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Merge pull request #49 from EOPF-Sample-Service/forman-x-release_0.1
Release 0.1
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CHANGES.md

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## Changes in 0.1.0 (under development)
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## Changes in 0.1.0 (from 2025-04-28)
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* Added initial analysis mode for Sentinel-2 L1C and L2A products.
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Using analysis mode (the default), users can open deeply nested
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products as a flat xarray dataset with all bands resampled to a single
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resolution.
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* Added CI for unit and integration tests
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* Added CodeCov report
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The analysis mode (the default mode) provides the following features:
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* Open the deeply nested EOPF products as flat `xarray.Dataset` objects.
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* All bands and quality images resampled to a single, user provided
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resolution, hence, spatial dimensions will be just `x` and `y`.
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* User-specified resampling by passing spline orders for up-scaling
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and aggregation methods for downscaling.
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* Attach CF-compliant spatial referencing of datasets using a shared grid
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mapping variable `spatial_ref`.
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* Attach other CF-compliant metadata enhancements such as flag values and
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meanings for pixel quality information, such as the Sentinel-2
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scene classification (variable `scl`).
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* Added notebook examples for accessing Sentinel-1 and Sentinel-2 using the
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`eopf-zarr` engine
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* Added CI for unit and integration tests
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* Added CodeCov report
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## Changes in 0.0.1

README.md

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and aggregation methods for downscaling.
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* CF-compliant spatial referencing of datasets using a shared grid
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mapping variable `spatial_ref`.
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* Attach other CF-compliant metadata enhancements such as flag values and
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meanings for pixel quality information, such as the Sentinel-2
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scene classification (variable `scl`).
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The analysis mode is currently implemented Sentinel-2 products only.
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Support for Sentinel-1 and Sentinel-3 is coming soon.
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More information can be found in the
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[package documentation](https://eopf-sample-service.github.io/xarray-eopf).
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## Usage
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The `xarray-eopf` package can be installed from PyPI (`pip install xarray-eopf`)
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or conda-forge (`conda install -c conda-forge xarray-eopf`).
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Now you can open EOPF sample products using xarray by specifying the
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`"eopf-zarr"` backend in your Python code:
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```python
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import xarray as xr
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s2_l2a_url = (
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"s3://e05ab01a9d56408d82ac32d69a5aae2a:202504-s02msil2a/15/products/cpm_v256/"
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"S2B_MSIL2A_20250415T142749_N0511_R139_T25WEV_20250415T181516.zarr"
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)
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s2_l2a_dataset = xr.open_dataset(s2_l2a_url, engine="eopf-zarr", resolution=10)
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```
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## Development
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### Setting up a development environment
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pytest --cov xarray_eopf --cov-report html tests
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```
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### Documentation
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### Setting up a documentation environment
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```shell

xarray_eopf/version.py

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# Permissions are hereby granted under the terms of the Apache 2.0 License:
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# https://opensource.org/license/apache-2-0.
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version = "0.1.0.dev0"
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version = "0.1.0"

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