environment using `pixi run -e test zsh`.\n\n## License\n\n`virtual-tiff` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.\n",
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