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Prepare for release, adjust extras (#750)
* simplify extras * update version * correct error messages * add changelog info
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CHANGELOG.md

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## [Version 1.5.0] - 2023-09-06
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The release focuses on making `eo-learn` much simpler to install, reducing the number of dependencies, and improving validation of soundness of `EOPatch` data.
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- `eo-learn` is now distributed as a single package. Installation of `eo-learn-mask` and similar is no longer necessary and users are warned when such installations are detected.
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- Changes to `timestamps` and `bbox` attributes of `EOPatch` objects:
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- `FeatureType.TIMESTAMPS` and `FeatureType.BBOX` have been deprecated, data should be accessed via attributes. Feature parsers no longer return these values (for instance when calling `EOPatch.get_features`).
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- EOPatches without temporal information now have a timestamp value of `None`, whereas a timestamp value `[]` signifies that the EOPatch has a temporal dimension of 0.
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- Introduced a `get_timestamps` method that will fail if `timestamps` are `None`. This can be used in cases where timestamps are assumed to be present (to avoid issues with type-checking and ill formed inputs).
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- Loading, saving, and copying of EOPatches will take `timestamps` into account either when processing the full eopatch (i.e. `features=...`) or if the selection contains a temporal feature. The behavior can be controlled via the `load_timestamps`/`save_timestamps`/`copy_timestamps` parameter.
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- Saving and loading of `FeatureType.META_INFO` now processes each feature as a separate file, allowing better filtering and preventing accidental overwriting.
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- The default backend for `SpatialResizeTask` has been switched to `cv2` to reduce the number of dependencies.
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- `eolearn.geometry.morphology` tasks now use `cv2` instead of `scikit-image` to reduce the number of dependencies. The task interfaces have been slightly adjusted.
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- Improved reports:
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- Exception grouping is now done by exception origin instead of exception message, resulting in shorter reports.
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- Added execution time statistics per node
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- `CloudMaskTask` has been restricted to mono-temporal predictions using the `s2cloudless` package. For the multi-temporal one check [here](https://github.com/sentinel-hub/eo-learn-examples/blob/main/extra-tasks/cloud_mask/cloud_mask.py).
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- Certain tasks (for instance `SaveTask` and `LoadTask`) no longer pass arguments to the super-class via **kwargs in order to improve documentation and type-checking.
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- `SaveTask` and `LoadTask` now raise `OSError` exceptions instead of `IOError`.
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- Project-specific and outdated EOTasks were moved to extras or to the example repository [eo-learn-examples/extra-tasks](https://github.com/sentinel-hub/eo-learn-examples/tree/main/extra-tasks).
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- The submodule `eolearn.features.bands_extraction` has been renamed to `eolearn.features.ndi`.
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- The submodule `eolearn.ml_tools.extra.plotting` has been moved to `eolearn.visualization.utils`.
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- Compression of EOPatch files has been hardcoded. The parameter `compression_level` has been deprecated and has no effect.
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- Introduced experimental `zarr` support for loading/saving temporal slices of temporal features. The API might be changed in future releases.
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- Limited `rasterio` to 1.3.7 due to an issue with importing rasters from AWS S3
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- Updated examples, simplified tests, various improvements.
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## [Version 1.4.2] - 2023-03-14
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- Introduced support for Python 3.11.

core/requirements.txt

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eo-learn[ALL]
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eo-learn

coregistration/requirements.txt

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eo-learn[ALL]
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eo-learn

eolearn/__init__.py

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"""Main module of the `eolearn` package."""
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__version__ = "1.4.2"
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__version__ = "1.5.0"
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import importlib.util
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import warnings

eolearn/core/eodata.py

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from eolearn.visualization.eopatch import plot_eopatch
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except ImportError:
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raise RuntimeError(
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"Subpackage eo-learn-visualization has to be installed in order to use EOPatch visualization method"
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"Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to use EOPatch plotting."
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)
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return plot_eopatch(

eolearn/core/eoexecution.py

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from eolearn.visualization.eoexecutor import EOExecutorVisualization
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except ImportError:
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raise RuntimeError(
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"Subpackage eo-learn-visualization has to be installed in order to create EOExecutor reports."
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"Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to create EOExecutor reports."
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)
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return EOExecutorVisualization(self).make_report(include_logs=include_logs)

eolearn/core/eoworkflow.py

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from eolearn.visualization.eoworkflow import EOWorkflowVisualization
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except ImportError:
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raise RuntimeError(
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"Subpackage eo-learn-visualization has to be installed in order to use EOWorkflow visualization methods"
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"Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to use EOWorkflow visualization."
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)
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return EOWorkflowVisualization(self._nodes)
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examples/visualization/EOPatchVisualization.ipynb

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"source": [
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"## Basics\n",
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"\n",
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"All visualizations can be done simply by calling `EOPatch.plot` method, however calling this method still requires that `eo-learn-visualization` subpackage is installed.\n",
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"All visualizations can be done simply by calling `EOPatch.plot` method, however calling this method still requires that Dependencies `eo-learn[VISUALIZATION]` are installed.\n",
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"\n",
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"Plotting a simple timeless single-channel feature produces a single-image plot. Plotting method always returns a 2D grid of `AxesSubplot` objects which can be further customized before a plot is produced."
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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"version": "3.8.10"
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},
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"vscode": {
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"interpreter": {

features/requirements.txt

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eo-learn[ALL]
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eo-learn

geometry/requirements.txt

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eo-learn[ALL]
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eo-learn

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