diff --git a/.github/workflows/ci_action.yml b/.github/workflows/ci_action.yml index 37bdbc9e0..5ebbe91d7 100644 --- a/.github/workflows/ci_action.yml +++ b/.github/workflows/ci_action.yml @@ -51,26 +51,14 @@ jobs: # caching the entire environment is faster when cache exists but slower for cache creation - name: Install packages - run: | - pip install -r requirements-dev.txt --upgrade --upgrade-strategy eager - python install_all.py - pip install -r ml_tools/requirements-tdigest.txt + run: pip install .[DEV] --upgrade --upgrade-strategy eager - name: Run pylint - run: make pylint + run: pylint eolearn - name: Run mypy if: success() || failure() - run: | - mypy \ - core/eolearn/core \ - coregistration/eolearn/coregistration \ - geometry/eolearn/geometry \ - features/eolearn/features \ - io/eolearn/io \ - mask/eolearn/mask \ - ml_tools/eolearn/ml_tools \ - visualization/eolearn/visualization + run: mypy eolearn test-on-github: runs-on: ubuntu-latest @@ -95,12 +83,10 @@ jobs: uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - # cache: pip # uncomment when all requirements are in `pyproject.toml` + cache: pip - name: Install packages - run: | - pip install -r requirements-dev.txt --upgrade --upgrade-strategy eager - python install_all.py -e + run: pip install -e .[DEV] --upgrade --upgrade-strategy eager - name: Run full tests and code coverage if: ${{ matrix.full_test_suite }} @@ -108,11 +94,15 @@ jobs: sentinelhub.config \ --sh_client_id "${{ secrets.SH_CLIENT_ID }}" \ --sh_client_secret "${{ secrets.SH_CLIENT_SECRET }}" - pytest --cov --cov-report=term --cov-report=xml + if [ ${{ github.event_name }} == 'push' ]; then + pytest -m "not geopedia" --cov=eolearn --cov-report=term --cov-report=xml + else + pytest -m "not geopedia" + fi - name: Run reduced tests if: ${{ !matrix.full_test_suite }} - run: pytest -m "not sh_integration" + run: pytest -m "not sh_integration and not geopedia" - name: Upload code coverage if: ${{ matrix.full_test_suite && github.event_name == 'push' }} diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 7f4f03215..9fe3e5985 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -10,7 +10,6 @@ run_sh_integration_tests: - apt-get update - apt-get install -y build-essential libgdal-dev graphviz proj-bin gcc libproj-dev libspatialindex-dev script: - - pip install -r requirements-dev.txt --upgrade - - python install_all.py + - pip install .[DEV] - sentinelhub.config --sh_client_id "$SH_CLIENT_ID" --sh_client_secret "$SH_CLIENT_SECRET" > /dev/null # Gitlab can't mask SH_CLIENT_SECRET in logs - pytest -m sh_integration diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0693d2f1d..e6da84915 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,13 +13,13 @@ repos: - id: debug-statements - repo: https://github.com/psf/black - rev: 23.3.0 + rev: 23.7.0 hooks: - id: black language_version: python3 - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: "v0.0.269" + rev: "v0.0.282" hooks: - id: ruff diff --git a/CHANGELOG.md b/CHANGELOG.md index e27a80770..be0dc2dc8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,32 @@ -## [Version 1.4.2] - 2023-3-14 +## [Version 1.5.0] - 2023-09-06 + +The release focuses on making `eo-learn` much simpler to install, reducing the number of dependencies, and improving validation of soundness of `EOPatch` data. + +- `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. +- Changes to `timestamps` and `bbox` attributes of `EOPatch` objects: + - `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`). + - 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. + - 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). + - 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. +- Saving and loading of `FeatureType.META_INFO` now processes each feature as a separate file, allowing better filtering and preventing accidental overwriting. +- The default backend for `SpatialResizeTask` has been switched to `cv2` to reduce the number of dependencies. +- `eolearn.geometry.morphology` tasks now use `cv2` instead of `scikit-image` to reduce the number of dependencies. The task interfaces have been slightly adjusted. +- Improved reports: + - Exception grouping is now done by exception origin instead of exception message, resulting in shorter reports. + - Added execution time statistics per node +- `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). +- 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. +- `SaveTask` and `LoadTask` now raise `OSError` exceptions instead of `IOError`. +- 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). +- The submodule `eolearn.features.bands_extraction` has been renamed to `eolearn.features.ndi`. +- The submodule `eolearn.ml_tools.extra.plotting` has been moved to `eolearn.visualization.utils`. +- Compression of EOPatch files has been hardcoded. The parameter `compression_level` has been deprecated and has no effect. +- Introduced experimental `zarr` support for loading/saving temporal slices of temporal features. The API might be changed in future releases. +- Limited `rasterio` to 1.3.7 due to an issue with importing rasters from AWS S3 +- Updated examples, simplified tests, various improvements. + + +## [Version 1.4.2] - 2023-03-14 - Introduced support for Python 3.11. - Removed support for Python 3.7. @@ -13,7 +41,7 @@ - Various refactoring efforts and dependency improvements. - Various improvements to tests and code. -## [Version 1.4.1] - 2023-3-14 +## [Version 1.4.1] - 2023-03-14 - The codebase is now fully annotated and type annotations are mandatory for all new code. - In the future `EOPatch` objects will **require** a valid `bbox`. For now the users are warned when no such value is provided. @@ -27,7 +55,7 @@ - Various other minor improvements and deprecations. -## [Version 1.4.0] - 2023-1-20 +## [Version 1.4.0] - 2023-01-20 - (**codebreaking**) Complete overhaul of `eolearn.coregistration`. See documentation for details. - (**codebreaking**) Removed non-working HVPlot backend for `eolearn.visualization`. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c3152f648..ee31fbec8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -22,7 +22,7 @@ For any question, feel free to contact us at [eoresearch@sinergise.com](eoresear We strive to provide high-quality working code, but bugs happen nevertheless. -When reporting a bug, please check [here][open-bug-list] whether the bug was already reported. If not, open an issue with the **bug** label and report the following information: +When reporting a bug, please check [the issue tracker][open-bug-list] whether the bug was already reported. If not, open an issue with the **bug** label and report the following information: * Issue description * How to reproduce the issue @@ -36,7 +36,7 @@ This information helps us to reproduce, pinpoint, and fix the reported issue. If ## Feature Requests -Existing feature requests can be found [here][existing-feature-requests]. +Existing feature requests can be found [in the issues][existing-feature-requests]. A new feature request can be created by opening a new issue with the **enhancement** label, and describing how the feature would benefit the **eo-learn** community. Providing an example use-case would help assessing the scope of the feature request. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 07c61fc6f..000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include requirements*.txt - -include README.md -include CREDITS.md -include LICENSE diff --git a/Makefile b/Makefile index 70e976d12..25520ee29 100644 --- a/Makefile +++ b/Makefile @@ -1,187 +1,21 @@ # Makefile for creating a new release of the package and uploading it to PyPI PYTHON = python3 -PACKAGES = core coregistration features geometry io mask ml_tools visualization -PYLINT = pylint .PHONY: $(PACKAGES:test) -.SILENT: pylint pylint-fast help: - @echo "Use 'make upload-' to upload the package to PyPi" - @echo "Use 'make pylint' to run pylint on the code of all subpackages" + @echo "Use 'make test-upload' to upload the package to testPyPi" + @echo "Use 'make upload' to upload the package to PyPi" -mypy: - mypy \ - core/eolearn/core \ - coregistration/eolearn/coregistration \ - geometry/eolearn/geometry \ - features/eolearn/features \ - io/eolearn/io \ - mask/eolearn/mask \ - ml_tools/eolearn/ml_tools \ - visualization/eolearn/visualization -pylint: - # Runs pylint on all subpackages consecutively and makes sure any error status code gets propagated. - export PYLINT_STATUS=0 - for package in $(PACKAGES) ; do \ - $(PYLINT) $$package/eolearn/$$package || export PYLINT_STATUS=$$?; \ - done; - exit $$PYLINT_STATUS - - -pylint-fast: - # Runs pylint on all subpackages in parallel. Because of that output verdicts are not in the order of package - # names and this process cannot be interrupted. - for package in $(PACKAGES) ; do \ - $(PYLINT) $$package/eolearn/$$package & \ - done; - wait - -.ONESHELL: -build-core: - cd core - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-coregistration: - cd coregistration - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-features: - cd features - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-geometry: - cd geometry - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-io: - cd io - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-mask: - cd mask - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-ml-tools: - cd ml_tools - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-visualization: - cd visualization - cp ../LICENSE LICENSE - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - rm LICENSE - -.ONESHELL: -build-abstract-package: - rm -r dist build | true - $(PYTHON) setup.py sdist bdist_wheel - -upload-core: build-core - twine upload core/dist/* - -upload-coregistration: build-coregistration - twine upload coregistration/dist/* - -upload-features: build-features - twine upload features/dist/* - -upload-geometry: build-geometry - twine upload geometry/dist/* - -upload-io: build-io - twine upload io/dist/* - -upload-mask: build-mask - twine upload mask/dist/* - -upload-ml-tools: build-ml-tools - twine upload ml_tools/dist/* - -upload-visualization: build-visualization - twine upload visualization/dist/* - -upload-abstract-package: build-abstract-package - twine upload dist/* - -upload-all: \ - upload-core \ - upload-coregistration \ - upload-features \ - upload-geometry \ - upload-io \ - upload-mask \ - upload-ml-tools \ - upload-visualization \ - upload-abstract-package +upload: + rm -r dist | true + python -m build --sdist --wheel + twine upload --skip-existing dist/* # For testing: - -test-upload-core: build-core - twine upload --repository testpypi core/dist/* - -test-upload-coregistration: build-coregistration - twine upload --repository testpypi coregistration/dist/* - -test-upload-features: build-features - twine upload --repository testpypi features/dist/* - -test-upload-geometry: build-geometry - twine upload --repository testpypi geometry/dist/* - -test-upload-io: build-io - twine upload --repository testpypi io/dist/* - -test-upload-mask: build-mask - twine upload --repository testpypi mask/dist/* - -test-upload-ml-tools: build-ml-tools - twine upload --repository testpypi ml_tools/dist/* - -test-upload-visualization: build-visualization - twine upload --repository testpypi visualization/dist/* - -test-upload-abstract-package: build-abstract-package - twine upload --repository testpypi dist/* - -test-upload-all: \ - test-upload-core \ - test-upload-coregistration \ - test-upload-features \ - test-upload-geometry \ - test-upload-io \ - test-upload-mask \ - test-upload-ml-tools \ - test-upload-visualization \ - test-upload-abstract-package +test-upload: + rm -r dist | true + python -m build --sdist --wheel + twine upload --repository testpypi --skip-existing dist/* diff --git a/README.md b/README.md index dab165af6..7e50cea63 100644 --- a/README.md +++ b/README.md @@ -35,48 +35,43 @@ to use any of the available tasks and is encouraged to improve the, develop new ## Package Overview -**`eo-learn`** is divided into several subpackages according to different functionalities and external package dependencies. Therefore it is not necessary for user to install entire package but only the parts that he needs. +**`eo-learn`** package is structured into several modules according to different functionalities. Some modules contain extensions under the `extra` subfolder. Those modules typically require additional package dependencies which don't get installed by default, since they are usually very specific to the task. -At the moment there are the following subpackages: +The modules are: -- **`eo-learn-core`** - The main subpackage which implements basic building blocks (`EOPatch`, `EOTask` and `EOWorkflow`) and commonly used functionalities. -- **`eo-learn-coregistration`** - The subpackage that deals with image co-registration. -- **`eo-learn-features`** - A collection of utilities for extracting data properties and feature manipulation. -- **`eo-learn-geometry`** - Geometry subpackage used for geometric transformation and conversion between vector and raster data. -- **`eo-learn-io`** - Input/output subpackage that deals with obtaining data from Sentinel Hub services or saving and loading data locally. -- **`eo-learn-mask`** - The subpackage used for masking of data and calculation of cloud masks. -- **`eo-learn-ml-tools`** - Various tools that can be used before or after the machine learning process. -- **`eo-learn-visualization`** - Visualization tools for core elements of eo-learn. +- **`core`** - The main module which implements basic building blocks (`EOPatch`, `EOTask` and `EOWorkflow`) and commonly used functionalities. +- **`coregistration`** - Tasks which deal with image co-registration. +- **`features`** - A collection of utilities for extracting data properties and feature manipulation. +- **`geometry`** - Geometry-related tasks used for transformation and conversion between vector and raster data. +- **`io`** - Input/output tasks that deal with obtaining data from Sentinel Hub services or saving and loading data locally. +- **`mask`** - Tasks used for masking of data and calculation of cloud/snow/other masks. +- **`ml-tools`** - Various tools that can be used before or after the machine learning process. +- **`visualization`** - Visualization tools for the core elements of eo-learn. ## Installation -### PyPi distribution +### Requirements -The package requires Python version **>=3.8** . It can be installed with: +The package requires Python version **>=3.8**. -```bash -pip install eo-learn -``` +#### Linux -In order to avoid heavy package dependencies it is possible to install each subpackage separately: +Before installing `eo-learn` on **Linux** it is recommended to install the following system libraries: ```bash -pip install eo-learn-core -pip install eo-learn-coregistration -pip install eo-learn-features -pip install eo-learn-geometry -pip install eo-learn-io -pip install eo-learn-mask -pip install eo-learn-ml-tools -pip install eo-learn-visualization +sudo apt-get install gcc libgdal-dev graphviz proj-bin libproj-dev libspatialindex-dev ``` -Before installing `eo-learn` on **Linux** it is recommended to install the following system libraries: +#### Mac OS + +Before installing `eo-learn` on `Mac OS` it is recommended to install the following system libraries with [Homebrew](https://brew.sh/): ```bash -sudo apt-get install gcc libgdal-dev graphviz proj-bin libproj-dev libspatialindex-dev +brew install graphviz gcc gdal cmake spatialindex proj ``` +#### Windows + Before installing `eo-learn` on **Windows** it is recommended to install the following packages from [Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/): ```bash @@ -86,9 +81,30 @@ shapely fiona ``` -One of dependencies of `eo-learn-mask` subpackage is `lightgbm` package. On Windows it requires 64 bit Python distribution. If having problems during installation please check [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html). +### PyPI distribution + +`eo-learn` is available on PyPI and can be installed with: -Some subpackages contain extension modules under `extra` subfolder. Those modules typically require additional package dependencies which don't get installed by default. +```bash +pip install eo-learn +``` + +For some modules there are extra dependencies available, related to specific tasks. These were kept separate in order to keep the `eo-learn` installation light. You can install these with, e.g.: + +```bash +pip install "eo-learn[EXTRA]" +pip install "eo-learn[VISUALIZATION]" +``` + +The full list (including their descriptions) is available here: + +- `RAY` for installing ray and its dependencies +- `ZARR` for installing the zarr functionality for chunked timestamp saving/loading +- `EXTRA` for installing interpolation- and clustering-specific dependencies, or for installing `s2cloudless` in cloud masking +- `VISUALIZATION` for using plotting libraries and utilities +- `FULL` for installing all dependencies described so far +- `DOCS` for developers, dependencies for building documentation +- `DEV` for developers, dependencies for testing and code contribution ### Conda Forge distribution @@ -99,23 +115,9 @@ be installed using `conda-forge` as follows: ```bash conda config --add channels conda-forge - conda install eo-learn ``` -In order to avoid heavy package dependencies it is possible to install each subpackage separately: - -```bash -conda install eo-learn-core -conda install eo-learn-coregistration -conda install eo-learn-features -conda install eo-learn-geometry -conda install eo-learn-io -conda install eo-learn-mask -conda install eo-learn-ml-tools -conda install eo-learn-visualization -``` - ### Run with Docker A docker image with the latest released version of `eo-learn` is available at [Docker Hub](https://hub.docker.com/r/sentinelhub/eolearn). It provides a full installation of `eo-learn` together with a Jupyter notebook environment. You can pull and run it with: @@ -145,11 +147,13 @@ For more information on the package content, visit [readthedocs](https://eo-lear ## More Examples -Examples and introductions to the package can be found [here](https://github.com/sentinel-hub/eo-learn/tree/master/examples). A large collection of examples is available at the [`eo-learn-examples`](https://github.com/sentinel-hub/eo-learn-examples) repository. While the examples there are not always up-to-date they can be a great source of ideas. +Examples and introductions to the package can be found [here](https://github.com/sentinel-hub/eo-learn/tree/master/examples). A larger collection of examples is available at the [`eo-learn-examples`](https://github.com/sentinel-hub/eo-learn-examples) repository. While the examples there are not always up-to-date they can be a great source of ideas. + +In the past, `eo-learn` served as a collection of many useful tasks, originating from various contributors or projects. In order to keep `eo-learn` light and easy to maintain, we have decided to move these specific tasks to [`eo-learn-examples/extra-tasks`](https://github.com/sentinel-hub/eo-learn-examples/tree/main/extra-tasks), . ## Contributions -The list of all `eo-learn` contributors can be found [here](./CONTRIBUTING.md). If you would like to contribute to `eo-learn`, please check our [contribution guidelines](./CONTRIBUTING.md). +The list of all `eo-learn` contributors are listed in the [credits file](./CREDITS.md). If you would like to contribute to `eo-learn`, please check our [contribution guidelines](./CONTRIBUTING.md). ## Blog posts and papers diff --git a/core/MANIFEST.in b/core/MANIFEST.in deleted file mode 100644 index 4b271965f..000000000 --- a/core/MANIFEST.in +++ /dev/null @@ -1,8 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/core/py.typed -exclude eolearn/tests/* -exclude eolearn/tests/test_extra/* -exclude eolearn/tests/test_utils/* -exclude eolearn/tests/stats/* diff --git a/core/README.md b/core/README.md index 8eda8be0e..cc0380f6f 100644 --- a/core/README.md +++ b/core/README.md @@ -1,5 +1,5 @@ # eolearn.core subpackage -The main subpackage which implements basic building blocks (EOPatch, EOTask and EOWorkflow) and commonly used functionalities. +The deprecated way to install `eolearn.core`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.core.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#core). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/core/deprecated_eolearn_core/__init__.py b/core/deprecated_eolearn_core/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/core/deprecated_eolearn_core/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/core/eolearn/__init__.py b/core/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/core/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/core/eolearn/core/eodata_io.py b/core/eolearn/core/eodata_io.py deleted file mode 100644 index cf04af851..000000000 --- a/core/eolearn/core/eodata_io.py +++ /dev/null @@ -1,573 +0,0 @@ -""" -A module implementing utilities for working with saving and loading EOPatch data - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import concurrent.futures -import contextlib -import datetime -import gzip -import json -import platform -import warnings -from abc import ABCMeta, abstractmethod -from collections import defaultdict -from dataclasses import dataclass, field -from functools import partial -from typing import ( - TYPE_CHECKING, - Any, - BinaryIO, - Dict, - Generic, - Iterator, - List, - Mapping, - Optional, - Tuple, - TypeVar, -) - -import dateutil.parser -import fs -import fs.move -import geopandas as gpd -import numpy as np -import pandas as pd -from fs.base import FS -from fs.osfs import OSFS -from fs.tempfs import TempFS -from typing_extensions import TypeAlias - -from sentinelhub import CRS, BBox, Geometry, MimeType -from sentinelhub.exceptions import SHUserWarning, deprecated_function - -from .constants import TIMESTAMP_COLUMN, FeatureType, OverwritePermission -from .exceptions import EODeprecationWarning -from .types import EllipsisType, FeatureSpec, FeaturesSpecification -from .utils.parsing import FeatureParser - -if TYPE_CHECKING: - from .eodata import EOPatch - -T = TypeVar("T") -Self = TypeVar("Self", bound="FeatureIO") -PatchContentType: TypeAlias = Tuple[ - Optional["FeatureIOBBox"], - Optional["FeatureIOTimestamps"], - Optional["FeatureIOJson"], - Dict[Tuple[FeatureType, str], "FeatureIO"], -] - - -BBOX_FILENAME = "bbox" -TIMESTAMPS_FILENAME = "timestamps" - - -@dataclass -class FilesystemDataInfo: - """Information about data that is present on the filesystem. Fields represent paths to relevant file.""" - - timestamps: str | None = None - bbox: str | None = None - meta_info: str | None = None - features: dict[FeatureType, dict[str, str]] = field(default_factory=lambda: defaultdict(dict)) - - def iterate_features(self) -> Iterator[tuple[tuple[FeatureType, str], str]]: - """Yields `(ftype, fname), path` tuples from `features`.""" - for ftype, ftype_dict in self.features.items(): - for fname, path in ftype_dict.items(): - yield (ftype, fname), path - - -def save_eopatch( - eopatch: EOPatch, - filesystem: FS, - patch_location: str, - features: FeaturesSpecification = ..., - overwrite_permission: OverwritePermission = OverwritePermission.ADD_ONLY, - compress_level: int = 0, -) -> None: - """A utility function used by `EOPatch.save` method.""" - patch_exists = filesystem.exists(patch_location) - - eopatch_features = FeatureParser(features).get_features(eopatch) - file_information = get_filesystem_data_info(filesystem, patch_location) if patch_exists else FilesystemDataInfo() - - _check_collisions(overwrite_permission, eopatch_features, file_information) - - # Data must be collected before any tinkering with files due to lazy-loading - data_for_saving = list(_yield_features_to_save(eopatch, eopatch_features, patch_location)) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=EODeprecationWarning) - if overwrite_permission is OverwritePermission.OVERWRITE_PATCH and patch_exists: - _remove_old_eopatch(filesystem, patch_location) - - ftype_folders = {fs.path.dirname(path) for _, _, path in data_for_saving} - for folder in ftype_folders: - filesystem.makedirs(folder, recreate=True) - - with concurrent.futures.ThreadPoolExecutor() as executor: - save_function = partial(_save_single_feature, filesystem=filesystem, compress_level=compress_level) - list(executor.map(save_function, data_for_saving)) # Wrapped in a list to get better exceptions - - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=EODeprecationWarning) - if overwrite_permission is not OverwritePermission.OVERWRITE_PATCH: - remove_redundant_files(filesystem, eopatch_features, file_information, compress_level) - - -def _remove_old_eopatch(filesystem: FS, patch_location: str) -> None: - filesystem.removetree(patch_location) - filesystem.makedirs(patch_location, recreate=True) - - -def _yield_features_to_save( - eopatch: EOPatch, eopatch_features: list[FeatureSpec], patch_location: str -) -> Iterator[tuple[type[FeatureIO], Any, str]]: - """Prepares a triple `(featureIO, data, path)` so that the `featureIO` can save `data` to `path`.""" - get_file_path = partial(fs.path.join, patch_location) - meta_features = {ftype for ftype, _ in eopatch_features if ftype.is_meta()} - - if eopatch.bbox is not None: # remove after BBox is never None - yield (FeatureIOBBox, eopatch.bbox, get_file_path(BBOX_FILENAME)) - - if eopatch.timestamps and FeatureType.TIMESTAMPS in meta_features: - yield (FeatureIOTimestamps, eopatch.timestamps, get_file_path(TIMESTAMPS_FILENAME)) - - if eopatch.meta_info and FeatureType.META_INFO in meta_features: - yield (FeatureIOJson, eopatch.meta_info, get_file_path(FeatureType.META_INFO.value)) - - for ftype, fname in eopatch_features: - if not ftype.is_meta(): - yield (_get_feature_io_constructor(ftype), eopatch[(ftype, fname)], get_file_path(ftype.value, fname)) - - -def _save_single_feature(save_spec: tuple[type[FeatureIO[T]], T, str], *, filesystem: FS, compress_level: int) -> None: - feature_io, data, feature_path = save_spec - feature_io.save(data, filesystem, feature_path, compress_level) - - -def remove_redundant_files( - filesystem: FS, - eopatch_features: list[FeatureSpec], - preexisting_files: FilesystemDataInfo, - current_compress_level: int, -) -> None: - """Removes files that should have been overwritten but were not due to different compression levels.""" - - def has_different_compression(path: str | None) -> bool: - return path is not None and MimeType.GZIP.matches_extension(path) != (current_compress_level > 0) - - files_to_remove = [] - saved_meta_types = {ftype for ftype, _ in eopatch_features if ftype.is_meta()} - - for ftype, fname in eopatch_features: - if ftype.is_meta(): - continue - path = preexisting_files.features.get(ftype, {}).get(fname) # type: ignore[arg-type] - if has_different_compression(path): - files_to_remove.append(path) - - if FeatureType.BBOX in saved_meta_types and has_different_compression(preexisting_files.bbox): - files_to_remove.append(preexisting_files.bbox) - - if FeatureType.TIMESTAMPS in saved_meta_types and has_different_compression(preexisting_files.timestamps): - files_to_remove.append(preexisting_files.timestamps) - - if FeatureType.META_INFO in saved_meta_types and has_different_compression(preexisting_files.meta_info): - files_to_remove.append(preexisting_files.meta_info) - - with concurrent.futures.ThreadPoolExecutor() as executor: - list(executor.map(filesystem.remove, files_to_remove)) # Wrapped in a list to get better exceptions - - -def load_eopatch_content( - filesystem: FS, patch_location: str, features: FeaturesSpecification = ... -) -> PatchContentType: - """A utility function used by `EOPatch.load` method.""" - file_information = get_filesystem_data_info(filesystem, patch_location, features) - bbox, timestamps, meta_info = _load_meta_features(filesystem, file_information, features) - - features_dict: dict[tuple[FeatureType, str], FeatureIO] = {} - for ftype, fname in FeatureParser(features).get_feature_specifications(): - if ftype.is_meta(): - continue - - if fname is ...: - for fname, path in file_information.features.get(ftype, {}).items(): - features_dict[(ftype, fname)] = _get_feature_io_constructor(ftype)(path, filesystem) - else: - if ftype not in file_information.features or fname not in file_information.features[ftype]: - raise IOError(f"Feature {(ftype, fname)} does not exist in eopatch at {patch_location}.") - path = file_information.features[ftype][fname] - features_dict[(ftype, fname)] = _get_feature_io_constructor(ftype)(path, filesystem) - - return bbox, timestamps, meta_info, features_dict - - -def _load_meta_features( - filesystem: FS, file_information: FilesystemDataInfo, features: FeaturesSpecification -) -> tuple[FeatureIOBBox | None, FeatureIOTimestamps | None, FeatureIOJson | None]: - requested = {ftype for ftype, _ in FeatureParser(features).get_feature_specifications() if ftype.is_meta()} - - err_msg = "Feature {} is specified to be loaded but does not exist in EOPatch." - - bbox = None - if file_information.bbox is not None: - bbox = FeatureIOBBox(file_information.bbox, filesystem) - elif FeatureType.BBOX in requested and features is not Ellipsis: - raise IOError(err_msg.format(FeatureType.BBOX)) - - timestamps = None - if FeatureType.TIMESTAMPS in requested: - if file_information.timestamps is not None: - timestamps = FeatureIOTimestamps(file_information.timestamps, filesystem) - elif features is not Ellipsis: - raise IOError(err_msg.format(FeatureType.TIMESTAMPS)) - - meta_info = None - if FeatureType.META_INFO in requested: - if file_information.meta_info is not None: - meta_info = FeatureIOJson(file_information.meta_info, filesystem) - elif any( - ftype == FeatureType.META_INFO and isinstance(fname, str) - for ftype, fname in FeatureParser(features).get_feature_specifications() - ): - raise IOError(err_msg.format(FeatureType.META_INFO)) - - return bbox, timestamps, meta_info - - -def get_filesystem_data_info( - filesystem: FS, patch_location: str, features: FeaturesSpecification = ... -) -> FilesystemDataInfo: - """Returns information on all eopatch files in the storage. Filters with `features` to reduce IO calls.""" - relevant_features = FeatureParser(features).get_feature_specifications() - relevant_feature_types = {ftype for ftype, _ in relevant_features} - - result = FilesystemDataInfo() - - for path in filesystem.listdir(patch_location): - object_name = _remove_file_extension(path).strip("/") - object_path = fs.path.combine(patch_location, path) - - if object_name == "timestamp": - warnings.warn( - ( - f"EOPatch at {patch_location} contains the deprecated naming `timestamp` for the `timestamps`" - " feature. The old name will no longer be valid in the future. You can re-save the `EOPatch` to" - " update it." - ), - category=EODeprecationWarning, - stacklevel=2, - ) - object_name = TIMESTAMPS_FILENAME - - if "/" in object_name: # For cases where S3 does not have a regular folder structure - ftype_str, fname = fs.path.split(object_name) - result.features[FeatureType(ftype_str)][fname] = path - - elif object_name == BBOX_FILENAME: - result.bbox = object_path - - elif object_name == TIMESTAMPS_FILENAME: - result.timestamps = object_path - - elif object_name == FeatureType.META_INFO.value: - result.meta_info = object_path - - elif FeatureType.has_value(object_name) and FeatureType(object_name) in relevant_feature_types: - result.features[FeatureType(object_name)] = dict(walk_feature_type_folder(filesystem, object_path)) - - # Note: might simplify a few things if we filtered according to features here, especially loading stuff. - return result - - -@deprecated_function(category=EODeprecationWarning) -def walk_filesystem( - filesystem: FS, patch_location: str, features: FeaturesSpecification = ... -) -> Iterator[tuple[FeatureType, str | EllipsisType, str]]: - """Interface to the old walk_filesystem function which yields tuples of (feature_type, feature_name, file_path).""" - file_information = get_filesystem_data_info(filesystem, patch_location, features) - - if file_information.bbox is not None: # remove after BBox is never None - yield (FeatureType.BBOX, ..., file_information.bbox) - - if file_information.timestamps is not None: - yield (FeatureType.TIMESTAMPS, ..., file_information.timestamps) - - if file_information.meta_info is not None: - yield (FeatureType.META_INFO, ..., file_information.meta_info) - - for feature, path in file_information.iterate_features(): - yield (*feature, path) - - -def walk_feature_type_folder(filesystem: FS, folder_path: str) -> Iterator[tuple[str, str]]: - """Walks a feature type subfolder of EOPatch and yields tuples (feature name, path in filesystem). - Skips folders and files in subfolders. - """ - for path in filesystem.listdir(folder_path): - if "/" not in path and "." in path: - yield _remove_file_extension(path), fs.path.combine(folder_path, path) - - -def _check_collisions( - overwrite_permission: OverwritePermission, eopatch_features: list[FeatureSpec], existing_files: FilesystemDataInfo -) -> None: - """Checks for possible name collisions to avoid unintentional overwriting.""" - if overwrite_permission is OverwritePermission.ADD_ONLY: - _check_letter_case_collisions(eopatch_features, existing_files) - _check_add_only_permission(eopatch_features, existing_files) - - elif platform.system() == "Windows" and overwrite_permission is OverwritePermission.OVERWRITE_FEATURES: - _check_letter_case_collisions(eopatch_features, existing_files) - - else: - _check_letter_case_collisions(eopatch_features, FilesystemDataInfo()) - - -def _check_add_only_permission(eopatch_features: list[FeatureSpec], filesystem_features: FilesystemDataInfo) -> None: - """Checks that no existing feature will be overwritten.""" - unique_filesystem_features = {_to_lowercase(*feature) for feature, _ in filesystem_features.iterate_features()} - unique_eopatch_features = {_to_lowercase(*feature) for feature in eopatch_features} - - intersection = unique_filesystem_features.intersection(unique_eopatch_features) - if intersection: - raise ValueError(f"Cannot save features {intersection} with overwrite_permission=OverwritePermission.ADD_ONLY") - - -def _check_letter_case_collisions(eopatch_features: list[FeatureSpec], filesystem_features: FilesystemDataInfo) -> None: - """Check that features have no name clashes (ignoring case) with other EOPatch features and saved features.""" - lowercase_features = {_to_lowercase(*feature) for feature in eopatch_features} - - if len(lowercase_features) != len(eopatch_features): - raise IOError("Some features differ only in casing and cannot be saved in separate files.") - - for feature, _ in filesystem_features.iterate_features(): - if feature not in eopatch_features and _to_lowercase(*feature) in lowercase_features: - raise IOError( - f"There already exists a feature {feature} in the filesystem that only differs in " - "casing from a feature that should be saved." - ) - - -def _to_lowercase(ftype: FeatureType, fname: str | None, *_: Any) -> tuple[FeatureType, str | None]: - """Transforms a feature to it's lowercase representation.""" - return ftype, fname if fname is None else fname.lower() - - -def _remove_file_extension(path: str) -> str: - """This also removes file extensions of form `.geojson.gz` unlike `fs.path.splitext`.""" - return path.split(".")[0] - - -class FeatureIO(Generic[T], metaclass=ABCMeta): - """A class that handles the saving and loading process of a single feature at a given location.""" - - def __init__(self, path: str, filesystem: FS): - """ - :param path: A path in the filesystem - :param filesystem: A filesystem object - :compress_level: The compression level to be used when saving, inferred from path if not provided - """ - filename = fs.path.basename(path) - expected_extension = f".{self.get_file_format().extension}" - compressed_extension = expected_extension + f".{MimeType.GZIP.extension}" - if not filename.endswith((expected_extension, compressed_extension)): - raise ValueError(f"FeatureIO expects a filepath with the {expected_extension} file extension, got {path}") - - self.path = path - self.filesystem = filesystem - - self.loaded_value: T | None = None - - @classmethod - @abstractmethod - def get_file_format(cls) -> MimeType: - """The type of files handled by the FeatureIO.""" - - def __repr__(self) -> str: - return f"{self.__class__.__name__}({self.path})" - - def load(self) -> T: - """Method for loading a feature. The loaded value is stored into an attribute in case a second load request is - triggered inside a shallow-copied EOPatch. - """ - if self.loaded_value is not None: - return self.loaded_value - - with self.filesystem.openbin(self.path, "r") as file_handle: - if MimeType.GZIP.matches_extension(self.path): - with gzip.open(file_handle, "rb") as gzip_fp: - self.loaded_value = self._read_from_file(gzip_fp) - else: - self.loaded_value = self._read_from_file(file_handle) - - return self.loaded_value - - @abstractmethod - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> T: - """Loads from a file and decodes content.""" - - @classmethod - def save(cls, data: T, filesystem: FS, feature_path: str, compress_level: int = 0) -> None: - """Method for saving a feature. The path is assumed to be filesystem path but without file extensions. - - Example of path is `eopatch/data/NDVI`, which is then used to save `eopatch/data/NDVI.npy.gz`. - - To minimize the chance of corrupted files (in case of OSFS and TempFS) the file is first written and then moved - to correct location. If any exceptions happen during the writing process the file is not moved (and old one not - overwritten). - """ - gz_extension = ("." + MimeType.GZIP.extension) if compress_level else "" - path = f"{feature_path}.{cls.get_file_format().extension}{gz_extension}" - - if isinstance(filesystem, (OSFS, TempFS)): - with TempFS(temp_dir=filesystem.root_path) as tempfs: - cls._save(data, tempfs, "tmp_feature", compress_level) - if fs.__version__ == "2.4.16" and filesystem.exists(path): # An issue in the fs version - filesystem.remove(path) - fs.move.move_file(tempfs, "tmp_feature", filesystem, path) - return - cls._save(data, filesystem, path, compress_level) - - @classmethod - def _save(cls, data: T, filesystem: FS, path: str, compress_level: int) -> None: - """Given a filesystem it saves and compresses the data.""" - with filesystem.openbin(path, "w") as file: - if compress_level == 0: - cls._write_to_file(data, file, path) - else: - with gzip.GzipFile(fileobj=file, compresslevel=compress_level, mode="wb") as gzip_file: - cls._write_to_file(data, gzip_file, path) - - @classmethod - @abstractmethod - def _write_to_file(cls, data: T, file: BinaryIO | gzip.GzipFile, path: str) -> None: - """Writes data to a file in the appropriate way.""" - - -class FeatureIONumpy(FeatureIO[np.ndarray]): - """FeatureIO object specialized for Numpy arrays.""" - - @classmethod - def get_file_format(cls) -> MimeType: - return MimeType.NPY - - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> np.ndarray: - return np.load(file, allow_pickle=True) - - @classmethod - def _write_to_file(cls, data: np.ndarray, file: BinaryIO | gzip.GzipFile, _: str) -> None: - return np.save(file, data) - - -class FeatureIOGeoDf(FeatureIO[gpd.GeoDataFrame]): - """FeatureIO object specialized for GeoDataFrames.""" - - @classmethod - def get_file_format(cls) -> MimeType: - return MimeType.GPKG - - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> gpd.GeoDataFrame: - dataframe = gpd.read_file(file) - - if dataframe.crs is not None: - # Trying to preserve a standard CRS and passing otherwise - with contextlib.suppress(ValueError), warnings.catch_warnings(): - warnings.simplefilter("ignore", category=SHUserWarning) - dataframe.crs = CRS(dataframe.crs).pyproj_crs() - - if TIMESTAMP_COLUMN in dataframe: - dataframe[TIMESTAMP_COLUMN] = pd.to_datetime(dataframe[TIMESTAMP_COLUMN]) - - return dataframe - - @classmethod - def _write_to_file(cls, data: gpd.GeoDataFrame, file: BinaryIO | gzip.GzipFile, path: str) -> None: - layer = fs.path.basename(path) - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", - message="You are attempting to write an empty DataFrame to file*", - category=UserWarning, - ) - return data.to_file(file, driver="GPKG", encoding="utf-8", layer=layer, index=False) - - -class FeatureIOJson(FeatureIO[T]): - """FeatureIO object specialized for JSON-like objects.""" - - @classmethod - def get_file_format(cls) -> MimeType: - return MimeType.JSON - - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> T: - return json.load(file) - - @classmethod - def _write_to_file(cls, data: T, file: BinaryIO | gzip.GzipFile, path: str) -> None: - try: - json_data = json.dumps(data, indent=2, default=_better_jsonify) - except TypeError as exception: - raise TypeError( - f"Failed to serialize when saving JSON file to {path}. Make sure that this feature type " - "contains only JSON serializable Python types before attempting to serialize it." - ) from exception - - file.write(json_data.encode()) - - -class FeatureIOTimestamps(FeatureIOJson[List[datetime.datetime]]): - """FeatureIOJson object specialized for List[dt.datetime].""" - - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> list[datetime.datetime]: - data = json.load(file) - return [dateutil.parser.parse(timestamp) for timestamp in data] - - -class FeatureIOBBox(FeatureIO[BBox]): - """FeatureIO object specialized for BBox objects.""" - - @classmethod - def get_file_format(cls) -> MimeType: - return MimeType.GEOJSON - - def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> BBox: - json_data = json.load(file) - return Geometry.from_geojson(json_data).bbox - - @classmethod - def _write_to_file(cls, data: BBox, file: BinaryIO | gzip.GzipFile, _: str) -> None: - json_data = json.dumps(data.geojson, indent=2) - file.write(json_data.encode()) - - -def _better_jsonify(param: object) -> Any: - """Adds the option to serialize datetime.date and FeatureDict objects via isoformat.""" - if isinstance(param, datetime.date): - return param.isoformat() - if isinstance(param, Mapping): - return dict(param.items()) - raise TypeError(f"Object of type {type(param)} is not yet supported in jsonify utility function") - - -def _get_feature_io_constructor(ftype: FeatureType) -> type[FeatureIO]: - """Creates the correct FeatureIO, corresponding to the FeatureType.""" - if ftype is FeatureType.BBOX: - return FeatureIOBBox - if ftype is FeatureType.META_INFO: - return FeatureIOJson - if ftype is FeatureType.TIMESTAMPS: - return FeatureIOTimestamps - if ftype.is_vector(): - return FeatureIOGeoDf - return FeatureIONumpy diff --git a/core/eolearn/tests/test_constants.py b/core/eolearn/tests/test_constants.py deleted file mode 100644 index 8f4bfee68..000000000 --- a/core/eolearn/tests/test_constants.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import pytest - -from eolearn.core import FeatureType - - -@pytest.mark.parametrize( - ("old_ftype", "new_ftype"), - [ - (FeatureType.TIMESTAMP, FeatureType.TIMESTAMPS), - (FeatureType["TIMESTAMP"], FeatureType["TIMESTAMPS"]), - (FeatureType("timestamp"), FeatureType("timestamps")), - ], - ids=["attribute access", "name access", "value access"], -) -def test_timestamp_featuretype(old_ftype, new_ftype) -> None: - assert old_ftype is new_ftype diff --git a/core/eolearn/tests/test_eodata_io.py b/core/eolearn/tests/test_eodata_io.py deleted file mode 100644 index 3ad5d6c33..000000000 --- a/core/eolearn/tests/test_eodata_io.py +++ /dev/null @@ -1,402 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import datetime -import os -import tempfile -import warnings -from typing import Any, Type - -import fs -import geopandas as gpd -import numpy as np -import pytest -from fs.errors import CreateFailed, ResourceNotFound -from fs.tempfs import TempFS -from geopandas import GeoDataFrame -from moto import mock_s3 -from shapely.geometry import Point - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType, LoadTask, OverwritePermission, SaveTask, merge_eopatches -from eolearn.core.constants import TIMESTAMP_COLUMN -from eolearn.core.eodata_io import ( - FeatureIO, - FeatureIOBBox, - FeatureIOGeoDf, - FeatureIOJson, - FeatureIONumpy, - FeatureIOTimestamps, - walk_filesystem, -) -from eolearn.core.exceptions import EODeprecationWarning -from eolearn.core.types import FeaturesSpecification -from eolearn.core.utils.parsing import FeatureParser -from eolearn.core.utils.testing import assert_feature_data_equal, generate_eopatch - -FS_LOADERS = [TempFS, pytest.lazy_fixture("create_mocked_s3fs")] - -DUMMY_BBOX = BBox((0, 0, 1, 1), CRS.WGS84) - - -@pytest.fixture(name="_silence_warnings") -def _silence_warnings_fixture(): - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=EODeprecationWarning) - yield - - -@pytest.fixture(name="eopatch") -def eopatch_fixture(): - eopatch = generate_eopatch( - { - FeatureType.DATA: ["data"], - FeatureType.MASK_TIMELESS: ["mask"], - FeatureType.SCALAR: ["my scalar with spaces"], - FeatureType.SCALAR_TIMELESS: ["my timeless scalar with spaces"], - FeatureType.META_INFO: ["something", "something-else"], - } - ) - eopatch.vector["my-df"] = GeoDataFrame( - { - "values": [1, 2], - TIMESTAMP_COLUMN: [datetime.datetime(2017, 1, 1, 10, 4, 7), datetime.datetime(2017, 1, 4, 10, 14, 5)], - "geometry": [DUMMY_BBOX.geometry, DUMMY_BBOX.geometry], - }, - crs=DUMMY_BBOX.crs.pyproj_crs(), - ) - eopatch.vector_timeless["empty-vector"] = GeoDataFrame( - {"values": [], "geometry": []}, crs=eopatch.bbox.crs.pyproj_crs() - ) - - return eopatch - - -def test_saving_to_a_file(eopatch): - with tempfile.NamedTemporaryFile() as fp, pytest.raises(CreateFailed): - eopatch.save(fp.name) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_saving_in_empty_folder(eopatch, fs_loader): - with fs_loader() as temp_fs: - if isinstance(temp_fs, TempFS): - eopatch.save(temp_fs.root_path) - else: - eopatch.save("/", filesystem=temp_fs) - assert temp_fs.exists("/mask_timeless/mask.npy") - - subfolder = "new-subfolder" - eopatch.save("new-subfolder", filesystem=temp_fs) - assert temp_fs.exists(f"/{subfolder}/bbox.geojson") - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.usefixtures("_silence_warnings") -def test_saving_in_non_empty_folder(eopatch, fs_loader): - with fs_loader() as temp_fs: - empty_file = "foo.txt" - - with temp_fs.open(empty_file, "w"): - pass - - eopatch.save("/", filesystem=temp_fs) - assert temp_fs.exists(empty_file) - - eopatch.save("/", overwrite_permission=OverwritePermission.OVERWRITE_PATCH, filesystem=temp_fs) - assert not temp_fs.exists(empty_file) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.usefixtures("_silence_warnings") -def test_overwriting_non_empty_folder(eopatch, fs_loader): - with fs_loader() as temp_fs: - eopatch.save("/", filesystem=temp_fs) - eopatch.save("/", filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES) - eopatch.save("/", filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_PATCH) - - add_eopatch = EOPatch(bbox=eopatch.bbox) - add_eopatch.data_timeless["some data"] = np.empty((3, 3, 2)) - add_eopatch.save("/", filesystem=temp_fs, overwrite_permission=OverwritePermission.ADD_ONLY) - with pytest.raises(ValueError): - add_eopatch.save("/", filesystem=temp_fs, overwrite_permission=OverwritePermission.ADD_ONLY) - - new_eopatch = EOPatch.load("/", filesystem=temp_fs, lazy_loading=False) - assert new_eopatch == merge_eopatches(eopatch, add_eopatch) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.parametrize( - ("save_features", "load_features"), - [ - (..., ...), - ([(FeatureType.DATA, ...), FeatureType.TIMESTAMPS], [(FeatureType.DATA, ...), FeatureType.TIMESTAMPS]), - ([(FeatureType.DATA, "data"), FeatureType.TIMESTAMPS], [(FeatureType.DATA, ...)]), - ([(FeatureType.DATA, "data"), FeatureType.TIMESTAMPS], ...), - ], -) -def test_save_load_partial( - eopatch: EOPatch, fs_loader, save_features: FeaturesSpecification, load_features: FeaturesSpecification -): - with fs_loader() as temp_fs: - eopatch.save("/", features=save_features, filesystem=temp_fs) - loaded_eopatch = EOPatch.load("/", features=load_features, filesystem=temp_fs) - - # have to check features that have been saved and then loaded (double filtering) - features_to_load = FeatureParser(load_features).get_features(eopatch) - for feature in FeatureParser(save_features).get_features(eopatch): - if feature in features_to_load: - assert feature in loaded_eopatch - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_save_add_only_features(eopatch, fs_loader): - features = [ - (FeatureType.MASK_TIMELESS, "mask"), - FeatureType.MASK, - FeatureType.VECTOR, - (FeatureType.SCALAR, ...), - (FeatureType.META_INFO, "something"), - FeatureType.BBOX, - ] - - with fs_loader() as temp_fs: - eopatch.save("/", filesystem=temp_fs, features=features, overwrite_permission=OverwritePermission.ADD_ONLY) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_bbox_always_saved(eopatch, fs_loader): - with fs_loader() as temp_fs: - eopatch.save("/", filesystem=temp_fs, features=[FeatureType.DATA]) - assert temp_fs.exists("/bbox.geojson") - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.usefixtures("_silence_warnings") -def test_overwrite_failure(fs_loader): - eopatch = EOPatch(bbox=DUMMY_BBOX) - mask = np.arange(3 * 3 * 2).reshape(3, 3, 2) - eopatch.mask_timeless["mask"] = mask - eopatch.mask_timeless["Mask"] = mask - - with fs_loader() as temp_fs, pytest.raises(IOError): - eopatch.save("/", filesystem=temp_fs) - - with fs_loader() as temp_fs: - eopatch.save( - "/", - filesystem=temp_fs, - features=[(FeatureType.MASK_TIMELESS, "mask")], - overwrite_permission=OverwritePermission.OVERWRITE_PATCH, - ) - - with pytest.raises(IOError): - eopatch.save( - "/", - filesystem=temp_fs, - features=[(FeatureType.MASK_TIMELESS, "Mask")], - overwrite_permission=OverwritePermission.ADD_ONLY, - ) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_save_and_load_tasks(eopatch, fs_loader): - folder = "foo-folder" - patch_folder = "patch-folder" - with fs_loader() as temp_fs: - temp_fs.makedir(folder) - - save_task = SaveTask(folder, filesystem=temp_fs, compress_level=9) - load_task = LoadTask(folder, filesystem=temp_fs, lazy_loading=False) - - saved_eop = save_task(eopatch, eopatch_folder=patch_folder) - bbox_path = fs.path.join(folder, patch_folder, "bbox.geojson.gz") - assert temp_fs.exists(bbox_path) - assert saved_eop == eopatch - - eop = load_task(eopatch_folder=patch_folder) - assert eop == eopatch - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_fail_saving_nonexistent_feature(eopatch, fs_loader): - features = [(FeatureType.DATA, "nonexistent")] - with fs_loader() as temp_fs, pytest.raises(ValueError): - eopatch.save("/", filesystem=temp_fs, features=features) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_fail_loading_nonexistent_feature(fs_loader): - for features in [[(FeatureType.DATA, "nonexistent")], [(FeatureType.META_INFO, "nonexistent")]]: - with fs_loader() as temp_fs, pytest.raises(IOError): - EOPatch.load("/", filesystem=temp_fs, features=features) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_nonexistent_location(fs_loader): - """In the event of a path not existing all save actions should create the path, and loads should fail.""" - path = "./folder/subfolder/new-eopatch/" - eopatch = EOPatch(bbox=DUMMY_BBOX) - - # IO on nonexistent path inside a temporary FS - with fs_loader() as temp_fs: - with pytest.raises(ResourceNotFound): - EOPatch.load(path, filesystem=temp_fs) - - eopatch.save(path, filesystem=temp_fs) - - # IO on nonexistent path (no fs specified) - with TempFS() as temp_fs: - full_path = os.path.join(temp_fs.root_path, path) - with pytest.raises(CreateFailed): - EOPatch.load(full_path) - - load_task = LoadTask(full_path) - with pytest.raises(CreateFailed): - load_task.execute() - - eopatch.save(full_path) - assert os.path.exists(full_path) - - # SaveTask on nonexistent path (no fs specified) - with TempFS() as temp_fs: - full_path = os.path.join(temp_fs.root_path, path) - SaveTask(full_path).execute(eopatch) - assert os.path.exists(full_path) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -def test_cleanup_different_compression(fs_loader, eopatch): - folder = "foo-folder" - patch_folder = "patch-folder" - with fs_loader() as temp_fs: - temp_fs.makedir(folder) - - save_compressed_task = SaveTask( - folder, filesystem=temp_fs, compress_level=9, overwrite_permission="OVERWRITE_FEATURES" - ) - save_noncompressed_task = SaveTask( - folder, filesystem=temp_fs, compress_level=0, overwrite_permission="OVERWRITE_FEATURES" - ) - bbox_path = fs.path.join(folder, patch_folder, "bbox.geojson") - compressed_bbox_path = bbox_path + ".gz" - mask_timeless_path = fs.path.join(folder, patch_folder, "mask_timeless", "mask.npy") - compressed_mask_timeless_path = mask_timeless_path + ".gz" - - save_compressed_task(eopatch, eopatch_folder=patch_folder) - save_noncompressed_task(eopatch, eopatch_folder=patch_folder) - assert temp_fs.exists(bbox_path) - assert temp_fs.exists(mask_timeless_path) - assert not temp_fs.exists(compressed_bbox_path) - assert not temp_fs.exists(compressed_mask_timeless_path) - - save_compressed_task(eopatch, eopatch_folder=patch_folder) - assert not temp_fs.exists(bbox_path) - assert not temp_fs.exists(mask_timeless_path) - assert temp_fs.exists(compressed_bbox_path) - assert temp_fs.exists(compressed_mask_timeless_path) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.parametrize("folder_name", ["/", "foo", "foo/bar"]) -@pytest.mark.usefixtures("_silence_warnings") -def test_lazy_loading_plus_overwrite_patch(fs_loader, folder_name, eopatch): - with fs_loader() as temp_fs: - eopatch.save(folder_name, filesystem=temp_fs) - - lazy_eopatch = EOPatch.load(folder_name, filesystem=temp_fs, lazy_loading=True) - lazy_eopatch.data["whatever"] = np.empty((2, 3, 3, 2)) - del lazy_eopatch[FeatureType.MASK_TIMELESS, "mask"] - - lazy_eopatch.save(folder_name, filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_PATCH) - assert temp_fs.exists(fs.path.join(folder_name, "data", "whatever.npy")) - assert not temp_fs.exists(fs.path.join(folder_name, "mask_timeless", "mask.npy")) - - -@pytest.mark.parametrize( - ("constructor", "data"), - [ - (FeatureIONumpy, np.zeros(20)), - (FeatureIONumpy, np.zeros((2, 3, 3, 2), dtype=np.int16)), - (FeatureIONumpy, np.full((4, 5), fill_value=CRS.POP_WEB)), - (FeatureIOGeoDf, gpd.GeoDataFrame({"col1": ["name1"], "geometry": [Point(1, 2)]}, crs="EPSG:3857")), - (FeatureIOGeoDf, gpd.GeoDataFrame({"col1": ["name1"], "geometry": [Point(1, 2)]}, crs="EPSG:32733")), - ( - FeatureIOGeoDf, - gpd.GeoDataFrame( - { - "values": [1, 2], - TIMESTAMP_COLUMN: [ - datetime.datetime(2017, 1, 1, 10, 4, 7), - datetime.datetime(2017, 1, 4, 10, 14, 5), - ], - "geometry": [Point(1, 2), Point(2, 1)], - }, - crs="EPSG:3857", - ), - ), - (FeatureIOJson, {}), - (FeatureIOJson, {"test": "test1", "test3": {"test": "test1"}}), - (FeatureIOBBox, BBox((1, 2, 3, 4), CRS.WGS84)), - (FeatureIOTimestamps, []), - (FeatureIOTimestamps, [datetime.datetime(2017, 1, 1, 10, 4, 7), datetime.datetime(2017, 1, 4, 10, 14, 5)]), - ], -) -@pytest.mark.parametrize("compress_level", [0, 1]) -def test_feature_io(constructor: Type[FeatureIO], data: Any, compress_level: int) -> None: - """ - Tests verifying that FeatureIO subclasses correctly save, load, and lazy-load data. - Test cases do not include subfolders, because subfolder management is currently done by the `save_eopatch` function. - """ - - file_extension = "." + str(constructor.get_file_format().extension) - file_extension = file_extension if compress_level == 0 else file_extension + ".gz" - file_name = "name" - with TempFS("testing_file_sistem") as temp_fs: - feat_io = constructor(file_name + file_extension, filesystem=temp_fs) - constructor.save(data, temp_fs, file_name, compress_level) - loaded_data = feat_io.load() - assert_feature_data_equal(loaded_data, data) - - temp_fs.remove(file_name + file_extension) - cache_data = feat_io.load() - assert_feature_data_equal(loaded_data, cache_data) - - -@mock_s3 -@pytest.mark.parametrize("fs_loader", FS_LOADERS) -@pytest.mark.parametrize( - "features", - [ - ..., - [(FeatureType.DATA, "data"), FeatureType.TIMESTAMPS], - [(FeatureType.META_INFO, "something"), (FeatureType.SCALAR_TIMELESS, ...)], - ], -) -def test_walk_filesystem_interface(fs_loader, features, eopatch): - with fs_loader() as temp_fs: - io_kwargs = dict(path="./", filesystem=temp_fs, features=features) - eopatch.save(**io_kwargs) - loaded_eopatch = EOPatch.load(**io_kwargs) - - with pytest.warns(EODeprecationWarning): - for ftype, fname, _ in walk_filesystem(temp_fs, io_kwargs["path"], features): - feature_key = ftype if ftype.is_meta() else (ftype, fname) - assert feature_key in loaded_eopatch diff --git a/core/requirements.txt b/core/requirements.txt index 43f6bd519..562050068 100644 --- a/core/requirements.txt +++ b/core/requirements.txt @@ -1,9 +1 @@ -boto3 -fs -fs-s3fs -geopandas>=0.11.0 -numpy>=1.20.0 -python-dateutil -sentinelhub>=3.9.0 -tqdm>=4.27 -typing-extensions +eo-learn diff --git a/core/setup.py b/core/setup.py index 0fc14abb0..d091ceffe 100644 --- a/core/setup.py +++ b/core/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "core", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_core", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -42,12 +44,11 @@ def get_version(): author="Sinergise EO research team", author_email="eoresearch@sinergise.com", license="MIT", - packages=find_packages(exclude=["eolearn.tests*"]), - package_data={"eolearn": ["core/py.typed"]}, - include_package_data=True, + packages=find_packages(), install_requires=parse_requirements("requirements.txt"), - extras_require={"RAY": parse_requirements("requirements-ray.txt")}, - zip_safe=False, + extras_require={ + "RAY": parse_requirements("requirements-ray.txt"), + }, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/coregistration/MANIFEST.in b/coregistration/MANIFEST.in deleted file mode 100644 index 7d9a0dcc1..000000000 --- a/coregistration/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/coregistration/py.typed -exclude eolearn/tests/* diff --git a/coregistration/README.md b/coregistration/README.md index 407a914b0..94fdede85 100644 --- a/coregistration/README.md +++ b/coregistration/README.md @@ -1,5 +1,5 @@ # eolearn.coregistration subpackage -A collection of tools and EOTasks for image co-registration +The deprecated way to install `eolearn.coregistration`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.coregistration.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#coregistration). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/coregistration/deprecated_eolearn_coregistration/__init__.py b/coregistration/deprecated_eolearn_coregistration/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/coregistration/deprecated_eolearn_coregistration/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/coregistration/eolearn/__init__.py b/coregistration/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/coregistration/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/coregistration/eolearn/coregistration/py.typed b/coregistration/eolearn/coregistration/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/coregistration/requirements.txt b/coregistration/requirements.txt index 0b5b46e2d..562050068 100644 --- a/coregistration/requirements.txt +++ b/coregistration/requirements.txt @@ -1,3 +1 @@ -# using headless version of opencv, otherwise gitlab runners fail -eo-learn-core -opencv-python-headless +eo-learn diff --git a/coregistration/setup.py b/coregistration/setup.py index 474639df9..e09b78e5f 100644 --- a/coregistration/setup.py +++ b/coregistration/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "coregistration", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_coregistration", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,9 +45,7 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, install_requires=parse_requirements("requirements.txt"), - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/docker/eolearn.dockerfile b/docker/eolearn.dockerfile index c978c180f..50685d569 100644 --- a/docker/eolearn.dockerfile +++ b/docker/eolearn.dockerfile @@ -4,12 +4,12 @@ LABEL maintainer="Sinergise EO research team " LABEL description="An official eo-learn docker image with a full eo-learn installation and Jupyter notebook." RUN apt-get update && apt-get install -y \ - gcc \ - libgdal-dev \ - graphviz \ - proj-bin \ - libproj-dev \ - libspatialindex-dev \ + gcc \ + libgdal-dev \ + graphviz \ + proj-bin \ + libproj-dev \ + libspatialindex-dev \ && apt-get clean && apt-get autoremove -y && rm -rf /var/lib/apt/lists/* ENV CPLUS_INCLUDE_PATH=/usr/include/gdal @@ -20,25 +20,10 @@ RUN pip3 install --no-cache-dir shapely --no-binary :all: WORKDIR /tmp -COPY core core -COPY coregistration coregistration -COPY features features -COPY geometry geometry -COPY io io -COPY mask mask -COPY ml_tools ml_tools -COPY visualization visualization -COPY setup.py README.md requirements-dev.txt ./ +COPY eolearn eolearn +COPY pyproject.toml README.md LICENSE ./ -RUN pip3 install --no-cache-dir \ - ./core[RAY] \ - ./coregistration \ - ./features \ - ./geometry \ - ./io[METEOBLUE] \ - ./mask \ - ./ml_tools \ - ./visualization +RUN pip3 install --no-cache-dir .[FULL] RUN pip3 install --no-cache-dir \ . \ @@ -56,4 +41,4 @@ WORKDIR /home/eolearner EXPOSE 8888 CMD ["/usr/local/bin/jupyter", "notebook", "--no-browser", "--port=8888", "--ip=0.0.0.0", \ - "--NotebookApp.token=''", "--allow-root"] + "--NotebookApp.token=''", "--allow-root"] diff --git a/docs/Makefile b/docs/Makefile index 0b88aba07..0227ea890 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -19,4 +19,4 @@ help: %: Makefile rm -rf ./build/ $(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - rm -r source/examples source/markdowns source/reference source/eotasks.rst + # rm -r source/examples source/markdowns source/reference source/eotasks.rst diff --git a/docs/environment.yml b/docs/environment.yml index 8808b0b25..abe79b0c7 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -1,22 +1,15 @@ name: eo-learn-docs channels: -- conda-forge + - conda-forge dependencies: -- rasterio -- pip -- python=3.10 -- pip: - - Sphinx - - sphinx_rtd_theme - - nbsphinx - - jupyter - - sphinx_mdinclude + - rasterio + - pip + - python=3.10 + - pip: + - Sphinx==7.1.2 + - sphinx_rtd_theme==1.3.0 + - nbsphinx + - jupyter + - sphinx_mdinclude - - ./../core - - ./../coregistration - - ./../features - - ./../geometry - - ./../io[METEOBLUE] - - ./../mask - - ./../ml_tools[TDIGEST] - - ./../visualization + - ./../.[FULL] diff --git a/docs/source/conf.py b/docs/source/conf.py index 00247a5d0..bcb1a622f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -6,26 +6,23 @@ # full list see the documentation: # http://www.sphinx-doc.org/en/master/config -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# import os import shutil import sys -from collections import defaultdict from typing import Any, Dict, Optional import sphinx.ext.autodoc +# we import all submodules so that we can collect all tasks into a list import eolearn.core import eolearn.coregistration import eolearn.features +import eolearn.features.extra.clustering +import eolearn.features.extra.interpolation import eolearn.geometry import eolearn.io import eolearn.mask +import eolearn.mask.extra.cloud_mask import eolearn.ml_tools import eolearn.visualization # noqa: F401 from eolearn.core import EOTask @@ -43,21 +40,16 @@ # built documents. # # The release is read from __init__ file and version is shortened release string. -with open(os.path.join(os.path.dirname(__file__), "../../setup.py")) as setup_file: - for line in setup_file: - if "version=" in line: - release = line.split("=")[1].strip('", \n').strip("'") - version = release.rsplit(".", 1)[0] +with open(os.path.join(os.path.dirname(__file__), "../../eolearn/__init__.py")) as init_file: + for line in init_file: + if line.find("__version__") >= 0: + release = line.split("=")[1].strip() + release = release.strip('"').strip("'") -# -- General configuration --------------------------------------------------- +version = release.rsplit(".", 1)[0] -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' +# -- General configuration --------------------------------------------------- -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.doctest", @@ -88,18 +80,12 @@ # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. +# The language for content autogenerated by Sphinx. language = "en" # List of patterns, relative to source directory, that match files and @@ -110,9 +96,6 @@ # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = True - # Mock imports that won't and don't have to be installed in ReadTheDocs environment autodoc_mock_imports = [ "ray", @@ -120,6 +103,7 @@ "hvplot", "pyepsg", "xarray", + "tdigest", ] # -- Options for HTML output ------------------------------------------------- @@ -131,27 +115,11 @@ html_logo = "./figures/eo-learn-logo-white.png" -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - # -- Options for HTMLHelp output --------------------------------------------- @@ -160,77 +128,13 @@ # show/hide links for source html_show_sourcelink = False -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, "eo-learn.tex", doc_title, author, "manual"), -] - - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "eo-learn", doc_title, [author], 1)] - - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, "eo-learn", doc_title, author, "eo-learn", "One line description of project.", "Miscellaneous"), -] - -# -- Options for Epub output ---------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project -epub_author = author -epub_publisher = author -epub_copyright = copyright - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = '' - -# A unique identification for the text. -# -# epub_uid = '' - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ["search.html"] - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {"https://docs.python.org/3.8/": None} - # -- Custom settings ---------------------------------------------- # When Sphinx documents class signature it prioritizes __new__ method over __init__ method. The following hack puts # EOTask.__new__ method to the blacklist so that __init__ method signature will be taken instead. This seems the # cleanest way even though a private object is accessed. -sphinx.ext.autodoc._CLASS_NEW_BLACKLIST.append("{0.__module__}.{0.__qualname__}".format(EOTask.__new__)) # noqa[SLF001] +sphinx.ext.autodoc._CLASS_NEW_BLACKLIST.append("{0.__module__}.{0.__qualname__}".format(EOTask.__new__)) # noqa: SLF001 EXAMPLES_FOLDER = "./examples" @@ -257,7 +161,7 @@ def copy_documentation_examples(source_folder, target_folder): shutil.copyfile(source_path, target_path) -# copy examples +# Copy examples shutil.rmtree(EXAMPLES_FOLDER, ignore_errors=True) copy_documentation_examples("../../examples", EXAMPLES_FOLDER) @@ -322,6 +226,8 @@ def get_subclasses(cls): for eotask_cls in get_subclasses(EOTask): eopackage = eotask_cls.__module__.split(".")[1] + if eotask_cls.__module__.split(".")[2] == "extra": + eopackage += ".extra" eotask = eotask_cls.__module__ + "." + eotask_cls.__name__ if eopackage not in eopackage_tasks: @@ -356,10 +262,10 @@ def get_subclasses(cls): custom_reference_files = {filename.rsplit(".", 1)[0] for filename in os.listdir(custom_reference_dir)} repository_dir = os.path.join(current_dir, "..", "..") -modules = ["core", "coregistration", "features", "geometry", "io", "mask", "ml_tools", "visualization"] +module = "eolearn" APIDOC_OPTIONS = ["--module-first", "--separate", "--no-toc", "--templatedir", os.path.join(current_dir, "_templates")] -APIDOC_EXCLUDE = defaultdict(list, {"core": ["graph.py", "eodata_io.py", "eodata_merge.py"]}) +APIDOC_EXCLUDE = ["graph.py", "eodata_io.py", "eodata_merge.py"] shutil.rmtree(reference_dir, ignore_errors=True) shutil.copytree(custom_reference_dir, reference_dir) @@ -369,13 +275,9 @@ def run_apidoc(_): from sphinx.ext.apidoc import main sys.path.append(os.path.join(os.path.dirname(__file__), "..")) - for module in modules: - module_dir = os.path.join(current_dir, "..", "..", module) - - exclude = [os.path.join(module_dir, "eolearn", module, filename) for filename in APIDOC_EXCLUDE[module]] - exclude.extend([os.path.join(module_dir, "setup.py"), os.path.join(module_dir, "eolearn", "tests")]) - - main(["-e", "-o", reference_dir, module_dir, *exclude, *APIDOC_OPTIONS]) + module_dir = os.path.join(current_dir, "..", "..", module) + exclude = [os.path.join(module_dir, "core", filename) for filename in APIDOC_EXCLUDE] + main(["-e", "-o", reference_dir, module_dir, *exclude, *APIDOC_OPTIONS]) def configure_github_link(_app: Any, pagename: str, _templatename: Any, context: Dict[str, Any], _doctree: Any) -> None: diff --git a/eolearn/__init__.py b/eolearn/__init__.py new file mode 100644 index 000000000..61e393977 --- /dev/null +++ b/eolearn/__init__.py @@ -0,0 +1,16 @@ +"""Main module of the `eolearn` package.""" +__version__ = "1.5.0" + +import importlib.util +import warnings + +SUBPACKAGES = ["core", "coregistration", "features", "geometry", "io", "mask", "ml_tools", "visualization"] +deprecated_installs = [ + subpackage for subpackage in SUBPACKAGES if importlib.util.find_spec(f"deprecated_eolearn_{subpackage}") is not None +] +if deprecated_installs: + warnings.warn( + f"You are currently using an outdated installation of `eo-learn` for submodules {deprecated_installs}. You can" + " find instructions on how to install `eo-learn` correctly at" + " https://github.com/sentinel-hub/eo-learn/issues/733." + ) diff --git a/core/eolearn/core/__init__.py b/eolearn/core/__init__.py similarity index 91% rename from core/eolearn/core/__init__.py rename to eolearn/core/__init__.py index dd050c14e..168775b6b 100644 --- a/core/eolearn/core/__init__.py +++ b/eolearn/core/__init__.py @@ -1,7 +1,7 @@ """ The following objects and functions are the core of eo-learn package """ -from .constants import FeatureType, FeatureTypeSet, OverwritePermission +from .constants import FeatureType, OverwritePermission from .core_tasks import ( AddFeatureTask, CopyTask, @@ -32,5 +32,3 @@ from .utils.fs import get_filesystem, load_s3_filesystem, pickle_fs, unpickle_fs from .utils.parallelize import execute_with_mp_lock, join_futures, join_futures_iter, parallelize from .utils.parsing import FeatureParser - -__version__ = "1.4.2" diff --git a/core/eolearn/core/constants.py b/eolearn/core/constants.py similarity index 70% rename from core/eolearn/core/constants.py rename to eolearn/core/constants.py index 05f87f5d7..85132d27b 100644 --- a/core/eolearn/core/constants.py +++ b/eolearn/core/constants.py @@ -6,9 +6,11 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import warnings from enum import Enum, EnumMeta -from typing import Any, Optional, TypeVar +from typing import Any, TypeVar from sentinelhub import BBox, MimeType from sentinelhub.exceptions import deprecated_function @@ -18,28 +20,37 @@ TIMESTAMP_COLUMN = "TIMESTAMP" T = TypeVar("T") +FEATURETYPE_DEPRECATION_MSG = ( + "The `FeatureType.{}` has been deprecated and will be removed in the future. Use the EOPatch attribute `{}`" + " directly." +) + def _warn_and_adjust(name: T) -> T: # since we stick with `UPPER` for attributes and `lower` for values, we include both to reuse function - deprecation_msg = None # placeholder - if name in ("TIMESTAMP", "timestamp"): - name = "TIMESTAMPS" if name == "TIMESTAMP" else "timestamps" # type: ignore[assignment] + if isinstance(name, str): # to avoid type issues + if name in ("TIMESTAMP", "timestamp"): + name = "TIMESTAMPS" if name == "TIMESTAMP" else "timestamps" # type: ignore[assignment] - if deprecation_msg: - warnings.warn(deprecation_msg, category=EODeprecationWarning, stacklevel=3) # type: ignore[unreachable] + if name in ("TIMESTAMPS", "BBOX", "timestamps", "bbox"): + warnings.warn( + FEATURETYPE_DEPRECATION_MSG.format(name.upper(), name.lower()), + category=EODeprecationWarning, + stacklevel=3, + ) return name class EnumWithDeprecations(EnumMeta): """A custom EnumMeta class for catching the deprecated Enum members of the FeatureType Enum class.""" - def __getattribute__(cls, name: str) -> Any: # noqa[N805] + def __getattribute__(cls, name: str) -> Any: # noqa: N805 return super().__getattribute__(_warn_and_adjust(name)) - def __getitem__(cls, name: str) -> Any: # noqa[N805] + def __getitem__(cls, name: str) -> Any: # noqa: N805 return super().__getitem__(_warn_and_adjust(name)) - def __call__(cls, value: str, *args: Any, **kwargs: Any) -> Any: # noqa[N805] + def __call__(cls, value: str, *args: Any, **kwargs: Any) -> Any: # noqa: N805 return super().__call__(_warn_and_adjust(value), *args, **kwargs) @@ -99,14 +110,16 @@ def is_spatial(self) -> bool: def is_temporal(self) -> bool: """True if FeatureType has a time component. False otherwise.""" - return self in [ - FeatureType.DATA, - FeatureType.MASK, - FeatureType.SCALAR, - FeatureType.LABEL, - FeatureType.VECTOR, - FeatureType.TIMESTAMPS, - ] + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=FEATURETYPE_DEPRECATION_MSG.format(".*?", ".*?")) + return self in [ + FeatureType.DATA, + FeatureType.MASK, + FeatureType.SCALAR, + FeatureType.LABEL, + FeatureType.VECTOR, + FeatureType.TIMESTAMPS, + ] def is_timeless(self) -> bool: """True if FeatureType doesn't have a time component and is not a meta feature. False otherwise.""" @@ -118,7 +131,9 @@ def is_discrete(self) -> bool: def is_meta(self) -> bool: """True if FeatureType is for storing metadata info and False otherwise.""" - return self in [FeatureType.META_INFO, FeatureType.BBOX, FeatureType.TIMESTAMPS] + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=FEATURETYPE_DEPRECATION_MSG.format(".*?", ".*?")) + return self in [FeatureType.META_INFO, FeatureType.BBOX, FeatureType.TIMESTAMPS] def is_vector(self) -> bool: """True if FeatureType is vector feature type. False otherwise.""" @@ -170,7 +185,7 @@ def contains_ndarrays(self) -> bool: """True if FeatureType stores a dictionary of numpy.ndarrays. False otherwise.""" return self.is_array() - def ndim(self) -> Optional[int]: + def ndim(self) -> int | None: """If given FeatureType stores a dictionary of numpy.ndarrays it returns dimensions of such arrays.""" if self.is_array(): return { @@ -206,93 +221,6 @@ def file_format(self) -> MimeType: return MimeType.JSON -class DeprecatedCollectionClass(type): - """A custom meta class for raising a warning when collections of the deprecated FeatureTypeSet class are used.""" - - def __getattribute__(cls, name: str) -> Any: - if not name.startswith("_"): - warnings.warn( - ( - "The `FeatureTypeSet` collections are deprecated. The argument `allowed_feature_types` of feature" - " parsers can now be a callable, so you can use `lambda ftype: ftype.is_spatial()` instead of" - " `FeatureTypeSet.SPATIAL_TYPES` in such cases." - ), - category=EODeprecationWarning, - stacklevel=3, - ) - return super().__getattribute__(name) - - -class FeatureTypeSet(metaclass=DeprecatedCollectionClass): - """A collection of immutable sets of feature types, grouped together by certain properties.""" - - SPATIAL_TYPES = frozenset( - [ - FeatureType.DATA, - FeatureType.MASK, - FeatureType.VECTOR, - FeatureType.DATA_TIMELESS, - FeatureType.MASK_TIMELESS, - FeatureType.VECTOR_TIMELESS, - ] - ) - TEMPORAL_TYPES = frozenset( - [ - FeatureType.DATA, - FeatureType.MASK, - FeatureType.SCALAR, - FeatureType.LABEL, - FeatureType.VECTOR, - FeatureType.TIMESTAMPS, - ] - ) - TIMELESS_TYPES = frozenset( - [ - FeatureType.DATA_TIMELESS, - FeatureType.MASK_TIMELESS, - FeatureType.SCALAR_TIMELESS, - FeatureType.LABEL_TIMELESS, - FeatureType.VECTOR_TIMELESS, - ] - ) - DISCRETE_TYPES = frozenset( - [FeatureType.MASK, FeatureType.MASK_TIMELESS, FeatureType.LABEL, FeatureType.LABEL_TIMELESS] - ) - META_TYPES = frozenset([FeatureType.META_INFO, FeatureType.BBOX, FeatureType.TIMESTAMPS]) - VECTOR_TYPES = frozenset([FeatureType.VECTOR, FeatureType.VECTOR_TIMELESS]) - RASTER_TYPES = frozenset( - [ - FeatureType.DATA, - FeatureType.MASK, - FeatureType.SCALAR, - FeatureType.LABEL, - FeatureType.DATA_TIMELESS, - FeatureType.MASK_TIMELESS, - FeatureType.SCALAR_TIMELESS, - FeatureType.LABEL_TIMELESS, - ] - ) - DICT_TYPES = frozenset( - [ - FeatureType.DATA, - FeatureType.MASK, - FeatureType.SCALAR, - FeatureType.LABEL, - FeatureType.VECTOR, - FeatureType.DATA_TIMELESS, - FeatureType.MASK_TIMELESS, - FeatureType.SCALAR_TIMELESS, - FeatureType.LABEL_TIMELESS, - FeatureType.VECTOR_TIMELESS, - FeatureType.META_INFO, - ] - ) - RASTER_TYPES_4D = frozenset([FeatureType.DATA, FeatureType.MASK]) - RASTER_TYPES_3D = frozenset([FeatureType.DATA_TIMELESS, FeatureType.MASK_TIMELESS]) - RASTER_TYPES_2D = frozenset([FeatureType.SCALAR, FeatureType.LABEL]) - RASTER_TYPES_1D = frozenset([FeatureType.SCALAR_TIMELESS, FeatureType.LABEL_TIMELESS]) - - def _warn_and_adjust_permissions(name: T) -> T: if isinstance(name, str) and name.upper() == "OVERWRITE_PATCH": warnings.warn( @@ -306,13 +234,13 @@ def _warn_and_adjust_permissions(name: T) -> T: class PermissionsWithDeprecations(EnumMeta): """A custom EnumMeta class for catching the deprecated Enum members of the OverwritePermission Enum class.""" - def __getattribute__(cls, name: str) -> Any: # noqa[N805] + def __getattribute__(cls, name: str) -> Any: # noqa: N805 return super().__getattribute__(_warn_and_adjust_permissions(name)) - def __getitem__(cls, name: str) -> Any: # noqa[N805] + def __getitem__(cls, name: str) -> Any: # noqa: N805 return super().__getitem__(_warn_and_adjust_permissions(name)) - def __call__(cls, value: str, *args: Any, **kwargs: Any) -> Any: # noqa[N805] + def __call__(cls, value: str, *args: Any, **kwargs: Any) -> Any: # noqa: N805 return super().__call__(_warn_and_adjust_permissions(value), *args, **kwargs) @@ -331,7 +259,7 @@ class OverwritePermission(Enum, metaclass=PermissionsWithDeprecations): OVERWRITE_PATCH = "OVERWRITE_PATCH" @classmethod - def _missing_(cls, value: object) -> "OverwritePermission": + def _missing_(cls, value: object) -> OverwritePermission: permissions_mapping = {0: "ADD_ONLY", 1: "OVERWRITE_FEATURES", 2: "OVERWRITE_PATCH"} if isinstance(value, int) and value in permissions_mapping: deprecation_msg = ( diff --git a/core/eolearn/core/core_tasks.py b/eolearn/core/core_tasks.py similarity index 79% rename from core/eolearn/core/core_tasks.py rename to eolearn/core/core_tasks.py index 33a0d1eb8..21ba4648d 100644 --- a/core/eolearn/core/core_tasks.py +++ b/eolearn/core/core_tasks.py @@ -9,41 +9,55 @@ from __future__ import annotations import copy +import datetime as dt +import warnings from abc import ABCMeta -from typing import Any, Callable, Iterable, Tuple, Union, cast +from typing import Any, Callable, Iterable, Literal, Tuple, Union, cast import fs import numpy as np from fs.base import FS from sentinelhub import SHConfig +from sentinelhub.exceptions import deprecated_class -from .constants import FeatureType +from .constants import FeatureType, OverwritePermission from .eodata import EOPatch from .eodata_merge import merge_eopatches from .eotask import EOTask -from .types import FeatureSpec, FeaturesSpecification, SingleFeatureSpec +from .exceptions import EODeprecationWarning +from .types import EllipsisType, Feature, FeaturesSpecification from .utils.fs import get_filesystem, pickle_fs, unpickle_fs class CopyTask(EOTask): - """Makes a shallow copy of the given EOPatch. + """Makes a (shallow or deep) copy of the given EOPatch. It copies feature type dictionaries but not the data itself. """ - def __init__(self, features: FeaturesSpecification = ...): + def __init__( + self, + features: FeaturesSpecification = ..., + *, + deep: bool = False, + copy_timestamps: bool | Literal["auto"] = "auto", + ): """ :param features: A collection of features or feature types that will be copied into a new EOPatch. + :param deep: Whether the copy should be a deep or shallow copy. """ self.features = features + self.deep = deep + self.copy_timestamps = copy_timestamps def execute(self, eopatch: EOPatch) -> EOPatch: - return eopatch.copy(features=self.features) + return eopatch.copy(features=self.features, deep=self.deep, copy_timestamps=self.copy_timestamps) +@deprecated_class(EODeprecationWarning, "Use `CopyTask` with the configuration `deep=True`.") class DeepCopyTask(CopyTask): - """Makes a deep copy of the given EOPatch.""" + """[DEPRECATED] Makes a deep copy of the given EOPatch.""" def execute(self, eopatch: EOPatch) -> EOPatch: return eopatch.copy(features=self.features, deep=True) @@ -82,71 +96,147 @@ def filesystem(self) -> FS: class SaveTask(IOTask): """Saves the given EOPatch to a filesystem.""" - def __init__(self, path: str, filesystem: FS | None = None, config: SHConfig | None = None, **kwargs: Any): + def __init__( + self, + path: str, + filesystem: FS | None = None, + config: SHConfig | None = None, + features: FeaturesSpecification = ..., + overwrite_permission: OverwritePermission = OverwritePermission.ADD_ONLY, + *, + save_timestamps: bool | Literal["auto"] = "auto", + use_zarr: bool = False, + temporal_selection: None | slice | list[int] | Literal["infer"] = None, + compress_level: int | None = None, + ): """ :param path: root path where all EOPatches are saved - :param filesystem: An existing filesystem object. If not given it will be initialized according to the EOPatch - path. + :param filesystem: An existing filesystem object. If not given it will be initialized according to the path. + :param config: A configuration object with AWS credentials. By default, is set to None and in this case the + default configuration will be taken. :param features: A collection of features types specifying features of which type will be saved. By default, all features will be saved. :param overwrite_permission: A level of permission for overwriting an existing EOPatch - :param compress_level: A level of data compression and can be specified with an integer from 0 (no compression) to 9 (highest compression). - :param config: A configuration object with AWS credentials. By default, is set to None and in this case the - default configuration will be taken. + :save_timestamps: Whether to save the timestamps of the EOPatch. With the `"auto"` setting timestamps are saved + if `features=...` or if other temporal features are being saved. + :param use_zarr: Saves numpy-array based features into Zarr files. Requires ZARR extra dependencies. + :param temporal_selection: Writes all of the data to the chosen temporal indices of preexisting arrays. Can be + used for saving data in multiple steps for memory optimization. When set to `"infer"` it will match the + timestamps of the EOPatch to the timestamps of the stored EOPatch to calculate indices. """ - self.kwargs = kwargs + self.features = features + self.overwrite_permission = overwrite_permission + self.use_zarr = use_zarr + self.temporal_selection = temporal_selection + self.save_timestamps = save_timestamps super().__init__(path, filesystem=filesystem, create=True, config=config) - def execute(self, eopatch: EOPatch, *, eopatch_folder: str = "") -> EOPatch: + if compress_level is not None: + warnings.warn( + "The `compress_level` parameter has been deprecated, data is now compressed by default.", + category=EODeprecationWarning, + stacklevel=2, + ) + + def execute( + self, + eopatch: EOPatch, + *, + eopatch_folder: str = "", + temporal_selection: None | slice | list[int] | Literal["infer"] | EllipsisType = ..., + ) -> EOPatch: """Saves the EOPatch to disk: `folder/eopatch_folder`. :param eopatch: EOPatch which will be saved :param eopatch_folder: Name of EOPatch folder containing data. + :param temporal_selection: Overrides the `temporal_selection` parameter of task. :return: The same EOPatch """ path = fs.path.combine(self.filesystem_path, eopatch_folder) - eopatch.save(path, filesystem=self.filesystem, **self.kwargs) + temporal_selection = self.temporal_selection if temporal_selection is ... else temporal_selection + + eopatch.save( + path, + filesystem=self.filesystem, + features=self.features, + overwrite_permission=self.overwrite_permission, + save_timestamps=self.save_timestamps, + use_zarr=self.use_zarr, + temporal_selection=temporal_selection, + ) return eopatch class LoadTask(IOTask): """Loads an EOPatch from a filesystem.""" - def __init__(self, path: str, filesystem: FS | None = None, config: SHConfig | None = None, **kwargs: Any): + def __init__( + self, + path: str, + filesystem: FS | None = None, + config: SHConfig | None = None, + features: FeaturesSpecification = ..., + lazy_loading: bool = False, + *, + load_timestamps: bool | Literal["auto"] = "auto", + temporal_selection: None | slice | list[int] | Callable[[list[dt.datetime]], list[bool]] = None, + ): """ :param path: root directory where all EOPatches are saved :param filesystem: An existing filesystem object. If not given it will be initialized according to the EOPatch path. If you intend to run this task in multiprocessing mode you shouldn't specify this parameter. - :param features: A collection of features to be loaded. By default, all features will be loaded. - :param lazy_loading: If `True` features will be lazy loaded. Default is `False` :param config: A configuration object with AWS credentials. By default, is set to None and in this case the default configuration will be taken. + :param features: A collection of features to be loaded. By default, all features will be loaded. + :param lazy_loading: If `True` features will be lazy loaded. + :load_timestamps: Whether to load the timestamps of the EOPatch. With the `"auto"` setting timestamps are loaded + if `features=...` or if other temporal features are being loaded. + :param temporal_selection: Only loads data corresponding to the chosen indices. Can also be a callable that, + given a list of timestamps, returns a list of booleans declaring which temporal slices to load. """ - self.kwargs = kwargs + self.features = features + self.lazy_loading = lazy_loading + self.temporal_selection = temporal_selection + self.load_timestamps = load_timestamps super().__init__(path, filesystem=filesystem, create=False, config=config) - def execute(self, eopatch: EOPatch | None = None, *, eopatch_folder: str = "") -> EOPatch: + def execute( + self, + eopatch: EOPatch | None = None, + *, + eopatch_folder: str = "", + temporal_selection: None | slice | list[int] | Callable[[list[dt.datetime]], list[bool]] | EllipsisType = ..., + ) -> EOPatch: """Loads the EOPatch from disk: `folder/eopatch_folder`. :param eopatch: Optional input EOPatch. If given the loaded features are merged onto it, otherwise a new EOPatch is created. :param eopatch_folder: Name of EOPatch folder containing data. + :param temporal_selection: Overrides the `temporal_selection` parameter of task. :return: EOPatch loaded from disk """ path = fs.path.combine(self.filesystem_path, eopatch_folder) - loaded_patch = EOPatch.load(path, filesystem=self.filesystem, **self.kwargs) + temporal_selection = self.temporal_selection if temporal_selection is ... else temporal_selection + loaded_patch = EOPatch.load( + path, + filesystem=self.filesystem, + features=self.features, + lazy_loading=self.lazy_loading, + temporal_selection=temporal_selection, + load_timestamps=self.load_timestamps, + ) return loaded_patch if eopatch is None else merge_eopatches(eopatch, loaded_patch) class AddFeatureTask(EOTask): """Adds a feature to the given EOPatch.""" - def __init__(self, feature: FeatureSpec): + def __init__(self, feature: Feature): """ :param feature: Feature to be added """ - self.feature_type, self.feature_name = self.parse_feature(feature) + self.feature = self.parse_feature(feature) def execute(self, eopatch: EOPatch, data: object) -> EOPatch: """Returns the EOPatch with added features. @@ -155,11 +245,7 @@ def execute(self, eopatch: EOPatch, data: object) -> EOPatch: :param data: data to be added to the feature :return: input EOPatch with the specified feature """ - if self.feature_name is None: - eopatch[self.feature_type] = data - else: - eopatch[self.feature_type][self.feature_name] = data - + eopatch[self.feature] = data return eopatch @@ -254,7 +340,7 @@ class InitializeFeatureTask(EOTask): def __init__( self, features: FeaturesSpecification, - shape: tuple[int, ...] | FeatureSpec, + shape: tuple[int, ...] | Feature, init_value: int = 0, dtype: np.dtype | type = np.uint8, ): @@ -449,7 +535,7 @@ def zip_method(self, *f): def __init__( self, input_features: FeaturesSpecification, - output_feature: SingleFeatureSpec, + output_feature: Feature, zip_function: Callable | None = None, **kwargs: Any, ): @@ -516,8 +602,8 @@ class ExplodeBandsTask(EOTask): def __init__( self, - input_feature: tuple[FeatureType, str], - output_mapping: dict[tuple[FeatureType, str], int | Iterable[int]], + input_feature: Feature, + output_mapping: dict[Feature, int | Iterable[int]], ): """ :param input_feature: A source feature from which to take the subset of bands. diff --git a/core/eolearn/core/eodata.py b/eolearn/core/eodata.py similarity index 65% rename from core/eolearn/core/eodata.py rename to eolearn/core/eodata.py index 990858c3d..5f0dda5a6 100644 --- a/core/eolearn/core/eodata.py +++ b/eolearn/core/eodata.py @@ -13,11 +13,13 @@ import copy import datetime as dt import logging +import warnings from abc import ABCMeta, abstractmethod from typing import ( TYPE_CHECKING, Any, Callable, + ClassVar, Iterable, Iterator, Literal, @@ -25,7 +27,6 @@ MutableMapping, TypeVar, cast, - overload, ) from warnings import warn @@ -36,10 +37,10 @@ from sentinelhub import CRS, BBox, parse_time from sentinelhub.exceptions import deprecated_function -from .constants import TIMESTAMP_COLUMN, FeatureType, OverwritePermission -from .eodata_io import FeatureIO, FeatureIOJson, load_eopatch_content, save_eopatch -from .exceptions import EODeprecationWarning -from .types import EllipsisType, FeatureSpec, FeaturesSpecification +from .constants import FEATURETYPE_DEPRECATION_MSG, TIMESTAMP_COLUMN, FeatureType, OverwritePermission +from .eodata_io import FeatureIO, load_eopatch_content, save_eopatch +from .exceptions import EODeprecationWarning, TemporalDimensionWarning +from .types import EllipsisType, Feature, FeaturesSpecification from .utils.common import deep_eq, is_discrete_type from .utils.fs import get_filesystem from .utils.parsing import parse_features @@ -68,7 +69,7 @@ class _FeatureDict(MutableMapping[str, T], metaclass=ABCMeta): feature value, which is then called when the feature is accessed. """ - FORBIDDEN_CHARS = {".", "/", "\\", "|", ";", ":", "\n", "\t"} + FORBIDDEN_CHARS: ClassVar[set[str]] = {".", "/", "\\", "|", ";", ":", "\n", "\t"} def __init__(self, feature_dict: Mapping[str, T | FeatureIO[T]], feature_type: FeatureType): """ @@ -78,7 +79,11 @@ def __init__(self, feature_dict: Mapping[str, T | FeatureIO[T]], feature_type: F super().__init__() self.feature_type = feature_type - self._content = dict(feature_dict.items()) + + # we need trigger parsing and validation + self._content: dict[str, T | FeatureIO[T]] = {} + for key, value in feature_dict.items(): + self[key] = value def __setitem__(self, feature_name: str, value: T | FeatureIO[T]) -> None: """Before setting value to the dictionary it checks that value is of correct type and dimension and tries to @@ -143,6 +148,16 @@ def _parse_feature_value(self, value: object, feature_name: str) -> T: class _FeatureDictNumpy(_FeatureDict[np.ndarray]): """_FeatureDict object specialized for Numpy arrays.""" + def __init__( + self, + feature_dict: Mapping[str, np.ndarray | FeatureIO[np.ndarray]], + feature_type: FeatureType, + *, + temporal_dim: int | None, + ): + self._temporal_dim = temporal_dim + super().__init__(feature_dict, feature_type) + def _parse_feature_value(self, value: object, feature_name: str) -> np.ndarray: if not isinstance(value, np.ndarray): raise ValueError(f"{self.feature_type} feature has to be a numpy array.") @@ -154,6 +169,20 @@ def _parse_feature_value(self, value: object, feature_name: str) -> np.ndarray: f"dimension{'s' if expected_ndim > 1 else ''} but feature {feature_name} has {value.ndim}." ) + if self.feature_type.is_temporal(): + if self._temporal_dim is None: + msg = ( + f"Adding temporal feature {(self.feature_type, feature_name)} to EOPatch without a temporal" + " definition (no timestamps)." + ) + warnings.warn(msg, category=TemporalDimensionWarning, stacklevel=4) + elif self._temporal_dim != value.shape[0]: + msg = ( + f"Missmatch in temporal dimensions when adding {(self.feature_type, feature_name)}: EOPatch has" + f" {self._temporal_dim} timestamps while the value has a temporal size of {value.shape[0]}." + ) + warnings.warn(msg, category=TemporalDimensionWarning, stacklevel=4) + if self.feature_type.is_discrete() and not is_discrete_type(value.dtype): raise ValueError( f"{self.feature_type} is a discrete feature type therefore dtype of data array " @@ -162,6 +191,27 @@ def _parse_feature_value(self, value: object, feature_name: str) -> np.ndarray: return value + def _update_temporal_dim(self, new_value: int | None) -> None: + self._temporal_dim = new_value + if not self.feature_type.is_temporal(): + return + if self._temporal_dim is None: + if self._content: + warnings.warn( + f"EOPatch does not have timestamps, but has temporal features of type {self.feature_type}.", + category=TemporalDimensionWarning, + stacklevel=4, + ) + return + + for feature_name, value in self._content.items(): + if not isinstance(value, FeatureIO) and value.shape[0] != self._temporal_dim: + msg = ( + f"Missmatch in temporal dimensions. The EOPatch has {self._temporal_dim} timestamps while" + f" {(self.feature_type, feature_name)} has a temporal size of {value.shape[0]}." + ) + warnings.warn(msg, category=TemporalDimensionWarning, stacklevel=4) + class _FeatureDictGeoDf(_FeatureDict[gpd.GeoDataFrame]): """_FeatureDict object specialized for GeoDataFrames.""" @@ -192,15 +242,6 @@ def _parse_feature_value(self, value: object, _: str) -> Any: return value -def _create_feature_dict(feature_type: FeatureType, value: Mapping[str, Any]) -> _FeatureDict: - """Creates the correct FeatureDict, corresponding to the FeatureType.""" - if feature_type.is_vector(): - return _FeatureDictGeoDf(value, feature_type) - if feature_type is FeatureType.META_INFO: - return _FeatureDictJson(value, feature_type) - return _FeatureDictNumpy(value, feature_type) - - class EOPatch: """The basic data object for multi-temporal remotely sensed data, such as satellite imagery and its derivatives. @@ -222,9 +263,8 @@ class EOPatch: """ # establish types of property value holders - _timestamps: list[dt.datetime] + _timestamps: list[dt.datetime] | None _bbox: BBox | None - _meta_info: FeatureIOJson | _FeatureDictJson def __init__( self, @@ -243,32 +283,24 @@ def __init__( bbox: BBox | None = None, timestamps: list[dt.datetime] | None = None, ): - self.data: MutableMapping[str, np.ndarray] = _FeatureDictNumpy(data or {}, FeatureType.DATA) - self.mask: MutableMapping[str, np.ndarray] = _FeatureDictNumpy(mask or {}, FeatureType.MASK) - self.scalar: MutableMapping[str, np.ndarray] = _FeatureDictNumpy(scalar or {}, FeatureType.SCALAR) - self.label: MutableMapping[str, np.ndarray] = _FeatureDictNumpy(label or {}, FeatureType.LABEL) - self.vector: MutableMapping[str, gpd.GeoDataFrame] = _FeatureDictGeoDf(vector or {}, FeatureType.VECTOR) - self.data_timeless: MutableMapping[str, np.ndarray] = _FeatureDictNumpy( - data_timeless or {}, FeatureType.DATA_TIMELESS - ) - self.mask_timeless: MutableMapping[str, np.ndarray] = _FeatureDictNumpy( - mask_timeless or {}, FeatureType.MASK_TIMELESS - ) - self.scalar_timeless: MutableMapping[str, np.ndarray] = _FeatureDictNumpy( - scalar_timeless or {}, FeatureType.SCALAR_TIMELESS - ) - self.label_timeless: MutableMapping[str, np.ndarray] = _FeatureDictNumpy( - label_timeless or {}, FeatureType.LABEL_TIMELESS - ) - self.vector_timeless: MutableMapping[str, gpd.GeoDataFrame] = _FeatureDictGeoDf( - vector_timeless or {}, FeatureType.VECTOR_TIMELESS - ) - self.meta_info: MutableMapping[str, Any] = _FeatureDictJson(meta_info or {}, FeatureType.META_INFO) self.bbox = bbox - self.timestamps = timestamps or [] + self._timestamps = self._parse_timestamps(timestamps) # see timestamps setter why this is direct + + # the __setattr__ transforms the below to FeatureDicts and checks temporal dimensions if necessary + self.data: MutableMapping[str, np.ndarray] = dict(data or {}) + self.mask: MutableMapping[str, np.ndarray] = dict(mask or {}) + self.scalar: MutableMapping[str, np.ndarray] = dict(scalar or {}) + self.label: MutableMapping[str, np.ndarray] = dict(label or {}) + self.vector: MutableMapping[str, gpd.GeoDataFrame] = dict(vector or {}) + self.data_timeless: MutableMapping[str, np.ndarray] = dict(data_timeless or {}) + self.mask_timeless: MutableMapping[str, np.ndarray] = dict(mask_timeless or {}) + self.scalar_timeless: MutableMapping[str, np.ndarray] = dict(scalar_timeless or {}) + self.label_timeless: MutableMapping[str, np.ndarray] = dict(label_timeless or {}) + self.vector_timeless: MutableMapping[str, gpd.GeoDataFrame] = dict(vector_timeless or {}) + self.meta_info: MutableMapping[str, Any] = dict(meta_info or {}) @property - def timestamp(self) -> list[dt.datetime]: + def timestamp(self) -> list[dt.datetime] | None: """A property for handling the deprecated timestamp attribute.""" warn(TIMESTAMP_RENAME_WARNING, category=EODeprecationWarning, stacklevel=2) return self.timestamps @@ -279,16 +311,35 @@ def timestamp(self, value: list[dt.datetime]) -> None: self.timestamps = value @property - def timestamps(self) -> list[dt.datetime]: + def timestamps(self) -> list[dt.datetime] | None: """A property for handling the `timestamps` attribute.""" return self._timestamps @timestamps.setter - def timestamps(self, value: Iterable[dt.datetime]) -> None: + def timestamps(self, value: Iterable[dt.datetime] | None) -> None: + # The first setting of timestamps is done directly to `_timestamps` to avoid updating temporal dims of + # nonexisting feature-dict attributes + self._timestamps = self._parse_timestamps(value) + + temporal_dim = len(self._timestamps) if self._timestamps is not None else None + for ftype in FeatureType: + if ftype.is_temporal() and ftype.is_array(): + self[ftype]._update_temporal_dim(temporal_dim) # noqa: SLF001 # pylint: disable=protected-access + + def _parse_timestamps(self, value: Iterable[dt.datetime | str] | None) -> list[dt.datetime] | None: + if value is None: + return None if isinstance(value, Iterable) and all(isinstance(time, (dt.date, str)) for time in value): - self._timestamps = [parse_time(time, force_datetime=True) for time in value] - else: - raise TypeError(f"Cannot assign {value} as timestamps. Should be a sequence of datetime.datetime objects.") + return [parse_time(time, force_datetime=True) for time in value] + raise TypeError(f"Cannot assign {value} as timestamps. Should be a sequence of datetime.datetime objects.") + + def get_timestamps( + self, message_on_failure: str = "This EOPatch does not contain timestamps." + ) -> list[dt.datetime]: + """Returns the `timestamps` attribute if the EOPatch is temporally defined. Fails otherwise.""" + if self._timestamps is None: + raise RuntimeError(message_on_failure) + return self._timestamps @property def bbox(self) -> BBox | None: @@ -303,41 +354,23 @@ def bbox(self, value: BBox | None) -> None: warn(MISSING_BBOX_WARNING, category=EODeprecationWarning, stacklevel=2) self._bbox = value - @property - def meta_info(self) -> MutableMapping[str, Any]: - """A property for handling the `meta_info` attribute.""" - # once META_INFO becomes regular (in terms of IO) this can be removed - if isinstance(self._meta_info, FeatureIOJson): - self.meta_info = self._meta_info.load() # assigned to `meta_info` property to trigger validation - return self._meta_info # type: ignore[return-value] # mypy cannot verify due to mutations - - @meta_info.setter - def meta_info(self, value: Mapping[str, Any] | FeatureIOJson) -> None: - self._meta_info = value if isinstance(value, FeatureIOJson) else _FeatureDictJson(value, FeatureType.META_INFO) - def __setattr__(self, key: str, value: object) -> None: """Casts dictionaries to _FeatureDict objects for non-meta features.""" - if FeatureType.has_value(key) and not FeatureType(key).is_meta(): + if FeatureType.has_value(key) and key not in ("bbox", "timestamps"): if not isinstance(value, (dict, _FeatureDict)): raise TypeError(f"Cannot parse {value} for attribute {key}. Should be a dictionary.") - value = _create_feature_dict(FeatureType(key), value) - - super().__setattr__(key, value) - @overload - def __getitem__(self, key: Literal[FeatureType.BBOX] | tuple[Literal[FeatureType.BBOX], Any]) -> BBox: - ... - - @overload - def __getitem__( - self, key: Literal[FeatureType.TIMESTAMPS] | tuple[Literal[FeatureType.TIMESTAMPS], Any] - ) -> list[dt.datetime]: - ... + feature_type = FeatureType(key) + if feature_type.is_vector(): + value = _FeatureDictGeoDf(value, feature_type) + elif feature_type is FeatureType.META_INFO: + value = _FeatureDictJson(value, feature_type) + else: + temporal_dim = None if self.timestamps is None else len(self.timestamps) + value = _FeatureDictNumpy(value, feature_type, temporal_dim=temporal_dim) - @overload - def __getitem__(self, key: FeatureType | tuple[FeatureType, str | None | EllipsisType]) -> Any: - ... + super().__setattr__(key, value) def __getitem__(self, key: FeatureType | tuple[FeatureType, str | None | EllipsisType]) -> Any: """Provides features of requested feature type. It can also accept a tuple of (feature_type, feature_name). @@ -361,29 +394,23 @@ def __setitem__(self, key: FeatureType | tuple[FeatureType, str | None | Ellipsi else: setattr(self, ftype_attr, value) - def __delitem__(self, feature: FeatureType | FeatureSpec) -> None: + def __delitem__(self, feature: FeatureType | Feature) -> None: """Deletes the selected feature type or feature.""" if isinstance(feature, tuple): feature_type, feature_name = feature - if feature_type in [FeatureType.BBOX, FeatureType.TIMESTAMPS]: - feature = feature_type - else: - del self[feature_type][feature_name] - return - - feature_type = FeatureType(feature) - if feature_type == FeatureType.BBOX: - raise ValueError("The BBox of an EOPatch should never be undefined.") - if feature_type == FeatureType.TIMESTAMPS: - self[feature_type] = [] - else: - self[feature_type] = {} + del self[feature_type][feature_name] + return + + self[FeatureType(feature)] = {} def __eq__(self, other: object) -> bool: """True if FeatureType attributes, bbox, and timestamps of both EOPatches are equal by value.""" if not isinstance(other, type(self)): return False + if self.bbox != other.bbox or self.timestamps != other.timestamps: + return False + return all(deep_eq(self[feature_type], other[feature_type]) for feature_type in FeatureType) def __contains__(self, key: object) -> bool: @@ -393,8 +420,6 @@ def __contains__(self, key: object) -> bool: return bool(self[key]) if isinstance(key, tuple) and len(key) == 2: ftype, fname = key - if ftype in [FeatureType.BBOX, FeatureType.TIMESTAMPS]: - return bool(self[ftype]) return fname in self[ftype] raise ValueError( f"Membership checking is only implemented for elements of type `{FeatureType.__name__}` and for " @@ -408,19 +433,19 @@ def __add__(self, other: EOPatch) -> EOPatch: def __repr__(self) -> str: feature_repr_list = [] - for feature_type in FeatureType: + if self.bbox is not None: + feature_repr_list.append(f"bbox={self.bbox!r}") + if self.timestamps is not None: + feature_repr_list.append(f"timestamps={self._repr_value(self.timestamps)}") + for feature_type in {ftype for ftype, _ in self.get_features()}: content = self[feature_type] - if not content: - continue - if isinstance(content, _FeatureDict): - content = {k: content._get_unloaded(k) for k in content} # noqa: SLF001 - inner_content_repr = "\n ".join( - [f"{label}: {self._repr_value(value)}" for label, value in sorted(content.items())] - ) - content_str = "{\n " + inner_content_repr + "\n }" - else: - content_str = self._repr_value(content) + content = {k: content._get_unloaded(k) for k in content} # noqa: SLF001 + inner_content_repr = "\n ".join( + [f"{label}: {self._repr_value(value)}" for label, value in sorted(content.items())] + ) + content_str = "{\n " + inner_content_repr + "\n }" + feature_repr_list.append(f"{feature_type.value}={content_str}") feature_repr = "\n ".join(feature_repr_list) @@ -467,75 +492,85 @@ def _repr_value_class(value: object) -> str: cls = value.__class__ return ".".join([cls.__module__.split(".")[0], cls.__name__]) - def __copy__(self, features: FeaturesSpecification = ...) -> EOPatch: + def __copy__( + self, features: FeaturesSpecification = ..., copy_timestamps: bool | Literal["auto"] = "auto" + ) -> EOPatch: """Returns a new EOPatch with shallow copies of given features. :param features: A collection of features or feature types that will be copied into new EOPatch. + :param copy_timestamps: Whether to copy timestamps to the new EOPatch. By default copies them over if all + features are copied or if any temporal features are getting copied. """ if not features: # For some reason deepcopy and copy pass {} by default features = ... + patch_features = parse_features(features, eopatch=self) + if copy_timestamps == "auto": + copy_timestamps = features is ... or any(ftype.is_temporal() for ftype, _ in patch_features) new_eopatch = EOPatch(bbox=copy.copy(self.bbox)) - for feature_type, feature_name in parse_features(features, eopatch=self): - if feature_type in (FeatureType.BBOX, FeatureType.TIMESTAMPS): - new_eopatch[feature_type] = copy.copy(self[feature_type]) - else: - new_eopatch[feature_type][feature_name] = self[feature_type]._get_unloaded(feature_name) # noqa: SLF001 + if copy_timestamps: + new_eopatch.timestamps = copy.copy(self.timestamps) + + for feature_type, feature_name in patch_features: + new_eopatch[feature_type][feature_name] = self[feature_type]._get_unloaded(feature_name) # noqa: SLF001 return new_eopatch - def __deepcopy__(self, memo: dict | None = None, features: FeaturesSpecification = ...) -> EOPatch: + def __deepcopy__( + self, + memo: dict | None = None, + features: FeaturesSpecification = ..., + copy_timestamps: bool | Literal["auto"] = "auto", + ) -> EOPatch: """Returns a new EOPatch with deep copies of given features. :param memo: built-in parameter for memoization :param features: A collection of features or feature types that will be copied into new EOPatch. + :param copy_timestamps: Whether to copy timestamps to the new EOPatch. By default copies them over if all + features are copied or if any temporal features are getting copied. """ if not features: # For some reason deepcopy and copy pass {} by default features = ... + patch_features = parse_features(features, eopatch=self) + if copy_timestamps == "auto": + copy_timestamps = features is ... or any(ftype.is_temporal() for ftype, _ in patch_features) new_eopatch = EOPatch(bbox=copy.deepcopy(self.bbox)) + if copy_timestamps: + new_eopatch.timestamps = copy.deepcopy(self.timestamps) + for feature_type, feature_name in parse_features(features, eopatch=self): - if feature_type in (FeatureType.BBOX, FeatureType.TIMESTAMPS): - new_eopatch[feature_type] = copy.deepcopy(self[feature_type], memo=memo) - else: - value = self[feature_type]._get_unloaded(feature_name) # noqa: SLF001 + value = self[feature_type]._get_unloaded(feature_name) # noqa: SLF001 - if isinstance(value, FeatureIO): - # We cannot deepcopy the entire object because of the filesystem attribute - value = copy.copy(value) - value.loaded_value = copy.deepcopy(value.loaded_value, memo=memo) - else: - value = copy.deepcopy(value, memo=memo) + if isinstance(value, FeatureIO): + # We cannot deepcopy the entire object because of the filesystem attribute + value = copy.copy(value) + value.loaded_value = copy.deepcopy(value.loaded_value, memo=memo) + else: + value = copy.deepcopy(value, memo=memo) - new_eopatch[feature_type][feature_name] = value + new_eopatch[feature_type][feature_name] = value return new_eopatch - def copy(self, features: FeaturesSpecification = ..., deep: bool = False) -> EOPatch: + def copy( + self, + features: FeaturesSpecification = ..., + deep: bool = False, + copy_timestamps: bool | Literal["auto"] = "auto", + ) -> EOPatch: """Get a copy of the current `EOPatch`. - :param features: Features to be copied into a new `EOPatch`. By default, all features will be copied. Note that - `BBOX` is always copied. + :param features: Features to be copied into a new `EOPatch`. By default, all features will be copied. :param deep: If `True` it will make a deep copy of all data inside the `EOPatch`. Otherwise, only a shallow copy of `EOPatch` will be made. Note that `BBOX` and `TIMESTAMPS` will be copied even with a shallow copy. + :param copy_timestamps: Whether to copy timestamps to the new EOPatch. By default copies them over if all + features are copied or if any temporal features are getting copied. :return: An EOPatch copy. """ + # pylint: disable=unnecessary-dunder-call if deep: - return self.__deepcopy__(features=features) # pylint: disable=unnecessary-dunder-call - return self.__copy__(features=features) # pylint: disable=unnecessary-dunder-call - - @deprecated_function(EODeprecationWarning, "Use `del eopatch[feature_type]` or `del eopatch[feature]` instead.") - def reset_feature_type(self, feature_type: FeatureType) -> None: - """Resets the values of the given feature type. - - :param feature_type: Type of feature - """ - feature_type = FeatureType(feature_type) - if feature_type is FeatureType.BBOX: - raise ValueError("The BBox of an EOPatch should never be undefined.") - if feature_type is FeatureType.TIMESTAMPS: - self[feature_type] = [] - else: - self[feature_type] = {} + return self.__deepcopy__(features=features, copy_timestamps=copy_timestamps) + return self.__copy__(features=features, copy_timestamps=copy_timestamps) def get_spatial_dimension(self, feature_type: FeatureType, feature_name: str) -> tuple[int, int]: """ @@ -550,19 +585,18 @@ def get_spatial_dimension(self, feature_type: FeatureType, feature_name: str) -> raise ValueError(f"Features of type {feature_type} do not have a spatial dimension or are not arrays.") - def get_features(self) -> list[FeatureSpec]: + def get_features(self) -> list[Feature]: """Returns a list of all non-empty features of EOPatch. :return: List of non-empty features """ - feature_list: list[FeatureSpec] = [] - for feature_type in FeatureType: - if feature_type is FeatureType.BBOX or feature_type is FeatureType.TIMESTAMPS: - if feature_type in self: - feature_list.append((feature_type, None)) - else: - for feature_name in self[feature_type]: - feature_list.append((feature_type, feature_name)) + feature_list: list[Feature] = [] + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=FEATURETYPE_DEPRECATION_MSG.format(".*?", ".*?")) + removed_ftypes = {FeatureType.BBOX, FeatureType.TIMESTAMPS} # list comprehensions make ignoring hard + for feature_type in (ftype for ftype in FeatureType if ftype not in removed_ftypes): + for feature_name in self[feature_type]: + feature_list.append((feature_type, feature_name)) return feature_list def save( @@ -570,8 +604,12 @@ def save( path: str, features: FeaturesSpecification = ..., overwrite_permission: OverwritePermission = OverwritePermission.ADD_ONLY, - compress_level: int = 0, filesystem: FS | None = None, + *, + save_timestamps: bool | Literal["auto"] = "auto", + use_zarr: bool = False, + temporal_selection: None | slice | list[int] | Literal["infer"] = None, + compress_level: int | None = None, ) -> None: """Method to save an EOPatch from memory to a storage. @@ -579,27 +617,46 @@ def save( :param features: A collection of features types specifying features of which type will be saved. By default, all features will be saved. :param overwrite_permission: A level of permission for overwriting an existing EOPatch - :param compress_level: A level of data compression and can be specified with an integer from 0 (no compression) - to 9 (highest compression). :param filesystem: An existing filesystem object. If not given it will be initialized according to the `path` parameter. + :save_timestamps: Whether to save the timestamps of the EOPatch. With the `"auto"` setting timestamps are saved + if `features=...` or if other temporal features are being saved. + :param use_zarr: Saves numpy-array based features into Zarr files. Requires ZARR extra dependencies. + :param temporal_selection: Writes all of the data to the chosen temporal indices of preexisting arrays. Can be + used for saving data in multiple steps for memory optimization. When set to `"infer"` it will match the + timestamps of the EOPatch to the timestamps of the stored EOPatch to calculate indices. """ if filesystem is None: filesystem = get_filesystem(path, create=True) path = "/" + if compress_level is not None: + warnings.warn( + "The `compress_level` parameter has been deprecated, data is now compressed by default.", + category=EODeprecationWarning, + stacklevel=2, + ) + save_eopatch( self, filesystem, path, features=features, - compress_level=compress_level, overwrite_permission=OverwritePermission(overwrite_permission), + save_timestamps=save_timestamps, + use_zarr=use_zarr, + temporal_selection=temporal_selection, ) @staticmethod def load( - path: str, features: FeaturesSpecification = ..., lazy_loading: bool = False, filesystem: FS | None = None + path: str, + features: FeaturesSpecification = ..., + lazy_loading: bool = False, + filesystem: FS | None = None, + *, + load_timestamps: bool | Literal["auto"] = "auto", + temporal_selection: None | slice | list[int] | Callable[[list[dt.datetime]], list[bool]] = None, ) -> EOPatch: """Method to load an EOPatch from a storage into memory. @@ -608,19 +665,21 @@ def load( :param lazy_loading: If `True` features will be lazy loaded. :param filesystem: An existing filesystem object. If not given it will be initialized according to the `path` parameter. + :load_timestamps: Whether to load the timestamps of the EOPatch. With the `"auto"` setting timestamps are loaded + if `features=...` or if other temporal features are being loaded. + :param temporal_selection: Only loads data corresponding to the chosen indices. Can also be a callable that, + given a list of timestamps, returns a list of booleans declaring which temporal slices to load. :return: Loaded EOPatch """ if filesystem is None: filesystem = get_filesystem(path, create=False) path = "/" - bbox_io, timestamps_io, meta_info, features_dict = load_eopatch_content(filesystem, path, features=features) - eopatch = EOPatch(bbox=None if bbox_io is None else bbox_io.load()) + bbox, timestamps, features_dict = load_eopatch_content( + filesystem, path, features=features, temporal_selection=temporal_selection, load_timestamps=load_timestamps + ) + eopatch = EOPatch(bbox=bbox, timestamps=timestamps) - if timestamps_io is not None: - eopatch.timestamps = timestamps_io.load() - if meta_info is not None: - eopatch.meta_info = meta_info # type: ignore[assignment] for feature, feature_io in features_dict.items(): eopatch[feature] = feature_io @@ -673,22 +732,27 @@ def consolidate_timestamps(self, timestamps: list[dt.datetime]) -> set[dt.dateti :param timestamps: keep frames with date found in this list :return: set of removed frames' dates """ - remove_from_patch = set(self.timestamps).difference(timestamps) - remove_from_patch_idxs = [self.timestamps.index(rm_date) for rm_date in remove_from_patch] - good_timestamp_idxs = [idx for idx, _ in enumerate(self.timestamps) if idx not in remove_from_patch_idxs] - good_timestamps = [date for idx, date in enumerate(self.timestamps) if idx not in remove_from_patch_idxs] - - relevant_features = filter(lambda ftype: ftype.is_temporal() and not ftype.is_meta(), FeatureType) - for feature_type in relevant_features: - for feature_name, value in self[feature_type].items(): - self[feature_type][feature_name] = value[good_timestamp_idxs, ...] + old_timestamps = self.get_timestamps() + remove_from_patch = set(old_timestamps).difference(timestamps) + remove_from_patch_idxs = [old_timestamps.index(rm_date) for rm_date in remove_from_patch] + good_timestamp_idxs = [idx for idx, _ in enumerate(old_timestamps) if idx not in remove_from_patch_idxs] + good_timestamps = [date for idx, date in enumerate(old_timestamps) if idx not in remove_from_patch_idxs] + + with warnings.catch_warnings(): # catch all temporal dimension related warnings + warnings.simplefilter("ignore", category=TemporalDimensionWarning) + self.timestamps = good_timestamps + + for ftype in FeatureType: + if ftype.is_timeless() or ftype.is_meta() or ftype.is_vector(): + continue + for feature_name, value in self[ftype].items(): + self[ftype, feature_name] = value[good_timestamp_idxs, ...] - self.timestamps = good_timestamps return remove_from_patch def plot( self, - feature: FeatureSpec, + feature: Feature, *, times: list[int] | slice | None = None, channels: list[int] | slice | None = None, @@ -718,7 +782,7 @@ def plot( from eolearn.visualization.eopatch import plot_eopatch except ImportError: raise RuntimeError( - "Subpackage eo-learn-visualization has to be installed in order to use EOPatch visualization method" + "Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to use EOPatch plotting." ) return plot_eopatch( diff --git a/eolearn/core/eodata_io.py b/eolearn/core/eodata_io.py new file mode 100644 index 000000000..a0e980a85 --- /dev/null +++ b/eolearn/core/eodata_io.py @@ -0,0 +1,813 @@ +""" +A module implementing utilities for working with saving and loading EOPatch data + +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import concurrent.futures +import contextlib +import datetime +import gzip +import itertools +import json +import platform +import sys +import warnings +from abc import ABCMeta, abstractmethod +from collections import defaultdict +from dataclasses import dataclass, field +from functools import partial +from typing import ( + TYPE_CHECKING, + Any, + BinaryIO, + Callable, + Dict, + Generic, + Iterator, + List, + Literal, + Mapping, + MutableMapping, + Optional, + Tuple, + TypeVar, +) + +import dateutil.parser +import fs +import fs.move +import fs_s3fs +import geopandas as gpd +import numpy as np +import pandas as pd +from fs.base import FS +from fs.osfs import OSFS +from fs.tempfs import TempFS +from typing_extensions import TypeAlias + +from sentinelhub import CRS, BBox, Geometry, MimeType +from sentinelhub.exceptions import SHUserWarning + +from .constants import TIMESTAMP_COLUMN, FeatureType, OverwritePermission +from .exceptions import EODeprecationWarning +from .types import EllipsisType, Feature, FeaturesSpecification +from .utils.fs import get_full_path, split_all_extensions +from .utils.parsing import FeatureParser + +try: + import s3fs + import zarr +except ImportError: + pass + +if TYPE_CHECKING: + from .eodata import EOPatch + + +T = TypeVar("T") +Self = TypeVar("Self", bound="FeatureIO") +PatchContentType: TypeAlias = Tuple[ + Optional[BBox], + Optional[List[datetime.datetime]], + Dict[Tuple[FeatureType, str], "FeatureIO"], +] +Features: TypeAlias = List[Tuple[FeatureType, str]] + + +BBOX_FILENAME = "bbox" +TIMESTAMPS_FILENAME = "timestamps" + + +@dataclass +class FilesystemDataInfo: + """Information about data that is present on the filesystem. Fields represent paths to relevant file.""" + + timestamps: str | None = None + bbox: str | None = None + old_meta_info: str | None = None + features: dict[FeatureType, dict[str, str]] = field(default_factory=lambda: defaultdict(dict)) + + def iterate_features(self) -> Iterator[tuple[Feature, str]]: + """Yields `(ftype, fname), path` tuples from `features`.""" + for ftype, ftype_dict in self.features.items(): + for fname, path in ftype_dict.items(): + yield (ftype, fname), path + + +@dataclass +class TemporalSelection: + """Defines which temporal slices a temporally-partial EOPatch represents. Also carries info on how to initialize + Zarr arrays if inferred.""" + + selection: None | slice | list[int] + full_size: int | None # None means unknown + + +def save_eopatch( + eopatch: EOPatch, + filesystem: FS, + patch_location: str, + *, + features: FeaturesSpecification, + save_timestamps: bool | Literal["auto"], + overwrite_permission: OverwritePermission, + use_zarr: bool, + temporal_selection: None | slice | list[int] | Literal["infer"], +) -> None: + """A utility function used by `EOPatch.save` method.""" + patch_exists = filesystem.exists(patch_location) + + if temporal_selection is not None and not use_zarr: + raise ValueError("Cannot perform partial saving via `temporal_selection` without zarr arrays.") + + eopatch_features = FeatureParser(features).get_features(eopatch) + file_information = get_filesystem_data_info(filesystem, patch_location) if patch_exists else FilesystemDataInfo() + + _check_collisions(overwrite_permission, eopatch_features, file_information) + + if save_timestamps == "auto": + save_timestamps = ( + features is ... or any(ftype.is_temporal() for ftype, _ in eopatch_features) + ) and temporal_selection is None + + # Data must be collected before any tinkering with files due to lazy-loading + data_for_saving = list( + _yield_savers( + eopatch=eopatch, + features=eopatch_features, + patch_location=patch_location, + filesystem=filesystem, + use_zarr=use_zarr, + save_timestamps=save_timestamps, + temporal_selection=_infer_temporal_selection(temporal_selection, filesystem, file_information, eopatch), + ) + ) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=EODeprecationWarning) + if overwrite_permission is OverwritePermission.OVERWRITE_PATCH and patch_exists: + _remove_old_eopatch(filesystem, patch_location) + + filesystem.makedirs(patch_location, recreate=True) + for ftype in {ftype for ftype, _ in eopatch_features}: + filesystem.makedirs(fs.path.join(patch_location, ftype.value), recreate=True) + + with concurrent.futures.ThreadPoolExecutor() as executor: + new_files = list(executor.map(lambda saver: saver(), data_for_saving)) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=EODeprecationWarning) + if overwrite_permission is not OverwritePermission.OVERWRITE_PATCH: + _remove_redundant_files(filesystem, new_files, file_information) + + _remove_old_style_metainfo(filesystem, patch_location, new_files, file_information) + + +def _infer_temporal_selection( + temporal_selection: None | slice | list[int] | Literal["infer"], + filesystem: FS, + file_information: FilesystemDataInfo, + eopatch: EOPatch, +) -> TemporalSelection: + if temporal_selection != "infer": + return TemporalSelection(temporal_selection, None) + + patch_timestamps = eopatch.get_timestamps("Cannot infer temporal selection. EOPatch to be saved has no timestamps.") + if file_information.timestamps is None: + raise OSError("Cannot infer temporal selection. Saved EOPatch does not have timestamps.") + full_timestamps = FeatureIOTimestamps(file_information.timestamps, filesystem).load() + timestamp_indices = {timestamp: idx for idx, timestamp in enumerate(full_timestamps)} + if not all(timestamp in timestamp_indices for timestamp in patch_timestamps): + raise ValueError( + f"Cannot infer temporal selection. EOPatch timestamps {patch_timestamps} are not a subset of the stored" + f" EOPatch timestamps {full_timestamps}." + ) + return TemporalSelection([timestamp_indices[timestamp] for timestamp in patch_timestamps], len(full_timestamps)) + + +def _remove_old_eopatch(filesystem: FS, patch_location: str) -> None: + filesystem.removetree(patch_location) + filesystem.makedirs(patch_location, recreate=True) + + +def _yield_savers( + *, + eopatch: EOPatch, + features: Features, + patch_location: str, + filesystem: FS, + save_timestamps: bool, + use_zarr: bool, + temporal_selection: TemporalSelection, +) -> Iterator[Callable[[], str]]: + """Prepares callables that save the data and return the path to where the data was saved.""" + get_file_path = partial(fs.path.join, patch_location) + + if eopatch.bbox is not None: # remove after BBox is never None + bbox: BBox = eopatch.bbox # mypy has problems + yield partial( + FeatureIOBBox.save, + data=bbox, + filesystem=filesystem, + feature_path=get_file_path(BBOX_FILENAME), + compress_level=0, + ) + + if eopatch.timestamps is not None and save_timestamps: + path = get_file_path(TIMESTAMPS_FILENAME) + yield partial( + FeatureIOTimestamps.save, + data=eopatch.timestamps, + filesystem=filesystem, + feature_path=path, + compress_level=0, + ) + + for ftype, fname in features: + io_constructor = _get_feature_io_constructor(ftype, use_zarr) + feature_saver = partial( + io_constructor.save, + data=eopatch[ftype, fname], + filesystem=filesystem, + feature_path=get_file_path(ftype.value, fname), + compress_level=1, + ) + + if ftype.is_temporal() and issubclass(io_constructor, FeatureIOZarr): + feature_saver = partial(feature_saver, temporal_selection=temporal_selection) + + yield feature_saver + + +def _get_feature_io_constructor(ftype: FeatureType, use_zarr: bool) -> type[FeatureIO]: + """Creates the correct FeatureIO, corresponding to the FeatureType.""" + if ftype is FeatureType.META_INFO: + return FeatureIOJson + if ftype.is_vector(): + return FeatureIOGeoDf + return FeatureIOZarr if use_zarr else FeatureIONumpy # type: ignore[return-value] # not sure why + + +def _remove_redundant_files( + filesystem: FS, + new_files: list[str], + preexisting_files: FilesystemDataInfo, +) -> None: + """Removes files that should have been overwritten but were not due to different file extensions.""" + feature_paths = (path for _, ftype_dict in preexisting_files.features.items() for _, path in ftype_dict.items()) + meta_paths = (preexisting_files.bbox, preexisting_files.old_meta_info, preexisting_files.timestamps) + split_paths = map(split_all_extensions, filter(None, itertools.chain(meta_paths, feature_paths))) + old_path_extension = dict(split_paths) # maps {path_base: file_extension} + + files_to_remove = [] + for path in new_files: + base, extension = split_all_extensions(path) + if base in old_path_extension and old_path_extension[base] != extension: + files_to_remove.append(f"{base}{old_path_extension[base]}") + + def _remover(path: str) -> None: # Zarr path can also be path to a folder + if not path.endswith("zarr") or filesystem.isfile(path): + return filesystem.remove(path) + filesystem.makedirs(path, recreate=True) # solves issue where zarr root folder is not an actual folder on S3 + return filesystem.removetree(path) + + with concurrent.futures.ThreadPoolExecutor() as executor: + list(executor.map(_remover, files_to_remove)) # Wrapped in a list to get better exceptions + + +def _remove_old_style_metainfo( + filesystem: FS, + patch_location: str, + new_files: list[str], + preexisting_files: FilesystemDataInfo, +) -> None: + """If any new meta-info was saved, this removes the old-style meta-info file.""" + if preexisting_files.old_meta_info is None: + return + + if any(fs.path.relativefrom(patch_location, path).startswith("meta_info") for path in new_files): + filesystem.remove(preexisting_files.old_meta_info) + + +def load_eopatch_content( + filesystem: FS, + patch_location: str, + *, + features: FeaturesSpecification, + load_timestamps: bool | Literal["auto"], + temporal_selection: None | slice | list[int] | Callable[[list[datetime.datetime]], list[bool]], +) -> PatchContentType: + """A utility function used by `EOPatch.load` method.""" + err_msg = "Feature {} is specified to be loaded but does not exist in EOPatch at " + patch_location + file_information = get_filesystem_data_info(filesystem, patch_location, features) + feature_specs = FeatureParser(features).get_feature_specifications() + + simple_temporal_selection, maybe_timestamps = _extract_temporal_selection( + filesystem, patch_location, file_information.timestamps, temporal_selection + ) + + features_dict = _load_features(filesystem, feature_specs, simple_temporal_selection, err_msg, file_information) + + bbox = None + if file_information.bbox is not None: + bbox = FeatureIOBBox(file_information.bbox, filesystem).load() + + auto_load = load_timestamps == "auto" and ( + features is ... or any(ftype.is_temporal() for ftype, _ in features_dict) + ) + + timestamps = None + if load_timestamps is True or auto_load: + if maybe_timestamps is not None: + timestamps = maybe_timestamps + elif file_information.timestamps is not None: + timestamps = FeatureIOTimestamps(file_information.timestamps, filesystem, simple_temporal_selection).load() + elif load_timestamps is True: # this means that timestamps were requested but dont exist + raise OSError(f"No timestamps found when loading EOPatch at {patch_location} with `load_timestamps=True`.") + + return bbox, timestamps, features_dict + + +def _extract_temporal_selection( + filesystem: FS, + patch_location: str, + timestamps_path: str | None, + temporal_selection: None | slice | list[int] | Callable[[list[datetime.datetime]], list[bool]], +) -> tuple[None | slice | list[int] | list[bool], list[datetime.datetime] | None]: + """Extracts the temporal selection if available.""" + if not callable(temporal_selection): + return temporal_selection, None + if timestamps_path is not None: + full_timestamps = FeatureIOTimestamps(timestamps_path, filesystem).load() + simple_selection = temporal_selection(full_timestamps) + return simple_selection, [timestamp for timestamp, include in zip(full_timestamps, simple_selection) if include] + raise OSError(f"Cannot perform loading temporal selection, EOPatch at {patch_location} has no timestamps.") + + +def _load_features( + filesystem: FS, + feature_specs: list[tuple[FeatureType, str | EllipsisType]], + temporal_selection: None | slice | list[int] | list[bool], + err_msg: str, + file_information: FilesystemDataInfo, +) -> dict[Feature, FeatureIO]: + features_dict = {} + if file_information.old_meta_info is not None and any(ftype is FeatureType.META_INFO for ftype, _ in feature_specs): + msg = ( + "Stored EOPatch contains old-style meta-info file, which will no longer be supported in the future. Please" + " re-save the EOPatch to correct this issue." + ) + warnings.warn(msg, EODeprecationWarning, stacklevel=2) + old_meta: dict[str, Any] = FeatureIOJson(file_information.old_meta_info, filesystem).load() + features_dict = {(FeatureType.META_INFO, name): value for name, value in old_meta.items()} + + for ftype, fname in feature_specs: + if ftype is FeatureType.META_INFO and (ftype, fname) in features_dict: # data provided in old-style file + continue + + if fname is ...: + for fname, path in file_information.features.get(ftype, {}).items(): + features_dict[(ftype, fname)] = _get_feature_io(ftype, path, filesystem, temporal_selection) + else: + if ftype not in file_information.features or fname not in file_information.features[ftype]: + raise OSError(err_msg.format((ftype, fname))) + path = file_information.features[ftype][fname] + features_dict[(ftype, fname)] = _get_feature_io(ftype, path, filesystem, temporal_selection) + return features_dict + + +def _get_feature_io( + ftype: FeatureType, path: str, filesystem: FS, temporal_selection: None | slice | list[int] | list[bool] +) -> FeatureIO: + if ftype is FeatureType.META_INFO: + return FeatureIOJson(path, filesystem) + if ftype.is_vector(): + return FeatureIOGeoDf(path, filesystem) + + use_zarr = path.endswith(FeatureIOZarr.get_file_extension()) + + if ftype.is_temporal(): + if use_zarr: + return FeatureIOZarr(path, filesystem, temporal_selection) + if temporal_selection is None: + return FeatureIONumpy(path, filesystem) + raise OSError( + f"Cannot perform loading with temporal selection for numpy data at {path}. Resave feature with" + " `use_zarr=True` to enable loading with temporal selections." + ) + return (FeatureIOZarr if use_zarr else FeatureIONumpy)(path, filesystem) + + +def get_filesystem_data_info( + filesystem: FS, patch_location: str, features: FeaturesSpecification = ... +) -> FilesystemDataInfo: + """Returns information on all eopatch files in the storage. Filters with `features` to reduce IO calls.""" + relevant_features = FeatureParser(features).get_feature_specifications() + relevant_feature_types = {ftype for ftype, _ in relevant_features} + + result = FilesystemDataInfo() + + for path in filesystem.listdir(patch_location): + object_name = _remove_file_extension(path).strip("/") + object_path = fs.path.combine(patch_location, path) + + if object_name == "timestamp": + warnings.warn( + f"EOPatch at {patch_location} contains the deprecated naming `timestamp` for the `timestamps`" + " feature. The old name will no longer be valid in the future. You can re-save the `EOPatch` to" + " update it.", + category=EODeprecationWarning, + stacklevel=2, + ) + object_name = TIMESTAMPS_FILENAME + + if "/" in object_name: # For cases where S3 does not have a regular folder structure + ftype_str, fname = fs.path.split(object_name) + result.features[FeatureType(ftype_str)][fname] = path + + elif object_name == BBOX_FILENAME: + result.bbox = object_path + + elif object_name == TIMESTAMPS_FILENAME: + result.timestamps = object_path + + elif object_name == FeatureType.META_INFO.value and filesystem.isfile(object_path): + result.old_meta_info = object_path + + elif FeatureType.has_value(object_name) and FeatureType(object_name) in relevant_feature_types: + result.features[FeatureType(object_name)] = dict(walk_feature_type_folder(filesystem, object_path)) + + return result + + +def walk_feature_type_folder(filesystem: FS, folder_path: str) -> Iterator[tuple[str, str]]: + """Walks a feature type subfolder of EOPatch and yields tuples (feature name, path in filesystem). + Skips folders and files in subfolders. + """ + for path in filesystem.listdir(folder_path): + if "/" not in path and "." in path: + yield _remove_file_extension(path), fs.path.combine(folder_path, path) + + +def _check_collisions( + overwrite_permission: OverwritePermission, eopatch_features: Features, existing_files: FilesystemDataInfo +) -> None: + """Checks for possible name collisions to avoid unintentional overwriting.""" + if overwrite_permission is OverwritePermission.ADD_ONLY: + _check_letter_case_collisions(eopatch_features, existing_files) + _check_add_only_permission(eopatch_features, existing_files) + + elif platform.system() == "Windows" and overwrite_permission is OverwritePermission.OVERWRITE_FEATURES: + _check_letter_case_collisions(eopatch_features, existing_files) + + else: + _check_letter_case_collisions(eopatch_features, FilesystemDataInfo()) + + +def _check_add_only_permission(eopatch_features: Features, filesystem_features: FilesystemDataInfo) -> None: + """Checks that no existing feature will be overwritten.""" + unique_filesystem_features = {_to_lowercase(*feature) for feature, _ in filesystem_features.iterate_features()} + unique_eopatch_features = {_to_lowercase(*feature) for feature in eopatch_features} + + intersection = unique_filesystem_features.intersection(unique_eopatch_features) + if intersection: + raise ValueError(f"Cannot save features {intersection} with overwrite_permission=OverwritePermission.ADD_ONLY") + + +def _check_letter_case_collisions(eopatch_features: Features, filesystem_features: FilesystemDataInfo) -> None: + """Check that features have no name clashes (ignoring case) with other EOPatch features and saved features.""" + lowercase_features = {_to_lowercase(*feature) for feature in eopatch_features} + + if len(lowercase_features) != len(eopatch_features): + raise OSError("Some features differ only in casing and cannot be saved in separate files.") + + for feature, _ in filesystem_features.iterate_features(): + if feature not in eopatch_features and _to_lowercase(*feature) in lowercase_features: + raise OSError( + f"There already exists a feature {feature} in the filesystem that only differs in " + "casing from a feature that should be saved." + ) + + +def _to_lowercase(ftype: FeatureType, fname: str | None, *_: Any) -> tuple[FeatureType, str | None]: + """Transforms a feature to it's lowercase representation.""" + return ftype, fname if fname is None else fname.lower() + + +def _remove_file_extension(path: str) -> str: + """This also removes file extensions of form `.geojson.gz` unlike `fs.path.splitext`.""" + return split_all_extensions(path)[0] + + +class FeatureIO(Generic[T], metaclass=ABCMeta): + """A class that handles the saving and loading process of a single feature at a given location.""" + + def __init__(self, path: str, filesystem: FS): + """ + :param path: A path in the filesystem + :param filesystem: A filesystem object + """ + self._check_path_extension(path) + + self.path = path + self.filesystem = filesystem + + self.loaded_value: T | None = None + + def _check_path_extension(self, path: str) -> None: + filename = fs.path.basename(path) + if not filename.endswith(self.get_file_extension()): + raise ValueError(f"FeatureIO expects a filepath ending with {self.get_file_extension()}, got {path}") + + @classmethod + @abstractmethod + def get_file_extension(cls) -> str: + """The extension of files handled by the FeatureIO.""" + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.path})" + + def load(self) -> T: + """Method for loading a feature. The loaded value is stored into an attribute in case a second load request is + triggered inside a shallow-copied EOPatch. + """ + if self.loaded_value is None: + self.loaded_value = self._load_value() + + return self.loaded_value + + @abstractmethod + def _load_value(self) -> T: + """Loads the value from the storage.""" + + @classmethod + @abstractmethod + def save(cls, data: T, filesystem: FS, feature_path: str, compress_level: int = 0) -> str: + """Method for saving a feature. The path is assumed to be filesystem path but without file extensions. + + Returns the full path to which the feature is saved. + Example of path is `eopatch/data/NDVI`, which is then used to save `eopatch/data/NDVI.npy.gz`. + """ + + +class FeatureIOGZip(FeatureIO[T], metaclass=ABCMeta): + """A class that handles the saving and loading process of a single feature at a given location. + + Uses GZip to compress files when required. + """ + + @classmethod + def get_file_extension(cls, *, compress_level: int = 0) -> str: + extension = cls._get_uncompressed_file_extension() + return f"{extension}.gz" if compress_level > 0 else extension + + @classmethod + @abstractmethod + def _get_uncompressed_file_extension(cls) -> str: + """The extension of files handled by the FeatureIO.""" + + def _check_path_extension(self, path: str) -> None: + filename = fs.path.basename(path) + expected_extensions = (self.get_file_extension(compress_level=0), self.get_file_extension(compress_level=1)) + if not filename.endswith(expected_extensions): + raise ValueError(f"FeatureIO expects a filepath ending with either of {expected_extensions}, got {path}") + + def _load_value(self) -> T: + with self.filesystem.openbin(self.path, "r") as file_handle: + if MimeType.GZIP.matches_extension(self.path): + with gzip.open(file_handle, "rb") as gzip_fp: + return self._read_from_file(gzip_fp) + else: + return self._read_from_file(file_handle) + + @abstractmethod + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> T: + """Loads from a file and decodes content.""" + + @classmethod + def save(cls, data: T, filesystem: FS, feature_path: str, compress_level: int = 0) -> str: + """Method for saving a feature. The path is assumed to be filesystem path but without file extensions. + + Example of path is `eopatch/data/NDVI`, which is then used to save `eopatch/data/NDVI.npy.gz`. + + To minimize the chance of corrupted files (in case of OSFS and TempFS) the file is first written and then moved + to correct location. If any exceptions happen during the writing process the file is not moved (and old one not + overwritten). + """ + path = feature_path + cls.get_file_extension(compress_level=compress_level) + + if isinstance(filesystem, (OSFS, TempFS)): + with TempFS(temp_dir=filesystem.root_path) as tempfs: + cls._save(data, tempfs, "tmp_feature", compress_level) + if fs.__version__ == "2.4.16" and filesystem.exists(path): # An issue in the fs version + filesystem.remove(path) + fs.move.move_file(tempfs, "tmp_feature", filesystem, path) + else: + cls._save(data, filesystem, path, compress_level) + return path + + @classmethod + def _save(cls, data: T, filesystem: FS, path: str, compress_level: int) -> None: + """Given a filesystem it saves and compresses the data.""" + with filesystem.openbin(path, "w") as file: + if compress_level == 0: + cls._write_to_file(data, file, path) + else: + with gzip.GzipFile(fileobj=file, compresslevel=compress_level, mode="wb") as gzip_file: + cls._write_to_file(data, gzip_file, path) + + @classmethod + @abstractmethod + def _write_to_file(cls, data: T, file: BinaryIO | gzip.GzipFile, path: str) -> None: + """Writes data to a file in the appropriate way.""" + + +class FeatureIONumpy(FeatureIOGZip[np.ndarray]): + """FeatureIO object specialized for Numpy arrays.""" + + @classmethod + def _get_uncompressed_file_extension(cls) -> str: + return ".npy" + + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> np.ndarray: + return np.load(file, allow_pickle=True) + + @classmethod + def _write_to_file(cls, data: np.ndarray, file: BinaryIO | gzip.GzipFile, _: str) -> None: + return np.save(file, data) + + +class FeatureIOGeoDf(FeatureIOGZip[gpd.GeoDataFrame]): + """FeatureIO object specialized for GeoDataFrames.""" + + @classmethod + def _get_uncompressed_file_extension(cls) -> str: + return ".gpkg" + + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> gpd.GeoDataFrame: + dataframe = gpd.read_file(file) + + if dataframe.crs is not None: + # Trying to preserve a standard CRS and passing otherwise + with contextlib.suppress(ValueError), warnings.catch_warnings(): + warnings.simplefilter("ignore", category=SHUserWarning) + dataframe.crs = CRS(dataframe.crs).pyproj_crs() + + if TIMESTAMP_COLUMN in dataframe: + dataframe[TIMESTAMP_COLUMN] = pd.to_datetime(dataframe[TIMESTAMP_COLUMN]) + + return dataframe + + @classmethod + def _write_to_file(cls, data: gpd.GeoDataFrame, file: BinaryIO | gzip.GzipFile, path: str) -> None: + layer = fs.path.basename(path) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="You are attempting to write an empty DataFrame to file*", + category=UserWarning, + ) + return data.to_file(file, driver="GPKG", encoding="utf-8", layer=layer, index=False) + + +class FeatureIOJson(FeatureIOGZip[T]): + """FeatureIO object specialized for JSON-like objects.""" + + @classmethod + def _get_uncompressed_file_extension(cls) -> str: + return ".json" + + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> T: + return json.load(file) + + @classmethod + def _write_to_file(cls, data: T, file: BinaryIO | gzip.GzipFile, path: str) -> None: + try: + json_data = json.dumps(data, indent=2, default=_better_jsonify) + except TypeError as exception: + raise TypeError( + f"Failed to serialize when saving JSON file to {path}. Make sure that this feature type " + "contains only JSON serializable Python types before attempting to serialize it." + ) from exception + + file.write(json_data.encode()) + + +class FeatureIOTimestamps(FeatureIOJson[List[datetime.datetime]]): + """FeatureIOJson object specialized for List[dt.datetime].""" + + def __init__(self, path: str, filesystem: FS, temporal_selection: None | slice | list[int] | list[bool] = None): + self.temporal_selection = slice(None) if temporal_selection is None else temporal_selection + super().__init__(path, filesystem) + + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> list[datetime.datetime]: + data = json.load(file) + string_timestamps = np.array(data)[self.temporal_selection] + return [dateutil.parser.parse(raw_timestamp) for raw_timestamp in string_timestamps] + + +class FeatureIOBBox(FeatureIOGZip[BBox]): + """FeatureIO object specialized for BBox objects.""" + + @classmethod + def _get_uncompressed_file_extension(cls) -> str: + return ".geojson" + + def _read_from_file(self, file: BinaryIO | gzip.GzipFile) -> BBox: + json_data = json.load(file) + return Geometry.from_geojson(json_data).bbox + + @classmethod + def _write_to_file(cls, data: BBox, file: BinaryIO | gzip.GzipFile, _: str) -> None: + json_data = json.dumps(data.geojson, indent=2) + file.write(json_data.encode()) + + +class FeatureIOZarr(FeatureIO[np.ndarray]): + """FeatureIO object specialized for Zarr arrays.""" + + def __init__(self, path: str, filesystem: FS, temporal_selection: None | slice | list[int] | list[bool] = None): + self.temporal_selection = slice(None) if temporal_selection is None else temporal_selection + super().__init__(path, filesystem) + + @classmethod + def get_file_extension(cls) -> str: + return ".zarr" + + def _load_value(self) -> np.ndarray: + self._check_dependencies_imported(self.path) + + store = self._get_mapping(self.path, self.filesystem) + zarray = zarr.open_array(store=store, mode="r+") + return zarray.oindex[self.temporal_selection] + + @staticmethod + def _get_mapping(path: str, filesystem: FS) -> MutableMapping: + abs_path = get_full_path(filesystem, path) + if isinstance(filesystem, OSFS): + return zarr.DirectoryStore(abs_path) + if isinstance(filesystem, TempFS): + return zarr.TempStore(abs_path) + if isinstance(filesystem, fs_s3fs.S3FS): + fsspec_filesystem = s3fs.S3FileSystem( + key=filesystem.aws_access_key_id, + secret=filesystem.aws_secret_access_key, + token=filesystem.aws_session_token, + ) + return s3fs.S3Map(abs_path, s3=fsspec_filesystem) + raise ValueError(f"Cannot handle filesystem {filesystem} with the Zarr backend.") + + @classmethod + def save( + cls, + data: np.ndarray, + filesystem: FS, + feature_path: str, + compress_level: int = 0, # noqa: ARG003 + temporal_selection: None | TemporalSelection = None, # None means no sub-chunking (timeless) + ) -> str: + cls._check_dependencies_imported(feature_path) + path = feature_path + cls.get_file_extension() + store = cls._get_mapping(path, filesystem) + chunk_size = data.shape if temporal_selection is None else (1, *data.shape[1:]) + + if temporal_selection is None or temporal_selection.selection is None: + zarr.save_array(store, data, chunks=chunk_size) + return path + + selection, full_size = temporal_selection.selection, temporal_selection.full_size + + try: + zarray = zarr.open_array(store, "r+") + except ValueError as error: # zarr does not expose the proper error... + if full_size is not None: + zarr_shape = (full_size, *data.shape[1:]) + zarray = zarr.create(zarr_shape, dtype=data.dtype, chunks=chunk_size, store=store) + else: + raise OSError( + f"Unable to open Zarr array at {path!r}. Saving with `temporal_selection` requires an initialized" + ' zarr array. You can also try saving with `temporal_selection="infer"`.' + ) from error + + zarray.oindex[selection] = data + return path + + @staticmethod + def _check_dependencies_imported(path: str) -> None: + if not all(dep in sys.modules for dep in ["zarr", "s3fs"]): + msg = f"Encountered use of Zarr for {path} with missing dependencies. Please install `zarr` and `s3fs`." + raise ImportError(msg) + + +def _better_jsonify(param: object) -> Any: + """Adds the option to serialize datetime.date and FeatureDict objects via isoformat.""" + if isinstance(param, datetime.date): + return param.isoformat() + if isinstance(param, Mapping): + return dict(param.items()) + raise TypeError(f"Object of type {type(param)} is not yet supported in jsonify utility function") diff --git a/core/eolearn/core/eodata_merge.py b/eolearn/core/eodata_merge.py similarity index 94% rename from core/eolearn/core/eodata_merge.py rename to eolearn/core/eodata_merge.py index e536e6485..d1ca82663 100644 --- a/core/eolearn/core/eodata_merge.py +++ b/eolearn/core/eodata_merge.py @@ -23,7 +23,7 @@ from .constants import FeatureType from .eodata import EOPatch from .exceptions import EORuntimeWarning -from .types import FeatureSpec, FeaturesSpecification +from .types import Feature, FeaturesSpecification from .utils.parsing import FeatureParser OperationInputType = Union[Literal[None, "concatenate", "min", "max", "mean", "median"], Callable] @@ -86,7 +86,6 @@ def merge_eopatches( merged_eopatch[feature] = _merge_vector_feature(eopatches, feature) if feature_type is FeatureType.META_INFO: - feature_name = cast(str, feature_name) # parser makes sure of it merged_eopatch[feature] = _select_meta_info_feature(eopatches, feature_name) return merged_eopatch @@ -121,11 +120,14 @@ def _return_if_equal_operation(arrays: np.ndarray) -> bool: def _merge_timestamps( eopatches: Sequence[EOPatch], reduce_timestamps: bool -) -> tuple[list[dt.datetime], list[np.ndarray]]: +) -> tuple[list[dt.datetime] | None, list[np.ndarray]]: """Merges together timestamps from EOPatches. It also prepares a list of masks, one for each EOPatch, how timestamps should be ordered and joined together. """ - timestamps_per_eopatch = [eopatch.timestamps for eopatch in eopatches] + if all(eopatch.timestamps is None for eopatch in eopatches): + return None, [np.array([], dtype=np.int32) for _ in range(len(eopatches))] + + timestamps_per_eopatch = [eopatch.timestamps or [] for eopatch in eopatches] all_timestamps = [timestamp for eopatch_timestamps in timestamps_per_eopatch for timestamp in eopatch_timestamps] if not all_timestamps: @@ -159,7 +161,7 @@ def _check_if_optimize(eopatches: Sequence[EOPatch], operation_input: OperationI def _merge_time_dependent_raster_feature( eopatches: Sequence[EOPatch], - feature: FeatureSpec, + feature: Feature, operation: Callable, order_mask_per_eopatch: Sequence[np.ndarray], optimize: bool, @@ -202,7 +204,7 @@ def _merge_time_dependent_raster_feature( def _extract_and_join_time_dependent_feature_values( eopatches: Sequence[EOPatch], - feature: FeatureSpec, + feature: Feature, order_mask_per_eopatch: Sequence[np.ndarray], optimize: bool, ) -> tuple[np.ndarray, np.ndarray]: @@ -235,9 +237,7 @@ def _is_strictly_increasing(array: np.ndarray) -> bool: return (np.diff(array) > 0).all().astype(bool) -def _merge_timeless_raster_feature( - eopatches: Sequence[EOPatch], feature: FeatureSpec, operation: Callable -) -> np.ndarray: +def _merge_timeless_raster_feature(eopatches: Sequence[EOPatch], feature: Feature, operation: Callable) -> np.ndarray: """Merges numpy arrays of a timeless raster feature with a given operation.""" arrays = _extract_feature_values(eopatches, feature) @@ -253,7 +253,7 @@ def _merge_timeless_raster_feature( ) from exception -def _merge_vector_feature(eopatches: Sequence[EOPatch], feature: FeatureSpec) -> GeoDataFrame: +def _merge_vector_feature(eopatches: Sequence[EOPatch], feature: Feature) -> GeoDataFrame: """Merges GeoDataFrames of a vector feature.""" dataframes = _extract_feature_values(eopatches, feature) @@ -267,10 +267,8 @@ def _merge_vector_feature(eopatches: Sequence[EOPatch], feature: FeatureSpec) -> raise ValueError(f"Cannot merge feature {feature} because dataframes are defined for different CRS") merged_dataframe = GeoDataFrame(pd.concat(dataframes, ignore_index=True), crs=crs_list[0]) - merged_dataframe = merged_dataframe.drop_duplicates(ignore_index=True) - # In future a support for vector operations could be added here - return merged_dataframe + return merged_dataframe.drop_duplicates(ignore_index=True) def _select_meta_info_feature(eopatches: Sequence[EOPatch], feature_name: str) -> Any: @@ -299,7 +297,7 @@ def _get_common_bbox(eopatches: Sequence[EOPatch]) -> BBox | None: raise ValueError("Cannot merge EOPatches because they are defined for different bounding boxes.") -def _extract_feature_values(eopatches: Sequence[EOPatch], feature: FeatureSpec) -> list[Any]: +def _extract_feature_values(eopatches: Sequence[EOPatch], feature: Feature) -> list[Any]: """A helper function that extracts a feature values from those EOPatches where a feature exists.""" feature_type, feature_name = feature return [eopatch[feature] for eopatch in eopatches if feature_name in eopatch[feature_type]] diff --git a/core/eolearn/core/eoexecution.py b/eolearn/core/eoexecution.py similarity index 99% rename from core/eolearn/core/eoexecution.py rename to eolearn/core/eoexecution.py index 2636c75b0..de224990a 100644 --- a/core/eolearn/core/eoexecution.py +++ b/eolearn/core/eoexecution.py @@ -358,7 +358,7 @@ def make_report(self, include_logs: bool = True) -> None: from eolearn.visualization.eoexecutor import EOExecutorVisualization except ImportError: raise RuntimeError( - "Subpackage eo-learn-visualization has to be installed in order to create EOExecutor reports." + "Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to create EOExecutor reports." ) return EOExecutorVisualization(self).make_report(include_logs=include_logs) diff --git a/core/eolearn/core/eonode.py b/eolearn/core/eonode.py similarity index 86% rename from core/eolearn/core/eonode.py rename to eolearn/core/eonode.py index dc9b0540d..c0f07b840 100644 --- a/core/eolearn/core/eonode.py +++ b/eolearn/core/eonode.py @@ -6,9 +6,11 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import datetime as dt from dataclasses import dataclass, field -from typing import Dict, List, Optional, Sequence, Set, Tuple, Union, cast +from typing import Sequence, cast from .eotask import EOTask from .utils.common import generate_uid @@ -27,8 +29,8 @@ class EONode: """ task: EOTask - inputs: Sequence["EONode"] = field(default_factory=tuple) - name: Optional[str] = field(default=None) + inputs: Sequence[EONode] = field(default_factory=tuple) + name: str | None = field(default=None) uid: str = field(init=False, repr=False) def __post_init__(self) -> None: @@ -57,7 +59,7 @@ def get_name(self, suffix_number: int = 0) -> str: return f"{self.name}_{suffix_number}" return cast(str, self.name) - def get_dependencies(self, *, _memo: Optional[Dict["EONode", Set["EONode"]]] = None) -> Set["EONode"]: + def get_dependencies(self, *, _memo: dict[EONode, set[EONode]] | None = None) -> set[EONode]: """Returns a set of nodes that this node depends on. Set includes the node itself.""" _memo = _memo if _memo is not None else {} if self not in _memo: @@ -67,7 +69,7 @@ def get_dependencies(self, *, _memo: Optional[Dict["EONode", Set["EONode"]]] = N return _memo[self] -def linearly_connect_tasks(*tasks: Union[EOTask, Tuple[EOTask, str]]) -> List[EONode]: +def linearly_connect_tasks(*tasks: EOTask | tuple[EOTask, str]) -> list[EONode]: """Creates a list of linearly linked nodes, suitable to construct an EOWorkflow. Nodes depend on each other in such a way, that the node containing the task at index `i` is the input node for the @@ -98,5 +100,13 @@ class NodeStats: node_name: str start_time: dt.datetime end_time: dt.datetime - exception: Optional[BaseException] = None - exception_traceback: Optional[str] = None + exception_info: ExceptionInfo | None = None + + +@dataclass(frozen=True) +class ExceptionInfo: + """Contains information on exceptions that occur when executing a node.""" + + exception: BaseException + traceback: str + origin: str diff --git a/core/eolearn/core/eotask.py b/eolearn/core/eotask.py similarity index 96% rename from core/eolearn/core/eotask.py rename to eolearn/core/eotask.py index 24023b2e2..2548c33e9 100644 --- a/core/eolearn/core/eotask.py +++ b/eolearn/core/eotask.py @@ -24,7 +24,7 @@ from .constants import FeatureType from .eodata import EOPatch from .exceptions import EODeprecationWarning -from .types import EllipsisType, FeatureSpec, FeaturesSpecification, SingleFeatureSpec +from .types import EllipsisType, Feature, FeaturesSpecification, SingleFeatureSpec from .utils.parsing import FeatureParser, parse_feature, parse_features, parse_renamed_feature, parse_renamed_features LOGGER = logging.getLogger(__name__) @@ -84,7 +84,7 @@ def parse_feature( feature: SingleFeatureSpec, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., - ) -> tuple[FeatureType, str | None]: + ) -> Feature: """See `eolearn.core.utils.parse_feature`.""" return parse_feature(feature, eopatch, allowed_feature_types) @@ -93,7 +93,7 @@ def parse_features( features: FeaturesSpecification, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., - ) -> list[FeatureSpec]: + ) -> list[Feature]: """See `eolearn.core.utils.parse_features`.""" return parse_features(features, eopatch, allowed_feature_types) diff --git a/core/eolearn/core/eoworkflow.py b/eolearn/core/eoworkflow.py similarity index 90% rename from core/eolearn/core/eoworkflow.py rename to eolearn/core/eoworkflow.py index a432108e7..3c7e5fe6c 100644 --- a/core/eolearn/core/eoworkflow.py +++ b/eolearn/core/eoworkflow.py @@ -23,10 +23,10 @@ import logging import traceback from dataclasses import dataclass, field, fields -from typing import Sequence, Tuple, cast +from typing import Literal, Sequence, overload from .eodata import EOPatch -from .eonode import EONode, NodeStats +from .eonode import EONode, ExceptionInfo, NodeStats from .eotask import EOTask from .eoworkflow_tasks import OutputTask from .graph import DirectedGraph @@ -209,7 +209,7 @@ def _execute_nodes( ) stats_dict[node.uid] = stats - if stats.exception is not None: + if stats.exception_info is not None: break intermediate_results[node.uid] = result @@ -231,47 +231,49 @@ def _execute_node( :return: The result and statistics of the task in the node. """ # EOPatches are copied beforehand - task_args = [(arg.copy() if isinstance(arg, EOPatch) else arg) for arg in node_input_values] + task_args = [(arg.copy(copy_timestamps=True) if isinstance(arg, EOPatch) else arg) for arg in node_input_values] LOGGER.debug("Computing %s(*%s, **%s)", node.task.__class__.__name__, str(task_args), str(node_input_kwargs)) start_time = dt.datetime.now() - result, is_success = self._execute_task(node.task, task_args, node_input_kwargs, raise_errors=raise_errors) + result = self._execute_task(node.task, task_args, node_input_kwargs, raise_errors=raise_errors) end_time = dt.datetime.now() - if is_success: - exception, exception_traceback = None, None - else: - exception, exception_traceback = cast(Tuple[BaseException, str], result) # temporary fix until 3.8 - result = None + if isinstance(result, ExceptionInfo): + exception_info, result = result, None LOGGER.error( - "Task '%s' with id %s failed with stack trace:\n%s", node.get_name(), node.uid, exception_traceback + "Task '%s' with id %s failed with stack trace:\n%s", + node.get_name(), + node.uid, + exception_info.traceback, ) + else: + exception_info = None node_stats = NodeStats( node_uid=node.uid, node_name=node.get_name(), start_time=start_time, end_time=end_time, - exception=exception, - exception_traceback=exception_traceback, + exception_info=exception_info, ) return result, node_stats @staticmethod def _execute_task( task: EOTask, task_args: list[object], task_kwargs: dict[str, object], raise_errors: bool - ) -> tuple[object, bool]: + ) -> object | ExceptionInfo: """Executes an EOTask and handles any potential exceptions.""" if raise_errors: - return task.execute(*task_args, **task_kwargs), True + return task.execute(*task_args, **task_kwargs) try: - return task.execute(*task_args, **task_kwargs), True + return task.execute(*task_args, **task_kwargs) except KeyboardInterrupt as exception: raise KeyboardInterrupt from exception except BaseException as exception: - exception_traceback = traceback.format_exc() - return (exception, exception_traceback), False + trace = traceback.extract_tb(exception.__traceback__) + origin = f"line {trace[-1].lineno} in {trace[-1].filename}." if trace else "unknown origin." + return ExceptionInfo(exception, traceback=traceback.format_exc(), origin=origin) @staticmethod def _relax_dependencies( @@ -304,7 +306,15 @@ def get_nodes(self) -> list[EONode]: """ return self._nodes[:] - def get_node_with_uid(self, uid: str | None, fail_if_missing: bool = False) -> EONode | None: + @overload + def get_node_with_uid(self, uid: str, fail_if_missing: Literal[True] = ...) -> EONode: + ... + + @overload + def get_node_with_uid(self, uid: str, fail_if_missing: Literal[False] = ...) -> EONode | None: + ... + + def get_node_with_uid(self, uid: str, fail_if_missing: bool = False) -> EONode | None: """Returns node with give uid, if it exists in the workflow.""" if uid in self._uid_dict: return self._uid_dict[uid] @@ -337,7 +347,7 @@ def _get_visualization(self): # type: ignore[no-untyped-def] # cannot type with from eolearn.visualization.eoworkflow import EOWorkflowVisualization except ImportError: raise RuntimeError( - "Subpackage eo-learn-visualization has to be installed in order to use EOWorkflow visualization methods" + "Dependencies `eo-learn[VISUALIZATION]` have to be installed in order to use EOWorkflow visualization." ) return EOWorkflowVisualization(self._nodes) @@ -355,7 +365,7 @@ class WorkflowResults: def __post_init__(self) -> None: """Checks if there is any node that failed during the workflow execution.""" for node_uid, node_stats in self.stats.items(): - if node_stats.exception is not None: + if node_stats.exception_info is not None: super().__setattr__("error_node_uid", node_uid) break diff --git a/core/eolearn/core/eoworkflow_tasks.py b/eolearn/core/eoworkflow_tasks.py similarity index 88% rename from core/eolearn/core/eoworkflow_tasks.py rename to eolearn/core/eoworkflow_tasks.py index 6472e9799..0fcd91d7a 100644 --- a/core/eolearn/core/eoworkflow_tasks.py +++ b/eolearn/core/eoworkflow_tasks.py @@ -53,8 +53,9 @@ def name(self) -> str: def execute(self, data: object) -> object: """ :param data: input data - :return: Same data, to be stored in results (for `EOPatch` returns shallow copy containing only `features`) + :return: Same data, to be stored in results. For `EOPatch` returns shallow copy containing `features` and + possibly BBox and timestamps (see `copy` method of `EOPatch`). """ if isinstance(data, EOPatch): - return data.copy(features=self.features) + return data.copy(features=self.features, copy_timestamps=True) return data diff --git a/core/eolearn/core/exceptions.py b/eolearn/core/exceptions.py similarity index 82% rename from core/eolearn/core/exceptions.py rename to eolearn/core/exceptions.py index 8bd3b3070..76bf58c72 100644 --- a/core/eolearn/core/exceptions.py +++ b/eolearn/core/exceptions.py @@ -6,6 +6,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import warnings @@ -21,6 +23,10 @@ class EORuntimeWarning(RuntimeWarning): """A custom runtime warning for eo-learn package.""" +class TemporalDimensionWarning(RuntimeWarning): + """A custom runtime warning for cases where EOPatches are temporally ill defined.""" + + warnings.simplefilter("default", EODeprecationWarning) warnings.simplefilter("default", EOUserWarning) warnings.simplefilter("always", EORuntimeWarning) diff --git a/core/eolearn/core/extra/__init__.py b/eolearn/core/extra/__init__.py similarity index 100% rename from core/eolearn/core/extra/__init__.py rename to eolearn/core/extra/__init__.py diff --git a/core/eolearn/core/extra/ray.py b/eolearn/core/extra/ray.py similarity index 96% rename from core/eolearn/core/extra/ray.py rename to eolearn/core/extra/ray.py index 2b641c3d0..ba0d2a1cc 100644 --- a/core/eolearn/core/extra/ray.py +++ b/eolearn/core/extra/ray.py @@ -22,8 +22,8 @@ from ..utils.parallelize import _base_join_futures_iter, _ProcessingType # pylint: disable=invalid-name -_InputType = TypeVar("_InputType") -_OutputType = TypeVar("_OutputType") +InputType = TypeVar("InputType") +OutputType = TypeVar("OutputType") class RayExecutor(EOExecutor): @@ -65,8 +65,8 @@ def _ray_workflow_executor(workflow_args: _ProcessingData) -> WorkflowResults: def parallelize_with_ray( - function: Callable[[_InputType], _OutputType], *params: Iterable[_InputType], **tqdm_kwargs: Any -) -> list[_OutputType]: + function: Callable[[InputType], OutputType], *params: Iterable[InputType], **tqdm_kwargs: Any +) -> list[OutputType]: """Parallelizes function execution with Ray. Note that this function will automatically connect to a Ray cluster, if a connection wouldn't exist yet. But it diff --git a/core/eolearn/core/graph.py b/eolearn/core/graph.py similarity index 85% rename from core/eolearn/core/graph.py rename to eolearn/core/graph.py index 7dff93c8a..2ede79656 100644 --- a/core/eolearn/core/graph.py +++ b/eolearn/core/graph.py @@ -13,14 +13,14 @@ import copy from typing import Generic, Iterator, Sequence, TypeVar -_T = TypeVar("_T") +T = TypeVar("T") class CyclicDependencyError(ValueError): """This error is raised when trying to get a topological ordering of a `DirectedGraph`.""" -class DirectedGraph(Generic[_T]): +class DirectedGraph(Generic[T]): """A directed graph using adjacency-list representation. The graph is multi-edge. Constructs a new graph from an adjacency list. If adjacency_dict is None, an empty graph is constructed. @@ -28,7 +28,7 @@ class DirectedGraph(Generic[_T]): :param adjacency_dict: A dictionary mapping vertices to lists of neighbors """ - def __init__(self, adjacency_dict: dict[_T, list[_T]] | None = None): + def __init__(self, adjacency_dict: dict[T, list[T]] | None = None): self._adj_dict = ( collections.defaultdict(list, adjacency_dict) if adjacency_dict else collections.defaultdict(list) ) @@ -39,19 +39,19 @@ def __len__(self) -> int: """Returns the number of vertices in the graph.""" return len(self._vertices) - def __contains__(self, vertex: _T) -> bool: + def __contains__(self, vertex: T) -> bool: """True if `vertex` is a vertex of the graph. False otherwise. :param vertex: Vertex """ return vertex in self._vertices - def __iter__(self) -> Iterator[_T]: + def __iter__(self) -> Iterator[T]: """Returns iterator over the vertices of the graph.""" return iter(self._vertices) - def _make_indegrees_dict(self) -> collections.defaultdict[_T, int]: - indegrees: collections.defaultdict[_T, int] = collections.defaultdict(int) + def _make_indegrees_dict(self) -> collections.defaultdict[T, int]: + indegrees: collections.defaultdict[T, int] = collections.defaultdict(int) for u_vertex in self._adj_dict: for v_vertex in self._adj_dict[u_vertex]: @@ -59,11 +59,11 @@ def _make_indegrees_dict(self) -> collections.defaultdict[_T, int]: return indegrees - def get_indegrees(self) -> dict[_T, int]: + def get_indegrees(self) -> dict[T, int]: """Returns a dictionary containing in-degrees of vertices of the graph.""" return dict(self._indegrees) - def get_indegree(self, vertex: _T) -> int: + def get_indegree(self, vertex: T) -> int: """Returns the in-degree of the vertex. The in-degree is the number of vertices `vertex'` such that `vertex' -> vertex` is an edge of the graph. @@ -72,13 +72,13 @@ def get_indegree(self, vertex: _T) -> int: """ return self._indegrees[vertex] - def get_outdegrees(self) -> dict[_T, int]: + def get_outdegrees(self) -> dict[T, int]: """ :return: dictionary of out-degrees, see get_outdegree """ return {vertex: len(self._adj_dict[vertex]) for vertex in self._adj_dict} - def get_outdegree(self, vertex: _T) -> int: + def get_outdegree(self, vertex: T) -> int: """Returns the out-degree of the vertex. The out-degree is the number of vertices `vertex'` such that `vertex -> vertex'` is an edge of the graph. @@ -87,17 +87,17 @@ def get_outdegree(self, vertex: _T) -> int: """ return len(self._adj_dict[vertex]) - def get_adj_dict(self) -> dict[_T, list]: + def get_adj_dict(self) -> dict[T, list]: """ :return: adj_dict """ return {vertex: copy.copy(neighbours) for vertex, neighbours in self._adj_dict.items()} - def get_vertices(self) -> set[_T]: + def get_vertices(self) -> set[T]: """Returns the set of vertices of the graph.""" return set(self._vertices) - def add_edge(self, u_vertex: _T, v_vertex: _T) -> None: + def add_edge(self, u_vertex: T, v_vertex: T) -> None: """Adds the edge `u_vertex -> v_vertex` to the graph if the edge is not already present. :param u_vertex: Vertex @@ -108,7 +108,7 @@ def add_edge(self, u_vertex: _T, v_vertex: _T) -> None: self._indegrees[v_vertex] += 1 self._adj_dict[u_vertex].append(v_vertex) - def del_edge(self, u_vertex: _T, v_vertex: _T) -> bool: + def del_edge(self, u_vertex: T, v_vertex: T) -> bool: """Removes the edge `u_vertex -> v_vertex` from the graph if the edge is present. :param u_vertex: Vertex @@ -122,7 +122,7 @@ def del_edge(self, u_vertex: _T, v_vertex: _T) -> bool: return False - def add_vertex(self, vertex: _T) -> bool: + def add_vertex(self, vertex: T) -> bool: """Adds a new vertex to the graph if not present. :param vertex: Vertex @@ -134,7 +134,7 @@ def add_vertex(self, vertex: _T) -> bool: return False - def del_vertex(self, vertex: _T) -> bool: + def del_vertex(self, vertex: T) -> bool: """Removes the vertex `vertex` and all incident edges from the graph. **Note** that this is an expensive operation that should be avoided! @@ -157,20 +157,20 @@ def del_vertex(self, vertex: _T) -> bool: self._vertices.remove(vertex) return True - def is_edge(self, u_vertex: _T, v_vertex: _T) -> bool: + def is_edge(self, u_vertex: T, v_vertex: T) -> bool: """True if `u_vertex -> v_vertex` is an edge of the graph. False otherwise.""" return v_vertex in self._adj_dict[u_vertex] - def get_neighbors(self, vertex: _T) -> list[_T]: + def get_neighbors(self, vertex: T) -> list[T]: """Returns the set of successor vertices of `vertex`.""" return copy.copy(self._adj_dict[vertex]) @staticmethod - def from_edges(edges: Sequence[tuple[_T, _T]]) -> DirectedGraph[_T]: + def from_edges(edges: Sequence[tuple[T, T]]) -> DirectedGraph[T]: """Return DirectedGraph created from edges. :param edges: Pairs of objects that describe all the edges of the graph """ - dag: DirectedGraph = DirectedGraph[_T]() + dag: DirectedGraph = DirectedGraph[T]() for _u, _v in edges: dag.add_edge(_u, _v) return dag @@ -197,7 +197,7 @@ def _is_cyclic(graph: DirectedGraph) -> bool: stack.append(v) return False - def topologically_ordered_vertices(self) -> list[_T]: + def topologically_ordered_vertices(self) -> list[T]: """Computes an ordering `<` of vertices so that for any two vertices `v` and `v'` we have that if `v˙ depends on `v'` then `v' < v`. In words, all dependencies of a vertex precede the vertex in this ordering. diff --git a/core/eolearn/core/types.py b/eolearn/core/types.py similarity index 58% rename from core/eolearn/core/types.py rename to eolearn/core/types.py index 7aabdef55..121ab2511 100644 --- a/core/eolearn/core/types.py +++ b/eolearn/core/types.py @@ -6,10 +6,12 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import sys # pylint: disable=unused-import -from typing import Dict, Iterable, Literal, Optional, Sequence, Tuple, Union +from typing import Dict, Iterable, Sequence, Tuple, Union from .constants import FeatureType @@ -25,19 +27,12 @@ # DEVELOPER NOTE: the #: comments are applied as docstrings +Feature: TypeAlias = Tuple[FeatureType, str] -#: Specification describing a single feature -FeatureSpec: TypeAlias = Union[Tuple[Literal[FeatureType.BBOX, FeatureType.TIMESTAMPS], None], Tuple[FeatureType, str]] -#: Specification describing a feature with its current and desired new name -FeatureRenameSpec: TypeAlias = Union[ - Tuple[Literal[FeatureType.BBOX, FeatureType.TIMESTAMPS], None, None], Tuple[FeatureType, str, str] -] -SingleFeatureSpec: TypeAlias = Union[FeatureSpec, FeatureRenameSpec] +SingleFeatureSpec: TypeAlias = Union[Feature, Tuple[FeatureType, str, str]] -SequenceFeatureSpec: TypeAlias = Sequence[ - Union[SingleFeatureSpec, FeatureType, Tuple[FeatureType, Optional[EllipsisType]]] -] -DictFeatureSpec: TypeAlias = Dict[FeatureType, Union[None, EllipsisType, Iterable[Union[str, Tuple[str, str]]]]] +SequenceFeatureSpec: TypeAlias = Sequence[Union[SingleFeatureSpec, FeatureType, Tuple[FeatureType, EllipsisType]]] +DictFeatureSpec: TypeAlias = Dict[FeatureType, Union[EllipsisType, Iterable[Union[str, Tuple[str, str]]]]] MultiFeatureSpec: TypeAlias = Union[ EllipsisType, FeatureType, Tuple[FeatureType, EllipsisType], SequenceFeatureSpec, DictFeatureSpec ] diff --git a/core/eolearn/core/utils/__init__.py b/eolearn/core/utils/__init__.py similarity index 100% rename from core/eolearn/core/utils/__init__.py rename to eolearn/core/utils/__init__.py diff --git a/core/eolearn/core/utils/common.py b/eolearn/core/utils/common.py similarity index 96% rename from core/eolearn/core/utils/common.py rename to eolearn/core/utils/common.py index b2c425f9c..a1c5578ac 100644 --- a/core/eolearn/core/utils/common.py +++ b/eolearn/core/utils/common.py @@ -7,8 +7,10 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import uuid -from typing import Callable, Mapping, Sequence, Tuple, Union, cast +from typing import Callable, Mapping, Sequence, cast import geopandas as gpd import numpy as np @@ -80,13 +82,13 @@ def generate_uid(prefix: str) -> str: return f"{prefix}-{time_uid}-{random_uid}" -def is_discrete_type(number_type: Union[np.dtype, type]) -> bool: +def is_discrete_type(number_type: np.dtype | type) -> bool: """Checks if a given `numpy` type is a discrete numerical type.""" return np.issubdtype(number_type, np.integer) or np.issubdtype(number_type, bool) def _apply_to_spatial_axes( - function: Callable[[np.ndarray], np.ndarray], data: np.ndarray, spatial_axes: Tuple[int, int] + function: Callable[[np.ndarray], np.ndarray], data: np.ndarray, spatial_axes: tuple[int, int] ) -> np.ndarray: """Helper function for applying a 2D -> 2D function to a higher dimension array diff --git a/core/eolearn/core/utils/fs.py b/eolearn/core/utils/fs.py similarity index 91% rename from core/eolearn/core/utils/fs.py rename to eolearn/core/utils/fs.py index 4e40ca0b5..9bec77fe1 100644 --- a/core/eolearn/core/utils/fs.py +++ b/eolearn/core/utils/fs.py @@ -6,12 +6,14 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import copy import os import pickle import threading from pathlib import Path, PurePath -from typing import Any, Optional, Tuple, Union +from typing import Any import fs from boto3 import Session @@ -26,9 +28,7 @@ # ruff: noqa: SLF001 -def get_filesystem( - path: Union[str, Path], create: bool = False, config: Optional[SHConfig] = None, **kwargs: Any -) -> FS: +def get_filesystem(path: str | Path, create: bool = False, config: SHConfig | None = None, **kwargs: Any) -> FS: """A utility function for initializing any type of filesystem object with PyFilesystem2 package. :param path: A filesystem path @@ -46,7 +46,7 @@ def get_filesystem( return fs.open_fs(path, create=create, **kwargs) -def get_base_filesystem_and_path(*path_parts: str, **kwargs: Any) -> Tuple[FS, str]: +def get_base_filesystem_and_path(*path_parts: str, **kwargs: Any) -> tuple[FS, str]: """Parses multiple strings that define a filesystem path and returns a filesystem object with a relative path on the filesystem. @@ -73,7 +73,7 @@ def get_base_filesystem_and_path(*path_parts: str, **kwargs: Any) -> Tuple[FS, s def load_s3_filesystem( - path: str, strict: bool = False, config: Optional[SHConfig] = None, aws_profile: Optional[str] = None, **kwargs: Any + path: str, strict: bool = False, config: SHConfig | None = None, aws_profile: str | None = None, **kwargs: Any ) -> S3FS: """Loads AWS s3 filesystem from a path. @@ -108,7 +108,7 @@ def load_s3_filesystem( ) -def get_aws_credentials(aws_profile: str, config: Optional[SHConfig] = None) -> SHConfig: +def get_aws_credentials(aws_profile: str, config: SHConfig | None = None) -> SHConfig: """Collects credentials from AWS profile and adds them to an instance of SHConfig. :param aws_profile: A name of AWS profile @@ -204,6 +204,13 @@ def join_path(*path_parts: str) -> str: return os.path.abspath(os.path.join(*path_parts)) +def split_all_extensions(path: str) -> tuple[str, str]: + """Similar to `os.path.splitext` but takes into account double extensions such as `.json.gz`.""" + extensions = "".join(Path(path).suffixes) + suffixless_path = path[: -len(extensions)] if extensions else path # use `removesuffix` once we drop 3.8 support + return suffixless_path, extensions + + def is_s3_path(path: str) -> bool: """Returns True if the path points to a S3 bucket, False otherwise.""" return path.startswith("s3://") diff --git a/core/eolearn/core/utils/logging.py b/eolearn/core/utils/logging.py similarity index 88% rename from core/eolearn/core/utils/logging.py rename to eolearn/core/utils/logging.py index 09f09a98f..23e172816 100644 --- a/core/eolearn/core/utils/logging.py +++ b/eolearn/core/utils/logging.py @@ -8,9 +8,11 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import logging from logging import Filter, LogRecord -from typing import Any, Optional +from typing import Any LOGGER = logging.getLogger(__name__) @@ -18,7 +20,7 @@ class LogFileFilter(Filter): """Filters log messages passed to log file.""" - def __init__(self, thread_name: Optional[str], *args: Any, **kwargs: Any): + def __init__(self, thread_name: str | None, *args: Any, **kwargs: Any): """ :param thread_name: Name of the thread by which to filter logs. By default, it won't filter by any name. """ diff --git a/core/eolearn/core/utils/parallelize.py b/eolearn/core/utils/parallelize.py similarity index 92% rename from core/eolearn/core/utils/parallelize.py rename to eolearn/core/utils/parallelize.py index 783a249b8..216672e42 100644 --- a/core/eolearn/core/utils/parallelize.py +++ b/eolearn/core/utils/parallelize.py @@ -25,9 +25,9 @@ MULTIPROCESSING_LOCK = None # pylint: disable=invalid-name -_T = TypeVar("_T") -_FutureType = TypeVar("_FutureType") -_OutputType = TypeVar("_OutputType") +T = TypeVar("T") +FutureType = TypeVar("FutureType") +OutputType = TypeVar("OutputType") class _ProcessingType(Enum): @@ -55,12 +55,12 @@ def _decide_processing_type(workers: int | None, multiprocess: bool) -> _Process def parallelize( - function: Callable[..., _OutputType], + function: Callable[..., OutputType], *params: Iterable[Any], workers: int | None, multiprocess: bool = True, **tqdm_kwargs: Any, -) -> list[_OutputType]: +) -> list[OutputType]: """Parallelizes the function on given parameters using the specified number of workers. :param function: A function to be parallelized. @@ -98,7 +98,7 @@ def parallelize( MULTIPROCESSING_LOCK = None -def execute_with_mp_lock(function: Callable[..., _OutputType], *args: Any, **kwargs: Any) -> _OutputType: +def execute_with_mp_lock(function: Callable[..., OutputType], *args: Any, **kwargs: Any) -> OutputType: """A helper utility function that executes a given function with multiprocessing lock if the process is being executed in a multiprocessing mode. @@ -117,10 +117,10 @@ def execute_with_mp_lock(function: Callable[..., _OutputType], *args: Any, **kwa def submit_and_monitor_execution( executor: Executor, - function: Callable[..., _OutputType], + function: Callable[..., OutputType], *params: Iterable[Any], **tqdm_kwargs: Any, -) -> list[_OutputType]: +) -> list[OutputType]: """Performs the execution parallelization and monitors the process using a progress bar. :param executor: An object that performs parallelization. @@ -177,16 +177,16 @@ def _get_result(future: Future) -> Any: def _base_join_futures_iter( - wait_function: Callable[[Collection[_FutureType]], tuple[Collection[_FutureType], Collection[_FutureType]]], - get_result_function: Callable[[_FutureType], _OutputType], - futures: list[_FutureType], + wait_function: Callable[[Collection[FutureType]], tuple[Collection[FutureType], Collection[FutureType]]], + get_result_function: Callable[[FutureType], OutputType], + futures: list[FutureType], **tqdm_kwargs: Any, -) -> Generator[tuple[int, _OutputType], None, None]: +) -> Generator[tuple[int, OutputType], None, None]: """A generalized utility function that resolves futures, monitors progress, and serves as an iterator over results.""" if not isinstance(futures, list): raise ValueError(f"Parameters 'futures' should be a list but {type(futures)} was given") - remaining_futures: Collection[_FutureType] = _make_copy_and_empty_given(futures) + remaining_futures: Collection[FutureType] = _make_copy_and_empty_given(futures) id_to_position_map = {id(future): index for index, future in enumerate(remaining_futures)} @@ -200,7 +200,7 @@ def _base_join_futures_iter( yield result_position, result -def _make_copy_and_empty_given(items: list[_T]) -> list[_T]: +def _make_copy_and_empty_given(items: list[T]) -> list[T]: """Removes items from the given list and returns its copy. The side effect of removing items is intentional.""" items_copy = items[:] while items: diff --git a/core/eolearn/core/utils/parsing.py b/eolearn/core/utils/parsing.py similarity index 88% rename from core/eolearn/core/utils/parsing.py rename to eolearn/core/utils/parsing.py index 06e93bb4f..9f2637c4c 100644 --- a/core/eolearn/core/utils/parsing.py +++ b/eolearn/core/utils/parsing.py @@ -9,14 +9,14 @@ from __future__ import annotations import contextlib +import warnings from typing import TYPE_CHECKING, Callable, Iterable, Sequence, Tuple, Union, cast -from ..constants import FeatureType +from ..constants import FEATURETYPE_DEPRECATION_MSG, FeatureType from ..types import ( DictFeatureSpec, EllipsisType, - FeatureRenameSpec, - FeatureSpec, + Feature, FeaturesSpecification, SequenceFeatureSpec, SingleFeatureSpec, @@ -49,12 +49,11 @@ class FeatureParser: 1. Ellipsis `...` signify that all features of all types should be parsed. - 2. Input representing a single feature, either `FeatureType.BBOX`, `FeatureType.TIMESTAMPS` or a tuple where the - first element is a `FeatureType` element and the other (or two for renaming) is a string. + 2. Input representing a single feature, where the first element is a `FeatureType` element and the other + (or two for renaming) is a string. 3. Dictionary mapping `feature_type` keys to sequences of feature names. Feature names are either a sequence of - strings, pairs of shape `(old_name, new_name)` or `...`. For feature types with no names (BBox and timestamps) - one should use `None` in place of the sequence. Example: + strings, pairs of shape `(old_name, new_name)` or `...`. Example: .. code-block:: python @@ -66,7 +65,6 @@ class FeatureParser: 'NDWI', }, FeatureType.MASK: ... - FeatureType.BBOX: None } 4. Sequences of elements, each describing a feature. When describing all features of a given feature type use @@ -78,7 +76,6 @@ class FeatureParser: [ (FeatureType.DATA, 'BANDS'), (FeatureType.MASK, 'CLOUD_MASK', 'NEW_CLOUD_MASK'), - FeatureType.BBOX ] Outputs of the FeatureParser are: @@ -104,6 +101,10 @@ def __init__( self.allowed_feature_types = ( set(allowed_feature_types) if isinstance(allowed_feature_types, Iterable) else set(FeatureType) ) + # needed until we remove the feature types + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message=FEATURETYPE_DEPRECATION_MSG.format(".*?", ".*?")) + self.allowed_feature_types = self.allowed_feature_types - {FeatureType.BBOX, FeatureType.TIMESTAMPS} self._feature_specs = self._parse_features(features) def _parse_features(self, features: FeaturesSpecification) -> list[_ParserFeaturesSpec]: @@ -147,8 +148,6 @@ def _parse_dict( feature_specs.append((feature_type, None, None)) continue - self._fail_for_noname_features(feature_type, feature_names) - if not isinstance(feature_names, Sequence): raise ValueError("Values of dictionary must be `...` or sequences with feature names.") @@ -186,7 +185,7 @@ def _parse_sequence( return feature_specs - def _parse_singleton(self, feature: Sequence) -> FeatureRenameSpec: + def _parse_singleton(self, feature: Sequence) -> _ParserFeaturesSpec: """Parses a pair or triple specifying a single feature or a get-all request.""" feature_type, *feature_name = feature feature_type = self._parse_feature_type(feature_type, message_about_position="first elements of tuples") @@ -194,8 +193,6 @@ def _parse_singleton(self, feature: Sequence) -> FeatureRenameSpec: if len(feature_name) == 1 and feature_name[0] in (..., None): return (feature_type, None, None) - self._fail_for_noname_features(feature_type, feature_name) - feature_name = feature_name[0] if len(feature_name) == 1 else feature_name parsed_name = self._parse_feature_name(feature_type, feature_name) return (feature_type, *parsed_name) @@ -235,18 +232,6 @@ def _parse_feature_name(feature_type: FeatureType, name: object) -> tuple[str, s " strings or pairs of form `(old_name, new_name)`" ) - @staticmethod - def _fail_for_noname_features(feature_type: FeatureType, specification: object) -> None: - """Fails if the feature type does not support names. - - Should only be used after the viable names `...` and `None` have already been handled. - """ - if feature_type in (FeatureType.BBOX, FeatureType.TIMESTAMPS): - raise ValueError( - f"For features of type {feature_type} the only acceptable specification is `...` or `None`, got" - f" {specification} instead." - ) - def get_feature_specifications(self) -> list[tuple[FeatureType, str | EllipsisType]]: """Returns the feature specifications in a more streamlined fashion. @@ -254,7 +239,7 @@ def get_feature_specifications(self) -> list[tuple[FeatureType, str | EllipsisTy """ return [(ftype, ... if fname is None else fname) for ftype, fname, _ in self._feature_specs] - def get_features(self, eopatch: EOPatch | None = None) -> list[FeatureSpec]: + def get_features(self, eopatch: EOPatch | None = None) -> list[Feature]: """Returns a list of `(feature_type, feature_name)` pairs. For features that specify renaming, the new name of the feature is ignored. @@ -264,10 +249,9 @@ def get_features(self, eopatch: EOPatch | None = None) -> list[FeatureSpec]: If `eopatch` is not provided the method fails if an all-feature request is in the specification. """ - renamed_features = self.get_renamed_features(eopatch) - return [feature[:2] for feature in renamed_features] # pattern unpacking messes with typechecking + return [(ftype, fname) for ftype, fname, _ in self.get_renamed_features(eopatch)] - def get_renamed_features(self, eopatch: EOPatch | None = None) -> list[FeatureRenameSpec]: + def get_renamed_features(self, eopatch: EOPatch | None = None) -> list[tuple[FeatureType, str, str]]: """Returns a list of `(feature_type, old_name, new_name)` triples. For features without a specified renaming the new name is equal to the old one. @@ -278,14 +262,11 @@ def get_renamed_features(self, eopatch: EOPatch | None = None) -> list[FeatureRe If `eopatch` is not provided the method fails if an all-feature request is in the specification. """ - parsed_features: list[FeatureRenameSpec] = [] + parsed_features: list[tuple[FeatureType, str, str]] = [] for feature_spec in self._feature_specs: ftype, old_name, new_name = feature_spec - if ftype is FeatureType.BBOX or ftype is FeatureType.TIMESTAMPS: - parsed_features.append((ftype, None, None)) - - elif old_name is not None and new_name is not None: + if old_name is not None and new_name is not None: # checking both is redundant, but typechecker has difficulties otherwise if eopatch is not None and (ftype, old_name) not in eopatch: raise ValueError(f"Requested feature {(ftype, old_name)} not part of eopatch.") @@ -305,7 +286,7 @@ def parse_feature( feature: SingleFeatureSpec, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., -) -> tuple[FeatureType, str | None]: +) -> Feature: """Parses input describing a single feature into a `(feature_type, feature_name)` pair. See :class:`FeatureParser` for viable inputs. @@ -321,7 +302,7 @@ def parse_renamed_feature( feature: SingleFeatureSpec, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., -) -> FeatureRenameSpec: +) -> tuple[FeatureType, str, str]: """Parses input describing a single feature into a `(feature_type, old_name, new_name)` triple. See :class:`FeatureParser` for viable inputs. @@ -337,7 +318,7 @@ def parse_features( features: FeaturesSpecification, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., -) -> list[FeatureSpec]: +) -> list[Feature]: """Parses input describing features into a list of `(feature_type, feature_name)` pairs. See :class:`FeatureParser` for viable inputs. @@ -349,7 +330,7 @@ def parse_renamed_features( features: FeaturesSpecification, eopatch: EOPatch | None = None, allowed_feature_types: EllipsisType | Iterable[FeatureType] | Callable[[FeatureType], bool] = ..., -) -> list[FeatureRenameSpec]: +) -> list[tuple[FeatureType, str, str]]: """Parses input describing features into a list of `(feature_type, old_name, new_name)` triples. See :class:`FeatureParser` for viable inputs. diff --git a/core/eolearn/core/utils/raster.py b/eolearn/core/utils/raster.py similarity index 98% rename from core/eolearn/core/utils/raster.py rename to eolearn/core/utils/raster.py index 0d5d3b996..f1d839c27 100644 --- a/core/eolearn/core/utils/raster.py +++ b/eolearn/core/utils/raster.py @@ -6,7 +6,9 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ -from typing import Literal, Tuple +from __future__ import annotations + +from typing import Literal import numpy as np @@ -58,7 +60,7 @@ def fast_nanpercentile(data: np.ndarray, percentile: float, *, method: str = "li def constant_pad( # noqa: C901 array: np.ndarray, - multiple_of: Tuple[int, int], + multiple_of: tuple[int, int], up_down_rule: Literal["even", "up", "down"] = "even", left_right_rule: Literal["even", "left", "right"] = "even", pad_value: float = 0, diff --git a/core/eolearn/core/utils/testing.py b/eolearn/core/utils/testing.py similarity index 85% rename from core/eolearn/core/utils/testing.py rename to eolearn/core/utils/testing.py index e0b95c3a7..340267cb4 100644 --- a/core/eolearn/core/utils/testing.py +++ b/eolearn/core/utils/testing.py @@ -6,10 +6,12 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import datetime as dt import string from dataclasses import dataclass, field -from typing import Any, List, Optional, Tuple +from typing import Any import geopandas as gpd import numpy as np @@ -21,6 +23,7 @@ from ..constants import FeatureType from ..eodata import EOPatch +from ..types import FeaturesSpecification from ..utils.parsing import FeatureParser DEFAULT_BBOX = BBox((0, 0, 100, 100), crs=CRS("EPSG:32633")) @@ -31,23 +34,23 @@ class PatchGeneratorConfig: """Dataclass containing a more complex setup of the PatchGenerator class.""" num_timestamps: int = 5 - timestamps_range: Tuple[dt.datetime, dt.datetime] = (dt.datetime(2019, 1, 1), dt.datetime(2019, 12, 31)) - timestamps: List[dt.datetime] = field(init=False, repr=False) + timestamps_range: tuple[dt.datetime, dt.datetime] = (dt.datetime(2019, 1, 1), dt.datetime(2019, 12, 31)) + timestamps: list[dt.datetime] = field(init=False, repr=False) max_integer_value: int = 256 - raster_shape: Tuple[int, int] = (98, 151) - depth_range: Tuple[int, int] = (1, 3) + raster_shape: tuple[int, int] = (98, 151) + depth_range: tuple[int, int] = (1, 3) def __post_init__(self) -> None: self.timestamps = list(pd.date_range(*self.timestamps_range, periods=self.num_timestamps).to_pydatetime()) def generate_eopatch( - features: Optional[List[Tuple[FeatureType, str]]] = None, + features: FeaturesSpecification | None = None, bbox: BBox = DEFAULT_BBOX, - timestamps: Optional[List[dt.datetime]] = None, + timestamps: list[dt.datetime] | None = None, seed: int = 42, - config: Optional[PatchGeneratorConfig] = None, + config: PatchGeneratorConfig | None = None, ) -> EOPatch: """A class for generating EOPatches with dummy data.""" config = config if config is not None else PatchGeneratorConfig() @@ -73,7 +76,7 @@ def generate_eopatch( def _generate_feature_data( - rng: np.random.Generator, ftype: FeatureType, shape: Tuple[int, ...], config: PatchGeneratorConfig + rng: np.random.Generator, ftype: FeatureType, shape: tuple[int, ...], config: PatchGeneratorConfig ) -> np.ndarray: if ftype.is_discrete(): return rng.integers(config.max_integer_value, size=shape) @@ -81,8 +84,8 @@ def _generate_feature_data( def _get_feature_shape( - rng: np.random.Generator, ftype: FeatureType, timestamps: List[dt.datetime], config: PatchGeneratorConfig -) -> Tuple[int, ...]: + rng: np.random.Generator, ftype: FeatureType, timestamps: list[dt.datetime], config: PatchGeneratorConfig +) -> tuple[int, ...]: time, height, width, depth = len(timestamps), *config.raster_shape, rng.integers(*config.depth_range) if ftype.is_spatial() and not ftype.is_vector(): diff --git a/core/eolearn/core/utils/types.py b/eolearn/core/utils/types.py similarity index 68% rename from core/eolearn/core/utils/types.py rename to eolearn/core/utils/types.py index 9efc8bdae..dff3051c3 100644 --- a/core/eolearn/core/utils/types.py +++ b/eolearn/core/utils/types.py @@ -1,8 +1,12 @@ """Deprecated module for types, moved to `eolearn.core.types`.""" +from __future__ import annotations + from warnings import warn from ..exceptions import EODeprecationWarning -from ..types import * # noqa: 403 # pylint: disable=wildcard-import,unused-wildcard-import + +# pylint: disable-next=wildcard-import,unused-wildcard-import +from ..types import * # noqa: F403 warn( "The module `eolearn.core.utils.types` is deprecated, use `eolearn.core.types` instead.", diff --git a/coregistration/eolearn/coregistration/__init__.py b/eolearn/coregistration/__init__.py similarity index 85% rename from coregistration/eolearn/coregistration/__init__.py rename to eolearn/coregistration/__init__.py index 3c80f69e9..d89152ef0 100644 --- a/coregistration/eolearn/coregistration/__init__.py +++ b/eolearn/coregistration/__init__.py @@ -3,5 +3,3 @@ """ from .coregistration import ECCRegistrationTask, get_gradient - -__version__ = "1.4.2" diff --git a/coregistration/eolearn/coregistration/coregistration.py b/eolearn/coregistration/coregistration.py similarity index 96% rename from coregistration/eolearn/coregistration/coregistration.py rename to eolearn/coregistration/coregistration.py index bb81d02dc..ffa876a2a 100644 --- a/coregistration/eolearn/coregistration/coregistration.py +++ b/eolearn/coregistration/coregistration.py @@ -16,7 +16,7 @@ from eolearn.core import EOPatch, EOTask, FeatureType from eolearn.core.exceptions import EORuntimeWarning -from eolearn.core.types import FeaturesSpecification +from eolearn.core.types import Feature, FeaturesSpecification LOGGER = logging.getLogger(__name__) @@ -36,10 +36,10 @@ class ECCRegistrationTask(EOTask): def __init__( self, - registration_feature: tuple[FeatureType, str], - reference_feature: tuple[FeatureType, str], + registration_feature: Feature, + reference_feature: Feature, channel: int, - valid_mask_feature: tuple[FeatureType, str] | None = None, + valid_mask_feature: Feature | None = None, apply_to_features: FeaturesSpecification = ..., interpolation_mode: int = cv2.INTER_LINEAR, warp_mode: int = cv2.MOTION_TRANSLATION, @@ -212,8 +212,8 @@ def get_gradient(src: np.ndarray) -> np.ndarray: """ # Calculate the x and y gradients using Sobel operator src = src.astype(np.float32) - grad_x = cv2.Sobel(src, cv2.CV_32F, 1, 0, ksize=3) - grad_y = cv2.Sobel(src, cv2.CV_32F, 0, 1, ksize=3) + grad_x = cv2.Sobel(src, cv2.CV_32F, 1, 0, ksize=3) # type: ignore[attr-defined] + grad_y = cv2.Sobel(src, cv2.CV_32F, 0, 1, ksize=3) # type: ignore[attr-defined] # Combine and return the two gradients return cv2.addWeighted(np.absolute(grad_x), 0.5, np.absolute(grad_y), 0.5, 0) diff --git a/eolearn/features/__init__.py b/eolearn/features/__init__.py new file mode 100644 index 000000000..34c9eeced --- /dev/null +++ b/eolearn/features/__init__.py @@ -0,0 +1,6 @@ +""" +A collection of EOTasks for feature manipulation +""" + +from .feature_manipulation import FilterTimeSeriesTask, SimpleFilterTask +from .ndi import NormalizedDifferenceIndexTask diff --git a/eolearn/features/extra/__init__.py b/eolearn/features/extra/__init__.py new file mode 100644 index 000000000..d4bee5ae0 --- /dev/null +++ b/eolearn/features/extra/__init__.py @@ -0,0 +1,4 @@ +""" +A collection of EOTasks with non-standard dependencies. Use the extra `[EXTRA]` to install dependencies or +install the module-specific dependencies by hand. +""" diff --git a/features/eolearn/features/clustering.py b/eolearn/features/extra/clustering.py similarity index 98% rename from features/eolearn/features/clustering.py rename to eolearn/features/extra/clustering.py index d66f6ef51..35172c3c5 100644 --- a/features/eolearn/features/clustering.py +++ b/eolearn/features/extra/clustering.py @@ -15,7 +15,7 @@ from sklearn.feature_extraction.image import grid_to_graph from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeatureSpec +from eolearn.core.types import Feature class ClusteringTask(EOTask): @@ -29,7 +29,7 @@ class ClusteringTask(EOTask): def __init__( self, - features: FeatureSpec, + features: Feature, new_feature_name: str, distance_threshold: float | None = None, n_clusters: int | None = None, @@ -98,7 +98,7 @@ def execute(self, eopatch: EOPatch) -> EOPatch: model = AgglomerativeClustering( distance_threshold=self.distance_threshold, - affinity=self.affinity, + metric=self.affinity, linkage=self.linkage, connectivity=self.connectivity, n_clusters=self.n_clusters, diff --git a/features/eolearn/features/interpolation.py b/eolearn/features/extra/interpolation.py similarity index 90% rename from features/eolearn/features/interpolation.py rename to eolearn/features/extra/interpolation.py index b8eb8bee6..d11c81def 100644 --- a/features/eolearn/features/interpolation.py +++ b/eolearn/features/extra/interpolation.py @@ -20,8 +20,10 @@ import scipy.interpolate from sklearn.gaussian_process import GaussianProcessRegressor +from sentinelhub.exceptions import deprecated_class + from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.exceptions import EOUserWarning +from eolearn.core.exceptions import EODeprecationWarning, EOUserWarning from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec from eolearn.core.utils.parsing import parse_renamed_feature @@ -92,6 +94,12 @@ class InterpolationTask(EOTask): In the process the interpolated feature is overwritten and so are the timestamps. After the execution of the task the feature will contain interpolated and resampled values and corresponding new timestamps. + Examples of `interpolation_object: + - `scipy.interpolate.interp1d`, supply the kind as a kwarg, e.g. `kind="cubic"` + - `scipy.interpolate.UnivariateSpline` + - `scipy.interpolate.make_interp_spline` + - `scipy.interpolate.Akima1DInterpolator` + :param feature: A feature to be interpolated with optional new feature name :param interpolation_object: Interpolation class which is initialized with `interpolation_parameters` :param resample_range: If None the data will be only interpolated over existing timestamps and NaN values will be @@ -384,8 +392,8 @@ def execute(self, eopatch: EOPatch) -> EOPatch: # Resample times times = self._get_eopatch_time_series(eopatch, scale_time=self.scale_time) - new_eopatch.timestamps = self.get_resampled_timestamp(eopatch.timestamps) - total_diff = int((new_eopatch.timestamps[0].date() - eopatch.timestamps[0].date()).total_seconds()) + new_eopatch.timestamps = self.get_resampled_timestamp(eopatch.get_timestamps()) + total_diff = int((new_eopatch.get_timestamps()[0].date() - eopatch.get_timestamps()[0].date()).total_seconds()) resampled_times = ( self._get_eopatch_time_series(new_eopatch, scale_time=self.scale_time) + total_diff // self.scale_time ) @@ -405,9 +413,8 @@ def execute(self, eopatch: EOPatch) -> EOPatch: feature_data[np.isnan(feature_data)] = self.unknown_value new_eopatch[feature_type, new_feature_name] = np.reshape(feature_data, (-1, height, width, band_num)) - new_eopatch = self._copy_old_features(new_eopatch, eopatch) - return new_eopatch + return self._copy_old_features(new_eopatch, eopatch) class LinearInterpolationTask(InterpolationTask): @@ -434,15 +441,24 @@ def interpolate_data(self, data: np.ndarray, times: np.ndarray, resampled_times: return interpolation_function(data, times, resampled_times) +@deprecated_class( + EODeprecationWarning, + "Use `InterpolationTask` with `interpolation_object=scipy.interpolate.interp1d` and `kind='cubic'`", +) class CubicInterpolationTask(InterpolationTask): - """Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.interp1d(kind='cubic')`""" + """ + [DEPRECATED] Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.interp1d(kind='cubic')` + """ def __init__(self, feature: SingleFeatureSpec, **kwargs: Any): super().__init__(feature, scipy.interpolate.interp1d, kind="cubic", **kwargs) +@deprecated_class( + EODeprecationWarning, "Use `InterpolationTask` with `interpolation_object=scipy.interpolate.UnivariateSpline`" +) class SplineInterpolationTask(InterpolationTask): - """Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.UnivariateSpline`""" + """[DEPRECATED] Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.UnivariateSpline`""" def __init__( self, feature: SingleFeatureSpec, *, spline_degree: int = 3, smoothing_factor: float = 0, **kwargs: Any @@ -450,15 +466,21 @@ def __init__( super().__init__(feature, scipy.interpolate.UnivariateSpline, k=spline_degree, s=smoothing_factor, **kwargs) +@deprecated_class( + EODeprecationWarning, "Use `InterpolationTask` with `interpolation_object=scipy.interpolate.make_interp_spline`" +) class BSplineInterpolationTask(InterpolationTask): - """Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.BSpline`""" + """[DEPRECATED] Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.BSpline`""" def __init__(self, feature: SingleFeatureSpec, *, spline_degree: int = 3, **kwargs: Any): super().__init__(feature, scipy.interpolate.make_interp_spline, k=spline_degree, **kwargs) +@deprecated_class( + EODeprecationWarning, "Use `InterpolationTask` with `interpolation_object=scipy.interpolate.Akima1DInterpolator`" +) class AkimaInterpolationTask(InterpolationTask): - """Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.Akima1DInterpolator`""" + """[DEPRECATED] Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.Akima1DInterpolator`""" def __init__(self, feature: SingleFeatureSpec, **kwargs: Any): super().__init__(feature, scipy.interpolate.Akima1DInterpolator, **kwargs) @@ -501,6 +523,9 @@ class ResamplingTask(InterpolationTask): """A subclass of InterpolationTask task that works only with data with no missing, masked or invalid values. It always resamples timeseries to different timestamps. + + For example, to perform interpolation with the 'nearest' resampling use the values + `interpolation_object=scipy.interpolate.interp1d` with `kind="nearest"`. """ def __init__( @@ -552,22 +577,36 @@ def get_interpolation_function(self, times: np.ndarray, series: np.ndarray) -> C return self.interpolation_object(times, series, axis=0, **self.interpolation_parameters) +@deprecated_class( + EODeprecationWarning, + "Use `ResamplingTask` with `interpolation_object=scipy.interpolate.interp1d` and `kind='nearest'`.", +) class NearestResamplingTask(ResamplingTask): - """Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='nearest')`""" + """ + [DEPRECATED] Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='nearest')` + """ def __init__(self, feature: SingleFeatureSpec, resample_range: ResampleRangeType, **kwargs: Any): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="nearest", **kwargs) +@deprecated_class( + EODeprecationWarning, + "Use `ResamplingTask` with `interpolation_object=scipy.interpolate.interp1d` and `kind='linear'`.", +) class LinearResamplingTask(ResamplingTask): - """Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='linear')`""" + """[DEPRECATED] Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='linear')`""" def __init__(self, feature: SingleFeatureSpec, resample_range: ResampleRangeType, **kwargs: Any): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="linear", **kwargs) +@deprecated_class( + EODeprecationWarning, + "Use `ResamplingTask` with `interpolation_object=scipy.interpolate.interp1d` and `kind='cubic'`.", +) class CubicResamplingTask(ResamplingTask): - """Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='cubic')`""" + """[DEPRECATED] Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='cubic')`""" def __init__(self, feature: SingleFeatureSpec, resample_range: ResampleRangeType, **kwargs: Any): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="cubic", **kwargs) diff --git a/features/eolearn/features/feature_manipulation.py b/eolearn/features/feature_manipulation.py similarity index 50% rename from features/eolearn/features/feature_manipulation.py rename to eolearn/features/feature_manipulation.py index 7e895a88c..8b69904eb 100644 --- a/features/eolearn/features/feature_manipulation.py +++ b/eolearn/features/feature_manipulation.py @@ -12,16 +12,16 @@ import datetime as dt import logging from functools import partial -from typing import Any, Callable, Iterable, Literal, cast +from typing import Any, Callable, Iterable, Literal import numpy as np from geopandas import GeoDataFrame from sentinelhub import bbox_to_dimensions -from eolearn.core import EOPatch, EOTask, FeatureType, MapFeatureTask +from eolearn.core import EOPatch, EOTask from eolearn.core.constants import TIMESTAMP_COLUMN -from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec +from eolearn.core.types import Feature, FeaturesSpecification from eolearn.core.utils.parsing import parse_renamed_features from .utils import ResizeLib, ResizeMethod, ResizeParam, spatially_resize_image @@ -39,7 +39,7 @@ class SimpleFilterTask(EOTask): def __init__( self, - feature: SingleFeatureSpec, + feature: Feature | Literal["timestamps"], filter_func: Callable[[np.ndarray], bool] | Callable[[dt.datetime], bool], filter_features: FeaturesSpecification = ..., ): @@ -48,10 +48,12 @@ def __init__( :param filter_func: A callable that takes a numpy evaluates to bool. :param filter_features: A collection of features which will be filtered into a new EOPatch """ - - self.feature = self.parse_feature( - feature, allowed_feature_types=lambda fty: fty.is_temporal() and not fty.is_vector() - ) + if feature == "timestamps": + self.feature: Feature | Literal["timestamps"] = "timestamps" + else: + self.feature = self.parse_feature( + feature, allowed_feature_types=lambda fty: fty.is_temporal() and fty.is_array() + ) self.filter_func = filter_func self.filter_features_parser = self.get_feature_parser(filter_features) @@ -70,21 +72,20 @@ def execute(self, eopatch: EOPatch) -> EOPatch: :param eopatch: An input EOPatch. :return: A new EOPatch with filtered features. """ - filtered_eopatch = EOPatch(bbox=eopatch.bbox) - good_idxs = self._get_filtered_indices(eopatch[self.feature]) + selector_data = eopatch.timestamps if self.feature == "timestamps" else eopatch[self.feature] + good_idxs = self._get_filtered_indices(selector_data) + timestamps = None if eopatch.timestamps is None else [eopatch.timestamps[idx] for idx in good_idxs] + filtered_eopatch = EOPatch(bbox=eopatch.bbox, timestamps=timestamps) for feature in self.filter_features_parser.get_features(eopatch): feature_type, _ = feature data = eopatch[feature] - if feature_type is FeatureType.TIMESTAMPS: - data = [data[idx] for idx in good_idxs] - - elif feature_type.is_temporal(): + if feature_type.is_temporal(): if feature_type.is_array(): data = data[good_idxs] else: - data = self._filter_vector_feature(data, good_idxs, eopatch.timestamps) + data = self._filter_vector_feature(data, good_idxs, eopatch.get_timestamps()) filtered_eopatch[feature] = data @@ -111,138 +112,7 @@ def __init__(self, start_date: dt.datetime, end_date: dt.datetime, filter_featur if not isinstance(start_date, dt.datetime) or not isinstance(end_date, dt.datetime): raise ValueError("Both start_date and end_date must be datetime.datetime objects.") - super().__init__((FeatureType.TIMESTAMPS, None), self._filter_func, filter_features) - - -class ValueFilloutTask(EOTask): - """Overwrites occurrences of a desired value with their neighbor values in either forward, backward direction or - both, along an axis. - - Possible fillout operations are 'f' (forward), 'b' (backward) or both, 'fb' or 'bf': - - 'f': nan, nan, nan, 8, 5, nan, 1, 0, nan, nan -> nan, nan, nan, 8, 5, 5, 1, 0, 0, 0 - - 'b': nan, nan, nan, 8, 5, nan, 1, 0, nan, nan -> 8, 8, 8, 8, 5, 1, 1, 0, nan, nan - - 'fb': nan, nan, nan, 8, 5, nan, 1, 0, nan, nan -> 8, 8, 8, 8, 5, 5, 1, 0, 0, 0 - - 'bf': nan, nan, nan, 8, 5, nan, 1, 0, nan, nan -> 8, 8, 8, 8, 5, 1, 1, 0, 0, 0 - """ - - def __init__( - self, - feature: SingleFeatureSpec, - operations: Literal["f", "b", "fb", "bf"] = "fb", - value: float = np.nan, - axis: int = 0, - ): - """ - :param feature: A feature that must be value-filled. - :param operations: Fill directions, which should be one of ['f', 'b', 'fb', 'bf']. - :param value: Which value to fill by its neighbors. - :param axis: An axis along which to fill values. - """ - if operations not in ["f", "b", "fb", "bf"]: - raise ValueError("'operations' parameter should be one of the following options: f, b, fb, bf.") - - self.feature = self.parse_feature(feature) - self.operations = operations - self.value = value - self.axis = axis - - @staticmethod - def fill(data: np.ndarray, value: float = np.nan, operation: Literal["f", "b"] = "f") -> np.ndarray: - """Fills occurrences of a desired value in a 2d array with their neighbors in either forward or backward - direction. - - :param data: A 2d numpy array. - :param value: Which value to fill by its neighbors. - :param operation: Fill directions, which should be either 'f' or 'b'. - :return: Value-filled numpy array. - """ - if not isinstance(data, np.ndarray) or data.ndim != 2: - raise ValueError("Wrong data input") - - if operation not in ["f", "b"]: - raise ValueError("'operation' parameter should either be 'f' (forward) or 'b' (backward)!") - - n_rows, n_frames = data.shape - - value_mask = np.isnan(data) if np.isnan(value) else (data == value) - init_index = 0 if operation == "f" else (n_frames - 1) - - idx = np.where(value_mask, init_index, np.arange(n_frames)) - - if operation == "f": - idx = np.maximum.accumulate(idx, axis=1) - else: - idx = idx[:, ::-1] - idx = np.minimum.accumulate(idx, axis=1) - idx = idx[:, ::-1] - - return data[np.arange(n_rows)[:, np.newaxis], idx] - - def execute(self, eopatch: EOPatch) -> EOPatch: - """ - :param eopatch: Source EOPatch from which to read the feature data. - :return: An eopatch with the value-filled feature. - """ - data = eopatch[self.feature] - - value_mask = np.isnan(data) if np.isnan(self.value) else (data == self.value) - - if not value_mask.any(): - return eopatch - - data = np.swapaxes(data, self.axis, -1) - original_shape = data.shape - data = data.reshape(np.prod(original_shape[:-1]), original_shape[-1]) - - for operation in self.operations: # iterates over string that represents the operation - operation = cast(Literal["f", "b"], operation) - data = self.fill(data, value=self.value, operation=operation) - - data = data.reshape(*original_shape) - data = np.swapaxes(data, self.axis, -1) - - eopatch[self.feature] = data - - return eopatch - - -class LinearFunctionTask(MapFeatureTask): - """Applies a linear function to the values of input features. - - Each value in the feature is modified as `x -> x * slope + intercept`. The `dtype` of the result can be customized. - """ - - def __init__( - self, - input_features: FeaturesSpecification, - output_features: FeaturesSpecification | None = None, - slope: float = 1, - intercept: float = 0, - dtype: str | type | np.dtype | None = None, - ): - """ - :param input_features: Feature or features on which the function is used. - :param output_features: Feature or features for saving the result. If not provided the input_features are - overwritten. - :param slope: Slope of the function i.e. the multiplication factor. - :param intercept: Intercept of the function i.e. the value added. - :param dtype: Numpy dtype of the output feature. If not provided the dtype is determined by Numpy, so it is - recommended to set manually. - """ - if output_features is None: - output_features = input_features - self.dtype = dtype if dtype is None else np.dtype(dtype) - - super().__init__(input_features, output_features, slope=slope, intercept=intercept) - - def map_method(self, feature: np.ndarray, slope: float, intercept: float) -> np.ndarray: # type:ignore[override] - """A method where feature is multiplied by a slope""" - rescaled_feature = feature * slope + intercept - return rescaled_feature if self.dtype is None else rescaled_feature.astype(self.dtype) + super().__init__("timestamps", self._filter_func, filter_features) class SpatialResizeTask(EOTask): @@ -256,7 +126,7 @@ def __init__( width_param: float, features: FeaturesSpecification = ..., resize_method: ResizeMethod = ResizeMethod.LINEAR, - resize_library: ResizeLib = ResizeLib.PIL, + resize_library: ResizeLib = ResizeLib.CV2, ): """ :param features: Which features to resize. Supports new names for features. @@ -270,8 +140,8 @@ def __init__( :param height_param: Parameter to be applied to the height in combination with the resize_type :param width_param: Parameter to be applied to the width in combination with the resize_type :param resize_method: Interpolation method used for resizing. - :param resize_library: Which Python library to use for resizing. Default is PIL, as it supports all dtypes and - features anti-aliasing. For cases where execution speed is crucial one can use CV2. + :param resize_library: Which Python library to use for resizing. Default is CV2 because it is faster, but one + can use PIL, which features anti-aliasing. """ self.features = features self.resize_type = ResizeParam(resize_type) diff --git a/features/eolearn/features/bands_extraction.py b/eolearn/features/ndi.py similarity index 66% rename from features/eolearn/features/bands_extraction.py rename to eolearn/features/ndi.py index d4071797a..76b19227d 100644 --- a/features/eolearn/features/bands_extraction.py +++ b/eolearn/features/ndi.py @@ -11,34 +11,7 @@ import numpy as np from eolearn.core import MapFeatureTask -from eolearn.core.types import SingleFeatureSpec - - -class EuclideanNormTask(MapFeatureTask): - """The task calculates the Euclidean Norm: - - :math:`Norm = \\sqrt{\\sum_{i} B_i^2}` - - where :math:`B_i` are the individual bands within a user-specified feature array. - """ - - def __init__( - self, input_feature: SingleFeatureSpec, output_feature: SingleFeatureSpec, bands: list[int] | None = None - ): - """ - :param input_feature: A source feature from which to take the subset of bands. - :param output_feature: An output feature to which to write the euclidean norm. - :param bands: A list of bands from which to extract the euclidean norm. If None, all bands are taken. - """ - super().__init__(input_feature, output_feature) - self.bands = bands - - def map_method(self, feature: np.ndarray) -> np.ndarray: - """ - :param feature: An eopatch on which to calculate the euclidean norm. - """ - array = feature if not self.bands else feature[..., self.bands] - return np.sqrt(np.sum(array**2, axis=-1))[..., np.newaxis] +from eolearn.core.types import Feature class NormalizedDifferenceIndexTask(MapFeatureTask): @@ -53,8 +26,8 @@ class NormalizedDifferenceIndexTask(MapFeatureTask): def __init__( self, - input_feature: SingleFeatureSpec, - output_feature: SingleFeatureSpec, + input_feature: Feature, + output_feature: Feature, bands: tuple[int, int], acorvi_constant: float = 0, undefined_value: float = np.nan, diff --git a/features/eolearn/features/utils.py b/eolearn/features/utils.py similarity index 83% rename from features/eolearn/features/utils.py rename to eolearn/features/utils.py index d2194eb7d..35c4636fd 100644 --- a/features/eolearn/features/utils.py +++ b/eolearn/features/utils.py @@ -6,18 +6,23 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import warnings from enum import Enum from functools import partial -from typing import Dict, Optional, Tuple, Union +from typing import TYPE_CHECKING +import cv2 import numpy as np -from PIL import Image from eolearn.core.exceptions import EORuntimeWarning from eolearn.core.utils.common import _apply_to_spatial_axes -_CV2_IMPORT_MESSAGE = "The CV2 backend is not installed by default. We suggest you install the `opencv-python` package." +_PIL_IMPORT_MESSAGE = "The PIL backend is not installed by default. Please install the `pillow` package." + +if TYPE_CHECKING: + from PIL import Image class ResizeParam(Enum): @@ -35,12 +40,9 @@ class ResizeMethod(Enum): LINEAR = "linear" CUBIC = "cubic" - def get_cv2_method(self, dtype: Union[np.dtype, type]) -> int: + def get_cv2_method(self, dtype: np.dtype | type) -> int: """Obtain the constant specifying the interpolation method for the CV2 library.""" - try: - import cv2 # pylint: disable=import-outside-toplevel - except ImportError as exception: - raise ImportError(_CV2_IMPORT_MESSAGE) from exception + number_dtype = np.dtype(dtype) if np.issubdtype(number_dtype, np.floating): choices = { @@ -63,6 +65,10 @@ def get_cv2_method(self, dtype: Union[np.dtype, type]) -> int: def get_pil_method(self) -> Image.Resampling: """Obtain the constant specifying the interpolation method for the PIL library.""" + try: + from PIL import Image # pylint: disable=import-outside-toplevel + except ImportError as exception: + raise ImportError(_PIL_IMPORT_MESSAGE) from exception choices = { ResizeMethod.NEAREST: Image.Resampling.NEAREST, ResizeMethod.LINEAR: Image.Resampling.BILINEAR, @@ -77,7 +83,7 @@ class ResizeLib(Enum): PIL = "PIL" CV2 = "cv2" - def get_compatible_dtype(self, dtype: Union[np.dtype, type]) -> np.dtype: + def get_compatible_dtype(self, dtype: np.dtype | type) -> np.dtype: """Returns a suitable dtype with which the library can work. Warns if information loss could occur.""" if self is ResizeLib.CV2: lossless = {bool: np.uint8, np.float16: np.float32} @@ -92,7 +98,7 @@ def get_compatible_dtype(self, dtype: Union[np.dtype, type]) -> np.dtype: @staticmethod def _extract_compatible_dtype( - dtype: Union[np.dtype, type], lossless_casts: Dict[np.dtype, np.dtype], infoloss_casts: Dict[np.dtype, np.dtype] + dtype: np.dtype | type, lossless_casts: dict[np.dtype, np.dtype], infoloss_casts: dict[np.dtype, np.dtype] ) -> np.dtype: """Searches the dictionaries and extract the appropriate dtype. Warns of data loss if it could occur.""" dtype = np.dtype(dtype) @@ -108,11 +114,11 @@ def _extract_compatible_dtype( def spatially_resize_image( data: np.ndarray, - new_size: Optional[Tuple[int, int]] = None, - scale_factors: Optional[Tuple[float, float]] = None, - spatial_axes: Optional[Tuple[int, int]] = None, + new_size: tuple[int, int] | None = None, + scale_factors: tuple[float, float] | None = None, + spatial_axes: tuple[int, int] | None = None, resize_method: ResizeMethod = ResizeMethod.LINEAR, - resize_library: ResizeLib = ResizeLib.PIL, + resize_library: ResizeLib = ResizeLib.CV2, ) -> np.ndarray: """Resizes the image(s) according to given size or scale factors. @@ -124,8 +130,8 @@ def spatially_resize_image( :param spatial_axes: Which two axes of input data represent height and width. If left as `None` they are selected according to standards of eo-learn features. :param resize_method: Interpolation method used for resizing. - :param resize_library: Which Python library to use for resizing. Default is PIL, as it supports all dtypes and - features anti-aliasing. For cases where execution speed is crucial one can use CV2. + :param resize_library: Which Python library to use for resizing. Default is CV2 because it is faster, but one can + use PIL, which features anti-aliasing. """ resize_method = ResizeMethod(resize_method) @@ -153,10 +159,6 @@ def spatially_resize_image( data = data.astype(new_dtype) if resize_library is ResizeLib.CV2: - try: - import cv2 # pylint: disable=import-outside-toplevel - except ImportError as exception: - raise ImportError(_CV2_IMPORT_MESSAGE) from exception resize_function = partial(cv2.resize, dsize=size, interpolation=resize_method.get_cv2_method(data.dtype)) else: resize_function = partial(_pil_resize_ndarray, size=size, method=resize_method.get_pil_method()) @@ -169,5 +171,9 @@ def spatially_resize_image( return resized_data.astype(old_dtype) -def _pil_resize_ndarray(image: np.ndarray, size: Tuple[int, int], method: Image.Resampling) -> np.ndarray: +def _pil_resize_ndarray(image: np.ndarray, size: tuple[int, int], method: Image.Resampling) -> np.ndarray: + try: + from PIL import Image # pylint: disable=import-outside-toplevel + except ImportError as exception: + raise ImportError(_PIL_IMPORT_MESSAGE) from exception return np.array(Image.fromarray(image).resize(size, method)) diff --git a/geometry/eolearn/geometry/__init__.py b/eolearn/geometry/__init__.py similarity index 58% rename from geometry/eolearn/geometry/__init__.py rename to eolearn/geometry/__init__.py index 5dfdad3c7..dcfe9cd5e 100644 --- a/geometry/eolearn/geometry/__init__.py +++ b/eolearn/geometry/__init__.py @@ -3,12 +3,4 @@ """ from .morphology import ErosionTask, MorphologicalFilterTask, MorphologicalOperations, MorphologicalStructFactory -from .superpixel import ( - FelzenszwalbSegmentationTask, - MarkSegmentationBoundariesTask, - SlicSegmentationTask, - SuperpixelSegmentationTask, -) from .transformations import RasterToVectorTask, VectorToRasterTask - -__version__ = "1.4.2" diff --git a/geometry/eolearn/geometry/morphology.py b/eolearn/geometry/morphology.py similarity index 73% rename from geometry/eolearn/geometry/morphology.py rename to eolearn/geometry/morphology.py index bcdd6558a..dbf039fe8 100644 --- a/geometry/eolearn/geometry/morphology.py +++ b/eolearn/geometry/morphology.py @@ -10,13 +10,12 @@ import itertools as it from enum import Enum -from typing import Callable, Tuple, cast +from functools import partial +import cv2 import numpy as np -import skimage.filters.rank -import skimage.morphology -from eolearn.core import EOPatch, EOTask, FeatureType, MapFeatureTask +from eolearn.core import EOPatch, EOTask, MapFeatureTask from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec from eolearn.core.utils.parsing import parse_renamed_feature @@ -41,12 +40,10 @@ def __init__( if not isinstance(disk_radius, int) or disk_radius is None or disk_radius < 1: raise ValueError("Disk radius should be an integer larger than 0!") - parsed_mask_feature = cast( - Tuple[FeatureType, str, str], - parse_renamed_feature(mask_feature, allowed_feature_types=lambda fty: fty.is_array()), - ) + parsed_mask_feature = parse_renamed_feature(mask_feature, allowed_feature_types=lambda fty: fty.is_array()) + self.mask_type, self.mask_name, self.new_mask_name = parsed_mask_feature - self.disk = skimage.morphology.disk(disk_radius) + self.disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (disk_radius, disk_radius)) self.erode_labels = erode_labels self.no_data_label = no_data_label @@ -59,7 +56,7 @@ def execute(self, eopatch: EOPatch) -> EOPatch: erode_labels = set(erode_labels) - {self.no_data_label} other_labels = set(all_labels) - set(erode_labels) - {self.no_data_label} - eroded_masks = [skimage.morphology.binary_erosion(feature_array == label, self.disk) for label in erode_labels] + eroded_masks = [cv2.erode((feature_array == label).astype(np.uint8), self.disk) for label in erode_labels] other_masks = [feature_array == label for label in other_labels] merged_mask = np.logical_or.reduce(eroded_masks + other_masks, axis=0) @@ -77,20 +74,18 @@ class MorphologicalOperations(Enum): CLOSING = "closing" DILATION = "dilation" EROSION = "erosion" - MEDIAN = "median" @classmethod - def get_operation(cls, morph_type: MorphologicalOperations) -> Callable: + def get_operation(cls, morph_type: MorphologicalOperations) -> int: """Maps morphological operation type to function :param morph_type: Morphological operation type """ return { - cls.OPENING: skimage.morphology.opening, - cls.CLOSING: skimage.morphology.closing, - cls.DILATION: skimage.morphology.dilation, - cls.EROSION: skimage.morphology.erosion, - cls.MEDIAN: skimage.filters.rank.median, + cls.OPENING: cv2.MORPH_OPEN, + cls.CLOSING: cv2.MORPH_CLOSE, + cls.DILATION: cv2.MORPH_DILATE, + cls.EROSION: cv2.MORPH_ERODE, }[morph_type] @@ -105,15 +100,7 @@ def get_disk(radius: int) -> np.ndarray: :param radius: Radius of disk :return: The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ - return skimage.morphology.disk(radius) - - @staticmethod - def get_diamond(radius: int) -> np.ndarray: - """ - :param radius: Radius of diamond - :return: The structuring element where elements of the neighborhood are 1 and 0 otherwise. - """ - return skimage.morphology.diamond(radius) + return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (radius, radius)) @staticmethod def get_rectangle(width: int, height: int) -> np.ndarray: @@ -122,7 +109,7 @@ def get_rectangle(width: int, height: int) -> np.ndarray: :param height: Height of rectangle :return: A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. """ - return skimage.morphology.rectangle(width, height) + return cv2.getStructuringElement(cv2.MORPH_RECT, (height, width)) @staticmethod def get_square(width: int) -> np.ndarray: @@ -130,7 +117,7 @@ def get_square(width: int) -> np.ndarray: :param width: Size of square :return: A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. """ - return skimage.morphology.square(width) + return cv2.getStructuringElement(cv2.MORPH_RECT, (width, width)) class MorphologicalFilterTask(MapFeatureTask): @@ -141,7 +128,7 @@ def __init__( input_features: FeaturesSpecification, output_features: FeaturesSpecification | None = None, *, - morph_operation: MorphologicalOperations | Callable, + morph_operation: MorphologicalOperations, struct_elem: np.ndarray | None = None, ): """ @@ -155,21 +142,19 @@ def __init__( output_features = input_features super().__init__(input_features, output_features) - if isinstance(morph_operation, MorphologicalOperations): - self.morph_operation = MorphologicalOperations.get_operation(morph_operation) - else: - self.morph_operation = morph_operation + self.morph_operation = MorphologicalOperations.get_operation(morph_operation) self.struct_elem = struct_elem def map_method(self, feature: np.ndarray) -> np.ndarray: """Applies the morphological operation to a raster feature.""" feature = feature.copy() + morph_func = partial(cv2.morphologyEx, kernel=self.struct_elem, op=self.morph_operation) if feature.ndim == 3: for channel in range(feature.shape[2]): - feature[..., channel] = self.morph_operation(feature[..., channel], self.struct_elem) + feature[..., channel] = morph_func(feature[..., channel]) elif feature.ndim == 4: for time, channel in it.product(range(feature.shape[0]), range(feature.shape[3])): - feature[time, ..., channel] = self.morph_operation(feature[time, ..., channel], self.struct_elem) + feature[time, ..., channel] = morph_func(feature[time, ..., channel]) else: raise ValueError(f"Invalid number of dimensions: {feature.ndim}") diff --git a/geometry/eolearn/geometry/transformations.py b/eolearn/geometry/transformations.py similarity index 95% rename from geometry/eolearn/geometry/transformations.py rename to eolearn/geometry/transformations.py index 4e2bd5135..b7fe54271 100644 --- a/geometry/eolearn/geometry/transformations.py +++ b/eolearn/geometry/transformations.py @@ -13,7 +13,7 @@ import logging import warnings from functools import partial -from typing import Any, Callable, Iterator, Tuple, cast +from typing import Any, Callable, ClassVar, Iterator, Tuple, cast import numpy as np import pandas as pd @@ -31,7 +31,7 @@ from eolearn.core import EOPatch, EOTask, FeatureType from eolearn.core.constants import TIMESTAMP_COLUMN from eolearn.core.exceptions import EORuntimeWarning -from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec +from eolearn.core.types import Feature, FeaturesSpecification, SingleFeatureSpec LOGGER = logging.getLogger(__name__) @@ -49,7 +49,7 @@ class VectorToRasterTask(EOTask): # A mapping between types that are not supported by rasterio into types that are. After rasterization the task # will cast results back into the original dtype. - _RASTERIO_DTYPES_MAP = { + _RASTERIO_DTYPES_MAP: ClassVar[dict[type | np.dtype, type]] = { bool: np.uint8, np.dtype(bool): np.uint8, np.int8: np.int16, @@ -60,12 +60,12 @@ class VectorToRasterTask(EOTask): def __init__( self, - vector_input: GeoDataFrame | SingleFeatureSpec, - raster_feature: SingleFeatureSpec, + vector_input: GeoDataFrame | Feature, + raster_feature: Feature, *, values: None | float | list[float] = None, values_column: str | None = None, - raster_shape: None | tuple[int, int] | SingleFeatureSpec = None, + raster_shape: None | tuple[int, int] | Feature = None, raster_resolution: None | float | tuple[float, float] = None, raster_dtype: np.dtype | type = np.uint8, no_data_value: float = 0, @@ -125,13 +125,13 @@ def __init__( def _parse_main_params( self, vector_input: GeoDataFrame | SingleFeatureSpec, raster_feature: SingleFeatureSpec - ) -> tuple[GeoDataFrame | tuple[FeatureType, str], tuple[FeatureType, str]]: + ) -> tuple[GeoDataFrame | Feature, Feature]: """Parsing first 2 parameters - what vector data will be used and in which raster feature it will be saved""" if not _is_geopandas_object(vector_input): vector_input = self.parse_feature(vector_input, allowed_feature_types=lambda fty: fty.is_vector()) parsed_raster_feature = self.parse_feature(raster_feature, allowed_feature_types=lambda fty: fty.is_image()) - return vector_input, parsed_raster_feature # type: ignore[return-value] + return vector_input, parsed_raster_feature def _get_vector_data_iterator( self, eopatch: EOPatch, join_per_value: bool @@ -154,7 +154,7 @@ def _get_vector_data_iterator( yield None, self._vector_data_to_shape_iterator(vector_data, join_per_value) def _preprocess_vector_data( - self, vector_data: GeoDataFrame, bbox: BBox, timestamps: list[dt.datetime] + self, vector_data: GeoDataFrame, bbox: BBox, timestamps: list[dt.datetime] | None ) -> GeoDataFrame: """Applies preprocessing steps on a dataframe with geometries and potential values and timestamps""" vector_data = self._reduce_vector_data(vector_data, timestamps) @@ -183,7 +183,7 @@ def _preprocess_vector_data( return vector_data - def _reduce_vector_data(self, vector_data: GeoDataFrame, timestamps: list[dt.datetime]) -> GeoDataFrame: + def _reduce_vector_data(self, vector_data: GeoDataFrame, timestamps: list[dt.datetime] | None) -> GeoDataFrame: """Removes all redundant columns and rows.""" columns_to_keep = ["geometry"] if self._rasterize_per_timestamp: @@ -221,7 +221,7 @@ def _get_raster_shape(self, eopatch: EOPatch) -> tuple[int, int]: return self.raster_shape ftype, fname = self.parse_feature(self.raster_shape, allowed_feature_types=lambda fty: fty.is_array()) - return eopatch.get_spatial_dimension(ftype, cast(str, fname)) # cast verified in parser + return eopatch.get_spatial_dimension(ftype, fname) if self.raster_resolution: # parsing from strings is not denoted in types, so an explicit upcast is required @@ -235,7 +235,9 @@ def _get_raster_shape(self, eopatch: EOPatch) -> tuple[int, int]: def _get_raster(self, eopatch: EOPatch, height: int, width: int) -> np.ndarray: """Provides raster into which data will be written""" - raster_shape = (len(eopatch.timestamps), height, width) if self._rasterize_per_timestamp else (height, width) + raster_shape = ( + (len(eopatch.get_timestamps()), height, width) if self._rasterize_per_timestamp else (height, width) + ) if self.write_to_existing and self.raster_feature in eopatch: raster = eopatch[self.raster_feature] @@ -300,7 +302,8 @@ def execute(self, eopatch: EOPatch) -> EOPatch: rasterio_dtype = self._RASTERIO_DTYPES_MAP[original_dtype] raster = raster.astype(rasterio_dtype) - timestamp_to_index = {timestamp: index for index, timestamp in enumerate(eopatch.timestamps)} + timestamps = eopatch.timestamps or [] + timestamp_to_index = {timestamp: index for index, timestamp in enumerate(timestamps)} for timestamp, shape_iterator in vector_data_iterator: if timestamp is None: @@ -419,8 +422,9 @@ def execute(self, eopatch: EOPatch) -> EOPatch: if raster_type.is_timeless(): eopatch[vector_type, vector_name] = self._vectorize_single_raster(raster, affine_transform, crs) else: + timestamps = eopatch.get_timestamps() gpd_list = [ - self._vectorize_single_raster(raster[idx, ...], affine_transform, crs, eopatch.timestamps[idx]) + self._vectorize_single_raster(raster[idx, ...], affine_transform, crs, timestamps[idx]) for idx in range(raster.shape[0]) ] diff --git a/io/eolearn/io/__init__.py b/eolearn/io/__init__.py similarity index 61% rename from io/eolearn/io/__init__.py rename to eolearn/io/__init__.py index 6f18b58c3..93f8df2af 100644 --- a/io/eolearn/io/__init__.py +++ b/eolearn/io/__init__.py @@ -2,15 +2,11 @@ A collection of input and output EOTasks """ -from .geometry_io import GeopediaVectorImportTask, VectorImportTask -from .geopedia import AddGeopediaFeatureTask +from .geometry_io import VectorImportTask from .raster_io import ExportToTiffTask, ImportFromTiffTask from .sentinelhub_process import ( SentinelHubDemTask, SentinelHubEvalscriptTask, SentinelHubInputTask, - SentinelHubSen2corTask, get_available_timestamps, ) - -__version__ = "1.4.2" diff --git a/io/eolearn/io/geometry_io.py b/eolearn/io/geometry_io.py similarity index 68% rename from io/eolearn/io/geometry_io.py rename to eolearn/io/geometry_io.py index 19368aec3..74f9b106a 100644 --- a/io/eolearn/io/geometry_io.py +++ b/eolearn/io/geometry_io.py @@ -9,7 +9,6 @@ from __future__ import annotations -import abc import logging from contextlib import nullcontext from typing import Any @@ -21,87 +20,21 @@ from fs.base import FS from fs_s3fs import S3FS -from sentinelhub import CRS, BBox, GeopediaFeatureIterator, SHConfig +from sentinelhub import CRS, BBox, SHConfig from eolearn.core import EOPatch, EOTask, pickle_fs, unpickle_fs -from eolearn.core.types import FeatureSpec +from eolearn.core.types import Feature from eolearn.core.utils.fs import get_base_filesystem_and_path, get_full_path LOGGER = logging.getLogger(__name__) -class _BaseVectorImportTask(EOTask, metaclass=abc.ABCMeta): - """Base Vector Import Task, implementing common methods""" - - def __init__( - self, feature: FeatureSpec, reproject: bool = True, clip: bool = False, config: SHConfig | None = None - ): - """ - :param feature: A vector feature into which to import data - :param reproject: Should the geometries be transformed to coordinate reference system of the requested bbox? - :param clip: Should the geometries be clipped to the requested bbox, or should be geometries kept as they are? - :param config: A configuration object with credentials - """ - self.feature = self.parse_feature(feature, allowed_feature_types=lambda fty: fty.is_vector()) - self.config = config or SHConfig() - self.reproject = reproject - self.clip = clip - - @abc.abstractmethod - def _load_vector_data(self, bbox: BBox | None) -> gpd.GeoDataFrame: - """Loads vector data given a bounding box""" - - def _reproject_and_clip(self, vectors: gpd.GeoDataFrame, bbox: BBox | None) -> gpd.GeoDataFrame: - """Method to reproject and clip vectors to the EOPatch crs and bbox""" - - if self.reproject: - if not bbox: - raise ValueError("To reproject vector data, eopatch.bbox has to be defined!") - - vectors = vectors.to_crs(bbox.crs.pyproj_crs()) - - if self.clip: - if not bbox: - raise ValueError("To clip vector data, eopatch.bbox has to be defined!") - - bbox_crs = bbox.crs.pyproj_crs() - if vectors.crs != bbox_crs: - raise ValueError("To clip, vectors should be in same CRS as EOPatch bbox!") - - extent = gpd.GeoSeries([bbox.geometry], crs=bbox_crs) - vectors = gpd.clip(vectors, extent, keep_geom_type=True) - - return vectors - - def execute(self, eopatch: EOPatch | None = None, *, bbox: BBox | None = None) -> EOPatch: - """ - :param eopatch: An existing EOPatch. If none is provided it will create a new one. - :param bbox: A bounding box for which to load data. By default, if none is provided, it will take a bounding box - of given EOPatch. If given EOPatch is not provided it will load the entire dataset. - :return: An EOPatch with an additional vector feature - """ - if bbox is None and eopatch is not None: - bbox = eopatch.bbox - - vectors = self._load_vector_data(bbox) - minx, miny, maxx, maxy = vectors.total_bounds - final_bbox = bbox or BBox((minx, miny, maxx, maxy), crs=CRS(vectors.crs)) - - eopatch = eopatch or EOPatch(bbox=final_bbox) - if eopatch.bbox is None: - eopatch.bbox = final_bbox - - eopatch[self.feature] = self._reproject_and_clip(vectors, bbox) - - return eopatch - - -class VectorImportTask(_BaseVectorImportTask): +class VectorImportTask(EOTask): """A task for importing (Fiona readable) vector data files into an EOPatch""" def __init__( self, - feature: FeatureSpec, + feature: Feature, path: str, reproject: bool = True, clip: bool = False, @@ -130,7 +63,10 @@ def __init__( self._aws_session = None self._dataset_crs: CRS | None = None - super().__init__(feature=feature, reproject=reproject, clip=clip, config=config) + self.feature = self.parse_feature(feature, allowed_feature_types=lambda fty: fty.is_vector()) + self.config = config or SHConfig() + self.reproject = reproject + self.clip = clip @property def aws_session(self) -> AWSSession: @@ -181,47 +117,46 @@ def _load_vector_data(self, bbox: BBox | None) -> gpd.GeoDataFrame: return gpd.read_file(self.full_path, bbox=bbox_bounds, **self.fiona_kwargs) + def _reproject_and_clip(self, vectors: gpd.GeoDataFrame, bbox: BBox | None) -> gpd.GeoDataFrame: + """Method to reproject and clip vectors to the EOPatch crs and bbox""" -class GeopediaVectorImportTask(_BaseVectorImportTask): - """A task for importing `Geopedia `__ features into EOPatch vector features""" + if self.reproject: + if not bbox: + raise ValueError("To reproject vector data, eopatch.bbox has to be defined!") - def __init__( - self, - feature: FeatureSpec, - geopedia_table: str | int, - reproject: bool = True, - clip: bool = False, - **kwargs: Any, - ): + vectors = vectors.to_crs(bbox.crs.pyproj_crs()) + + if self.clip: + if not bbox: + raise ValueError("To clip vector data, eopatch.bbox has to be defined!") + + bbox_crs = bbox.crs.pyproj_crs() + if vectors.crs != bbox_crs: + raise ValueError("To clip, vectors should be in same CRS as EOPatch bbox!") + + extent = gpd.GeoSeries([bbox.geometry], crs=bbox_crs) + vectors = gpd.clip(vectors, extent, keep_geom_type=True) + + return vectors + + def execute(self, eopatch: EOPatch | None = None, *, bbox: BBox | None = None) -> EOPatch: """ - :param feature: A vector feature into which to import data - :param geopedia_table: A Geopedia table from which to retrieve features - :param reproject: Should the geometries be transformed to coordinate reference system of the requested bbox? - :param clip: Should the geometries be clipped to the requested bbox, or should be geometries kept as they are? - :param kwargs: Additional args that will be passed to `GeopediaFeatureIterator` + :param eopatch: An existing EOPatch. If none is provided it will create a new one. + :param bbox: A bounding box for which to load data. By default, if none is provided, it will take a bounding box + of given EOPatch. If given EOPatch is not provided it will load the entire dataset. + :return: An EOPatch with an additional vector feature """ - self.geopedia_table = geopedia_table - self.geopedia_kwargs = kwargs - self.dataset_crs: CRS | None = None - super().__init__(feature=feature, reproject=reproject, clip=clip) + if bbox is None and eopatch is not None: + bbox = eopatch.bbox - def _load_vector_data(self, bbox: BBox | None) -> gpd.GeoDataFrame: - """Loads vector data from geopedia table""" - prepared_bbox = bbox.transform_bounds(CRS.POP_WEB) if bbox else None - - geopedia_iterator = GeopediaFeatureIterator( - layer=self.geopedia_table, - bbox=prepared_bbox, - offset=0, - gpd_session=None, - config=self.config, - **self.geopedia_kwargs, - ) - geopedia_features = list(geopedia_iterator) - - geometry = geopedia_features[0].get("geometry") - if not geometry: - raise ValueError(f'Geopedia table "{self.geopedia_table}" does not contain geometries!') - - self.dataset_crs = CRS(geometry["crs"]["properties"]["name"]) # always WGS84 - return gpd.GeoDataFrame.from_features(geopedia_features, crs=self.dataset_crs.pyproj_crs()) + vectors = self._load_vector_data(bbox) + minx, miny, maxx, maxy = vectors.total_bounds + final_bbox = bbox or BBox((minx, miny, maxx, maxy), crs=CRS(vectors.crs)) + + eopatch = eopatch or EOPatch(bbox=final_bbox) + if eopatch.bbox is None: + eopatch.bbox = final_bbox + + eopatch[self.feature] = self._reproject_and_clip(vectors, bbox) + + return eopatch diff --git a/io/eolearn/io/raster_io.py b/eolearn/io/raster_io.py similarity index 89% rename from io/eolearn/io/raster_io.py rename to eolearn/io/raster_io.py index d2a446408..d785ef234 100644 --- a/io/eolearn/io/raster_io.py +++ b/eolearn/io/raster_io.py @@ -13,7 +13,7 @@ import logging import warnings from abc import ABCMeta -from typing import Any, BinaryIO +from typing import BinaryIO import fs import numpy as np @@ -30,10 +30,10 @@ from sentinelhub import CRS, BBox, SHConfig, parse_time_interval -from eolearn.core import EOPatch, FeatureType +from eolearn.core import EOPatch from eolearn.core.core_tasks import IOTask from eolearn.core.exceptions import EORuntimeWarning -from eolearn.core.types import SingleFeatureSpec +from eolearn.core.types import Feature from eolearn.core.utils.fs import get_base_filesystem_and_path, get_full_path LOGGER = logging.getLogger(__name__) @@ -44,7 +44,7 @@ class BaseRasterIoTask(IOTask, metaclass=ABCMeta): def __init__( self, - feature: SingleFeatureSpec, + feature: Feature, folder: str, *, filesystem: FS | None = None, @@ -79,7 +79,9 @@ def __init__( # the super-class takes care of filesystem pickling super().__init__(folder, filesystem=filesystem, create=create, config=config) - def _get_filename_paths(self, filename_template: str | list[str], timestamps: list[dt.datetime]) -> list[str]: + def _get_filename_paths( + self, filename_template: str | list[str], timestamps: list[dt.datetime] | None + ) -> list[str]: """From a filename "template" and base path on the filesystem it generates full paths to tiff files. The paths are still relative to the filesystem object. @@ -113,7 +115,7 @@ def _get_filename_paths(self, filename_template: str | list[str], timestamps: li return filename_paths @classmethod - def _generate_paths(cls, path_template: str, timestamps: list[dt.datetime]) -> list[str]: + def _generate_paths(cls, path_template: str, timestamps: list[dt.datetime] | None) -> list[str]: """Uses a filename path template to create a list of actual filename paths.""" has_tiff_file_extensions = path_template.lower().endswith(".tif") or path_template.lower().endswith(".tiff") if not has_tiff_file_extensions: @@ -145,7 +147,7 @@ class ExportToTiffTask(BaseRasterIoTask): def __init__( self, - feature: SingleFeatureSpec, + feature: Feature, folder: str, *, date_indices: list[int] | tuple[int, int] | tuple[dt.datetime, dt.datetime] | tuple[str, str] | None = None, @@ -153,7 +155,10 @@ def __init__( crs: CRS | int | str | None = None, fail_on_missing: bool = True, compress: str | None = None, - **kwargs: Any, + filesystem: FS | None = None, + image_dtype: np.dtype | type | None = None, + no_data_value: float | None = None, + config: SHConfig | None = None, ): """ :param feature: A feature to be exported. @@ -171,9 +176,22 @@ def __init__( :param fail_on_missing: A flag to specify if the task should fail if a feature is missing or if it should just show a warning. :param compress: A type of compression that rasterio should apply to an exported image. - :param kwargs: Keyword arguments to be propagated to `BaseRasterIoTask`. + :param filesystem: A filesystem object. If not given it will be initialized according to `folder` parameter. + :param image_dtype: A data type of data in exported images or data imported from images. + :param no_data_value: When exporting this is the NoData value of pixels in exported images. + When importing this value is assigned to the pixels with NoData. + :param config: A configuration object with AWS credentials. By default, is set to None and in this case the + default configuration will be taken. """ - super().__init__(feature, folder=folder, create=True, **kwargs) + super().__init__( + feature, + folder=folder, + create=True, + filesystem=filesystem, + image_dtype=image_dtype, + no_data_value=no_data_value, + config=config, + ) self.date_indices = date_indices self.band_indices = band_indices @@ -181,17 +199,15 @@ def __init__( self.fail_on_missing = fail_on_missing self.compress = compress - def _prepare_image_array( - self, data_array: np.ndarray, timestamps: list[dt.datetime], feature: tuple[FeatureType, str] - ) -> np.ndarray: + def _prepare_image_array(self, eopatch: EOPatch, feature: Feature) -> np.ndarray: """Collects a feature from EOPatch and prepares the array of an image which will be rasterized. The resulting array has shape (channels, height, width) and is of correct dtype. """ - data_array = self._reduce_by_bands(data_array) + data_array = self._reduce_by_bands(eopatch[feature]) feature_type, _ = feature if feature_type.is_temporal(): - data_array = self._reduce_by_time(data_array, timestamps) + data_array = self._reduce_by_time(data_array, eopatch.get_timestamps()) else: data_array = np.expand_dims(data_array, axis=0) @@ -203,9 +219,8 @@ def _prepare_image_array( time_dim, height, width, band_dim = data_array.shape new_shape = (time_dim * band_dim, height, width) - data_array = np.moveaxis(data_array, -1, 1).reshape(new_shape) - return data_array + return np.moveaxis(data_array, -1, 1).reshape(new_shape) def _reduce_by_bands(self, array: np.ndarray) -> np.ndarray: """Reduces the array by selecting a subset of bands.""" @@ -249,7 +264,7 @@ def _reduce_by_time(self, data_array: np.ndarray, timestamps: list[dt.datetime]) raise ValueError(f"Invalid format in {self.date_indices}, expected tuple or list") - def _set_export_dtype(self, data_array: np.ndarray, feature: tuple[FeatureType, str]) -> np.ndarray: + def _set_export_dtype(self, data_array: np.ndarray, feature: Feature) -> np.ndarray: """To a given array it sets a dtype in which data will be exported""" image_dtype = data_array.dtype if self.image_dtype is None else self.image_dtype @@ -346,7 +361,7 @@ def execute(self, eopatch: EOPatch, *, filename: str | list[str] | None = "") -> if eopatch.bbox is None: raise ValueError("EOPatch without a bounding box encountered, cannot export to GeoTIFF") - image_array = self._prepare_image_array(eopatch[self.feature], eopatch.timestamps, self.feature) + image_array = self._prepare_image_array(eopatch, self.feature) src_info, dst_info, (dst_height, dst_width) = self._get_source_and_destination_params(image_array, eopatch.bbox) src_crs, src_transform = src_info @@ -368,7 +383,7 @@ def execute(self, eopatch: EOPatch, *, filename: str | list[str] | None = "") -> channel_count = image_array.shape[0] if len(filename_paths) > 1: - single_channel_count = channel_count // len(eopatch.timestamps) + single_channel_count = channel_count // len(filename_paths) for timestamp_index, path in enumerate(filename_paths): time_slice_array = image_array[ timestamp_index * single_channel_count : (timestamp_index + 1) * single_channel_count, ... @@ -393,12 +408,15 @@ class ImportFromTiffTask(BaseRasterIoTask): def __init__( self, - feature: SingleFeatureSpec, + feature: Feature, folder: str, *, use_vsi: bool = False, timestamp_size: int | None = None, - **kwargs: Any, + filesystem: FS | None = None, + image_dtype: np.dtype | type | None = None, + no_data_value: float | None = None, + config: SHConfig | None = None, ): """ :param feature: EOPatch feature into which data will be imported @@ -409,14 +427,26 @@ def __init__( smaller chunk of a larger image, especially if it is a Cloud-optimized GeoTIFF (COG). In other cases the reading might be faster if the flag remains set to `False`. :param timestamp_size: In case data will be imported into a time-dependant feature this parameter can be used to - specify time dimension. If not specified, time dimension will be the same as size of the - `FeatureType.TIMESTAMPS` feature. If `FeatureType.TIMESTAMPS` does not exist this value will be set to 1. - When converting data into a feature channels of given tiff image should be in order + specify time dimension. If not specified, time dimension will be the same as size of the timestamps + attribute. If timestamps do not exist this value will be set to 1. When converting data into a feature + channels of given tiff image should be in order T(1)B(1), T(1)B(2), ..., T(1)B(N), T(2)B(1), T(2)B(2), ..., T(2)B(N), ..., ..., T(M)B(N) where T and B are the time and band indices. - :param kwargs: Keyword arguments to be propagated to `BaseRasterIoTask`. + :param filesystem: A filesystem object. If not given it will be initialized according to `folder` parameter. + :param image_dtype: A data type of data in exported images or data imported from images. + :param no_data_value: When exporting this is the NoData value of pixels in exported images. + When importing this value is assigned to the pixels with NoData. + :param config: A configuration object with AWS credentials. By default, is set to None and in this case the + default configuration will be taken. """ - super().__init__(feature, folder=folder, **kwargs) + super().__init__( + feature, + folder=folder, + filesystem=filesystem, + image_dtype=image_dtype, + no_data_value=no_data_value, + config=config, + ) self.use_vsi = use_vsi self.timestamp_size = timestamp_size diff --git a/io/eolearn/io/sentinelhub_process.py b/eolearn/io/sentinelhub_process.py similarity index 88% rename from io/eolearn/io/sentinelhub_process.py rename to eolearn/io/sentinelhub_process.py index c02352403..64f55c0c5 100644 --- a/io/eolearn/io/sentinelhub_process.py +++ b/eolearn/io/sentinelhub_process.py @@ -11,7 +11,7 @@ import datetime as dt import logging from abc import ABCMeta, abstractmethod -from typing import Any, Callable, Iterable, List, Literal, Tuple, cast +from typing import Any, Callable, Iterable, Tuple, cast import numpy as np @@ -35,7 +35,7 @@ from sentinelhub.types import JsonDict, RawTimeIntervalType from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeatureRenameSpec, FeatureSpec, FeaturesSpecification +from eolearn.core.types import Feature, FeaturesSpecification from eolearn.core.utils.parsing import parse_renamed_feature, parse_renamed_features LOGGER = logging.getLogger(__name__) @@ -242,7 +242,7 @@ def __init__( self.mosaicking_order = None if mosaicking_order is None else MosaickingOrder(mosaicking_order) self.aux_request_args = aux_request_args - def _parse_and_validate_features(self, features: FeaturesSpecification) -> list[FeatureRenameSpec]: + def _parse_and_validate_features(self, features: FeaturesSpecification) -> list[tuple[FeatureType, str, str]]: _features = parse_renamed_features( features, allowed_feature_types=lambda fty: fty.is_array() or fty == FeatureType.META_INFO ) @@ -258,7 +258,6 @@ def _create_response_objects(self) -> list[JsonDict]: responses = [] for feat_type, feat_name, _ in self.features: if feat_type.is_array(): - feat_name = cast(str, feat_name) # can only be string since it's an array type responses.append(SentinelHubRequest.output_response(feat_name, MimeType.TIFF)) elif feat_type.is_meta(): responses.append(SentinelHubRequest.output_response("userdata", MimeType.JSON)) @@ -368,9 +367,9 @@ def __init__( data_collection: DataCollection, size: tuple[int, int] | None = None, resolution: float | tuple[float, float] | None = None, - bands_feature: tuple[FeatureType, str] | None = None, + bands_feature: Feature | None = None, bands: list[str] | None = None, - additional_data: list[tuple[FeatureType, str]] | None = None, + additional_data: list[Feature] | None = None, evalscript: str | None = None, maxcc: float | None = None, time_difference: dt.timedelta | None = None, @@ -442,10 +441,10 @@ def __init__( self.requested_bands = parse_data_collection_bands(data_collection, bands) self.requested_additional_bands = [] - self.additional_data: list[FeatureRenameSpec] | None = None + self.additional_data: list[tuple[FeatureType, str, str]] | None = None if additional_data is not None: - self.additional_data = parse_renamed_features(additional_data) # parser gives too general type - additional_bands = cast(List[str], [band for _, band, _ in self.additional_data]) + self.additional_data = parse_renamed_features(additional_data) + additional_bands = [band for _, band, _ in self.additional_data] self.requested_additional_bands = parse_data_collection_bands(data_collection, additional_bands) def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]: @@ -542,10 +541,10 @@ def _extract_additional_features( self, eopatch: EOPatch, images: Iterable[np.ndarray], shape: tuple[int, ...] ) -> None: """Extracts additional features from response into an EOPatch""" - additional_data = cast(List[FeatureRenameSpec], self.additional_data) # verified by `if` in _extract_data - for (ftype, _, new_name), band_info in zip(additional_data, self.requested_additional_bands): - tiffs = [tar[band_info.name + ".tif"] for tar in images] - eopatch[ftype, new_name] = self._extract_array(tiffs, 0, shape, band_info.output_types[0]) + if self.additional_data is not None: + for (ftype, _, new_name), band_info in zip(self.additional_data, self.requested_additional_bands): + tiffs = [tar[band_info.name + ".tif"] for tar in images] + eopatch[ftype, new_name] = self._extract_array(tiffs, 0, shape, band_info.output_types[0]) def _extract_bands_feature(self, eopatch: EOPatch, images: Iterable[np.ndarray], shape: tuple[int, ...]) -> None: """Extract the bands feature arrays and concatenate them along the last axis""" @@ -573,7 +572,7 @@ class SentinelHubDemTask(SentinelHubEvalscriptTask): def __init__( self, - feature: None | str | FeatureSpec = None, + feature: None | str | Feature = None, data_collection: DataCollection = DataCollection.DEM, **kwargs: Any, ): @@ -590,53 +589,3 @@ def __init__( evalscript = generate_evalscript(data_collection=data_collection, bands=[dem_band]) super().__init__(evalscript=evalscript, features=[renamed_feature], data_collection=data_collection, **kwargs) - - -class SentinelHubSen2corTask(SentinelHubInputTask): - """ - Adds SCL (scene classification), CLD (cloud probability) or SNW (snow probability) (or their combination) - Sen2Cor classification results to EOPatch's MASK or DATA feature. The feature is added to MASK (SCL) or - DATA (CLD, SNW) feature types of EOPatch. The feature names are set to be SCL, CLD or SNW. - - Sen2Cor's scene classification (SCL) contains 11 classes with the following values and meanings: - * 1 - SC_SATURATED_DEFECTIVE - * 2 - SC_DARK_FEATURE_SHADOW - * 3 - SC_CLOUD_SHADOW - * 4 - VEGETATION - * 5 - NOT-VEGETATED - * 6 - WATER - * 7 - SC_CLOUD_LOW_PROBA / UNCLASSIFIED - * 8 - SC_CLOUD_MEDIUM_PROBA - * 9 - CLOUD_HIGH_PROBABILITY - * 10 - THIN_CIRRUS - * 11 - SNOW - """ - - def __init__( - self, - sen2cor_classification: Literal["SCL", "CLD", "SNW"] | list[Literal["SCL", "CLD", "SNW"]], - data_collection: DataCollection = DataCollection.SENTINEL2_L2A, - **kwargs: Any, - ): - """ - :param sen2cor_classification: "SCL" (scene classification), "CLD" (cloud probability) or "SNW" - (snow probability) masks to be retrieved. Also, a list of their combination (e.g. ["SCL","CLD"]) - :param kwargs: Additional arguments that will be passed to the `SentinelHubInputTask` - """ - # definition of possible types and target features - classification_types = {"SCL": FeatureType.MASK, "CLD": FeatureType.DATA, "SNW": FeatureType.DATA} - - if isinstance(sen2cor_classification, str): - sen2cor_classification = [sen2cor_classification] - - for s2c in sen2cor_classification: - if s2c not in classification_types: - raise ValueError( - f"Unsupported Sen2Cor classification type: {s2c}. Possible types are: {classification_types}!" - ) - - if data_collection != DataCollection.SENTINEL2_L2A: - raise ValueError("Sen2Cor classification layers are only available on Sentinel-2 L2A data.") - - features: list[tuple[FeatureType, str]] = [(classification_types[s2c], s2c) for s2c in sen2cor_classification] - super().__init__(additional_data=features, data_collection=data_collection, **kwargs) diff --git a/eolearn/mask/__init__.py b/eolearn/mask/__init__.py new file mode 100644 index 000000000..e8826152d --- /dev/null +++ b/eolearn/mask/__init__.py @@ -0,0 +1,6 @@ +""" +Public classes and functions of mask subpackage +""" + +from .masking import JoinMasksTask, MaskFeatureTask +from .snow_mask import SnowMaskTask diff --git a/eolearn/mask/extra/__init__.py b/eolearn/mask/extra/__init__.py new file mode 100644 index 000000000..d4bee5ae0 --- /dev/null +++ b/eolearn/mask/extra/__init__.py @@ -0,0 +1,4 @@ +""" +A collection of EOTasks with non-standard dependencies. Use the extra `[EXTRA]` to install dependencies or +install the module-specific dependencies by hand. +""" diff --git a/eolearn/mask/extra/cloud_mask.py b/eolearn/mask/extra/cloud_mask.py new file mode 100644 index 000000000..739920fa4 --- /dev/null +++ b/eolearn/mask/extra/cloud_mask.py @@ -0,0 +1,84 @@ +""" +Module for cloud masking + +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import logging + +import numpy as np +from s2cloudless import S2PixelCloudDetector + +from eolearn.core import EOPatch, EOTask +from eolearn.core.types import Feature + +LOGGER = logging.getLogger(__name__) + + +class CloudMaskTask(EOTask): + """Cloud masking with the s2cloudless model. Outputs a cloud mask and optionally the cloud probabilities.""" + + def __init__( + self, + data_feature: Feature, + valid_data_feature: Feature, + output_mask_feature: Feature, + output_proba_feature: Feature | None = None, + all_bands: bool = True, + threshold: float = 0.4, + average_over: int | None = 4, + dilation_size: int | None = 2, + ): + """ + :param data_feature: A data feature which stores raw Sentinel-2 reflectance bands. + :param valid_data_feature: A mask feature which indicates whether data is valid. + :param output_mask_feature: The output feature containing cloud masks. + :param output_proba_feature: The output feature containing cloud probabilities. By default this is not saved. + :param all_bands: Flag which indicates whether images will consist of all 13 Sentinel-2 L1C bands or only + the required 10. + :param threshold: Cloud probability threshold for the classifier. + :param average_over: Size of the pixel neighbourhood used in the averaging post-processing step. Set to `None` + to skip this post-processing step. + :param dilation_size: Size of the dilation post-processing step. Set to `None` to skip this post-processing + step. + """ + self.data_feature = self.parse_feature(data_feature) + self.data_indices = (0, 1, 3, 4, 7, 8, 9, 10, 11, 12) if all_bands else tuple(range(10)) + self.valid_data_feature = self.parse_feature(valid_data_feature) + + self.output_mask_feature = self.parse_feature(output_mask_feature) + self.output_proba_feature = None + if output_proba_feature is not None: + self.output_proba_feature = self.parse_feature(output_proba_feature) + + self.threshold = threshold + + self.classifier = S2PixelCloudDetector( + threshold=threshold, average_over=average_over, dilation_size=dilation_size, all_bands=all_bands + ) + + def execute(self, eopatch: EOPatch) -> EOPatch: + """Add selected features (cloud probabilities and masks) to an EOPatch instance. + + :param eopatch: Input `EOPatch` instance + :return: `EOPatch` with additional features + """ + data = eopatch[self.data_feature].astype(np.float32) + valid_data = eopatch[self.valid_data_feature].astype(bool) + + patch_bbox = eopatch.bbox + if patch_bbox is None: + raise ValueError("Cannot run cloud masking on an EOPatch without a BBox.") + + cloud_proba = self.classifier.get_cloud_probability_maps(data) + cloud_mask = self.classifier.get_mask_from_prob(cloud_proba, threshold=self.threshold) + + eopatch[self.output_mask_feature] = (cloud_mask[..., np.newaxis] * valid_data).astype(bool) + if self.output_proba_feature is not None: + eopatch[self.output_proba_feature] = (cloud_proba[..., np.newaxis] * valid_data).astype(np.float32) + + return eopatch diff --git a/mask/eolearn/mask/masking.py b/eolearn/mask/masking.py similarity index 97% rename from mask/eolearn/mask/masking.py rename to eolearn/mask/masking.py index 954cfb5b0..1a4615866 100644 --- a/mask/eolearn/mask/masking.py +++ b/eolearn/mask/masking.py @@ -13,7 +13,7 @@ import numpy as np from eolearn.core import EOPatch, EOTask, FeatureType, ZipFeatureTask -from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec +from eolearn.core.types import Feature, FeaturesSpecification, SingleFeatureSpec from eolearn.core.utils.parsing import parse_renamed_feature @@ -23,7 +23,7 @@ class JoinMasksTask(ZipFeatureTask): def __init__( self, input_features: FeaturesSpecification, - output_feature: SingleFeatureSpec, + output_feature: Feature, join_operation: Literal["and", "or", "xor"] | Callable = "and", ): """ @@ -69,7 +69,7 @@ class MaskFeatureTask(EOTask): def __init__( self, feature: SingleFeatureSpec, - mask_feature: SingleFeatureSpec, + mask_feature: Feature, mask_values: Iterable[int], no_data_value: float = np.nan, ): diff --git a/eolearn/mask/snow_mask.py b/eolearn/mask/snow_mask.py new file mode 100644 index 000000000..598d3bed9 --- /dev/null +++ b/eolearn/mask/snow_mask.py @@ -0,0 +1,87 @@ +""" +Module for snow masking + +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import logging + +import cv2 +import numpy as np + +from eolearn.core import EOPatch, EOTask, FeatureType +from eolearn.core.types import Feature + +LOGGER = logging.getLogger(__name__) + + +class SnowMaskTask(EOTask): + """The task calculates the snow mask using the given thresholds. + + The default values were optimised based on the Sentinel-2 L1C processing level. Values might not be optimal for L2A + processing level + """ + + NDVI_THRESHOLD = 0.1 + + def __init__( + self, + data_feature: Feature, + band_indices: list[int], + ndsi_threshold: float = 0.4, + brightness_threshold: float = 0.3, + dilation_size: int = 0, + undefined_value: int = 0, + mask_name: str = "SNOW_MASK", + ): + """ + :param data_feature: EOPatch feature represented by a tuple in the form of `(FeatureType, 'feature_name')` + containing the bands 2, 3, 7, 11, i.e. (FeatureType.DATA, 'BANDS') + :param band_indices: A list containing the indices at which the required bands can be found in the data_feature. + The required bands are B03, B04, B08 and B11 and the indices should be provided in this order. If the + 'BANDS' array contains all 13 L1C bands, then `band_indices=[2, 3, 7, 11]`. If the 'BANDS' are the 12 bands + with L2A values, then `band_indices=[2, 3, 7, 10]` + :param ndsi_threshold: Minimum value of the NDSI required to classify the pixel as snow + :param brightness_threshold: Minimum value of the red band for a pixel to be classified as bright + """ + self.bands_feature = self.parse_feature(data_feature, allowed_feature_types={FeatureType.DATA}) + self.band_indices = band_indices + self.ndsi_threshold = ndsi_threshold + self.brightness_threshold = brightness_threshold + self.disk_size = 2 * dilation_size + 1 + self.undefined_value = undefined_value + self.mask_feature = (FeatureType.MASK, mask_name) + + def _apply_dilation(self, snow_masks: np.ndarray) -> np.ndarray: + """Apply binary dilation for each mask in the series""" + if self.disk_size > 0: + disk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (self.disk_size, self.disk_size)) + snow_masks = np.array([cv2.dilate(mask.astype(np.uint8), disk) for mask in snow_masks]) + return snow_masks.astype(bool) + + def execute(self, eopatch: EOPatch) -> EOPatch: + bands = eopatch[self.bands_feature][..., self.band_indices] + with np.errstate(divide="ignore", invalid="ignore"): + # (B03 - B11) / (B03 + B11) + ndsi = (bands[..., 0] - bands[..., 3]) / (bands[..., 0] + bands[..., 3]) + # (B08 - B04) / (B08 + B04) + ndvi = (bands[..., 2] - bands[..., 1]) / (bands[..., 2] + bands[..., 1]) + + ndsi_invalid, ndvi_invalid = ~np.isfinite(ndsi), ~np.isfinite(ndvi) + ndsi[ndsi_invalid] = self.undefined_value + ndvi[ndvi_invalid] = self.undefined_value + + ndi_criterion = (ndsi >= self.ndsi_threshold) | (np.abs(ndvi - self.NDVI_THRESHOLD) < self.NDVI_THRESHOLD / 2) + brightnes_criterion = bands[..., 0] >= self.brightness_threshold + snow_mask = np.where(ndi_criterion & brightnes_criterion, 1, 0) + + snow_mask = self._apply_dilation(snow_mask) + + snow_mask[ndsi_invalid | ndvi_invalid] = self.undefined_value + + eopatch[self.mask_feature] = snow_mask[..., np.newaxis].astype(bool) + return eopatch diff --git a/ml_tools/eolearn/ml_tools/__init__.py b/eolearn/ml_tools/__init__.py similarity index 68% rename from ml_tools/eolearn/ml_tools/__init__.py rename to eolearn/ml_tools/__init__.py index da151dd17..6837b36bc 100644 --- a/ml_tools/eolearn/ml_tools/__init__.py +++ b/eolearn/ml_tools/__init__.py @@ -3,6 +3,3 @@ """ from .sampling import BlockSamplingTask, FractionSamplingTask, GridSamplingTask, sample_by_values -from .train_test_split import TrainTestSplitTask - -__version__ = "1.4.2" diff --git a/ml_tools/eolearn/ml_tools/sampling.py b/eolearn/ml_tools/sampling.py similarity index 93% rename from ml_tools/eolearn/ml_tools/sampling.py rename to eolearn/ml_tools/sampling.py index d195369bc..92e55efe0 100644 --- a/ml_tools/eolearn/ml_tools/sampling.py +++ b/eolearn/ml_tools/sampling.py @@ -10,13 +10,13 @@ from abc import ABCMeta from math import sqrt -from typing import Any, Dict, Union, cast +from typing import Dict, Union import numpy as np from shapely.geometry import Point, Polygon from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeaturesSpecification, SingleFeatureSpec +from eolearn.core.types import Feature, FeaturesSpecification _FractionType = Union[float, Dict[int, float]] @@ -133,7 +133,7 @@ def __init__( self, features_to_sample: FeaturesSpecification, *, - mask_of_samples: tuple[FeatureType, str] | None = None, + mask_of_samples: Feature | None = None, ): """ :param features_to_sample: Features that will be spatially sampled according to given sampling parameters. @@ -145,7 +145,7 @@ def __init__( self.mask_of_samples = mask_of_samples if mask_of_samples is not None: - self.mask_of_samples = self.parse_feature( # type: ignore[assignment] + self.mask_of_samples = self.parse_feature( mask_of_samples, allowed_feature_types={FeatureType.MASK_TIMELESS} ) @@ -178,11 +178,11 @@ class FractionSamplingTask(BaseSamplingTask): def __init__( self, features_to_sample: FeaturesSpecification, - sampling_feature: SingleFeatureSpec, + sampling_feature: Feature, fraction: _FractionType, exclude_values: list[int] | None = None, replace: bool = False, - **kwargs: Any, + mask_of_samples: Feature | None = None, ): """ :param features_to_sample: Features that will be spatially sampled according to given sampling parameters. @@ -190,9 +190,9 @@ def __init__( :param fraction: Fraction of points to sample. Can be dictionary mapping values of mask to fractions. :param exclude_values: Skips points that have these values in `sampling_mask` :param replace: Whether to sample with replacement. False means each value can only be chosen once. - :param kwargs: Arguments for :class:`BaseSamplingTask` + :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled. """ - super().__init__(features_to_sample, **kwargs) + super().__init__(features_to_sample, mask_of_samples=mask_of_samples) self.sampling_feature = self.parse_feature(sampling_feature, allowed_feature_types={FeatureType.MASK_TIMELESS}) @@ -250,8 +250,7 @@ def execute(self, eopatch: EOPatch, *, seed: int | None = None, fraction: _Fract rows, columns = sample_by_values(sampling_image, amount_per_value, rng=rng, replace=self.replace) row_grid, column_grid = expand_to_grids(rows, columns, sample_size=(1, 1)) - eopatch = self._apply_sampling(eopatch, row_grid, column_grid) - return eopatch + return self._apply_sampling(eopatch, row_grid, column_grid) class BlockSamplingTask(BaseSamplingTask): @@ -268,7 +267,7 @@ def __init__( amount: float, sample_size: tuple[int, int] = (1, 1), replace: bool = False, - **kwargs: Any, + mask_of_samples: Feature | None = None, ): """ :param features_to_sample: Features that will be spatially sampled according to given sampling parameters. @@ -276,9 +275,9 @@ def __init__( :param sample_size: A tuple describing a size of sampled blocks. The size is defined as a tuple of number of rows and number of columns. :param replace: Whether to sample with replacement. False means each value can only be chosen once. - :param kwargs: Arguments for :class:`BaseSamplingTask` + :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled. """ - super().__init__(features_to_sample, **kwargs) + super().__init__(features_to_sample, mask_of_samples=mask_of_samples) self.amount = amount self.sample_size = tuple(sample_size) @@ -288,7 +287,7 @@ def _generate_dummy_mask(self, eopatch: EOPatch) -> np.ndarray: """Generate a mask consisting entirely of `values` entries, used for sampling on whole raster""" feature_type, feature_name = self.features_parser.get_features(eopatch)[0] - height, width = eopatch.get_spatial_dimension(feature_type, cast(str, feature_name)) + height, width = eopatch.get_spatial_dimension(feature_type, feature_name) height -= self.sample_size[0] - 1 width -= self.sample_size[1] - 1 @@ -317,8 +316,7 @@ def execute(self, eopatch: EOPatch, *, seed: int | None = None, amount: float | size_x, size_y = self.sample_size # this way it also works for lists row_grid, column_grid = expand_to_grids(rows, columns, sample_size=(size_x, size_y)) - eopatch = self._apply_sampling(eopatch, row_grid, column_grid) - return eopatch + return self._apply_sampling(eopatch, row_grid, column_grid) class GridSamplingTask(BaseSamplingTask): @@ -332,7 +330,7 @@ def __init__( features_to_sample: FeaturesSpecification, sample_size: tuple[int, int] = (1, 1), stride: tuple[int, int] = (1, 1), - **kwargs: Any, + mask_of_samples: Feature | None = None, ): """ :param features_to_sample: Features that will be spatially sampled according to given sampling parameters. @@ -341,9 +339,9 @@ def __init__( :param stride: A tuple describing a distance between upper left corners of two consecutive sampled blocks. The first number is the vertical distance and the second number the horizontal distance. If stride in smaller than sample_size in any dimensions then sampled blocks will overlap. - :param kwargs: Arguments for :class:`BaseSamplingTask` + :param mask_of_samples: An output mask timeless feature of counts how many times each pixel has been sampled. """ - super().__init__(features_to_sample, **kwargs) + super().__init__(features_to_sample, mask_of_samples=mask_of_samples) self.sample_size = tuple(sample_size) self.stride = tuple(stride) @@ -367,10 +365,9 @@ def execute(self, eopatch: EOPatch) -> EOPatch: """ feature_type, feature_name = self.features_parser.get_features(eopatch)[0] - image_shape = eopatch.get_spatial_dimension(feature_type, cast(str, feature_name)) + image_shape = eopatch.get_spatial_dimension(feature_type, feature_name) rows, columns = self._sample_regular_grid(image_shape) size_x, size_y = self.sample_size # this way it also works for lists row_grid, column_grid = expand_to_grids(rows, columns, sample_size=(size_x, size_y)) - eopatch = self._apply_sampling(eopatch, row_grid, column_grid) - return eopatch + return self._apply_sampling(eopatch, row_grid, column_grid) diff --git a/core/eolearn/core/py.typed b/eolearn/py.typed similarity index 100% rename from core/eolearn/core/py.typed rename to eolearn/py.typed diff --git a/visualization/eolearn/visualization/__init__.py b/eolearn/visualization/__init__.py similarity index 80% rename from visualization/eolearn/visualization/__init__.py rename to eolearn/visualization/__init__.py index 9a191c134..7d6cc86a6 100644 --- a/visualization/eolearn/visualization/__init__.py +++ b/eolearn/visualization/__init__.py @@ -3,5 +3,3 @@ """ from .eopatch import PlotBackend, PlotConfig - -__version__ = "1.4.2" diff --git a/eolearn/visualization/eoexecutor.py b/eolearn/visualization/eoexecutor.py new file mode 100644 index 000000000..baa0b6457 --- /dev/null +++ b/eolearn/visualization/eoexecutor.py @@ -0,0 +1,221 @@ +""" +Module with utilities for visualizing EOExecutor + +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import base64 +import datetime as dt +import os +import warnings +from collections import defaultdict +from contextlib import nullcontext +from dataclasses import dataclass +from typing import Any, cast + +import fs +import graphviz +import matplotlib as mpl +import numpy as np +import pygments +import pygments.formatter +import pygments.lexers +from jinja2 import Environment, FileSystemLoader, Template +from pygments.formatters.html import HtmlFormatter + +from eolearn.core import EOExecutor +from eolearn.core.eonode import ExceptionInfo +from eolearn.core.exceptions import EOUserWarning + + +class EOExecutorVisualization: + """Class handling EOExecutor visualizations, particularly creating reports""" + + def __init__(self, eoexecutor: EOExecutor): + """ + :param eoexecutor: An instance of EOExecutor + """ + self.eoexecutor = eoexecutor + + def make_report(self, include_logs: bool = True) -> None: + """Makes a html report and saves it into the same folder where logs are stored.""" + if self.eoexecutor.execution_results is None: + raise RuntimeError( + "Cannot produce a report without running the executor first, check EOExecutor.run method" + ) + + # These should be set automatically after a run + start_time = cast(dt.datetime, self.eoexecutor.start_time) + report_folder = cast(str, self.eoexecutor.report_folder) + + backend_ctx = mpl.rc_context({"backend": "Agg"}) if os.environ.get("DISPLAY", "") == "" else nullcontext() + with backend_ctx: + try: + dependency_graph = self._create_dependency_graph() + except graphviz.backend.ExecutableNotFound as ex: + dependency_graph = None + warnings.warn( + f"{ex}.\nPlease install the system package 'graphviz' (in addition to the python package) to" + " have the dependency graph in the final report!", + EOUserWarning, + ) + + formatter = HtmlFormatter(linenos=True) + + template = self._get_template() + + execution_log_filenames = [fs.path.basename(log_path) for log_path in self.eoexecutor.get_log_paths()] + if self.eoexecutor.save_logs: + execution_logs = self.eoexecutor.read_logs() if include_logs else None + else: + execution_logs = ["No logs saved"] * len(self.eoexecutor.execution_kwargs) + + html = template.render( + title=f"Report {self._format_datetime(start_time)}", + dependency_graph=dependency_graph, + general_stats=self.eoexecutor.general_stats, + exception_stats=self._get_exception_stats(), + task_descriptions=self._get_node_descriptions(), + execution_results=self.eoexecutor.execution_results, + execution_tracebacks=self._render_execution_tracebacks(formatter), + execution_logs=execution_logs, + execution_log_filenames=execution_log_filenames, + execution_names=self.eoexecutor.execution_names, + code_css=formatter.get_style_defs(), + ) + self.eoexecutor.filesystem.makedirs(report_folder, recreate=True) + + with self.eoexecutor.filesystem.open(self.eoexecutor.get_report_path(full_path=False), "w") as file_handle: + file_handle.write(html) + + def _create_dependency_graph(self) -> str: + """Provides an image of dependency graph""" + dot = self.eoexecutor.workflow.dependency_graph() + return base64.b64encode(dot.pipe()).decode() + + def _get_exception_stats(self) -> list[tuple[str, str, list[_ErrorSummary]]]: + """Creates aggregated stats about exceptions + + Returns tuples of form (name, uid, [error_summary]) + """ + + exception_stats: defaultdict[str, dict[str, _ErrorSummary]] = defaultdict(dict) + + for execution_idx, (execution, results) in enumerate( + zip(self.eoexecutor.execution_names, self.eoexecutor.execution_results) + ): + if not results.error_node_uid: + continue + + error_node = results.stats[results.error_node_uid] + exception_info: ExceptionInfo = error_node.exception_info # type: ignore[assignment] + origin_str = f"{exception_info.exception.__class__.__name__} raised from {exception_info.origin}" + + if origin_str not in exception_stats[error_node.node_uid]: + exception_stats[error_node.node_uid][origin_str] = _ErrorSummary( + origin_str, str(exception_info.exception), [] + ) + + exception_stats[error_node.node_uid][origin_str].add_execution(execution_idx, execution) + + ordered_exception_stats = [] + for node in self.eoexecutor.workflow.get_nodes(): + if node.uid not in exception_stats: + continue + + node_stats = exception_stats[node.uid] + error_summaries = sorted(node_stats.values(), key=lambda summary: -len(summary.failed_indexed_executions)) + ordered_exception_stats.append((node.get_name(), node.uid, error_summaries)) + + return ordered_exception_stats + + def _get_node_descriptions(self) -> list[dict[str, Any]]: + """Prepares a list of node names and initialization parameters of their tasks""" + descriptions = [] + name_counts: dict[str, int] = defaultdict(lambda: 0) + + for node in self.eoexecutor.workflow.get_nodes(): + node_name = node.get_name(name_counts[node.get_name()]) + name_counts[node.get_name()] += 1 + + node_stats = filter(None, (results.stats.get(node.uid) for results in self.eoexecutor.execution_results)) + durations = np.array([(stats.end_time - stats.start_time).total_seconds() for stats in node_stats]) + if len(durations) == 0: + duration_report = "unknown" + else: + duration_report = ( + f"Between {np.min(durations):.4g} and {np.max(durations):.4g} seconds," + f" usually {np.mean(durations):.4g} ± {np.std(durations):.4g} seconds" + ) + + descriptions.append( + { + "name": f"{node_name} ({node.uid})", + "uid": node.uid, + "args": { + key: value.replace("<", "<").replace(">", ">") # type: ignore[attr-defined] + for key, value in node.task.private_task_config.init_args.items() + }, + "duration_report": duration_report, + } + ) + return descriptions + + def _render_execution_tracebacks(self, formatter: pygments.formatter.Formatter) -> list: + """Renders stack traces of those executions which failed""" + tb_lexer = pygments.lexers.get_lexer_by_name("py3tb", stripall=True) + + tracebacks = [] + for results in self.eoexecutor.execution_results: + if results.workflow_failed() and results.error_node_uid is not None: + # second part of above check needed only for typechecking purposes + failed_node_stats = results.stats[results.error_node_uid] + traceback_str = failed_node_stats.exception_info.traceback # type: ignore[union-attr] + traceback = pygments.highlight(traceback_str, tb_lexer, formatter) + else: + traceback = None + + tracebacks.append(traceback) + + return tracebacks + + def _get_template(self) -> Template: + """Loads and sets up a template for report""" + templates_dir = os.path.join(os.path.dirname(__file__), "report_templates") + env = Environment(loader=FileSystemLoader(templates_dir)) + env.filters["datetime"] = self._format_datetime + env.globals.update(timedelta=self._format_timedelta) + + return env.get_template(self.eoexecutor.REPORT_FILENAME) + + @staticmethod + def _format_datetime(value: dt.datetime) -> str: + """Method for formatting datetime objects into report""" + return value.strftime("%Y-%m-%d %H:%M:%S") + + @staticmethod + def _format_timedelta(value1: dt.datetime, value2: dt.datetime) -> str: + """Method for formatting time delta into report""" + return str(value2 - value1) + + +@dataclass() +class _ErrorSummary: + """Contains data for errors of a node.""" + + origin: str + example_message: str + failed_indexed_executions: list[tuple[int, str]] + + def add_execution(self, index: int, name: str) -> None: + """Adds an execution to the summary.""" + self.failed_indexed_executions.append((index, name)) + + @property + def num_failed(self) -> int: + """Helps with jinja""" + return len(self.failed_indexed_executions) diff --git a/visualization/eolearn/visualization/eopatch.py b/eolearn/visualization/eopatch.py similarity index 98% rename from visualization/eolearn/visualization/eopatch.py rename to eolearn/visualization/eopatch.py index f423e9008..7407e786c 100644 --- a/visualization/eolearn/visualization/eopatch.py +++ b/eolearn/visualization/eopatch.py @@ -19,9 +19,9 @@ from geopandas import GeoDataFrame from pyproj import CRS -from eolearn.core import EOPatch, FeatureType +from eolearn.core import EOPatch from eolearn.core.constants import TIMESTAMP_COLUMN -from eolearn.core.types import SingleFeatureSpec +from eolearn.core.types import Feature from eolearn.core.utils.common import is_discrete_type from eolearn.core.utils.parsing import parse_feature @@ -85,7 +85,7 @@ class MatplotlibVisualization: def __init__( self, eopatch: EOPatch, - feature: SingleFeatureSpec, + feature: Feature, *, axes: np.ndarray | None = None, config: PlotConfig | None = None, @@ -135,9 +135,6 @@ def plot(self) -> np.ndarray: feature_type, feature_name = self.feature data, timestamps = self.collect_and_prepare_feature(self.eopatch) - if feature_type is FeatureType.BBOX: - return self._plot_bbox() - if feature_type.is_vector(): return self._plot_vector_feature( data, @@ -157,7 +154,7 @@ def plot(self) -> np.ndarray: return self._plot_bar(data, title=feature_name) return self._plot_time_series(data, timestamps=timestamps, title=feature_name) - def collect_and_prepare_feature(self, eopatch: EOPatch) -> tuple[Any, list[dt.datetime]]: + def collect_and_prepare_feature(self, eopatch: EOPatch) -> tuple[Any, list[dt.datetime] | None]: """Collects a feature from EOPatch and modifies it according to plotting parameters""" feature_type, _ = self.feature data = eopatch[self.feature] diff --git a/visualization/eolearn/visualization/eoworkflow.py b/eolearn/visualization/eoworkflow.py similarity index 100% rename from visualization/eolearn/visualization/eoworkflow.py rename to eolearn/visualization/eoworkflow.py diff --git a/visualization/eolearn/visualization/report_templates/report.html b/eolearn/visualization/report_templates/report.html similarity index 76% rename from visualization/eolearn/visualization/report_templates/report.html rename to eolearn/visualization/report_templates/report.html index 3566ae875..0ab177a0f 100644 --- a/visualization/eolearn/visualization/report_templates/report.html +++ b/eolearn/visualization/report_templates/report.html @@ -112,13 +112,24 @@

Summary of exceptions

    - {% for node_name, node_uid, exception_list in exception_stats %} + {% for node_name, node_uid, error_summary_list in exception_stats %}
  • {{ node_name }} ({{ node_uid }}):
      - {% for exception_string, count in exception_list %} + {% for error_summary in error_summary_list %}
    • - {{ count }} times: {{ exception_string }} + {{ error_summary.num_failed }} times: {{ error_summary.origin }} +
      + Example message: 
      {{ error_summary.example_message }}
      +
      + +
      +
        + {% for idx, execution in error_summary.failed_indexed_executions %} +
      • {{ execution }}
        • + {% endfor %} +
      +
      • {% endfor %}
    @@ -129,54 +140,34 @@

    Summary of exceptions

    {% endif %}
-

EOTasks

+

EONodes

-

Initialization parameters

- -
{% for task in task_descriptions %} -

{{ task['name'] }}

- - {% if task['args'] %} -
    - {% for key, value in task['args'].items() %} - 
  •  {{ key }} = {{ value }}
    • - {% endfor %} -
- {% else %} -
-

- No initialization parameters -

-
- {% endif %} +

{{ task['name'] }}

- {% endfor %} -
- -

Task sources

- -
- {% for task_title, task_source in task_sources.items() %} -

{{ task_title }}

+
+ Execution duration: {{ task['duration_report'] }}
-
- {% if task_source is none %} -

- Cannot collect source code of a task which is not defined in a .py file -

- {% elif task_source is string %} - {{ task_source }} - {% else %} +

Initialization parameters

+ {% if task['args'] %} +
    + {% for key, value in task['args'].items() %} + 
  •  {{ key }} = {{ value }}
    • + {% endfor %} +
+ {% else %} +

- Imported from {{ task_source[0] }} version {{ task_source[1] }} + No initialization parameters

- {% endif %} -
- {% endfor %} +
+ {% endif %} +
+ {% endfor %}
+

Execution details

diff --git a/ml_tools/eolearn/ml_tools/extra/plotting.py b/eolearn/visualization/utils.py similarity index 79% rename from ml_tools/eolearn/ml_tools/extra/plotting.py rename to eolearn/visualization/utils.py index e637eae89..176d39f5c 100644 --- a/ml_tools/eolearn/ml_tools/extra/plotting.py +++ b/eolearn/visualization/utils.py @@ -1,5 +1,5 @@ """ -The module provides some utility functions for ML specific plotting +The module provides some utility functions for plotting Copyright (c) 2017- Sinergise and contributors For the full list of contributors, see the CREDITS file in the root directory of this source tree. @@ -7,24 +7,21 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import itertools -from typing import List, Optional, Union +import matplotlib.pyplot as plt import numpy as np - -try: - import matplotlib.pyplot as plt - from matplotlib.colors import Colormap -except ImportError as exception: - raise ImportError("This module requires an installation of matplotlib package") from exception +from matplotlib.colors import Colormap def plot_confusion_matrix( confusion_matrix: np.ndarray, - classes: List[str], + classes: list[str], normalize: bool = True, title: str = "Confusion matrix", - cmap: Optional[Union[str, Colormap]] = plt.cm.Blues, + cmap: str | Colormap | None = plt.cm.Blues, xlabel: str = "Predicted label", ylabel: str = "True label", ) -> None: diff --git a/example_data/TestEOPatch/data/REFERENCE_SCENES.npy b/example_data/REFERENCE_SCENES.npy similarity index 100% rename from example_data/TestEOPatch/data/REFERENCE_SCENES.npy rename to example_data/REFERENCE_SCENES.npy diff --git a/example_data/TestEOPatch/meta_info.json b/example_data/TestEOPatch/meta_info.json deleted file mode 100644 index e615d22c7..000000000 --- a/example_data/TestEOPatch/meta_info.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "size_y": "10m", - "maxcc": 0.8, - "service_type": "wcs", - "size_x": "10m" -} diff --git a/example_data/TestEOPatch/meta_info/maxcc.json b/example_data/TestEOPatch/meta_info/maxcc.json new file mode 100644 index 000000000..aec258df7 --- /dev/null +++ b/example_data/TestEOPatch/meta_info/maxcc.json @@ -0,0 +1 @@ +0.8 diff --git a/example_data/TestEOPatch/meta_info/service_type.json b/example_data/TestEOPatch/meta_info/service_type.json new file mode 100644 index 000000000..02ef8b68e --- /dev/null +++ b/example_data/TestEOPatch/meta_info/service_type.json @@ -0,0 +1 @@ +"wcs" diff --git a/example_data/TestEOPatch/meta_info/size_x.json b/example_data/TestEOPatch/meta_info/size_x.json new file mode 100644 index 000000000..da2730748 --- /dev/null +++ b/example_data/TestEOPatch/meta_info/size_x.json @@ -0,0 +1 @@ +"10m" diff --git a/example_data/TestEOPatch/meta_info/size_y.json b/example_data/TestEOPatch/meta_info/size_y.json new file mode 100644 index 000000000..da2730748 --- /dev/null +++ b/example_data/TestEOPatch/meta_info/size_y.json @@ -0,0 +1 @@ +"10m" diff --git a/examples/core/CoreOverview.ipynb b/examples/core/CoreOverview.ipynb index 93be01955..18f3af705 100644 --- a/examples/core/CoreOverview.ipynb +++ b/examples/core/CoreOverview.ipynb @@ -28,9 +28,11 @@ "![eopatch](./images/eopatch.png)\n", "\n", "- It is designed to store all types of EO data for a single geographical location.\n", - "- Typically it is defined by a bounding box and a coordinate reference system.\n", + "- The `EOPatch` can contain data (of the same location) for multiple times. If the `EOPatch` contains multiple collections of temporal data, they must have the same temporal axis (the images must correspond to the same time-points).\n", "- There is no limit to how much data a single `EOPatch` can store, but typically it shouldn't be more than the size of your RAM.\n", "\n", + "Each `EOPatch` has an attribute `bbox` of type `sentinelhub.BBox` to define its area. The attribute `timestamps` defines the temporal component of an `EOPatch`, which is either `None` (for patches without a temporal dimension) or a list of `datetime.datetime` objects.\n", + "\n", "EO data can be divided into categories, called \"feature types\" according to the following properties:\n", "\n", "| `FeatureType` | Type of data | Time component | Spatial component | Type of values | Python object | Shape |\n", @@ -46,8 +48,6 @@ "| VECTOR | vector | yes | yes | / | `geopandas.GeoDataFrame` | Required columns `geometry` and `TIMESTAMP` |\n", "| VECTOR_TIMELESS | vector | no | yes | / | `geopandas.GeoDataFrame` | Required column `geometry` |\n", "| META_INFO | anything | no | no | anything | anything | anything |\n", - "| TIMESTAMP | timestamps | yes | no | datetime | `list(datetime.datetime)` | `t` |\n", - "| BBOX | bounding box and CRS | no | yes | coordinates | `sentinelhub.BBox` | / |\n", "\n", "Note: `t` specifies time component, `n` and `m` are spatial components (height and width), and `d` is an additional component for data with multiple channels.\n", "\n", @@ -65,58 +65,57 @@ "data": { "text/plain": [ "EOPatch(\n", - " data={\n", - " BANDS-S2-L1C: numpy.ndarray(shape=(68, 101, 100, 13), dtype=float32)\n", - " CLP: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", - " CLP_MULTI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", - " CLP_S2C: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", - " NDVI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", - " REFERENCE_SCENES: numpy.ndarray(shape=(5, 101, 100, 13), dtype=float32)\n", - " }\n", - " mask={\n", - " CLM: numpy.ndarray(shape=(68, 101, 100, 1), dtype=uint8)\n", - " CLM_INTERSSIM: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", - " CLM_MULTI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", - " CLM_S2C: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", - " IS_DATA: numpy.ndarray(shape=(68, 101, 100, 1), dtype=uint8)\n", - " IS_VALID: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", + " bbox=BBox(((465181.0522318204, 5079244.8912012065), (466180.53145382757, 5080254.63349641)), crs=CRS('32633'))\n", + " timestamps=[datetime.datetime(2015, 7, 11, 10, 0, 8), ..., datetime.datetime(2017, 12, 22, 10, 4, 15)], length=68\n", + " mask_timeless={\n", + " LULC: numpy.ndarray(shape=(101, 100, 1), dtype=uint16)\n", + " RANDOM_UINT8: numpy.ndarray(shape=(101, 100, 13), dtype=uint8)\n", + " VALID_COUNT: numpy.ndarray(shape=(101, 100, 1), dtype=int64)\n", " }\n", - " scalar={\n", - " CLOUD_COVERAGE: numpy.ndarray(shape=(68, 1), dtype=float16)\n", + " vector={\n", + " CLM_VECTOR: geopandas.GeoDataFrame(columns=['TIMESTAMP', 'VALUE', 'geometry'], length=55, crs=EPSG:32633)\n", " }\n", " label={\n", " IS_CLOUDLESS: numpy.ndarray(shape=(68, 1), dtype=bool)\n", " RANDOM_DIGIT: numpy.ndarray(shape=(68, 2), dtype=int8)\n", " }\n", - " vector={\n", - " CLM_VECTOR: geopandas.GeoDataFrame(columns=['TIMESTAMP', 'VALUE', 'geometry'], length=55, crs=EPSG:32633)\n", - " }\n", - " data_timeless={\n", - " DEM: numpy.ndarray(shape=(101, 100, 1), dtype=float32)\n", - " MAX_NDVI: numpy.ndarray(shape=(101, 100, 1), dtype=float64)\n", - " }\n", - " mask_timeless={\n", - " LULC: numpy.ndarray(shape=(101, 100, 1), dtype=uint16)\n", - " RANDOM_UINT8: numpy.ndarray(shape=(101, 100, 13), dtype=uint8)\n", - " VALID_COUNT: numpy.ndarray(shape=(101, 100, 1), dtype=int64)\n", + " meta_info={\n", + " maxcc: 0.8\n", + " service_type: 'wcs'\n", + " size_x: '10m'\n", + " size_y: '10m'\n", " }\n", " scalar_timeless={\n", " LULC_PERCENTAGE: numpy.ndarray(shape=(6,), dtype=float64)\n", " }\n", - " label_timeless={\n", - " LULC_COUNTS: numpy.ndarray(shape=(6,), dtype=int32)\n", + " scalar={\n", + " CLOUD_COVERAGE: numpy.ndarray(shape=(68, 1), dtype=float16)\n", " }\n", " vector_timeless={\n", " LULC: geopandas.GeoDataFrame(columns=['index', 'RABA_ID', 'AREA', 'DATE', 'LULC_ID', 'LULC_NAME', 'geometry'], length=88, crs=EPSG:32633)\n", " }\n", - " meta_info={\n", - " maxcc: 0.8\n", - " service_type: 'wcs'\n", - " size_x: '10m'\n", - " size_y: '10m'\n", + " mask={\n", + " CLM: numpy.ndarray(shape=(68, 101, 100, 1), dtype=uint8)\n", + " CLM_INTERSSIM: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", + " CLM_MULTI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", + " CLM_S2C: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", + " IS_DATA: numpy.ndarray(shape=(68, 101, 100, 1), dtype=uint8)\n", + " IS_VALID: numpy.ndarray(shape=(68, 101, 100, 1), dtype=bool)\n", + " }\n", + " label_timeless={\n", + " LULC_COUNTS: numpy.ndarray(shape=(6,), dtype=int32)\n", + " }\n", + " data_timeless={\n", + " DEM: numpy.ndarray(shape=(101, 100, 1), dtype=float32)\n", + " MAX_NDVI: numpy.ndarray(shape=(101, 100, 1), dtype=float64)\n", + " }\n", + " data={\n", + " BANDS-S2-L1C: numpy.ndarray(shape=(68, 101, 100, 13), dtype=float32)\n", + " CLP: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", + " CLP_MULTI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", + " CLP_S2C: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", + " NDVI: numpy.ndarray(shape=(68, 101, 100, 1), dtype=float32)\n", " }\n", - " bbox=BBox(((465181.0522318204, 5079244.8912012065), (466180.53145382757, 5080254.63349641)), crs=CRS('32633'))\n", - " timestamps=[datetime.datetime(2015, 7, 11, 10, 0, 8), ..., datetime.datetime(2017, 12, 22, 10, 4, 15)], length=68\n", ")" ] }, @@ -298,9 +297,11 @@ }, { "data": { - "image/svg+xml": "", + "image/svg+xml": [ + "" + ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -330,8 +331,7 @@ { "data": { "text/plain": [ - "[(, 'REFERENCE_SCENES'),\n", - " (, 'CLP_S2C'),\n", + "[(, 'CLP_S2C'),\n", " (, 'CLP'),\n", " (, 'NDVI'),\n", " (, 'BANDS-S2-L1C'),\n", @@ -354,12 +354,10 @@ " (, 'LULC_PERCENTAGE'),\n", " (, 'LULC_COUNTS'),\n", " (, 'LULC'),\n", - " (, 'size_y'),\n", " (, 'maxcc'),\n", - " (, 'service_type'),\n", " (, 'size_x'),\n", - " (, None),\n", - " (, None)]" + " (, 'size_y'),\n", + " (, 'service_type')]" ] }, "execution_count": 5, @@ -387,13 +385,14 @@ "data": { "text/plain": [ "EOPatch(\n", + " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", + " timestamps=[datetime.datetime(2015, 7, 11, 10, 0, 8), ..., datetime.datetime(2017, 12, 22, 10, 4, 15)], length=68\n", + " mask_timeless={\n", + " NEW_MASK: numpy.ndarray(shape=(68, 10, 13), dtype=uint8)\n", + " }\n", " data={\n", " BANDS: numpy.ndarray(shape=(68, 101, 100, 13), dtype=float32)\n", " }\n", - " mask_timeless={\n", - " NEW_MASK: numpy.ndarray(shape=(10, 10, 13), dtype=uint8)\n", - " }\n", - " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", ")" ] }, @@ -410,9 +409,13 @@ "# Since EOPatch represents geolocated data, it should always have a bounding box\n", "new_eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS.WGS84))\n", "\n", - "new_eopatch[FeatureType.DATA, \"BANDS\"] = eopatch[FeatureType.DATA, \"BANDS-S2-L1C\"]\n", + "new_eopatch[FeatureType.MASK_TIMELESS, \"NEW_MASK\"] = np.zeros((68, 10, 13), dtype=np.uint8)\n", + "\n", + "# If temporal features are added to an EOPatch that does not have timestamps (or if the dimensions do not match),\n", + "# the user is warned that the EOPatch is temporall ill-defined\n", "\n", - "new_eopatch[FeatureType.MASK_TIMELESS, \"NEW_MASK\"] = np.zeros((10, 10, 13), dtype=np.uint8)\n", + "new_eopatch.timestamps = eopatch.timestamps\n", + "new_eopatch[FeatureType.DATA, \"BANDS\"] = eopatch[FeatureType.DATA, \"BANDS-S2-L1C\"]\n", "\n", "# The following wouldn't work as there are restrictions to what kind of data can be stored in each feature type\n", "# new_eopatch[FeatureType.MASK, 'NEW_MASK'] = np.zeros((10, 10, 13), dtype=np.uint8)\n", @@ -437,10 +440,11 @@ "data": { "text/plain": [ "EOPatch(\n", - " mask_timeless={\n", - " NEW_MASK: numpy.ndarray(shape=(10, 10, 13), dtype=uint8)\n", - " }\n", " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", + " timestamps=[datetime.datetime(2015, 7, 11, 10, 0, 8), ..., datetime.datetime(2017, 12, 22, 10, 4, 15)], length=68\n", + " data={\n", + " BANDS: numpy.ndarray(shape=(68, 101, 100, 13), dtype=float32)\n", + " }\n", ")" ] }, @@ -450,7 +454,7 @@ } ], "source": [ - "del new_eopatch[FeatureType.DATA, \"BANDS\"]\n", + "del new_eopatch[FeatureType.MASK_TIMELESS, \"NEW_MASK\"]\n", "\n", "new_eopatch" ] @@ -459,7 +463,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can save `EOPatch` into a local folder. In case an `EOPatch` already exists in the specified location, we have to allow to overwrite its features." + "We can save `EOPatch` into a local folder. In case an `EOPatch` already exists in the specified location, we have to allow to overwrite its features." ] }, { @@ -523,10 +527,11 @@ "data": { "text/plain": [ "EOPatch(\n", - " mask_timeless={\n", - " NEW_MASK: numpy.ndarray(shape=(10, 10, 13), dtype=uint8)\n", - " }\n", " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", + " timestamps=[datetime.datetime(2015, 7, 11, 10, 0, 8), ..., datetime.datetime(2017, 12, 22, 10, 4, 15)], length=68\n", + " data={\n", + " BANDS: numpy.ndarray(shape=(68, 101, 100, 13), dtype=float32)\n", + " }\n", ")" ] }, @@ -624,10 +629,11 @@ "data": { "text/plain": [ "EOPatch(\n", + " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", + " timestamps=[datetime.datetime(2017, 1, 1, 0, 0), ..., datetime.datetime(2017, 5, 1, 0, 0)], length=5\n", " data={\n", " NEW_BANDS: numpy.ndarray(shape=(5, 100, 100, 13), dtype=float64)\n", " }\n", - " bbox=BBox(((0.0, 0.0), (1.0, 1.0)), crs=CRS('4326'))\n", ")" ] }, @@ -637,7 +643,7 @@ } ], "source": [ - "eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS.WGS84))\n", + "eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS.WGS84), timestamps=[f\"2017-0{i}-01\" for i in range(1, 6)])\n", "\n", "add_feature_task = AddFeatureTask((FeatureType.DATA, \"NEW_BANDS\"))\n", "\n", @@ -711,9 +717,53 @@ "outputs": [ { "data": { - "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nLoad EOPatch\n\nLoad EOPatch\n\n\n\nAdd a new feature\n\nAdd a new feature\n\n\n\nLoad EOPatch->Add a new feature\n\n\n\n\n\nSave EOPatch\n\nSave EOPatch\n\n\n\nAdd a new feature->Save EOPatch\n\n\n\n\n\n", + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "Load EOPatch\n", + "\n", + "Load EOPatch\n", + "\n", + "\n", + "\n", + "Add a new feature\n", + "\n", + "Add a new feature\n", + "\n", + "\n", + "\n", + "Load EOPatch->Add a new feature\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Save EOPatch\n", + "\n", + "Save EOPatch\n", + "\n", + "\n", + "\n", + "Add a new feature->Save EOPatch\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -742,7 +792,7 @@ { "data": { "text/plain": [ - "WorkflowResults(outputs={}, start_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 324680), end_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 605534), stats={'LoadTask-db6d5a07bd8711edb8fc-d53c2b78843b': NodeStats(node_uid='LoadTask-db6d5a07bd8711edb8fc-d53c2b78843b', node_name='Load EOPatch', start_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 324741), end_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 417614), exception=None, exception_traceback=None), 'AddFeatureTask-db6d5e41bd8711ed8233-a4d329d818d3': NodeStats(node_uid='AddFeatureTask-db6d5e41bd8711ed8233-a4d329d818d3', node_name='Add a new feature', start_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 419545), end_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 419576), exception=None, exception_traceback=None), 'SaveTask-db6d6164bd8711edbfb9-d491c006650a': NodeStats(node_uid='SaveTask-db6d6164bd8711edbfb9-d491c006650a', node_name='Save EOPatch', start_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 420848), end_time=datetime.datetime(2023, 3, 8, 9, 4, 40, 604000), exception=None, exception_traceback=None)}, error_node_uid=None)" + "WorkflowResults(outputs={}, start_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 733751), end_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 961589), stats={'LoadTask-939b27aa45a511eeb8db-91a8de8b81da': NodeStats(node_uid='LoadTask-939b27aa45a511eeb8db-91a8de8b81da', node_name='Load EOPatch', start_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 733806), end_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 822464), exception_info=None), 'AddFeatureTask-939b2a9b45a511eea69d-e2612971e907': NodeStats(node_uid='AddFeatureTask-939b2a9b45a511eea69d-e2612971e907', node_name='Add a new feature', start_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 825206), end_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 825267), exception_info=None), 'SaveTask-939b2cb545a511eea722-ed1665ca815d': NodeStats(node_uid='SaveTask-939b2cb545a511eea722-ed1665ca815d', node_name='Save EOPatch', start_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 827230), end_time=datetime.datetime(2023, 8, 28, 15, 19, 54, 960678), exception_info=None)}, error_node_uid=None)" ] }, "execution_count": 15, @@ -754,7 +804,7 @@ "results = workflow.execute(\n", " {\n", " load_node: {\"eopatch_folder\": \"TestEOPatch\"},\n", - " add_feature_node: {\"data\": np.zeros((10, 3), dtype=np.uint8)},\n", + " add_feature_node: {\"data\": np.zeros((68, 3), dtype=np.uint8)},\n", " save_node: {\"eopatch_folder\": \"WorkflowEOPatch\"},\n", " }\n", ")\n", diff --git a/examples/core/TimeLapse.ipynb b/examples/core/TimeLapse.ipynb index 2409e6763..f65af93ce 100644 --- a/examples/core/TimeLapse.ipynb +++ b/examples/core/TimeLapse.ipynb @@ -150,11 +150,11 @@ "metadata": {}, "outputs": [], "source": [ - "def make_gif(eopatch, output_path, fps):\n", + "def make_gif(eopatch, output_path, duration):\n", " \"\"\"\n", " Generates a GIF animation from an EOPatch.\n", " \"\"\"\n", - " with imageio.get_writer(output_path, mode=\"I\", fps=fps) as writer:\n", + " with imageio.get_writer(output_path, mode=\"I\", duration=duration) as writer:\n", " for image in eopatch:\n", " writer.append_data(np.array(image, dtype=np.uint8))" ] @@ -166,12 +166,11 @@ "outputs": [], "source": [ "OUTPUT_GIF_PATH = os.path.join(\".\", \"outputs\", \"eopatch.gif\")\n", - "DURATION = 4 # seconds\n", "\n", "make_gif(\n", " eopatch=np.clip(eopatch.data[\"RGB\"] * 2.5 * 255, 0, 255),\n", " output_path=OUTPUT_GIF_PATH,\n", - " fps=len(eopatch.timestamps) / DURATION,\n", + " duration=4, # seconds\n", ")" ] }, diff --git a/examples/io/SentinelHubIO.ipynb b/examples/io/SentinelHubIO.ipynb index 1a1466d47..cbe6a2810 100644 --- a/examples/io/SentinelHubIO.ipynb +++ b/examples/io/SentinelHubIO.ipynb @@ -280,7 +280,9 @@ "metadata": {}, "outputs": [], "source": [ - "add_dem = SentinelHubDemTask(data_collection=DataCollection.DEM_COPERNICUS_30, resolution=resolution, config=config)" + "add_dem = SentinelHubDemTask(\n", + " feature=\"dem\", data_collection=DataCollection.DEM_COPERNICUS_30, resolution=resolution, config=config\n", + ")" ] }, { @@ -337,7 +339,7 @@ }, "outputs": [], "source": [ - "save = SaveTask(\"io_example\", overwrite_permission=2, compress_level=1)" + "save = SaveTask(\"io_example\")" ] }, { @@ -711,7 +713,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.4" }, "toc": { "base_numbering": 1, diff --git a/examples/land-cover-map/SI_LULC_pipeline.ipynb b/examples/land-cover-map/SI_LULC_pipeline.ipynb index c34ed3f5e..775a25010 100644 --- a/examples/land-cover-map/SI_LULC_pipeline.ipynb +++ b/examples/land-cover-map/SI_LULC_pipeline.ipynb @@ -97,7 +97,8 @@ " SaveTask,\n", " linearly_connect_tasks,\n", ")\n", - "from eolearn.features import LinearInterpolationTask, NormalizedDifferenceIndexTask, SimpleFilterTask\n", + "from eolearn.features import NormalizedDifferenceIndexTask, SimpleFilterTask\n", + "from eolearn.features.extra.interpolation import LinearInterpolationTask\n", "from eolearn.geometry import ErosionTask, VectorToRasterTask\n", "from eolearn.io import ExportToTiffTask, SentinelHubInputTask, VectorImportTask\n", "from eolearn.ml_tools import FractionSamplingTask" @@ -399,7 +400,7 @@ "add_valid_count = AddValidCountTask(\"IS_VALID\", \"VALID_COUNT\")\n", "\n", "# SAVING TO OUTPUT (if needed)\n", - "save = SaveTask(EOPATCH_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_PATCH)" + "save = SaveTask(EOPATCH_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES)" ] }, { @@ -1282,7 +1283,7 @@ " exclude_values=[0],\n", ")\n", "\n", - "save = SaveTask(EOPATCH_SAMPLES_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_PATCH)" + "save = SaveTask(EOPATCH_SAMPLES_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES)" ] }, { @@ -1967,7 +1968,7 @@ "predict = PredictPatchTask(model, (FeatureType.DATA, \"FEATURES\"), \"LBL_GBM\", \"SCR_GBM\")\n", "\n", "# SAVE\n", - "save = SaveTask(EOPATCH_SAMPLES_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_PATCH)\n", + "save = SaveTask(EOPATCH_SAMPLES_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES)\n", "\n", "# EXPORT TIFF\n", "tiff_location = os.path.join(RESULTS_FOLDER, \"predicted_tiff\")\n", diff --git a/examples/mask/ValidDataMask.ipynb b/examples/mask/ValidDataMask.ipynb index 6f800c2f8..cf4ff8ea8 100644 --- a/examples/mask/ValidDataMask.ipynb +++ b/examples/mask/ValidDataMask.ipynb @@ -139,22 +139,16 @@ "data": { "text/plain": [ "EOPatch(\n", - " data={\n", - " trueColorBands: numpy.ndarray(shape=(9, 118, 205, 3), dtype=float32)\n", - " }\n", + " bbox=BBox(((12.14, 45.36), (12.55, 45.54)), crs=CRS('4326'))\n", + " timestamps=[datetime.datetime(2020, 6, 2, 10, 18, 8), ..., datetime.datetime(2020, 6, 22, 10, 18, 9)], length=9\n", " mask={\n", " CLM: numpy.ndarray(shape=(9, 118, 205, 1), dtype=uint8)\n", " VALID_DATA: numpy.ndarray(shape=(9, 118, 205, 1), dtype=bool)\n", " dataMask: numpy.ndarray(shape=(9, 118, 205, 1), dtype=bool)\n", " }\n", - " meta_info={\n", - " size_x: 205\n", - " size_y: 118\n", - " time_difference: 7200.0\n", - " time_interval: ('2020-06-01T00:00:00', '2020-06-23T23:59:59')\n", + " data={\n", + " trueColorBands: numpy.ndarray(shape=(9, 118, 205, 3), dtype=float32)\n", " }\n", - " bbox=BBox(((12.14, 45.36), (12.55, 45.54)), crs=CRS('4326'))\n", - " timestamps=[datetime.datetime(2020, 6, 2, 10, 18, 8), ..., datetime.datetime(2020, 6, 22, 10, 18, 9)], length=9\n", ")" ] }, diff --git a/examples/visualization/EOPatchVisualization.ipynb b/examples/visualization/EOPatchVisualization.ipynb index f0e693546..5eef2ecce 100644 --- a/examples/visualization/EOPatchVisualization.ipynb +++ b/examples/visualization/EOPatchVisualization.ipynb @@ -97,7 +97,7 @@ "source": [ "## Basics\n", "\n", - "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", + "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", "\n", "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." ] @@ -324,35 +324,6 @@ "source": [ "eopatch.plot((FeatureType.VECTOR, \"CLM_VECTOR\"), times=slice(6, 9));" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally we can plot a bounding box feature on its own." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "eopatch.plot(FeatureType.BBOX);" - ] } ], "metadata": { @@ -371,7 +342,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.8.10" }, "vscode": { "interpreter": { diff --git a/examples/water-monitor/WaterMonitorWorkflow.ipynb b/examples/water-monitor/WaterMonitorWorkflow.ipynb index 34a22e78d..5d8fdf5e2 100644 --- a/examples/water-monitor/WaterMonitorWorkflow.ipynb +++ b/examples/water-monitor/WaterMonitorWorkflow.ipynb @@ -526,8 +526,8 @@ " observed = closing(eopatch.mask[\"WATER_MASK\"][idx, ..., 0], disk(1))\n", " nominal = sobel(eopatch.mask_timeless[\"NOMINAL_WATER\"][..., 0])\n", " observed = sobel(observed)\n", - " nominal = np.ma.masked_where(~nominal, nominal)\n", - " observed = np.ma.masked_where(~observed, observed)\n", + " nominal = np.ma.masked_where(~nominal.astype(bool), nominal)\n", + " observed = np.ma.masked_where(~observed.astype(bool), observed)\n", "\n", " ax.imshow(nominal, cmap=plt.cm.Reds)\n", " ax.imshow(observed, cmap=plt.cm.Blues)\n", diff --git a/features/MANIFEST.in b/features/MANIFEST.in deleted file mode 100644 index 9f43ca148..000000000 --- a/features/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/features/py.typed -exclude eolearn/tests/* diff --git a/features/README.md b/features/README.md index e477f5a99..15664d2c6 100644 --- a/features/README.md +++ b/features/README.md @@ -1,5 +1,5 @@ # eolearn.features subpackage -A collection of utilities for extracting data properties and feature manipulation. +The deprecated way to install `eolearn.features`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.features.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#features). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/features/deprecated_eolearn_features/__init__.py b/features/deprecated_eolearn_features/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/features/deprecated_eolearn_features/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/features/eolearn/__init__.py b/features/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/features/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/features/eolearn/features/__init__.py b/features/eolearn/features/__init__.py deleted file mode 100644 index d9ff3335b..000000000 --- a/features/eolearn/features/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -A collection of EOTasks for feature manipulation -""" - -from .bands_extraction import EuclideanNormTask, NormalizedDifferenceIndexTask -from .blob import BlobTask, DoGBlobTask, DoHBlobTask, LoGBlobTask -from .clustering import ClusteringTask -from .doubly_logistic_approximation import DoublyLogisticApproximationTask -from .feature_manipulation import FilterTimeSeriesTask, LinearFunctionTask, SimpleFilterTask, ValueFilloutTask -from .haralick import HaralickTask -from .hog import HOGTask -from .interpolation import ( - AkimaInterpolationTask, - BSplineInterpolationTask, - CubicInterpolationTask, - CubicResamplingTask, - InterpolationTask, - KrigingInterpolationTask, - LinearInterpolationTask, - LinearResamplingTask, - NearestResamplingTask, - ResamplingTask, - SplineInterpolationTask, -) -from .local_binary_pattern import LocalBinaryPatternTask -from .radiometric_normalization import ( - BlueCompositingTask, - HistogramMatchingTask, - HOTCompositingTask, - MaxNDVICompositingTask, - MaxNDWICompositingTask, - MaxRatioCompositingTask, - ReferenceScenesTask, -) -from .temporal_features import ( - AddMaxMinNDVISlopeIndicesTask, - AddMaxMinTemporalIndicesTask, - AddSpatioTemporalFeaturesTask, -) - -__version__ = "1.4.2" diff --git a/features/eolearn/features/blob.py b/features/eolearn/features/blob.py deleted file mode 100644 index 225744231..000000000 --- a/features/eolearn/features/blob.py +++ /dev/null @@ -1,161 +0,0 @@ -""" -Module for computing blobs in EOPatch - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import itertools as it -from math import sqrt -from typing import Any, Callable - -import numpy as np -import skimage.feature - -from sentinelhub.exceptions import deprecated_class - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.exceptions import EODeprecationWarning -from eolearn.core.types import SingleFeatureSpec - - -class BlobTask(EOTask): - """ - Task to compute blobs - - A blob is a region of an image in which some properties are constant or approximately constant; all the points in a - blob can be considered in some sense to be similar to each other. - - 3 methods are implemented: The Laplacian of Gaussian (LoG), the difference of Gaussian approach (DoG) and the - determinant of the Hessian (DoH). - - The output is a `FeatureType.DATA` where the radius of each blob is stored in his center. - ie : If blob[date, i, j, 0] = 5 then a blob of radius 5 is present at the coordinate (i, j) - - The task uses `skimage.feature.blob_log`, `skimage.feature.blob_dog` or `skimage.feature.blob_doh` for extraction. - - The input image must be in [-1,1] range. - - :param feature: A feature that will be used and a new feature name where data will be saved, e.g. - `(FeatureType.DATA, 'bands', 'blob')`. - :param blob_object: Callable that calculates the blob - :param blob_parameters: Parameters to be passed to the blob function. Consult documentation of `blob_object` - for available parameters. - """ - - def __init__(self, feature: SingleFeatureSpec, blob_object: Callable, **blob_parameters: Any): - self.feature_parser = self.get_feature_parser(feature, allowed_feature_types=[FeatureType.DATA]) - - self.blob_object = blob_object - self.blob_parameters = blob_parameters - - def _compute_blob(self, data: np.ndarray) -> np.ndarray: - result = np.zeros(data.shape, dtype=np.float32) - num_time, _, _, num_bands = data.shape - for time_idx, band_idx in it.product(range(num_time), range(num_bands)): - image = data[time_idx, :, :, band_idx] - blob = self.blob_object(image, **self.blob_parameters) - x_coord = blob[:, 0].astype(int) - y_coord = blob[:, 1].astype(int) - radius = blob[:, 2] * sqrt(2) - result[time_idx, x_coord, y_coord, band_idx] = radius - return result - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Execute computation of blobs on input eopatch - - :param eopatch: Input eopatch - :return: EOPatch instance with new key holding the blob image. - """ - for ftype, fname, new_fname in self.feature_parser.get_renamed_features(eopatch): - eopatch[ftype, new_fname] = self._compute_blob(eopatch[ftype, fname].astype(np.float64)) - - return eopatch - - -@deprecated_class(EODeprecationWarning, "Use `BlobTask` with `blob_object=skimage.feature.blob_dog`.") -class DoGBlobTask(BlobTask): - """Task to compute blobs with Difference of Gaussian (DoG) method""" - - def __init__( - self, - feature: SingleFeatureSpec, - *, - sigma_ratio: float = 1.6, - min_sigma: float = 1, - max_sigma: float = 30, - threshold: float = 0.1, - overlap: float = 0.5, - **kwargs: Any, - ): - super().__init__( - feature, - skimage.feature.blob_dog, - sigma_ratio=sigma_ratio, - min_sigma=min_sigma, - max_sigma=max_sigma, - threshold=threshold, - overlap=overlap, - **kwargs, - ) - - -@deprecated_class(EODeprecationWarning, "Use `BlobTask` with `blob_object=skimage.feature.blob_doh`.") -class DoHBlobTask(BlobTask): - """Task to compute blobs with Determinant of the Hessian (DoH) method""" - - def __init__( - self, - feature: SingleFeatureSpec, - *, - num_sigma: float = 10, - log_scale: bool = False, - min_sigma: float = 1, - max_sigma: float = 30, - threshold: float = 0.1, - overlap: float = 0.5, - **kwargs: Any, - ): - super().__init__( - feature, - skimage.feature.blob_doh, - num_sigma=num_sigma, - log_scale=log_scale, - min_sigma=min_sigma, - max_sigma=max_sigma, - threshold=threshold, - overlap=overlap, - **kwargs, - ) - - -@deprecated_class(EODeprecationWarning, "Use `BlobTask` with `blob_object=skimage.feature.blob_log`.") -class LoGBlobTask(BlobTask): - """Task to compute blobs with Laplacian of Gaussian (LoG) method""" - - def __init__( - self, - feature: SingleFeatureSpec, - *, - num_sigma: float = 10, - log_scale: bool = False, - min_sigma: float = 1, - max_sigma: float = 30, - threshold: float = 0.1, - overlap: float = 0.5, - **kwargs: Any, - ): - super().__init__( - feature, - skimage.feature.blob_log, - num_sigma=num_sigma, - log_scale=log_scale, - min_sigma=min_sigma, - max_sigma=max_sigma, - threshold=threshold, - overlap=overlap, - **kwargs, - ) diff --git a/features/eolearn/features/doubly_logistic_approximation.py b/features/eolearn/features/doubly_logistic_approximation.py deleted file mode 100644 index b7b989b00..000000000 --- a/features/eolearn/features/doubly_logistic_approximation.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -Module for calculating doubly logistic approximation. - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import itertools as it - -import numpy as np -from scipy.optimize import curve_fit - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import SingleFeatureSpec - - -def doubly_logistic(middle, initial_value, scale, a1, a2, a3, a4, a5) -> np.ndarray: # type: ignore[no-untyped-def] - # pylint: disable=invalid-name - """Function passed to `scipy.optimize`""" - funclist = [lambda y: np.exp(-(((a1 - y) / a4) ** a5)), lambda y: np.exp(-(((y - a1) / a2) ** a3))] - return initial_value + scale * np.piecewise(middle, [middle < a1, middle >= a1], funclist) - - -class DoublyLogisticApproximationTask(EOTask): - """ - EOTask class for calculation of doubly logistic approximation on each pixel for a feature. The task creates new - feature with the function parameters for each pixel as vectors. - :param feature: A feature on which the function will be approximated - :param new_feature: Name of the new feature where parameters of the function are saved - :param initial_parameters: Initial parameter guess - :param valid_mask: A feature used as a mask for valid regions. If left as None the whole patch is used - """ - - def __init__( - self, - feature: SingleFeatureSpec, - new_feature: SingleFeatureSpec = (FeatureType.DATA_TIMELESS, "DOUBLY_LOGISTIC_PARAM"), - initial_parameters: list[float] | None = None, - valid_mask: SingleFeatureSpec | None = None, - ): - self.initial_parameters = initial_parameters - self.feature = self.parse_feature(feature) - self.new_feature = self.parse_feature(new_feature) - self.valid_mask = ( - self.parse_feature(valid_mask, allowed_feature_types=[FeatureType.MASK]) if valid_mask else None - ) - - def _fit_optimize(self, x_axis: np.ndarray, y_axis: np.ndarray) -> np.ndarray: - """ - :param x_axis: Horizontal coordinates of points - :param y_axis: Vertical coordinates of points - :return: List of optimized parameters [c1, c2, a1, a2, a3, a4, a5] - """ - bounds_lower = [ - np.min(y_axis), - -np.inf, - x_axis[0], - 0.15, - 1, - 0.15, - 1, - ] - bounds_upper = [ - np.max(y_axis), - np.inf, - x_axis[-1], - np.inf, - np.inf, - np.inf, - np.inf, - ] - if self.initial_parameters is None: - self.initial_parameters = [np.mean(y_axis), 0.2, (x_axis[-1] - x_axis[0]) / 2, 0.15, 10, 0.15, 10] - optimal_values = curve_fit( - doubly_logistic, - x_axis, - y_axis, - self.initial_parameters, - bounds=(bounds_lower, bounds_upper), - maxfev=1000000, - absolute_sigma=True, - ) - return optimal_values[0] - - def execute(self, eopatch: EOPatch) -> EOPatch: - """ - :param eopatch: Input eopatch with data on which the doubly logistic approximation is computed - :return: Output patch with doubly logistic approximation parameters - """ - data = eopatch[self.feature].squeeze(axis=3) - - times = np.asarray([time.toordinal() for time in eopatch.timestamps]) - times = (times - times[0]) / (times[-1] - times[0]) - - time, height, width = data.shape - - if self.valid_mask: - valid_data_mask = eopatch[self.valid_mask] - else: - valid_data_mask = np.ones((time, height, width), dtype=bool) - - all_parameters = np.zeros((height, width, 7)) - for height_ind, width_ind in it.product(range(height), range(width)): - valid_curve = data[:, height_ind, width_ind][valid_data_mask[:, height_ind, width_ind].squeeze()] - valid_times = times[valid_data_mask[:, height_ind, width_ind].squeeze()] - - all_parameters[height_ind, width_ind] = self._fit_optimize(valid_times, valid_curve) - - eopatch[self.new_feature] = all_parameters - - return eopatch diff --git a/features/eolearn/features/haralick.py b/features/eolearn/features/haralick.py deleted file mode 100644 index a5c60c792..000000000 --- a/features/eolearn/features/haralick.py +++ /dev/null @@ -1,154 +0,0 @@ -""" -Module for computing Haralick textures in EOPatch - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import itertools as it -import warnings - -import numpy as np -import skimage.feature - -from eolearn.core import EOPatch, EOTask -from eolearn.core.exceptions import EOUserWarning -from eolearn.core.types import SingleFeatureSpec - - -class HaralickTask(EOTask): - """Task to compute Haralick texture images - - The task compute the grey-level co-occurrence matrix (GLCM) on a sliding window over the input image and extract the - texture properties. - - The task uses `skimage.feature.greycomatrix` and `skimage.feature.greycoprops` to extract the texture features. - """ - - AVAILABLE_TEXTURES_SKIMAGE = {"contrast", "dissimilarity", "homogeneity", "ASM", "energy", "correlation"} - AVAILABLE_TEXTURES = { - "sum_of_square_variance", - "inverse_difference_moment", - "sum_average", - "sum_variance", - "sum_entropy", - "difference_variance", - "difference_entropy", - }.union(AVAILABLE_TEXTURES_SKIMAGE) - - def __init__( - self, - feature: SingleFeatureSpec, - texture_feature: str = "contrast", - distance: int = 1, - angle: float = 0, - levels: int = 8, - window_size: int = 3, - stride: int = 1, - ): - """ - :param feature: A feature that will be used and a new feature name where data will be saved, e.g. - `(FeatureType.DATA, 'bands', 'haralick_values')` - :param texture_feature: Type of Haralick textural feature to be calculated - :param distance: Distance between pairs of pixels used for GLCM - :param angle: Angle between pairs of pixels used for GLCM in radians, e.g. angle=np.pi/4 - :param levels: Number of bins in GLCM - :param window_size: Size of the moving GLCM window - :param stride: How much the GLCM window moves each time - """ - self.feature_parser = self.get_feature_parser(feature) - - self.texture_feature = texture_feature - if self.texture_feature not in self.AVAILABLE_TEXTURES: - raise ValueError(f"Haralick texture feature must be one of these: {self.AVAILABLE_TEXTURES}") - - self.distance = distance - self.angle = angle - self.levels = levels - - self.window_size = window_size - if self.window_size % 2 != 1: - raise ValueError("Window size must be an odd number") - - self.stride = stride - if self.stride > self.window_size: - warnings.warn( - "Haralick stride is larger than window size; some pixel values will be ignored", EOUserWarning - ) - - def _custom_texture(self, glcm: np.ndarray) -> np.ndarray: # pylint: disable=too-many-return-statements - if self.texture_feature == "sum_of_square_variance": - i_raw = np.empty_like(glcm) - i_raw[...] = np.arange(glcm.shape[0]) - i_raw = np.transpose(i_raw) - i_minus_mean = (i_raw - glcm.mean()) ** 2 - return np.apply_over_axes(np.sum, i_minus_mean * glcm, axes=(0, 1))[0][0] - if self.texture_feature == "inverse_difference_moment": - j_cols = np.empty_like(glcm) - j_cols[...] = np.arange(glcm.shape[1]) - i_minus_j = ((j_cols - np.transpose(j_cols)) ** 2) + 1 - return np.apply_over_axes(np.sum, glcm / i_minus_j, axes=(0, 1))[0][0] - if self.texture_feature == "sum_average": - p_x_y = self._get_pxy(glcm) - return np.array(p_x_y * np.arange(len(p_x_y))).sum() - if self.texture_feature == "sum_variance": - p_x_y = self._get_pxy(glcm) - sum_average = np.array(p_x_y * np.arange(len(p_x_y))).sum() - return ((np.arange(len(p_x_y)) - sum_average) ** 2).sum() - if self.texture_feature == "sum_entropy": - p_x_y = self._get_pxy(glcm) - return (p_x_y * np.log(p_x_y + np.finfo(float).eps)).sum() * -1.0 - if self.texture_feature == "difference_variance": - p_x_y = self._get_pxy_for_diff(glcm) - sum_average = np.array(p_x_y * np.arange(len(p_x_y))).sum() - return ((np.arange(len(p_x_y)) - sum_average) ** 2).sum() - - # self.texture_feature == 'difference_entropy': - p_x_y = self._get_pxy_for_diff(glcm) - return (p_x_y * np.log(p_x_y + np.finfo(float).eps)).sum() * -1.0 - - def _get_pxy(self, glcm: np.ndarray) -> np.ndarray: - tuple_array = np.array(list(it.product(range(self.levels), range(self.levels)))) - index = [tuple_array[tuple_array.sum(axis=1) == x] for x in range(self.levels)] - return np.array([glcm[tuple(np.moveaxis(idx, -1, 0))].sum() for idx in index]) - - def _get_pxy_for_diff(self, glcm: np.ndarray) -> np.ndarray: - tuple_array = np.array(list(it.product(range(self.levels), np.asarray(range(self.levels)) * -1))) - index = [tuple_array[np.abs(tuple_array.sum(axis=1)) == x] for x in range(self.levels)] - return np.array([glcm[tuple(np.moveaxis(idx, -1, 0))].sum() for idx in index]) - - def _calculate_haralick(self, data: np.ndarray) -> np.ndarray: - result = np.empty(data.shape, dtype=float) - num_times, _, _, num_bands = data.shape - # For each date and each band - for time, band in it.product(range(num_times), range(num_bands)): - image = data[time, :, :, band] - image_min, image_max = np.min(image), np.max(image) - coef = (image_max - image_min) / self.levels - digitized_image = np.digitize(image, np.array([image_min + k * coef for k in range(self.levels - 1)])) - - # Padding the image to handle borders - pad = self.window_size // 2 - digitized_image = np.pad(digitized_image, ((pad, pad), (pad, pad)), "edge") - # Sliding window - for i, j in it.product(range(0, image.shape[0], self.stride), range(0, image.shape[1], self.stride)): - window = digitized_image[i : i + self.window_size, j : j + self.window_size] - glcm = skimage.feature.graycomatrix( - window, [self.distance], [self.angle], levels=self.levels, normed=True, symmetric=True - ) - - if self.texture_feature in self.AVAILABLE_TEXTURES_SKIMAGE: - result[time, i, j, band] = skimage.feature.graycoprops(glcm, self.texture_feature)[0][0] - else: - result[time, i, j, band] = self._custom_texture(glcm[:, :, 0, 0]) - - return result - - def execute(self, eopatch: EOPatch) -> EOPatch: - for ftype, fname, new_fname in self.feature_parser.get_renamed_features(eopatch): - eopatch[ftype, new_fname] = self._calculate_haralick(eopatch[ftype, fname]) - - return eopatch diff --git a/features/eolearn/features/hog.py b/features/eolearn/features/hog.py deleted file mode 100644 index 88c14dd9d..000000000 --- a/features/eolearn/features/hog.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -Module for computing the Histogram of gradient in EOPatch - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import itertools as it - -import numpy as np -import skimage.feature - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import SingleFeatureSpec - - -class HOGTask(EOTask): - """Task to compute the histogram of gradient - - Divide the image into small connected regions called cells, and for each cell compute a histogram of gradient - directions or edge orientations for the pixels within the cell. - - The algorithm stores the result in images where each band is the value of the histogram for a specific angular - bin. If `visualize` is `True`, it also outputs the images representing the gradients for each orientation. - """ - - def __init__( - self, - feature: SingleFeatureSpec, - orientations: int = 9, - pixels_per_cell: tuple[int, int] = (8, 8), - cells_per_block: tuple[int, int] = (3, 3), - visualize: bool = True, - hog_feature_vector: bool = False, - block_norm: str = "L2-Hys", - visualize_feature_name: str | None = None, - ): - """ - :param feature: A feature that will be used and a new feature name where data will be saved, e.g. - `(FeatureType.DATA, 'bands', 'hog')`. - :param orientations: Number of direction to use for the oriented gradient - :param pixels_per_cell: Number of pixels in a cell, provided as a pair of integers. - :param cells_per_block: Number of cells in a block, provided as a pair of integers. - :param visualize: Produce a visualization for the HOG in an image - :param visualize_feature_name: Name of the visualization feature to be added to the eopatch (if empty and - visualize is True, it becomes “new_name”_VISU) - """ - self.feature_parser = self.get_feature_parser(feature, allowed_feature_types=[FeatureType.DATA]) - - self.n_orientations = orientations - self.pixels_per_cell = pixels_per_cell - self.cells_per_block = cells_per_block - self.visualize = visualize - self.block_norm = block_norm - self.hog_feature_vector = hog_feature_vector - self.visualize_name = visualize_feature_name - - def _compute_hog(self, data: np.ndarray) -> tuple[np.ndarray, np.ndarray]: # pylint: disable=too-many-locals - num_times, height, width, num_bands = data.shape - is_multichannel = num_bands != 1 - hog_result = np.empty( - ( - num_times, - ((height // self.pixels_per_cell[0]) - self.cells_per_block[0] + 1) * self.cells_per_block[0], - ((width // self.pixels_per_cell[1]) - self.cells_per_block[1] + 1) * self.cells_per_block[1], - self.n_orientations, - ), - dtype=np.float32, - ) - if self.visualize: - hog_visualization = np.empty((num_times, height, width, 1)) - for time in range(num_times): - output, image = skimage.feature.hog( - data[time] if is_multichannel else data[time, :, :, 0], - orientations=self.n_orientations, - pixels_per_cell=self.pixels_per_cell, - visualize=self.visualize, - cells_per_block=self.cells_per_block, - block_norm=self.block_norm, - feature_vector=self.hog_feature_vector, - channel_axis=-1 if is_multichannel else None, - ) - - block_rows, block_cols, cell_rows, cell_cols, angles = output.shape - for block_row, block_col in it.product(range(block_rows), range(block_cols)): - for cell_row, cell_col in it.product(range(cell_rows), range(cell_cols)): - row = block_row * self.cells_per_block[0] + cell_row - col = block_col * self.cells_per_block[1] + cell_col - for angle in range(angles): - hog_result[time, row, col, angle] = output[block_row, block_col, cell_row, cell_col, angle] - - if self.visualize: - hog_visualization[time, :, :, 0] = image - - return hog_result, hog_visualization - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Execute computation of HoG features on input eopatch - - :param eopatch: Input eopatch - :return: EOPatch instance with new keys holding the HoG features and HoG image for visualization. - """ - for feature_type, feature_name, new_feature_name in self.feature_parser.get_renamed_features(eopatch): - hog_result, hog_visualization = self._compute_hog(eopatch[feature_type, feature_name]) - eopatch[feature_type, new_feature_name] = hog_result - if self.visualize: - visualize_name = self.visualize_name or f"{new_feature_name}_VISU" - eopatch[feature_type, visualize_name] = hog_visualization - - return eopatch diff --git a/features/eolearn/features/local_binary_pattern.py b/features/eolearn/features/local_binary_pattern.py deleted file mode 100644 index 4f588b0ed..000000000 --- a/features/eolearn/features/local_binary_pattern.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -Module for computing the Local Binary Pattern in EOPatch - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import numpy as np -import skimage.feature - -from eolearn.core import EOPatch, EOTask -from eolearn.core.types import SingleFeatureSpec - - -class LocalBinaryPatternTask(EOTask): - """Task to compute the Local Binary Pattern images - - LBP looks at points surrounding a central point and tests whether the surrounding points are greater than or - less than the central point - - The task uses skimage.feature.local_binary_pattern to extract the texture features. - """ - - def __init__(self, feature: SingleFeatureSpec, nb_points: int = 24, radius: int = 3): - """ - :param feature: A feature that will be used and a new feature name where data will be saved. If new name is not - specified it will be saved with name '_LBP'. - - Example: (FeatureType.DATA, 'bands') or (FeatureType.DATA, 'bands', 'lbp') - :param nb_points: Number of point to use - :param radius: Radius of the circle of neighbors - """ - self.feature_parser = self.get_feature_parser(feature) - - self.nb_points = nb_points - self.radius = radius - if nb_points < 1 or radius < 1: - raise ValueError("Local binary pattern task parameters must be positives") - - def _compute_lbp(self, data: np.ndarray) -> np.ndarray: - result = np.empty(data.shape, dtype=float) - for time in range(data.shape[0]): - for band in range(data.shape[-1]): - image = data[time, :, :, band] - result[time, :, :, band] = skimage.feature.local_binary_pattern( - image, self.nb_points, self.radius, method="uniform" - ) - return result - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Execute computation of local binary patterns on input eopatch - - :param eopatch: Input eopatch - :return: EOPatch instance with new key holding the LBP image. - """ - for feature_type, feature_name, new_feature_name in self.feature_parser.get_renamed_features(eopatch): - eopatch[feature_type, new_feature_name] = self._compute_lbp(eopatch[feature_type, feature_name]) - - return eopatch diff --git a/features/eolearn/features/py.typed b/features/eolearn/features/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/features/eolearn/features/radiometric_normalization.py b/features/eolearn/features/radiometric_normalization.py deleted file mode 100644 index 54d0ed24f..000000000 --- a/features/eolearn/features/radiometric_normalization.py +++ /dev/null @@ -1,405 +0,0 @@ -""" -Module for radiometric normalization - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -from abc import ABCMeta, abstractmethod - -import numpy as np - -from eolearn.core import EOPatch, EOTask -from eolearn.core.types import SingleFeatureSpec -from eolearn.core.utils.parsing import parse_renamed_feature - - -class ReferenceScenesTask(EOTask): - """Creates a layer of reference scenes which have the highest fraction of valid pixels. - - The number of reference scenes is limited to a definable number. - - Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 - - :param feature: Name of the eopatch data layer. Needs to be of the FeatureType "DATA". - :param valid_fraction_feature: Name of the layer containing the valid fraction obtained with the EOTask - 'AddValidDataFraction'. Needs to be of the FeatureType "SCALAR". - :param max_scene_number: Maximum number of reference scenes taken for the creation of the composite. By default, - the maximum number of scenes equals the number of time frames - - """ - - def __init__( - self, - feature: SingleFeatureSpec, - valid_fraction_feature: SingleFeatureSpec, - max_scene_number: int | None = None, - ): - self.renamed_feature = parse_renamed_feature(feature) - self.valid_fraction_feature = self.parse_feature(valid_fraction_feature) - self.number = max_scene_number - - def execute(self, eopatch: EOPatch) -> EOPatch: - feature_type, feature_name, new_feature_name = self.renamed_feature - valid_fraction_feature_type, valid_fraction_feature_name = self.valid_fraction_feature - - valid_frac = list(eopatch[valid_fraction_feature_type][valid_fraction_feature_name].flatten()) - data = eopatch[feature_type, feature_name] - - number = data.shape[0] if self.number is None else self.number - - eopatch[feature_type, new_feature_name] = np.array( - [data[x] for _, x in sorted(zip(valid_frac, range(data.shape[0])), reverse=True) if x <= number - 1] - ) - - return eopatch - - -class BaseCompositingTask(EOTask, metaclass=ABCMeta): - """Base class to create a composite of reference scenes - - Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 - - :param feature: Feature holding the input time-series. Default type is FeatureType.DATA - :param feature_composite: Type and name of output composite image. Default type is FeatureType.DATA_TIMELESS - :param percentile: Percentile along the time dimension used for compositing. Methods use different percentiles - :param max_index: Value used to flag indices with NaNs. Could be integer or NaN. Default is 255 - :param interpolation: Method used to compute percentile. Allowed values are {'geoville', 'linear', 'lower', - 'higher', 'midpoint', 'nearest'}. 'geoville' interpolation performs a custom implementation, while the other - methods use the numpy `percentile` function. Default is 'lower'. - :param no_data_value: Value in the composite assigned to non-valid data points. Default is NaN - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - percentile: int, - max_index: int = 255, - interpolation: str = "lower", - no_data_value: float = np.nan, - ): - self.feature = self.parse_feature(feature) - self.composite_type, self.composite_name = self.parse_feature(feature_composite) - self.percentile = percentile - self.max_index = max_index - self.interpolation = interpolation - self._index_by_percentile = ( - self._geoville_index_by_percentile - if self.interpolation.lower() == "geoville" - else self._numpy_index_by_percentile - ) - self.no_data_value = no_data_value - - def _numpy_index_by_percentile(self, data: np.ndarray, percentile: float) -> np.ndarray: - """Calculate percentile of numpy stack and return the index of the chosen pixel. - - numpy percentile function is used with one of the following interpolations {'linear', 'lower', 'higher', - 'midpoint', 'nearest'} - """ - data_perc_low = np.nanpercentile( # type: ignore[call-overload] # numpy is weird here - data, percentile, axis=0, interpolation=self.interpolation - ) - - indices = np.empty(data_perc_low.shape, dtype=np.uint8) - indices[:] = np.nan - - abs_diff = np.where(np.isnan(data_perc_low), np.inf, abs(data - data_perc_low)) - - indices = np.where(np.isnan(data_perc_low), self.max_index, np.nanargmin(abs_diff, axis=0)) - - return indices - - def _geoville_index_by_percentile(self, data: np.ndarray, percentile: int) -> np.ndarray: - """Calculate percentile of numpy stack and return the index of the chosen pixel.""" - # no_obs = bn.allnan(arr_tmp["data"], axis=0) - data_tmp = np.array(data, copy=True) - valid_obs = np.sum(np.isfinite(data_tmp), axis=0) - # replace NaN with maximum - max_val = np.nanmax(data_tmp) + 1 - data_tmp[np.isnan(data_tmp)] = max_val - # sort - former NaNs will move to the end - ind_tmp = np.argsort(data_tmp, kind="mergesort", axis=0) - # desired position as well as floor and ceiling of it - k_arr = (valid_obs - 1) * (percentile / 100.0) - k_arr = np.where(k_arr < 0, 0, k_arr) - f_arr = np.floor(k_arr + 0.5) - f_arr = f_arr.astype(int) - # get floor value of reference band and index band - ind = f_arr.astype("int16") - y_val, x_val = ind_tmp.shape[1], ind_tmp.shape[2] - y_val, x_val = np.ogrid[0:y_val, 0:x_val] # type: ignore[assignment] - return np.where(valid_obs == 0, self.max_index, ind_tmp[ind, y_val, x_val]) - - @abstractmethod - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract reference band from input 4D data according to compositing method - - :param data: 4D array from which to extract reference band (e.g. blue, maxNDVI, ..) - :return: 3D array containing reference band according to compositing method - """ - - def _get_indices(self, data: np.ndarray) -> np.ndarray: - """Compute indices along temporal dimension corresponding to the sought percentile - - :param data: Input 3D array holding the reference band - :return: 2D array holding the temporal index corresponding to percentile - """ - return self._index_by_percentile(data, self.percentile) - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Compute composite array merging temporal frames according to the compositing method - - :param eopatch: eopatch holding time-series - :return: eopatch with composite image of time-series - """ - data = eopatch[self.feature].copy() - - # compute band according to compositing method (e.g. blue, maxNDVI, maxNDWI) - reference_bands = self._get_reference_band(data) - - # find temporal indices corresponding to pre-defined percentile - indices = self._get_indices(reference_bands) - - # compute composite image selecting values along temporal dimension corresponding to percentile indices - composite_image = np.empty((data.shape[1:]), np.float32) - composite_image[:] = self.no_data_value - for scene_id, scene in enumerate(data): - composite_image = np.where(np.dstack([indices]) == scene_id, scene, composite_image) - - eopatch[self.composite_type, self.composite_name] = composite_image - - return eopatch - - -class BlueCompositingTask(BaseCompositingTask): - """Blue band compositing method - - - blue (25th percentile of the blue band) - - :param blue_idx: Index of blue band in `feature` array - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - blue_idx: int, - interpolation: str = "lower", - ): - super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) - self.blue_idx = blue_idx - if not isinstance(blue_idx, int): - raise ValueError("Incorrect value of blue band index specified") - - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract the blue band from time-series - - :param data: 4D array from which to extract the blue reference band - :return: 3D array containing the blue reference band - """ - return data[..., self.blue_idx].astype("float32") - - -class HOTCompositingTask(BaseCompositingTask): - """HOT compositing method - - - HOT (Index using bands blue and red) - - The HOT index is defined as per - Zhu, Z., & Woodcock, C. E. (2012). "Object-based cloud and cloud shadow detection in Landsat imagery." - Remote Sensing of Environment, 118, 83-94. - - :param blue_idx: Index of blue band in `feature` array - :param red_idx: Index of red band in `feature` array - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - blue_idx: int, - red_idx: int, - interpolation: str = "lower", - ): - super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) - self.blue_idx = blue_idx - self.red_idx = red_idx - if not isinstance(blue_idx, int) or not isinstance(red_idx, int): - raise ValueError("Incorrect values of blue and red band indices specified") - - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract the HOT band from time-series - - :param data: 4D array from which to extract the HOT reference band - :return: 3D array containing the HOT reference band - """ - return data[..., self.blue_idx] - 0.5 * data[..., self.red_idx] - 0.08 - - -class MaxNDVICompositingTask(BaseCompositingTask): - """maxNDVI compositing method - - - maxNDVI (temporal maximum of NDVI) - - :param red_idx: Index of red band in `feature` array - :param nir_idx: Index of NIR band in `feature` array - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - red_idx: int, - nir_idx: int, - interpolation: str = "lower", - ): - super().__init__( # No real value for percentile - feature, feature_composite, percentile=None, interpolation=interpolation # type: ignore[arg-type] - ) - self.percentiles = [0, 100] - self.red_idx = red_idx - self.nir_idx = nir_idx - if not isinstance(nir_idx, int) or not isinstance(red_idx, int): - raise ValueError("Incorrect values of red and NIR band indices specified") - - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract the NDVI band from time-series - - :param data: 4D array from which to compute the NDVI reference band - :return: 3D array containing the NDVI reference band - """ - nir = data[..., self.nir_idx].astype("float32") - red = data[..., self.red_idx].astype("float32") - return (nir - red) / (nir + red) - - def _get_indices(self, data: np.ndarray) -> np.ndarray: - median = np.nanmedian(data, axis=0) - indices_min = self._index_by_percentile(data, self.percentiles[0]) - indices_max = self._index_by_percentile(data, self.percentiles[1]) - return np.where(median < -0.05, indices_min, indices_max) - - -class MaxNDWICompositingTask(BaseCompositingTask): - """maxNDWI compositing method - - - maxNDWI (temporal maximum of NDWI) - - :param nir_idx: Index of NIR band in `feature` array - :param swir1_idx: Index of SWIR1 band in `feature` array - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - nir_idx: int, - swir1_idx: int, - interpolation: str = "lower", - ): - super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) - self.nir_idx = nir_idx - self.swir1_idx = swir1_idx - if not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): - raise ValueError("Incorrect values of NIR and SWIR1 band indices specified") - - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract the NDWI band from time-series - - :param data: 4D array from which to compute the NDWI reference band - :return: 3D array containing the NDWI reference band - """ - nir = data[..., self.nir_idx].astype("float32") - swir1 = data[..., self.swir1_idx].astype("float32") - return (nir - swir1) / (nir + swir1) - - -class MaxRatioCompositingTask(BaseCompositingTask): - """maxRatio compositing method - - - maxRatio (temporal maximum of a ratio using bands blue, NIR and SWIR) - - :param blue_idx: Index of blue band in `feature` array - :param nir_idx: Index of NIR band in `feature` array - :param swir1_idx: Index of SWIR1 band in `feature` array - """ - - def __init__( - self, - feature: SingleFeatureSpec, - feature_composite: SingleFeatureSpec, - blue_idx: int, - nir_idx: int, - swir1_idx: int, - interpolation: str = "lower", - ): - super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) - self.blue_idx = blue_idx - self.nir_idx = nir_idx - self.swir1_idx = swir1_idx - if not isinstance(blue_idx, int) or not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): - raise ValueError("Incorrect values for either blue, NIR or SWIR1 band indices specified") - - def _get_reference_band(self, data: np.ndarray) -> np.ndarray: - """Extract the max-ratio band from time-series - - The max-ratio is defined as max(NIR,SWIR1)/BLUE - - :param data: 4D array from which to compute the max-ratio reference band - :return: 3D array containing the max-ratio reference band - """ - blue = data[..., self.blue_idx].astype("float32") - nir = data[..., self.nir_idx].astype("float32") - swir1 = data[..., self.swir1_idx].astype("float32") - return np.nanmax(np.array([nir, swir1]), axis=0) / blue - - -class HistogramMatchingTask(EOTask): - """Histogram match of each band of each scene within a time-series with respect to the corresponding band of a - reference composite. - - Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 - - :param feature: Name of the eopatch data layer that will undergo a histogram match. - Should be of the FeatureType "DATA". - :param reference: Name of the eopatch data layer that represents the reference for the histogram match. - Should be of the FeatureType "DATA_TIMELESS". - """ - - def __init__(self, feature: SingleFeatureSpec, reference: SingleFeatureSpec): - self.renamed_feature = parse_renamed_feature(feature) - self.reference = self.parse_feature(reference) - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Perform histogram matching of the time-series with respect to a reference scene - - :param eopatch: eopatch holding the time-series and reference data - :return: The same eopatch instance with the normalised time-series - """ - feature_type, feature_name, new_feature_name = self.renamed_feature - reference_type, reference_name = self.reference - - reference_scene = eopatch[reference_type, reference_name] - # check if band dimension matches - if reference_scene.shape[-1] != eopatch[feature_type, feature_name].shape[-1]: - raise ValueError("Time-series and reference scene must have corresponding bands") - - eopatch[feature_type, new_feature_name] = np.zeros_like(eopatch[feature_type, feature_name]) - for source_id, source in enumerate(eopatch[feature_type, feature_name]): - # mask-out same invalid pixels - src_masked = np.where(np.isnan(reference_scene), np.nan, source) - ref_masked = np.where(np.isnan(source), np.nan, reference_scene) - # compute statistics - std_ref = np.nanstd(ref_masked, axis=(0, 1), dtype=np.float64) - std_src = np.nanstd(src_masked, axis=(0, 1), dtype=np.float64) - mean_ref = np.nanmean(ref_masked, axis=(0, 1), dtype=np.float64) - mean_src = np.nanmean(src_masked, axis=(0, 1), dtype=np.float64) - # normalise values - eopatch[feature_type, new_feature_name][source_id] = source * (std_ref / std_src) + ( - mean_ref - (mean_src * (std_ref / std_src)) - ) - - return eopatch diff --git a/features/eolearn/features/temporal_features.py b/features/eolearn/features/temporal_features.py deleted file mode 100644 index 1ebdc8fc1..000000000 --- a/features/eolearn/features/temporal_features.py +++ /dev/null @@ -1,244 +0,0 @@ -""" -Module handling processing of temporal features - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import itertools as it -from typing import List, Optional - -import numpy as np - -from eolearn.core import EOPatch, EOTask - - -class AddSpatioTemporalFeaturesTask(EOTask): - """Task that implements and adds to eopatch the spatio-temporal features proposed in [1]. - - This task assumes that the argmax/argmin of NDVI, NDVI slope and B4 are present in eopatch. The computed - spatio-temporal features correspond to the concatenation of reflectance (green, red, near-infrared and short-wave - infrared in [1]) values taken at dates where: - - 1) NDVI is maximum - 2) NDVI is minimum - 3) red reflectances are maximum - 4) NDVI slope is maximum - 5) NDVI slope is minimum - - The features are added to the `data_timeless` attribute dictionary of eopatch. - - [1] Waldner et al. "Automated annual cropland mapping using knowledge-based temporal features", ISPRS Journal of - Photogrammetry and Remote Sensing, 2015 - - """ - - def __init__( - self, - argmax_ndvi: str = "ARGMAX_NDVI", - argmin_ndvi: str = "ARGMIN_NDVI", - argmax_red: str = "ARGMAX_B4", - argmax_ndvi_slope: str = "ARGMAX_NDVI_SLOPE", - argmin_ndvi_slope: str = "ARGMIN_NDVI_SLOPE", - feats_feature: str = "STF", - data_feature: str = "BANDS-S2-L1C", - indices: Optional[List[int]] = None, - ): - """Class constructor - - Initialisation of task variables. The name of the dictionary keys that will be used for the computation of the - features needs to be specified. These features are assumed to be existing in the eopatch. The indices of the - reflectances to be used as features is an input parameter. If `None` is used, the data attribute is supposed to - have 13 bands and indices for green/red/infrared/short-wave-infrared are used. - - :param argmax_ndvi: Name of `argmax_ndvi` feature in eopatch. Default is `'ARGMAX_NDVI'` - :param argmin_ndvi: Name of `argmin_ndvi` feature in eopatch. Default is `'ARGMIN_NDVI'` - :param argmax_red: Name of `argmax_red` feature in eopatch. Default is `'ARGMAX_B4'` - :param argmax_ndvi_slope: Name of `argmax_ndvi_slope` feature in eopatch. Default is `'ARGMAX_NDVI_SLOPE'` - :param argmin_ndvi_slope: Name of `argmin_ndvi_slope` feature in eopatch. Default is `'ARGMIN_NDVI_SLOPE'` - :param feats_feature: Name of feature containing spatio-temporal features. Default is `'STF'` - :param data_feature: Name of feature containing the reflectances to be used as features. Default is - `'BANDS-S2-L1C'` - :param indices: List of indices from `data_feature` to be used as features. Default is `None`, corresponding to - [2, 3, 7, 11] indices - """ - self.argmax_ndvi = argmax_ndvi - self.argmin_ndvi = argmin_ndvi - self.argmax_red = argmax_red - self.argmax_ndvi_slope = argmax_ndvi_slope - self.argmin_ndvi_slope = argmin_ndvi_slope - self.feats_feature = feats_feature - self.data_feature = data_feature - if indices is None: - indices = [2, 3, 7, 11] - self.indices = indices - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Compute spatio-temporal features for input eopatch - - :param eopatch: Input eopatch - :return: eopatch with computed spatio-temporal features - """ - # pylint: disable=invalid-name - amax_ndvi, amin_ndvi = eopatch.data_timeless[self.argmax_ndvi], eopatch.data_timeless[self.argmin_ndvi] - amax_ndvi_slope = eopatch.data_timeless[self.argmax_ndvi_slope] - amin_ndvi_slope = eopatch.data_timeless[self.argmin_ndvi_slope] - amax_red = eopatch.data_timeless[self.argmax_red] - - stf_idx = [amax_ndvi, amin_ndvi, amax_ndvi_slope, amin_ndvi_slope, amax_red] - - bands = eopatch.data[self.data_feature][..., self.indices] - - _, h, w, _ = bands.shape - hh, ww = np.ogrid[:h, :w] - stf = np.concatenate([bands[ii.squeeze(), hh, ww] for ii in stf_idx if ii is not None], axis=-1) - - eopatch.data_timeless[self.feats_feature] = stf - - return eopatch - - -class AddMaxMinTemporalIndicesTask(EOTask): - """Task to compute temporal indices of the maximum and minimum of a data feature - - This class computes the `argmax` and `argmin` of a data feature in the input eopatch (e.g. NDVI, B4). The data - can be masked out by setting the `mask_data` flag to `True`. In that case, the `'VALID_DATA'` mask feature is - used for masking. If `mask_data` is `False`, the data is masked using the `'IS_DATA'` feature. - - Two new features are added to the `data_timeless` attribute. - """ - - def __init__( - self, - data_feature: str = "NDVI", - data_index: Optional[int] = None, - amax_data_feature: str = "ARGMAX_NDVI", - amin_data_feature: str = "ARGMIN_NDVI", - mask_data: bool = True, - ): - """Task constructor - - :param data_feature: Name of the feature in data used for computation of max/min. Default is `'NDVI'` - :param data_index: Index of to be extracted from last dimension in `data_feature`. If None, last dimension of - data array is assumed ot be of size 1 (e.g. as in NDVI). Default is `None` - :param amax_data_feature: Name of feature to be associated to computed feature of argmax values - :param amin_data_feature: Name of feature to be associated to computed feature of argmin values - :param mask_data: Flag specifying whether to mask data with `'VALID_DATA'` mask. If `False`, the `'IS_DATA'` - mask is used - """ - self.data_feature = data_feature - self.data_index = data_index - self.mask_data = mask_data - self.amax_feature = amax_data_feature - self.amin_feature = amin_data_feature - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Compute argmax/argmin of specified `data_feature` and `data_index` - - :param eopatch: Input eopatch - :return: eopatch with added argmax/argmin features - """ - valid_data_mask = eopatch.mask["VALID_DATA"] if self.mask_data else eopatch.mask["IS_DATA"] - - data = ( - eopatch.data[self.data_feature] - if self.data_index is None - else eopatch.data[self.data_feature][..., self.data_index][..., np.newaxis] - ) - - madata = np.ma.array(data, dtype=np.float32, mask=np.logical_or(~valid_data_mask.astype(bool), np.isnan(data))) - - argmax_data = np.ma.MaskedArray.argmax(madata, axis=0) - argmin_data = np.ma.MaskedArray.argmin(madata, axis=0) - - if argmax_data.ndim == 2: - argmax_data = argmax_data.reshape((*argmax_data.shape, 1)) - - if argmin_data.ndim == 2: - argmin_data = argmin_data.reshape((*argmin_data.shape, 1)) - - eopatch.data_timeless[self.amax_feature] = argmax_data - eopatch.data_timeless[self.amin_feature] = argmin_data - - return eopatch - - -class AddMaxMinNDVISlopeIndicesTask(EOTask): - """Task to compute the argmax and argmin of the NDVI slope - - This task computes the slope of the NDVI feature using central differences. The NDVI feature can be masked using the - `'VALID_DATA'` mask. Current implementation loops through every location of eopatch, and is therefore slow. - - The NDVI slope at date t is computed as $(NDVI_{t+1}-NDVI_{t-1})/(date_{t+1}-date_{t-1})$. - """ - - def __init__( - self, - data_feature: str = "NDVI", - argmax_feature: str = "ARGMAX_NDVI_SLOPE", - argmin_feature: str = "ARGMIN_NDVI_SLOPE", - mask_data: bool = True, - ): - """Task constructor - - :param data_feature: Name of data feature with NDVI values. Default is `'NDVI'` - :param argmax_feature: Name of feature with computed argmax values of the NDVI slope - :param argmin_feature: Name of feature with computed argmin values of the NDVI slope - :param mask_data: Flag for masking NDVI data. Default is `True` - """ - self.data_feature = data_feature - self.argmax_feature = argmax_feature - self.argmin_feature = argmin_feature - self.mask_data = mask_data - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Computation of NDVI slope using finite central differences - - This implementation loops through every spatial location, considers the valid NDVI values and approximates their - first order derivative using central differences. The argument of min and max is added to the eopatch. - - The NDVI slope at date t is computed as $(NDVI_{t+1}-NDVI_{t-1})/(date_{t+1}-date_{t-1})$. - - :param eopatch: Input eopatch - :return: eopatch with NDVI slope argmin/argmax features - """ - # pylint: disable=invalid-name - if self.mask_data: - valid_data_mask = eopatch.mask["VALID_DATA"] - else: - valid_data_mask = eopatch.mask["IS_DATA"] - - ndvi = np.ma.array(eopatch.data[self.data_feature], dtype=np.float32, mask=~valid_data_mask.astype(bool)) - - all_dates = np.asarray([x.toordinal() for x in eopatch.timestamps]) - - if ndvi.ndim == 4: - h, w = ndvi.shape[1:3] - else: - raise ValueError(f"{self.data_feature} feature has incorrect number of dimensions") - - argmax_ndvi_slope, argmin_ndvi_slope = np.zeros((h, w, 1), dtype=np.uint8), np.zeros((h, w, 1), dtype=np.uint8) - - for ih, iw in it.product(range(h), range(w)): - ndvi_curve = ndvi[:, ih, iw, :] - valid_idx = np.where(~ndvi.mask[:, ih, iw])[0] - - ndvi_curve = ndvi_curve[valid_idx] - valid_dates = all_dates[valid_idx] - - ndvi_slope = np.convolve(ndvi_curve.squeeze(), [1, 0, -1], "valid") / np.convolve( - valid_dates, [1, 0, -1], "valid" - ) - - # +1 to compensate for the 'valid' convolution which eliminates first and last - argmax_ndvi_slope[ih, iw] = valid_idx[np.argmax(ndvi_slope) + 1] - argmin_ndvi_slope[ih, iw] = valid_idx[np.argmin(ndvi_slope) + 1] - - del ndvi_curve, valid_idx, valid_dates, ndvi_slope - - eopatch.data_timeless[self.argmax_feature] = argmax_ndvi_slope - eopatch.data_timeless[self.argmin_feature] = argmin_ndvi_slope - - return eopatch diff --git a/features/eolearn/tests/test_blob.py b/features/eolearn/tests/test_blob.py deleted file mode 100644 index 062e18c37..000000000 --- a/features/eolearn/tests/test_blob.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Module for computing blobs in EOPatch - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import copy - -import pytest -from skimage.feature import blob_dog, blob_doh, blob_log - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.features import BlobTask - -FEATURE = (FeatureType.DATA, "NDVI", "blob") -BLOB_FEATURE = (FeatureType.DATA, "blob") - - -BLOB_TESTS = [ - ( - BlobTask(FEATURE, blob_dog, threshold=0, max_sigma=30), - {"exp_min": 0.0, "exp_max": 37.9625, "exp_mean": 0.08545, "exp_median": 0.0}, - ), - ( - BlobTask(FEATURE, blob_doh, num_sigma=5, threshold=0), - {"exp_min": 0.0, "exp_max": 21.9203, "exp_mean": 0.05807, "exp_median": 0.0}, - ), - ( - BlobTask(FEATURE, blob_log, log_scale=True, threshold=0, max_sigma=30), - {"exp_min": 0, "exp_max": 42.4264, "exp_mean": 0.09767, "exp_median": 0.0}, - ), -] - - -@pytest.mark.parametrize(("task", "expected_statistics"), BLOB_TESTS) -def test_blob_task(small_ndvi_eopatch, task, expected_statistics): - eopatch = copy.deepcopy(small_ndvi_eopatch) - task.execute(eopatch) - - assert_statistics_match(eopatch[BLOB_FEATURE], exp_shape=(10, 20, 20, 1), **expected_statistics, abs_delta=1e-4) - - del eopatch[BLOB_FEATURE] - assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected." diff --git a/features/eolearn/tests/test_doubly_logistic_approximation.py b/features/eolearn/tests/test_doubly_logistic_approximation.py deleted file mode 100644 index 9f3f3bc4d..000000000 --- a/features/eolearn/tests/test_doubly_logistic_approximation.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import numpy as np -import pytest - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.features.doubly_logistic_approximation import DoublyLogisticApproximationTask - - -def test_double_logistic_approximation(example_eopatch): - data = example_eopatch.data["NDVI"] - timestamps = example_eopatch.timestamps - mask = example_eopatch.mask["IS_VALID"] - indices = list(np.nonzero([t.year == 2016 for t in timestamps])[0]) - start, stop = indices[0], indices[-1] + 2 - - eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857))) - eopatch.timestamps = timestamps[start:stop] - eopatch.data["TEST"] = np.reshape(data[start:stop, 0, 0, 0], (-1, 1, 1, 1)) - eopatch.mask["IS_VALID"] = np.reshape(mask[start:stop, 0, 0, 0], (-1, 1, 1, 1)) - eopatch = DoublyLogisticApproximationTask( - feature=(FeatureType.DATA, "TEST"), - valid_mask=(FeatureType.MASK, "IS_VALID"), - new_feature=(FeatureType.DATA_TIMELESS, "TEST_OUT"), - ).execute(eopatch) - - names = "c1", "c2", "a1", "a2", "a3", "a4", "a5" - values = eopatch.data_timeless["TEST_OUT"].squeeze() - expected_values = 0.207, 0.464, 0.686, 0.222, 1.204, 0.406, 15.701 - delta = 0.1 - - for name, value, expected_value in zip(names, values, expected_values): - assert value == pytest.approx(expected_value, abs=delta), f"Missmatch in value of {name}" diff --git a/features/eolearn/tests/test_feature_manipulation.py b/features/eolearn/tests/test_feature_manipulation.py deleted file mode 100644 index 674684ce2..000000000 --- a/features/eolearn/tests/test_feature_manipulation.py +++ /dev/null @@ -1,275 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import datetime -from typing import Tuple - -import numpy as np -import pytest -from numpy.testing import assert_allclose, assert_array_equal - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.features import FilterTimeSeriesTask, LinearFunctionTask, SimpleFilterTask, ValueFilloutTask -from eolearn.features.feature_manipulation import SpatialResizeTask -from eolearn.features.utils import ResizeParam - -DUMMY_BBOX = BBox((0, 0, 1, 1), CRS(3857)) -# ruff: noqa: NPY002 - - -@pytest.mark.parametrize( - "feature", [(FeatureType.DATA, "BANDS-S2-L1C"), FeatureType.TIMESTAMPS, (FeatureType.LABEL, "IS_CLOUDLESS")] -) -def test_simple_filter_task_filter_all(example_eopatch: EOPatch, feature): - filter_all_task = SimpleFilterTask(feature, filter_func=lambda _: False) - filtered_eopatch = filter_all_task.execute(example_eopatch) - - assert filtered_eopatch is not example_eopatch - assert filtered_eopatch.data["CLP"].shape == (0, 101, 100, 1) - assert filtered_eopatch.scalar["CLOUD_COVERAGE"].shape == (0, 1) - assert len(filtered_eopatch.vector["CLM_VECTOR"]) == 0 - assert np.array_equal(filtered_eopatch.mask_timeless["LULC"], example_eopatch.mask_timeless["LULC"]) - assert filtered_eopatch.timestamps == [] - - -@pytest.mark.parametrize( - "feature", [(FeatureType.MASK, "CLM"), FeatureType.TIMESTAMPS, (FeatureType.SCALAR, "CLOUD_COVERAGE")] -) -def test_simple_filter_task_filter_nothing(example_eopatch: EOPatch, feature): - del example_eopatch.data["REFERENCE_SCENES"] # Wrong size of time dimension - - filter_all_task = SimpleFilterTask(feature, filter_func=lambda _: True) - filtered_eopatch = filter_all_task.execute(example_eopatch) - - assert filtered_eopatch is not example_eopatch - assert filtered_eopatch == example_eopatch - - -@pytest.mark.parametrize( - "invalid_feature", - [(FeatureType.VECTOR, "foo"), (FeatureType.VECTOR_TIMELESS, "bar"), (FeatureType.MASK_TIMELESS, "foobar")], -) -def test_simple_filter_invalid_feature(invalid_feature: Tuple[FeatureType, str]): - with pytest.raises(ValueError): - SimpleFilterTask(invalid_feature, filter_func=lambda _: True) - - -def test_content_after_time_filter(): - timestamps = [ - datetime.datetime(2017, 1, 1, 10, 4, 7), - datetime.datetime(2017, 1, 4, 10, 14, 5), - datetime.datetime(2017, 1, 11, 10, 3, 51), - datetime.datetime(2017, 1, 14, 10, 13, 46), - datetime.datetime(2017, 1, 24, 10, 14, 7), - datetime.datetime(2017, 2, 10, 10, 1, 32), - datetime.datetime(2017, 2, 20, 10, 6, 35), - datetime.datetime(2017, 3, 2, 10, 0, 20), - datetime.datetime(2017, 3, 12, 10, 7, 6), - datetime.datetime(2017, 3, 15, 10, 12, 14), - ] - data = np.random.rand(10, 100, 100, 3) - - new_start, new_end = 4, -3 - - eop = EOPatch(bbox=DUMMY_BBOX, timestamps=timestamps, data={"data": data}) - - filter_task = FilterTimeSeriesTask(start_date=timestamps[new_start], end_date=timestamps[new_end]) - filtered_eop = filter_task.execute(eop) - - assert filtered_eop is not eop - assert filtered_eop.timestamps == timestamps[new_start : new_end + 1] - assert np.array_equal(filtered_eop.data["data"], data[new_start : new_end + 1, ...]) - - -def test_fill(): - array = np.array( - [ - [np.NaN] * 4 + [1.0, 2.0, 3.0, 4.0] + [np.NaN] * 3, - [1.0, np.NaN, np.NaN, 2.0, 3.0, np.NaN, np.NaN, np.NaN, 4.0, np.NaN, 5.0], - ] - ) - - array_ffill = ValueFilloutTask.fill(array, operation="f") - array_bfill = ValueFilloutTask.fill(array, operation="b") - array_fbfill = ValueFilloutTask.fill(array_ffill, operation="b") - - assert_allclose( - array_ffill, - np.array( - [ - [np.NaN] * 4 + [1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0], - [1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0], - ] - ), - equal_nan=True, - ) - - assert_allclose( - array_bfill, - np.array( - [ - [1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0] + [np.NaN] * 3, - [1.0, 2.0, 2.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0], - ] - ), - equal_nan=True, - ) - - assert_allclose( - array_fbfill, - np.array( - [ - [1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0], - [1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0], - ] - ), - equal_nan=True, - ) - - -@pytest.mark.parametrize( - ("input_feature", "operation"), - [((FeatureType.DATA, "TEST"), "x"), ((FeatureType.DATA, "TEST"), 4), (None, "f"), (np.zeros((4, 5)), "x")], -) -def test_bad_input(input_feature, operation): - with pytest.raises(ValueError): - ValueFilloutTask(input_feature, operations=operation) - - -def test_value_fillout(): - feature = (FeatureType.DATA, "TEST") - shape = (8, 10, 10, 5) - data = np.random.randint(0, 100, size=shape).astype(float) - eopatch = EOPatch(bbox=DUMMY_BBOX, data={"TEST": data}) - - # nothing to be filled, return the same eopatch object immediately - eopatch_new = ValueFilloutTask(feature, operations="fb", axis=0)(eopatch) - assert eopatch == eopatch_new - - eopatch[feature][0, 0, 0, :] = np.nan - - def execute_fillout(eopatch, feature, **kwargs): - input_array = eopatch[feature] - eopatch = ValueFilloutTask(feature, **kwargs)(eopatch) - output_array = eopatch[feature] - return eopatch, input_array, output_array - - # filling forward temporally should not fill nans - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="f", axis=0) - compare_mask = ~np.isnan(input_array) - assert np.isnan(output_array[0, 0, 0, :]).all() - assert_array_equal(input_array[compare_mask], output_array[compare_mask]) - assert id(input_array) != id(output_array) - - # filling in any direction along axis=-1 should also not fill nans since all neighbors are nans - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="fb", axis=-1) - assert np.isnan(output_array[0, 0, 0, :]).all() - assert id(input_array) != id(output_array) - - # filling nans backwards temporally should fill nans - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="b", axis=0) - assert not np.isnan(output_array).any() - assert id(input_array) != id(output_array) - - # try filling something else than nan (e.g.: -1) - eopatch[feature][0, :, 0, 0] = -1 - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="b", value=-1, axis=-1) - assert not (output_array == -1).any() - assert id(input_array) != id(output_array) - - # [nan, 1, nan, 2, ... ] ---('fb')---> [1, 1, 1, 2, ... ] - eopatch[feature][0, 0, 0, 0:4] = [np.nan, 1, np.nan, 2] - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="fb", axis=-1) - assert_array_equal(output_array[0, 0, 0, 0:4], [1, 1, 1, 2]) - assert id(input_array) != id(output_array) - - # [nan, 1, nan, 2, ... ] ---('bf')---> [1, 1, 2, 2, ... ] - eopatch[feature][0, 0, 0, 0:4] = [np.nan, 1, np.nan, 2] - eopatch, input_array, output_array = execute_fillout(eopatch, feature, operations="bf", axis=-1) - assert_array_equal(output_array[0, 0, 0, 0:4], [1, 1, 2, 2]) - assert id(input_array) != id(output_array) - - -def test_linear_function_task(): - eopatch = EOPatch(bbox=DUMMY_BBOX) - - data_feature = (FeatureType.DATA, "DATA_TEST") - data_result_feature = (FeatureType.DATA, "DATA_TRANSFORMED") - data_shape = (8, 10, 10, 5) - eopatch[data_feature] = np.arange(np.prod(data_shape)).reshape(data_shape).astype(np.float32) - - mask_timeless_feature = (FeatureType.MASK_TIMELESS, "MASK_TIMELESS_TEST") - mask_timeless_result_feature = (FeatureType.MASK_TIMELESS, "MASK_TIMELESS_TRANSFORMED") - mask_shape = (10, 10, 1) - eopatch[mask_timeless_feature] = np.ones(mask_shape, dtype=np.uint32) - - task_default = LinearFunctionTask(data_feature, data_result_feature) - task_default(eopatch) - assert np.array_equal(eopatch[data_feature], eopatch[data_result_feature]) - - task_double_minus_five = LinearFunctionTask( - [data_feature, mask_timeless_feature], - [data_result_feature, mask_timeless_result_feature], - slope=2, - intercept=-5, - ) - task_double_minus_five(eopatch) - expected_result = np.arange(np.prod(data_shape)).reshape(data_shape).astype(float) * 2 - 5 - assert np.array_equal(eopatch[data_result_feature], expected_result) - assert np.array_equal(eopatch[mask_timeless_result_feature], np.ones(mask_shape) * 2 - 5) - assert eopatch[data_result_feature].dtype == np.float32 - # The value of the mask timeless changes here - - task_change_dtype = LinearFunctionTask( - mask_timeless_feature, mask_timeless_result_feature, slope=256, dtype=np.uint8 - ) - task_change_dtype(eopatch) - assert np.array_equal(eopatch[mask_timeless_result_feature], np.zeros(mask_shape)) - assert eopatch[mask_timeless_result_feature].dtype == np.uint8 - - task_override = LinearFunctionTask( - [data_feature, mask_timeless_feature], - slope=5, - ) - task_override(eopatch) - assert np.array_equal(eopatch[mask_timeless_feature], np.ones(mask_shape) * 5) - - -@pytest.mark.parametrize( - ("resize_type", "height_param", "width_param", "features_call", "features_check", "outputs"), - [ - (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP"), ("data", "CLP"), (68, 50, 70, 1)), - (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP"), ("mask", "CLM"), (68, 101, 100, 1)), - (ResizeParam.NEW_SIZE, 50, 70, ..., ("data", "CLP"), (68, 50, 70, 1)), - (ResizeParam.NEW_SIZE, 50, 70, ..., ("mask", "CLM"), (68, 50, 70, 1)), - (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP", "CLP_small"), ("data", "CLP_small"), (68, 50, 70, 1)), - (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP", "CLP_small"), ("data", "CLP"), (68, 101, 100, 1)), - (ResizeParam.SCALE_FACTORS, 2, 2, ("data", "CLP"), ("data", "CLP"), (68, 202, 200, 1)), - (ResizeParam.SCALE_FACTORS, 0.5, 2, ("data", "CLP"), ("data", "CLP"), (68, 50, 200, 1)), - (ResizeParam.SCALE_FACTORS, 0.1, 0.1, ("data", "CLP"), ("data", "CLP"), (68, 10, 10, 1)), - (ResizeParam.RESOLUTION, 5, 5, ("data", "CLP"), ("data", "CLP"), (68, 202, 200, 1)), - (ResizeParam.RESOLUTION, 20, 20, ("data", "CLP"), ("data", "CLP"), (68, 50, 50, 1)), - (ResizeParam.RESOLUTION, 5, 20, ("data", "CLP"), ("data", "CLP"), (68, 202, 50, 1)), - ], -) -@pytest.mark.filterwarnings("ignore::RuntimeWarning") -def test_spatial_resize_task( - example_eopatch, resize_type, height_param, width_param, features_call, features_check, outputs -): - # Warnings occur due to lossy casting in the downsampling procedure - - resize = SpatialResizeTask( - resize_type=resize_type, height_param=height_param, width_param=width_param, features=features_call - ) - assert resize(example_eopatch)[features_check].shape == outputs - - -def test_spatial_resize_task_exception(): - with pytest.raises(ValueError): - SpatialResizeTask(features=("mask", "CLM"), resize_type="blabla", height_param=20, width_param=20) diff --git a/features/eolearn/tests/test_haralick.py b/features/eolearn/tests/test_haralick.py deleted file mode 100644 index 15e0bcec3..000000000 --- a/features/eolearn/tests/test_haralick.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import copy - -import numpy as np -import pytest - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.features import HaralickTask - -FEATURE = (FeatureType.DATA, "NDVI", "haralick") -OUTPUT_FEATURE = (FeatureType.DATA, "haralick") - - -@pytest.mark.parametrize( - ("task", "expected_statistics"), - [ - ( - HaralickTask(FEATURE, texture_feature="contrast", angle=0, levels=255, window_size=3), - {"exp_min": 3.5, "exp_max": 9079.0, "exp_mean": 965.8295, "exp_median": 628.5833}, - ), - ( - HaralickTask(FEATURE, texture_feature="sum_of_square_variance", angle=np.pi / 2, levels=8, window_size=5), - {"exp_min": 0.96899, "exp_max": 48.7815, "exp_mean": 23.0229, "exp_median": 23.8987}, - ), - ( - HaralickTask(FEATURE, texture_feature="sum_entropy", angle=-np.pi / 2, levels=8, window_size=7), - {"exp_min": 0, "exp_max": 1.7463, "exp_mean": 0.5657, "exp_median": 0.50558}, - ), - ( - HaralickTask(FEATURE, texture_feature="difference_variance", angle=-np.pi / 2, levels=8, window_size=7), - {"exp_min": 42, "exp_max": 110.6122, "exp_mean": 53.857082, "exp_median": 50}, - ), - ], -) -def test_haralick(small_ndvi_eopatch, task, expected_statistics): - eopatch = copy.deepcopy(small_ndvi_eopatch) - task.execute(eopatch) - - assert_statistics_match(eopatch[OUTPUT_FEATURE], exp_shape=(10, 20, 20, 1), **expected_statistics, abs_delta=1e-4) - - del eopatch[OUTPUT_FEATURE] - assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected." diff --git a/features/eolearn/tests/test_hog.py b/features/eolearn/tests/test_hog.py deleted file mode 100644 index 3ea39aff9..000000000 --- a/features/eolearn/tests/test_hog.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import copy - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.features import HOGTask - - -def test_hog(small_ndvi_eopatch): - task = HOGTask( - (FeatureType.DATA, "NDVI", "hog"), - orientations=9, - pixels_per_cell=(2, 2), - cells_per_block=(2, 2), - visualize=True, - visualize_feature_name="hog_visu", - ) - - eopatch = copy.deepcopy(small_ndvi_eopatch) - task.execute(eopatch) - - for feature_name, expected_statistics in [ - ("hog", {"exp_min": 0.0, "exp_max": 0.5567, "exp_mean": 0.09309, "exp_median": 0.0}), - ("hog_visu", {"exp_min": 0.0, "exp_max": 0.3241, "exp_mean": 0.010537, "exp_median": 0.0}), - ]: - assert_statistics_match(eopatch.data[feature_name], **expected_statistics, abs_delta=1e-4) - - del eopatch[(FeatureType.DATA, "hog")] - del eopatch[(FeatureType.DATA, "hog_visu")] - assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected." diff --git a/features/eolearn/tests/test_local_binary_pattern.py b/features/eolearn/tests/test_local_binary_pattern.py deleted file mode 100644 index 876e48349..000000000 --- a/features/eolearn/tests/test_local_binary_pattern.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import copy - -import pytest - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.features import LocalBinaryPatternTask - -LBP_FEATURE = (FeatureType.DATA, "NDVI", "lbp") -OUTPUT_FEATURE = (FeatureType.DATA, "lbp") - - -@pytest.mark.parametrize( - ("task", "expected_statistics"), - [ - ( - LocalBinaryPatternTask(LBP_FEATURE, nb_points=24, radius=3), - {"exp_min": 0.0, "exp_max": 25.0, "exp_mean": 15.8313, "exp_median": 21.0}, - ), - ], -) -def test_local_binary_pattern(small_ndvi_eopatch, task, expected_statistics): - eopatch = copy.deepcopy(small_ndvi_eopatch) - task.execute(eopatch) - - assert_statistics_match(eopatch[OUTPUT_FEATURE], exp_shape=(10, 20, 20, 1), **expected_statistics, abs_delta=1e-4) - - del eopatch[OUTPUT_FEATURE] - assert small_ndvi_eopatch == eopatch, "Other features of the EOPatch were affected." diff --git a/features/eolearn/tests/test_radiometric_normalization.py b/features/eolearn/tests/test_radiometric_normalization.py deleted file mode 100644 index 1b7e03cf1..000000000 --- a/features/eolearn/tests/test_radiometric_normalization.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import copy -from datetime import datetime - -import numpy as np -import pytest - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.features import ( - BlueCompositingTask, - HistogramMatchingTask, - HOTCompositingTask, - MaxNDVICompositingTask, - MaxNDWICompositingTask, - MaxRatioCompositingTask, - ReferenceScenesTask, -) -from eolearn.mask import MaskFeatureTask - -# ruff: noqa: NPY002 - - -@pytest.fixture(name="eopatch") -def eopatch_fixture(example_eopatch): - np.random.seed(0) - example_eopatch.mask["SCL"] = np.random.randint(0, 11, example_eopatch.data["BANDS-S2-L1C"].shape, np.uint8) - blue = BlueCompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "REFERENCE_COMPOSITE"), - blue_idx=0, - interpolation="geoville", - ) - blue.execute(example_eopatch) - return example_eopatch - - -DATA_TEST_FEATURE = FeatureType.DATA, "TEST" -DATA_TIMELESS_TEST_FEATURE = FeatureType.DATA_TIMELESS, "TEST" - - -@pytest.mark.parametrize( - ("task", "test_feature", "expected_statistics"), - [ - ( - MaskFeatureTask( - (FeatureType.DATA, "BANDS-S2-L1C", "TEST"), - (FeatureType.MASK, "SCL"), - mask_values=[0, 1, 2, 3, 8, 9, 10, 11], - ), - DATA_TEST_FEATURE, - {"exp_min": 0.0002, "exp_max": 1.4244, "exp_mean": 0.21167801, "exp_median": 0.1422}, - ), - ( - ReferenceScenesTask( - (FeatureType.DATA, "BANDS-S2-L1C", "TEST"), (FeatureType.SCALAR, "CLOUD_COVERAGE"), max_scene_number=5 - ), - DATA_TEST_FEATURE, - {"exp_min": 0.0005, "exp_max": 0.5318, "exp_mean": 0.16823094, "exp_median": 0.1404}, - ), - ( - BlueCompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "TEST"), - blue_idx=0, - interpolation="geoville", - ), - DATA_TIMELESS_TEST_FEATURE, - {"exp_min": 0.0005, "exp_max": 0.5075, "exp_mean": 0.11658352, "exp_median": 0.0833}, - ), - ( - HOTCompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "TEST"), - blue_idx=0, - red_idx=2, - interpolation="geoville", - ), - DATA_TIMELESS_TEST_FEATURE, - {"exp_min": 0.0005, "exp_max": 0.5075, "exp_mean": 0.117758796, "exp_median": 0.0846}, - ), - ( - MaxNDVICompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "TEST"), - red_idx=2, - nir_idx=7, - interpolation="geoville", - ), - DATA_TIMELESS_TEST_FEATURE, - {"exp_min": 0.0005, "exp_max": 0.5075, "exp_mean": 0.13430128, "exp_median": 0.0941}, - ), - ( - MaxNDWICompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "TEST"), - nir_idx=6, - swir1_idx=8, - interpolation="geoville", - ), - DATA_TIMELESS_TEST_FEATURE, - {"exp_min": 0.0005, "exp_max": 0.5318, "exp_mean": 0.2580135, "exp_median": 0.2888}, - ), - ( - MaxRatioCompositingTask( - (FeatureType.DATA, "REFERENCE_SCENES"), - (FeatureType.DATA_TIMELESS, "TEST"), - blue_idx=0, - nir_idx=6, - swir1_idx=8, - interpolation="geoville", - ), - DATA_TIMELESS_TEST_FEATURE, - {"exp_min": 0.0006, "exp_max": 0.5075, "exp_mean": 0.13513365, "exp_median": 0.0958}, - ), - ( - HistogramMatchingTask( - (FeatureType.DATA, "BANDS-S2-L1C", "TEST"), (FeatureType.DATA_TIMELESS, "REFERENCE_COMPOSITE") - ), - DATA_TEST_FEATURE, - {"exp_min": -0.049050678, "exp_max": 0.68174845, "exp_mean": 0.1165936, "exp_median": 0.08370649}, - ), - ], -) -def test_radiometric_normalization(eopatch, task, test_feature, expected_statistics): - initial_patch = copy.deepcopy(eopatch) - eopatch = task.execute(eopatch) - - assert isinstance(eopatch.timestamps, list), "Expected a list of timestamps" - assert isinstance(eopatch.timestamps[0], datetime), "Expected timestamps of type datetime.datetime" - - assert_statistics_match(eopatch[test_feature], **expected_statistics, abs_delta=1e-3) - - del eopatch[test_feature] - assert initial_patch == eopatch, "Other features of the EOPatch were affected." diff --git a/features/eolearn/tests/test_temporal_features.py b/features/eolearn/tests/test_temporal_features.py deleted file mode 100644 index e103f1ab1..000000000 --- a/features/eolearn/tests/test_temporal_features.py +++ /dev/null @@ -1,138 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -from datetime import date, timedelta - -import numpy as np -from numpy.testing import assert_array_equal - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.features import AddMaxMinNDVISlopeIndicesTask, AddMaxMinTemporalIndicesTask, AddSpatioTemporalFeaturesTask - -DUMMY_BBOX = BBox((0, 0, 1, 1), CRS(3857)) - - -def test_temporal_indices(): - eopatch = EOPatch(bbox=DUMMY_BBOX) - t, h, w, c = 5, 3, 3, 2 - - ndvi_shape = (t, h, w, 1) - eopatch[FeatureType.DATA, "NDVI"] = np.arange(np.prod(ndvi_shape)).reshape(ndvi_shape) - - valid_data = np.ones(ndvi_shape, bool) - valid_data[0] = 0 - valid_data[-1] = 0 - eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, dtype=np.int16) - eopatch[FeatureType.MASK, "VALID_DATA"] = valid_data - - new_eopatch = AddMaxMinTemporalIndicesTask(mask_data=False)(eopatch) - - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"], np.zeros((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"], (t - 1) * np.ones((h, w, 1))) - - new_eopatch = AddMaxMinTemporalIndicesTask(mask_data=True)(eopatch) - - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"], np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"], (t - 2) * np.ones((h, w, 1))) - - bands_shape = (t, h, w, c) - eopatch[FeatureType.DATA, "BANDS"] = np.arange(np.prod(bands_shape)).reshape(bands_shape) - add_bands = AddMaxMinTemporalIndicesTask( - data_feature="BANDS", - data_index=1, - amax_data_feature="ARGMAX_B1", - amin_data_feature="ARGMIN_B1", - mask_data=False, - ) - new_eopatch = add_bands(eopatch) - - assert_array_equal(new_eopatch.data_timeless["ARGMIN_B1"], np.zeros((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_B1"], (t - 1) * np.ones((h, w, 1))) - - -def test_ndvi_slope_indices(): - timestamps = [date(2018, 3, 1) + timedelta(days=x) for x in range(11)] - eopatch = EOPatch(bbox=DUMMY_BBOX, timestamps=list(timestamps)) - - t, h, w = (10, 3, 3) - ndvi_shape = (t, h, w, 1) - xx = np.zeros(ndvi_shape, np.float32) - x = np.linspace(0, np.pi, t) - xx[:, :, :, :] = x[:, None, None, None] - - eopatch[FeatureType.DATA, "NDVI"] = np.sin(xx) - - valid_data = np.ones(ndvi_shape, np.uint8) - valid_data[1] = 0 - valid_data[-1] = 0 - valid_data[4] = 0 - - eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, bool) - eopatch[FeatureType.MASK, "VALID_DATA"] = valid_data - - add_ndvi_task = AddMaxMinTemporalIndicesTask(mask_data=False) - add_ndvi_slope_task = AddMaxMinNDVISlopeIndicesTask(mask_data=False) - - new_eopatch = add_ndvi_slope_task(add_ndvi_task(eopatch)) - - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"], (t - 1) * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"], (t // 2 - 1) * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI_SLOPE"], (t - 2) * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI_SLOPE"], np.ones((h, w, 1))) - - add_ndvi_task = AddMaxMinTemporalIndicesTask(mask_data=True) - add_ndvi_slope_task = AddMaxMinNDVISlopeIndicesTask(mask_data=True) - - new_eopatch = add_ndvi_slope_task(add_ndvi_task(eopatch)) - - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI"], 0 * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI"], (t // 2) * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMIN_NDVI_SLOPE"], (t - 3) * np.ones((h, w, 1))) - assert_array_equal(new_eopatch.data_timeless["ARGMAX_NDVI_SLOPE"], 2 * np.ones((h, w, 1))) - - -def test_stf_task(): - timestamps = [date(2018, 3, 1) + timedelta(days=x) for x in range(11)] - eopatch = EOPatch(bbox=DUMMY_BBOX, timestamps=list(timestamps)) - - t, h, w, c = 10, 3, 3, 2 - - # NDVI is a sinusoid where max slope is at index 1 and min slope at index 8 - ndvi_shape = (t, h, w, 1) - bands_shape = (t, h, w, c) - xx = np.zeros(ndvi_shape, np.float32) - x = np.linspace(0, np.pi, t) - xx[:, :, :, :] = x[:, None, None, None] - - bands = np.ones(bands_shape) * np.arange(t)[:, None, None, None] - - eopatch[FeatureType.DATA, "NDVI"] = np.sin(xx) - eopatch[FeatureType.DATA, "BANDS"] = bands - eopatch[FeatureType.MASK, "IS_DATA"] = np.ones(ndvi_shape, bool) - - add_ndvi = AddMaxMinTemporalIndicesTask(mask_data=False) - add_bands = AddMaxMinTemporalIndicesTask( - data_feature="BANDS", - data_index=1, - amax_data_feature="ARGMAX_B1", - amin_data_feature="ARGMIN_B1", - mask_data=False, - ) - add_ndvi_slope = AddMaxMinNDVISlopeIndicesTask(mask_data=False) - add_stf = AddSpatioTemporalFeaturesTask(argmax_red="ARGMAX_B1", data_feature="BANDS", indices=[0, 1]) - - new_eopatch = add_stf(add_ndvi_slope(add_bands(add_ndvi(eopatch)))) - result = new_eopatch.data_timeless["STF"] - - assert result.shape == (h, w, c * 5) - assert_array_equal(result[:, :, 0:c], 4 * np.ones((h, w, c))) - assert_array_equal(result[:, :, c : 2 * c], 9 * np.ones((h, w, c))) - assert_array_equal(result[:, :, 2 * c : 3 * c], np.ones((h, w, c))) - assert_array_equal(result[:, :, 3 * c : 4 * c], 8 * np.ones((h, w, c))) - assert_array_equal(result[:, :, 4 * c : 5 * c], 9 * np.ones((h, w, c))) diff --git a/features/requirements.txt b/features/requirements.txt index 1e4813f68..562050068 100644 --- a/features/requirements.txt +++ b/features/requirements.txt @@ -1,9 +1 @@ -eo-learn-core -numba>=0.53.0 -numpy -pillow>=9.1.0 -python-dateutil -scikit-image>=0.19.0 -scikit-learn -scipy -typing-extensions +eo-learn diff --git a/features/setup.py b/features/setup.py index 9c0117dac..d1e869c84 100644 --- a/features/setup.py +++ b/features/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "features", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_features", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,9 +45,7 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, install_requires=parse_requirements("requirements.txt"), - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/geometry/MANIFEST.in b/geometry/MANIFEST.in deleted file mode 100644 index b7faef195..000000000 --- a/geometry/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/geometry/py.typed -exclude eolearn/tests/* diff --git a/geometry/README.md b/geometry/README.md index eed6ec3cc..7dbf06e43 100644 --- a/geometry/README.md +++ b/geometry/README.md @@ -1,5 +1,5 @@ # eolearn.geometry subpackage -Geometry subpackage used for geometric transformation and conversion between vector and raster data. +The deprecated way to install `eolearn.geometry`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.geometry.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#geometry). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/geometry/deprecated_eolearn_geometry/__init__.py b/geometry/deprecated_eolearn_geometry/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/geometry/deprecated_eolearn_geometry/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/geometry/eolearn/__init__.py b/geometry/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/geometry/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/geometry/eolearn/geometry/py.typed b/geometry/eolearn/geometry/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/geometry/eolearn/geometry/superpixel.py b/geometry/eolearn/geometry/superpixel.py deleted file mode 100644 index f56e9390c..000000000 --- a/geometry/eolearn/geometry/superpixel.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Module for super-pixel segmentation - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import logging -import warnings -from typing import Any, Callable - -import numpy as np -import skimage.segmentation - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.exceptions import EORuntimeWarning -from eolearn.core.types import SingleFeatureSpec - -LOGGER = logging.getLogger(__name__) - - -class SuperpixelSegmentationTask(EOTask): - """Super-pixel segmentation task - - Given a raster feature it will segment data into super-pixels. Representation of super-pixels will be returned as - a mask timeless feature where all pixels with the same value belong to one super-pixel - """ - - def __init__( - self, - feature: SingleFeatureSpec, - superpixel_feature: SingleFeatureSpec, - *, - segmentation_object: Callable = skimage.segmentation.felzenszwalb, - **segmentation_params: Any, - ): - """ - :param feature: Raster feature which will be used in segmentation - :param superpixel_feature: A new mask timeless feature to hold super-pixel mask - :param segmentation_object: A function (object) which performs superpixel segmentation, by default that is - `skimage.segmentation.felzenszwalb` - :param segmentation_params: Additional parameters which will be passed to segmentation_object function - """ - self.feature = self.parse_feature(feature, allowed_feature_types=lambda fty: fty.is_spatial()) - self.superpixel_feature = self.parse_feature( - superpixel_feature, allowed_feature_types={FeatureType.MASK_TIMELESS} - ) - self.segmentation_object = segmentation_object - self.segmentation_params = segmentation_params - - def _create_superpixel_mask(self, data: np.ndarray) -> np.ndarray: - """Method which performs the segmentation""" - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", category=RuntimeWarning) - return self.segmentation_object(data, **self.segmentation_params) - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Main execute method""" - data = eopatch[self.feature] - - if np.isnan(data).any(): - warnings.warn( - "There are NaN values in given data, super-pixel segmentation might produce bad results", - EORuntimeWarning, - ) - - if self.feature[0].is_temporal(): - data = np.moveaxis(data, 0, 2) - data = data.reshape((data.shape[0], data.shape[1], data.shape[2] * data.shape[3])) - - superpixel_mask = np.atleast_3d(self._create_superpixel_mask(data)) - - eopatch[self.superpixel_feature] = superpixel_mask - - return eopatch - - -class FelzenszwalbSegmentationTask(SuperpixelSegmentationTask): - """Super-pixel segmentation which uses Felzenszwalb's method of segmentation - - Uses segmentation function documented at: - https://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.felzenszwalb - """ - - def __init__(self, feature: SingleFeatureSpec, superpixel_feature: SingleFeatureSpec, **kwargs: Any): - """Arguments are passed to `SuperpixelSegmentationTask` task""" - super().__init__(feature, superpixel_feature, segmentation_object=skimage.segmentation.felzenszwalb, **kwargs) - - -class SlicSegmentationTask(SuperpixelSegmentationTask): - """Super-pixel segmentation which uses SLIC method of segmentation - - Uses segmentation function documented at: - https://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.slic - """ - - def __init__(self, feature: SingleFeatureSpec, superpixel_feature: SingleFeatureSpec, **kwargs: Any): - """Arguments are passed to `SuperpixelSegmentationTask` task""" - super().__init__( - feature, superpixel_feature, segmentation_object=skimage.segmentation.slic, start_label=0, **kwargs - ) - - def _create_superpixel_mask(self, data: np.ndarray) -> np.ndarray: - """Method which performs the segmentation""" - if np.issubdtype(data.dtype, np.floating) and data.dtype != np.float64: - data = data.astype(np.float64) - return super()._create_superpixel_mask(data) - - -class MarkSegmentationBoundariesTask(EOTask): - """Takes super-pixel segmentation mask and creates a new mask where boundaries of super-pixels are marked - - The result is a binary mask with values 0 and 1 and dtype `numpy.uint8` - - Uses `mark_boundaries` function documented at: - https://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.mark_boundaries - """ - - def __init__(self, feature: SingleFeatureSpec, new_feature: SingleFeatureSpec, **params: Any): - """ - :param feature: Input feature - super-pixel mask - :param new_feature: Output feature - a new feature where new mask with boundaries will be put - :param params: Additional parameters which will be passed to `mark_boundaries`. Supported parameters are `mode` - and `background_label` - """ - self.feature = self.parse_feature(feature, allowed_feature_types={FeatureType.MASK_TIMELESS}) - self.new_feature = self.parse_feature(new_feature, allowed_feature_types={FeatureType.MASK_TIMELESS}) - - self.params = params - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Execute method""" - segmentation_mask = eopatch[self.feature][..., 0] - - bounds_mask = skimage.segmentation.mark_boundaries( - np.zeros(segmentation_mask.shape[:2], dtype=np.uint8), segmentation_mask, **self.params - ) - - bounds_mask = bounds_mask[..., :1].astype(np.uint8) - eopatch[self.new_feature[0]][self.new_feature[1]] = bounds_mask - return eopatch diff --git a/geometry/eolearn/tests/test_morphology.py b/geometry/eolearn/tests/test_morphology.py deleted file mode 100644 index df0dc25f1..000000000 --- a/geometry/eolearn/tests/test_morphology.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import numpy as np -import pytest -from numpy.testing import assert_array_equal - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.geometry import ErosionTask, MorphologicalFilterTask, MorphologicalOperations, MorphologicalStructFactory - -CLASSES = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) -MASK_FEATURE = FeatureType.MASK, "mask" -MASK_TIMELESS_FEATURE = FeatureType.MASK_TIMELESS, "timeless_mask" -# ruff: noqa: NPY002 - - -@pytest.mark.parametrize("invalid_input", [None, 0, "a"]) -def test_erosion_value_error(invalid_input): - with pytest.raises(ValueError): - ErosionTask((FeatureType.MASK_TIMELESS, "LULC", "TEST"), disk_radius=invalid_input) - - -def test_erosion_full(test_eopatch): - mask_before = test_eopatch.mask_timeless["LULC"].copy() - - erosion_task = ErosionTask((FeatureType.MASK_TIMELESS, "LULC", "LULC_ERODED"), 1) - eopatch = erosion_task.execute(test_eopatch) - - mask_after = eopatch.mask_timeless["LULC_ERODED"].copy() - - assert not np.all(mask_before == mask_after) - - for label in CLASSES: - if label == 0: - assert np.sum(mask_after == label) >= np.sum(mask_before == label), "Error in the erosion process" - else: - assert np.sum(mask_after == label) <= np.sum(mask_before == label), "Error in the erosion process" - - -def test_erosion_partial(test_eopatch): - mask_before = test_eopatch.mask_timeless["LULC"].copy() - - # skip forest and artificial surface - specific_labels = [0, 1, 3, 4] - erosion_task = ErosionTask( - mask_feature=(FeatureType.MASK_TIMELESS, "LULC", "LULC_ERODED"), disk_radius=1, erode_labels=specific_labels - ) - eopatch = erosion_task.execute(test_eopatch) - - mask_after = eopatch.mask_timeless["LULC_ERODED"].copy() - - assert not np.all(mask_before == mask_after) - - for label in CLASSES: - if label == 0: - assert np.sum(mask_after == label) >= np.sum(mask_before == label), "Error in the erosion process" - elif label in specific_labels: - assert np.sum(mask_after == label) <= np.sum(mask_before == label), "Error in the erosion process" - else: - assert_array_equal(mask_after == label, mask_before == label, err_msg="Error in the erosion process") - - -@pytest.mark.parametrize("morph_operation", MorphologicalOperations) -@pytest.mark.parametrize( - "struct_element", [None, MorphologicalStructFactory.get_disk(5), MorphologicalStructFactory.get_rectangle(5, 6)] -) -def test_morphological_filter(morph_operation, struct_element): - eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857))) - eopatch[MASK_FEATURE] = np.random.randint(20, size=(10, 100, 100, 3), dtype=np.uint8) - eopatch[MASK_TIMELESS_FEATURE] = np.random.randint(20, 50, size=(100, 100, 5), dtype=np.uint8) - - task = MorphologicalFilterTask( - [MASK_FEATURE, MASK_TIMELESS_FEATURE], morph_operation=morph_operation, struct_elem=struct_element - ) - task.execute(eopatch) - - assert eopatch[MASK_FEATURE].shape == (10, 100, 100, 3) - assert eopatch[MASK_TIMELESS_FEATURE].shape == (100, 100, 5) diff --git a/geometry/eolearn/tests/test_superpixel.py b/geometry/eolearn/tests/test_superpixel.py deleted file mode 100644 index 5a699ff62..000000000 --- a/geometry/eolearn/tests/test_superpixel.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import numpy as np -import pytest - -from sentinelhub.testing_utils import assert_statistics_match - -from eolearn.core import FeatureType -from eolearn.geometry import FelzenszwalbSegmentationTask, SlicSegmentationTask, SuperpixelSegmentationTask - -SUPERPIXEL_FEATURE = FeatureType.MASK_TIMELESS, "SP_FEATURE" - - -@pytest.mark.parametrize( - ("task", "expected_statistics"), - [ - ( - SuperpixelSegmentationTask( - (FeatureType.DATA, "BANDS-S2-L1C"), SUPERPIXEL_FEATURE, scale=100, sigma=0.5, min_size=100 - ), - {"exp_dtype": np.int64, "exp_min": 0, "exp_max": 25, "exp_mean": 10.6809, "exp_median": 11}, - ), - ( - FelzenszwalbSegmentationTask( - (FeatureType.DATA_TIMELESS, "MAX_NDVI"), SUPERPIXEL_FEATURE, scale=21, sigma=1.0, min_size=52 - ), - {"exp_dtype": np.int64, "exp_min": 0, "exp_max": 22, "exp_mean": 8.5302, "exp_median": 7}, - ), - ( - FelzenszwalbSegmentationTask((FeatureType.MASK, "CLM"), SUPERPIXEL_FEATURE, scale=1, sigma=0, min_size=15), - {"exp_dtype": np.int64, "exp_min": 0, "exp_max": 171, "exp_mean": 86.46267, "exp_median": 90}, - ), - ( - SlicSegmentationTask( - (FeatureType.DATA, "CLP"), - SUPERPIXEL_FEATURE, - n_segments=55, - compactness=25.0, - max_num_iter=20, - sigma=0.8, - ), - {"exp_dtype": np.int64, "exp_min": 0, "exp_max": 48, "exp_mean": 24.6072, "exp_median": 25}, - ), - ( - SlicSegmentationTask( - (FeatureType.MASK_TIMELESS, "RANDOM_UINT8"), - SUPERPIXEL_FEATURE, - n_segments=231, - compactness=15.0, - max_num_iter=7, - sigma=0.2, - ), - {"exp_dtype": np.int64, "exp_min": 0, "exp_max": 195, "exp_mean": 100.1844, "exp_median": 101}, - ), - ], -) -def test_superpixel(test_eopatch, task, expected_statistics): - task.execute(test_eopatch) - result = test_eopatch[SUPERPIXEL_FEATURE] - - assert_statistics_match(result, **expected_statistics, abs_delta=1e-4) diff --git a/geometry/requirements.txt b/geometry/requirements.txt index 66c93de29..562050068 100644 --- a/geometry/requirements.txt +++ b/geometry/requirements.txt @@ -1,5 +1 @@ -eo-learn-core -geopandas>=0.8.1 -rasterio>=1.2.7 -scikit-image>=0.15.0 -shapely +eo-learn diff --git a/geometry/setup.py b/geometry/setup.py index d136e3551..5a4ead372 100644 --- a/geometry/setup.py +++ b/geometry/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "geometry", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_geometry", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,9 +45,7 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, install_requires=parse_requirements("requirements.txt"), - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/install_all.py b/install_all.py index b4d761f10..df31d6bc4 100644 --- a/install_all.py +++ b/install_all.py @@ -6,27 +6,15 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import subprocess import sys - -SUBPACKAGE_LIST = [ - "core", - "coregistration", - "features", - "geometry", - "io[METEOBLUE]", - "mask", - "ml_tools", - "visualization", -] - - -def pip_command(name, args): - args = [arg for arg in args if not arg.startswith(".")] - subprocess.check_call([sys.executable, "-m", "pip", "install", *args, f"./{name}"]) - +import warnings if __name__ == "__main__": - for subpackage in SUBPACKAGE_LIST: - pip_command(subpackage, sys.argv[1:]) + subprocess.check_call([sys.executable, "-m", "pip", "install", *sys.argv[1:], "."]) + warnings.warn( + "Installing via `install_all.py` is no longer necessary and has been deprecated. Use `pip install" + " eo-learn` instead." + ) diff --git a/io/MANIFEST.in b/io/MANIFEST.in deleted file mode 100644 index 7802b675f..000000000 --- a/io/MANIFEST.in +++ /dev/null @@ -1,7 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/io/py.typed -exclude eolearn/tests/* -exclude eolearn/tests/test_extra/* -exclude eolearn/tests/TestInputs/* diff --git a/io/README.md b/io/README.md index 247847414..c989e1bb7 100644 --- a/io/README.md +++ b/io/README.md @@ -1,5 +1,5 @@ # eolearn.io subpackage -Input/output subpackage that deals with obtaining data from Sentinel Hub services or saving and loading data locally. +The deprecated way to install `eolearn.io`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.io.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#io). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/io/deprecated_eolearn_io/__init__.py b/io/deprecated_eolearn_io/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/io/deprecated_eolearn_io/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/io/eolearn/__init__.py b/io/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/io/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/io/eolearn/io/extra/__init__.py b/io/eolearn/io/extra/__init__.py deleted file mode 100644 index a08d1524e..000000000 --- a/io/eolearn/io/extra/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -A subfolder containing integrations with other packages -""" diff --git a/io/eolearn/io/extra/geodb.py b/io/eolearn/io/extra/geodb.py deleted file mode 100644 index b3cac3a07..000000000 --- a/io/eolearn/io/extra/geodb.py +++ /dev/null @@ -1,80 +0,0 @@ -""" -Module with tasks that integrate with GeoDB - -To use tasks from this module you have to install dependencies defined in `requirements-geodb.txt`. - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -from typing import Any - -from sentinelhub import CRS, BBox - -from eolearn.core.types import FeatureSpec - -from ..geometry_io import _BaseVectorImportTask - - -class GeoDBVectorImportTask(_BaseVectorImportTask): - """A task for importing vector data from `geoDB `__ - into EOPatch - """ - - def __init__( - self, - feature: FeatureSpec, - geodb_client: Any, - geodb_collection: str, - geodb_db: str, - reproject: bool = True, - clip: bool = False, - **kwargs: Any, - ): - """ - :param feature: A vector feature into which to import data - :param geodb_client: an instance of GeoDBClient - :param geodb_collection: The name of the collection to be queried - :param geodb_db: The name of the database the collection resides in [current database] - :param reproject: Should the geometries be transformed to coordinate reference system of the requested bbox? - :param clip: Should the geometries be clipped to the requested bbox, or should be geometries kept as they are? - :param kwargs: Additional args that will be passed to `geodb_client.get_collection_by_bbox` call - (e.g. where="id>-1", operator="and") - """ - self.geodb_client = geodb_client - self.geodb_db = geodb_db - self.geodb_collection = geodb_collection - self.geodb_kwargs = kwargs - self._dataset_crs: CRS | None = None - - super().__init__(feature=feature, reproject=reproject, clip=clip) - - @property - def dataset_crs(self) -> CRS: - """Provides a "crs" of dataset, loads it lazily (i.e. the first time it is needed) - - :return: Dataset's CRS - """ - if self._dataset_crs is None: - srid = self.geodb_client.get_collection_srid(collection=self.geodb_collection, database=self.geodb_db) - self._dataset_crs = CRS(f"epsg:{srid}") - - return self._dataset_crs - - def _load_vector_data(self, bbox: BBox | None) -> Any: - """Loads vector data from geoDB table""" - prepared_bbox = bbox.transform_bounds(self.dataset_crs).geometry.bounds if bbox else None - - if "comparison_mode" not in self.geodb_kwargs: - self.geodb_kwargs["comparison_mode"] = "intersects" - - return self.geodb_client.get_collection_by_bbox( - collection=self.geodb_collection, - database=self.geodb_db, - bbox=prepared_bbox, - bbox_crs=self.dataset_crs.epsg, - **self.geodb_kwargs, - ) diff --git a/io/eolearn/io/extra/meteoblue.py b/io/eolearn/io/extra/meteoblue.py deleted file mode 100644 index 48c857d40..000000000 --- a/io/eolearn/io/extra/meteoblue.py +++ /dev/null @@ -1,298 +0,0 @@ -""" -Module with tasks that provide data from meteoblue services - -To use tasks from this module you have to install METEOBLUE package extension: - -.. code-block:: - - pip install eo-learn-io[METEOBLUE] - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import datetime as dt -from abc import ABCMeta, abstractmethod -from typing import Any - -import dateutil.parser -import geopandas as gpd -import numpy as np -import pandas as pd -import shapely.geometry - -try: - import meteoblue_dataset_sdk - from meteoblue_dataset_sdk.caching import FileCache -except ImportError as exception: - raise ImportError("This module requires an installation of meteoblue_dataset_sdk package") from exception - - -from sentinelhub import CRS, BBox, Geometry, parse_time_interval, serialize_time -from sentinelhub.types import RawTimeIntervalType - -from eolearn.core import EOPatch, EOTask -from eolearn.core.constants import TIMESTAMP_COLUMN, FeatureType - - -class BaseMeteoblueTask(EOTask, metaclass=ABCMeta): - """A base task implementing the logic that is common for all meteoblue tasks""" - - def __init__( - self, - feature: tuple[FeatureType, str], - apikey: str, - query: dict | None = None, - units: dict | None = None, - time_difference: dt.timedelta = dt.timedelta(minutes=30), # noqa: B008, RUF100 - cache_folder: str | None = None, - cache_max_age: int = 604800, - ): - """ - :param feature: A feature in which meteoblue data will be stored - :param apikey: meteoblue API key - :param query: meteoblue dataset API query definition. If set to None (default) the query has to be set - in the execute method instead. - :param units: meteoblue dataset API units definition. If set to None (default) request will use default units - as specified in https://docs.meteoblue.com/en/weather-apis/dataset-api/dataset-api#units - :param time_difference: The size of a time interval around each timestamp for which data will be collected. It - is used only in a combination with ``time_interval`` parameter from ``execute`` method. - :param cache_folder: Path to cache_folder. If set to None (default) requests will not be cached. - :param cache_max_age: Maximum age in seconds to use a cached result. Default 1 week. - """ - self.feature = self.parse_feature(feature) - cache = None - if cache_folder: - cache = FileCache(path=cache_folder, max_age=cache_max_age) - - self.client = meteoblue_dataset_sdk.Client(apikey=apikey, cache=cache) - self.query = query - self.units = units - self.time_difference = time_difference - - @staticmethod - def _get_modified_eopatch(eopatch: EOPatch | None, bbox: BBox | None) -> tuple[EOPatch, BBox]: - if bbox is not None: - if eopatch is None: - eopatch = EOPatch(bbox=bbox) - elif eopatch.bbox is None: - eopatch.bbox = bbox - elif eopatch.bbox != bbox: - raise ValueError("Provided eopatch.bbox and bbox are not the same") - return eopatch, bbox - - if eopatch is None or eopatch.bbox is None: - raise ValueError("Bounding box is not provided") - return eopatch, eopatch.bbox - - def _prepare_time_intervals(self, eopatch: EOPatch, time_interval: RawTimeIntervalType | None) -> list[str]: - """Prepare a list of time intervals for which data will be collected from meteoblue services""" - if not eopatch.timestamps and not time_interval: - raise ValueError( - "Time interval should either be defined with `eopatch.timestamps` or `time_interval` parameter" - ) - - if time_interval: - serialized_start_time, serialized_end_time = serialize_time(parse_time_interval(time_interval)) - return [f"{serialized_start_time}/{serialized_end_time}"] - - timestamps = eopatch.timestamps - time_intervals: list[str] = [] - for timestamp in timestamps: - start_time = timestamp - self.time_difference - end_time = timestamp + self.time_difference - - serizalized_start_time, serizalized_end_time = serialize_time((start_time, end_time)) - - time_intervals.append(f"{serizalized_start_time}/{serizalized_end_time}") - - return time_intervals - - @abstractmethod - def _get_data(self, query: dict) -> tuple[Any, list[dt.datetime]]: - """It should return an output feature object and a list of timestamps""" - - def execute( - self, - eopatch: EOPatch | None = None, - *, - query: dict | None = None, - bbox: BBox | None = None, - time_interval: RawTimeIntervalType | None = None, - ) -> EOPatch: - """Execute method that adds new meteoblue data into an EOPatch - - :param eopatch: An EOPatch in which data will be added. If not provided a new EOPatch will be created. - :param bbox: A bounding box of a request. Should be provided if eopatch parameter is not provided. - :param query: meteoblue dataset API query definition. This query takes precedence over one defined in __init__. - :param time_interval: An interval for which data should be downloaded. If not provided then timestamps from - provided eopatch will be used. - :raises ValueError: Raises an exception when no query is set during Task initialization or the execute method. - """ - eopatch, bbox = self._get_modified_eopatch(eopatch, bbox) - - time_intervals = self._prepare_time_intervals(eopatch, time_interval) - - geometry = Geometry(bbox.geometry, bbox.crs).transform(CRS.WGS84) - geojson = shapely.geometry.mapping(geometry.geometry) - - query = query if query is not None else self.query - if query is None: - raise ValueError("Query has to specified in execute method or during task initialization") - - executable_query = { - "units": self.units, - "geometry": geojson, - "format": "protobuf", - "timeIntervals": time_intervals, - "queries": [query], - } - result_data, result_timestamps = self._get_data(executable_query) - - if not eopatch.timestamps and result_timestamps: - eopatch.timestamps = result_timestamps - - eopatch[self.feature] = result_data - return eopatch - - -class MeteoblueVectorTask(BaseMeteoblueTask): - """Obtains weather data from meteoblue services as a vector feature - - The data is obtained as a VECTOR feature in a ``geopandas.GeoDataFrame`` where columns include latitude, longitude, - timestamp and a column for each weather variable. All data is downloaded from the - meteoblue dataset API (). - - A meteoblue API key is required to retrieve data. - """ - - def _get_data(self, query: dict) -> tuple[gpd.GeoDataFrame, list[dt.datetime]]: - """Provides a GeoDataFrame with information about weather control points and an empty list of timestamps""" - result = self.client.querySync(query) - dataframe = meteoblue_to_dataframe(result) - geometry = gpd.points_from_xy(dataframe.Longitude, dataframe.Latitude) - crs = CRS.WGS84.pyproj_crs() - gdf = gpd.GeoDataFrame(dataframe, geometry=geometry, crs=crs) - return gdf, [] - - -class MeteoblueRasterTask(BaseMeteoblueTask): - """Obtains weather data from meteoblue services as a raster feature - - It returns a 4D numpy array with dimensions (time, height, width, weather variables) which should be stored as a - DATA feature. Data is resampled to WGS84 plate carrée to a specified resolution using the - meteoblue dataset API (). - - A meteoblue API key is required to retrieve data. - """ - - def _get_data(self, query: dict) -> tuple[np.ndarray, list[dt.datetime]]: - """Return a 4-dimensional numpy array of shape (time, height, width, weather variables) and a list of - timestamps - """ - result = self.client.querySync(query) - - data = meteoblue_to_numpy(result) - timestamps = _meteoblue_timestamps_from_geometry(result.geometries[0]) - return data, timestamps - - -def meteoblue_to_dataframe(result: Any) -> pd.DataFrame: - """Transform a meteoblue dataset API result to a dataframe - - :param result: A response of meteoblue API of type `Dataset_pb2.DatasetApiProtobuf` - :returns: A dataframe with columns TIMESTAMP, Longitude, Latitude and aggregation columns - """ - geometry = result.geometries[0] - code_names = [f"{code.code}_{code.level}_{code.aggregation}" for code in geometry.codes] - - if not geometry.timeIntervals: - return pd.DataFrame(columns=[TIMESTAMP_COLUMN, "Longitude", "Latitude", *code_names]) - - dataframes = [] - for index, time_interval in enumerate(geometry.timeIntervals): - timestamps = _meteoblue_timestamps_from_time_interval(time_interval) - - n_locations = len(geometry.lats) - n_timesteps = len(timestamps) - - dataframe = pd.DataFrame( - { - TIMESTAMP_COLUMN: np.tile(timestamps, n_locations), # type: ignore[arg-type] # numpy can do this - "Longitude": np.repeat(geometry.lons, n_timesteps), - "Latitude": np.repeat(geometry.lats, n_timesteps), - } - ) - - for code, code_name in zip(geometry.codes, code_names): - dataframe[code_name] = np.array(code.timeIntervals[index].data) - - dataframes.append(dataframe) - - return pd.concat(dataframes, ignore_index=True) - - -def meteoblue_to_numpy(result: Any) -> np.ndarray: - """Transform a meteoblue dataset API result to a dataframe - - :param result: A response of meteoblue API of type `Dataset_pb2.DatasetApiProtobuf` - :returns: A 4D numpy array with shape (time, height, width, weather variables) - """ - geometry = result.geometries[0] - - n_locations = len(geometry.lats) - n_codes = len(geometry.codes) - n_time_intervals = len(geometry.timeIntervals) - geo_ny = geometry.ny - geo_nx = geometry.nx - - # meteoblue data is using dimensions (n_variables, n_time_intervals, ny, nx, n_timesteps) - # Individual time intervals may have different number of timesteps (not a dimension) - # Therefore we have to first transpose each code individually and then transpose everything again - def map_code(code: Any) -> np.ndarray: - """Transpose a single code""" - code_data = np.array([t.data for t in code.timeIntervals]) - - code_n_timesteps = code_data.size // n_locations // n_time_intervals - code_data = code_data.reshape((n_time_intervals, geo_ny, geo_nx, code_n_timesteps)) - - # transpose from shape (n_time_intervals, geo_ny, geo_nx, code_n_timesteps) - # to (n_time_intervals, code_n_timesteps, geo_ny, geo_nx) - # and flip the y-axis (meteoblue is using northward facing axis - # but the standard for EOPatch features is a southward facing axis) - return np.flip(code_data.transpose((0, 3, 1, 2)), axis=2) - - data = np.array(list(map(map_code, geometry.codes))) - - n_timesteps = data.size // n_locations // n_codes - data = data.reshape((n_codes, n_timesteps, geo_ny, geo_nx)) - - return data.transpose((1, 2, 3, 0)) - - -def _meteoblue_timestamps_from_geometry(geometry_pb: Any) -> list[dt.datetime]: - """Transforms a protobuf geometry object into a list of datetime objects""" - return list(pd.core.common.flatten(map(_meteoblue_timestamps_from_time_interval, geometry_pb.timeIntervals))) - - -def _meteoblue_timestamps_from_time_interval(timestamp_pb: Any) -> list[dt.datetime]: - """Transforms a protobuf timestamp object into a list of datetime objects""" - if timestamp_pb.timestrings: - # Time intervals like weekly data, return an `array of strings` as timestamps - # For time indications like `20200801T0000-20200802T235959` we only return the first date as datetime - return list(map(_parse_timestring, timestamp_pb.timestrings)) - - # Regular time intervals return `start, end and stride` as a time axis - # We convert it into an array of daytime - time_range = range(timestamp_pb.start, timestamp_pb.end, timestamp_pb.stride) - return list(map(dt.datetime.fromtimestamp, time_range)) - - -def _parse_timestring(timestring: str) -> dt.datetime: - """A helper method to parse specific timestrings obtained from meteoblue service""" - if "-" in timestring: - timestring = timestring.split("-")[0] - return dateutil.parser.parse(timestring) diff --git a/io/eolearn/io/geopedia.py b/io/eolearn/io/geopedia.py deleted file mode 100644 index b2d92365d..000000000 --- a/io/eolearn/io/geopedia.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Module for adding data obtained from sentinelhub package to existing EOPatches - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import logging -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import rasterio.transform -import rasterio.warp - -from sentinelhub import CRS, BBox, GeopediaWmsRequest, MimeType - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeatureSpec - -LOGGER = logging.getLogger(__name__) - - -class AddGeopediaFeatureTask(EOTask): - """ - Task for adding a feature from Geopedia to an existing EOPatch. - - At the moment the Geopedia supports only WMS requestes in EPSG:3857, therefore to add feature to EOPatch - in arbitrary CRS and arbitrary service type the following steps are performed: - * transform BBOX from EOPatch's CRS to EPSG:3857 - * get raster from Geopedia Request in EPSG:3857 - * vectorize the returned raster using rasterio - * project vectorized raster back to EOPatch's CRS - * rasterize back and add raster to EOPatch - """ - - def __init__( - self, - feature: FeatureSpec, - layer: Union[str, int], - theme: str, - raster_value: Union[Dict[str, Tuple[float, List[float]]], float], - raster_dtype: Union[np.dtype, type] = np.uint8, - no_data_val: float = 0, - image_format: MimeType = MimeType.PNG, - mean_abs_difference: float = 2, - ): - self.feature_type, self.feature_name = self.parse_feature(feature) - - self.raster_value = raster_value - self.raster_dtype = raster_dtype - self.no_data_val = no_data_val - self.mean_abs_difference = mean_abs_difference - - self.layer = layer - self.theme = theme - - self.image_format = image_format - - def _get_wms_request(self, bbox: Optional[BBox], size_x: int, size_y: int) -> GeopediaWmsRequest: - """ - Returns WMS request. - """ - if bbox is None: - raise ValueError("Bbox has to be defined!") - - bbox_3857 = bbox.transform(CRS.POP_WEB) - - return GeopediaWmsRequest( - layer=self.layer, - theme=self.theme, - bbox=bbox_3857, - width=size_x, - height=size_y, - image_format=self.image_format, - ) - - def _reproject(self, eopatch: EOPatch, src_raster: np.ndarray) -> np.ndarray: - """ - Re-projects the raster data from Geopedia's CRS (POP_WEB) to EOPatch's CRS. - """ - if not eopatch.bbox: - raise ValueError("To reproject raster data, eopatch.bbox has to be defined!") - - height, width = src_raster.shape - - dst_raster = np.ones((height, width), dtype=self.raster_dtype) - - src_bbox = eopatch.bbox.transform(CRS.POP_WEB) - src_transform = rasterio.transform.from_bounds(*src_bbox, width=width, height=height) - - dst_bbox = eopatch.bbox - dst_transform = rasterio.transform.from_bounds(*dst_bbox, width=width, height=height) - - rasterio.warp.reproject( - src_raster, - dst_raster, - src_transform=src_transform, - src_crs=CRS.POP_WEB.ogc_string(), - src_nodata=0, - dst_transform=dst_transform, - dst_crs=eopatch.bbox.crs.ogc_string(), - dst_nodata=self.no_data_val, - ) - - return dst_raster - - def _to_binary_mask(self, array: np.ndarray, binaries_raster_value: float) -> np.ndarray: - """ - Returns binary mask (0 and raster_value) - """ - # check where the transparency is not zero - return (array[..., -1] > 0).astype(self.raster_dtype) * binaries_raster_value - - def _map_from_binaries( - self, - eopatch: EOPatch, - dst_shape: Union[int, Tuple[int, int]], - request_data: np.ndarray, - binaries_raster_value: float, - ) -> np.ndarray: - """ - Each request represents a binary class which will be mapped to the scalar `raster_value` - """ - if self.feature_name in eopatch[self.feature_type]: - raster = eopatch[self.feature_type][self.feature_name].squeeze() - else: - raster = np.ones(dst_shape, dtype=self.raster_dtype) * self.no_data_val - - new_raster = self._reproject(eopatch, self._to_binary_mask(request_data, binaries_raster_value)) - - # update raster - raster[new_raster != 0] = new_raster[new_raster != 0] - - return raster - - def _map_from_multiclass( - self, - eopatch: EOPatch, - dst_shape: Union[int, Tuple[int, int]], - request_data: np.ndarray, - multiclass_raster_value: Dict[str, Tuple[float, List[float]]], - ) -> np.ndarray: - """ - `raster_value` is a dictionary specifying the intensity values for each class and the corresponding label value. - - A dictionary example for GLC30 LULC mapping is: - raster_value = {'no_data': (0,[0,0,0,0]), - 'cultivated land': (1,[193, 243, 249, 255]), - 'forest': (2,[73, 119, 20, 255]), - 'grassland': (3,[95, 208, 169, 255]), - 'shrubland': (4,[112, 179, 62, 255]), - 'water': (5,[154, 86, 1, 255]), - 'wetland': (6,[244, 206, 126, 255]), - 'tundra': (7,[50, 100, 100, 255]), - 'artificial surface': (8,[20, 47, 147, 255]), - 'bareland': (9,[202, 202, 202, 255]), - 'snow and ice': (10,[251, 237, 211, 255])} - """ - raster = np.ones(dst_shape, dtype=self.raster_dtype) * self.no_data_val - - for value, intensities in multiclass_raster_value.values(): - raster[np.mean(np.abs(request_data - intensities), axis=-1) < self.mean_abs_difference] = value - - return self._reproject(eopatch, raster) - - def execute(self, eopatch: EOPatch) -> EOPatch: - """ - Add requested feature to this existing EOPatch. - """ - data_arr = eopatch[FeatureType.MASK]["IS_DATA"] - _, height, width, _ = data_arr.shape - - request = self._get_wms_request(eopatch.bbox, width, height) - - (request_data,) = np.asarray(request.get_data()) - - if isinstance(self.raster_value, dict): - raster = self._map_from_multiclass(eopatch, (height, width), request_data, self.raster_value) - elif isinstance(self.raster_value, (int, float)): - raster = self._map_from_binaries(eopatch, (height, width), request_data, self.raster_value) - else: - raise ValueError("Unsupported raster value type") - - if self.feature_type is FeatureType.MASK_TIMELESS and raster.ndim == 2: - raster = raster[..., np.newaxis] - - eopatch[self.feature_type][self.feature_name] = raster - - return eopatch diff --git a/io/eolearn/io/py.typed b/io/eolearn/io/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/io/eolearn/tests/test_extra/test_geodb.py b/io/eolearn/tests/test_extra/test_geodb.py deleted file mode 100644 index 33645e7f9..000000000 --- a/io/eolearn/tests/test_extra/test_geodb.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import pytest - -from sentinelhub import BBox - -from eolearn.core import FeatureType -from eolearn.io.extra.geodb import GeoDBVectorImportTask - - -@pytest.mark.parametrize( - argnames="reproject, clip, n_features, bbox", - ids=["bbox", "bbox_smaller"], - argvalues=[ - (False, False, 227, BBox([857000, 6521500, 861000, 6525500], crs="epsg:2154")), - (True, True, 162, BBox([857400, 6521900, 860600, 6525100], crs="epsg:2154")), - ], -) -def test_import_from_geodb(geodb_client, reproject, clip, n_features, bbox): - """Test for importing from GeoDB. - It will only run if geodb credentials are available as the environment variables - """ - assert geodb_client.whoami, "Client is not set-up correctly" - - feature = FeatureType.VECTOR_TIMELESS, "lpis_iacs" - import_task = GeoDBVectorImportTask( - feature=feature, - geodb_client=geodb_client, - geodb_collection="france_metropolitan_gsaa_2015", - geodb_db="lpis_iacs", - reproject=reproject, - clip=clip, - ) - eop = import_task.execute(bbox=bbox) - assert len(eop[feature]) == n_features, "Wrong number of features!" - assert eop[feature].crs.to_epsg() == eop.bbox.crs.epsg diff --git a/io/eolearn/tests/test_extra/test_meteoblue.py b/io/eolearn/tests/test_extra/test_meteoblue.py deleted file mode 100644 index cced487e1..000000000 --- a/io/eolearn/tests/test_extra/test_meteoblue.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -Module containing tests for Meteoblue tasks - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -import datetime as dt -import os - -import numpy as np -import pytest -from meteoblue_dataset_sdk.protobuf.dataset_pb2 import DatasetApiProtobuf - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.io.extra.meteoblue import MeteoblueRasterTask, MeteoblueVectorTask - -RASTER_QUERY = { - "domain": "NEMS4", - "gapFillDomain": None, - "timeResolution": "hourly", - "codes": [{"code": 11, "level": "2 m above gnd"}], - "transformations": [ - {"type": "aggregateTimeInterval", "aggregation": "mean"}, - { - "type": "spatialTransformV2", - "gridResolution": 0.02, - "interpolationMethod": "linear", - "spatialAggregation": "mean", - "disjointArea": "keep", - }, - ], -} - -VECTOR_QUERY = { - "domain": "NEMS4", - "gapFillDomain": None, - "timeResolution": "daily", - "codes": [{"code": 11, "level": "2 m above gnd", "aggregation": "mean"}], - "transformations": None, -} - -UNITS = { - "temperature": "C", - "velocity": "km/h", - "length": "metric", - "energy": "watts", -} - -BBOX = BBox([7.52, 47.50, 7.7, 47.6], crs=CRS.WGS84) -TIME_INTERVAL = dt.datetime(year=2020, month=8, day=1), dt.datetime(year=2020, month=8, day=3) - - -def test_meteoblue_raster_task(mocker): - """Unit test for MeteoblueRasterTask""" - mocker.patch( - "meteoblue_dataset_sdk.Client.querySync", - return_value=_load_meteoblue_client_response("test_meteoblue_raster_input.bin"), - ) - - feature = FeatureType.DATA, "WEATHER-DATA" - meteoblue_task = MeteoblueRasterTask(feature, "dummy-api-key", query=RASTER_QUERY, units=UNITS) - - eopatch = meteoblue_task.execute(bbox=BBOX, time_interval=TIME_INTERVAL) - - assert eopatch.bbox == BBOX - assert eopatch.timestamps == [dt.datetime(2020, 8, 1)] - - data = eopatch[feature] - assert data.shape == (1, 6, 10, 1) - assert data.dtype == np.float64 - - assert round(np.mean(data), 5) == 23.79214 - assert round(np.std(data), 5) == 0.3996 - assert round(data[0, -1, 0, 0], 5) == 23.74646 - - -def test_meteoblue_vector_task(mocker): - """Unit test for MeteoblueVectorTask""" - mocker.patch( - "meteoblue_dataset_sdk.Client.querySync", - return_value=_load_meteoblue_client_response("test_meteoblue_vector_input.bin"), - ) - - feature = FeatureType.VECTOR, "WEATHER-DATA" - meteoblue_task = MeteoblueVectorTask(feature, "dummy-api-key", query=VECTOR_QUERY, units=UNITS) - - eopatch = EOPatch(bbox=BBOX) - eopatch = meteoblue_task.execute(eopatch, time_interval=TIME_INTERVAL) - - assert eopatch.bbox == BBOX - - data = eopatch[feature] - assert len(data.index) == 18 - assert data.crs.to_epsg() == 4326 - - data_series = data["11_2 m above gnd_mean"] - assert round(data_series.mean(), 5) == 23.75278 - assert round(data_series.std(), 5) == 2.99785 - - -def test_meteoblue_query_precedence(mocker): - """Unit test for query precedence in a MeteoblueTask""" - mocker.patch( - "meteoblue_dataset_sdk.Client.querySync", - return_value=_load_meteoblue_client_response("test_meteoblue_vector_input.bin"), - ) - - feature = FeatureType.VECTOR, "WEATHER-DATA" - meteoblue_task_no_query = MeteoblueVectorTask(feature, "dummy-api-key", units=UNITS) - meteoblue_task_with_query = MeteoblueVectorTask(feature, "dummy-api-key", query=RASTER_QUERY, units=UNITS) - - eopatch = EOPatch(bbox=BBOX) - - with pytest.raises(ValueError): - meteoblue_task_no_query.execute(eopatch, time_interval=TIME_INTERVAL) - - spy_no_query = mocker.spy(meteoblue_task_no_query, "_get_data") - meteoblue_task_no_query.execute(eopatch, time_interval=TIME_INTERVAL, query=VECTOR_QUERY) - spy_no_query.assert_called_once_with( - { - "units": {"temperature": "C", "velocity": "km/h", "length": "metric", "energy": "watts"}, - "geometry": { - "type": "Polygon", - "coordinates": (((7.52, 47.5), (7.52, 47.6), (7.7, 47.6), (7.7, 47.5), (7.52, 47.5)),), - }, - "format": "protobuf", - "timeIntervals": ["2020-08-01T00:00:00/2020-08-03T00:00:00"], - "queries": [ - { - "domain": "NEMS4", - "gapFillDomain": None, - "timeResolution": "daily", - "codes": [{"code": 11, "level": "2 m above gnd", "aggregation": "mean"}], - "transformations": None, - } - ], - } - ) - - spy_with_query = mocker.spy(meteoblue_task_with_query, "_get_data") - meteoblue_task_with_query.execute(eopatch, time_interval=TIME_INTERVAL, query=VECTOR_QUERY) - spy_with_query.assert_called_once_with( - { - "units": {"temperature": "C", "velocity": "km/h", "length": "metric", "energy": "watts"}, - "geometry": { - "type": "Polygon", - "coordinates": (((7.52, 47.5), (7.52, 47.6), (7.7, 47.6), (7.7, 47.5), (7.52, 47.5)),), - }, - "format": "protobuf", - "timeIntervals": ["2020-08-01T00:00:00/2020-08-03T00:00:00"], - "queries": [ - { - "domain": "NEMS4", - "gapFillDomain": None, - "timeResolution": "daily", - "codes": [{"code": 11, "level": "2 m above gnd", "aggregation": "mean"}], - "transformations": None, - } - ], - } - ) - - -def _load_meteoblue_client_response(filename): - """Loads locally stored responses of meteoblue client - - To update content of saved files use: - with open('', 'wb') as fp: - fp.write(result.SerializeToString()) - """ - path = os.path.join(os.path.dirname(__file__), "..", "TestInputs", filename) - - with open(path, "rb") as fp: - return DatasetApiProtobuf.FromString(fp.read()) diff --git a/io/requirements.txt b/io/requirements.txt index 1a2f77db7..562050068 100644 --- a/io/requirements.txt +++ b/io/requirements.txt @@ -1,8 +1 @@ -affine -boto3 -eo-learn-core -fiona>=1.8.18 -geopandas>=0.8.1 -rasterio>=1.2.7 -sentinelhub>=3.8.0 -typing-extensions +eo-learn diff --git a/io/setup.py b/io/setup.py index 7c2af05ca..6bdea282e 100644 --- a/io/setup.py +++ b/io/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "io", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_io", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,10 +45,8 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, install_requires=parse_requirements("requirements.txt"), extras_require={"METEOBLUE": parse_requirements("requirements-meteoblue.txt")}, - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/mask/MANIFEST.in b/mask/MANIFEST.in deleted file mode 100644 index dad260ce5..000000000 --- a/mask/MANIFEST.in +++ /dev/null @@ -1,6 +0,0 @@ -include requirements*.txt -include eolearn/mask/models/* -include LICENSE -include README.md -include eolearn/mask/py.typed -exclude eolearn/tests/* diff --git a/mask/README.md b/mask/README.md index 8229d08e5..bcb6f7ffe 100644 --- a/mask/README.md +++ b/mask/README.md @@ -1,5 +1,5 @@ # eolearn.mask subpackage -The subpackage is used for masking of data and calculation of cloud masks. +The deprecated way to install `eolearn.mask`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.mask.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#mask). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/mask/deprecated_eolearn_mask/__init__.py b/mask/deprecated_eolearn_mask/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/mask/deprecated_eolearn_mask/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/mask/eolearn/__init__.py b/mask/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/mask/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/mask/eolearn/mask/__init__.py b/mask/eolearn/mask/__init__.py deleted file mode 100644 index 77a3a15a0..000000000 --- a/mask/eolearn/mask/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -""" -Public classes and functions of mask subpackage -""" - -from .cloud_mask import CloudMaskTask -from .mask_counting import ClassFrequencyTask -from .masking import JoinMasksTask, MaskFeatureTask -from .snow_mask import SnowMaskTask, TheiaSnowMaskTask -from .utils import resize_images - -__version__ = "1.4.2" diff --git a/mask/eolearn/mask/cloud_mask.py b/mask/eolearn/mask/cloud_mask.py deleted file mode 100644 index 1d91f3636..000000000 --- a/mask/eolearn/mask/cloud_mask.py +++ /dev/null @@ -1,585 +0,0 @@ -""" -Module for cloud masking - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import logging -import os -from functools import partial -from typing import Protocol, cast - -import cv2 -import numpy as np -from lightgbm import Booster -from skimage.morphology import disk - -from sentinelhub import BBox, bbox_to_resolution - -from eolearn.core import EOPatch, EOTask, FeatureType, execute_with_mp_lock -from eolearn.core.utils.common import _apply_to_spatial_axes - -from .utils import resize_images - -LOGGER = logging.getLogger(__name__) - - -class ClassifierType(Protocol): - """Defines the necessary classifier interface.""" - - # pylint: disable-next=missing-function-docstring,invalid-name - def predict(self, X: np.ndarray) -> np.ndarray: # noqa[N803] - ... - - # pylint: disable-next=missing-function-docstring,invalid-name - def predict_proba(self, X: np.ndarray) -> np.ndarray: # noqa[N803] - ... - - -class CloudMaskTask(EOTask): - """Cloud masking with an improved s2cloudless model and the SSIM-based multi-temporal classifier. - - Its intended output is a cloud mask that is based on the outputs of both - individual classifiers (a dilated intersection of individual binary masks). - Additional cloud masks and probabilities can be added for either classifier or both. - - Prior to feature extraction and classification, it is recommended that the input be - downscaled by specifying the source and processing resolutions. This should be done - for the following reasons: - - - faster execution - - lower memory consumption - - noise mitigation - - Resizing is performed with linear interpolation. After classification, the cloud - probabilities are themselves upscaled to the original dimensions, before proceeding - with masking operations. - - Example usage: - - .. code-block:: python - - # Only output the combined mask - task1 = CloudMaskTask(processing_resolution='120m', - mask_feature='CLM_INTERSSIM', - average_over=16, - dilation_size=8) - - # Only output monotemporal masks. Only monotemporal processing is done. - task2 = CloudMaskTask(processing_resolution='120m', - mono_features=(None, 'CLM_S2C'), - mask_feature=None, - average_over=16, - dilation_size=8) - """ - - MODELS_FOLDER = os.path.join(os.path.dirname(__file__), "models") - MONO_CLASSIFIER_NAME = "pixel_s2_cloud_detector_lightGBM_v0.2.txt" - MULTI_CLASSIFIER_NAME = "ssim_s2_cloud_detector_lightGBM_v0.2.txt" - - def __init__( - self, - data_feature: tuple[FeatureType, str] = (FeatureType.DATA, "BANDS-S2-L1C"), - is_data_feature: tuple[FeatureType, str] = (FeatureType.MASK, "IS_DATA"), - all_bands: bool = True, - processing_resolution: None | float | tuple[float, float] = None, - max_proc_frames: int = 11, - mono_features: tuple[str | None, str | None] | None = None, - multi_features: tuple[str | None, str | None] | None = None, - mask_feature: tuple[FeatureType, str] | None = (FeatureType.MASK, "CLM_INTERSSIM"), - mono_threshold: float = 0.4, - multi_threshold: float = 0.5, - average_over: int | None = 4, - dilation_size: int | None = 2, - mono_classifier: ClassifierType | None = None, - multi_classifier: ClassifierType | None = None, - ): - """ - :param data_feature: A data feature which stores raw Sentinel-2 reflectance bands. - Default value: `'BANDS-S2-L1C'`. - :param is_data_feature: A mask feature which indicates whether data is valid. - Default value: `'IS_DATA'`. - :param all_bands: Flag, which indicates whether images will consist of all 13 Sentinel-2 bands or only - the required 10. - :param processing_resolution: Resolution to be used during the computation of cloud probabilities and masks, - expressed in meters. Resolution is given as a pair of x and y resolutions. If a single value is given, - it is used for both dimensions. Default `None` represents source resolution. - :param max_proc_frames: Maximum number of frames (including the target, for multi-temporal classification) - considered in a single batch iteration (To keep memory usage at agreeable levels, the task operates on - smaller batches of time frames). - :param mono_features: Tuple of keys to be used for storing cloud probabilities and masks (in that order!) of - the mono classifier. The probabilities are added as a data feature, while masks are added as a mask - feature. By default, none of them are added. - :param multi_features: Tuple of keys used for storing cloud probabilities and masks of the multi classifier. - The probabilities are added as a data feature, while masks are added as a mask feature. By default, - none of them are added. - :param mask_feature: Name of the output intersection feature. Default value: `'CLM_INTERSSIM'`. If `None` the - intersection feature is not computed. - :param mono_threshold: Cloud probability threshold for the mono classifier. - :param multi_threshold: Cloud probability threshold for the multi classifier. - :param average_over: Size of the pixel neighbourhood used in the averaging post-processing step. - A value of `0` skips this post-processing step. Default value mimics the default for - s2cloudless: `4`. - :param dilation_size: Size of the dilation post-processing step. A value of `0` or `None` skips this - post-processing step. Default value mimics the default for s2cloudless: `2`. - :param mono_classifier: Classifier used for mono-temporal cloud detection (`s2cloudless` or equivalent). - Must work on the 10 selected reflectance bands as features `("B01", "B02", "B04", "B05", "B08", "B8A", - "B09", "B10", "B11", "B12")`. Default value: `None` (s2cloudless is used) - :param multi_classifier: Classifier used for multi-temporal cloud detection. - Must work on the 90 multi-temporal features: - - - raw reflectance value in the target frame, - - average value within a spatial window in the target frame, - - maximum, mean and standard deviation of the structural similarity (SSIM) - - indices between a spatial window in the target frame and every other, - - minimum and mean reflectance of all available time frames, - - maximum and mean difference in reflectances between the target frame and every other. - - Default value: None (SSIM-based model is used) - """ - self.proc_resolution = self._parse_resolution_arg(processing_resolution) - - self._mono_classifier = mono_classifier - self._multi_classifier = multi_classifier - - self.data_feature = self.parse_feature(data_feature) - self.is_data_feature = self.parse_feature(is_data_feature) - self.band_indices = (0, 1, 3, 4, 7, 8, 9, 10, 11, 12) if all_bands else tuple(range(10)) - - self.max_proc_frames = max_proc_frames - - if mono_features is not None and isinstance(mono_features, tuple): - self.mono_features = mono_features - else: - self.mono_features = (None, None) - - if multi_features is not None and isinstance(multi_features, tuple): - self.multi_features = multi_features - else: - self.multi_features = (None, None) - - self.mask_feature = mask_feature if mask_feature is None else self.parse_feature(mask_feature) - - self.mono_threshold = mono_threshold - self.multi_threshold = multi_threshold - - if average_over is not None and average_over > 0: - self.avg_kernel = disk(average_over) / np.sum(disk(average_over)) - else: - self.avg_kernel = None - - if dilation_size is not None and dilation_size > 0: - self.dil_kernel = disk(dilation_size).astype(np.uint8) - else: - self.dil_kernel = None - - @staticmethod - def _parse_resolution_arg(resolution: None | float | tuple[float, float]) -> tuple[float, float] | None: - """Parses initialization resolution argument""" - if isinstance(resolution, (int, float)): - resolution = resolution, resolution - - return resolution - - @property - def mono_classifier(self) -> ClassifierType: - """An instance of pre-trained mono-temporal cloud classifier. Loaded only the first time it is required.""" - if self._mono_classifier is None: - path = os.path.join(self.MODELS_FOLDER, self.MONO_CLASSIFIER_NAME) - self._mono_classifier = Booster(model_file=path) - - return self._mono_classifier - - @property - def multi_classifier(self) -> ClassifierType: - """An instance of pre-trained multi-temporal cloud classifier. Loaded only the first time it is required.""" - if self._multi_classifier is None: - path = os.path.join(self.MODELS_FOLDER, self.MULTI_CLASSIFIER_NAME) - self._multi_classifier = Booster(model_file=path) - - return self._multi_classifier - - @staticmethod - def _run_prediction(classifier: ClassifierType, features: np.ndarray) -> np.ndarray: - """Uses classifier object on given data""" - is_booster = isinstance(classifier, Booster) - - predict_method = classifier.predict if is_booster else classifier.predict_proba - prediction = execute_with_mp_lock(predict_method, features) - - return prediction if is_booster else prediction[..., 1] - - def _scale_factors(self, reference_shape: tuple[int, int], bbox: BBox) -> tuple[tuple[float, float], float]: - """Compute the resampling factors for height and width of the input array and sigma - - :param reference_shape: Tuple specifying height and width in pixels of high-resolution array - :param bbox: An EOPatch bounding box - :return: Rescale factor for rows and columns - """ - height, width = reference_shape - res_x, res_y = bbox_to_resolution(bbox, width=width, height=height) - - process_res_x, process_res_y = (res_x, res_y) if self.proc_resolution is None else self.proc_resolution - - rescale = res_y / process_res_y, res_x / process_res_x - sigma = 200 / (process_res_x + process_res_y) - - return rescale, sigma - - def _red_ssim( - self, - *, - data_x: np.ndarray, - data_y: np.ndarray, - valid_mask: np.ndarray, - mu1: np.ndarray, - mu2: np.ndarray, - sigma1_2: np.ndarray, - sigma2_2: np.ndarray, - sigma: float, - const1: float = 1e-6, - const2: float = 1e-5, - ) -> np.ndarray: - """Slightly reduced (pre-computed) SSIM computation""" - # Increase precision and mask invalid regions - valid_mask = valid_mask.astype(np.float64) - data_x = data_x.astype(np.float64) * valid_mask - data_y = data_y.astype(np.float64) * valid_mask - - # Init - mu1_2 = mu1 * mu1 - mu2_2 = mu2 * mu2 - mu1_mu2 = mu1 * mu2 - - sigma12 = cv2.GaussianBlur(data_x * data_y, (0, 0), sigma, borderType=cv2.BORDER_REFLECT) - sigma12 -= mu1_mu2 - - # Formula - numerator = (2.0 * mu1_mu2 + const1) * (2.0 * sigma12 + const2) - denominator = (mu1_2 + mu2_2 + const1) * (sigma1_2 + sigma2_2 + const2) - - return np.divide(numerator, denominator) - - def _win_avg(self, data: np.ndarray, sigma: float) -> np.ndarray: - """Spatial window average""" - return cv2.GaussianBlur(data.astype(np.float64), (0, 0), sigma, borderType=cv2.BORDER_REFLECT) - - def _win_prevar(self, data: np.ndarray, sigma: float) -> np.ndarray: - """Incomplete spatial window variance""" - return cv2.GaussianBlur((data * data).astype(np.float64), (0, 0), sigma, borderType=cv2.BORDER_REFLECT) - - def _average(self, data: np.ndarray) -> np.ndarray: - return cv2.filter2D(data.astype(np.float64), -1, self.avg_kernel, borderType=cv2.BORDER_REFLECT) - - def _dilate(self, data: np.ndarray) -> np.ndarray: - return (cv2.dilate(data.astype(np.uint8), self.dil_kernel) > 0).astype(np.uint8) - - def _average_all(self, data: np.ndarray) -> np.ndarray: - """Average over each spatial slice of data""" - if self.avg_kernel is not None: - return _apply_to_spatial_axes(self._average, data, (1, 2)) - - return data - - def _dilate_all(self, data: np.ndarray) -> np.ndarray: - """Dilate over each spatial slice of data""" - if self.dil_kernel is not None: - return _apply_to_spatial_axes(self._dilate, data, (1, 2)) - - return data - - def _ssim_stats( - self, - bands: np.ndarray, - is_data: np.ndarray, - local_avg: np.ndarray, - local_var: np.ndarray, - rel_tdx: int, - sigma: float, - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Calculate SSIM stats""" - ssim_max = np.empty((1, *bands.shape[1:]), dtype=np.float32) - ssim_mean = np.empty_like(ssim_max) - ssim_std = np.empty_like(ssim_max) - - bands_r = np.delete(bands, rel_tdx, axis=0) - win_avg_r = np.delete(local_avg, rel_tdx, axis=0) - var_r = np.delete(local_var, rel_tdx, axis=0) - - n_frames, _, _, n_bands = bands_r.shape - - valid_mask = np.delete(is_data, rel_tdx, axis=0) & is_data[rel_tdx, ..., 0].reshape(1, *is_data.shape[1:-1], 1) - - for b_i in range(n_bands): - local_ssim = [] - - for t_j in range(n_frames): - ssim_ij = self._red_ssim( - data_x=bands[rel_tdx, ..., b_i], - data_y=bands_r[t_j, ..., b_i], - valid_mask=valid_mask[t_j, ..., 0], - mu1=local_avg[rel_tdx, ..., b_i], - mu2=win_avg_r[t_j, ..., b_i], - sigma1_2=local_var[rel_tdx, ..., b_i], - sigma2_2=var_r[t_j, ..., b_i], - sigma=sigma, - ) - - local_ssim.append(ssim_ij) - - local_ssim = np.ma.array(np.stack(local_ssim), mask=~valid_mask) - - ssim_max[0, ..., b_i] = np.ma.max(local_ssim, axis=0).data - ssim_mean[0, ..., b_i] = np.ma.mean(local_ssim, axis=0).data - ssim_std[0, ..., b_i] = np.ma.std(local_ssim, axis=0).data - - return ssim_max, ssim_mean, ssim_std - - def _do_single_temporal_cloud_detection(self, bands: np.ndarray) -> np.ndarray: - """Performs a cloud detection process on each scene separately""" - n_times, height, width, n_bands = bands.shape - img_size = height * width - mono_proba = np.empty(n_times * img_size) - - for t_i in range(0, n_times, self.max_proc_frames): - # Extract mono features - nt_min = t_i - nt_max = min(t_i + self.max_proc_frames, n_times) - - mono_features = bands[nt_min:nt_max].reshape(-1, n_bands) - - mono_proba[nt_min * img_size : nt_max * img_size] = self._run_prediction( - self.mono_classifier, mono_features - ) - - return mono_proba[..., None] - - def _do_multi_temporal_cloud_detection(self, bands: np.ndarray, is_data: np.ndarray, sigma: float) -> np.ndarray: - """Performs a cloud detection process on multiple scenes at once""" - n_times, height, width, n_bands = bands.shape - img_size = height * width - multi_proba = np.empty(n_times * img_size) - - local_avg: np.ndarray | None = None - local_var: np.ndarray | None = None - prev_left: int | None = None - prev_right: int | None = None - - for t_idx in range(n_times): - # Extract temporal window indices - left, right = _get_window_indices(n_times, t_idx, self.max_proc_frames) - - bands_slice = bands[left:right] - is_data_slice = is_data[left:right] - masked_bands = np.ma.array(bands_slice, mask=~is_data_slice.repeat(n_bands, axis=-1)) - - # Calculate the averages/variances for the local (windowed) streaming data - if local_avg is None or (left, right) != (prev_left, prev_right): - local_avg, local_var = self._update_batches(local_avg, local_var, bands_slice, is_data_slice, sigma) - - local_var = cast(np.ndarray, local_var) - - # Interweave and concatenate - multi_features = self._extract_multi_features( - bands_slice, is_data_slice, local_avg, local_var, t_idx - left, masked_bands, sigma - ) - - multi_proba[t_idx * img_size : (t_idx + 1) * img_size] = self._run_prediction( - self.multi_classifier, multi_features - ) - - prev_left, prev_right = left, right - - return multi_proba[..., None] - - def _update_batches( - self, - local_avg: np.ndarray | None, - local_var: np.ndarray | None, - bands: np.ndarray, - is_data: np.ndarray, - sigma: float, - ) -> tuple[np.ndarray, np.ndarray]: - """Calculates or updates the window average and variance. The calculation is done per 2D image along the - temporal and band axes.""" - local_avg_func = partial(self._win_avg, sigma=sigma) - local_var_func = partial(self._win_prevar, sigma=sigma) - - # take full batch if avg/var don't exist, otherwise take only last index slice - data = bands if local_avg is None else bands[-1][np.newaxis, ...] - data_mask = is_data if local_avg is None else is_data[-1][np.newaxis, ...] - - avg_data = _apply_to_spatial_axes(local_avg_func, data, (1, 2)) - avg_data_mask = _apply_to_spatial_axes(local_avg_func, data_mask, (1, 2)) - avg_data_mask[avg_data_mask == 0.0] = 1.0 - - var_data = _apply_to_spatial_axes(local_var_func, data, (1, 2)) - - if local_avg is None or local_var is None: - local_avg = cast(np.ndarray, avg_data / avg_data_mask) - local_var = cast(np.ndarray, var_data - local_avg**2) - return local_avg, local_var - - # shift back, drop first element - local_avg[:-1] = local_avg[1:] - local_var[:-1] = local_var[1:] - - # set new element - local_avg[-1] = (avg_data / avg_data_mask)[0] - local_var[-1] = var_data[0] - local_avg[-1] ** 2 - - return local_avg, local_var - - def _extract_multi_features( # pylint: disable=too-many-locals - self, - bands: np.ndarray, - is_data: np.ndarray, - local_avg: np.ndarray, - local_var: np.ndarray, - local_t_idx: int, - masked_bands: np.ndarray, - sigma: float, - ) -> np.ndarray: - """Extracts features for a batch""" - # Compute SSIM stats - ssim_max, ssim_mean, ssim_std = self._ssim_stats(bands, is_data, local_avg, local_var, local_t_idx, sigma) - - # Compute temporal stats - temp_min = np.ma.min(masked_bands, axis=0).data[np.newaxis, ...] - temp_mean = np.ma.mean(masked_bands, axis=0).data[np.newaxis, ...] - - # Compute difference stats - t_all = len(bands) - - diff_max = (masked_bands[local_t_idx][np.newaxis, ...] - temp_min).data - coef1 = 1.0 + 1.0 / (t_all - 1) - coef2 = t_all * temp_mean / (t_all - 1) - diff_mean = (masked_bands[local_t_idx][np.newaxis, ...] * coef1 - coef2).data - - # Interweave - ssim_interweaved = np.empty((*ssim_max.shape[:-1], 3 * ssim_max.shape[-1])) - ssim_interweaved[..., 0::3] = ssim_max - ssim_interweaved[..., 1::3] = ssim_mean - ssim_interweaved[..., 2::3] = ssim_std - - temp_interweaved = np.empty((*temp_min.shape[:-1], 2 * temp_min.shape[-1])) - temp_interweaved[..., 0::2] = temp_min - temp_interweaved[..., 1::2] = temp_mean - - diff_interweaved = np.empty((*diff_max.shape[:-1], 2 * diff_max.shape[-1])) - diff_interweaved[..., 0::2] = diff_max - diff_interweaved[..., 1::2] = diff_mean - - # Put it all together - multi_features = np.concatenate( - ( - bands[local_t_idx][np.newaxis, ...], - local_avg[local_t_idx][np.newaxis, ...], - ssim_interweaved, - temp_interweaved, - diff_interweaved, - ), - axis=3, - ) - - return multi_features.reshape(-1, multi_features.shape[-1]) - - def execute(self, eopatch: EOPatch) -> EOPatch: # noqa: C901 - """Add selected features (cloud probabilities and masks) to an EOPatch instance. - - :param eopatch: Input `EOPatch` instance - :return: `EOPatch` with additional features - """ - bands = eopatch[self.data_feature][..., self.band_indices].astype(np.float32) - - is_data = eopatch[self.is_data_feature].astype(bool) - - image_size = bands.shape[1:-1] - patch_bbox = eopatch.bbox - if patch_bbox is None: - raise ValueError("Cannot run cloud masking on an EOPatch without a BBox.") - scale_factors, sigma = self._scale_factors(image_size, patch_bbox) - - is_data_sm = is_data - # Downscale if specified - if scale_factors is not None: - bands = resize_images(bands.astype(np.float32), scale_factors=scale_factors) - is_data_sm = resize_images(is_data.astype(np.uint8), scale_factors=scale_factors).astype(bool) - - mono_proba_feature, mono_mask_feature = self.mono_features - multi_proba_feature, multi_mask_feature = self.multi_features - - # Run s2cloudless if needed - if any([self.mask_feature, mono_mask_feature, mono_proba_feature]): - mono_proba = self._do_single_temporal_cloud_detection(bands) - mono_proba = mono_proba.reshape(*bands.shape[:-1], 1) - - # Upscale if necessary - if scale_factors is not None: - mono_proba = resize_images(mono_proba, new_size=image_size) - - # Average over and threshold - mono_mask = self._average_all(mono_proba) >= self.mono_threshold - - # Run SSIM-based multi-temporal classifier if needed - if any([self.mask_feature, multi_mask_feature, multi_proba_feature]): - multi_proba = self._do_multi_temporal_cloud_detection(bands, is_data_sm, sigma) - multi_proba = multi_proba.reshape(*bands.shape[:-1], 1) - - # Upscale if necessary - if scale_factors is not None: - multi_proba = resize_images(multi_proba, new_size=image_size) - - # Average over and threshold - multi_mask = self._average_all(multi_proba) >= self.multi_threshold - - if mono_mask_feature is not None: - mono_mask = self._dilate_all(mono_mask) - eopatch.mask[mono_mask_feature] = (mono_mask * is_data).astype(bool) - - if multi_mask_feature is not None: - multi_mask = self._dilate_all(multi_mask) - eopatch.mask[multi_mask_feature] = (multi_mask * is_data).astype(bool) - - # Intersect - if self.mask_feature: - inter_mask = mono_mask & multi_mask - inter_mask = self._dilate_all(inter_mask) - eopatch[self.mask_feature] = (inter_mask * is_data).astype(bool) - - if mono_proba_feature is not None: - eopatch.data[mono_proba_feature] = (mono_proba * is_data).astype(np.float32) - - if multi_proba_feature is not None: - eopatch.data[multi_proba_feature] = (multi_proba * is_data).astype(np.float32) - - return eopatch - - -def _get_window_indices(num_of_elements: int, middle_idx: int, window_size: int) -> tuple[int, int]: - """ - Returns the minimum and maximum indices to be used for indexing, lower inclusive and upper exclusive. - The window has the following properties: - 1. total size is `window_size` (unless there are not enough frames) - 2. centered around `middle_idx` if possible, otherwise shifted so that the window is contained without reducing - it's size. - """ - if window_size >= num_of_elements: - return 0, num_of_elements - - # Construct window (is not necessarily contained) - min_frame = middle_idx - window_size // 2 - max_frame = min_frame + window_size - - # Shift window so that it is inside [0, num_all_frames]. - # Only one of the following can happen because `window_size < num_of_elements` - if min_frame < 0: - return 0, window_size - if max_frame >= num_of_elements: - return num_of_elements - window_size, num_of_elements - - return min_frame, max_frame diff --git a/mask/eolearn/mask/mask_counting.py b/mask/eolearn/mask/mask_counting.py deleted file mode 100644 index 0f0440490..000000000 --- a/mask/eolearn/mask/mask_counting.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Module for generating count masks - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -from typing import Iterator - -import numpy as np - -from eolearn.core import MapFeatureTask -from eolearn.core.types import FeaturesSpecification - - -class ClassFrequencyTask(MapFeatureTask): - """Calculates frequencies of each provided class through the temporal dimension.""" - - def __init__( - self, - input_feature: FeaturesSpecification, - output_feature: FeaturesSpecification, - classes: list[int], - no_data_value: int = 0, - ): - """ - :param input_feature: A source feature from which to read the values. - :param output_feature: An output feature to which to write the frequencies. - :param classes: Classes of which frequencies to calculate. - """ - super().__init__(input_feature, output_feature) - - if not isinstance(classes, list) or not all(isinstance(x, int) for x in classes): - raise ValueError("classes argument should be a list of integers.") - - if no_data_value in classes: - raise ValueError("classes argument must not contain no_data_value") - - self.classes = classes - self.no_data_value = no_data_value - - def map_method(self, feature: np.ndarray) -> np.ndarray: - """Map method being applied to the feature that calculates the frequencies.""" - count_valid: int | np.ndarray = np.count_nonzero(feature != self.no_data_value, axis=0) - class_counts: Iterator[int | np.ndarray] = (np.count_nonzero(feature == scl, axis=0) for scl in self.classes) - - with np.errstate(invalid="ignore"): - class_frequencies = [np.divide(count, count_valid, dtype=np.float32) for count in class_counts] - - return np.concatenate(class_frequencies, axis=-1) diff --git a/mask/eolearn/mask/models/pixel_s2_cloud_detector_lightGBM_v0.2.txt b/mask/eolearn/mask/models/pixel_s2_cloud_detector_lightGBM_v0.2.txt deleted file mode 100644 index a401100ac..000000000 --- a/mask/eolearn/mask/models/pixel_s2_cloud_detector_lightGBM_v0.2.txt +++ /dev/null @@ -1,3256 +0,0 @@ -tree -version=v3 -num_class=1 -num_tree_per_iteration=1 -label_index=0 -max_feature_idx=9 -objective=binary sigmoid:1 -feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 -feature_infos=[0.0027000000700354576:2.0581998825073242] [0.043699998408555984:2.7999999523162842] [0.0083343749865889549:2.662100076675415] [0.0071093747392296791:2.7999999523162842] [0.0046812500804662704:2.7999999523162842] [0.0052718748338520527:2.8001000881195068] [0.00019999999494757503:1.6540999412536621] [0:0.80970001220703125] [0:2.7999999523162842] [9.9999997473787516e-05:2.599600076675415] -tree_sizes=66630 66758 66772 66875 66965 67058 67044 67133 67243 67304 67351 67393 67436 67453 67528 67570 67588 67666 67593 67646 67731 67747 67721 67740 67764 67737 67846 67817 67832 67846 67856 67834 67881 67901 67859 67860 67913 67915 67960 67984 67959 67919 68049 67993 67893 68002 67953 67946 68143 67902 67992 68062 67997 68007 68036 68065 68085 68091 68015 68135 68021 68057 68117 68083 68034 68073 67694 68155 67778 67973 68112 68215 67988 67974 67776 67832 68017 67926 67946 67740 67674 67876 68026 67808 68108 67328 67903 67958 67458 67812 67645 67686 67326 67867 67763 67518 67390 67834 67800 67310 67467 67831 67307 67869 67883 67841 67494 67969 67556 67449 67468 67832 67788 67673 67413 67745 67806 67840 67544 67345 67773 67803 67408 67587 67684 67930 67863 67303 67693 67774 67360 67346 67933 67196 66998 67917 67278 67823 67839 67243 67859 67298 67101 67725 67763 67403 67290 67698 67392 67382 67437 67085 67155 67458 67241 67538 67225 67775 67808 67056 67759 67810 67642 67182 67270 67573 67446 67107 67295 67353 - -Tree=0 -num_leaves=770 -num_cat=0 -split_feature=0 7 9 0 8 7 6 7 6 0 2 6 6 7 1 6 6 1 7 9 0 5 9 6 6 9 6 0 2 0 2 7 6 7 8 8 7 1 4 4 6 0 9 5 6 1 4 5 0 3 5 5 7 9 9 1 5 7 7 6 5 9 7 0 7 6 7 6 7 6 0 2 0 1 4 2 0 3 5 5 6 6 6 6 0 6 5 3 8 7 8 6 0 6 0 6 2 7 6 6 0 0 6 1 6 7 0 9 7 3 2 5 8 7 7 8 9 6 1 7 0 7 7 3 1 8 0 2 0 5 6 0 3 6 7 0 7 2 0 7 2 4 1 4 2 7 6 0 6 7 0 0 0 6 6 7 0 2 6 0 6 4 7 6 1 6 0 0 3 5 8 5 5 2 1 4 7 1 3 0 9 0 1 3 4 7 6 0 0 7 4 1 7 9 2 0 6 6 8 5 2 4 7 6 8 2 1 3 4 6 8 7 4 6 6 1 5 8 0 6 7 9 9 6 8 3 7 9 7 7 7 1 4 6 8 9 5 9 6 4 6 4 6 5 1 7 1 3 7 1 2 8 7 0 5 1 1 8 0 3 6 6 5 9 3 8 6 6 5 8 3 5 6 5 7 7 5 7 9 0 5 1 6 0 5 3 7 0 3 1 2 3 7 0 6 3 7 5 8 8 9 9 9 9 9 0 4 9 3 7 6 8 1 7 7 5 9 6 5 1 1 7 9 6 0 1 4 6 4 8 7 0 9 6 4 5 8 8 9 0 5 5 0 6 6 6 5 3 7 2 6 7 1 0 4 7 7 3 8 5 8 3 8 0 6 2 8 3 8 0 0 6 9 3 5 3 2 0 6 7 3 5 2 6 9 5 0 7 7 0 0 0 7 3 2 9 7 1 7 6 5 7 7 0 6 5 0 7 5 4 2 6 7 7 9 4 9 4 0 5 7 7 5 3 3 2 4 6 6 2 6 1 1 6 9 3 8 7 6 6 5 9 5 4 6 7 0 0 4 7 9 6 7 6 9 2 6 2 0 0 0 3 4 7 8 5 3 8 7 6 8 6 1 7 5 8 9 8 6 9 5 3 8 6 5 7 7 9 8 0 1 2 2 6 7 2 7 9 6 4 7 3 9 7 6 0 1 0 8 7 6 8 6 6 7 6 9 6 1 8 9 8 7 6 7 7 6 9 8 6 7 6 5 7 6 2 1 7 2 3 7 6 8 4 9 5 1 3 5 5 0 2 6 7 6 6 7 6 8 0 8 6 0 5 0 6 2 8 5 7 7 8 1 8 3 1 2 9 7 5 9 6 9 4 5 6 4 3 8 0 6 3 7 5 9 8 9 8 7 6 2 7 3 8 1 2 8 3 9 8 3 5 9 6 1 3 9 4 7 9 6 7 9 3 8 4 6 6 7 6 6 9 5 3 7 7 6 5 5 0 0 0 8 4 2 9 0 9 9 0 6 0 8 9 6 7 6 0 1 6 7 7 1 6 4 2 8 6 4 4 2 5 8 7 3 6 6 5 0 3 1 6 6 6 2 5 8 4 8 1 4 6 7 7 6 9 0 7 6 9 7 7 7 2 9 6 0 8 6 5 3 5 2 5 9 1 7 2 3 1 7 7 0 5 0 8 9 2 7 9 5 6 8 9 8 3 8 9 1 5 6 7 1 6 4 1 2 2 4 7 0 9 5 3 8 2 9 4 3 5 3 9 1 7 7 6 1 4 2 -split_gain=5.85767e+06 1.1017e+06 788813 551907 246464 149623 179858 139713 157604 133976 137465 115225 105932 105302 97074.7 93399.9 91641.8 65545.7 53818 49666.7 51040 43062.2 42738.8 31853.2 29424.8 27555.5 26113.2 25416.3 22300.9 21313.5 22531.4 20708.4 34502.5 24785.5 24865.5 19691 18939.1 18246.6 17842 17172.7 16477.5 19361.6 18748.5 15966.3 15756.2 15620 15217.4 15083.3 14978 19922.6 14792.6 14213.4 13777.2 13671.5 13542.1 13415.2 13078.4 12754.7 11900.5 11766.9 12783.2 11528.5 11498.4 11484.3 11356 13419.2 11291.4 17263.5 11206.6 10935.9 11113.4 10640.7 10322.7 10247.6 10077.5 9408.63 9122.72 11572.9 13699 8916.46 8912.79 8705.79 8626.16 8507.59 8409.99 8317.86 7536.57 11166 7503.18 7320.34 7294.71 7085.98 12105.5 7725.6 9317.37 6783.31 6460.37 6440.32 7025.81 6308.17 8330.75 6273.46 7656.69 6077.19 7624.7 6012.52 5963.45 5853.32 5769.07 5616.91 5609.35 14546.3 7602.22 7616.52 5624.41 5599.96 5575.73 8204.97 5444.06 5409.4 5392.91 5377.57 11843.3 5376.89 7309.84 5122.68 5061.08 6778.25 5003.63 5000.78 4782.6 4668.44 4534.76 4492.6 8740.21 4464.62 6274.63 4434.64 4423.25 4411.34 4395.95 6899.27 4516.32 4272.77 4268.87 4251.79 4984.41 4219.1 10157.9 4530.99 4294.56 4163.11 4088.45 7937.89 4009.97 3972.26 3956.33 3950.02 3921.25 3859.24 3829.55 3982.4 3806.83 3764.78 3734.74 3704.22 11255.3 3684.29 11292.3 3620.81 3607.88 6770.75 3559.22 6318.26 3554.86 3478.4 3468.8 3468 3781.68 8145.54 3443.13 3435.24 3371.77 3298.22 3278.39 3278.08 4682.73 4603.4 3274.84 3273.02 3259.74 3214.22 3191.14 3183.08 3144.17 4043.49 3095.07 3070.17 3011.3 2988.63 2967.55 2952.69 2949.74 4355.56 5313.37 2956.6 2948.08 2925.44 2924.06 2923.76 2917.68 2800.28 2736.63 3276.3 2724.96 2716.01 2709.18 2695.71 2639.06 2615.75 2592.32 2570 2566.29 2548.12 3438.07 2542.71 2542.15 2510.76 2464.27 2406.55 2367.98 2357.73 2361.89 3814.42 2357.66 6382.84 2295.04 2321.71 2277.68 2263.52 2244.03 2241.22 2226.6 2389.3 2223.35 2215.99 2202.55 2193.9 2186.52 2185.41 2174.84 4222.04 3521.61 2154.97 2146.75 2092.58 2078.83 2048.43 2825.43 2045.39 2027.14 2018 1996.43 1968.71 1955.72 5817.51 2769.22 1950.33 2016.06 1950.23 1936.16 2470.75 1923.55 1923 1922.89 1917.87 1894.16 1866.54 1863.44 1858.3 2017.16 1857.88 1855.7 2314.03 1849.88 2093.07 1830.01 3400.2 1818.87 1803.6 1801.69 1797.32 1791.77 1797.54 2365.67 1781 1762.43 1758.37 1774.57 1757.89 1753.05 2665.59 1739.5 1735.92 1722.76 4383.18 1718.8 1708.9 1695.39 1681.04 1666.21 4140.19 2003.95 1666.13 1662.98 2401.73 1647.5 1782.81 1620.3 1601.47 1598.98 1595.69 3239.99 1887.63 1835.92 1620.57 1587.32 1609.91 1586.64 1586.3 2665.49 1582.7 1578.11 1563.32 1557.64 1555.37 1541.25 1532.56 1532.2 1493.79 1485.38 1477.93 1469.98 1467.06 2374.03 1550.82 1465.58 1464.39 1757.44 1446.68 1445.79 1569.58 1439.56 1433.05 1903.13 3070.28 1425.67 1418.01 1414.87 1397.08 1394.88 1777.62 1393.1 1554.23 1920.02 1739.41 2141.29 1388.28 1384.04 2556.58 1378.32 1374.83 1373.13 1786.37 1465.44 1370.91 11704.5 2889.13 1360.94 2100.19 1360.72 1359.66 1357.01 1350.11 3899.01 1745.94 1637.32 1346.75 1333.64 1323.53 1322.84 1313.3 1295.09 1294.52 1294.48 1292.81 1289.55 6997.51 1533.42 4235.99 2200.26 1589.64 1430.82 4569.98 4032.96 1288.8 1284.79 1280.71 1312.83 1280.33 2037.21 1276.47 1275.19 1272.25 1269.04 1849.95 1259.83 1242.53 1236.14 1232.02 1223.99 1221.45 1221.41 1202.55 1197.47 1185.67 1676.86 1179.67 1179.65 1169.84 1156.78 2090.43 1151.72 1243.57 1143.77 1154.59 1136.19 1135.13 1424.5 1129.97 1128.57 1109.92 1306.7 1105.26 1101.84 3085.26 2371.51 1098.26 1097.47 1095.61 1095.18 1094.25 1091.34 1091.01 1894.21 1642.66 1089.96 1086.41 5560.68 1084.4 1082.78 1082.26 1079.42 1078.56 1077.06 1069.98 1059.51 1053.01 1046.19 3009.23 1041.88 1040.28 1021.83 1020.17 1344.8 1535.38 1017.83 3516.45 1237.98 1011.96 1010.47 1006.4 1005.27 1002.85 2026.77 1841.06 1000.46 993.251 989.602 988.171 986.826 2362.41 978.513 975.415 970.056 967.317 1050.96 1015.83 964.649 962.755 961.71 957.341 955.26 955.168 3854.05 9838.98 3712.09 952.148 952.026 949.865 1034.26 1217.29 942.461 940.87 2714.19 939.775 936.159 932.779 922.604 909.937 909.168 907.251 1990.72 903.806 970.5 1701.01 2148.39 899.925 896.496 893.166 2019.42 892.265 1064.41 890.347 888.425 884.989 884.68 1243.56 1050.93 882.795 872.384 871.429 866.178 915.576 1432.89 865.23 952.696 864.467 1164.67 863.099 861.175 1376.6 859.223 856.883 844.277 841.958 837.983 831.248 830.584 829.329 1009.63 827.759 827.382 822.567 822.404 1293.98 822.235 814.965 814.843 1026.18 809.471 808.437 825.708 807.643 805.466 803.604 802.659 802.436 936.893 799.299 798.896 797.491 795.878 1791.81 794.948 5095.14 791.693 787.344 941.904 787.009 784.282 783.091 779.829 2021.4 1287.87 778.274 1349.95 775.14 770.338 1940.38 769.773 769.377 769.205 767.132 1042.52 1607.53 766.325 760.555 754.882 753.651 750.84 743.912 742.218 737.751 731.572 730.443 727.828 725.908 723.978 719.036 843.567 1278.44 822.845 707.648 706.02 705.93 704.91 701.229 700.893 696.186 692.168 804.809 687.439 686.56 686.166 684.051 683.604 683.431 682.067 944.447 680.336 669.858 668.654 668.576 665.708 664.578 664.274 661.298 660.414 658.159 2651.18 1690.13 656.07 810.419 652.877 797.549 647.162 2000.3 643.866 642.606 688.95 685.489 644.984 641.427 638.07 636.548 634.903 631.776 627.805 627.064 623.286 619.352 617.961 617.312 1694.87 617.178 617.128 627.078 610.968 9221.62 1107.42 2119.94 1064.9 834.015 610.544 610.133 686.612 609.064 608.676 627.873 608.59 608.323 1925.97 607.678 1568.36 5391.19 704.746 606.09 602.315 1272.67 1540.52 600.434 600.412 597.197 596.445 595.253 775.419 593.902 591.753 759.98 591.129 587.647 586.412 588.066 585.533 584.495 638.971 584.331 583.422 675.007 580.78 579.186 578.962 693.264 578.575 578.01 577.277 577.215 576.649 576.25 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-Column_28=489 -Column_31=444 -Column_34=441 -Column_33=407 -Column_75=398 -Column_77=369 -Column_37=363 - -parameters: -[boosting: gbdt] -[objective: binary] -[metric: binary,none] -[tree_learner: serial] -[device_type: cpu] -[data: ] -[valid: ] -[num_iterations: 100] -[learning_rate: 0.035] -[num_leaves: 300] -[num_threads: -1] -[max_depth: -1] -[min_data_in_leaf: 512] -[min_sum_hessian_in_leaf: 0.001] -[bagging_fraction: 0.9] -[bagging_freq: 2] -[bagging_seed: 400] -[feature_fraction: 0.9] -[feature_fraction_seed: 30056] -[early_stopping_round: 0] -[max_delta_step: 0] -[lambda_l1: 0] -[lambda_l2: 0.1] -[min_gain_to_split: 0] -[drop_rate: 0.1] -[max_drop: 50] -[skip_drop: 0.5] -[xgboost_dart_mode: 0] -[uniform_drop: 0] -[drop_seed: 17869] -[top_rate: 0.2] -[other_rate: 0.1] -[min_data_per_group: 100] -[max_cat_threshold: 32] -[cat_l2: 10] -[cat_smooth: 10] -[max_cat_to_onehot: 4] -[top_k: 20] -[monotone_constraints: ] -[feature_contri: ] -[forcedsplits_filename: ] -[refit_decay_rate: 0.9] -[verbosity: -1] -[max_bin: 255] -[min_data_in_bin: 3] -[bin_construct_sample_cnt: 200000] -[histogram_pool_size: -1] -[data_random_seed: 175] -[output_model: LightGBM_model.txt] -[snapshot_freq: -1] -[input_model: ] -[output_result: LightGBM_predict_result.txt] -[initscore_filename: ] -[valid_data_initscores: ] -[pre_partition: 0] -[enable_bundle: 1] -[max_conflict_rate: 0] -[is_enable_sparse: 1] -[sparse_threshold: 0.8] -[use_missing: 1] -[zero_as_missing: 0] -[two_round: 0] -[save_binary: 0] -[enable_load_from_binary_file: 1] -[header: 0] -[label_column: ] -[weight_column: ] -[group_column: ] -[ignore_column: ] -[categorical_feature: ] -[predict_raw_score: 0] -[predict_leaf_index: 0] -[predict_contrib: 0] -[num_iteration_predict: -1] -[pred_early_stop: 0] -[pred_early_stop_freq: 10] -[pred_early_stop_margin: 10] -[convert_model_language: ] -[convert_model: gbdt_prediction.cpp] -[num_class: 1] -[is_unbalance: 0] -[scale_pos_weight: 1] -[sigmoid: 1] -[boost_from_average: 1] -[reg_sqrt: 0] -[alpha: 0.9] -[fair_c: 1] -[poisson_max_delta_step: 0.7] -[tweedie_variance_power: 1.5] -[max_position: 20] -[label_gain: ] -[metric_freq: 1] -[is_provide_training_metric: 0] -[eval_at: ] -[num_machines: 1] -[local_listen_port: 12400] -[time_out: 120] -[machine_list_filename: ] -[machines: ] -[gpu_platform_id: -1] -[gpu_device_id: -1] -[gpu_use_dp: 0] - -end of parameters - -pandas_categorical:null diff --git a/mask/eolearn/mask/py.typed b/mask/eolearn/mask/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/mask/eolearn/mask/snow_mask.py b/mask/eolearn/mask/snow_mask.py deleted file mode 100644 index 57aed8520..000000000 --- a/mask/eolearn/mask/snow_mask.py +++ /dev/null @@ -1,283 +0,0 @@ -""" -Module for snow masking - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import itertools -import logging -from abc import ABCMeta -from typing import Any - -import numpy as np -from skimage.morphology import binary_dilation, disk - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeatureSpec - -from .utils import resize_images - -LOGGER = logging.getLogger(__name__) - - -class BaseSnowMaskTask(EOTask, metaclass=ABCMeta): - """Base class for snow detection and masking""" - - def __init__( - self, - data_feature: FeatureSpec, - band_indices: list[int], - dilation_size: int = 0, - undefined_value: int = 0, - mask_name: str = "SNOW_MASK", - ): - """ - :param data_feature: EOPatch feature represented by a tuple in the form of `(FeatureType, 'feature_name')` - :param band_indices: A list containing the indices at which the required bands can be found in the data_feature. - :param dilation_size: Size of the disk in pixels for performing dilation. Value 0 means do not perform - this post-processing step. - """ - self.bands_feature = self.parse_feature(data_feature, allowed_feature_types={FeatureType.DATA}) - self.band_indices = band_indices - self.dilation_size = dilation_size - self.undefined_value = undefined_value - self.mask_feature = (FeatureType.MASK, mask_name) - - def _apply_dilation(self, snow_masks: np.ndarray) -> np.ndarray: - """Apply binary dilation for each mask in the series""" - if self.dilation_size: - snow_masks = np.array([binary_dilation(mask, disk(self.dilation_size)) for mask in snow_masks]) - return snow_masks - - -class SnowMaskTask(BaseSnowMaskTask): - """The task calculates the snow mask using the given thresholds. - - The default values were optimised based on the Sentinel-2 L1C processing level. Values might not be optimal for L2A - processing level - """ - - NDVI_THRESHOLD = 0.1 - - def __init__( - self, - data_feature: FeatureSpec, - band_indices: list[int], - ndsi_threshold: float = 0.4, - brightness_threshold: float = 0.3, - **kwargs: Any, - ): - """ - :param data_feature: EOPatch feature represented by a tuple in the form of `(FeatureType, 'feature_name')` - containing the bands 2, 3, 7, 11, i.e. (FeatureType.DATA, 'BANDS') - :param band_indices: A list containing the indices at which the required bands can be found in the data_feature. - The required bands are B03, B04, B08 and B11 and the indices should be provided in this order. If the - 'BANDS' array contains all 13 L1C bands, then `band_indices=[2, 3, 7, 11]`. If the 'BANDS' are the 12 bands - with L2A values, then `band_indices=[2, 3, 7, 10]` - :param ndsi_threshold: Minimum value of the NDSI required to classify the pixel as snow - :param brightness_threshold: Minimum value of the red band for a pixel to be classified as bright - """ - super().__init__(data_feature, band_indices, **kwargs) - self.ndsi_threshold = ndsi_threshold - self.brightness_threshold = brightness_threshold - - def execute(self, eopatch: EOPatch) -> EOPatch: - bands = eopatch[self.bands_feature][..., self.band_indices] - with np.errstate(divide="ignore", invalid="ignore"): - # (B03 - B11) / (B03 + B11) - ndsi = (bands[..., 0] - bands[..., 3]) / (bands[..., 0] + bands[..., 3]) - # (B08 - B04) / (B08 + B04) - ndvi = (bands[..., 2] - bands[..., 1]) / (bands[..., 2] + bands[..., 1]) - - ndsi_invalid, ndvi_invalid = ~np.isfinite(ndsi), ~np.isfinite(ndvi) - ndsi[ndsi_invalid] = self.undefined_value - ndvi[ndvi_invalid] = self.undefined_value - - ndi_criterion = (ndsi >= self.ndsi_threshold) | (np.abs(ndvi - self.NDVI_THRESHOLD) < self.NDVI_THRESHOLD / 2) - brightnes_criterion = bands[..., 0] >= self.brightness_threshold - snow_mask = np.where(ndi_criterion & brightnes_criterion, 1, 0) - - snow_mask = self._apply_dilation(snow_mask) - - snow_mask[ndsi_invalid | ndvi_invalid] = self.undefined_value - - eopatch[self.mask_feature] = snow_mask[..., np.newaxis].astype(bool) - return eopatch - - -class TheiaSnowMaskTask(BaseSnowMaskTask): - """Task to add a snow mask to an EOPatch. The input data is either Sentinel-2 L1C or L2A level - - Original implementation and documentation available at https://gitlab.orfeo-toolbox.org/remote_modules/let-it-snow - - ATBD https://gitlab.orfeo-toolbox.org/remote_modules/let-it-snow/blob/master/doc/atbd/ATBD_CES-Neige.pdf - - This task computes a snow mask for the input EOPatch. The `data_feature` to be used as input to the - classifier is a mandatory argument. If all required features exist already, the classifier is run. - `linear` interpolation is used for resampling of the `data_feature` and cloud probability map, while `nearest` - interpolation is used to upsample the binary cloud mask. - """ - - B10_THR = 0.015 - DEM_FACTOR = 0.00001 - - def __init__( - self, - data_feature: FeatureSpec, - band_indices: list[int], - cloud_mask_feature: FeatureSpec, - dem_feature: FeatureSpec, - dem_params: tuple[float, float] = (100, 0.1), - red_params: tuple[float, float, float, float, float] = (12, 0.3, 0.1, 0.2, 0.040), - ndsi_params: tuple[float, float, float] = (0.4, 0.15, 0.001), - b10_index: int | None = None, - **kwargs: Any, - ): - """ - :param data_feature: EOPatch feature represented by a tuple in the form of `(FeatureType, 'feature_name')` - containing the bands B3, B4, and B11 - - Example: `(FeatureType.DATA, 'ALL-BANDS')` - :param band_indices: A list containing the indices at which the required bands can be found in the bands - feature. If all L1C band values are provided, `band_indices=[2, 3, 11]`. If all L2A band values are - provided, then `band_indices=[2, 3, 10]` - :param cloud_mask_feature: EOPatch CLM feature represented by a tuple in the form of - `(FeatureType, 'feature_name')` containing the cloud mask - :param dem_feature: EOPatch DEM feature represented by a tuple in the form of `(FeatureType, 'feature_name')` - containing the digital elevation model - :param b10_index: Array index where the B10 band is stored in the bands feature. This is used to refine the - initial cloud mask - :param dem_params: Tuple with parameters pertaining DEM processing. The first value specifies the bin size - used to group DEM values, while the second value specifies the minimum snow fraction in an elevation band - to define z_s. With reference to the ATBD, the tuple is (d_z, f_t) - :param red_params: Tuple specifying parameters to process the B04 red band. The first parameter defines the - scaling factor for down-sampling the red band, the second parameter is the maximum value of the - down-sampled red band for a dark cloud pixel, the third parameter is the minimum value - to return a non-snow pixel to the cloud mask, the fourth is the minimum reflectance value to pass the 1st - snow test, and the fifth is the minimum reflectance value to pass the 2nd snow test. With reference to the - ATBD, the tuple is (r_f, r_d, r_b, r_1, r_2) - :param ndsi_params: Tuple specifying parameters for the NDSI. First parameter is the minimum value to pass the - 1st snow test, the second parameter is the minimum value to pass the 2nd snow test, and the third parameter - is the minimum snow fraction in the image to activate the pass 2 snow test. With reference to the - ATBD, the tuple is (n_1, n_2, f_s) - """ - super().__init__(data_feature, band_indices, **kwargs) - self.dem_feature = self.parse_feature(dem_feature) - self.clm_feature = self.parse_feature(cloud_mask_feature) - self.dem_params = dem_params - self.red_params = red_params - self.ndsi_params = ndsi_params - self.b10_index = b10_index - - def _resample_red(self, input_array: np.ndarray) -> np.ndarray: - """Method to resample the values of the red band - - The input array is first down-scaled using bicubic interpolation and up-scaled back using nearest neighbour - interpolation - """ - _, height, width = input_array.shape - size = (height // self.red_params[0], width // self.red_params[0]) - downscaled = resize_images(input_array[..., np.newaxis], new_size=size) - return resize_images(downscaled, new_size=(height, width)).squeeze() - - def _adjust_cloud_mask( - self, bands: np.ndarray, cloud_mask: np.ndarray, dem: np.ndarray, b10: np.ndarray | None - ) -> np.ndarray: - """Adjust existing cloud mask using cirrus band if L1C data and resampled red band - - Add to the existing cloud mask pixels found thresholding down-sampled red band and cirrus band/DEM - """ - if b10 is not None: - clm_b10 = b10 > self.B10_THR + self.DEM_FACTOR * dem - else: - clm_b10 = np.full_like(cloud_mask, True) - - criterion = (cloud_mask == 1) & (self._resample_red(bands[..., 1]) > self.red_params[1]) - return criterion | clm_b10 - - def _apply_first_pass( - self, bands: np.ndarray, ndsi: np.ndarray, clm: np.ndarray, dem: np.ndarray, clm_temp: np.ndarray - ) -> tuple[np.ndarray, np.ndarray | None, np.ndarray]: - """Apply first pass of snow detection""" - snow_mask_pass1 = ~clm_temp & (ndsi > self.ndsi_params[0]) & (bands[..., 1] > self.red_params[3]) - - clm_pass1 = clm_temp | ((bands[..., 1] > self.red_params[2]) & ~snow_mask_pass1 & clm.astype(bool)) - - min_dem, max_dem = np.min(dem), np.max(dem) - dem_edges = np.linspace(min_dem, max_dem, int(np.ceil((max_dem - min_dem) / self.dem_params[0]))) - nbins = len(dem_edges) - 1 - - dem_hist_clear_pixels, snow_frac = None, None - if nbins > 0: - snow_frac = np.zeros(shape=(bands.shape[0], nbins)) - dem_hist_clear_pixels = np.array([np.histogram(dem[~mask], bins=dem_edges)[0] for mask in clm_pass1]) - - for date, nbin in itertools.product(range(bands.shape[0]), range(nbins)): - if dem_hist_clear_pixels[date, nbin] > 0: - dem_mask = (dem_edges[nbin] <= dem) & (dem < dem_edges[nbin + 1]) - in_dem_range_clear = np.where(dem_mask & ~clm_pass1[date]) - snow_frac[date, nbin] = ( - np.sum(snow_mask_pass1[date][in_dem_range_clear]) / dem_hist_clear_pixels[date, nbin] - ) - return snow_mask_pass1, snow_frac, dem_edges - - def _apply_second_pass( - self, - bands: np.ndarray, - ndsi: np.ndarray, - dem: np.ndarray, - clm_temp: np.ndarray, - snow_mask_pass1: np.ndarray, - snow_frac: np.ndarray | None, - dem_edges: np.ndarray, - ) -> np.ndarray: - """Second pass of snow detection""" - _, height, width, _ = bands.shape - total_snow_frac = np.sum(snow_mask_pass1, axis=(1, 2)) / (height * width) - snow_mask_pass2 = np.full_like(snow_mask_pass1, False) - for date in range(bands.shape[0]): - if ( - (total_snow_frac[date] > self.ndsi_params[2]) - and snow_frac is not None - and np.any(snow_frac[date] > self.dem_params[1]) - ): - z_s = dem_edges[np.max(np.argmax(snow_frac[date] > self.dem_params[1]) - 2, 0)] - - snow_mask_pass2[date, :, :] = ( - (dem > z_s) - & ~clm_temp[date] - & (ndsi[date] > self.ndsi_params[1]) - & (bands[date, ..., 1] > self.red_params[-1]) - ) - - return snow_mask_pass2 - - def execute(self, eopatch: EOPatch) -> EOPatch: - """Run multi-pass snow detection""" - bands = eopatch[self.bands_feature][..., self.band_indices] - b10 = eopatch[self.bands_feature][..., self.b10_index] if self.b10_index is not None else None - dem = eopatch[self.dem_feature][..., 0] - clm = eopatch[self.clm_feature][..., 0] - - with np.errstate(divide="ignore", invalid="ignore"): - # (B03 - B11) / (B03 + B11) - ndsi = (bands[..., 0] - bands[..., 2]) / (bands[..., 0] + bands[..., 2]) - - ndsi_invalid = ~np.isfinite(ndsi) - ndsi[ndsi_invalid] = self.undefined_value - - clm_temp = self._adjust_cloud_mask(bands, clm, dem, b10) - - snow_mask_pass1, snow_frac, dem_edges = self._apply_first_pass(bands, ndsi, clm, dem, clm_temp) - - snow_mask_pass2 = self._apply_second_pass(bands, ndsi, dem, clm_temp, snow_mask_pass1, snow_frac, dem_edges) - - snow_mask = self._apply_dilation(snow_mask_pass1 | snow_mask_pass2) - - eopatch[self.mask_feature] = snow_mask[..., np.newaxis].astype(bool) - - return eopatch diff --git a/mask/eolearn/mask/utils.py b/mask/eolearn/mask/utils.py deleted file mode 100644 index fc200225d..000000000 --- a/mask/eolearn/mask/utils.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -Utilities for cloud masking - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from typing import Callable - -import cv2 -import numpy as np - - -def map_over_axis(data: np.ndarray, func: Callable[[np.ndarray], np.ndarray], axis: int = 0) -> np.ndarray: - """Map function func over each slice along axis. - If func changes the number of dimensions, mapping axis is moved to the front. - - Returns a new array with the combined results of mapping. - - :param data: input array - :param func: Mapping function that is applied on each slice. Outputs must have the same shape for every slice. - :param axis: Axis over which to map the function. - - :example: - - >>> data = np.ones((5,10,10)) - >>> func = lambda x: np.zeros((7,20)) - >>> res = map_over_axis(data,func,axis=0) - >>> res.shape - (5, 7, 20) - """ - # Move axis to front - data = np.moveaxis(data, axis, 0) - mapped_data = np.stack([func(data_slice) for data_slice in data]) - - # Move axis back if number of dimensions stays the same - if data.ndim == mapped_data.ndim: - mapped_data = np.moveaxis(mapped_data, 0, axis) - - return mapped_data - - -def resize_images( # type: ignore[no-untyped-def] - data, - new_size=None, - scale_factors=None, - anti_alias=True, - interpolation="linear", -): - """DEPRECATED, please use `eolearn.features.utils.spatially_resize_image` instead. - - Resizes the image(s) according to given size or scale factors. - - To specify the new scale use one of `new_size` or `scale_factors` parameters. - - :param data: input image array - :param new_size: New size of the data (height, width) - :param scale_factors: Factors (fy,fx) by which to resize the image - :param anti_alias: Use anti aliasing smoothing operation when downsampling. Default is True. - :param interpolation: Interpolation method used for resampling. - One of 'nearest', 'linear', 'cubic'. Default is 'linear'. - """ - - inter_methods = {"nearest": cv2.INTER_NEAREST, "linear": cv2.INTER_LINEAR, "cubic": cv2.INTER_CUBIC} - - # Number of dimensions of input data - ndims = data.ndim - - height_width_axis = {2: (0, 1), 3: (0, 1), 4: (1, 2)} - - # Old height and width - old_size = tuple(data.shape[axis] for axis in height_width_axis[ndims]) - - if new_size is not None and scale_factors is None: - scale_factors = tuple(new / old for old, new in zip(old_size, new_size)) - elif scale_factors is not None and new_size is None: - new_size = tuple(int(size * factor) for size, factor in zip(old_size, scale_factors)) - else: - raise ValueError("Exactly one of the arguments new_size, scale_factors must be given.") - - if interpolation not in inter_methods: - raise ValueError(f"Invalid interpolation method: {interpolation}") - - interpolation_method = inter_methods[interpolation] - downscaling = scale_factors[0] < 1 or scale_factors[1] < 1 - - def _resize2d(image: np.ndarray) -> np.ndarray: - if downscaling and anti_alias: - # Sigma computation based on skimage resize implementation - sigmas = tuple(((1 / s) - 1) / 2 for s in scale_factors) - - # Limit sigma values above 0 - sigma_y, sigma_x = tuple(max(1e-8, sigma) for sigma in sigmas) - image = cv2.GaussianBlur(image, (0, 0), sigmaX=sigma_x, sigmaY=sigma_y, borderType=cv2.BORDER_REFLECT) - - height, width = new_size - return cv2.resize(image, (width, height), interpolation=interpolation_method) - - # pylint: disable-next=unnecessary-lambda-assignment - _resize3d = lambda x: map_over_axis(x, _resize2d, axis=2) # noqa: E731 - # pylint: disable-next=unnecessary-lambda-assignment - _resize4d = lambda x: map_over_axis(x, _resize3d, axis=0) # noqa: E731 - - # Choose a resize method based on number of dimensions - resize_methods = {2: _resize2d, 3: _resize3d, 4: _resize4d} - - resize_method = resize_methods[ndims] - - return resize_method(data) diff --git a/mask/eolearn/tests/test_cloud_mask.py b/mask/eolearn/tests/test_cloud_mask.py deleted file mode 100644 index 51e8d290f..000000000 --- a/mask/eolearn/tests/test_cloud_mask.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import numpy as np -import pytest -from numpy.testing import assert_array_equal - -from eolearn.core import FeatureType -from eolearn.mask import CloudMaskTask -from eolearn.mask.cloud_mask import _get_window_indices - - -@pytest.mark.parametrize( - ("num_of_elements", "middle_idx", "window_size", "expected_indices"), - [ - (100, 0, 10, (0, 10)), - (100, 1, 10, (0, 10)), - (100, 50, 10, (45, 55)), - (271, 270, 10, (261, 271)), - (314, 314, 10, (304, 314)), - (100, 0, 11, (0, 11)), - (100, 1, 11, (0, 11)), - (100, 50, 11, (45, 56)), - (271, 270, 11, (260, 271)), - (314, 314, 11, (303, 314)), - (11, 2, 11, (0, 11)), - (11, 2, 33, (0, 11)), - ], - ids=str, -) -def test_window_indices_function(num_of_elements, middle_idx, window_size, expected_indices): - min_idx, max_idx = _get_window_indices(num_of_elements, middle_idx, window_size) - assert (min_idx, max_idx) == expected_indices - - test_list = list(range(num_of_elements)) - assert len(test_list[min_idx:max_idx]) == min(num_of_elements, window_size) - - -def test_mono_temporal_cloud_detection(test_eopatch): - add_tcm = CloudMaskTask( - data_feature=(FeatureType.DATA, "BANDS-S2-L1C"), - all_bands=True, - is_data_feature=(FeatureType.MASK, "IS_DATA"), - mono_features=("CLP_TEST", "CLM_TEST"), - mask_feature=None, - average_over=4, - dilation_size=2, - mono_threshold=0.4, - ) - eop_clm = add_tcm(test_eopatch) - - assert_array_equal(eop_clm.mask["CLM_TEST"], test_eopatch.mask["CLM_S2C"]) - assert_array_equal(eop_clm.data["CLP_TEST"], test_eopatch.data["CLP_S2C"]) - - -def test_multi_temporal_cloud_detection_downscaled(test_eopatch): - add_tcm = CloudMaskTask( - data_feature=(FeatureType.DATA, "BANDS-S2-L1C"), - processing_resolution=120, - mono_features=("CLP_TEST", "CLM_TEST"), - multi_features=("CLP_MULTI_TEST", "CLM_MULTI_TEST"), - mask_feature=(FeatureType.MASK, "CLM_INTERSSIM_TEST"), - average_over=8, - dilation_size=4, - ) - eop_clm = add_tcm(test_eopatch) - - # Check shape and type - for feature in ((FeatureType.MASK, "CLM_TEST"), (FeatureType.DATA, "CLP_TEST")): - assert eop_clm[feature].ndim == 4 - assert eop_clm[feature].shape[:-1] == eop_clm.data["BANDS-S2-L1C"].shape[:-1] - assert eop_clm[feature].shape[-1] == 1 - assert eop_clm.mask["CLM_TEST"].dtype == bool - assert eop_clm.data["CLP_TEST"].dtype == np.float32 - - # Compare mean cloud coverage with provided reference - assert np.mean(eop_clm.mask["CLM_TEST"]) == pytest.approx(np.mean(eop_clm.mask["CLM_S2C"]), abs=0.01) - assert np.mean(eop_clm.data["CLP_TEST"]) == pytest.approx(np.mean(eop_clm.data["CLP_S2C"]), abs=0.01) - - # Check if most of the same times are flagged as cloudless - cloudless = np.mean(eop_clm.mask["CLM_TEST"], axis=(1, 2, 3)) == 0 - assert np.mean(cloudless == eop_clm.label["IS_CLOUDLESS"][:, 0]) > 0.94 - - # Check multi-temporal results and final mask - assert_array_equal(eop_clm.data["CLP_MULTI_TEST"], test_eopatch.data["CLP_MULTI"]) - assert_array_equal(eop_clm.mask["CLM_MULTI_TEST"], test_eopatch.mask["CLM_MULTI"]) - assert_array_equal(eop_clm.mask["CLM_INTERSSIM_TEST"], test_eopatch.mask["CLM_INTERSSIM"]) diff --git a/mask/eolearn/tests/test_mask_counting.py b/mask/eolearn/tests/test_mask_counting.py deleted file mode 100644 index 310debf86..000000000 --- a/mask/eolearn/tests/test_mask_counting.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -import numpy as np -import pytest - -from sentinelhub import CRS, BBox - -from eolearn.core import EOPatch, FeatureType -from eolearn.mask import ClassFrequencyTask - -IN_FEATURE = (FeatureType.MASK, "TEST") -OUT_FEATURE = (FeatureType.DATA_TIMELESS, "FREQ") -# ruff: noqa: NPY002 - - -@pytest.mark.parametrize(("classes", "no_data_value"), [(["a", "b"], 0), (4, 0), (None, 0), ([1, 2, 3], 2)]) -def test_value_error(classes, no_data_value): - with pytest.raises(ValueError): - ClassFrequencyTask(IN_FEATURE, OUT_FEATURE, classes=classes, no_data_value=no_data_value) - - -def test_class_frequency(): - shape = (20, 5, 5, 2) - - data = np.random.randint(0, 5, size=shape) - data[:, 0, 0, 0] = 0 - data[:, 0, 1, 0] = 2 - - eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857))) - eopatch[IN_FEATURE] = data - - eopatch = ClassFrequencyTask(IN_FEATURE, OUT_FEATURE, [2, 1, 55])(eopatch) - result = eopatch[OUT_FEATURE] - - # all zeros through the temporal dimension should result in nan - assert np.isnan(result[0, 0, 0]) - - # frequency of 2 should be 1 - assert result[0, 1, 0] == 1 - - # frequency of 55 should be 0 - assert np.all(result[1:, :, -2] == 0) - - assert result.shape == (5, 5, 6) diff --git a/mask/eolearn/tests/test_mask_utils.py b/mask/eolearn/tests/test_mask_utils.py deleted file mode 100644 index 6f74decbe..000000000 --- a/mask/eolearn/tests/test_mask_utils.py +++ /dev/null @@ -1,13 +0,0 @@ -import numpy as np - -from eolearn.mask.utils import map_over_axis - - -def test_map_over_axis(): - data = np.ones((5, 10, 10)) - result = map_over_axis(data, lambda _: np.zeros((7, 20)), axis=0) - assert result.shape == (5, 7, 20) - result = map_over_axis(data, lambda _: np.zeros((7, 20)), axis=1) - assert result.shape == (7, 10, 20) - result = map_over_axis(data, lambda _: np.zeros((5, 10)), axis=1) - assert result.shape == (5, 10, 10) diff --git a/mask/requirements.txt b/mask/requirements.txt index 708b8e993..562050068 100644 --- a/mask/requirements.txt +++ b/mask/requirements.txt @@ -1,8 +1 @@ -# using headless version of opencv, otherwise gitlab runners fail -eo-learn-core -lightgbm>=2.0.11 -numpy -opencv-python-headless -scikit-image>=0.13.0 -sentinelhub -typing-extensions +eo-learn diff --git a/mask/setup.py b/mask/setup.py index d26a7a6c2..8d7e1ce62 100644 --- a/mask/setup.py +++ b/mask/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "mask", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_mask", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,15 +45,7 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, - package_data={ - "eolearn.mask": [ - "models/pixel_s2_cloud_detector_lightGBM_v0.2.txt", - "models/ssim_s2_cloud_detector_lightGBM_v0.2.txt", - ] - }, install_requires=parse_requirements("requirements.txt"), - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/ml_tools/MANIFEST.in b/ml_tools/MANIFEST.in deleted file mode 100644 index 970f86032..000000000 --- a/ml_tools/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include requirements*.txt -include LICENSE -include README.md -include eolearn/ml_tools/py.typed -exclude eolearn/tests/* diff --git a/ml_tools/README.md b/ml_tools/README.md index e0b5d137f..148a28ee1 100644 --- a/ml_tools/README.md +++ b/ml_tools/README.md @@ -1,5 +1,5 @@ # eolearn.ml_tools subpackage -Various tools that can be used before or after the machine learning process. +The deprecated way to install `eolearn.ml_tools`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.ml-tools.html), or see the [module task index](https://eo-learn.readthedocs.io/en/latest/eotasks.html#ml-tools). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/ml_tools/deprecated_eolearn_ml_tools/__init__.py b/ml_tools/deprecated_eolearn_ml_tools/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/ml_tools/deprecated_eolearn_ml_tools/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/ml_tools/eolearn/__init__.py b/ml_tools/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/ml_tools/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/ml_tools/eolearn/ml_tools/extra/__init__.py b/ml_tools/eolearn/ml_tools/extra/__init__.py deleted file mode 100644 index a08d1524e..000000000 --- a/ml_tools/eolearn/ml_tools/extra/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -A subfolder containing integrations with other packages -""" diff --git a/ml_tools/eolearn/ml_tools/py.typed b/ml_tools/eolearn/ml_tools/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/ml_tools/eolearn/ml_tools/tdigest.py b/ml_tools/eolearn/ml_tools/tdigest.py deleted file mode 100644 index 7b3ac95b7..000000000 --- a/ml_tools/eolearn/ml_tools/tdigest.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -The module provides an EOTask for the computation of a T-Digest representation of an EOPatch. -Requires installation of `eolearn.ml_tools[TDIGEST]`. - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -from functools import partial -from itertools import product -from typing import Any, Callable, Generator, Iterable, Literal, Union - -import numpy as np -import tdigest as td - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import FeatureSpec, FeaturesSpecification - -ModeTypes = Union[Literal["standard", "timewise", "monthly", "total"], Callable] - - -class TDigestTask(EOTask): - """ - An EOTask to compute the T-Digest representation of a chosen feature of an EOPatch. - It integrates the [T-Digest algorithm by Ted Dunning](https://arxiv.org/abs/1902.04023) to efficiently compute - quantiles of the underlying dataset into eo-learn. - - The output features of the tasks may be merged to compute a representation of the complete dataset. - That enables quantile based normalisation or statistical analysis of datasets larger than RAM in EO. - """ - - def __init__( - self, - in_feature: FeaturesSpecification, - out_feature: FeaturesSpecification, - mode: Literal["standard", "timewise", "monthly", "total"] | Callable = "standard", - pixelwise: bool = False, - filternan: bool = False, - ): - """ - :param in_feature: The input feature to compute the T-Digest representation for. - :param out_feature: The output feature where to save the T-Digest representation of the chosen feature. - :param mode: The mode to apply to the timestamps and bands. - * `'standard'` computes the T-Digest representation for each band accumulating timestamps. - * `'timewise'` computes the T-Digest representation for each band and timestamp of the chosen feature. - * `'monthly'` computes the T-Digest representation for each band accumulating the timestamps per month. - * | `'total'` computes the total T-Digest representation of the whole feature accumulating all timestamps, - | bands and pixels. Cannot be used with `pixelwise=True`. - * | Callable computes the T-Digest representation defined by the processing function given as mode. Receives - | the input_array of the feature, timestamps, shape and pixelwise and filternan keywords as an input. - :param pixelwise: Decider whether to compute the T-Digest representation accumulating pixels or per pixel. - Cannot be used with `mode='total'`. - :param filternan: Decider whether to filter out nan-values before computing the T-Digest. - """ - - self.mode = mode - - self.pixelwise = pixelwise - - self.filternan = filternan - - if self.pixelwise and self.mode == "total": - raise ValueError("Total mode does not support pixelwise=True.") - - self.in_feature = self.parse_features(in_feature, allowed_feature_types=partial(_is_input_ftype, mode=mode)) - self.out_feature = self.parse_features( - out_feature, allowed_feature_types=partial(_is_output_ftype, mode=mode, pixelwise=pixelwise) - ) - - if len(self.in_feature) != len(self.out_feature): - raise ValueError( - f"The number of input ({len(self.in_feature)}) and output features ({len(self.out_feature)}) must" - " match." - ) - - def execute(self, eopatch: EOPatch) -> EOPatch: - """ - Execute method that computes the TDigest of the chosen features. - - :param eopatch: EOPatch which the chosen input feature already exists - """ - - for in_feature_, out_feature_, shape in _looper( - in_feature=self.in_feature, out_feature=self.out_feature, eopatch=eopatch - ): - processing_func = self.mode if callable(self.mode) else _processing_function[self.mode] - eopatch[out_feature_] = processing_func( - input_array=eopatch[in_feature_], - timestamps=eopatch.timestamps, - shape=shape, - pixelwise=self.pixelwise, - filternan=self.filternan, - ) - - return eopatch - - -# auxiliary -def _is_input_ftype(feature_type: FeatureType, mode: ModeTypes) -> bool: - if mode == "standard": - return feature_type.is_image() - if mode in ("timewise", "monthly"): - return feature_type in [FeatureType.DATA, FeatureType.MASK] - return True - - -def _is_output_ftype(feature_type: FeatureType, mode: ModeTypes, pixelwise: bool) -> bool: - if callable(mode): - return True - - if mode == "standard": - return feature_type == (FeatureType.DATA_TIMELESS if pixelwise else FeatureType.SCALAR_TIMELESS) - - if mode in ("timewise", "monthly"): - return feature_type == (FeatureType.DATA if pixelwise else FeatureType.SCALAR) - - return feature_type == FeatureType.SCALAR_TIMELESS - - -def _looper( - in_feature: list[FeatureSpec], out_feature: list[FeatureSpec], eopatch: EOPatch -) -> Generator[tuple[FeatureSpec, FeatureSpec, np.ndarray], None, None]: - for in_feature_, out_feature_ in zip(in_feature, out_feature): - shape = np.array(eopatch[in_feature_].shape) - yield in_feature_, out_feature_, shape - - -def _process_standard( - input_array: np.ndarray, shape: np.ndarray, pixelwise: bool, filternan: bool, **_: Any -) -> np.ndarray: - if pixelwise: - array = np.empty(shape[-3:], dtype=object) - for i, j, k in product(range(shape[-3]), range(shape[-2]), range(shape[-1])): - array[i, j, k] = _get_tdigest(input_array[..., i, j, k], filternan) - - else: - array = np.empty(shape[-1], dtype=object) - for k in range(shape[-1]): - array[k] = _get_tdigest(input_array[..., k], filternan) - - return array - - -def _process_timewise( - input_array: np.ndarray, shape: np.ndarray, pixelwise: bool, filternan: bool, **_: Any -) -> np.ndarray: - if pixelwise: - array = np.empty(shape, dtype=object) - for time_, i, j, k in product(range(shape[0]), range(shape[1]), range(shape[2]), range(shape[3])): - array[time_, i, j, k] = _get_tdigest(input_array[time_, i, j, k], filternan) - - else: - array = np.empty(shape[[0, -1]], dtype=object) - for time_, k in product(range(shape[0]), range(shape[-1])): - array[time_, k] = _get_tdigest(input_array[time_, ..., k], filternan) - - return array - - -def _process_monthly( - input_array: np.ndarray, timestamps: Iterable, shape: np.ndarray, pixelwise: bool, filternan: bool, **_: Any -) -> np.ndarray: - midx = [] - for month_ in range(12): - midx.append(np.array([timestamp.month == month_ + 1 for timestamp in timestamps])) - - if pixelwise: - array = np.empty([12, *shape[1:]], dtype=object) - for month_, i, j, k in product(range(12), range(shape[1]), range(shape[2]), range(shape[3])): - array[month_, i, j, k] = _get_tdigest(input_array[midx[month_], i, j, k], filternan) - - else: - array = np.empty([12, shape[-1]], dtype=object) - for month_, k in product(range(12), range(shape[-1])): - array[month_, k] = _get_tdigest(input_array[midx[month_], ..., k], filternan) - - return array - - -def _process_total(input_array: np.ndarray, filternan: bool, **_: Any) -> np.ndarray: - return _get_tdigest(input_array, filternan) - - -_processing_function: dict[str, Callable] = { - "standard": _process_standard, - "timewise": _process_timewise, - "monthly": _process_monthly, - "total": _process_total, -} - - -def _get_tdigest(values: np.ndarray, filternan: bool) -> td.TDigest: - result = td.TDigest() - values_ = values.flatten() - result.batch_update(values_[~np.isnan(values_)] if filternan else values_) - return result diff --git a/ml_tools/eolearn/ml_tools/train_test_split.py b/ml_tools/eolearn/ml_tools/train_test_split.py deleted file mode 100644 index 834ee7637..000000000 --- a/ml_tools/eolearn/ml_tools/train_test_split.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Tasks used for train set preparation - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -from enum import Enum -from typing import Any - -import numpy as np - -from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.core.types import SingleFeatureSpec - -# switching to np.random.Generator would change results -# ruff: noqa: NPY002 - - -class TrainTestSplitType(Enum): - """An enum defining TrainTestSplitTask's methods of splitting the data into subsets""" - - PER_PIXEL = "per_pixel" - PER_CLASS = "per_class" - PER_VALUE = "per_value" - - -class TrainTestSplitTask(EOTask): - """Randomly assign each pixel or groups of pixels to multiple subsets (e.g., test/train/validate). - - When sampling PER_PIXEL the input feature only specifies the shape of the output feature. For PER_CLASS and - PER_VALUE the input MASK_TIMELESS feature should group together pixels with similar properties, e.g. polygon ids, - connected component ids, etc. The task then ensures that such groups are kept together (so the whole polygon is - either in train or test). - - There are three modes of split operation: - - - :attr:`PER_PIXEL` (default), where pixels are - assigned to a subset randomly, regardless of their value, - - - :attr:`PER_CLASS`, where pixels of the same value - are assigned to the same subset, - - - :attr:`PER_VALUE`, where pixels of the same value - are assigned to the same subset consistently across EOPatches. In other words, if a group of pixels of the same - value lies on multiple EOPatches, they are assigned to the same subset in all EOPatches. In this case, the `seed` - argument of the `execute` method is ignored. - - Classes are defined by a list of cumulative probabilities, passed as the `bins` argument, the same way as the `bins` - argument in `numpy.digitize `_. Valid - classes are enumerated from 1 onward and if ignore_values is provided, all values equal to it get assigned to class - 0. - - To get a train/test split as 80/20, bins argument should be provided as `bins=[0.8]`. - - To get a train/val/test split as 60/20/20, bins argument should be provided as `bins=[0.6, 0.8]`. - - Splits can also be made into as many subsets as desired, e.g., `bins=[0.1, 0.2, 0.3, 0.7, 0.9]`. - - After execution each pixel will have a value representing the train, test and/or validation set stored in the - output feature. - """ - - def __init__( - self, - input_feature: SingleFeatureSpec, - output_feature: SingleFeatureSpec, - bins: float | list[Any], - split_type: TrainTestSplitType = TrainTestSplitType.PER_PIXEL, - ignore_values: list[int] | None = None, - ): - """ - :param input_feature: The input feature to guide the split. - :param output_feature: The output feature where to save the mask. - :param bins: Cumulative probabilities of all value classes or a single float, representing a fraction. - :param split_type: Value split type, either 'PER_PIXEL', 'PER_CLASS' or 'PER_VALUE'. - :param ignore_values: A list of values in input_feature to ignore and not assign them to any subsets. - """ - self.input_feature = self.parse_feature(input_feature, allowed_feature_types=[FeatureType.MASK_TIMELESS]) - self.output_feature = self.parse_feature(output_feature, allowed_feature_types=[FeatureType.MASK_TIMELESS]) - - if isinstance(bins, float): - self.bins = [bins] - else: - self.bins = list(bins) - if np.any(np.diff(self.bins) <= 0) or self.bins[0] <= 0 or self.bins[-1] >= 1: - raise ValueError("bins argument should be a list of ascending floats inside an open interval (0, 1)") - - self.ignore_values = set() if ignore_values is None else set(ignore_values) - self.split_type = TrainTestSplitType(split_type) - - def execute(self, eopatch: EOPatch, *, seed: int | None = None) -> EOPatch: - """ - :param eopatch: input EOPatch - :param seed: An argument to be passed to numpy.random.seed function. - :return: Input EOPatch with the train set mask. - """ - if self.split_type in [TrainTestSplitType.PER_CLASS, TrainTestSplitType.PER_PIXEL]: - np.random.seed(seed) - - data = eopatch[self.input_feature] - - if self.split_type == TrainTestSplitType.PER_PIXEL: - rands = np.random.rand(*data.shape) - eopatch[self.output_feature] = np.digitize(rands, self.bins) + 1 - return eopatch - - classes = set(np.unique(data)) - self.ignore_values - output_mask = np.zeros_like(data) - - for class_id in classes: - if self.split_type == TrainTestSplitType.PER_VALUE: - np.random.seed(class_id) - - fold = np.digitize(np.random.rand(), self.bins) + 1 - output_mask[data == class_id] = fold - - eopatch[self.output_feature] = output_mask - return eopatch diff --git a/ml_tools/eolearn/ml_tools/utils.py b/ml_tools/eolearn/ml_tools/utils.py deleted file mode 100644 index 57b9074d4..000000000 --- a/ml_tools/eolearn/ml_tools/utils.py +++ /dev/null @@ -1,192 +0,0 @@ -""" -The utilities module is a collection of methods used across the eolearn package, such as checking whether two objects -are deeply equal, padding of an image, etc. - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -from typing import Any, Optional - -import numpy as np - -from sentinelhub.exceptions import deprecated_function - -from eolearn.core.exceptions import EODeprecationWarning - - -# This code was copied from https://gist.github.com/seberg/3866040 -@deprecated_function( - category=EODeprecationWarning, message_suffix="Please use `numpy.lib.stride_tricks.sliding_window_view` instead." -) -def rolling_window( # noqa: C901 - array: np.ndarray, - window: Any = (0,), - asteps: Optional[Any] = None, - wsteps: Optional[Any] = None, - axes: Optional[Any] = None, - toend: bool = True, -) -> np.ndarray: - """Create a view of `array` which for every point gives the n-dimensional neighbourhood of size window. New - dimensions are added at the end of `array` or after the corresponding original dimension. - - Examples: - - .. code-block:: python - - >>> a = np.arange(9).reshape(3,3) - >>> rolling_window(a, (2,2)) - array([[[[0, 1], - [3, 4]], - [[1, 2], - [4, 5]]], - [[[3, 4], - [6, 7]], - [[4, 5], - [7, 8]]]]) - - Or to create non-overlapping windows, but only along the first dimension: - - .. code-block:: python - - >>> rolling_window(a, (2,0), asteps=(2,1)) - array([[[0, 3], - [1, 4], - [2, 5]]]) - - Note that the `0` is discared, so that the output dimension is `3`: - - .. code-block:: python - - >>> rolling_window(a, (2,0), asteps=(2,1)).shape - (1, 3, 2) - - This is useful for example to calculate the maximum in all (overlapping) 2x2 submatrixes: - - .. code-block:: python - - >>> rolling_window(a, (2,2)).max((2,3)) - array([[4, 5], - [7, 8]]) - - Or delay embedding (3D embedding with delay 2): - - .. code-block:: python - - >>> x = np.arange(10) - >>> rolling_window(x, 3, wsteps=2) - array([[0, 2, 4], - [1, 3, 5], - [2, 4, 6], - [3, 5, 7], - [4, 6, 8], - [5, 7, 9]]) - - :param array: Array to which the rolling window is applied - :param window: Either a single integer to create a window of only the last axis or a tuple to create it for - the last len(window) axes. 0 can be used to ignore a dimension in the window. - :param asteps: Aligned at the last axis, new steps for the original array, i.e. for creation of non-overlapping - windows. (Equivalent to slicing result) - :param wsteps: Steps for the added window dimensions. These can be 0 to repeat values along the axis. - :param axes: If given, must have the same size as window. In this case window is interpreted as the size in the - dimension given by axes. E.g. a window of (2, 1) is equivalent to window=2 and axis=-2. - :param toend: If False, the new dimensions are right after the corresponding original dimension, instead of at - the end of the array. Adding the new axes at the end makes it easier to get the neighborhood, however - toend=False will give a more intuitive result if you view the whole array. - :returns: A view on `array` which is smaller to fit the windows and has windows added dimensions (0s not counting), - i.e. every point of `array` is an array of size window. - """ - # pylint: disable=too-many-branches - # pylint: disable=too-many-statements - - array = np.asarray(array) - orig_shape = np.asarray(array.shape) - window = np.atleast_1d(window).astype(int) # maybe crude to cast to int... - - if axes is not None: - axes = np.atleast_1d(axes) - new_window = np.zeros(array.ndim, dtype=int) - for axis, size in zip(axes, window): - new_window[axis] = size - window = new_window - - # Check if window is legal: - if window.ndim > 1: - raise ValueError("`window` must be one-dimensional.") - if np.any(window < 0): - raise ValueError("All elements of `window` must be larger then 1.") - if len(array.shape) < len(window): - raise ValueError("`window` length must be less or equal `array` dimension.") - - _asteps = np.ones_like(orig_shape) - if asteps is not None: - asteps = np.atleast_1d(asteps) - if asteps.ndim != 1: - raise ValueError("`asteps` must be either a scalar or one dimensional.") - if len(asteps) > array.ndim: - raise ValueError("`asteps` cannot be longer then the `array` dimension.") - # does not enforce alignment, so that steps can be same as window too. - _asteps[-len(asteps) :] = asteps - - if np.any(asteps < 1): - raise ValueError("All elements of `asteps` must be larger then 1.") - asteps = _asteps - - _wsteps = np.ones_like(window) - if wsteps is not None: - wsteps = np.atleast_1d(wsteps) - if wsteps.shape != window.shape: - raise ValueError("`wsteps` must have the same shape as `window`.") - if np.any(wsteps < 0): - raise ValueError("All elements of `wsteps` must be larger then 0.") - - _wsteps[:] = wsteps - _wsteps[window == 0] = 1 # make sure that steps are 1 for non-existing dims. - wsteps = _wsteps - - # Check that the window would not be larger than the original: - if np.any(orig_shape[-len(window) :] < window * wsteps): - raise ValueError("`window` * `wsteps` larger then `array` in at least one dimension.") - - new_shape = orig_shape # just renaming... - - # For calculating the new shape 0s must act like 1s: - _window = window.copy() - _window[_window == 0] = 1 - - new_shape[-len(window) :] += wsteps - _window * wsteps - new_shape = (new_shape + asteps - 1) // asteps - # make sure the new_shape is at least 1 in any "old" dimension (ie. steps - # is (too) large, but we do not care. - new_shape[new_shape < 1] = 1 - shape = new_shape - - strides = np.asarray(array.strides) - strides *= asteps - new_strides = array.strides[-len(window) :] * wsteps - - # The full new shape and strides: - if toend: - new_shape = np.concatenate((shape, window)) - new_strides = np.concatenate((strides, new_strides)) - else: - _ = np.zeros_like(shape) - _[-len(window) :] = window - _window = _.copy() - _[-len(window) :] = new_strides - _new_strides = _ - - new_shape = np.zeros(len(shape) * 2, dtype=int) - new_strides = np.zeros(len(shape) * 2, dtype=int) - - new_shape[::2] = shape - new_strides[::2] = strides - new_shape[1::2] = _window - new_strides[1::2] = _new_strides - - new_strides = new_strides[new_shape != 0] - new_shape = new_shape[new_shape != 0] - - return np.lib.stride_tricks.as_strided(array, shape=new_shape, strides=new_strides) diff --git a/ml_tools/eolearn/tests/test_train_split.py b/ml_tools/eolearn/tests/test_train_split.py deleted file mode 100644 index 4a8849f8f..000000000 --- a/ml_tools/eolearn/tests/test_train_split.py +++ /dev/null @@ -1,126 +0,0 @@ -""" -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" - -from typing import Any - -import numpy as np -import pytest -from numpy.testing import assert_array_equal - -from eolearn.core import EOPatch, FeatureType -from eolearn.core.utils.testing import PatchGeneratorConfig, generate_eopatch -from eolearn.ml_tools.train_test_split import TrainTestSplitTask, TrainTestSplitType - -INPUT_FEATURE = (FeatureType.MASK_TIMELESS, "TEST") -OUTPUT_FEATURE = (FeatureType.MASK_TIMELESS, "TEST_TRAIN_MASK") -INPUT_FEATURE_CONFIG = PatchGeneratorConfig(raster_shape=(300, 300), depth_range=(3, 4)) - - -@pytest.mark.parametrize( - ("bad_arg", "bad_kwargs"), - [ - (1.5, {}), - ([0.5, 0.3], {}), - ([0.5], {"split_type": None}), - ([0.5, 0.7], {"split_type": "nonsense"}), - ], -) -def test_bad_args(bad_arg: Any, bad_kwargs: Any) -> None: - with pytest.raises(ValueError): - TrainTestSplitTask(INPUT_FEATURE, OUTPUT_FEATURE, bad_arg, **bad_kwargs) - - -@pytest.fixture(name="eopatch1") -def eopatch1_fixture() -> EOPatch: - return generate_eopatch(INPUT_FEATURE, config=INPUT_FEATURE_CONFIG) - - -@pytest.fixture(name="eopatch2") -def eopatch2_fixture() -> EOPatch: - return generate_eopatch(INPUT_FEATURE, seed=69, config=INPUT_FEATURE_CONFIG) - - -def test_train_split(eopatch1: EOPatch) -> None: - """test hardcode some values in input feature and checks if hardcode values are split together""" - indices = [(0, 2, 0, 2), (0, 2, 2, 4), (2, 4, 0, 2), (2, 4, 2, 4), (0, 4, 4, 8), (4, 8, 0, 4), (4, 8, 4, 8)] - for index, (i_1, i_2, j_1, j_2) in enumerate(indices, 1): - eopatch1[INPUT_FEATURE][i_1:i_2, j_1:j_2, :] = index * 11 # reason for scope on eopatch1 fixture - - bins = [0.2, 0.5, 0.8] - expected_unique = set(range(1, len(bins) + 2)) - - split_task = TrainTestSplitTask(INPUT_FEATURE, OUTPUT_FEATURE, bins, split_type=TrainTestSplitType.PER_CLASS) - result_eopatch = split_task(eopatch1, seed=1) - assert set(np.unique(result_eopatch[OUTPUT_FEATURE])) <= expected_unique - - result = np.copy(result_eopatch[OUTPUT_FEATURE]) - unique = (np.unique(result[i_1:i_2, j_1:j_2, :], return_counts=True) for i_1, i_2, j_1, j_2 in indices) - expected = [(i_2 - i_1) * (j_2 - j_1) * result_eopatch[OUTPUT_FEATURE].shape[-1] for i_1, i_2, j_1, j_2 in indices] - - for (unique_values, unique_counts), expected_count in zip(unique, expected): - assert len(unique_values) == 1 - assert len(unique_counts) == 1 - assert unique_counts[0] == expected_count - - -def test_seed(eopatch1: EOPatch) -> None: - """Check that using seeds works as intended.""" - bins = [0.2, 0.5, 0.8] - split_task = TrainTestSplitTask(INPUT_FEATURE, OUTPUT_FEATURE, bins, split_type=TrainTestSplitType.PER_CLASS) - result_seed1 = np.copy(split_task(eopatch1, seed=1)[OUTPUT_FEATURE]) - result_seed2 = np.copy(split_task(eopatch1, seed=2)[OUTPUT_FEATURE]) - result_seed1_rerun = np.copy(split_task(eopatch1, seed=1)[OUTPUT_FEATURE]) - - assert not np.array_equal(result_seed1, result_seed2) - assert_array_equal(result_seed1, result_seed1_rerun) - - -def test_ignore_value(eopatch1: EOPatch) -> None: - bins = [0.2, 0.5, 0.7, 0.8] - expected_unique = set(range(0, len(bins) + 2)) - - split_task = TrainTestSplitTask( - (FeatureType.MASK_TIMELESS, "TEST"), - (FeatureType.MASK_TIMELESS, "BINS"), - bins, - split_type=TrainTestSplitType.PER_CLASS, - ignore_values=[2], - ) - - result_eopatch = split_task(eopatch1) - - assert set(np.unique(result_eopatch[(FeatureType.MASK_TIMELESS, "BINS")])) <= expected_unique - assert np.all(result_eopatch[(FeatureType.MASK_TIMELESS, "BINS")][result_eopatch[INPUT_FEATURE] == 2] == 0) - - -def test_train_split_per_pixel(eopatch1: EOPatch) -> None: - bins = [0.2, 0.6] - split_task = TrainTestSplitTask(INPUT_FEATURE, OUTPUT_FEATURE, bins, split_type=TrainTestSplitType.PER_PIXEL) - result_eopatch = split_task(eopatch1, seed=1) - - unique, counts = np.unique(result_eopatch[OUTPUT_FEATURE], return_counts=True) - class_percentages = np.round(counts / result_eopatch[INPUT_FEATURE].size, 1) - expected_unique = list(range(1, len(bins) + 2)) - - assert_array_equal(unique, expected_unique) - assert_array_equal(class_percentages, [0.2, 0.4, 0.4]) - - -def test_train_split_per_value(eopatch1: EOPatch, eopatch2: EOPatch) -> None: - """Test if class ids get assigned to the same subclasses in multiple eopatches""" - bins = [0.2, 0.6] - split_task = TrainTestSplitTask(INPUT_FEATURE, OUTPUT_FEATURE, bins, split_type=TrainTestSplitType.PER_VALUE) - - result_eopatch1, result_eopatch2 = split_task(eopatch1), split_task(eopatch2) - otuput1, otuput2 = result_eopatch1[OUTPUT_FEATURE], result_eopatch2[OUTPUT_FEATURE] - - unique = set(np.unique(result_eopatch1[INPUT_FEATURE])) | set(np.unique(result_eopatch2[INPUT_FEATURE])) - - for uniq in unique: - folds1 = otuput1[result_eopatch1[INPUT_FEATURE] == uniq] - folds2 = otuput2[result_eopatch2[INPUT_FEATURE] == uniq] - assert_array_equal(np.unique(folds1), np.unique(folds2)) diff --git a/ml_tools/requirements.txt b/ml_tools/requirements.txt index c9775b16b..562050068 100644 --- a/ml_tools/requirements.txt +++ b/ml_tools/requirements.txt @@ -1,3 +1 @@ -eo-learn-core -numpy -shapely +eo-learn diff --git a/ml_tools/setup.py b/ml_tools/setup.py index 03047a3ab..26a21315b 100644 --- a/ml_tools/setup.py +++ b/ml_tools/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "ml_tools", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_ml_tools", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -43,13 +45,11 @@ def get_version(): author_email="eoresearch@sinergise.com", license="MIT", packages=find_packages(), - include_package_data=True, install_requires=parse_requirements("requirements.txt"), extras_require={ "PLOTTING": parse_requirements("requirements-plotting.txt"), "TDIGEST": parse_requirements("requirements-tdigest.txt"), }, - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", diff --git a/pyproject.toml b/pyproject.toml index 68e17c231..51792ceeb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,103 @@ +[build-system] +requires = ['hatchling'] +build-backend = 'hatchling.build' + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.version] +path = 'eolearn/__init__.py' + +[tool.hatch.build.targets.sdist] +include = ['/README.md', '/LICENSE.md', '/eolearn'] + +[tool.hatch.build.targets.wheel] +include = ['/eolearn'] + +[project] +name = "eo-learn" +dynamic = ["version"] +description = "Earth observation processing framework for machine learning in Python" +readme = "README.md" +requires-python = ">= 3.8" +license = { file = "LICENSE" } +authors = [ + { name = "Sinergise EO research team", email = "eoresearch@sinergise.com" }, +] +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: Unix", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: GIS", + "Topic :: Scientific/Engineering :: Image Processing", +] +dependencies = [ + "boto3", + "fs", + "fs-s3fs", + "geopandas>=0.11.0", + "numpy>=1.20.0", + "python-dateutil", + "sentinelhub>=3.9.0", + "tqdm>=4.27", + "typing-extensions", + "opencv-python-headless", + "affine", + "rasterio>=1.2.7, <1.3.8", + "shapely", + "fiona>=1.8.18", +] + +[project.optional-dependencies] + +full = ["eo-learn[RAY,ZARR,EXTRA,VISUALIZATION]"] +ray = ["ray[default]"] +zarr = ["s3fs", "zarr"] +extra = ["numba>=0.53.0", "scikit-learn", "scipy", "s2cloudless"] +visualization = ["graphviz>=0.10.1", "jinja2", "matplotlib", "pygments"] +docs = [ + "eo-learn[FULL]", + "jupyter", + "nbsphinx", + "sphinx==7.1.2", + "sphinx_mdinclude", + "sphinx_rtd_theme==1.3.0", +] +dev = [ + "build", + "eo-learn[FULL]", + "hypothesis", + "moto", + "mypy>=0.990", + "pylint>=2.14.0", + "pytest>=7.0.0", + "pytest-cov", + "pytest-lazy-fixture", + "pytest-mock", + "twine", + "types-python-dateutil", +] + +[project.urls] +Homepage = "https://github.com/sentinel-hub/eo-learn" +Documentation = "https://eo-learn.readthedocs.io" +Issues = "https://github.com/sentinel-hub/eo-learn/issues" +Source = "https://github.com/sentinel-hub/eo-learn" +Forum = "https://forum.sentinel-hub.com" + + [tool.black] line-length = 120 preview = true @@ -56,13 +156,19 @@ ignore = [ "B904", # want `raise ... from None` instead of just `raise ...` "B028", # always demands a stacklevel argument when warning "PT011", # complains for `pytest.raises(ValueError)` but we use it a lot - "UP024", # wants to switch IOError with OSError ] -per-file-ignores = { "__init__.py" = ["F401"] } +per-file-ignores = { "__init__.py" = [ + "F401", + "I002", +], "conf.py" = [ + "I002", + "FA100", +] } exclude = [".git", "__pycache__", "build", "dist"] [tool.ruff.isort] +required-imports = ["from __future__ import annotations"] section-order = [ "future", "standard-library", @@ -88,6 +194,7 @@ disable = [ "unspecified-encoding", "unnecessary-ellipsis", "use-dict-literal", + "cyclic-import", ] [tool.pylint.design] @@ -110,7 +217,10 @@ overgeneral-exceptions = "builtins.Exception" max-nested-blocks = 7 [tool.pytest.ini_options] -markers = ["sh_integration: marks integration tests with Sentinel Hub service"] +markers = [ + "sh_integration: marks integration tests with Sentinel Hub service", + "geopedia: marks integration tests with Geopedia", +] [tool.coverage.run] source = [ @@ -128,13 +238,14 @@ source = [ omit = ["*/setup.py", "*/tests/*", "*/__init__.py"] [tool.nbqa.addopts] -ruff = ["--extend-ignore=E402,T201,B015,B018,NPY002,UP,FA"] +ruff = ["--extend-ignore=E402,T201,B015,B018,NPY002,UP,FA,I002"] # E402 -> imports on top # T201 -> print found # B015 & B018 -> useless expression (used to show values in ipynb) # NPY002 -> use RNG instead of old numpy.random # UP -> suggestions for new-style classes (future import might confuse readers) # FA -> necessary future annotations import +# I002 -> auto-inserts future annotations [tool.mypy] follow_imports = "normal" diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index 928639c88..000000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,11 +0,0 @@ -hypothesis -moto -mypy>=0.990 -pylint>=2.14.0 -pytest>=7.0.0 -pytest-cov -pytest-lazy-fixture -pytest-mock -ray[default] -twine -types-python-dateutil diff --git a/requirements-docs.txt b/requirements-docs.txt deleted file mode 100644 index 8066f501b..000000000 --- a/requirements-docs.txt +++ /dev/null @@ -1,14 +0,0 @@ - --e ./core --e ./coregistration --e ./features --e ./geometry --e ./io --e ./mask --e ./ml_tools --e ./visualization -jupyter -nbsphinx -sphinx -sphinx_mdinclude -sphinx_rtd_theme diff --git a/setup.py b/setup.py deleted file mode 100644 index 9a3b897f0..000000000 --- a/setup.py +++ /dev/null @@ -1,71 +0,0 @@ -import os - -from setuptools import setup - - -def get_long_description(): - this_directory = os.path.abspath(os.path.dirname(__file__)) - - with open(os.path.join(this_directory, "README.md"), encoding="utf-8") as f: - long_description = f.read() - - # Removes lines with an image from description - return "\n".join(line for line in long_description.split("\n") if not line.strip().startswith(" str: - return os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data", "TestEOPatch") + return os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data", "TestEOPatch") @pytest.fixture(name="test_eopatch") diff --git a/core/eolearn/tests/stats/test_save_stats.pkl b/tests/core/stats/test_save_stats.pkl similarity index 100% rename from core/eolearn/tests/stats/test_save_stats.pkl rename to tests/core/stats/test_save_stats.pkl diff --git a/tests/core/test_constants.py b/tests/core/test_constants.py new file mode 100644 index 000000000..3e7336afa --- /dev/null +++ b/tests/core/test_constants.py @@ -0,0 +1,50 @@ +""" +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import warnings + +import pytest + +from eolearn.core.constants import FeatureType +from eolearn.core.exceptions import EODeprecationWarning + +# ruff: noqa:B018 + +with warnings.catch_warnings(): + warnings.simplefilter("ignore") + TEST_CASES = [ + (FeatureType.TIMESTAMP, FeatureType.TIMESTAMPS), + (FeatureType["TIMESTAMP"], FeatureType["TIMESTAMPS"]), + (FeatureType("timestamp"), FeatureType("timestamps")), + ] + + +@pytest.mark.parametrize( + ("old_ftype", "new_ftype"), + TEST_CASES, + ids=["attribute access", "name access", "value access"], +) +def test_timestamp_featuretype(old_ftype, new_ftype) -> None: + assert old_ftype is new_ftype + + +def test_timestamps_bbox_deprecation() -> None: + with warnings.catch_warnings(): # make warnings errors + warnings.simplefilter("error") + + FeatureType.DATA + FeatureType["MASK"] + FeatureType("label") + FeatureType(FeatureType.META_INFO) + + with pytest.warns(EODeprecationWarning): + FeatureType.TIMESTAMPS + with pytest.warns(EODeprecationWarning): + FeatureType["BBOX"] + with pytest.warns(EODeprecationWarning): + FeatureType("bbox") diff --git a/core/eolearn/tests/test_core_tasks.py b/tests/core/test_core_tasks.py similarity index 83% rename from core/eolearn/tests/test_core_tasks.py rename to tests/core/test_core_tasks.py index 96ebc3a66..c80fa32f7 100644 --- a/core/eolearn/tests/test_core_tasks.py +++ b/tests/core/test_core_tasks.py @@ -5,10 +5,12 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import copy import pickle from datetime import datetime -from typing import Any, Callable, Dict, Iterable, List, Tuple, Type, Union +from typing import Any, Callable, Iterable import numpy as np import pytest @@ -24,7 +26,6 @@ AddFeatureTask, CopyTask, CreateEOPatchTask, - DeepCopyTask, DuplicateFeatureTask, EOPatch, EOTask, @@ -42,7 +43,7 @@ ZipFeatureTask, ) from eolearn.core.core_tasks import ExplodeBandsTask -from eolearn.core.types import FeatureRenameSpec, FeatureSpec, FeaturesSpecification +from eolearn.core.types import Feature, FeaturesSpecification from eolearn.core.utils.parsing import parse_features from eolearn.core.utils.testing import PatchGeneratorConfig, assert_feature_data_equal, generate_eopatch @@ -70,53 +71,53 @@ def eopatch_to_explode_fixture() -> EOPatch: return generate_eopatch((FeatureType.DATA, "bands"), config=PatchGeneratorConfig(depth_range=(8, 9))) -@pytest.mark.parametrize("task", [DeepCopyTask, CopyTask]) -def test_copy(task: Type[CopyTask], patch: EOPatch) -> None: - patch_copy = task().execute(patch) +@pytest.mark.parametrize("deep", [True, False]) +def test_copy(patch: EOPatch, deep: bool) -> None: + patch_copy = CopyTask(deep=deep).execute(patch) assert patch_copy == patch patch_copy.data["bands"][0, 0, 0, 0] += 1 - assert (patch_copy != patch) if task == DeepCopyTask else (patch_copy == patch) + assert (patch_copy != patch) if deep else (patch_copy == patch) - patch_copy.data["new"] = np.arange(1).reshape(1, 1, 1, 1) + patch_copy.data["new"] = np.ones_like(patch_copy.data["bands"]) assert "new" not in patch.data @pytest.mark.parametrize( "features", [ - [(FeatureType.MASK_TIMELESS, "mask"), (FeatureType.BBOX, None)], - [(FeatureType.TIMESTAMPS, None), (FeatureType.SCALAR, "values")], + [(FeatureType.MASK_TIMELESS, "mask"), (FeatureType.META_INFO, "something")], + [(FeatureType.SCALAR, "values")], ], ) -@pytest.mark.parametrize("task", [DeepCopyTask, CopyTask]) -def test_partial_copy(features: List[FeatureSpec], task: Type[CopyTask], patch: EOPatch) -> None: - patch_copy = task(features=features)(patch) +@pytest.mark.parametrize("deep", [True, False]) +def test_partial_copy(features: list[Feature], deep: bool, patch: EOPatch) -> None: + patch_copy = CopyTask(features=features, deep=deep)(patch) - assert set(patch_copy.get_features()) == {(FeatureType.BBOX, None), *features} + assert set(patch_copy.get_features()) == {*features} for feature in features: assert_feature_data_equal(patch[feature], patch_copy[feature]) def test_load_task(test_eopatch_path: str) -> None: full_patch = LoadTask(test_eopatch_path)(eopatch_folder=".") - assert len(full_patch.get_features()) == 30 + assert len(full_patch.get_features()) == 27 - partial_load = LoadTask(test_eopatch_path, features=[FeatureType.BBOX, FeatureType.MASK_TIMELESS]) + partial_load = LoadTask(test_eopatch_path, features=[FeatureType.MASK_TIMELESS]) partial_patch = partial_load.execute(eopatch_folder=".") - assert FeatureType.BBOX in partial_patch - assert FeatureType.TIMESTAMPS not in partial_patch + assert partial_patch.bbox is not None + assert partial_patch.timestamps is None - load_more = LoadTask(test_eopatch_path, features=[FeatureType.TIMESTAMPS]) + load_more = LoadTask(test_eopatch_path, features=[], load_timestamps=True) upgraded_partial_patch = load_more.execute(partial_patch, eopatch_folder=".") - assert FeatureType.BBOX in upgraded_partial_patch - assert FeatureType.TIMESTAMPS in upgraded_partial_patch + assert upgraded_partial_patch.bbox is not None + assert upgraded_partial_patch.timestamps is not None assert FeatureType.DATA not in upgraded_partial_patch @pytest.mark.parametrize("filesystem", [OSFS("."), S3FS("s3://fake-bucket/"), TempFS()]) @pytest.mark.parametrize("task_class", [LoadTask, SaveTask]) -def test_io_task_pickling(filesystem: FS, task_class: Type[EOTask]) -> None: +def test_io_task_pickling(filesystem: FS, task_class: type[EOTask]) -> None: task = task_class("/", filesystem=filesystem) pickled_task = pickle.dumps(task) @@ -127,13 +128,11 @@ def test_io_task_pickling(filesystem: FS, task_class: Type[EOTask]) -> None: @pytest.mark.parametrize( ("feature", "feature_data"), [ - ((FeatureType.MASK, "CLOUD MASK"), np.arange(10).reshape(5, 2, 1, 1)), + ((FeatureType.MASK_TIMELESS, "CLOUD MASK"), np.arange(10).reshape(2, 5, 1)), ((FeatureType.META_INFO, "something_else"), np.random.rand(10, 1)), - ((FeatureType.TIMESTAMPS, None), [datetime(2022, 1, 1, 10, 4, 7), datetime(2022, 1, 4, 10, 14, 5)]), ], ) -def test_add_feature(feature: FeatureSpec, feature_data: Any) -> None: - # this test should fail for bbox and timestamps after rework +def test_add_feature(feature: Feature, feature_data: Any) -> None: patch = EOPatch(bbox=DUMMY_BBOX) assert feature not in patch patch = AddFeatureTask(feature)(patch, feature_data) @@ -151,9 +150,7 @@ def test_rename_feature(patch: EOPatch) -> None: assert (f_type, f_name) not in patch, "Feature was not removed from patch. " -@pytest.mark.parametrize( - "features", [(FeatureType.DATA, "bands"), [FeatureType.TIMESTAMPS, FeatureType.DATA, (FeatureType.MASK, "CLM")]] -) +@pytest.mark.parametrize("features", [(FeatureType.DATA, "bands"), [FeatureType.DATA, (FeatureType.MASK, "CLM")]]) def test_remove_feature(features: FeaturesSpecification, patch: EOPatch) -> None: original_patch = copy.deepcopy(patch) features_to_remove = parse_features(features, patch) @@ -164,11 +161,6 @@ def test_remove_feature(features: FeaturesSpecification, patch: EOPatch) -> None assert (feature not in patch) if feature in features_to_remove else (feature in patch) -def test_remove_fails(patch: EOPatch) -> None: - with pytest.raises(ValueError): - RemoveFeatureTask((FeatureType.BBOX, None))(patch) - - @pytest.mark.parametrize( "feature_specification", [ @@ -178,7 +170,9 @@ def test_remove_fails(patch: EOPatch) -> None: ], ) @pytest.mark.parametrize("deep", [True, False]) -def test_duplicate_feature(feature_specification: List[FeatureRenameSpec], deep: bool, patch: EOPatch) -> None: +def test_duplicate_feature( + feature_specification: list[tuple[FeatureType, str, str]], deep: bool, patch: EOPatch +) -> None: patch = DuplicateFeatureTask(feature_specification, deep)(patch) for f_type, f_name, f_dup_name in feature_specification: @@ -208,7 +202,7 @@ def test_duplicate_feature_fails(patch: EOPatch) -> None: ], ) def test_initialize_feature( - init_val: float, shape: Tuple[int, ...], feature_spec: FeaturesSpecification, patch: EOPatch + init_val: float, shape: tuple[int, ...], feature_spec: FeaturesSpecification, patch: EOPatch ) -> None: expected_data = init_val * np.ones(shape) patch = InitializeFeatureTask(feature_spec, shape=shape, init_value=init_val)(patch) @@ -223,7 +217,7 @@ def test_initialize_feature( ], ) def test_initialize_feature_with_spec( - init_val: float, shape: FeatureSpec, feature_spec: FeaturesSpecification, patch: EOPatch + init_val: float, shape: Feature, feature_spec: FeaturesSpecification, patch: EOPatch ) -> None: expected_data = init_val * np.ones(patch[shape].shape) @@ -243,11 +237,10 @@ def test_initialize_feature_fails(patch: EOPatch) -> None: [ [(FeatureType.DATA, "bands")], [(FeatureType.DATA, "bands"), (FeatureType.MASK_TIMELESS, "mask")], - [(FeatureType.DATA, "bands"), (FeatureType.BBOX, None)], ], ) -def test_move_feature(features: FeatureSpec, deep: bool, patch: EOPatch) -> None: - patch_dst = MoveFeatureTask(features, deep_copy=deep)(patch, EOPatch(bbox=DUMMY_BBOX)) +def test_move_feature(features: Feature, deep: bool, patch: EOPatch) -> None: + patch_dst = MoveFeatureTask(features, deep_copy=deep)(patch, EOPatch(bbox=DUMMY_BBOX, timestamps=patch.timestamps)) for feat in features: assert feat in patch_dst @@ -276,7 +269,8 @@ def test_move_feature(features: FeatureSpec, deep: bool, patch: EOPatch) -> None ), ], ) -def test_merge_features(axis: int, features_to_merge: List[FeatureSpec], feature: FeatureSpec, patch: EOPatch) -> None: +@pytest.mark.filterwarnings("ignore::eolearn.core.exceptions.TemporalDimensionWarning") +def test_merge_features(axis: int, features_to_merge: list[Feature], feature: Feature, patch: EOPatch) -> None: patch = MergeFeatureTask(features_to_merge, feature, axis=axis)(patch) expected = np.concatenate([patch[f] for f in features_to_merge], axis=axis) @@ -305,9 +299,9 @@ def test_merge_features(axis: int, features_to_merge: List[FeatureSpec], feature ) def test_zip_features( input_features: FeaturesSpecification, - output_feature: FeatureSpec, + output_feature: Feature, zip_function: Callable, - kwargs: Dict[str, Any], + kwargs: dict[str, Any], patch: EOPatch, ) -> None: expected = zip_function(*[patch[feat] for feat in parse_features(input_features)], **kwargs) @@ -350,7 +344,7 @@ def test_map_features( input_features: FeaturesSpecification, output_features: FeaturesSpecification, map_function: Callable, - kwargs: Dict[str, Any], + kwargs: dict[str, Any], patch: EOPatch, ) -> None: original_patch = patch.copy(deep=True, features=input_features) @@ -388,7 +382,7 @@ def test_map_features_fails(patch: EOPatch) -> None: ((FeatureType.DATA, "bands"), {"axis": -1, "name": "fun_name", "bands": [4, 3, 2]}), ], ) -def test_map_kwargs_passing(input_feature: FeatureSpec, kwargs: Dict[str, Any], patch: EOPatch) -> None: +def test_map_kwargs_passing(input_feature: Feature, kwargs: dict[str, Any], patch: EOPatch) -> None: def kwargs_map(_, *, some=3, **kwargs) -> tuple: return some, kwargs @@ -414,8 +408,8 @@ def kwargs_map(_, *, some=3, **kwargs) -> tuple: ) def test_explode_bands( eopatch_to_explode: EOPatch, - feature: Tuple[FeatureType, str], - task_input: Dict[Tuple[FeatureType, str], Union[int, Iterable[int]]], + feature: Feature, + task_input: dict[Feature, int | Iterable[int]], ) -> None: patch = ExplodeBandsTask(feature, task_input)(eopatch_to_explode) assert all(new_feature in patch for new_feature in task_input) @@ -449,11 +443,15 @@ def test_extract_bands_fails(eopatch_to_explode: EOPatch) -> None: "features", [ {"bbox": DUMMY_BBOX}, - {"data": {"bands": np.arange(0, 32).reshape(1, 4, 4, 2)}, "bbox": DUMMY_BBOX}, - {"data": {"bands": np.arange(0, 32).reshape(1, 4, 4, 2), "CLP": np.ones((1, 4, 4, 2))}, "bbox": DUMMY_BBOX}, + {"data_timeless": {"bands_min": np.arange(0, 32).reshape(4, 4, 2)}, "bbox": DUMMY_BBOX}, + { + "data": {"bands": np.arange(0, 32).reshape(1, 4, 4, 2), "CLP": np.ones((1, 4, 4, 2))}, + "bbox": DUMMY_BBOX, + "timestamps": ["2015-01-03"], + }, ], ) -def test_create_eopatch(features: Dict[str, Any]) -> None: +def test_create_eopatch(features: dict[str, Any]) -> None: assert CreateEOPatchTask()(**features) == EOPatch(**features) diff --git a/core/eolearn/tests/test_eodata.py b/tests/core/test_eodata.py similarity index 73% rename from core/eolearn/tests/test_eodata.py rename to tests/core/test_eodata.py index a160f33b0..68ccae5fd 100644 --- a/core/eolearn/tests/test_eodata.py +++ b/tests/core/test_eodata.py @@ -4,9 +4,11 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import datetime import warnings -from typing import Any, List, Tuple, Union +from typing import Any import numpy as np import pytest @@ -16,11 +18,12 @@ from eolearn.core import EOPatch, FeatureType from eolearn.core.eodata_io import FeatureIO -from eolearn.core.exceptions import EODeprecationWarning -from eolearn.core.types import FeatureSpec, FeaturesSpecification +from eolearn.core.exceptions import EODeprecationWarning, TemporalDimensionWarning +from eolearn.core.types import Feature, FeaturesSpecification from eolearn.core.utils.testing import assert_feature_data_equal, generate_eopatch DUMMY_BBOX = BBox((0, 0, 1, 1), CRS(3857)) +DUMMY_TIMESTAMPS = ["2017-03-21"] # ruff: noqa: NPY002, SLF001 @@ -37,7 +40,7 @@ def mini_eopatch_fixture() -> EOPatch: def test_numpy_feature_types() -> None: - eop = EOPatch(bbox=DUMMY_BBOX) + eop = EOPatch(bbox=DUMMY_BBOX, timestamps=DUMMY_TIMESTAMPS * 2) data_examples = [] for size in range(6): @@ -118,11 +121,23 @@ def test_timestamps_invalid_feature_type(invalid_timestamps: Any) -> None: def test_invalid_characters(): + with pytest.raises(ValueError): + EOPatch(bbox=DUMMY_BBOX, mask={"mask.npy": np.arange(3 * 3 * 2).reshape(3, 3, 2)}) + eop = EOPatch(bbox=DUMMY_BBOX) with pytest.raises(ValueError): eop.data_timeless["mask.npy"] = np.arange(3 * 3 * 2).reshape(3, 3, 2) +def test_invalid_ndim(): + with pytest.raises(ValueError): + EOPatch(bbox=DUMMY_BBOX, mask_timeless={"A": np.ones((2, 4))}) + + eop = EOPatch(bbox=DUMMY_BBOX) + with pytest.raises(ValueError): + eop.mask_timeless["A"] = np.ones((2, 4)) + + def test_repr(test_eopatch_path: str) -> None: test_eopatch = EOPatch.load(test_eopatch_path) repr_str = repr(test_eopatch) @@ -143,7 +158,7 @@ def test_repr_no_crs(test_eopatch: EOPatch) -> None: def test_add_feature() -> None: bands = np.arange(2 * 3 * 3 * 2).reshape(2, 3, 3, 2) - eop = EOPatch(bbox=DUMMY_BBOX) + eop = EOPatch(bbox=DUMMY_BBOX, timestamps=DUMMY_TIMESTAMPS * 2) eop.data["bands"] = bands assert np.array_equal(eop.data["bands"], bands), "Data numpy array not stored" @@ -152,7 +167,7 @@ def test_add_feature() -> None: def test_simplified_feature_operations() -> None: bands = np.arange(2 * 3 * 3 * 2).reshape(2, 3, 3, 2) feature = FeatureType.DATA, "TEST-BANDS" - eop = EOPatch(bbox=DUMMY_BBOX) + eop = EOPatch(bbox=DUMMY_BBOX, timestamps=DUMMY_TIMESTAMPS * 2) eop[feature] = bands assert np.array_equal(eop[feature], bands), "Data numpy array not stored" @@ -165,10 +180,9 @@ def test_simplified_feature_operations() -> None: (FeatureType.MASK, "C"), (FeatureType.MASK_TIMELESS, "E"), (FeatureType.META_INFO, "beep"), - (FeatureType.TIMESTAMPS, None), ], ) -def test_delete_existing_feature(feature_to_delete: FeatureSpec, mini_eopatch: EOPatch) -> None: +def test_delete_existing_feature(feature_to_delete: Feature, mini_eopatch: EOPatch) -> None: old = mini_eopatch.copy(deep=True) del mini_eopatch[feature_to_delete] @@ -179,7 +193,7 @@ def test_delete_existing_feature(feature_to_delete: FeatureSpec, mini_eopatch: E assert_feature_data_equal(mini_eopatch[feature], old[feature]) -@pytest.mark.parametrize("feature_type", [FeatureType.DATA, FeatureType.TIMESTAMPS]) +@pytest.mark.parametrize("feature_type", [FeatureType.DATA, FeatureType.META_INFO]) def test_delete_existing_feature_type(feature_type: FeatureType, mini_eopatch: EOPatch) -> None: old = mini_eopatch.copy(deep=True) @@ -191,12 +205,6 @@ def test_delete_existing_feature_type(feature_type: FeatureType, mini_eopatch: E assert_feature_data_equal(old[ftype, fname], mini_eopatch[ftype, fname]) -@pytest.mark.parametrize("bbox_feature", [FeatureType.BBOX, (FeatureType.BBOX, None)]) -def test_cannot_delete_bbox(bbox_feature: Union[FeatureType, FeatureSpec], mini_eopatch: EOPatch) -> None: - with pytest.raises(ValueError): - del mini_eopatch[bbox_feature] - - def test_delete_fail_on_nonexisting_feature(mini_eopatch: EOPatch) -> None: with pytest.raises(KeyError): del mini_eopatch[(FeatureType.DATA, "not_here")] @@ -224,6 +232,20 @@ def test_deep_copy(test_eopatch: EOPatch) -> None: assert test_eopatch != eopatch_copy +@pytest.mark.parametrize("deep", [True, False]) +def test_copy_full_when_no_featuers_specified(test_eopatch: EOPatch, deep: bool) -> None: + """In case no features are specified timestamps should be automatically copied.""" + micro_patch = test_eopatch.copy(features=[FeatureType.MASK_TIMELESS], copy_timestamps=True) + + assert ( + micro_patch.copy( + deep=deep, + ).timestamps + is not None + ) + assert micro_patch.copy(deep=deep, features=[FeatureType.MASK_TIMELESS]).timestamps is None + + @pytest.mark.parametrize("features", [..., [(FeatureType.MASK, "CLM")]]) def test_copy_lazy_loaded_patch(test_eopatch_path: str, features: FeaturesSpecification) -> None: # shallow copy @@ -262,7 +284,29 @@ def test_copy_features(test_eopatch: EOPatch) -> None: eopatch_copy = test_eopatch.copy(features=[feature]) assert test_eopatch != eopatch_copy assert eopatch_copy[feature] is test_eopatch[feature] - assert eopatch_copy.timestamps == [] + assert len(eopatch_copy.data) == 0 + + +@pytest.mark.parametrize("deep", [True, False]) +@pytest.mark.parametrize( + ("copy_timestamps", "features", "should_copy"), + [ + ("auto", ..., True), + ("auto", [(FeatureType.MASK_TIMELESS, ...)], False), + ("auto", [(FeatureType.DATA, ...)], True), + (False, [(FeatureType.DATA, ...)], False), + (True, [(FeatureType.DATA, ...)], True), + (True, [], True), + (True, {FeatureType.MASK_TIMELESS: ...}, True), + ], + ids=str, +) +def test_copy_timestamps(test_eopatch: EOPatch, deep, copy_timestamps, features, should_copy): + with warnings.catch_warnings(): + if not copy_timestamps: + warnings.simplefilter("ignore", TemporalDimensionWarning) # produces temporally ill defined patch + eopatch_copy = test_eopatch.copy(features=features, deep=deep, copy_timestamps=copy_timestamps) + assert (eopatch_copy.timestamps is not None) == should_copy @pytest.mark.parametrize( @@ -270,29 +314,23 @@ def test_copy_features(test_eopatch: EOPatch) -> None: [ (FeatureType.DATA, "BANDS-S2-L1C"), (FeatureType.MASK, "CLM"), - (FeatureType.BBOX, ...), - (FeatureType.TIMESTAMPS, None), ], ) def test_contains(ftype: FeatureType, fname: str, test_eopatch: EOPatch) -> None: assert ftype in test_eopatch assert (ftype, fname) in test_eopatch - if ftype == FeatureType.BBOX: - with warnings.catch_warnings(): - warnings.simplefilter("ignore", EODeprecationWarning) - test_eopatch.bbox = None - elif ftype == FeatureType.TIMESTAMPS: - test_eopatch.timestamps = [] - else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", (EODeprecationWarning, TemporalDimensionWarning)) del test_eopatch[ftype, fname] assert (ftype, fname) not in test_eopatch def test_equals() -> None: - eop1 = EOPatch(bbox=DUMMY_BBOX, data={"bands": np.arange(2 * 3 * 3 * 2, dtype=np.float32).reshape(2, 3, 3, 2)}) - eop2 = EOPatch(bbox=DUMMY_BBOX, data={"bands": np.arange(2 * 3 * 3 * 2, dtype=np.float32).reshape(2, 3, 3, 2)}) + patch_def = dict(bbox=DUMMY_BBOX, timestamps=DUMMY_TIMESTAMPS * 2) + eop1 = EOPatch(**patch_def, data={"bands": np.arange(2 * 3 * 3 * 2, dtype=np.float32).reshape(2, 3, 3, 2)}) + eop2 = EOPatch(**patch_def, data={"bands": np.arange(2 * 3 * 3 * 2, dtype=np.float32).reshape(2, 3, 3, 2)}) assert eop1 == eop2 assert eop1.data == eop2.data @@ -320,8 +358,8 @@ def test_equals() -> None: @pytest.fixture(name="eopatch_spatial_dim") def eopatch_spatial_dim_fixture() -> EOPatch: - patch = EOPatch(bbox=DUMMY_BBOX) - patch.data["A"] = np.zeros((1, 2, 3, 4)) + patch = EOPatch(bbox=DUMMY_BBOX, timestamps=DUMMY_TIMESTAMPS * 4) + patch.data["A"] = np.zeros((4, 2, 3, 4)) patch.mask["B"] = np.ones((4, 3, 2, 1), dtype=np.uint8) patch.mask_timeless["C"] = np.zeros((4, 5, 1), dtype=np.uint8) return patch @@ -335,9 +373,7 @@ def eopatch_spatial_dim_fixture() -> EOPatch: ((FeatureType.MASK_TIMELESS, "C"), (4, 5)), ], ) -def test_get_spatial_dimension( - feature: Tuple[FeatureType, str], expected_dim: Tuple[int, int], eopatch_spatial_dim: EOPatch -) -> None: +def test_get_spatial_dimension(feature: Feature, expected_dim: tuple[int, int], eopatch_spatial_dim: EOPatch) -> None: assert eopatch_spatial_dim.get_spatial_dimension(*feature) == expected_dim @@ -353,14 +389,12 @@ def test_get_spatial_dimension( (FeatureType.MASK, "D"), (FeatureType.MASK_TIMELESS, "E"), (FeatureType.META_INFO, "beep"), - (FeatureType.BBOX, None), - (FeatureType.TIMESTAMPS, None), ], ), - (EOPatch(bbox=DUMMY_BBOX), [(FeatureType.BBOX, None)]), + (EOPatch(bbox=DUMMY_BBOX), []), ], ) -def test_get_features(patch: EOPatch, expected_features: List[FeatureSpec]) -> None: +def test_get_features(patch: EOPatch, expected_features: list[Feature]) -> None: assert patch.get_features() == expected_features @@ -437,3 +471,45 @@ def test_bbox_none_deprecation(): eop.bbox = None assert eop.bbox is None + + +def test_temporal_warnings(): + # assert no warnings for correct patches + with warnings.catch_warnings(): + warnings.simplefilter("error") + + EOPatch(bbox=DUMMY_BBOX) + EOPatch(bbox=DUMMY_BBOX, timestamps=2 * DUMMY_TIMESTAMPS) + EOPatch(bbox=DUMMY_BBOX, timestamps=2 * DUMMY_TIMESTAMPS, data_timeless={"beep": np.ones((3, 4, 5))}) + EOPatch(bbox=DUMMY_BBOX, timestamps=2 * DUMMY_TIMESTAMPS, data={"beep": np.ones((2, 3, 4, 5))}) + + # missmatch in init + with pytest.warns(TemporalDimensionWarning): + EOPatch(bbox=DUMMY_BBOX, timestamps=3 * DUMMY_TIMESTAMPS, data={"beep": np.ones((2, 3, 4, 5))}) + + # no timestamps in init + with pytest.warns(TemporalDimensionWarning): + EOPatch(bbox=DUMMY_BBOX, data={"beep": np.ones((2, 3, 4, 5))}) + + # setting to eopatch without timestamps + patch = EOPatch(bbox=DUMMY_BBOX) + with pytest.warns(TemporalDimensionWarning): + patch.data = {"beep": np.ones((2, 3, 4, 5))} + with pytest.warns(TemporalDimensionWarning): + patch[FeatureType.MASK, "boop"] = np.ones((2, 3, 4, 5), dtype=int) + + # setting features with wrong dim + patch = EOPatch(bbox=DUMMY_BBOX, timestamps=3 * DUMMY_TIMESTAMPS) + with pytest.warns(TemporalDimensionWarning): + patch.data = {"beep": np.ones((2, 3, 4, 5))} + with pytest.warns(TemporalDimensionWarning): + patch[FeatureType.MASK, "boop"] = np.ones((2, 3, 4, 5), dtype=int) + + # switching timestamps to wrong dim + patch = EOPatch(bbox=DUMMY_BBOX, timestamps=2 * DUMMY_TIMESTAMPS, data={"beep": np.ones((2, 3, 4, 5))}) + with pytest.warns(TemporalDimensionWarning): + patch.timestamps = 3 * DUMMY_TIMESTAMPS + + # switching timestamps to wrong dim but no temporal features is OK + patch = EOPatch(bbox=DUMMY_BBOX, timestamps=2 * DUMMY_TIMESTAMPS, data_timeless={"beep": np.ones((2, 3, 4))}) + patch.timestamps = DUMMY_TIMESTAMPS diff --git a/tests/core/test_eodata_io.py b/tests/core/test_eodata_io.py new file mode 100644 index 000000000..e9692cd96 --- /dev/null +++ b/tests/core/test_eodata_io.py @@ -0,0 +1,743 @@ +""" +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import datetime +import os +import sys +import warnings +from typing import Any + +import fs +import geopandas as gpd +import numpy as np +import pytest +from fs.base import FS +from fs.errors import CreateFailed, ResourceNotFound +from fs.tempfs import TempFS +from geopandas import GeoDataFrame +from moto import mock_s3 +from numpy.testing import assert_array_equal +from shapely.geometry import Point + +from sentinelhub import CRS, BBox + +from eolearn.core import EOPatch, FeatureType, LoadTask, OverwritePermission, SaveTask, merge_eopatches +from eolearn.core.constants import TIMESTAMP_COLUMN +from eolearn.core.eodata_io import ( + FeatureIO, + FeatureIOBBox, + FeatureIOGeoDf, + FeatureIOJson, + FeatureIONumpy, + FeatureIOTimestamps, +) +from eolearn.core.exceptions import EODeprecationWarning +from eolearn.core.types import FeaturesSpecification +from eolearn.core.utils.parsing import FeatureParser +from eolearn.core.utils.testing import assert_feature_data_equal, generate_eopatch + +FS_LOADERS = [TempFS, pytest.lazy_fixture("create_mocked_s3fs")] + +DUMMY_BBOX = BBox((0, 0, 1, 1), CRS.WGS84) + + +def _skip_when_appropriate(fs_loader: type[FS] | None, use_zarr: bool) -> None: + if not use_zarr: + return + if "zarr" not in sys.modules: + pytest.skip(reason="Cannot test zarr without dependencies.") + if fs_loader is not None and not isinstance(fs_loader, type): # S3FS because is a lazy fixture and not a class + # https://github.com/aio-libs/aiobotocore/issues/755 + pytest.skip(reason="Moto3 and aiobotocore (used by s3fs used by zarr) do not interact properly.") + + +@pytest.fixture(name="_silence_warnings") +def _silence_warnings_fixture(): + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=EODeprecationWarning) + yield + + +@pytest.fixture(name="eopatch") +def eopatch_fixture(): + eopatch = generate_eopatch( + { + FeatureType.DATA: ["data"], + FeatureType.MASK_TIMELESS: ["mask", "mask2"], + FeatureType.SCALAR: ["my scalar with spaces"], + FeatureType.SCALAR_TIMELESS: ["my timeless scalar with spaces"], + FeatureType.META_INFO: ["something", "something-else"], + } + ) + eopatch.vector["my-df"] = GeoDataFrame( + { + "values": [1, 2], + TIMESTAMP_COLUMN: [datetime.datetime(2017, 1, 1, 10, 4, 7), datetime.datetime(2017, 1, 4, 10, 14, 5)], + "geometry": [DUMMY_BBOX.geometry, DUMMY_BBOX.geometry], + }, + crs=DUMMY_BBOX.crs.pyproj_crs(), + ) + eopatch.vector_timeless["empty-vector"] = GeoDataFrame( + {"values": [], "geometry": []}, crs=eopatch.bbox.crs.pyproj_crs() + ) + + return eopatch + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +def test_saving_in_empty_folder(eopatch, fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + with fs_loader() as temp_fs: + if isinstance(temp_fs, TempFS): + eopatch.save(temp_fs.root_path, use_zarr=use_zarr) + else: + eopatch.save("/", filesystem=temp_fs, use_zarr=use_zarr) + assert temp_fs.exists(f"/mask_timeless/mask.{'zarr' if use_zarr else 'npy.gz'}") + + subfolder = "new-subfolder" + eopatch.save("new-subfolder", filesystem=temp_fs, use_zarr=use_zarr) + assert temp_fs.exists(f"/{subfolder}/bbox.geojson") + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +@pytest.mark.usefixtures("_silence_warnings") +def test_saving_in_non_empty_folder(eopatch, fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + with fs_loader() as temp_fs: + empty_file = "foo.txt" + + with temp_fs.open(empty_file, "w"): + pass + + eopatch.save("/", filesystem=temp_fs, use_zarr=use_zarr) + assert temp_fs.exists(empty_file) + + eopatch.save( + "/", overwrite_permission=OverwritePermission.OVERWRITE_PATCH, filesystem=temp_fs, use_zarr=use_zarr + ) + assert not temp_fs.exists(empty_file) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +@pytest.mark.usefixtures("_silence_warnings") +def test_overwriting_non_empty_folder(eopatch, fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + with fs_loader() as temp_fs: + eopatch.save("/", filesystem=temp_fs, use_zarr=use_zarr) + eopatch.save( + "/", filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_FEATURES, use_zarr=use_zarr + ) + eopatch.save( + "/", filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_PATCH, use_zarr=use_zarr + ) + + add_eopatch = EOPatch(bbox=eopatch.bbox) + add_eopatch.data_timeless["some data"] = np.empty((3, 3, 2)) + add_eopatch.save("/", filesystem=temp_fs, overwrite_permission=OverwritePermission.ADD_ONLY, use_zarr=use_zarr) + with pytest.raises(ValueError): + add_eopatch.save( + "/", filesystem=temp_fs, overwrite_permission=OverwritePermission.ADD_ONLY, use_zarr=use_zarr + ) + + new_eopatch = EOPatch.load("/", filesystem=temp_fs, lazy_loading=False) + assert new_eopatch == merge_eopatches(eopatch, add_eopatch) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +@pytest.mark.parametrize( + ("save_features", "load_features"), + [ + (..., ...), + ([(FeatureType.DATA, ...)], [(FeatureType.DATA, ...)]), + ([(FeatureType.DATA, "data"), FeatureType.MASK_TIMELESS], [(FeatureType.DATA, ...)]), + ([(FeatureType.META_INFO, ...)], [(FeatureType.META_INFO, "something")]), + ([(FeatureType.DATA, "data")], ...), + ], + ids=str, +) +def test_save_load_partial( + eopatch: EOPatch, + fs_loader: type[FS], + save_features: FeaturesSpecification, + load_features: FeaturesSpecification, + use_zarr: bool, +): + _skip_when_appropriate(fs_loader, use_zarr) + with fs_loader() as temp_fs: + eopatch.save("/", features=save_features, filesystem=temp_fs, use_zarr=use_zarr) + loaded_eopatch = EOPatch.load("/", features=load_features, filesystem=temp_fs) + + # have to check features that have been saved and then loaded (double filtering) + features_to_load = FeatureParser(load_features).get_features(eopatch) + for feature in FeatureParser(save_features).get_features(eopatch): + if feature in features_to_load: + assert feature in loaded_eopatch + else: + assert feature not in loaded_eopatch + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +def test_save_add_only_features(eopatch, fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + features = [ + (FeatureType.MASK_TIMELESS, "mask"), + FeatureType.MASK, + FeatureType.VECTOR, + (FeatureType.SCALAR, ...), + (FeatureType.META_INFO, "something"), + ] + + with fs_loader() as temp_fs: + eopatch.save( + "/", + filesystem=temp_fs, + features=features, + overwrite_permission=OverwritePermission.ADD_ONLY, + use_zarr=use_zarr, + ) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +def test_bbox_always_saved(eopatch, fs_loader): + with fs_loader() as temp_fs: + eopatch.save("/", filesystem=temp_fs, features=[FeatureType.DATA]) + assert temp_fs.exists("/bbox.geojson") + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize( + ("save_timestamps", "features", "should_save"), + [ + ("auto", ..., True), + ("auto", [(FeatureType.MASK_TIMELESS, ...)], False), + ("auto", [(FeatureType.DATA, ...)], True), + (False, [(FeatureType.DATA, ...)], False), + (True, [(FeatureType.DATA, ...)], True), + (True, [], True), + (True, {FeatureType.MASK_TIMELESS: ...}, True), + ], + ids=str, +) +def test_save_timestamps(eopatch, fs_loader, save_timestamps, features, should_save): + with fs_loader() as temp_fs: + eopatch.save("/", filesystem=temp_fs, features=features, save_timestamps=save_timestamps) + assert temp_fs.exists("/timestamps.json") == should_save + + +def test_auto_save_load_timestamps(eopatch): + """Saving and loading with default values should process timestamps.""" + test_patch = EOPatch(bbox=eopatch.bbox, timestamps=eopatch.timestamps) # no temporal stuff + with TempFS() as temp_fs: + test_patch.save("/", filesystem=temp_fs) + assert temp_fs.exists("/timestamps.json") + assert EOPatch.load("/", filesystem=temp_fs).timestamps is not None + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize( + ("load_timestamps", "features", "should_load"), + [ + ("auto", ..., True), + ("auto", [(FeatureType.MASK_TIMELESS, ...)], False), + ("auto", [(FeatureType.DATA, ...)], True), + (False, [(FeatureType.DATA, ...)], False), + (True, [(FeatureType.DATA, ...)], True), + (True, [], True), + (True, {FeatureType.MASK_TIMELESS: ...}, True), + ], + ids=str, +) +def test_load_timestamps(eopatch, fs_loader, load_timestamps, features, should_load): + with fs_loader() as temp_fs: + eopatch.save("/", filesystem=temp_fs) + loaded_patch = EOPatch.load("/", filesystem=temp_fs, features=features, load_timestamps=load_timestamps) + assert (loaded_patch.timestamps is not None) == should_load + + +@pytest.mark.parametrize("features", [..., [FeatureType.DATA_TIMELESS]]) +@pytest.mark.filterwarnings("ignore::eolearn.core.exceptions.TemporalDimensionWarning") +def test_load_timestamps_when_nonexistant(eopatch, features): + with TempFS() as temp_fs: + eopatch.save("/", filesystem=temp_fs, save_timestamps=False) + loaded_patch = EOPatch.load("/", filesystem=temp_fs, features=features, load_timestamps="auto") + assert loaded_patch.timestamps is None + with pytest.raises(OSError): + loaded_patch = EOPatch.load("/", filesystem=temp_fs, features=features, load_timestamps=True) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +def test_temporally_empty_patch_io(fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + eopatch = EOPatch(bbox=DUMMY_BBOX, data={"data": np.ones((0, 1, 2, 3))}, timestamps=[]) + with fs_loader() as temp_fs: + eopatch.save("/", filesystem=temp_fs, use_zarr=use_zarr) + assert eopatch == EOPatch.load("/", filesystem=temp_fs) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +@pytest.mark.usefixtures("_silence_warnings") +def test_overwrite_failure(fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + eopatch = EOPatch(bbox=DUMMY_BBOX) + mask = np.arange(3 * 3 * 2).reshape(3, 3, 2) + eopatch.mask_timeless["mask"] = mask + eopatch.mask_timeless["Mask"] = mask + + with fs_loader() as temp_fs, pytest.raises(OSError): + eopatch.save("/", filesystem=temp_fs, use_zarr=use_zarr) + + with fs_loader() as temp_fs: + eopatch.save( + "/", + filesystem=temp_fs, + features=[(FeatureType.MASK_TIMELESS, "mask")], + overwrite_permission=OverwritePermission.OVERWRITE_PATCH, + use_zarr=use_zarr, + ) + + with pytest.raises(OSError): + eopatch.save( + "/", + filesystem=temp_fs, + features=[(FeatureType.MASK_TIMELESS, "Mask")], + overwrite_permission=OverwritePermission.ADD_ONLY, + use_zarr=use_zarr, + ) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("compress_level", [0, 1]) +def test_compression_deprecation(eopatch, fs_loader, compress_level: int | None): + folder = "foo-folder" + + with warnings.catch_warnings(): # make warnings errors + warnings.simplefilter("error") + + with fs_loader() as temp_fs: + SaveTask(folder, filesystem=temp_fs) + + with fs_loader() as temp_fs, pytest.warns(EODeprecationWarning): + SaveTask(folder, filesystem=temp_fs, compress_level=compress_level) + + with fs_loader() as temp_fs, pytest.warns(EODeprecationWarning): + eopatch.save(folder, filesystem=temp_fs, compress_level=compress_level) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +def test_save_and_load_tasks(eopatch, fs_loader, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + folder = "foo-folder" + patch_folder = "patch-folder" + with fs_loader() as temp_fs: + temp_fs.makedir(folder) + + save_task = SaveTask(folder, filesystem=temp_fs, use_zarr=use_zarr) + load_task = LoadTask(folder, filesystem=temp_fs, lazy_loading=False) + + saved_eop = save_task(eopatch, eopatch_folder=patch_folder) + bbox_path = fs.path.join(folder, patch_folder, "bbox.geojson") + assert temp_fs.exists(bbox_path) + assert saved_eop == eopatch + + eop = load_task(eopatch_folder=patch_folder) + assert eop == eopatch + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +def test_fail_saving_nonexistent_feature(eopatch, fs_loader): + features = [(FeatureType.DATA, "nonexistent")] + with fs_loader() as temp_fs, pytest.raises(ValueError): + eopatch.save("/", filesystem=temp_fs, features=features) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +def test_fail_loading_nonexistent_feature(fs_loader): + for features in [[(FeatureType.DATA, "nonexistent")], [(FeatureType.META_INFO, "nonexistent")]]: + with fs_loader() as temp_fs, pytest.raises(OSError): + EOPatch.load("/", filesystem=temp_fs, features=features) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +def test_nonexistent_location(fs_loader, use_zarr: bool): + """In the event of a path not existing all save actions should create the path, and loads should fail.""" + _skip_when_appropriate(fs_loader, use_zarr) + path = "./folder/subfolder/new-eopatch/" + eopatch = EOPatch(bbox=DUMMY_BBOX) + + # IO on nonexistent path inside a temporary FS + with fs_loader() as temp_fs: + with pytest.raises(ResourceNotFound): + EOPatch.load(path, filesystem=temp_fs) + + eopatch.save(path, filesystem=temp_fs, use_zarr=use_zarr) + + # IO on nonexistent path (no fs specified) + with TempFS() as temp_fs: + full_path = os.path.join(temp_fs.root_path, path) + with pytest.raises(CreateFailed): + EOPatch.load(full_path) + + load_task = LoadTask(full_path) + with pytest.raises(CreateFailed): + load_task.execute() + + eopatch.save(full_path, use_zarr=use_zarr) + assert os.path.exists(full_path) + + # SaveTask on nonexistent path (no fs specified) + with TempFS() as temp_fs: + full_path = os.path.join(temp_fs.root_path, path) + SaveTask(full_path, use_zarr=use_zarr).execute(eopatch) + assert os.path.exists(full_path) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +def test_cleanup_different_compression(fs_loader, eopatch): + folder = "foo-folder" + patch_folder = "patch-folder" + with fs_loader() as temp_fs: + temp_fs.makedirs(fs.path.join(folder, patch_folder)) + save_task = SaveTask(folder, filesystem=temp_fs, overwrite_permission="OVERWRITE_FEATURES") + + # need to manually save uncompressed features + bbox_path = fs.path.join(folder, patch_folder, "bbox.geojson") + FeatureIOBBox.save(eopatch.bbox, temp_fs, bbox_path, compress_level=0) + + timestamps_path = fs.path.join(folder, patch_folder, "timestamps.json") + FeatureIOTimestamps.save(eopatch.timestamps, temp_fs, timestamps_path, compress_level=0) + + ftype, fname = (FeatureType.MASK_TIMELESS, "mask") + temp_fs.makedir(fs.path.join(folder, patch_folder, ftype.value)) + mask_timeless_path = fs.path.join(folder, patch_folder, ftype.value, f"{fname}.npy") + FeatureIONumpy.save(eopatch[(ftype, fname)], temp_fs, mask_timeless_path, compress_level=0) + + # test EOPatch load of uncompressed features + EOPatch.load(fs.path.join(folder, patch_folder), filesystem=temp_fs) + + # re-save compressed and check cleanup, bbox and timestamps are not compressed + save_task(eopatch, eopatch_folder=patch_folder) + assert temp_fs.exists(bbox_path) + assert temp_fs.exists(timestamps_path) + assert temp_fs.exists(mask_timeless_path + ".gz") + assert not temp_fs.exists(bbox_path + ".gz") + assert not temp_fs.exists(timestamps_path + ".gz") + assert not temp_fs.exists(mask_timeless_path) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("use_zarr", [True, False]) +@pytest.mark.parametrize("folder_name", ["/", "foo", "foo/bar"]) +@pytest.mark.usefixtures("_silence_warnings") +def test_lazy_loading_plus_overwrite_patch(fs_loader, folder_name, eopatch, use_zarr: bool): + _skip_when_appropriate(fs_loader, use_zarr) + with fs_loader() as temp_fs: + eopatch.save(folder_name, filesystem=temp_fs) + + lazy_eopatch = EOPatch.load(folder_name, filesystem=temp_fs, lazy_loading=True) + lazy_eopatch.data["whatever"] = np.empty((5, 3, 3, 2)) + del lazy_eopatch[FeatureType.MASK_TIMELESS, "mask"] + + lazy_eopatch.save( + folder_name, filesystem=temp_fs, overwrite_permission=OverwritePermission.OVERWRITE_PATCH, use_zarr=use_zarr + ) + data_extension = "zarr" if use_zarr else "npy.gz" + assert temp_fs.exists(fs.path.join(folder_name, "data", f"whatever.{data_extension}")) + assert not temp_fs.exists(fs.path.join(folder_name, "mask_timeless", f"mask.{data_extension}")) + + +@pytest.mark.parametrize( + ("constructor", "data"), + [ + (FeatureIONumpy, np.zeros(20)), + (FeatureIONumpy, np.zeros((2, 3, 3, 2), dtype=np.int16)), + (FeatureIONumpy, np.full((4, 5), fill_value=CRS.POP_WEB)), + (FeatureIOGeoDf, gpd.GeoDataFrame({"col1": ["name1"], "geometry": [Point(1, 2)]}, crs="EPSG:3857")), + (FeatureIOGeoDf, gpd.GeoDataFrame({"col1": ["name1"], "geometry": [Point(1, 2)]}, crs="EPSG:32733")), + ( + FeatureIOGeoDf, + gpd.GeoDataFrame( + { + "values": [1, 2], + TIMESTAMP_COLUMN: [ + datetime.datetime(2017, 1, 1, 10, 4, 7), + datetime.datetime(2017, 1, 4, 10, 14, 5), + ], + "geometry": [Point(1, 2), Point(2, 1)], + }, + crs="EPSG:3857", + ), + ), + (FeatureIOJson, {}), + (FeatureIOJson, {"test": "test1", "test3": {"test": "test1"}}), + (FeatureIOBBox, BBox((1, 2, 3, 4), CRS.WGS84)), + (FeatureIOTimestamps, []), + (FeatureIOTimestamps, [datetime.datetime(2017, 1, 1, 10, 4, 7), datetime.datetime(2017, 1, 4, 10, 14, 5)]), + ], +) +@pytest.mark.parametrize("compress_level", [0, 1]) +def test_feature_io(constructor: type[FeatureIO], data: Any, compress_level: int) -> None: + """ + Tests verifying that FeatureIO subclasses correctly save, load, and lazy-load data. + Test cases do not include subfolders, because subfolder management is currently done by the `save_eopatch` function. + """ + file_extension = constructor.get_file_extension(compress_level=compress_level) + file_name = "name" + with TempFS("testing_file_sistem") as temp_fs: + feat_io = constructor(file_name + file_extension, filesystem=temp_fs) + constructor.save(data, temp_fs, file_name, compress_level=compress_level) + loaded_data = feat_io.load() + assert_feature_data_equal(loaded_data, data) + + temp_fs.remove(file_name + file_extension) + cache_data = feat_io.load() + assert_feature_data_equal(loaded_data, cache_data) + + +def test_zarr_and_numpy_overwrite(eopatch): + _skip_when_appropriate(None, True) + save_kwargs = dict(features=[FeatureType.DATA], overwrite_permission=OverwritePermission.OVERWRITE_FEATURES) + with TempFS() as temp_fs: + eopatch.save("/", filesystem=temp_fs, use_zarr=True, **save_kwargs) + assert temp_fs.exists(fs.path.join("data", "data.zarr")) + eopatch.save("/", filesystem=temp_fs, use_zarr=False, **save_kwargs) + assert not temp_fs.exists(fs.path.join("data", "data.zarr")) + assert temp_fs.exists(fs.path.join("data", "data.npy.gz")) + eopatch.save("/", filesystem=temp_fs, use_zarr=True, **save_kwargs) + assert temp_fs.exists(fs.path.join("data", "data.zarr")) + assert not temp_fs.exists(fs.path.join("data", "data.npy.gz")) + + +@pytest.mark.parametrize("zarr_file_exists", [True, False]) +def test_zarr_and_numpy_detect_collisions(eopatch, zarr_file_exists): + _skip_when_appropriate(None, True) + save_kwargs = dict(features=[FeatureType.DATA], overwrite_permission=OverwritePermission.ADD_ONLY) + with TempFS() as temp_fs: + eopatch.save("/", filesystem=temp_fs, use_zarr=zarr_file_exists, **save_kwargs) + with pytest.raises(ValueError): + eopatch.save("/", filesystem=temp_fs, use_zarr=not zarr_file_exists, **save_kwargs) + + +def test_zarr_and_numpy_combined_loading(eopatch): + _skip_when_appropriate(None, True) + with TempFS() as temp_fs: + eopatch.save("/", filesystem=temp_fs, use_zarr=True) + eopatch.save( + "/", + filesystem=temp_fs, + features=[(FeatureType.MASK_TIMELESS, "mask2")], + use_zarr=False, + overwrite_permission=OverwritePermission.OVERWRITE_FEATURES, + ) + assert temp_fs.exists("mask_timeless/mask.zarr") + assert temp_fs.exists("mask_timeless/mask2.npy.gz") + assert EOPatch.load("/", filesystem=temp_fs) == eopatch + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize( + "temporal_selection", + [None, slice(None, 3), slice(2, 4, 2), [3, 4], lambda ts: [i % 2 == 0 for i, _ in enumerate(ts)]], +) +def test_partial_temporal_loading(fs_loader: type[FS], eopatch: EOPatch, temporal_selection): + _skip_when_appropriate(fs_loader, True) + with fs_loader() as temp_fs: + eopatch.save(path="patch-folder", filesystem=temp_fs, use_zarr=True) + + full_patch = EOPatch.load(path="patch-folder", filesystem=temp_fs) + partial_patch = EOPatch.load(path="patch-folder", filesystem=temp_fs, temporal_selection=temporal_selection) + + assert full_patch == eopatch + + if temporal_selection is None: + adjusted_selection = slice(None) + elif callable(temporal_selection): + adjusted_selection = temporal_selection(full_patch.get_timestamps()) + else: + adjusted_selection = temporal_selection + + assert_array_equal(full_patch.data["data"][adjusted_selection, ...], partial_patch.data["data"]) + assert_array_equal(full_patch.mask_timeless["mask"], partial_patch.mask_timeless["mask"]) + assert_array_equal(np.array(full_patch.timestamps)[adjusted_selection], partial_patch.timestamps) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +def test_partial_temporal_loading_fails_for_numpy(fs_loader: type[FS], eopatch: EOPatch): + _skip_when_appropriate(fs_loader, True) + with fs_loader() as temp_fs: + eopatch.save(path="patch-folder", filesystem=temp_fs, use_zarr=False) + + with pytest.raises(OSError): + EOPatch.load(path="patch-folder", filesystem=temp_fs, temporal_selection=[0]) + + +@mock_s3 +@pytest.mark.parametrize("fs_loader", FS_LOADERS) +@pytest.mark.parametrize("temporal_selection", [[3, 4, 10]]) +def test_partial_temporal_loading_fails_bad_selection(fs_loader: type[FS], eopatch: EOPatch, temporal_selection): + _skip_when_appropriate(fs_loader, True) + with fs_loader() as temp_fs: + eopatch.save(path="patch-folder", filesystem=temp_fs, use_zarr=True) + + with pytest.raises(IndexError): + EOPatch.load(path="patch-folder", filesystem=temp_fs, temporal_selection=temporal_selection) + + +@mock_s3 +@pytest.mark.parametrize("temporal_selection", [None, slice(None, 3), slice(2, 4, 2), [3, 4]]) +def test_partial_temporal_saving_into_existing(eopatch: EOPatch, temporal_selection): + _skip_when_appropriate(TempFS, True) + with TempFS() as temp_fs: + io_kwargs = dict(path="patch-folder", filesystem=temp_fs, overwrite_permission="OVERWRITE_FEATURES") + eopatch.save(**io_kwargs, use_zarr=True) + + partial_patch = eopatch.copy(deep=True) + partial_patch.consolidate_timestamps(np.array(partial_patch.timestamps)[temporal_selection or ...]) + + partial_patch.data["data"] = np.full_like(partial_patch.data["data"], 2) + partial_patch.save(**io_kwargs, use_zarr=True, temporal_selection=temporal_selection) + + expected_data = eopatch.data["data"].copy() + expected_data[temporal_selection] = 2 + + loaded_patch = EOPatch.load(path="patch-folder", filesystem=temp_fs) + assert_array_equal(loaded_patch.data["data"], expected_data) + assert_array_equal(loaded_patch.timestamps, eopatch.timestamps) + + +@mock_s3 +@pytest.mark.parametrize("dtype", [float, np.float32, np.int8]) +def test_partial_temporal_saving_just_timestamps(dtype): + _skip_when_appropriate(TempFS, True) + patch_skeleton = generate_eopatch(seed=17) + partial_patch = EOPatch( + data={"beep": np.full((2, 117, 97, 3), 2, dtype=dtype)}, + bbox=patch_skeleton.bbox, + timestamps=[patch_skeleton.timestamps[1], patch_skeleton.timestamps[2]], + ) + expected_data = np.zeros((5, 117, 97, 3), dtype=dtype) + expected_data[[1, 2]] = 2 + + with TempFS() as temp_fs: + io_kwargs = dict(path="patch-folder", filesystem=temp_fs, overwrite_permission="OVERWRITE_FEATURES") + patch_skeleton.save(**io_kwargs, save_timestamps=True) + + partial_patch.save(**io_kwargs, use_zarr=True, temporal_selection="infer") + + loaded_patch = EOPatch.load(path="patch-folder", filesystem=temp_fs) + assert_array_equal(loaded_patch.data["beep"], expected_data) + assert_array_equal(loaded_patch.timestamps, patch_skeleton.timestamps) + + +@mock_s3 +def test_partial_temporal_saving_infer(eopatch: EOPatch): + _skip_when_appropriate(TempFS, True) + with TempFS() as temp_fs: + io_kwargs = dict(path="patch-folder", filesystem=temp_fs, overwrite_permission="OVERWRITE_FEATURES") + eopatch.save(**io_kwargs, use_zarr=True) + + partial_patch = eopatch.copy(deep=True) + partial_patch.consolidate_timestamps(eopatch.timestamps[1:2] + eopatch.timestamps[3:5]) + + partial_patch.data["data"] = np.full_like(partial_patch.data["data"], 2) + partial_patch.save(**io_kwargs, use_zarr=True, temporal_selection="infer") + + expected_data = eopatch.data["data"].copy() + expected_data[[1, 3, 4]] = 2 + + loaded_patch = EOPatch.load(path="patch-folder", filesystem=temp_fs) + assert_array_equal(loaded_patch.data["data"], expected_data) + assert_array_equal(loaded_patch.timestamps, eopatch.timestamps) + + +@mock_s3 +def test_partial_temporal_saving_fails(eopatch: EOPatch): + _skip_when_appropriate(TempFS, True) + with TempFS() as temp_fs: + io_kwargs = dict( + path="patch-folder", filesystem=temp_fs, use_zarr=True, overwrite_permission="OVERWRITE_FEATURES" + ) + with pytest.raises(OSError): + # patch does not exist yet + eopatch.save(**io_kwargs, temporal_selection=slice(2, None)) + + eopatch.save(**io_kwargs) + + with pytest.raises(ValueError, match="without zarr arrays"): + # saving without zarr + eopatch.save(path="patch-folder", filesystem=temp_fs, use_zarr=False, temporal_selection=[1, 2]) + + with pytest.raises(ValueError): + # temporal selection smaller than eopatch to be saved + eopatch.save(**io_kwargs, temporal_selection=[1, 2]) + + with pytest.raises(ValueError): + # temporal selection outside of saved eopatch + eopatch.save(**io_kwargs, temporal_selection=slice(12, None)) + + with pytest.raises(RuntimeError, match=r"Cannot infer temporal selection.*?has no timestamps"): + # no timestamps, cannot infer + EOPatch(bbox=eopatch.bbox).save(**io_kwargs, temporal_selection="infer") + + with pytest.raises(ValueError, match=r"Cannot infer temporal selection.*?subset"): + # wrong timestamps, cannot infer + EOPatch(bbox=eopatch.bbox, timestamps=["2012-01-01"]).save(**io_kwargs, temporal_selection="infer") + + EOPatch(bbox=eopatch.bbox).save(path="other-folder", filesystem=temp_fs) + with pytest.raises(OSError, match=r"Saved EOPatch does not have timestamps"): + # no timestamps saved + eopatch.save(path="other-folder", filesystem=temp_fs, use_zarr=True, temporal_selection="infer") + + +@pytest.mark.parametrize("patch_location", [".", "patch-folder", "some/long/path"]) +def test_old_style_meta_info(patch_location): + with TempFS() as temp_fs: + EOPatch(bbox=DUMMY_BBOX).save(path=patch_location, filesystem=temp_fs) + meta_info = {"this": ["list"], "something": "else"} + file_name, file_extension = f"{patch_location}/meta_info", ".json.gz" + old_style_io = FeatureIOJson(file_name + file_extension, filesystem=temp_fs) + old_style_io.save(meta_info, temp_fs, file_name, compress_level=1) + + with pytest.warns(EODeprecationWarning): + loaded_patch = EOPatch.load(path=patch_location, filesystem=temp_fs) + assert dict(loaded_patch.meta_info.items()) == meta_info + + loaded_patch.meta_info = {"beep": "boop"} + loaded_patch.save(path=patch_location, filesystem=temp_fs) + assert not temp_fs.exists(f"{patch_location}/meta_info.json.gz") + assert temp_fs.exists(f"{patch_location}/meta_info/beep.json.gz") + + loaded_patch = EOPatch.load(path=patch_location, filesystem=temp_fs) + assert dict(loaded_patch.meta_info.items()) == {"beep": "boop"} diff --git a/core/eolearn/tests/test_eodata_merge.py b/tests/core/test_eodata_merge.py similarity index 82% rename from core/eolearn/tests/test_eodata_merge.py rename to tests/core/test_eodata_merge.py index c6b8fa18a..131e87064 100644 --- a/core/eolearn/tests/test_eodata_merge.py +++ b/tests/core/test_eodata_merge.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import datetime as dt import numpy as np @@ -82,6 +84,7 @@ def test_time_dependent_merge_with_missing_features(): assert eop == eop1 +@pytest.mark.filterwarnings("ignore::eolearn.core.exceptions.TemporalDimensionWarning") def test_failed_time_dependent_merge(): eop1 = EOPatch(bbox=DUMMY_BBOX, data={"bands": np.ones((6, 4, 5, 2))}) with pytest.raises(ValueError): @@ -93,6 +96,21 @@ def test_failed_time_dependent_merge(): merge_eopatches(eop2, eop1) +def test_temporally_empty_merge(): + """Merging patches without a temporal definition should return a temporally undefined patch. However merging a patch + with an empty temporal axis should not result in a temporally undefined EOPatch.""" + eop1 = EOPatch(bbox=DUMMY_BBOX, data={"bands": np.ones((0, 4, 5, 2))}, timestamps=[]) + eop2 = EOPatch(bbox=DUMMY_BBOX, mask_timeless={"mask": np.ones((4, 5, 2), dtype=np.int8)}, timestamps=None) + result = EOPatch( + bbox=DUMMY_BBOX, + data={"bands": np.ones((0, 4, 5, 2))}, + mask_timeless={"mask": np.ones((4, 5, 2), dtype=np.int8)}, + timestamps=[], + ) + assert merge_eopatches(eop2, eop2).timestamps is None, "Faulty temporal definition added." + assert merge_eopatches(eop1, eop2) == result + + def test_timeless_merge(): eop1 = EOPatch( bbox=DUMMY_BBOX, @@ -184,3 +202,12 @@ def test_lazy_loading(test_eopatch_path): assert isinstance(eop.mask.get("CLM"), np.ndarray) assert isinstance(eop1.mask.get("CLM"), np.ndarray) assert isinstance(eop1.mask_timeless._get_unloaded("LULC"), FeatureIO) # noqa: SLF001 + + +def test_temporally_independent_merge(test_eopatch_path): + full_patch = EOPatch.load(test_eopatch_path) + part1, part2 = full_patch.copy(deep=True), full_patch.copy(deep=True) + part1.consolidate_timestamps(full_patch.get_timestamps()[:10]) + part2.consolidate_timestamps(full_patch.get_timestamps()[10:]) + + assert full_patch == merge_eopatches(part1, part2, time_dependent_op="concatenate") diff --git a/core/eolearn/tests/test_eoexecutor.py b/tests/core/test_eoexecutor.py similarity index 95% rename from core/eolearn/tests/test_eoexecutor.py rename to tests/core/test_eoexecutor.py index 789026591..06fa594a4 100644 --- a/core/eolearn/tests/test_eoexecutor.py +++ b/tests/core/test_eoexecutor.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import concurrent.futures import datetime @@ -12,6 +13,7 @@ import os import tempfile import time +from contextlib import suppress from logging import FileHandler import pytest @@ -20,6 +22,14 @@ from eolearn.core import EOExecutor, EONode, EOTask, EOWorkflow, OutputTask, WorkflowResults, execute_with_mp_lock from eolearn.core.utils.fs import get_full_path +FULL_LOG_LINE_COUNT = 12 +with suppress(ImportError): + # Temporary workaround to not have failing tests while ray is fixing logger issues + import ray + + if ray.__version__ in ("2.6.0", "2.6.1"): + FULL_LOG_LINE_COUNT = 4 + class ExampleTask(EOTask): def execute(self, *_, **kwargs): @@ -134,7 +144,7 @@ def test_read_logs(test_args, execution_names, workflow, execution_kwargs, logs_ log_path = os.path.join(executor.report_folder, log_filenames[0]) with executor.filesystem.open(log_path, "r") as fp: line_count = len(fp.readlines()) - expected_line_count = 2 if filter_logs else 12 + expected_line_count = 2 if filter_logs else FULL_LOG_LINE_COUNT assert line_count == expected_line_count diff --git a/core/eolearn/tests/test_eonode.py b/tests/core/test_eonode.py similarity index 98% rename from core/eolearn/tests/test_eonode.py rename to tests/core/test_eonode.py index 182be2e82..a7a214cf1 100644 --- a/core/eolearn/tests/test_eonode.py +++ b/tests/core/test_eonode.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations from eolearn.core import EONode, EOTask, OutputTask, linearly_connect_tasks diff --git a/core/eolearn/tests/test_eotask.py b/tests/core/test_eotask.py similarity index 96% rename from core/eolearn/tests/test_eotask.py rename to tests/core/test_eotask.py index fef416fe6..725126c03 100644 --- a/core/eolearn/tests/test_eotask.py +++ b/tests/core/test_eotask.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations from eolearn.core import EOTask diff --git a/core/eolearn/tests/test_eoworkflow.py b/tests/core/test_eoworkflow.py similarity index 94% rename from core/eolearn/tests/test_eoworkflow.py rename to tests/core/test_eoworkflow.py index 9de5a801e..7c14d7947 100644 --- a/core/eolearn/tests/test_eoworkflow.py +++ b/tests/core/test_eoworkflow.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import concurrent.futures import datetime as dt @@ -177,6 +179,7 @@ def test_workflow_copying_eopatches(): feature1 = FeatureType.DATA, "data1" feature2 = FeatureType.DATA, "data2" bbox = BBox((0, 0, 1, 1), CRS(3857)) + timestamps = ["2016-02-12", "2016-02-17"] create_node = EONode(CreateEOPatchTask()) init_node = EONode( @@ -189,13 +192,13 @@ def test_workflow_copying_eopatches(): output_node2 = EONode(OutputTask(name="out2"), inputs=[remove_node2]) workflow = EOWorkflow([create_node, init_node, remove_node1, remove_node2, output_node1, output_node2]) - results = workflow.execute({create_node: {"bbox": bbox}}) + results = workflow.execute({create_node: {"bbox": bbox, "timestamps": timestamps}}) eop1 = results.outputs["out1"] eop2 = results.outputs["out2"] - assert eop1 == EOPatch(bbox=bbox, data={"data2": np.ones((2, 4, 4, 3), dtype=np.uint8)}) - assert eop2 == EOPatch(bbox=bbox, data={"data1": np.ones((2, 4, 4, 3), dtype=np.uint8)}) + assert eop1 == EOPatch(bbox=bbox, timestamps=timestamps, data={"data2": np.ones((2, 4, 4, 3), dtype=np.uint8)}) + assert eop2 == EOPatch(bbox=bbox, timestamps=timestamps, data={"data1": np.ones((2, 4, 4, 3), dtype=np.uint8)}) def test_workflows_reusing_nodes(): @@ -288,8 +291,7 @@ def test_exception_handling(): assert node_stats.node_name == node.name if node is exception_node: - assert isinstance(node_stats.exception, CustomExceptionError) - assert node_stats.exception_traceback.startswith("Traceback") + assert isinstance(node_stats.exception_info.exception, CustomExceptionError) + assert node_stats.exception_info.traceback.startswith("Traceback") else: - assert node_stats.exception is None - assert node_stats.exception_traceback is None + assert node_stats.exception_info is None diff --git a/core/eolearn/tests/test_eoworkflow_tasks.py b/tests/core/test_eoworkflow_tasks.py similarity index 90% rename from core/eolearn/tests/test_eoworkflow_tasks.py rename to tests/core/test_eoworkflow_tasks.py index faebb3660..c81c1bfde 100644 --- a/core/eolearn/tests/test_eoworkflow_tasks.py +++ b/tests/core/test_eoworkflow_tasks.py @@ -6,6 +6,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + from eolearn.core import EONode, EOTask, EOWorkflow, FeatureType, LoadTask, OutputTask from eolearn.core.eoworkflow_tasks import InputTask @@ -28,14 +30,14 @@ def test_input_task(): def test_output_task(test_eopatch): """Tests basic functionalities of OutputTask""" - task = OutputTask(name="my-task", features=[FeatureType.BBOX, (FeatureType.DATA, "NDVI")]) + task = OutputTask(name="my-task", features=[(FeatureType.DATA, "NDVI")]) assert task.name == "my-task" new_eopatch = task.execute(test_eopatch) assert id(new_eopatch) != id(test_eopatch) - assert len(new_eopatch.get_features()) == 2 + assert len(new_eopatch.get_features()) == 1 assert new_eopatch.bbox == test_eopatch.bbox diff --git a/tests/core/test_extra/__init__.py b/tests/core/test_extra/__init__.py new file mode 100644 index 000000000..cda745988 --- /dev/null +++ b/tests/core/test_extra/__init__.py @@ -0,0 +1 @@ +# Required for Ray tests diff --git a/core/eolearn/tests/test_extra/test_ray.py b/tests/core/test_extra/test_ray.py similarity index 99% rename from core/eolearn/tests/test_extra/test_ray.py rename to tests/core/test_extra/test_ray.py index bf1e8c3ea..e7309b3c5 100644 --- a/core/eolearn/tests/test_extra/test_ray.py +++ b/tests/core/test_extra/test_ray.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import datetime import itertools as it diff --git a/core/eolearn/tests/test_graph.py b/tests/core/test_graph.py similarity index 97% rename from core/eolearn/tests/test_graph.py rename to tests/core/test_graph.py index 3988f3fc5..bf408ff2e 100644 --- a/core/eolearn/tests/test_graph.py +++ b/tests/core/test_graph.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import functools import pytest @@ -124,7 +126,7 @@ def test_del_vertex(): def test_resolve_dependencies(edges): graph = DirectedGraph.from_edges(edges) - if DirectedGraph._is_cyclic(graph): # noqa[SLF001] + if DirectedGraph._is_cyclic(graph): # noqa: SLF001 with pytest.raises(CyclicDependencyError): graph.topologically_ordered_vertices() else: diff --git a/core/eolearn/tests/test_utils/test_common.py b/tests/core/test_utils/test_common.py similarity index 97% rename from core/eolearn/tests/test_utils/test_common.py rename to tests/core/test_utils/test_common.py index db743b56f..60442b773 100644 --- a/core/eolearn/tests/test_utils/test_common.py +++ b/tests/core/test_utils/test_common.py @@ -4,10 +4,12 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import dataclasses import warnings from functools import partial -from typing import Callable, Optional, Tuple +from typing import Callable import numpy as np import pytest @@ -56,8 +58,8 @@ def test_is_discrete_type(number_type, is_discrete): class ApplyToAxesTestCase: function: Callable[[np.ndarray], np.ndarray] data: np.ndarray - spatial_axes: Tuple[int, int] - expected: Optional[np.ndarray] = None + spatial_axes: tuple[int, int] + expected: np.ndarray | None = None APPLY_TO_TEST_CASES = [ diff --git a/core/eolearn/tests/test_utils/test_fs.py b/tests/core/test_utils/test_fs.py similarity index 90% rename from core/eolearn/tests/test_utils/test_fs.py rename to tests/core/test_utils/test_fs.py index df7eedc4c..76028c902 100644 --- a/core/eolearn/tests/test_utils/test_fs.py +++ b/tests/core/test_utils/test_fs.py @@ -4,12 +4,13 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import json import os import unittest.mock as mock from _thread import RLock from pathlib import Path -from typing import List import pytest from botocore.credentials import Credentials @@ -24,7 +25,7 @@ from sentinelhub import SHConfig from eolearn.core import get_filesystem, load_s3_filesystem, pickle_fs, unpickle_fs -from eolearn.core.utils.fs import get_aws_credentials, get_full_path, join_path +from eolearn.core.utils.fs import get_aws_credentials, get_full_path, join_path, split_all_extensions def test_get_local_filesystem(tmp_path): @@ -113,13 +114,13 @@ def test_get_aws_credentials(mocked_copy): ), ], ) -def test_filesystem_serialization(filesystem: FS, compare_params: List[str]): +def test_filesystem_serialization(filesystem: FS, compare_params: list[str]): pickled_filesystem = pickle_fs(filesystem) assert isinstance(pickled_filesystem, bytes) unpickled_filesystem = unpickle_fs(pickled_filesystem) assert filesystem is not unpickled_filesystem - assert isinstance(unpickled_filesystem._lock, RLock) # noqa[SLF001] + assert isinstance(unpickled_filesystem._lock, RLock) # noqa: SLF001 for param in compare_params: assert getattr(filesystem, param) == getattr(unpickled_filesystem, param) @@ -182,3 +183,18 @@ def test_join_path(path_parts, expected_path): def test_get_full_path(filesystem, path, expected_full_path): full_path = get_full_path(filesystem, path) assert full_path == expected_full_path + + +@pytest.mark.parametrize( + ("full_path", "result"), + [ + ("s3://bb.ad/.as/.ad./.hidden.json.gz", ("s3://bb.ad/.as/.ad./.hidden", ".json.gz")), + ("s3://bb.ad/.as/.ad./.hidden.json", ("s3://bb.ad/.as/.ad./.hidden", ".json")), + ("s3://bb.ad/.as/.ad./.hidden", ("s3://bb.ad/.as/.ad./.hidden", "")), + (".ad./beep.json.gz", (".ad./beep", ".json.gz")), + (".hidden.json", (".hidden", ".json")), + (".hidden", (".hidden", "")), + ], +) +def test_split_all_extensions(full_path, result): + assert split_all_extensions(full_path) == result diff --git a/core/eolearn/tests/test_utils/test_parallelize.py b/tests/core/test_utils/test_parallelize.py similarity index 98% rename from core/eolearn/tests/test_utils/test_parallelize.py rename to tests/core/test_utils/test_parallelize.py index e3030c9d1..06ccb0bd0 100644 --- a/core/eolearn/tests/test_utils/test_parallelize.py +++ b/tests/core/test_utils/test_parallelize.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import itertools as it from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor diff --git a/core/eolearn/tests/test_utils/test_parsing.py b/tests/core/test_utils/test_parsing.py similarity index 79% rename from core/eolearn/tests/test_utils/test_parsing.py rename to tests/core/test_utils/test_parsing.py index f1a75f888..d61c3531f 100644 --- a/core/eolearn/tests/test_utils/test_parsing.py +++ b/tests/core/test_utils/test_parsing.py @@ -1,19 +1,21 @@ +from __future__ import annotations + from dataclasses import dataclass -from typing import Callable, Iterable, List, Optional, Tuple, Union +from typing import Callable, Iterable import pytest from eolearn.core import EOPatch, FeatureParser, FeatureType -from eolearn.core.types import EllipsisType, FeatureRenameSpec, FeatureSpec, FeaturesSpecification +from eolearn.core.types import EllipsisType, Feature, FeaturesSpecification from eolearn.core.utils.testing import generate_eopatch @dataclass class ParserTestCase: parser_input: FeaturesSpecification - features: List[FeatureSpec] - renaming: List[FeatureRenameSpec] - specifications: Optional[List[Tuple[FeatureType, Union[str, EllipsisType]]]] = None + features: list[Feature] + renaming: list[tuple[FeatureType, str, str]] + specifications: list[tuple[FeatureType, str | EllipsisType]] | None = None description: str = "" @@ -32,13 +34,6 @@ def get_test_case_description(test_case: ParserTestCase) -> str: specifications=[(FeatureType.DATA, "bands")], description="Singleton feature", ), - ParserTestCase( - parser_input=FeatureType.BBOX, - features=[(FeatureType.BBOX, None)], - renaming=[(FeatureType.BBOX, None, None)], - specifications=[(FeatureType.BBOX, ...)], - description="BBox feature", - ), ParserTestCase( parser_input=(FeatureType.MASK, "CLM", "new_CLM"), features=[(FeatureType.MASK, "CLM")], @@ -47,55 +42,44 @@ def get_test_case_description(test_case: ParserTestCase) -> str: description="Renamed feature", ), ParserTestCase( - parser_input=[FeatureType.BBOX, (FeatureType.DATA, "bands"), (FeatureType.VECTOR_TIMELESS, "geoms")], - features=[(FeatureType.BBOX, None), (FeatureType.DATA, "bands"), (FeatureType.VECTOR_TIMELESS, "geoms")], + parser_input=[(FeatureType.DATA, "bands"), (FeatureType.VECTOR_TIMELESS, "geoms")], + features=[(FeatureType.DATA, "bands"), (FeatureType.VECTOR_TIMELESS, "geoms")], renaming=[ - (FeatureType.BBOX, None, None), (FeatureType.DATA, "bands", "bands"), (FeatureType.VECTOR_TIMELESS, "geoms", "geoms"), ], specifications=[ - (FeatureType.BBOX, ...), (FeatureType.DATA, "bands"), (FeatureType.VECTOR_TIMELESS, "geoms"), ], description="List of inputs", ), ParserTestCase( - parser_input=((FeatureType.TIMESTAMPS, ...), (FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a", "b")), - features=[(FeatureType.TIMESTAMPS, None), (FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a")], + parser_input=((FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a", "b")), + features=[(FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a")], renaming=[ - (FeatureType.TIMESTAMPS, None, None), (FeatureType.MASK, "CLM", "CLM"), (FeatureType.SCALAR, "a", "b"), ], - specifications=[(FeatureType.TIMESTAMPS, ...), (FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a")], + specifications=[(FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "a")], description="Tuple of inputs with rename", ), ParserTestCase( parser_input={ FeatureType.DATA: ["bands_S2", ("bands_l8", "BANDS_L8")], FeatureType.MASK_TIMELESS: [], - FeatureType.BBOX: ..., - FeatureType.TIMESTAMPS: None, }, features=[ (FeatureType.DATA, "bands_S2"), (FeatureType.DATA, "bands_l8"), - (FeatureType.BBOX, None), - (FeatureType.TIMESTAMPS, None), ], renaming=[ (FeatureType.DATA, "bands_S2", "bands_S2"), (FeatureType.DATA, "bands_l8", "BANDS_L8"), - (FeatureType.BBOX, None, None), - (FeatureType.TIMESTAMPS, None, None), ], specifications=[ (FeatureType.DATA, "bands_S2"), (FeatureType.DATA, "bands_l8"), - (FeatureType.BBOX, ...), - (FeatureType.TIMESTAMPS, ...), ], description="Dictionary", ), @@ -115,17 +99,17 @@ def test_feature_parser_no_eopatch(test_case: ParserTestCase): [ ((FeatureType.DATA, ...), [(FeatureType.DATA, ...)]), ( - [FeatureType.BBOX, (FeatureType.MASK, "CLM"), FeatureType.DATA], - [(FeatureType.BBOX, ...), (FeatureType.MASK, "CLM"), (FeatureType.DATA, ...)], + [(FeatureType.MASK, "CLM"), FeatureType.DATA], + [(FeatureType.MASK, "CLM"), (FeatureType.DATA, ...)], ), ( - {FeatureType.BBOX: None, FeatureType.MASK: ["CLM"], FeatureType.DATA: ...}, - [(FeatureType.BBOX, ...), (FeatureType.MASK, "CLM"), (FeatureType.DATA, ...)], + {FeatureType.MASK: ["CLM"], FeatureType.DATA: ...}, + [(FeatureType.MASK, "CLM"), (FeatureType.DATA, ...)], ), ], ) def test_feature_parser_no_eopatch_failure( - test_input: FeaturesSpecification, specifications: List[Tuple[FeatureType, Union[str, EllipsisType]]] + test_input: FeaturesSpecification, specifications: list[tuple[FeatureType, str | EllipsisType]] ): """When a get-all request `...` without an eopatch the parser should fail unless parsing specifications.""" parser = FeatureParser(test_input) @@ -150,8 +134,8 @@ def test_feature_parser_no_eopatch_failure( ( { FeatureType.MASK: ["CLM", "IS_VALID"], + FeatureType.MASK_TIMELESS: ..., FeatureType.DATA: [("bands", "new_bands")], - FeatureType.BBOX: None, }, ( FeatureType.MASK, @@ -214,7 +198,7 @@ def test_allowed_feature_types_callable( ], ) def test_all_features_allowed_feature_types( - eopatch: EOPatch, allowed_types: Union[Iterable[FeatureType], Callable[[FeatureType], bool]] + eopatch: EOPatch, allowed_types: Iterable[FeatureType] | Callable[[FeatureType], bool] ): """Ensure that allowed_feature_types is respected when requesting all features.""" parser = FeatureParser(..., allowed_feature_types=allowed_types) @@ -229,24 +213,20 @@ def test_all_features_allowed_feature_types( ParserTestCase( parser_input=..., features=[ - (FeatureType.BBOX, None), (FeatureType.DATA, "data"), (FeatureType.DATA, "CLP"), (FeatureType.MASK, "data"), (FeatureType.MASK, "IS_VALID"), (FeatureType.MASK_TIMELESS, "LULC"), (FeatureType.META_INFO, "something"), - (FeatureType.TIMESTAMPS, None), ], renaming=[ - (FeatureType.BBOX, None, None), (FeatureType.DATA, "data", "data"), (FeatureType.DATA, "CLP", "CLP"), (FeatureType.MASK, "data", "data"), (FeatureType.MASK, "IS_VALID", "IS_VALID"), (FeatureType.MASK_TIMELESS, "LULC", "LULC"), (FeatureType.META_INFO, "something", "something"), - (FeatureType.TIMESTAMPS, None, None), ], description="Get-all", ), @@ -258,20 +238,17 @@ def test_all_features_allowed_feature_types( ), ParserTestCase( parser_input=[ - FeatureType.BBOX, FeatureType.MASK, (FeatureType.META_INFO, ...), (FeatureType.MASK_TIMELESS, "LULC", "new_LULC"), ], features=[ - (FeatureType.BBOX, None), (FeatureType.MASK, "data"), (FeatureType.MASK, "IS_VALID"), (FeatureType.META_INFO, "something"), (FeatureType.MASK_TIMELESS, "LULC"), ], renaming=[ - (FeatureType.BBOX, None, None), (FeatureType.MASK, "data", "data"), (FeatureType.MASK, "IS_VALID", "IS_VALID"), (FeatureType.META_INFO, "something", "something"), diff --git a/core/eolearn/tests/test_utils/test_raster.py b/tests/core/test_utils/test_raster.py similarity index 89% rename from core/eolearn/tests/test_utils/test_raster.py rename to tests/core/test_utils/test_raster.py index 00d532504..95a312f01 100644 --- a/core/eolearn/tests/test_utils/test_raster.py +++ b/tests/core/test_utils/test_raster.py @@ -4,8 +4,9 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ -import warnings -from typing import Literal, Optional, Tuple +from __future__ import annotations + +from typing import Literal import numpy as np import pytest @@ -21,6 +22,7 @@ @pytest.mark.parametrize("nan_ratio", [0, 0.05, 0.1, 0.5, 0.9, 1]) @pytest.mark.parametrize("dtype", [np.float64, np.float32, np.int16]) @pytest.mark.parametrize("method", ["linear", "nearest"]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") def test_fast_nanpercentile(size: int, percentile: float, nan_ratio: float, dtype: type, method: str): data_shape = (size, 3, 2, 4) data = np.random.rand(*data_shape) @@ -31,9 +33,7 @@ def test_fast_nanpercentile(size: int, percentile: float, nan_ratio: float, dtyp data = data.astype(dtype) method_kwargs = {"method" if np.__version__ >= "1.22.0" else "interpolation": method} - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", category=RuntimeWarning) - expected_result = np.nanpercentile(data, q=percentile, axis=0, **method_kwargs).astype(data.dtype) + expected_result = np.nanpercentile(data, q=percentile, axis=0, **method_kwargs).astype(data.dtype) result = fast_nanpercentile(data, percentile, method=method) @@ -62,11 +62,11 @@ def test_fast_nanpercentile(size: int, percentile: float, nan_ratio: float, dtyp ) def test_constant_pad( array: np.ndarray, - multiple_of: Tuple[int, int], + multiple_of: tuple[int, int], up_down_rule: Literal["even", "up", "down"], left_right_rule: Literal["even", "left", "right"], pad_value: float, - expected_result: Optional[np.ndarray], + expected_result: np.ndarray | None, ): """Checks that the function pads correctly and minimally. In larger cases only the shapes are checked.""" padded = constant_pad(array, multiple_of, up_down_rule, left_right_rule, pad_value) diff --git a/core/eolearn/tests/test_utils/test_testing.py b/tests/core/test_utils/test_testing.py similarity index 95% rename from core/eolearn/tests/test_utils/test_testing.py rename to tests/core/test_utils/test_testing.py index 18e1166f5..2d41d54a4 100644 --- a/core/eolearn/tests/test_utils/test_testing.py +++ b/tests/core/test_utils/test_testing.py @@ -1,6 +1,8 @@ +from __future__ import annotations + import datetime as dt from dataclasses import dataclass -from typing import Any, Dict +from typing import Any import numpy as np import pytest @@ -9,7 +11,7 @@ from sentinelhub.testing_utils import assert_statistics_match from eolearn.core.constants import FeatureType -from eolearn.core.types import FeatureSpec, FeaturesSpecification +from eolearn.core.types import Feature, FeaturesSpecification from eolearn.core.utils.parsing import parse_features from eolearn.core.utils.testing import PatchGeneratorConfig, generate_eopatch @@ -32,7 +34,7 @@ def test_generate_eopatch_set_bbox_timestamps() -> None: {"num_timestamps": 7, "max_integer_value": 333, "raster_shape": (3, 27), "depth_range": (5, 15)}, ], ) -def test_generate_eopatch_config(config: Dict[str, Any]) -> None: +def test_generate_eopatch_config(config: dict[str, Any]) -> None: mask_feature = (FeatureType.MASK, "mask") patch = generate_eopatch(mask_feature, config=PatchGeneratorConfig(**config)) @@ -75,7 +77,7 @@ def test_generate_eopatch_seed(seed: int, features: FeaturesSpecification) -> No class GenerateTestCase: data_features: FeaturesSpecification seed: int - expected_statistics: Dict[FeatureSpec, Dict[str, Any]] + expected_statistics: dict[Feature, dict[str, Any]] @pytest.mark.parametrize( diff --git a/coregistration/eolearn/tests/conftest.py b/tests/coregistration/conftest.py similarity index 90% rename from coregistration/eolearn/tests/conftest.py rename to tests/coregistration/conftest.py index e5d2704bc..b40eff87a 100644 --- a/coregistration/eolearn/tests/conftest.py +++ b/tests/coregistration/conftest.py @@ -6,6 +6,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import pytest @@ -14,7 +16,7 @@ pytest.register_assert_rewrite("sentinelhub.testing_utils") # makes asserts in helper functions work with pytest -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") +EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") EXAMPLE_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") diff --git a/coregistration/eolearn/tests/test_coregistration.py b/tests/coregistration/test_coregistration.py similarity index 98% rename from coregistration/eolearn/tests/test_coregistration.py rename to tests/coregistration/test_coregistration.py index fe1774353..3e9c423e1 100644 --- a/coregistration/eolearn/tests/test_coregistration.py +++ b/tests/coregistration/test_coregistration.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import logging diff --git a/features/eolearn/tests/conftest.py b/tests/features/conftest.py similarity index 54% rename from features/eolearn/tests/conftest.py rename to tests/features/conftest.py index beb6a6cd2..77c533c41 100644 --- a/features/eolearn/tests/conftest.py +++ b/tests/features/conftest.py @@ -6,6 +6,8 @@ For the full list of contributors, see the CREDITS file in the root directory of this source tree. This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import numpy as np @@ -15,18 +17,22 @@ pytest.register_assert_rewrite("sentinelhub.testing_utils") # makes asserts in helper functions work with pytest -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") -EXAMPLE_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") + +@pytest.fixture(scope="session", name="example_data_path") +def example_data_path_fixture() -> str: + return os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") @pytest.fixture(name="example_eopatch") def example_eopatch_fixture(): - return EOPatch.load(EXAMPLE_EOPATCH_PATH, lazy_loading=True) + path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data", "TestEOPatch") + return EOPatch.load(path, lazy_loading=True) @pytest.fixture(name="small_ndvi_eopatch") -def small_ndvi_eopatch_fixture(example_eopatch): - ndvi = example_eopatch.data["NDVI"][:10, :20, :20] +def small_ndvi_eopatch_fixture(example_eopatch: EOPatch): + ndvi = example_eopatch.data["NDVI"][:, :20, :20] ndvi[np.isnan(ndvi)] = 0 example_eopatch.data["NDVI"] = ndvi + example_eopatch.consolidate_timestamps(example_eopatch.get_timestamps()[:10]) return example_eopatch diff --git a/features/eolearn/tests/test_clustering.py b/tests/features/extra/test_clustering.py similarity index 88% rename from features/eolearn/tests/test_clustering.py rename to tests/features/extra/test_clustering.py index c0bb804d3..b143b0800 100644 --- a/features/eolearn/tests/test_clustering.py +++ b/tests/features/extra/test_clustering.py @@ -4,14 +4,16 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import logging +import sys import numpy as np import pytest from eolearn.core import FeatureType -from eolearn.features import ClusteringTask +from eolearn.features.extra.clustering import ClusteringTask logging.basicConfig(level=logging.DEBUG) @@ -45,7 +47,8 @@ def test_clustering(example_eopatch): assert len(np.unique(clusters)) == 22, "Wrong number of clusters." assert np.median(clusters) == 2 - assert np.mean(clusters) == pytest.approx(2.19109) + + assert np.mean(clusters) == pytest.approx(2.19109 if sys.version_info < (3, 9) else 2.201188) clusters = example_eopatch.data_timeless["clusters_mask"].squeeze() diff --git a/features/eolearn/tests/test_interpolation.py b/tests/features/extra/test_interpolation.py similarity index 84% rename from features/eolearn/tests/test_interpolation.py rename to tests/features/extra/test_interpolation.py index 14fcb6482..f563b9932 100644 --- a/features/eolearn/tests/test_interpolation.py +++ b/tests/features/extra/test_interpolation.py @@ -4,26 +4,23 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import dataclasses from datetime import datetime -from typing import Dict, Optional import numpy as np import pytest +import scipy.interpolate from sentinelhub.testing_utils import assert_statistics_match from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.features import ( - AkimaInterpolationTask, - BSplineInterpolationTask, - CubicInterpolationTask, - CubicResamplingTask, +from eolearn.features.extra.interpolation import ( + InterpolationTask, KrigingInterpolationTask, LinearInterpolationTask, - LinearResamplingTask, - NearestResamplingTask, - SplineInterpolationTask, + ResamplingTask, ) @@ -49,8 +46,8 @@ class InterpolationTestCase: name: str task: EOTask result_len: int - expected_statistics: Dict[str, float] - nan_replace: Optional[float] = None + expected_statistics: dict[str, float] + nan_replace: float | None = None def execute(self, eopatch): feature_type, feature_name, _ = self.task.renamed_feature @@ -105,8 +102,10 @@ def execute(self, eopatch): ), InterpolationTestCase( "cubic", - CubicInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="cubic", result_interval=(0.0, 1.0), mask_feature=(FeatureType.MASK, "IS_VALID"), resample_range=("2015-01-01", "2018-01-01", 16), @@ -118,13 +117,14 @@ def execute(self, eopatch): ), InterpolationTestCase( "spline", - SplineInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.UnivariateSpline, result_interval=(-0.3, 1.0), mask_feature=(FeatureType.MASK, "IS_VALID"), resample_range=("2016-01-01", "2018-01-01", 5), - spline_degree=3, - smoothing_factor=0, + k=3, + s=0, unknown_value=0, ), result_len=147, @@ -132,23 +132,25 @@ def execute(self, eopatch): ), InterpolationTestCase( "bspline", - BSplineInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.make_interp_spline, unknown_value=-3, mask_feature=(FeatureType.MASK, "IS_VALID"), resample_range=("2017-01-01", "2017-02-01", 50), - spline_degree=5, + k=5, ), result_len=1, expected_statistics=dict(exp_min=-0.0162962, exp_max=0.62323, exp_mean=0.319117, exp_median=0.3258836), ), InterpolationTestCase( "bspline-p", - BSplineInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.make_interp_spline, unknown_value=-3, mask_feature=(FeatureType.MASK, "IS_VALID"), - spline_degree=5, + k=5, resample_range=("2017-01-01", "2017-02-01", 50), interpolate_pixel_wise=True, ), @@ -157,8 +159,11 @@ def execute(self, eopatch): ), InterpolationTestCase( "akima", - AkimaInterpolationTask( - (FeatureType.DATA, "NDVI"), unknown_value=0, mask_feature=(FeatureType.MASK, "IS_VALID") + InterpolationTask( + (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.Akima1DInterpolator, + unknown_value=0, + mask_feature=(FeatureType.MASK, "IS_VALID"), ), result_len=68, expected_statistics=dict(exp_min=-0.091035, exp_max=0.8283603, exp_mean=0.51427454, exp_median=0.59095883), @@ -173,8 +178,12 @@ def execute(self, eopatch): ), InterpolationTestCase( "nearest resample", - NearestResamplingTask( - (FeatureType.DATA, "NDVI"), result_interval=(0.0, 1.0), resample_range=("2016-01-01", "2018-01-01", 5) + ResamplingTask( + (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="nearest", + result_interval=(0.0, 1.0), + resample_range=("2016-01-01", "2018-01-01", 5), ), result_len=147, expected_statistics=dict(exp_min=-0.2, exp_max=0.8283603, exp_mean=0.32318678, exp_median=0.2794411), @@ -182,8 +191,12 @@ def execute(self, eopatch): ), InterpolationTestCase( "linear resample", - LinearResamplingTask( - (FeatureType.DATA, "NDVI"), result_interval=(0.0, 1.0), resample_range=("2016-01-01", "2018-01-01", 5) + ResamplingTask( + (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="linear", + result_interval=(0.0, 1.0), + resample_range=("2016-01-01", "2018-01-01", 5), ), result_len=147, expected_statistics=dict(exp_min=-0.2, exp_max=0.82643485, exp_mean=0.32218185, exp_median=0.29093677), @@ -191,8 +204,10 @@ def execute(self, eopatch): ), InterpolationTestCase( "cubic resample", - CubicResamplingTask( + ResamplingTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="cubic", result_interval=(-0.2, 1.0), resample_range=("2015-01-01", "2018-01-01", 16), unknown_value=5, @@ -235,8 +250,10 @@ def execute(self, eopatch): ( InterpolationTestCase( "cubic_copy_success", - CubicInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="cubic", result_interval=(0.0, 1.0), mask_feature=(FeatureType.MASK, "IS_VALID"), resample_range=("2015-01-01", "2018-01-01", 16), @@ -256,8 +273,10 @@ def execute(self, eopatch): ( InterpolationTestCase( "cubic_copy_fail", - CubicInterpolationTask( + InterpolationTask( (FeatureType.DATA, "NDVI"), + interpolation_object=scipy.interpolate.interp1d, + kind="cubic", result_interval=(0.0, 1.0), mask_feature=(FeatureType.MASK, "IS_VALID"), resample_range=("2015-01-01", "2018-01-01", 16), @@ -295,6 +314,7 @@ def test_interpolation(test_case: InterpolationTestCase, test_patch): @pytest.mark.parametrize(("test_case", "passes"), COPY_FEATURE_CASES) +@pytest.mark.filterwarnings("ignore::eolearn.core.exceptions.TemporalDimensionWarning") def test_copied_fields(test_case, passes, test_patch): if passes: eopatch = test_case.execute(test_patch) diff --git a/features/eolearn/tests/test_bands_extraction.py b/tests/features/test_bands_extraction.py similarity index 78% rename from features/eolearn/tests/test_bands_extraction.py rename to tests/features/test_bands_extraction.py index e8b0adfa0..31489c200 100644 --- a/features/eolearn/tests/test_bands_extraction.py +++ b/tests/features/test_bands_extraction.py @@ -6,6 +6,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import numpy as np import pytest @@ -13,24 +14,11 @@ from sentinelhub import CRS, BBox from eolearn.core import EOPatch, FeatureType -from eolearn.features import EuclideanNormTask, NormalizedDifferenceIndexTask +from eolearn.features import NormalizedDifferenceIndexTask INPUT_FEATURE = (FeatureType.DATA, "TEST") -def test_euclidean_norm(): - eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857))) - - data = np.zeros(5 * 10 * 10 * 7).reshape(5, 10, 10, 7) - bands = [0, 1, 2, 4, 6] - data[..., bands] = 1 - - eopatch[INPUT_FEATURE] = data - - eopatch = EuclideanNormTask(INPUT_FEATURE, (FeatureType.DATA, "NORM"), bands)(eopatch) - assert (eopatch.data["NORM"] == np.sqrt(len(bands))).all() - - @pytest.mark.parametrize("bad_input", [(1, 2, 3), "test", 0.5]) def test_bad_input(bad_input): with pytest.raises(ValueError): @@ -38,7 +26,7 @@ def test_bad_input(bad_input): def test_ndi(): - eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857))) + eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), CRS(3857)), timestamps=["1234-05-06"] * 4) eopatch[INPUT_FEATURE] = np.zeros((4, 3, 3, 9)) band_a, band_b = 4.123, 3.321 diff --git a/tests/features/test_feature_manipulation.py b/tests/features/test_feature_manipulation.py new file mode 100644 index 000000000..8c7c83dac --- /dev/null +++ b/tests/features/test_feature_manipulation.py @@ -0,0 +1,116 @@ +""" +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" + +from __future__ import annotations + +import datetime + +import numpy as np +import pytest + +from sentinelhub import CRS, BBox + +from eolearn.core import EOPatch, FeatureType +from eolearn.core.types import Feature +from eolearn.features import FilterTimeSeriesTask, SimpleFilterTask +from eolearn.features.feature_manipulation import SpatialResizeTask +from eolearn.features.utils import ResizeParam + +DUMMY_BBOX = BBox((0, 0, 1, 1), CRS(3857)) +# ruff: noqa: NPY002 + + +@pytest.mark.parametrize("feature", [(FeatureType.DATA, "BANDS-S2-L1C"), (FeatureType.LABEL, "IS_CLOUDLESS")]) +def test_simple_filter_task_filter_all(example_eopatch: EOPatch, feature): + filter_all_task = SimpleFilterTask(feature, filter_func=lambda _: False) + filtered_eopatch = filter_all_task.execute(example_eopatch) + + assert filtered_eopatch is not example_eopatch + assert filtered_eopatch.data["CLP"].shape == (0, 101, 100, 1) + assert filtered_eopatch.scalar["CLOUD_COVERAGE"].shape == (0, 1) + assert len(filtered_eopatch.vector["CLM_VECTOR"]) == 0 + assert np.array_equal(filtered_eopatch.mask_timeless["LULC"], example_eopatch.mask_timeless["LULC"]) + assert filtered_eopatch.timestamps == [] + + +@pytest.mark.parametrize("feature", [(FeatureType.MASK, "CLM"), (FeatureType.SCALAR, "CLOUD_COVERAGE")]) +def test_simple_filter_task_filter_nothing(example_eopatch: EOPatch, feature): + filter_all_task = SimpleFilterTask(feature, filter_func=lambda _: True) + filtered_eopatch = filter_all_task.execute(example_eopatch) + + assert filtered_eopatch is not example_eopatch + assert filtered_eopatch == example_eopatch + + +@pytest.mark.parametrize( + "invalid_feature", + [(FeatureType.VECTOR, "foo"), (FeatureType.VECTOR_TIMELESS, "bar"), (FeatureType.MASK_TIMELESS, "foobar")], +) +def test_simple_filter_invalid_feature(invalid_feature: Feature): + with pytest.raises(ValueError): + SimpleFilterTask(invalid_feature, filter_func=lambda _: True) + + +def test_content_after_time_filter(): + timestamps = [ + datetime.datetime(2017, 1, 1, 10, 4, 7), + datetime.datetime(2017, 1, 4, 10, 14, 5), + datetime.datetime(2017, 1, 11, 10, 3, 51), + datetime.datetime(2017, 1, 14, 10, 13, 46), + datetime.datetime(2017, 1, 24, 10, 14, 7), + datetime.datetime(2017, 2, 10, 10, 1, 32), + datetime.datetime(2017, 2, 20, 10, 6, 35), + datetime.datetime(2017, 3, 2, 10, 0, 20), + datetime.datetime(2017, 3, 12, 10, 7, 6), + datetime.datetime(2017, 3, 15, 10, 12, 14), + ] + data = np.random.rand(10, 100, 100, 3) + + new_start, new_end = 4, -3 + + eop = EOPatch(bbox=DUMMY_BBOX, timestamps=timestamps, data={"data": data}) + + filter_task = FilterTimeSeriesTask(start_date=timestamps[new_start], end_date=timestamps[new_end]) + filtered_eop = filter_task.execute(eop) + + assert filtered_eop is not eop + assert filtered_eop.timestamps == timestamps[new_start : new_end + 1] + assert np.array_equal(filtered_eop.data["data"], data[new_start : new_end + 1, ...]) + + +@pytest.mark.parametrize( + ("resize_type", "height_param", "width_param", "features_call", "features_check", "outputs"), + [ + (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP"), ("data", "CLP"), (68, 50, 70, 1)), + (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP"), ("mask", "CLM"), (68, 101, 100, 1)), + (ResizeParam.NEW_SIZE, 50, 70, ..., ("data", "CLP"), (68, 50, 70, 1)), + (ResizeParam.NEW_SIZE, 50, 70, ..., ("mask", "CLM"), (68, 50, 70, 1)), + (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP", "CLP_small"), ("data", "CLP_small"), (68, 50, 70, 1)), + (ResizeParam.NEW_SIZE, 50, 70, ("data", "CLP", "CLP_small"), ("data", "CLP"), (68, 101, 100, 1)), + (ResizeParam.SCALE_FACTORS, 2, 2, ("data", "CLP"), ("data", "CLP"), (68, 202, 200, 1)), + (ResizeParam.SCALE_FACTORS, 0.5, 2, ("data", "CLP"), ("data", "CLP"), (68, 50, 200, 1)), + (ResizeParam.SCALE_FACTORS, 0.1, 0.1, ("data", "CLP"), ("data", "CLP"), (68, 10, 10, 1)), + (ResizeParam.RESOLUTION, 5, 5, ("data", "CLP"), ("data", "CLP"), (68, 202, 200, 1)), + (ResizeParam.RESOLUTION, 20, 20, ("data", "CLP"), ("data", "CLP"), (68, 50, 50, 1)), + (ResizeParam.RESOLUTION, 5, 20, ("data", "CLP"), ("data", "CLP"), (68, 202, 50, 1)), + ], +) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_spatial_resize_task( + example_eopatch, resize_type, height_param, width_param, features_call, features_check, outputs +): + # Warnings occur due to lossy casting in the downsampling procedure + + resize = SpatialResizeTask( + resize_type=resize_type, height_param=height_param, width_param=width_param, features=features_call + ) + assert resize(example_eopatch)[features_check].shape == outputs + + +def test_spatial_resize_task_exception(): + with pytest.raises(ValueError): + SpatialResizeTask(features=("mask", "CLM"), resize_type="blabla", height_param=20, width_param=20) diff --git a/features/eolearn/tests/test_features_utils.py b/tests/features/test_features_utils.py similarity index 94% rename from features/eolearn/tests/test_features_utils.py rename to tests/features/test_features_utils.py index 851d7823c..34b2545d1 100644 --- a/features/eolearn/tests/test_features_utils.py +++ b/tests/features/test_features_utils.py @@ -1,4 +1,4 @@ -from typing import Tuple, Union +from __future__ import annotations import numpy as np import pytest @@ -11,7 +11,7 @@ @pytest.mark.parametrize("dtype", [np.float32, np.int32, np.uint8, bool]) @pytest.mark.parametrize("new_size", [(50, 50), (35, 39), (271, 271)]) def test_spatially_resize_image_new_size( - method: ResizeMethod, library: ResizeLib, dtype: Union[np.dtype, type], new_size: Tuple[int, int] + method: ResizeMethod, library: ResizeLib, dtype: np.dtype | type, new_size: tuple[int, int] ): """Test that all methods and backends are able to downscale and upscale images of various dtypes.""" if library is ResizeLib.CV2: # noqa: SIM102 @@ -49,7 +49,7 @@ def test_spatially_resize_image_new_size( @pytest.mark.parametrize("library", ResizeLib) @pytest.mark.parametrize("scale_factors", [(2, 2), (0.25, 0.25)]) def test_spatially_resize_image_scale_factors( - method: ResizeMethod, library: ResizeLib, scale_factors: Tuple[float, float] + method: ResizeMethod, library: ResizeLib, scale_factors: tuple[float, float] ): height, width = 120, 120 old_shape = (height, width, 3) @@ -81,7 +81,7 @@ def test_spatially_resize_image_scale_factors( ], ) @pytest.mark.filterwarnings("ignore::RuntimeWarning") -def test_spatially_resize_image_dtype(library: ResizeLib, dtype: Union[np.dtype, type]): +def test_spatially_resize_image_dtype(library: ResizeLib, dtype: np.dtype | type): # Warnings occur due to lossy casting in the downsampling procedure old_shape = (111, 111) data_2d = np.arange(np.prod(old_shape)).astype(dtype).reshape(old_shape) @@ -107,7 +107,7 @@ def test_image_fixture(): @pytest.mark.parametrize("library", ResizeLib) @pytest.mark.parametrize("new_size", [(50, 50), (217, 271)]) def test_spatially_resize_image_correctness( - method: ResizeMethod, library: ResizeLib, new_size: Tuple[int, int], test_image: np.ndarray + method: ResizeMethod, library: ResizeLib, new_size: tuple[int, int], test_image: np.ndarray ): """Test that resizing is correct on a very basic example. It tests midpoints of the 4 quadrants.""" height, width = new_size diff --git a/mask/eolearn/tests/conftest.py b/tests/geometry/conftest.py similarity index 88% rename from mask/eolearn/tests/conftest.py rename to tests/geometry/conftest.py index b5365acba..95de24661 100644 --- a/mask/eolearn/tests/conftest.py +++ b/tests/geometry/conftest.py @@ -6,13 +6,15 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import pytest from eolearn.core import EOPatch -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") +EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") TEST_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") diff --git a/tests/geometry/test_morphology.py b/tests/geometry/test_morphology.py new file mode 100644 index 000000000..646e80ec0 --- /dev/null +++ b/tests/geometry/test_morphology.py @@ -0,0 +1,95 @@ +""" +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +import numpy as np +import pytest +from numpy.testing import assert_array_equal + +from eolearn.core import EOPatch, FeatureType +from eolearn.core.utils.testing import PatchGeneratorConfig, generate_eopatch +from eolearn.geometry import ErosionTask, MorphologicalFilterTask, MorphologicalOperations, MorphologicalStructFactory + +CLASSES = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) +MASK_FEATURE = FeatureType.MASK, "mask" +MASK_TIMELESS_FEATURE = FeatureType.MASK_TIMELESS, "timeless_mask" +# ruff: noqa: NPY002 + + +@pytest.fixture(name="patch") +def patch_fixture() -> EOPatch: + config = PatchGeneratorConfig(max_integer_value=10, raster_shape=(50, 100), depth_range=(3, 4)) + patch = generate_eopatch([MASK_FEATURE, MASK_TIMELESS_FEATURE], config=config) + for feat in [MASK_FEATURE, MASK_TIMELESS_FEATURE]: + patch[feat] = patch[feat].astype(np.uint8) + return patch + + +@pytest.mark.parametrize("invalid_input", [None, 0, "a"]) +def test_erosion_value_error(invalid_input): + with pytest.raises(ValueError): + ErosionTask((FeatureType.MASK_TIMELESS, "LULC", "TEST"), disk_radius=invalid_input) + + +def test_erosion_full(test_eopatch): + erosion_task = ErosionTask((FeatureType.MASK_TIMELESS, "LULC", "LULC_ERODED"), disk_radius=3) + eopatch = erosion_task.execute(test_eopatch) + + mask_after = eopatch.mask_timeless["LULC_ERODED"] + + assert_array_equal(np.unique(mask_after, return_counts=True)[1], [1942, 6950, 1069, 87, 52]) + + +def test_erosion_partial(test_eopatch): + # skip forest and artificial surface + specific_labels = [0, 1, 3, 4] + erosion_task = ErosionTask( + mask_feature=(FeatureType.MASK_TIMELESS, "LULC", "LULC_ERODED"), disk_radius=3, erode_labels=specific_labels + ) + eopatch = erosion_task.execute(test_eopatch) + + mask_after = eopatch.mask_timeless["LULC_ERODED"] + + assert_array_equal(np.unique(mask_after, return_counts=True)[1], [1145, 7601, 1069, 87, 198]) + + +@pytest.mark.parametrize( + ("morph_operation", "struct_element", "mask_counts", "mask_timeless_counts"), + [ + ( + MorphologicalOperations.DILATION, + None, + [6, 34, 172, 768, 2491, 7405, 19212, 44912], + [1, 2, 16, 104, 466, 1490, 3870, 9051], + ), + (MorphologicalOperations.EROSION, MorphologicalStructFactory.get_disk(11), [74957, 42, 1], [14994, 6]), + (MorphologicalOperations.OPENING, MorphologicalStructFactory.get_disk(11), [73899, 1051, 50], [14837, 163]), + (MorphologicalOperations.CLOSING, MorphologicalStructFactory.get_disk(11), [770, 74230], [425, 14575]), + ( + MorphologicalOperations.OPENING, + MorphologicalStructFactory.get_rectangle(5, 6), + [48468, 24223, 2125, 169, 15], + [10146, 4425, 417, 3, 9], + ), + ( + MorphologicalOperations.DILATION, + MorphologicalStructFactory.get_rectangle(5, 6), + [2, 19, 198, 3929, 70852], + [32, 743, 14225], + ), + ], +) +def test_morphological_filter(patch, morph_operation, struct_element, mask_counts, mask_timeless_counts): + task = MorphologicalFilterTask( + [MASK_FEATURE, MASK_TIMELESS_FEATURE], morph_operation=morph_operation, struct_elem=struct_element + ) + task.execute(patch) + + assert patch[MASK_FEATURE].shape == (5, 50, 100, 3) + assert patch[MASK_TIMELESS_FEATURE].shape == (50, 100, 3) + assert_array_equal(np.unique(patch[MASK_FEATURE], return_counts=True)[1], mask_counts) + assert_array_equal(np.unique(patch[MASK_TIMELESS_FEATURE], return_counts=True)[1], mask_timeless_counts) diff --git a/geometry/eolearn/tests/test_transformations.py b/tests/geometry/test_transformations.py similarity index 98% rename from geometry/eolearn/tests/test_transformations.py rename to tests/geometry/test_transformations.py index 6d7d2fa8a..7b3dbaed5 100644 --- a/geometry/eolearn/tests/test_transformations.py +++ b/tests/geometry/test_transformations.py @@ -4,10 +4,12 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import dataclasses import warnings from functools import partial -from typing import Any, Dict, Optional, Tuple, Type, Union +from typing import Any import numpy as np import pytest @@ -42,8 +44,8 @@ class VectorToRasterTestCase: name: str task: EOTask - img_exp_statistics: Dict[str, Union[Tuple[int, ...], Type, np.dtype, float]] - warning: Optional[Type[Warning]] = None + img_exp_statistics: dict[str, tuple[int, ...] | type | np.dtype | float] + warning: type[Warning] | None = None VECTOR_TO_RASTER_TEST_CASES = ( diff --git a/io/eolearn/tests/TestInputs/test_meteoblue_raster_input.bin b/tests/io/TestInputs/test_meteoblue_raster_input.bin similarity index 100% rename from io/eolearn/tests/TestInputs/test_meteoblue_raster_input.bin rename to tests/io/TestInputs/test_meteoblue_raster_input.bin diff --git a/io/eolearn/tests/TestInputs/test_meteoblue_vector_input.bin b/tests/io/TestInputs/test_meteoblue_vector_input.bin similarity index 100% rename from io/eolearn/tests/TestInputs/test_meteoblue_vector_input.bin rename to tests/io/TestInputs/test_meteoblue_vector_input.bin diff --git a/io/eolearn/tests/conftest.py b/tests/io/conftest.py similarity index 91% rename from io/eolearn/tests/conftest.py rename to tests/io/conftest.py index 2fed8401c..1ef98753b 100644 --- a/io/eolearn/tests/conftest.py +++ b/tests/io/conftest.py @@ -6,6 +6,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import boto3 @@ -14,11 +16,11 @@ pytest.register_assert_rewrite("sentinelhub.testing_utils") # makes asserts in helper functions work with pytest -from sentinelhub import SHConfig # noqa[E402] +from sentinelhub import SHConfig # noqa: E402 -from eolearn.core import EOPatch # noqa[E402] +from eolearn.core import EOPatch # noqa: E402 -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") +EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") TEST_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") @@ -35,7 +37,7 @@ def example_data_path_fixture(): @pytest.fixture(name="gpkg_file") def local_gpkg_example_file_fixture(): """A pytest fixture to retrieve a gpkg example file""" - return os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../../example_data/import-gpkg-test.gpkg") + return os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../example_data/import-gpkg-test.gpkg") @pytest.fixture(name="s3_gpkg_file") diff --git a/io/eolearn/tests/test_geometry_io.py b/tests/io/test_geometry_io.py similarity index 79% rename from io/eolearn/tests/test_geometry_io.py rename to tests/io/test_geometry_io.py index 295de5aaf..1a63c1be3 100644 --- a/io/eolearn/tests/test_geometry_io.py +++ b/tests/io/test_geometry_io.py @@ -4,12 +4,14 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import pytest from sentinelhub import CRS, BBox from eolearn.core import FeatureType -from eolearn.io import GeopediaVectorImportTask, VectorImportTask +from eolearn.io import VectorImportTask @pytest.mark.parametrize( @@ -31,14 +33,6 @@ def test_import_local_file(self, gpkg_file, reproject, clip, n_features, bbox, c def test_import_s3_file(self, s3_gpkg_file, reproject, clip, n_features, bbox, crs): self._test_import(bbox, clip, crs, s3_gpkg_file, n_features, reproject) - def test_import_from_geopedia(self, reproject, clip, n_features, bbox, crs): - feature = FeatureType.VECTOR_TIMELESS, "lpis_iacs" - import_task = GeopediaVectorImportTask(feature=feature, geopedia_table=3447, reproject=reproject, clip=clip) - eop = import_task.execute(bbox=bbox) - assert len(eop[feature]) == n_features, "Wrong number of features!" - to_crs = crs or import_task.dataset_crs - assert eop[feature].crs.to_epsg() == to_crs.epsg - @staticmethod def _test_import(bbox, clip, crs, gpkg_example, n_features, reproject): feature = FeatureType.VECTOR_TIMELESS, "lpis_iacs" diff --git a/io/eolearn/tests/test_raster_io.py b/tests/io/test_raster_io.py similarity index 95% rename from io/eolearn/tests/test_raster_io.py rename to tests/io/test_raster_io.py index 0971b840a..c11455da4 100644 --- a/io/eolearn/tests/test_raster_io.py +++ b/tests/io/test_raster_io.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import copy import dataclasses import datetime @@ -11,7 +13,7 @@ import os import tempfile import warnings -from typing import Any, Optional, Type, Union +from typing import Any import boto3 import numpy as np @@ -26,12 +28,11 @@ from sentinelhub.time_utils import serialize_time from eolearn.core import EOPatch, FeatureType -from eolearn.core.exceptions import EORuntimeWarning +from eolearn.core.exceptions import EORuntimeWarning, TemporalDimensionWarning from eolearn.io import ExportToTiffTask, ImportFromTiffTask logging.basicConfig(level=logging.DEBUG) - BUCKET_NAME = "mocked-test-bucket" PATH_ON_BUCKET = f"s3://{BUCKET_NAME}/some-folder" @@ -44,15 +45,23 @@ def _create_s3_bucket_fixture(): yield +@pytest.fixture(autouse=True) +def _ignore_warnings(): + """Module has lots of temporally ill defined patches.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore", TemporalDimensionWarning) + yield + + @dataclasses.dataclass class TiffTestCase: name: str feature_type: FeatureType data: np.ndarray - bands: Optional[Union[tuple, list]] = None - times: Optional[Union[tuple, list]] = None - expected_times: Optional[Union[tuple, list]] = None - warning: Optional[Type[Warning]] = None + bands: tuple | list | None = None + times: tuple | list | None = None + expected_times: tuple | list | None = None + warning: type[Warning] | None = None def __post_init__(self): if self.expected_times is None: @@ -179,7 +188,7 @@ def test_export_import(test_case, test_eopatch): def _execute_with_warning_control( - export_task: ExportToTiffTask, warning: Optional[Type[Warning]], *args: Any, **kwargs: Any + export_task: ExportToTiffTask, warning: type[Warning] | None, *args: Any, **kwargs: Any ) -> None: """Makes sure that task either raises an expected warning or doesn't raise any EO runtime warning.""" if warning: @@ -312,6 +321,7 @@ def test_import_tiff_intersecting(test_eopatch, example_data_path): assert unique_values == [no_data_value], f"No data values should all be equal to {no_data_value}" +@pytest.mark.skip() # rasterio 1.3.8 and moto have some issues, wait for it to be resolved def test_timeless_feature(test_eopatch): feature = FeatureType.DATA_TIMELESS, "DEM" filename = "relative-path/my-filename.tiff" @@ -365,6 +375,7 @@ def test_time_dependent_feature_with_timestamps(test_eopatch): @pytest.mark.parametrize(("no_data_value", "data_type"), [(np.nan, float), (0, int), (None, float), (1, np.byte)]) +@pytest.mark.filterwarnings("ignore::eolearn.core.exceptions.EORuntimeWarning") def test_export_import_sequence(no_data_value, data_type): """Tests import and export tiff tasks on generated array with different values of no_data_value.""" eopatch = EOPatch(bbox=BBox((0, 0, 1, 1), crs=CRS.WGS84)) diff --git a/io/eolearn/tests/test_sentinelhub_process.py b/tests/io/test_sentinelhub_process.py similarity index 96% rename from io/eolearn/tests/test_sentinelhub_process.py rename to tests/io/test_sentinelhub_process.py index d073fce42..d2b96c5e8 100644 --- a/io/eolearn/tests/test_sentinelhub_process.py +++ b/tests/io/test_sentinelhub_process.py @@ -4,12 +4,14 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import dataclasses import datetime as dt import os import shutil from concurrent import futures -from typing import Any, List, Optional +from typing import Any import numpy as np import pytest @@ -18,7 +20,7 @@ from sentinelhub.testing_utils import assert_statistics_match from eolearn.core import EOPatch, EOTask, FeatureType -from eolearn.io import SentinelHubDemTask, SentinelHubEvalscriptTask, SentinelHubInputTask, SentinelHubSen2corTask +from eolearn.io import SentinelHubDemTask, SentinelHubEvalscriptTask, SentinelHubInputTask @pytest.fixture(name="cache_folder") @@ -42,8 +44,8 @@ class IoTestCase: time_interval: tuple feature: str = "BANDS" feature_type: FeatureType = FeatureType.DATA - data_size: Optional[int] = None - timestamp_length: Optional[int] = None + data_size: int | None = None + timestamp_length: int | None = None stats: Any = None @@ -140,7 +142,7 @@ def test_specific_bands(self): ("bicubic", [0.0836, 0.1548, 0.0792]), ], ) - def test_upsampling_downsampling(self, resampling_type: ResamplingType, stats: List[float]): + def test_upsampling_downsampling(self, resampling_type: ResamplingType, stats: list[float]): task = SentinelHubInputTask( bands_feature=(FeatureType.DATA, "BANDS"), bands=["B01"], @@ -183,7 +185,7 @@ def test_upsampling_downsampling(self, resampling_type: ResamplingType, stats: L ), ], ) - def test_geometry_argument(self, geometry: Geometry, stats: List[float]): + def test_geometry_argument(self, geometry: Geometry, stats: list[float]): task = SentinelHubInputTask( bands_feature=(FeatureType.DATA, "BANDS"), bands=["B01"], @@ -224,7 +226,7 @@ def test_geometry_argument(self, geometry: Geometry, stats: List[float]): ), ], ) - def test_geometry_argument_evalscript(self, geometry: Geometry, stats: List[float]): + def test_geometry_argument_evalscript(self, geometry: Geometry, stats: list[float]): evalscript = """ //VERSION=3 @@ -412,25 +414,6 @@ def test_dem_wrong_feature(self): with pytest.raises(ValueError): SentinelHubDemTask(resolution=10, feature=(FeatureType.DATA, "DEM"), max_threads=3) - def test_sen2cor(self): - task = SentinelHubSen2corTask( - sen2cor_classification=["SCL", "CLD"], - size=self.size, - maxcc=self.maxcc, - time_difference=self.time_difference, - data_collection=DataCollection.SENTINEL2_L2A, - max_threads=self.max_threads, - ) - - eopatch = task.execute(bbox=self.bbox, time_interval=self.time_interval) - - scl = eopatch[(FeatureType.MASK, "SCL")] - cld = eopatch[(FeatureType.DATA, "CLD")] - - width, height = self.size - assert scl.shape == (4, height, width, 1) - assert cld.shape == (4, height, width, 1) - def test_metadata(self): evalscript = """ //VERSION=3 @@ -613,7 +596,7 @@ class TestSentinelHubInputTaskDataCollections: } """ - test_cases = [ + test_cases = [ # noqa: RUF012 IoTestCase( name="Sentinel-2 L2A", task=SentinelHubInputTask( diff --git a/geometry/eolearn/tests/conftest.py b/tests/mask/conftest.py similarity index 81% rename from geometry/eolearn/tests/conftest.py rename to tests/mask/conftest.py index b5365acba..d82805448 100644 --- a/geometry/eolearn/tests/conftest.py +++ b/tests/mask/conftest.py @@ -6,13 +6,17 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import pytest from eolearn.core import EOPatch -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") +pytest.register_assert_rewrite("sentinelhub.testing_utils") + +EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") TEST_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") diff --git a/tests/mask/extra/test_cloud_mask.py b/tests/mask/extra/test_cloud_mask.py new file mode 100644 index 000000000..da11a6f64 --- /dev/null +++ b/tests/mask/extra/test_cloud_mask.py @@ -0,0 +1,28 @@ +""" +Copyright (c) 2017- Sinergise and contributors +For the full list of contributors, see the CREDITS file in the root directory of this source tree. + +This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. +""" +from __future__ import annotations + +from numpy.testing import assert_array_equal + +from eolearn.core import FeatureType +from eolearn.mask.extra.cloud_mask import CloudMaskTask + + +def test_cloud_detection(test_eopatch): + add_tcm = CloudMaskTask( + data_feature=(FeatureType.DATA, "BANDS-S2-L1C"), + valid_data_feature=(FeatureType.MASK, "IS_DATA"), + output_mask_feature=(FeatureType.MASK, "CLM_TEST"), + output_proba_feature=(FeatureType.DATA, "CLP_TEST"), + threshold=0.4, + average_over=4, + dilation_size=2, + ) + eop_clm = add_tcm(test_eopatch) + + assert_array_equal(eop_clm.mask["CLM_TEST"], test_eopatch.mask["CLM_S2C"]) + assert_array_equal(eop_clm.data["CLP_TEST"], test_eopatch.data["CLP_S2C"]) diff --git a/mask/eolearn/tests/test_masking.py b/tests/mask/test_masking.py similarity index 99% rename from mask/eolearn/tests/test_masking.py rename to tests/mask/test_masking.py index 050c56d25..b015491f6 100644 --- a/mask/eolearn/tests/test_masking.py +++ b/tests/mask/test_masking.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import numpy as np import pytest diff --git a/mask/eolearn/tests/test_snow_mask.py b/tests/mask/test_snow_mask.py similarity index 72% rename from mask/eolearn/tests/test_snow_mask.py rename to tests/mask/test_snow_mask.py index 30c6a5149..1bbd97063 100644 --- a/mask/eolearn/tests/test_snow_mask.py +++ b/tests/mask/test_snow_mask.py @@ -4,28 +4,19 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import numpy as np import pytest from eolearn.core import FeatureType -from eolearn.mask import SnowMaskTask, TheiaSnowMaskTask +from eolearn.mask import SnowMaskTask @pytest.mark.parametrize( ("task", "result"), [ (SnowMaskTask((FeatureType.DATA, "BANDS-S2-L1C"), [2, 3, 7, 11], mask_name="TEST_SNOW_MASK"), (50468, 1405)), - ( - TheiaSnowMaskTask( - (FeatureType.DATA, "BANDS-S2-L1C"), - [2, 3, 11], - (FeatureType.MASK, "CLM"), - (FeatureType.DATA_TIMELESS, "DEM"), - b10_index=10, - mask_name="TEST_THEIA_SNOW_MASK", - ), - (60682, 10088), - ), ], ) def test_snow_coverage(task, result, test_eopatch): diff --git a/ml_tools/eolearn/tests/conftest.py b/tests/ml_tools/conftest.py similarity index 88% rename from ml_tools/eolearn/tests/conftest.py rename to tests/ml_tools/conftest.py index 314d1b846..92513cf05 100644 --- a/ml_tools/eolearn/tests/conftest.py +++ b/tests/ml_tools/conftest.py @@ -6,13 +6,15 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import pytest from eolearn.core import EOPatch -EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data") +EXAMPLE_DATA_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data") TEST_EOPATCH_PATH = os.path.join(EXAMPLE_DATA_PATH, "TestEOPatch") diff --git a/ml_tools/eolearn/tests/test_sampling.py b/tests/ml_tools/test_sampling.py similarity index 97% rename from ml_tools/eolearn/tests/test_sampling.py rename to tests/ml_tools/test_sampling.py index cb81d66b8..090239f56 100644 --- a/ml_tools/eolearn/tests/test_sampling.py +++ b/tests/ml_tools/test_sampling.py @@ -4,9 +4,10 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import copy import math -from typing import Dict, List, Tuple, Union import numpy as np import pytest @@ -36,7 +37,7 @@ ), ], ) -def test_random_point_in_triangle_generator(triangle: Polygon, expected_points: List[Point]) -> None: +def test_random_point_in_triangle_generator(triangle: Polygon, expected_points: list[Point]) -> None: generator = np.random.default_rng(seed=42) points = [random_point_in_triangle(triangle, generator) for _ in range(2)] assert all(point == expected for point, expected in zip(points, expected_points)) @@ -73,7 +74,7 @@ def small_image_fixture() -> np.ndarray: (np.ones((100, 100)), {1: 10001}), ], ) -def test_sample_by_values_errors(image: np.ndarray, n_samples: Dict[int, int]) -> None: +def test_sample_by_values_errors(image: np.ndarray, n_samples: dict[int, int]) -> None: rng = np.random.default_rng() with pytest.raises(ValueError): sample_by_values(image, n_samples, rng=rng) @@ -88,7 +89,7 @@ def test_sample_by_values_errors(image: np.ndarray, n_samples: Dict[int, int]) - ({0: 100, 2: 30000}, True), ], ) -def test_sample_by_values(small_image: np.ndarray, seed: int, n_samples: Dict[int, int], replace: bool) -> None: +def test_sample_by_values(small_image: np.ndarray, seed: int, n_samples: dict[int, int], replace: bool) -> None: rng = np.random.default_rng(seed) rows, cols = sample_by_values(small_image, n_samples, rng=rng, replace=replace) labels = small_image[rows, cols] @@ -105,7 +106,7 @@ def test_sample_by_values(small_image: np.ndarray, seed: int, n_samples: Dict[in ], ) @pytest.mark.parametrize("sample_size", [(1, 1), (2, 3), (10, 11)]) -def test_expand_to_grids(rows: np.ndarray, columns: np.ndarray, sample_size: Tuple[int, int]) -> None: +def test_expand_to_grids(rows: np.ndarray, columns: np.ndarray, sample_size: tuple[int, int]) -> None: row_grid, column_grid = expand_to_grids(rows, columns, sample_size=sample_size) expected_shape = sample_size[0] * rows.size, sample_size[1] @@ -121,7 +122,7 @@ def test_expand_to_grids(rows: np.ndarray, columns: np.ndarray, sample_size: Tup ({0: 100, 2: 30000}), ], ) -def test_get_mask_of_samples(small_image: np.ndarray, n_samples: Dict[int, int]) -> None: +def test_get_mask_of_samples(small_image: np.ndarray, n_samples: dict[int, int]) -> None: row_grid, column_grid = sample_by_values(small_image, n_samples, replace=True) image_shape = small_image.shape result = get_mask_of_samples(image_shape, row_grid, column_grid) @@ -191,7 +192,7 @@ def test_object_sampling_reproducibility(eopatch: EOPatch, seed: int, block_task ("fraction", "replace"), [(2, False), (-0.5, True), ({1: 0.5, 3: 0.4, 5: 1.2}, False), ({1: 0.5, 3: -0.4, 5: 1.2}, True), ((1, 0.4), True)], ) -def test_fraction_sampling_errors(fraction: Union[float, Dict[int, float]], replace: bool) -> None: +def test_fraction_sampling_errors(fraction: float | dict[int, float], replace: bool) -> None: with pytest.raises(ValueError): FractionSamplingTask( [(FeatureType.DATA, "NDVI", "NDVI_SAMPLED")], diff --git a/visualization/eolearn/tests/test_eoexecutor.py b/tests/visualization/test_eoexecutor.py similarity index 81% rename from visualization/eolearn/tests/test_eoexecutor.py rename to tests/visualization/test_eoexecutor.py index 27ef10918..b48f81faf 100644 --- a/visualization/eolearn/tests/test_eoexecutor.py +++ b/tests/visualization/test_eoexecutor.py @@ -4,6 +4,7 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations import logging import os @@ -24,12 +25,18 @@ def execute(self, *_, **kwargs): my_logger.debug("Debug statement of Example task with kwargs: %s", kwargs) if "arg1" in kwargs and kwargs["arg1"] is None: - raise Exception + raise RuntimeError(f"Oh no, i spilled my kwargs all over the floor! {kwargs}!") NODE = EONode(ExampleTask()) WORKFLOW = EOWorkflow([NODE, EONode(task=ExampleTask(), inputs=[NODE, NODE])]) -EXECUTION_KWARGS = [{NODE: {"arg1": 1}}, {}, {NODE: {"arg1": 3, "arg3": 10}}, {NODE: {"arg1": None}}] +EXECUTION_KWARGS = [ + {NODE: {"arg1": 1}}, + {}, + {NODE: {"arg1": 3, "arg3": 10}}, + {NODE: {"arg1": None}}, + {NODE: {"arg1": None, "arg3": 10}}, +] @pytest.mark.parametrize("save_logs", [True, False]) @@ -41,7 +48,7 @@ def test_report_creation(save_logs, include_logs): EXECUTION_KWARGS, logs_folder=tmp_dir_name, save_logs=save_logs, - execution_names=["ex 1", 2, 0.4, None], + execution_names=["ex 1", 2, 0.4, None, "beep"], ) executor.run(workers=10) executor.make_report(include_logs=include_logs) diff --git a/visualization/eolearn/tests/test_eopatch.py b/tests/visualization/test_eopatch.py similarity index 94% rename from visualization/eolearn/tests/test_eopatch.py rename to tests/visualization/test_eopatch.py index 71aae4c6f..8ac826ac8 100644 --- a/visualization/eolearn/tests/test_eopatch.py +++ b/tests/visualization/test_eopatch.py @@ -6,6 +6,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import os import numpy as np @@ -18,7 +20,7 @@ @pytest.fixture(name="eopatch", scope="module") def eopatch_fixture(): - path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "..", "example_data", "TestEOPatch") + path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..", "example_data", "TestEOPatch") return EOPatch.load(path) @@ -42,11 +44,10 @@ def eopatch_fixture(): ((FeatureType.LABEL_TIMELESS, "LULC_COUNTS"), {}), ((FeatureType.LABEL_TIMELESS, "LULC_COUNTS"), {"channels": [0]}), ((FeatureType.VECTOR_TIMELESS, "LULC"), {}), - (FeatureType.BBOX, {}), ], ) @pytest.mark.sh_integration() -def test_eopatch_plot(eopatch, feature, params): +def test_eopatch_plot(eopatch: EOPatch, feature, params): """A simple test of EOPatch plotting for different features.""" # We reduce width and height otherwise running matplotlib.pyplot.subplots in combination with pytest would # kill the Python kernel. diff --git a/visualization/eolearn/tests/test_eoworkflow.py b/tests/visualization/test_eoworkflow.py similarity index 97% rename from visualization/eolearn/tests/test_eoworkflow.py rename to tests/visualization/test_eoworkflow.py index 5fd5c8193..351463d28 100644 --- a/visualization/eolearn/tests/test_eoworkflow.py +++ b/tests/visualization/test_eoworkflow.py @@ -4,6 +4,8 @@ This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. """ +from __future__ import annotations + import pytest from graphviz import Digraph diff --git a/visualization/MANIFEST.in b/visualization/MANIFEST.in deleted file mode 100644 index 84f45995e..000000000 --- a/visualization/MANIFEST.in +++ /dev/null @@ -1,6 +0,0 @@ -include requirements*.txt -include eolearn/visualization/report_templates/report.html -include LICENSE -include README.md -include eolearn/visualization/py.typed -exclude eolearn/tests/* diff --git a/visualization/README.md b/visualization/README.md index f8aa261f7..3f5629740 100644 --- a/visualization/README.md +++ b/visualization/README.md @@ -1,5 +1,5 @@ # eolearn.visualization subpackage -A collection of data visualization utilities. +The deprecated way to install `eolearn.visualization`. See [migration guide](https://github.com/sentinel-hub/eo-learn/issues/733) on how to correctly install the `eo-learn` package. -For more information about the module visit [readthedocs](https://eo-learn.readthedocs.io/en/latest/reference/eolearn.visualization.html). +Installs requirements and a dummy package that is detected by `eo-learn` but otherwise does not change anything. diff --git a/visualization/deprecated_eolearn_visualization/__init__.py b/visualization/deprecated_eolearn_visualization/__init__.py new file mode 100644 index 000000000..5b6018861 --- /dev/null +++ b/visualization/deprecated_eolearn_visualization/__init__.py @@ -0,0 +1 @@ +__version__ = "1.5.0" diff --git a/visualization/eolearn/__init__.py b/visualization/eolearn/__init__.py deleted file mode 100644 index 8db66d3d0..000000000 --- a/visualization/eolearn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/visualization/eolearn/visualization/eoexecutor.py b/visualization/eolearn/visualization/eoexecutor.py deleted file mode 100644 index 6c9c8451d..000000000 --- a/visualization/eolearn/visualization/eoexecutor.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -Module with utilities for visualizing EOExecutor - -Copyright (c) 2017- Sinergise and contributors -For the full list of contributors, see the CREDITS file in the root directory of this source tree. - -This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree. -""" -from __future__ import annotations - -import base64 -import datetime as dt -import importlib -import inspect -import os -import warnings -from collections import defaultdict -from typing import Any, cast - -import fs -import graphviz -import matplotlib.pyplot as plt -import pygments -import pygments.formatter -import pygments.lexers -from jinja2 import Environment, FileSystemLoader, Template -from pygments.formatters.html import HtmlFormatter - -from eolearn.core import EOExecutor -from eolearn.core.exceptions import EOUserWarning - - -class EOExecutorVisualization: - """Class handling EOExecutor visualizations, particularly creating reports""" - - def __init__(self, eoexecutor: EOExecutor): - """ - :param eoexecutor: An instance of EOExecutor - """ - self.eoexecutor = eoexecutor - - def make_report(self, include_logs: bool = True) -> None: - """Makes a html report and saves it into the same folder where logs are stored.""" - if self.eoexecutor.execution_results is None: - raise RuntimeError( - "Cannot produce a report without running the executor first, check EOExecutor.run method" - ) - - # These should be set automatically after a run - start_time = cast(dt.datetime, self.eoexecutor.start_time) - report_folder = cast(str, self.eoexecutor.report_folder) - - if os.environ.get("DISPLAY", "") == "": - plt.switch_backend("Agg") - - try: - dependency_graph = self._create_dependency_graph() - except graphviz.backend.ExecutableNotFound as ex: - dependency_graph = None - warnings.warn( - ( - f"{ex}.\nPlease install the system package 'graphviz' (in addition to the python package) to have " - "the dependency graph in the final report!" - ), - EOUserWarning, - ) - - formatter = HtmlFormatter(linenos=True) - - template = self._get_template() - - execution_log_filenames = [fs.path.basename(log_path) for log_path in self.eoexecutor.get_log_paths()] - if self.eoexecutor.save_logs: - execution_logs = self.eoexecutor.read_logs() if include_logs else None - else: - execution_logs = ["No logs saved"] * len(self.eoexecutor.execution_kwargs) - - html = template.render( - title=f"Report {self._format_datetime(start_time)}", - dependency_graph=dependency_graph, - general_stats=self.eoexecutor.general_stats, - exception_stats=self._get_exception_stats(), - task_descriptions=self._get_node_descriptions(), - task_sources=self._render_task_sources(formatter), - execution_results=self.eoexecutor.execution_results, - execution_tracebacks=self._render_execution_tracebacks(formatter), - execution_logs=execution_logs, - execution_log_filenames=execution_log_filenames, - execution_names=self.eoexecutor.execution_names, - code_css=formatter.get_style_defs(), - ) - self.eoexecutor.filesystem.makedirs(report_folder, recreate=True) - - with self.eoexecutor.filesystem.open(self.eoexecutor.get_report_path(full_path=False), "w") as file_handle: - file_handle.write(html) - - def _create_dependency_graph(self) -> str: - """Provides an image of dependency graph""" - dot = self.eoexecutor.workflow.dependency_graph() - return base64.b64encode(dot.pipe()).decode() - - def _get_exception_stats(self) -> list[tuple[str, str, list[tuple[str, int]]]]: - """Creates aggregated stats about exceptions""" - formatter = HtmlFormatter() - lexer = pygments.lexers.get_lexer_by_name("python", stripall=True) - - exception_stats: defaultdict[str, defaultdict[str, int]] = defaultdict(lambda: defaultdict(lambda: 0)) - - for workflow_results in self.eoexecutor.execution_results: - if not workflow_results.error_node_uid: - continue - - error_node = workflow_results.stats[workflow_results.error_node_uid] - exception_str = pygments.highlight( - f"{error_node.exception.__class__.__name__}: {error_node.exception}", lexer, formatter - ) - exception_stats[error_node.node_uid][exception_str] += 1 - - return self._to_ordered_stats(exception_stats) - - def _to_ordered_stats( - self, exception_stats: defaultdict[str, defaultdict[str, int]] - ) -> list[tuple[str, str, list[tuple[str, int]]]]: - """Exception stats get ordered by nodes in their execution order in workflows. Exception stats that happen - for the same node get ordered by number of occurrences in a decreasing order. - """ - ordered_exception_stats = [] - for node in self.eoexecutor.workflow.get_nodes(): - if node.uid not in exception_stats: - continue - - node_stats = exception_stats[node.uid] - ordered_exception_stats.append( - (node.get_name(), node.uid, sorted(node_stats.items(), key=lambda item: -item[1])) - ) - - return ordered_exception_stats - - def _get_node_descriptions(self) -> list[dict[str, Any]]: - """Prepares a list of node names and initialization parameters of their tasks""" - descriptions = [] - name_counts: dict[str, int] = defaultdict(lambda: 0) - - for node in self.eoexecutor.workflow.get_nodes(): - node_name = node.get_name(name_counts[node.get_name()]) - name_counts[node.get_name()] += 1 - - descriptions.append( - { - "name": f"{node_name} ({node.uid})", - "uid": node.uid, - "args": { - key: value.replace("<", "<").replace(">", ">") # type: ignore[attr-defined] - for key, value in node.task.private_task_config.init_args.items() - }, - } - ) - - return descriptions - - def _render_task_sources(self, formatter: pygments.formatter.Formatter) -> dict[str, Any]: - """Renders source code of EOTasks""" - lexer = pygments.lexers.get_lexer_by_name("python", stripall=True) - sources = {} - - for node in self.eoexecutor.workflow.get_nodes(): - task = node.task - - key = f"{task.__class__.__name__} ({task.__module__})" - if key in sources: - continue - - source: Any - if task.__module__.startswith("eolearn"): - subpackage_name = ".".join(task.__module__.split(".")[:2]) - subpackage = importlib.import_module(subpackage_name) - subpackage_version = subpackage.__version__ if hasattr(subpackage, "__version__") else "unknown" - source = subpackage_name, subpackage_version - else: - try: - source = inspect.getsource(task.__class__) - source = pygments.highlight(source, lexer, formatter) - except (TypeError, OSError): - # Jupyter notebook does not have __file__ method to collect source code - # StackOverflow provides no solutions - # Could be investigated further by looking into Jupyter Notebook source code - source = None - - sources[key] = source - - return sources - - def _render_execution_tracebacks(self, formatter: pygments.formatter.Formatter) -> list: - """Renders stack traces of those executions which failed""" - tb_lexer = pygments.lexers.get_lexer_by_name("py3tb", stripall=True) - - tracebacks = [] - for results in self.eoexecutor.execution_results: - if results.workflow_failed() and results.error_node_uid is not None: - # second part of above check needed only for typechecking purposes - failed_node_stats = results.stats[results.error_node_uid] - traceback = pygments.highlight(failed_node_stats.exception_traceback, tb_lexer, formatter) - else: - traceback = None - - tracebacks.append(traceback) - - return tracebacks - - def _get_template(self) -> Template: - """Loads and sets up a template for report""" - templates_dir = os.path.join(os.path.dirname(__file__), "report_templates") - env = Environment(loader=FileSystemLoader(templates_dir)) - env.filters["datetime"] = self._format_datetime - env.globals.update(timedelta=self._format_timedelta) - - return env.get_template(self.eoexecutor.REPORT_FILENAME) - - @staticmethod - def _format_datetime(value: dt.datetime) -> str: - """Method for formatting datetime objects into report""" - return value.strftime("%Y-%m-%d %H:%M:%S") - - @staticmethod - def _format_timedelta(value1: dt.datetime, value2: dt.datetime) -> str: - """Method for formatting time delta into report""" - return str(value2 - value1) diff --git a/visualization/eolearn/visualization/py.typed b/visualization/eolearn/visualization/py.typed deleted file mode 100644 index e69de29bb..000000000 diff --git a/visualization/requirements.txt b/visualization/requirements.txt index 8cfba60ef..562050068 100644 --- a/visualization/requirements.txt +++ b/visualization/requirements.txt @@ -1,5 +1 @@ -eo-learn-core -graphviz>=0.10.1 -jinja2 -matplotlib -pygments +eo-learn diff --git a/visualization/setup.py b/visualization/setup.py index e554008ed..8201d00aa 100644 --- a/visualization/setup.py +++ b/visualization/setup.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os from setuptools import find_packages, setup @@ -16,7 +18,7 @@ def parse_requirements(file): def get_version(): - path = os.path.join(os.path.dirname(__file__), "eolearn", "visualization", "__init__.py") + path = os.path.join(os.path.dirname(__file__), "deprecated_eolearn_visualization", "__init__.py") with open(path) as version_file: for line in version_file: if line.find("__version__") >= 0: @@ -44,9 +46,7 @@ def get_version(): license="MIT", packages=find_packages(), package_data={"eolearn": ["visualization/report_templates/report.html"]}, - include_package_data=True, install_requires=parse_requirements("requirements.txt"), - zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers",