From 8a4c13ac60a30a6c6c65a598ef78c4b28d080644 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 13:55:03 +0000 Subject: [PATCH 1/9] build: use 12rambau/pypackage as a tempalte --- .copier-answers.yml | 13 + .devcontainer/devcontainer.json | 5 +- .../PULL_REQUEST_TEMPLATE/pr_template.md | 23 + .github/ISSUE_TEMPLATE/bug_report.md | 24 + .github/ISSUE_TEMPLATE/feature_request.md | 19 + .github/workflows/prerelease.yaml | 27 - .github/workflows/pypackage_check.yaml | 53 + .github/workflows/release.yaml | 26 +- .github/workflows/unit.yaml | 32 +- .gitignore | 26 +- .pre-commit-config.yaml | 40 +- AUTHORS.rst | 45 +- CHANGELOG.rst | 19 - CITATION.cff | 11 + CODE_OF_CONDUCT.rst | 140 + CONTRIBUTING.rst | 51 +- LICENSE | 4 +- README.rst | 87 +- RELEASE.rst | 35 - codecov.yml | 2 + docs/_extension/api_admonition.py | 73 - docs/_extension/docstring.py | 59 - docs/_static/banner.html | 7 - docs/_static/custom-icon.js | 2 +- docs/_static/custom.css | 44 +- 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tests/test_deprecated.py delete mode 100644 tests/test_deprecated/test_deprecated_cast_image.yml delete mode 100644 tests/test_deprecated/test_merge_geometry.yml delete mode 100644 tests/test_deprecated/test_tobands.yml create mode 100644 tests/test_geetools.py diff --git a/.copier-answers.yml b/.copier-answers.yml new file mode 100644 index 00000000..a57ccd37 --- /dev/null +++ b/.copier-answers.yml @@ -0,0 +1,13 @@ +# Changes here will be overwritten by Copier +_commit: 0.1.16 +_src_path: gh:12rambau/pypackage +author_email: pierrick.rambaud49@gmail.com +author_first_name: Pierrick +author_last_name: Rambaud +author_orcid: 0000-0001-8764-5749 +creation_year: "2017" +github_repo_name: geetools +github_user: gee-community +project_name: geetools +project_slug: geetools +short_description: A collection of tools to work with Google Earth Engine Python API diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index d21f0b86..0288d0c1 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -3,8 +3,7 @@ "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye", "features": { "ghcr.io/devcontainers-contrib/features/nox:2": {}, - "ghcr.io/devcontainers-contrib/features/pre-commit:2": {}, - "ghcr.io/rocker-org/devcontainer-features/pandoc:1": {} + "ghcr.io/devcontainers-contrib/features/pre-commit:2": {} }, - "postCreateCommand": "pre-commit install" + "postCreateCommand": "python -m pip install commitizen uv && pre-commit install" } diff --git a/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE/pr_template.md b/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE/pr_template.md new file mode 100644 index 00000000..f9c309e0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE/pr_template.md @@ -0,0 +1,23 @@ +--- +name: Pull request template +about: Create a pull request +title: "" +labels: "" +assignees: "" +--- + +## reference the related issue + +PR should answer problem stated in the issue tracker. please open one before starting a PR + +## description of the changes + +Describe the changes you propose + +## mention + +@mentions of the person or team responsible for reviewing proposed changes + +## comments + +any other comments we should pay attention to diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..25ea0037 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,24 @@ +--- +name: Bug report +about: Create a report to help us improve +title: "" +labels: "" +assignees: "" +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: + +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..2bc5d5f7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,19 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: "" +labels: "" +assignees: "" +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/prerelease.yaml b/.github/workflows/prerelease.yaml deleted file mode 100644 index 6926d965..00000000 --- a/.github/workflows/prerelease.yaml +++ /dev/null @@ -1,27 +0,0 @@ -name: Schedule - -on: - schedule: - # Runs every Sunday at 18:00 (UTC time) - - cron: "0 18 * * 0" - workflow_dispatch: - -env: - EARTHENGINE_SERVICE_ACCOUNT: ${{ secrets.EARTHENGINE_SERVICE_ACCOUNT }} - EARTHENGINE_PROJECT: ${{ secrets.EARTHENGINE_PROJECT }} - -jobs: - prerelease: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python 3.11 - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - name: Install dependencies - run: pip install .[test] - - name: Install EE pre-release - run: pip install --pre --upgrade --force-reinstall earthengine-api - - name: test with pytest - run: pytest --color=yes tests diff --git a/.github/workflows/pypackage_check.yaml b/.github/workflows/pypackage_check.yaml new file mode 100644 index 00000000..d9dd5891 --- /dev/null +++ b/.github/workflows/pypackage_check.yaml @@ -0,0 +1,53 @@ +name: template update check + +on: + workflow_dispatch: + +env: + PIP_ROOT_USER_ACTION: ignore + +jobs: + check_version: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: install dependencies + run: pip install requests + - name: get latest pypackage release + id: get_latest_release + run: | + RELEASE=$(curl -s https://api.github.com/repos/12rambau/pypackage/releases | jq -r '.[0].tag_name') + echo "latest=$RELEASE" >> $GITHUB_OUTPUT + echo "latest release: $RELEASE" + - name: get current pypackage version + id: get_current_version + run: | + RELEASE=$(yq -r "._commit" .copier-answers.yml) + echo "current=$RELEASE" >> $GITHUB_OUTPUT + echo "current release: $RELEASE" + - name: open issue + if: steps.get_current_version.outputs.current != steps.get_latest_release.outputs.latest + uses: rishabhgupta/git-action-issue@v2 + with: + token: ${{ secrets.GITHUB_TOKEN }} + title: "Update template to ${{ steps.get_latest_release.outputs.latest }}" + body: | + The package is based on the ${{ steps.get_current_version.outputs.current }} version of [@12rambau/pypackage](https://github.com/12rambau/pypackage). + + The latest version of the template is ${{ steps.get_latest_release.outputs.latest }}. + + Please consider updating the template to the latest version to include all the latest developments. + + Run the following code in your project directory to update the template: + + ``` + copier update --trust --defaults --vcs-ref ${{ steps.get_latest_release.outputs.latest }} + ``` + + > **Note** + > You may need to reinstall ``copier`` and ``jinja2-time`` if they are not available in your environment. + + After solving the merging issues you can push back the changes to your main branch. diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index e053807f..75830dde 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -1,26 +1,30 @@ -name: Release +name: Upload Python Package on: - workflow_dispatch: release: types: [created] +env: + PIP_ROOT_USER_ACTION: ignore + jobs: + tests: + uses: ./.github/workflows/unit.yaml + deploy: + needs: [tests] runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - #- uses: ./.github/workflows/unit.yaml - - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install dependencies - run: pip install twine build nox + run: pip install twine build nox[uv] + - name: update citation date + run: nox -s release-date - name: Build and publish env: TWINE_USERNAME: __token__ TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} - run: | - python -m build - twine upload dist/* + run: python -m build && twine upload dist/* diff --git a/.github/workflows/unit.yaml b/.github/workflows/unit.yaml index 2e6e2b2a..5bc1935d 100644 --- a/.github/workflows/unit.yaml +++ b/.github/workflows/unit.yaml @@ -1,15 +1,15 @@ -name: Unit +name: Unit tests on: + workflow_call: push: branches: - main pull_request: - workflow_dispatch: env: - EARTHENGINE_SERVICE_ACCOUNT: ${{ secrets.EARTHENGINE_SERVICE_ACCOUNT }} - EARTHENGINE_PROJECT: ${{ secrets.EARTHENGINE_PROJECT }} + FORCE_COLOR: 1 + PIP_ROOT_USER_ACTION: ignore jobs: lint: @@ -18,8 +18,8 @@ jobs: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: - python-version: "3.10" - - uses: pre-commit/action@v3.0.1 + python-version: "3.11" + - uses: pre-commit/action@v3.0.0 mypy: runs-on: ubuntu-latest @@ -27,34 +27,32 @@ jobs: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install nox - run: pip install nox + run: pip install nox[uv] - name: run mypy checks run: nox -s mypy docs: - needs: [lint] + needs: [lint, mypy] runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: 12rambau/setup-pandoc@test - uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.11" - name: Install nox - run: pip install nox + run: pip install nox[uv] - name: build static docs run: nox -s docs build: needs: [lint, mypy] strategy: - fail-fast: false + fail-fast: true matrix: os: [ubuntu-latest] - # dropping 3.12 for now because of https://github.com/r-earthengine/ee_extra/issues/56 - python-version: ["3.9", "3.10", "3.11"] + python-version: ["3.8", "3.9", "3.10", "3.11"] include: - os: macos-latest # macos test python-version: "3.11" @@ -68,7 +66,7 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install nox - run: pip install nox + run: pip install nox[uv] - name: test with pytest run: nox -s ci-test - name: assess dead fixtures @@ -88,10 +86,10 @@ jobs: - uses: actions/download-artifact@v4 with: name: coverage + path: coverage.xml - name: codecov uses: codecov/codecov-action@v4 with: file: ./coverage.xml token: ${{ secrets.CODECOV_TOKEN }} verbose: true - fail_ci_if_error: true diff --git a/.gitignore b/.gitignore index 511f0094..d8b224aa 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ parts/ sdist/ var/ wheels/ +pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg @@ -49,7 +50,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ -cover/ +.ruff_cache/ # Translations *.mo @@ -70,10 +71,9 @@ instance/ # Sphinx documentation docs/_build/ -warnings.txt +docs/api/ # PyBuilder -.pybuilder/ target/ # Jupyter Notebook @@ -84,9 +84,7 @@ profile_default/ ipython_config.py # pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version +.python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. @@ -95,7 +93,7 @@ ipython_config.py # install all needed dependencies. #Pipfile.lock -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +# PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff @@ -132,15 +130,11 @@ dmypy.json # Pyre type checker .pyre/ -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# vscode config +# system IDE .vscode/ -# sandbox Notebook -sandbox.ipynb +# image tmp file +*Zone.Identifier + +# debugging notebooks test.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6136f96c..c4a21ce4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,36 +1,38 @@ default_install_hook_types: [pre-commit, commit-msg] repos: - - repo: "https://github.com/psf/black" - rev: "22.3.0" - hooks: - - id: black - stages: [pre-commit] - - repo: "https://github.com/commitizen-tools/commitizen" rev: "v2.18.0" hooks: - id: commitizen stages: [commit-msg] - - repo: "https://github.com/pre-commit/mirrors-prettier" - rev: "v2.7.1" + - repo: "https://github.com/kynan/nbstripout" + rev: "0.5.0" + hooks: + - id: nbstripout + stages: [pre-commit] + + - repo: "https://github.com/pycontribs/mirrors-prettier" + rev: "v3.4.2" hooks: - id: prettier stages: [pre-commit] exclude: tests\/test_.+\. - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: "v0.0.215" + rev: "v0.7.0" hooks: - id: ruff stages: [pre-commit] + - id: ruff-format + stages: [pre-commit] - # - repo: https://github.com/PyCQA/doc8 - # rev: "v1.1.1" - # hooks: - # - id: doc8 - # stages: [pre-commit] + - repo: https://github.com/sphinx-contrib/sphinx-lint + rev: "v1.0.0" + hooks: + - id: sphinx-lint + stages: [pre-commit] - repo: https://github.com/codespell-project/codespell rev: v2.2.4 @@ -40,10 +42,10 @@ repos: additional_dependencies: - tomli - # waiting for https://github.com/gee-community/geetools/issues/337 - # so notebooks can again be executed from the documentation - - repo: "https://github.com/kynan/nbstripout" - rev: "0.5.0" + # Prevent committing inline conflict markers + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 hooks: - - id: nbstripout + - id: check-merge-conflict stages: [pre-commit] + args: [--assume-in-merge] diff --git a/AUTHORS.rst b/AUTHORS.rst index 30bba2ab..d655e1ff 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -6,50 +6,11 @@ Thanks goes to these wonderful people (`emoji key - - 12rambau
- Rodrigo E. Principe - - - - - 12rambau
+ + gee-community
Pierrick Rambaud - - - - 12rambau
- Marc Rußwurm - - - - - - - 12rambau
- hubert-crea - - - - - 12rambau
- Sam Murphy - - - - - 12rambau
- lumbric - - - - - - - 12rambau
- emmanuel-ferdman - + 💻 diff --git a/CHANGELOG.rst b/CHANGELOG.rst deleted file mode 100644 index 12208ffc..00000000 --- a/CHANGELOG.rst +++ /dev/null @@ -1,19 +0,0 @@ -Changelog -========= - -This is an old legacy changelog system, for newer versions, please refer to the changelog associated with github releases. - -New version 0.3.0 ------------------ - -I have split this package in two. This ``geetools`` will contain functions and methods related to Google Earth Engine exclusively, so you can use this module in any python environment you like. For working in Jupyter I have made another package called ``ipygee`` available `here `__. - -New version 0.5.0 (breaking changes) ------------------------------------- - -I have split this package in two (again). Now the functions to make a strip of images using Pillow is available as a different package called `geepillow `__. - -New version 0.6.0 (breaking changes) ------------------------------------- - -I have split this package in two (again x2). The module ``geetools.collection`` in an independent package called `geedataset `__. \ No newline at end of file diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 00000000..4daaf511 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,11 @@ +cff-version: "1.2.0" +message: "If you use this software, please cite it as below." +authors: + - family-names: "Rambaud" + given-names: "Pierrick" + orcid: "https://orcid.org/0000-0001-8764-5749" +title: "geetools" +version: "0.0.0" +doi: "" +date-released: "2024-12-09" +url: "https://github.com/gee-community/geetools" diff --git a/CODE_OF_CONDUCT.rst b/CODE_OF_CONDUCT.rst new file mode 100644 index 00000000..8d623501 --- /dev/null +++ b/CODE_OF_CONDUCT.rst @@ -0,0 +1,140 @@ +Contributor Covenant Code of Conduct +==================================== + +Our Pledge +---------- + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +Our Standards +------------- + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +Enforcement Responsibilities +---------------------------- + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +Scope +----- + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +Enforcement +----------- + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the FAO team responsible for enforcement at +pierrick.rambaud49@gmail.com. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +Enforcement Guidelines +---------------------- + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +Correction +^^^^^^^^^^ + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +Warning +^^^^^^^ + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +Temporary Ban +^^^^^^^^^^^^^ + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +Permanent Ban +^^^^^^^^^^^^^ + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +Attribution +----------- + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index d56adef7..a10bc443 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -66,47 +66,6 @@ Lastly, activate the pre-commit hooks by running: This will install the necessary dependencies to run pre-commit every time you make a commit with Git. -Initialize GEE available -^^^^^^^^^^^^^^^^^^^^^^^^ - -All the ``geetools`` package is build around the Google Earth Engine API. It is thus impossible to work wihin it if you are not registered. Please follow `Google instructions `__ to use the tool. - -Once you get access to GEE, you have 2 authentication options to work within `geetools`: - -#. Specify your project -#. use a service account - -Specify your project -#################### - -Here we assume your machine is already authenticated to GEE and some credentials are saved in your computer in the confi folder: ``~/.config/earthengine/credentials``. - -In this case ``geetools`` ``docs`` and ``test`` session will only need to get the name of the project to use. Specify it in a environment variable: - -.. code-block:: console - - export EARTHENGINE_PROJECT= - - -Service account -############### - -.. note:: - - This is the method used by all the CI/CD project pipeline from documentation to deployment. - -If your machine is not authenticated you can use a service account from the GCP console and save its json API key in an environment variable: - -.. code-block:: console - - export EARTHENGINE_SERVICE_ACCOUNT= - -The key should have the following format and is generated from the `GCP console `__: - -.. code-block:: json - - {"client_id": "value", "client_secret": "value", "refresh_token": "value", "project": "value"} - Contribute to the codebase ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -138,10 +97,12 @@ For each pull request, the documentation is built and deployed to make it easier Release new version ^^^^^^^^^^^^^^^^^^^ -.. danger:: +To release a new version, start by pushing a new bump from the local directory: + +.. code-block:: - Only maintainers can release new versions of **geetools**. + cz bump -To release a new version, open an issue with the new version number e.g. ``RLS: 1.0.0``. copy/paste the instructions from the `release instructions `__ and follow the presented workflow. +The commitizen-tool will detect the semantic version name based on the existing commits messages. -Once you are done you can close the issue and celebrate! +Then push to Github. In Github design a new release using the same tag name nad the ``release.yaml`` job will send it to pipy. diff --git a/LICENSE b/LICENSE index 148be08d..a1ef2a38 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2017 Rodrigo E. Principe +Copyright (c) 2017 Pierrick Rambaud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal @@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +SOFTWARE. diff --git a/README.rst b/README.rst index d207fa06..ccb5842c 100644 --- a/README.rst +++ b/README.rst @@ -1,31 +1,31 @@ + +geetools +======== + .. |license| image:: https://img.shields.io/badge/License-MIT-yellow.svg?logo=opensourceinitiative&logoColor=white :target: LICENSE :alt: License: MIT .. |commit| image:: https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg?logo=git&logoColor=white - :target: https://conventionalcommits.org - :alt: conventional commit + :target: https://conventionalcommits.org + :alt: conventional commit -.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg - :target: https://github.com/psf/black - :alt: Black badge +.. |ruff| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json + :target: https://github.com/astral-sh/ruff + :alt: ruff badge .. |prettier| image:: https://img.shields.io/badge/code_style-prettier-ff69b4.svg?logo=prettier&logoColor=white - :target: https://github.com/prettier/prettier - :alt: prettier badge + :target: https://github.com/prettier/prettier + :alt: prettier badge -.. |pre-commit| image:: https://img.shields.io/badge/pre--commit-active-yellow?logo=pre-commit&logoColor=white +.. |pre-commmit| image:: https://img.shields.io/badge/pre--commit-active-yellow?logo=pre-commit&logoColor=white :target: https://pre-commit.com/ :alt: pre-commit -.. |pypi| image:: https://img.shields.io/pypi/v/geetools?color=blue&logo=python&logoColor=white +.. |pypi| image:: https://img.shields.io/pypi/v/geetools?color=blue&logo=pypi&logoColor=white :target: https://pypi.org/project/geetools/ :alt: PyPI version -.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/geetools?color=blue&logo=anaconda&logoColor=white - :target: https://github.com/conda-forge/geetools-feedstock - :alt: Conda-forge version - .. |build| image:: https://img.shields.io/github/actions/workflow/status/gee-community/geetools/unit.yaml?logo=github&logoColor=white :target: https://github.com/gee-community/geetools/actions/workflows/unit.yaml :alt: build @@ -35,62 +35,17 @@ :alt: Test Coverage .. |docs| image:: https://img.shields.io/readthedocs/geetools?logo=readthedocs&logoColor=white - :target: https://app.readthedocs.org/projects/geetools/ + :target: https://geetools.readthedocs.io/en/latest/ :alt: Documentation Status +|license| |commit| |ruff| |prettier| |pre-commmit| |pypi| |build| |coverage| |docs| +Overview +-------- -geetools -======== - -|license| |commit| |black| |prettier| |pre-commit| |pypi| |conda| |build| |coverage| |docs| - - -Google Earth Engine tools -------------------------- - -.. image:: https://raw.githubusercontent.com/gee-community/geetools/main/docs/_static/logo.svg - :width: 20% - :align: right - -`Google Earth Engine `__ is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a `JavaScript and a Python API `__ with different methods to process geospatial objects. - -The **geetools** package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different Earth Engine Objects that are friendly with the Python method chaining using the ``geetools`` namespace. - -There is JavaScript module that you can import from the code editor that has -similar functions (not exactly the same) and it's available `here `__. - -Installation ------------- - -.. code-block:: python - - pip install geetools - -Why using it ? --------------- - -New utility methods and constructors are added to most of the GEE classes. They can be simple wrapper for repetitive tasks, complex algorithm or mandatory preprocessing steps. The goal is to make the code more fluid and easy to read for researchers, students and analysts. - -The package design is mostly performing server-side computation making it also very friendly with commercial users of Earth Engine. - -This small example wrapping of the excellent ``ee_extra`` package functionalities shows how to preprocess sentinel 2 data in 5 lines of code: - -.. code-block:: python - - import ee - import geetools #noqa: F401 - import pygaul # another gee-community package to access FAO GAUl 2015 dataset - - # we assume you are already authenticated to GEE - ee.Initialize.geetools.from_account("toto") # yes we also support multi users +A collection of tools to work with Google Earth Engine Python API - amazonas = pygaul.Items(name="Amazonas").centroid() +Credits +------- - S2 = ( - ee.ImageCollection('COPERNICUS/S2_SR') - .filterBounds(point) - .geetools.closest('2020-10-15') # Extended (pre-processing) - .geetools.maskClouds(prob = 70) # Extended (pre-processing) - .geetools.scaleAndOffset() # Extended (pre-processing) - .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) +This package was created with `Copier `__ and the `@12rambau/pypackage `__ 0.1.16 project template. diff --git a/RELEASE.rst b/RELEASE.rst deleted file mode 100644 index 28565e44..00000000 --- a/RELEASE.rst +++ /dev/null @@ -1,35 +0,0 @@ -Release instructions -==================== - -This page contains the steps to make a release and some helpful resources to get you started. - -Create an issue and copy/paste the steps below to release a new version. Close the issue when it is done. - -These steps should be taken in order to create a new release! - -.. code-block:: md - - **Double check for quality-control** - - - [ ] There are no [open issues with a `impact: block-release` label](https://github.com/gee-community/geetools/labels/impact%3A%20block-release) - - **Prepare the codebase for a new version** - - - [ ] Bump a new version using `commitizen`. use the `--pre-release` flag if it's a **release candidate**. - - [ ] Push the RLS commit `git push upstream main` - - [ ] If a **release candidate** is needed, tick this box when we're now ready for a full release. - - **Make the release** - - - [ ] [Start a new GitHub release](https://github.com/gee-community/geetools/releases/new) - - Call the release the current version, e.g. `v0.2.0` - - In the **`Choose a Tag:`** dropdown, type in the release name (e.g., `v0.2.0`) and click "Create new tag" - - In the **`Target:`** dropdown, pin it to the release commit that you've just pushed. - - Generate the automatic release notes, eventually manually specify the previous version (useful when several release candidate have been made) - - [ ] Confirm that the release completed - - [The `publish` github action job](https://github.com/gee-community/geetools/actions/workflows/release.yaml) has completed successfully in the [actions tab](https://github.com/gee-community/geetools/actions). - - [The PyPI version is updated](https://pypi.org/project/geetools/) - - [ ] Hide the previous patch version build in the RDT interface if needed. - - [ ] Celebrate, you're done! - - Taken from [the release checklist](https://github.com/gee-community/geetools/blob/main/RELEASE.rst). diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 00000000..de09d048 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,2 @@ +# disable the treemap comment and report in PRs +comment: false diff --git a/docs/_extension/api_admonition.py b/docs/_extension/api_admonition.py deleted file mode 100644 index 7e096053..00000000 --- a/docs/_extension/api_admonition.py +++ /dev/null @@ -1,73 +0,0 @@ -"""A directive to generate an API admonition.""" -from __future__ import annotations - -from docutils import nodes -from docutils.parsers.rst import directives -from docutils.parsers.rst.directives.admonitions import BaseAdmonition -from sphinx.application import Sphinx -from sphinx.util import logging -from sphinx.util.docutils import SphinxDirective -from sphinx.writers.html5 import HTML5Translator - -logger = logging.getLogger(__name__) - - -class api_node(nodes.Admonition, nodes.Element): - pass - - -def visit_api_node(self: HTML5Translator, node: api_node) -> None: - self.visit_admonition(node) - - -def depart_api_node(self: HTML5Translator, node: api_node) -> None: - self.depart_admonition(node) - - -class APIAdmonitionDirective(BaseAdmonition, SphinxDirective): - """An API entry, displayed (if configured) in the form of an admonition.""" - - node_class = api_node - has_content = True - required_arguments = 0 - optional_arguments = 0 - final_argument_whitespace = False - option_spec = { - "class": directives.class_option, - "name": directives.unchanged, - } - - def run(self) -> list[nodes.Node]: - if not self.options.get("class"): - self.options["class"] = ["admonition-api"] - - (api,) = super().run() - if isinstance(api, nodes.system_message): - return [api] - elif isinstance(api, api_node): - api.insert(0, nodes.title(text="See API")) - api["docname"] = self.env.docname - self.add_name(api) - self.set_source_info(api) - self.state.document.note_explicit_target(api) - return [api] - else: - raise RuntimeError # never reached here - - -def setup(app: Sphinx) -> dict[str, object]: - """Add custom configuration to sphinx app. - - Args: - app: the Sphinx application - - Returns: - the 2 parallel parameters set to ``True``. - """ - app.add_directive("api", APIAdmonitionDirective) - app.add_node(api_node, html=(visit_api_node, depart_api_node)) - - return { - "parallel_read_safe": True, - "parallel_write_safe": True, - } diff --git a/docs/_extension/docstring.py b/docs/_extension/docstring.py deleted file mode 100644 index 50d01ade..00000000 --- a/docs/_extension/docstring.py +++ /dev/null @@ -1,59 +0,0 @@ -"""A docstring role to read the docstring from a Python method.""" -from __future__ import annotations - -import inspect -from functools import reduce - -import ee -from docutils import nodes -from sphinx.application import Sphinx -from sphinx.util import logging -from sphinx.util.docutils import SphinxRole - -import geetools # noqa: F401 - -logger = logging.getLogger(__name__) - - -class DocstringRole(SphinxRole): - """The docstring role interpreter.""" - - def run(self) -> tuple[list[nodes.Node], list[str]]: - """Setup the role in the builder context.""" - # retrieve the environment from the node members - env = self.inliner.document.settings.env - builder = env.app.builder - current_doc = self.env.docname - - # extract the members we try to reach from the ee lib - members = self.text.split(".")[1:] - - # reach the final object using getattr. It will allow us to access the complete information - # of the object (docstring, qualname, name, source module) - try: - o = reduce(getattr, members, ee) - modules = inspect.getmodule(o).__name__.split(".") - except Exception as e: - logger.warning(f"Failed to retrieve {members}: {e}") - return [nodes.Text(f"{self.text} not found")], [] - - # create the docstring node - docstring = nodes.Text(f": {o.__doc__.splitlines()[0]}") - - # create a complete link to the object using the url and the name of the object - target_doc = f"autoapi/{'/'.join(modules)}/{o.__qualname__}" - refuri = builder.get_relative_uri(current_doc, target_doc) - inline_node = nodes.literal(members[-1], members[-1], classes=["py", "py-meth"]) - link = nodes.reference("", "", inline_node, internal=True, refuri=refuri) - - return [link, docstring], [] - - -def setup(app: Sphinx) -> dict[str, object]: - """Add custom configuration to sphinx application.""" - app.add_role("docstring", DocstringRole()) - - return { - "parallel_read_safe": True, - "parallel_write_safe": True, - } diff --git a/docs/_static/banner.html b/docs/_static/banner.html deleted file mode 100644 index 55183f3a..00000000 --- a/docs/_static/banner.html +++ /dev/null @@ -1,7 +0,0 @@ -
- This is a community-supported extension. If you'd like to contribute, - check out our GitHub repository. - Your contributions are welcome! -
diff --git a/docs/_static/custom-icon.js b/docs/_static/custom-icon.js index e080c65d..d947d8a5 100644 --- a/docs/_static/custom-icon.js +++ b/docs/_static/custom-icon.js @@ -12,5 +12,5 @@ FontAwesome.library.add( "e001", // unicode codepoint - private use area "M12.045.033a12.181 12.182 0 00-1.361.078 17.512 17.513 0 011.813 1.433l.48.438-.465.45a15.047 15.048 0 00-1.126 1.205l-.178.215a8.527 8.527 0 01.86-.05 8.154 8.155 0 11-4.286 15.149 15.764 15.765 0 01-1.841.106h-.86a21.847 21.848 0 00.264 2.866 11.966 11.967 0 106.7-21.89zM8.17.678a12.181 12.182 0 00-2.624 1.275 15.506 15.507 0 011.813.43A18.551 18.552 0 018.17.678zM9.423.75a16.237 16.238 0 00-.995 1.998 16.15 16.152 0 011.605.66 6.98 6.98 0 01.43-.509c.234-.286.472-.559.716-.817A15.047 15.048 0 009.423.75zM4.68 2.949a14.969 14.97 0 000 2.336c.587-.065 1.196-.1 1.812-.107a16.617 16.617 0 01.48-1.748 16.48 16.481 0 00-2.292-.481zM3.62 3.5A11.938 11.938 0 001.762 5.88a17.004 17.004 0 011.877-.444A17.39 17.391 0 013.62 3.5zm4.406.287c-.143.437-.265.888-.38 1.347a8.255 8.255 0 011.67-.803c-.423-.2-.845-.38-1.29-.544zM6.3 6.216a14.051 14.052 0 00-1.555.108c.064.523.157 1.038.272 1.554a8.39 8.391 0 011.283-1.662zm-2.55.137a15.313 15.313 0 00-2.602.716h-.078v.079a17.104 17.105 0 001.267 2.544l.043.071.072-.049a16.309 16.31 0 011.734-1.083l.057-.035V8.54a16.867 16.868 0 01-.408-2.094v-.092zM.644 8.095l-.063.2A11.844 11.845 0 000 11.655v.209l.143-.152a17.706 17.707 0 011.584-1.447l.057-.043-.043-.064a16.18 16.18 0 01-1.025-1.87zm3.77 1.253l-.18.1c-.465.273-.93.573-1.375.889l-.065.05.05.064c.309.437.645.867.996 1.276l.137.165v-.208a8.176 8.176 0 01.364-2.15zM2.2 10.853l-.072.05a16.574 16.574 0 00-1.813 1.734l-.058.058.066.057a15.449 15.45 0 001.991 1.483l.072.05.043-.08a16.738 16.74 0 011.053-1.64v-.05l-.043-.05a16.99 16.99 0 01-1.19-1.54zm1.855 2.071l-.121.172a15.363 15.363 0 00-.917 1.433l-.043.072.071.043a16.61 16.61 0 001.562.766l.193.086-.086-.193a8.04 8.04 0 01-.66-2.172zm-3.976.48v.2a11.758 11.759 0 00.946 3.326l.078.186.072-.194a16.215 16.216 0 01.845-2l.057-.063-.064-.043a17.197 17.198 0 01-1.776-1.284zm2.543 1.805l-.035.08a15.764 15.765 0 00-.983 2.479v.08h.086a16.15 16.152 0 002.688.5l.072.007v-.086a17.562 17.563 0 01.164-2.056v-.065H4.55a16.266 16.266 0 01-1.849-.896zm2.544 1.169v.114a17.254 17.255 0 00-.151 1.828v.078h.931c.287 0 .624.014.946 0h.209l-.166-.129a8.011 8.011 0 01-1.64-1.834zm-3.29 2.1l.115.172a11.988 11.988 0 002.502 2.737l.157.129v-.201a22.578 22.58 0 01-.2-2.336v-.071h-.072a16.23 16.23 0 01-2.3-.387z", // svg path (https://simpleicons.org/icons/anaconda.svg) ], - }) + }), ); diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 77c3b154..0af9db66 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -1,48 +1,6 @@ -/******************************************************************************* -* add dollar sign in console code-block -*/ +/* add dollar sign in console code-block */ div.highlight-console pre span.go::before { content: "$"; margin-right: 10px; margin-left: 5px; } - -/******************************************************************************* -* Create a custom api admonition -*/ -div.admonition.admonition-api > .admonition-title::after { - content: "\f121"; /* the fa-code icon */ -} - -/******************************************************************************* -* custom sizing -*/ -.bd-main .bd-content .bd-article-container { - max-width: 100%; /* default is 60em */ -} - -.bd-page-width { - max-width: 110rem; /* default is 88rem */ -} - -/******************************************************************************* -* custom article footer rendering -*/ -footer.bd-footer-article { - background-color: transparent; - border-top: 1px solid var(--pst-color-border); - margin-top: 2em; -} - -.bd-footer-article .footer-article-items { - flex-direction: row; -} - -.bd-footer-article .footer-article-items .footer-article-item { - flex-grow: 1; -} - -.bd-footer-article .last-updated { - 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own_page_children = visible_children|selectattr("type", "in", own_page_types)|list %} - {% if is_own_page and own_page_children %} -.. toctree:: - :hidden: - - {% for child in own_page_children %} - {{ child.include_path }} - {% endfor %} - - {% endif %} -.. py:{{ obj.type }}:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}{% if obj.args %}({{ obj.args }}){% endif %} - - {% for (args, return_annotation) in obj.overloads %} - {{ " " * (obj.type | length) }} {{ obj.short_name }}{% if args %}({{ args }}){% endif %} - - {% endfor %} - {% if obj.bases %} - {% if "show-inheritance" in autoapi_options %} - - Bases: {% for base in obj.bases %}{{ base|link_objs }}{% if not loop.last %}, {% endif %}{% endfor %} - {% endif %} - - - {% if "show-inheritance-diagram" in autoapi_options and obj.bases != ["object"] %} - .. autoapi-inheritance-diagram:: {{ obj.obj["full_name"] }} - :parts: 1 - {% if "private-members" in autoapi_options %} - :private-bases: - {% endif %} - - {% endif %} - {% endif %} - {% if obj.docstring %} - - {{ obj.docstring|indent(3) }} - {% endif %} - {% for obj_item in visible_children %} - {% if obj_item.type not in own_page_types %} - - {{ obj_item.render()|indent(3) }} - {% endif %} - {% endfor %} - {% if is_own_page and own_page_children %} - {% set visible_attributes = own_page_children|selectattr("type", "in", ("attribute", "property"))|list %} - {% if visible_attributes %} -Attributes ----------- - -.. autoapisummary:: - - {% for attribute in visible_attributes %} - {{ attribute.id }} - {% endfor %} - - - {% endif %} - {% set visible_exceptions = own_page_children|selectattr("type", "equalto", "exception")|list %} - {% if visible_exceptions %} -Exceptions ----------- - -.. autoapisummary:: - - {% for exception in visible_exceptions %} - {{ exception.id }} - {% endfor %} - - - {% endif %} - {% set visible_classes = own_page_children|selectattr("type", "equalto", "class")|list %} - {% if visible_classes %} -Classes -------- - -.. autoapisummary:: - - {% for klass in visible_classes %} - {{ klass.id }} - {% endfor %} - - - {% endif %} - {% set visible_methods = own_page_children|selectattr("type", "equalto", "method")|list %} - {% if visible_methods %} -Methods -------- - -.. autoapisummary:: - - {% for method in visible_methods %} - {{ method.id }} - {% endfor %} - - - {% endif %} - {% endif %} -{% endif %} \ No newline at end of file diff --git a/docs/_templates/python/function.rst b/docs/_templates/python/function.rst deleted file mode 100644 index 3381ee4f..00000000 --- a/docs/_templates/python/function.rst +++ /dev/null @@ -1,23 +0,0 @@ -{% if obj.display %} - {% if is_own_page %} -:html_theme.sidebar_secondary.remove: - -{{ obj.id.split(".")[-1] }} -{{ "=" * obj.id.split(".")[-1] | length }} - - {% endif %} -.. py:function:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %} - {% for (args, return_annotation) in obj.overloads %} - - {%+ if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %} - {% endfor %} - {% for property in obj.properties %} - - :{{ property }}: - {% endfor %} - - {% if obj.docstring %} - - {{ obj.docstring|indent(3) }} - {% endif %} -{% endif %} \ No newline at end of file diff --git a/docs/_templates/python/method.rst b/docs/_templates/python/method.rst deleted file mode 100644 index b7cc9721..00000000 --- a/docs/_templates/python/method.rst +++ /dev/null @@ -1,23 +0,0 @@ -{% if obj.display %} - {% if is_own_page %} -:html_theme.sidebar_secondary.remove: - -{{ obj.id.split(".")[-1] }} -{{ "=" * obj.id.split(".")[-1] | length }} - - {% endif %} -.. py:method:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %} - {% for (args, return_annotation) in obj.overloads %} - - {%+ if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %} - {% endfor %} - {% for property in obj.properties %} - - :{{ property }}: - {% endfor %} - - {% if obj.docstring %} - - {{ obj.docstring|indent(3) }} - {% endif %} -{% endif %} \ No newline at end of file diff --git a/docs/_templates/python/module.rst b/docs/_templates/python/module.rst deleted file mode 100644 index d45d1945..00000000 --- a/docs/_templates/python/module.rst +++ /dev/null @@ -1,180 +0,0 @@ -{% if obj.display %} - {% if is_own_page %} -:html_theme.sidebar_secondary.remove: - -{{ obj.id.split(".")[-1] }} -{{ "=" * obj.id.split(".")[-1] | length }} - -.. py:module:: {{obj.name}} - - {% if obj.docstring %} -.. autoapi-nested-parse:: - - {{obj.docstring|indent(3)}} - - {% endif %} - - {% block subpackages %} - {% set visible_subpackages = obj.subpackages|selectattr("display")|list %} - {% if visible_subpackages %} - -Subpackages ------------ - -.. toctree:: - :maxdepth: 1 - - {% for subpackage in visible_subpackages %} - {{subpackage.include_path}} - {% endfor %} - - {% endif %} - {% endblock %} - {% block submodules %} - {% set visible_submodules = obj.submodules|selectattr("display")|list %} - {% if visible_submodules %} - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - {% for submodule in visible_submodules %} - {{submodule.include_path}} - {% endfor %} - - - {% endif %} - {% endblock %} - {% block content %} - {% if obj.all is not none %} - {% set visible_children = obj.children|selectattr("short_name", "in", obj.all)|list %} - {% elif obj.type is equalto("package") %} - {% set visible_children = obj.children|selectattr("display")|list %} - {% else %} - {% set visible_children = obj.children|selectattr("display")|rejectattr("imported")|list %} - {% endif %} - {% if visible_children %} - {% set visible_attributes = visible_children|selectattr("type", "equalto", "data")|list %} - {% if visible_attributes %} - {% if "attribute" in own_page_types or "show-module-summary" in autoapi_options %} - -Attributes ----------- - - {% if "attribute" in own_page_types %} -.. toctree:: - :hidden: - - {% for attribute in visible_attributes %} - {{attribute.include_path}} - {% endfor %} - - {% endif %} -.. autoapisummary:: - - {% for attribute in visible_attributes %} - {{attribute.id}} - {% endfor %} - {% endif %} - - - {% endif %} - {% set visible_exceptions = visible_children|selectattr("type", "equalto", "exception")|list %} - {% if visible_exceptions %} - {% if "exception" in own_page_types or "show-module-summary" in autoapi_options %} -Exceptions ----------- - - {% if "exception" in own_page_types %} -.. toctree:: - :hidden: - - {% for exception in visible_exceptions %} - {{exception.include_path}} - {% endfor %} - - {% endif %} -.. autoapisummary:: - - {% for exception in visible_exceptions %} - {{exception.id}} - {% endfor %} - {% endif %} - - - {% endif %} - {% set visible_classes = visible_children|selectattr("type", "equalto", "class")|list %} - {% if visible_classes %} - {% if "class" in own_page_types or "show-module-summary" in autoapi_options %} -Classes -------- - - {% if "class" in own_page_types %} -.. toctree:: - :hidden: - - {% for klass in visible_classes %} - {{klass.include_path}} - {% endfor %} - - {% endif %} -.. autoapisummary:: - - {% for klass in visible_classes %} - {{klass.id}} - {% endfor %} - {% endif %} - - - {% endif %} - {% set visible_functions = visible_children|selectattr("type", "equalto", "function")|list %} - {% if visible_functions %} - {% if "function" in own_page_types or "show-module-summary" in autoapi_options %} -Functions ---------- - - {% if "function" in own_page_types %} -.. toctree:: - :hidden: - - {% for function in visible_functions %} - {{function.include_path}} - {% endfor %} - - {% endif %} -.. autoapisummary:: - - {% for function in visible_functions %} - {{function.id}} - {% endfor %} - {% endif %} - - - {% endif %} - {% set this_page_children = visible_children|rejectattr("type", "in", own_page_types)|list %} - {% if this_page_children %} -{{obj.type|title}} Contents -{{"-" * obj.type|length}}--------- - - {% for obj_item in this_page_children %} -{{obj_item.render()|indent(0)}} - {% endfor %} - {% endif %} - {% endif %} - {% endblock %} - {% else %} -.. py:module:: {{obj.name}} - - {% if obj.docstring %} - .. autoapi-nested-parse:: - - {{obj.docstring|indent(6)}} - - {% endif %} - {% for obj_item in visible_children %} - {{obj_item.render()|indent(3)}} - {% endfor %} - {% endif %} -{% endif %} \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 0514089f..911498b0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,76 +6,45 @@ """ # -- Path setup ---------------------------------------------------------------- -import os -import sys from datetime import datetime -from pathlib import Path - -import geetools as geetools - -# add . to sys to import local extensions -sys.path.append(str(Path(".").resolve())) # -- Project information ------------------------------------------------------- project = "geetools" -author = "Rodrigo E. Principe" +author = "Pierrick Rambaud" copyright = f"2017-{datetime.now().year}, {author}" -release = "1.9.1" +release = "0.0.0" # -- General configuration ----------------------------------------------------- extensions = [ + "sphinx_copybutton", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.intersphinx", - "sphinx.ext.autosectionlabel", - "sphinxcontrib.icon", "sphinx_design", - "sphinx_last_updated_by_git", - "sphinx_copybutton", "autoapi.extension", - "jupyter_sphinx", - "myst_nb", - "_extension.docstring", - "_extension.api_admonition", ] exclude_patterns = ["**.ipynb_checkpoints"] +templates_path = ["_template"] # -- Options for HTML output --------------------------------------------------- -# Define the json_url for our version switcher. -json_url = "https://geetools.readthedocs.io/en/latest/_static/switcher.json" - -# Define the version we use for matching in the version switcher. -version_match = os.environ.get("READTHEDOCS_VERSION") - -# If READTHEDOCS_VERSION doesn't exist, we're not on RTD -# for local development and the latest dev build use the local file instead of the distant one. -if not version_match or version_match.isdigit() or version_match == "latest": - version_match = "dev" - json_url = "_static/switcher.json" -elif version_match == "stable": - version_match = f"v{release}" - html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] -html_logo = "_static/logo.png" -html_favicon = "_static/logo.png" html_theme_options = { "logo": { "text": project, }, "use_edit_page_button": True, + "footer_end": ["theme-version", "pypackage-credit"], "icon_links": [ { "name": "GitHub", "url": "https://github.com/gee-community/geetools", "icon": "fa-brands fa-github", - "type": "fontawesome", }, { "name": "Pypi", "url": "https://pypi.org/project/geetools/", "icon": "fa-brands fa-python", - "type": "fontawesome", }, { "name": "Conda", @@ -84,56 +53,20 @@ "type": "fontawesome", }, ], - "announcement": "https://raw.githubusercontent.com/gee-community/geetools/main/docs/_static/banner.html", - "secondary_sidebar_items": [ - "page-toc.html", - "edit-this-page.html", - ], - "article_footer_items": ["last-updated"], - # remove the switcher for now as the version management is not satisfying - # "switcher": { - # "json_url": json_url, - # "version_match": version_match, - # }, - "show_toc_level": 2, } html_context = { "github_user": "gee-community", "github_repo": "geetools", - "github_version": "main", + "github_version": "", "doc_path": "docs", } html_css_files = ["custom.css"] -html_js_files = ["custom-icon.js"] # -- Options for autosummary/autodoc output ------------------------------------ autodoc_typehints = "description" autoapi_dirs = ["../geetools"] -autoapi_python_class_content = "both" +autoapi_python_class_content = "init" autoapi_member_order = "groupwise" -autoapi_template_dir = "_templates" -autoapi_options = [ - "members", - "undoc-members", - "show-inheritance", - "show-module-summary", - "special-members", -] -autoapi_own_page_level = "method" -autoapi_keep_files = False # -- Options for intersphinx output -------------------------------------------- -# fmt: off -intersphinx_mapping = { - "python": ("https://docs.python.org/3", None), - "matplotlib": ("https://matplotlib.org/stable/", None), - "ee": ("https://developers.google.com/earth-engine/apidocs", "https://raw.githubusercontent.com/gee-community/sphinx-inventory/refs/heads/main/inventory/earthengine-api.inv"), -} -# fmt: on - -# -- options for the autolabel extension --------------------------------------- -autosectionlabel_prefix_document = True - -# -- options for myst-nb ------------------------------------------------------ -nb_execution_mode = "force" -nb_execution_timeout = 120 +intersphinx_mapping = {} diff --git a/docs/contribute.rst b/docs/contribute.rst new file mode 100644 index 00000000..5b77c9c9 --- /dev/null +++ b/docs/contribute.rst @@ -0,0 +1,5 @@ +Contribute +========== + +.. include:: ../CONTRIBUTING.rst + :start-line: 3 diff --git a/docs/index.rst b/docs/index.rst index f04a0ef0..a6c81f2a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,82 +7,33 @@ geetools .. toctree:: :hidden: - setup/index - usage/index - Reference - Changelogs - earth-engine API + usage + contribute -Overview --------- +Documentation contents +---------------------- -.. image:: _static/logo.png - :width: 20% - :align: right - :class: dark-light - -`Google Earth Engine `__ is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a `JavaScript and a Python API `__ with different methods to process geospatial objects. - -The **geetools** package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different Earth Engine Objects that are friendly with the Python method chaining using the ``geetools`` namespace. - -content -------- +The documentation contains 3 main sections: .. grid:: 1 2 3 3 .. grid-item:: - .. card:: :icon:`fa-solid fa-chart-simple` plot - :link: usage/plot/index.html - - Display EE objects directly as static plots. - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-folder` asset - :link: usage/asset.html + .. card:: Usage + :link: usage.html - Manage your assets as a object-oriented file system. + Usage and installation .. grid-item:: - .. card:: :icon:`fa-solid fa-images` export ImageCollections - :link: usage/export.html - - Useful wrapper to export ImageCollections as simply as Images. - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-handshake-angle` Contribute - :link: setup/contribute.html + .. card:: Contribute + :link: contribute.html Help us improve the lib. -Why using it ? --------------- - -New utility methods and constructors are added to most of the GEE classes. They can be simple wrapper for repetitive tasks, complex algorithm or mandatory preprocessing steps. The goal is to make the code more fluid and easy to read for researchers, students and analysts. - -The package design is mostly performing server-side computation making it also very friendly with commercial users of Earth Engine. - -This small example wrapping of the excellent ``ee_extra`` package functionalities shows how to preprocess sentinel 2 data in 5 lines of code: - -.. code-block:: python - - import ee - import geetools #noqa: F401 - import pygaul # another gee-community package to access FAO GAUl 2015 dataset - - # we assume you are already authenticated to GEE - ee.Initialize.geetools.from_account("toto") # yes we also support multi users - - amazonas = pygaul.Items(name="Amazonas").centroid() + .. grid-item:: - S2 = ( - ee.ImageCollection('COPERNICUS/S2_SR') - .filterBounds(point) - .geetools.closest('2020-10-15') # Extended (pre-processing) - .geetools.maskClouds(prob = 70) # Extended (pre-processing) - .geetools.scaleAndOffset() # Extended (pre-processing) - .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) + .. card:: API + :link: autoapi/index.html + Discover the lib API. diff --git a/docs/setup/author.rst b/docs/setup/author.rst deleted file mode 100644 index f3e97086..00000000 --- a/docs/setup/author.rst +++ /dev/null @@ -1,4 +0,0 @@ -Authors -======= - -.. include:: ../../AUTHORS.rst \ No newline at end of file diff --git a/docs/setup/contribute.rst b/docs/setup/contribute.rst deleted file mode 100644 index 3e1236cd..00000000 --- a/docs/setup/contribute.rst +++ /dev/null @@ -1,5 +0,0 @@ -Contributing workflow -===================== - -.. include:: ../../CONTRIBUTING.rst - :start-line: 3 diff --git a/docs/setup/index.rst b/docs/setup/index.rst deleted file mode 100644 index 9e54a1c1..00000000 --- a/docs/setup/index.rst +++ /dev/null @@ -1,46 +0,0 @@ -Setup -===== - -Overview --------- - -The User Guide covers all of **geetools** by topic area. The :doc:`quickstart` page is a good place to start if you are new to the package or just want to refresh your memory. The :doc:`layout` page provides a high-level overview of the package's layout, and the :doc:`pattern` page provides a high-level overview of the package's design decsisions. - -The use of the package requires a basic understanding of the **Python** programming language and the **GEE Python API**. Users brand-new to Earth Engine should refer to the `Google documentation `__ first. - -Further hands-on example of specific tasks can be found in the :doc:`../usage/index` section. and for the most advance user please refe to the :doc:`../autoapi/index` section for a complete description of each individual functionality. - -Refactoring ------------ - -Since version v1.0.0, the package has been drastically modified to adopt the extension pattern (see :doc:`pattern` for more information). Many functions have also bee dropped or fully refactored to improve overall performances, and to make the package more consistent and easy to use. For more information about the miregation process please refer to the :doc:`migration` page. - -.. important:: - - The refactoring process is not finished yet, we will progressively reintegrate all the methods in the new pattern and add many cool functionalities. If any of your previous is not working anymore and the :doc:`migration` page did not provided any solution, please open an issue in the `GitHub repository `__. - -.. toctree:: - :hidden: - :caption: Get started - - install - quickstart - layout - -.. toctree:: - :hidden: - :caption: Extension Layout - - pattern - migration - inspiration - -.. toctree:: - :hidden: - :caption: Contributor guide - - contribute - author - license - - diff --git a/docs/setup/inspiration.rst b/docs/setup/inspiration.rst deleted file mode 100644 index 353fa4f8..00000000 --- a/docs/setup/inspiration.rst +++ /dev/null @@ -1,20 +0,0 @@ -Aknowledgment -============= - -This project was inspired by other very cool initiatives and we would like to acknowledge them here. - -xarray ------- - -the `xarray `__ lib is a great lib to handle n-dimensional data and has very well documented the use of the extension pattern. Without their guidances, `documentation `__ and `sphinx-extention `__, this project would have not been possible. - -eemont ------- - -the `eemont `__ lib has already implemented the extension pattern and allow the users to perform many different operations from preprocessing to extra construction methods. We loved some of them so much that we rewired within **geetools** every method available in `ee_extra `__. - -Our implementations diverge on 2 main points: - -- We decided to be more careful with the extensions and avoid adding them directly after the ``earthengine-api`` objects. We preferred to follow ``xarray`` recommendation and create systematically a ``geetools`` intermediate member to notify to the user that no, these methods are not from the vanilla ``earthengine-api`` but from the ``geetools`` lib. - -- We decided not to reimplement the Python magic methods. Now that EarthEngine is a commercial product, the users need to always be in control of what is performed server-side and what is performed client-side. We believe that spercharging magic method (although they allow cool things as ``ee.Image(1) + ee.image(2)``) make it even more confusing for new users to understand what is performed server-side and what is performed client-side. That's why all our method are using camel case naming convention and only return ``ee.ComputedObject``. Few exceptions are made for converters and plotting method that are forced to run an interactive ``getInfo()`` but they use snake case convention to notify their user and have a disclaimer in the docstring. diff --git a/docs/setup/install.rst b/docs/setup/install.rst deleted file mode 100644 index 3a08ea96..00000000 --- a/docs/setup/install.rst +++ /dev/null @@ -1,42 +0,0 @@ -Installation instructions -========================= - -The package is a pure python package, installation is thus very straight forward. - -pypi ----- - -the python package is available on `pypi `__ and can be installed it using ``pip``: - -.. code-block:: console - - pip install geetools - -conda ------ - -The python package is available on `conda `__ and can be installed using ``conda`` and the ``conda-forge`` channel: - -.. code-block:: console - - conda install -c conda-forge geetools - -from source ------------ - -You can also install the package from source. First clone the repository: - -.. code-block:: console - - git clone https://github.com/gee-community/geetools.git - -Then, navigate to the folder and install the package using pip: - -.. code-block:: console - - cd geetools - pip install . - -Multiple version of the package requirements are available and are specifically important for local development. -See the :doc:`contribute` page for more information. - diff --git a/docs/setup/layout.rst b/docs/setup/layout.rst deleted file mode 100644 index b4209bf7..00000000 --- a/docs/setup/layout.rst +++ /dev/null @@ -1,383 +0,0 @@ -Layout of the extensions -======================== - -Overview --------- - -This section is a glossary of all the methods that are added to ``ee`` objects by the extension. -They are gathered by sections and link to the API reference for detailed examples but give a good overview of what can be done. - -If you search for a method in the package, note that we decided to use names that are meaningful for python users so if you know what you are looking for in ``rasterio``, ``pandas`` or ``numpy`` you should be able to find it here under the same name. - -.. warning:: - - Method that were replaced during refactoring or that are still not reimplemented are not listed here. - They remain fully accecible in the geetools package. If they raise a deprecation warning, it means that they are still available but will be removed in the future by their extension equivalent. - -Earth Engine classes --------------------- - -ee.Array -^^^^^^^^ - -constructor -########### -- :docstring:`ee.Array.geetools.full` - - -data manipulation -################# - -- :docstring:`ee.Array.geetools.set` - -ee.Authenticate -^^^^^^^^^^^^^^^ - -- :docstring:`ee.Authenticate.geetools.new_user` -- :docstring:`ee.Authenticate.geetools.delete_user` -- :docstring:`ee.Authenticate.geetools.list_user` -- :docstring:`ee.Authenticate.geetools.rename_user` - - -ee.ComputedObject -^^^^^^^^^^^^^^^^^ - -The ``ee.ComputedObject`` is the base object of all API object. The methods added here can thus be used in every object of the API. -That's also the only Object where the methods are directly added as members without the need to call `geetools` before. - -Types management -################ - -- :docstring:`ee.ComputedObject.isInstance` - -save json representations -######################### - -- :docstring:`ee.ComputedObject.save` -- :docstring:`ee.ComputedObject.open` - -ee.Date -^^^^^^^ - -Constructors -############ - -- :docstring:`ee.Date.geetools.fromEpoch` -- :docstring:`ee.Date.geetools.fromDOY` - -Extra operations -################ - -- :docstring:`ee.Date.geetools.getUnitSinceEpoch` -- :docstring:`ee.Date.geetools.isLeap` - -Exportation -########### - -.. warning:: - - As the snake case suggests, this method is client side. - -- :docstring:`ee.Date.geetools.to_datetime` - -helper -###### - -- :docstring:`ee.Date.geetools.check_unit` - -ee.DateRange -^^^^^^^^^^^^ - -Extra operations -################ - -- :docstring:`ee.DateRange.geetools.split` - -Helper -###### - -- :docstring:`ee.DateRange.geetools.check_unit` -- :docstring:`ee.DateRange.geetools.unitMillis` - -ee.Dictionary -^^^^^^^^^^^^^ - -Constructors -############ - -- :docstring:`ee.Dictionary.geetools.fromPairs` - -Extra operations -################ - -- :docstring:`ee.Dictionary.geetools.sort` -- :docstring:`ee.Dictionary.geetools.getMany` - -ee.Feature -^^^^^^^^^^ - -- :docstring:`ee.Feature.geetools.toFeatureCollection` -- :docstring:`ee.Feature.geetools.removeProperties` - -ee.FeatureCollection -^^^^^^^^^^^^^^^^^^^^ - -Properties management -##################### - -- :docstring:`ee.FeatureCollection.geetools.addId` - -Geometry management -################### - -- :docstring:`ee.FeatureCollection.geetools.mergeGeometries` -- :docstring:`ee.FeatureCollection.geetools.toPolygons` - -Converter -######### - -- :docstring:`ee.FeatureCollection.geetools.toImage` -- :docstring:`ee.FeatureCollection.geetools.byFeatures` -- :docstring:`ee.FeatureCollection.geetools.byProperties` - -Plotting -######## - -- :docstring:`ee.FeatureCollection.geetools.plot_by_features` -- :docstring:`ee.FeatureCollection.geetools.plot_by_properties` -- :docstring:`ee.FeatureCollection.geetools.plot_hist` - -ee.Filter -^^^^^^^^^ - -- :docstring:`ee.Filter.geetools.dateRange` - - -ee.Geometry -^^^^^^^^^^^ - -- :docstring:`ee.Geometry.geetools.keepType` - -ee.Image -^^^^^^^^ - -Constructor -########### - -- :docstring:`ee.Image.geetools.full` -- :docstring:`ee.Image.geetools.fullLike` - -Band manipulation -################# - -- :docstring:`ee.Image.geetools.addDate` -- :docstring:`ee.Image.geetools.addSuffix` -- :docstring:`ee.Image.geetools.addPrefix` -- :docstring:`ee.Image.geetools.rename` -- :docstring:`ee.Image.geetools.remove` -- :docstring:`ee.Image.geetools.doyToDate` -- :docstring:`ee.Image.geetools.negativeClip` -- :docstring:`ee.Image.geetools.gauss` -- :docstring:`ee.Image.geetools.repeat` - -Data extraction -############### - -- :docstring:`ee.Image.geetools.getValues` -- :docstring:`ee.Image.geetools.minScale` -- :docstring:`ee.Image.geetools.reduceBands` -- :docstring:`ee.Image.geetools.format` -- :docstring:`ee.Image.geetools.index_list` -- :docstring:`ee.Image.geetools.spectralIndices` -- :docstring:`ee.Image.geetools.getScaleParams` -- :docstring:`ee.Image.geetools.getOffsetParams` -- :docstring:`ee.Image.geetools.getSTAC` -- :docstring:`ee.Image.geetools.getDOI` -- :docstring:`ee.Image.geetools.getCitation` - - -Data manipulation -################# - -- :docstring:`ee.Image.geetools.doyToDate` -- :docstring:`ee.Image.geetools.clipOnCollection` -- :docstring:`ee.Image.geetools.bufferMask` -- :docstring:`ee.Image.geetools.removeZeros` -- :docstring:`ee.Image.geetools.interpolateBands` -- :docstring:`ee.Image.geetools.isletMask` -- :docstring:`ee.Image.geetools.scaleAndOffset` -- :docstring:`ee.Image.geetools.preprocess` -- :docstring:`ee.Image.geetools.panSharpen` -- :docstring:`ee.Image.geetools.tasseledCap` -- :docstring:`ee.Image.geetools.matchHistogram` -- :docstring:`ee.Image.geetools.maskClouds` - -Converter -######### - -- :docstring:`ee.Image.geetools.toGrid` - -Properties -########## - -- :docstring:`ee.Image.geetools.removeProperties` - -ee.ImageCollection -^^^^^^^^^^^^^^^^^^ - -Data manipulation -################# - -- :docstring:`ee.ImageCollection.geetools.maskClouds` -- :docstring:`ee.ImageCollection.geetools.closest` -- :docstring:`ee.ImageCollection.geetools.scaleAndOffset` -- :docstring:`ee.ImageCollection.geetools.preprocess` -- :docstring:`ee.ImageCollection.geetools.panSharpen` -- :docstring:`ee.ImageCollection.geetools.tasseledCap` -- :docstring:`ee.ImageCollection.geetools.append` -- :docstring:`ee.ImageCollection.geetools.outliers` - -Data extraction -############### - -- :docstring:`ee.ImageCollection.geetools.spectralIndices` -- :docstring:`ee.ImageCollection.geetools.getScaleParams` -- :docstring:`ee.ImageCollection.geetools.getOffsetParams` -- :docstring:`ee.ImageCollection.geetools.getDOI` -- :docstring:`ee.ImageCollection.geetools.getCitation` -- :docstring:`ee.ImageCollection.geetools.getSTAC` -- :docstring:`ee.ImageCollection.geetools.collectionMask` -- :docstring:`ee.ImageCollection.geetools.iloc` -- :docstring:`ee.ImageCollection.geetools.integral` -- :docstring:`ee.ImageCollection.geetools.aggregateArray` -- :docstring:`ee.ImageCollection.geetools.validPixel` - -Filter -###### - -- :docstring:`ee.ImageCollection.geetools.containsBandNames` -- :docstring:`ee.ImageCollection.geetools.containsAllBands` -- :docstring:`ee.ImageCollection.geetools.containsAnyBands` - -Converter -######### - -- :docstring:`ee.ImageCollection.geetools.to_xarray` - -ee.Initialize -^^^^^^^^^^^^^ - -- :docstring:`ee.Initialize.geetools.from_user` -- :docstring:`ee.Initialize.geetools.project_id` - -ee.Join -^^^^^^^ - -- :docstring:`ee.Join.geetools.byProperty` - -ee.List -^^^^^^^ - -Constructor -########### - -- :docstring:`ee.List.geetools.sequence` -- :docstring:`ee.List.geetools.zip` - -operations -########## - -- :docstring:`ee.List.geetools.product` -- :docstring:`ee.List.geetools.complement` -- :docstring:`ee.List.geetools.intersection` -- :docstring:`ee.List.geetools.union` -- :docstring:`ee.List.geetools.delete` -- :docstring:`ee.List.geetools.replaceMany` - -Converter -######### - -- :docstring:`ee.List.geetools.join` -- :docstring:`ee.List.geetools.toStrings` - -ee.Number -^^^^^^^^^ - -- :docstring:`ee.Number.geetools.truncate` - -ee.String -^^^^^^^^^ - -- :docstring:`ee.String.geetools.eq` -- :docstring:`ee.String.geetools.format` - -Added classes -------------- - -ee.Float -^^^^^^^^ - -.. note:: - - This object does not exist in the original API. It is a custom Placeholder Float class to be used in the :py:meth:`ee.ComputedObject.isInstance` method. - -ee.Integer -^^^^^^^^^^ - -.. note:: - - This object does not exist in the original API. It is a custom Placeholder Float class to be used in the :py:meth:`ee.ComputedObject.isInstance` method. - -ee.Asset -^^^^^^^^ - -.. note:: - - This object is overriding most of the pathlib methods. We are simply gathering them here for convenience. - -Constructor -########### - -- :docstring:`ee.Asset.home` - -Operation -######### - -- :docstring:`ee.Asset.as_posix` -- :docstring:`ee.Asset.as_uri` -- :docstring:`ee.Asset.is_absolute` -- :docstring:`ee.Asset.is_user_project` -- :docstring:`ee.Asset.expanduser` -- :docstring:`ee.Asset.exists` -- :docstring:`ee.Asset.is_relative_to` -- :docstring:`ee.Asset.joinpath` -- :docstring:`ee.Asset.match` -- :docstring:`ee.Asset.with_name` -- :docstring:`ee.Asset.is_image` -- :docstring:`ee.Asset.is_image_collection` -- :docstring:`ee.Asset.is_feature_collection` -- :docstring:`ee.Asset.is_folder` -- :docstring:`ee.Asset.is_project` -- :docstring:`ee.Asset.is_type` -- :docstring:`ee.Asset.iterdir` -- :docstring:`ee.Asset.mkdir` -- :docstring:`ee.Asset.move` -- :docstring:`ee.Asset.rmdir` -- :docstring:`ee.Asset.unlink` -- :docstring:`ee.Asset.delete` -- :docstring:`ee.Asset.copy` -- :docstring:`ee.Asset.glob` -- :docstring:`ee.Asset.rglob` -- :docstring:`ee.Asset.setProperties` - -Property -######## - -- :docstring:`ee.Asset.parts` -- :docstring:`ee.Asset.parent` -- :docstring:`ee.Asset.parents` -- :docstring:`ee.Asset.name` -- :docstring:`ee.Asset.st_size` -- :docstring:`ee.Asset.type` -- :docstring:`ee.Asset.owner` - diff --git a/docs/setup/license.rst b/docs/setup/license.rst deleted file mode 100644 index 01f3963c..00000000 --- a/docs/setup/license.rst +++ /dev/null @@ -1,4 +0,0 @@ -License -======= - -.. include:: ../../LICENSE \ No newline at end of file diff --git a/docs/setup/migration.rst b/docs/setup/migration.rst deleted file mode 100644 index 30dcbe11..00000000 --- a/docs/setup/migration.rst +++ /dev/null @@ -1,51 +0,0 @@ -Upgrade from v0 to v1 -===================== - -in v1 **geetoold** has fully embraced the extension pattern and has revamped most of it's functionalities. -This page will gather the changes that you need to make to your code to upgrade from v0 to v1. - -The lib is following a deprecation cycle of several month so no function has been directly removed. -They will simply raised a deprecation warning and provide some suggestion on the potential replacement. - -None the less, as previous implementation was leading to internal issues such as circular imports, it is recommended to update your code to the new pattern as soon as possible to avoid any future breakage. - -.. warning:: - - This documentation is gathering problems faced by the community and the solutions that were found. - If you have a problem that is not listed here, please open an item in our `issue tracker `__. - - -Import the modules ------------------- - -If in the previous implementation, most of the modules were brought back to the main ``__init.py`` which was causing circular import issues. -In v1 the internal structure was revisited and some modules are no longer accecible from ``geetools``. - -A v0 implementation would look like this: - -.. code-block:: python - - import ee - from geetools.tools import geometry - - image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first()) - tools.geometry.getRegion(image) - -It will raise an error as the "tools" file has been replaced by a "_deprecated_tools" one. - -Now to run the same code you should do: - -.. code-block:: python - - import ee - from geetools import tools - - image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first()) - tools.geometry.getRegion(image) - -This will simply raise a deprecation warning and will work as expected. - - - - - diff --git a/docs/setup/pattern.rst b/docs/setup/pattern.rst deleted file mode 100644 index a7c7a17b..00000000 --- a/docs/setup/pattern.rst +++ /dev/null @@ -1,70 +0,0 @@ -The extension pattern -===================== - -.. note:: - - This page is vastly inspired from the ``xarray`` `documentation `__ that is the inspiration for the ``geetools`` implementation. - -**Google Earth Engine** is designed as a general purpose library and hence tries to avoid including overly user specific functionality. But inevitably, the need for open-source community to contribute arises. This is where the extension pattern comes in. - -Composition over Inheritance ----------------------------- - -One potential solution to this problem is to subclass every ``ee.ComputedObject`` to add user specific functionality. However, inheritance is not very robust. It’s easy to inadvertently use internal APIs when subclassing, which means that your code may break when ``earthengine-api`` upgrades. Furthermore, many builtin methods will only return native ``ee.ComputedObject`` objects. - -The standard advice is to use composition over inheritance, but reimplementing an API as large as ``earthengine-api`` on your own objects can be an onerous task, even if most methods are only forwarding to ``earthengine-api`` implementations (That was the technical choice made prior in v0 of ``geetools``). - - -Writing Custom Accessors ------------------------- - -To resolve this issue for more complex cases, ``geetools`` has implemented 3 decorators: - -- A class decorator: :py:meth:`register_class_accessor ` -- A function decorator: :py:meth:`register_function_accessor ` - -They are used to add custom “accessors” on objects/functions/modules thereby “extending” the functionality of your ``ee`` object. - -Here’s how we use these decorators to write a custom “geetools” accessor implementing a extra method to ``ee.Number`` object: - -.. code-block:: python - - import ee - from geetools.accessor import register_class_accessor - - @register_class_accessor(ee.Number, "geetools") - class NumberAccessor: - - def __init__(self, obj: ee.Number): - self._obj = obj - - def truncate(self, nbDecimals = 2): - """Truncate a number to a given number of decimals.""" - nbDecimals = ee.Number(nbDecimals).toInt() - factor = ee.Number(10).pow(nbDecimals) - return self._obj.multiply(factor).toInt().divide(factor) - -In general, the only restriction on the accessor class is that the ``__init__`` method must have a single parameter: the object it is supposed to work on. - -This achieves the same result as if the Dataset class had a cached property defined that returns an instance of your class: - -.. code-block:: python - - class Number: - ... - - @property - def geetools(self): - return NumberAccessor(self) - -However, using the register accessor decorators is preferable to simply adding your own ad-hoc property (i.e., ``ee.number.geetools = property(...)``), for several reasons: - -- It ensures that the name of your property does not accidentally conflict with any other attributes or methods (including other accessors). -- Instances of accessor object will be cached on the object that creates them. This means you can save state on them (e.g., to cache computed properties). -- Using an accessor provides an implicit namespace for your custom functionality that clearly identifies it as separate from built-in ``earthengine-api`` methods. - -.. note:: - - Accessors are created once per object instance. New instances, like those created from mapping operations or when accessing a ``ee.Feature`` from a ``ee.FeatureCollection`` (ex. ``fc.first()``), will have new accessors created. - -The intent here is that libraries that extend ``earthengine-api`` could add such an accessor to implement subclass specific functionality rather than using actual subclasses or patching in a large number of domain specific methods. For further reading on ways to write new accessors and the philosophy behind the approach, see https://github.com/pydata/xarray/issues/1080. \ No newline at end of file diff --git a/docs/setup/quickstart.rst b/docs/setup/quickstart.rst deleted file mode 100644 index d9cfc446..00000000 --- a/docs/setup/quickstart.rst +++ /dev/null @@ -1,75 +0,0 @@ -Quickstart -========== - - - -An extension ? --------------- - -**geetools** is an extension package. It means that it cannot be used without the **Google Earth Engine Python API** but also that you don't need to call it explicitly to use all the methods and functions available. - -To summarize, these functions are added to GEE objects inside a member called ``geetools``. - -The first step is as always to authenticate to GEE and import the **geetools** package. - -.. code-block:: python - - import ee - import geetools #noqa: F401 - - ee.Initialize() - -At this stage all the methods of the package have been added to the ``ee`` objects. You can use them as if they were part of the original API in the ``geetools`` member. - -The following example will use the ``truncate`` method to truncate a number to a given number of decimals. - -.. code-block:: python - :emphasize-lines: 2, 5 - - import ee - import geetools #noqa: F401 - - number = ee.Number(3.14159265359) - truncated = number.geetools.truncate(2) - truncated.getInfo() - -Real life example ------------------ - -This small example shows how **geetools** is wrapping the excellent ``ee_extra`` package functionalities to preprocess sentinel 2 data in 5 lines of code: - -.. code-block:: python - - import ee - import geetools #noqa: F401 - import pygaul # another gee-community package to access FAO GAUl 2015 dataset - - # we assume you are already authenticated to GEE - ee.Initialize.geetools.from_account("toto") # yes we also support multi users - - amazonas = pygaul.Items(name="Amazonas").centroid() - - S2 = ( - ee.ImageCollection('COPERNICUS/S2_SR') - .filterBounds(point) - .geetools.closest('2020-10-15') # Extended (pre-processing) - .geetools.maskClouds(prob = 70) # Extended (pre-processing) - .geetools.scaleAndOffset() # Extended (pre-processing) - .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) - -More examples of more complex and meaningful analysis can be found in the :doc:`../usage/index` gallery. - -F401 ? ------- - -In Python it's recommended to clean you code using automatic tools like ``flake8``, ``ruff``, ``isort``...etc - -These tool will raise an error if you import a package and don't use it, it's the ``F401`` error. In some tools the erroring lines can be deleted from the file. This will break your code as even if the **geetools** package is never called it's required to import it to extend the ``ee`` package. - -The ``#noqa: F401`` comment is used to avoid the linter to raise an error when the package is imported but not used. It is not mandatory if you don't use linters but it is a good practice to keep your code clean. - -.. note:: - - as per flake8 `documentation `__: - - F401: A module has been imported but is not used anywhere in the file. The module should either be used or the import should be removed. diff --git a/docs/usage.rst b/docs/usage.rst new file mode 100644 index 00000000..81cf3d6d --- /dev/null +++ b/docs/usage.rst @@ -0,0 +1,4 @@ +Usage +===== + +**geetools** usage documentation. diff --git a/docs/usage/asset.ipynb b/docs/usage/asset.ipynb deleted file mode 100644 index b69ebefc..00000000 --- a/docs/usage/asset.ipynb +++ /dev/null @@ -1,621 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Object-oriented asset file system" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/asset.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/asset.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the required libs if necessary and perform the import statements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if initialization is required\n", - "# ee.Initialize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The `Asset` object\n", - "\n", - "In Google Earth Engine API, users are working with Assets. An asset is a filelike object that englobes a wide variety of types: IMAGE, IMAGE_COLLECTION, FOLDER, TABLE, FEATURE_COLLECTION, etc.\n", - "\n", - "They are identified by a unique ID, which is a string that looks like: `projects/username/assets/foo`. Using the vanila Earthengine API, They can be modified using the `ee.data` module. This module has been proven complicated when dealing with basic file manipulation operation such as listing, moving, copying, etc.\n", - "\n", - "`geetools` provides a simple way to manage assets as an object-oriented filesystem paths using the `Asset` object. This object is a subclass of the `pathlib.Path` object, which is a powerful way to manage file paths in Python. Most of the methods and properties are overwritten to work with the Google Earth Engine context.\n", - "\n", - "`ee.Asset` objects implement the os.PathLike interface, allowing them to be used anywhere the interface is accepted." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic use\n", - "\n", - "Importing the main class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee, geetools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create asset objects \n", - "\n", - "The Asset objects etend the pathlib.Path object and thus behave exactly the same when dealing with constructor. THe only differnece is that asset path only supports posix-like file separator: `/`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each element of pathsegments can be either a string representing a path segment, or an object implementing the os.PathLike interface where the __fspath__() method returns a string, such as another path object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Asset(\"projects\", \"ee-geetools\", \"assets\", \"documentation\", \"image1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Asset(\"projects/ee-geetools/assets/documentation\") / \"image1\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Asset(\"projects/ee-geetools/assets/documentation\").joinpath(\"image1\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Listing subdirectories" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# a public folder created for this docuemntation\n", - "folder = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", - "\n", - "# list all its direct subdirectories\n", - "[a for a in folder.iterdir() if a.is_folder()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.Asset.iterdir`\n", - "- {{docstring}`ee.Asset.is_folder`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Listing Image in this folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[a for a in folder.iterdir() if a.is_image()]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[a for a in folder.glob(\"**/image*\")]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.Asset.iterdir`\n", - "- {docstring}`ee.Asset.glob`\n", - "- {docstring}`ee.Asset.is_image`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Querying asset properties" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "folder.exists()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fakeImage = folder / \"image6\"\n", - "fakeImage.exists()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.Asset.exists`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## General properties\n", - "\n", - "Paths are immutable and hashable. Paths of a same flavour are comparable and orderable. These properties respect the flavour’s case-folding semantics:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "folder = ee.Asset(\"projects/ee-geetools/assets/documentation\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "folder == ee.Asset(\"projects/ee-geetools/assets/DOCUMENTATION\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "folder in { ee.Asset(\"projects/ee-geetools/assets/documentation\")}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The slash operator helps create child asset, like `os.path.join()`. If the argument is an absolute asset, the previous path is ignored." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Asset(\"projects/ee-geetools/assets/documentation\") / \"image1\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An asset object can be used anywhere an object implementing `os.PathLike` is accepted." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "a = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", - "os.fspath(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The string representation of an asset is the asset id itself, which you can pass to any function taking an asset id as a string:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")\n", - "str(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Accessing individual parts\n", - "\n", - "To access the individual “parts” (components) of a path, use the following property:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")\n", - "a.parts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.Asset.parts`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### access parent container\n", - "\n", - "Asset parent containers can be access either by the `parent` property or the `parents` property. Note This is a purely lexical operation and the parent is not checked to exist.\n", - "\n", - "```{api}\n", - "- {docstring}`ee.Asset.parent`\n", - "- {docstring}`ee.Asset.parents`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.parent" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.parents" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Name of the asset\n", - "\n", - "A string representing the final path component can be used to get the name of the asset.add\n", - "\n", - "```{api}\n", - "- {docstring}`ee.Asset.name`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## General Methods\n", - "\n", - "Pure paths provide the following methods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### evaluate relation between assets\n", - "\n", - "It's possible to check if files are related between one another using the following methods:\n", - "\n", - "```{api}\n", - "- {docstring}`ee.Asset.is_relative_to`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "b = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", - "a.is_relative_to(b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### create a siblings\n", - "\n", - "One can create a siblings asset in the same container by using the `with_name()` method:\n", - "\n", - "```{api} \n", - "- {docstring}`ee.Asset.with_name`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.with_name(\"image2\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### resolve unix like symbols\n", - "\n", - "One can use some unix-like descriptors in it's Asset constructor parameters. If so before using the Asset object, it is necessary to resolve these symbols. The method `expanduser` does that.\n", - "\n", - "```{api}\n", - "- {py:meth}`expanduser `: {docstring}`ee.Asset.expanduser`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"~/documentation/subfolder1/image1\")\n", - "a.expanduser()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### check existence\n", - "\n", - "One can check if an asset exists using the `exists` method:\n", - "\n", - "```{api}\n", - "- {py:meth}`exists `: {docstring}`ee.Asset.exists`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.exists()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image10\")\n", - "a.exists()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate asset type\n", - "\n", - "As Earth Engine is not using any file extention to differentiate the asset type, one can use the `is_type` method with any of the following types: `IMAGE`, `IMAGE_COLLECTION`, `FOLDER`, `TABLE`, `FEATURE_COLLECTION`, `UNKNOWN`.\n", - "\n", - "```{api}\n", - "- {py:meth}`is_type `: {docstring}`ee.Asset.is_type`\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", - "a.is_type(\"IMAGE\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All type checks are available in dedicated wrapped methods like `is_image`, `is_folder`, `is_table` ...etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a.is_image()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many other useful methods are available and are described in the {py:class}`API documentation `." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/export.ipynb b/docs/usage/export.ipynb deleted file mode 100644 index 617eb720..00000000 --- a/docs/usage/export.ipynb +++ /dev/null @@ -1,187 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exporting ImageCollections\n", - "\n", - "Earth Engine provides numbers of ways to export `ee.Image` as explained in their [documentation](https://developers.google.com/earth-engine/guides/image_export). `geetools`provides an extention to the `ee.Export` class to export `ee.ImageCollection` as well. This is useful when you have a collection of images and you want to export them all at once.\n", - "\n", - "As the vanilla Earth Engine methods were returning Task objects, these method will return lists of Task objects. This ensures that Once the task are launched they can be fully monitored outside from your initial script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/export.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/export.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example Set up \n", - "\n", - "Start by defining the image data that will be exported." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee, geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load a landsat image and select three bands over the whole mont of january 2023\n", - "landsat = (\n", - " ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\")\n", - " .select(['B4', 'B3', 'B2'])\n", - " .filterDate('2023-01-01', '2023-01-31')\n", - ")\n", - "\n", - "# Create a geometry representing an export region.\n", - "geometry = ee.Geometry.Rectangle([116.2621, 39.8412, 116.4849, 40.01236])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## to Drive\n", - "\n", - "To export an imageCollection to your Drive account, use `ee.batch.Export.geetools.imagecollection.toDrive()`. For example, to export portions of a Landsat collection, define a region to export, then call Export:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python \n", - "# Export the image to Cloud Storage.\n", - "ee.batch.Export.geetools.imagecollection.toDrive(\n", - " imagecollection = landsat,\n", - " index_property = \"system:id\",\n", - " description = 'imageCollectionToDriveExample',\n", - " scale = 30,\n", - " region = geometry,\n", - " folder = 'geetools_example',\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## to Cloud Storage\n", - "\n", - "To export an ImageCollection to a Google Cloud Storage bucket, use `ee.batch.Export.geetools.imagecollection.toCloudStorage()`. To export the Landsat image in the previous example to Cloud Storage instead of Drive, use:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python \n", - "# Export the image to Cloud Storage.\n", - "ee.batch.Export.image.toCloudStorage(\n", - " imagecollection = landsat,\n", - " index_property = \"system:id\",\n", - " description = 'imageToCloudExample',\n", - " bucket = 'your-bucket-name',\n", - " scale = 30,\n", - " region = geometry\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## To Asset\n", - "\n", - "To export an ImageCollection to an Earth Engine asset, use `ee.batch.Export.geetools.imagecollection.toAsset()`. To export the Landsat image in the previous example to an asset, use:\n", - "\n", - "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# Start the export process.\n", - "ee.batch.Export.geetools.imagecollection.toAsset(\n", - " imagecollection = landsat,\n", - " index_property = \"system:id\",\n", - " assetId = 'projects/username/ladnsat_collection',\n", - " scale = 30,\n", - " region = geometry,\n", - " maxPixels = 1e13,\n", - " pyramidingPolicy = {\n", - " 'b4': 'mean',\n", - " 'b3': 'mean',\n", - " 'b2': 'mean'\n", - " }\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For all function please refer to offcial documentation for complete list of parameters of the `ee.batch.Export.image` methods." - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/index.rst b/docs/usage/index.rst deleted file mode 100644 index 5246df20..00000000 --- a/docs/usage/index.rst +++ /dev/null @@ -1,47 +0,0 @@ -Guides -====== - -Overview --------- - -This section gathered many real life examples of the Lib usage gathered by the community. -If you think your workflow should be shared please open a PR and follow the contribution guildelines shared in the next section. - -.. warning:: - - The example gallery is a work in progress as the library was recently refactored. - All contributions are welcolmed! - -Add a new example ------------------ - -.. image:: /_static/we-need-you.jpg - :alt: We need you! - :align: center - -Currently most of the examples built by `@Rodrigo `__ are still using the old implementation of the library. -They should be transformed into modern example and moved from the old `notebook `__ folder to the new `example `__ one to be displayed in our doc. - -The examples are regular notebook files that are interpreted by the ``myst-nb`` lib and displayed in the doc, clicking on the :guilabel:`open in colab` button will open a colab notebook with the code ready to be executed and the :guilabel:`view source` will bring you back to github. - -To add a new example, you can use the `example template `__ and replace things with your code. - -Adapt the code of the 2 first buttons to your file so users can lunch it in collab and view the source in github. - -.. code-block:: md - - [![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/template.ipynb) - [![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/template.ipynb) - - -Then you can open a PR with the new file and it will be reviewed and merged. - -.. toctree:: - :hidden: - - template - export - plot/index - asset - profile - reduce \ No newline at end of file diff --git a/docs/usage/plot/index.rst b/docs/usage/plot/index.rst deleted file mode 100644 index 6d244b4f..00000000 --- a/docs/usage/plot/index.rst +++ /dev/null @@ -1,99 +0,0 @@ -Plotting -======== - -We embed some plotting capabilities in the library to help you visualize your data. For simplicity we decided to map all the plotting function to the :doc:`matplotlib ` library as it's the most used static plotting library in the Python ecosystem. - -.. toctree:: - :hidden: - :maxdepth: 1 - - plot-featurecollection - plot-image - plot-imagecollection - map-image - map-featurecollection - -.. grid:: 1 2 3 3 - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-chart-simple` FeatureCollection - :link: plot-featurecollection.html - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-chart-simple` Image - :link: plot-image.html - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-chart-simple` ImageCollection - :link: plot-imagecollection.html - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-image` Image - :link: map-image.html - - .. grid-item:: - - .. card:: :icon:`fa-solid fa-map` FeatureCollection - :link: map-featurecollection.html - - - -In all these examples we will use the object interface of matplotlib creating the :py:class:`Figure ` and :py:class:`Axes ` object before plotting the data. This is the recommended way to use matplotlib as it gives you more control over the plot and the figure. - -.. code-block:: python - - # custom image for this specific chart - modisSr = ( - ee.ImageCollection("MODIS/061/MOD09A1") - .filter(ee.Filter.date("2018-06-01", "2018-09-01")) - .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"]) - .mean() - ) - histRegion = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22]) - - #create a matplotlib figure - fig, ax = plt.subplots(figsize=(10, 4)) - - # plot the histogram of the reds - modisSr.geetools.plot_hist( - bands = ["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"], - labels = [['Red', 'NIR', 'SWIR']], - colors = ["#cf513e", "#1d6b99", "#f0af07"], - ax = ax, - bins = 100, - scale = 500, - region = histRegion, - ) - - # once created the axes can be modified as needed using pure matplotlib functions - ax.set_title("Modis SR Reflectance Histogram") - ax.set_xlabel("Reflectance (x1e4)") - -.. image:: ../../_static/usage/plot/index/histogram.png - :alt: Modis SR Reflectance Histogram - :align: center - -If you are used to the :py:mod:`pyplot ` interface of matplotlib you can still use it with the state-base module of matplotlib. Just be aware that the module is a stateful interface and you will have less control over the figure and the plot. - -.. code-block:: python - - # get all hydroshed from the the south amercias within the WWF/HydroATLAS dataset. - region = ee.Geometry.BBox(-80, -60, -20, 20); - fc = ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level04').filterBounds(region) - - # create the plot - fc.geetools.plot(property="UP_AREA", cmap="viridis") - - # Customized display - plt.colorbar(ax.collections[0], label="Upstream area (km²)") - plt.title("HydroATLAS basins of level4") - plt.xlabel("Longitude (°)") - plt.ylabel("Latitude (°)") - -.. image:: ../../_static/usage/plot/index/hydroshed.png - :alt: HydroATLAS basins of level4 - :align: center \ No newline at end of file diff --git a/docs/usage/plot/map-featurecollection.ipynb b/docs/usage/plot/map-featurecollection.ipynb deleted file mode 100644 index 816e9954..00000000 --- a/docs/usage/plot/map-featurecollection.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Map FeatureCollection\n", - "\n", - "The `geetools` extension contains a set of functions for rendering maps from `ee.FeatureCollection` objects. Use the following function descriptions and examples to determine the best function and chart type for your purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/map-featurecollection.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/map-featurecollection.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the required packages and perform the import statement upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data \n", - "\n", - "The following examples rely on a `ee.FeatureCollection` composed of all the hydroshed bassins from south america." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "region = ee.Geometry.BBox(-80, -60, -20, 20);\n", - "fc = ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level04').filterBounds(region)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map Vector\n", - "\n", - "```{api}\n", - "{docstring}`ee.FeatureCollection.geetools.plot`\n", - "```\n", - "\n", - "An `ee.FeatureCollection` is a vector representation of geographical properties. A user can be interested by either the property evolution across the landscape or the geometries associated with it. The {py:meth}`plot ` is coverinig both use cases. \n", - "\n", - "### Map a property\n", - "\n", - "A single property can be ploted on a map using matplotlib. The following example is showing the bassin area in km².\n", - "\n", - "First create a matplotlib figure and axis, then you can add the bassins to the map using the `plot` method. By default it will display the first property of the features. In our case we will opt to display the area of the bassins in km² i.e. the \"UP_AREA\" property. Finally that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create the plot\n", - "fig, ax = plt.subplots(figsize=(10, 10))\n", - "\n", - "# generate the graph\n", - "fc.geetools.plot(ax=ax, property=\"UP_AREA\", cmap=\"viridis\")\n", - "\n", - "# you can then customize the figure as you would for any other matplotlib object\n", - "fig.colorbar(ax.collections[0], label=\"Upstream area (km²)\")\n", - "ax.set_title(\"HydroATLAS basins of level4\")\n", - "ax.set_xlabel(\"Longitude (°)\")\n", - "ax.set_ylabel(\"Latitude (°)\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Map geometries\n", - "\n", - "Alternatively if you only want to plot the geometries of the featurecollection on a map, you can use the `plot` method with the `boundares` parameter set to `True`.\n", - "\n", - "Similarly to the previous example we start by creating a pyplot figure and axis, then you can start plotting the geometries and finally customize the plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.ioff() # remove interactive for the sake of the example\n", - "fig, ax = plt.subplots(figsize=(10, 10))\n", - "\n", - "# create the graph\n", - "fc.geetools.plot(ax=ax, boundaries=True)\n", - "\n", - "# you can then customize the figure as you would for any other matplotlib object\n", - "ax.set_title(\"Borders of the HydroATLAS basins of level4\")\n", - "ax.set_xlabel(\"Longitude (°)\")\n", - "ax.set_ylabel(\"Latitude (°)\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/plot/map-image.ipynb b/docs/usage/plot/map-image.ipynb deleted file mode 100644 index 6d060079..00000000 --- a/docs/usage/plot/map-image.ipynb +++ /dev/null @@ -1,218 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Map Image\n", - "\n", - "The `geetools` extension contains a set of functions for rendering maps from `ee.Image` objects. Use the following function descriptions and examples to determine the best function and chart type for your purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/map-image.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/map-image.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install the required packages and authenticate your Earth Engine account." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data \n", - "\n", - "The following examples rely on the \"COPERNICUS/S2_HARMONIZED\" `ee.ImageCollection` filtered between 2022-06-01 and 2022-06-30. We then build the NDVI spectral indice and use mosaic to get an `ee.Image` object. This object is clipped over the Vatican city as it's one of the smallest country in the world." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load the vatican\n", - "level0 = ee.FeatureCollection(\"FAO/GAUL/2015/level0\")\n", - "vatican = level0.filter(ee.Filter.eq(\"ADM0_NAME\", \"Holy See\"))\n", - "\n", - "# pre-process the imagecollection and mosaic the month of June 2022\n", - "image = (\n", - " ee.ImageCollection('COPERNICUS/S2_HARMONIZED')\n", - " .filterDate('2022-06-01', '2022-06-30')\n", - " .filterBounds(vatican)\n", - " .geetools.maskClouds()\n", - " .geetools.spectralIndices(\"NDVI\")\n", - " .mosaic()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map Raster\n", - "\n", - "```{api}\n", - "{py:meth}`plot `: \n", - " {docstring}`geetools.ImageAccessor.plot`\n", - "```\n", - "\n", - "An `ee.image` is a raster representation of the Earth's surface. The `plot` function allows you to visualize the raster data on a map. The function provides options to customize the visualization, such as the color palette, opacity, and the visualization range.\n", - "\n", - "### Map pseudo color\n", - "\n", - "A pseudo-color image is a single-band raster image that uses a color palette to represent the data. The following example demonstrates how to plot the NDVI pseudo-color image using the `plot` function.\n", - "\n", - "First create a matplotlib figure and axis. Then you can add the map to the axis. Provide a single element list in the bands parameter to plot the NDVI image. \n", - "As per interactive representation an image needs to be reduced to a region, here \"Vatican City\". In this example we also select a pseudo-mercator projection and we displayed the `ee.FeatureCollection` on top of it. Now that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar. Now that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "\n", - "image.geetools.plot(\n", - " bands = [\"NDVI\"],\n", - " ax=ax,\n", - " region=vatican.geometry(),\n", - " crs=\"EPSG:3857\",\n", - " scale=10,\n", - " fc=vatican,\n", - " cmap=\"viridis\",\n", - " color=\"k\"\n", - ")\n", - "\n", - "# as it's a figure you can then edit the information as you see fit\n", - "ax.set_title(\"NDVI in Vatican City\")\n", - "ax.set_xlabel(\"x coordinates (m)\")\n", - "ax.set_ylabel(\"y coordinates (m)\")\n", - "fig.colorbar(ax.images[0], label=\"NDVI\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Map RGB combo\n", - "\n", - "An RGB image is a three-band raster image that uses the red, green, and blue bands to represent the data. The following example demonstrates how to plot the RGB image using the `plot` function.\n", - "\n", - "First create a matplotlib figure and axis. Then you can add the map to the axis. Provide a 3 elements list in the bands parameter to plot the NDVI image. \n", - "As per interactive representation an image needs to be reduced to a region, here \"Vatican City\". In this example we displayed the `ee.FeatureCollection` on top of it. Finally customize the plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the plot figure\n", - "fig, ax = plt.subplots()\n", - "\n", - "# Create the graph\n", - "image.geetools.plot(\n", - " bands = [\"B4\", \"B3\", \"B2\"],\n", - " ax=ax,\n", - " region=vatican.geometry(),\n", - " fc=vatican,\n", - " color=\"k\"\n", - ")\n", - "\n", - "# as it's a figure you can then edit the information as you see fit\n", - "ax.set_title(\"Sentinel 2 composite in Vatican City\")\n", - "ax.set_xlabel(\"longitude (°)\")\n", - "ax.set_ylabel(\"latitude (°)\")\n", - "\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/plot/plot-featurecollection.ipynb b/docs/usage/plot/plot-featurecollection.ipynb deleted file mode 100644 index c612083a..00000000 --- a/docs/usage/plot/plot-featurecollection.ipynb +++ /dev/null @@ -1,579 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot FeatureCollection\n", - "\n", - "The `geetools` extension contains a set of functions for rendering charts from `ee.FeatureCollection` objects. The choice of function determines the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function and chart type for your purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-featurecollection.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-featurecollection.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the required libs if necessary and perform the import statements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data\n", - "\n", - "The following examples rely on a FeatureCollection composed of three ecoregion features with properties that describe climate normals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the example feature collection.\n", - "ecoregions = ee.FeatureCollection('projects/google/charts_feature_example')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot by features\n", - "\n", - "Features are plotted along the x-axis by values of a selected property. Series are defined by a list of property names whose values are plotted along the y-axis. The type of produced chart can be controlled by the `type` parameter as shown in the following examples.\n", - "\n", - "If you want to use another plotting library you can get the raw data using the `byFeatures` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Data for the chart\n", - "features = ['f1', 'f2', 'f3']\n", - "p1_values = [0.5, 2.5, 4.5]\n", - "p2_values = [1.5, 3.5, 5.5]\n", - "p3_values = [2.5, 4.0, 6.5]\n", - "\n", - "# Set the width of the bars\n", - "bar_width = 0.25\n", - "index = np.arange(len(features))\n", - "offset = 0.005\n", - "\n", - "# Create the plot\n", - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Plotting the bars\n", - "rects1 = ax.bar(index, p1_values, bar_width, label='p1', color='#1d6b99')\n", - "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label='p2', color='#cf513e')\n", - "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label='p3', color='#f0af07')\n", - "\n", - "# Add labels, title, and custom x-axis tick labels\n", - "ax.set_xlabel('Features by property value')\n", - "ax.set_ylabel('Series property value')\n", - "ax.set_xticks(index + bar_width)\n", - "ax.set_xticklabels(features)\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Property names')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`geetools.FeatureCollectionAccessor.plot_by_features`\n", - "- {docstring}`ee.FeatureCollection.geetools.byFeatures`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Column chart\n", - "\n", - "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by adjacent columns defined by a list of property names whose values are plotted along the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize the plot with the ecoregions data\n", - "ecoregions.geetools.plot_by_features(\n", - " type = \"bar\",\n", - " featureId = \"label\",\n", - " properties = ['01_tmean', '02_tmean', '03_tmean', '04_tmean', '05_tmean', '06_tmean', '07_tmean', '08_tmean', '09_tmean', '10_tmean', '11_tmean', '12_tmean'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", - "ax.set_xlabel(\"Ecoregion\")\n", - "ax.set_ylabel(\"Temperature (°C)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stacked column chart\n", - "\n", - "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by stacked columns defined by a list of property names whose values are plotted along the y-axis as the cumulative series sum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ecoregions.geetools.plot_by_features(\n", - " type = \"stacked\",\n", - " featureId = \"label\",\n", - " properties = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Ecoregion\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scatter chart\n", - "\n", - "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by points defined by a list of property names whose values are plotted along the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ecoregions.geetools.plot_by_features(\n", - " type = \"scatter\",\n", - " featureId = \"label\",\n", - " properties = ['01_ppt', '06_ppt', '09_ppt'],\n", - " labels = [\"jan\", \"jun\", \"sep\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Ecoregion\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pie chart\n", - "\n", - "The pie is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the pie." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ecoregions.geetools.plot_by_features(\n", - " type = \"pie\",\n", - " featureId = \"label\",\n", - " properties = ['06_ppt'],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Share of precipitation in June by Ecoregion\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Donut chart\n", - "\n", - "The donut is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the donut." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ecoregions.geetools.plot_by_features(\n", - " type = \"donut\",\n", - " featureId = \"label\",\n", - " properties = ['07_ppt'],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Share of precipitation in July by Ecoregion\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot by properties\n", - "\n", - "Feature properties are plotted along the x-axis by name; values of the given properties are plotted along the y-axis. Series are features labeled by values of a selected property. The type of produced chart can be controlled by the `type` parameter as shown in the following examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Data for the chart\n", - "features = ['p1', 'p2', 'p3']\n", - "p1_values = [0.5, 2.5, 4.5]\n", - "p2_values = [1.5, 3.5, 5.5]\n", - "p3_values = [2.5, 4.0, 6.5]\n", - "\n", - "# Set the width of the bars\n", - "bar_width = 0.25\n", - "index = np.arange(len(features))\n", - "offset = 0.005\n", - "\n", - "# Create the plot\n", - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Plotting the bars\n", - "rects1 = ax.bar(index, p1_values, bar_width, label='f1', color='#1d6b99')\n", - "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label='f2', color='#cf513e')\n", - "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label='f3', color='#f0af07')\n", - "\n", - "# Add labels, title, and custom x-axis tick labels\n", - "ax.set_xlabel('Property names')\n", - "ax.set_ylabel('Series property value')\n", - "ax.set_xticks(index + bar_width)\n", - "ax.set_xticklabels(features)\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Features by property value')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.FeatureCollection.geetools.plot_by_properties`\n", - "- {docstring}`ee.FeatureCollection.geetools.byProperties`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Column chart\n", - "\n", - "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ax = ecoregions.geetools.plot_by_properties(\n", - " type = \"bar\",\n", - " properties = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " featureId = \"label\",\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Line chart\n", - "\n", - "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize theplot with the ecoregions data\n", - "ax = ecoregions.geetools.plot_by_properties(\n", - " type = \"plot\",\n", - " properties = [\"01_ppt\", \"02_ppt\", \"03_ppt\", \"04_ppt\", \"05_ppt\", \"06_ppt\", \"07_ppt\", \"08_ppt\", \"09_ppt\", \"10_ppt\", \"11_ppt\", \"12_ppt\"],\n", - " featureId = \"label\",\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Area chart \n", - "\n", - "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by lines and shaded areas, labeled by values of a selected property." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize the plot with the ecoregions data\n", - "ax = ecoregions.geetools.plot_by_properties(\n", - " type = \"fill_between\",\n", - " properties = [\"01_ppt\", \"02_ppt\", \"03_ppt\", \"04_ppt\", \"05_ppt\", \"06_ppt\", \"07_ppt\", \"08_ppt\", \"09_ppt\", \"10_ppt\", \"11_ppt\", \"12_ppt\"],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " featureId = \"label\",\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot hist\n", - "\n", - "```{api}\n", - "{docstring}`ee.FeatureCollection.geetools.plot_hist`\n", - "```\n", - "\n", - "The x-axis is defined by value bins for the range of values of a selected property; the y-axis is the number of elements in the given bin." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# load some data\n", - "normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands()\n", - "\n", - "# Make a point sample of climate variables for a region in western USA.\n", - "region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14)\n", - "climSamp = normClim.sample(region, 5000)\n", - "\n", - "\n", - "# initialize the plot with the ecoregions data\n", - "ax = climSamp.geetools.plot_hist(\n", - " property = \"07_ppt\",\n", - " label = \"July Precipitation (mm)\",\n", - " color = '#1d6b99',\n", - " ax = ax,\n", - " bins = 30\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"July Precipitation Distribution for NW USA\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/plot/plot-image.ipynb b/docs/usage/plot/plot-image.ipynb deleted file mode 100644 index cf18b020..00000000 --- a/docs/usage/plot/plot-image.ipynb +++ /dev/null @@ -1,587 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Image\n", - "\n", - "The `geetools` extention contains a set of functions for reducing `ee.Image` objects by region(s) and rendering charts from the results. The choice of function dictates the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function and chart type for your purpose.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-image.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-image.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the requireed libs if necessary. and perform the import satements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data\n", - "\n", - "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The Image data are PRISM climate normals, where bands describe climate variables per month; e.g., July precipitation or January mean temperature.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\").select([\"label\", \"value\",\"warm\"])\n", - "normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot by regions\n", - "\n", - "Reduction regions are plotted along the x-axis, labeled by values of a selected feature property. Series are defined by band names whose region reduction results are plotted along the y-axis.\n", - "\n", - "If you want to use another plotting library, you can use the `byRegions` function to get the data and plot it with your favorite library.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Data for the chart\n", - "features = ['r1', 'r2', 'r3']\n", - "p1_values = [0.5, 2.5, 4.5]\n", - "p2_values = [1.5, 3.5, 5.5]\n", - "p3_values = [2.5, 4.0, 6.5]\n", - "\n", - "# Set the width of the bars\n", - "bar_width = 0.25\n", - "index = np.arange(len(features))\n", - "offset = 0.005\n", - "\n", - "# Create the plot\n", - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Plotting the bars\n", - "rects1 = ax.bar(index, p1_values, bar_width, label='b1', color='#1d6b99')\n", - "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label='b2', color='#cf513e')\n", - "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label='b3', color='#f0af07')\n", - "\n", - "# Add labels, title, and custom x-axis tick labels\n", - "ax.set_xlabel('Regions by feature property value')\n", - "ax.set_ylabel('Series reduction value')\n", - "ax.set_xticks(index + bar_width)\n", - "ax.set_xticklabels(features)\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Band names')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.Image.geetools.plot_by_regions`\n", - "- {docstring}`ee.Image.geetools.byRegions`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Column chart\n", - "\n", - "In this example, image bands representing average monthly temperature are reduced to the mean among pixels intersecting each of three ecoregions. The results are plotted as columns per month by ecoregion, where column height indicates the respective mean monthly temperature." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "normClim.geetools.plot_by_regions(\n", - " type = \"bar\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = [\"01_tmean\", \"02_tmean\", \"03_tmean\", \"04_tmean\", \"05_tmean\", \"06_tmean\", \"07_tmean\", \"08_tmean\", \"09_tmean\", \"10_tmean\", \"11_tmean\", \"12_tmean\"],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", - "ax.set_xlabel(\"Ecoregion\")\n", - "ax.set_ylabel(\"Temperature (°C)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bar chart \n", - "\n", - "The previous column chart can be swiped from vertical column to horizontal bars a bar chart by changing the `type` input from 'bar' to 'barh'." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_regions(\n", - " type = \"barh\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = [\"01_tmean\", \"02_tmean\", \"03_tmean\", \"04_tmean\", \"05_tmean\", \"06_tmean\", \"07_tmean\", \"08_tmean\", \"09_tmean\", \"10_tmean\", \"11_tmean\", \"12_tmean\"],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", - "ax.set_ylabel(\"Ecoregion\")\n", - "ax.set_xlabel(\"Temperature (°C)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stacked bar chart \n", - "\n", - "An absolute stacked bar chart relates the total of a numeric variable by increments of a contributing categorical variable series. For instance, in this example, total precipitation is plotted as the accumulation of monthly precipitation over a year, by ecoregion. Monthly precipitation totals are derived from image bands, where each band represents a grid of average total precipitation for a given month, reduced to the mean of the pixels intersecting each of three ecoregions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_regions(\n", - " type = \"stacked\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", - "ax.set_xlabel(\"Ecoregion\")\n", - "ax.set_ylabel(\"Temperature (°C)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot by bands\n", - "\n", - "Bands are plotted along the x-axis. Series are labeled by values of a feature property. Reduction of the region defined by the geometry of respective series features are plotted along the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Data for the chart\n", - "features = ['b1', 'b2', 'b3']\n", - "p1_values = [0.5, 2.5, 4.5]\n", - "p2_values = [1.5, 3.5, 5.5]\n", - "p3_values = [2.5, 4.0, 6.5]\n", - "\n", - "# Set the width of the bars\n", - "bar_width = 0.25\n", - "index = np.arange(len(features))\n", - "offset = 0.005\n", - "\n", - "# Create the plot\n", - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Plotting the bars\n", - "rects1 = ax.bar(index, p1_values, bar_width, label='r1', color='#1d6b99')\n", - "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label='r2', color='#cf513e')\n", - "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label='r3', color='#f0af07')\n", - "\n", - "# Add labels, title, and custom x-axis tick labels\n", - "ax.set_xlabel('band names')\n", - "ax.set_ylabel('Series reduction value')\n", - "ax.set_xticks(index + bar_width)\n", - "ax.set_xticklabels(features)\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Regions by feature property value')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Colmun chart \n", - "\n", - "This chart shows total average precipitation per month for three ecoregions. The results are derived from the region reduction of an image where each band is a grid of average total precipitation for a given month. Bands are plotted along the x-axis and regions define the series." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_bands(\n", - " type = \"bar\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Line chart \n", - "\n", - "The previous column chart can be rendered as a line chart by changing the `type` input from \"bar\" to \"plot\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_bands(\n", - " type = \"plot\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Area chart \n", - "\n", - "The previous column chart can be rendered as a line chart by changing the `type` input from \"plot\" to \"fill_between\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_bands(\n", - " type = \"fill_between\",\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", - "ax.set_xlabel(\"Month\")\n", - "ax.set_ylabel(\"Precipitation (mm)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pie chart \n", - "\n", - "Average monthly precipitation is displayed as a proportion of the average total annual precipitation for a forest ecoregion. Image bands representing monthly precipitation are subset from a climate normals dataset and reduced to the mean of pixels intersecting the ecoregion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "normClim.geetools.plot_by_bands(\n", - " type = \"pie\",\n", - " regions = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation in Forest\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Donuts chart \n", - "\n", - "The previous chart can be represented as a donut by replacing the `type` parameter with `donut`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "fc = normClim.geetools.plot_by_bands(\n", - " type = \"donut\",\n", - " regions = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'],\n", - " labels = [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'],\n", - " ax = ax\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Average Monthly Precipitation in Forest\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## histogram plot \n", - "\n", - "A histogram of pixel values within a region surrounding Salt Lake City, Utah, USA are displayed for three MODIS surface reflectance bands. The histogram is plotted as a line chart, where x-axis values are pixel values and y-axis values are the frequency of pixels with the respective value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# custom image for this specific chart\n", - "modisSr = (\n", - " ee.ImageCollection(\"MODIS/061/MOD09A1\")\n", - " .filter(ee.Filter.date(\"2018-06-01\", \"2018-09-01\"))\n", - " .select([\"sur_refl_b01\", \"sur_refl_b02\", \"sur_refl_b06\"])\n", - " .mean()\n", - ")\n", - "histRegion = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# initialize the plot with the ecoregions data\n", - "modisSr.geetools.plot_hist(\n", - " bands = [\"sur_refl_b01\", \"sur_refl_b02\", \"sur_refl_b06\"],\n", - " labels = [['Red', 'NIR', 'SWIR']],\n", - " colors = [\"#cf513e\", \"#1d6b99\", \"#f0af07\"],\n", - " ax = ax,\n", - " bins = 100,\n", - " scale = 500,\n", - " region = histRegion,\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_title(\"Modis SR Reflectance Histogram\")\n", - "ax.set_xlabel(\"Reflectance (x1e4)\")\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/plot/plot-imagecollection.ipynb b/docs/usage/plot/plot-imagecollection.ipynb deleted file mode 100644 index 2d652c18..00000000 --- a/docs/usage/plot/plot-imagecollection.ipynb +++ /dev/null @@ -1,709 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot ImageCollection\n", - "\n", - "The `geetools` extention contains a set of functions for rendering charts from the results of spatiotemporal reduction of images within an `ee.ImageCollection`. The choice of function dictates the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function for your purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-imagecollection.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-imagecollection.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the required libs if necessary and perform the import satements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "from datetime import datetime as dt\n", - "\n", - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data \n", - "\n", - "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The ImageCollection data loads the modis vegetation indicies and subset the 2010 2020 decade of images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Import the example feature collection and drop the data property.\n", - "ecoregions = (\n", - " ee.FeatureCollection(\"projects/google/charts_feature_example\")\n", - " .select([\"label\", \"value\", \"warm\"])\n", - ")\n", - "\n", - "\n", - "## Load MODIS vegetation indices data and subset a decade of images.\n", - "vegIndices = (\n", - " ee.ImageCollection(\"MODIS/061/MOD13A1\")\n", - " .filter(ee.Filter.date(\"2010-01-01\", \"2020-01-01\"))\n", - " .select([\"NDVI\", \"EVI\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot dates\n", - "\n", - "The `plot_dates*` methods will plot the values of the image collection using their dates as x-axis values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### series by bands \n", - "\n", - "Image date is plotted along the x-axis according to the `dateProperty` property. Series are defined by image bands. Y-axis values are the reduction of images, by date, for a single region." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Sample data (replace these with your actual data)\n", - "dates = [\"date1\", \"date2\", \"date3\"]\n", - "b1 = [1, 2, 1]\n", - "b2 = [2, 3, 2]\n", - "b3 = [3, 4, 3]\n", - "\n", - "# Create the plot\n", - "ax.plot(dates, b1, label=\"b1\", color=\"#1d6b99\")\n", - "ax.plot(dates, b2, label=\"b2\", color=\"#cf513e\")\n", - "ax.plot(dates, b3, label=\"b3\", color=\"#f0af07\")\n", - "\n", - "# Add titles and labels\n", - "ax.set_title('Single-region spatial reduction')\n", - "ax.set_xlabel('Image date')\n", - "ax.set_ylabel('Spatial reduction')\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Band names')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_ylim(0, 5)\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plot_series_by_bands` to display an image time series for a given region; each image band is presented as a unique series. It is useful for comparing the time series of individual image bands. Here, a MODIS image collection with bands representing NDVI and EVI vegetation indices are plotted. The date of every image observation is included along the x-axis, while the mean reduction of pixels intersecting a forest ecoregion defines the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "region = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\"))\n", - "vegIndices.geetools.plot_dates_by_bands(\n", - " region = region.geometry(),\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " bands = [\"NDVI\", \"EVI\"],\n", - " ax = ax,\n", - " dateProperty = \"system:time_start\",\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", - "ax.set_title(\"Average Vegetation index Values by date in the Forest ecoregion\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_dates_by_bands`\n", - "- {docstring}`ee.ImageCollection.geetools.datesByBands`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot series by region\n", - "\n", - "Image date is plotted along the x-axis according to the `dateProperty` property. Series are defined by regions. Y-axis values are the reduction of images, by date, for a single image band." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Sample data (replace these with your actual data)\n", - "dates = [\"date1\", \"date2\", \"date3\"]\n", - "b1 = [1, 2, 1]\n", - "b2 = [2, 3, 2]\n", - "b3 = [3, 4, 3]\n", - "\n", - "# Create the plot\n", - "ax.plot(dates, b1, label=\"r1\", color=\"#1d6b99\")\n", - "ax.plot(dates, b2, label=\"r2\", color=\"#cf513e\")\n", - "ax.plot(dates, b3, label=\"r3\", color=\"#f0af07\")\n", - "\n", - "# Add titles and labels\n", - "ax.set_title(\"Single-band spatial reduction\")\n", - "ax.set_xlabel(\"Image date\")\n", - "ax.set_ylabel(\"Spatial reduction\")\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Regions')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_ylim(0, 5)\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plot_dates_by_regions` to display a single image band time series for multiple regions; each region is presented as a unique series. It is useful for comparing the time series of a single band among several regions. Here, a MODIS image collection representing an NDVI time series is plotted for three ecoregions. The date of every image observation is included along the x-axis, while mean reduction of pixels intersecting forest, desert, and grasslands ecoregions define y-axis series." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "region = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\"))\n", - "vegIndices.geetools.plot_dates_by_regions(\n", - " band = \"NDVI\",\n", - " regions = ecoregions,\n", - " label = \"label\",\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " ax = ax,\n", - " dateProperty = \"system:time_start\",\n", - " colors = ['#f0af07', '#0f8755', '#76b349']\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", - "ax.set_title(\"Average Vegetation index Values by date in the Forest ecoregion\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_dates_by_regions`\n", - "- {docstring}`ee.ImageCollection.geetools.datesByRegions`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PLot DOY\n", - "\n", - "DOY stands for day of year. The `plot_doyseries*` methods will plot the values of the image collection using the day of year as x-axis values.\n", - "\n", - "Note that `.plot_doyseries*` functions take two reducers: one for region reduction (`regionReducer`) and another for intra-annual coincident day-of-year reduction (`yearReducer`). Examples in the following sections use `ee.Reducer.mean()` as the argument for both of these parameters." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot DOY by bands \n", - "\n", - "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by image bands. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Sample data (replace these with your actual data)\n", - "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", - "b1 = [1, 2, 1]\n", - "b2 = [2, 3, 2]\n", - "b3 = [3, 4, 3]\n", - "\n", - "# Create the plot\n", - "ax.plot(dates, b1, label=\"b1\", color=\"#1d6b99\")\n", - "ax.plot(dates, b2, label=\"b2\", color=\"#cf513e\")\n", - "ax.plot(dates, b3, label=\"b3\", color=\"#f0af07\")\n", - "\n", - "# Add titles and labels\n", - "ax.set_title(\"Single-region spatiotemporal reduction\")\n", - "ax.set_xlabel(\"Image date\")\n", - "ax.set_ylabel(\"Reduced values\")\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='Band names')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_ylim(0, 5)\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plot_doy_by_bands` to display a day-of-year time series for a given region; each image band is presented as a unique series. It is useful for reducing observations occurring on the same day-of-year, across multiple years, to compare e.g. average annual NDVI and EVI profiles from MODIS, as in this example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10,4))\n", - "\n", - "vegIndices.geetools.plot_doy_by_bands(\n", - " region = ecoregions.filter(ee.Filter.eq(\"label\", \"Grassland\")).geometry(),\n", - " spatialReducer = \"mean\",\n", - " timeReducer = \"mean\",\n", - " scale = 500,\n", - " bands = [\"NDVI\", \"EVI\"],\n", - " ax = ax,\n", - " dateProperty = \"system:time_start\",\n", - " colors = ['#e37d05', '#1d6b99']\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", - "ax.set_title(\"Average Vegetation index Values by doy in the Grassland ecoregion\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_bands`\n", - "- {docstring}`ee.ImageCollection.geetools.doyByBands`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot doy by regions \n", - "\n", - "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by regions. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year, for a selected image band.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Sample data (replace these with your actual data)\n", - "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", - "b1 = [1, 2, 1]\n", - "b2 = [2, 3, 2]\n", - "b3 = [3, 4, 3]\n", - "\n", - "# Create the plot\n", - "ax.plot(dates, b1, label=\"r1\", color=\"#1d6b99\")\n", - "ax.plot(dates, b2, label=\"r2\", color=\"#cf513e\")\n", - "ax.plot(dates, b3, label=\"r3\", color=\"#f0af07\")\n", - "\n", - "# Add titles and labels\n", - "ax.set_title(\"Single-region spatiotemporal reduction\")\n", - "ax.set_xlabel(\"Image date\")\n", - "ax.set_ylabel(\"Reduced values\")\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='regions')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_ylim(0, 5)\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plot_doy_by_regions` to display a single image band day-of-year time series for multiple regions, where each distinct region is presented as a unique series. It is useful for comparing annual single-band time series among regions. For instance, in this example, annual MODIS-derived NDVI profiles for forest, desert, and grassland ecoregions are plotted, providing a convenient comparison of NDVI response by region. Note that intra-annual observations occurring on the same day-of-year are reduced by their mean." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10,4))\n", - "\n", - "vegIndices.geetools.plot_doy_by_regions(\n", - " regions = ecoregions,\n", - " label = \"label\",\n", - " spatialReducer = \"mean\",\n", - " timeReducer = \"mean\",\n", - " scale = 500,\n", - " band = \"NDVI\",\n", - " ax = ax,\n", - " dateProperty = \"system:time_start\",\n", - " colors = ['#f0af07', '#0f8755', '#76b349']\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"NDVI (x1e4)\")\n", - "ax.set_title(\"Average NDVI Values by doy in each ecoregion\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_regions`\n", - "- {docstring}`ee.ImageCollection.geetools.doyByRegions`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### plot doy by year \n", - "\n", - "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by years present in the ImageCollection. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year, for a selected image band." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10, 4))\n", - "\n", - "# Sample data (replace these with your actual data)\n", - "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", - "b1 = [1, 2, 1]\n", - "b2 = [2, 3, 2]\n", - "b3 = [3, 4, 3]\n", - "\n", - "# Create the plot\n", - "ax.plot(dates, b1, label=\"year1\", color=\"#1d6b99\")\n", - "ax.plot(dates, b2, label=\"year2\", color=\"#cf513e\")\n", - "ax.plot(dates, b3, label=\"year3\", color=\"#f0af07\")\n", - "\n", - "# Add titles and labels\n", - "ax.set_title(\"Single-region/band spatiotemporal reduction\")\n", - "ax.set_xlabel(\"Image date\")\n", - "ax.set_ylabel(\"Reduced values\")\n", - "\n", - "# Add a legend\n", - "ax.legend(loc='upper center', bbox_to_anchor=(0.85, 1.15), ncol=3, title='iage years')\n", - "\n", - "# set the grid display\n", - "ax.grid(axis=\"y\")\n", - "ax.set_ylim(0, 5)\n", - "ax.set_axisbelow(True)\n", - "ax.spines[\"top\"].set_visible(False)\n", - "ax.spines[\"right\"].set_visible(False)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plot_doy_by_years` to display a day-of-year time series for a given region and image band, where each distinct year in the image collection is presented as a unique series. It is useful for comparing annual time series among years. For instance, in this example, annual MODIS-derived NDVI profiles for a grassland ecoregion are plotted for years 2012 and 2019, providing convenient year-over-year interpretation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# reduce the regions to grassland\n", - "grassland = ecoregions.filter(ee.Filter.eq(\"label\", \"Grassland\"))\n", - "\n", - "# for plot speed and lisibility only keep 2 years (2010 and 2020) for the example\n", - "indices = vegIndices.filter(\n", - " ee.Filter.Or(\n", - " ee.Filter.date(\"2012-01-01\", \"2012-12-31\"),\n", - " ee.Filter.date(\"2019-01-01\", \"2019-12-31\"),\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10,4))\n", - "\n", - "indices.geetools.plot_doy_by_years(\n", - " band = \"NDVI\",\n", - " region = grassland.geometry(),\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " ax = ax,\n", - " colors = ['#39a8a7', '#9c4f97']\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"NDVI (x1e4)\")\n", - "ax.set_title(\"Average NDVI Values by day of year for Grassland\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_years`\n", - "- {docstring}`ee.ImageCollection.geetools.doyByYears`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### plot doy by seasons \n", - "\n", - "In case the observation you want to analyse are only meaningful on a subset of the year a variant of the previous method allows you to plot the data by season. The season is defined by the `seasonStart` and `seasonEnd` parameters, which are 2 numbers between 1 and 366 representing the start and end of the season. To set them, the user can use the {py:method}`ee.Date.getRelative` or {py:class}`time.struct_time` method to get the day of the year." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.Date(\"2022-06-01\").getRelative(\"day\", \"year\").getInfo()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt(2022, 6, 1).timetuple().tm_yday" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10,4))\n", - "\n", - "indices.geetools.plot_doy_by_seasons(\n", - " band = \"NDVI\",\n", - " region = grassland.geometry(),\n", - " seasonStart = ee.Date(\"2022-04-15\").getRelative(\"day\", \"year\"),\n", - " seasonEnd = ee.Date(\"2022-09-15\").getRelative(\"day\", \"year\"),\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " ax = ax,\n", - " colors = ['#39a8a7', '#9c4f97']\n", - ")\n", - "\n", - "# once created the axes can be modified as needed using pure matplotlib functions\n", - "ax.set_ylabel(\"NDVI (x1e4)\")\n", - "ax.set_title(\"Average NDVI Values during growing season in Grassland\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{api}\n", - "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_seasons`\n", - "- {docstring}`ee.ImageCollection.geetools.doyBySeasons`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/profile.ipynb b/docs/usage/profile.ipynb deleted file mode 100644 index a51cbce7..00000000 --- a/docs/usage/profile.ipynb +++ /dev/null @@ -1,217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Profile Earth Engine computation\n", - "\n", - "The Earth Engine API provides tools for profiling the performance of your computations but they are not always the easiest to use to get the number you are looking for. The `geetools` library supercharge the original profiler to make any computation evaluation the easiest possible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/profile.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/profile.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the requireed libs if necessary. and perform the import satements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "import geetools\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()\n", - "# ee.Intialize(project=\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data\n", - "\n", - "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The Image data are PRISM climate normals, where bands describe climate variables per month; e.g., July precipitation or January mean temperature.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ecoregions = (\n", - " ee.FeatureCollection(\"projects/google/charts_feature_example\")\n", - " .select([\"label\", \"value\",\"warm\"])\n", - ")\n", - "\n", - "normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## default profiler \n", - "\n", - "The default profiler from Earth Engine can be called as a context manager, it will print at the end of the cell the extensive description of your computation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ee.profilePrinting():\n", - " normClim.geetools.byBands(\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = [f\"{i:02d}_tmean\" for i in range(1,13)],\n", - " ).getInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This result is extremely useful but cannot be further explored in the notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## geetools profiler\n", - "\n", - "The `geetools` profiler is a context manager object that fill a dictionary member (`profile`) with the content of the string profile. This dictionary can be transformed into a table easily." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# example with a simple function\n", - "with ee.geetools.Profiler() as p:\n", - " ee.Number(3.14).add(0.00159).getInfo()\n", - "p.profile" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With a bigger method we can valorized the results as a pandas dataframe and extract key informations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with ee.geetools.Profiler() as p:\n", - " normClim.geetools.byBands(\n", - " regions = ecoregions,\n", - " reducer = \"mean\",\n", - " scale = 500,\n", - " regionId = \"label\",\n", - " bands = [f\"{i:02d}_tmean\" for i in range(1,13)],\n", - " ).getInfo()\n", - "df = pd.DataFrame(p.profile)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# total EECU cost of the computation\n", - "float(df[\"EECU-s\"].sum())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/reduce.ipynb b/docs/usage/reduce.ipynb deleted file mode 100644 index c8e7f7a2..00000000 --- a/docs/usage/reduce.ipynb +++ /dev/null @@ -1,167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reduce ImageCollection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/reduce.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/reduce.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "THe Earth Engine API provides 2 ways to reduce images: `reduceRegion` and `reduceRegion`. `geetools` is making these methods also available for `ee.ImageCollection` objects." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environment\n", - "\n", - "Install all the required libs if necessary and perform the import statements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if initialization is required\n", - "# ee.Initialize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example data \n", - "\n", - "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The ImageCollection data loads the modis vegetation indicies and subset the 2010 2020 decade of images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Import the example feature collection and drop the data property.\n", - "ecoregions = (\n", - " ee.FeatureCollection(\"projects/google/charts_feature_example\")\n", - " .select([\"label\", \"value\", \"warm\"])\n", - ")\n", - "\n", - "\n", - "## Load MODIS vegetation indices data and subset of 4 images.\n", - "vegIndices = (\n", - " ee.ImageCollection(\"MODIS/061/MOD13A1\")\n", - " .filter(ee.Filter.date(\"2010-01-01\", \"2010-02-28\"))\n", - " .select([\"NDVI\", \"EVI\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reduce over single region\n", - "\n", - "Using `reduceRegion` you can reduce an `ee.ImageCollection` over a single region. The function will return a `ee.Dictionary` with the reduced values of each band grouped under each image Id as key.\n", - "\n", - "It will return a `ee.Dictionary` with the following shape: \n", - "\n", - "```\n", - "{\n", - " \"image1\": {\"band1\": value1, \"band2\": value2, ...},\n", - " \"image2\": {\"band1\": value1, \"band2\": value2, ...},\n", - "}\n", - "```\n", - "\n", - "where `image*`is the id of the image as per specified property (casted to string) and `band*` is the name of the band." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vegIndices.geetools.reduceRegion(\n", - " reducer = ee.Reducer.mean(),\n", - " idProperty = \"system:time_start\",\n", - " idType = ee.Date,\n", - " geometry = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")).geometry(),\n", - " scale = 500\n", - ").getInfo()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geetools", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/usage/template.ipynb b/docs/usage/template.ipynb deleted file mode 100644 index faa27def..00000000 --- a/docs/usage/template.ipynb +++ /dev/null @@ -1,123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# example Template" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "import ee, geetools\n", - "from geetools.utils import initialize_documentation\n", - "\n", - "initialize_documentation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/template.ipynb)\n", - "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/template.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up environement\n", - "\n", - "Install all the required libs if necessary and perform the import statements upstream." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if installation of libs is necessary\n", - "# !pip install earthengine-api geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "import geetools #noqa: F401" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if authetication to GEE is needed\n", - "# ee.Authenticate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncomment if initialization is required\n", - "# ee.Initialize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Truncate a number\n", - "\n", - "This template is demonstrating how to truncate a EE number to a given number of decimal places." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# initialize your GEE object\n", - "pi = 3.14159265359\n", - "ee_pi = ee.Number(pi)\n", - "ee_pi.getInfo()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# truncate the number to 3 decimals directly in GEE\n", - "ee_pi_truncated = ee_pi.geetools.truncate(3)\n", - "ee_pi_truncated.getInfo()" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/geetools/__init__.py b/geetools/__init__.py index 80b83e81..d33f2b08 100644 --- a/geetools/__init__.py +++ b/geetools/__init__.py @@ -1,51 +1,20 @@ -"""A toolbox to use with Google Earth Engine Python API. +"""The init file of the package.""" -The ``geetools`` package extends the Google Earth Engine Python API with pre-processing and -processing tools for the most used satellite platforms by adding utility methods for different Earth -Engine Objects that are friendly with the Python method chaining using the geetools namespace. -""" -import ee +__version__ = "0.0.0" +__author__ = "Pierrick Rambaud" +__email__ = "pierrick.rambaud49@gmail.com" -# import the accessor namespace -from .accessors import geetools -# it needs to be imported first as it's the mother class -from .ee_computed_object import * +class Hello: + """Hello world class.""" -# reproduce older structure of the lib (deprecated) -# will be removed along the deprecation cycle -from . import _deprecated_algorithms as algorithms -from . import _deprecated_composite as composite -from .tools import imagecollection + msg = "hello world !" + "the message to print" -# then we extend all the other classes -from .ee_asset import Asset -from .ee_date import DateAccessor -from .ee_dictionary import DictionaryAccessor -from .ee_feature import FeatureAccessor -from .ee_feature_collection import FeatureCollectionAccessor -from .ee_filter import FilterAccessor -from .ee_geometry import GeometryAccessor -from .ee_image import ImageAccessor -from .ee_join import JoinAccessor -from .ee_list import ListAccessor -from .ee_number import NumberAccessor -from .ee_string import StringAccessor -from .ee_image_collection import ImageCollectionAccessor -from .ee_initialize import InitializeAccessor -from .ee_authenticate import AuthenticateAccessor -from .ee_array import ArrayAccessor -from .ee_date_range import DateRangeAccessor -from .ee_export import ExportAccessor -from .ee_profiler import Profiler + def hello_world(self) -> str: + """Hello world demo method. -__title__ = "geetools" -__summary__ = "A set of useful tools to use with Google Earth Engine Python" "API" -__uri__ = "http://geetools.readthedocs.io" -__version__ = "1.9.1" - -__author__ = "Rodrigo E. Principe" -__email__ = "fitoprincipe82@gmail.com" - -__license__ = "MIT" -__copyright__ = "2017 Rodrigo E. Principe" + Returns: + the hello world string + """ + return self.msg diff --git a/geetools/_deprecated_algorithms.py b/geetools/_deprecated_algorithms.py deleted file mode 100644 index 7b99c5bc..00000000 --- a/geetools/_deprecated_algorithms.py +++ /dev/null @@ -1,60 +0,0 @@ -# coding=utf-8 -"""Module holding misc algorithms.""" - -import ee -import ee.data -from deprecated.sphinx import deprecated - - -@deprecated(version="1.4.0", reason="Use ee.Image.geetools.distanceToMask instead.") -def distanceToMask( - image, - mask, - kernel=None, - radius=1000, - unit="meters", - scale=None, - geometry=None, - band_name="distance_to_mask", - normalize=False, -): - """Compute the distance to the mask in meters.""" - return ( - ee.Image(image) - .geetools.distanceToMask(mask, radius=radius, band_name=band_name) - .select(band_name) - ) - - -@deprecated(version="1.5.0", reason="Use ee.Image.geetools.maskCover instead.") -def maskCover( - image, - geometry=None, - scale=None, - property_name="MASK_COVER", - crs=None, - crsTransform=None, - bestEffort=False, - maxPixels=1e13, - tileScale=1, -): - """Percentage of masked pixels (masked/total * 100) as an Image property.""" - return ee.Image(image).geetools.maskCover() - - -@deprecated(version="1.5.0", reason="Use ee.Image.geetools.distance instead.") -def euclideanDistance(image1, image2, bands=None, discard_zeros=False, name="distance"): - """Compute the Euclidean distance between two images.""" - return ee.Image(image1).geetools.distance(image2) - - -@deprecated(version="1.4.0", reason="It's included in the ee_extra bindings.") -def pansharpenKernel(image, pan, rgb=None, kernel=None): - """Compute the per-pixel means of the unsharpened bands.""" - raise NotImplementedError("This function is deprecated. Use the one in ee_extra.") - - -@deprecated(version="1.4.0", reason="It's included in the ee_extra bindings.") -def pansharpenIhsFusion(image, pan=None, rgb=None): - """HSV-based Pan-Sharpening.""" - raise NotImplementedError("This function is deprecated. Use the one in ee_extra.") diff --git a/geetools/_deprecated_composite.py b/geetools/_deprecated_composite.py deleted file mode 100644 index f04d95c4..00000000 --- a/geetools/_deprecated_composite.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Module holding tools for creating composites.""" - -import ee -from deprecated.sphinx import deprecated - - -@deprecated(version="1.5.0", reason="Only used to build the medoid composite.") -def medoidScore(collection, bands=None, discard_zeros=False, bandname="sumdist", normalize=True): - """Compute a score to reflect 'how far' is from the medoid.""" - raise NotImplementedError( - "This method was only used to build the medoid composite. " - "The medoid composite is still available in the lib." - ) - - -@deprecated(version="1.5.0", reason="Use ee.imageCollection.geetools.medoid instead") -def medoid(collection, bands=None, discard_zeros=False): - """Medoid Composite. Adapted from https://www.mdpi.com/2072-4292/5/12/6481.""" - return ee.ImageCollection(collection).geetools.medoid() - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.closestDate instead") -def closestDate(col, clip_to_first=False): - """Make a composite in which masked pixels are filled with the last available pixel.""" - return ee.ImageCollection(col).geetools.closestDate() - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead") -def compositeRegularIntervals( - collection, - interval=1, - unit="month", - date_range=(1, 0), - date_range_unit="month", - direction="backward", - start=None, - end=None, - composite_function=None, - composite_args=None, - composite_kwargs=None, -): - """Make a composite at regular intervals parsing a composite.""" - return ee.ImageCollection(collection).geetools.reduceInterval(unit=unit) - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead") -def compositeByMonth( - collection, composite_function=None, composite_args=None, composite_kwargs=None -): - """Make a composite at regular intervals parsing a composite.""" - return ee.ImageCollection(collection).geetools.reduceInterval(unit="month") - - -@deprecated(version="1.4.0", reason="Use the vanilla Earth Engine API") -def max(collection, band=None): - """Make a max composite using the specified band.""" - return collection.max() diff --git a/geetools/accessors.py b/geetools/accessors.py deleted file mode 100644 index 31a2cce9..00000000 --- a/geetools/accessors.py +++ /dev/null @@ -1,81 +0,0 @@ -"""Generic accessor to add extra function to the base GEE API classes.""" -from __future__ import annotations - -from typing import Callable - -import ee - - -def register_class_accessor(klass: type, name: str) -> Callable: - """Create an accessor through the provided namespace to a given class. - - Parameters: - klass: The class to set the accessor to. - name: The name of the accessor namespace - - Returns: - The accessor function to to the class. - """ - - def decorator(accessor: Callable) -> object: - class ClassAccessor: - def __init__(self, name: str, accessor: Callable): - self.name, self.accessor = name, accessor - - def __get__(self, obj: object, *args) -> object: - return self.accessor(obj) - - # check if the accessor already exists for this class - if hasattr(klass, name): - raise AttributeError(f"Accessor {name} already exists for {klass}") - - # register the accessor to the class - setattr(klass, name, ClassAccessor(name, accessor)) - - return accessor - - return decorator - - -def register_function_accessor(func: type, name: str) -> Callable: - """Add a Accessor class to function through the provided namespace. - - Parameters: - func: The function to set the accessor to. - name: The name of the accessor namespace. - - Returns: - The accessor function to to the function. - """ - - def decorator(accessor: Callable) -> object: - - # check if the accessor already exists for this class - if hasattr(func, name): - raise AttributeError(f"Member {name} already exists for {func}") - else: - setattr(func, name, accessor()) - - return accessor - - return decorator - - -# this private method should not be exposed to end user as it perform 0 checks it can overwrite -# existing methods/class/member. Only used in the lib for the Computed object as the method need -# to be shared by every other child of the class. -def _register_extention(obj: object) -> Callable: - """Add the function to any object.""" - return lambda f: (setattr(obj, f.__name__, f) or f) # type: ignore - - -# create a geetools namespace that can be use directly on the ee module - - -@_register_extention(ee) -class geetools: - """Namespace class for the geetools library.""" - - def __init__(self): - """The geetools namespace cannot be instantiated.""" - raise AttributeError("Cannot instantiate geetools") diff --git a/geetools/ee_array.py b/geetools/ee_array.py deleted file mode 100644 index a9a19ef9..00000000 --- a/geetools/ee_array.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Extra methods for the ``ee.Array`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Array, "geetools") -class ArrayAccessor: - """Toolbox for the ``ee.Array`` class.""" - - def __init__(self, obj: ee.Array): - """Initialize the Array class.""" - self._obj = obj - - def full( - self, - width: float | int | ee.Number, - height: float | int | ee.Number, - value: float | int | ee.Number, - ) -> ee.Array: - """Create an :py:class:`ee.Array` with the given dimensions, initialized to the given value. - - Parameters: - width: The width of the array. - height: The height of the array. - value: The value to initialize the array with. - - Returns: - An array with the given dimensions, initialized to the given value. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - array = ee.Array.geetools.full(3, 3, 1) - array.getInfo() - """ - width, height = ee.Number(width).toInt(), ee.Number(height).toInt() - return ee.Array(ee.List.repeat(ee.List.repeat(value, width), height)) - - def set( - self, - x: int | ee.Number, - y: int | ee.Number, - value: float | int | ee.Number, - ) -> ee.Array: - """Set the value of a cell in an array. - - Parameters: - x: The x coordinate of the cell. - y: The y coordinate of the cell. - value: The value to set the cell to. - - Returns: - The array with the cell set to the given value. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - array = ee.Array.geetools.full(3, 3, 1) - array = array.geetools.set(1, 1, 0) - array.getInfo() - """ - xPos, yPos = ee.Number(x).toInt(), ee.Number(y).toInt() - row = ee.List(self._obj.toList().get(yPos)).set(xPos, ee.Number(value)) - return ee.Array(self._obj.toList().set(yPos, row)) diff --git a/geetools/ee_asset.py b/geetools/ee_asset.py deleted file mode 100644 index 6c81a931..00000000 --- a/geetools/ee_asset.py +++ /dev/null @@ -1,792 +0,0 @@ -"""An Asset management class mimicking the ``pathlib.Path`` class behaviour.""" -from __future__ import annotations - -import os -import re -from pathlib import PurePosixPath - -import ee -import ee.data - -from .accessors import _register_extention -from .utils import format_description - - -@_register_extention(ee) -class Asset(os.PathLike): - """An Asset management class mimicking the ``pathlib.Path`` class behaviour.""" - - def __init__(self, *args): - """Initialize the Asset class. - - .. note:: - An asset cannot be an absolute path like in a normal filesystem and thus any trailing "/" will be removed. - """ - if len(args) == 0: - self._path = f"projects/{ee.data._cloud_api_user_project}/assets/" - else: - self._path = args[0]._path if isinstance(args[0], Asset) else PurePosixPath(*args) - project_assets = PurePosixPath(str(self._path)[1:]) - self._path = project_assets if self._path.is_absolute() else self._path - - def __str__(self): - """Transform the asset id to a string.""" - return self.as_posix() - - def __repr__(self): - """Return the asset object representation as a string.""" - return f"ee.{type(self).__name__}('{self.as_posix()}')" - - def __truediv__(self, other: os.PathLike) -> Asset: - """Override the division operator to join the asset with other paths.""" - return Asset(self._path / str(other)) - - def __lt__(self, other: os.PathLike) -> bool: - """Override the less than operator to compare the asset with other paths.""" - return self._path < PurePosixPath(str(other)) - - def __gt__(self, other: os.PathLike) -> bool: - """Override the greater than operator to compare the asset with other paths.""" - return self._path > PurePosixPath(str(other)) - - def __le__(self, other: os.PathLike) -> bool: - """Override the less than or equal operator to compare the asset with other paths.""" - return self._path <= PurePosixPath(str(other)) - - def __ge__(self, other: os.PathLike) -> bool: - """Override the greater than or equal operator to compare the asset with other paths.""" - return self._path >= PurePosixPath(str(other)) - - def __eq__(self, other: object) -> bool: - """Override the equal operator to compare the asset with other paths.""" - return self._path == PurePosixPath(str(other)) - - def __ne__(self, other: object) -> bool: - """Override the not equal operator to compare the asset with other paths.""" - return self._path != PurePosixPath(str(other)) - - def __idiv__(self, other: os.PathLike) -> Asset: - """Override the in-place division operator to join the asset with other paths.""" - return Asset(self._path / str(other)) - - def __fspath__(self): - """Implement the os.Pathlike interface.""" - return self.as_posix() - - def __hash__(self): - """make the Asset object hashable.""" - return hash(self.as_posix()) - - def __getattr__(self, name): - """Return the attribute of the path object.""" - return getattr(self, name) - - @classmethod - def home(cls) -> Asset: - """Return the root asset folder of the used cloud project. - - Returns: - The root asset folder. - - Examples: - .. code-block:: python - - ee.Asset.home() - """ - return cls(f"projects/{ee.data._cloud_api_user_project}/assets/") - - def as_posix(self) -> str: - """Return the asset id as a posix path. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.as_posix() - - # equivalent to - str(asset) - """ - return self._path.as_posix() - - def as_uri(self) -> str: - """Return the asset id as a uri. - - The uri can be directly copy/pasted to your browser to see the asset in the GEE code editor. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.as_uri() - """ - return f"https://code.earthengine.google.com/?asset={self.as_posix()}" - - def is_absolute(self, raised: bool = False) -> bool: - """Return True if the asset is absolute. - - An absolute asset path starts with "projects" and contains "assets" at the 3rd position. - We don't check if the project name exist in this method, simply the sctructure of the path. - - Args: - raised: If True, raise an exception if the asset is not absolute. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.is_absolute() - """ - # we decided not to enforce the length of the parts to still be able to use the - # relative_to method of the Path class. Consequence is tis little trick in case - # the asset is not absolute at all. - parts = dict(enumerate(self.parts)) - if parts.get(0) == "projects" and parts.get(2) == "assets": - return True - else: - if raised is True: - raise ValueError(f"Asset {self.as_posix()} is not absolute.") - else: - return False - - def is_user_project(self, raised: bool = False) -> bool: - """Check if the current asset is in the same project as the user. - - Args: - raised: If True, raise an exception if the asset is not in the same project. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.is_user_project() - """ - if self.is_relative_to(self.home()._path): - return True - else: - if raised is True: - user_project = ee.data._cloud_api_user_project - msg = f"Asset {self.as_posix()} is not in the same project as the user ({user_project})" - raise ValueError(msg) - else: - return False - - def expanduser(self) -> Asset: - """Return a new path with expanded ~ constructs. - - If one don't want to write the path with the complete project name, the method will build it for you. - - Examples: - .. code-block:: python - - asset = ee.Asset("~/assets/folder/image") - asset.expanduser() - """ - return Asset(self.as_posix().replace("~", self.home().as_posix(), 1)) - - def exists(self, raised: bool = False) -> bool: - """Return True if the asset exists and/or the user has access to it. - - Args: - raised: If True, raise an exception if the asset does not exist. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.exists() - """ - try: - ee.data.getAsset(self.as_posix()) - return True - except ee.EEException: - if raised is True: - raise ValueError(f"Asset {self.as_posix()} does not exist.") - else: - return False - - @property - def parts(self): - """Return the asset parts of the path. - - We will show all the parts from the root to the asset name. - Remember that projects/user/assets is not part of the asset name but is part of the path. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.parts - """ - return self._path.parts - - @property - def parent(self): - """Return the direct parent directory. - - It can go further up than the root folder if the asset is not absolute. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.parent - """ - return Asset(self._path.parent) - - @property - def parents(self): - """Return the parent directories from the root folder. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.parents - """ - # we remove the files that are not assets but are parsed by parents method - parents = self._path.parents - patterns = [r"^\.$", "^projects$", r"^projects/[^/]+$", r"^projects/[^/]+/assets$"] - return [Asset(a) for a in parents if not any(re.match(p, str(a)) for p in patterns)] - - @property - def name(self): - """Return the asset name. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.name - """ - return self._path.name - - @property - def st_size(self): - """Return the byte size of the file. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.st_size - """ - # sanity checks - self.exists(raised=True) - if self.is_folder(): - raise ValueError(f"Asset {self.as_posix()} is a folder.") - - return int(ee.data.getAsset(self.as_posix())["sizeBytes"]) - - def is_relative_to(self, other: os.PathLike) -> bool: - """Return True if the asset is relative to another asset. - - Args: - other: The other asset to compare with. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.is_relative_to("projects/ee-geetools/assets") - """ - return self._path.is_relative_to(PurePosixPath(str(other))) - - def joinpath(self, *args) -> Asset: - """Join the asset with other paths. - - Args: - *args: The other paths to join with the asset. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.joinpath("other", "path") - """ - return Asset(self._path.joinpath(*args)) - - def match(self, *patterns) -> bool: - """Return True if the asset matches the patterns. - - patterns: The patterns to match with the asset name. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.match("**/image") - """ - return self._path.match(*patterns) - - def with_name(self, name: str) -> Asset: - """Return the asset with the given name. - - Args: - name: The new name for the asset. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.with_name("new_image") - """ - return Asset(self._path.with_name(name)) - - def is_image(self, raised: bool = False) -> bool: - """Return ``True`` if the asset is an image. - - Args: - raised: If True, raise an exception if the asset is not an image. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.is_image() - """ - return self.is_type("IMAGE", raised) - - def is_image_collection(self, raised: bool = False) -> bool: - """Return ``True`` if the asset is an image collection. - - Args: - raised: If True, raise an exception if the asset is not an image collection. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image_collection") - asset.is_image_collection() - """ - return self.is_type("IMAGE_COLLECTION", raised) - - def is_feature_collection(self, raised: bool = False) -> bool: - """Return ``True`` if the asset is a feature collection. - - Args: - raised: If True, raise an exception if the asset is not a feature collection. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/feature_collection") - asset.is_feature_collection() - """ - return self.is_type("FEATURE_COLLECTION", raised) or self.is_type("TABLE", raised) - - def is_folder(self, raised: bool = False) -> bool: - """Return ``True`` if the asset is a folder. - - Args: - raised: If True, raise an exception if the asset is not a folder. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.is_folder() - """ - return self.is_type("FOLDER", raised) - - @property - def type(self) -> str: - """Return the asset type. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.type - """ - self.exists(raised=True) - return ee.data.getAsset(self.as_posix())["type"] - - def is_project(self, raised: bool = False) -> bool: - """Return ``True`` if the asset is a project. - - As project path are not assets, we cannot check their existence. We only check the path structure. - - Args: - raised: If True, raise an exception if the asset is not a project. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets") - asset.is_project() - """ - if self.is_absolute() and len(self.parts) == 3: - return True - else: - if raised is True: - raise ValueError(f"Asset {self.as_posix()} is not a project.") - else: - return False - - def is_type(self, asset_type: str, raised=False) -> bool: - """Return ``True`` if the asset is of the specified type. - - Args: - asset_type: The asset type to check for. - raised: If True, raise an exception if the asset is not corresponding to the type. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.is_type("IMAGE") - """ - self.exists(raised=True) - if self.type == asset_type: - return True - else: - if raised is True: - raise ValueError(f"Asset {self.as_posix()} is not a {asset_type}.") - else: - return False - - def iterdir(self, recursive: bool = False) -> list: - """Get the list of children of a container. - - Note: - A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. - - Args: - recursive: If True, get all the children recursively. Defaults to False. - - See Also: - - :docstring:`ee.Asset.glob` - - :docstring:`ee.Asset.rglob` - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.iterdir(recursive=True) - """ - # sanity check on variables - if not (self.is_project() or self.is_folder() or self.is_image_collection()): - raise ValueError( - f"Asset {self.as_posix()} is not a container and cannot contain other assets." - ) - - # no need for recursion if recursive is false we directly return the result of th API call - if recursive is False: - asset_ids = ee.data.listAssets({"parent": self.as_posix()})["assets"] - return [Asset(asset["id"]) for asset in asset_ids] - - # recursive function to get all the assets - def _recursive_get(folder, asset_list): - for asset in ee.data.listAssets({"parent": str(folder)})["assets"]: - asset_list.append(Asset(asset["id"])) - if asset["type"] in ["FOLDER", "IMAGE_COLLECTION"] and recursive is True: - asset_list = _recursive_get(asset["id"], asset_list) - return asset_list - - return _recursive_get(self, []) - - def mkdir(self, parents=False, exist_ok=False, image_collection: bool = False) -> Asset: - """Create a container asset from the Asset path. - - Note: - A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. - - Args: - parents: If True, create all the parents of the folder. Defaults to False. - exist_ok: If True, do not raise an error if the folder already exists. Defaults to False. - image_collection: If True, create an image collection asset. Otherwise create a folder asset. Defaults to False. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.mkdir(parents=True, exist_ok=True) - """ - # check if the root is the same as home (only place where we can write to) - self.is_absolute(raised=True) - - # if the complete one is in the list and exist_ok is True remove it from the list and - # proceed else raise an error - if self.exists() and exist_ok is False: - raise ValueError(f"Asset {self.as_posix()} already exists.") - - # list the non-existing parents of the folder to create - to_be_created = [p for p in self.parents if not p.exists()] - - # if parents is True, create all the parts that are in the list - # else raise an error with the 1st parent name - if len(to_be_created) > 0 and parents is False: - raise ValueError(f'Parent Asset "{to_be_created[-1]}" does not exist.') - - # 2 option either there is 1 single element in the list or all the parents are included - # we need to walk it in reversed to make sure the parents are build first. - for p in reversed(to_be_created): - ee.data.createFolder(p.as_posix()) - - # now that all the parents are there, we can create the requested container - if not self.exists(): - asset_type = "IMAGE_COLLECTION" if image_collection is True else "FOLDER" - ee.data.createAsset({"type": asset_type}, self.as_posix()) - - return self - - @property - def owner(self): - """Return the asset owner (project name). - - This method is only parsing the asset path and is not checking asset existence. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.owner - """ - self.is_absolute(raised=True) - return self.parts[1] - - def move(self, new_asset: Asset, overwrite: bool = False) -> Asset: - """Move the asset to a target destination. - - Move this asset (any type) to the given target, and return a new ``Asset`` instance - pointing to target. If target exists and overwrite is False the method will raise an - error. Else it will silently delete the existing file. If the asset is a container the whole - content will be moved as well. The initial content is removed after the move. - - Args: - new_asset: The destination asset. - overwrite: If True, overwrite the destination asset if it exists. Defaults to False. - - Returns: - The new asset instance. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - new_asset = ee.Asset("projects/ee-geetools/assets/folder/new_image") - asset.move(new_asset, overwrite=False) - """ - # copy the assets - self.copy(new_asset, overwrite=overwrite) - - # delete the original - self.delete(recursive=True, dry_run=False) - - return new_asset - - def delete(self, recursive: bool = False, dry_run: bool | None = None) -> list: - """Remove the asset. - - This method will delete an asset (any type) asset and all its potential children. by default it is not recursive and will raise an error if the container is not empty. - By setting the recursive argument to True, the method will delete all the children and the container asset (including potential subfolders). - To avoid deleting important assets by accident the method is set to dry_run by default. - - Note: - A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. - - Args: - recursive: If True, delete all the children and the container asset. Defaults to False. - dry_run: If True, do not delete the asset simply pass them to the output list. Defaults to True. - - Returns: - The list of deleted assets. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.delete(recursive=True) - """ - # init if it should be a dry-run or not - # if we run a recursive rmdir the dry_run is set to True to avoid deleting too many things by accident - # if we run a non-recursive rmdir the dry_run is set to False to delete the folder only - dry_run = dry_run if dry_run is not None else recursive - - # define a delete function to change the behaviour of the method depending of the mode - # in dry mode, the function only store the assets to be destroyed as a dictionary. - # in non dry mode, the function store the asset names in a dictionary AND delete them. - output = [] - - def delete(asset): - output.append(str(asset)) - dry_run is True or ee.data.deleteAsset(str(asset)) - - is_container = self.is_folder() or self.is_image_collection() - if recursive is True and is_container: - - # get all the assets - asset_list = self.iterdir(recursive=True) - - # split the files by nesting levels - # we will need to delete the more nested files first - assets_ordered: dict = {} - for asset in asset_list: - lvl = len(asset.parts) - assets_ordered.setdefault(lvl, []) - assets_ordered[lvl].append(asset) - - # delete all items starting from the more nested ones - assets_ordered = dict(sorted(assets_ordered.items(), reverse=True)) - for lvl in assets_ordered: - [delete(asset) for asset in assets_ordered[lvl]] - - # delete the initial folder/asset - delete(self) - - return output - - # aliases - def unlink(self) -> list: - """``delete`` alias for singular assets.""" - # sanity check on variables - if self.is_project() or self.is_folder() or self.is_image_collection(): - raise ValueError(f"Asset {self.as_posix()} is a container, use rmdir instead.") - self.exists(raised=True) - return self.delete() - - def rmdir(self, recursive: bool = False, dry_run: bool | None = None) -> list: - """``delete`` alias for containers.""" - if not (self.is_project() or self.is_folder() or self.is_image_collection()): - raise ValueError(f"Asset {self.as_posix()} is not a container, use unlink instead.") - self.exists(raised=True) - return self.delete(recursive, dry_run) - - def copy(self, new_asset: Asset, overwrite: bool = False) -> Asset: - """Copy the asset to a target destination. - - Copy this asset (any type) to the given target, and return a new ``Asset`` instance - pointing to target. If target exists and overwrite is False the method will raise an - error. Else it will silently delete the existing asset. If the asset is a container the whole - content will be moved as well. - - Args: - new_asset: The destination asset. - overwrite: If True, overwrite the destination asset if it exists. Defaults to False. - - Returns: - The new asset instance. - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - new_asset = ee.Asset("projects/ee-geetools/assets/folder/new_image") - asset.copy(new_asset, overwrite=False) - """ - # exit if the destination asset exist and overwrite is False - if new_asset.exists() and overwrite is False: - raise ValueError(f"Asset {new_asset.as_posix()} already exists.") - - # make all the parents of the target asset if necessary - if len(new_asset.parents) != 0: - new_asset.parent.mkdir(parents=True, exist_ok=True) - - # copy the asset to the new destination. If the asset is a container, we need to move all its - # content recursively to the new destination we recursively call this method on each - # children of the asset if it's a folder it will loop again. - if self.is_folder() or self.is_image_collection(): - new_asset.mkdir(True, True, self.is_image_collection()) - - # if the asset is an image collection we need to copy the properties of the collection - if self.is_image_collection(): - original_dict = ee.data.getAsset(self.as_posix()) - props = original_dict["properties"] - if "startTime" in original_dict: - props["system:time_start"] = original_dict["startTime"] - if "endTime" in original_dict: - props["system:time_end"] = original_dict["endTime"] - new_asset.setProperties(**props) - - # copy the children objects - for asset in self.iterdir(): - loc_asset = new_asset / asset._path.relative_to(self._path) - asset.copy(loc_asset, overwrite=overwrite) - else: - ee.data.copyAsset(self.as_posix(), new_asset.as_posix(), allowOverwrite=True) - - return new_asset - - def glob(self, pattern: str) -> list: - """Return a list of assets matching the pattern. - - Args: - pattern: The pattern to match with the asset name. - - See Also: - - :docstring:`ee.Asset.iterdir` - - :docstring:`ee.Asset.rglob` - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.glob("image_*") - """ - return [a for a in self.iterdir(recursive=False) if a.match(pattern)] - - def rglob(self, pattern: str) -> list: - """Return a list of assets matching the pattern recursively. - - Args: - pattern: The pattern to match with the asset name. - - See Also: - - :docstring:`ee.Asset.glob` - - :docstring:`ee.Asset.iterdir` - - Examples: - .. code-block:: python - - asset = ee.Asset("projects/ee-geetools/assets/folder") - asset.rglob("image_*") - """ - return [a for a in self.iterdir(recursive=True) if a.match(pattern)] - - def as_description(self) -> str: - """Transform the name of the Asset in to a description compatible string for a Task. - - Returns: - The formatted description. - """ - return format_description(self.name) - - def setProperties(self, **kwargs) -> ee.Asset: - """Set properties of the asset. - - Args: - **kwargs: The properties to set key, value pairs. To name normal properties simply use the name as key. For system properties, prefix it with "system:". Note that only the "time_start" and "time_end" are editable. - - Examples: - .. code-block:: python - - # dates need to be set in milliseconds - start = dt.datetime(2021, 1, 1).timestamp() * 1000 - asset = ee.Asset("projects/ee-geetools/assets/folder/image") - asset.setProperties(**{"description": "new_description", "system:time_start": start}) - """ - # We need to retrieve the system properties. - # They are named as in the server API and renamed inside this function. - # The method raise error when we try to set something else that the authorized one. - legit_keys = {"system:time_start": "start_time", "system:time_end": "end_time"} - system = {k: v for k, v in kwargs.items() if k.startswith("system:")} - for key in system.keys(): - if key not in legit_keys: - raise ValueError(f"Property {key} is not a valid system property.") - system = {legit_keys[k]: v for k, v in system.items()} - - # Specifying an update mask of 'properties' results in full replacement, - # which isn't what we want. Instead, we name each property that we'll be - # updating. - props = {k: v for k, v in kwargs.items() if not k.startswith("system:")} - update_mask = [f"properties.{k}" for k in props] - - # we can now update the asset by setting both system and asset properties - ee.data.updateAsset( - asset_id=self.as_posix(), - asset={**system, "properties": props}, - update_mask=list(system.keys()) + update_mask, - ) - - return self diff --git a/geetools/ee_authenticate.py b/geetools/ee_authenticate.py deleted file mode 100644 index 6ee59656..00000000 --- a/geetools/ee_authenticate.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Toolbox for the ``ee.Authenticate`` function.""" -from __future__ import annotations - -from contextlib import suppress -from pathlib import Path -from shutil import move -from tempfile import TemporaryDirectory - -import ee - -from .accessors import register_function_accessor - - -@register_function_accessor(ee.Authenticate, "geetools") -class AuthenticateAccessor: - """Create an accessor for the ``ee.Authenticate`` function.""" - - @staticmethod - def new_user(name: str = "", credential_pathname: str = "") -> None: - """Authenticate the user and save the credentials in a specific folder. - - Equivalent to ee.Authenticate but where the registered user will not be the default one (the one you get when running :py:meth:`ee.Initialize`) - - Args: - name: The name of the user. If not set, it will reauthenticate default. - credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path - - Example: - .. code-block:: python - - import ee - import geetools - - # cannot be displayed in the documentation as the creation - # of a new user requires user interaction - ee.Authenticate.geetools.new_user("secondary") - ee.Initialize.geetools.from_user("secondary") - ee.Number(1).getInfo() - """ - name = f"credentials{name}" - credential_pathname = credential_pathname or ee.oauth.get_credentials_path() - credential_path = Path(credential_pathname).parent - - # the authenticate method will write the credentials in the default - # folder and with the default name. We have to save the existing one in tmp, - # and then exchange places between the newly created and the existing one - default = Path(ee.oauth.get_credentials_path()) - - with TemporaryDirectory() as dir: - with suppress(FileNotFoundError): - move(default, Path(dir) / default.name) - ee.Authenticate() - move(default, credential_path / name) - with suppress(FileNotFoundError): - move(Path(dir) / default.name, default) - - @staticmethod - def delete_user(name: str = "", credential_pathname: str = "") -> None: - """Delete a user credential file. - - Args: - name: The name of the user. If not set, it will delete the default user - credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path - - Example: - .. code-block:: python - - import ee - import geetools - - # cannot be displayed in the documentation as the creation - # of a new user requires user interaction - ee.Authenticate.geetools.new_user("secondary") - ee.Authenticate.geetools.delete_user("secondary") - """ - name = f"credentials{name}" - credential_pathname = credential_pathname or ee.oauth.get_credentials_path() - credential_path = Path(credential_pathname).parent - with suppress(FileNotFoundError): - (credential_path / name).unlink() - - @staticmethod - def list_user(credential_pathname: str = "") -> list: - """return all the available users in the set folder. - - To reach "default" simply omit the ``name`` parameter in the User methods - - Args: - credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path - - Returns: - A list of strings with the names of the users - - Example: - .. code-block:: python - - import ee - import geetools - - ee.Authenticate.geetools.list_user() - """ - credential_pathname = credential_pathname or ee.oauth.get_credentials_path() - credential_path = Path(credential_pathname).parent - files = [f for f in credential_path.glob("credentials*") if f.is_file()] - return [f.name.replace("credentials", "") or "default" for f in files] - - @staticmethod - def rename_user(new: str, old: str = "", credential_pathname: str = "") -> None: - """Rename a user without changing the credentials. - - Args: - new: The new name of the user - old: The name of the user to rename - credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path - - Example: - .. code-block:: python - - import ee - import geetools - - ee.Authenticate.geetools.new_user("old") - ee.Authenticate.geetools.rename_user("new", "old") - ee.Initialize.geetools.from_user("new") - ee.Number(1).getInfo() - """ - old = f"credentials{old}" - new = f"credentials{new}" - credential_pathname = credential_pathname or ee.oauth.get_credentials_path() - credential_path = Path(credential_pathname).parent - with suppress(FileNotFoundError): - (credential_path / old).rename(credential_path / new) diff --git a/geetools/ee_computed_object.py b/geetools/ee_computed_object.py deleted file mode 100644 index 94a02f64..00000000 --- a/geetools/ee_computed_object.py +++ /dev/null @@ -1,139 +0,0 @@ -"""Extra tools for the ``ee.ComputedObject`` class.""" -from __future__ import annotations - -import json -import os -from pathlib import Path - -import ee - -from .accessors import _register_extention - - -# -- types management ---------------------------------------------------------- -@_register_extention(ee.ComputedObject) -def isInstance(self, klass: type) -> ee.Number: - """Return 1 if the element is the passed type or 0 if not. - - Parameters: - klass: The class to check the instance of. - - Returns: - ``1`` if the element is the passed type or ``0`` if not. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - # test if a String is a ee.String - s = ee.String("foo") - isString = ee.String("foo").isInstance(ee.String) - print(f"{s.getInfo()} is a earthengine string: {isString.getInfo()}") - - # test if a Number is a ee.String - n = ee.Number(1) - isString = ee.Number(1).isInstance(ee.String) - print(f"{n.getInfo()} is a earthengine string: {isString.getInfo()}") - """ - return ee.Algorithms.ObjectType(self).compareTo(klass.__name__).eq(0) - - -# -- .gee files ---------------------------------------------------------------- -@_register_extention(ee.ComputedObject) # type: ignore -def save(self, path: os.PathLike) -> Path: - """Save a ``ComputedObject`` to a .gee file. - - The file contains the JSON representation of the object. it still need to be computed via ``getInfo()`` to be used. - - Parameters: - path: The path to save the object to. - - Returns: - The path to the saved file. - - Examples: - .. jupyter-execute:: - - from tempfile import TemporaryDirectory - from pathlib import Path - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - img = ee.Image("COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM") - - with TemporaryDirectory() as tmp: - file = Path(tmp) / "test.gee" - img.save(file) - print(file.read_text()) - """ - path = Path(path).with_suffix(".gee") - path.write_text(json.dumps(ee.serializer.encode(self))) - return path - - -@staticmethod # type: ignore -@_register_extention(ee.ComputedObject) # type: ignore -def open(path: os.PathLike) -> ee.ComputedObject: - """Open a .gee file as a ComputedObject. - - Parameters: - path: The path to the file to open. - - Returns: - The ComputedObject instance. - - Examples: - .. jupyter-execute:: - - from tempfile import TemporaryDirectory - from pathlib import Path - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - img = ee.Image("COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM") - - with TemporaryDirectory() as tmp: - file = Path(tmp) / "test.gee" - img.save(file) - obj = ee.Image.open(file) - print(obj.getInfo()) - """ - if (path := Path(path)).suffix != ".gee": - raise ValueError("File must be a .gee file") - - return ee.deserializer.decode(json.loads(path.read_text())) - - -# placeholder classes for the isInstance method -------------------------------- -@_register_extention(ee) -class Float: - """Placeholder Float class to be used in the isInstance method.""" - - def __init__(self): - """Avoid initializing the class.""" - raise NotImplementedError("This class is a placeholder, it should not be initialized") - - def __name__(self): - """Return the class name.""" - return "Float" - - -@_register_extention(ee) -class Integer: - """Placeholder Integer class to be used in the isInstance method.""" - - def __init__(self): - """Avoid initializing the class.""" - raise NotImplementedError("This class is a placeholder, it should not be initialized") - - def __name__(self): - """Return the class name.""" - return "Integer" diff --git a/geetools/ee_date.py b/geetools/ee_date.py deleted file mode 100644 index 45a392f8..00000000 --- a/geetools/ee_date.py +++ /dev/null @@ -1,172 +0,0 @@ -"""Extra methods for the ``ee.Date`` class.""" -from __future__ import annotations - -from datetime import datetime - -import ee - -from .accessors import register_class_accessor - -EE_EPOCH = datetime(1970, 1, 1, 0, 0, 0) - - -@register_class_accessor(ee.Date, "geetools") -class DateAccessor: - """Toolbox for the ``ee.Date`` class.""" - - def __init__(self, obj: ee.Date): - """Initialize the Date class.""" - self._obj = obj - - @classmethod - def fromEpoch(cls, number: int, unit: str = "day") -> ee.Date: - """Set an the number of units since epoch (1970-01-01). - - Parameters: - number: The number of units since the epoch. - unit: The unit to return the number of. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. - - Returns: - The date as a ``ee.Date`` object. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Date.geetools.fromEpoch(49, 'year') - d.format("YYYY-MM-DD").getInfo() - """ - cls.check_unit(unit) - return ee.Date(EE_EPOCH.isoformat()).advance(number, unit) - - @classmethod - def fromDOY(cls, doy: int, year: int) -> ee.Date: - """Create a date from a day of year and a year. - - Parameters: - doy: The day of year. - year: The year. - - Returns: - The date as a ``ee.Date`` object. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Date.geetools.fromDOY(1, 2020) - d.format("YYYY-MM-DD").getInfo() - """ - d, y = ee.Number(doy).toInt(), ee.Number(year).toInt() - return ee.Date.fromYMD(y, 1, 1).advance(d.subtract(1), "day") - - @classmethod - def now(cls) -> ee.Date: - """Create a date on current date. - - Returns: - The current date. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Date.geetools.now() - d.format("YYYY-MM-dd").getInfo() - """ - return ee.Date(datetime.now().isoformat()) - - def to_datetime(self) -> datetime: - """Convert a ``ee.Date`` to a ``datetime.datetime``. - - Returns: - The ``datetime.datetime`` representation of the ``ee.Date``. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Date('2020-01-01').geetools.to_datetime() - d.strftime('%Y-%m-%d') - - """ - return datetime.fromtimestamp(self._obj.millis().getInfo() / 1000.0) - - def getUnitSinceEpoch(self, unit: str = "day") -> ee.Number: - """Get the number of units since epoch (1970-01-01). - - Parameters: - unit: The unit to return the number of. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. - - Returns: - The number of units since the epoch. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Date('2020-01-01').geetools.getUnitSinceEpoch('year') - d.getInfo() - """ - self.check_unit(unit) - return self._obj.difference(EE_EPOCH, unit).toInt() - - def isLeap(self) -> ee.Number: - """Check if the year of the date is a leap year. - - Returns: - ``1`` if the year is a leap year, ``0`` otherwise. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - isLeap = ee.Date('2020-01-01').geetools.isLeap() - isLeap.getInfo() - """ - year = self._obj.get("year") - divisibleBy4 = year.mod(4).eq(0) - divisibleBy100 = year.mod(100).eq(0) - divisibleBy400 = year.mod(400).eq(0) - - # d400 or (d4 and not d100) - isLeap = divisibleBy400.Or(divisibleBy4.And(divisibleBy100.Not())) - - return isLeap.toInt() - - @staticmethod - def check_unit(unit: str) -> None: - """Check if the provided value is a valid time unit. - - Parameters: - unit: The unit to check. - - Raises: - ValueError: If the unit is not valid. - """ - if unit not in (units := ["second", "minute", "hour", "day", "month", "year"]): - raise ValueError(f"unit must be one of: {','.join(units)}") diff --git a/geetools/ee_date_range.py b/geetools/ee_date_range.py deleted file mode 100644 index 18bdd40a..00000000 --- a/geetools/ee_date_range.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Extra tools for the ``ee.DateRange`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.DateRange, "geetools") -class DateRangeAccessor: - """Toolbox for the ``ee.DateRange`` class.""" - - def __init__(self, obj: ee.DateRange): - """Initialize the DateRange class.""" - self._obj = obj - - # -- date range operations ------------------------------------------------- - def split(self, interval: int | ee.Number, unit: str = "day") -> ee.List: - """Convert a ``ee.DateRange`` to a list of ``ee.DateRange``. - - The DateRange will be split in multiple DateRanges of the specified interval and Unit. - For example "1", "day". if the end date is not included the last dateRange length will be adapted. - - Parameters: - interval: The interval to split the DateRange - unit: The unit to split the DateRange. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. - - Returns: - The list of DateRanges - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - dateList = ee.DateRange('2020-01-01', '2020-01-31').geetools.split(1, 'day') - dateList.getInfo() - """ - self.check_unit(unit) - interval = ee.Number(interval).toInt().multiply(self.unitMillis(unit)) - start, end = self._obj.start().millis(), self._obj.end().millis() - - timestampList = ee.List.sequence(start, end, interval) - timestampList = timestampList.add(ee.Number(end).toFloat()).distinct() - indexList = ee.List.sequence(0, timestampList.size().subtract(2)) - - return indexList.map( - lambda i: ee.DateRange(timestampList.get(i), timestampList.get(ee.Number(i).add(1))) - ) - - # -- utils ----------------------------------------------------------------- - @staticmethod - def check_unit(unit: str) -> None: - """Check if the unit is valid.""" - if unit not in (units := ["second", "minute", "hour", "day", "month", "year"]): - raise ValueError(f"unit must be one of: {','.join(units)}") - - @staticmethod - def unitMillis(unit: str) -> ee.Number: - """Get the milliseconds of a unit. - - Parameters: - unit: The unit to get the milliseconds. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. - - Returns: - The number of milliseconds in the unit - """ - millis = { - "second": 1000, - "minute": 1000 * 60, - "hour": 1000 * 60 * 60, - "day": 1000 * 60 * 60 * 24, - "month": 1000 * 60 * 60 * 24 * 30, - "year": 1000 * 60 * 60 * 24 * 365, - } - return ee.Number(millis[unit]) diff --git a/geetools/ee_dictionary.py b/geetools/ee_dictionary.py deleted file mode 100644 index 49320bd2..00000000 --- a/geetools/ee_dictionary.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Extra methods for the ``ee.Dictionary`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Dictionary, "geetools") -class DictionaryAccessor: - """Toolbox for the ``ee.Dictionary`` class.""" - - def __init__(self, obj: ee.Dictionary): - """Initialize the Dictionary class.""" - self._obj = obj - - # -- alternative constructor ----------------------------------------------- - def fromPairs(self, list: list | ee.List) -> ee.Dictionary: - """Create a dictionary from a list of [[key, value], ...]] pairs. - - Parameters: - list: A list of pairs (key, value). - - Returns: - A dictionary using the pairs. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Dictionary.geetools.fromPairs([["foo", 1], ["bar", 2]]) - d.getInfo() - """ - list = ee.List(list) - keys = list.map(lambda pair: ee.List(pair).get(0)) - values = list.map(lambda pair: ee.List(pair).get(1)) - return ee.Dictionary.fromLists(keys, values) - - # -- dictionary operations ------------------------------------------------- - def sort(self) -> ee.Dictionary: - """Sort the dictionary by keys in ascending order. - - Returns: - The sorted dictionary. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.sort() - d.getInfo() - """ - orderededKeys = self._obj.keys().sort() - values = orderededKeys.map(lambda key: self._obj.get(key)) - return ee.Dictionary.fromLists(orderededKeys, values) - - def getMany(self, list: list | ee.List) -> ee.List: - """Extract values from a list of keys. - - Parameters: - list: A list of keys. - - Returns: - A list of values. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - d = ee.Dictionary({"foo": 1, "bar": 2, "baz": 3}) - d = d.geetools.getMany(["foo", "bar"]) - d.getInfo() - """ - return ee.List(list).map(lambda key: self._obj.get(key)) diff --git a/geetools/ee_export.py b/geetools/ee_export.py deleted file mode 100644 index 206dabab..00000000 --- a/geetools/ee_export.py +++ /dev/null @@ -1,220 +0,0 @@ -"""Toolbox for the ``ee.Export`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor -from .utils import format_asset_id, format_description - - -@register_class_accessor(ee.batch.Export, "geetools") -class ExportAccessor: - """Toolbox for the ``ee.batch.Export`` class.""" - - def __init__(self, obj: ee.batch.Export): - """Initialize the ExportAccessor class.""" - self._obj = obj - - # this pattern is not pythonic but I mimic the content of the Export class from GEE - # I know as a namespace it should be a module. - class imagecollection: - """A static class with methods to start imagecollection export tasks.""" - - def __init__(self): - """Forbids class instantiation.""" - raise AssertionError("This class cannot be instantiated.") - - @staticmethod - def toAsset( - imagecollection: ee.ImageCollection, - index_property: str = "system:id", - description: str = "", - assetId: str = "", - **kwargs, - ) -> list[ee.batch.Task]: - """Creates a task to export an EE ImageCollection to an EE Asset. - - The method will create the imagecollection asset beforehand and launching the task will - Populate the said image collection with the exported images. Each image in the Collection - Will be named using the index_property value of the image. - If no asset Id is provided the asset will be created at the root of the current project assets. - - Parameters: - imagecollection: The image collection to export. - index_property: The property of the image to use as name. Default is "system:id". - description: The description of the task. - assetId: The asset id where to export the image collection. - **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toAsset - - Returns: - The task created. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - # create a test image collection - collection = ee.ImageCollection("COPERNICUS/S2").limit(5) - - # export the collection - tasks = geetools.batch.Export.imagecollection.toAsset(collection, "system:index", "test export") - ``` - """ - # sanity check on parameters - # renaming them for mypy type reassignment and compactness - desc = description if description else ee.Asset(assetId).name - aid = ee.Asset(assetId) if assetId else ee.Asset("~").expanduser() / description - - # create the ImageCollection asset - ee.data.createAsset({"type": "IMAGE_COLLECTION"}, aid.as_posix()) - - # loop over the collection and export each image - nb_images = imagecollection.size().getInfo() - imageList = imagecollection.toList(nb_images) - task_list = [] - for i in range(nb_images): - # extract image information - locImage = ee.Image(imageList.get(i)) - loc_id = locImage.get(index_property).getInfo() - - # override the parameters related to the image itself - kwargs["image"] = locImage - kwargs["description"] = format_description(f"{desc}_{loc_id}") - kwargs["assetId"] = (aid / format_asset_id(loc_id)).as_posix() - - # create the task - task_list.append(ee.batch.Export.image.toAsset(**kwargs)) - - return task_list - - @staticmethod - def toDrive( - imagecollection: ee.ImageCollection, - index_property: str = "system:id", - description: str = "", - folder: str = "", - **kwargs, - ) -> list[ee.batch.Task]: - """Creates a list of tasks to export an EE ImageCollection to Google Drive. - - The method will create a folder in Google Drive with the description value and populate - it with the exported images. Each image in the Collection Will be named using the - index_property value of the image. If no Folder is provided the folder will be created at the root - of the current drive and use the description name. - - Parameters: - imagecollection: The image collection to export. - index_property: The property of the image to use as name. Default is "system:id". - description: The description of the task. - folder: The folder id where to export the image collection. It will be stored at the root of the drive. - **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toDrive - - Returns: - The list of created tasks - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - # create a test image collection - collection = ee.ImageCollection("COPERNICUS/S2").limit(5) - - # export the collection - tasks = geetools.batch.Export.imagecollection.toDrive(collection, "system:index", "test export") - """ - # sanity check on parameters - # renaming them for mypy type reassignment and compactness - desc = description if description else folder - fid = folder if folder else description - - # loop over the collection and export each image - nb_images = imagecollection.size().getInfo() - imageList = imagecollection.toList(nb_images) - task_list = [] - for i in range(nb_images): - # extract image information - locImage = ee.Image(imageList.get(i)) - loc_id = locImage.get(index_property).getInfo() - - # override the parameters related to the image itself - # the folder will be created by the first task - kwargs["image"] = locImage - kwargs["folder"] = format_asset_id(f"{fid}") - kwargs["description"] = format_description(f"{desc}_{loc_id}") - - # create the task - task_list.append(ee.batch.Export.image.toDrive(**kwargs)) - - return task_list - - @staticmethod - def toCloudStorage( - imagecollection: ee.ImageCollection, - index_property: str = "system:id", - description: str = "", - folder: str = "", - **kwargs, - ) -> list[ee.batch.Task]: - """Creates a list of tasks to export an EE ImageCollection to Google cloud. - - The method will create a folder in Google cloud bucket with the description value and populate - it with the exported images. Each image in the Collection Will be named using the - index_property value of the image. If no Folder is provided the folder will be created at the root - of the bucket and use the description name. - - Parameters: - imagecollection: The image collection to export. - index_property: The property of the image to use as name. Default is "system:id". - description: The description of the task. - folder: The folder id where to export the image collection. It will be stored at the root of the drive. - **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toCloudStorage - - Returns: - The list of created tasks - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - # create a test image collection - collection = ee.ImageCollection("COPERNICUS/S2").limit(5) - - # export the collection - tasks = geetools.batch.Export.imagecollection.toDrive(collection, "system:index", "test export") - """ - # sanity check on parameters - # renaming them for mypy type reassignment and compactness - desc = description if description else folder - fid = folder if folder else description - - # loop over the collection and export each image - nb_images = imagecollection.size().getInfo() - imageList = imagecollection.toList(nb_images) - task_list = [] - for i in range(nb_images): - # extract image information - locImage = ee.Image(imageList.get(i)) - loc_id = locImage.get(index_property).getInfo() - - # override the parameters related to the image itself - # the folder will be created by the first task - kwargs["image"] = locImage - kwargs["fileNamePrefix"] = format_asset_id(f"{fid}") + "/" - kwargs["description"] = format_description(f"{desc}_{loc_id}") - - # create the task - task_list.append(ee.batch.Export.image.toCloudStorage(**kwargs)) - - return task_list diff --git a/geetools/ee_feature.py b/geetools/ee_feature.py deleted file mode 100644 index aae47a12..00000000 --- a/geetools/ee_feature.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Toolbox for the ``ee.Feature`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Feature, "geetools") -class FeatureAccessor: - """Toolbox for the ``ee.Feature`` class.""" - - def __init__(self, obj: ee.Feature): - """Initialize the class.""" - self._obj = obj - - def toFeatureCollection(self) -> ee.FeatureCollection: - """Convert a :py:class:`ee.Feature` composed of a multiGeometry geometry into a :py:class:`ee.FeatureCollection`. - - Returns: - The FeatureCollection - - Example: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - geoms = ee.Geometry.MultiPoint([[0,0], [0,1]]) - feature = ee.Feature(geoms).set("foo", "bar") - fc = feature.geetools.toFeatureCollection() - fc.getInfo() - """ - geoms = self._obj.geometry().geometries() - fc = geoms.map(lambda g: self._obj.setGeometry(g)) - return ee.FeatureCollection(fc) - - def removeProperties(self, properties: list | ee.List) -> ee.Feature: - """Remove properties from a feature. - - Args: - properties : List of properties to remove - - Returns: - The feature without the properties - - Example: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - feature = ee.Feature(None).set("foo", "bar", "baz", "foo") - feature = feature.geetools.removeProperties(["foo"]) - feature.getInfo() - """ - properties = ee.List(properties) - proxy = ee.Feature(self._obj.geometry()) # drop properties - return proxy.copyProperties(self._obj, exclude=properties) diff --git a/geetools/ee_feature_collection.py b/geetools/ee_feature_collection.py deleted file mode 100644 index ead0dada..00000000 --- a/geetools/ee_feature_collection.py +++ /dev/null @@ -1,671 +0,0 @@ -"""Toolbox for the `ee.FeatureCollection` class.""" -from __future__ import annotations - -import ee -import geopandas as gpd -from matplotlib import pyplot as plt -from matplotlib.axes import Axes - -from .accessors import register_class_accessor -from .utils import plot_data - - -@register_class_accessor(ee.FeatureCollection, "geetools") -class FeatureCollectionAccessor: - """Toolbox for the `ee.FeatureCollection` class.""" - - def __init__(self, obj: ee.FeatureCollection): - """Initialize the FeatureCollection class.""" - self._obj = obj - - def toImage( - self, - color: str | ee.String | int | ee.Number = 0, - width: str | ee.String | int | ee.Number = "", - ) -> ee.Image: - """Paint the current FeatureCollection to an Image. - - It's a simple wrapper on Image.paint() method - - Args: - color: The pixel value to paint into every band of the input image, either as a number which will be used for all features, or the name of a numeric property to take from each feature in the collection. - width: Line width, either as a number which will be the line width for all geometries, or the name of a numeric property to take from each feature in the collection. If unspecified, the geometries will be filled instead of outlined. - - Returns: - The painted image - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - from matplotlib.colors import ListedColormap - - initialize_documentation() - - # extract the featureCollection of the Vatican from the FAO gaul dataset - vatican = ( - ee.FeatureCollection("FAO/GAUL/2015/level0") - .filter(ee.Filter.eq("ADM0_NAME", "Holy See")) - ) - - # transform the featureCollection into an image - img = vatican.geetools.toImage(color=1).rename("gaul") - - # Define a custom colormap ffor the raster representation - # it will only have 1 color: teal for the first value and white for everything else - cmap = ListedColormap(['teal', 'white']) - - # create the axes for the plots - fig, axes = plt.subplots(1, 2, figsize=(10, 5)) - - # customize the layout of the 2 plots - for ax in axes: - ax.set_xlabel("Longitude (°)") - ax.set_ylabel("Latitude (°)") - ax.set_xticks([]) - ax.set_yticks([]) - - # add the vector on the first plot - axes[0].set_title("Vector") - vatican.geetools.plot(ax=axes[0], color="teal", boundaries=True) - - # add the raster on the second plot - axes[1].set_title("Raster") - img.geetools.plot(region=vatican.bounds(), bands=["gaul"], ax=axes[1], cmap=cmap) - - fig.show() - """ - params = {"color": color} - width == "" or params.update(width=width) - return ee.Image().paint(self._obj, **params) - - def toDictionary( - self, keyColumn: str | ee.String = "system:index", selectors: list | ee.List = [] - ) -> ee.Dictionary: - """Convert to Dictionary. - - Parameters: - keyColumn: the column to use as keys. Must contain unique values, if not it will fail. - selectors: a list of properties to add in the output. If the list is empty all properties will be added. - - Returns: - a ee.Dictionary with values of keyColumn as keys and ee.Dictionary as values. The output will look like: - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - import json - - initialize_documentation() - - # Extracting the first 3 countries from the FAO GAUL dataset. - # and transform them into dictionary - countries = ( - ee.FeatureCollection("FAO/GAUL/2015/level0") - .select(["ADM0_NAME", "ADM0_CODE"]) - .limit(3) - .geetools.toDictionary() - ) - - print(json.dumps(countries.getInfo(), indent=2)) - """ - uniqueIds = self._obj.aggregate_array(keyColumn) - selectors = ee.List(selectors) if selectors else self._obj.first().propertyNames() - keyColumn = ee.String(keyColumn) - - features = self._obj.toList(self._obj.size()) - values = features.map(lambda feat: ee.Feature(feat).toDictionary(selectors)) - keys = uniqueIds.map(lambda uid: ee.String(ee.Algorithms.String(uid))) - return ee.Dictionary.fromLists(keys, values) - - def addId( - self, name: str | ee.String = "id", start: int | ee.Number = 1 - ) -> ee.FeatureCollection: - """Add a unique numeric identifier, starting from parameter ``start``. - - Args: - name: The name of the property to add. Defaults to "id". - start: The starting value of the id. Defaults to 1. - - Returns: - The parsed collection with a new id property - - Example: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - from matplotlib.colors import ListedColormap - - initialize_documentation() - - # create a featureCollection from the 3 first countries of the FAO GAUL dataset - # then add an id property to each feature and show them in the console - fc = ( - ee.FeatureCollection("FAO/GAUL/2015/level0") - .filter(ee.Filter.inList("ADM0_NAME", ["France", "Germany", "Italy"])) - .select(["ADM0_NAME", "ADM0_CODE"]) - .geetools.addId() - ) - - # create a figure to show the created featureCollection generated "id" property - fig, ax = plt.subplots(figsize=(10, 5)) - cmap = ListedColormap(["#3AA3FF", "#F3FF3B", "#FF433B"]) - fc.geetools.plot(ax=ax, property="id", cmap=cmap) - - fig.colorbar(ax.collections[0], label="id value", ticks=[1, 2, 3]) - ax.set_title("generated id of FAO countries") - ax.set_xlabel("Longitude (°)") - ax.set_ylabel("Latitude (°)") - ax.set_xticks([]) - ax.set_yticks([]) - - fig.show() - """ - start, name = ee.Number(start).toInt(), ee.String(name) - - indexes = ee.List(self._obj.aggregate_array("system:index")) - ids = ee.List.sequence(start, start.add(self._obj.size()).subtract(1)) - idByIndex = ee.Dictionary.fromLists(indexes, ids) - return self._obj.map(lambda f: f.set(name, idByIndex.get(f.get("system:index")))) - - def mergeGeometries(self, maxError: float | int | ee.number | None = None) -> ee.Geometry: - """Merge the geometries included in the features. - - Args: - maxError: The maximum amount of error tolerated when performing any necessary reprojection. - - Returns: - the dissolved geometry - - Example: - .. code-block:: python - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - # create a featurecollection containing 2 bounding boxes - fc = ee.FeatureCollection([ - ee.Geometry.BBox(-1, -1, 1, 1), - ee.Geometry.BBox(0, 0, 2, 2) - ]) - - # merge them into a single geometry - geometry = fc.geetools.mergeGeometries(maxError=.1) - - # print the geometry on a matplotlib graph - fig, ax = plt.subplots(figsize=(10, 5)) - c = ee.FeatureCollection(geometry).geetools.plot(boundaries=True, color="teal", ax=ax) - - fig.show() - """ - first = self._obj.first().geometry() - union = self._obj.iterate(lambda f, g: f.geometry().union(g, maxError=maxError), first) - return ee.Geometry(union).dissolve(maxError=maxError) - - def toPolygons(self) -> ee.FeatureCollection: - """Drop any geometry that is not a Polygon or a multipolygon. - - This method is made to avoid errors when performing zonal statistics and/or other surfaces operations. - These operations won't work on geometries that are Lines or points. The methods remove these geometry - types from GEometryCollections and rremove features that don't have any polygon geometry. - - Returns: - The parsed collection with only polygon/MultiPolygon geometries - - Example: - .. code-block:: python - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - point0 = ee.Geometry.Point([0,0], proj="EPSG:4326") - point1 = ee.Geometry.Point([0,1], proj="EPSG:4326") - poly0 = point0.buffer(1, proj="EPSG:4326") - poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") - line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") - multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") - geometryCol = ee.Algorithms.GeometryConstructors.MultiGeometry([multiPoly, poly0, poly1, point0, line], crs="EPSG:4326", geodesic=True, maxError=1) - - fc = ee.FeatureCollection([geometryCol]) - fc = fc.geetools.toPolygons() - - fig, ax = plt.subplots(figsize=(5, 10)) - fc.geetools.plot(boundaries=True, ax=ax) - """ - - def filterGeom(geom): - geom = ee.Geometry(geom) - return ee.Algorithms.If(geom.type().compareTo("Polygon"), None, geom) - - def removeNonPoly(feat): - filteredGeoms = feat.geometry().geometries().map(filterGeom, True) - proj = feat.geometry().projection() - return feat.setGeometry(ee.Geometry.MultiPolygon(filteredGeoms, proj)) - - return self._obj.map(removeNonPoly) - - def byProperties( - self, - featureId: str | ee.String = "system:index", - properties: list | ee.List = [], - labels: list = [], - ) -> ee.Dictionary: - """Get a dictionary with all feature values for each properties. - - This method is returning a dictionary with all the properties as keys and their values in each feaure as a list. - - .. code-block:: - - { - "property1": {"feature1": value1, "feature2": value2, ...}, - "property2": {"feature1": value1, "feature2": value2, ...}, - ... - } - - The output remain server side and can be used to create a client side plot. - - Args: - featureId: The property used to label features. Defaults to "system:index". - properties: A list of properties to get the values from. - labels: A list of names to replace properties names. Default to the properties names. - - Returns: - A dictionary with all the properties as keys and their values in each feaure as a list. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.byFeatures` - - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` - - Example: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(3) - d = fc.geetools.byProperties(properties=["ADM1_CODE", "ADM2_CODE"]) - d.getInfo() - """ - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = self._obj.aggregate_array(featureId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - - # retrieve properties for each feature - properties = ee.List(properties) if properties else self._obj.first().propertyNames() - properties = properties.remove(featureId) - values = properties.map( - lambda p: ee.Dictionary.fromLists(features, self._obj.aggregate_array(p)) - ) - - # get the label to use in the dictionary if requested - labels = ee.List(labels) if labels else properties - - return ee.Dictionary.fromLists(labels, values) - - def byFeatures( - self, - featureId: str | ee.String = "system:index", - properties: list | ee.List = [], - labels: list = [], - ) -> ee.Dictionary: - """Get a dictionary with all property values for each feature. - - This method is returning a dictionary with all the feature ids as keys and their properties as a dictionary. - - .. code-block:: - - { - "feature1": {"property1": value1, "property2": value2, ...}, - "feature2": {"property1": value1, "property2": value2, ...}, - ... - } - - The output remain server side and can be used to create a client side plot. - - Args: - featureId: The property to use as the feature id. Defaults to "system:index". This property needs to be a string property. - properties: A list of properties to get the values from. - labels: A list of names to replace properties names. Default to the properties names. - - Returns: - A dictionary with all the feature ids as keys and their properties as a dictionary. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.byProperties` - - :docstring:`ee.FeatureCollection.geetools.plot_by_features` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(3) - d = fc.geetools.byFeatures(properties=["ADM0_CODE", "ADM1_CODE", "ADM2_CODE"]) - d.getInfo() - - - """ - # compute the properties and their labels - props = ee.List(properties) if properties else self._obj.first().propertyNames() - props = props.remove(featureId) - labels = ee.List(labels) if labels else props - - # create a function to get the properties of a feature - # we need to map the featureCollection into a list as it's not possible to return something else than a - # featureCollection mapping a FeatureCollection. very expensive process but we don't have any other choice. - fc = self._obj.select(propertySelectors=props, newProperties=props) - fc_list = fc.toList(self._obj.size()) - values = fc_list.map(lambda f: ee.Feature(f).select(props, labels).toDictionary(labels)) - - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = self._obj.aggregate_array(featureId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - - return ee.Dictionary.fromLists(features, values) - - def plot_by_features( - self, - type: str = "bar", - featureId: str = "system:index", - properties: list = [], - labels: list = [], - colors: list = [], - ax: Axes | None = None, - **kwargs, - ) -> Axes: - """Plot the values of a ``ee.FeatureCollection`` by feature. - - Each feature property selected in properties will be plotted using the ``featureId`` as the x-axis. - If no ``properties`` are provided, all properties will be plotted. - If no ``featureId`` is provided, the "system:index" property will be used. - - Warning: - This function is a client-side function. - - Args: - type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! - featureId: The property to use as the x-axis (name the features). Defaults to "system:index". - properties: A list of properties to plot. Defaults to all properties. - labels: A list of labels to use for plotting the properties. If not provided, the default labels will be used. It needs to match the properties length. - colors: A list of colors to use for plotting the properties. If not provided, the default colors from the matplotlib library will be used. - ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. - kwargs: Additional arguments from the ``pyplot`` function. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.byFeatures` - - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` - - :docstring:`ee.FeatureCollection.geetools.plot_hist` - - :docstring:`ee.FeatureCollection.geetools.plot` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - # start a plot object from matplotlib library - fig, ax = plt.subplots(figsize=(10, 5)) - - # plot on this object the 10 first items of the FAO GAUL level 2 feature collection - # for each one of them (marked with it's "ADM0_NAME" property) we plot the value of the "ADM1_CODE" and "ADM2_CODE" properties - fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(10) - fc.geetools.plot_by_features(featureId="ADM2_NAME", properties=["ADM1_CODE", "ADM2_CODE"], colors=["#61A0D4", "#D49461"], ax=ax) - - # Modify the rotation of existing x-axis tick labels - for label in ax.get_xticklabels(): - label.set_rotation(45) - """ - # Get the features and properties - props = ee.List(properties) if properties else self._obj.first().propertyNames().getInfo() - props = props.remove(featureId) - - # get the data from server - data = self.byProperties(featureId, props, labels).getInfo() - - # reorder the data according to the labels or properties set by the user - labels = labels if labels else props.getInfo() - data = {k: data[k] for k in labels} - - return plot_data(type=type, data=data, label_name=featureId, colors=colors, ax=ax, **kwargs) - - def plot_by_properties( - self, - type: str = "bar", - featureId: str = "system:index", - properties: list | ee.List = [], - labels: list = [], - colors: list = [], - ax: Axes | None = None, - **kwargs, - ) -> Axes: - """Plot the values of a FeatureCollection by property. - - Each features will be represented by a color and each property will be a bar of the bar chart. - - Warning: - This function is a client-side function. - - Args: - type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! - featureId: The property to use as the y-axis (name the features). Defaults to "system:index". - properties: A list of properties to plot. Defaults to all properties. - labels: A list of labels to use for plotting the properties. If not provided, the default labels will be used. It needs to match the properties length. - colors: A list of colors to use for plotting the properties. If not provided, the default colors from the matplotlib library will be used. - ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. - kwargs: Additional arguments from the ``pyplot`` function. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.byProperties` - - :docstring:`ee.FeatureCollection.geetools.plot_by_features` - - :docstring:`ee.FeatureCollection.geetools.plot_hist` - - :docstring:`ee.FeatureCollection.geetools.plot` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - # start a plot object from matplotlib library - fig, ax = plt.subplots(figsize=(10, 5)) - - # plot on this object the 10 first items of the FAO GAUL level 2 feature collection - # for each one of them (marked with it's "ADM2_NAME" property) we plot the value of the "ADM1_CODE" property - fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(10) - fc.geetools.plot_by_properties(featureId="ADM2_NAME", properties=["ADM1_CODE"], ax=ax) - """ - # Get the features and properties - fc = self._obj - props = ee.List(properties) if properties else fc.first().propertyNames() - props = props.remove(featureId) - - # get the data from server - data = self.byFeatures(featureId, props, labels).getInfo() - - # reorder the data according to the lapbes or properties set by the user - labels = labels if labels else props.getInfo() - data = {f: {k: data[f][k] for k in labels} for f in data.keys()} - - return plot_data(type=type, data=data, label_name=featureId, colors=colors, ax=ax, **kwargs) - - def plot_hist( - self, - property: str | ee.String, - label: str = "", - ax: Axes | None = None, - color=None, - **kwargs, - ) -> Axes: - """Plot the histogram of a specific property. - - Warning: - This function is a client-side function. - - Args: - property: The property to display - label: The label to use for the property. If not provided, the property name will be used. - ax: The matplotlib axes to use. If not provided, the plot will be send to the current axes (``plt.gca()``) - color: The color to use for the plot. If not provided, the default colors from the matplotlib library will be used. - kwargs: Additional arguments from the ``pyplot.hist`` function. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.plot_by_features` - - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` - - :docstring:`ee.FeatureCollection.geetools.plot` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - # start a plot object from matplotlib library - fig, ax = plt.subplots(figsize=(10, 5)) - ax.set_title('Histogram of Precipitation in July') - ax.set_xlabel('Precipitation (mm)') - - - # build the histogram of the precipitation band for the month of july in the PRISM dataset - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm81m').toBands() - region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14) - climSamp = normClim.sample(region, 5000) - climSamp.geetools.plot_hist("07_ppt", ax=ax, bins=20) - - fig.show() - """ - # gather the data from parameters - properties, labels = ee.List([property]), ee.List([label]) - - # get the data from the server - data = self.byProperties(properties=properties, labels=labels).getInfo() - - # define the ax if not provided by the user - if ax is None: - fig, ax = plt.subplots() - - # gather the data from the data variable - labels = list(data.keys()) - if len(labels) != 1: - raise ValueError("Pie chart can only be used with one property") - - kwargs["rwidth"] = kwargs.get("rwidth", 0.9) - kwargs["color"] = color or plt.get_cmap("tab10").colors[0] - ax.hist(list(data[labels[0]].values()), **kwargs) - ax.set_xlabel(labels[0]) - ax.set_ylabel("frequency") - - # customize the layout of the axis - ax.grid(axis="y") - ax.set_axisbelow(True) - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - - # make sure the canvas is only rendered once. - ax.figure.canvas.draw_idle() - - return ax - - def plot( - self, - ax: Axes | None = None, - property: str = "", - crs: str = "EPSG:4326", - cmap: str = "viridis", - boundaries: bool = False, - color: str = "k", - ): - """Plot the featureCollection on a map using the provided property. - - Warning: - This function is a client-side function. - - Parameters: - property: The property to use to color the features. - ax: The axes to plot the map on. - crs: The CRS to use for the map. - cmap: The colormap to use for the colors. - boundaries: Whether to plot the features values or only the boundaries. - color: The color to use for the boundaries. - - See Also: - - :docstring:`ee.FeatureCollection.geetools.plot_by_features` - - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` - - :docstring:`ee.FeatureCollection.geetools.plot_hist` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - from matplotlib import pyplot as plt - - initialize_documentation() - - # start a plot object from matplotlib library - fig, ax = plt.subplots(figsize=(10, 5)) - - # plot france geometry on the map - france = ( - ee.FeatureCollection("FAO/GAUL/2015/level2") - .filter(ee.Filter.eq("ADM0_NAME", "France")) - .geetools.plot(boundaries=True, color="teal", ax=ax) - ) - - # style the figure - ax.set_title("France departements") - ax.set_xlabel("Longitude (°)") - ax.set_ylabel("Latitude (°)") - ax.set_xticks([]) - ax.set_yticks([]) - - fig.show() - """ - if ax is None: - fig, ax = plt.subplots() - - # get the data from the server - names = self._obj.first().propertyNames() - names = names.filter(ee.Filter.stringStartsWith("item", "system:").Not()) - property = property if property != "" else names.get(0).getInfo() - data = self._obj.select([property]).getInfo() - - # transform the data to a geodataframe and reproject it to the destination crs - gdf = gpd.GeoDataFrame.from_features(data["features"]).set_crs(4326).to_crs(crs) - - # plot the data on the map either as contours or a valued features - if boundaries is True: - gdf.boundary.plot(ax=ax, color=color) - else: - gdf.plot(column=property, ax=ax, cmap=cmap) diff --git a/geetools/ee_filter.py b/geetools/ee_filter.py deleted file mode 100644 index d60c923e..00000000 --- a/geetools/ee_filter.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Extra method for the ``ee.Filter`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Filter, "geetools") -class FilterAccessor: - """Toolbox for the ``ee.Filter`` class.""" - - def __init__(self, obj: ee.Filter): - """Initialize the Filter class.""" - self._obj = obj - - def dateRange(self, range: ee.DateRange) -> ee.Filter: - """Filter by daterange. - - Parameters: - range: The date range to filter by. - - Returns: - The filter to apply to a collection. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - # Create a collection and filter it by a date range - collection = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") - - # filter by date range - range = ee.DateRange("2018-01-01", "2019-01-01") - filteredCollection = collection.filter(ee.Filter.geetools.dateRange(range)) - - # print the total size of the collections - print(f"landsat full collection: {collection.size().getInfo()}") - print(f"landsat filtered collection: {filteredCollection.size().getInfo()}") - """ - return ee.Filter.date(range.start(), range.end()) diff --git a/geetools/ee_geometry.py b/geetools/ee_geometry.py deleted file mode 100644 index 70b37551..00000000 --- a/geetools/ee_geometry.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Toolbox for the ``ee.Geometry`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Geometry, "geetools") -class GeometryAccessor: - """Toolbox for the ``ee.Geometry`` class.""" - - def __init__(self, obj: ee.Geometry): - """Initialize the Geometry class.""" - self._obj = obj - - def keepType(self, type: str) -> ee.Geometry: - """Only keep the geometries of the given type from a GeometryCollection. - - Args: - type: The type of geometries to keep. Can be one of: Point, LineString, LineRing Polygon. - - Returns: - The geometries of the given type. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - # generate multiple geometries of different types - point0 = ee.Geometry.Point([0,0], proj="EPSG:4326") - point1 = ee.Geometry.Point([0,1], proj="EPSG:4326") - poly0 = point0.buffer(1, proj="EPSG:4326") - poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") - line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") - multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") - - # create a geometry collection from them - geometryColllection = ee.Algorithms.GeometryConstructors.MultiGeometry( - [multiPoly, poly0, poly1, point0, line], - crs="EPSG:4326", - geodesic=True, - maxError=1 - ) - - # extract only the LineString geometries from the collection - geom = geometryColllection.geetools.keepType('LineString') - geom.getInfo() - """ - # will raise an error if self is not a GeometryCollection - error_msg = "This method can only be used with GeometryCollections" - assert self._obj.type().getInfo() == "GeometryCollection", error_msg - - def filterType(geom): - geom = ee.Geometry(geom) - return ee.Algorithms.If(geom.type().compareTo(type), None, geom) - - geometries = self._obj.geometries().map(filterType, True) - return getattr(ee.Geometry, "Multi" + type)(geometries, self._obj.projection()) diff --git a/geetools/ee_image.py b/geetools/ee_image.py deleted file mode 100644 index f176fe4c..00000000 --- a/geetools/ee_image.py +++ /dev/null @@ -1,2089 +0,0 @@ -"""Toolbox for the ``ee.Image`` class.""" -from __future__ import annotations - -from typing import Optional - -import ee -import ee_extra -import ee_extra.Algorithms.core -import geopandas as gpd -import numpy as np -import requests -import xarray -from matplotlib import pyplot as plt -from matplotlib.axes import Axes -from matplotlib.colors import to_rgba -from pyproj import CRS, Transformer -from xee.ext import REQUEST_BYTE_LIMIT - -from .accessors import register_class_accessor -from .utils import plot_data - - -@register_class_accessor(ee.Image, "geetools") -class ImageAccessor: - """Toolbox for the ``ee.Image`` class.""" - - def __init__(self, obj: ee.Image): - """Initialize the Image class.""" - self._obj = obj - - # -- band manipulation ----------------------------------------------------- - def addDate(self, format: str | ee.String = "") -> ee.Image: - """Add a band with the date of the image in the provided format. - - If no format is provided, the date is stored as a Timestamp in millisecond in a band "date". If format band is provided, the date is store in a int8 band with the date in the provided format. This format needs to be a string that can be converted to a number. - If not an error will be thrown. - - Args: - format: A date pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html - - Returns: - The image with the date band added. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image.geetools.addDate() - date = image.select('date') - buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(100) - value = date.reduceRegion(ee.Reducer.first(), buffer, 10).get("date") - ee.Date(value).format('YYYY-MM-dd').getInfo() - """ - # parse the inputs - isMillis = ee.String(format).equals(ee.String("")) - format = ee.String(format) if format else ee.String("YYYYMMdd") - - # extract the date from the object and create a image band from it - date = self._obj.date() - date = ee.Algorithms.If(isMillis, date.millis(), ee.Number.parse(date.format(format))) - dateBand = ee.Image.constant(ee.Number(date)).rename("date") - - return self._obj.addBands(dateBand) - - def addSuffix(self, suffix: str | ee.String, bands: list | ee.List = []) -> ee.Image: - """Add a suffix to the image selected band. - - Add a suffix to the selected band. If no band is specified, the suffix is added to all bands. - - Parameters: - suffix: The suffix to add to the band. - bands: The bands to add the suffix to. If None, all bands are selected. - - Returns: - The image with the suffix added to the selected bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image.geetools.addSuffix('_suffix') - print(image.bandNames().getInfo()) - """ - suffix = ee.String(suffix) - bands = self._obj.bandNames() if bands == [] else ee.List(bands) - bandNames = bands.iterate( - lambda b, n: ee.List(n).replace(b, ee.String(b).cat(suffix)), - self._obj.bandNames(), - ) - return self._obj.rename(bandNames) - - def addPrefix(self, prefix: str | ee.String, bands: list | ee.List = []): - """Add a prefix to the image selected band. - - Add a prefix to the selected band. If no band is specified, the prefix is added to all bands. - - Parameters: - prefix: The prefix to add to the band. - bands: The bands to add the prefix to. If None, all bands are selected. - - Returns: - The image with the prefix added to the selected bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image.geetools.addPrefix('prefix_') - print(image.bandNames().getInfo()) - """ - prefix = ee.String(prefix) - bands = self._obj.bandNames() if bands == [] else ee.List(bands) - bandNames = bands.iterate( - lambda b, n: ee.List(n).replace(b, prefix.cat(ee.String(b))), - self._obj.bandNames(), - ) - return self._obj.rename(bandNames) - - def rename(self, names: dict | ee.Dictionary) -> ee.Image: - """Rename the bands of the image based on a dictionary. - - It's the same function as the one from GEE but it takes a dictionary as input. - Keys are the old names and values are the new names. - - Parameters: - names: The new names of the bands. - - Returns: - The image with the new band names. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' - image = ee.Image(src).select(['B1', 'B2', 'B3']) - image = image.geetools.rename({'B1': 'Aerosol', 'B2': 'Blue'}) - print(image.bandNames().getInfo()) - """ - names = ee.Dictionary(names) - bands = names.keys().iterate( - lambda b, n: ee.List(n).replace(b, names.get(b)), self._obj.bandNames() - ) - return self._obj.rename(bands) - - def remove(self, bands: list | ee.List) -> ee.Image: - """Remove bands from the image. - - Parameters: - bands: The bands to remove. - - Returns: - The image without the specified bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' - image = ee.Image(src).select(['B1', 'B2', 'B3']) - image = image.geetools.remove(['B1', 'B2']) - print(image.bandNames().getInfo()) - """ - bands = self._obj.bandNames().removeAll(ee.List(bands)) - return self._obj.select(bands) - - def doyToDate( - self, - year, - dateFormat: str | ee.String = "yyyyMMdd", - band: str | ee.String = "", - ) -> ee.Image: - """Convert the DOY band to a date band. - - This method only work with date formats that can be converted to numbers as earthengine images don't accept string bands. - - Args: - year: The year to use for the date - dateFormat: The date format to use for the date band - band: The band to use as DOY band. If empty, the first one is selected. - - Returns: - The original image with the DOY band converted to a date band. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image.random().multiply(365).toInt() - vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) - - image = image.geetools.doyToDate(2023) - print(image.reduceRegion(ee.Reducer.min(), vatican, 1).getInfo()) - """ - year = ee.Number(year) - band = ee.String(band) if band else ee.String(self._obj.bandNames().get(0)) - dateFormat = ee.String(dateFormat) - - doyList = ee.List.sequence(0, 365) - remapList = doyList.map( - lambda d: ee.Number.parse( - ee.Date.fromYMD(year, 1, 1).advance(d, "day").format(dateFormat) - ) - ) - return self._obj.remap(doyList, remapList, bandName=band).rename(band) - - # -- the rest -------------------------------------------------------------- - - def getValues(self, point: ee.Geometry.Point, scale: int | ee.Number = 0) -> ee.Dictionary: - """Get the value of the image at the given point using specified geometry. - - The result is presented as a dictionary where the keys are the bands name and the value the mean value of the band at the given point. - - Parameters: - point: The geometry to get the value from. - - Returns: - A dictionary with the band names and the value at the given point. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - point = ee.Geometry.Point([11.0, 45.0]) - value = image.geetools.getValues(point, 10) - print(value.getInfo()) - """ - scale = self._obj.select(0).projection().nominalScale() if scale == 0 else ee.Number(scale) - return self._obj.reduceRegion(ee.Reducer.mean(), point, scale) - - def minScale(self) -> ee.Number: - """Return the minimum scale of the image. - - It will be looking at all bands available so Select specific values before using this method. - - Returns: - The minimum scale of the image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image.geetools.minScale().getInfo() - """ - bandNames = self._obj.bandNames() - scales = bandNames.map(lambda b: self._obj.select(ee.String(b)).projection().nominalScale()) - return ee.Number(scales.sort().get(0)) - - def merge(self, images: list | ee.List) -> ee.Image: - """Merge images into a single image. - - Parameters: - images: The images to merge. - - Returns: - The merged image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image1 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image2 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQL') - image3 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image1.geetools.merge([image2, image3]) - print(image.bandNames().getInfo()) - """ - images = ee.List(images) - merged = images.iterate(lambda dst, src: ee.Image(src).addBands(dst), self._obj) - return ee.Image(merged) - - def toGrid( - self, - size: int | ee.Number = 1, - band: str | ee.String = "", - geometry: ee.Geometry | None = None, - ) -> ee.FeatureCollection: - """Convert an image to a grid of polygons. - - Based on the size given by the user, the tool will build a grid of size*pixelSize x size * pixelSize cells. Each cell will be a polygon. Note that for images that have multiple scale depending on the band, we will use the first one or the one stated in the parameters. - - Parameters: - size: The size of the grid. It will be size * pixelSize x size * pixelSize cells. - band: The band to burn into the grid. - geometry: The geometry to use as reference for the grid. If None, the image footprint will be used. - - Returns: - The grid as a FeatureCollection. - - Note: - The method has a known bug when the projection of the image is different than 3857. As we use a buffer, the grid cells can slightly overlap. Feel free to open a Issue and contribute if you feel it needs improvements. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' - image = ee.Image(src) - buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(100) - grid = image.geetools.toGrid(1, 'B2', buffer) - print(grid.getInfo()) - """ - band = ee.String(band) if band else self._obj.bandNames().get(0) - projection = self._obj.select(band).projection() - size = projection.nominalScale().multiply(ee.Number(size).toInt()) - - # extract the centers at the correct resolution - lonLat = ee.Image.pixelLonLat().reproject(projection) - coords = lonLat.reduceRegion(ee.Reducer.toList(), geometry, size) - - # extract them as lists - lat, lon = ee.List(coords.get("latitude")), ee.List(coords.get("longitude")) - - # we can now map them user their index to point -> buffer -> square - index = ee.List.sequence(0, lat.size().subtract(2)) - squares = index.map( - lambda i: ( - ee.Geometry.Point([lon.get(i), lat.get(i)]) - .buffer(size.divide(2)) - .bounds(0.01, projection) - ) - ) - - # make the grid - features = ee.List(squares).map(lambda g: ee.Feature(ee.Geometry(g))) - - return ee.FeatureCollection(features) - - def clipOnCollection( - self, fc: ee.FeatureCollection, keepProperties: int | ee.Number = 1 - ) -> ee.ImageCollection: - """Clip an image to a FeatureCollection. - - The image will be clipped to every single features of the featureCollection as one independent image. - - Parameters: - fc: The featureCollection to clip to. - keepProperties: If True, the properties of the featureCollection will be added to the clipped image. - - Returns: - The clipped imageCollection. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' - image = ee.Image(src) - fc = ee.FeatureCollection('FAO/GAUL/2015/level0') - clipped = image.geetools.clipOnCollection(fc) - print(clipped.size().getInfo()) - """ - - def fcClip(feat): - image = self._obj.clip(feat.geometry()) - return ee.Algorithms.If( - ee.Number(keepProperties).toInt(), image.copyProperties(feat), image - ) - - return ee.ImageCollection(fc.map(fcClip)) - - def bufferMask( - self, - radius: int | ee.Number = 1.5, - kernelType: str | ee.String = "square", - units: str | ee.String = "pixels", - ) -> ee.Image: - """Make a buffer around every masked pixel of the Image. - - The buffer will be made using the specified radius, kernelType and units and will mask surrounfing pixels. - - Parameters: - radius: The radius of the buffer. - kernelType: The kernel type of the buffer. One of: ``square``, ``circle``, ``diamond``, ``octagon``, ``plus``, ``square``. - units: The units of the radius. One of: ``pixels``, ``meters``. - - Returns: - The image with the buffer mask applied. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' - image = ee.Image(src) - image = image.geetools.bufferMask(1.5, 'square', 'pixels') - print(image.bandNames().getInfo()) - """ - radius, kernelType = ee.Number(radius), ee.String(kernelType) - units = ee.String(units) - masked = self._obj.mask().Not() - buffer = masked.focalMax(radius, kernelType, units) - return self._obj.updateMask(buffer.Not()) - - @classmethod - def full( - self, - values: list | ee.List = [0], - names: list | ee.List = ["constant"], - ) -> ee.Image: - """Create an image with the given values and names. - - Parameters: - values: The values to initialize the image with. If one value is given, it will be used for all bands. - names: The names of the bands. By default it uses the earthen engine default value, "constant". - - Returns: - An image with the given values and names. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image.geetools.full([1, 2, 3], ['a', 'b', 'c']) - print(image.bandNames().getInfo()) - """ - values, names = ee.List(values), ee.List(names) - - # resize value to the same length as names - values = ee.List( - ee.Algorithms.If( - values.size().eq(1), - ee.List.repeat(ee.Number(values.get(0)), names.size()), - values, - ) - ) - return ee.Image.constant(values).rename(names) - - def fullLike( - self, - fillValue: float | int | ee.Number, - copyProperties: int | ee.Number = 0, - keepMask: int | ee.Number = 0, - keepFootprint: int | ee.Number = 1, - ) -> ee.Image: - """Create an image with the same band names, projection and scale as the original image. - - The projection is computed on the first band, make sure all bands have the same. - The procduced image can also copy the properties of the original image and keep the mask. - - Parameters: - fillValue: The value to fill the image with. - copyProperties: If True, the properties of the original image will be copied to the new one. - keepMask: If True, the mask of the original image will be copied to the new one. - keepFootprint: If True, the footprint of the original image will be used to clip the new image. - - Returns: - An image with the same band names, projection and scale as the original image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image.geetools.fullLike(0) - print(image.bandNames().getInfo()) - """ - # function params as GEE objects - keepMask, copyProperties = ee.Number(keepMask), ee.Number(copyProperties) - keepFootprint = ee.Number(keepFootprint) - # get geometry, band names and property names - footprint, bandNames = self._obj.geometry(), self._obj.bandNames() - properties = self._obj.propertyNames().remove( - "system:footprint" - ) # remove footprint as a "normal" property - # list of values to fill the image - fillValue = ee.List.repeat(fillValue, bandNames.size()) - # filled image - image = self.full(fillValue, bandNames) - # handler projection - projected_list = bandNames.map( - lambda b: image.select([b]).reproject(self._obj.select([b]).projection()) - ) - image = ee.ImageCollection.fromImages(projected_list).toBands().rename(bandNames) - # handle footprint - image_footprint = image.clip(footprint) # sets system:footprint property - image = ee.Image(ee.Algorithms.If(keepFootprint, image_footprint, image)) - # handle properties - withProperties = image.copyProperties(self._obj, properties) - image = ee.Algorithms.If(copyProperties, withProperties, image) - # handle mask - withMask = ee.Image(image).updateMask(self._obj.mask()) - image = ee.Image(ee.Algorithms.If(keepMask, withMask, image)) - # handle band types - return ee.Image(image.cast(self._obj.bandTypes())) - - def reduceBands( - self, - reducer: str | ee.Reducer, - bands: list | ee.List = [], - name: str | ee.String = "", - ) -> ee.Image: - """Reduce the image using the selected reducer and adding the result as a band using the selected name. - - Args: - bands: The bands to reduce - reducer: The reducer to use - name: The name of the new band - - Returns: - The image with the new reduced band added - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - image = image.geetools.reduceBands("mean", ['B1', 'B2']) - print(image.bandNames().getInfo()) - """ - # the reduce method only accept client side string - if not isinstance(reducer, str): - raise TypeError("reducer must be a Python string") - - bands, name = ee.List(bands), ee.String(name) - bands = ee.Algorithms.If(bands.size().eq(0), self._obj.bandNames(), bands) - name = ee.Algorithms.If(name.equals(ee.String("")), reducer, name) - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - reduceImage = self._obj.select(ee.List(bands)).reduce(red).rename([name]) - return self._obj.addBands(reduceImage) - - def negativeClip(self, geometry: ee.Geometry | ee.Feature | ee.FeatureCollection) -> ee.Image: - """The opposite of the clip method. - - The inside of the geometry will be masked from the image. - - Args: - geometry: The geometry to mask from the image. - - Returns: - The image with the geometry masked. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - src, bands = "COPERNICUS/S2_SR_HARMONIZED", ["B1", "B2", "B3"] - vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) - - image = ee.ImageCollection(src).filterBounds(vatican).first().select(bands) - image = image.geetools.negativeClip(vatican) - print(image.reduceRegion(ee.Reducer.mean(), vatican, 1).getInfo()) - """ - return self._obj.updateMask(self._obj.clip(geometry).mask().Not()) - - def format( - self, - string: str | ee.String, - dateFormat: str | ee.String = "yyyy-MM-dd", - ) -> ee.String: - """Create a string from using the given pattern and using the image properties. - - The ``system_date`` property is special cased to fit the dateFormat parameter. - - Args: - string: The pattern to use for the string - dateFormat: The date format to use for the system_date property - - Returns: - The string corresponding to the image - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') - string = image.geetools.format('this is the image date: {system_date}') - print(string.getInfo()) - """ - dateFormat, string = ee.String(dateFormat), ee.String(string) - - patternList = string.match(r"\{([^}]+)\}", "g") - - def replaceProperties(p, s): - p = ee.String(p) - prop = self._obj.get(p.slice(1, -1)) - date = self._obj.date().format(dateFormat) - prop = ee.Algorithms.If(p.equals("{system_date}"), date, prop) - # return ee.String(s).cat(date) - return ee.String(s).replace(p, ee.String(prop)) - - return patternList.iterate(replaceProperties, string) - - def gauss(self, band: str | ee.String = "") -> ee.Image: - """Apply a gaussian filter to the image. - - We apply the following function to the image: "exp(((val-mean)**2)/(-2*(std**2)))" - where val is the value of the pixel, mean is the mean of the image, std is the standard deviation of the image. - - See the `Gaussian filter `_ Wikipedia page for more information. - - Args: - band: The band to apply the gaussian filter to. If empty, the first one is selected. - - Returns: - The image with the gaussian filter applied.An single band image with the gaussian filter applied. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() - image = image.geetools.gauss() - print(image.bandNames().getInfo()) - """ - band = ee.String(band) if band else ee.String(self._obj.bandNames().get(0)) - image = self._obj.select(band) - - kwargs = {"geometry": image.geometry(), "bestEffort": True} - mean = image.reduceRegion(ee.Reducer.mean(), **kwargs).get(band) - std = image.reduceRegion(ee.Reducer.stdDev(), **kwargs).get(band) - - return image.expression( - "exp(((val-mean)**2)/(-2*(std**2)))", - { - "val": image, - "mean": ee.Image.constant(mean), - "std": ee.Image.constant(std), - }, - ).rename(band.cat("_gauss")) - - def repeat(self, band, repeats: int | ee.Number) -> ee.image: - """Repeat a band of the image. - - Args: - band: The band to repeat - repeats: The number of times to repeat the band - - Returns: - The image with the band repeated - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() - image = image.geetools.repeat('B1', 2) - print(image.bandNames().getInfo()) - """ - band, repeats = ee.String(band), ee.Number(repeats).toInt() - - sequence = ee.List.sequence(1, repeats) - image = self._obj.select(band) - - def addBand(n, i): - name = band.cat("_").cat(ee.Number(n).toInt().format()) - return ee.Image(i).addBands(image.rename(name)) - - return ee.Image(sequence.iterate(addBand, self._obj)) - - def removeZeros(self) -> ee.Image: - """Return an image array with non-zero values extracted from each band. - - This function processes a multi-band image array, where each band represents different data. - It removes zero values from each band independently and then combines the non-zero values from all bands into a single image. - The resulting image may have inconsistent array lengths for each pixel, as the number of zero values removed can vary across bands. - - Returns: - The image with the zero values removed from each band. - - Example: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) - image = ee.Image([0, 1, 2]).toArray() - image = image.geetools.removeZeros() - values = image.reduceRegion(ee.Reducer.first(), vatican, 1) - print(values.getInfo()) - """ - bands = self._obj.bandNames() - - def remove(band): - image = self._obj.select([band]) - isZero = image.divide(image) - countZeros = isZero.arrayReduce(ee.Reducer.sum(), [0]).multiply(-1) - nbZeros = countZeros.arrayProject([0]).arrayFlatten([["n"]]).toInt() - return image.arraySort().arraySlice(0, nbZeros) - - return ee.ImageCollection(bands.map(remove)).toBands().rename(bands) - - def interpolateBands(self, src: list | ee.List, to: list | ee.List) -> ee.Image: - """Interpolate bands from the "src" value range to the "to" value range. - - The Interpolation is performed linearly using the "extrapolate" option of the "interpolate" method. - - Args: - src: The source value range - to: The target value range - - Returns: - The image with the interpolated bands - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) - image = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterBounds(vatican).first() - image = image.select(["B4", "B3", "B2"]).geetools.interpolateBands([0, 3000], [0, 30]) - values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) - print(values.getInfo()) - """ - bands = self._obj.bandNames() - src, to = ee.List(src), ee.List(to) - - def interpolate(band): - original = self._obj.select([band]) - normalized = original.unitScale(src.get(0), src.get(1)) - return normalized.interpolate([0, 1], to) - - return ee.ImageCollection(bands.map(interpolate)).toBands().rename(bands) - - def isletMask(self, offset: float | int | ee.Number) -> ee.Image: - """Compute the islet mask from an image. - - An islet is a set of non-masked pixels connected together by their edges of very small surface. The user define the offset of the island size and we compute the max number of pixels to improve computation speed. The inpt Image needs to be a single band binary image. - - Args: - offset: The limit of the islet size in square metters - - Returns: - The island mask - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() - mask = image.select('SCL').eq(4) - mask = mask.geetools.islandMask(100) - print(mask.bandNames().getInfo()) - """ - offset = ee.Number(offset) - scale = self._obj.projection().nominalScale() - pixelsLimit = offset.multiply(2).sqrt().divide(scale).max(ee.Number(2)).toInt() - area = ee.Image.pixelArea().rename("area") - isletArea = ( - self._obj.select(0).mask().toInt().connectedPixelCount(pixelsLimit).multiply(area) - ) - return isletArea.lt(offset).rename("mask").selfMask() - - # -- ee-extra wrapper ------------------------------------------------------ - def index_list(cls) -> dict: - """Return the list of indices implemented in this module. - - Returns: - List of indices implemented in this module - - Examples: - .. code-block:: python - - import ee, geetools - - ind = ee.Image.geetools.indices()["BAIS2"] - print(ind["long_name"]) - print(ind["formula"]) - print(ind["reference"]) - """ - return ee_extra.Spectral.core.indices() - - def spectralIndices( - self, - index: str = "NDVI", - G: float | int = 2.5, - C1: float | int = 6.0, - C2: float | int = 7.5, - L: float | int = 1.0, - cexp: float | int = 1.16, - nexp: float | int = 2.0, - alpha: float | int = 0.1, - slope: float | int = 1.0, - intercept: float | int = 0.0, - gamma: float | int = 1.0, - omega: float | int = 2.0, - beta: float | int = 0.05, - k: float | int = 0.0, - fdelta: float | int = 0.581, - kernel: str = "RBF", - sigma: str = "0.5 * (a + b)", - p: float | int = 2.0, - c: float | int = 1.0, - lambdaN: float | int = 858.5, - lambdaR: float | int = 645.0, - lambdaG: float | int = 555.0, - online: float | int = False, - ) -> ee.Image: - """Computes one or more spectral indices (indices are added as bands) for an image from the Awesome List of Spectral Indices. - - Parameters: - self: Image to compute indices on. Must be scaled to [0,1]. - index: Index or list of indices to compute, default = 'NDVI' - Available options: - - 'vegetation' : Compute all vegetation indices. - - 'burn' : Compute all burn indices. - - 'water' : Compute all water indices. - - 'snow' : Compute all snow indices. - - 'urban' : Compute all urban (built-up) indices. - - 'kernel' : Compute all kernel indices. - - 'all' : Compute all indices listed below. - - Awesome Spectral Indices for GEE: Check the complete list of indices `here `_. - G: Gain factor. Used just for index = 'EVI', default = 2.5 - C1: Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI', default = 6.0 - C2: Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI', default = 7.5 - L: Canopy background adjustment. Used just for index = ['EVI','SAVI'], default = 1.0 - cexp: Exponent used for OCVI, default = 1.16 - nexp: Exponent used for GDVI, default = 2.0 - alpha: Weighting coefficient used for WDRVI, default = 0.1 - slope: Soil line slope, default = 1.0 - intercept: Soil line intercept, default = 0.0 - gamma: Weighting coefficient used for ARVI, default = 1.0 - omega: Weighting coefficient used for MBWI, default = 2.0 - beta: Calibration parameter used for NDSIns, default = 0.05 - k: Slope parameter by soil used for NIRvH2, default = 0.0 - fdelta: Adjustment factor used for SEVI, default = 0.581 - kernel: Kernel used for kernel indices, default = 'RBF' - Available options: - - 'linear' : Linear Kernel. - - 'RBF' : Radial Basis Function (RBF) Kernel. - - 'poly' : Polynomial Kernel. - sigma: Length-scale parameter. Used for kernel = 'RBF', default = '0.5 * (a + b)'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive. - p: Kernel degree. Used for kernel = 'poly', default = 2.0 - c: Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel. Used for kernel = 'poly', default = 1.0. This must be greater than or equal to 0. - lambdaN: NIR wavelength used for NIRvH2 and NDGI, default = 858.5 - lambdaR: Red wavelength used for NIRvH2 and NDGI, default = 645.0 - lambdaG: Green wavelength used for NDGI, default = 555.0 - drop: Whether to drop all bands except the new spectral indices, default = False - - Returns: - Image with the computed spectral index, or indices, as new bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - image = image.specralIndices(["NDVI", "NDFI"]) - """ - # fmt: off - return ee_extra.Spectral.core.spectralIndices( - self._obj, index, G, C1, C2, L, cexp, nexp, alpha, slope, intercept, gamma, omega, - beta, k, fdelta, kernel, sigma, p, c, lambdaN, lambdaR, lambdaG, online, - drop=False, - ) - # fmt: on - - def getScaleParams(self) -> dict: - """Gets the scale parameters for each band of the image. - - Returns: - Dictionary with the scale parameters for each band. - - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('MODIS/006/MOD11A2').first().geetools.getScaleParams() - """ - return ee_extra.STAC.core.getScaleParams(self._obj) - - def getOffsetParams(self) -> dict: - """Gets the offset parameters for each band of the image. - - Returns: - Dictionary with the offset parameters for each band. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('MODIS/006/MOD11A2').first().getOffsetParams() - """ - return ee_extra.STAC.core.getOffsetParams(self._obj) - - def scaleAndOffset(self) -> ee.Image: - """Scales bands on an image according to their scale and offset parameters. - - Returns: - Scaled image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - S2 = ee.ImageCollection('COPERNICUS/S2_SR').first().scaleAndOffset() - """ - return ee_extra.STAC.core.scaleAndOffset(self._obj) - - def preprocess(self, **kwargs) -> ee.Image: - """Pre-processes the image: masks clouds and shadows, and scales and offsets the image. - - Parameters: - **kwargs: Keywords arguments for ``maskClouds`` method. - - Returns: - Pre-processed image. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - S2 = ee.ImageCollection('COPERNICUS/S2_SR').first().preprocess() - """ - return ee_extra.QA.pipelines.preprocess(self._obj, **kwargs) - - def getSTAC(self) -> dict: - """Gets the STAC of the image. - - Returns: - STAC of the image. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('COPERNICUS/S2_SR').first().getSTAC() - """ - # extract the Asset id from the imagecollection - assetId = self._obj.get("system:id").getInfo() - - # search for the project in the GEE catalog and extract the project catalog URL - project = assetId.split("/")[0] - catalog = "https://earthengine-stac.storage.googleapis.com/catalog/catalog.json" - links = requests.get(catalog).json()["links"] - project_catalog = next((i["href"] for i in links if i.get("title") == project), None) - if project_catalog is None: - raise ValueError(f"Project {project} not found in the catalog") - - # search for the collection in the project catalog and extract the collection STAC URL - collection = "_".join(assetId.split("/")[:-1]) - links = requests.get(project_catalog).json()["links"] - collection_stac = next((i["href"] for i in links if i.get("title") == collection), None) - if collection_stac is None: - raise ValueError(f"Collection {collection} not found in the {project} catalog") - - return requests.get(collection_stac).json() - - def getDOI(self) -> str: - """Gets the DOI of the image, if available. - - Returns: - DOI of the ee.Image dataset. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('NASA/GPM_L3/IMERG_V06').first().getDOI() - """ - return ee_extra.STAC.core.getDOI(self._obj) - - def getCitation(self) -> str: - """Gets the citation of the image, if available. - - Returns: - Citation of the ee.Image dataset. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('NASA/GPM_L3/IMERG_V06').first().getCitation() - """ - return ee_extra.STAC.core.getCitation(self._obj) - - def panSharpen(self, method: str = "SFIM", qa: str = "", **kwargs) -> ee.Image: - """Apply panchromatic sharpening to the Image. - - Optionally, run quality assessments between the original and sharpened Image to - measure spectral distortion and set results as properties of the sharpened Image. - - Parameters: - method: The sharpening algorithm to apply. Current options are "SFIM" (Smoothing Filter-based Intensity Modulation), "HPFA" (High Pass Filter Addition), "PCS" (Principal Component Substitution), and "SM" (simple mean). Different sharpening methods will produce different quality sharpening results in different scenarios. - qa: One or more optional quality assessment names to apply after sharpening. Results will be stored as image properties with the pattern `geetools:metric`, e.g. `geetools:RMSE`. - **kwargs: Keyword arguments passed to ee.Image.reduceRegion() such as "geometry", "maxPixels", "bestEffort", etc. These arguments are only used for PCS sharpening and quality assessments. - - Returns: - The Image with all sharpenable bands sharpened to the panchromatic resolution and quality assessments run and set as properties. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") - sharp = source.panSharpen(method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13) - """ - return ee_extra.Algorithms.core.panSharpen( - img=self._obj, method=method, qa=qa, prefix="geetools", **kwargs - ) - - def tasseledCap(self) -> ee.Image: - """Calculates tasseled cap brightness, wetness, and greenness components. - - Tasseled cap transformations are applied using coefficients published for these - supported platforms: - - * Sentinel-2 MSI Level 1C - * Landsat 9 OLI-2 SR - * Landsat 9 OLI-2 TOA - * Landsat 8 OLI SR - * Landsat 8 OLI TOA - * Landsat 7 ETM+ TOA - * Landsat 5 TM Raw DN - * Landsat 4 TM Raw DN - * Landsat 4 TM Surface Reflectance - * MODIS NBAR - - Parameters: - self: ee.Image to calculate tasseled cap components for. Must belong to a supported platform. - - Returns: - Image with the tasseled cap components as new bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - img = img.tasseledCap() - """ - return ee_extra.Spectral.core.tasseledCap(self._obj) - - def matchHistogram( - self, - target: ee.Image, - bands: dict, - geometry: ee.Geometry | None = None, - maxBuckets: int = 256, - ) -> ee.Image: - """Adjust the image's histogram to match a target image. - - Parameters: - target: Image to match. - bands: A dictionary of band names to match, with source bands as keys and target bands as values. - geometry: The region to match histograms in that overlaps both images. If none is provided, the geometry of the source image will be used. - maxBuckets: The maximum number of buckets to use when building histograms. Will be rounded to the nearest power of 2. - - Returns: - The adjusted image containing the matched source bands. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") - target = ee.Image("LANDSAT/LE07/C01/T1_TOA/LE07_046027_20150701") - bands = { - "B4": "B3", - "B3": "B2", - "B2": "B1" - } - matched = source.matchHistogram(target, bands) - """ - return ee_extra.Spectral.core.matchHistogram( - source=self._obj, - target=target, - bands=bands, - geometry=geometry, - maxBuckets=maxBuckets, - ) - - def maskClouds( - self, - method: str = "cloud_prob", - prob: int = 60, - maskCirrus: bool = True, - maskShadows: bool = True, - scaledImage: bool = False, - dark: float = 0.15, - cloudDist: int = 1000, - buffer: int = 250, - cdi: int | None = None, - ): - """Masks clouds and shadows in an image (valid just for Surface Reflectance products). - - Parameters: - self: Image to mask. - method: Method used to mask clouds. This parameter is ignored for Landsat products. - Available options: - - 'cloud_prob' : Use cloud probability. - - 'qa' : Use Quality Assessment band. - prob: Cloud probability threshold. Valid just for method = 'cloud_prob'. This parameter is ignored for Landsat products. - maskCirrus: Whether to mask cirrus clouds. Default to ``True``. Valid just for method = 'qa'. This parameter is ignored for Landsat products. - maskShadows: Whether to mask cloud shadows. Default to ``True`` This parameter is ignored for Landsat products. - scaledImage: Whether the pixel values are scaled to the range [0,1] (reflectance values). This parameter is ignored for Landsat products. - dark: NIR threshold. NIR values below this threshold are potential cloud shadows. This parameter is ignored for Landsat products. - cloudDist: Maximum distance in meters (m) to look for cloud shadows from cloud edges. This parameter is ignored for Landsat products. - buffer: Distance in meters (m) to dilate cloud and cloud shadows objects. This parameter is ignored for Landsat products. - cdi: Cloud Displacement Index threshold. Values below this threshold are considered potential clouds. A cdi = None means that the index is not used. This parameter is ignored for Landsat products. - - Returns: - Cloud-shadow masked image. - - Notes: - This method may mask water as well as clouds for the Sentinel-3 Radiance product. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - S2 = ( - ee.ImageCollection('COPERNICUS/S2_SR') - .first() - .maskClouds(prob = 75,buffer = 300,cdi = -0.5)) - """ - return ee_extra.QA.clouds.maskClouds( - self._obj, - method, - prob, - maskCirrus, - maskShadows, - scaledImage, - dark, - cloudDist, - buffer, - cdi, - ) - - def removeProperties(self, properties: list | ee.List) -> ee.Image: - """Remove a list of properties from an image. - - Args: - properties: List of properties to remove from the image. - - Returns: - Image with the specified properties removed. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - image = image.removeProperties(["system:time_start"]) - """ - properties = ee.List(properties) - proxy = self._obj.multiply(1) # drop properties - return ee.Image(proxy.copyProperties(self._obj, exclude=properties)) - - def distanceToMask( - self, - mask: ee.Image, - kernel: str = "euclidean", - radius: int = 1000, - band_name: str | ee.String = "distance_to_mask", - ) -> ee.Image: - """Compute the distance from each pixel to the nearest non-masked pixel. - - Parameters: - mask: The mask to compute the distance to. - kernel: The kernel type to use for the distance computation default to "euclidean". - radius: The radius of the kernel. - band_name: The name of the band to store the distance values. - - Returns: - The original images with the distance band added. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - centerBuffer = image.geometry().centroid().buffer(100) - BufferMask = ee.Image.constant(1).clip(centerBuffer) - mask = ee.Image.constant(0).where(BufferMask, 1).clip(image.geometry()) - image = image.distanceToMask(mask) - """ - # gather the parameters - kernel = getattr(ee.Kernel, kernel)(radius, "meters") - bandName = ee.String(band_name) - - # compute the distance - distance = self._obj.select(0).mask().Not().distance(kernel).rename(bandName) - distMask = distance.mask().Not().remap([0, 1], [0, radius]) - final = distance.unmask().add(distMask) - - return self._obj.addBands(final) - - def distance(self, other: ee.image) -> ee.Image: - """Compute the sum of all spectral distance between two images. - - Parameters: - other: The image to compute the distance to. - - Returns: - and Image with the euclidean distance between the two images for each band. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - other = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - image = image.distance(other) - """ - # compute the distance - distance = self._obj.subtract(other).pow(2).reduce("sum").sqrt().rename("sum_distance") - - return ee.Image(distance) - - def maskCoverRegion( - self, - region: ee.Geometry, - scale: Optional[int | ee.Number] = None, - band: Optional[str | ee.String] = None, - proxyValue: int | ee.Number = -999, - **kwargs, - ) -> ee.Number: - """Compute the coverage of masked pixels inside a Geometry. - - Parameters: - region: The region to compute the mask coverage. - scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. - band: The band to use. Defaults to the first band. - proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. In most cases the user should not change this value, but in case of conflicts, choose a value that is out of the range of the image values. - - Kwargs: - maxPixels: The maximum number of pixels to reduce. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - The percentage of masked pixels within the region - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) - image = image.maskCoverRegion(aoi) - """ - # compute the mask cover - image = self._obj.select(band or 0) - scale = scale or image.projection().nominalScale() - unmasked = image.unmask(proxyValue) - mask = unmasked.eq(proxyValue) - cover = mask.reduceRegion( - ee.Reducer.frequencyHistogram(), region, scale=scale, bestEffort=True, **kwargs - ) - # The cover result is a dictionary with each band as key (in our case the first one). - # For each band key the number of 0 and 1 is stored in a dictionary. - # We need to extract the number of 1 and 0 to compute the ratio which implys lots of casting. - values = ee.Dictionary(cover.values().get(0)) - zeros, ones = ee.Number(values.get("0", 0)), ee.Number(values.get("1", 0)) - ratio = ones.divide(zeros.add(ones)).multiply(100) - - # we want to display this result as a 1 digit float - return ratio - - def maskCoverRegions( - self, - collection: ee.FeatureCollection, - scale: Optional[int | ee.Number] = None, - band: Optional[str | ee.String] = None, - proxyValue: int | ee.Number = -999, - columnName: str | ee.String = "mask_cover", - **kwargs, - ) -> ee.FeatureCollection: - """Compute the coverage of masked pixels inside a Geometry. - - Parameters: - collection: The collection to compute the mask coverage (in each Feature). - scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. - band: The band to use. Defaults to the first band. - proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. In most cases the user should not change this value, but in case of conflicts, choose a value that is out of the range of the image values. - columnName: name of the column that will hold the value. - - Kwargs: - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - The passed table with the new column containing the percentage of masked pixels within the region - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - reg = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) - aoi = ee.FeatureCollection([ee.Feature(reg)]) - image = image.maskCoverRegions(aoi) - """ - # compute the mask cover - properties = collection.propertyNames() # original properties - image = self._obj.select(band or 0) - scale = scale or image.projection().nominalScale() - unmasked = image.unmask(proxyValue) - mask = unmasked.eq(proxyValue) - column = "_geetools_histo_" - cover = mask.reduceRegions( - collection=collection, - reducer=ee.Reducer.frequencyHistogram().setOutputs([column]), - scale=scale, - **kwargs, - ) - - def compute_percentage(feat: ee.Feature) -> ee.Feature: - histo = ee.Dictionary(feat.get(column)) - zeros, ones = ee.Number(histo.get("0", 0)), ee.Number(histo.get("1", 0)) - ratio = ones.divide(zeros.add(ones)).multiply(100) - return feat.select(properties).set(columnName, ratio) - - return cover.map(compute_percentage) - - def maskCover( - self, - scale: Optional[int] = None, - proxyValue: int = -999, - propertyName: str = "mask_cover", - **kwargs, - ) -> ee.Image: - """Compute the percentage of masked pixels inside the image. - - It will use the geometry and the first band of the image. - - Parameters: - scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. - proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. Choose a value that is out of the range of the image values. - propertyName: the name of the property where the value will be saved - - Kwargs: - maxPixels: The maximum number of pixels to reduce. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - The same image with the percentage of masked pixels as a property - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) - image = image.maskCoverRegion(aoi) - """ - region = self._obj.geometry() - value = self.maskCoverRegion(region, scale, None, proxyValue, **kwargs) - return self._obj.set(propertyName, value) - - def plot( - self, - bands: list, - region: ee.Geometry, - ax: Axes | None = None, - fc: ee.FeatureCollection = None, - cmap: str = "viridis", - crs: str = "EPSG:4326", - scale: float = 0.0001, # 0.0001 is the default scale for Sentinel-2 - color="k", - ) -> Axes: - """Plot the image on a matplotlib axis. - - Parameters: - bands: The bands to plot. - region: The geometry borders to plot the image on. - ax: The matplotlib axis to plot the image on. - fc: a FeatureCollection object to overlay on top of the image. Default is None, it can be a different object from the region. - cmap: The colormap to use for the image. Default is 'viridis'. can only ber used for single band images. - crs: The coordinate reference system of the image. if not set we use the projection of the first band. - scale: The scale of the image. - color: The color of the overlaid feature collection. Default is "k" (black). - - Examples: - .. code-block:: python - - import ee, geetools - import matplotlib.pyplot as plt - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - fig, ax = plt.subplots() - image.plot(["B2", "B3", "B4"], image.geometry(), ax) - """ - if ax is None: - fig, ax = plt.subplots() - - # compute the crs from the image if necessary - if crs == "": - crs = self._obj.projection().crs().getInfo() - - # extract the image as a xarray dataset - ds = xarray.open_dataset( - ee.ImageCollection([self._obj]), - engine="ee", - crs=crs, - scale=scale, - geometry=region.bounds(), - request_byte_limit=REQUEST_BYTE_LIMIT, - ) - - # extract all the bands as dataarrays objects - # x and y coordinates need to be transposed to match imshow requirements - bands_da = [ds[b][0, :, :].transpose() for b in bands] - - # compute the extend of the image so the unit displayed for x and y are matching the required crs - proj = Transformer.from_crs(CRS("EPSG:4326"), CRS(crs), always_xy=False) - region_bounds = region.bounds().coordinates().get(0).getInfo() - min_x, min_y = proj.transform(*region_bounds[0]) - max_x, max_y = proj.transform(*region_bounds[2]) - - # set the parameters that will be use for single and multi-band display - params = dict(extent=[min_x, max_x, min_y, max_y], origin="lower") - - # For single band image, we use the data array directly as source image - # for multi band image, we need to stack the dataarrays to create a RGB image - # and normalized them - if len(bands) == 1: - ax.imshow(bands_da[0], cmap=cmap, **params) - else: - da = np.dstack(bands_da) - rgb_image = (da - np.min(da)) / (np.max(da) - np.min(da)) - ax.imshow(rgb_image, **params) - - # add the feature collection if provided - # we need to extract the geometries and plot them - if fc is not None: - gdf = gpd.GeoDataFrame.from_features(fc.getInfo()["features"]) - gdf = gdf.set_crs("EPSG:4326").to_crs(crs) - gdf.boundary.plot(ax=ax, color=color) - - # The default aspect for map plots is 'auto'; if however data are not projected (coordinates are long/lat), - # the aspect is by default set to 1/cos(s_y * pi/180) with s_y the y coordinate of the middle of the - # region (the mean of the y range of bounding box) so that a long/lat square appears square in the - # middle of the plot. This implies an Equirectangular projection. - if CRS(crs).is_geographic: - y_coord = np.mean([min_y, max_y]) - ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) - else: - ax.set_aspect("auto") - - # make sure the canvas is only rendered once. - ax.figure.canvas.draw_idle() - - return ax - - @classmethod - def fromList(cls, images: ee.List | list): - """Create a single image by passing a list of images. - - Warning: The bands cannot have repeated names, if so, it will throw an error (see examples). - - Parameters: - images: a list of ee.Image - - Returns: - A single ee.Image with one band per image in the passed list - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - sequence = ee.List([1, 2, 3]) - images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) - image = ee.Image.geetools.fromList(images) - print(image.bandNames().getInfo()) - - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - sequence = ee.List([1, 2, 2, 3]) - images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) - image = ee.Image.geetools.fromList(images) - print(image.bandNames().getInfo()) - > ee.ee_exception.EEException: Image.rename: Can't add a band named '2' to image because a band with this name already exists. Existing bands: [1, 2]. - """ - bandNames = ee.List(images).map(lambda i: ee.Image(i).bandNames()).flatten() - ic = ee.ImageCollection.fromImages(images) - return ic.toBands().rename(bandNames) - - def byBands( - self, - regions: ee.featurecollection, - reducer: str | ee.Reducer = "mean", - bands: list = [], - regionId: str = "system:index", - labels: list = [], - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> ee.Dictionary: - """Compute a reducer for each band of the image in each region. - - This method is returning a dictionary with all the bands as keys and their reduced value in each region as values. - - .. code-block:: - - { - "band1": {"feature1": value1, "feature2": value2, ...}, - "band2": {"feature1": value1, "feature2": value2, ...}, - ... - } - - Parameters: - regions: The regions to compute the reducer in. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - regionId: The property used to label region. Defaults to "system:index". - labels: The labels to use for the output dictionary. Default to the band names. - bands: The bands to compute the reducer on. Default to all bands. - scale: The scale to use for the computation. Default is 10000m. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with all the bands as keys and their values in each region as a list. - - See Also: - - :docstring:`ee.Image.geetools.byRegions` - - :docstring:`ee.Image.geetools.plot_by_bands` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() - d = normClim.byBands(ecoregions, ee.Reducer.mean(), scale=10000) - print(d.getInfo()) - """ - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = regions.aggregate_array(regionId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - - # get the bands to be used in the reducer - eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() - - # retrieve the label to use for each bands if provided - eeLabels = ee.List(labels) if len(labels) else eeBands - - # by default for 1 band image, the reducers are renaming the output band. To ensure it keeps - # the original band name we add setOutputs that is ignored for multi band images. - # This is currently hidden because of https://issuetracker.google.com/issues/374285504 - # It will have no impact on most of the cases as plt_hist should be used for single band images - # reducer = reducer.setOutputs(labels) - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - - # retrieve the reduce bands for each feature - image = self._obj.select(eeBands).rename(eeLabels) - fc = image.reduceRegions( - collection=regions, - reducer=red, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ) - - # extract the data as a list of dictionaries (one for each label) aggregating - # the values for each feature - values = eeLabels.map(lambda b: ee.Dictionary.fromLists(features, fc.aggregate_array(b))) - - return ee.Dictionary.fromLists(eeLabels, values) - - def byRegions( - self, - regions: ee.featurecollection, - reducer: str | ee.Reducer = "mean", - bands: list = [], - regionId: str = "system:index", - labels: list = [], - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> ee.Dictionary: - """Compute a reducer in each region of the image for eah band. - - This method is returning a dictionary with all the features as keys and their reduced value for each band as values. - - .. code-block:: - - { - "feature1": {"band1": value1, "band2": value2, ...}, - "feature2": {"bands1": value1, "band2": value2, ...}, - ... - } - - Parameters: - regions: The regions to compute the reducer in. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - regionId: The property used to label region. Defaults to "system:index". - labels: The labels to use for the output dictionary. Default to the band names. - bands: The bands to compute the reducer on. Default to all bands. - scale: The scale to use for the computation. Default is 10000m. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - - Returns: - A dictionary with all the bands as keys and their values in each region as a list. - - See Also: - - :docstring:`ee.Image.geetools.byBands` - - :docstring:`ee.Image.geetools.plot_by_regions` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() - d = normClim.byregions(ecoregions, ee.Reducer.mean(), scale=10000) - print(d.getInfo()) - """ - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = regions.aggregate_array(regionId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - - # get the bands to be used in the reducer - bands = ee.List(bands) if len(bands) else self._obj.bandNames() - - # retrieve the label to use for each bands if provided - labels = ee.List(labels) if len(labels) else bands - - # by default for 1 band image, the reducers are renaming the output band. To ensure it keeps - # the original band name we add setOutputs that is ignored for multi band images. - # This is currently hidden because of https://issuetracker.google.com/issues/374285504 - # It will have no impact on most of the cases as plt_hist should be used for single band images - # reducer = reducer.setOutputs(labels) - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - - # retrieve the reduce bands for each feature - image = self._obj.select(bands).rename(labels) - fc = image.reduceRegions( - collection=regions, - reducer=red, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ) - - # extract the data as a list of dictionaries (one for each label) aggregating - # we are force to turn the fc into a list because GEE don't accept to map a featureCollection - # into something else (in our a case a dict) - fcList = fc.toList(fc.size()) - values = fcList.map(lambda f: ee.Feature(f).select(labels).toDictionary()) - - return ee.Dictionary.fromLists(features, values) - - def plot_by_regions( - self, - type: str, - regions: ee.FeatureCollection, - reducer: str | ee.Reducer = "mean", - bands: list = [], - regionId: str = "system:index", - labels: list = [], - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced values for each region. - - Each region will be plotted using the ``regionId`` as x-axis label defauting to "system:index" if not provided. - If no ``bands`` are provided, all bands will be plotted. - If no ``labels`` are provided, the band names will be used. - - Warning: - This method is client-side. - - Parameters: - type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! - regions: The regions to compute the reducer in. - rreducer: The name of the reducer or a reducer object to use. Default is "mean". - bands: The bands to compute the reducer on. Default to all bands. - regionId: The property used to label region. Defaults to "system:index". - labels: The labels to use for the output dictionary. Default to the band names. - colors: The colors to use for the plot. Default to the default matplotlib colors. - ax: The matplotlib axis to plot the data on. If None, a new figure is created. - scale: The scale to use for the computation. Default is 10000m. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - - Returns: - The matplotlib axis with the plot. - - See Also: - - :docstring:`ee.Image.geetools.byRegions` - - :docstring:`ee.Image.geetools.byBands` - - :docstring:`ee.Image.geetools.plot_by_bands` - - :docstring:`ee.Image.geetools.plot_hist - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() - - - normClim.plot_by_regions(ecoregions, ee.Reducer.mean(), scale=10000) - """ - # get the data from the server - data = self.byBands( - regions=regions, - reducer=reducer, - bands=bands, - regionId=regionId, - labels=labels, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ).getInfo() - - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = regions.aggregate_array(regionId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - features = features.getInfo() - - # extract the labels from the parameters - eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() - labels = labels if len(labels) else eeBands.getInfo() - - # reorder the data according to the labels id set by the user - data = {b: {f: data[b][f] for f in features} for b in labels} - - ax = plot_data(type=type, data=data, label_name=regionId, colors=colors, ax=ax) - - return ax - - def plot_by_bands( - self, - type: str, - regions: ee.FeatureCollection, - reducer: str | ee.Reducer = "mean", - bands: list = [], - regionId: str = "system:index", - labels: list = [], - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced values for each bands. - - Each band will be plotted using the ``labels`` as x-axis label defauting to band names if not provided. - If no ``bands`` are provided, all bands will be plotted. - If no ``regionId`` are provided, the "system;index" property will be used. - - - Warning: - This method is client-side. - - Parameters: - type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! - regions: The regions to compute the reducer in. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - bands: The bands to compute the reducer on. Default to all bands. - regionId: The property used to label region. Defaults to "system:index". - labels: The labels to use for the output dictionary. Default to the band names. - colors: The colors to use for the plot. Default to the default matplotlib colors. - ax: The matplotlib axis to plot the data on. If None, a new figure is created. - scale: The scale to use for the computation. Default is 10000m. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - The matplotlib axis with the plot. - - See Also: - - :docstring:`ee.Image.geetools.byRegions` - - :docstring:`ee.Image.geetools.byBands` - - :docstring:`ee.Image.geetools.plot_by_regions` - - :docstring:`ee.Image.geetools.plot_hist - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() - - - normClim.plot_by_bands(ecoregions, ee.Reducer.mean(), scale=10000) - """ - # get the data from the server - data = self.byRegions( - regions=regions, - reducer=reducer, - bands=bands, - regionId=regionId, - labels=labels, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ).getInfo() - - # get all the id values, they must be string so we are forced to cast them manually - # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 - features = regions.aggregate_array(regionId) - isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 - features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) - features = features.getInfo() - - # extract the labels from the parameters - eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() - labels = labels if len(labels) else eeBands.getInfo() - - # reorder the data according to the labels id set by the user - data = {f: {b: data[f][b] for b in labels} for f in features} - - ax = plot_data(type=type, data=data, label_name=regionId, colors=colors, ax=ax) - - return ax - - def plot_hist( - self, - bins: int = 30, - region: ee.Geometry | None = None, - bands: list = [], - labels: list = [], - colors: list = [], - precision: int = 2, - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int = 10**7, - tileScale: float = 1, - **kwargs, - ) -> Axes: - """Plot the histogram of the image bands. - - Parameters: - bins: The number of bins to use for the histogram. Default is 30. - region: The region to compute the histogram in. Default is the image geometry. - bands: The bands to plot the histogram for. Default to all bands. - labels: The labels to use for the output dictionary. Default to the band names. - colors: The colors to use for the plot. Default to the default matplotlib colors. - prescision: The number of decimal to keep for the histogram bins values. Default is 2. - ax: The matplotlib axis to plot the data on. If None, a new figure is created. - scale: The scale to use for the computation. Default is 10,000m. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. default to 10**7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - kwargs: Keyword arguments passed to the matplotlib fill_between() function. - - Returns: - The matplotlib axis with the plot. - - See Also: - - :docstring:`ee.Image.geetools.byRegions` - - :docstring:`ee.Image.geetools.byBands` - - :docstring:`ee.Image.geetools.plot_by_bands` - - :docstring:`ee.Image.geetools.plot_by_regions - - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() - normClim.plot_hist() - """ - # extract the bands from the image - eeBands = ee.List(bands) if len(bands) == 0 else self._obj.bandNames() - eeLabels = ee.List(labels).flatten() if len(labels) == 0 else eeBands - labels = eeLabels.getInfo() - - # retrieve the region from the parameters - region = region if region else self._obj.geometry() - - # extract the data from the server - image = self._obj.select(eeBands).rename(eeLabels).clip(region) - - # set the common parameters of the 3 reducers - params = { - "geometry": region, - "scale": scale, - "crs": crs, - "crsTransform": crsTransform, - "bestEffort": bestEffort, - "maxPixels": maxPixels, - "tileScale": tileScale, - } - - # compute the min and max values of the bands so w can scale the bins of the histogram - min = image.reduceRegion(**{"reducer": ee.Reducer.min(), **params}) - min = min.values().reduce(ee.Reducer.min()) - - max = image.reduceRegion(**{"reducer": ee.Reducer.max(), **params}) - max = max.values().reduce(ee.Reducer.max()) - - # compute the histogram. The result is a dictionary with each band as key and the histogram - # as values. The histograp is a list of [start of bin, value] pairs - reducer = ee.Reducer.fixedHistogram(min, max, bins) - raw_data = image.reduceRegion(**{"reducer": reducer, **params}).getInfo() - - # massage raw data to reshape them as usable source for an Axes plot - # first extract the x coordinates of the plot as a list of bins borders - # every value is duplicated but the first one to create a scale like display. - # the values are treated the same way we simply drop the last duplication to get the same size. - p = 10**precision # multiplier use to truncate the float values - x = [int(d[0] * p) / p for d in raw_data[labels[0]] for _ in range(2)][1:] - data = {l: [int(d[1]) for d in raw_data[l] for _ in range(2)][:-1] for l in labels} - - # create the graph objcet if not provided - if ax is None: - fig, ax = plt.subplots() - - # display the histogram as a fill_between plot to respect GEE lib design - for i, label in enumerate(labels): - kwargs["facecolor"] = to_rgba(colors[i], 0.2) - kwargs["edgecolor"] = to_rgba(colors[i], 1) - ax.fill_between(x, data[label], label=label, **kwargs) - - # customize the layout of the axis - ax.set_ylabel("Count") - ax.grid(axis="x" if type in ["barh"] else "y") - ax.set_axisbelow(True) - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.legend(bbox_to_anchor=(1.02, 1), loc="upper left") - - return ax diff --git a/geetools/ee_image_collection.py b/geetools/ee_image_collection.py deleted file mode 100644 index 9d92aca1..00000000 --- a/geetools/ee_image_collection.py +++ /dev/null @@ -1,2370 +0,0 @@ -"""Toolbox for the ``ee.ImageCollection`` class.""" -from __future__ import annotations - -import uuid -from datetime import datetime as dt - -import ee -import ee_extra -import requests -import xarray -from ee import apifunction -from matplotlib.axes import Axes -from xarray import Dataset -from xee.ext import REQUEST_BYTE_LIMIT - -from .accessors import register_class_accessor -from .utils import plot_data - -PY_DATE_FORMAT = "%Y-%m-%dT%H-%M-%S" -"The python format to use to parse dates coming from GEE." - -EE_DATE_FORMAT = "YYYY-MM-dd'T'HH-mm-ss" -"The javascript format to use to burn date object in GEE." - - -@register_class_accessor(ee.ImageCollection, "geetools") -class ImageCollectionAccessor: - """Toolbox for the ``ee.ImageCollection`` class.""" - - def __init__(self, obj: ee.ImageCollection): - """Instantiate the class.""" - self._obj = obj - - # -- ee-extra wrapper ------------------------------------------------------ - def maskClouds( - self, - method: str = "cloud_prob", - prob: int = 60, - maskCirrus: bool = True, - maskShadows: bool = True, - scaledImage: bool = False, - dark: float = 0.15, - cloudDist: int = 1000, - buffer: int = 250, - cdi: int | None = None, - ) -> ee.ImageCollection: - """Masks clouds and shadows in each image of an ImageCollection (valid just for Surface Reflectance products). - - Parameters: - self: ImageCollection to mask. - method: Method used to mask clouds. This parameter is ignored for Landsat products. - Available options: - - 'cloud_prob' : Use cloud probability. - - 'qa' : Use Quality Assessment band. - prob: Cloud probability threshold. Valid just for method = 'cloud_prob'. This parameter is ignored for Landsat products. - maskCirrus: Whether to mask cirrus clouds. Default to ``True``. Valid just for method = 'qa'. This parameter is ignored for Landsat products. - maskShadows: Whether to mask cloud shadows. Default to ``True`` This parameter is ignored for Landsat products. - scaledImage: Whether the pixel values are scaled to the range [0,1] (reflectance values). This parameter is ignored for Landsat products. - dark: NIR threshold. NIR values below this threshold are potential cloud shadows. This parameter is ignored for Landsat products. - cloudDist: Maximum distance in meters (m) to look for cloud shadows from cloud edges. This parameter is ignored for Landsat products. - buffer: Distance in meters (m) to dilate cloud and cloud shadows objects. This parameter is ignored for Landsat products. - cdi: Cloud Displacement Index threshold. Values below this threshold are considered potential clouds. A cdi = None means that the index is not used. This parameter is ignored for Landsat products. - - Returns: - Cloud-shadow masked image. - - Notes: - This method may mask water as well as clouds for the Sentinel-3 Radiance product. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - S2 = ( - ee.ImageCollection('COPERNICUS/S2_SR') - .maskClouds(prob = 75,buffer = 300,cdi = -0.5) - .first() - ) - - """ - return ee_extra.QA.clouds.maskClouds( - self._obj, - method, - prob, - maskCirrus, - maskShadows, - scaledImage, - dark, - cloudDist, - buffer, - cdi, - ) - - def closest( - self, date: ee.Date | str, tolerance: int = 1, unit: str = "month" - ) -> ee.ImageCollection: - """Gets the closest image (or set of images if the collection intersects a region that requires multiple scenes) to the specified date. - - Parameters: - date: Date of interest. The method will look for images closest to this date. - tolerance: Filter the collection to [date - tolerance, date + tolerance) before searching the closest image. This speeds up the searching process for collections with a high temporal resolution. - unit: Units for tolerance. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. - - Returns: - Closest images to the specified date. - - Examples: - .. code-block:: python - - import ee - import geetools - - s2 = ee.ImageCollection('COPERNICUS/S2_SR').closest('2020-10-15') - s2.size().getInfo() - """ - return ee_extra.ImageCollection.core.closest(self._obj, date, tolerance, unit) - - def spectralIndices( - self, - index: str = "NDVI", - G: float | int = 2.5, - C1: float | int = 6.0, - C2: float | int = 7.5, - L: float | int = 1.0, - cexp: float | int = 1.16, - nexp: float | int = 2.0, - alpha: float | int = 0.1, - slope: float | int = 1.0, - intercept: float | int = 0.0, - gamma: float | int = 1.0, - omega: float | int = 2.0, - beta: float | int = 0.05, - k: float | int = 0.0, - fdelta: float | int = 0.581, - kernel: str = "RBF", - sigma: str = "0.5 * (a + b)", - p: float | int = 2.0, - c: float | int = 1.0, - lambdaN: float | int = 858.5, - lambdaR: float | int = 645.0, - lambdaG: float | int = 555.0, - online: bool = False, - ) -> ee.ImageCollection: - """Computes one or more spectral indices (indices are added as bands) for an image from the Awesome List of Spectral Indices. - - Parameters: - self: Image to compute indices on. Must be scaled to [0,1]. - index: Index or list of indices to compute, default = 'NDVI' - Available options: - - 'vegetation' : Compute all vegetation indices. - - 'burn' : Compute all burn indices. - - 'water' : Compute all water indices. - - 'snow' : Compute all snow indices. - - 'urban' : Compute all urban (built-up) indices. - - 'kernel' : Compute all kernel indices. - - 'all' : Compute all indices listed below. - - Awesome Spectral Indices for GEE: Check the complete list of indices `here `_. - G: Gain factor. Used just for index = 'EVI', default = 2.5 - C1: Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI', default = 6.0 - C2: Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI', default = 7.5 - L: Canopy background adjustment. Used just for index = ['EVI','SAVI'], default = 1.0 - cexp: Exponent used for OCVI, default = 1.16 - nexp: Exponent used for GDVI, default = 2.0 - alpha: Weighting coefficient used for WDRVI, default = 0.1 - slope: Soil line slope, default = 1.0 - intercept: Soil line intercept, default = 0.0 - gamma: Weighting coefficient used for ARVI, default = 1.0 - omega: Weighting coefficient used for MBWI, default = 2.0 - beta: Calibration parameter used for NDSIns, default = 0.05 - k: Slope parameter by soil used for NIRvH2, default = 0.0 - fdelta: Adjustment factor used for SEVI, default = 0.581 - kernel: Kernel used for kernel indices, default = 'RBF' - Available options: - - 'linear' : Linear Kernel. - - 'RBF' : Radial Basis Function (RBF) Kernel. - - 'poly' : Polynomial Kernel. - sigma: Length-scale parameter. Used for kernel = 'RBF', default = '0.5 * (a + b)'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive. - p: Kernel degree. Used for kernel = 'poly', default = 2.0 - c: Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel. Used for kernel = 'poly', default = 1.0. This must be greater than or equal to 0. - lambdaN: NIR wavelength used for NIRvH2 and NDGI, default = 858.5 - lambdaR: Red wavelength used for NIRvH2 and NDGI, default = 645.0 - lambdaG: Green wavelength used for NDGI, default = 555.0 - drop: Whether to drop all bands except the new spectral indices, default = False - - Returns: - Image with the computed spectral index, or indices, as new bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') - image = image.geetools.specralIndices(["NDVI", "NDFI"]) - """ - # fmt: off - return ee_extra.Spectral.core.spectralIndices( - self._obj, index, G, C1, C2, L, cexp, nexp, alpha, slope, intercept, gamma, omega, - beta, k, fdelta, kernel, sigma, p, c, lambdaN, lambdaR, lambdaG, online, - drop=False, - ) - # fmt: on - - def getScaleParams(self) -> dict: - """Gets the scale parameters for each band of the image. - - Returns: - Dictionary with the scale parameters for each band. - - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('MODIS/006/MOD11A2').geetools.getScaleParams() - """ - return ee_extra.STAC.core.getScaleParams(self._obj) - - def getOffsetParams(self) -> dict: - """Gets the offset parameters for each band of the image. - - Returns: - Dictionary with the offset parameters for each band. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('MODIS/006/MOD11A2').getOffsetParams() - """ - return ee_extra.STAC.core.getOffsetParams(self._obj) - - def scaleAndOffset(self) -> ee.ImageCollection: - """Scales bands on an image according to their scale and offset parameters. - - Returns: - Scaled image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - S2 = ee.ImageCollection('COPERNICUS/S2_SR').scaleAndOffset() - """ - return ee_extra.STAC.core.scaleAndOffset(self._obj) - - def preprocess(self, **kwargs) -> ee.ImageCollection: - """Pre-processes the image: masks clouds and shadows, and scales and offsets the image. - - Parameters: - **kwargs: Keywords arguments for ``maskClouds`` method. - - Returns: - Pre-processed image. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - S2 = ee.ImageCollection('COPERNICUS/S2_SR').preprocess() - """ - return ee_extra.QA.pipelines.preprocess(self._obj, **kwargs) - - def getSTAC(self) -> dict: - """Gets the STAC of the image. - - Returns: - STAC of the image. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('COPERNICUS/S2_SR').getSTAC() - """ - # extract the Asset id from the imagecollection - assetId = self._obj.get("system:id").getInfo() - - # search for the project in the GEE catalog and extract the project catalog URL - project = assetId.split("/")[0] - catalog = "https://earthengine-stac.storage.googleapis.com/catalog/catalog.json" - links = requests.get(catalog).json()["links"] - project_catalog = next((i["href"] for i in links if i.get("title") == project), None) - if project_catalog is None: - raise ValueError(f"Project {project} not found in the catalog") - - # search for the collection in the project catalog and extract the collection STAC URL - collection = "_".join(assetId.split("/")) - links = requests.get(project_catalog).json()["links"] - collection_stac = next((i["href"] for i in links if i.get("title") == collection), None) - if collection_stac is None: - raise ValueError(f"Collection {collection} not found in the {project} catalog") - - return requests.get(collection_stac).json() - - def getDOI(self) -> str: - """Gets the DOI of the collection, if available. - - Returns: - DOI of the ee.Image dataset. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getDOI() - """ - return ee_extra.STAC.core.getDOI(self._obj) - - def getCitation(self) -> str: - """Gets the citation of the image, if available. - - Returns: - Citation of the ee.Image dataset. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getCitation() - """ - return ee_extra.STAC.core.getCitation(self._obj) - - def panSharpen(self, method: str = "SFIM", qa: str = "", **kwargs) -> ee.ImageCollection: - """Apply panchromatic sharpening to the ImageCollection images. - - Optionally, run quality assessments between the original and sharpened Image to - measure spectral distortion and set results as properties of the sharpened Image. - - Parameters: - method: The sharpening algorithm to apply. Current options are "SFIM" (Smoothing Filter-based Intensity Modulation), "HPFA" (High Pass Filter Addition), "PCS" (Principal Component Substitution), and "SM" (simple mean). Different sharpening methods will produce different quality sharpening results in different scenarios. - qa: One or more optional quality assessment names to apply after sharpening. Results will be stored as image properties with the pattern `geetools:metric`, e.g. `geetools:RMSE`. - **kwargs: Keyword arguments passed to ee.Image.reduceRegion() such as "geometry", "maxPixels", "bestEffort", etc. These arguments are only used for PCS sharpening and quality assessments. - - Returns: - The ImageCollections with all sharpenable bands sharpened to the panchromatic resolution and quality assessments run and set as properties. - - Examples: - .. code-block:: python - - import ee - import geetools - - ee.Initialize() - - source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") - sharp = source.panSharpen(method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13) - """ - return ee_extra.Algorithms.core.panSharpen( - img=self._obj, method=method, qa=qa or None, prefix="geetools", **kwargs - ) - - def tasseledCap(self) -> ee.ImageCollection: - """Calculates tasseled cap brightness, wetness, and greenness components. - - Tasseled cap transformations are applied using coefficients published for these - supported platforms: - - * Sentinel-2 MSI Level 1C - * Landsat 9 OLI-2 SR - * Landsat 9 OLI-2 TOA - * Landsat 8 OLI SR - * Landsat 8 OLI TOA - * Landsat 7 ETM+ TOA - * Landsat 5 TM Raw DN - * Landsat 4 TM Raw DN - * Landsat 4 TM Surface Reflectance - * MODIS NBAR - - Parameters: - self: ee.ImageCollection to calculate tasseled cap components for. Must belong to a supported platform. - - Returns: - ImageCollections with the tasseled cap components as new bands. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - image = ee.Image('COPERNICUS/S2_SR') - img = img.tasseledCap() - """ - return ee_extra.Spectral.core.tasseledCap(self._obj) - - def append(self, image: ee.Image) -> ee.ImageCollection: - """Append an image to the existing image collection. - - Args: - image: Image to append to the collection. - - Returns: - ImageCollection with the new image appended. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ic = ee.ImageCollection('COPERNICUS/S2_SR'); - - geom = ee.Geometry.Point(-122.196, 41.411); - ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') - ic2021 = ic.filterBounds(geom).filterDate('2021-07-01', '2021-10-01') - - ic = ic2018.append(ic2021.first()) - ic.getInfo() - """ - return self._obj.merge(ee.ImageCollection([image])) - - def collectionMask(self) -> ee.Image: - """A binary ee.Image where only pixels that are masked in all images of the collection get masked. - - Returns: - ee.Image of the mask. 1 where at least 1 pixel is valid 0 elswere - - Examples: - .. code-block:: - - import ee, geetools - - ee.Initialize() - - ic = ee.ImageCollection('COPERNICUS/S2_SR'); - - geom = ee.Geometry.Point(-122.196, 41.411); - ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') - ic = ic2018.geetools.collectionMask() - ic.getInfo() - """ - masks = self._obj.map(lambda i: i.mask()) - return ee.Image(masks.sum().gt(0)) - - def iloc(self, index: int) -> ee.Image: - """Get Image from the ImageCollection by index. - - Args: - index: Index of the image to get. - - Returns: - ee.Image at the specified index. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - ic = ee.ImageCollection('COPERNICUS/S2_SR'); - - geom = ee.Geometry.Point(-122.196, 41.411); - ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') - ic2018.geetools.iloc(0).getInfo() - """ - return ee.Image(self._obj.toList(self._obj.size()).get(index)) - - def integral(self, band: str, time: str = "system:time_start", unit: str = "") -> ee.Image: - """Compute the integral of a band over time or a specified property. - - Args: - band: the name of the band to integrate - time: the name of the property to use as time. It must be a date property of the images. - unit: the time unit use to compute the integral. It can be one of the following: ["year", "month", "day", "hour", "minute", "second"]. If non is set, the time will be normalized on the integral length. - - Returns: - An Image object with the integrated band for each pixel - - Examples: - .. code-block:: python - - import ee, LDCGEETools - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - integral = collection.geetools.integral("B1") - print(integral.getInfo()) - """ - # compute the intervals along the x axis - # the GEE time is stored as a milliseconds timestamp. If the time unit is not set, - # the integral is normalized on the total time length of the time series - minTime = self._obj.aggregate_min(time) - maxTime = self._obj.aggregate_max(time) - intervals = { - "year": ee.Number(1000 * 60 * 60 * 24 * 365), # 1 year in milliseconds - "month": ee.Number(1000 * 60 * 60 * 24 * 30), # 1 month in milliseconds - "day": ee.Number(1000 * 60 * 60 * 24), # 1 day in milliseconds - "hour": ee.Number(1000 * 60 * 60), # 1 hour in milliseconds - "minute": ee.Number(1000 * 60), # 1 minute in milliseconds - "second": ee.Number(1000), # 1 second in milliseconds - "": ee.Number(maxTime).subtract(ee.Number(minTime)), - } - interval = intervals[unit] - - # initialize the sum with a 0 value initial item - # all the properties of the first image of the collection are copied - first = self._obj.first() - zero = ee.Image.constant(0).copyProperties(first, first.propertyNames()) - s = ee.Image(zero).rename("integral").set("last", zero) - - # compute the approximation of the integral using the trapezoidal method - # each local interval is aproximated by the corresponding trapez and the - # sum is updated - def computeIntegral(image, integral): - image = ee.Image(image).select(band) - integral = ee.Image(integral) - last = ee.Image(integral.get("last")) - locMinTime = ee.Number(last.get(time)) - locMaxTime = ee.Number(image.get(time)) - locInterval = locMaxTime.subtract(locMinTime).divide(interval) - locIntegral = last.add(image).multiply(locInterval).divide(2) - return integral.add(locIntegral).set("last", image) - - return ee.Image(self._obj.iterate(computeIntegral, s)) - - def outliers( - self, bands: list | ee.List = [], sigma: float | int | ee.Number = 2, drop: bool = False - ) -> ee.ImageCollection: - """Compute the outlier for each pixel in the specified bands. - - A pixel is considered as an outlier if: - - .. code-block:: - - outlier = value > mean+(sigma*stddev) - outlier = value < mean-(sigma*stddev) - - In a 1D example it would be: - - values = [1, 5, 6, 4, 7, 10] - - mean = 5.5 - - std dev = 3 - - mean + (sigma*stddev) = 8.5 - - mean - (sigma*stddev) = 2.5 - - outliers = values between 2.5 and 8.5 = [1, 10] - - Here in this function an extra band is added to each image for each of the evaluated bands with the outlier status. The band name is the original band name with the suffix "_outlier". A value of 1 means that the pixel is an outlier, 0 means that it is not. - - Optionally users can discard this band by setting ``drop`` to ``True`` and the outlier will simply be masked from each ilmage. This is useful when the outlier band is not needed and the user wants to save space. - - idea from: https://www.kdnuggets.com/2017/02/removing-outliers-standard-deviation-python.html - - Args: - bands: the bands to evaluate for outliers. If empty, all bands are evaluated - sigma: the number of standard deviations to use to compute the outlier - drop: whether to drop the outlier band from the images - - Returns: - an ImageCollection with the outlier band added to each image or masked if ``drop`` is ``True`` - - Examples: - .. code-block:: python - - import ee, LDCGEETools - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - outliers = collection.geetools.outliers(["B1", "B2"], 2) - print(outliers.getInfo()) - """ - # cast parameters and compute the outlier band names - initBands = self._obj.first().bandNames() - statBands = ee.List(bands) if bands else initBands - outBands = statBands.map(lambda b: ee.String(b).cat("_outlier")) - - # compute the mean and std dev for each band - statCollection = self._obj.select(statBands) - mean = statCollection.mean() - stdDev = statCollection.reduce(ee.Reducer.stdDev()) - minValues = mean.subtract(stdDev.multiply(sigma)) - maxValues = mean.add(stdDev.multiply(sigma)) - - # compute the outlier band for each image - def computeOutlierBands(i): - outImage = i.select(statBands) - outImage = outImage.gt(maxValues).Or(outImage.lt(minValues)) - return i.addBands(outImage.rename(outBands)) - - ic = self._obj.map(computeOutlierBands) - - # drop the outlier band and mask each image if requested - def maskOutliers(i): - maskedBands = i.select(statBands).updateMask(i.select(outBands).Not()) - return i.addBands(maskedBands, overwrite=True).select(initBands) - - ic = ic if drop is False else ic.map(maskOutliers) - - return ee.ImageCollection(ic) - - def to_xarray( - self, - drop_variables: tuple[str, ...] | None = None, - io_chunks: object = None, - n_images: int = -1, - mask_and_scale: bool = True, - decode_times: bool = True, - decode_timedelta: bool | None = None, - use_cftime: bool | None = None, - concat_characters: bool = True, - decode_coords: bool = True, - crs: str | None = None, - scale: float | int | None = None, - projection: ee.Projection | None = None, - geometry: ee.Geometry | None = None, - primary_dim_name: str | None = None, - primary_dim_property: str | None = None, - ee_mask_value: float | None = None, - request_byte_limit: int = REQUEST_BYTE_LIMIT, - ) -> Dataset: - """Open an Earth Engine ImageCollection as an ``xarray.Dataset``. - - Args: - drop_variables: Variables or bands to drop before opening. - io_chunks: Specifies the chunking strategy for loading data from EE. By default, this automatically calculates optional chunks based on the ``request_byte_limit``. - n_images: The max number of EE images in the collection to open. Useful when there are a large number of images in the collection since calculating collection size can be slow. -1 indicates that all images should be included. - mask_and_scale: Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). - decode_times: Decode cf times (e.g., integers since "hours since 2000-01-01") to np.datetime64. - decode_timedelta: If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. - use_cftime: Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. - concat_characters: Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" - decode_coords: bool or {"coordinates", "all"}, Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the ``'coordinates'`` attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as coordinate variables. - crs: The coordinate reference system (a CRS code or WKT string). This defines the frame of reference to coalesce all variables upon opening. By default, data is opened with 'EPSG:4326'. - scale: The scale in the ``crs`` or ``projection``'s units of measure -- either meters or degrees. This defines the scale that all data is represented in upon opening. By default, the scale is 1° when the CRS is in degrees or 10,000 when in meters. - projection: Specify an ``ee.Projection`` object to define the ``scale`` and ``crs`` (or other coordinate reference system) with which to coalesce all variables upon opening. By default, the scale and reference system is set by the the ``crs`` and ``scale`` arguments. - geometry: Specify an ``ee.Geometry`` to define the regional bounds when opening the data. When not set, the bounds are defined by the CRS's ``area_of_use`` boundaries. If those aren't present, the bounds are derived from the geometry of the first image of the collection. - primary_dim_name: Override the name of the primary dimension of the output Dataset. By default, the name is 'time'. - primary_dim_property: Override the ``ee.Image`` property for which to derive the values of the primary dimension. By default, this is 'system:time_start'. - ee_mask_value: Value to mask to EE nodata values. By default, this is 'np.iinfo(np.int32).max' i.e. 2147483647. - request_byte_limit: the max allowed bytes to request at a time from Earth Engine. By default, it is 48MBs. - - Returns: - An ``xarray.Dataset`` that streams in remote data from Earth Engine. - """ - return xarray.open_dataset( - self._obj, - engine="ee", - drop_variables=drop_variables, - io_chunks=io_chunks, - n_images=n_images, - mask_and_scale=mask_and_scale, - decode_times=decode_times, - decode_timedelta=decode_timedelta, - use_cftime=use_cftime, - concat_characters=concat_characters, - decode_coords=decode_coords, - crs=crs, - scale=scale, - projection=projection, - geometry=geometry, - primary_dim_name=primary_dim_name, - primary_dim_property=primary_dim_property, - ee_mask_value=ee_mask_value, - request_byte_limit=request_byte_limit, - ) - - def validPixel(self, band: str | ee.String = "") -> ee.Image: - """Compute the number of valid pixels in the specified band. - - Compute the number of valid pixels in the specified band. 2 bands will be created: - one with the number of valid pixels (``valid``) and another with the percentage of valid pixels (``pct_valid``). - - Args: - band: the band to evaluate for valid pixels. If empty, use the first band - Returns: - an Image with the number of valid pixels or the percentage of valid pixels. - - Examples: - .. code-block:: python - import ee, LDCGEETools - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - valid = collection.geetools.validPixels("B1") - print(valid.getInfo()). - """ - # compute the mask for the specified band - band = self._obj.first().bandNames().get(0) if band == "" else ee.String(band) - masks = self._obj.select([band]).map(lambda i: i.mask().eq(1)) - validPixel = masks.sum().rename("valid").clip(self._obj.geometry()) - validPct = validPixel.divide(self._obj.size()).multiply(100).rename("pct_valid") - return validPixel.addBands(validPct) - - def containsBandNames(self, bandNames: list | ee.List, filter: str) -> ee.ImageCollection: - """Filter the ImageCollection by band names using the provided filter. - - Args: - bandNames: list of band names to filter - filter: type of filter to apply. To keep images that contains all the specified bands use "ALL". To get the images including at least one of the specified band use "ANY". - - Returns: - A filtered ImageCollection - - Examples: - .. code-block:: python - - import ee, LDCGEETools - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - filtered = collection.geetools.containsBandNames(["B1", "B2"], "ALL") - print(filtered.getInfo()) - """ - # cast parameters - filter = {"ALL": "Filter.and", "ANY": "Filter.or"}[filter] - bandNames = ee.List(bandNames) - - # add bands as metadata in a temporary property - band_name = uuid.uuid4().hex - ic = self._obj.map(lambda i: i.set(band_name, i.bandNames())) - - # create a filter by combining a listContain filter over all the band names from the - # user list. Combine them with a "Or" to get a "any" filter and "And" to get a "all". - # We use a workaround until this is solved: https://issuetracker.google.com/issues/322838709 - filterList = bandNames.map(lambda b: ee.Filter.listContains(band_name, b)) - filterCombination = apifunction.ApiFunction.call_(filter, ee.List(filterList)) - - # apply this filter and remove the temporary property. Exclude parameter is additive so - # we do a blank multiplication to remove all the properties beforhand - ic = ee.ImageCollection(ic.filter(filterCombination)) - ic = ic.map(lambda i: ee.Image(i.multiply(1).copyProperties(i, exclude=[band_name]))) - - return ee.ImageCollection(ic) - - def containsAllBands(self, bandNames: list | ee.List) -> ee.ImageCollection: - """Filter the ImageCollection keeping only the images with all the provided bands. - - Args: - bandNames: list of band names to filter - - Returns: - A filtered ImageCollection - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - filtered = collection.geetools.containsAllBands(["B1", "B2"]) - print(filtered.getInfo()) - """ - return self.containsBandNames(bandNames, "ALL") - - def containsAnyBands(self, bandNames: list | ee.List) -> ee.ImageCollection: - """Filter the ImageCollection keeping only the images with any of the provided bands. - - Args: - bandNames: list of band names to filter - - Returns: - A filtered ImageCollection - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - filtered = collection.geetools.containsAnyBands(["B1", "B2"]) - print(filtered.getInfo()) - """ - return self.containsBandNames(bandNames, "ANY") - - def aggregateArray(self, properties: list | ee.List | None = None) -> ee.Dict: - """Aggregate the ImageCollection selected properties into a dictionary. - - Args: - properties: list of properties to aggregate. If None, all properties are aggregated. - - Returns: - A dictionary with the properties as keys and the aggregated values as values. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - aggregated = collection.geetools.aggregateArray(["CLOUD_COVER", "system:time_start"]) - print(aggregated.getInfo()) - """ - keys = ee.List(properties) if properties is not None else self._obj.first().propertyNames() - values = keys.map(lambda p: self._obj.aggregate_array(p)) - return ee.Dictionary.fromLists(keys, values) - - def groupInterval(self, unit: str = "month", duration: int = 1) -> ee.List: - """Transform the ImageCollection into a list of smaller collection of the specified duration. - - For example using unit as "month" and duration as 1, the ImageCollection will be transformed - into a list of ImageCollection with each ImageCollection containing images for each month. - Make sure the collection is filtered beforeend to reduce the number of images that needs to be - processed. - - Args: - unit: The unit of time to split the collection. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. - duration: The duration of each split. - - Returns: - A list of imagecollection grouped by interval - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - split = collection.geetools.groupInterval("month", 1) - print(split.getInfo()) - """ - # as everything is relyin on the "system:time_start" property - # we sort the image collection in the first place. In most collection it will change nothing - # so free of charge unless for plumbing - ic = self._obj.sort("system:time_start") - - # transform the interval into a duration in milliseconds - # I can use the DateRangeAccessor as it's imported earlier in the __init__.py file - # I don't know if it should be properly imported here, let's see with user feedback - timeList = ic.aggregate_array("system:time_start") - start, end = timeList.get(0), timeList.get(-1) - DateRangeList = ee.DateRange(start, end).geetools.split(duration, unit) - imageCollectionList = DateRangeList.map( - lambda dr: ic.filterDate(ee.DateRange(dr).start(), ee.DateRange(dr).end()) - ) - - return ee.List(imageCollectionList) - - def reduceInterval( - self, - reducer: str | ee.Reducer = "mean", - unit: str = "month", - duration: int = 1, - ) -> ee.ImageCollection: - """Reduce the images included in the same duration interval using the provided reducer. - - For example using unit as "month" and duration as 1, the ImageCollection will be reduced - into a new ImageCollection with each image containing the reduced values for each month. - Make sure the collection is filtered beforehand to reduce the number of images that needs to be - processed. - - Args: - reducer: The name of the reducer to use or a Reducer object. Default is "mean". - unit: The unit of time to split the collection. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. - duration: The duration of each split. - - Returns: - A new ImageCollection with the reduced images. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - reduced = collection.geetools.reduceInterval("mean", "month", 1) - print(reduced.getInfo()) - """ - # create a list of image collections to be reduced - # Every subcollection is sorted in case one use the "first" reducer - imageCollectionList = self.groupInterval(unit, duration) - - # create a reducer from user parameters - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - - def reduce(ic): - timeList = ee.ImageCollection(ic).aggregate_array("system:time_start") - start, end = timeList.get(0), timeList.get(-1) - bandNames = ee.ImageCollection(ic).first().bandNames() - image = ee.ImageCollection(ic).reduce(red).rename(bandNames) - return image.set("system:time_start", start, "system:time_end", end) - - reducedImagesList = imageCollectionList.map(reduce) - - # set back the original properties - ic = ee.ImageCollection(reducedImagesList).copyProperties(self._obj) - - return ee.ImageCollection(ic) - - def closestDate(self) -> ee.ImageCollection: - """Fill masked pixels with the first valid pixel in the stack of images. - - The method will for every image, fill all the pixels with the latest nono masked pixel in the stack of images. - I requires the image to have a valid "system:time_start" property. - As the imageCollection will need to be sorted limit the analysis to a reasonable number of image by filtering your data beforehand. - - Returns: - An ImageCollection with all pixels unmasked in every image. - - Examples: - .. code:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - filled = collection.geetools.fillWithFirst() - print(filled.getInfo()) - """ - # retrieve all the time starts as an ordered list to iterate - timeList = self._obj.aggregate_array("system:time_start").sort() - - # for each time start find all the images thata are before and use the mosaic reducer - # to only keep the first one with a non masked pixel - def fill(date): - return self._obj.filter(ee.Filter.lte("system:time_start", date)).mosaic() - - imageList = timeList.map(fill) - - return ee.ImageCollection(imageList) - - def medoid(self) -> ee.image: - """Compute the medoid of the ImageCollection. - - The medoid is the image that has the smallest sum of distances to all other images in the collection. - The distance is computed using the Euclidean distance between the pixels of the images. - - Returns: - An Image that is the medoid of the ImageCollection. - - Examples: - .. code:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - medoid = collection.geetools.medoid() - print(medoid.getInfo()) - """ - # create a random name for the sum of distances band to avoid conflicts - sumOfDistancesName = uuid.uuid4().hex - - # discover bandname from the first image of the collection - bandNames = self._obj.first().bandNames() - - # normalize the band used to compute the distance - # first extract the min and max value of each band pixelwizse along the stac and then - # normalize the pixel values. - minMax = self._obj.reduce(ee.Reducer.minMax()) - - def normalizeBands(image): - def normalizeBand(bandName): - band = image.select([bandName]) - bandMin = minMax.select(ee.String(bandName).cat("_min")) - bandMax = minMax.select(ee.String(bandName).cat("_max")) - return band.subtract(bandMin).divide(bandMax.subtract(bandMin)) - - return ee.ImageCollection(bandNames.map(normalizeBand)).toBands().rename(bandNames) - - normalized = self._obj.map(normalizeBands) - - # compute the distance between each image and all the others - def computeSumDistance(image): - def computeDistance(other): - return image.subtract(other).pow(2).reduce(ee.Reducer.sum()).sqrt() - - sumDistances = normalized.map(computeDistance).reduce(ee.Reducer.sum()) - return image.addBands(sumDistances.rename(sumOfDistancesName)) - - sumDistance = normalized.map(computeSumDistance) - - # use the computed sum of distances as a sorting band for a quality mossaic - # to get the image with the smallest sum of distances - medoid = sumDistance.qualityMosaic(sumOfDistancesName) - - return ee.Image(medoid).select(bandNames) - - def datesByBands( - self, - region: ee.Geometry, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - bands: list = [], - labels: list = [], - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> ee.Dictionary: - """Reduce the data for each image in the collection by bands on a specific region. - - This method is returning a dictionary with all the bands as keys and their reduced value for each date over the specified region as value. - - .. code-block:: - - { - "band1": {"date1": value1, "date2": value2, ...}, - "band2": {"date1": value1, "date2": value2, ...}, - ... - } - - Parameters: - region: The region to reduce the data on. - reducer: The name of the reducer or a reducer object use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - bands: The bands to reduce. If empty, all bands are reduced. - labels: The labels to use for the bands. If empty, the bands names are used. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each band and each date. - - See Also: - - :docstring:`ee.ImageCollection.geetools.datesByRegions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) - reduced = collection.geetools.datesByBands(region, "mean", 10000, "system:time_start") - print(reduced.getInfo()) - """ - # cast parameters - eeBands = ee.List(bands) if len(bands) else self._obj.first().bandNames() - eeLabels = ee.List(labels) if len(labels) else eeBands - - # recast band names as labels in the source collection - ic = self._obj.select(eeBands).map(lambda i: i.rename(eeLabels)) - - # aggregate all the dates contained in the collection - dateList = ic.aggregate_array(dateProperty).map(lambda d: ee.Date(d).format(EE_DATE_FORMAT)) - - # create a reducer from the specified parameters - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - - # create a list of dictionaries with the reduced values for each band - def reduce(lbl: ee.String) -> ee.Dictionary: - image = ic.select([lbl]).toBands().rename(dateList) - return image.reduceRegion( - reducer=red, - geometry=region, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - - return ee.Dictionary.fromLists(eeLabels, eeLabels.map(reduce)) - - def datesByRegions( - self, - band: str, - regions: ee.FeatureCollection, - label: str = "system:index", - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> ee.Dictionary: - """Reduce the data for each image in the collection by regions for a single band. - - This method is returning a dictionary with all the regions as keys and their reduced value for each date over the specified region for a specific band as value. - - .. code-block:: - - { - "region1": {"date1": value1, "date2": value2, ...}, - "region2": {"date1": value1, "date2": value2, ...}, - ... - } - - Parameters: - band: The band to reduce. - regions: The regions to reduce the data on. - label: The property to use as label for each region. Default is "system:index". - reducer: The name of the reducer or a reducer object use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each region and each date. - - See Also: - - :docstring:`ee.ImageCollection.geetools.datesByBands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - regions = ee.FeatureCollection([ - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) - ]) - - reduced = collection.geetools.datesByRegions("B1", regions, "name", "mean", 10000, "system:time_start") - print(reduced.getInfo()) - """ - # aggregate all the dates of the image collection into bands of a single image - def to_string(date: ee.Date) -> ee.String: - return ee.Date(date).format(EE_DATE_FORMAT) - - dateList = self._obj.aggregate_array(dateProperty).map(to_string) - - # reduce the data for each region - image = self._obj.select([band]).toBands().rename(dateList) - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - reduced = image.reduceRegions( - collection=regions, - reducer=red, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ) - - # create a list of dictionaries for each region and aggregate them into a dictionary - values = reduced.toList(regions.size()).map(lambda f: ee.Feature(f).toDictionary(dateList)) - keys = ee.List(regions.aggregate_array(label)) - - return ee.Dictionary.fromLists(keys, values) - - def doyByBands( - self, - region: ee.Geometry, - spatialReducer: str | ee.Reducer = "mean", - timeReducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - bands: list = [], - labels: list = [], - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> ee.Dictionary: - """Aggregate the images that occurs on the same day and then reduce each band on a single region. - - This method is returning a dictionary with all the bands as keys and their reduced value for each day over the specified region as value. - - .. code-block:: - - { - "band1": {"doy1": value1, "doy2": value2, ...}, - "band2": {"doy1": value1, "doy2": value2, ...}, - ... - } - - Parameters: - region: The region to reduce the data on. - spatialReducer: The name of the reducer or a reducer object to use for spatial reduction. Default is "mean". - timeReducer: The name of the reducer or a reducer object to use for time reduction. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - bands: The bands to reduce. If empty, all bands are reduced. - labels: The labels to use for the bands. If empty, the bands names are used. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each band and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - """ - # cast parameters - bands = ee.List(bands) if len(bands) else self._obj.first().bandNames() - labels = ee.List(labels) if len(labels) else bands - - # recast band names as labels in the source collection - ic = self._obj.select(bands).map(lambda i: i.rename(labels)) - - # create 2 metadata name as random string to avoid any risk of conflicts - doy_metadata, size_metadata = uuid.uuid4().hex, uuid.uuid4().hex - - # add the day of year as metadata to each image - def doy_tag(i: ee.Image) -> ee.Image: - doy = ee.Date(i.get(dateProperty)).getRelative("day", "year") - return i.set(doy_metadata, doy) - - ic = self._obj.map(doy_tag) - - # create a list of ImageCollection where every images of the same day are grouped together - dayList = ee.List.sequence(0, 366) - - def filter_doy(d: ee.Number) -> ee.ImageCollection: - c = ic.filter(ee.Filter.eq(doy_metadata, d)) - c = c.set(size_metadata, c.size()) - return c.set(doy_metadata, d) - - icList = dayList.map(filter_doy) - - # reduce every sub ImageCollection in the list into images (it's the temporal reduction) - # and aggregate the result as a single ImageCollection - timeRed = ( - getattr(ee.Reducer, timeReducer)() if isinstance(timeReducer, str) else timeReducer - ) - - def timeReduce(c: ee.imageCollection) -> ee.image: - c = ee.ImageCollection(c) - i = c.reduce(timeRed).rename(labels) - i = i.set(size_metadata, c.get(size_metadata)) - return i.set(doy_metadata, c.get(doy_metadata)) - - ic = ee.ImageCollection(icList.map(timeReduce)).filter(ee.Filter.gt(size_metadata, 0)) - - # spatially reduce the generated imagecollection over the region for each band - doyList = ic.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) - spatialRed = ( - getattr(ee.Reducer, spatialReducer)() - if isinstance(spatialReducer, str) - else spatialReducer - ) - - def spatialReduce(label: ee.String) -> ee.Dictionary: - image = ic.select([label]).toBands().rename(doyList) - return image.reduceRegion( - reducer=spatialRed, - geometry=region, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - - return ee.Dictionary.fromLists(labels, ee.List(labels).map(spatialReduce)) - - def doyByRegions( - self, - band: str, - regions: ee.FeatureCollection, - label: str = "system:index", - spatialReducer: str | ee.Reducer = "mean", - timeReducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> ee.Dictionary: - """Aggregate the images that occurs on the same day and then reduce a single band on multiple regions. - - This method is returning a dictionary with all the regions as keys and their reduced value for each day over the specified region for a specific band as value. - - .. code-block:: - - { - "region1": {"doy1": value1, "doy2": value2, ...}, - "region2": {"doy1": value1, "doy2": value2, ...}, - ... - } - - Parameters: - band: The band to reduce. - regions: The regions to reduce the data on. - label: The property to use as label for each region. Default is "system:index". - spatialReducer: The name of the reducer or a reducer object to use for spatial reduction. Default is "mean". - timeReducer: The name of the reducer or a reducer object to use for time reduction. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each region and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - """ - # create 2 metadata name as random string to avoid any risk of conflicts - doy_metadata, size_metadata = uuid.uuid4().hex, uuid.uuid4().hex - - # add the day of year as metadata to each image - def doy_tag(i: ee.Image) -> ee.Image: - doy = ee.Date(i.get(dateProperty)).getRelative("day", "year") - return i.set(doy_metadata, doy) - - ic = self._obj.select([band]).map(doy_tag) - - # create a list of ImageCollection where every images of the same day are grouped together - dayList = ee.List.sequence(0, 366) - - def filter_doy(d: ee.Number) -> ee.ImageCollection: - c = ic.filter(ee.Filter.eq(doy_metadata, d)) - c = c.set(size_metadata, c.size()) - return c.set(doy_metadata, d) - - icList = dayList.map(filter_doy) - - # reduce every sub ImageCollection in the list into images (it's the temporal reduction) - # and aggregate the result as a single ImageCollection - timeRed = ( - getattr(ee.Reducer, timeReducer)() if isinstance(timeReducer, str) else timeReducer - ) - - def timeReduce(c: ee.imageCollection) -> ee.image: - c = ee.ImageCollection(c) - i = c.reduce(timeRed).rename([band]) - i = i.set(size_metadata, c.get(size_metadata)) - return i.set(doy_metadata, c.get(doy_metadata)) - - ic = ee.ImageCollection(icList.map(timeReduce)).filter(ee.Filter.gt(size_metadata, 0)) - - # reduce the data for each region - doyList = ic.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) - spatialRed = ( - getattr(ee.Reducer, spatialReducer)() - if isinstance(spatialReducer, str) - else spatialReducer - ) - image = ic.toBands().rename(doyList) - reduced = image.reduceRegions( - collection=regions, - reducer=spatialRed, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ) - - # create a list of dictionaries for each region and aggregate them into a dictionary - values = reduced.toList(regions.size()).map(lambda f: ee.Feature(f).toDictionary(doyList)) - keys = ee.List(regions.aggregate_array(label)) - - return ee.Dictionary.fromLists(keys, values) - - def doyBySeasons( - self, - band: str, - region: ee.Geometry, - seasonStart: int | ee.Number, - seasonEnd: int | ee.Number, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> ee.Dictionary: - """Aggregate for each year on a single region a single band. - - This method is returning a dictionary with all the years as keys and their reduced value for each day of the season over the specified region for a specific band as value. - To set the start and end of the season, use the :py:method:`ee.Date.getRelative` or :py:class:`time.struct_time` method to get the day of the year. - - .. code-block:: - - { - "year1": {"doy1": value1, "doy2": value2, ...}, - "year2": {"doy1": value1, "doy2": value2, ...}, - ... - } - - Parameters: - band: The band to reduce. - region: The region to reduce the data on. - seasonStart: The day of the year that marks the start of the season. - seasonEnd: The day of the year that marks the end of the season. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each year and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filter(ee.Filter.Or( - ee.Filter.date("2022-01-01", "2022-12-31"), - ee.Filter.date("2016-01-01", "2016-12-31"), - )) - .map(lambda i: ee.Image(i).addBands( - ee.Image(i) - .normalizedDifference(["B5", "B4"]) - .rename("NDVI") - )) - ) - - reduced = collection.geetools.doyBySeasons( - band = "NDVI", - region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), - seasonStart = ee.Date("2016-05-01").getRelative("day", "year"), - seasonEnd = ee.Date("2016-10-31").getRelative("day", "year"), - reducer = "mean", - dateProperty = "system:time_start", - scale = 10000 - ) - reduced.getInfo() - """ - # force cast the start and end of season as ee.Number - seasonStart, seasonEnd = ee.Number(seasonStart), ee.Number(seasonEnd) - - # add a doy metadata to the images - doy_metadata, year_metadata = uuid.uuid4().hex, uuid.uuid4().hex - - def date_tag(i: ee.Image) -> ee.Image: - date = ee.Date(i.get(dateProperty)) - doy = date.getRelative("day", "year") - year = date.get("year") - return i.set(doy_metadata, doy).set(year_metadata, year) - - ic = self._obj.select([band]).map(date_tag) - - # create a List of image collection where every images from the same year are grouped together - yearList = ic.aggregate_array(year_metadata).distinct().sort() - yearKeys = yearList.map(lambda y: ee.Number(y).int().format()) - red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer - - def reduce(year: ee.Number) -> ee.Dictionary: - c = ic.filter(ee.Filter.eq(year_metadata, year)) - c = c.filter(ee.Filter.rangeContains(doy_metadata, seasonStart, seasonEnd)) - doyList = c.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) - return ( - c.toBands() - .rename(doyList) - .reduceRegion( - reducer=red, - geometry=region, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - ) - - return ee.Dictionary.fromLists(yearKeys, yearList.map(reduce)) - - def doyByYears( - self, - band: str, - region: ee.Geometry, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> ee.Dictionary: - """Aggregate for each year on a single region a single band. - - This method is returning a dictionary with all the years as keys and their reduced value for each day over the specified region for a specific band as value. - - .. code-block:: - - { - "year1": {"doy1": value1, "doy2": value2, ...}, - "year2": {"doy1": value1, "doy2": value2, ...}, - ... - } - - Parameters: - band: The band to reduce. - region: The region to reduce the data on. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each year and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filter(ee.Filter.Or( - ee.Filter.date("2022-01-01", "2022-12-31"), - ee.Filter.date("2016-01-01", "2016-12-31"), - )) - .map(lambda i: ee.Image(i).addBands( - ee.Image(i) - .normalizedDifference(["B5", "B4"]) - .rename("NDVI") - )) - ) - - reduced = collection.geetools.doyByYears( - band = "NDVI", - region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), - reducer = "mean", - dateProperty = "system:time_start", - scale = 10000 - ) - reduced.getInfo() - """ - return self.doyBySeasons( - band=band, - region=region, - seasonStart=ee.Number(0), - seasonEnd=ee.Number(366), - reducer=reducer, - dateProperty=dateProperty, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - - def plot_dates_by_bands( - self, - region: ee.Geometry, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - bands: list = [], - labels: list = [], - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by bands on a specific region. - - This method is plotting the reduced data for each image in the collection by bands on a specific region. - - Parameters: - region: The region to reduce the data on. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - bands: The bands to reduce. If empty, all bands are reduced. - labels: The labels to use for the bands. If empty, the bands names are used. - colors: The colors to use for the bands. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each band and each date. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) - collection.geetools.plot_dates_by_bands(region, "mean", 10000, "system:time_start") - """ - # get the reduced data - raw_data = self.datesByBands( - region=region, - reducer=reducer, - dateProperty=dateProperty, - bands=bands, - labels=labels, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ).getInfo() - - # transform all the dates int datetime objects - def to_date(dict): - return {dt.strptime(d, PY_DATE_FORMAT): v for d, v in dict.items()} - - data = {l: to_date(dict) for l, dict in raw_data.items()} - - # create the plot - ax = plot_data("date", data, "Date", colors, ax) - - return ax - - def plot_dates_by_regions( - self, - band: str, - regions: ee.FeatureCollection, - label: str = "system:index", - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by regions for a single band. - - This method is plotting the reduced data for each image in the collection by regions for a single band. - - Parameters: - band: The band to reduce. - regions: The regions to reduce the data on. - label: The property to use as label for each region. Default is "system:index". - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - colors: The colors to use for the regions. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each region and each date. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - regions = ee.FeatureCollection([ - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) - ]) - - collection.geetools.plot_dates_by_regions("B1", regions, "name", "mean", 10000, "system:time_start") - """ - # get the reduced data - raw_data = self.datesByRegions( - band=band, - regions=regions, - label=label, - reducer=reducer, - dateProperty=dateProperty, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ).getInfo() - - # transform all the dates int datetime objects - def to_date(dict): - return {dt.strptime(d, PY_DATE_FORMAT): v for d, v in dict.items()} - - data = {l: to_date(dict) for l, dict in raw_data.items()} - - # create the plot - ax = plot_data("date", data, "Date", colors, ax) - - return ax - - def plot_doy_by_bands( - self, - region: ee.Geometry, - spatialReducer: str | ee.Reducer = "mean", - timeReducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - bands: list = [], - labels: list = [], - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by bands on a specific region. - - This method is plotting the reduced data for each image in the collection by bands on a specific region. - - Parameters: - region: The region to reduce the data on. - spatialReducer: The name of the reducer or a reducer object to use. Default is "mean". - timeReducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - bands: The bands to reduce. If empty, all bands are reduced. - labels: The labels to use for the bands. If empty, the bands names are used. - colors: The colors to use for the bands. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each band and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) - collection.geetools.plot_doy_by_bands(region, "mean", "mean", 10000, "system:time_start") - """ - # get the reduced data - raw_data = self.doyByBands( - region=region, - spatialReducer=spatialReducer, - timeReducer=timeReducer, - dateProperty=dateProperty, - bands=bands, - labels=labels, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ).getInfo() - - # transform all the dates strings into int object and reorder the dictionary - def to_int(d): - return {int(k): v for k, v in d.items()} - - data = {l: dict(sorted(to_int(raw_data[l]).items())) for l in raw_data} - - # create the plot - ax = plot_data("doy", data, "Day of Year", colors, ax) - - return ax - - def plot_doy_by_regions( - self, - band: str, - regions: ee.FeatureCollection, - label: str = "system:index", - spatialReducer: str | ee.Reducer = "mean", - timeReducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by regions for a single band. - - This method is plotting the reduced data for each image in the collection by regions for a single band. - - Parameters: - band: The band to reduce. - regions: The regions to reduce the data on. - label: The property to use as label for each region. Default is "system:index". - spatialReducer: The name of the reducer or a reducer object to use. Default is "mean". - timeReducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - colors: The colors to use for the regions. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each region and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - - regions = ee.FeatureCollection([ - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), - ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) - ]) - - collection.geetools.plot_doy_by_regions("B1", regions, "name", "mean", "mean", 10000, "system:time_start") - """ - # get the reduced data - raw_data = self.doyByRegions( - band=band, - regions=regions, - label=label, - spatialReducer=spatialReducer, - timeReducer=timeReducer, - dateProperty=dateProperty, - scale=scale, - crs=crs, - crsTransform=crsTransform, - tileScale=tileScale, - ).getInfo() - - # transform all the dates strings into int object and reorder the dictionary - def to_int(d): - return {int(k): v for k, v in d.items()} - - data = {l: dict(sorted(to_int(raw_data[l]).items())) for l in raw_data} - - # create the plot - ax = plot_data("doy", data, "Day of Year", colors, ax) - - return ax - - def plot_doy_by_seasons( - self, - band: str, - region: ee.Geometry, - seasonStart: int | ee.Number, - seasonEnd: int | ee.Number, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by years for a single band. - - This method is plotting the reduced data for each image in the collection by years for a single band. - To set the start and end of the season, use the :py:method:`ee.Date.getRelative` or :py:class:`time.struct_time` method to get the day of the year. - - Parameters: - band: The band to reduce. - region: The region to reduce the data on. - seasonStart: The day of the year that marks the start of the season. - seasonEnd: The day of the year that marks the end of the season. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - colors: The colors to use for the regions. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each year and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filter(ee.Filter.Or( - ee.Filter.date("2022-01-01", "2022-12-31"), - ee.Filter.date("2016-01-01", "2016-12-31"), - )) - .map(lambda i: ee.Image(i).addBands( - ee.Image(i) - .normalizedDifference(["B5", "B4"]) - .rename("NDVI") - )) - ) - - collection.geetools.plot_doy_by_seasons( - band = "NDVI", - region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), - seasonStart = ee.Date("2016-05-01").getRelative("day", "year"), - seasonEnd = ee.Date("2016-10-31").getRelative("day", "year"), - reducer = "mean", - dateProperty = "system:time_start", - scale = 10000 - ) - """ - # get the reduced data - raw_data = self.doyBySeasons( - band=band, - region=region, - seasonStart=seasonStart, - seasonEnd=seasonEnd, - reducer=reducer, - dateProperty=dateProperty, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ).getInfo() - - # transform all the dates strings into int object and reorder the dictionary - def to_int(d): - return {int(k): v for k, v in d.items()} - - data = {l: dict(sorted(to_int(raw_data[l]).items())) for l in raw_data} - - # create the plot - ax = plot_data("doy", data, "Day of Year", colors, ax) - - return ax - - def plot_doy_by_years( - self, - band: str, - region: ee.Geometry, - reducer: str | ee.Reducer = "mean", - dateProperty: str = "system:time_start", - colors: list = [], - ax: Axes | None = None, - scale: int = 10000, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = 10**7, - tileScale: float = 1, - ) -> Axes: - """Plot the reduced data for each image in the collection by years for a single band. - - This method is plotting the reduced data for each image in the collection by years for a single band. - - Parameters: - band: The band to reduce. - region: The region to reduce the data on. - reducer: The name of the reducer or a reducer object to use. Default is "mean". - dateProperty: The property to use as date for each image. Default is "system:time_start". - colors: The colors to use for the regions. If empty, the default colors are used. - ax: The matplotlib axes to plot the data on. If None, a new figure is created. - scale: The scale in meters to use for the reduction. default is 10000m - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A matplotlib axes with the reduced values for each year and each day. - - See Also: - - :docstring:`ee.ImageCollection.geetools.doyByBands` - - :docstring:`ee.ImageCollection.geetools.doyByRegions` - - :docstring:`ee.ImageCollection.geetools.doyBySeasons` - - :docstring:`ee.ImageCollection.geetools.doyByYears` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` - - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filter(ee.Filter.Or( - ee.Filter.date("2022-01-01", "2022-12-31"), - ee.Filter.date("2016-01-01", "2016-12-31"), - )) - .map(lambda i: ee.Image(i).addBands( - ee.Image(i) - .normalizedDifference(["B5", "B4"]) - .rename("NDVI") - )) - ) - - collection.geetools.plot_doy_by_years( - band = "NDVI", - region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), - reducer = "mean", - dateProperty = "system:time_start", - scale = 10000 - ) - """ - return self.plot_doy_by_seasons( - band=band, - region=region, - seasonStart=ee.Number(0), - seasonEnd=ee.Number(366), - reducer=reducer, - dateProperty=dateProperty, - colors=colors, - ax=ax, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - - def reduceRegion( - self, - reducer: str, - geometry: ee.Geometry, - idProperty: str = "system:index", - idType: type = ee.Number, - idReducer: str | ee.Reducer = "first", - idFormat: str | ee.String | None = None, - scale: int | float | None = None, - crs: str | None = None, - crsTransform: list | None = None, - bestEffort: bool = False, - maxPixels: int | None = None, - tileScale: float = 1, - ) -> ee.Dictionary: - """Apply a reducer to all the pixels in a specific region on each image of the collection. - - The result will be shaped as a dictionary with the idProperty as key and for each f them the reduced band values. - - .. code-block:: - - { - "image1": {"band1": value1, "band2": value2, ...}, - "image2": {"band1": value1, "band2": value2, ...}, - } - - Warning: - The method makes a call to the pure Python ``uuid`` package so it cannot be used in a server-side ``map`` function. - - Parameters: - idProperty: The property to use as the key of the resulting dictionary. If not specified, the key of the dictionary is the index of the image in the collection. One should use a meaningful property to avoid conflicts. in case of conflicts, the images with the same property will be mosaicked together (e.g. all raw satellite imagery with the same date) to make sure the final reducer have 1 single entry per idProperty. - reducer: THe reducer to apply. - idType: The type of the idProperty. Default is ee.Number. As Dates are stored as numbers in metadata, we need to know what parsing to apply to the property in advance. - idReducer: If the multiple images have the same idProperty, they will be aggregated beforehand using the provided reducer. default to a mosaic behaviour to match most of the satellite imagery collection where the world is split for each date between multiple images. - idFormat: If a date format is used for the IdProperty, the values will be formatted as "YYYY-MM-ddThh-mm-ss". If a number format is used for the IdProperty, the values will be formatted as a string ("%s"). You can specify any other format compatible with band names. - geometry: The region over which to reduce the data. - scale: A nominal scale in meters to work in. - crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. - crstransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. - bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. - maxPixels: The maximum number of pixels to reduce. - tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. - - Returns: - A dictionary with the reduced values for each image. - - Examples: - .. code-block:: python - - import ee, geetools - - ee.Initialize() - - collection = ( - ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") - .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) - .filterDate("2014-01-01", "2014-12-31") - ) - data = collection.geetools.reduceRegion("mean", geometry=ee.Geometry.Point(-122.262, 37.8719), scale=30) - print(data.getInfo()) - """ - # filter the imageCollection with the region parameter to reduce the number of manipulated images and speed up the computation - ic = self._obj.filterBounds(geometry) - - # raise an error if the idType is not supported - if idType not in [ee.String, ee.Number, ee.Date]: - msg = f"idPropertyType format {idType} not supported (yet)!" - raise ValueError(msg) - - # create a unique property name to avoid conflict with any - # existing property in the image collection - pname = uuid.uuid4().hex - - # add to each image the idProperty as metadata converted to string according - # to the idPropertyType parameter - def addIdProperty(i: ee.Image) -> ee.Image: - p = i.get(idProperty) - if idType == ee.String: - p = ee.String(p) - elif idType == ee.Number: - p = ee.Number(p).format(idFormat or "%s") - elif idType == ee.Date: - p = ee.Date(p).format(idFormat or EE_DATE_FORMAT) - return i.set(pname, p) - - ic = ic.map(addIdProperty) - - # reduce the images collection to an collection of image with unique idproperty - # in case of duplication the images arereduced together using the idReducer - idRed = idReducer # renaming of the variable to save space - red = getattr(ee.Reducer, idRed)() if isinstance(idRed, str) else idRed - pList = ic.aggregate_array(pname).distinct() - bands = ic.first().bandNames() - iList = pList.map(lambda p: ic.filter(ee.Filter.eq(pname, p)).reduce(red).rename(bands)) - ic = ee.ImageCollection(iList) - - # The tobands method will produce an image with the following band names: _ - # What we want is: _ so we can make more advance filtering downstream. - bandNames = pList.map(lambda p: bands.map(lambda b: ee.String(p).cat("_").cat(b))) - bandNames = bandNames.flatten() - - # reduce the collection to a single image and run the reducer on it - image = ic.toBands().rename(bandNames) - reduced = image.reduceRegion( - reducer=reducer, - geometry=geometry, - scale=scale, - crs=crs, - crsTransform=crsTransform, - bestEffort=bestEffort, - maxPixels=maxPixels, - tileScale=tileScale, - ) - - # reshape the result dictionary into the desired structure - def getProp(p: ee.String) -> ee.Dictionary: - p = ee.String(p) - keys = reduced.keys().filter(ee.Filter.stringStartsWith("item", p)) - values = reduced.select(keys).values() - keys = keys.map(lambda k: ee.String(k).replace(p, "").slice(1)) - return ee.Dictionary.fromLists(keys, values) - - values = pList.map(lambda p: getProp(p)) - - return ee.Dictionary.fromLists(pList, values) diff --git a/geetools/ee_initialize.py b/geetools/ee_initialize.py deleted file mode 100644 index 8aa2d276..00000000 --- a/geetools/ee_initialize.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Tools for the ``ee.Initialize`` function.""" -from __future__ import annotations - -import json -import tempfile -from pathlib import Path - -import ee -import httplib2 -from google.oauth2.credentials import Credentials - -from .accessors import register_function_accessor - -_project_id: str | None = None -"The project Id used by the current user." - - -@register_function_accessor(ee.Initialize, "geetools") -class InitializeAccessor: - """Toolbox for the ``ee.Initialize`` function.""" - - @staticmethod - def from_user(name: str = "", credential_pathname: str = "", project: str = "") -> None: - """Initialize Earthengine API using a specific user. - - Equivalent to the ``ee.initialize`` function but with a specific credential file stored in - the machine by the :py:meth:`ee.Authenticate.geetools.new_user ` - function. - - Args: - name: The name of the user as saved when created. use default if not set - credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path - project: The project_id to use. If not set, it uses the default project_id of the saved credentials. - - Example: - .. code-block:: python - - import ee - import geetools - - ee.Initialize.from_user("") - """ - # gather global variable to be modified - global _project_id - - # set the user profile information - name = f"credentials{name}" - credential_pathname = credential_pathname or ee.oauth.get_credentials_path() - credential_folder = Path(credential_pathname).parent - credential_path = credential_folder / name - - # check if the user exists - if not credential_path.exists(): - msg = "Please register this user first by using geetools.User.create first" - raise ee.EEException(msg) - - # Set the credential object and Init GEE API - tokens = json.loads((credential_path / name).read_text()) - credentials = Credentials( - None, - refresh_token=tokens["refresh_token"], - token_uri=ee.oauth.TOKEN_URI, - client_id=tokens["client_id"], - client_secret=tokens["client_secret"], - scopes=ee.oauth.SCOPES, - ) - ee.Initialize(credentials) - - # save the project_id in a dedicated global variable as it's not saved - # from GEE side - _project_id = project or tokens["project_id"] - - @staticmethod - def from_service_account(private_key: str) -> None: - """Initialize Earthengine API using a service account. - - Equivalent to the ``ee.initialize`` function but with a specific service account json key. - - Args: - private_key: The private key of the service account in json format. - - Example: - .. code-block:: python - - import ee - import geetools - - private_key = "your_private_key" - - ee.Initialize.from_service_account(private_key) - """ - # gather global variable to be modified - global _project_id - - # connect to GEE using a temp file to avoid writing the key to disk - with tempfile.TemporaryDirectory() as temp_dir: - file = Path(temp_dir) / "private_key.json" - file.write_text(private_key) - ee_user = json.loads(private_key)["client_email"] - _project_id = json.loads(private_key)["project_id"] - credentials = ee.ServiceAccountCredentials(ee_user, str(file)) - ee.Initialize(credentials=credentials, http_transport=httplib2.Http()) - - @staticmethod - def project_id() -> str: - """Get the project_id of the current account. - - Returns: - The project_id of the connected profile - - Raises: - RuntimeError: If the account is not initialized. - - Examples: - .. code-block:: - - import ee, geetools - - ee.Initialize.geetools.project_id() - """ - if _project_id is None: - raise RuntimeError("The GEE account is not initialized") - return _project_id diff --git a/geetools/ee_join.py b/geetools/ee_join.py deleted file mode 100644 index 7cfff2ca..00000000 --- a/geetools/ee_join.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Extra methods for the ``ee.Join`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Join, "geetools") -class JoinAccessor: - """Toolbox for the ``ee.Join`` class.""" - - def __init__(self, obj: ee.join): - """Initialize the Join class.""" - self._obj = obj - - @staticmethod - def byProperty( - primary: ee.Collection, - secondary: ee.Collection, - field: str | ee.String, - outer: bool = False, - ) -> ee.Collection: - """Join 2 collections by a given property field. - - It assumes ids are unique so uses ee.Join.saveFirst. - - Args: - primary: the first collection - secondary: the second collection - field: the field to join by - outer: whether to keep non matching features - - Returns: - the joined collection - - - Example: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - # build fake featureCollections on the same point - point = ee.Geometry.Point([0,0]) - prop1 = {'id': 1, 'prop_from_fc1': 'I am from fc1'} - prop2 = {'id': 1, 'prop_from_fc2': 'I am from fc2'} - fc1 = ee.FeatureCollection([ee.Feature(point, prop1)]) - fc2 = ee.FeatureCollection([ee.Feature(point, prop2)]) - - # join them together in the same featureCollection - joined = ee.Join.geetools.byProperty(fc1, fc2, 'id') - joined.getInfo() - """ - field = ee.String(field) - primary, secondary = ee.FeatureCollection(primary), ee.FeatureCollection(secondary) - Filter = ee.Filter.equals(leftField=field, rightField=field) - join = ee.Join.saveFirst(matchKey="match", outer=outer) - joined = join.apply(primary, secondary, Filter) - - def cleanJoin(feat): - primaryProp = feat.propertyNames().remove("match") - secondaryProp = ee.Feature(feat.get("match")).toDictionary() - return feat.select(primaryProp).setMulti(secondaryProp) - - return ee.FeatureCollection(joined.map(cleanJoin)) diff --git a/geetools/ee_list.py b/geetools/ee_list.py deleted file mode 100644 index ebe31cea..00000000 --- a/geetools/ee_list.py +++ /dev/null @@ -1,289 +0,0 @@ -"""Extra methods for the ``ee.List`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.List, "geetools") -class ListAccessor: - """Toolbox for the ``ee.List`` class.""" - - def __init__(self, obj: ee.List): - """Initialize the List class.""" - self._obj = obj - - def product(self, other: list | ee.List) -> ee.List: - """Compute the cartesian product of 2 list. - - Values will all be considered as string and will be joined with **no spaces**. - - Parameters: - other: The list to compute the cartesian product with. - - Returns: - A list of strings corresponding to the cartesian product. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l1 = ee.List(["1", "2", "3"]) - l2 = ee.List(["a", "b", "c"]) - - l1.geetools.product(l2).getInfo() - """ - l1 = ee.List(self._obj).map(lambda e: ee.String(e)) - l2 = ee.List(other).map(lambda e: ee.String(e)) - product = l1.map( - lambda e: l2.map(lambda f: ee.Algorithms.String(e).cat(ee.Algorithms.String(f))) - ) - return product.flatten() - - def complement(self, other: list | ee.List) -> ee.List: - """Compute the complement of the current list and the ``other`` list. - - The mathematical complement is the list of elements that are in the current list but not in the ``other`` list and vice-versa. - - Parameters: - other: The list to compute the complement with. - - Returns: - A list of strings corresponding to the complement of the current list and the ``other`` list. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l1 = ee.List(["1", "2", "3"]) - l2 = ee.List(["2", "3", "4"]) - - l1.geetools.complement(l2).getInfo() - """ - l1, l2 = ee.List(self._obj), ee.List(other) - return l1.removeAll(l2).cat(l2.removeAll(l1)) - - def intersection(self, other: list | ee.List) -> ee.List: - """Compute the intersection of the current list and the ``other`` list. - - The intersection is the list of elements that are in both lists. - - Parameters: - other: The list to compute the intersection with. - - Returns: - A list of strings corresponding to the intersection of the current list and the ``other`` list. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l1 = ee.List(["1", "2", "3"]) - l2 = ee.List(["2", "3", "4"]) - - l1.geetools.intersection(l2).getInfo() - """ - l1, l2 = ee.List(self._obj), ee.List(other) - return l1.removeAll(l1.removeAll(l2)) - - def union(self, other: list | ee.List) -> ee.List: - """Compute the union of the current list and the ``other`` list. - - This list will drop duplicated items. - - Parameters: - other: The list to compute the union with. - - Returns: - A list of strings corresponding to the union of the current list and the ``other`` list. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l1 = ee.List(["1", "2", "3"]) - l2 = ee.List(["2", "3", "4"]) - - l1.geetools.union(l2).getInfo() - """ - l1, l2 = ee.List(self._obj), ee.List(other) - return l1.cat(l2).distinct() - - # this method is simply a del but the name is protected in the GEE context - def delete(self, index: int | ee.Number) -> ee.List: - """Delete an element from a list. - - Parameters: - index: The index of the element to delete. - - Returns: - The list without the element at the given index. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List(["a", "b", "c"]) - l.geetools.delete(1).getInfo() - """ - index = ee.Number(index).toInt() - return self._obj.slice(0, index).cat(self._obj.slice(index.add(1))) - - @classmethod - def sequence( - cls, - ini: int | ee.Number, - end: int | ee.Number, - step: int | ee.Number = 1, - ) -> ee.List: - """Create a sequence from ini to end by step. - - Similar to ``ee.List.sequence``, but if end != last item then adds the end to the end of the resuting list. - - Parameters: - ini: The initial value of the sequence. - end: The final value of the sequence. - step: The step of the sequence. - - Returns: - A list of numbers corresponding to the sequence. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List.geetools.sequence(0, 11, 2) - l.getInfo() - """ - ini, end = ee.Number(ini), ee.Number(end) - step = ee.Number(step).toInt().max(1) - return ee.List.sequence(ini, end, step).add(end.toFloat()).distinct() - - def replaceMany(self, replace: dict | ee.Dictionary) -> ee.List: - """Replace many values in a list. - - Parameters: - replace: the dictionary with the values to replace. the keys are the values to replace and the values are the new values. - - Returns: - A list with the values replaced - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List(["a", "b", "c"]) - replace = ee.Dictionary({"a": "foo", "c": "bar"}) - l = l.geetools.replaceMany(replace) - l.getInfo() - """ - replace = ee.Dictionary(replace) - keys = replace.keys() - list = keys.iterate(lambda k, p: ee.List(p).replace(k, replace.get(k)), self._obj) - return ee.List(list) # to avoid returning a ComputedObject - - def join(self, separator: str | ee.String = ", ") -> ee.string: - """Format a list to a string. - - Same as the join method but elements that cannot be stringified will be returned as the object type. - - Parameters: - separator: The separator to use. - - Returns: - A string with the list elements separated by commas. - - Examples: - .. juptyer-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List(['a', 1, ee.Image(0)]) - l = l.geetools.join() - l.getInfo() - """ - return self.toStrings().join(separator) - - def toStrings(self) -> ee.List: - """Convert elements of a list into Strings. - - If the list contains other elements that are not strings or numbers, it will return the object type. For example, ['a', 1, ee.Image(0)] -> ['a', '1', 'Image']. - - Returns: - A list of strings corresponding to the elements of the list. - - Examples: - .. juptyer-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List(["a", 1, ee.Image(0)]) - l = l.geetools.toStrings() - l.getInfo() - """ - klasses = ee.List(["Float", "Integer", "String"]) - - def getString(el): - otype = ee.Algorithms.ObjectType(el) - stringReady = klasses.contains(otype) - return ee.Algorithms.If(stringReady, ee.Algorithms.String(el), otype) - - return self._obj.map(getString) - - def zip(self) -> ee.List: - """Zip a list of lists. - - The nested lists need to all have the same size. The size of the first element will be taken as reference. - - Returns: - A list of lists with the zipped elements - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - l = ee.List([[1,2,3], [4,5,6], [7,8,9]]) - l = l.geetools.zip() - l.getInfo() - """ - indices = ee.List.sequence(0, ee.List(self._obj.get(0)).size().subtract(1)) - return indices.map(lambda i: self._obj.map(lambda j: ee.List(j).get(i))) diff --git a/geetools/ee_number.py b/geetools/ee_number.py deleted file mode 100644 index a996dd1c..00000000 --- a/geetools/ee_number.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Extra methods for the ``ee.Number`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.Number, "geetools") -class NumberAccessor: - """toolbox for the ``ee.Number`` class.""" - - def __init__(self, obj: ee.Number): - """Initialize the Number class.""" - self._obj = obj - - def truncate(self, nbDecimals: int | ee.Number = 2) -> ee.Number: - """Truncate a number to a given number of decimals. - - Parameters: - nbDecimals: The number of decimals to truncate to. - - Returns: - The truncated number. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - n = ee.Number(1.23456).geetools.truncate(3) - n.getInfo() - """ - nbDecimals = ee.Number(nbDecimals).toInt() - factor = ee.Number(10).pow(nbDecimals) - return self._obj.multiply(factor).toInt().divide(factor) diff --git a/geetools/ee_profiler.py b/geetools/ee_profiler.py deleted file mode 100644 index 6ff812d9..00000000 --- a/geetools/ee_profiler.py +++ /dev/null @@ -1,104 +0,0 @@ -"""A profiler context manager for Earth Engine Python API.""" -from __future__ import annotations - -import io -import re - -import ee -from anyascii import anyascii - -from .accessors import _register_extention - - -@_register_extention(ee.geetools) -class Profiler: - """A profiler context manager for Earth Engine Python API. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - with ee.geetools.Profiler() as p: - ee.Number(3.14).add(0.00159).getInfo() - res = p.profile - res - """ - - _output_capture: io.StringIO | None = None - "The output of the profiler." - - _profile_context: ee.profilePrinting | None = None - "The raw profile context." - - profile: dict | None = None - "The profile data as a dictionary." - - def __enter__(self): - """Enter the context manager.""" - self._output_capture = io.StringIO() - self._profile_context = ee.profilePrinting(destination=self._output_capture) - self._profile_context.__enter__() - return self - - def __exit__(self, *args): - """Exit the context manager.""" - self._profile_context.__exit__(*args) - - # Check if there's anything captured - profile_output = self._output_capture.getvalue() - if profile_output: - self.profile = self._to_dict(profile_output) - else: - self.profile = None # Handle the case where no output is captured - print("Warning: No profile output was captured.") - - self._output_capture.close() - - def _memory(self, mem_str: str) -> int: - """Transform a memory string to an integer.""" - mapping = {"": 1, "k": 3, "M": 6, "G": 9, "T": 12} - - # Match numbers with optional multipliers (k, M, etc.) - # and apply the multiplier to the number - match = re.match(r"([\d.]+)([kMGT]?)", mem_str) - if match is None: - raise ValueError(f"Invalid memory string: {mem_str}") - - number, multiplier = float(match.group(1)), match.group(2) - - return int(number * 10 ** mapping[multiplier]) - - def _to_dict(self, input: str) -> dict: - """Transform the output of a Earthengine profiler into a dictionary compatible with pandas DataFrame.""" - # Split the string into lines - lines = input.strip().splitlines() - - # First line contains column headers - # Initialize a dictionary to hold lists for each column - headers = [anyascii(h.strip()) for h in lines[0].split()] - result: dict = {header: [] for header in headers} - # functions to process/format each header - process = { - "EECU-s": lambda eecus: float(eecus) if eecus != "-" else None, - "CurrMem": lambda mem: self._memory(mem), # Mem is a string to convert - "PeakMem": lambda mem: self._memory(mem), # Mem is a string to convert - "Count": lambda count: int(count), # Count is an integer - "Description": lambda desc: " ".join(desc) - if isinstance(desc, list) - else desc, # Description can have multiple words - } - - # Process each line of data after the header - for line in lines[1:]: - # Split the line by spaces, considering multiple spaces as a separator - # Handle missing values denoted by "-" - parts = line.split() - part_result = dict(zip(headers, parts)) - # Populate the dictionary with values for each column - for head in headers: - result[head].append(process[head](part_result[head])) - return result diff --git a/geetools/ee_string.py b/geetools/ee_string.py deleted file mode 100644 index 00d099ca..00000000 --- a/geetools/ee_string.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Extra methods for the ``ee.String`` class.""" -from __future__ import annotations - -import ee - -from .accessors import register_class_accessor - - -@register_class_accessor(ee.String, "geetools") -class StringAccessor: - """Toolbox for the ``ee.String`` class.""" - - def __init__(self, obj: ee.String): - """Initialize the String class.""" - self._obj = obj - - def eq(self, other: str | ee.String) -> ee.Number: - """Compare two strings and return a ``ee.Number``. - - Parameters: - other: The string to compare with. - - Returns: - ``1`` if the strings are equal, ``0`` otherwise. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - isEqual = ee.String("foo").geetools.eq("foo") - isEqual.getInfo() - """ - return self._obj.compareTo(ee.String(other)).Not() - - def format(self, template: dict | ee.Dictionary) -> ee.String: - """Format a string with a dictionary. - - Replace the keys in the string using the values provided in the dictionary. Follow the same pattern: value format as Python string.format method. - - Parameters: - template: A dictionary with the values to replace. - - Returns: - The formatted string. - - Examples: - .. jupyter-execute:: - - import ee, geetools - from geetools.utils import initialize_documentation - - initialize_documentation() - - s = ee.String("{greeting} {name} !") - s = s.geetools.format({"greeting": "Hello", "name": "bob"}) - s.getInfo() - """ - template = ee.Dictionary(template) - templateList = template.keys().zip(template.values()) - - def replace_format(kv, s): - kv = ee.List(kv) - key, value = ee.String(kv.get(0)), ee.String(kv.get(1)) - pattern = ee.String("{").cat(key).cat(ee.String("}")) - return ee.String(s).replace(pattern, value) - - return ee.String(templateList.iterate(replace_format, self._obj)) diff --git a/geetools/py.typed b/geetools/py.typed new file mode 100644 index 00000000..2c85c451 --- /dev/null +++ b/geetools/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. The mypy package uses inline types. \ No newline at end of file diff --git a/geetools/tools/__init__.py b/geetools/tools/__init__.py deleted file mode 100644 index 12fcdb77..00000000 --- a/geetools/tools/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Legacy import package for tools.""" -from . import _deprecated_imagecollection as imagecollection # noqa: F401 diff --git a/geetools/tools/_deprecated_imagecollection.py b/geetools/tools/_deprecated_imagecollection.py deleted file mode 100644 index 26c6b514..00000000 --- a/geetools/tools/_deprecated_imagecollection.py +++ /dev/null @@ -1,117 +0,0 @@ -# coding=utf-8 -"""legacy Module holding tools for ee.ImageCollections.""" -import ee -from deprecated.sphinx import deprecated - -import geetools # noqa: F401 - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.closestDate instead.") -def fillWithLast(collection, reverse=False, proxy=-999): - """Fill each masked pixels with the last available not masked pixel.""" - return ee.ImageCollection(collection).geetools.closestDate() - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") -def mosaicSameDay(collection, qualityBand=""): - """Return a collection where images from the same day are mosaicked.""" - reducer = "mosaic" if qualityBand == "" else "qualityMosaic" - return ee.ImageCollection(collection).geetools.reduceInterval(reducer, "day", 1, qualityBand) - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") -def reduceEqualInterval( - collection, interval=30, unit="day", reducer=None, start_date=None, end_date=None -): - """Reduce an ImageCollection into a new one that has one image per reduced interval.""" - return ee.ImageCollection(collection).geetools.reduceInterval(reducer, unit, interval) - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.groupInterval instead.") -def makeEqualInterval(collection, interval=1, unit="month"): - """Make a list of image collections filtered by the given interval.""" - return ee.ImageCollection(collection).geetools.groupInterval(unit, interval) - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.groupInterval instead..") -def makeDayIntervals(collection, interval=30, reverse=False, buffer="second"): - """Make day intervals.""" - return ee.ImageCollection(collection).geetools.groupInterval("day", 1) - - -@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") -def reduceDayIntervals(collection, reducer, interval=30, reverse=False, buffer="second"): - """Reduce Day Intervals.""" - return ee.ImageCollection(collection).geetools.reduceInterval(reducer, "day", 1) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested") -def linearFunctionBand(*args, **kwargs): - """Apply a linear function over the bands across every image of the ImageCollection""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested") -def linearFunctionProperty(*args, **kwargs): - """Apply a linear function over the properties across every image of the ImageCollection""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested.") -def linearInterpolation(collection, date_property="system:time_start"): - """TODO missing docstring.""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested.") -def gaussFunctionBand(*args, **kwargs): - """Compute a Gauss function using a specified band over an ImageCollection, See: https://en.wikipedia.org/wiki/Gaussian_function.""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested.") -def gaussFunctionProperty(*args, **kwargs): - """Compute a Gauss function using a specified property over an ImageCollection.""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested.") -def normalDistributionProperty(*args, **kwargs): - """Compute a normal distribution using a specified property, over an ImageCollection""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.4.0", reason="Removed from the lib as untested.") -def normalDistributionBand(collection, band, mean=None, std=None, name="normal_distribution"): - """Compute a normal distribution using a specified band, over an ImageCollection.""" - raise NotImplementedError( - "As it was vastly untested this method has been removed from the lib." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) - - -@deprecated(version="1.5.0", reason="The output format is unclear.") -def moving_average(collection, back=5, reducer=None, use_original=True): - """Compute the moving average over a time series.""" - raise NotImplementedError( - "This method has been deprecated as the output format is unclear." - "If a real use case for this method can be provided, please open an issue and we'll reimplement it." - ) diff --git a/geetools/utils.py b/geetools/utils.py deleted file mode 100644 index 7328655b..00000000 --- a/geetools/utils.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Utils methods for file and asset manipulation in the context of batch processing.""" -from __future__ import annotations - -import os -import re -from datetime import datetime as dt - -import ee -import httplib2 -import numpy as np -from anyascii import anyascii -from matplotlib import pyplot as plt -from matplotlib.axes import Axes -from matplotlib.colors import to_rgba - - -def format_description(description: str) -> str: - """Format a name to be accepted as a Task description. - - The rule is: - The description must contain only the following characters: a..z, A..Z,0..9, ".", ",", ":", ";", - "_" or "-". The description must be at most 100 characters long. - - Args: - description: The description to format. - - Returns: - The formatted description. - """ - replacements = [ - [[" "], "_"], - [["/"], "-"], - [["?", "!", "¿", "*"], "."], - [["(", ")", "[", "]", "{", "}"], ":"], - ] - - desc = anyascii(description) - for chars, rep in replacements: - pattern = "|".join(re.escape(c) for c in chars) - desc = re.sub(pattern, rep, desc) # type: ignore - - return desc[:100] - - -def format_asset_id(description: str) -> str: - """Format a name to be accepted as an asset Id. - - The rule is: - Each segment must contain only the following characters: a..z, A..Z, 0..9, "_" or "-". - Each segment must be at least 1 character long and at most 100 characters long. - - Args: - description: The description to format. - - Returns: - The formatted description. - """ - replacements = [ - [[" "], "_"], - [["/"], "-"], - [["?", "!", "¿", "*"], "."], - [["(", ")", "[", "]", "{", "}", ";", ":", ",", "."], "_"], - ] - - desc = anyascii(description) - for chars, rep in replacements: - pattern = "|".join(re.escape(c) for c in chars) - desc = re.sub(pattern, rep, desc) # type: ignore - - return desc - - -def plot_data( - type: str, - data: dict, - label_name: str, - colors: list = [], - ax: Axes | None = None, - **kwargs, -) -> Axes: - """Plotting mechanism used in all the plotting functions. - - It binds the matplotlib capabilities with the data aggregated by different xes. - the shape of the data should as follows: - - .. code-block:: - - { - "label1": {"properties1": value1, "properties2": value2, ...} - "label2": {"properties1": value1, "properties2": value2, ...}, - ... - } - - Args: - type: The type of plot to use. can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! - data: the data to use as inputs of the graph. please follow the fomrmat specified in the documentation. - label_name: The name of the property that was used to generate the labels - property_names: The list of names that was used to name the values. They will be used to order the keys of the data dictionary. - colors: A list of colors to use for the plot. If not provided, the default colors from the matplotlib library will be used. - ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. - kwargs: Additional arguments from the ``pyplot`` chat type selected. - """ - # define the ax if not provided by the user - if ax is None: - _, ax = plt.subplots() - - # gather the data from parameters - labels = list(data.keys()) - props = list(data[labels[0]].keys()) - colors = colors if colors else plt.get_cmap("tab10").colors - - # draw the chart based on the type - if type == "plot": - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - name = props[0] if len(props) == 1 else "Properties values" - values = list(data[label].values()) - ax.plot(props, values, label=label, **kwargs) - ax.set_ylabel(name) - ax.set_xlabel(f"Features (labeled by {label_name})") - grid_axis = "y" - - elif type == "scatter": - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - name = props[0] if len(props) == 1 else "Properties values" - values = list(data[label].values()) - ax.scatter(props, values, label=label, **kwargs) - ax.set_ylabel(name) - ax.set_xlabel(f"Features (labeled by {label_name})") - grid_axis = "y" - - elif type == "fill_between": - for i, label in enumerate(labels): - kwargs["facecolor"] = to_rgba(colors[i], 0.2) - kwargs["edgecolor"] = to_rgba(colors[i], 1) - name = props[0] if len(props) == 1 else "Properties values" - values = list(data[label].values()) - ax.fill_between(props, values, label=label, **kwargs) - ax.set_ylabel(name) - ax.set_xlabel(f"Features (labeled by {label_name})") - grid_axis = "y" - - elif type == "bar": - x = np.arange(len(props)) - width = 1 / (len(labels) + 0.8) - margin = width / 10 - kwargs["width"] = width - margin - ax.set_xticks(x + width * len(labels) / 2, props) - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - values = list(data[label].values()) - ax.bar(x + width * i, values, label=label, **kwargs) - grid_axis = "y" - - elif type == "barh": - y = np.arange(len(props)) - height = 1 / (len(labels) + 0.8) - margin = height / 10 - kwargs["height"] = height - margin - ax.set_yticks(y + height * len(labels) / 2, props) - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - values = list(data[label].values()) - ax.barh(y + height * i, values, label=label, **kwargs) - grid_axis = "x" - - elif type == "stacked": - x = np.arange(len(props)) - bottom = np.zeros(len(props)) - ax.set_xticks(x, props) - for i, label in enumerate(labels): - kwargs.update(color=colors[i], bottom=bottom) - values = list(data[label].values()) - ax.bar(x, values, label=label, **kwargs) - bottom += values - grid_axis = "y" - - elif type == "pie": - if len(labels) != 1: - raise ValueError("Pie chart can only be used with one property") - kwargs["autopct"] = kwargs.get("autopct", "%1.1f%%") - kwargs["normalize"] = kwargs.get("normalize", True) - kwargs["labeldistance"] = kwargs.get("labeldistance", None) - kwargs["wedgeprops"] = kwargs.get("wedgeprops", {"edgecolor": "w"}) - kwargs["textprops"] = kwargs.get("textprops", {"color": "w"}) - kwargs.update(autopct="%1.1f%%", colors=colors) - values = [data[labels[0]][p] for p in props] - ax.pie(values, labels=props, **kwargs) - grid_axis = "y" - - elif type == "donut": - if len(labels) != 1: - raise ValueError("Pie chart can only be used with one property") - kwargs["autopct"] = kwargs.get("autopct", "%1.1f%%") - kwargs["normalize"] = kwargs.get("normalize", True) - kwargs["labeldistance"] = kwargs.get("labeldistance", None) - kwargs["wedgeprops"] = kwargs.get("wedgeprops", {"width": 0.6, "edgecolor": "w"}) - kwargs["textprops"] = kwargs.get("textprops", {"color": "w"}) - kwargs["pctdistance"] = kwargs.get("pctdistance", 0.7) - kwargs.update(autopct="%1.1f%%", colors=colors) - values = [data[labels[0]][p] for p in props] - ax.pie(values, labels=props, **kwargs) - grid_axis = "y" - - elif type == "date": - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - x, y = list(data[label].keys()), list(data[label].values()) - ax.plot(x, y, label=label, **kwargs) - ax.set_xlabel("Date") - grid_axis = "both" - - elif type == "doy": - xmin, xmax = 366, 0 # inverted initialization to get the first iteration values - for i, label in enumerate(labels): - kwargs["color"] = colors[i] - x, y = list(data[label].keys()), list(data[label].values()) - ax.plot(x, y, label=label, **kwargs) - ax.set_xlabel("Day of year") - grid_axis = "both" - dates = [dt(2023, i + 1, 1) for i in range(12)] - idates = [int(d.strftime("%j")) - 1 for d in dates] - ndates = [d.strftime("%B")[:3] for d in dates] - ax.set_xticks(idates, ndates) - xmin, xmax = min(xmin, min(x)), max(xmax, max(x)) - ax.set_xlim(xmin - 5, xmax + 5) - - else: - raise ValueError(f"Type {type} is not (yet?) supported") - - # customize the layout of the axis - ax.grid(axis=grid_axis) - ax.set_axisbelow(True) - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - ax.legend(bbox_to_anchor=(1.02, 1), loc="upper left") - - # make sure the canvas is only rendered once. - ax.figure.canvas.draw_idle() - - return ax - - -def initialize_documentation(): - """Initialize Earthe Engine Python API in the context of the Documentation build. - - Warning: - This method is only used in the documentation build and should not be used in a production environment. - ``geetools`` need to be imported prior to import this function. - """ - # use a saved service account key if available - if "EARTHENGINE_SERVICE_ACCOUNT" in os.environ: - private_key = os.environ["EARTHENGINE_SERVICE_ACCOUNT"] - # small massage of the key to remove the quotes coming from RDT - private_key = ( - private_key[1:-1] if re.compile(r"^'[^']*'$").match(private_key) else private_key - ) - ee.Initialize.geetools.from_service_account(private_key) - - elif "EARTHENGINE_PROJECT" in os.environ: - ee.Initialize(project=os.environ["EARTHENGINE_PROJECT"], http_transport=httplib2.Http()) - - else: - raise ValueError( - "EARTHENGINE_SERVICE_ACCOUNT or EARTHENGINE_PROJECT environment variable is missing" - ) - - pass diff --git a/notebooks/algorithms/brdf.ipynb b/notebooks/algorithms/brdf.ipynb deleted file mode 100644 index 90c0553d..00000000 --- a/notebooks/algorithms/brdf.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, algorithms, collection, ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipygee import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## BRDF correction in Landsat 8 TOA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('LANDSAT/LC8_L1T_TOA_FMASK/LC81780592016030LGN00').select(\n", - " ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B9', 'B10', 'fmask'],\n", - " ['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'cirrus', 'thermal', 'fmask']\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ic = algorithms.Landsat.brdfCorrect(i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([22.0227, 1.4801])\n", - "Map.centerObject(p, zoom=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ival = 0.10098563879728317\n", - "icval = 0.10012929931321499" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ival_here = tools.image.getValue(i, p, 30, 'client')['blue']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ival_here == ival" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "icval_here = tools.image.getValue(ic, p, 30, 'client')['blue']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "icval_here == icval" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = {'bands':['nir', 'swir1','red'], 'min':0, 'max': 0.5}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i, vis, 'original')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(ic, vis, 'corrected')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using `collection` module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8toa = collection.Landsat8TOA()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = l8toa.visualization('NSR')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([22.21, 1.5])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = l8toa.collection.filterBounds(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i2 = col.first()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "proxy = ee.Image.constant(0).rename('proxy')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colr = col.map(lambda img: l8toa.rename(img)).map(lambda img: img.addBands(proxy))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colbrdf = colr.map(lambda img: l8toa.brdf(img, True))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i2, vis, 'Landsat 8 TOA')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i2c = colbrdf.first()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i2c, l8toa.visualization('NSR', True), 'Landsat 8 TOA BRDF Corrected')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "diff = i2.subtract(i2c).multiply(100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(diff, vis.update(dict(min=-1, max=1)), 'BRDF difference')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/algorithms/distance_to_mask.ipynb b/notebooks/algorithms/distance_to_mask.ipynb deleted file mode 100644 index 30196c12..00000000 --- a/notebooks/algorithms/distance_to_mask.ipynb +++ /dev/null @@ -1,157 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distance to Mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import algorithms, ui, cloud_mask, tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipygee import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Area of Interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-71.33972167968751, -42.737619925503054])\n", - "aoi = p.buffer(8000).bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Masked image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT')\n", - "masked = cloud_mask.sentinel2()(i)\n", - "masked = masked.clip(aoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Distance to mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_distance = 2000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distance = algorithms.distanceToMask(masked, radius=max_distance, geometry=aoi)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(masked, {'bands':['B8', 'B11', 'B4'], 'min':0, 'max':5000}, 'Masked image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.centerObject(aoi)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(distance, {'min':0, 'max':max_distance}, 'distance')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/algorithms/euclidean_distance.ipynb b/notebooks/algorithms/euclidean_distance.ipynb deleted file mode 100644 index 1daf39d5..00000000 --- a/notebooks/algorithms/euclidean_distance.ipynb +++ /dev/null @@ -1,466 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Euclidean distance between two images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, algorithms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bands = ['B1','B2','B3']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image1 = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_232089_20170126').select(bands)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image2 = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_232089_20170211').select(bands)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image3 = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_232089_20170227').select(bands)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point(-72, -42)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data1 = tools.image.getValue(image1, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data2 = tools.image.getValue(image2, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data3 = tools.image.getValue(image3, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distance = algorithms.euclideanDistance(image1, image2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_dist = tools.image.getValue(distance, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_dist" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute locally to compare" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Euclidean distance\n", - "def distanceF(arr1, arr2):\n", - " zipped = zip(arr1, arr2)\n", - " accum = 0\n", - " for a, b in zipped:\n", - " rest = (a-b)\n", - " calc = rest*rest\n", - " accum += calc\n", - " return math.sqrt(accum)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr1 = data1.values()\n", - "arr1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr2 = data2.values()\n", - "arr2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_distance = distanceF(arr1, arr2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_distance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Distance sum between one image and a collection of images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection.fromImages([image2, image3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed = algorithms.sumDistance(image1, col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed_data = tools.image.getValue(summed, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute locally to compare results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import copy\n", - "# Sum of distances\n", - "def sum_distances(values):\n", - " results = {}\n", - " for i, val in enumerate(values):\n", - " val = list(val)\n", - " cop = copy(values)\n", - " cop = [list(a) for a in cop]\n", - " cop.remove(val)\n", - " dist = 0\n", - " for r in cop:\n", - " r = list(r)\n", - " d = distanceF(val, r)\n", - " dist += d\n", - " results[i] = dist\n", - "\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values = [list(data1.values()), list(data2.values()), list(data3.values())]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum_distances(values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Handling mask values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "masked_image = image2.updateMask(image2.gte(0).Not())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_masked = tools.image.getValue(masked_image, p, 30, side='client')\n", - "data_masked" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distance_masked = algorithms.euclideanDistance(image1, masked_image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_dist_masked = tools.image.getValue(distance_masked, p, 30, side='client')\n", - "data_dist_masked" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distanceF(data1.values(), [0, 0, 0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "outside = image1\n", - "inside = masked_image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_masked = ee.ImageCollection.fromImages([inside])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed_masked = algorithms.sumDistance(outside, col_masked)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed_data_masked = tools.image.getValue(summed_masked, p, 30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summed_data_masked" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/algorithms/harmonize.ipynb b/notebooks/algorithms/harmonize.ipynb deleted file mode 100644 index 94e9ca6c..00000000 --- a/notebooks/algorithms/harmonize.ipynb +++ /dev/null @@ -1,116 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui\n", - "import geedatasets\n", - "import geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8 = geedatasets.landsat.Landsat8SR()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = l8.collection().filterBounds(ee.Geometry.Point([-72,-42])).first()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i, l8.visualization('NSR'), 'Original L8 Image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i_harmonized = geetools.algorithms.Landsat.harmonization(i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i_harmonized, l8.visualization('NSR'), 'Harmonized L8 Image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/algorithms/mask_cover.ipynb b/notebooks/algorithms/mask_cover.ipynb deleted file mode 100644 index 78563e27..00000000 --- a/notebooks/algorithms/mask_cover.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mask Cover\n", - "Percentage of masked pixels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import algorithms, ui, cloud_mask, tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipygee import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Area of Interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-71.33972167968751, -42.737619925503054])\n", - "aoi = p.buffer(8000).bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Masked image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT')\n", - "masked = cloud_mask.sentinel2()(i)\n", - "masked = masked.clip(aoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Mask Cover" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cover = algorithms.maskCover(masked, aoi, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eprint(cover.get('MASK_COVER'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Show Image on Map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = {'bands': ['B8', 'B11','B4'], 'min':0, 'max':5000}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(masked, vis, 'Masked image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.centerObject(aoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make it an image band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metadata = cover.metadata('MASK_COVER')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.image.getValue(metadata, aoi.centroid(1), 10, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/algorithms/pansharpen.ipynb b/notebooks/algorithms/pansharpen.ipynb deleted file mode 100644 index 2114633c..00000000 --- a/notebooks/algorithms/pansharpen.ipynb +++ /dev/null @@ -1,238 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, batch, algorithms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipygee import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-72, -42])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.centerObject(p, zoom=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA').filterBounds(p).filterMetadata('CLOUD_COVER', 'less_than', 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8i = ee.Image(l8.first())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rgb = ['B4', 'B3', 'B2']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nsr = ['B5', 'B6', 'B4']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "visrgb = {'bands': rgb, 'min':0, 'max':0.3}\n", - "visnsr = {'bands': nsr, 'min':0, 'max':0.5}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(l8i, visrgb, 'original RGB')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(l8i, visnsr, 'original NSR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "pansharpening hsv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pan_hsv_rgb = algorithms.pansharpenIhsFusion(l8i, 'B8', rgb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(pan_hsv_rgb, {'bands':['red', 'green', 'blue'], 'min':0, 'max':0.2}, 'HSV pansharpen RGB')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pan_hsv_nsr = algorithms.pansharpenIhsFusion(l8i, 'B8', nsr)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(pan_hsv_nsr, {'bands':['red', 'green', 'blue'], 'min':0, 'max':0.2}, 'HSV pansharpen NSR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "pansharpening kernel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pan_kernel_rgb = algorithms.pansharpenKernel(l8i, 'B8', rgb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(pan_kernel_rgb, {'bands':rgb, 'min':0, 'max':0.3}, 'kernel pansharpen RGB')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pan_kernel_nsr = algorithms.pansharpenKernel(l8i, 'B8', nsr)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(pan_kernel_nsr, {'bands':nsr, 'min':0, 'max':0.5}, 'kernel pansharpen NSR')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/cloud_mask/cloud_masking.ipynb b/notebooks/cloud_mask/cloud_masking.ipynb deleted file mode 100644 index ea4819e5..00000000 --- a/notebooks/cloud_mask/cloud_masking.ipynb +++ /dev/null @@ -1,325 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cloud Masking" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make all imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import ui, cloud_mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Landsat 8 SR" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "visL8 = {'bands':['B5','B6','B4'],'min':0, 'max':5000}\n", - " \n", - "image = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_232090_20140915')\n", - "Map.addLayer(image, visL8, 'Landsat 8 SR Original')\n", - "Map.centerObject(image, zoom=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using BQA band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask_l8SR_all = cloud_mask.landsatSR()\n", - "mask_l8SR_cloud = cloud_mask.landsatSR(['cloud'])\n", - "mask_l8SR_shadow = cloud_mask.landsatSR(['shadow'])\n", - "mask_l8SR_snow = cloud_mask.landsatSR(['snow'])\n", - "\n", - "l8sr_masked_all = mask_l8SR_all(image)\n", - "l8sr_masked_cloud = mask_l8SR_cloud(image)\n", - "l8sr_masked_shadow = mask_l8SR_shadow(image)\n", - "l8sr_masked_snow = mask_l8SR_snow(image)\n", - "\n", - "Map.addLayer(l8sr_masked_all, visL8, 'L8SR masked all')\n", - "Map.addLayer(l8sr_masked_cloud, visL8, 'L8SR masked cloud')\n", - "Map.addLayer(l8sr_masked_shadow, visL8, 'L8SR masked shadow')\n", - "Map.addLayer(l8sr_masked_snow, visL8, 'L8SR masked snow')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Landsat TOA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MapTOA = ui.Map()\n", - "MapTOA.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "visTOA = {'bands': visL8['bands'], 'min':0, 'max':0.5}\n", - "i_toa = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_232090_20140915')\n", - "MapTOA.addLayer(i_toa, visTOA, 'Landsat 8 TOA Original')\n", - "MapTOA.centerObject(i_toa, zoom=12)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using BQA band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask_l8TOA_all = cloud_mask.landsat8ToaBQA()\n", - "mask_l8TOA_cloud = cloud_mask.landsat8ToaBQA(['cloud'])\n", - "mask_l8TOA_shadow = cloud_mask.landsat8ToaBQA(['shadow'])\n", - "mask_l8TOA_snow = cloud_mask.landsat8ToaBQA(['snow'])\n", - "\n", - "l8toa_masked_all = mask_l8TOA_all(i_toa)\n", - "l8toa_masked_cloud = mask_l8TOA_cloud(i_toa)\n", - "l8toa_masked_shadow = mask_l8TOA_shadow(i_toa)\n", - "l8toa_masked_snow = mask_l8TOA_snow(i_toa)\n", - "\n", - "MapTOA.addLayer(l8toa_masked_all, visTOA, 'L8TOA masked all')\n", - "MapTOA.addLayer(l8toa_masked_cloud, visTOA, 'L8TOA masked cloud')\n", - "MapTOA.addLayer(l8toa_masked_shadow, visTOA, 'L8TOA masked shadow')\n", - "MapTOA.addLayer(l8toa_masked_snow, visTOA, 'L8TOA masked snow')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sentinel 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MapS2 = ui.Map()\n", - "MapS2.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "visS2 = {'bands':['B8','B11','B4'],'min':0, 'max':5000}\n", - "is2 = ee.Image('COPERNICUS/S2/20151123T142942_20170221T180430_T18GYT')\n", - "MapS2.centerObject(is2, zoom=12)\n", - "MapS2.addLayer(is2, visS2, 'Sentinel 2 Original')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ESA Cloud Masking" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ESA_mask_all = cloud_mask.sentinel2()\n", - "is2_ESA = ESA_mask_all(is2)\n", - "MapS2.addLayer(is2_ESA, visS2, 'Sentinel 2 ESA maked')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hollstein Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "is2_Holl_all = cloud_mask.applyHollstein(is2)\n", - "is2_Holl_cloud = cloud_mask.applyHollstein(is2, ['cloud'])\n", - "is2_Holl_shadow = cloud_mask.applyHollstein(is2, ['shadow'])\n", - "is2_Holl_snow = cloud_mask.applyHollstein(is2, ['snow'])\n", - "is2_Holl_water = cloud_mask.applyHollstein(is2, ['water'])\n", - "is2_Holl_cirrus = cloud_mask.applyHollstein(is2, ['cirrus'])\n", - "\n", - "MapS2.addLayer(is2_Holl_all, visS2, 'Sentinel 2 Hollstein all')\n", - "MapS2.addLayer(is2_Holl_cloud, visS2, 'Sentinel 2 Hollstein cloud')\n", - "MapS2.addLayer(is2_Holl_shadow, visS2, 'Sentinel 2 Hollstein shadow')\n", - "MapS2.addLayer(is2_Holl_snow, visS2, 'Sentinel 2 Hollstein snow')\n", - "MapS2.addLayer(is2_Holl_water, visS2, 'Sentinel 2 Hollstein water')\n", - "MapS2.addLayer(is2_Holl_cirrus, visS2, 'Sentinel 2 Hollstein cirrus')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MODIS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modis = ee.ImageCollection('MODIS/006/MOD09GA')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MapMOD = ui.Map()\n", - "MapMOD.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "site = ee.Geometry.Point([-71.8, -43])\n", - "date = ee.Date('2017-08-01')\n", - "visMOD = {'bands':['sur_refl_b02', 'sur_refl_b06', 'sur_refl_b01'],'min':0, 'max':5000}\n", - "modis = modis.filterDate(date, date.advance(4, 'month'))\n", - "i_mod = ee.Image(modis.first())\n", - "MapMOD.addLayer(i_mod, visMOD, 'MODIS TERRA Original Image')\n", - "MapMOD.centerObject(site, zoom=8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using `state_1km` band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mod_mask = cloud_mask.modis09ga()\n", - "i_masked = mod_mask(i_mod)\n", - "MapMOD.addLayer(i_masked, visMOD, 'Masked MODIS')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/collection/joinByProperty.ipynb b/notebooks/collection/joinByProperty.ipynb deleted file mode 100644 index 81516ea5..00000000 --- a/notebooks/collection/joinByProperty.ipynb +++ /dev/null @@ -1,104 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Join the data of two collections that share a common property, commonly an id property" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()\n", - "import geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nullGeom = ee.Geometry.Point([0, 0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col1 = ee.FeatureCollection([\n", - " ee.Feature(nullGeom, {'fid': 1, 'a': 10, 'b': 20}),\n", - " ee.Feature(nullGeom, {'fid': 2, 'a': 20, 'b': 10}),\n", - " ee.Feature(nullGeom, {'fid': 3, 'a': 5, 'b': 5}),\n", - " ee.Feature(nullGeom, {'fid': 4, 'a': 30, 'b': 40})\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col2 = ee.FeatureCollection([\n", - " ee.Feature(nullGeom, {'fid': 2, 'c': 1000, 'd': 2000}),\n", - " ee.Feature(nullGeom, {'fid': 3, 'c': 3000, 'd': 4000}),\n", - " ee.Feature(nullGeom, {'fid': 5, 'c': 5000, 'd': 6000})\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "joined = geetools.tools.collection.joinByProperty(col1, col2, 'fid')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "joined.toDictionary().getInfo()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/composite/closest_date.ipynb b/notebooks/composite/closest_date.ipynb deleted file mode 100644 index 3c12ac2e..00000000 --- a/notebooks/composite/closest_date.ipynb +++ /dev/null @@ -1,251 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Composite \"Closest Date\"\n", - "Takes the last image of the given collection and fills the masked pixels with the last available pixel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, composite, cloud_mask, algorithms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geedatasets\n", - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8 = geedatasets.landsat.Landsat8SR()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = l8.visualization('NSR')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8col = l8.collection()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-70.72, -41.92]).buffer(50000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.centerObject(p, zoom=8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### add date band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = l8col.map(lambda img: img.addBands(tools.date.makeDateBand(img)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter and mask clouds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = col.filterBounds(p)\\\n", - " .filterDate('2017-01-01', '2017-03-01')\\\n", - " .map(lambda img: l8.applyMask(img, 'pixel_qa', ['cloud', 'shadow', 'snow']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(col.size())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### add all images to the map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addImageCollection(col, vis, '{system_date} {id}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make composite" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "comp = composite.closestDate(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(comp, vis, 'composite')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(comp.select('date').randomVisualizer(), {'bands':['viz-red', 'viz-green', 'viz-blue'], 'min':0, 'max':255}, 'dates')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### clip to first image (target date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "comp_clip = composite.closestDate(col, clip_to_first=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(comp_clip, vis, 'composite 2017-02-25 clip to first')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/composite/medoid.ipynb b/notebooks/composite/medoid.ipynb deleted file mode 100644 index 77f8a526..00000000 --- a/notebooks/composite/medoid.ipynb +++ /dev/null @@ -1,607 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Medoid Compositing\n", - "*Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median), Neil Flood, 2013, doi:10.3390/rs5126481*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, composite, cloud_mask, indices" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build a collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point(-72, -42)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\\\n", - " .filterBounds(p).filterDate('2017-01-01', '2017-12-01')\\\n", - " .map(cloud_mask.landsat8SRPixelQA())\\\n", - " .map(lambda img: img.addBands(indices.ndvi(img,'B5', 'B4')))\\\n", - " .limit(7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(col.size())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Other simple composites to compare" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_ndvi = col.qualityMosaic('ndvi')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mosaic = col.mosaic()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Medoid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### add date band before compositing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def add_date(img):\n", - " date = tools.date.getDateBand(img)\n", - " return img.addBands(date).copyProperties(date, ['day_since_epoch'])\n", - "col = col.map(add_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "minval = ee.Number(col.aggregate_min('day_since_epoch'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "maxval = ee.Number(col.aggregate_max('day_since_epoch'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid = composite.medoid(col, bands=bands)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid = medoid.set('min_date', minval, 'max_date', maxval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Medoid without taking in count zero values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_no_zeros = composite.medoid(col, bands=bands, discard_zeros=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_no_zeros = medoid_no_zeros.set('min_date', minval, 'max_date', maxval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show on Map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = {'bands':['B5', 'B6','B4'], 'min':0, 'max':5000}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(p)\n", - "Map.centerObject(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(max_ndvi, vis, 'max NDVI')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(mosaic, vis, 'simply Mosaic')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(medoid, vis, 'Medoid')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(medoid.select('date').randomVisualizer(), {'bands':['viz-red', 'viz-green', 'viz-blue'], 'min':0, 'max':255}, 'dates')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(medoid_no_zeros, vis, 'Medoid without zero values')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(medoid_no_zeros.select('date').randomVisualizer(), {'bands':['viz-red', 'viz-green', 'viz-blue'], 'min':0, 'max':255}, 'dates of medoid without zero values')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract data from images and compute locally to compare" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract medoid values in point" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values = tools.image.getValue(medoid.select(bands), p, scale=30, side='client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "List of values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values_list = [val for _, val in medoid_values.items()]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract values at point in each image of the collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_values = tools.imagecollection.getValues(col.select(bands), p, scale=30, side='client')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get bandnames" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_key_list = []\n", - "for _, d in col_values.items():\n", - " keys = []\n", - " for k, v in d.items():\n", - " keys.append(k) \n", - " col_key_list.append(keys)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_key_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get values as a list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_values_list = []\n", - "for _, d in col_values.items():\n", - " values = []\n", - " for _, v in d.items():\n", - " if v:\n", - " values.append(v)\n", - " else:\n", - " values.append(0)\n", - " col_values_list.append(values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_values_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Medoid Method locally" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def local_medoid(values):\n", - " from copy import copy\n", - " import math\n", - "\n", - " def distance(arr1, arr2):\n", - " zipped = zip(arr1, arr2)\n", - " accum = 0\n", - " for a, b in zipped:\n", - " calc = (a-b)*(a-b)\n", - " accum += calc\n", - " return math.sqrt(accum)\n", - "\n", - " def med(values):\n", - " results = {}\n", - " for i, val in enumerate(values):\n", - " val = list(val)\n", - " cop = copy(values)\n", - " cop = [list(a) for a in cop]\n", - " cop.remove(val)\n", - " dist = 0\n", - " for r in cop:\n", - " r = list(r)\n", - " d = distance(val, r)\n", - " dist += d\n", - " results[i] = dist\n", - "\n", - " return results\n", - " \n", - " def getmin(d):\n", - " minval = min(d.values())\n", - " for k, v in d.items():\n", - " if v == minval:\n", - " return k\n", - " \n", - " values = med(values)\n", - " min_value = getmin(values)\n", - " \n", - " # return the index of the minimized sum as first argument, and all options as second\n", - " return min_value, values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute medoid locally and compare" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local = local_medoid(col_values_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the values that correspond to the medoid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "min_values = col_values_list[local[0]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "min_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Match bands with values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_medoid = dict(zip(col_key_list[0], min_values))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "local_medoid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finally, compare values from medoid mosaic against locally computed medoid (from images values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medoid_values == local_medoid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/composite/medoid_score.ipynb b/notebooks/composite/medoid_score.ipynb deleted file mode 100644 index 46e588e4..00000000 --- a/notebooks/composite/medoid_score.ipynb +++ /dev/null @@ -1,142 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compute a medoid score of 'how far is from medoid'\n", - "This is the previous step to compute the medoid composite\n", - "\n", - "When `normalize` parameter is `True`, pixels with value 1 are the actual medoid, and 0 is the further" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import composite, tools, cloud_mask, indices" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point(-72, -42)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\\\n", - " .filterBounds(p).filterDate('2017-01-01', '2017-12-01')\\\n", - " .map(cloud_mask.landsat8SRPixelQA())\\\n", - " .map(lambda img: img.addBands(indices.ndvi(img, 'B5', 'B4')))\\\n", - " .limit(7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bands for medoid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Medoid score including zero values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "medscore = composite.medoidScore(col, bands, False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values = tools.imagecollection.getValues(medscore, p, scale=30)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/date/dayRangeIntervals.ipynb b/notebooks/date/dayRangeIntervals.ipynb deleted file mode 100644 index 2c428ced..00000000 --- a/notebooks/date/dayRangeIntervals.ipynb +++ /dev/null @@ -1,108 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Divide a date range into equal day intervals" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()\n", - "import geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "start = '2020-01-01'\n", - "end = '2020-06-07'\n", - "# make 30 day intervals\n", - "interval = 30" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "intervals = geetools.tools.date.dayRangeIntervals(\n", - " start, end, \n", - " interval=interval,\n", - " reverse=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(intervals)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "intervals_inverse = geetools.tools.date.dayRangeIntervals(\n", - " start, end, \n", - " interval=interval,\n", - " reverse=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(intervals_inverse)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/date/since_epoch.ipynb b/notebooks/date/since_epoch.ipynb deleted file mode 100644 index 898e73f8..00000000 --- a/notebooks/date/since_epoch.ipynb +++ /dev/null @@ -1,157 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_image = ee.Image('LANDSAT/LT05/C01/T1_SR/LT05_226087_20000102')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `get_date_band`\n", - "Get the date of an image, compute how many `units` (for example `day`) has ellpsed since the epoch (1970-01-01) and set it to a band (called `date`) and a property (called `unit_since_epoch`, for example, `day_since_epoch`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date_band = tools.date.getDateBand(test_image, 'day')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(date_band)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `date_since_epoch`\n", - "Given an elapsed time since epoch (for example the result of `get_date_band`) compute what day it is" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image_date = date_band.get('day_since_epoch')\n", - "date_since_epoch = tools.date.dateSinceEpoch(image_date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(date_since_epoch)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `unit_since_epoch`\n", - "Return the number of `unit` (for example, `day`) since the epoch (1970-1-1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = ee.Date('2000-01-02')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "days = tools.date.unitSinceEpoch(date)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(days)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/geometry/.ipynb_checkpoints/getRegion-checkpoint.ipynb b/notebooks/geometry/.ipynb_checkpoints/getRegion-checkpoint.ipynb deleted file mode 100644 index dec1dd87..00000000 --- a/notebooks/geometry/.ipynb_checkpoints/getRegion-checkpoint.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# getRegion(eeObject, bounds)\n", - "Get the `region` for functions that need it as an argument (like Export.image)\n", - "\n", - "**params**\n", - "- eeObject: any eeObject that has a geometry\n", - "- bounds: return only bounds (rectangle). Defaults to `False`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bounded Images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unbounded Images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uimage = ee.Image.constant(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(uimage)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Geometry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pol = ee.Geometry.Polygon([[0,0], [10,10], [20,0], [0, 0]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(pol)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(pol, True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "feat = ee.Feature(pol, {'test':'getRegion'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(feat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FeatureCollection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fc = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filterMetadata('wld_rgn', 'equals', 'South America')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(fc, True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/geometry/getRegion.ipynb b/notebooks/geometry/getRegion.ipynb deleted file mode 100644 index dec1dd87..00000000 --- a/notebooks/geometry/getRegion.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# getRegion(eeObject, bounds)\n", - "Get the `region` for functions that need it as an argument (like Export.image)\n", - "\n", - "**params**\n", - "- eeObject: any eeObject that has a geometry\n", - "- bounds: return only bounds (rectangle). Defaults to `False`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bounded Images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unbounded Images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uimage = ee.Image.constant(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(uimage)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Geometry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pol = ee.Geometry.Polygon([[0,0], [10,10], [20,0], [0, 0]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(pol)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(pol, True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "feat = ee.Feature(pol, {'test':'getRegion'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(feat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FeatureCollection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fc = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filterMetadata('wld_rgn', 'equals', 'South America')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.geometry.getRegion(fc, True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/addConstantBand.ipynb b/notebooks/image/addConstantBand.ipynb deleted file mode 100644 index 274aafab..00000000 --- a/notebooks/image/addConstantBand.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# addConstantBands(value, *names, **pairs)\n", - "Adds bands with a constant value\n", - "\n", - "- names: final names for the additional bands\n", - "- value: constant value\n", - "- pairs: keywords for the bands (see example)\n", - "\n", - "return the function for ee.ImageCollection.map()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('COPERNICUS/S2').select(['B1', 'B2', 'B3']).limit(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "helper function to print values for the centroid of the first image of a collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def print_center(collection):\n", - " first = ee.Image(collection.first())\n", - " p = first.geometry().centroid()\n", - " return tools.image.getValue(first, p, scale=10, side='client')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option 1 - arguments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newcol = col.map(lambda i: tools.image.addConstantBands(i, 0, \"a\", \"b\", \"c\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_center(newcol)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option 2 - keyword arguments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newcolCK = col.map(lambda i: tools.image.addConstantBands(i, a=0, b=1, c=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_center(newcolCK)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Option 3 - combined" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newcolCC = col.map(lambda i:tools.image.addConstantBands(i, 0, \"a\", \"b\", \"c\", d=1, e=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_center(newcolCC)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/addSuffix_addPrefix.ipynb b/notebooks/image/addSuffix_addPrefix.ipynb deleted file mode 100644 index 64acf4c6..00000000 --- a/notebooks/image/addSuffix_addPrefix.ipynb +++ /dev/null @@ -1,210 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add a suffix or a prefix to bands" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT').select('B.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(i.bandNames())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## add a suffix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "suffix = '_suffix'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed_s = tools.image.addSuffix(i, suffix, ['B2', 'B4', 'B8'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed_s.bandNames())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## add prefix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prefix = 'prefix_'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed_p = tools.image.addPrefix(i, prefix, ['B2', 'B4', 'B8'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed_p.bandNames())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In a collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('COPERNICUS/S2').limit(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def print_bands(col):\n", - " info = col.getInfo()\n", - " images = info['features']\n", - " for image in images:\n", - " bands = image['bands']\n", - " print([band['id'] for band in bands])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed_col_s = col.map(lambda img: tools.image.addSuffix(img, suffix, ['B2', 'B4', 'B8']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(renamed_col_s)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/bufferMask.ipynb b/notebooks/image/bufferMask.ipynb deleted file mode 100644 index 6fcaab98..00000000 --- a/notebooks/image/bufferMask.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()\n", - "import geetools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Site" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "site = ee.Geometry.Point([-71.5, -41.7]).buffer(2000)\n", - "Map.centerObject(site)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Masked image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image = ee.Image('COPERNICUS/S2/20151123T142942_20170221T180430_T18GYU')\n", - "fclouds = geetools.cloud_mask.sentinel2()\n", - "masked = fclouds(image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = dict(bands=['B4', 'B3', 'B2'], min=0, max=2500)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(masked, vis, 'Not buffered')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Buffer Mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "buffer = geetools.tools.image.bufferMask(masked, 500, units='meters')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(buffer, vis, 'buffered 500 m')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/image/clipToCollection.ipynb b/notebooks/image/clipToCollection.ipynb deleted file mode 100644 index 8d7219f5..00000000 --- a/notebooks/image/clipToCollection.ipynb +++ /dev/null @@ -1,167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clip an Image using each Feature of a FeatureCollection and return an ImageCollection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geetools\n", - "import geedatasets as dsets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-71, -43]).buffer(1000)\n", - "p2 = ee.Geometry.Point([-70.8, -42.8]).buffer(1000)\n", - "p3 = ee.Geometry.Point([-71.4, -42.8]).buffer(1000)\n", - "p4 = ee.Geometry.Point([-71, -42.5]).buffer(1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fc = ee.FeatureCollection([\n", - " ee.Feature(p, {'ID': 1}),\n", - " ee.Feature(p2, {'ID': 2}),\n", - " ee.Feature(p3, {'ID': 3}),\n", - " ee.Feature(p4, {'ID': 4}),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(fc, name='Feature Collection')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s2 = dsets.fromShortName('S2TOA')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = s2.collection().filterBounds(p).filterDate('2018-01-01', '2018-02-01')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image = col.first()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(image, s2.visualization('NSR'), 'image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "clipped = geetools.image.clipToCollection(image, fc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addImageCollection(clipped, s2.visualization('NSR'), 'image {ID}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/image/distributions.ipynb b/notebooks/image/distributions.ipynb deleted file mode 100644 index 4feeba2c..00000000 --- a/notebooks/image/distributions.ipynb +++ /dev/null @@ -1,250 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools, utils\n", - "import geedatasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get a NDVI Image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8 = geedatasets.landsat.Landsat8SR()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-72,-42])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = l8.collection().filterBounds(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image(col.first())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ndvi = l8.ndvi.apply(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get point grid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "points = ndvi.sample(scale=10000, geometries=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear Function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = tools.image.linearFunction(ndvi, 'NDVI', mean=0.3, range_min=0, range_max=1, min=2, max=5)\n", - "result = ndvi.addBands(d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chart = ui.chart.Image.bandsByRegion(result, points.limit(100), xProperty='NDVI')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gauss Function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss = tools.image.gaussFunction(ndvi, 'NDVI', 0, 1, mean=0.3, output_min=0, output_max=1, stretch=2)\n", - "gauss = ndvi.addBands(gauss)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chart_gauss = ui.chart.Image.bandsByRegion(gauss, points.limit(100), xProperty='NDVI', bands=['gauss'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chart_gauss.renderWidget(width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Normal Distribution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal = tools.image.normalDistribution(ndvi, 'NDVI', scale=30, maxPixels=1e13)\n", - "result_n = ndvi.addBands(normal)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chart_normal = ui.chart.Image.bandsByRegion(result_n, points.limit(100), xProperty='NDVI', bands=['normal_distribution'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Charts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "charts = chart.cat(chart_gauss, chart_normal)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "charts.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "charts.dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/parametrize.ipynb b/notebooks/image/parametrize.ipynb deleted file mode 100644 index ef3f1f05..00000000 --- a/notebooks/image/parametrize.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parametrize pixel values to fit a desired range\n", - "You must know the actual range (computing it could take too much EE memory capacity)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT').select('B1')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i, {'min':0, 'max': 6000}, 'Original')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As values in Sentinel 2 go from 0 to 10000, parametrize values to be between 0 and 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parametrized = tools.image.parametrize(i, (0, 10000), (0, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(parametrized, {'min':0, 'max': 0.6}, 'Parametrized')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "BEFORE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.image.getValue(i, i.geometry().centroid(), 10, 'client')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "AFTER" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.image.getValue(parametrized, i.geometry().centroid(), 10, 'client')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Range can be switched" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "switch_parametrized = tools.image.parametrize(i, (0, 10000), (1, 0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "AFTER\n", - "\n", - " 1 - 0.4712 = 0.5288" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tools.image.getValue(switch_parametrized, i.geometry().centroid(), 10, 'client')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/removeBands.ipynb b/notebooks/image/removeBands.ipynb deleted file mode 100644 index 7e2c1268..00000000 --- a/notebooks/image/removeBands.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT').select('B.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(i.bandNames())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remove some bands" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "removed = tools.image.removeBands(i, ['B1', 'B2', 'B7'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(removed.bandNames())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In a collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('COPERNICUS/S2').limit(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def print_bands(col):\n", - " info = col.getInfo()\n", - " images = info['features']\n", - " for image in images:\n", - " bands = image['bands']\n", - " print([band['id'] for band in bands])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "removed_col = col.map(lambda img: tools.image.removeBands(img, ['B1', 'B2', 'B7']))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(removed_col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/renameDict.ipynb b/notebooks/image/renameDict.ipynb deleted file mode 100644 index b13e7c87..00000000 --- a/notebooks/image/renameDict.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rename bands using a dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT').select('B.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bands = i.bandNames()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(bands)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed = tools.image.renameDict(i, {'B2':'BLUE', 'B3':'GREEN', 'B4':'RED'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed_bands = renamed.bandNames()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed_bands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## In collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ee.ImageCollection('COPERNICUS/S2').limit(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def print_bands(col):\n", - " info = col.getInfo()\n", - " images = info['features']\n", - " for image in images:\n", - " bands = image['bands']\n", - " print([band['id'] for band in bands])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed_col = col.map(lambda img: tools.image.renameDict(img, {'B2':'BLUE', 'B3':'GREEN', 'B4':'RED'}))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print_bands(renamed_col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/renamePattern.ipynb b/notebooks/image/renamePattern.ipynb deleted file mode 100644 index 59f7f46a..00000000 --- a/notebooks/image/renamePattern.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_i = ee.Image([0,1,2,3]).rename('a', 'b', 'c', 'd')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(test_i.bandNames())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pattern = 'new pattern {band} '" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed = tools.image.renamePattern(test_i, pattern)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed.bandNames())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed2 = tools.image.renamePattern(test_i, pattern, bands=['a', 'b'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed2.bandNames())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pattern2 = 'no{band}'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed3 = tools.image.renamePattern(test_i, pattern2, bands=['a', 'b'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed3.bandNames())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pattern3 = 'nothing'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renamed4 = tools.image.renamePattern(test_i, pattern3, bands=['a', 'b'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(renamed4.bandNames())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/image/toGrid.ipynb b/notebooks/image/toGrid.ipynb deleted file mode 100644 index b9a5c336..00000000 --- a/notebooks/image/toGrid.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make a grid out of Image pixels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ipygee import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Geometry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-71.33972167968751, -42.737619925503054])\n", - "aoi = p.buffer(8000).bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = ee.Image('COPERNICUS/S2/20181122T142749_20181122T143353_T18GYT').clip(aoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make Grid" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grid = tools.image.toGrid(i, 3, geometry=aoi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show on Map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = Map()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(i, {'bands':['B2'], 'min':0, 'max':3000}, 'Image')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(grid, None, 'Grid')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eprint(ee.Feature(grid.first()).geometry().projection())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eprint(i.select(0).projection())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(ee.Feature(grid.first()).geometry().transform(i.select(0).projection(), 1), None, 'reprojected')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(ee.Feature(grid.first()), None, 'first')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eprint(Map.getObject('reprojected'))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/imagecollection/distributions.ipynb b/notebooks/imagecollection/distributions.ipynb deleted file mode 100644 index 1fff0e07..00000000 --- a/notebooks/imagecollection/distributions.ipynb +++ /dev/null @@ -1,630 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Applying different distributions to collections and images" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([0, 0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build a Test Collection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# values = ee.List([0, 2, 5, 8, 10, 12, 15, 18, 22, 25, 28, 31, 35, 39, 42, 48, 51, 55])\n", - "# values = ee.List([-10, -8, -7, -1, -2, 0, 1, 5, 7, 8, 10, 12, 14, 16, 18, 20, 22, 24]) # With negative values\n", - "values = ee.List([-20, -32, -15, -8, -5, 0, 2, 4, 6, 8, 9, 10, 12, 14, 16, 18, 20, 23])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def build_collection(n):\n", - " n = ee.Number(n)\n", - " return ee.Image.constant(n).rename('test').set('test', n).toFloat()\n", - "images = values.map(build_collection)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "collection = ee.ImageCollection.fromImages(images)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear Function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "help(tools.imagecollection.linearFunctionBand)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band1 = tools.imagecollection.linearFunctionBand(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band1_chart = ui.chart.Image.series(linear_band1, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['default values'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band2 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band2_chart = ui.chart.Image.series(linear_band2, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band3 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10, output_min=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band3_chart = ui.chart.Image.series(linear_band3, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10 min=0'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band4 = tools.imagecollection.linearFunctionBand(collection, 'test', mean=10, output_min=10, output_max=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_band4_chart = ui.chart.Image.series(linear_band4, p, scale=1000, xProperty='test', bands=['linear_function'], label_bands=['mean=10 min=0 max=12'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_chart = linear_band1_chart.cat(linear_band2_chart, linear_band3_chart, linear_band4_chart)\n", - "linear_chart.renderWidget(width=800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a Property" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop1 = tools.imagecollection.linearFunctionProperty(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop1_chart = ui.chart.Image.series(linear_prop1, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['default values'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop2 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop2_chart = ui.chart.Image.series(linear_prop2, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop3 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop3_chart = ui.chart.Image.series(linear_prop3, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=0'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop4 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=0, output_max=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop4_chart = ui.chart.Image.series(linear_prop4, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=0 max=10'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop5 = tools.imagecollection.linearFunctionProperty(collection, 'test', mean=10, output_min=5, output_max=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_prop5_chart = ui.chart.Image.series(linear_prop5, p, scale=1000, xProperty='test', properties=['LINEAR_FUNCTION'], label_properties=['mean=10 min=5 max=20'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_chart_prop = linear_prop1_chart.cat(linear_prop2_chart, linear_prop3_chart, linear_prop4_chart, linear_prop5_chart)\n", - "linear_chart_prop.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "linear_chart_prop.dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gauss Function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over a band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band1 = tools.imagecollection.gaussFunctionBand(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band1_chart = ui.chart.Image.series(gauss_band1, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['default'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band2 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band2_chart = ui.chart.Image.series(gauss_band2, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['mean=10'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band3 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=10, output_min=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band3_chart = ui.chart.Image.series(gauss_band3, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['mean=10 min=0.5'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exponential function using the gauss function\n", - "Use as mean value the range_max" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band4 = tools.imagecollection.gaussFunctionBand(collection, 'test', mean=23, output_min=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band4_chart = ui.chart.Image.series(gauss_band4, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['gauss'], label_bands=['Exponential'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band_chart = gauss_band1_chart.cat(gauss_band2_chart, gauss_band3_chart, gauss_band4_chart)\n", - "gauss_band_chart.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_band_chart.dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over a Property" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop1 = tools.imagecollection.gaussFunctionProperty(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop1_chart = ui.chart.Image.series(gauss_prop1, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['default'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop2 = tools.imagecollection.gaussFunctionProperty(collection, 'test', mean=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop2_chart = ui.chart.Image.series(gauss_prop2, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['mean=10'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop3 = tools.imagecollection.gaussFunctionProperty(collection, 'test', mean=10, output_min=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop3_chart = ui.chart.Image.series(gauss_prop3, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['GAUSS'], label_properties=['mean=10 min=0.5'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop = gauss_prop1_chart.cat(gauss_prop2_chart, gauss_prop3_chart)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gauss_prop.dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Normal Distribution (applies the Gauss function using a computed stdDev and mean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over a Property" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_prop = tools.imagecollection.normalDistributionProperty(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_prop_chart = ui.chart.Image.series(normal_prop, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', properties=['NORMAL_DISTRIBUTION'], label_properties=['default'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_prop_chart.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_prop_chart.dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over a Band" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_band = tools.imagecollection.normalDistributionBand(collection, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_band_chart = ui.chart.Image.series(normal_band, ee.Geometry.Point([0, 0]), scale=1000, xProperty='test', bands=['normal_distribution'], label_bands=['default'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_band_chart.renderWidget(width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal_band_chart.dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/imagecollection/mosaicSameDay.ipynb b/notebooks/imagecollection/mosaicSameDay.ipynb deleted file mode 100644 index 3fdb9282..00000000 --- a/notebooks/imagecollection/mosaicSameDay.ipynb +++ /dev/null @@ -1,196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mosaic Same Day\n", - "Images inside an ImageCollection that where taken in the same day are fusioned into one image using a reducer (default: mean). The resulting images will have 2 properties only:\n", - "\n", - "- system:time_start\n", - "- system:footprint (this will contain the fusioned geometries)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geedatasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map = ui.Map()\n", - "Map.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sat = geedatasets.sentinel.Sentinel2SR()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis = sat.visualization('NSR')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = ee.Geometry.Point([-72, -42])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = sat.collection().filterBounds(p).filterDate('2019-01-01', '2019-05-01')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(col.size())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get dates\n", - "def get_dates(col):\n", - " dates = ee.List(col.toList(col.size()).map(lambda img: ee.Image(img).date().format()))\n", - " return dates\n", - "\n", - "ui.eprint(get_dates(col))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mosaics = tools.imagecollection.mosaicSameDay(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(mosaics.size())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(get_dates(mosaics))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addImageCollection(col.limit(2), vis)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(mosaics.first(), sat.visualization('NSR'), 'Day 1 mosaicked')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Map.addLayer(mosaics.first().geometry(), name='Footprint day 1 mosaicked')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/imagecollection/parametrizeProperty.ipynb b/notebooks/imagecollection/parametrizeProperty.ipynb deleted file mode 100644 index c0da2e28..00000000 --- a/notebooks/imagecollection/parametrizeProperty.ipynb +++ /dev/null @@ -1,147 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ee\n", - "ee.Initialize()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from geetools import tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipygee as ui\n", - "import geedatasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l8 = geedatasets.landsat.Landsat8SR()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = l8.collection().limit(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parametrize CLOUD_COVER from 0-100 to 0-1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parametrized = tools.imagecollection.parametrizeProperty(col, 'CLOUD_COVER', [0, 100], [0, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cloud_cover = parametrized.toList(10).map(lambda img: ee.Image(img).get('CLOUD_COVER_PARAMETRIZED'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(cloud_cover)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Values out of range" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parametrized2 = tools.imagecollection.parametrizeProperty(col, 'CLOUD_COVER', [0, 80], [0, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "out_of_range = parametrized2.toList(10).map(lambda img: ee.Image(img).get('CLOUD_COVER_PARAMETRIZED'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ui.eprint(out_of_range)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/noxfile.py b/noxfile.py index d2b79b12..4fd34326 100644 --- a/noxfile.py +++ b/noxfile.py @@ -3,63 +3,54 @@ The nox run are build in isolated environment that will be stored in .nox. to force the venv update, remove the .nox/xxx folder. """ +import datetime +import fileinput + import nox nox.options.sessions = ["lint", "test", "docs", "mypy"] -@nox.session(reuse_venv=True) +@nox.session(reuse_venv=True, venv_backend="uv") def lint(session): """Apply the pre-commits.""" session.install("pre-commit") session.run("pre-commit", "run", "--all-files", *session.posargs) -@nox.session(reuse_venv=True, name="ci-test") -def ci_test(session): - """Run all the test using the environment variable of the running machine.""" +@nox.session(reuse_venv=True, venv_backend="uv") +def test(session): + """Run the selected tests and report coverage in html.""" session.install(".[test]") test_files = session.posargs or ["tests"] - session.run( - "pytest", - "--color=yes", - "--cov", - "--cov-report=xml", - *test_files, - ) + session.run("pytest", "--cov", "--cov-report=html", *test_files) -@nox.session(reuse_venv=True) -def test(session): - """Run all the test using the environment variable of the running machine.""" +@nox.session(reuse_venv=True, name="ci-test", venv_backend="uv") +def ci_test(session): + """Run all the test and report coverage in xml.""" session.install(".[test]") - test_files = session.posargs or ["tests"] - session.run( - "pytest", - "--color=yes", - "--cov", - "--cov-report=html", - *test_files, - ) + session.run("pytest", "--cov", "--cov-report=xml") -@nox.session(reuse_venv=True, name="dead-fixtures") +@nox.session(reuse_venv=True, name="dead-fixtures", venv_backend="uv") def dead_fixtures(session): """Check for dead fixtures within the tests.""" session.install(".[test]") session.run("pytest", "--dead-fixtures") -@nox.session(reuse_venv=True) +@nox.session(reuse_venv=True, venv_backend="uv") def docs(session): """Build the documentation.""" build = session.posargs.pop() if session.posargs else "html" session.install(".[doc]") dst, warn = f"docs/_build/{build}", "warnings.txt" session.run("sphinx-build", "-v", "-b", build, "docs", dst, "-w", warn) + session.run("python", "tests/check_warnings.py") -@nox.session(name="mypy", reuse_venv=True) +@nox.session(name="mypy", reuse_venv=True, venv_backend="uv") def mypy(session): """Run a mypy check of the lib.""" session.install("mypy") @@ -67,9 +58,22 @@ def mypy(session): session.run("mypy", *test_files) -@nox.session(reuse_venv=True) +@nox.session(reuse_venv=True, venv_backend="uv") def stubgen(session): """Generate stub files for the lib but requires human attention before merge.""" session.install("mypy") package = session.posargs or ["geetools"] session.run("stubgen", "-p", package[0], "-o", "stubs", "--include-private") + + +@nox.session(name="release-date", reuse_venv=True, venv_backend="uv") +def release_date(session): + """Update the release date of the citation file.""" + current_date = datetime.datetime.now().strftime("%Y-%m-%d") + + with fileinput.FileInput("CITATION.cff", inplace=True) as file: + for line in file: + if line.startswith("date-released:"): + print(f'date-released: "{current_date}"') + else: + print(line, end="") diff --git a/pyproject.toml b/pyproject.toml index 2f7bab33..e547cda5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,41 +4,29 @@ build-backend = "hatchling.build" [project] name = "geetools" -version = "1.9.1" +version = "0.0.0" description = "A collection of tools to work with Google Earth Engine Python API" keywords = [ - "python", - "geospatial", - "remote-sensing", - "google-earth-engine", - "earthengine", + "skeleton", + "Python" ] classifiers = [ - "Development Status :: 5 - Production/Stable", + "Development Status :: 3 - Alpha", "Intended Audience :: Developers", - "Topic :: Software Development :: Build Tools", "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ] -requires-python = ">=3.9" +requires-python = ">=3.8" dependencies = [ - "earthengine-api>=1", # fully static implementation - "requests", - "pandas", - "geopandas", - "deprecated", - "ee-extra", - "xee>=0.0.11", # xee change dtype management - "yamlable", - "matplotlib", - "anyascii", + "deprecated>=1.2.14" ] [[project.authors]] -name = "Rodrigo E. Principe" -email = "fitoprincipe82@gmail.com" +name = "Pierrick Rambaud" +email = "pierrick.rambaud49@gmail.com" [project.license] text = "MIT" @@ -51,34 +39,17 @@ content-type = "text/x-rst" Homepage = "https://github.com/gee-community/geetools" [project.optional-dependencies] -"dev" = [ - "pre-commit", - "nox", - "commitizen" -] -"test" = [ - "pytest", - "pytest-cov", - "pytest-deadfixtures", - "httplib2", - "pytest-regressions", - "Pillow", - "pytest-gee>=0.3.5", # to make IC creation possible in the test suit - "jsonschema", +test = [ + "pytest", + "pytest-cov", + "pytest-deadfixtures" ] doc = [ "sphinx>=6.2.1", "pydata-sphinx-theme", "sphinx-copybutton", "sphinx-design", - "sphinx-autoapi==3.1.0a3", # until v3.1 is released with the single page fix - "ipykernel", - "httplib2", - "jupyter-sphinx", - "myst-nb", - "pytest-gee>=0.3.7", # avoid issue with rdt env variables - "sphinx-icon", - "sphinx-last-updated-by-git", + "sphinx-autoapi" ] [tool.hatch.build.targets.wheel] @@ -88,56 +59,44 @@ only-include = ["geetools"] dependencies = [ "pre-commit", "commitizen", - "nox" + "nox[uv]" ] post-install-commands = ["pre-commit install"] -[tool.licensecheck] -using = "PEP631:test;dev;doc" +[tool.commitizen] +tag_format = "v$major.$minor.$patch$prerelease" +update_changelog_on_bump = false +version = "0.0.0" +version_files = [ + "pyproject.toml:version", + "geetools/__init__.py:__version__", + "docs/conf.py:release", + "CITATION.cff:version" +] + +[tool.pytest.ini_options] +testpaths = "tests" [tool.ruff] -ignore-init-module-imports = true +line-length = 100 fix = true + +[tool.ruff.lint] select = ["E", "F", "W", "I", "D", "RUF"] -extend-exclude = ["geetools/__init__.py", "geetools/tools/_deprecated_imagecollection.py"] -force-exclude = true # to make sure exclude is respected when used via pre-commit ignore = [ "E501", # line too long | Black take care of it - "D213", # Multi-line docstring | We use D212 - "E741", # Ambiguous variable name: `l` | TODO + "D212", # Multi-line docstring | We use D213 + "D101", # Missing docstring in public class | We use D106 ] -[tool.ruff.flake8-quotes] +[tool.ruff.lint.flake8-quotes] docstring-quotes = "double" -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "google" -[tool.ruff.per-file-ignores] -"tests/*" = ["D102"] # Missing docstring in public method | this is test who cares - -[tool.pytest.ini_options] -testpaths = "tests" - [tool.coverage.run] source = ["geetools"] -branch = true - -[tool.commitizen] -tag_format = "v$major.$minor.$patch$prerelease" -update_changelog_on_bump = false -version = "1.9.1" -version_files = [ - "pyproject.toml:version", - "geetools/__init__.py:__version__", - "docs/conf.py:release", -] - -[tool.codespell] -skip = "geetools/tools/*,geetools/classification.py,**/*.ipynb,**/*.yml" - -[tool.black] -line-length = 100 [tool.mypy] scripts_are_modules = true diff --git a/tests/check_warnings.py b/tests/check_warnings.py new file mode 100644 index 00000000..04db76e0 --- /dev/null +++ b/tests/check_warnings.py @@ -0,0 +1,59 @@ +"""Check the warnings from doc builds.""" + +import sys +from pathlib import Path + + +def check_warnings(file: Path) -> int: + """Check the list of warnings produced by the CI tests. + + Raises errors if there are unexpected ones and/or if some are missing. + + Args: + file: the path to the generated warning.txt file from + the CI build + + Returns: + 0 if the warnings are all there + 1 if some warning are not registered or unexpected + """ + # print some log + print("\n=== Sphinx Warnings test ===\n") + + # find the file where all the known warnings are stored + warning_file = Path(__file__).parent / "data" / "warning_list.txt" + + test_warnings = file.read_text().strip().split("\n") + ref_warnings = warning_file.read_text().strip().split("\n") + + print( + f'Checking build warnings in file: "{file}" and comparing to expected ' + f'warnings defined in "{warning_file}"\n\n' + ) + + # find all the missing warnings + missing_warnings = [] + for wa in ref_warnings: + index = [i for i, twa in enumerate(test_warnings) if wa in twa] + if len(index) == 0: + missing_warnings += [wa] + print(f"Warning was not raised: {wa}") + else: + test_warnings.pop(index[0]) + + # the remaining one are unexpected + for twa in test_warnings: + print(f"Unexpected warning: {twa}") + + # delete the tmp warnings file + file.unlink() + + return len(missing_warnings) != 0 or len(test_warnings) != 0 + + +if __name__ == "__main__": + # cast the file to path and resolve to an absolute one + file = Path.cwd() / "warnings.txt" + + # execute the test + sys.exit(check_warnings(file)) diff --git a/tests/conftest.py b/tests/conftest.py index c71f89d4..23c19ef8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,268 +1 @@ """Pytest session configuration.""" - -import string - -import ee -import pytest -import pytest_gee -import requests - -S2_BAND_COMBO = ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12", "SCL"] -"""Sentinel-2 band combination.""" - -L8_BAND_COMBO = ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"] -"""Landsat-8 band combination.""" - - -def pytest_configure() -> None: - """Initialize earth engine according to the environment.""" - pytest_gee.init_ee_from_service_account() - - -@pytest.fixture(scope="session") -def gee_folder_structure(): - """Override the default test folder structure.""" - point = ee.Geometry.Point([0, 0]) - return { - "folder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - "subfolder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - }, - }, - "rmdir_folder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - "subfolder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - }, - }, - "move_folder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - "subfolder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - }, - }, - "copy_folder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - "subfolder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - }, - }, - "unlink_folder::Folder": { - "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), - }, - } - - -# -- fixtures that will be used throughout the tests --------------------------- -@pytest.fixture -def amazonas() -> ee.FeatureCollection: - """Return the Amazonas state from colombia.""" - level2 = ee.FeatureCollection("FAO/GAUL/2015/level2") - colombia = level2.filter(ee.Filter.eq("ADM0_NAME", "Colombia")) - return colombia.filter(ee.Filter.eq("ADM1_NAME", "Amazonas")) - - -@pytest.fixture -def s2_sr(amazonas) -> ee.ImageCollection: - """Return a copernicus based collection. - - the 100 first images of the Sentinel-2 Surface Reflectance ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. - """ - return ( - ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") - .select(S2_BAND_COMBO) - .filterBounds(amazonas) - .filterDate("2021-01-01", "2021-12-01") - ) - - -@pytest.fixture -def vatican(): - """Return the vatican city.""" - level0 = ee.FeatureCollection("FAO/GAUL/2015/level0") - return level0.filter(ee.Filter.eq("ADM0_NAME", "Holy See")) - - -@pytest.fixture -def vatican_buffer(): - """A 100 buffer around vatican city.""" - return ee.Geometry.Point([12.4534, 41.9033]).buffer(100) - - -@pytest.fixture -def s2_sr_vatican_2020(): - """A single image from 2020 on top of vatican city from S2 SR collection.""" - src = "COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM" - return ee.Image(src).select(S2_BAND_COMBO) - - -@pytest.fixture -def l8_sr_vatican_2020(): - """A single image from 2020 on top of vatican city from L8 SR collection.""" - src = "LANDSAT/LC08/C02/T1/LC08_191031_20130711" - return ee.Image(src).select(L8_BAND_COMBO) - - -@pytest.fixture -def l8_toa(amazonas) -> ee.ImageCollection: - """Return a landsat based collection. - - the 100 first images of the landast 8 TOA ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. - """ - return ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_RT_TOA") - .select(L8_BAND_COMBO) - .filterBounds(amazonas) - .filterDate("2021-01-01", "2021-12-01") - ) - - -@pytest.fixture -def l8_sr(amazonas): - """Return a landsat based collection. - - the 100 first images of the landast 8 SR ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. - """ - return ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") - .filterBounds(amazonas) - .filterDate("2021-01-01", "2021-12-01") - ) - - -@pytest.fixture -def l8_sr_raw(): - """Return a defined image collection.""" - return ee.ImageCollection("LANDSAT/LC08/C02/T1").select(L8_BAND_COMBO) - - -@pytest.fixture -def date_instance(): - """Return a defined date instance.""" - return ee.Date("2020-01-01") - - -@pytest.fixture -def daterange_instance(): - """Return a DateRange instance.""" - return ee.DateRange("2020-01-01", "2020-01-31") - - -@pytest.fixture -def letter_list(): - """Return a defined list instance.""" - return ee.List([*string.ascii_lowercase[:3]]) - - -@pytest.fixture -def int_list(): - """Return a defined list instance.""" - return ee.List([*range(1, 3)]) - - -@pytest.fixture -def mix_list(): - """Return a defined list instance.""" - return ee.List(["a", 1, ee.Image(1)]) - - -@pytest.fixture -def number_instance(): - """Return a defined number instance.""" - return ee.Number(1234.56785678) - - -@pytest.fixture -def string_instance(): - """Return a defined string instance.""" - return ee.String("foo") - - -@pytest.fixture -def format_string_instance(): - """Return a defined string instance.""" - return ee.String("{greeting} {name} !") - - -@pytest.fixture -def geom_instance(): - """Set a geometryCollection instance.""" - point0 = ee.Geometry.Point([0, 0], proj="EPSG:4326") - point1 = ee.Geometry.Point([0, 1], proj="EPSG:4326") - poly0 = point0.buffer(1, proj="EPSG:4326") - poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") - line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") - multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") - return ee.Algorithms.GeometryConstructors.MultiGeometry( - [multiPoly, poly0, poly1, point0, line], - crs="EPSG:4326", - geodesic=True, - maxError=1, - ) - - -@pytest.fixture -def fc_instance(): - """Return a fc collection containing 1 single geometryCollection.""" - point0 = ee.Geometry.Point([0, 0], proj="EPSG:4326") - point1 = ee.Geometry.Point([0, 1], proj="EPSG:4326") - poly0 = point0.buffer(1, proj="EPSG:4326") - poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") - line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") - multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") - geometryCollection = ee.Algorithms.GeometryConstructors.MultiGeometry( - [multiPoly, poly0, poly1, point0, line], - crs="EPSG:4326", - geodesic=True, - maxError=1, - ) - return ee.FeatureCollection([geometryCollection]) - - -@pytest.fixture -def gaul_3_countries(): - """Return Italy switzerland and France.""" - fc = ee.FeatureCollection("FAO/GAUL/2015/level0") - return fc.filter(ee.Filter.inList("ADM0_CODE", [122, 237, 85])) - - -@pytest.fixture -def doy_image(): - """Return an Image instance with 2 random doy bands.""" - doy = ee.Image.random(seed=0).multiply(365).toInt().rename("doy1") - return doy.rename("doy1").addBands(doy.rename("doy2")) - - -@pytest.fixture -def multipoint_feature(): - """Return a ``Feature`` instance.""" - geoms = ee.Geometry.MultiPoint([[0, 0], [0, 1]]) - return ee.Feature(geoms).set({"foo": "bar", "bar": "foo"}) - - -@pytest.fixture -def ecoregions(): - """Return the ecoregion collection.""" - return ee.FeatureCollection("projects/google/charts_feature_example") - - -@pytest.fixture -def climSamp(): - """Return the climate sample collection.""" - normClim = ee.ImageCollection("OREGONSTATE/PRISM/Norm81m").toBands() - region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14) - return normClim.sample(region, 5000) - - -@pytest.fixture(scope="session") -def stac_schema(): - """Return the STAC collection schema.""" - url = "https://raw.githubusercontent.com/radiantearth/stac-spec/v1.0.0/collection-spec/json-schema/collection.json" - return requests.get(url).json() - - -@pytest.fixture(scope="session") -def jaxa_rainfall(): - """Return the JAXA rain collection.""" - return ee.ImageCollection("JAXA/GPM_L3/GSMaP/v6/operational") diff --git a/tests/data/warning_list.txt b/tests/data/warning_list.txt new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_Array.py b/tests/test_Array.py deleted file mode 100644 index 8af04926..00000000 --- a/tests/test_Array.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Test the Array class methods.""" -import ee - - -class TestFull: - """Test the full method.""" - - def test_full_with_integers(self): - full_array = ee.Array.geetools.full(3, 3, 1) - assert full_array.getInfo() == [[1, 1, 1], [1, 1, 1], [1, 1, 1]] - - def test_full_with_floats(self): - full_array = ee.Array.geetools.full(3.1, 3.1, 1.0) - assert full_array.getInfo() == [ - [1.0, 1.0, 1.0], - [1.0, 1.0, 1.0], - [1.0, 1.0, 1.0], - ] - - def test_full_with_ee_numbers(self): - full_array = ee.Array.geetools.full(ee.Number(3), ee.Number(3), ee.Number(1)) - assert full_array.getInfo() == [[1, 1, 1], [1, 1, 1], [1, 1, 1]] - - -class TestSet: - """Test the set method.""" - - def test_set_with_integers(self): - array = ee.Array.geetools.full(3, 3, 1) - set_array = array.geetools.set(1, 1, 0) - assert set_array.getInfo() == [[1, 1, 1], [1, 0, 1], [1, 1, 1]] - - def test_set_with_floats(self): - array = ee.Array.geetools.full(3.1, 3.1, 1.0) - set_array = array.geetools.set(1, 1, 0.0) - assert set_array.getInfo() == [ - [1.0, 1.0, 1.0], - [1.0, 0.0, 1.0], - [1.0, 1.0, 1.0], - ] - - def test_set_with_ee_numbers(self): - array = ee.Array.geetools.full(3, 3, ee.Number(1)) - set_array = array.geetools.set(1, 1, ee.Number(0)) - assert set_array.getInfo() == [[1, 1, 1], [1, 0, 1], [1, 1, 1]] diff --git a/tests/test_Asset.py b/tests/test_Asset.py deleted file mode 100644 index e3955a16..00000000 --- a/tests/test_Asset.py +++ /dev/null @@ -1,356 +0,0 @@ -"""Test cases for the Asset class.""" -import os -from unittest.mock import patch - -import ee -import pytest - -import geetools # noqa F401 - -EARTHENGINE_PROJECT = os.environ.get("EARTHENGINE_PROJECT") - - -class TestConstructors: - """Test the constructors of the Asset class.""" - - @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) - def test_home(self): - asset = ee.Asset.home() - assert asset == f"projects/{EARTHENGINE_PROJECT}/assets" - - def test_not_absolute(self): - asset = ee.Asset("/projects/foo/bar") - assert asset == "projects/foo/bar" - - -class TestStr: - """Test the to_string method.""" - - def test_str(self): - asset = ee.Asset("projects/bar") - assert str(asset) == "projects/bar" - - def test_repr(self): - asset = ee.Asset("projects/bar") - assert repr(asset) == "ee.Asset('projects/bar')" - - def test_as_posix(self): - asset = ee.Asset("projects/bar") - assert asset.as_posix() == "projects/bar" - - def test_as_uri(self): - asset = ee.Asset("projects/bar") - assert asset.as_uri() == "https://code.earthengine.google.com/?asset=projects/bar" - - def test_as_description(self): - asset = ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets/a weird name") - assert asset.as_description() == "a_weird_name" - - -class TestOperations: - """Test the operations that can be run on the asset.""" - - def test_truediv(self): - asset = ee.Asset("projects/bar") - assert asset / "foo" == "projects/bar/foo" - - def test_lt(self): - asset = ee.Asset("projects/bar/foo") - assert (asset < "projects/bar/foo/bar") is True - assert (asset < "projects/bar/foo") is False - assert (asset < "projects/bar") is False - - def test_gt(self): - asset = ee.Asset("projects/bar/foo") - assert (asset > "projects/bar/foo/bar") is False - assert (asset > "projects/bar/foo") is False - assert (asset > "projects/bar") is True - - def test_le(self): - asset = ee.Asset("projects/bar/foo") - assert (asset <= "projects/bar/foo/bar") is True - assert (asset <= "projects/bar/foo") is True - assert (asset <= "projects/bar") is False - - def test_ge(self): - asset = ee.Asset("projects/bar/foo") - assert (asset >= "projects/bar/foo/bar") is False - assert (asset >= "projects/bar/foo") is True - assert (asset >= "projects/bar") is True - - def test_eq(self): - asset = ee.Asset("projects/bar") - assert (asset == "projects/bar") is True - assert (asset == "projects/bar/foo") is False - - def test_ne(self): - asset = ee.Asset("projects/bar") - assert (asset != "projects/bar") is False - assert (asset != "projects/bar/foo") is True - - def test_idiv(self): - asset = ee.Asset("projects/bar") - asset /= "foo" - assert asset == "projects/bar/foo" - - def test_is_absolute(self): - assert ee.Asset("projects/bar/assets/foo").is_absolute() is True - assert ee.Asset("projects/bar/foo").is_absolute() is False - assert ee.Asset("projects/assets/foo").is_absolute() is False - assert ee.Asset("bar/assets/foo").is_absolute() is False - with pytest.raises(ValueError): - ee.Asset("projects/bar").is_absolute(raised=True) - - @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) - def test_is_user_project(self): - assert ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets/foo").is_user_project() is True - assert ee.Asset("projects/foo").is_user_project() is False - with pytest.raises(ValueError): - ee.Asset("projects/foo").is_user_project(raised=True) - - @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) - def test_expanduser(self): - asset = ee.Asset("~/foo").expanduser() - assert asset == f"projects/{EARTHENGINE_PROJECT}/assets/foo" - - def test_is_relative_to(self): - asset1 = ee.Asset("projects/bar") - asset2 = ee.Asset("projects/bar/foo") - assert asset2.is_relative_to(asset1) is True - assert asset1.is_relative_to(asset2) is False - - def test_joinpath(self): - asset = ee.Asset("projects/bar") - assert asset.joinpath("foo") == "projects/bar/foo" - - def test_match(self): - asset = ee.Asset("projects/bar/foo") - assert asset.match("projects/bar/*") is True - assert asset.match("projects/bar") is False - assert asset.match("**/foo") is True - - def test_with_name(self): - asset = ee.Asset("projects/bar") - assert asset.with_name("foo") == "projects/foo" - - -class TestProperties: - """Test the properties of the Asset class.""" - - def test_parts(self): - asset = ee.Asset("projects/bar/foo") - assert asset.parts == ("projects", "bar", "foo") - - def test_parent(self): - asset = ee.Asset("projects/test/assets/foo/bar/baz") - assert asset.parent == "projects/test/assets/foo/bar" - - def test_parents(self, data_regression): - asset = ee.Asset("projects/test/assets/foo/bar/baz") - parents = [str(p) for p in asset.parents] - data_regression.check(parents) - - def test_name(self): - asset = ee.Asset("projects/bar/foo") - assert asset.name == "foo" - - def test_st_size(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - assert (gee_test_folder / "folder/image").st_size == 31 - with pytest.raises(ValueError): - gee_test_folder.st_size - - def test_owner(self): - assert ee.Asset("projects/bar/assets/foo").owner == "bar" - with pytest.raises(ValueError): - ee.Asset("projects/foo").owner - - -class TestServerMethods: - """Test methods that are run on the server.""" - - def test_exists(self, gee_test_folder): - # cast to asset as it's just a regular path in pytest-gee - gee_test_folder = ee.Asset(gee_test_folder) - - assert gee_test_folder.exists() is True - assert (gee_test_folder / "folder").exists() is True - assert (gee_test_folder / "folder" / "image").exists() is True - assert (gee_test_folder / "folder" / "fake").exists() is False - - def test_is_project(self): - assert ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets").is_project() is True - assert ee.Asset("projects/bar").is_project() is False - with pytest.raises(ValueError): - ee.Asset("projects/bar").is_project(raised=True) - - def test_is_folder(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - assert (gee_test_folder / "folder").is_folder() is True - assert (gee_test_folder / "folder" / "image").is_folder() is False - - def test_is_image(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - assert (gee_test_folder / "folder" / "image").is_image() is True - assert (gee_test_folder / "folder").is_feature_collection() is False - - def test_is_type(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - assert gee_test_folder.is_type("FOLDER") is True - with pytest.raises(ValueError): - gee_test_folder.is_type("IMAGE", raised=True) - - def test_iterdir(self, gee_hash, gee_test_folder, data_regression): - folder = ee.Asset(gee_test_folder) / "folder" - assets = [ - str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") - for a in folder.iterdir() - ] - data_regression.check(assets) - - def test_iterdir_recursive(self, gee_hash, gee_test_folder, data_regression): - folder = ee.Asset(gee_test_folder) / "folder" - assets = [ - str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") - for a in folder.iterdir(recursive=True) - ] - data_regression.check(assets) - - def test_iterdir_nodir(self, gee_test_folder): - with pytest.raises(ValueError): - (ee.Asset(gee_test_folder) / "folder" / "image").iterdir() - - def test_glob(self, gee_test_folder, gee_hash, data_regression): - folder = ee.Asset(gee_test_folder) / "folder" - assets = [ - str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") - for a in folder.glob("*/image") - ] - data_regression.check(assets) - - def test_rglob(self, gee_test_folder, gee_hash, data_regression): - folder = ee.Asset(gee_test_folder) / "folder" - assets = [ - str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") - for a in folder.rglob("*/image") - ] - data_regression.check(assets) - - def test_mkdir(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = (gee_test_folder / "new_mkdir_folder").mkdir() - assert asset.is_folder() is True - ee.data.deleteAsset(str(asset)) - - def test_mkdir_exists(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - with pytest.raises(ValueError): - (gee_test_folder / "folder").mkdir() - asset = (gee_test_folder / "folder").mkdir(exist_ok=True) - assert asset.is_folder() is True - - def test_mkdir_parents(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - with pytest.raises(ValueError): - (gee_test_folder / "new_mkdir_parent" / "subfolder").mkdir() - asset = (gee_test_folder / "new_mkdir_parent" / "subfolder").mkdir(parents=True) - assert asset.parent.is_folder() is True - assert asset.is_folder() is True - ee.data.deleteAsset(str(asset)) - ee.data.deleteAsset(str(asset.parent)) - - def test_unlink(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "unlink_folder" / "image" - assert asset.is_image() is True - asset.unlink() - assert asset.exists() is False - - def test_unlink_not_exists(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - with pytest.raises(ValueError): - (gee_test_folder / "new_unlink_folder").unlink() - - def test_delete(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = (gee_test_folder / "new_delete_folder").mkdir() - assert asset.is_folder() is True - asset.delete() - assert asset.exists() is False - - def test_rmdir_dry_run(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = (gee_test_folder / "test_rmdir_folder").mkdir() - assert asset.is_folder() is True - assets = asset.rmdir(dry_run=True) - assert assets == [str(asset)] - assert asset.exists() is True - ee.data.deleteAsset(str(asset)) - - def test_rmdir(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = (gee_test_folder / "test_rmdir_folder").mkdir() - assert asset.is_folder() is True - asset.rmdir() - assert asset.exists() is False - - def test_rmdir_not_folder(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - with pytest.raises(ValueError): - (gee_test_folder / "folder" / "image").rmdir() - - def test_rmdir_recursive_dry_run(self, gee_hash, gee_test_folder, data_regression): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "rmdir_folder" - assets = [ - str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") - for a in asset.rmdir(recursive=True) - ] - data_regression.check(assets) - - def test_rmdir_recursive(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "rmdir_folder" - asset.rmdir(recursive=True, dry_run=False) - assert asset.exists() is False - - def test_copy(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "copy_folder" / "image" - new_asset = gee_test_folder / "copy_folder" / "new_image" - asset.copy(new_asset) - assert asset.exists() is True - assert new_asset.exists() is True - - def test_copy_folder(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "copy_folder" - new_asset = gee_test_folder / "new_copy_folder" - asset.copy(new_asset) - assert asset.exists() is True - assert new_asset.exists() is True - - def test_move(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "move_folder" / "image" - new_asset = gee_test_folder / "move_folder" / "new_image" - asset.move(new_asset) - assert asset.exists() is False - assert new_asset.exists() is True - - def test_move_folder(self, gee_test_folder): - gee_test_folder = ee.Asset(gee_test_folder) - asset = gee_test_folder / "move_folder" - new_asset = gee_test_folder / "new_move_folder" - asset.move(new_asset) - assert asset.exists() is False - assert new_asset.exists() is True - - -class TestSetProperties: - """Test the ``set_properties`` method.""" - - def test_set_properties(self, gee_test_folder): - asset = ee.Asset(gee_test_folder) / "folder" / "image" - asset.setProperties(foo="bar") - assert ee.Image(asset.as_posix()).get("foo").getInfo() == "bar" diff --git a/tests/test_Asset/test_glob.yml b/tests/test_Asset/test_glob.yml deleted file mode 100644 index 5f21f759..00000000 --- a/tests/test_Asset/test_glob.yml +++ /dev/null @@ -1 +0,0 @@ -- projects/ee-project/assets/hash/folder/image diff --git a/tests/test_Asset/test_iterdir.yml b/tests/test_Asset/test_iterdir.yml deleted file mode 100644 index 08bc8524..00000000 --- a/tests/test_Asset/test_iterdir.yml +++ /dev/null @@ -1,2 +0,0 @@ -- projects/ee-project/assets/hash/folder/image -- projects/ee-project/assets/hash/folder/subfolder diff --git a/tests/test_Asset/test_iterdir_recursive.yml b/tests/test_Asset/test_iterdir_recursive.yml deleted file mode 100644 index 059ac2d3..00000000 --- a/tests/test_Asset/test_iterdir_recursive.yml +++ /dev/null @@ -1,3 +0,0 @@ -- projects/ee-project/assets/hash/folder/image -- projects/ee-project/assets/hash/folder/subfolder -- projects/ee-project/assets/hash/folder/subfolder/image diff --git a/tests/test_Asset/test_parents.yml b/tests/test_Asset/test_parents.yml deleted file mode 100644 index 2852d6bb..00000000 --- a/tests/test_Asset/test_parents.yml +++ /dev/null @@ -1,2 +0,0 @@ -- projects/test/assets/foo/bar -- projects/test/assets/foo diff --git a/tests/test_Asset/test_rglob.yml b/tests/test_Asset/test_rglob.yml deleted file mode 100644 index 8f013e97..00000000 --- a/tests/test_Asset/test_rglob.yml +++ /dev/null @@ -1,2 +0,0 @@ -- projects/ee-project/assets/hash/folder/image -- projects/ee-project/assets/hash/folder/subfolder/image diff --git a/tests/test_Asset/test_rmdir_recursive_dry_run.yml b/tests/test_Asset/test_rmdir_recursive_dry_run.yml deleted file mode 100644 index 29fa7a73..00000000 --- a/tests/test_Asset/test_rmdir_recursive_dry_run.yml +++ /dev/null @@ -1,4 +0,0 @@ -- projects/ee-project/assets/hash/rmdir_folder/subfolder/image -- projects/ee-project/assets/hash/rmdir_folder/image -- projects/ee-project/assets/hash/rmdir_folder/subfolder -- projects/ee-project/assets/hash/rmdir_folder diff --git a/tests/test_ComputedObect.py b/tests/test_ComputedObect.py deleted file mode 100644 index 32cc4854..00000000 --- a/tests/test_ComputedObect.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Test the ComputedObject class methods.""" - -import ee -import pytest - - -class TestIsinstance: - """Test the isInstance method.""" - - def test_isinstance_with_string(self): - assert ee.String("").isInstance(ee.String).getInfo() == 1 - - def test_isinstance_with_integer(self): - assert ee.Number(1).isInstance(ee.Integer).getInfo() == 1 - - def test_isinstance_with_float(self): - assert ee.Number(1.1).isInstance(ee.Float).getInfo() == 1 - - def test_isinstance_with_image(self): - assert ee.Image().isInstance(ee.Image).getInfo() == 1 - - def test_isinstance_with_imagecollection(self): - ic = ee.ImageCollection([ee.Image()]) - assert ic.isInstance(ee.ImageCollection).getInfo() == 1 - - def test_isinstance_with_feature(self): - assert ee.Feature(None).isInstance(ee.Feature).getInfo() == 1 - - def test_isinstance_with_geometry(self): - assert ee.Geometry.Point([0, 0]).isInstance(ee.Geometry).getInfo() == 1 - - -class TestSave: - """Test the ``save`` method.""" - - def test_save(self, tmp_path): - file = tmp_path / "test.gee" - ee.Number(1.1).save(file) - assert file.exists() - - -class TestOpen: - """Test the ``open`` method.""" - - def test_open(self, tmp_path): - (object := ee.Number(1.1)).save((file := tmp_path / "test.gee")) - opened = ee.Number.open(file) - assert object.eq(opened).getInfo() - - def test_open_not_correct_suffix(self): - with pytest.raises(ValueError): - ee.Number.open("file.toto") diff --git a/tests/test_Date.py b/tests/test_Date.py deleted file mode 100644 index 1f60205f..00000000 --- a/tests/test_Date.py +++ /dev/null @@ -1,81 +0,0 @@ -"""Test the Date class methods.""" -import ee -import pytest - -import geetools # noqa: F401 - - -class TestToDatetime: - """Test the toDatetime method.""" - - def test_to_datetime(self, date_instance): - datetime = date_instance.geetools.to_datetime() - assert datetime.year == 2020 - assert datetime.month == 1 - assert datetime.day == 1 - - -class TestGetUnitSinceEpoch: - """Test the getUnitSinceEpoch method.""" - - def test_unit_since_epoch(self, date_instance): - unit = date_instance.geetools.getUnitSinceEpoch("year") - assert unit.getInfo() >= 49 # 2020 - 1970 - - def test_wrong_unit(self, date_instance): - with pytest.raises(ValueError): - date_instance.geetools.getUnitSinceEpoch("foo") - - -class TestFromEpoch: - """Test the fromEpoch method.""" - - def test_from_epoch(self): - date = ee.Date.geetools.fromEpoch(49, "year") - assert date.format("YYYY-MM-DD").getInfo() == "2019-01-01" - - def test_wrong_unit(self): - with pytest.raises(ValueError): - ee.Date.geetools.fromEpoch(49, "foo") - - -class TestFromDOY: - """Test the fromDOY method.""" - - def test_from_doy(self): - date = ee.Date.geetools.fromDOY(1, 2020) - assert date.format("YYYY-MM-DD").getInfo() == "2020-01-01" - - def test_wrong_year(self): - # check GEE can use year < EPOCH - date = ee.Date.geetools.fromDOY(1, 3) - assert date.format("YYYY-MM-DD").getInfo() == "0003-01-01" - - def test_wrong_doy(self): - # check that GEE can use > 365 doy - date = ee.Date.geetools.fromDOY(367, 2020) - assert date.format("YYYY-MM-DD").getInfo() == "2021-01-01" - - -class TestIsLeap: - """Test the isLeap method.""" - - def test_is_leap_1992(self): - leap = ee.Date("1992-01-01").geetools.isLeap() - assert leap.getInfo() == 1 - - def test_is_leap_2000(self): - leap = ee.Date("2000-01-01").geetools.isLeap() - assert leap.getInfo() == 1 - - def test_is_leap_1900(self): - leap = ee.Date("1900-01-01").geetools.isLeap() - assert leap.getInfo() == 0 - - -class TestNow: - """Test the now method.""" - - def test_now(self): - date = ee.Date.geetools.now() - assert date.getInfo() is not None diff --git a/tests/test_DateRange.py b/tests/test_DateRange.py deleted file mode 100644 index 6d05c9a9..00000000 --- a/tests/test_DateRange.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Test the ``DateRange`` class.""" -import ee -import pytest - - -class TestSplit: - """Test the ``split`` method.""" - - def test_split(self, daterange_instance): - list = daterange_instance.geetools.split(1, "day") - first = ee.DateRange(list.get(0)).start() - last = ee.DateRange(list.get(-1)).end() - assert list.size().getInfo() == 30 - assert first.format("YYYY-MM-dd").getInfo() == "2020-01-01" - assert last.format("YYYY-MM-dd").getInfo() == "2020-01-31" - - def split_with_end_outside(self, daterange_instance): - list = daterange_instance.geetools.split(2, "month") - first = ee.DateRange(list.get(0)).start() - last = ee.DateRange(list.get(-1)).end() - assert list.size().getInfo() == 1 - assert first.format("YYYY-MM-dd").getInfo() == "2020-01-01" - assert last.format("YYYY-MM-dd").getInfo() == "2020-01-31" - - -class TestCheckUnit: - """Test the ``check_unit`` method exception.""" - - def test_check_unit(self): - with pytest.raises(ValueError): - ee.DateRange.geetools.check_unit("toto") diff --git a/tests/test_Dictionary.py b/tests/test_Dictionary.py deleted file mode 100644 index 9e410cea..00000000 --- a/tests/test_Dictionary.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Test the Dictionary class methods.""" -import ee - - -class TestFromPairs: - """Test the fromPairs method.""" - - def test_from_pairs_with_list(self): - d = ee.Dictionary.geetools.fromPairs([["foo", 1], ["bar", 2]]) - assert d.getInfo() == {"foo": 1, "bar": 2} - - def test_from_pairs_with_ee_list(self): - d = ee.Dictionary.geetools.fromPairs(ee.List([["foo", 1], ["bar", 2]])) - assert d.getInfo() == {"foo": 1, "bar": 2} - - -class TestSort: - """Test the sort method.""" - - def test_sort(self): - d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.sort() - assert d.getInfo() == {"bar": 2, "foo": 1} - - -class TestGetMany: - """Test the getMany method.""" - - def test_getMany(self): - d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.getMany(["foo"]) - assert d.getInfo() == [1] diff --git a/tests/test_Export.py b/tests/test_Export.py deleted file mode 100644 index 31543653..00000000 --- a/tests/test_Export.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Test the ``Export`` class.""" -import ee -import pytest -from ee.cli.utils import wait_for_task - -import geetools # noqa F401 - - -class TestImageCollection: - """Test the ``imagecollection`` namespace.""" - - @pytest.mark.skip(reason="The export task timeout when to many tests are run at the same time") - def test_toAsset(self, gee_test_folder): - task_list = ee.batch.Export.geetools.imagecollection.toAsset( - imagecollection=self.ic, - description="ic_to_asset", - index_property="index", - assetId=(gee_test_folder / "ic_to_asset").as_posix(), - region=ee.Geometry.Point([0, 0]).buffer(100).bounds(), - scale=50, - ) - [task.start() for task in task_list] - [wait_for_task(task.id, timeout=50) for task in task_list] - - ic = ee.ImageCollection((gee_test_folder / "ic_to_asset").as_posix()) - assert ic.size().getInfo() == 2 - - @property - def ic(self): - """Return a test image collection.""" - image_list = [ee.Image(i).set("index", f"image_{i}") for i in range(2)] - return ee.ImageCollection(image_list) diff --git a/tests/test_Feature.py b/tests/test_Feature.py deleted file mode 100644 index c0515e7c..00000000 --- a/tests/test_Feature.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Test the ``Feature`` class.""" - - -class TestToFeatureCollection: - """Test the ``toFeatureCollection`` method.""" - - def test_to_feature_collection(self, multipoint_feature, data_regression): - fc = multipoint_feature.geetools.toFeatureCollection() - data_regression.check(fc.getInfo()) - - -class TestRemoveProperties: - """Test the ``removeProperties`` method.""" - - def test_remove_properties(self, multipoint_feature, data_regression): - feature = multipoint_feature.geetools.removeProperties(["foo"]) - data_regression.check(feature.getInfo()) diff --git a/tests/test_Feature/test_remove_properties.yml b/tests/test_Feature/test_remove_properties.yml deleted file mode 100644 index b7943b95..00000000 --- a/tests/test_Feature/test_remove_properties.yml +++ /dev/null @@ -1,10 +0,0 @@ -geometry: - coordinates: - - - 0 - - 0 - - - 0 - - 1 - type: MultiPoint -properties: - bar: foo -type: Feature diff --git a/tests/test_Feature/test_to_feature_collection.yml b/tests/test_Feature/test_to_feature_collection.yml deleted file mode 100644 index 49ad297e..00000000 --- a/tests/test_Feature/test_to_feature_collection.yml +++ /dev/null @@ -1,26 +0,0 @@ -columns: - bar: String - foo: String - system:index: String -features: -- geometry: - coordinates: - - 0 - - 0 - type: Point - id: '0' - properties: - bar: foo - foo: bar - type: Feature -- geometry: - coordinates: - - 0 - - 1 - type: Point - id: '1' - properties: - bar: foo - foo: bar - type: Feature -type: FeatureCollection diff --git a/tests/test_FeatureCollection.py b/tests/test_FeatureCollection.py deleted file mode 100644 index 19f7f9aa..00000000 --- a/tests/test_FeatureCollection.py +++ /dev/null @@ -1,294 +0,0 @@ -"""Test the ``FeatureCollection`` class.""" -import io - -import ee -import geopandas as gpd -import pytest -from matplotlib import pyplot as plt - -import geetools # noqa: F401 - - -class TestToImage: - """Test the ``toImage`` method.""" - - def test_to_image(self, fc_instance, vatican): - image = fc_instance.geetools.toImage() - values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) - assert values.getInfo() == {"constant": 0} - - def test_to_image_with_color(self, fc_instance, vatican): - image = fc_instance.geetools.toImage(color="ADM0_CODE") - values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) - assert values.getInfo() == {"constant": 110} - - @pytest.fixture - def fc_instance(self): - gaul = ee.FeatureCollection("FAO/GAUL/2015/level0") - return gaul.filter(ee.Filter.eq("ADM0_CODE", 110)) - - @pytest.fixture - def vatican(self): - """Return a buffer around the Vatican City.""" - return ee.Geometry.Point([12.453386, 41.903282]).buffer(1) - - -class TestToDictionary: - """Test the ``toDictionary`` method.""" - - def test_to_dictionary(self, data_regression): - output = self.table.geetools.toDictionary("ADM0_NAME", ["ADM0_CODE", "Shape_Area"]) - data_regression.check(output.getInfo()) - - @property - def table(self): - return ee.FeatureCollection("FAO/GAUL/2015/level0").filter( - ee.Filter.stringStartsWith(leftField="ADM0_NAME", rightValue="Ar") - ) - - -class TestAddId: - """Test the ``addId`` method.""" - - def test_add_id(self, fc_instance): - fc = fc_instance.geetools.addId() - assert fc.first().get("id").getInfo() == 1 - - @pytest.fixture - def fc_instance(self): - return ee.FeatureCollection("FAO/GAUL/2015/level0").limit(10) - - -class TestMergeGeometries: - """Test the ``mergeGeometries`` method.""" - - def test_merge_geometries(self, gaul_3_countries, data_regression): - geom = gaul_3_countries.geetools.mergeGeometries() - data_regression.check(geom.getInfo()) - - -class TestToPolygons: - """Test the ``toPolygons`` method.""" - - def test_to_polygons(self, fc_instance, dataframe_regression): - fc = fc_instance.geetools.toPolygons() - gdf = gpd.GeoDataFrame.from_features(fc.getInfo()) - vertex = gdf.geometry.apply(lambda g: sum(len(p.exterior.coords) for p in g.geoms)) - assert vertex.sum() == 66 - - -class TestByProperties: - """Test the ``byProperties`` method.""" - - def test_by_properties(self, ecoregions, data_regression): - fc = ecoregions.geetools.byProperties() - data_regression.check(fc.getInfo()) - - def test_by_properties_with_id(self, ecoregions, data_regression): - fc = ecoregions.geetools.byProperties("label") - data_regression.check(fc.getInfo()) - - def test_by_properties_with_properties(self, ecoregions, data_regression): - fc = ecoregions.geetools.byProperties("label", properties=["01_tmean", "02_tmean"]) - data_regression.check(fc.getInfo()) - - -class TestByFeatures: - """Test the ``byFeatures`` method.""" - - def test_by_features(self, ecoregions, data_regression): - fc = ecoregions.geetools.byFeatures() - data_regression.check(fc.getInfo()) - - def test_by_features_with_id(self, ecoregions, data_regression): - fc = ecoregions.geetools.byFeatures("label") - data_regression.check(fc.getInfo()) - - def test_by_features_with_properties(self, ecoregions, data_regression): - fc = ecoregions.geetools.byFeatures("label", properties=["01_tmean", "02_tmean"]) - data_regression.check(fc.getInfo()) - - -class TestPlotByFeatures: - """Test the ``plot_by_features`` method.""" - - def test_plot_by_features_bar(self, ecoregions, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregions.geetools.plot_by_features( - type="bar", - featureId="label", - properties=["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean","11_tmean", "12_tmean"], - labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors=["#604791", "#1d6b99", "#39a8a7", "#0f8755", "#76b349", "#f0af07", "#e37d05", "#cf513e", "#96356f", "#724173", "#9c4f97", "#696969"], - ax=ax, - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_features_stacked(self, ecoregions, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregions.geetools.plot_by_features( - type="stacked", - featureId="label", - properties=["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean","11_tmean", "12_tmean"], - labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors=["#604791", "#1d6b99", "#39a8a7", "#0f8755", "#76b349", "#f0af07", "#e37d05", "#cf513e", "#96356f", "#724173", "#9c4f97", "#696969"], - ax=ax, - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_features_scatter(self, ecoregions, image_regression): - fig, ax = plt.subplots() - ecoregions.geetools.plot_by_features( - type="scatter", - featureId="label", - properties=["01_ppt", "06_ppt", "09_ppt"], - labels=["jan", "jun", "sep"], - ax=ax, - ) - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_features_pie(self, ecoregions, image_regression): - fig, ax = plt.subplots() - ecoregions.geetools.plot_by_features( - type="pie", - featureId="label", - properties=["06_ppt"], - colors=["#f0af07", "#0f8755", "#76b349"], - ax=ax, - ) - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_features_donut(self, ecoregions, image_regression): - fig, ax = plt.subplots() - ecoregions.geetools.plot_by_features( - type="donut", - featureId="label", - properties=["06_ppt"], - colors=["#f0af07", "#0f8755", "#76b349"], - ax=ax, - ) - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - -class TestPlotByPropperties: - """Test the ``plot_by_properties`` method.""" - - def test_plot_by_properties_bar(self, ecoregions, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregions.geetools.plot_by_properties( - type="bar", - properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], - labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - featureId="label", - colors=["#f0af07", "#0f8755", "#76b349"], - ax=ax, - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_properties_plot(self, ecoregions, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregions.geetools.plot_by_properties( - type="plot", - properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], - labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - featureId="label", - colors=["#f0af07", "#0f8755", "#76b349"], - ax=ax, - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_properties_area(self, ecoregions, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregions.geetools.plot_by_properties( - type="fill_between", - properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], - labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - featureId="label", - colors=["#f0af07", "#0f8755", "#76b349"], - ax=ax, - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - -class TestPlotHist: - """Test the ``plot_hist`` method.""" - - def test_plot_hist(self, climSamp, image_regression): - fig, ax = plt.subplots() - climSamp.geetools.plot_hist( - property="07_ppt", label="July Precipitation (mm)", color="#1d6b99", ax=ax, bins=30 - ) - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - -class TestPlot: - """Test the ``plot`` method.""" - - def test_plot(self, image_regression): - fig, ax = plt.subplots() - self.hydroshed.select(["UP_AREA"]).geetools.plot(ax=ax) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_with_property(self, image_regression): - fig, ax = plt.subplots() - self.hydroshed.geetools.plot(ax=ax, property="UP_AREA") - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_with_cmap(self, image_regression): - fig, ax = plt.subplots() - self.hydroshed.geetools.plot(ax=ax, property="UP_AREA", cmap="magma") - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_with_boundaries(self, image_regression): - fig, ax = plt.subplots() - self.hydroshed.geetools.plot( - ax=ax, property="UP_AREA", cmap="magma", boundaries=True, color="g" - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def hydroshed(self): - """The level 4 hydroshed of South america.""" - dataset = "WWF/HydroATLAS/v1/Basins/level04" - region = ee.Geometry.BBox(-80, -60, -20, 20) - return ee.FeatureCollection(dataset).filterBounds(region) diff --git a/tests/test_FeatureCollection/test_by_features.yml b/tests/test_FeatureCollection/test_by_features.yml deleted file mode 100644 index de724356..00000000 --- a/tests/test_FeatureCollection/test_by_features.yml +++ /dev/null @@ -1,264 +0,0 @@ -'00000000000000000000': - 01_ppt: 27.95434081213815 - 01_tdmean: -5.938981422015599 - 01_tmax: 14.05185354641506 - 01_tmean: 5.791035977772305 - 01_tmin: -2.469781775474549 - 01_vpdmax: 12.487591886520388 - 01_vpdmin: 1.3548382043838505 - 02_ppt: 21.858468787329542 - 02_tdmean: -5.516507593563626 - 02_tmax: 16.323491311754502 - 02_tmean: 7.6450114270619 - 02_tmin: -1.0334683659672739 - 02_vpdmax: 15.010737727028985 - 02_vpdmin: 1.6896190329960419 - 03_ppt: 17.579124287196574 - 03_tdmean: -5.821656778880528 - 03_tmax: 19.68566257476807 - 03_tmean: 10.454658155441285 - 03_tmin: 1.2236530038288664 - 03_vpdmax: 19.560661269596647 - 03_vpdmin: 2.66291476556233 - 04_ppt: 8.252543340410508 - 04_tdmean: -5.8467324072974085 - 04_tmax: 24.170578130994524 - 04_tmean: 14.251640262603761 - 04_tmin: 4.332702394894191 - 04_vpdmax: 26.725532964978907 - 04_vpdmin: 4.364877526419504 - 05_ppt: 8.372215850693841 - 05_tdmean: -3.4355346179008484 - 05_tmax: 29.04781422478813 - 05_tmean: 19.0328149005345 - 05_tmin: 9.017815884181434 - 05_vpdmax: 35.71408178601947 - 05_vpdmin: 6.715028816631862 - 06_ppt: 14.802122592926029 - 06_tdmean: 0.9473899156706674 - 06_tmax: 33.579262025015694 - 06_tmean: 23.790505815233505 - 06_tmin: 14.001748946053644 - 06_vpdmax: 45.70917290278844 - 06_vpdmin: 9.571135237557549 - 07_ppt: 80.38940920148579 - 07_tdmean: 10.336808161054341 - 07_tmax: 33.16552563803537 - 07_tmean: 25.06657654081073 - 07_tmin: 16.967626991271974 - 07_vpdmax: 39.50814271654403 - 07_vpdmin: 5.347937705857414 - 08_ppt: 79.32688581194199 - 08_tdmean: 13.058130756105697 - 08_tmax: 31.466057499476847 - 08_tmean: 23.845259066990447 - 08_tmin: 16.224460500989643 - 08_vpdmax: 33.170324205671044 - 08_vpdmin: 3.090083679471698 - 09_ppt: 37.412246641431544 - 09_tdmean: 8.827760635103498 - 09_tmax: 29.893881457192563 - 09_tmean: 21.454794812883655 - 09_tmin: 13.015708386557444 - 09_vpdmax: 31.64241425105504 - 09_vpdmin: 3.494040838650295 - 10_ppt: 32.728693926674985 - 10_tdmean: 1.6853334009647374 - 10_tmax: 25.111735046931678 - 10_tmean: 15.997987973349439 - 10_tmin: 6.884241169520789 - 10_vpdmax: 26.030083217620856 - 10_vpdmin: 3.16713012967791 - 11_ppt: 22.80909897395543 - 11_tdmean: -3.7638271621295383 - 11_tmax: 18.830497095925473 - 11_tmean: 9.84932084628514 - 11_tmin: 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-'00000000000000000002': - 01_ppt: 9.63689298170773 - 01_tdmean: -9.243994949608927 - 01_tmax: 2.920940618828293 - 01_tmean: -3.7566084167809777 - 01_tmin: -10.434157329993546 - 01_vpdmax: 4.750900714792671 - 01_vpdmin: 0.4877849314347581 - 02_ppt: 13.915104321071084 - 02_tdmean: -7.86542825028585 - 02_tmax: 4.8533627908588635 - 02_tmean: -1.9902201443796765 - 02_tmin: -8.833802803132807 - 02_vpdmax: 5.8019529070173 - 02_vpdmin: 0.4764236839014306 - 03_ppt: 29.761295011154655 - 03_tdmean: -4.456489591037527 - 03_tmax: 9.703265936890116 - 03_tmean: 2.5701463153291244 - 03_tmin: -4.5629730873020495 - 03_vpdmax: 8.605489207734765 - 03_vpdmin: 0.5928395878184926 - 04_ppt: 57.73611168682895 - 04_tdmean: -0.680647569817382 - 04_tmax: 15.133965780755052 - 04_tmean: 7.7213063400332675 - 04_tmin: 0.30864710750085333 - 04_vpdmax: 12.6060700930194 - 04_vpdmin: 0.9239025479332014 - 05_ppt: 84.27645039394486 - 05_tdmean: 5.660218238830567 - 05_tmax: 20.88765926667076 - 05_tmean: 13.643875216053496 - 05_tmin: 6.400091244848381 - 05_vpdmax: 16.249302510727084 - 05_vpdmin: 1.2595413990654596 - 06_ppt: 86.13685962277756 - 06_tdmean: 10.866694089745275 - 06_tmax: 26.368349470192467 - 06_tmean: 19.033557568368703 - 06_tmin: 11.6987659317378 - 06_vpdmax: 21.888229037897567 - 06_vpdmin: 1.705698052101048 - 07_ppt: 76.18175452387544 - 07_tdmean: 13.60701877238461 - 07_tmax: 30.4872358918099 - 07_tmean: 22.753059371178896 - 07_tmin: 15.01888324105166 - 07_vpdmax: 29.400374446778564 - 07_vpdmin: 2.106498198531073 - 08_ppt: 60.18963010480515 - 08_tdmean: 13.20663212454893 - 08_tmax: 29.572255087226655 - 08_tmean: 21.848346125906527 - 08_tmin: 14.124437596617629 - 08_vpdmax: 27.547342451953817 - 08_vpdmin: 1.7171582686618778 - 09_ppt: 45.833161420363155 - 09_tdmean: 7.35217793321136 - 09_tmax: 24.46398764188459 - 09_tmean: 16.401770091220747 - 09_tmin: 8.339552018185259 - 09_vpdmax: 21.816946184116073 - 09_vpdmin: 1.3778070207767767 - 10_ppt: 38.1349073574687 - 10_tdmean: 0.7420330415867593 - 10_tmax: 17.179647020204452 - 10_tmean: 9.263020804676962 - 10_tmin: 1.3463946990697235 - 10_vpdmax: 14.014356020118012 - 10_vpdmin: 0.8949369789535895 - 11_ppt: 20.085923063017376 - 11_tdmean: -4.703556506664723 - 11_tmax: 8.99501460959112 - 11_tmean: 2.0219183634764555 - 11_tmin: -4.951177818161373 - 11_vpdmax: 8.34751803425089 - 11_vpdmin: 0.6549484433337329 - 12_ppt: 11.198114620264112 - 12_tdmean: -8.863682889683362 - 12_tmax: 3.1774102388400896 - 12_tmean: -3.4267057673087833 - 12_tmin: -10.030821691677716 - 12_vpdmax: 5.038355772690158 - 12_vpdmin: 0.48811269459877427 - label: Grassland - value: 2 - warm: 0 diff --git a/tests/test_FeatureCollection/test_by_features_with_id.yml b/tests/test_FeatureCollection/test_by_features_with_id.yml deleted file mode 100644 index e6ad65aa..00000000 --- a/tests/test_FeatureCollection/test_by_features_with_id.yml +++ /dev/null @@ -1,264 +0,0 @@ -Desert: - 01_ppt: 27.95434081213815 - 01_tdmean: -5.938981422015599 - 01_tmax: 14.05185354641506 - 01_tmean: 5.791035977772305 - 01_tmin: -2.469781775474549 - 01_vpdmax: 12.487591886520388 - 01_vpdmin: 1.3548382043838505 - 02_ppt: 21.858468787329542 - 02_tdmean: -5.516507593563626 - 02_tmax: 16.323491311754502 - 02_tmean: 7.6450114270619 - 02_tmin: -1.0334683659672739 - 02_vpdmax: 15.010737727028985 - 02_vpdmin: 1.6896190329960419 - 03_ppt: 17.579124287196574 - 03_tdmean: -5.821656778880528 - 03_tmax: 19.68566257476807 - 03_tmean: 10.454658155441285 - 03_tmin: 1.2236530038288664 - 03_vpdmax: 19.560661269596647 - 03_vpdmin: 2.66291476556233 - 04_ppt: 8.252543340410508 - 04_tdmean: -5.8467324072974085 - 04_tmax: 24.170578130994524 - 04_tmean: 14.251640262603761 - 04_tmin: 4.332702394894191 - 04_vpdmax: 26.725532964978907 - 04_vpdmin: 4.364877526419504 - 05_ppt: 8.372215850693841 - 05_tdmean: -3.4355346179008484 - 05_tmax: 29.04781422478813 - 05_tmean: 19.0328149005345 - 05_tmin: 9.017815884181434 - 05_vpdmax: 35.71408178601947 - 05_vpdmin: 6.715028816631862 - 06_ppt: 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file mode 100644 index 870de6d3..00000000 --- a/tests/test_FeatureCollection/test_by_features_with_properties.yml +++ /dev/null @@ -1,9 +0,0 @@ -Desert: - 01_tmean: 5.791035977772305 - 02_tmean: 7.6450114270619 -Forest: - 01_tmean: 2.792466754200815 - 02_tmean: 3.609074035652296 -Grassland: - 01_tmean: -3.7566084167809777 - 02_tmean: -1.9902201443796765 diff --git a/tests/test_FeatureCollection/test_by_properties.yml b/tests/test_FeatureCollection/test_by_properties.yml deleted file mode 100644 index 2c2d2e53..00000000 --- a/tests/test_FeatureCollection/test_by_properties.yml +++ /dev/null @@ -1,348 +0,0 @@ -01_ppt: - '00000000000000000000': 27.95434081213815 - '00000000000000000001': 235.3735403009758 - '00000000000000000002': 9.63689298170773 -01_tdmean: - '00000000000000000000': -5.938981422015599 - '00000000000000000001': -0.6960752072005435 - '00000000000000000002': -9.243994949608927 -01_tmax: - '00000000000000000000': 14.05185354641506 - '00000000000000000001': 6.547829357600303 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zOibUX&$W8o*YSI*lxXx4wj3dAxt&f(+~50!+;gK_Bbb;b!v9pgRsF061$wFOS0*#F zv*4sZ(E&S7l_3@;IE1Kz1EZ?QqAkI{0x7Pc+763~^l3d77S_XbSSXqNh%rYNzq*oB z31L=&Ks!FYvJMAvL4Ll@{!I0QIt?a}lB!WOWJD(%!Op;=iXm}qDUg4ff-h~NGuD(ZJNXz8vrv3vT>-Ms}0 z{TCNn?{-_Epf%sl^3}z77I;Z?CLeX_FW#eV^8Er#X(VgKFCN)Dr!5e$0RiW;NAf9wst`LnOIuVnOI!Z z)@*jJz5?c+a0&Jpa4@-3WbOWL41RNTtlK#}EJZx^Z`RiEcyKOlDBk0-sH4}>lQDVH!VISXvz%n`SDXGaS4@spV9PV z?GjH{5nICaQ>x91Ync_7+bk=ttz1tS_1Fx4Y}ha1<^1#8i$C9L|CRdi@bDRYeNi)` siXp(8kx7>s)s-OYK~CX^Is-&CFoH-lHwSpLvVnvdfzTF6F9C5F0EN~~xBvhE diff --git a/tests/test_Image.py b/tests/test_Image.py deleted file mode 100644 index 978eb561..00000000 --- a/tests/test_Image.py +++ /dev/null @@ -1,906 +0,0 @@ -"""Test the ``Image`` class.""" -import io -import zipfile -from io import BytesIO -from math import isclose -from pathlib import Path -from tempfile import TemporaryDirectory -from urllib.request import urlretrieve - -import ee -import numpy as np -import pytest -from jsonschema import validate -from matplotlib import pyplot as plt - -import geetools - - -class TestAddDate: - """Test the ``addDate`` method.""" - - def test_add_date(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.addDate() - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - def test_add_date_format(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.addDate("yyyyMMdd") - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - -class TestAddSuffix: - """Test the ``addSuffix`` method.""" - - def test_add_suffix_to_all(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addSuffix("_suffix") - data_regression.check(image.bandNames().getInfo()) - - def test_add_suffix_to_selected(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addSuffix("_suffix", bands=["B1", "B2"]) - data_regression.check(image.bandNames().getInfo()) - - -class TestAddPrefix: - """Test the ``addPrefix`` method.""" - - def test_add_prefix_to_all(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addPrefix("prefix_") - data_regression.check(image.bandNames().getInfo()) - - def test_add_prefix_to_selected(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addPrefix("prefix_", bands=["B1", "B2"]) - data_regression.check(image.bandNames().getInfo()) - - -class TestGetValues: - """Test the ``getValues`` method.""" - - def test_get_values(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - values = s2_sr_vatican_2020.geetools.getValues(vatican_buffer.centroid()) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - def test_get_values_with_scale(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - values = s2_sr_vatican_2020.geetools.getValues(vatican_buffer.centroid(), scale=100) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - -class TestMinScale: - """Test the ``minScale`` method.""" - - def test_min_scale(self, s2_sr_vatican_2020): - scale = s2_sr_vatican_2020.geetools.minScale() - assert scale.getInfo() == 10 - - -class TestMerge: - """Test the ``merge`` method.""" - - def test_merge(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.merge([s2_sr_vatican_2020, s2_sr_vatican_2020]) - data_regression.check(image.bandNames().getInfo()) - - -class TestRename: - """Test the ``rename`` method.""" - - def test_rename(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.rename({"B1": "newB1", "B2": "newB2"}) - data_regression.check(image.bandNames().getInfo()) - - -class TestRemove: - """Test the ``remove`` method.""" - - def test_remove(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.remove(["B1", "B2"]) - data_regression.check(image.bandNames().getInfo()) - - -class TestToGrid: - """Test the ``toGrid`` method.""" - - def test_to_grid(self, s2_sr_vatican_2020, vatican_buffer, ndarrays_regression): - grid = s2_sr_vatican_2020.geetools.toGrid(1, "B2", vatican_buffer) - grid = [f["geometry"]["coordinates"] for f in grid.getInfo()["features"]] - grid = {f"geometry_{i}": np.array(c) for i, c in enumerate(grid)} - ndarrays_regression.check(grid) - - -class TestClipOnCollection: - """Test the ``clipOnCollection`` method.""" - - def test_clip_on_collection(self, s2_sr_vatican_2020, fc_instance, data_regression): - clipped = s2_sr_vatican_2020.geetools.clipOnCollection(fc_instance) - name = "test_clip_on_collection" - data_regression.check(clipped.first().bandNames().getInfo(), f"{name}_bands") - data_regression.check(clipped.first().propertyNames().getInfo(), f"{name}_property") - - def test_clip_on_collection_without_properties( - self, s2_sr_vatican_2020, fc_instance, data_regression - ): - clipped = s2_sr_vatican_2020.geetools.clipOnCollection(fc_instance, 0) - name = "test_clip_on_collection_without_properties" - data_regression.check(clipped.first().bandNames().getInfo(), f"{name}_bands") - data_regression.check(clipped.first().propertyNames().getInfo(), f"{name}_property") - - @pytest.fixture - def fc_instance(self): - """Return 2 little buffers in vaticanc city as a featurecollection.""" - return ee.FeatureCollection( - [ - ee.Feature(ee.Geometry.Point([12.4534, 41.9029]).buffer(50), {"Id": 1}), - ee.Feature(ee.Geometry.Point([12.4534, 41.9029]).buffer(100), {"Id": 2}), - ] - ) - - -class TestBufferMask: - """Test the ``bufferMask`` method.""" - - @pytest.mark.xfail - def test_buffer_mask(self): - """I don't know what to test here.""" - assert False - - -class TestFull: - """Test the ``full`` method.""" - - def test_full(self, vatican_buffer, num_regression): - image = ee.Image.geetools.full() - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_full_with_value(self, vatican_buffer, num_regression): - image = ee.Image.geetools.full([1]) - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_full_with_name(self, vatican_buffer, num_regression): - image = ee.Image.geetools.full([1], ["toto"]) - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_full_with_lists(self, vatican_buffer, num_regression): - image = ee.Image.geetools.full([1, 2, 3], ["toto", "titi", "tata"]) - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestFullLike: - """Test the ``fullLike`` method.""" - - def test_full_like(self, vatican_buffer, s2_sr_vatican_2020, num_regression): - image = s2_sr_vatican_2020.set({"props": "toto"}) - image = image.geetools.fullLike(0) - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - assert "props" not in image.propertyNames().getInfo() - num_regression.check(values.getInfo()) - - def test_full_like_with_properties(self, s2_sr_vatican_2020): - image = s2_sr_vatican_2020.set({"props": "toto"}) - image = image.geetools.fullLike(0, copyProperties=1) - assert "props" in image.propertyNames().getInfo() - - def test_full_like_with_mask(self, s2_sr_vatican_2020, num_regression): - image = s2_sr_vatican_2020.geetools.fullLike(0, keepMask=1) - values = image.geetools.getValues(ee.Geometry.Point(0, 0)) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - -class TestReduceBands: - """Test the ``reduceBands`` method.""" - - def test_reduce_bands(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.reduceBands("sum") - values = image.select("sum").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_reduce_bands_with_bands(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.reduceBands("sum", ["B1", "B2"]) - values = image.select("sum").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_reduce_bands_with_name(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.reduceBands("sum", name="toto") - values = image.select("toto").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestNegativeClip: - """Test the ``negativeClip`` method.""" - - def test_negative_clip(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.negativeClip(vatican_buffer) - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - -class testFormat: - """Test the ``toString`` method.""" - - def test_to_string(self, s2_sr_vatican_2020): - string = s2_sr_vatican_2020.geetools.toString("date: {system_date}") - assert string.getInfo() == "date: 2020-01-01" - - -class TestPrefixSuffix: - """Test the ``prefix`` and ``suffix`` methods.""" - - def test_prefix(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addPrefix("prefix_") - data_regression.check(image.bandNames().getInfo()) - - def test_suffix(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.addSuffix("_suffix") - data_regression.check(image.bandNames().getInfo()) - - -class TestGauss: - """Test the ``gauss`` method.""" - - def test_gauss(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.gauss() - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_gauss_with_band(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.gauss("B2") - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestDoyToDate: - """Test the ``doyToDate`` method.""" - - def test_doy_to_date(self, doy_image, vatican_buffer, num_regression): - image = doy_image.geetools.doyToDate(2023) - values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_doy_to_date_with_format(self, doy_image, vatican_buffer, num_regression): - image = doy_image.geetools.doyToDate(2023, dateFormat="yyyy.DDD") - values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_doy_to_date_with_band(self, doy_image, vatican_buffer, num_regression): - image = doy_image.geetools.doyToDate(2023, band="doy2") - values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestRepeat: - """Test the ``repeat`` method.""" - - def test_repeat(self, image_instance): - image = image_instance.geetools.repeat("B1", 2) - assert image.bandNames().getInfo() == ["B1", "B2", "B3", "B1_1", "B1_2"] - - @pytest.fixture - def image_instance(self): - """Return an Image instance.""" - src = "COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM" - return ee.Image(src).select(["B1", "B2", "B3"]) - - -class TestmatchHistogram: - """Test the ``histogramMatch`` method.""" - - def test_histogram_match(self, image_source, image_target, vatican_buffer, num_regression): - bands = {"R": "R", "G": "G", "B": "B"} - image = image_source.geetools.matchHistogram(image_target, bands) - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - @pytest.fixture - def dates(self): - """The dates of my imagery.""" - return "2023-06-01", "2023-06-30" - - @pytest.fixture - def image_source(self, vatican_buffer, dates): - """image from the S2 copernicus program over vatican city.""" - return ( - ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") - .filterBounds(vatican_buffer) - .filterDate(*dates) - .first() - .select("B4", "B3", "B2") - .rename("R", "G", "B") - ) - - @pytest.fixture - def image_target(self, vatican_buffer, dates): - """image from the L8 Landsat program over vatican city.""" - return ( - ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") - .filterBounds(vatican_buffer) - .filterDate(*dates) - .first() - .select("SR_B4", "SR_B3", "SR_B2") - .rename("R", "G", "B") - ) - - -class TestRemoveZeros: - """Test the ``removeZeros`` method.""" - - def test_remove_zeros(self, image_instance, vatican_buffer): - image = image_instance.geetools.removeZeros() - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - assert values.getInfo() == {"array": [1, 2]} - - @pytest.fixture - def image_instance(self): - """A random image instance with array data containing zeros.""" - return ee.Image([0, 1, 2]).toArray() - - -class TestInterpolateBands: - """Test the ``interpolateBands`` method.""" - - def test_interpolate_bands(self, image_instance, vatican_buffer, num_regression): - image = image_instance.geetools.interpolateBands([0, 3000], [0, 30]) - values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - @pytest.fixture - def image_instance(self, vatican_buffer): - """A sentinel 2 single image centered on the vatican.""" - src = "COPERNICUS/S2_SR_HARMONIZED" - return ee.ImageCollection(src).filterBounds(vatican_buffer).first().select(["B4", "B2"]) - - -class TestIsletMask: - """Test the ``isletMask`` method.""" - - def test_islet_mask(self, image_instance, tmp_path, image_regression): - image = image_instance.geetools.isletMask(20) - file = self.get_image(image, tmp_path / "test.tif") - image_regression.check(file.read_bytes()) - - def get_image(self, image, dst): - - link = image.getDownloadURL( - { - "name": "test", - "region": ee.Geometry.Point([12.4534, 41.9033]).buffer(1000), - "filePerBand": False, - "scale": 10, - } - ) - - with TemporaryDirectory() as dir: - tmp = Path(dir) / "tmp.zip" - urlretrieve(link, tmp) - with zipfile.ZipFile(tmp, "r") as zip_: - dst.write_bytes(zip_.read(zip_.namelist()[0])) - - return dst - - @pytest.fixture - def image_instance(self): - """An image on top of the buffer.""" - buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(1000) - return ( - ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") - .filterBounds(buffer) - .filterDate("2023-01-01", "2023-01-31") - .first() - .select("B4", "B3", "B2") - ) - - -class TestIndicexList: - """Test the ``index_list`` method.""" - - def test_indices(self): - indices = ee.Image.geetools.index_list() - assert "NDVI" in indices.keys() - assert len(indices) == 228 - - -class TestSpectralIndices: - """Test the ``spectralIndices`` method.""" - - def test_default_spectral_indices(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.spectralIndices("all") - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) - - -class TestMaskClouds: - """Test the ``maskClouds`` method.""" - - def test_mask_S2_clouds(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - image = s2_sr_vatican_2020.geetools.maskClouds() - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - values = {k: np.nan if v is None else v for k, v in values.getInfo().items()} - num_regression.check(values) - - -class TestGetscaleParams: - """Test the ``getScaleParams`` method.""" - - def test_get_scale_params(self, s2_sr_vatican_2020, data_regression): - params = s2_sr_vatican_2020.geetools.getScaleParams() - data_regression.check(params) - - -class TestGetOffsetParams: - """Test the ``getOffsetParams`` method.""" - - def get_offset_params(self, s2_sr_vatican_2020, data_regression): - params = s2_sr_vatican_2020.geetools.getOffsetParams() - data_regression.check(params) - - -class TestScaleAndOffset: - """Test the ``scaleAndOffset`` method.""" - - def test_scale_and_offset(self, vatican_buffer, s2_sr_vatican_2020, num_regression): - image = s2_sr_vatican_2020.geetools.scaleAndOffset() - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestPreprocess: - """Test the ``preprocess`` method.""" - - def test_preprocess(self, vatican_buffer, s2_sr_vatican_2020, num_regression): - image = s2_sr_vatican_2020.geetools.preprocess() - values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestGetSTAC: - """Test the ``getSTAC`` method.""" - - def test_get_stac_schema(self, s2_sr_vatican_2020, stac_schema): - stac = s2_sr_vatican_2020.geetools.getSTAC() - validate(stac, stac_schema) - - def test_get_stac(self, s2_sr_vatican_2020): - stac = s2_sr_vatican_2020.geetools.getSTAC() - assert stac["id"] == "COPERNICUS/S2_SR_HARMONIZED" - - -class TestGetDOI: - """Test the ``getDOI`` method.""" - - def get_doi(self, s2_sr_vatican_2020, data_regression): - doi = s2_sr_vatican_2020.geetools.getDOI() - data_regression.check(doi) - - -class TestGetCitation: - """Test the ``getCitation`` method.""" - - def get_citation(self, s2_sr_vatican_2020, data_regression): - citation = s2_sr_vatican_2020.first().geetools.getCitation() - data_regression.check(citation) - - -class TestPanSharpen: - """Test the panSharpen method.""" - - @pytest.mark.xfail( - reason="This test is failing because the panSharpen method is not implemented for this platform." - ) - def test_pan_sharpen(self, l8_sr_vatican_2020, num_regression): - sharp = l8_sr_vatican_2020.geetools.panSharpen( - method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13 - ) - centroid = sharp.geometry().centroid().buffer(100) - values = sharp.reduceRegion(ee.Reducer.mean(), centroid, 1) - num_regression.check(values.getInfo()) - - -class TestTasseledCap: - """Test the tasseledCap method.""" - - @pytest.mark.xfail( - reason="This test is failing because the tasseledCap method is not implemented for this platform." - ) - def test_tasseled_cap(self, l8_sr_vatican_2020, num_regression): - img = l8_sr_vatican_2020.geetools.tasseledCap() - centroid = img.geometry().centroid().buffer(100) - values = img.reduceRegion(ee.Reducer.mean(), centroid, 1) - num_regression.check(values.getInfo()) - - -class TestRemoveProperties: - """Test the removeProperties method.""" - - def test_remove_properties(self, s2_sr_vatican_2020, data_regression): - image = s2_sr_vatican_2020.geetools.removeProperties(["system:time_start"]) - data_regression.check(image.propertyNames().getInfo()) - - -class TestDistanceToMask: - """Test the distanceToMask method.""" - - def test_distance_to_mask(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - centerBuffer = vatican_buffer.centroid().buffer(100) - BufferMask = ee.Image.constant(1).clip(centerBuffer) - mask = ee.Image.constant(0).where(BufferMask, 1) - distance = s2_sr_vatican_2020.geetools.distanceToMask(mask) - values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_deprecated_distance_to_mask(self, s2_sr_vatican_2020, vatican_buffer, num_regression): - centerBuffer = vatican_buffer.centroid().buffer(100) - BufferMask = ee.Image.constant(1).clip(centerBuffer) - mask = ee.Image.constant(0).where(BufferMask, 1) - with pytest.deprecated_call(): - distance = geetools.algorithms.distanceToMask(s2_sr_vatican_2020, mask) - values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - -class TestDistance: - """Test the ``distance`` method.""" - - def test_distance(self, vatican_buffer, num_regression): - # 2 images from june in vatican - distance = self.image.geetools.distance(self.other) - values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - def test_deprecated_euclidian_distance(self, vatican_buffer, num_regression): - # 2 images from june in vatican - with pytest.deprecated_call(): - distance = geetools.algorithms.euclideanDistance(self.image, self.other) - values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) - num_regression.check(values.getInfo()) - - @property - def image(self): - """Return an image from june in vatican.""" - image_id = "COPERNICUS/S2_SR_HARMONIZED/20210604T100029_20210604T100027_T32TQM" - return ee.Image(image_id).select(["B4", "B3", "B2"]) - - @property - def other(self): - """Return another image from june in vatican.""" - other_id = "COPERNICUS/S2_SR_HARMONIZED/20210604T100029_20210604T100027_T33TTG" - return ee.Image(other_id).select(["B4", "B3", "B2"]) - - -class TestMaskCover: - """Test the ``maskCoverRegion`` method.""" - - def test_mask_cover_region(self): - aoi = ee.Geometry.Point([12.210900891755129, 41.928551351175386]).buffer(2200) - ratio = self.image.geetools.maskCoverRegion(aoi, scale=10) - assert isclose(ratio.getInfo(), 9.99, abs_tol=0.01) - - def test_mask_cover_region_zero(self): - aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(1000) - ratio = self.image.geetools.maskCoverRegion(aoi, scale=10) - assert isclose(ratio.getInfo(), 0) - - def test_mask_cover_regions(self): - geom = ee.Geometry.Point([12.210900891755129, 41.928551351175386]).buffer(2200) - aoi = ee.FeatureCollection([ee.Feature(geom, {"test_property": 1})]) - result = self.image.geetools.maskCoverRegions(aoi, scale=10) - feat = ee.Feature(result.first()) - ratio = feat.getInfo()["properties"]["mask_cover"] - # ratio = ee.Number(feat.get('mask_cover')) - # the last line should work, but it doesn't, I don't know why - assert isclose(ratio, 9.99, abs_tol=0.01) - - def test_mask_cover_regions_zero(self): - geom = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(1000) - aoi = ee.FeatureCollection([ee.Feature(geom, {"test_property": 1})]) - result = self.image.geetools.maskCoverRegions(aoi, scale=10) - feat = ee.Feature(result.first()) - ratio = feat.getInfo()["properties"]["mask_cover"] - # ratio = ee.Number(feat.get('mask_cover')) - # the last line should work, but it doesn't, I don't know why - assert isclose(ratio, 0) - - def test_deprecated_mask_cover(self): - with pytest.deprecated_call(): - image = geetools.algorithms.maskCover(self.image) - assert isclose(image.get("mask_cover").getInfo(), 18.04, rel_tol=0.01) - - @property - def image(self): - image_id = "COPERNICUS/S2_SR_HARMONIZED/20180401T100019_20180401T100022_T32TQM" - image = ee.Image(image_id) - qa = image.select("QA60") - cloudBitMask, cirrusBitMask = 1 << 10, 1 << 11 - mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0)) - image = image.updateMask(mask) - return image.select(["B4", "B3", "B2"]) - - -class TestPlot: - """Test the ``plot`` method.""" - - def test_plot(self, s2_sr_vatican_2020, vatican, image_regression): - fig, ax = plt.subplots() - s2_sr_vatican_2020.geetools.plot(["B4", "B3", "B2"], vatican.geometry(), ax) - - with BytesIO() as image_byte: - fig.savefig(image_byte, format="png") - image_byte.seek(0) - image_regression.check(image_byte.getvalue()) - - def test_plot_one_band(self, s2_sr_vatican_2020, vatican, image_regression): - fig, ax = plt.subplots() - ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") - ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax) - - with BytesIO() as image_byte: - fig.savefig(image_byte, format="png") - image_byte.seek(0) - image_regression.check(image_byte.getvalue()) - - def test_plot_one_band_cmap(self, s2_sr_vatican_2020, vatican, image_regression): - fig, ax = plt.subplots() - ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") - ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax, cmap="RdYlGn") - - with BytesIO() as image_byte: - fig.savefig(image_byte, format="png") - image_byte.seek(0) - image_regression.check(image_byte.getvalue()) - - def test_plot_with_fc(self, s2_sr_vatican_2020, vatican, image_regression): - fig, ax = plt.subplots() - s2_sr_vatican_2020.geetools.plot(["B4", "B3", "B2"], vatican.geometry(), ax, fc=vatican) - - with BytesIO() as image_byte: - fig.savefig(image_byte, format="png") - image_byte.seek(0) - image_regression.check(image_byte.getvalue()) - - def test_plot_with_crs(self, s2_sr_vatican_2020, vatican, image_regression): - fig, ax = plt.subplots() - ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") - ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax, crs="EPSG:3857", scale=10) - - with BytesIO() as image_byte: - fig.savefig(image_byte, format="png") - image_byte.seek(0) - image_regression.check(image_byte.getvalue()) - - -class TestFromList: - """Test ``fromList`` method.""" - - def test_from_list_unique(self): - """Test using a list of unique band names.""" - sequence = ee.List([1, 2, 3]) - images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) - image = ee.Image.geetools.fromList(images) - assert image.bandNames().getInfo() == ["1", "2", "3"] - - def test_from_list_multiband(self): - """Test using a list of multiband images.""" - images = ee.List( - [ - ee.Image([1, 2, 3]).rename(["1", "2", "3"]), - ee.Image([4, 5]).rename(["4", "5"]), - ] - ) - image = ee.Image.geetools.fromList(images) - assert image.bandNames().getInfo() == ["1", "2", "3", "4", "5"] - - -class TestPlotByRegions: - """Test the ``plot_by_regions`` method.""" - - def test_plot_by_regions_bar(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_regions( - type = "bar", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_regions_barh(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_regions( - type = "barh", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_regions_stacked(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_regions( - type = "stacked", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def ecoregions(self): - return ee.FeatureCollection("projects/google/charts_feature_example").select( - ["label", "value", "warm"] - ) - - @property - def image(self): - return ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands() - - -class TestPlotByBands: - """Test the ``plot_by_bands`` method.""" - - def test_plot_by_bands_bar(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_bands( - type = "bar", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ["#f0af07", "#0f8755", "#76b349"], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_bands_plot(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_bands( - type = "plot", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ["#f0af07", "#0f8755", "#76b349"], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_bands_area(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - self.image.geetools.plot_by_bands( - type = "fill_between", - regions = self.ecoregions, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ["#f0af07", "#0f8755", "#76b349"], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_bands_pie(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregion = self.ecoregions.filter(ee.Filter.eq("label", "Forest")) - self.image.geetools.plot_by_bands( - type = "pie", - regions = ecoregion, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - def test_plot_by_bands_donut(self, image_regression): - fig, ax = plt.subplots() - # fmt: off - ecoregion = self.ecoregions.filter(ee.Filter.eq("label", "Forest")) - self.image.geetools.plot_by_bands( - type = "donut", - regions = ecoregion, - reducer = "mean", - scale = 500, - regionId = "label", - bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], - labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], - colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], - ax = ax - ) - # fmt: on - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def ecoregions(self): - return ee.FeatureCollection("projects/google/charts_feature_example").select( - ["label", "value", "warm"] - ) - - @property - def image(self): - return ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands() - - -class TestPlotHist: - """Test the ``plot_hist`` method.""" - - def test_plot_hist(self, image_regression): - fig, ax = plt.subplots() - self.image.geetools.plot_hist( - bands=["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"], - labels=[["Red", "NIR", "SWIR"]], - colors=["#cf513e", "#1d6b99", "#f0af07"], - ax=ax, - bins=100, - scale=500, - region=self.region, - ) - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue(), diff_threshold=0.2) - - @property - def image(self): - return ( - ee.ImageCollection("MODIS/061/MOD09A1") - .filter(ee.Filter.date("2018-06-01", "2018-09-01")) - .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"]) - .mean() - ) - - @property - def region(self): - return ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22]) diff --git a/tests/test_Image/test_add_date.csv b/tests/test_Image/test_add_date.csv deleted file mode 100644 index c29eacba..00000000 --- a/tests/test_Image/test_add_date.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP,date -0,72,214,1175,958,188,366,285,910,1232,1328,1216,1431,2104,,,,2,0,0,0,0,5,21,38,29,588,1577873347042 diff --git a/tests/test_Image/test_add_date_format.csv b/tests/test_Image/test_add_date_format.csv deleted file mode 100644 index 81f8da1c..00000000 --- a/tests/test_Image/test_add_date_format.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP,date -0,72,214,1175,958,188,366,285,910,1232,1328,1216,1431,2104,2,0,0,0,0,5,21,38,29,588,20200101 diff --git a/tests/test_Image/test_add_prefix_to_all.yml b/tests/test_Image/test_add_prefix_to_all.yml deleted file mode 100644 index 1319709a..00000000 --- a/tests/test_Image/test_add_prefix_to_all.yml +++ /dev/null @@ -1,13 +0,0 @@ -- prefix_B1 -- prefix_B2 -- prefix_B3 -- prefix_B4 -- prefix_B5 -- prefix_B6 -- prefix_B7 -- prefix_B8 -- prefix_B8A -- prefix_B9 -- prefix_B11 -- prefix_B12 -- prefix_SCL diff --git a/tests/test_Image/test_add_prefix_to_selected.yml b/tests/test_Image/test_add_prefix_to_selected.yml deleted file mode 100644 index b2245db3..00000000 --- a/tests/test_Image/test_add_prefix_to_selected.yml +++ /dev/null @@ -1,13 +0,0 @@ -- prefix_B1 -- prefix_B2 -- B3 -- B4 -- B5 -- B6 -- B7 -- B8 -- B8A -- B9 -- B11 -- B12 -- SCL diff --git a/tests/test_Image/test_add_suffix_to_all.yml b/tests/test_Image/test_add_suffix_to_all.yml deleted file mode 100644 index 4130bb9e..00000000 --- a/tests/test_Image/test_add_suffix_to_all.yml +++ /dev/null @@ -1,13 +0,0 @@ -- B1_suffix -- B2_suffix -- B3_suffix -- B4_suffix -- B5_suffix -- B6_suffix -- B7_suffix -- B8_suffix -- B8A_suffix -- B9_suffix -- B11_suffix -- B12_suffix -- SCL_suffix diff --git a/tests/test_Image/test_add_suffix_to_selected.yml b/tests/test_Image/test_add_suffix_to_selected.yml deleted file mode 100644 index 73df0092..00000000 --- a/tests/test_Image/test_add_suffix_to_selected.yml +++ /dev/null @@ -1,13 +0,0 @@ -- B1_suffix -- B2_suffix -- B3 -- B4 -- B5 -- B6 -- B7 -- B8 -- B8A -- B9 -- B11 -- B12 -- SCL diff --git a/tests/test_Image/test_clip_on_collection_bands.yml b/tests/test_Image/test_clip_on_collection_bands.yml deleted file mode 100644 index 2a05ac63..00000000 --- a/tests/test_Image/test_clip_on_collection_bands.yml +++ /dev/null @@ -1,13 +0,0 @@ -- B1 -- B2 -- B3 -- B4 -- B5 -- B6 -- B7 -- B8 -- B8A -- B9 -- B11 -- B12 -- SCL diff --git a/tests/test_Image/test_clip_on_collection_property.yml b/tests/test_Image/test_clip_on_collection_property.yml deleted file mode 100644 index 6db17f8d..00000000 --- a/tests/test_Image/test_clip_on_collection_property.yml +++ /dev/null @@ -1,86 +0,0 @@ -- system:footprint -- system:version -- system:id -- Id -- system:index -- DATATAKE_IDENTIFIER -- AOT_RETRIEVAL_ACCURACY -- SPACECRAFT_NAME -- SATURATED_DEFECTIVE_PIXEL_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8A -- CLOUD_SHADOW_PERCENTAGE -- MEAN_SOLAR_AZIMUTH_ANGLE -- VEGETATION_PERCENTAGE -- SOLAR_IRRADIANCE_B12 -- SOLAR_IRRADIANCE_B10 -- SENSOR_QUALITY -- SOLAR_IRRADIANCE_B11 -- GENERATION_TIME -- SOLAR_IRRADIANCE_B8A -- FORMAT_CORRECTNESS -- CLOUD_COVERAGE_ASSESSMENT -- THIN_CIRRUS_PERCENTAGE -- system:time_end -- WATER_VAPOUR_RETRIEVAL_ACCURACY -- system:time_start -- DATASTRIP_ID -- PROCESSING_BASELINE -- SENSING_ORBIT_NUMBER -- NODATA_PIXEL_PERCENTAGE -- SENSING_ORBIT_DIRECTION -- GENERAL_QUALITY -- GRANULE_ID -- REFLECTANCE_CONVERSION_CORRECTION -- MEDIUM_PROBA_CLOUDS_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8 -- DATATAKE_TYPE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B9 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B6 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B7 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B4 -- MEAN_INCIDENCE_ZENITH_ANGLE_B1 -- NOT_VEGETATED_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B5 -- RADIOMETRIC_QUALITY -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B2 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B3 -- MEAN_INCIDENCE_ZENITH_ANGLE_B5 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B1 -- MEAN_INCIDENCE_ZENITH_ANGLE_B4 -- MEAN_INCIDENCE_ZENITH_ANGLE_B3 -- MEAN_INCIDENCE_ZENITH_ANGLE_B2 -- MEAN_INCIDENCE_ZENITH_ANGLE_B9 -- MEAN_INCIDENCE_ZENITH_ANGLE_B8 -- MEAN_INCIDENCE_ZENITH_ANGLE_B7 -- DARK_FEATURES_PERCENTAGE -- HIGH_PROBA_CLOUDS_PERCENTAGE -- MEAN_INCIDENCE_ZENITH_ANGLE_B6 -- UNCLASSIFIED_PERCENTAGE -- MEAN_SOLAR_ZENITH_ANGLE -- MEAN_INCIDENCE_ZENITH_ANGLE_B8A -- RADIATIVE_TRANSFER_ACCURACY -- MGRS_TILE -- CLOUDY_PIXEL_PERCENTAGE -- PRODUCT_ID -- MEAN_INCIDENCE_ZENITH_ANGLE_B10 -- SOLAR_IRRADIANCE_B9 -- SNOW_ICE_PERCENTAGE -- DEGRADED_MSI_DATA_PERCENTAGE -- MEAN_INCIDENCE_ZENITH_ANGLE_B11 -- MEAN_INCIDENCE_ZENITH_ANGLE_B12 -- SOLAR_IRRADIANCE_B6 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B10 -- SOLAR_IRRADIANCE_B5 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B11 -- SOLAR_IRRADIANCE_B8 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B12 -- SOLAR_IRRADIANCE_B7 -- SOLAR_IRRADIANCE_B2 -- SOLAR_IRRADIANCE_B1 -- SOLAR_IRRADIANCE_B4 -- GEOMETRIC_QUALITY -- SOLAR_IRRADIANCE_B3 -- system:asset_size -- WATER_PERCENTAGE -- system:bands -- system:band_names diff --git a/tests/test_Image/test_clip_on_collection_without_properties_bands.yml b/tests/test_Image/test_clip_on_collection_without_properties_bands.yml deleted file mode 100644 index 2a05ac63..00000000 --- a/tests/test_Image/test_clip_on_collection_without_properties_bands.yml +++ /dev/null @@ -1,13 +0,0 @@ -- B1 -- B2 -- B3 -- B4 -- B5 -- B6 -- B7 -- B8 -- B8A -- B9 -- B11 -- B12 -- SCL diff --git a/tests/test_Image/test_clip_on_collection_without_properties_property.yml b/tests/test_Image/test_clip_on_collection_without_properties_property.yml deleted file mode 100644 index b6dadcb9..00000000 --- a/tests/test_Image/test_clip_on_collection_without_properties_property.yml +++ /dev/null @@ -1,85 +0,0 @@ -- system:footprint -- system:version -- system:id -- system:index -- DATATAKE_IDENTIFIER -- AOT_RETRIEVAL_ACCURACY -- SPACECRAFT_NAME -- SATURATED_DEFECTIVE_PIXEL_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8A -- CLOUD_SHADOW_PERCENTAGE -- MEAN_SOLAR_AZIMUTH_ANGLE -- VEGETATION_PERCENTAGE -- SOLAR_IRRADIANCE_B12 -- SOLAR_IRRADIANCE_B10 -- SENSOR_QUALITY -- SOLAR_IRRADIANCE_B11 -- GENERATION_TIME -- SOLAR_IRRADIANCE_B8A -- FORMAT_CORRECTNESS -- CLOUD_COVERAGE_ASSESSMENT -- THIN_CIRRUS_PERCENTAGE -- system:time_end -- WATER_VAPOUR_RETRIEVAL_ACCURACY -- system:time_start -- DATASTRIP_ID -- PROCESSING_BASELINE -- SENSING_ORBIT_NUMBER -- NODATA_PIXEL_PERCENTAGE -- SENSING_ORBIT_DIRECTION -- GENERAL_QUALITY -- GRANULE_ID -- REFLECTANCE_CONVERSION_CORRECTION -- MEDIUM_PROBA_CLOUDS_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8 -- DATATAKE_TYPE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B9 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B6 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B7 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B4 -- MEAN_INCIDENCE_ZENITH_ANGLE_B1 -- NOT_VEGETATED_PERCENTAGE -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B5 -- RADIOMETRIC_QUALITY -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B2 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B3 -- MEAN_INCIDENCE_ZENITH_ANGLE_B5 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B1 -- MEAN_INCIDENCE_ZENITH_ANGLE_B4 -- MEAN_INCIDENCE_ZENITH_ANGLE_B3 -- MEAN_INCIDENCE_ZENITH_ANGLE_B2 -- MEAN_INCIDENCE_ZENITH_ANGLE_B9 -- MEAN_INCIDENCE_ZENITH_ANGLE_B8 -- MEAN_INCIDENCE_ZENITH_ANGLE_B7 -- DARK_FEATURES_PERCENTAGE -- HIGH_PROBA_CLOUDS_PERCENTAGE -- MEAN_INCIDENCE_ZENITH_ANGLE_B6 -- UNCLASSIFIED_PERCENTAGE -- MEAN_SOLAR_ZENITH_ANGLE -- MEAN_INCIDENCE_ZENITH_ANGLE_B8A -- RADIATIVE_TRANSFER_ACCURACY -- MGRS_TILE -- CLOUDY_PIXEL_PERCENTAGE -- PRODUCT_ID -- MEAN_INCIDENCE_ZENITH_ANGLE_B10 -- SOLAR_IRRADIANCE_B9 -- SNOW_ICE_PERCENTAGE -- DEGRADED_MSI_DATA_PERCENTAGE -- MEAN_INCIDENCE_ZENITH_ANGLE_B11 -- MEAN_INCIDENCE_ZENITH_ANGLE_B12 -- SOLAR_IRRADIANCE_B6 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B10 -- SOLAR_IRRADIANCE_B5 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B11 -- SOLAR_IRRADIANCE_B8 -- MEAN_INCIDENCE_AZIMUTH_ANGLE_B12 -- SOLAR_IRRADIANCE_B7 -- SOLAR_IRRADIANCE_B2 -- SOLAR_IRRADIANCE_B1 -- SOLAR_IRRADIANCE_B4 -- GEOMETRIC_QUALITY -- SOLAR_IRRADIANCE_B3 -- system:asset_size -- WATER_PERCENTAGE -- system:bands -- system:band_names diff --git a/tests/test_Image/test_default_spectral_indices.csv b/tests/test_Image/test_default_spectral_indices.csv deleted file mode 100644 index cc877775..00000000 --- a/tests/test_Image/test_default_spectral_indices.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AFRI1600,AFRI2100,ANDWI,AOT,ARI,ARI2,ARVI,ATSAVI,AVI,AWEInsh,AWEIsh,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,BAI,BAIM,BAIS2,BCC,BI,BITM,BIXS,BLFEI,BNDVI,BRBA,BWDRVI,BaI,CIG,CIRE,CSI,CVI,DBSI,DSI,DSWI1,DSWI2,DSWI3,DSWI4,DSWI5,DVI,DVIplus,EMBI,EVI,EVI2,ExG,ExGR,ExR,FCVI,GARI,GBNDVI,GCC,GDVI,GEMI,GLI,GM1,GM2,GNDVI,GOSAVI,GRNDVI,GRVI,GSAVI,GVMI,IAVI,IBI,IKAW,IPVI,IRECI,LSWI,MBI,MBWI,MCARI,MCARI1,MCARI2,MCARI705,MCARIOSAVI,MCARIOSAVI705,MGRVI,MIRBI,MLSWI26,MLSWI27,MNDVI,MNDWI,MNLI,MRBVI,MSAVI,MSI,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,MSR,MSR705,MTCI,MTVI1,MTVI2,MuWIR,NBAI,NBR,NBR2,NBRSWIR,NBRplus,NBSIMS,ND705,NDBI,NDCI,NDDI,NDGI,NDGlaI,NDII,NDMI,NDPI,NDPonI,NDREI,NDSI,NDSII,NDSInw,NDSWIR,NDSaII,NDSoI,NDTI,NDVI,NDVI705,NDVIMNDWI,NDWI,NDWIns,NDYI,NGRDI,NHFD,NIRv,NIRvH2,NLI,NMDI,NRFIg,NRFIr,NSDS,NSDSI1,NSDSI2,NSDSI3,NWI,NormG,NormNIR,NormR,OCVI,OSAVI,PISI,PSRI,QA10,QA20,QA60,RCC,RDVI,REDSI,RENDVI,RGBVI,RGRI,RI,RI4XS,RVI,S2REP,S2WI,S3,SARVI,SAVI,SAVI2,SCL,SEVI,SI,SIPI,SR,SR2,SR3,SR555,SR705,SWI,SWM,SeLI,TCARI,TCARIOSAVI,TCARIOSAVI705,TCI,TCI_B,TCI_G,TCI_R,TDVI,TGI,TRRVI,TSAVI,TTVI,TVI,TriVI,UI,VARI,VARI700,VI700,VIBI,VIG,VgNIRBI,VrNIRBI,WDRVI,WDVI,WI1,WI2,WI2015,WRI,WVP,kEVI,kIPVI,kNDVI,kRVI,kVARI,mND705,mSR705 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diff --git a/tests/test_Image/test_deprecated_distance_to_mask.csv b/tests/test_Image/test_deprecated_distance_to_mask.csv deleted file mode 100644 index a230510b..00000000 --- a/tests/test_Image/test_deprecated_distance_to_mask.csv +++ /dev/null @@ -1,2 +0,0 @@ -,distance_to_mask -0,1000 diff --git a/tests/test_Image/test_deprecated_euclidian_distance.csv b/tests/test_Image/test_deprecated_euclidian_distance.csv deleted file mode 100644 index a4033ae1..00000000 --- a/tests/test_Image/test_deprecated_euclidian_distance.csv +++ /dev/null @@ -1,2 +0,0 @@ -,sum_distance -0,157.25661609998866 diff --git a/tests/test_Image/test_distance.csv b/tests/test_Image/test_distance.csv deleted file mode 100644 index a4033ae1..00000000 --- a/tests/test_Image/test_distance.csv +++ /dev/null @@ -1,2 +0,0 @@ -,sum_distance -0,157.25661609998866 diff --git a/tests/test_Image/test_distance_to_mask.csv b/tests/test_Image/test_distance_to_mask.csv deleted file mode 100644 index a7c4d6cb..00000000 --- a/tests/test_Image/test_distance_to_mask.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL,distance_to_mask -0,238.7171666246534,948.66361230148675,750.75646584320634,320.95785228132081,415.14734055961685,426.16661204940726,604.81486009578975,1007.6004411393997,1140.2466599445429,1217.7626543987888,1200.1272498109399,1389.4672800604985,3.3245021426770851,1000 diff --git a/tests/test_Image/test_doy_to_date.csv b/tests/test_Image/test_doy_to_date.csv deleted file mode 100644 index a14db8df..00000000 --- a/tests/test_Image/test_doy_to_date.csv +++ /dev/null @@ -1,2 +0,0 @@ -,doy1 -0,20230101 diff --git a/tests/test_Image/test_doy_to_date_with_band.csv b/tests/test_Image/test_doy_to_date_with_band.csv deleted file mode 100644 index bd3ec55f..00000000 --- a/tests/test_Image/test_doy_to_date_with_band.csv +++ /dev/null @@ -1,2 +0,0 @@ -,doy2 -0,20230101 diff --git a/tests/test_Image/test_doy_to_date_with_format.csv b/tests/test_Image/test_doy_to_date_with_format.csv deleted file mode 100644 index 150d4f03..00000000 --- a/tests/test_Image/test_doy_to_date_with_format.csv +++ /dev/null @@ -1,2 +0,0 @@ -,doy1 -0,2023.001 diff --git a/tests/test_Image/test_full.csv b/tests/test_Image/test_full.csv deleted file mode 100644 index 69e59a7f..00000000 --- a/tests/test_Image/test_full.csv +++ /dev/null @@ -1,2 +0,0 @@ -,constant -0,0 diff --git a/tests/test_Image/test_full_like.csv b/tests/test_Image/test_full_like.csv deleted file mode 100644 index 698b9a23..00000000 --- a/tests/test_Image/test_full_like.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/tests/test_Image/test_full_like_with_mask.csv b/tests/test_Image/test_full_like_with_mask.csv deleted file mode 100644 index e9999946..00000000 --- a/tests/test_Image/test_full_like_with_mask.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_Image/test_full_with_lists.csv b/tests/test_Image/test_full_with_lists.csv deleted file mode 100644 index db6d556e..00000000 --- a/tests/test_Image/test_full_with_lists.csv +++ /dev/null @@ -1,2 +0,0 @@ -,tata,titi,toto -0,3,2,1 diff --git a/tests/test_Image/test_full_with_name.csv b/tests/test_Image/test_full_with_name.csv deleted file mode 100644 index f39e2049..00000000 --- a/tests/test_Image/test_full_with_name.csv +++ /dev/null @@ -1,2 +0,0 @@ -,toto -0,1 diff --git a/tests/test_Image/test_full_with_value.csv b/tests/test_Image/test_full_with_value.csv deleted file mode 100644 index 03fb3c1f..00000000 --- a/tests/test_Image/test_full_with_value.csv +++ /dev/null @@ -1,2 +0,0 @@ -,constant -0,1 diff --git a/tests/test_Image/test_gauss.csv b/tests/test_Image/test_gauss.csv deleted file mode 100644 index 2597d3a7..00000000 --- a/tests/test_Image/test_gauss.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1_gauss -0,0.66461378242572766 diff --git a/tests/test_Image/test_gauss_with_band.csv b/tests/test_Image/test_gauss_with_band.csv deleted file mode 100644 index 41577224..00000000 --- a/tests/test_Image/test_gauss_with_band.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B2_gauss -0,0.50929481299884138 diff --git a/tests/test_Image/test_get_scale_params.yml b/tests/test_Image/test_get_scale_params.yml deleted file mode 100644 index 46506551..00000000 --- a/tests/test_Image/test_get_scale_params.yml +++ /dev/null @@ -1,23 +0,0 @@ -AOT: 0.001 -B1: 0.0001 -B11: 0.0001 -B12: 0.0001 -B2: 0.0001 -B3: 0.0001 -B4: 0.0001 -B5: 0.0001 -B6: 0.0001 -B7: 0.0001 -B8: 0.0001 -B8A: 0.0001 -B9: 0.0001 -MSK_CLDPRB: 1.0 -MSK_SNWPRB: 1.0 -QA10: 1.0 -QA20: 1.0 -QA60: 1.0 -SCL: 1.0 -TCI_B: 1.0 -TCI_G: 1.0 -TCI_R: 1.0 -WVP: 0.001 diff --git a/tests/test_Image/test_get_values.csv b/tests/test_Image/test_get_values.csv deleted file mode 100644 index 1c5c1dc8..00000000 --- a/tests/test_Image/test_get_values.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,72,159,1341,1105,487,641,714,861,1356,1538,1622,1617,1298,,,,0,0,0,0,0,4,50,66,73,789 diff --git a/tests/test_Image/test_get_values_with_scale.csv b/tests/test_Image/test_get_values_with_scale.csv deleted file mode 100644 index 17592770..00000000 --- a/tests/test_Image/test_get_values_with_scale.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,72,117,553,332,161,247,175,384,952,1071,1168,1145,1523,,,,0,0,0,0,0,4,18,26,19,693 diff --git a/tests/test_Image/test_histogram_match.csv b/tests/test_Image/test_histogram_match.csv deleted file mode 100644 index 81755659..00000000 --- a/tests/test_Image/test_histogram_match.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B,G,R -0,7500.2627678346307,8388.3192336778411,8269.7086967481646 diff --git a/tests/test_Image/test_interpolate_bands.csv b/tests/test_Image/test_interpolate_bands.csv deleted file mode 100644 index 24364761..00000000 --- a/tests/test_Image/test_interpolate_bands.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B2,B4 -0,4.5200000000000005,5.8399999999999999 diff --git a/tests/test_Image/test_islet_mask.png b/tests/test_Image/test_islet_mask.png deleted file mode 100644 index af07c93307c1fec2bd256f936a12cf1ff7774842..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119 zcmeAS@N?(olHy`uVBq!ia0vp^CxG}Q2asTJ&R2R4r0hIh978JRygg{h2;?1F@YO$; fmwy3}NeuAgoqLgi@&86$ZjfqES3j3^P6$U{`q6hF&$$9e83MHUQ#_MDINbVkVL$`Aj5hapWt! 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zhFgNd=%+Lq)EaKk38Vc|sIft<;dYrY+WIzy2DOHpSHentPoqJt;ZBk;I`j@THmEh+ z))8ht>Mn%_wT7E4!aA>{(V*6F!$WxNllQ2xL9OBTgD`q(ss_5I8DZVJ0d58eqnoBt zXi#goH6M(Qq0yk$aHBjJy*Qm38`K(ZTL+_my-%S*t>GqeF#0Zy2DOG;y1`oWen5>4 zY7O^SgVoaOA%zCDhI^gC%vCfR)Ee$Q2BQNWQDcKz!+pSDwEVFq8m=4Qu3Rv>fJWD7 Xt)Y$<`eR+sx?bneT3X>x@c;Wie+gBV diff --git a/tests/test_ImageCollection.py b/tests/test_ImageCollection.py deleted file mode 100644 index 6a7c6525..00000000 --- a/tests/test_ImageCollection.py +++ /dev/null @@ -1,713 +0,0 @@ -"""Test the ImageCollection class.""" -from __future__ import annotations - -import io - -import ee -import numpy as np -import pytest -from jsonschema import validate -from matplotlib import pyplot as plt - -import geetools - - -def reduce( - collection: ee.ImageCollection, geometry: ee.Geometry | None = None, reducer: str = "first" -) -> ee.Dictionary: - """Compute the mean reduction on the first image of the imageCollection.""" - image = getattr(collection, reducer)() - geometry = image.geometry() if geometry is None else geometry.geometry() - geometry = geometry.centroid(1).buffer(100) - return image.reduceRegion(ee.Reducer.mean(), geometry, 1) - - -def round_dict(d: dict = None, decimals: int = 2) -> dict: - """Round all the values of a dictionary.""" - d = d or {} - for k, v in d.items(): - if isinstance(v, dict): - round_dict(v, decimals) - else: - d[k] = round(v, decimals) - return d - - -class TestMaskClouds: - """Test the ``maskClouds`` method.""" - - @pytest.mark.xfail( - reason="ee_extra is joining ImgeCollection which is not compatible with ee v1.x." - ) - def test_mask_s2_sr(self, s2_sr, num_regression): - masked = s2_sr.geetools.maskClouds(prob=75, buffer=300, cdi=-0.5) - num_regression.check(reduce(masked).getInfo()) - - -class TestClosest: - """Test the ``closest`` method.""" - - def test_closest_s2_sr(self, s2_sr, data_regression): - closest = s2_sr.geetools.closest("2021-10-01") - data_regression.check(closest.size().getInfo()) - - -class TestSpectralIndices: - """Test the ``spectralIndices`` method.""" - - def test_spectral_indices(self, s2_sr, num_regression): - indices = s2_sr.geetools.spectralIndices(["NDVI", "NDWI"]) - num_regression.check(reduce(indices).getInfo()) - - -class TestGetScaleParams: - """Test the ``getScaleParams`` method.""" - - def test_get_scale_params(self, s2_sr, data_regression): - scale_params = s2_sr.geetools.getScaleParams() - data_regression.check(scale_params) - - -class TestGetOffsetParams: - """Test the ``getOffsetParams`` method.""" - - def test_get_offset_params(self, s2_sr, data_regression): - offset_params = s2_sr.geetools.getOffsetParams() - data_regression.check(offset_params) - - -class TestScaleAndOffset: - """Test the ``scaleAndOffset`` method.""" - - def test_scale_and_offset(self, s2_sr, num_regression): - scaled = s2_sr.geetools.scaleAndOffset() - num_regression.check(reduce(scaled).getInfo()) - - -class TestPreprocess: - """Test the ``preprocess`` method.""" - - @pytest.mark.xfail( - reason="ee_extra is joining ImgeCollection which is not compatible with ee v1.x." - ) - def test_preprocess(self, s2_sr, num_regression): - preprocessed = s2_sr.geetools.preprocess() - values = {k: np.nan if v is None else v for k, v in reduce(preprocessed).getInfo().items()} - num_regression.check(values) - - -class TestGetSTAC: - """Test the ``getSTAC`` method.""" - - def test_get_stac(self, s2_sr): - stac = s2_sr.geetools.getSTAC() - assert stac["id"] == "COPERNICUS/S2_SR_HARMONIZED" - - def test_get_stac_schema(self, s2_sr, stac_schema): - stac = s2_sr.geetools.getSTAC() - validate(stac, stac_schema) - - -class TestGetDOI: - """Test the ``getDOI`` method.""" - - def test_get_doi(self, s2_sr, data_regression): - doi = s2_sr.geetools.getDOI() - data_regression.check(doi) - - -class TestGetCitation: - """Test the ``getCitation`` method.""" - - def test_get_citation(self, s2_sr, data_regression): - citation = s2_sr.geetools.getCitation() - data_regression.check(citation) - - -class TestPanSharpen: - """Test the ``panSharpen`` method.""" - - @pytest.mark.xfail(reason="ee_extra does not accept C02 L08 collection yet.") - def test_pan_sharpen(self, l8_toa, num_regression): - sharpened = l8_toa.geetools.panSharpen() - num_regression.check(reduce(sharpened).getInfo()) - - -class TestTasseledCap: - """Test the ``tasseledCap`` method.""" - - def test_tasseled_cap(self, l8_sr, num_regression): - tc = l8_sr.geetools.tasseledCap() - num_regression.check(reduce(tc).getInfo()) - - -class TestAppend: - """Test the ``append`` method.""" - - def test_append(self, s2_sr, data_regression): - appended = s2_sr.geetools.append(s2_sr.first()) - data_regression.check(appended.size().getInfo()) - - -class TestcollectionMask: - """Test the ``collectionMask`` method.""" - - def test_collection_mask(self, s2_sr, amazonas, num_regression): - masked = s2_sr.geetools.collectionMask() - num_regression.check(reduce(ee.ImageCollection([masked]), amazonas).getInfo()) - - -class TestIloc: - """Test the iloc class.""" - - def test_iloc(self, s2_sr, num_regression): - ic = ee.ImageCollection([s2_sr.geetools.iloc(0).subtract(s2_sr.first())]) - values = {k: np.nan if v is None else v for k, v in reduce(ic).getInfo().items()} - num_regression.check(values) - - -class TestIntegral: - """Test the ``integral`` method.""" - - def test_integral(self, s2_sr, amazonas, num_regression): - integral = s2_sr.limit(10).geetools.integral("B4").select("integral") - ic = ee.ImageCollection([integral]) - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - -class TestOutliers: - """Test the ``outliers`` method.""" - - def test_outliers(self, s2_sr, amazonas, num_regression): - ic = s2_sr.limit(10).geetools.outliers() - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - def test_outliers_with_bands(self, s2_sr, amazonas, num_regression): - ic = s2_sr.limit(10).geetools.outliers(bands=["B4", "B2"]) - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - def test_outliers_with_sigma(self, s2_sr, amazonas, num_regression): - ic = s2_sr.limit(10).geetools.outliers(sigma=3) - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - def test_outliers_with_drop(self, s2_sr, amazonas, num_regression): - ic = s2_sr.limit(10).geetools.outliers(drop=True) - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - -class TestToXarray: - """Test the ``toXarray`` method.""" - - def test_to_xarray(self, s2_sr, data_regression): - ds = s2_sr.geetools.to_xarray() - - # drop all the dtype as they are not consistently setup depending on the xarray version - def drop_dtype(d=ds): - for k, v in ds.items(): - if isinstance(v, dict): - drop_dtype(v) - elif k == "dtype": - del ds[k] - - drop_dtype() - - # ds = ds.astype(np.float64) - data_regression.check(ds.to_dict(data=False)) - - -class TestValidPixel: - """Test the ``validPixel`` method.""" - - def test_validPixel(self, s2_sr, amazonas, num_regression): - s2_sr = s2_sr.filterDate("2021-01-01", "2021-01-31") - ic = ee.ImageCollection([s2_sr.geetools.validPixel("B1")]) - values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} - num_regression.check(values) - - -class TestContainsBandNames: - """Test the ``containsBandNames`` method and derivated.""" - - def test_contains_all(self, s2_sr): - ic = s2_sr.select(["B2", "B3", "B4"]) - ic = ic.geetools.containsAllBands(["B2", "B3"]) - assert ic.size().getInfo() == 2449 - - def test_contains_all_mismatch(self, s2_sr): - ic = s2_sr.select(["B2", "B3", "B4"]) - ic = ic.geetools.containsAllBands(["B2", "B3", "B5"]) - assert ic.size().getInfo() == 0 - - def test_contains_any(self, s2_sr): - ic = s2_sr.select(["B2", "B3", "B4"]) - ic = ic.geetools.containsAnyBands(["B2", "B3", "B5"]) - assert ic.size().getInfo() == 2449 - - def test_contains_any_mismatch(self, s2_sr): - ic = s2_sr.select(["B2", "B3", "B4"]) - ic = ic.geetools.containsAnyBands(["B5", "B6"]) - assert ic.size().getInfo() == 0 - - -class TestAggregateArray: - """Test the ``aggregateArray`` method.""" - - def test_aggregate_array(self, s2_sr, data_regression): - # reduce the number of properties beforehand to avoid the test to fail - keys = s2_sr.first().propertyNames() - keys = keys.filter(ee.Filter.stringStartsWith("item", "system:")).remove("system:version") - s2_sr_filtered = s2_sr.limit(3).map( - lambda i: ee.Image().addBands(i).copyProperties(i, keys) - ) - aggregated = s2_sr_filtered.geetools.aggregateArray() - data_regression.check(aggregated.getInfo()) - - def test_aggregate_array_with_properties(self, s2_sr, data_regression): - aggregated = s2_sr.limit(10).geetools.aggregateArray(["system:time_start", "system:index"]) - data_regression.check(aggregated.getInfo()) - - -class TestGroupInterval: - """Test the ``groupInterval`` method.""" - - def test_group_interval(self, jaxa_rainfall): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - grouped = ic.geetools.groupInterval() - assert grouped.size().getInfo() == 3 - assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 720 - - def test_group_interval_with_interval(self, jaxa_rainfall): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - grouped = ic.geetools.groupInterval(duration=2) - assert grouped.size().getInfo() == 2 - assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 1440 - assert ee.ImageCollection(grouped.get(1)).size().getInfo() == 719 - - def test_group_interval_with_interval_and_unit(self, jaxa_rainfall): - # get 3 days worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") - grouped = ic.geetools.groupInterval(duration=1, unit="day") - assert grouped.size().getInfo() == 3 - assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 24 - - def test_deprecated_make_equal_interval(self, jaxa_rainfall): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - with pytest.deprecated_call(): - grouped = geetools.imagecollection.makeEqualInterval(ic) - assert grouped.size().getInfo() == 3 - assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 720 - - def test_deprecated_make_day_intervals(self, jaxa_rainfall): - # get 3 days worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") - with pytest.deprecated_call(): - grouped = geetools.imagecollection.makeDayIntervals(ic) - assert grouped.size().getInfo() == 3 - assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 24 - - -class TestReduceInterval: - """Test the ``reduceInterval`` method.""" - - def test_reduce_interval(self, jaxa_rainfall, amazonas, num_regression): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - reduced = ic.geetools.reduceInterval() - values = { - k: np.nan if v is None else v for k, v in reduce(reduced, amazonas).getInfo().items() - } - num_regression.check(values) - - def test_reduce_interval_with_reducer(self, jaxa_rainfall, amazonas, num_regression): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - reduced = ic.geetools.reduceInterval("max") - values = reduce(reduced, amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_reduce_interval_with_non_existing_reducer_and_properties(self, jaxa_rainfall): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - with pytest.raises(AttributeError): - ic.geetools.reduceInterval("toto") - - def test_deprecated_reduce_equal_interval(self, jaxa_rainfall, amazonas, num_regression): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") - with pytest.deprecated_call(): - reduced = geetools.imagecollection.reduceEqualInterval(ic, reducer="mean") - values = reduce(reduced, amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_reduce_day_intervals(self, jaxa_rainfall, amazonas, num_regression): - # get 3 days worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") - with pytest.deprecated_call(): - reduced = geetools.imagecollection.reduceDayIntervals(ic, reducer="mean") - values = reduce(reduced, amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_composite_regular_intervals(self, jaxa_rainfall, amazonas, num_regression): - # get 3 days worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") - with pytest.deprecated_call(): - reduced = geetools.composite.compositeRegularIntervals(ic, unit="day") - values = reduce(reduced, amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_composite_by_month(self, jaxa_rainfall, amazonas, num_regression): - # get 3 month worth of data and group it with default parameters - ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-01") - with pytest.deprecated_call(): - reduced = geetools.composite.compositeByMonth(ic) - values = reduce(reduced, amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - -class TestClosestDate: - """Test the ``closestDate`` method.""" - - def test_closest_date(self, s2_sr, amazonas, num_regression): - # we need less images as the test will fail otherwise - filled = s2_sr.filterDate("2021-01-01", "2021-01-15").geetools.closestDate() - values = reduce(filled, amazonas, "mean").getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_fill_with_last(self, s2_sr, amazonas, num_regression): - with pytest.deprecated_call(): - filled = geetools.imagecollection.fillWithLast( - s2_sr.filterDate("2021-01-01", "2021-01-15") - ) - values = reduce(filled, amazonas, "mean").getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_closest_date(self, s2_sr, amazonas, num_regression): - with pytest.deprecated_call(): - filled = geetools.composite.closestDate(s2_sr.filterDate("2021-01-01", "2021-01-15")) - values = reduce(filled, amazonas, "mean").getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - -class TestMedoid: - """Test the ``medoid`` method.""" - - def test_medoid(self, s2_sr, amazonas, num_regression): - # we need less images as the test will fail otherwise - medoid = s2_sr.filterDate("2021-01-01", "2021-01-05").geetools.medoid() - values = reduce(ee.ImageCollection(medoid), amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - def test_deprecated_medoid(self, s2_sr, amazonas, num_regression): - with pytest.deprecated_call(): - # we need less images as the test will fail otherwise - medoid = geetools.composite.medoid(s2_sr.filterDate("2021-01-01", "2021-01-05")) - values = reduce(ee.ImageCollection(medoid), amazonas).getInfo() - values = {k: np.nan if v is None else v for k, v in values.items()} - num_regression.check(values) - - -class TestPlotDatesByBands: - """Test the ``plot_dates_by_bands`` method.""" - - def test_plot_dates_by_bands(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_dates_by_bands( - region=self.region.geometry(), - reducer="mean", - scale=500, - bands=["NDVI", "EVI"], - ax=ax, - dateProperty="system:time_start", - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def region(self): - return ( - ee.FeatureCollection("projects/google/charts_feature_example") - .select(["label", "value", "warm"]) - .filter(ee.Filter.eq("label", "Forest")) - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .filter(ee.Filter.date("2010-01-01", "2020-01-01")) - .select(["NDVI", "EVI"]) - ) - - -class TestPlotDatesByRegions: - """Test the ``plot_dates_by_regions`` method.""" - - def test_plot_dates_by_regions(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_dates_by_regions( - regions=self.regions, - label="label", - band="NDVI", - reducer="mean", - scale=500, - ax=ax, - dateProperty="system:time_start", - colors=["#f0af07", "#0f8755", "#76b349"], - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def regions(self): - return ee.FeatureCollection("projects/google/charts_feature_example").select( - ["label", "value", "warm"] - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .filter(ee.Filter.date("2010-01-01", "2020-01-01")) - .select(["NDVI", "EVI"]) - ) - - -class TestPlotDoyByBands: - """Test the ``plot_doy_by_bands`` method.""" - - def test_plot_doy_by_bands(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_doy_by_bands( - region=self.region.geometry(), - spatialReducer="mean", - timeReducer="mean", - scale=500, - bands=["NDVI", "EVI"], - ax=ax, - dateProperty="system:time_start", - colors=["#e37d05", "#1d6b99"], - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def region(self): - return ( - ee.FeatureCollection("projects/google/charts_feature_example") - .select(["label", "value", "warm"]) - .filter(ee.Filter.eq("label", "Grassland")) - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .filter(ee.Filter.date("2010-01-01", "2020-01-01")) - .select(["NDVI", "EVI"]) - ) - - -class TestPlotDoyByRegions: - """Test the ``plot_doy_by_regions`` method.""" - - def test_plot_doy_by_regions(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_doy_by_regions( - regions=self.regions, - label="label", - band="NDVI", - spatialReducer="mean", - timeReducer="mean", - scale=500, - ax=ax, - dateProperty="system:time_start", - colors=["#f0af07", "#0f8755", "#76b349"], - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def regions(self): - return ee.FeatureCollection("projects/google/charts_feature_example").select( - ["label", "value", "warm"] - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .filter(ee.Filter.date("2010-01-01", "2020-01-01")) - .select(["NDVI", "EVI"]) - ) - - -class TestPlotDoyByYears: - """Test the ``plot_doy_by_years`` method.""" - - def test_plot_doy_by_years(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_doy_by_years( - region=self.region.geometry(), - band="NDVI", - reducer="mean", - scale=500, - ax=ax, - colors=["#39a8a7", "#9c4f97"], - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def region(self): - return ( - ee.FeatureCollection("projects/google/charts_feature_example") - .select(["label", "value", "warm"]) - .filter(ee.Filter.eq("label", "Grassland")) - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .select(["NDVI", "EVI"]) - .filter( - ee.Filter.Or( - ee.Filter.date("2012-01-01", "2012-12-31"), - ee.Filter.date("2019-01-01", "2019-12-31"), - ) - ) - ) - - -class TestPlotDoyBySeasons: - """Test the ``plot_doy_by_seasons`` method.""" - - def test_plot_doy_by_seasons(self, image_regression): - fig, ax = plt.subplots() - self.collection.geetools.plot_doy_by_seasons( - region=self.region.geometry(), - seasonStart=ee.Date("2019-04-15").getRelative("day", "year"), - seasonEnd=ee.Date("2019-09-15").getRelative("day", "year"), - band="NDVI", - reducer="mean", - scale=500, - ax=ax, - colors=["#39a8a7", "#9c4f97"], - ) - - with io.BytesIO() as buffer: - fig.savefig(buffer) - image_regression.check(buffer.getvalue()) - - @property - def region(self): - return ( - ee.FeatureCollection("projects/google/charts_feature_example") - .select(["label", "value", "warm"]) - .filter(ee.Filter.eq("label", "Grassland")) - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .select(["NDVI", "EVI"]) - .filter( - ee.Filter.Or( - ee.Filter.date("2012-01-01", "2012-12-31"), - ee.Filter.date("2019-01-01", "2019-12-31"), - ) - ) - ) - - -class TestReduceRegion: - """Test the reduceRegion method.""" - - def test_reduce_region_by_dates(self, data_regression): - values = self.collection.geetools.reduceRegion( - reducer=ee.Reducer.mean(), - idProperty="system:time_start", - idType=ee.Date, - geometry=self.region.geometry(), - scale=500, - ).getInfo() - data_regression.check(round_dict(values, 4)) - - def test_reduce_region_by_date_property(self, data_regression): - values = self.collection.geetools.reduceRegion( - reducer=ee.Reducer.mean(), - idProperty="system:time_start", - idType=ee.Date, - idReducer="mean", - geometry=self.region.geometry(), - scale=500, - ).getInfo() - data_regression.check(round_dict(values, 4)) - - def test_reduce_region_by_doy(self, data_regression): - values = self.year_collection.geetools.reduceRegion( - reducer=ee.Reducer.mean(), - idProperty="system:time_start", - idType=ee.Date, - idFormat="DDD", - geometry=self.region.geometry(), - scale=500, - ).getInfo() - data_regression.check(round_dict(values, 4)) - - @property - def region(self): - return ( - ee.FeatureCollection("projects/google/charts_feature_example") - .select(["label", "value", "warm"]) - .filter(ee.Filter.eq("label", "Forest")) - ) - - @property - def collection(self): - return ( - ee.ImageCollection("MODIS/061/MOD13A1") - .filter(ee.Filter.date("2010-01-01", "2010-02-28")) - .select(["NDVI", "EVI"]) - ) - - @property - def year_collection(self): - return ( - ee.ImageCollection("MODIS/006/MOD13Q1") - .filter( - ee.Filter.Or( - ee.Filter.date("2010-01-01", "2010-02-28"), - ee.Filter.date("2011-01-01", "2011-02-28"), - ) - ) - .select(["NDVI", "EVI"]) - ) diff --git a/tests/test_ImageCollection/test_aggregate_array.yml b/tests/test_ImageCollection/test_aggregate_array.yml deleted file mode 100644 index 0c1a0660..00000000 --- a/tests/test_ImageCollection/test_aggregate_array.yml +++ /dev/null @@ -1,845 +0,0 @@ -system:asset_size: -- 361149596 -- 207607455 -- 1145104866 -system:band_names: -- - B1 - - B2 - - B3 - - B4 - - B5 - - B6 - - B7 - - B8 - - B8A - - B9 - - B11 - - B12 - - SCL -- - B1 - - B2 - - B3 - - B4 - - B5 - - B6 - - B7 - - B8 - - B8A - - B9 - - B11 - - B12 - - SCL -- - B1 - - B2 - - B3 - - B4 - - B5 - - B6 - - B7 - - B8 - - B8A - - B9 - - B11 - - B12 - - SCL -system:bands: -- B1: - crs: EPSG:32718 - crs_transform: - - 60 - - 0 - - 699960 - - 0 - - -60 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 1830 - - 1830 - B11: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B12: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B2: - crs: EPSG:32718 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 10980 - - 10980 - B3: - crs: EPSG:32718 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 10980 - - 10980 - B4: - crs: EPSG:32718 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 10980 - - 10980 - B5: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B6: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B7: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B8: - crs: EPSG:32718 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 10980 - - 10980 - B8A: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B9: - crs: EPSG:32718 - crs_transform: - - 60 - - 0 - - 699960 - - 0 - - -60 - - 9800020 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 1830 - - 1830 - SCL: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: - max: 255 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 -- B1: - crs: EPSG:32718 - crs_transform: - - 60 - - 0 - - 699960 - - 0 - - -60 - - 9900040 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 1830 - - 1830 - B11: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9900040 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B12: - crs: EPSG:32718 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9900040 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - dimensions: - - 5490 - - 5490 - B2: - crs: EPSG:32718 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9900040 - data_type: - max: 65535 - min: 0 - precision: int - type: PixelType - 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@@ -1,22 +0,0 @@ -system:index: -- 20210102T150719_20210102T150942_T18MYC -- 20210102T150719_20210102T150942_T18MYD -- 20210102T150719_20210102T150942_T18MZC -- 20210102T150719_20210102T150942_T18MZD -- 20210102T150719_20210102T150942_T18MZE -- 20210102T150719_20210102T150942_T18NZF -- 20210102T150719_20210102T150942_T19MBT -- 20210102T150719_20210102T150942_T19MBU -- 20210102T150719_20210102T150942_T19MBV -- 20210102T150719_20210102T150942_T19MCR -system:time_start: -- 1609600422628 -- 1609600409366 -- 1609600419630 -- 1609600405212 -- 1609600390766 -- 1609600376122 -- 1609600417688 -- 1609600403048 -- 1609600388626 -- 1609600443330 diff --git a/tests/test_ImageCollection/test_append.yml b/tests/test_ImageCollection/test_append.yml deleted file mode 100644 index 1644dd3f..00000000 --- a/tests/test_ImageCollection/test_append.yml +++ /dev/null @@ -1,2 +0,0 @@ -2450 -... diff --git a/tests/test_ImageCollection/test_closest_date.csv b/tests/test_ImageCollection/test_closest_date.csv deleted file mode 100644 index 118aae1b..00000000 --- a/tests/test_ImageCollection/test_closest_date.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL -0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_closest_s2_sr.yml b/tests/test_ImageCollection/test_closest_s2_sr.yml deleted file mode 100644 index ece6e09e..00000000 --- a/tests/test_ImageCollection/test_closest_s2_sr.yml +++ /dev/null @@ -1,2 +0,0 @@ -8 -... diff --git a/tests/test_ImageCollection/test_collection_mask.csv b/tests/test_ImageCollection/test_collection_mask.csv deleted file mode 100644 index 398ec81d..00000000 --- a/tests/test_ImageCollection/test_collection_mask.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 diff --git a/tests/test_ImageCollection/test_deprecated_closest_date.csv b/tests/test_ImageCollection/test_deprecated_closest_date.csv deleted file mode 100644 index 118aae1b..00000000 --- a/tests/test_ImageCollection/test_deprecated_closest_date.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL -0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_deprecated_composite_by_month.csv b/tests/test_ImageCollection/test_deprecated_composite_by_month.csv deleted file mode 100644 index 8fe785e6..00000000 --- a/tests/test_ImageCollection/test_deprecated_composite_by_month.csv +++ /dev/null @@ -1,2 +0,0 @@ -,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag -0,0,0.18768584728240967,0.1922166645526886,-0.0021944569889456034,9149832.9472222216 diff --git a/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv b/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv deleted file mode 100644 index 1a9c76e2..00000000 --- a/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv +++ /dev/null @@ -1,2 +0,0 @@ -,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag -0,0,0,0.16104844212532043,-0.16916666924953461,6294879.5 diff --git a/tests/test_ImageCollection/test_deprecated_fill_with_last.csv b/tests/test_ImageCollection/test_deprecated_fill_with_last.csv deleted file mode 100644 index 118aae1b..00000000 --- a/tests/test_ImageCollection/test_deprecated_fill_with_last.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL -0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_deprecated_medoid.csv b/tests/test_ImageCollection/test_deprecated_medoid.csv deleted file mode 100644 index cf49bd0b..00000000 --- a/tests/test_ImageCollection/test_deprecated_medoid.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL -0,0.6177746823789384,0.79167387556605839,0.75502788442188418,0.60222084925854291,0.63257736183390867,0.60337927161154525,0.63801273546323556,0.79096266749901301,0.85579134939633583,0.79265361382284005,0.87208650729675008,1,0 diff --git a/tests/test_ImageCollection/test_deprecated_mosaic_same_day.csv b/tests/test_ImageCollection/test_deprecated_mosaic_same_day.csv deleted file mode 100644 index 3f7fc7cd..00000000 --- a/tests/test_ImageCollection/test_deprecated_mosaic_same_day.csv +++ /dev/null @@ -1,2 +0,0 @@ -,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag -0,0,0,0.17295975983142853,-1.1200000047683716,-1 diff --git a/tests/test_ImageCollection/test_deprecated_reduce_day_intervals.csv b/tests/test_ImageCollection/test_deprecated_reduce_day_intervals.csv deleted file mode 100644 index 1a9c76e2..00000000 --- a/tests/test_ImageCollection/test_deprecated_reduce_day_intervals.csv +++ /dev/null @@ -1,2 +0,0 @@ -,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag -0,0,0,0.16104844212532043,-0.16916666924953461,6294879.5 diff --git a/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv b/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv deleted file mode 100644 index 8fe785e6..00000000 --- a/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv +++ /dev/null @@ -1,2 +0,0 @@ -,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag -0,0,0.18768584728240967,0.1922166645526886,-0.0021944569889456034,9149832.9472222216 diff --git a/tests/test_ImageCollection/test_get_citation.yml b/tests/test_ImageCollection/test_get_citation.yml deleted file mode 100644 index 866e45cf..00000000 --- a/tests/test_ImageCollection/test_get_citation.yml +++ /dev/null @@ -1,2 +0,0 @@ -Citation not available -... diff --git a/tests/test_ImageCollection/test_get_doi.yml b/tests/test_ImageCollection/test_get_doi.yml deleted file mode 100644 index 57e63a82..00000000 --- a/tests/test_ImageCollection/test_get_doi.yml +++ /dev/null @@ -1,2 +0,0 @@ -DOI not available -... diff --git a/tests/test_ImageCollection/test_get_offset_params.yml b/tests/test_ImageCollection/test_get_offset_params.yml deleted file mode 100644 index a3314864..00000000 --- a/tests/test_ImageCollection/test_get_offset_params.yml +++ /dev/null @@ -1,23 +0,0 @@ -AOT: 0.0 -B1: 0.0 -B11: 0.0 -B12: 0.0 -B2: 0.0 -B3: 0.0 -B4: 0.0 -B5: 0.0 -B6: 0.0 -B7: 0.0 -B8: 0.0 -B8A: 0.0 -B9: 0.0 -MSK_CLDPRB: 0.0 -MSK_SNWPRB: 0.0 -QA10: 0.0 -QA20: 0.0 -QA60: 0.0 -SCL: 0.0 -TCI_B: 0.0 -TCI_G: 0.0 -TCI_R: 0.0 -WVP: 0.0 diff --git a/tests/test_ImageCollection/test_get_scale_params.yml b/tests/test_ImageCollection/test_get_scale_params.yml deleted file mode 100644 index 46506551..00000000 --- a/tests/test_ImageCollection/test_get_scale_params.yml +++ /dev/null @@ -1,23 +0,0 @@ -AOT: 0.001 -B1: 0.0001 -B11: 0.0001 -B12: 0.0001 -B2: 0.0001 -B3: 0.0001 -B4: 0.0001 -B5: 0.0001 -B6: 0.0001 -B7: 0.0001 -B8: 0.0001 -B8A: 0.0001 -B9: 0.0001 -MSK_CLDPRB: 1.0 -MSK_SNWPRB: 1.0 -QA10: 1.0 -QA20: 1.0 -QA60: 1.0 -SCL: 1.0 -TCI_B: 1.0 -TCI_G: 1.0 -TCI_R: 1.0 -WVP: 0.001 diff --git a/tests/test_ImageCollection/test_iloc.csv b/tests/test_ImageCollection/test_iloc.csv deleted file mode 100644 index 52a10b2b..00000000 --- a/tests/test_ImageCollection/test_iloc.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP -0,,,,,,,,,,,,,,0,0,0,0,0,,,,, diff --git a/tests/test_ImageCollection/test_integral.csv b/tests/test_ImageCollection/test_integral.csv deleted file mode 100644 index 5268a191..00000000 --- a/tests/test_ImageCollection/test_integral.csv +++ /dev/null @@ -1,2 +0,0 @@ -,integral -0, diff --git a/tests/test_ImageCollection/test_mask_s2.yml b/tests/test_ImageCollection/test_mask_s2.yml deleted file mode 100644 index eb745b9d..00000000 --- a/tests/test_ImageCollection/test_mask_s2.yml +++ /dev/null @@ -1,27 +0,0 @@ -AOT: 354 -B1: 590.1282626148882 -B11: 1986.408679038172 -B12: 1522.041930986203 -B2: 564.3067665633657 -B3: 784.1374447316587 -B4: 821.3239234989915 -B5: 1217.444006928795 -B6: 2003.3571632505652 -B7: 2236.471328753445 -B8: 2390.463396773381 -B8A: 2407.3933334274057 -B9: 2422.9298342189186 -CLOUD_MASK: 0 -CLOUD_MASK_CDI: 0 -CLOUD_SHADOW_MASK: 0 -MSK_CLDPRB: null -MSK_SNWPRB: null -QA10: 0 -QA20: 0 -QA60: 0 -SCL: 4.598658742666373 -SHADOW_MASK: 0 -TCI_B: 57.67908547074245 -TCI_G: 80.82178790722993 -TCI_R: 83.79020123070153 -WVP: 602.9321499075425 diff --git a/tests/test_ImageCollection/test_mask_s2_sr.csv b/tests/test_ImageCollection/test_mask_s2_sr.csv deleted file mode 100644 index 72e1d438..00000000 --- a/tests/test_ImageCollection/test_mask_s2_sr.csv +++ /dev/null @@ -1,2 +0,0 @@ -,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,CLOUD_MASK,CLOUD_MASK_CDI,CLOUD_SHADOW_MASK,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,SHADOW_MASK,TCI_B,TCI_G,TCI_R,WVP -0,199,16640.094805201559,1568.290431797825,1968.3295548610204,15419.765396984198,14101.613293902175,12931.892357709678,14237.406449381348,12795.376786673241,11806.409740942607,12098.870170407052,11006.397712738837,15899,1,0,0,0,0,0,0,1024,8,0,255,255,255,3645 diff --git a/tests/test_ImageCollection/test_medoid.csv b/tests/test_ImageCollection/test_medoid.csv deleted file mode 100644 index cf49bd0b..00000000 --- a/tests/test_ImageCollection/test_medoid.csv +++ /dev/null @@ -1,2 +0,0 @@ -,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL -0,0.6177746823789384,0.79167387556605839,0.75502788442188418,0.60222084925854291,0.63257736183390867,0.60337927161154525,0.63801273546323556,0.79096266749901301,0.85579134939633583,0.79265361382284005,0.87208650729675008,1,0 diff --git a/tests/test_ImageCollection/test_outliers.csv b/tests/test_ImageCollection/test_outliers.csv deleted file mode 100644 index 7abbfc1a..00000000 --- a/tests/test_ImageCollection/test_outliers.csv +++ /dev/null @@ -1,2 +0,0 @@ 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zz(jrjfD!Dq+0K}A@CW4`8F|^#%4%cyc^$*eo7uxFUWW_JaQFj2`9ZYv+Gve#b3G+m z^j{18Kb_*(sPgjiB-lxj#%fUAs|-6VxTubUQ86({32NY{PdC8gy$o9j7rQ!Bgh6kL z%)JK>8i9&EK0p5&+Bx#_^CRfynp5h6f&v?7XK-59=S?AVLIgf0Hy@u2n9?6(V{H#M zjeqs^89?}oQtX8QViS6uc_Q5NF^@f(FlxacOO%x8&vJ)Vq}R}vHo<{`45;(o+uPgU ze*Adn>+8!REF3#DJlyviS^}~mha4C;{(gQvu!-QI`uh5^a-DaVpJGZt{OX1A!NZ3S z$>`{m;9x?G4GatnhlL-EC5~>lai_EJNSS&-*Ho$V0V)+>Q@!!_9vG@2Op0;$0xr^# ze_&>0d<#Z22y>KSB3ccXhQeaIDmH{tWIPhU3DF9>p1(Z;Gd^|pZn9x9- zm$jM3}k|-YhU|4ie*VpYKV&_wb<%v^v;Cgx!Lp x7ORsIP9OenHU#wloISentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging\ - \ mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring\ - \ of vegetation, soil and water cover,\nas well as observation of inland waterways\ - \ and coastal areas.

The Sentinel-2 L2 data are downloaded from scihub.\ - \ They were\ncomputed by running sen2cor. WARNING: ESA did not produce L2 data\n\ - for all L1 assets, and earlier L2 coverage is not global.

The assets contain\n\ - 12 UINT16 spectral bands representing SR scaled by 10000 (unlike in L1 data,\n\ - there is no B10). There are also several more L2-specific bands (see band\nlist\ - \ for details). See the\nSentinel-2 User Handbook\nfor details. In addition, three QA bands are present\ - \ where one\n(QA60) is a bitmask band with cloud mask information. For more\n\ - details, see the full explanation of how cloud masks are computed.

EE asset\ - \ ids for Sentinel-2 L2 assets have the following format:\nCOPERNICUS/S2_SR/20151128T002653_20151128T102149_T56MNN.\ - \ Here the\nfirst numeric part represents the sensing date and time, the\nsecond\ - \ numeric part represents the product generation date and\ntime, and the final\ - \ 6-character string is a unique granule identifier\nindicating its UTM grid reference\ - \ (see MGRS).

Clouds can be removed by using\nCOPERNICUS/S2_CLOUD_PROBABILITY.\nSee\nthis tutorial\nexplaining how to apply the cloud mask.

For more details\ - \ on Sentinel-2 radiometric resolution, see this page.

Provider: European Union/ESA/Copernicus

Revisit Interval
\n 5\ - \ days\n

BandsDescription
Name
B1

Aerosols

B2

Blue

B3

Green

B4

Red

B5

Red\ - \ Edge 1

B6

Red Edge 2

B7

Red\ - \ Edge 3

B8

NIR

B8A

Red\ - \ Edge 4

B9

Water vapor

B11

SWIR\ - \ 1

B12

SWIR 2

AOT

Aerosol\ - \ Optical Thickness

WVP

Water Vapor Pressure.\ - \ The height the water would occupy if the vapor were condensed into\nliquid and\ - \ spread evenly across the column.

SCL

Scene Classification\ - \ Map (The "No Data" value of 0 is masked out)

TCI_R

True\ - \ Color Image, Red channel

TCI_G

True Color Image,\ - \ Green channel

TCI_B

True Color Image, Blue channel

MSK_CLDPRB

Cloud\ - \ Probability Map (missing in some products)

MSK_SNWPRB

Snow\ - \ Probability Map (missing in some products)

QA10

Always\ - \ empty

QA20

Always empty

QA60

Cloud\ - \ mask

\n Bitmask for QA60\n
  • \n \ - \ Bit 10: Opaque clouds\n
    • 0: No opaque clouds
    • 1: Opaque\ - \ clouds present
  • \n Bit 11: Cirrus clouds\n
    • 0:\ - \ No cirrus clouds
    • 1: Cirrus clouds present

Image\ - \ Properties
NameTypeDescription
AOT_RETRIEVAL_ACCURACYDOUBLE

Accuracy\ - \ of Aerosol Optical thickness model

CLOUDY_PIXEL_PERCENTAGEDOUBLE

Granule-specific\ - \ cloudy pixel percentage taken from the original metadata

CLOUD_COVERAGE_ASSESSMENTDOUBLE

Cloudy\ - \ pixel percentage for the whole archive that\ncontains this granule. Taken from\ - \ the original metadata

CLOUDY_SHADOW_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as cloud shadow

DARK_FEATURES_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as dark features or shadows

DATASTRIP_IDSTRING

Unique\ - \ identifier of the datastrip Product Data Item (PDI)

DATATAKE_IDENTIFIERSTRING

Uniquely\ - \ identifies a given Datatake. The ID contains\nthe Sentinel-2 satellite, start\ - \ date and time, absolute orbit\nnumber, and processing baseline.

DATATAKE_TYPESTRING

MSI\ - \ operation mode

DEGRADED_MSI_DATA_PERCENTAGEDOUBLE

Percentage\ - \ of degraded MSI and ancillary data

FORMAT_CORRECTNESSSTRING

Synthesis\ - \ of the On-Line Quality Control (OLQC) checks\nperformed at granule (Product_Syntax)\ - \ and datastrip (Product\nSyntax and DS_Consistency) levels

GENERAL_QUALITYSTRING

Synthesis\ - \ of the OLQC checks performed at the datastrip level (Relative_Orbit_Number)

GENERATION_TIMEDOUBLE

Product\ - \ generation time

GEOMETRIC_QUALITYSTRING

Synthesis\ - \ of the OLQC checks performed at the datastrip level (Attitude_Quality_Indicator)

GRANULE_IDSTRING

Unique\ - \ identifier of the granule PDI (PDI_ID)

HIGH_PROBA_CLOUDS_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as high probability clouds

MEAN_INCIDENCE_AZIMUTH_ANGLE_B1DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B1 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B2DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B2 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B3DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B3 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B4DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B4 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B5DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B5 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B6DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B6 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B7DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B7 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B8DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B8 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B8ADOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B8a and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B9DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B9 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B10DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B10 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B11DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B11 and for\ - \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B12DOUBLE

Mean\ - \ value containing viewing incidence azimuth angle average for band B12 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B1DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B1 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B2DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B2 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B3DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B3 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B4DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B4 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B5DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B5 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B6DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B6 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B7DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B7 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B8DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B8 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B8ADOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B8a and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B9DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B9 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B10DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B10 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B11DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B11 and for\ - \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B12DOUBLE

Mean\ - \ value containing viewing incidence zenith angle average for band B12 and for\ - \ all detectors

MEAN_SOLAR_AZIMUTH_ANGLEDOUBLE

Mean\ - \ value containing sun azimuth angle average for all bands and detectors

MEAN_SOLAR_ZENITH_ANGLEDOUBLE

Mean\ - \ value containing sun zenith angle average for all bands and detectors

MEDIUM_PROBA_CLOUDS_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as medium probability clouds

MGRS_TILESTRING

US-Military\ - \ Grid Reference System (MGRS) tile

NODATA_PIXEL_PERCENTAGEDOUBLE

Percentage\ - \ of No Data pixels

NOT_VEGETATED_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as non-vegetated

PROCESSING_BASELINESTRING

Configuration\ - \ baseline used at the time of the product\ngeneration in terms of processor software\ - \ version and major Ground\nImage Processing Parameters (GIPP) version

PRODUCT_IDSTRING

The\ - \ full id of the original Sentinel-2 product

RADIATIVE_TRANSFER_ACCURACYDOUBLE

Accuracy\ - \ of radiative transfer model

RADIOMETRIC_QUALITYSTRING

Based\ - \ on the OLQC reports contained in the Datastrips/QI_DATA with RADIOMETRIC_QUALITY\ - \ checklist name

REFLECTANCE_CONVERSION_CORRECTIONDOUBLE

Earth-Sun\ - \ distance correction factor

SATURATED_DEFECTIVE_PIXEL_PERCENTAGEDOUBLE

Percentage\ - \ of saturated or defective pixels

SENSING_ORBIT_DIRECTIONSTRING

Imaging\ - \ orbit direction

SENSING_ORBIT_NUMBERDOUBLE

Imaging\ - \ orbit number

SENSOR_QUALITYSTRING

Synthesis\ - \ of the OLQC checks performed at granule\n(Missing_Lines, Corrupted_ISP, and\ - \ Sensing_Time) and datastrip\n(Degraded_SAD and Datation_Model) levels

SOLAR_IRRADIANCE_B1DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B1

SOLAR_IRRADIANCE_B2DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B2

SOLAR_IRRADIANCE_B3DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B3

SOLAR_IRRADIANCE_B4DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B4

SOLAR_IRRADIANCE_B5DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B5

SOLAR_IRRADIANCE_B6DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B6

SOLAR_IRRADIANCE_B7DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B7

SOLAR_IRRADIANCE_B8DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B8

SOLAR_IRRADIANCE_B8ADOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B8a

SOLAR_IRRADIANCE_B9DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B9

SOLAR_IRRADIANCE_B10DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B10

SOLAR_IRRADIANCE_B11DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B11

SOLAR_IRRADIANCE_B12DOUBLE

Mean\ - \ solar exoatmospheric irradiance for band B12

SNOW_ICE_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as snow or ice

SPACECRAFT_NAMESTRING

Sentinel-2\ - \ spacecraft name: Sentinel-2A, Sentinel-2B

THIN_CIRRUS_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as thin cirrus clouds

UNCLASSIFIED_PERCENTAGEDOUBLE

Percentage\ - \ of unclassified pixels

VEGETATION_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as vegetation

WATER_PERCENTAGEDOUBLE

Percentage\ - \ of pixels classified as water

WATER_VAPOUR_RETRIEVAL_ACCURACYDOUBLE

Declared\ - \ accuracy of the Water Vapor model

Terms of Use

The\ - \ use of Sentinel data is governed by the Copernicus\nSentinel Data Terms and Conditions.

" - keywords: - - copernicus - - esa - - eu - - msi - - reflectance - - sentinel - - sr - period: 0 - product_tags: - - msi - - sr - - reflectance - provider: European Union/ESA/Copernicus - provider_url: https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a - sample: https://mw1.google.com/ges/dd/images/COPERNICUS_S2_SR_sample.png - source_tags: - - eu - - esa - - copernicus - - sentinel - tags: - - copernicus - - esa - - eu - - msi - - reflectance - - sentinel - - sr - thumb: https://mw1.google.com/ges/dd/images/COPERNICUS_S2_SR_thumb.png - title: 'Sentinel-2 MSI: MultiSpectral Instrument, Level-2A' - type_name: ImageCollection - visualization_0_bands: B4,B3,B2 - visualization_0_max: '3000.0' - visualization_0_min: '0.0' - visualization_0_name: RGB -coords: - lat: - attrs: {} - dims: - - lat - dtype: float64 - shape: - - 180 - lon: - attrs: {} - dims: - - lon - dtype: float64 - shape: - - 360 - time: - attrs: {} - dims: - - time - dtype: datetime64[ns] - shape: - - 2449 -data_vars: - B1: - attrs: - crs: EPSG:4326 - crs_transform: - - 60 - - 0 - - 699960 - - 0 - - -60 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 1830 - - 1830 - id: B1 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B11: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B11 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B12: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B12 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B2: - attrs: - crs: EPSG:4326 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 10980 - - 10980 - id: B2 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B3: - attrs: - crs: EPSG:4326 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 10980 - - 10980 - id: B3 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B4: - attrs: - crs: EPSG:4326 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 10980 - - 10980 - id: B4 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B5: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B5 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B6: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B6 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B7: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B7 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B8: - attrs: - crs: EPSG:4326 - crs_transform: - - 10 - - 0 - - 699960 - - 0 - - -10 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 10980 - - 10980 - id: B8 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B8A: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 5490 - - 5490 - id: B8A - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - B9: - attrs: - crs: EPSG:4326 - crs_transform: - - 60 - - 0 - - 699960 - - 0 - - -60 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 65535}' - dimensions: - - 1830 - - 1830 - id: B9 - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 - SCL: - attrs: - crs: EPSG:4326 - crs_transform: - - 20 - - 0 - - 699960 - - 0 - - -20 - - 9800020 - data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': - 255}' - dimensions: - - 5490 - - 5490 - id: SCL - dims: - - time - - lon - - lat - dtype: float32 - shape: - - 2449 - - 360 - - 180 -dims: - lat: 180 - lon: 360 - time: 2449 diff --git a/tests/test_ImageCollection/test_validPixel.csv b/tests/test_ImageCollection/test_validPixel.csv deleted file mode 100644 index 1c1f49db..00000000 --- a/tests/test_ImageCollection/test_validPixel.csv +++ /dev/null @@ -1,2 +0,0 @@ -,pct_valid,valid -0,5.4298642533936654,12 diff --git a/tests/test_ImageCollection/test_valid_pixels.csv b/tests/test_ImageCollection/test_valid_pixels.csv deleted file mode 100644 index 2a50ff96..00000000 --- a/tests/test_ImageCollection/test_valid_pixels.csv +++ /dev/null @@ -1,2 +0,0 @@ -,pct_valid,valid -0,100,12 diff --git a/tests/test_Integer.py b/tests/test_Integer.py deleted file mode 100644 index 1a80f041..00000000 --- a/tests/test_Integer.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Test the Integer placeholder object.""" -import ee -import pytest - - -class TestInteger: - """Test the Integer placeholder class.""" - - def test_init(self): - with pytest.raises(NotImplementedError): - ee.Integer() - - def test_name(self): - assert ee.Integer.__name__ == "Integer" diff --git a/tests/test_Join.py b/tests/test_Join.py deleted file mode 100644 index 3545f434..00000000 --- a/tests/test_Join.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Test the ``Join`` class.""" -import ee -import pytest - - -class TestByProperty: - """Test the ``byProperty`` method.""" - - def test_by_property(self, fc1, fc2, data_regression): - joined = ee.Join.geetools.byProperty(fc1, fc2, "id") - data_regression.check(joined.getInfo()) - - def test_by_property_outer(self, fc1, fc2, data_regression): - joined = ee.Join.geetools.byProperty(fc1, fc2, "id", outer=True) - data_regression.check(joined.getInfo()) - - @pytest.fixture - def fc1(self): - point = ee.Geometry.Point([0, 0]) - prop1 = {"id": 1, "prop_from_fc1": "I am from fc1"} - return ee.FeatureCollection([ee.Feature(point, prop1)]) - - @pytest.fixture - def fc2(self): - point = ee.Geometry.Point([0, 0]) - prop2 = {"id": 1, "prop_from_fc2": "I am from fc2"} - return ee.FeatureCollection([ee.Feature(point, prop2)]) diff --git a/tests/test_Join/test_by_property.yml b/tests/test_Join/test_by_property.yml deleted file mode 100644 index dc663ee8..00000000 --- a/tests/test_Join/test_by_property.yml +++ /dev/null @@ -1,14 +0,0 @@ -columns: {} -features: -- geometry: - coordinates: - - 0 - - 0 - type: Point - id: '0' - properties: - id: 1 - prop_from_fc1: I am from fc1 - prop_from_fc2: I am from fc2 - type: Feature -type: FeatureCollection diff --git a/tests/test_Join/test_by_property_outer.yml b/tests/test_Join/test_by_property_outer.yml deleted file mode 100644 index dc663ee8..00000000 --- a/tests/test_Join/test_by_property_outer.yml +++ /dev/null @@ -1,14 +0,0 @@ -columns: {} -features: -- geometry: - coordinates: - - 0 - - 0 - type: Point - id: '0' - properties: - id: 1 - prop_from_fc1: I am from fc1 - prop_from_fc2: I am from fc2 - type: Feature -type: FeatureCollection diff --git a/tests/test_Join/test_deprecated_join.yml b/tests/test_Join/test_deprecated_join.yml deleted file mode 100644 index dc663ee8..00000000 --- a/tests/test_Join/test_deprecated_join.yml +++ /dev/null @@ -1,14 +0,0 @@ -columns: {} -features: -- geometry: - coordinates: - - 0 - - 0 - type: Point - id: '0' - properties: - id: 1 - prop_from_fc1: I am from fc1 - prop_from_fc2: I am from fc2 - type: Feature -type: FeatureCollection diff --git a/tests/test_List.py b/tests/test_List.py deleted file mode 100644 index df0d5a29..00000000 --- a/tests/test_List.py +++ /dev/null @@ -1,114 +0,0 @@ -"""Test the List class methods.""" -import ee - - -class TestProduct: - """Test the product method.""" - - def test_product_with_same_type(self, letter_list, data_regression): - product_list = letter_list.geetools.product(letter_list) - data_regression.check(product_list.getInfo()) - - def test_product_with_different_type(self, letter_list, int_list, data_regression): - product_list = letter_list.geetools.product(int_list) - data_regression.check(product_list.getInfo()) - - -class TestComplement: - """Test the complement method.""" - - def test_complement_with_same_type(self, letter_list, data_regression): - complement_list = letter_list.geetools.complement(letter_list) - data_regression.check(complement_list.getInfo()) - - def test_complement_with_different_type(self, letter_list, int_list, data_regression): - complement_list = letter_list.geetools.complement(int_list) - data_regression.check(complement_list.getInfo()) - - -class TestIntersection: - """Test the intersection method.""" - - def test_intersection_with_same_type(self, letter_list, data_regression): - intersection_list = letter_list.geetools.intersection(letter_list) - data_regression.check(intersection_list.getInfo()) - - def test_intersection_with_different_type(self, letter_list, int_list, data_regression): - intersection_list = letter_list.geetools.intersection(int_list) - data_regression.check(intersection_list.getInfo()) - - -class TestUnion: - """Test the union method.""" - - def test_union_with_duplicate(self, letter_list, data_regression): - union_list = letter_list.geetools.union(letter_list) - data_regression.check(union_list.getInfo()) - - def test_union_without_dupplicates(self, letter_list, int_list, data_regression): - union_list = letter_list.geetools.union(int_list) - data_regression.check(union_list.getInfo()) - - -class TestDelete: - """Test the delete method.""" - - def test_delete(self, letter_list, data_regression): - deleted_list = letter_list.geetools.delete(1) - data_regression.check(deleted_list.getInfo()) - - -class TestSequence: - """Test the sequence method.""" - - def test_sequence(self): - seq = ee.List.geetools.sequence(1, 10) - assert seq.getInfo() == list(range(1, 11)) - - def test_sequence_with_step(self): - seq = ee.List.geetools.sequence(1, 10, 2) - assert seq.getInfo() == list(range(1, 11, 2)) + [10] - - def test_sequence_with_uneven_step(self): - seq = ee.List.geetools.sequence(1, 10, 3) - assert seq.getInfo() == list(range(1, 10, 3)) + [10] - - def test_sequence_with_0_step(self): - seq = ee.List.geetools.sequence(1, 10, 0) - assert seq.getInfo() == list(range(1, 11)) - - -class TestReplaceMany: - """Test the replaceMany method.""" - - def test_replace_many(self, letter_list, data_regression): - replaced_list = letter_list.geetools.replaceMany({"a": "foo", "c": "bar"}) - data_regression.check(replaced_list.getInfo()) - - -class TestZip: - """Test the zip method.""" - - def test_zip(self, letter_list, data_regression): - zipped_list = ee.List([letter_list, letter_list]).geetools.zip() - data_regression.check(zipped_list.getInfo()) - - -class TestToStrings: - """Test the toStrings method.""" - - def test_to_strings(self, mix_list, data_regression): - strings = mix_list.geetools.toStrings() - data_regression.check(strings.getInfo()) - - -class TestJoin: - """Test the join method.""" - - def test_join(self, mix_list): - formatted = mix_list.geetools.join() - assert formatted.getInfo() == "a, 1, Image" - - def test_join_with_separator(self, mix_list): - formatted = mix_list.geetools.join(separator="; ") - assert formatted.getInfo() == "a; 1; Image" diff --git a/tests/test_List/test_complement_with_different_type.yml b/tests/test_List/test_complement_with_different_type.yml deleted file mode 100644 index 1606269f..00000000 --- a/tests/test_List/test_complement_with_different_type.yml +++ /dev/null @@ -1,5 +0,0 @@ -- a -- b -- c -- 1 -- 2 diff --git a/tests/test_List/test_complement_with_same_type.yml b/tests/test_List/test_complement_with_same_type.yml deleted file mode 100644 index fe51488c..00000000 --- a/tests/test_List/test_complement_with_same_type.yml +++ /dev/null @@ -1 +0,0 @@ -[] diff --git a/tests/test_List/test_delete.yml b/tests/test_List/test_delete.yml deleted file mode 100644 index a7cc122f..00000000 --- a/tests/test_List/test_delete.yml +++ /dev/null @@ -1,2 +0,0 @@ -- a -- c diff --git a/tests/test_List/test_intersection_with_different_type.yml b/tests/test_List/test_intersection_with_different_type.yml deleted file mode 100644 index fe51488c..00000000 --- a/tests/test_List/test_intersection_with_different_type.yml +++ /dev/null @@ -1 +0,0 @@ -[] diff --git a/tests/test_List/test_intersection_with_same_type.yml b/tests/test_List/test_intersection_with_same_type.yml deleted file mode 100644 index d729eb69..00000000 --- a/tests/test_List/test_intersection_with_same_type.yml +++ /dev/null @@ -1,3 +0,0 @@ -- a -- b -- c diff --git a/tests/test_List/test_product_with_different_type.yml b/tests/test_List/test_product_with_different_type.yml deleted file mode 100644 index b41eee5c..00000000 --- a/tests/test_List/test_product_with_different_type.yml +++ /dev/null @@ -1,6 +0,0 @@ -- a1 -- a2 -- b1 -- b2 -- c1 -- c2 diff --git a/tests/test_List/test_product_with_same_type.yml b/tests/test_List/test_product_with_same_type.yml deleted file mode 100644 index c4c0d3f6..00000000 --- a/tests/test_List/test_product_with_same_type.yml +++ /dev/null @@ -1,9 +0,0 @@ -- aa -- ab -- ac -- ba -- bb -- bc -- ca -- cb -- cc diff --git a/tests/test_List/test_replace_many.yml b/tests/test_List/test_replace_many.yml deleted file mode 100644 index de49b690..00000000 --- a/tests/test_List/test_replace_many.yml +++ /dev/null @@ -1,3 +0,0 @@ -- foo -- b -- bar diff --git a/tests/test_List/test_to_strings.yml b/tests/test_List/test_to_strings.yml deleted file mode 100644 index ead42ea7..00000000 --- a/tests/test_List/test_to_strings.yml +++ /dev/null @@ -1,3 +0,0 @@ -- a -- '1' -- Image diff --git a/tests/test_List/test_union_with_duplicate.yml b/tests/test_List/test_union_with_duplicate.yml deleted file mode 100644 index d729eb69..00000000 --- a/tests/test_List/test_union_with_duplicate.yml +++ /dev/null @@ -1,3 +0,0 @@ -- a -- b -- c diff --git a/tests/test_List/test_union_without_dupplicates.yml b/tests/test_List/test_union_without_dupplicates.yml deleted file mode 100644 index 1606269f..00000000 --- a/tests/test_List/test_union_without_dupplicates.yml +++ /dev/null @@ -1,5 +0,0 @@ -- a -- b -- c -- 1 -- 2 diff --git a/tests/test_List/test_zip.yml b/tests/test_List/test_zip.yml deleted file mode 100644 index ff5bbe0c..00000000 --- a/tests/test_List/test_zip.yml +++ /dev/null @@ -1,6 +0,0 @@ -- - a - - a -- - b - - b -- - c - - c diff --git a/tests/test_Number.py b/tests/test_Number.py deleted file mode 100644 index 0d4bd9cf..00000000 --- a/tests/test_Number.py +++ /dev/null @@ -1,21 +0,0 @@ -"""test the Number class methods.""" - - -class TestTruncate: - """Test the truncate method.""" - - def test_truncate_with_default_decimals(self, number_instance): - truncated_number = number_instance.geetools.truncate() - assert truncated_number.getInfo() == 1234.56 - - def test_truncate_with_custom_decimals(self, number_instance): - truncated_number = number_instance.geetools.truncate(1) - assert truncated_number.getInfo() == 1234.5 - - def test_truncate_with_zero_decimals(self, number_instance): - truncated_number = number_instance.geetools.truncate(0) - assert truncated_number.getInfo() == 1234.0 - - def test_truncate_with_large_decimals(self, number_instance): - truncated_number = number_instance.geetools.truncate(5) - assert truncated_number.getInfo() == 1234.56785 diff --git a/tests/test_Profiler.py b/tests/test_Profiler.py deleted file mode 100644 index 54d1db6a..00000000 --- a/tests/test_Profiler.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Test the ee_profiler module.""" -import ee - -import geetools # noqa: F401 - - -class TestProfiler: - """Test the Profiler class.""" - - def test_profiler(self): - """Test the Profiler class.""" - - with ee.geetools.Profiler() as p: - ee.Number(3.14).add(0.00159).getInfo() - assert [k for k in p.profile] == ["EECU-s", "PeakMem", "Count", "Description"] diff --git a/tests/test_String.py b/tests/test_String.py deleted file mode 100644 index 6fbe5f85..00000000 --- a/tests/test_String.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Test the String class methods.""" -import ee - - -class TestEq: - """Test the eq method.""" - - def test_eq_with_same_string(self, string_instance): - eq_number = string_instance.geetools.eq("foo") - assert eq_number.getInfo() == 1 - - def test_eq_with_different_string(self, string_instance): - eq_number = string_instance.geetools.eq("bar") - assert eq_number.getInfo() == 0 - - -class TestFormat: - """test the format method.""" - - def test_format_with_dictionary(self, format_string_instance): - params = {"greeting": "Hello", "name": "bob"} - formatted_string = format_string_instance.geetools.format(params) - assert formatted_string.getInfo() == "Hello bob !" - - def test_with_number(self, format_string_instance): - params = {"greeting": "Hello", "name": ee.Number(1)} - formatted_string = format_string_instance.geetools.format(params) - assert formatted_string.getInfo() == "Hello 1 !" diff --git a/tests/test_batch/test_utils.py b/tests/test_batch/test_utils.py deleted file mode 100644 index 09b2ba57..00000000 --- a/tests/test_batch/test_utils.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Test the utils module.""" - -from geetools import utils - - -class TestFormatDescription: - """Test the utils.format_description function.""" - - def test_valid_description(self): - """Test a valid description.""" - description = "This is a valid description 123.,:;_-" - result = utils.format_description(description) - assert result == "This_is_a_valid_description_123.,:;_-" - - def test_replacements(self): - """Test replacements.""" - description = "Testing / replacements ?!{}()" - result = utils.format_description(description) - assert result == "Testing_-_replacements_..::::" - - def test_long_description(self): - description = "A" * 150 - result = utils.format_description(description) - assert len(result) == 100 - - def test_unicode_characters(self): - description = "Unicode characters like é, ä, and ñ should be changed" - result = utils.format_description(description) - assert result == "Unicode_characters_like_e,_a,_and_n_should_be_changed" - - -class TestFormatAssetID: - """Test the utils.format_asset_id function.""" - - def test_valid_description(self): - """Test a valid description.""" - description = "This is a valid description 123.,:;_-" - result = utils.format_asset_id(description) - assert result == "This_is_a_valid_description_123_____-" - - def test_replacements(self): - """Test replacements.""" - description = "Testing / replacements ?!{}()" - result = utils.format_asset_id(description) - assert result == "Testing_-_replacements_______" - - def test_long_description(self): - description = "A" * 150 - result = utils.format_asset_id(description) - assert len(result) == 150 - - def test_unicode_characters(self): - description = "Unicode characters like é, ä, and ñ should be changed" - result = utils.format_asset_id(description) - assert result == "Unicode_characters_like_e__a__and_n_should_be_changed" diff --git a/tests/test_deprecated.py b/tests/test_deprecated.py deleted file mode 100644 index 3de8a9f7..00000000 --- a/tests/test_deprecated.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Test all the deprecated methods that have not been kept in the new implementation.""" - - -import pytest - -import geetools - - -class TestImageCollection: - """Test the deprecated_imagecollection module.""" - - def test_linear_function_band(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.linearFunctionBand(None, None, None, None) - - def test_linear_function_property(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.linearFunctionProperty(None, None, None, None) - - def linear_interpolation_property(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.linearInterpolationProperty(None, None, None, None) - - def test_gauss_function_band(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.gaussFunctionBand(None, None, None, None, None) - - def test_gauss_function_property(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.gaussFunctionProperty(None, None, None, None, None) - - def testnormal_distribution_property(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.normalDistributionProperty(None, None, None, None, None) - - def test_normal_distribution_band(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.normalDistributionBand(None, None, None, None, None) - - def test_moving_average(self): - with pytest.raises(NotImplementedError): - geetools.imagecollection.moving_average(None) - - -class TestAlgorithm: - """Test the deprecated_algorithms module.""" - - def test_pansharpenkernel(self): - with pytest.raises(NotImplementedError): - geetools.algorithms.pansharpenKernel(None, None) - - def test_pansharpenihsFusion(self): - with pytest.raises(NotImplementedError): - geetools.algorithms.pansharpenIhsFusion(None) - - -class TestComposite: - """Test the deprecated_composite module.""" - - def test_max(self, s2_sr): - with pytest.deprecated_call(): - geetools.composite.max(s2_sr) - - def test_medoidScore(self, s2_sr): - with pytest.raises(NotImplementedError): - geetools.composite.medoidScore(s2_sr) diff --git a/tests/test_deprecated/test_deprecated_cast_image.yml b/tests/test_deprecated/test_deprecated_cast_image.yml deleted file mode 100644 index 461ce28f..00000000 --- a/tests/test_deprecated/test_deprecated_cast_image.yml +++ /dev/null @@ -1 +0,0 @@ -constant: 1 diff --git a/tests/test_deprecated/test_merge_geometry.yml b/tests/test_deprecated/test_merge_geometry.yml deleted file mode 100644 index cda39444..00000000 --- a/tests/test_deprecated/test_merge_geometry.yml +++ /dev/null @@ -1,394 +0,0 @@ -coordinates: -- - 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- - -70.38699378219829 - - -4.610386099396646 - - - -70.38694973988952 - - -4.610377526369904 - - - -70.38666677932247 - - -4.609718586257469 - - - -70.38448291091515 - - -4.599946978971725 - - - -70.37630066015568 - - -4.563062563122359 - - - -70.27848180749106 - - -4.116352120648472 - - - -70.18084559826353 - - -3.669590752536642 - - - -70.16997653207238 - - -3.619673170172604 - - - -70.16970336763899 - - -3.6177694683796666 - - - -70.16976913786948 - - -3.6176711420508956 -type: MultiPolygon diff --git a/tests/test_deprecated/test_tobands.yml b/tests/test_deprecated/test_tobands.yml deleted file mode 100644 index 364e6d22..00000000 --- a/tests/test_deprecated/test_tobands.yml +++ /dev/null @@ -1,39 +0,0 @@ -- 20210102T150719_20210102T150942_T18MYC_B1 -- 20210102T150719_20210102T150942_T18MYC_B2 -- 20210102T150719_20210102T150942_T18MYC_B3 -- 20210102T150719_20210102T150942_T18MYC_B4 -- 20210102T150719_20210102T150942_T18MYC_B5 -- 20210102T150719_20210102T150942_T18MYC_B6 -- 20210102T150719_20210102T150942_T18MYC_B7 -- 20210102T150719_20210102T150942_T18MYC_B8 -- 20210102T150719_20210102T150942_T18MYC_B8A -- 20210102T150719_20210102T150942_T18MYC_B9 -- 20210102T150719_20210102T150942_T18MYC_B11 -- 20210102T150719_20210102T150942_T18MYC_B12 -- 20210102T150719_20210102T150942_T18MYC_SCL -- 20210102T150719_20210102T150942_T18MYD_B1 -- 20210102T150719_20210102T150942_T18MYD_B2 -- 20210102T150719_20210102T150942_T18MYD_B3 -- 20210102T150719_20210102T150942_T18MYD_B4 -- 20210102T150719_20210102T150942_T18MYD_B5 -- 20210102T150719_20210102T150942_T18MYD_B6 -- 20210102T150719_20210102T150942_T18MYD_B7 -- 20210102T150719_20210102T150942_T18MYD_B8 -- 20210102T150719_20210102T150942_T18MYD_B8A -- 20210102T150719_20210102T150942_T18MYD_B9 -- 20210102T150719_20210102T150942_T18MYD_B11 -- 20210102T150719_20210102T150942_T18MYD_B12 -- 20210102T150719_20210102T150942_T18MYD_SCL -- 20210102T150719_20210102T150942_T18MZC_B1 -- 20210102T150719_20210102T150942_T18MZC_B2 -- 20210102T150719_20210102T150942_T18MZC_B3 -- 20210102T150719_20210102T150942_T18MZC_B4 -- 20210102T150719_20210102T150942_T18MZC_B5 -- 20210102T150719_20210102T150942_T18MZC_B6 -- 20210102T150719_20210102T150942_T18MZC_B7 -- 20210102T150719_20210102T150942_T18MZC_B8 -- 20210102T150719_20210102T150942_T18MZC_B8A -- 20210102T150719_20210102T150942_T18MZC_B9 -- 20210102T150719_20210102T150942_T18MZC_B11 -- 20210102T150719_20210102T150942_T18MZC_B12 -- 20210102T150719_20210102T150942_T18MZC_SCL diff --git a/tests/test_geetools.py b/tests/test_geetools.py new file mode 100644 index 00000000..bb90f44f --- /dev/null +++ b/tests/test_geetools.py @@ -0,0 +1,8 @@ +"""Test the geetools package.""" + +import geetools + + +def test_hello_world(): + """Hello world test.""" + assert geetools.Hello().hello_world() == "hello world !" From 473f337d5a8115bffe28fa686dc12e3eb35abeb4 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 14:16:44 +0000 Subject: [PATCH 2/9] fix: rollback geetools --- geetools/__init__.py | 53 +- geetools/_deprecated_algorithms.py | 60 + geetools/_deprecated_composite.py | 57 + geetools/accessors.py | 81 + geetools/ee_array.py | 78 + geetools/ee_asset.py | 792 ++++++ geetools/ee_authenticate.py | 133 + geetools/ee_computed_object.py | 140 + geetools/ee_date.py | 173 ++ geetools/ee_date_range.py | 80 + geetools/ee_dictionary.py | 87 + geetools/ee_export.py | 221 ++ geetools/ee_feature.py | 64 + geetools/ee_feature_collection.py | 672 +++++ geetools/ee_filter.py | 46 + geetools/ee_geometry.py | 64 + geetools/ee_image.py | 2090 +++++++++++++++ geetools/ee_image_collection.py | 2372 +++++++++++++++++ geetools/ee_initialize.py | 124 + geetools/ee_join.py | 69 + geetools/ee_list.py | 290 ++ geetools/ee_number.py | 40 + geetools/ee_profiler.py | 105 + geetools/ee_string.py | 72 + geetools/py.typed | 1 - geetools/tools/__init__.py | 3 + geetools/tools/_deprecated_imagecollection.py | 118 + geetools/utils.py | 270 ++ pyproject.toml | 3 + 29 files changed, 8343 insertions(+), 15 deletions(-) create mode 100644 geetools/_deprecated_algorithms.py create mode 100644 geetools/_deprecated_composite.py create mode 100644 geetools/accessors.py create mode 100644 geetools/ee_array.py create mode 100644 geetools/ee_asset.py create mode 100644 geetools/ee_authenticate.py create mode 100644 geetools/ee_computed_object.py create mode 100644 geetools/ee_date.py create mode 100644 geetools/ee_date_range.py create mode 100644 geetools/ee_dictionary.py create mode 100644 geetools/ee_export.py create mode 100644 geetools/ee_feature.py create mode 100644 geetools/ee_feature_collection.py create mode 100644 geetools/ee_filter.py create mode 100644 geetools/ee_geometry.py create mode 100644 geetools/ee_image.py create mode 100644 geetools/ee_image_collection.py create mode 100644 geetools/ee_initialize.py create mode 100644 geetools/ee_join.py create mode 100644 geetools/ee_list.py create mode 100644 geetools/ee_number.py create mode 100644 geetools/ee_profiler.py create mode 100644 geetools/ee_string.py delete mode 100644 geetools/py.typed create mode 100644 geetools/tools/__init__.py create mode 100644 geetools/tools/_deprecated_imagecollection.py create mode 100644 geetools/utils.py diff --git a/geetools/__init__.py b/geetools/__init__.py index d33f2b08..a9143982 100644 --- a/geetools/__init__.py +++ b/geetools/__init__.py @@ -1,20 +1,45 @@ -"""The init file of the package.""" +"""A toolbox to use with Google Earth Engine Python API. -__version__ = "0.0.0" -__author__ = "Pierrick Rambaud" -__email__ = "pierrick.rambaud49@gmail.com" +The ``geetools`` package extends the Google Earth Engine Python API with pre-processing and +processing tools for the most used satellite platforms by adding utility methods for different Earth +Engine Objects that are friendly with the Python method chaining using the geetools namespace. +""" +import ee -class Hello: - """Hello world class.""" +# reproduce older structure of the lib (deprecated) +# will be removed along the deprecation cycle +from . import _deprecated_algorithms as algorithms +from . import _deprecated_composite as composite - msg = "hello world !" - "the message to print" +# import the accessor namespace +from .accessors import geetools +from .ee_array import ArrayAccessor - def hello_world(self) -> str: - """Hello world demo method. +# then we extend all the other classes +from .ee_asset import Asset +from .ee_authenticate import AuthenticateAccessor - Returns: - the hello world string - """ - return self.msg +# it needs to be imported first as it's the mother class +from .ee_computed_object import * # noqa: F403 +from .ee_date import DateAccessor +from .ee_date_range import DateRangeAccessor +from .ee_dictionary import DictionaryAccessor +from .ee_export import ExportAccessor +from .ee_feature import FeatureAccessor +from .ee_feature_collection import FeatureCollectionAccessor +from .ee_filter import FilterAccessor +from .ee_geometry import GeometryAccessor +from .ee_image import ImageAccessor +from .ee_image_collection import ImageCollectionAccessor +from .ee_initialize import InitializeAccessor +from .ee_join import JoinAccessor +from .ee_list import ListAccessor +from .ee_number import NumberAccessor +from .ee_profiler import Profiler +from .ee_string import StringAccessor +from .tools import imagecollection + +__version__ = "1.9.1" +__author__ = "Rodrigo E. Principe" +__email__ = "fitoprincipe82@gmail.com" diff --git a/geetools/_deprecated_algorithms.py b/geetools/_deprecated_algorithms.py new file mode 100644 index 00000000..7b99c5bc --- /dev/null +++ b/geetools/_deprecated_algorithms.py @@ -0,0 +1,60 @@ +# coding=utf-8 +"""Module holding misc algorithms.""" + +import ee +import ee.data +from deprecated.sphinx import deprecated + + +@deprecated(version="1.4.0", reason="Use ee.Image.geetools.distanceToMask instead.") +def distanceToMask( + image, + mask, + kernel=None, + radius=1000, + unit="meters", + scale=None, + geometry=None, + band_name="distance_to_mask", + normalize=False, +): + """Compute the distance to the mask in meters.""" + return ( + ee.Image(image) + .geetools.distanceToMask(mask, radius=radius, band_name=band_name) + .select(band_name) + ) + + +@deprecated(version="1.5.0", reason="Use ee.Image.geetools.maskCover instead.") +def maskCover( + image, + geometry=None, + scale=None, + property_name="MASK_COVER", + crs=None, + crsTransform=None, + bestEffort=False, + maxPixels=1e13, + tileScale=1, +): + """Percentage of masked pixels (masked/total * 100) as an Image property.""" + return ee.Image(image).geetools.maskCover() + + +@deprecated(version="1.5.0", reason="Use ee.Image.geetools.distance instead.") +def euclideanDistance(image1, image2, bands=None, discard_zeros=False, name="distance"): + """Compute the Euclidean distance between two images.""" + return ee.Image(image1).geetools.distance(image2) + + +@deprecated(version="1.4.0", reason="It's included in the ee_extra bindings.") +def pansharpenKernel(image, pan, rgb=None, kernel=None): + """Compute the per-pixel means of the unsharpened bands.""" + raise NotImplementedError("This function is deprecated. Use the one in ee_extra.") + + +@deprecated(version="1.4.0", reason="It's included in the ee_extra bindings.") +def pansharpenIhsFusion(image, pan=None, rgb=None): + """HSV-based Pan-Sharpening.""" + raise NotImplementedError("This function is deprecated. Use the one in ee_extra.") diff --git a/geetools/_deprecated_composite.py b/geetools/_deprecated_composite.py new file mode 100644 index 00000000..f04d95c4 --- /dev/null +++ b/geetools/_deprecated_composite.py @@ -0,0 +1,57 @@ +"""Module holding tools for creating composites.""" + +import ee +from deprecated.sphinx import deprecated + + +@deprecated(version="1.5.0", reason="Only used to build the medoid composite.") +def medoidScore(collection, bands=None, discard_zeros=False, bandname="sumdist", normalize=True): + """Compute a score to reflect 'how far' is from the medoid.""" + raise NotImplementedError( + "This method was only used to build the medoid composite. " + "The medoid composite is still available in the lib." + ) + + +@deprecated(version="1.5.0", reason="Use ee.imageCollection.geetools.medoid instead") +def medoid(collection, bands=None, discard_zeros=False): + """Medoid Composite. Adapted from https://www.mdpi.com/2072-4292/5/12/6481.""" + return ee.ImageCollection(collection).geetools.medoid() + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.closestDate instead") +def closestDate(col, clip_to_first=False): + """Make a composite in which masked pixels are filled with the last available pixel.""" + return ee.ImageCollection(col).geetools.closestDate() + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead") +def compositeRegularIntervals( + collection, + interval=1, + unit="month", + date_range=(1, 0), + date_range_unit="month", + direction="backward", + start=None, + end=None, + composite_function=None, + composite_args=None, + composite_kwargs=None, +): + """Make a composite at regular intervals parsing a composite.""" + return ee.ImageCollection(collection).geetools.reduceInterval(unit=unit) + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead") +def compositeByMonth( + collection, composite_function=None, composite_args=None, composite_kwargs=None +): + """Make a composite at regular intervals parsing a composite.""" + return ee.ImageCollection(collection).geetools.reduceInterval(unit="month") + + +@deprecated(version="1.4.0", reason="Use the vanilla Earth Engine API") +def max(collection, band=None): + """Make a max composite using the specified band.""" + return collection.max() diff --git a/geetools/accessors.py b/geetools/accessors.py new file mode 100644 index 00000000..9827c815 --- /dev/null +++ b/geetools/accessors.py @@ -0,0 +1,81 @@ +"""Generic accessor to add extra function to the base GEE API classes.""" + +from __future__ import annotations + +from typing import Callable + +import ee + + +def register_class_accessor(klass: type, name: str) -> Callable: + """Create an accessor through the provided namespace to a given class. + + Parameters: + klass: The class to set the accessor to. + name: The name of the accessor namespace + + Returns: + The accessor function to to the class. + """ + + def decorator(accessor: Callable) -> object: + class ClassAccessor: + def __init__(self, name: str, accessor: Callable): + self.name, self.accessor = name, accessor + + def __get__(self, obj: object, *args) -> object: + return self.accessor(obj) + + # check if the accessor already exists for this class + if hasattr(klass, name): + raise AttributeError(f"Accessor {name} already exists for {klass}") + + # register the accessor to the class + setattr(klass, name, ClassAccessor(name, accessor)) + + return accessor + + return decorator + + +def register_function_accessor(func: type, name: str) -> Callable: + """Add a Accessor class to function through the provided namespace. + + Parameters: + func: The function to set the accessor to. + name: The name of the accessor namespace. + + Returns: + The accessor function to to the function. + """ + + def decorator(accessor: Callable) -> object: + # check if the accessor already exists for this class + if hasattr(func, name): + raise AttributeError(f"Member {name} already exists for {func}") + else: + setattr(func, name, accessor()) + + return accessor + + return decorator + + +# this private method should not be exposed to end user as it perform 0 checks it can overwrite +# existing methods/class/member. Only used in the lib for the Computed object as the method need +# to be shared by every other child of the class. +def _register_extention(obj: object) -> Callable: + """Add the function to any object.""" + return lambda f: (setattr(obj, f.__name__, f) or f) # type: ignore + + +# create a geetools namespace that can be use directly on the ee module + + +@_register_extention(ee) +class geetools: + """Namespace class for the geetools library.""" + + def __init__(self): + """The geetools namespace cannot be instantiated.""" + raise AttributeError("Cannot instantiate geetools") diff --git a/geetools/ee_array.py b/geetools/ee_array.py new file mode 100644 index 00000000..739dc871 --- /dev/null +++ b/geetools/ee_array.py @@ -0,0 +1,78 @@ +"""Extra methods for the ``ee.Array`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Array, "geetools") +class ArrayAccessor: + """Toolbox for the ``ee.Array`` class.""" + + def __init__(self, obj: ee.Array): + """Initialize the Array class.""" + self._obj = obj + + def full( + self, + width: float | int | ee.Number, + height: float | int | ee.Number, + value: float | int | ee.Number, + ) -> ee.Array: + """Create an :py:class:`ee.Array` with the given dimensions, initialized to the given value. + + Parameters: + width: The width of the array. + height: The height of the array. + value: The value to initialize the array with. + + Returns: + An array with the given dimensions, initialized to the given value. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + array = ee.Array.geetools.full(3, 3, 1) + array.getInfo() + """ + width, height = ee.Number(width).toInt(), ee.Number(height).toInt() + return ee.Array(ee.List.repeat(ee.List.repeat(value, width), height)) + + def set( + self, + x: int | ee.Number, + y: int | ee.Number, + value: float | int | ee.Number, + ) -> ee.Array: + """Set the value of a cell in an array. + + Parameters: + x: The x coordinate of the cell. + y: The y coordinate of the cell. + value: The value to set the cell to. + + Returns: + The array with the cell set to the given value. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + array = ee.Array.geetools.full(3, 3, 1) + array = array.geetools.set(1, 1, 0) + array.getInfo() + """ + xPos, yPos = ee.Number(x).toInt(), ee.Number(y).toInt() + row = ee.List(self._obj.toList().get(yPos)).set(xPos, ee.Number(value)) + return ee.Array(self._obj.toList().set(yPos, row)) diff --git a/geetools/ee_asset.py b/geetools/ee_asset.py new file mode 100644 index 00000000..3d1ae0c6 --- /dev/null +++ b/geetools/ee_asset.py @@ -0,0 +1,792 @@ +"""An Asset management class mimicking the ``pathlib.Path`` class behaviour.""" + +from __future__ import annotations + +import os +import re +from pathlib import PurePosixPath + +import ee +import ee.data + +from .accessors import _register_extention +from .utils import format_description + + +@_register_extention(ee) +class Asset(os.PathLike): + """An Asset management class mimicking the ``pathlib.Path`` class behaviour.""" + + def __init__(self, *args): + """Initialize the Asset class. + + .. note:: + An asset cannot be an absolute path like in a normal filesystem and thus any trailing "/" will be removed. + """ + if len(args) == 0: + self._path = f"projects/{ee.data._cloud_api_user_project}/assets/" + else: + self._path = args[0]._path if isinstance(args[0], Asset) else PurePosixPath(*args) + project_assets = PurePosixPath(str(self._path)[1:]) + self._path = project_assets if self._path.is_absolute() else self._path + + def __str__(self): + """Transform the asset id to a string.""" + return self.as_posix() + + def __repr__(self): + """Return the asset object representation as a string.""" + return f"ee.{type(self).__name__}('{self.as_posix()}')" + + def __truediv__(self, other: os.PathLike) -> Asset: + """Override the division operator to join the asset with other paths.""" + return Asset(self._path / str(other)) + + def __lt__(self, other: os.PathLike) -> bool: + """Override the less than operator to compare the asset with other paths.""" + return self._path < PurePosixPath(str(other)) + + def __gt__(self, other: os.PathLike) -> bool: + """Override the greater than operator to compare the asset with other paths.""" + return self._path > PurePosixPath(str(other)) + + def __le__(self, other: os.PathLike) -> bool: + """Override the less than or equal operator to compare the asset with other paths.""" + return self._path <= PurePosixPath(str(other)) + + def __ge__(self, other: os.PathLike) -> bool: + """Override the greater than or equal operator to compare the asset with other paths.""" + return self._path >= PurePosixPath(str(other)) + + def __eq__(self, other: object) -> bool: + """Override the equal operator to compare the asset with other paths.""" + return self._path == PurePosixPath(str(other)) + + def __ne__(self, other: object) -> bool: + """Override the not equal operator to compare the asset with other paths.""" + return self._path != PurePosixPath(str(other)) + + def __idiv__(self, other: os.PathLike) -> Asset: + """Override the in-place division operator to join the asset with other paths.""" + return Asset(self._path / str(other)) + + def __fspath__(self): + """Implement the os.Pathlike interface.""" + return self.as_posix() + + def __hash__(self): + """Make the Asset object hashable.""" + return hash(self.as_posix()) + + def __getattr__(self, name): + """Return the attribute of the path object.""" + return getattr(self, name) + + @classmethod + def home(cls) -> Asset: + """Return the root asset folder of the used cloud project. + + Returns: + The root asset folder. + + Examples: + .. code-block:: python + + ee.Asset.home() + """ + return cls(f"projects/{ee.data._cloud_api_user_project}/assets/") + + def as_posix(self) -> str: + """Return the asset id as a posix path. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.as_posix() + + # equivalent to + str(asset) + """ + return self._path.as_posix() + + def as_uri(self) -> str: + """Return the asset id as a uri. + + The uri can be directly copy/pasted to your browser to see the asset in the GEE code editor. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.as_uri() + """ + return f"https://code.earthengine.google.com/?asset={self.as_posix()}" + + def is_absolute(self, raised: bool = False) -> bool: + """Return True if the asset is absolute. + + An absolute asset path starts with "projects" and contains "assets" at the 3rd position. + We don't check if the project name exist in this method, simply the sctructure of the path. + + Args: + raised: If True, raise an exception if the asset is not absolute. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.is_absolute() + """ + # we decided not to enforce the length of the parts to still be able to use the + # relative_to method of the Path class. Consequence is tis little trick in case + # the asset is not absolute at all. + parts = dict(enumerate(self.parts)) + if parts.get(0) == "projects" and parts.get(2) == "assets": + return True + else: + if raised is True: + raise ValueError(f"Asset {self.as_posix()} is not absolute.") + else: + return False + + def is_user_project(self, raised: bool = False) -> bool: + """Check if the current asset is in the same project as the user. + + Args: + raised: If True, raise an exception if the asset is not in the same project. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.is_user_project() + """ + if self.is_relative_to(self.home()._path): + return True + else: + if raised is True: + user_project = ee.data._cloud_api_user_project + msg = f"Asset {self.as_posix()} is not in the same project as the user ({user_project})" + raise ValueError(msg) + else: + return False + + def expanduser(self) -> Asset: + """Return a new path with expanded ~ constructs. + + If one don't want to write the path with the complete project name, the method will build it for you. + + Examples: + .. code-block:: python + + asset = ee.Asset("~/assets/folder/image") + asset.expanduser() + """ + return Asset(self.as_posix().replace("~", self.home().as_posix(), 1)) + + def exists(self, raised: bool = False) -> bool: + """Return True if the asset exists and/or the user has access to it. + + Args: + raised: If True, raise an exception if the asset does not exist. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.exists() + """ + try: + ee.data.getAsset(self.as_posix()) + return True + except ee.EEException: + if raised is True: + raise ValueError(f"Asset {self.as_posix()} does not exist.") + else: + return False + + @property + def parts(self): + """Return the asset parts of the path. + + We will show all the parts from the root to the asset name. + Remember that projects/user/assets is not part of the asset name but is part of the path. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.parts + """ + return self._path.parts + + @property + def parent(self): + """Return the direct parent directory. + + It can go further up than the root folder if the asset is not absolute. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.parent + """ + return Asset(self._path.parent) + + @property + def parents(self): + """Return the parent directories from the root folder. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.parents + """ + # we remove the files that are not assets but are parsed by parents method + parents = self._path.parents + patterns = [r"^\.$", "^projects$", r"^projects/[^/]+$", r"^projects/[^/]+/assets$"] + return [Asset(a) for a in parents if not any(re.match(p, str(a)) for p in patterns)] + + @property + def name(self): + """Return the asset name. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.name + """ + return self._path.name + + @property + def st_size(self): + """Return the byte size of the file. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.st_size + """ + # sanity checks + self.exists(raised=True) + if self.is_folder(): + raise ValueError(f"Asset {self.as_posix()} is a folder.") + + return int(ee.data.getAsset(self.as_posix())["sizeBytes"]) + + def is_relative_to(self, other: os.PathLike) -> bool: + """Return True if the asset is relative to another asset. + + Args: + other: The other asset to compare with. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.is_relative_to("projects/ee-geetools/assets") + """ + return self._path.is_relative_to(PurePosixPath(str(other))) + + def joinpath(self, *args) -> Asset: + """Join the asset with other paths. + + Args: + *args: The other paths to join with the asset. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.joinpath("other", "path") + """ + return Asset(self._path.joinpath(*args)) + + def match(self, *patterns) -> bool: + """Return True if the asset matches the patterns. + + patterns: The patterns to match with the asset name. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.match("**/image") + """ + return self._path.match(*patterns) + + def with_name(self, name: str) -> Asset: + """Return the asset with the given name. + + Args: + name: The new name for the asset. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.with_name("new_image") + """ + return Asset(self._path.with_name(name)) + + def is_image(self, raised: bool = False) -> bool: + """Return ``True`` if the asset is an image. + + Args: + raised: If True, raise an exception if the asset is not an image. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.is_image() + """ + return self.is_type("IMAGE", raised) + + def is_image_collection(self, raised: bool = False) -> bool: + """Return ``True`` if the asset is an image collection. + + Args: + raised: If True, raise an exception if the asset is not an image collection. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image_collection") + asset.is_image_collection() + """ + return self.is_type("IMAGE_COLLECTION", raised) + + def is_feature_collection(self, raised: bool = False) -> bool: + """Return ``True`` if the asset is a feature collection. + + Args: + raised: If True, raise an exception if the asset is not a feature collection. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/feature_collection") + asset.is_feature_collection() + """ + return self.is_type("FEATURE_COLLECTION", raised) or self.is_type("TABLE", raised) + + def is_folder(self, raised: bool = False) -> bool: + """Return ``True`` if the asset is a folder. + + Args: + raised: If True, raise an exception if the asset is not a folder. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.is_folder() + """ + return self.is_type("FOLDER", raised) + + @property + def type(self) -> str: + """Return the asset type. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.type + """ + self.exists(raised=True) + return ee.data.getAsset(self.as_posix())["type"] + + def is_project(self, raised: bool = False) -> bool: + """Return ``True`` if the asset is a project. + + As project path are not assets, we cannot check their existence. We only check the path structure. + + Args: + raised: If True, raise an exception if the asset is not a project. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets") + asset.is_project() + """ + if self.is_absolute() and len(self.parts) == 3: + return True + else: + if raised is True: + raise ValueError(f"Asset {self.as_posix()} is not a project.") + else: + return False + + def is_type(self, asset_type: str, raised=False) -> bool: + """Return ``True`` if the asset is of the specified type. + + Args: + asset_type: The asset type to check for. + raised: If True, raise an exception if the asset is not corresponding to the type. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.is_type("IMAGE") + """ + self.exists(raised=True) + if self.type == asset_type: + return True + else: + if raised is True: + raise ValueError(f"Asset {self.as_posix()} is not a {asset_type}.") + else: + return False + + def iterdir(self, recursive: bool = False) -> list: + """Get the list of children of a container. + + Note: + A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. + + Args: + recursive: If True, get all the children recursively. Defaults to False. + + See Also: + - :docstring:`ee.Asset.glob` + - :docstring:`ee.Asset.rglob` + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.iterdir(recursive=True) + """ + # sanity check on variables + if not (self.is_project() or self.is_folder() or self.is_image_collection()): + raise ValueError( + f"Asset {self.as_posix()} is not a container and cannot contain other assets." + ) + + # no need for recursion if recursive is false we directly return the result of th API call + if recursive is False: + asset_ids = ee.data.listAssets({"parent": self.as_posix()})["assets"] + return [Asset(asset["id"]) for asset in asset_ids] + + # recursive function to get all the assets + def _recursive_get(folder, asset_list): + for asset in ee.data.listAssets({"parent": str(folder)})["assets"]: + asset_list.append(Asset(asset["id"])) + if asset["type"] in ["FOLDER", "IMAGE_COLLECTION"] and recursive is True: + asset_list = _recursive_get(asset["id"], asset_list) + return asset_list + + return _recursive_get(self, []) + + def mkdir(self, parents=False, exist_ok=False, image_collection: bool = False) -> Asset: + """Create a container asset from the Asset path. + + Note: + A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. + + Args: + parents: If True, create all the parents of the folder. Defaults to False. + exist_ok: If True, do not raise an error if the folder already exists. Defaults to False. + image_collection: If True, create an image collection asset. Otherwise create a folder asset. Defaults to False. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.mkdir(parents=True, exist_ok=True) + """ + # check if the root is the same as home (only place where we can write to) + self.is_absolute(raised=True) + + # if the complete one is in the list and exist_ok is True remove it from the list and + # proceed else raise an error + if self.exists() and exist_ok is False: + raise ValueError(f"Asset {self.as_posix()} already exists.") + + # list the non-existing parents of the folder to create + to_be_created = [p for p in self.parents if not p.exists()] + + # if parents is True, create all the parts that are in the list + # else raise an error with the 1st parent name + if len(to_be_created) > 0 and parents is False: + raise ValueError(f'Parent Asset "{to_be_created[-1]}" does not exist.') + + # 2 option either there is 1 single element in the list or all the parents are included + # we need to walk it in reversed to make sure the parents are build first. + for p in reversed(to_be_created): + ee.data.createFolder(p.as_posix()) + + # now that all the parents are there, we can create the requested container + if not self.exists(): + asset_type = "IMAGE_COLLECTION" if image_collection is True else "FOLDER" + ee.data.createAsset({"type": asset_type}, self.as_posix()) + + return self + + @property + def owner(self): + """Return the asset owner (project name). + + This method is only parsing the asset path and is not checking asset existence. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.owner + """ + self.is_absolute(raised=True) + return self.parts[1] + + def move(self, new_asset: Asset, overwrite: bool = False) -> Asset: + """Move the asset to a target destination. + + Move this asset (any type) to the given target, and return a new ``Asset`` instance + pointing to target. If target exists and overwrite is False the method will raise an + error. Else it will silently delete the existing file. If the asset is a container the whole + content will be moved as well. The initial content is removed after the move. + + Args: + new_asset: The destination asset. + overwrite: If True, overwrite the destination asset if it exists. Defaults to False. + + Returns: + The new asset instance. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + new_asset = ee.Asset("projects/ee-geetools/assets/folder/new_image") + asset.move(new_asset, overwrite=False) + """ + # copy the assets + self.copy(new_asset, overwrite=overwrite) + + # delete the original + self.delete(recursive=True, dry_run=False) + + return new_asset + + def delete(self, recursive: bool = False, dry_run: bool | None = None) -> list: + """Remove the asset. + + This method will delete an asset (any type) asset and all its potential children. by default it is not recursive and will raise an error if the container is not empty. + By setting the recursive argument to True, the method will delete all the children and the container asset (including potential subfolders). + To avoid deleting important assets by accident the method is set to dry_run by default. + + Note: + A container is an asset containing other assets, it can be a ``Folder`` or an ``ImageCollection``. + + Args: + recursive: If True, delete all the children and the container asset. Defaults to False. + dry_run: If True, do not delete the asset simply pass them to the output list. Defaults to True. + + Returns: + The list of deleted assets. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.delete(recursive=True) + """ + # init if it should be a dry-run or not + # if we run a recursive rmdir the dry_run is set to True to avoid deleting too many things by accident + # if we run a non-recursive rmdir the dry_run is set to False to delete the folder only + dry_run = dry_run if dry_run is not None else recursive + + # define a delete function to change the behaviour of the method depending of the mode + # in dry mode, the function only store the assets to be destroyed as a dictionary. + # in non dry mode, the function store the asset names in a dictionary AND delete them. + output = [] + + def delete(asset): + output.append(str(asset)) + dry_run is True or ee.data.deleteAsset(str(asset)) + + is_container = self.is_folder() or self.is_image_collection() + if recursive is True and is_container: + # get all the assets + asset_list = self.iterdir(recursive=True) + + # split the files by nesting levels + # we will need to delete the more nested files first + assets_ordered: dict = {} + for asset in asset_list: + lvl = len(asset.parts) + assets_ordered.setdefault(lvl, []) + assets_ordered[lvl].append(asset) + + # delete all items starting from the more nested ones + assets_ordered = dict(sorted(assets_ordered.items(), reverse=True)) + for lvl in assets_ordered: + [delete(asset) for asset in assets_ordered[lvl]] + + # delete the initial folder/asset + delete(self) + + return output + + # aliases + def unlink(self) -> list: + """``delete`` alias for singular assets.""" + # sanity check on variables + if self.is_project() or self.is_folder() or self.is_image_collection(): + raise ValueError(f"Asset {self.as_posix()} is a container, use rmdir instead.") + self.exists(raised=True) + return self.delete() + + def rmdir(self, recursive: bool = False, dry_run: bool | None = None) -> list: + """``delete`` alias for containers.""" + if not (self.is_project() or self.is_folder() or self.is_image_collection()): + raise ValueError(f"Asset {self.as_posix()} is not a container, use unlink instead.") + self.exists(raised=True) + return self.delete(recursive, dry_run) + + def copy(self, new_asset: Asset, overwrite: bool = False) -> Asset: + """Copy the asset to a target destination. + + Copy this asset (any type) to the given target, and return a new ``Asset`` instance + pointing to target. If target exists and overwrite is False the method will raise an + error. Else it will silently delete the existing asset. If the asset is a container the whole + content will be moved as well. + + Args: + new_asset: The destination asset. + overwrite: If True, overwrite the destination asset if it exists. Defaults to False. + + Returns: + The new asset instance. + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + new_asset = ee.Asset("projects/ee-geetools/assets/folder/new_image") + asset.copy(new_asset, overwrite=False) + """ + # exit if the destination asset exist and overwrite is False + if new_asset.exists() and overwrite is False: + raise ValueError(f"Asset {new_asset.as_posix()} already exists.") + + # make all the parents of the target asset if necessary + if len(new_asset.parents) != 0: + new_asset.parent.mkdir(parents=True, exist_ok=True) + + # copy the asset to the new destination. If the asset is a container, we need to move all its + # content recursively to the new destination we recursively call this method on each + # children of the asset if it's a folder it will loop again. + if self.is_folder() or self.is_image_collection(): + new_asset.mkdir(True, True, self.is_image_collection()) + + # if the asset is an image collection we need to copy the properties of the collection + if self.is_image_collection(): + original_dict = ee.data.getAsset(self.as_posix()) + props = original_dict["properties"] + if "startTime" in original_dict: + props["system:time_start"] = original_dict["startTime"] + if "endTime" in original_dict: + props["system:time_end"] = original_dict["endTime"] + new_asset.setProperties(**props) + + # copy the children objects + for asset in self.iterdir(): + loc_asset = new_asset / asset._path.relative_to(self._path) + asset.copy(loc_asset, overwrite=overwrite) + else: + ee.data.copyAsset(self.as_posix(), new_asset.as_posix(), allowOverwrite=True) + + return new_asset + + def glob(self, pattern: str) -> list: + """Return a list of assets matching the pattern. + + Args: + pattern: The pattern to match with the asset name. + + See Also: + - :docstring:`ee.Asset.iterdir` + - :docstring:`ee.Asset.rglob` + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.glob("image_*") + """ + return [a for a in self.iterdir(recursive=False) if a.match(pattern)] + + def rglob(self, pattern: str) -> list: + """Return a list of assets matching the pattern recursively. + + Args: + pattern: The pattern to match with the asset name. + + See Also: + - :docstring:`ee.Asset.glob` + - :docstring:`ee.Asset.iterdir` + + Examples: + .. code-block:: python + + asset = ee.Asset("projects/ee-geetools/assets/folder") + asset.rglob("image_*") + """ + return [a for a in self.iterdir(recursive=True) if a.match(pattern)] + + def as_description(self) -> str: + """Transform the name of the Asset in to a description compatible string for a Task. + + Returns: + The formatted description. + """ + return format_description(self.name) + + def setProperties(self, **kwargs) -> ee.Asset: + """Set properties of the asset. + + Args: + **kwargs: The properties to set key, value pairs. To name normal properties simply use the name as key. For system properties, prefix it with "system:". Note that only the "time_start" and "time_end" are editable. + + Examples: + .. code-block:: python + + # dates need to be set in milliseconds + start = dt.datetime(2021, 1, 1).timestamp() * 1000 + asset = ee.Asset("projects/ee-geetools/assets/folder/image") + asset.setProperties(**{"description": "new_description", "system:time_start": start}) + """ + # We need to retrieve the system properties. + # They are named as in the server API and renamed inside this function. + # The method raise error when we try to set something else that the authorized one. + legit_keys = {"system:time_start": "start_time", "system:time_end": "end_time"} + system = {k: v for k, v in kwargs.items() if k.startswith("system:")} + for key in system.keys(): + if key not in legit_keys: + raise ValueError(f"Property {key} is not a valid system property.") + system = {legit_keys[k]: v for k, v in system.items()} + + # Specifying an update mask of 'properties' results in full replacement, + # which isn't what we want. Instead, we name each property that we'll be + # updating. + props = {k: v for k, v in kwargs.items() if not k.startswith("system:")} + update_mask = [f"properties.{k}" for k in props] + + # we can now update the asset by setting both system and asset properties + ee.data.updateAsset( + asset_id=self.as_posix(), + asset={**system, "properties": props}, + update_mask=list(system.keys()) + update_mask, + ) + + return self diff --git a/geetools/ee_authenticate.py b/geetools/ee_authenticate.py new file mode 100644 index 00000000..c65880c8 --- /dev/null +++ b/geetools/ee_authenticate.py @@ -0,0 +1,133 @@ +"""Toolbox for the ``ee.Authenticate`` function.""" + +from __future__ import annotations + +from contextlib import suppress +from pathlib import Path +from shutil import move +from tempfile import TemporaryDirectory + +import ee + +from .accessors import register_function_accessor + + +@register_function_accessor(ee.Authenticate, "geetools") +class AuthenticateAccessor: + """Create an accessor for the ``ee.Authenticate`` function.""" + + @staticmethod + def new_user(name: str = "", credential_pathname: str = "") -> None: + """Authenticate the user and save the credentials in a specific folder. + + Equivalent to ee.Authenticate but where the registered user will not be the default one (the one you get when running :py:meth:`ee.Initialize`) + + Args: + name: The name of the user. If not set, it will reauthenticate default. + credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path + + Example: + .. code-block:: python + + import ee + import geetools + + # cannot be displayed in the documentation as the creation + # of a new user requires user interaction + ee.Authenticate.geetools.new_user("secondary") + ee.Initialize.geetools.from_user("secondary") + ee.Number(1).getInfo() + """ + name = f"credentials{name}" + credential_pathname = credential_pathname or ee.oauth.get_credentials_path() + credential_path = Path(credential_pathname).parent + + # the authenticate method will write the credentials in the default + # folder and with the default name. We have to save the existing one in tmp, + # and then exchange places between the newly created and the existing one + default = Path(ee.oauth.get_credentials_path()) + + with TemporaryDirectory() as dir: + with suppress(FileNotFoundError): + move(default, Path(dir) / default.name) + ee.Authenticate() + move(default, credential_path / name) + with suppress(FileNotFoundError): + move(Path(dir) / default.name, default) + + @staticmethod + def delete_user(name: str = "", credential_pathname: str = "") -> None: + """Delete a user credential file. + + Args: + name: The name of the user. If not set, it will delete the default user + credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path + + Example: + .. code-block:: python + + import ee + import geetools + + # cannot be displayed in the documentation as the creation + # of a new user requires user interaction + ee.Authenticate.geetools.new_user("secondary") + ee.Authenticate.geetools.delete_user("secondary") + """ + name = f"credentials{name}" + credential_pathname = credential_pathname or ee.oauth.get_credentials_path() + credential_path = Path(credential_pathname).parent + with suppress(FileNotFoundError): + (credential_path / name).unlink() + + @staticmethod + def list_user(credential_pathname: str = "") -> list: + """Return all the available users in the set folder. + + To reach "default" simply omit the ``name`` parameter in the User methods + + Args: + credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path + + Returns: + A list of strings with the names of the users + + Example: + .. code-block:: python + + import ee + import geetools + + ee.Authenticate.geetools.list_user() + """ + credential_pathname = credential_pathname or ee.oauth.get_credentials_path() + credential_path = Path(credential_pathname).parent + files = [f for f in credential_path.glob("credentials*") if f.is_file()] + return [f.name.replace("credentials", "") or "default" for f in files] + + @staticmethod + def rename_user(new: str, old: str = "", credential_pathname: str = "") -> None: + """Rename a user without changing the credentials. + + Args: + new: The new name of the user + old: The name of the user to rename + credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path + + Example: + .. code-block:: python + + import ee + import geetools + + ee.Authenticate.geetools.new_user("old") + ee.Authenticate.geetools.rename_user("new", "old") + ee.Initialize.geetools.from_user("new") + ee.Number(1).getInfo() + """ + old = f"credentials{old}" + new = f"credentials{new}" + credential_pathname = credential_pathname or ee.oauth.get_credentials_path() + credential_path = Path(credential_pathname).parent + with suppress(FileNotFoundError): + (credential_path / old).rename(credential_path / new) diff --git a/geetools/ee_computed_object.py b/geetools/ee_computed_object.py new file mode 100644 index 00000000..2cfa91b2 --- /dev/null +++ b/geetools/ee_computed_object.py @@ -0,0 +1,140 @@ +"""Extra tools for the ``ee.ComputedObject`` class.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import ee + +from .accessors import _register_extention + + +# -- types management ---------------------------------------------------------- +@_register_extention(ee.ComputedObject) +def isInstance(self, klass: type) -> ee.Number: + """Return 1 if the element is the passed type or 0 if not. + + Parameters: + klass: The class to check the instance of. + + Returns: + ``1`` if the element is the passed type or ``0`` if not. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + # test if a String is a ee.String + s = ee.String("foo") + isString = ee.String("foo").isInstance(ee.String) + print(f"{s.getInfo()} is a earthengine string: {isString.getInfo()}") + + # test if a Number is a ee.String + n = ee.Number(1) + isString = ee.Number(1).isInstance(ee.String) + print(f"{n.getInfo()} is a earthengine string: {isString.getInfo()}") + """ + return ee.Algorithms.ObjectType(self).compareTo(klass.__name__).eq(0) + + +# -- .gee files ---------------------------------------------------------------- +@_register_extention(ee.ComputedObject) # type: ignore +def save(self, path: os.PathLike) -> Path: + """Save a ``ComputedObject`` to a .gee file. + + The file contains the JSON representation of the object. it still need to be computed via ``getInfo()`` to be used. + + Parameters: + path: The path to save the object to. + + Returns: + The path to the saved file. + + Examples: + .. jupyter-execute:: + + from tempfile import TemporaryDirectory + from pathlib import Path + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + img = ee.Image("COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM") + + with TemporaryDirectory() as tmp: + file = Path(tmp) / "test.gee" + img.save(file) + print(file.read_text()) + """ + path = Path(path).with_suffix(".gee") + path.write_text(json.dumps(ee.serializer.encode(self))) + return path + + +@staticmethod # type: ignore +@_register_extention(ee.ComputedObject) # type: ignore +def open(path: os.PathLike) -> ee.ComputedObject: + """Open a .gee file as a ComputedObject. + + Parameters: + path: The path to the file to open. + + Returns: + The ComputedObject instance. + + Examples: + .. jupyter-execute:: + + from tempfile import TemporaryDirectory + from pathlib import Path + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + img = ee.Image("COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM") + + with TemporaryDirectory() as tmp: + file = Path(tmp) / "test.gee" + img.save(file) + obj = ee.Image.open(file) + print(obj.getInfo()) + """ + if (path := Path(path)).suffix != ".gee": + raise ValueError("File must be a .gee file") + + return ee.deserializer.decode(json.loads(path.read_text())) + + +# placeholder classes for the isInstance method -------------------------------- +@_register_extention(ee) +class Float: + """Placeholder Float class to be used in the isInstance method.""" + + def __init__(self): + """Avoid initializing the class.""" + raise NotImplementedError("This class is a placeholder, it should not be initialized") + + def __name__(self): + """Return the class name.""" + return "Float" + + +@_register_extention(ee) +class Integer: + """Placeholder Integer class to be used in the isInstance method.""" + + def __init__(self): + """Avoid initializing the class.""" + raise NotImplementedError("This class is a placeholder, it should not be initialized") + + def __name__(self): + """Return the class name.""" + return "Integer" diff --git a/geetools/ee_date.py b/geetools/ee_date.py new file mode 100644 index 00000000..9454cf92 --- /dev/null +++ b/geetools/ee_date.py @@ -0,0 +1,173 @@ +"""Extra methods for the ``ee.Date`` class.""" + +from __future__ import annotations + +from datetime import datetime + +import ee + +from .accessors import register_class_accessor + +EE_EPOCH = datetime(1970, 1, 1, 0, 0, 0) + + +@register_class_accessor(ee.Date, "geetools") +class DateAccessor: + """Toolbox for the ``ee.Date`` class.""" + + def __init__(self, obj: ee.Date): + """Initialize the Date class.""" + self._obj = obj + + @classmethod + def fromEpoch(cls, number: int, unit: str = "day") -> ee.Date: + """Set an the number of units since epoch (1970-01-01). + + Parameters: + number: The number of units since the epoch. + unit: The unit to return the number of. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. + + Returns: + The date as a ``ee.Date`` object. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Date.geetools.fromEpoch(49, 'year') + d.format("YYYY-MM-DD").getInfo() + """ + cls.check_unit(unit) + return ee.Date(EE_EPOCH.isoformat()).advance(number, unit) + + @classmethod + def fromDOY(cls, doy: int, year: int) -> ee.Date: + """Create a date from a day of year and a year. + + Parameters: + doy: The day of year. + year: The year. + + Returns: + The date as a ``ee.Date`` object. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Date.geetools.fromDOY(1, 2020) + d.format("YYYY-MM-DD").getInfo() + """ + d, y = ee.Number(doy).toInt(), ee.Number(year).toInt() + return ee.Date.fromYMD(y, 1, 1).advance(d.subtract(1), "day") + + @classmethod + def now(cls) -> ee.Date: + """Create a date on current date. + + Returns: + The current date. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Date.geetools.now() + d.format("YYYY-MM-dd").getInfo() + """ + return ee.Date(datetime.now().isoformat()) + + def to_datetime(self) -> datetime: + """Convert a ``ee.Date`` to a ``datetime.datetime``. + + Returns: + The ``datetime.datetime`` representation of the ``ee.Date``. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Date('2020-01-01').geetools.to_datetime() + d.strftime('%Y-%m-%d') + + """ + return datetime.fromtimestamp(self._obj.millis().getInfo() / 1000.0) + + def getUnitSinceEpoch(self, unit: str = "day") -> ee.Number: + """Get the number of units since epoch (1970-01-01). + + Parameters: + unit: The unit to return the number of. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. + + Returns: + The number of units since the epoch. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Date('2020-01-01').geetools.getUnitSinceEpoch('year') + d.getInfo() + """ + self.check_unit(unit) + return self._obj.difference(EE_EPOCH, unit).toInt() + + def isLeap(self) -> ee.Number: + """Check if the year of the date is a leap year. + + Returns: + ``1`` if the year is a leap year, ``0`` otherwise. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + isLeap = ee.Date('2020-01-01').geetools.isLeap() + isLeap.getInfo() + """ + year = self._obj.get("year") + divisibleBy4 = year.mod(4).eq(0) + divisibleBy100 = year.mod(100).eq(0) + divisibleBy400 = year.mod(400).eq(0) + + # d400 or (d4 and not d100) + isLeap = divisibleBy400.Or(divisibleBy4.And(divisibleBy100.Not())) + + return isLeap.toInt() + + @staticmethod + def check_unit(unit: str) -> None: + """Check if the provided value is a valid time unit. + + Parameters: + unit: The unit to check. + + Raises: + ValueError: If the unit is not valid. + """ + if unit not in (units := ["second", "minute", "hour", "day", "month", "year"]): + raise ValueError(f"unit must be one of: {','.join(units)}") diff --git a/geetools/ee_date_range.py b/geetools/ee_date_range.py new file mode 100644 index 00000000..d9f3c02a --- /dev/null +++ b/geetools/ee_date_range.py @@ -0,0 +1,80 @@ +"""Extra tools for the ``ee.DateRange`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.DateRange, "geetools") +class DateRangeAccessor: + """Toolbox for the ``ee.DateRange`` class.""" + + def __init__(self, obj: ee.DateRange): + """Initialize the DateRange class.""" + self._obj = obj + + # -- date range operations ------------------------------------------------- + def split(self, interval: int | ee.Number, unit: str = "day") -> ee.List: + """Convert a ``ee.DateRange`` to a list of ``ee.DateRange``. + + The DateRange will be split in multiple DateRanges of the specified interval and Unit. + For example "1", "day". if the end date is not included the last dateRange length will be adapted. + + Parameters: + interval: The interval to split the DateRange + unit: The unit to split the DateRange. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. + + Returns: + The list of DateRanges + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + dateList = ee.DateRange('2020-01-01', '2020-01-31').geetools.split(1, 'day') + dateList.getInfo() + """ + self.check_unit(unit) + interval = ee.Number(interval).toInt().multiply(self.unitMillis(unit)) + start, end = self._obj.start().millis(), self._obj.end().millis() + + timestampList = ee.List.sequence(start, end, interval) + timestampList = timestampList.add(ee.Number(end).toFloat()).distinct() + indexList = ee.List.sequence(0, timestampList.size().subtract(2)) + + return indexList.map( + lambda i: ee.DateRange(timestampList.get(i), timestampList.get(ee.Number(i).add(1))) + ) + + # -- utils ----------------------------------------------------------------- + @staticmethod + def check_unit(unit: str) -> None: + """Check if the unit is valid.""" + if unit not in (units := ["second", "minute", "hour", "day", "month", "year"]): + raise ValueError(f"unit must be one of: {','.join(units)}") + + @staticmethod + def unitMillis(unit: str) -> ee.Number: + """Get the milliseconds of a unit. + + Parameters: + unit: The unit to get the milliseconds. One of: ``second``, ``minute``, ``hour``, ``day``, ``month``, ``year``. + + Returns: + The number of milliseconds in the unit + """ + millis = { + "second": 1000, + "minute": 1000 * 60, + "hour": 1000 * 60 * 60, + "day": 1000 * 60 * 60 * 24, + "month": 1000 * 60 * 60 * 24 * 30, + "year": 1000 * 60 * 60 * 24 * 365, + } + return ee.Number(millis[unit]) diff --git a/geetools/ee_dictionary.py b/geetools/ee_dictionary.py new file mode 100644 index 00000000..7219d676 --- /dev/null +++ b/geetools/ee_dictionary.py @@ -0,0 +1,87 @@ +"""Extra methods for the ``ee.Dictionary`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Dictionary, "geetools") +class DictionaryAccessor: + """Toolbox for the ``ee.Dictionary`` class.""" + + def __init__(self, obj: ee.Dictionary): + """Initialize the Dictionary class.""" + self._obj = obj + + # -- alternative constructor ----------------------------------------------- + def fromPairs(self, list: list | ee.List) -> ee.Dictionary: + """Create a dictionary from a list of [[key, value], ...]] pairs. + + Parameters: + list: A list of pairs (key, value). + + Returns: + A dictionary using the pairs. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Dictionary.geetools.fromPairs([["foo", 1], ["bar", 2]]) + d.getInfo() + """ + list = ee.List(list) + keys = list.map(lambda pair: ee.List(pair).get(0)) + values = list.map(lambda pair: ee.List(pair).get(1)) + return ee.Dictionary.fromLists(keys, values) + + # -- dictionary operations ------------------------------------------------- + def sort(self) -> ee.Dictionary: + """Sort the dictionary by keys in ascending order. + + Returns: + The sorted dictionary. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.sort() + d.getInfo() + """ + orderededKeys = self._obj.keys().sort() + values = orderededKeys.map(lambda key: self._obj.get(key)) + return ee.Dictionary.fromLists(orderededKeys, values) + + def getMany(self, list: list | ee.List) -> ee.List: + """Extract values from a list of keys. + + Parameters: + list: A list of keys. + + Returns: + A list of values. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + d = ee.Dictionary({"foo": 1, "bar": 2, "baz": 3}) + d = d.geetools.getMany(["foo", "bar"]) + d.getInfo() + """ + return ee.List(list).map(lambda key: self._obj.get(key)) diff --git a/geetools/ee_export.py b/geetools/ee_export.py new file mode 100644 index 00000000..6ac6a8d7 --- /dev/null +++ b/geetools/ee_export.py @@ -0,0 +1,221 @@ +"""Toolbox for the ``ee.Export`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor +from .utils import format_asset_id, format_description + + +@register_class_accessor(ee.batch.Export, "geetools") +class ExportAccessor: + """Toolbox for the ``ee.batch.Export`` class.""" + + def __init__(self, obj: ee.batch.Export): + """Initialize the ExportAccessor class.""" + self._obj = obj + + # this pattern is not pythonic but I mimic the content of the Export class from GEE + # I know as a namespace it should be a module. + class imagecollection: + """A static class with methods to start imagecollection export tasks.""" + + def __init__(self): + """Forbids class instantiation.""" + raise AssertionError("This class cannot be instantiated.") + + @staticmethod + def toAsset( + imagecollection: ee.ImageCollection, + index_property: str = "system:id", + description: str = "", + assetId: str = "", + **kwargs, + ) -> list[ee.batch.Task]: + """Creates a task to export an EE ImageCollection to an EE Asset. + + The method will create the imagecollection asset beforehand and launching the task will + Populate the said image collection with the exported images. Each image in the Collection + Will be named using the index_property value of the image. + If no asset Id is provided the asset will be created at the root of the current project assets. + + Parameters: + imagecollection: The image collection to export. + index_property: The property of the image to use as name. Default is "system:id". + description: The description of the task. + assetId: The asset id where to export the image collection. + **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toAsset + + Returns: + The task created. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + # create a test image collection + collection = ee.ImageCollection("COPERNICUS/S2").limit(5) + + # export the collection + tasks = geetools.batch.Export.imagecollection.toAsset(collection, "system:index", "test export") + ``` + """ + # sanity check on parameters + # renaming them for mypy type reassignment and compactness + desc = description if description else ee.Asset(assetId).name + aid = ee.Asset(assetId) if assetId else ee.Asset("~").expanduser() / description + + # create the ImageCollection asset + ee.data.createAsset({"type": "IMAGE_COLLECTION"}, aid.as_posix()) + + # loop over the collection and export each image + nb_images = imagecollection.size().getInfo() + imageList = imagecollection.toList(nb_images) + task_list = [] + for i in range(nb_images): + # extract image information + locImage = ee.Image(imageList.get(i)) + loc_id = locImage.get(index_property).getInfo() + + # override the parameters related to the image itself + kwargs["image"] = locImage + kwargs["description"] = format_description(f"{desc}_{loc_id}") + kwargs["assetId"] = (aid / format_asset_id(loc_id)).as_posix() + + # create the task + task_list.append(ee.batch.Export.image.toAsset(**kwargs)) + + return task_list + + @staticmethod + def toDrive( + imagecollection: ee.ImageCollection, + index_property: str = "system:id", + description: str = "", + folder: str = "", + **kwargs, + ) -> list[ee.batch.Task]: + """Creates a list of tasks to export an EE ImageCollection to Google Drive. + + The method will create a folder in Google Drive with the description value and populate + it with the exported images. Each image in the Collection Will be named using the + index_property value of the image. If no Folder is provided the folder will be created at the root + of the current drive and use the description name. + + Parameters: + imagecollection: The image collection to export. + index_property: The property of the image to use as name. Default is "system:id". + description: The description of the task. + folder: The folder id where to export the image collection. It will be stored at the root of the drive. + **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toDrive + + Returns: + The list of created tasks + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + # create a test image collection + collection = ee.ImageCollection("COPERNICUS/S2").limit(5) + + # export the collection + tasks = geetools.batch.Export.imagecollection.toDrive(collection, "system:index", "test export") + """ + # sanity check on parameters + # renaming them for mypy type reassignment and compactness + desc = description if description else folder + fid = folder if folder else description + + # loop over the collection and export each image + nb_images = imagecollection.size().getInfo() + imageList = imagecollection.toList(nb_images) + task_list = [] + for i in range(nb_images): + # extract image information + locImage = ee.Image(imageList.get(i)) + loc_id = locImage.get(index_property).getInfo() + + # override the parameters related to the image itself + # the folder will be created by the first task + kwargs["image"] = locImage + kwargs["folder"] = format_asset_id(f"{fid}") + kwargs["description"] = format_description(f"{desc}_{loc_id}") + + # create the task + task_list.append(ee.batch.Export.image.toDrive(**kwargs)) + + return task_list + + @staticmethod + def toCloudStorage( + imagecollection: ee.ImageCollection, + index_property: str = "system:id", + description: str = "", + folder: str = "", + **kwargs, + ) -> list[ee.batch.Task]: + """Creates a list of tasks to export an EE ImageCollection to Google cloud. + + The method will create a folder in Google cloud bucket with the description value and populate + it with the exported images. Each image in the Collection Will be named using the + index_property value of the image. If no Folder is provided the folder will be created at the root + of the bucket and use the description name. + + Parameters: + imagecollection: The image collection to export. + index_property: The property of the image to use as name. Default is "system:id". + description: The description of the task. + folder: The folder id where to export the image collection. It will be stored at the root of the drive. + **kwargs: every parameter that you would use for a vanilla ee.batch.Export.image.toCloudStorage + + Returns: + The list of created tasks + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + # create a test image collection + collection = ee.ImageCollection("COPERNICUS/S2").limit(5) + + # export the collection + tasks = geetools.batch.Export.imagecollection.toDrive(collection, "system:index", "test export") + """ + # sanity check on parameters + # renaming them for mypy type reassignment and compactness + desc = description if description else folder + fid = folder if folder else description + + # loop over the collection and export each image + nb_images = imagecollection.size().getInfo() + imageList = imagecollection.toList(nb_images) + task_list = [] + for i in range(nb_images): + # extract image information + locImage = ee.Image(imageList.get(i)) + loc_id = locImage.get(index_property).getInfo() + + # override the parameters related to the image itself + # the folder will be created by the first task + kwargs["image"] = locImage + kwargs["fileNamePrefix"] = format_asset_id(f"{fid}") + "/" + kwargs["description"] = format_description(f"{desc}_{loc_id}") + + # create the task + task_list.append(ee.batch.Export.image.toCloudStorage(**kwargs)) + + return task_list diff --git a/geetools/ee_feature.py b/geetools/ee_feature.py new file mode 100644 index 00000000..dd36ea00 --- /dev/null +++ b/geetools/ee_feature.py @@ -0,0 +1,64 @@ +"""Toolbox for the ``ee.Feature`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Feature, "geetools") +class FeatureAccessor: + """Toolbox for the ``ee.Feature`` class.""" + + def __init__(self, obj: ee.Feature): + """Initialize the class.""" + self._obj = obj + + def toFeatureCollection(self) -> ee.FeatureCollection: + """Convert a :py:class:`ee.Feature` composed of a multiGeometry geometry into a :py:class:`ee.FeatureCollection`. + + Returns: + The FeatureCollection + + Example: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + geoms = ee.Geometry.MultiPoint([[0,0], [0,1]]) + feature = ee.Feature(geoms).set("foo", "bar") + fc = feature.geetools.toFeatureCollection() + fc.getInfo() + """ + geoms = self._obj.geometry().geometries() + fc = geoms.map(lambda g: self._obj.setGeometry(g)) + return ee.FeatureCollection(fc) + + def removeProperties(self, properties: list | ee.List) -> ee.Feature: + """Remove properties from a feature. + + Args: + properties : List of properties to remove + + Returns: + The feature without the properties + + Example: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + feature = ee.Feature(None).set("foo", "bar", "baz", "foo") + feature = feature.geetools.removeProperties(["foo"]) + feature.getInfo() + """ + properties = ee.List(properties) + proxy = ee.Feature(self._obj.geometry()) # drop properties + return proxy.copyProperties(self._obj, exclude=properties) diff --git a/geetools/ee_feature_collection.py b/geetools/ee_feature_collection.py new file mode 100644 index 00000000..f5f4b43d --- /dev/null +++ b/geetools/ee_feature_collection.py @@ -0,0 +1,672 @@ +"""Toolbox for the `ee.FeatureCollection` class.""" + +from __future__ import annotations + +import ee +import geopandas as gpd +from matplotlib import pyplot as plt +from matplotlib.axes import Axes + +from .accessors import register_class_accessor +from .utils import plot_data + + +@register_class_accessor(ee.FeatureCollection, "geetools") +class FeatureCollectionAccessor: + """Toolbox for the `ee.FeatureCollection` class.""" + + def __init__(self, obj: ee.FeatureCollection): + """Initialize the FeatureCollection class.""" + self._obj = obj + + def toImage( + self, + color: str | ee.String | int | ee.Number = 0, + width: str | ee.String | int | ee.Number = "", + ) -> ee.Image: + """Paint the current FeatureCollection to an Image. + + It's a simple wrapper on Image.paint() method + + Args: + color: The pixel value to paint into every band of the input image, either as a number which will be used for all features, or the name of a numeric property to take from each feature in the collection. + width: Line width, either as a number which will be the line width for all geometries, or the name of a numeric property to take from each feature in the collection. If unspecified, the geometries will be filled instead of outlined. + + Returns: + The painted image + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + from matplotlib.colors import ListedColormap + + initialize_documentation() + + # extract the featureCollection of the Vatican from the FAO gaul dataset + vatican = ( + ee.FeatureCollection("FAO/GAUL/2015/level0") + .filter(ee.Filter.eq("ADM0_NAME", "Holy See")) + ) + + # transform the featureCollection into an image + img = vatican.geetools.toImage(color=1).rename("gaul") + + # Define a custom colormap ffor the raster representation + # it will only have 1 color: teal for the first value and white for everything else + cmap = ListedColormap(['teal', 'white']) + + # create the axes for the plots + fig, axes = plt.subplots(1, 2, figsize=(10, 5)) + + # customize the layout of the 2 plots + for ax in axes: + ax.set_xlabel("Longitude (°)") + ax.set_ylabel("Latitude (°)") + ax.set_xticks([]) + ax.set_yticks([]) + + # add the vector on the first plot + axes[0].set_title("Vector") + vatican.geetools.plot(ax=axes[0], color="teal", boundaries=True) + + # add the raster on the second plot + axes[1].set_title("Raster") + img.geetools.plot(region=vatican.bounds(), bands=["gaul"], ax=axes[1], cmap=cmap) + + fig.show() + """ + params = {"color": color} + width == "" or params.update(width=width) + return ee.Image().paint(self._obj, **params) + + def toDictionary( + self, keyColumn: str | ee.String = "system:index", selectors: list | ee.List = [] + ) -> ee.Dictionary: + """Convert to Dictionary. + + Parameters: + keyColumn: the column to use as keys. Must contain unique values, if not it will fail. + selectors: a list of properties to add in the output. If the list is empty all properties will be added. + + Returns: + a ee.Dictionary with values of keyColumn as keys and ee.Dictionary as values. The output will look like: + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + import json + + initialize_documentation() + + # Extracting the first 3 countries from the FAO GAUL dataset. + # and transform them into dictionary + countries = ( + ee.FeatureCollection("FAO/GAUL/2015/level0") + .select(["ADM0_NAME", "ADM0_CODE"]) + .limit(3) + .geetools.toDictionary() + ) + + print(json.dumps(countries.getInfo(), indent=2)) + """ + uniqueIds = self._obj.aggregate_array(keyColumn) + selectors = ee.List(selectors) if selectors else self._obj.first().propertyNames() + keyColumn = ee.String(keyColumn) + + features = self._obj.toList(self._obj.size()) + values = features.map(lambda feat: ee.Feature(feat).toDictionary(selectors)) + keys = uniqueIds.map(lambda uid: ee.String(ee.Algorithms.String(uid))) + return ee.Dictionary.fromLists(keys, values) + + def addId( + self, name: str | ee.String = "id", start: int | ee.Number = 1 + ) -> ee.FeatureCollection: + """Add a unique numeric identifier, starting from parameter ``start``. + + Args: + name: The name of the property to add. Defaults to "id". + start: The starting value of the id. Defaults to 1. + + Returns: + The parsed collection with a new id property + + Example: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + from matplotlib.colors import ListedColormap + + initialize_documentation() + + # create a featureCollection from the 3 first countries of the FAO GAUL dataset + # then add an id property to each feature and show them in the console + fc = ( + ee.FeatureCollection("FAO/GAUL/2015/level0") + .filter(ee.Filter.inList("ADM0_NAME", ["France", "Germany", "Italy"])) + .select(["ADM0_NAME", "ADM0_CODE"]) + .geetools.addId() + ) + + # create a figure to show the created featureCollection generated "id" property + fig, ax = plt.subplots(figsize=(10, 5)) + cmap = ListedColormap(["#3AA3FF", "#F3FF3B", "#FF433B"]) + fc.geetools.plot(ax=ax, property="id", cmap=cmap) + + fig.colorbar(ax.collections[0], label="id value", ticks=[1, 2, 3]) + ax.set_title("generated id of FAO countries") + ax.set_xlabel("Longitude (°)") + ax.set_ylabel("Latitude (°)") + ax.set_xticks([]) + ax.set_yticks([]) + + fig.show() + """ + start, name = ee.Number(start).toInt(), ee.String(name) + + indexes = ee.List(self._obj.aggregate_array("system:index")) + ids = ee.List.sequence(start, start.add(self._obj.size()).subtract(1)) + idByIndex = ee.Dictionary.fromLists(indexes, ids) + return self._obj.map(lambda f: f.set(name, idByIndex.get(f.get("system:index")))) + + def mergeGeometries(self, maxError: float | int | ee.number | None = None) -> ee.Geometry: + """Merge the geometries included in the features. + + Args: + maxError: The maximum amount of error tolerated when performing any necessary reprojection. + + Returns: + the dissolved geometry + + Example: + .. code-block:: python + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + # create a featurecollection containing 2 bounding boxes + fc = ee.FeatureCollection([ + ee.Geometry.BBox(-1, -1, 1, 1), + ee.Geometry.BBox(0, 0, 2, 2) + ]) + + # merge them into a single geometry + geometry = fc.geetools.mergeGeometries(maxError=.1) + + # print the geometry on a matplotlib graph + fig, ax = plt.subplots(figsize=(10, 5)) + c = ee.FeatureCollection(geometry).geetools.plot(boundaries=True, color="teal", ax=ax) + + fig.show() + """ + first = self._obj.first().geometry() + union = self._obj.iterate(lambda f, g: f.geometry().union(g, maxError=maxError), first) + return ee.Geometry(union).dissolve(maxError=maxError) + + def toPolygons(self) -> ee.FeatureCollection: + """Drop any geometry that is not a Polygon or a multipolygon. + + This method is made to avoid errors when performing zonal statistics and/or other surfaces operations. + These operations won't work on geometries that are Lines or points. The methods remove these geometry + types from GEometryCollections and rremove features that don't have any polygon geometry. + + Returns: + The parsed collection with only polygon/MultiPolygon geometries + + Example: + .. code-block:: python + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + point0 = ee.Geometry.Point([0,0], proj="EPSG:4326") + point1 = ee.Geometry.Point([0,1], proj="EPSG:4326") + poly0 = point0.buffer(1, proj="EPSG:4326") + poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") + line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") + multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") + geometryCol = ee.Algorithms.GeometryConstructors.MultiGeometry([multiPoly, poly0, poly1, point0, line], crs="EPSG:4326", geodesic=True, maxError=1) + + fc = ee.FeatureCollection([geometryCol]) + fc = fc.geetools.toPolygons() + + fig, ax = plt.subplots(figsize=(5, 10)) + fc.geetools.plot(boundaries=True, ax=ax) + """ + + def filterGeom(geom): + geom = ee.Geometry(geom) + return ee.Algorithms.If(geom.type().compareTo("Polygon"), None, geom) + + def removeNonPoly(feat): + filteredGeoms = feat.geometry().geometries().map(filterGeom, True) + proj = feat.geometry().projection() + return feat.setGeometry(ee.Geometry.MultiPolygon(filteredGeoms, proj)) + + return self._obj.map(removeNonPoly) + + def byProperties( + self, + featureId: str | ee.String = "system:index", + properties: list | ee.List = [], + labels: list = [], + ) -> ee.Dictionary: + """Get a dictionary with all feature values for each properties. + + This method is returning a dictionary with all the properties as keys and their values in each feaure as a list. + + .. code-block:: + + { + "property1": {"feature1": value1, "feature2": value2, ...}, + "property2": {"feature1": value1, "feature2": value2, ...}, + ... + } + + The output remain server side and can be used to create a client side plot. + + Args: + featureId: The property used to label features. Defaults to "system:index". + properties: A list of properties to get the values from. + labels: A list of names to replace properties names. Default to the properties names. + + Returns: + A dictionary with all the properties as keys and their values in each feaure as a list. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.byFeatures` + - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` + + Example: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(3) + d = fc.geetools.byProperties(properties=["ADM1_CODE", "ADM2_CODE"]) + d.getInfo() + """ + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = self._obj.aggregate_array(featureId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + + # retrieve properties for each feature + properties = ee.List(properties) if properties else self._obj.first().propertyNames() + properties = properties.remove(featureId) + values = properties.map( + lambda p: ee.Dictionary.fromLists(features, self._obj.aggregate_array(p)) + ) + + # get the label to use in the dictionary if requested + labels = ee.List(labels) if labels else properties + + return ee.Dictionary.fromLists(labels, values) + + def byFeatures( + self, + featureId: str | ee.String = "system:index", + properties: list | ee.List = [], + labels: list = [], + ) -> ee.Dictionary: + """Get a dictionary with all property values for each feature. + + This method is returning a dictionary with all the feature ids as keys and their properties as a dictionary. + + .. code-block:: + + { + "feature1": {"property1": value1, "property2": value2, ...}, + "feature2": {"property1": value1, "property2": value2, ...}, + ... + } + + The output remain server side and can be used to create a client side plot. + + Args: + featureId: The property to use as the feature id. Defaults to "system:index". This property needs to be a string property. + properties: A list of properties to get the values from. + labels: A list of names to replace properties names. Default to the properties names. + + Returns: + A dictionary with all the feature ids as keys and their properties as a dictionary. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.byProperties` + - :docstring:`ee.FeatureCollection.geetools.plot_by_features` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(3) + d = fc.geetools.byFeatures(properties=["ADM0_CODE", "ADM1_CODE", "ADM2_CODE"]) + d.getInfo() + + + """ + # compute the properties and their labels + props = ee.List(properties) if properties else self._obj.first().propertyNames() + props = props.remove(featureId) + labels = ee.List(labels) if labels else props + + # create a function to get the properties of a feature + # we need to map the featureCollection into a list as it's not possible to return something else than a + # featureCollection mapping a FeatureCollection. very expensive process but we don't have any other choice. + fc = self._obj.select(propertySelectors=props, newProperties=props) + fc_list = fc.toList(self._obj.size()) + values = fc_list.map(lambda f: ee.Feature(f).select(props, labels).toDictionary(labels)) + + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = self._obj.aggregate_array(featureId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + + return ee.Dictionary.fromLists(features, values) + + def plot_by_features( + self, + type: str = "bar", + featureId: str = "system:index", + properties: list = [], + labels: list = [], + colors: list = [], + ax: Axes | None = None, + **kwargs, + ) -> Axes: + """Plot the values of a ``ee.FeatureCollection`` by feature. + + Each feature property selected in properties will be plotted using the ``featureId`` as the x-axis. + If no ``properties`` are provided, all properties will be plotted. + If no ``featureId`` is provided, the "system:index" property will be used. + + Warning: + This function is a client-side function. + + Args: + type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! + featureId: The property to use as the x-axis (name the features). Defaults to "system:index". + properties: A list of properties to plot. Defaults to all properties. + labels: A list of labels to use for plotting the properties. If not provided, the default labels will be used. It needs to match the properties length. + colors: A list of colors to use for plotting the properties. If not provided, the default colors from the matplotlib library will be used. + ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. + kwargs: Additional arguments from the ``pyplot`` function. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.byFeatures` + - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` + - :docstring:`ee.FeatureCollection.geetools.plot_hist` + - :docstring:`ee.FeatureCollection.geetools.plot` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + # start a plot object from matplotlib library + fig, ax = plt.subplots(figsize=(10, 5)) + + # plot on this object the 10 first items of the FAO GAUL level 2 feature collection + # for each one of them (marked with it's "ADM0_NAME" property) we plot the value of the "ADM1_CODE" and "ADM2_CODE" properties + fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(10) + fc.geetools.plot_by_features(featureId="ADM2_NAME", properties=["ADM1_CODE", "ADM2_CODE"], colors=["#61A0D4", "#D49461"], ax=ax) + + # Modify the rotation of existing x-axis tick labels + for label in ax.get_xticklabels(): + label.set_rotation(45) + """ + # Get the features and properties + props = ee.List(properties) if properties else self._obj.first().propertyNames().getInfo() + props = props.remove(featureId) + + # get the data from server + data = self.byProperties(featureId, props, labels).getInfo() + + # reorder the data according to the labels or properties set by the user + labels = labels if labels else props.getInfo() + data = {k: data[k] for k in labels} + + return plot_data(type=type, data=data, label_name=featureId, colors=colors, ax=ax, **kwargs) + + def plot_by_properties( + self, + type: str = "bar", + featureId: str = "system:index", + properties: list | ee.List = [], + labels: list = [], + colors: list = [], + ax: Axes | None = None, + **kwargs, + ) -> Axes: + """Plot the values of a FeatureCollection by property. + + Each features will be represented by a color and each property will be a bar of the bar chart. + + Warning: + This function is a client-side function. + + Args: + type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! + featureId: The property to use as the y-axis (name the features). Defaults to "system:index". + properties: A list of properties to plot. Defaults to all properties. + labels: A list of labels to use for plotting the properties. If not provided, the default labels will be used. It needs to match the properties length. + colors: A list of colors to use for plotting the properties. If not provided, the default colors from the matplotlib library will be used. + ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. + kwargs: Additional arguments from the ``pyplot`` function. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.byProperties` + - :docstring:`ee.FeatureCollection.geetools.plot_by_features` + - :docstring:`ee.FeatureCollection.geetools.plot_hist` + - :docstring:`ee.FeatureCollection.geetools.plot` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + # start a plot object from matplotlib library + fig, ax = plt.subplots(figsize=(10, 5)) + + # plot on this object the 10 first items of the FAO GAUL level 2 feature collection + # for each one of them (marked with it's "ADM2_NAME" property) we plot the value of the "ADM1_CODE" property + fc = ee.FeatureCollection("FAO/GAUL/2015/level2").limit(10) + fc.geetools.plot_by_properties(featureId="ADM2_NAME", properties=["ADM1_CODE"], ax=ax) + """ + # Get the features and properties + fc = self._obj + props = ee.List(properties) if properties else fc.first().propertyNames() + props = props.remove(featureId) + + # get the data from server + data = self.byFeatures(featureId, props, labels).getInfo() + + # reorder the data according to the lapbes or properties set by the user + labels = labels if labels else props.getInfo() + data = {f: {k: data[f][k] for k in labels} for f in data.keys()} + + return plot_data(type=type, data=data, label_name=featureId, colors=colors, ax=ax, **kwargs) + + def plot_hist( + self, + property: str | ee.String, + label: str = "", + ax: Axes | None = None, + color=None, + **kwargs, + ) -> Axes: + """Plot the histogram of a specific property. + + Warning: + This function is a client-side function. + + Args: + property: The property to display + label: The label to use for the property. If not provided, the property name will be used. + ax: The matplotlib axes to use. If not provided, the plot will be send to the current axes (``plt.gca()``) + color: The color to use for the plot. If not provided, the default colors from the matplotlib library will be used. + kwargs: Additional arguments from the ``pyplot.hist`` function. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.plot_by_features` + - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` + - :docstring:`ee.FeatureCollection.geetools.plot` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + # start a plot object from matplotlib library + fig, ax = plt.subplots(figsize=(10, 5)) + ax.set_title('Histogram of Precipitation in July') + ax.set_xlabel('Precipitation (mm)') + + + # build the histogram of the precipitation band for the month of july in the PRISM dataset + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm81m').toBands() + region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14) + climSamp = normClim.sample(region, 5000) + climSamp.geetools.plot_hist("07_ppt", ax=ax, bins=20) + + fig.show() + """ + # gather the data from parameters + properties, labels = ee.List([property]), ee.List([label]) + + # get the data from the server + data = self.byProperties(properties=properties, labels=labels).getInfo() + + # define the ax if not provided by the user + if ax is None: + fig, ax = plt.subplots() + + # gather the data from the data variable + labels = list(data.keys()) + if len(labels) != 1: + raise ValueError("Pie chart can only be used with one property") + + kwargs["rwidth"] = kwargs.get("rwidth", 0.9) + kwargs["color"] = color or plt.get_cmap("tab10").colors[0] + ax.hist(list(data[labels[0]].values()), **kwargs) + ax.set_xlabel(labels[0]) + ax.set_ylabel("frequency") + + # customize the layout of the axis + ax.grid(axis="y") + ax.set_axisbelow(True) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + + # make sure the canvas is only rendered once. + ax.figure.canvas.draw_idle() + + return ax + + def plot( + self, + ax: Axes | None = None, + property: str = "", + crs: str = "EPSG:4326", + cmap: str = "viridis", + boundaries: bool = False, + color: str = "k", + ): + """Plot the featureCollection on a map using the provided property. + + Warning: + This function is a client-side function. + + Parameters: + property: The property to use to color the features. + ax: The axes to plot the map on. + crs: The CRS to use for the map. + cmap: The colormap to use for the colors. + boundaries: Whether to plot the features values or only the boundaries. + color: The color to use for the boundaries. + + See Also: + - :docstring:`ee.FeatureCollection.geetools.plot_by_features` + - :docstring:`ee.FeatureCollection.geetools.plot_by_properties` + - :docstring:`ee.FeatureCollection.geetools.plot_hist` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + from matplotlib import pyplot as plt + + initialize_documentation() + + # start a plot object from matplotlib library + fig, ax = plt.subplots(figsize=(10, 5)) + + # plot france geometry on the map + france = ( + ee.FeatureCollection("FAO/GAUL/2015/level2") + .filter(ee.Filter.eq("ADM0_NAME", "France")) + .geetools.plot(boundaries=True, color="teal", ax=ax) + ) + + # style the figure + ax.set_title("France departements") + ax.set_xlabel("Longitude (°)") + ax.set_ylabel("Latitude (°)") + ax.set_xticks([]) + ax.set_yticks([]) + + fig.show() + """ + if ax is None: + fig, ax = plt.subplots() + + # get the data from the server + names = self._obj.first().propertyNames() + names = names.filter(ee.Filter.stringStartsWith("item", "system:").Not()) + property = property if property != "" else names.get(0).getInfo() + data = self._obj.select([property]).getInfo() + + # transform the data to a geodataframe and reproject it to the destination crs + gdf = gpd.GeoDataFrame.from_features(data["features"]).set_crs(4326).to_crs(crs) + + # plot the data on the map either as contours or a valued features + if boundaries is True: + gdf.boundary.plot(ax=ax, color=color) + else: + gdf.plot(column=property, ax=ax, cmap=cmap) diff --git a/geetools/ee_filter.py b/geetools/ee_filter.py new file mode 100644 index 00000000..6b97724d --- /dev/null +++ b/geetools/ee_filter.py @@ -0,0 +1,46 @@ +"""Extra method for the ``ee.Filter`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Filter, "geetools") +class FilterAccessor: + """Toolbox for the ``ee.Filter`` class.""" + + def __init__(self, obj: ee.Filter): + """Initialize the Filter class.""" + self._obj = obj + + def dateRange(self, range: ee.DateRange) -> ee.Filter: + """Filter by daterange. + + Parameters: + range: The date range to filter by. + + Returns: + The filter to apply to a collection. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + # Create a collection and filter it by a date range + collection = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") + + # filter by date range + range = ee.DateRange("2018-01-01", "2019-01-01") + filteredCollection = collection.filter(ee.Filter.geetools.dateRange(range)) + + # print the total size of the collections + print(f"landsat full collection: {collection.size().getInfo()}") + print(f"landsat filtered collection: {filteredCollection.size().getInfo()}") + """ + return ee.Filter.date(range.start(), range.end()) diff --git a/geetools/ee_geometry.py b/geetools/ee_geometry.py new file mode 100644 index 00000000..84f3a2ab --- /dev/null +++ b/geetools/ee_geometry.py @@ -0,0 +1,64 @@ +"""Toolbox for the ``ee.Geometry`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Geometry, "geetools") +class GeometryAccessor: + """Toolbox for the ``ee.Geometry`` class.""" + + def __init__(self, obj: ee.Geometry): + """Initialize the Geometry class.""" + self._obj = obj + + def keepType(self, type: str) -> ee.Geometry: + """Only keep the geometries of the given type from a GeometryCollection. + + Args: + type: The type of geometries to keep. Can be one of: Point, LineString, LineRing Polygon. + + Returns: + The geometries of the given type. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + # generate multiple geometries of different types + point0 = ee.Geometry.Point([0,0], proj="EPSG:4326") + point1 = ee.Geometry.Point([0,1], proj="EPSG:4326") + poly0 = point0.buffer(1, proj="EPSG:4326") + poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") + line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") + multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") + + # create a geometry collection from them + geometryColllection = ee.Algorithms.GeometryConstructors.MultiGeometry( + [multiPoly, poly0, poly1, point0, line], + crs="EPSG:4326", + geodesic=True, + maxError=1 + ) + + # extract only the LineString geometries from the collection + geom = geometryColllection.geetools.keepType('LineString') + geom.getInfo() + """ + # will raise an error if self is not a GeometryCollection + error_msg = "This method can only be used with GeometryCollections" + assert self._obj.type().getInfo() == "GeometryCollection", error_msg + + def filterType(geom): + geom = ee.Geometry(geom) + return ee.Algorithms.If(geom.type().compareTo(type), None, geom) + + geometries = self._obj.geometries().map(filterType, True) + return getattr(ee.Geometry, "Multi" + type)(geometries, self._obj.projection()) diff --git a/geetools/ee_image.py b/geetools/ee_image.py new file mode 100644 index 00000000..74132ba4 --- /dev/null +++ b/geetools/ee_image.py @@ -0,0 +1,2090 @@ +"""Toolbox for the ``ee.Image`` class.""" + +from __future__ import annotations + +from typing import Optional + +import ee +import ee_extra +import ee_extra.Algorithms.core +import geopandas as gpd +import numpy as np +import requests +import xarray +from matplotlib import pyplot as plt +from matplotlib.axes import Axes +from matplotlib.colors import to_rgba +from pyproj import CRS, Transformer +from xee.ext import REQUEST_BYTE_LIMIT + +from .accessors import register_class_accessor +from .utils import plot_data + + +@register_class_accessor(ee.Image, "geetools") +class ImageAccessor: + """Toolbox for the ``ee.Image`` class.""" + + def __init__(self, obj: ee.Image): + """Initialize the Image class.""" + self._obj = obj + + # -- band manipulation ----------------------------------------------------- + def addDate(self, format: str | ee.String = "") -> ee.Image: + """Add a band with the date of the image in the provided format. + + If no format is provided, the date is stored as a Timestamp in millisecond in a band "date". If format band is provided, the date is store in a int8 band with the date in the provided format. This format needs to be a string that can be converted to a number. + If not an error will be thrown. + + Args: + format: A date pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html + + Returns: + The image with the date band added. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image.geetools.addDate() + date = image.select('date') + buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(100) + value = date.reduceRegion(ee.Reducer.first(), buffer, 10).get("date") + ee.Date(value).format('YYYY-MM-dd').getInfo() + """ + # parse the inputs + isMillis = ee.String(format).equals(ee.String("")) + format = ee.String(format) if format else ee.String("YYYYMMdd") + + # extract the date from the object and create a image band from it + date = self._obj.date() + date = ee.Algorithms.If(isMillis, date.millis(), ee.Number.parse(date.format(format))) + dateBand = ee.Image.constant(ee.Number(date)).rename("date") + + return self._obj.addBands(dateBand) + + def addSuffix(self, suffix: str | ee.String, bands: list | ee.List = []) -> ee.Image: + """Add a suffix to the image selected band. + + Add a suffix to the selected band. If no band is specified, the suffix is added to all bands. + + Parameters: + suffix: The suffix to add to the band. + bands: The bands to add the suffix to. If None, all bands are selected. + + Returns: + The image with the suffix added to the selected bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image.geetools.addSuffix('_suffix') + print(image.bandNames().getInfo()) + """ + suffix = ee.String(suffix) + bands = self._obj.bandNames() if bands == [] else ee.List(bands) + bandNames = bands.iterate( + lambda b, n: ee.List(n).replace(b, ee.String(b).cat(suffix)), + self._obj.bandNames(), + ) + return self._obj.rename(bandNames) + + def addPrefix(self, prefix: str | ee.String, bands: list | ee.List = []): + """Add a prefix to the image selected band. + + Add a prefix to the selected band. If no band is specified, the prefix is added to all bands. + + Parameters: + prefix: The prefix to add to the band. + bands: The bands to add the prefix to. If None, all bands are selected. + + Returns: + The image with the prefix added to the selected bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image.geetools.addPrefix('prefix_') + print(image.bandNames().getInfo()) + """ + prefix = ee.String(prefix) + bands = self._obj.bandNames() if bands == [] else ee.List(bands) + bandNames = bands.iterate( + lambda b, n: ee.List(n).replace(b, prefix.cat(ee.String(b))), + self._obj.bandNames(), + ) + return self._obj.rename(bandNames) + + def rename(self, names: dict | ee.Dictionary) -> ee.Image: + """Rename the bands of the image based on a dictionary. + + It's the same function as the one from GEE but it takes a dictionary as input. + Keys are the old names and values are the new names. + + Parameters: + names: The new names of the bands. + + Returns: + The image with the new band names. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' + image = ee.Image(src).select(['B1', 'B2', 'B3']) + image = image.geetools.rename({'B1': 'Aerosol', 'B2': 'Blue'}) + print(image.bandNames().getInfo()) + """ + names = ee.Dictionary(names) + bands = names.keys().iterate( + lambda b, n: ee.List(n).replace(b, names.get(b)), self._obj.bandNames() + ) + return self._obj.rename(bands) + + def remove(self, bands: list | ee.List) -> ee.Image: + """Remove bands from the image. + + Parameters: + bands: The bands to remove. + + Returns: + The image without the specified bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' + image = ee.Image(src).select(['B1', 'B2', 'B3']) + image = image.geetools.remove(['B1', 'B2']) + print(image.bandNames().getInfo()) + """ + bands = self._obj.bandNames().removeAll(ee.List(bands)) + return self._obj.select(bands) + + def doyToDate( + self, + year, + dateFormat: str | ee.String = "yyyyMMdd", + band: str | ee.String = "", + ) -> ee.Image: + """Convert the DOY band to a date band. + + This method only work with date formats that can be converted to numbers as earthengine images don't accept string bands. + + Args: + year: The year to use for the date + dateFormat: The date format to use for the date band + band: The band to use as DOY band. If empty, the first one is selected. + + Returns: + The original image with the DOY band converted to a date band. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image.random().multiply(365).toInt() + vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) + + image = image.geetools.doyToDate(2023) + print(image.reduceRegion(ee.Reducer.min(), vatican, 1).getInfo()) + """ + year = ee.Number(year) + band = ee.String(band) if band else ee.String(self._obj.bandNames().get(0)) + dateFormat = ee.String(dateFormat) + + doyList = ee.List.sequence(0, 365) + remapList = doyList.map( + lambda d: ee.Number.parse( + ee.Date.fromYMD(year, 1, 1).advance(d, "day").format(dateFormat) + ) + ) + return self._obj.remap(doyList, remapList, bandName=band).rename(band) + + # -- the rest -------------------------------------------------------------- + + def getValues(self, point: ee.Geometry.Point, scale: int | ee.Number = 0) -> ee.Dictionary: + """Get the value of the image at the given point using specified geometry. + + The result is presented as a dictionary where the keys are the bands name and the value the mean value of the band at the given point. + + Parameters: + point: The geometry to get the value from. + + Returns: + A dictionary with the band names and the value at the given point. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + point = ee.Geometry.Point([11.0, 45.0]) + value = image.geetools.getValues(point, 10) + print(value.getInfo()) + """ + scale = self._obj.select(0).projection().nominalScale() if scale == 0 else ee.Number(scale) + return self._obj.reduceRegion(ee.Reducer.mean(), point, scale) + + def minScale(self) -> ee.Number: + """Return the minimum scale of the image. + + It will be looking at all bands available so Select specific values before using this method. + + Returns: + The minimum scale of the image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image.geetools.minScale().getInfo() + """ + bandNames = self._obj.bandNames() + scales = bandNames.map(lambda b: self._obj.select(ee.String(b)).projection().nominalScale()) + return ee.Number(scales.sort().get(0)) + + def merge(self, images: list | ee.List) -> ee.Image: + """Merge images into a single image. + + Parameters: + images: The images to merge. + + Returns: + The merged image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image1 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image2 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQL') + image3 = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image1.geetools.merge([image2, image3]) + print(image.bandNames().getInfo()) + """ + images = ee.List(images) + merged = images.iterate(lambda dst, src: ee.Image(src).addBands(dst), self._obj) + return ee.Image(merged) + + def toGrid( + self, + size: int | ee.Number = 1, + band: str | ee.String = "", + geometry: ee.Geometry | None = None, + ) -> ee.FeatureCollection: + """Convert an image to a grid of polygons. + + Based on the size given by the user, the tool will build a grid of size*pixelSize x size * pixelSize cells. Each cell will be a polygon. Note that for images that have multiple scale depending on the band, we will use the first one or the one stated in the parameters. + + Parameters: + size: The size of the grid. It will be size * pixelSize x size * pixelSize cells. + band: The band to burn into the grid. + geometry: The geometry to use as reference for the grid. If None, the image footprint will be used. + + Returns: + The grid as a FeatureCollection. + + Note: + The method has a known bug when the projection of the image is different than 3857. As we use a buffer, the grid cells can slightly overlap. Feel free to open a Issue and contribute if you feel it needs improvements. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' + image = ee.Image(src) + buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(100) + grid = image.geetools.toGrid(1, 'B2', buffer) + print(grid.getInfo()) + """ + band = ee.String(band) if band else self._obj.bandNames().get(0) + projection = self._obj.select(band).projection() + size = projection.nominalScale().multiply(ee.Number(size).toInt()) + + # extract the centers at the correct resolution + lonLat = ee.Image.pixelLonLat().reproject(projection) + coords = lonLat.reduceRegion(ee.Reducer.toList(), geometry, size) + + # extract them as lists + lat, lon = ee.List(coords.get("latitude")), ee.List(coords.get("longitude")) + + # we can now map them user their index to point -> buffer -> square + index = ee.List.sequence(0, lat.size().subtract(2)) + squares = index.map( + lambda i: ( + ee.Geometry.Point([lon.get(i), lat.get(i)]) + .buffer(size.divide(2)) + .bounds(0.01, projection) + ) + ) + + # make the grid + features = ee.List(squares).map(lambda g: ee.Feature(ee.Geometry(g))) + + return ee.FeatureCollection(features) + + def clipOnCollection( + self, fc: ee.FeatureCollection, keepProperties: int | ee.Number = 1 + ) -> ee.ImageCollection: + """Clip an image to a FeatureCollection. + + The image will be clipped to every single features of the featureCollection as one independent image. + + Parameters: + fc: The featureCollection to clip to. + keepProperties: If True, the properties of the featureCollection will be added to the clipped image. + + Returns: + The clipped imageCollection. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' + image = ee.Image(src) + fc = ee.FeatureCollection('FAO/GAUL/2015/level0') + clipped = image.geetools.clipOnCollection(fc) + print(clipped.size().getInfo()) + """ + + def fcClip(feat): + image = self._obj.clip(feat.geometry()) + return ee.Algorithms.If( + ee.Number(keepProperties).toInt(), image.copyProperties(feat), image + ) + + return ee.ImageCollection(fc.map(fcClip)) + + def bufferMask( + self, + radius: int | ee.Number = 1.5, + kernelType: str | ee.String = "square", + units: str | ee.String = "pixels", + ) -> ee.Image: + """Make a buffer around every masked pixel of the Image. + + The buffer will be made using the specified radius, kernelType and units and will mask surrounfing pixels. + + Parameters: + radius: The radius of the buffer. + kernelType: The kernel type of the buffer. One of: ``square``, ``circle``, ``diamond``, ``octagon``, ``plus``, ``square``. + units: The units of the radius. One of: ``pixels``, ``meters``. + + Returns: + The image with the buffer mask applied. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src = 'COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM' + image = ee.Image(src) + image = image.geetools.bufferMask(1.5, 'square', 'pixels') + print(image.bandNames().getInfo()) + """ + radius, kernelType = ee.Number(radius), ee.String(kernelType) + units = ee.String(units) + masked = self._obj.mask().Not() + buffer = masked.focalMax(radius, kernelType, units) + return self._obj.updateMask(buffer.Not()) + + @classmethod + def full( + self, + values: list | ee.List = [0], + names: list | ee.List = ["constant"], + ) -> ee.Image: + """Create an image with the given values and names. + + Parameters: + values: The values to initialize the image with. If one value is given, it will be used for all bands. + names: The names of the bands. By default it uses the earthen engine default value, "constant". + + Returns: + An image with the given values and names. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image.geetools.full([1, 2, 3], ['a', 'b', 'c']) + print(image.bandNames().getInfo()) + """ + values, names = ee.List(values), ee.List(names) + + # resize value to the same length as names + values = ee.List( + ee.Algorithms.If( + values.size().eq(1), + ee.List.repeat(ee.Number(values.get(0)), names.size()), + values, + ) + ) + return ee.Image.constant(values).rename(names) + + def fullLike( + self, + fillValue: float | int | ee.Number, + copyProperties: int | ee.Number = 0, + keepMask: int | ee.Number = 0, + keepFootprint: int | ee.Number = 1, + ) -> ee.Image: + """Create an image with the same band names, projection and scale as the original image. + + The projection is computed on the first band, make sure all bands have the same. + The procduced image can also copy the properties of the original image and keep the mask. + + Parameters: + fillValue: The value to fill the image with. + copyProperties: If True, the properties of the original image will be copied to the new one. + keepMask: If True, the mask of the original image will be copied to the new one. + keepFootprint: If True, the footprint of the original image will be used to clip the new image. + + Returns: + An image with the same band names, projection and scale as the original image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image.geetools.fullLike(0) + print(image.bandNames().getInfo()) + """ + # function params as GEE objects + keepMask, copyProperties = ee.Number(keepMask), ee.Number(copyProperties) + keepFootprint = ee.Number(keepFootprint) + # get geometry, band names and property names + footprint, bandNames = self._obj.geometry(), self._obj.bandNames() + properties = self._obj.propertyNames().remove( + "system:footprint" + ) # remove footprint as a "normal" property + # list of values to fill the image + fillValue = ee.List.repeat(fillValue, bandNames.size()) + # filled image + image = self.full(fillValue, bandNames) + # handler projection + projected_list = bandNames.map( + lambda b: image.select([b]).reproject(self._obj.select([b]).projection()) + ) + image = ee.ImageCollection.fromImages(projected_list).toBands().rename(bandNames) + # handle footprint + image_footprint = image.clip(footprint) # sets system:footprint property + image = ee.Image(ee.Algorithms.If(keepFootprint, image_footprint, image)) + # handle properties + withProperties = image.copyProperties(self._obj, properties) + image = ee.Algorithms.If(copyProperties, withProperties, image) + # handle mask + withMask = ee.Image(image).updateMask(self._obj.mask()) + image = ee.Image(ee.Algorithms.If(keepMask, withMask, image)) + # handle band types + return ee.Image(image.cast(self._obj.bandTypes())) + + def reduceBands( + self, + reducer: str | ee.Reducer, + bands: list | ee.List = [], + name: str | ee.String = "", + ) -> ee.Image: + """Reduce the image using the selected reducer and adding the result as a band using the selected name. + + Args: + bands: The bands to reduce + reducer: The reducer to use + name: The name of the new band + + Returns: + The image with the new reduced band added + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + image = image.geetools.reduceBands("mean", ['B1', 'B2']) + print(image.bandNames().getInfo()) + """ + # the reduce method only accept client side string + if not isinstance(reducer, str): + raise TypeError("reducer must be a Python string") + + bands, name = ee.List(bands), ee.String(name) + bands = ee.Algorithms.If(bands.size().eq(0), self._obj.bandNames(), bands) + name = ee.Algorithms.If(name.equals(ee.String("")), reducer, name) + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + reduceImage = self._obj.select(ee.List(bands)).reduce(red).rename([name]) + return self._obj.addBands(reduceImage) + + def negativeClip(self, geometry: ee.Geometry | ee.Feature | ee.FeatureCollection) -> ee.Image: + """The opposite of the clip method. + + The inside of the geometry will be masked from the image. + + Args: + geometry: The geometry to mask from the image. + + Returns: + The image with the geometry masked. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + src, bands = "COPERNICUS/S2_SR_HARMONIZED", ["B1", "B2", "B3"] + vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) + + image = ee.ImageCollection(src).filterBounds(vatican).first().select(bands) + image = image.geetools.negativeClip(vatican) + print(image.reduceRegion(ee.Reducer.mean(), vatican, 1).getInfo()) + """ + return self._obj.updateMask(self._obj.clip(geometry).mask().Not()) + + def format( + self, + string: str | ee.String, + dateFormat: str | ee.String = "yyyy-MM-dd", + ) -> ee.String: + """Create a string from using the given pattern and using the image properties. + + The ``system_date`` property is special cased to fit the dateFormat parameter. + + Args: + string: The pattern to use for the string + dateFormat: The date format to use for the system_date property + + Returns: + The string corresponding to the image + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM') + string = image.geetools.format('this is the image date: {system_date}') + print(string.getInfo()) + """ + dateFormat, string = ee.String(dateFormat), ee.String(string) + + patternList = string.match(r"\{([^}]+)\}", "g") + + def replaceProperties(p, s): + p = ee.String(p) + prop = self._obj.get(p.slice(1, -1)) + date = self._obj.date().format(dateFormat) + prop = ee.Algorithms.If(p.equals("{system_date}"), date, prop) + # return ee.String(s).cat(date) + return ee.String(s).replace(p, ee.String(prop)) + + return patternList.iterate(replaceProperties, string) + + def gauss(self, band: str | ee.String = "") -> ee.Image: + """Apply a gaussian filter to the image. + + We apply the following function to the image: "exp(((val-mean)**2)/(-2*(std**2)))" + where val is the value of the pixel, mean is the mean of the image, std is the standard deviation of the image. + + See the `Gaussian filter `_ Wikipedia page for more information. + + Args: + band: The band to apply the gaussian filter to. If empty, the first one is selected. + + Returns: + The image with the gaussian filter applied.An single band image with the gaussian filter applied. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() + image = image.geetools.gauss() + print(image.bandNames().getInfo()) + """ + band = ee.String(band) if band else ee.String(self._obj.bandNames().get(0)) + image = self._obj.select(band) + + kwargs = {"geometry": image.geometry(), "bestEffort": True} + mean = image.reduceRegion(ee.Reducer.mean(), **kwargs).get(band) + std = image.reduceRegion(ee.Reducer.stdDev(), **kwargs).get(band) + + return image.expression( + "exp(((val-mean)**2)/(-2*(std**2)))", + { + "val": image, + "mean": ee.Image.constant(mean), + "std": ee.Image.constant(std), + }, + ).rename(band.cat("_gauss")) + + def repeat(self, band, repeats: int | ee.Number) -> ee.image: + """Repeat a band of the image. + + Args: + band: The band to repeat + repeats: The number of times to repeat the band + + Returns: + The image with the band repeated + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() + image = image.geetools.repeat('B1', 2) + print(image.bandNames().getInfo()) + """ + band, repeats = ee.String(band), ee.Number(repeats).toInt() + + sequence = ee.List.sequence(1, repeats) + image = self._obj.select(band) + + def addBand(n, i): + name = band.cat("_").cat(ee.Number(n).toInt().format()) + return ee.Image(i).addBands(image.rename(name)) + + return ee.Image(sequence.iterate(addBand, self._obj)) + + def removeZeros(self) -> ee.Image: + """Return an image array with non-zero values extracted from each band. + + This function processes a multi-band image array, where each band represents different data. + It removes zero values from each band independently and then combines the non-zero values from all bands into a single image. + The resulting image may have inconsistent array lengths for each pixel, as the number of zero values removed can vary across bands. + + Returns: + The image with the zero values removed from each band. + + Example: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) + image = ee.Image([0, 1, 2]).toArray() + image = image.geetools.removeZeros() + values = image.reduceRegion(ee.Reducer.first(), vatican, 1) + print(values.getInfo()) + """ + bands = self._obj.bandNames() + + def remove(band): + image = self._obj.select([band]) + isZero = image.divide(image) + countZeros = isZero.arrayReduce(ee.Reducer.sum(), [0]).multiply(-1) + nbZeros = countZeros.arrayProject([0]).arrayFlatten([["n"]]).toInt() + return image.arraySort().arraySlice(0, nbZeros) + + return ee.ImageCollection(bands.map(remove)).toBands().rename(bands) + + def interpolateBands(self, src: list | ee.List, to: list | ee.List) -> ee.Image: + """Interpolate bands from the "src" value range to the "to" value range. + + The Interpolation is performed linearly using the "extrapolate" option of the "interpolate" method. + + Args: + src: The source value range + to: The target value range + + Returns: + The image with the interpolated bands + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + vatican = ee.Geometry.Point([12.4534, 41.9033]).buffer(1) + image = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterBounds(vatican).first() + image = image.select(["B4", "B3", "B2"]).geetools.interpolateBands([0, 3000], [0, 30]) + values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) + print(values.getInfo()) + """ + bands = self._obj.bandNames() + src, to = ee.List(src), ee.List(to) + + def interpolate(band): + original = self._obj.select([band]) + normalized = original.unitScale(src.get(0), src.get(1)) + return normalized.interpolate([0, 1], to) + + return ee.ImageCollection(bands.map(interpolate)).toBands().rename(bands) + + def isletMask(self, offset: float | int | ee.Number) -> ee.Image: + """Compute the islet mask from an image. + + An islet is a set of non-masked pixels connected together by their edges of very small surface. The user define the offset of the island size and we compute the max number of pixels to improve computation speed. The inpt Image needs to be a single band binary image. + + Args: + offset: The limit of the islet size in square metters + + Returns: + The island mask + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").first() + mask = image.select('SCL').eq(4) + mask = mask.geetools.islandMask(100) + print(mask.bandNames().getInfo()) + """ + offset = ee.Number(offset) + scale = self._obj.projection().nominalScale() + pixelsLimit = offset.multiply(2).sqrt().divide(scale).max(ee.Number(2)).toInt() + area = ee.Image.pixelArea().rename("area") + isletArea = ( + self._obj.select(0).mask().toInt().connectedPixelCount(pixelsLimit).multiply(area) + ) + return isletArea.lt(offset).rename("mask").selfMask() + + # -- ee-extra wrapper ------------------------------------------------------ + def index_list(cls) -> dict: + """Return the list of indices implemented in this module. + + Returns: + List of indices implemented in this module + + Examples: + .. code-block:: python + + import ee, geetools + + ind = ee.Image.geetools.indices()["BAIS2"] + print(ind["long_name"]) + print(ind["formula"]) + print(ind["reference"]) + """ + return ee_extra.Spectral.core.indices() + + def spectralIndices( + self, + index: str = "NDVI", + G: float | int = 2.5, + C1: float | int = 6.0, + C2: float | int = 7.5, + L: float | int = 1.0, + cexp: float | int = 1.16, + nexp: float | int = 2.0, + alpha: float | int = 0.1, + slope: float | int = 1.0, + intercept: float | int = 0.0, + gamma: float | int = 1.0, + omega: float | int = 2.0, + beta: float | int = 0.05, + k: float | int = 0.0, + fdelta: float | int = 0.581, + kernel: str = "RBF", + sigma: str = "0.5 * (a + b)", + p: float | int = 2.0, + c: float | int = 1.0, + lambdaN: float | int = 858.5, + lambdaR: float | int = 645.0, + lambdaG: float | int = 555.0, + online: float | int = False, + ) -> ee.Image: + """Computes one or more spectral indices (indices are added as bands) for an image from the Awesome List of Spectral Indices. + + Parameters: + self: Image to compute indices on. Must be scaled to [0,1]. + index: Index or list of indices to compute, default = 'NDVI' + Available options: + - 'vegetation' : Compute all vegetation indices. + - 'burn' : Compute all burn indices. + - 'water' : Compute all water indices. + - 'snow' : Compute all snow indices. + - 'urban' : Compute all urban (built-up) indices. + - 'kernel' : Compute all kernel indices. + - 'all' : Compute all indices listed below. + - Awesome Spectral Indices for GEE: Check the complete list of indices `here `_. + G: Gain factor. Used just for index = 'EVI', default = 2.5 + C1: Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI', default = 6.0 + C2: Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI', default = 7.5 + L: Canopy background adjustment. Used just for index = ['EVI','SAVI'], default = 1.0 + cexp: Exponent used for OCVI, default = 1.16 + nexp: Exponent used for GDVI, default = 2.0 + alpha: Weighting coefficient used for WDRVI, default = 0.1 + slope: Soil line slope, default = 1.0 + intercept: Soil line intercept, default = 0.0 + gamma: Weighting coefficient used for ARVI, default = 1.0 + omega: Weighting coefficient used for MBWI, default = 2.0 + beta: Calibration parameter used for NDSIns, default = 0.05 + k: Slope parameter by soil used for NIRvH2, default = 0.0 + fdelta: Adjustment factor used for SEVI, default = 0.581 + kernel: Kernel used for kernel indices, default = 'RBF' + Available options: + - 'linear' : Linear Kernel. + - 'RBF' : Radial Basis Function (RBF) Kernel. + - 'poly' : Polynomial Kernel. + sigma: Length-scale parameter. Used for kernel = 'RBF', default = '0.5 * (a + b)'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive. + p: Kernel degree. Used for kernel = 'poly', default = 2.0 + c: Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel. Used for kernel = 'poly', default = 1.0. This must be greater than or equal to 0. + lambdaN: NIR wavelength used for NIRvH2 and NDGI, default = 858.5 + lambdaR: Red wavelength used for NIRvH2 and NDGI, default = 645.0 + lambdaG: Green wavelength used for NDGI, default = 555.0 + drop: Whether to drop all bands except the new spectral indices, default = False + + Returns: + Image with the computed spectral index, or indices, as new bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + image = image.specralIndices(["NDVI", "NDFI"]) + """ + # fmt: off + return ee_extra.Spectral.core.spectralIndices( + self._obj, index, G, C1, C2, L, cexp, nexp, alpha, slope, intercept, gamma, omega, + beta, k, fdelta, kernel, sigma, p, c, lambdaN, lambdaR, lambdaG, online, + drop=False, + ) + # fmt: on + + def getScaleParams(self) -> dict: + """Gets the scale parameters for each band of the image. + + Returns: + Dictionary with the scale parameters for each band. + + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('MODIS/006/MOD11A2').first().geetools.getScaleParams() + """ + return ee_extra.STAC.core.getScaleParams(self._obj) + + def getOffsetParams(self) -> dict: + """Gets the offset parameters for each band of the image. + + Returns: + Dictionary with the offset parameters for each band. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('MODIS/006/MOD11A2').first().getOffsetParams() + """ + return ee_extra.STAC.core.getOffsetParams(self._obj) + + def scaleAndOffset(self) -> ee.Image: + """Scales bands on an image according to their scale and offset parameters. + + Returns: + Scaled image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + S2 = ee.ImageCollection('COPERNICUS/S2_SR').first().scaleAndOffset() + """ + return ee_extra.STAC.core.scaleAndOffset(self._obj) + + def preprocess(self, **kwargs) -> ee.Image: + """Pre-processes the image: masks clouds and shadows, and scales and offsets the image. + + Parameters: + **kwargs: Keywords arguments for ``maskClouds`` method. + + Returns: + Pre-processed image. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + S2 = ee.ImageCollection('COPERNICUS/S2_SR').first().preprocess() + """ + return ee_extra.QA.pipelines.preprocess(self._obj, **kwargs) + + def getSTAC(self) -> dict: + """Gets the STAC of the image. + + Returns: + STAC of the image. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('COPERNICUS/S2_SR').first().getSTAC() + """ + # extract the Asset id from the imagecollection + assetId = self._obj.get("system:id").getInfo() + + # search for the project in the GEE catalog and extract the project catalog URL + project = assetId.split("/")[0] + catalog = "https://earthengine-stac.storage.googleapis.com/catalog/catalog.json" + links = requests.get(catalog).json()["links"] + project_catalog = next((i["href"] for i in links if i.get("title") == project), None) + if project_catalog is None: + raise ValueError(f"Project {project} not found in the catalog") + + # search for the collection in the project catalog and extract the collection STAC URL + collection = "_".join(assetId.split("/")[:-1]) + links = requests.get(project_catalog).json()["links"] + collection_stac = next((i["href"] for i in links if i.get("title") == collection), None) + if collection_stac is None: + raise ValueError(f"Collection {collection} not found in the {project} catalog") + + return requests.get(collection_stac).json() + + def getDOI(self) -> str: + """Gets the DOI of the image, if available. + + Returns: + DOI of the ee.Image dataset. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('NASA/GPM_L3/IMERG_V06').first().getDOI() + """ + return ee_extra.STAC.core.getDOI(self._obj) + + def getCitation(self) -> str: + """Gets the citation of the image, if available. + + Returns: + Citation of the ee.Image dataset. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('NASA/GPM_L3/IMERG_V06').first().getCitation() + """ + return ee_extra.STAC.core.getCitation(self._obj) + + def panSharpen(self, method: str = "SFIM", qa: str = "", **kwargs) -> ee.Image: + """Apply panchromatic sharpening to the Image. + + Optionally, run quality assessments between the original and sharpened Image to + measure spectral distortion and set results as properties of the sharpened Image. + + Parameters: + method: The sharpening algorithm to apply. Current options are "SFIM" (Smoothing Filter-based Intensity Modulation), "HPFA" (High Pass Filter Addition), "PCS" (Principal Component Substitution), and "SM" (simple mean). Different sharpening methods will produce different quality sharpening results in different scenarios. + qa: One or more optional quality assessment names to apply after sharpening. Results will be stored as image properties with the pattern `geetools:metric`, e.g. `geetools:RMSE`. + **kwargs: Keyword arguments passed to ee.Image.reduceRegion() such as "geometry", "maxPixels", "bestEffort", etc. These arguments are only used for PCS sharpening and quality assessments. + + Returns: + The Image with all sharpenable bands sharpened to the panchromatic resolution and quality assessments run and set as properties. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") + sharp = source.panSharpen(method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13) + """ + return ee_extra.Algorithms.core.panSharpen( + img=self._obj, method=method, qa=qa, prefix="geetools", **kwargs + ) + + def tasseledCap(self) -> ee.Image: + """Calculates tasseled cap brightness, wetness, and greenness components. + + Tasseled cap transformations are applied using coefficients published for these + supported platforms: + + * Sentinel-2 MSI Level 1C + * Landsat 9 OLI-2 SR + * Landsat 9 OLI-2 TOA + * Landsat 8 OLI SR + * Landsat 8 OLI TOA + * Landsat 7 ETM+ TOA + * Landsat 5 TM Raw DN + * Landsat 4 TM Raw DN + * Landsat 4 TM Surface Reflectance + * MODIS NBAR + + Parameters: + self: ee.Image to calculate tasseled cap components for. Must belong to a supported platform. + + Returns: + Image with the tasseled cap components as new bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + img = img.tasseledCap() + """ + return ee_extra.Spectral.core.tasseledCap(self._obj) + + def matchHistogram( + self, + target: ee.Image, + bands: dict, + geometry: ee.Geometry | None = None, + maxBuckets: int = 256, + ) -> ee.Image: + """Adjust the image's histogram to match a target image. + + Parameters: + target: Image to match. + bands: A dictionary of band names to match, with source bands as keys and target bands as values. + geometry: The region to match histograms in that overlaps both images. If none is provided, the geometry of the source image will be used. + maxBuckets: The maximum number of buckets to use when building histograms. Will be rounded to the nearest power of 2. + + Returns: + The adjusted image containing the matched source bands. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") + target = ee.Image("LANDSAT/LE07/C01/T1_TOA/LE07_046027_20150701") + bands = { + "B4": "B3", + "B3": "B2", + "B2": "B1" + } + matched = source.matchHistogram(target, bands) + """ + return ee_extra.Spectral.core.matchHistogram( + source=self._obj, + target=target, + bands=bands, + geometry=geometry, + maxBuckets=maxBuckets, + ) + + def maskClouds( + self, + method: str = "cloud_prob", + prob: int = 60, + maskCirrus: bool = True, + maskShadows: bool = True, + scaledImage: bool = False, + dark: float = 0.15, + cloudDist: int = 1000, + buffer: int = 250, + cdi: int | None = None, + ): + """Masks clouds and shadows in an image (valid just for Surface Reflectance products). + + Parameters: + self: Image to mask. + method: Method used to mask clouds. This parameter is ignored for Landsat products. + Available options: + - 'cloud_prob' : Use cloud probability. + - 'qa' : Use Quality Assessment band. + prob: Cloud probability threshold. Valid just for method = 'cloud_prob'. This parameter is ignored for Landsat products. + maskCirrus: Whether to mask cirrus clouds. Default to ``True``. Valid just for method = 'qa'. This parameter is ignored for Landsat products. + maskShadows: Whether to mask cloud shadows. Default to ``True`` This parameter is ignored for Landsat products. + scaledImage: Whether the pixel values are scaled to the range [0,1] (reflectance values). This parameter is ignored for Landsat products. + dark: NIR threshold. NIR values below this threshold are potential cloud shadows. This parameter is ignored for Landsat products. + cloudDist: Maximum distance in meters (m) to look for cloud shadows from cloud edges. This parameter is ignored for Landsat products. + buffer: Distance in meters (m) to dilate cloud and cloud shadows objects. This parameter is ignored for Landsat products. + cdi: Cloud Displacement Index threshold. Values below this threshold are considered potential clouds. A cdi = None means that the index is not used. This parameter is ignored for Landsat products. + + Returns: + Cloud-shadow masked image. + + Notes: + This method may mask water as well as clouds for the Sentinel-3 Radiance product. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + S2 = ( + ee.ImageCollection('COPERNICUS/S2_SR') + .first() + .maskClouds(prob = 75,buffer = 300,cdi = -0.5)) + """ + return ee_extra.QA.clouds.maskClouds( + self._obj, + method, + prob, + maskCirrus, + maskShadows, + scaledImage, + dark, + cloudDist, + buffer, + cdi, + ) + + def removeProperties(self, properties: list | ee.List) -> ee.Image: + """Remove a list of properties from an image. + + Args: + properties: List of properties to remove from the image. + + Returns: + Image with the specified properties removed. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + image = image.removeProperties(["system:time_start"]) + """ + properties = ee.List(properties) + proxy = self._obj.multiply(1) # drop properties + return ee.Image(proxy.copyProperties(self._obj, exclude=properties)) + + def distanceToMask( + self, + mask: ee.Image, + kernel: str = "euclidean", + radius: int = 1000, + band_name: str | ee.String = "distance_to_mask", + ) -> ee.Image: + """Compute the distance from each pixel to the nearest non-masked pixel. + + Parameters: + mask: The mask to compute the distance to. + kernel: The kernel type to use for the distance computation default to "euclidean". + radius: The radius of the kernel. + band_name: The name of the band to store the distance values. + + Returns: + The original images with the distance band added. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + centerBuffer = image.geometry().centroid().buffer(100) + BufferMask = ee.Image.constant(1).clip(centerBuffer) + mask = ee.Image.constant(0).where(BufferMask, 1).clip(image.geometry()) + image = image.distanceToMask(mask) + """ + # gather the parameters + kernel = getattr(ee.Kernel, kernel)(radius, "meters") + bandName = ee.String(band_name) + + # compute the distance + distance = self._obj.select(0).mask().Not().distance(kernel).rename(bandName) + distMask = distance.mask().Not().remap([0, 1], [0, radius]) + final = distance.unmask().add(distMask) + + return self._obj.addBands(final) + + def distance(self, other: ee.image) -> ee.Image: + """Compute the sum of all spectral distance between two images. + + Parameters: + other: The image to compute the distance to. + + Returns: + and Image with the euclidean distance between the two images for each band. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + other = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + image = image.distance(other) + """ + # compute the distance + distance = self._obj.subtract(other).pow(2).reduce("sum").sqrt().rename("sum_distance") + + return ee.Image(distance) + + def maskCoverRegion( + self, + region: ee.Geometry, + scale: Optional[int | ee.Number] = None, + band: Optional[str | ee.String] = None, + proxyValue: int | ee.Number = -999, + **kwargs, + ) -> ee.Number: + """Compute the coverage of masked pixels inside a Geometry. + + Parameters: + region: The region to compute the mask coverage. + scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. + band: The band to use. Defaults to the first band. + proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. In most cases the user should not change this value, but in case of conflicts, choose a value that is out of the range of the image values. + + Kwargs: + maxPixels: The maximum number of pixels to reduce. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + The percentage of masked pixels within the region + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) + image = image.maskCoverRegion(aoi) + """ + # compute the mask cover + image = self._obj.select(band or 0) + scale = scale or image.projection().nominalScale() + unmasked = image.unmask(proxyValue) + mask = unmasked.eq(proxyValue) + cover = mask.reduceRegion( + ee.Reducer.frequencyHistogram(), region, scale=scale, bestEffort=True, **kwargs + ) + # The cover result is a dictionary with each band as key (in our case the first one). + # For each band key the number of 0 and 1 is stored in a dictionary. + # We need to extract the number of 1 and 0 to compute the ratio which implys lots of casting. + values = ee.Dictionary(cover.values().get(0)) + zeros, ones = ee.Number(values.get("0", 0)), ee.Number(values.get("1", 0)) + ratio = ones.divide(zeros.add(ones)).multiply(100) + + # we want to display this result as a 1 digit float + return ratio + + def maskCoverRegions( + self, + collection: ee.FeatureCollection, + scale: Optional[int | ee.Number] = None, + band: Optional[str | ee.String] = None, + proxyValue: int | ee.Number = -999, + columnName: str | ee.String = "mask_cover", + **kwargs, + ) -> ee.FeatureCollection: + """Compute the coverage of masked pixels inside a Geometry. + + Parameters: + collection: The collection to compute the mask coverage (in each Feature). + scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. + band: The band to use. Defaults to the first band. + proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. In most cases the user should not change this value, but in case of conflicts, choose a value that is out of the range of the image values. + columnName: name of the column that will hold the value. + + Kwargs: + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + The passed table with the new column containing the percentage of masked pixels within the region + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + reg = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) + aoi = ee.FeatureCollection([ee.Feature(reg)]) + image = image.maskCoverRegions(aoi) + """ + # compute the mask cover + properties = collection.propertyNames() # original properties + image = self._obj.select(band or 0) + scale = scale or image.projection().nominalScale() + unmasked = image.unmask(proxyValue) + mask = unmasked.eq(proxyValue) + column = "_geetools_histo_" + cover = mask.reduceRegions( + collection=collection, + reducer=ee.Reducer.frequencyHistogram().setOutputs([column]), + scale=scale, + **kwargs, + ) + + def compute_percentage(feat: ee.Feature) -> ee.Feature: + histo = ee.Dictionary(feat.get(column)) + zeros, ones = ee.Number(histo.get("0", 0)), ee.Number(histo.get("1", 0)) + ratio = ones.divide(zeros.add(ones)).multiply(100) + return feat.select(properties).set(columnName, ratio) + + return cover.map(compute_percentage) + + def maskCover( + self, + scale: Optional[int] = None, + proxyValue: int = -999, + propertyName: str = "mask_cover", + **kwargs, + ) -> ee.Image: + """Compute the percentage of masked pixels inside the image. + + It will use the geometry and the first band of the image. + + Parameters: + scale: The scale of the computation. In case you need a rough estimation use a higher scale than the original from the image. + proxyValue: the value to use for counting the mask and avoid confusing 0s to masked values. Choose a value that is out of the range of the image values. + propertyName: the name of the property where the value will be saved + + Kwargs: + maxPixels: The maximum number of pixels to reduce. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + The same image with the percentage of masked pixels as a property + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(2000) + image = image.maskCoverRegion(aoi) + """ + region = self._obj.geometry() + value = self.maskCoverRegion(region, scale, None, proxyValue, **kwargs) + return self._obj.set(propertyName, value) + + def plot( + self, + bands: list, + region: ee.Geometry, + ax: Axes | None = None, + fc: ee.FeatureCollection = None, + cmap: str = "viridis", + crs: str = "EPSG:4326", + scale: float = 0.0001, # 0.0001 is the default scale for Sentinel-2 + color="k", + ) -> Axes: + """Plot the image on a matplotlib axis. + + Parameters: + bands: The bands to plot. + region: The geometry borders to plot the image on. + ax: The matplotlib axis to plot the image on. + fc: a FeatureCollection object to overlay on top of the image. Default is None, it can be a different object from the region. + cmap: The colormap to use for the image. Default is 'viridis'. can only ber used for single band images. + crs: The coordinate reference system of the image. if not set we use the projection of the first band. + scale: The scale of the image. + color: The color of the overlaid feature collection. Default is "k" (black). + + Examples: + .. code-block:: python + + import ee, geetools + import matplotlib.pyplot as plt + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + fig, ax = plt.subplots() + image.plot(["B2", "B3", "B4"], image.geometry(), ax) + """ + if ax is None: + fig, ax = plt.subplots() + + # compute the crs from the image if necessary + if crs == "": + crs = self._obj.projection().crs().getInfo() + + # extract the image as a xarray dataset + ds = xarray.open_dataset( + ee.ImageCollection([self._obj]), + engine="ee", + crs=crs, + scale=scale, + geometry=region.bounds(), + request_byte_limit=REQUEST_BYTE_LIMIT, + ) + + # extract all the bands as dataarrays objects + # x and y coordinates need to be transposed to match imshow requirements + bands_da = [ds[b][0, :, :].transpose() for b in bands] + + # compute the extend of the image so the unit displayed for x and y are matching the required crs + proj = Transformer.from_crs(CRS("EPSG:4326"), CRS(crs), always_xy=False) + region_bounds = region.bounds().coordinates().get(0).getInfo() + min_x, min_y = proj.transform(*region_bounds[0]) + max_x, max_y = proj.transform(*region_bounds[2]) + + # set the parameters that will be use for single and multi-band display + params = dict(extent=[min_x, max_x, min_y, max_y], origin="lower") + + # For single band image, we use the data array directly as source image + # for multi band image, we need to stack the dataarrays to create a RGB image + # and normalized them + if len(bands) == 1: + ax.imshow(bands_da[0], cmap=cmap, **params) + else: + da = np.dstack(bands_da) + rgb_image = (da - np.min(da)) / (np.max(da) - np.min(da)) + ax.imshow(rgb_image, **params) + + # add the feature collection if provided + # we need to extract the geometries and plot them + if fc is not None: + gdf = gpd.GeoDataFrame.from_features(fc.getInfo()["features"]) + gdf = gdf.set_crs("EPSG:4326").to_crs(crs) + gdf.boundary.plot(ax=ax, color=color) + + # The default aspect for map plots is 'auto'; if however data are not projected (coordinates are long/lat), + # the aspect is by default set to 1/cos(s_y * pi/180) with s_y the y coordinate of the middle of the + # region (the mean of the y range of bounding box) so that a long/lat square appears square in the + # middle of the plot. This implies an Equirectangular projection. + if CRS(crs).is_geographic: + y_coord = np.mean([min_y, max_y]) + ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) + else: + ax.set_aspect("auto") + + # make sure the canvas is only rendered once. + ax.figure.canvas.draw_idle() + + return ax + + @classmethod + def fromList(cls, images: ee.List | list): + """Create a single image by passing a list of images. + + Warning: The bands cannot have repeated names, if so, it will throw an error (see examples). + + Parameters: + images: a list of ee.Image + + Returns: + A single ee.Image with one band per image in the passed list + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + sequence = ee.List([1, 2, 3]) + images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) + image = ee.Image.geetools.fromList(images) + print(image.bandNames().getInfo()) + + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + sequence = ee.List([1, 2, 2, 3]) + images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) + image = ee.Image.geetools.fromList(images) + print(image.bandNames().getInfo()) + > ee.ee_exception.EEException: Image.rename: Can't add a band named '2' to image because a band with this name already exists. Existing bands: [1, 2]. + """ + bandNames = ee.List(images).map(lambda i: ee.Image(i).bandNames()).flatten() + ic = ee.ImageCollection.fromImages(images) + return ic.toBands().rename(bandNames) + + def byBands( + self, + regions: ee.featurecollection, + reducer: str | ee.Reducer = "mean", + bands: list = [], + regionId: str = "system:index", + labels: list = [], + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> ee.Dictionary: + """Compute a reducer for each band of the image in each region. + + This method is returning a dictionary with all the bands as keys and their reduced value in each region as values. + + .. code-block:: + + { + "band1": {"feature1": value1, "feature2": value2, ...}, + "band2": {"feature1": value1, "feature2": value2, ...}, + ... + } + + Parameters: + regions: The regions to compute the reducer in. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + regionId: The property used to label region. Defaults to "system:index". + labels: The labels to use for the output dictionary. Default to the band names. + bands: The bands to compute the reducer on. Default to all bands. + scale: The scale to use for the computation. Default is 10000m. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with all the bands as keys and their values in each region as a list. + + See Also: + - :docstring:`ee.Image.geetools.byRegions` + - :docstring:`ee.Image.geetools.plot_by_bands` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() + d = normClim.byBands(ecoregions, ee.Reducer.mean(), scale=10000) + print(d.getInfo()) + """ + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = regions.aggregate_array(regionId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + + # get the bands to be used in the reducer + eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() + + # retrieve the label to use for each bands if provided + eeLabels = ee.List(labels) if len(labels) else eeBands + + # by default for 1 band image, the reducers are renaming the output band. To ensure it keeps + # the original band name we add setOutputs that is ignored for multi band images. + # This is currently hidden because of https://issuetracker.google.com/issues/374285504 + # It will have no impact on most of the cases as plt_hist should be used for single band images + # reducer = reducer.setOutputs(labels) + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + + # retrieve the reduce bands for each feature + image = self._obj.select(eeBands).rename(eeLabels) + fc = image.reduceRegions( + collection=regions, + reducer=red, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ) + + # extract the data as a list of dictionaries (one for each label) aggregating + # the values for each feature + values = eeLabels.map(lambda b: ee.Dictionary.fromLists(features, fc.aggregate_array(b))) + + return ee.Dictionary.fromLists(eeLabels, values) + + def byRegions( + self, + regions: ee.featurecollection, + reducer: str | ee.Reducer = "mean", + bands: list = [], + regionId: str = "system:index", + labels: list = [], + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> ee.Dictionary: + """Compute a reducer in each region of the image for eah band. + + This method is returning a dictionary with all the features as keys and their reduced value for each band as values. + + .. code-block:: + + { + "feature1": {"band1": value1, "band2": value2, ...}, + "feature2": {"bands1": value1, "band2": value2, ...}, + ... + } + + Parameters: + regions: The regions to compute the reducer in. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + regionId: The property used to label region. Defaults to "system:index". + labels: The labels to use for the output dictionary. Default to the band names. + bands: The bands to compute the reducer on. Default to all bands. + scale: The scale to use for the computation. Default is 10000m. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + + Returns: + A dictionary with all the bands as keys and their values in each region as a list. + + See Also: + - :docstring:`ee.Image.geetools.byBands` + - :docstring:`ee.Image.geetools.plot_by_regions` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() + d = normClim.byregions(ecoregions, ee.Reducer.mean(), scale=10000) + print(d.getInfo()) + """ + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = regions.aggregate_array(regionId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + + # get the bands to be used in the reducer + bands = ee.List(bands) if len(bands) else self._obj.bandNames() + + # retrieve the label to use for each bands if provided + labels = ee.List(labels) if len(labels) else bands + + # by default for 1 band image, the reducers are renaming the output band. To ensure it keeps + # the original band name we add setOutputs that is ignored for multi band images. + # This is currently hidden because of https://issuetracker.google.com/issues/374285504 + # It will have no impact on most of the cases as plt_hist should be used for single band images + # reducer = reducer.setOutputs(labels) + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + + # retrieve the reduce bands for each feature + image = self._obj.select(bands).rename(labels) + fc = image.reduceRegions( + collection=regions, + reducer=red, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ) + + # extract the data as a list of dictionaries (one for each label) aggregating + # we are force to turn the fc into a list because GEE don't accept to map a featureCollection + # into something else (in our a case a dict) + fcList = fc.toList(fc.size()) + values = fcList.map(lambda f: ee.Feature(f).select(labels).toDictionary()) + + return ee.Dictionary.fromLists(features, values) + + def plot_by_regions( + self, + type: str, + regions: ee.FeatureCollection, + reducer: str | ee.Reducer = "mean", + bands: list = [], + regionId: str = "system:index", + labels: list = [], + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced values for each region. + + Each region will be plotted using the ``regionId`` as x-axis label defauting to "system:index" if not provided. + If no ``bands`` are provided, all bands will be plotted. + If no ``labels`` are provided, the band names will be used. + + Warning: + This method is client-side. + + Parameters: + type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! + regions: The regions to compute the reducer in. + rreducer: The name of the reducer or a reducer object to use. Default is "mean". + bands: The bands to compute the reducer on. Default to all bands. + regionId: The property used to label region. Defaults to "system:index". + labels: The labels to use for the output dictionary. Default to the band names. + colors: The colors to use for the plot. Default to the default matplotlib colors. + ax: The matplotlib axis to plot the data on. If None, a new figure is created. + scale: The scale to use for the computation. Default is 10000m. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + + Returns: + The matplotlib axis with the plot. + + See Also: + - :docstring:`ee.Image.geetools.byRegions` + - :docstring:`ee.Image.geetools.byBands` + - :docstring:`ee.Image.geetools.plot_by_bands` + - :docstring:`ee.Image.geetools.plot_hist + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() + + + normClim.plot_by_regions(ecoregions, ee.Reducer.mean(), scale=10000) + """ + # get the data from the server + data = self.byBands( + regions=regions, + reducer=reducer, + bands=bands, + regionId=regionId, + labels=labels, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ).getInfo() + + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = regions.aggregate_array(regionId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + features = features.getInfo() + + # extract the labels from the parameters + eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() + labels = labels if len(labels) else eeBands.getInfo() + + # reorder the data according to the labels id set by the user + data = {b: {f: data[b][f] for f in features} for b in labels} + + ax = plot_data(type=type, data=data, label_name=regionId, colors=colors, ax=ax) + + return ax + + def plot_by_bands( + self, + type: str, + regions: ee.FeatureCollection, + reducer: str | ee.Reducer = "mean", + bands: list = [], + regionId: str = "system:index", + labels: list = [], + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced values for each bands. + + Each band will be plotted using the ``labels`` as x-axis label defauting to band names if not provided. + If no ``bands`` are provided, all bands will be plotted. + If no ``regionId`` are provided, the "system;index" property will be used. + + + Warning: + This method is client-side. + + Parameters: + type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! + regions: The regions to compute the reducer in. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + bands: The bands to compute the reducer on. Default to all bands. + regionId: The property used to label region. Defaults to "system:index". + labels: The labels to use for the output dictionary. Default to the band names. + colors: The colors to use for the plot. Default to the default matplotlib colors. + ax: The matplotlib axis to plot the data on. If None, a new figure is created. + scale: The scale to use for the computation. Default is 10000m. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + The matplotlib axis with the plot. + + See Also: + - :docstring:`ee.Image.geetools.byRegions` + - :docstring:`ee.Image.geetools.byBands` + - :docstring:`ee.Image.geetools.plot_by_regions` + - :docstring:`ee.Image.geetools.plot_hist + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ecoregions = ee.FeatureCollection("projects/google/charts_feature_example").select(["label", "value","warm"]) + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() + + + normClim.plot_by_bands(ecoregions, ee.Reducer.mean(), scale=10000) + """ + # get the data from the server + data = self.byRegions( + regions=regions, + reducer=reducer, + bands=bands, + regionId=regionId, + labels=labels, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ).getInfo() + + # get all the id values, they must be string so we are forced to cast them manually + # the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322 + features = regions.aggregate_array(regionId) + isString = lambda i: ee.Algorithms.ObjectType(i).compareTo("String").eq(0) # noqa: E731 + features = features.map(lambda i: ee.Algorithms.If(isString(i), i, ee.Number(i).format())) + features = features.getInfo() + + # extract the labels from the parameters + eeBands = ee.List(bands) if len(bands) else self._obj.bandNames() + labels = labels if len(labels) else eeBands.getInfo() + + # reorder the data according to the labels id set by the user + data = {f: {b: data[f][b] for b in labels} for f in features} + + ax = plot_data(type=type, data=data, label_name=regionId, colors=colors, ax=ax) + + return ax + + def plot_hist( + self, + bins: int = 30, + region: ee.Geometry | None = None, + bands: list = [], + labels: list = [], + colors: list = [], + precision: int = 2, + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int = 10**7, + tileScale: float = 1, + **kwargs, + ) -> Axes: + """Plot the histogram of the image bands. + + Parameters: + bins: The number of bins to use for the histogram. Default is 30. + region: The region to compute the histogram in. Default is the image geometry. + bands: The bands to plot the histogram for. Default to all bands. + labels: The labels to use for the output dictionary. Default to the band names. + colors: The colors to use for the plot. Default to the default matplotlib colors. + prescision: The number of decimal to keep for the histogram bins values. Default is 2. + ax: The matplotlib axis to plot the data on. If None, a new figure is created. + scale: The scale to use for the computation. Default is 10,000m. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. default to 10**7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + kwargs: Keyword arguments passed to the matplotlib fill_between() function. + + Returns: + The matplotlib axis with the plot. + + See Also: + - :docstring:`ee.Image.geetools.byRegions` + - :docstring:`ee.Image.geetools.byBands` + - :docstring:`ee.Image.geetools.plot_by_bands` + - :docstring:`ee.Image.geetools.plot_by_regions + + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + normClim = ee.ImageCollection('OREGONSTATE/PRISM/Norm91m').toBands() + normClim.plot_hist() + """ + # extract the bands from the image + eeBands = ee.List(bands) if len(bands) == 0 else self._obj.bandNames() + eeLabels = ee.List(labels).flatten() if len(labels) == 0 else eeBands + labels = eeLabels.getInfo() + + # retrieve the region from the parameters + region = region if region else self._obj.geometry() + + # extract the data from the server + image = self._obj.select(eeBands).rename(eeLabels).clip(region) + + # set the common parameters of the 3 reducers + params = { + "geometry": region, + "scale": scale, + "crs": crs, + "crsTransform": crsTransform, + "bestEffort": bestEffort, + "maxPixels": maxPixels, + "tileScale": tileScale, + } + + # compute the min and max values of the bands so w can scale the bins of the histogram + min = image.reduceRegion(**{"reducer": ee.Reducer.min(), **params}) + min = min.values().reduce(ee.Reducer.min()) + + max = image.reduceRegion(**{"reducer": ee.Reducer.max(), **params}) + max = max.values().reduce(ee.Reducer.max()) + + # compute the histogram. The result is a dictionary with each band as key and the histogram + # as values. The histograp is a list of [start of bin, value] pairs + reducer = ee.Reducer.fixedHistogram(min, max, bins) + raw_data = image.reduceRegion(**{"reducer": reducer, **params}).getInfo() + + # massage raw data to reshape them as usable source for an Axes plot + # first extract the x coordinates of the plot as a list of bins borders + # every value is duplicated but the first one to create a scale like display. + # the values are treated the same way we simply drop the last duplication to get the same size. + p = 10**precision # multiplier use to truncate the float values + x = [int(d[0] * p) / p for d in raw_data[labels[0]] for _ in range(2)][1:] + data = {e: [int(d[1]) for d in raw_data[e] for _ in range(2)][:-1] for e in labels} + + # create the graph objcet if not provided + if ax is None: + fig, ax = plt.subplots() + + # display the histogram as a fill_between plot to respect GEE lib design + for i, label in enumerate(labels): + kwargs["facecolor"] = to_rgba(colors[i], 0.2) + kwargs["edgecolor"] = to_rgba(colors[i], 1) + ax.fill_between(x, data[label], label=label, **kwargs) + + # customize the layout of the axis + ax.set_ylabel("Count") + ax.grid(axis="x" if type in ["barh"] else "y") + ax.set_axisbelow(True) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.legend(bbox_to_anchor=(1.02, 1), loc="upper left") + + return ax diff --git a/geetools/ee_image_collection.py b/geetools/ee_image_collection.py new file mode 100644 index 00000000..4aa57125 --- /dev/null +++ b/geetools/ee_image_collection.py @@ -0,0 +1,2372 @@ +"""Toolbox for the ``ee.ImageCollection`` class.""" + +from __future__ import annotations + +import uuid +from datetime import datetime as dt + +import ee +import ee_extra +import requests +import xarray +from ee import apifunction +from matplotlib.axes import Axes +from xarray import Dataset +from xee.ext import REQUEST_BYTE_LIMIT + +from .accessors import register_class_accessor +from .utils import plot_data + +PY_DATE_FORMAT = "%Y-%m-%dT%H-%M-%S" +"The python format to use to parse dates coming from GEE." + +EE_DATE_FORMAT = "YYYY-MM-dd'T'HH-mm-ss" +"The javascript format to use to burn date object in GEE." + + +@register_class_accessor(ee.ImageCollection, "geetools") +class ImageCollectionAccessor: + """Toolbox for the ``ee.ImageCollection`` class.""" + + def __init__(self, obj: ee.ImageCollection): + """Instantiate the class.""" + self._obj = obj + + # -- ee-extra wrapper ------------------------------------------------------ + def maskClouds( + self, + method: str = "cloud_prob", + prob: int = 60, + maskCirrus: bool = True, + maskShadows: bool = True, + scaledImage: bool = False, + dark: float = 0.15, + cloudDist: int = 1000, + buffer: int = 250, + cdi: int | None = None, + ) -> ee.ImageCollection: + """Masks clouds and shadows in each image of an ImageCollection (valid just for Surface Reflectance products). + + Parameters: + self: ImageCollection to mask. + method: Method used to mask clouds. This parameter is ignored for Landsat products. + Available options: + - 'cloud_prob' : Use cloud probability. + - 'qa' : Use Quality Assessment band. + prob: Cloud probability threshold. Valid just for method = 'cloud_prob'. This parameter is ignored for Landsat products. + maskCirrus: Whether to mask cirrus clouds. Default to ``True``. Valid just for method = 'qa'. This parameter is ignored for Landsat products. + maskShadows: Whether to mask cloud shadows. Default to ``True`` This parameter is ignored for Landsat products. + scaledImage: Whether the pixel values are scaled to the range [0,1] (reflectance values). This parameter is ignored for Landsat products. + dark: NIR threshold. NIR values below this threshold are potential cloud shadows. This parameter is ignored for Landsat products. + cloudDist: Maximum distance in meters (m) to look for cloud shadows from cloud edges. This parameter is ignored for Landsat products. + buffer: Distance in meters (m) to dilate cloud and cloud shadows objects. This parameter is ignored for Landsat products. + cdi: Cloud Displacement Index threshold. Values below this threshold are considered potential clouds. A cdi = None means that the index is not used. This parameter is ignored for Landsat products. + + Returns: + Cloud-shadow masked image. + + Notes: + This method may mask water as well as clouds for the Sentinel-3 Radiance product. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + S2 = ( + ee.ImageCollection('COPERNICUS/S2_SR') + .maskClouds(prob = 75,buffer = 300,cdi = -0.5) + .first() + ) + + """ + return ee_extra.QA.clouds.maskClouds( + self._obj, + method, + prob, + maskCirrus, + maskShadows, + scaledImage, + dark, + cloudDist, + buffer, + cdi, + ) + + def closest( + self, date: ee.Date | str, tolerance: int = 1, unit: str = "month" + ) -> ee.ImageCollection: + """Gets the closest image (or set of images if the collection intersects a region that requires multiple scenes) to the specified date. + + Parameters: + date: Date of interest. The method will look for images closest to this date. + tolerance: Filter the collection to [date - tolerance, date + tolerance) before searching the closest image. This speeds up the searching process for collections with a high temporal resolution. + unit: Units for tolerance. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. + + Returns: + Closest images to the specified date. + + Examples: + .. code-block:: python + + import ee + import geetools + + s2 = ee.ImageCollection('COPERNICUS/S2_SR').closest('2020-10-15') + s2.size().getInfo() + """ + return ee_extra.ImageCollection.core.closest(self._obj, date, tolerance, unit) + + def spectralIndices( + self, + index: str = "NDVI", + G: float | int = 2.5, + C1: float | int = 6.0, + C2: float | int = 7.5, + L: float | int = 1.0, + cexp: float | int = 1.16, + nexp: float | int = 2.0, + alpha: float | int = 0.1, + slope: float | int = 1.0, + intercept: float | int = 0.0, + gamma: float | int = 1.0, + omega: float | int = 2.0, + beta: float | int = 0.05, + k: float | int = 0.0, + fdelta: float | int = 0.581, + kernel: str = "RBF", + sigma: str = "0.5 * (a + b)", + p: float | int = 2.0, + c: float | int = 1.0, + lambdaN: float | int = 858.5, + lambdaR: float | int = 645.0, + lambdaG: float | int = 555.0, + online: bool = False, + ) -> ee.ImageCollection: + """Computes one or more spectral indices (indices are added as bands) for an image from the Awesome List of Spectral Indices. + + Parameters: + self: Image to compute indices on. Must be scaled to [0,1]. + index: Index or list of indices to compute, default = 'NDVI' + Available options: + - 'vegetation' : Compute all vegetation indices. + - 'burn' : Compute all burn indices. + - 'water' : Compute all water indices. + - 'snow' : Compute all snow indices. + - 'urban' : Compute all urban (built-up) indices. + - 'kernel' : Compute all kernel indices. + - 'all' : Compute all indices listed below. + - Awesome Spectral Indices for GEE: Check the complete list of indices `here `_. + G: Gain factor. Used just for index = 'EVI', default = 2.5 + C1: Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI', default = 6.0 + C2: Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI', default = 7.5 + L: Canopy background adjustment. Used just for index = ['EVI','SAVI'], default = 1.0 + cexp: Exponent used for OCVI, default = 1.16 + nexp: Exponent used for GDVI, default = 2.0 + alpha: Weighting coefficient used for WDRVI, default = 0.1 + slope: Soil line slope, default = 1.0 + intercept: Soil line intercept, default = 0.0 + gamma: Weighting coefficient used for ARVI, default = 1.0 + omega: Weighting coefficient used for MBWI, default = 2.0 + beta: Calibration parameter used for NDSIns, default = 0.05 + k: Slope parameter by soil used for NIRvH2, default = 0.0 + fdelta: Adjustment factor used for SEVI, default = 0.581 + kernel: Kernel used for kernel indices, default = 'RBF' + Available options: + - 'linear' : Linear Kernel. + - 'RBF' : Radial Basis Function (RBF) Kernel. + - 'poly' : Polynomial Kernel. + sigma: Length-scale parameter. Used for kernel = 'RBF', default = '0.5 * (a + b)'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive. + p: Kernel degree. Used for kernel = 'poly', default = 2.0 + c: Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel. Used for kernel = 'poly', default = 1.0. This must be greater than or equal to 0. + lambdaN: NIR wavelength used for NIRvH2 and NDGI, default = 858.5 + lambdaR: Red wavelength used for NIRvH2 and NDGI, default = 645.0 + lambdaG: Green wavelength used for NDGI, default = 555.0 + drop: Whether to drop all bands except the new spectral indices, default = False + + Returns: + Image with the computed spectral index, or indices, as new bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + image = ee.Image('COPERNICUS/S2_SR/20190828T151811_20190828T151809_T18GYT') + image = image.geetools.specralIndices(["NDVI", "NDFI"]) + """ + # fmt: off + return ee_extra.Spectral.core.spectralIndices( + self._obj, index, G, C1, C2, L, cexp, nexp, alpha, slope, intercept, gamma, omega, + beta, k, fdelta, kernel, sigma, p, c, lambdaN, lambdaR, lambdaG, online, + drop=False, + ) + # fmt: on + + def getScaleParams(self) -> dict: + """Gets the scale parameters for each band of the image. + + Returns: + Dictionary with the scale parameters for each band. + + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('MODIS/006/MOD11A2').geetools.getScaleParams() + """ + return ee_extra.STAC.core.getScaleParams(self._obj) + + def getOffsetParams(self) -> dict: + """Gets the offset parameters for each band of the image. + + Returns: + Dictionary with the offset parameters for each band. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('MODIS/006/MOD11A2').getOffsetParams() + """ + return ee_extra.STAC.core.getOffsetParams(self._obj) + + def scaleAndOffset(self) -> ee.ImageCollection: + """Scales bands on an image according to their scale and offset parameters. + + Returns: + Scaled image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + S2 = ee.ImageCollection('COPERNICUS/S2_SR').scaleAndOffset() + """ + return ee_extra.STAC.core.scaleAndOffset(self._obj) + + def preprocess(self, **kwargs) -> ee.ImageCollection: + """Pre-processes the image: masks clouds and shadows, and scales and offsets the image. + + Parameters: + **kwargs: Keywords arguments for ``maskClouds`` method. + + Returns: + Pre-processed image. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + S2 = ee.ImageCollection('COPERNICUS/S2_SR').preprocess() + """ + return ee_extra.QA.pipelines.preprocess(self._obj, **kwargs) + + def getSTAC(self) -> dict: + """Gets the STAC of the image. + + Returns: + STAC of the image. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('COPERNICUS/S2_SR').getSTAC() + """ + # extract the Asset id from the imagecollection + assetId = self._obj.get("system:id").getInfo() + + # search for the project in the GEE catalog and extract the project catalog URL + project = assetId.split("/")[0] + catalog = "https://earthengine-stac.storage.googleapis.com/catalog/catalog.json" + links = requests.get(catalog).json()["links"] + project_catalog = next((i["href"] for i in links if i.get("title") == project), None) + if project_catalog is None: + raise ValueError(f"Project {project} not found in the catalog") + + # search for the collection in the project catalog and extract the collection STAC URL + collection = "_".join(assetId.split("/")) + links = requests.get(project_catalog).json()["links"] + collection_stac = next((i["href"] for i in links if i.get("title") == collection), None) + if collection_stac is None: + raise ValueError(f"Collection {collection} not found in the {project} catalog") + + return requests.get(collection_stac).json() + + def getDOI(self) -> str: + """Gets the DOI of the collection, if available. + + Returns: + DOI of the ee.Image dataset. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getDOI() + """ + return ee_extra.STAC.core.getDOI(self._obj) + + def getCitation(self) -> str: + """Gets the citation of the image, if available. + + Returns: + Citation of the ee.Image dataset. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getCitation() + """ + return ee_extra.STAC.core.getCitation(self._obj) + + def panSharpen(self, method: str = "SFIM", qa: str = "", **kwargs) -> ee.ImageCollection: + """Apply panchromatic sharpening to the ImageCollection images. + + Optionally, run quality assessments between the original and sharpened Image to + measure spectral distortion and set results as properties of the sharpened Image. + + Parameters: + method: The sharpening algorithm to apply. Current options are "SFIM" (Smoothing Filter-based Intensity Modulation), "HPFA" (High Pass Filter Addition), "PCS" (Principal Component Substitution), and "SM" (simple mean). Different sharpening methods will produce different quality sharpening results in different scenarios. + qa: One or more optional quality assessment names to apply after sharpening. Results will be stored as image properties with the pattern `geetools:metric`, e.g. `geetools:RMSE`. + **kwargs: Keyword arguments passed to ee.Image.reduceRegion() such as "geometry", "maxPixels", "bestEffort", etc. These arguments are only used for PCS sharpening and quality assessments. + + Returns: + The ImageCollections with all sharpenable bands sharpened to the panchromatic resolution and quality assessments run and set as properties. + + Examples: + .. code-block:: python + + import ee + import geetools + + ee.Initialize() + + source = ee.Image("LANDSAT/LC08/C01/T1_TOA/LC08_047027_20160819") + sharp = source.panSharpen(method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13) + """ + return ee_extra.Algorithms.core.panSharpen( + img=self._obj, method=method, qa=qa or None, prefix="geetools", **kwargs + ) + + def tasseledCap(self) -> ee.ImageCollection: + """Calculates tasseled cap brightness, wetness, and greenness components. + + Tasseled cap transformations are applied using coefficients published for these + supported platforms: + + * Sentinel-2 MSI Level 1C + * Landsat 9 OLI-2 SR + * Landsat 9 OLI-2 TOA + * Landsat 8 OLI SR + * Landsat 8 OLI TOA + * Landsat 7 ETM+ TOA + * Landsat 5 TM Raw DN + * Landsat 4 TM Raw DN + * Landsat 4 TM Surface Reflectance + * MODIS NBAR + + Parameters: + self: ee.ImageCollection to calculate tasseled cap components for. Must belong to a supported platform. + + Returns: + ImageCollections with the tasseled cap components as new bands. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + image = ee.Image('COPERNICUS/S2_SR') + img = img.tasseledCap() + """ + return ee_extra.Spectral.core.tasseledCap(self._obj) + + def append(self, image: ee.Image) -> ee.ImageCollection: + """Append an image to the existing image collection. + + Args: + image: Image to append to the collection. + + Returns: + ImageCollection with the new image appended. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ic = ee.ImageCollection('COPERNICUS/S2_SR'); + + geom = ee.Geometry.Point(-122.196, 41.411); + ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') + ic2021 = ic.filterBounds(geom).filterDate('2021-07-01', '2021-10-01') + + ic = ic2018.append(ic2021.first()) + ic.getInfo() + """ + return self._obj.merge(ee.ImageCollection([image])) + + def collectionMask(self) -> ee.Image: + """A binary ee.Image where only pixels that are masked in all images of the collection get masked. + + Returns: + ee.Image of the mask. 1 where at least 1 pixel is valid 0 elswere + + Examples: + .. code-block:: + + import ee, geetools + + ee.Initialize() + + ic = ee.ImageCollection('COPERNICUS/S2_SR'); + + geom = ee.Geometry.Point(-122.196, 41.411); + ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') + ic = ic2018.geetools.collectionMask() + ic.getInfo() + """ + masks = self._obj.map(lambda i: i.mask()) + return ee.Image(masks.sum().gt(0)) + + def iloc(self, index: int) -> ee.Image: + """Get Image from the ImageCollection by index. + + Args: + index: Index of the image to get. + + Returns: + ee.Image at the specified index. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + ic = ee.ImageCollection('COPERNICUS/S2_SR'); + + geom = ee.Geometry.Point(-122.196, 41.411); + ic2018 = ic.filterBounds(geom).filterDate('2019-07-01', '2019-10-01') + ic2018.geetools.iloc(0).getInfo() + """ + return ee.Image(self._obj.toList(self._obj.size()).get(index)) + + def integral(self, band: str, time: str = "system:time_start", unit: str = "") -> ee.Image: + """Compute the integral of a band over time or a specified property. + + Args: + band: the name of the band to integrate + time: the name of the property to use as time. It must be a date property of the images. + unit: the time unit use to compute the integral. It can be one of the following: ["year", "month", "day", "hour", "minute", "second"]. If non is set, the time will be normalized on the integral length. + + Returns: + An Image object with the integrated band for each pixel + + Examples: + .. code-block:: python + + import ee, LDCGEETools + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + integral = collection.geetools.integral("B1") + print(integral.getInfo()) + """ + # compute the intervals along the x axis + # the GEE time is stored as a milliseconds timestamp. If the time unit is not set, + # the integral is normalized on the total time length of the time series + minTime = self._obj.aggregate_min(time) + maxTime = self._obj.aggregate_max(time) + intervals = { + "year": ee.Number(1000 * 60 * 60 * 24 * 365), # 1 year in milliseconds + "month": ee.Number(1000 * 60 * 60 * 24 * 30), # 1 month in milliseconds + "day": ee.Number(1000 * 60 * 60 * 24), # 1 day in milliseconds + "hour": ee.Number(1000 * 60 * 60), # 1 hour in milliseconds + "minute": ee.Number(1000 * 60), # 1 minute in milliseconds + "second": ee.Number(1000), # 1 second in milliseconds + "": ee.Number(maxTime).subtract(ee.Number(minTime)), + } + interval = intervals[unit] + + # initialize the sum with a 0 value initial item + # all the properties of the first image of the collection are copied + first = self._obj.first() + zero = ee.Image.constant(0).copyProperties(first, first.propertyNames()) + s = ee.Image(zero).rename("integral").set("last", zero) + + # compute the approximation of the integral using the trapezoidal method + # each local interval is aproximated by the corresponding trapez and the + # sum is updated + def computeIntegral(image, integral): + image = ee.Image(image).select(band) + integral = ee.Image(integral) + last = ee.Image(integral.get("last")) + locMinTime = ee.Number(last.get(time)) + locMaxTime = ee.Number(image.get(time)) + locInterval = locMaxTime.subtract(locMinTime).divide(interval) + locIntegral = last.add(image).multiply(locInterval).divide(2) + return integral.add(locIntegral).set("last", image) + + return ee.Image(self._obj.iterate(computeIntegral, s)) + + def outliers( + self, bands: list | ee.List = [], sigma: float | int | ee.Number = 2, drop: bool = False + ) -> ee.ImageCollection: + """Compute the outlier for each pixel in the specified bands. + + A pixel is considered as an outlier if: + + .. code-block:: + + outlier = value > mean+(sigma*stddev) + outlier = value < mean-(sigma*stddev) + + In a 1D example it would be: + - values = [1, 5, 6, 4, 7, 10] + - mean = 5.5 + - std dev = 3 + - mean + (sigma*stddev) = 8.5 + - mean - (sigma*stddev) = 2.5 + - outliers = values between 2.5 and 8.5 = [1, 10] + + Here in this function an extra band is added to each image for each of the evaluated bands with the outlier status. The band name is the original band name with the suffix "_outlier". A value of 1 means that the pixel is an outlier, 0 means that it is not. + + Optionally users can discard this band by setting ``drop`` to ``True`` and the outlier will simply be masked from each ilmage. This is useful when the outlier band is not needed and the user wants to save space. + + idea from: https://www.kdnuggets.com/2017/02/removing-outliers-standard-deviation-python.html + + Args: + bands: the bands to evaluate for outliers. If empty, all bands are evaluated + sigma: the number of standard deviations to use to compute the outlier + drop: whether to drop the outlier band from the images + + Returns: + an ImageCollection with the outlier band added to each image or masked if ``drop`` is ``True`` + + Examples: + .. code-block:: python + + import ee, LDCGEETools + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + outliers = collection.geetools.outliers(["B1", "B2"], 2) + print(outliers.getInfo()) + """ + # cast parameters and compute the outlier band names + initBands = self._obj.first().bandNames() + statBands = ee.List(bands) if bands else initBands + outBands = statBands.map(lambda b: ee.String(b).cat("_outlier")) + + # compute the mean and std dev for each band + statCollection = self._obj.select(statBands) + mean = statCollection.mean() + stdDev = statCollection.reduce(ee.Reducer.stdDev()) + minValues = mean.subtract(stdDev.multiply(sigma)) + maxValues = mean.add(stdDev.multiply(sigma)) + + # compute the outlier band for each image + def computeOutlierBands(i): + outImage = i.select(statBands) + outImage = outImage.gt(maxValues).Or(outImage.lt(minValues)) + return i.addBands(outImage.rename(outBands)) + + ic = self._obj.map(computeOutlierBands) + + # drop the outlier band and mask each image if requested + def maskOutliers(i): + maskedBands = i.select(statBands).updateMask(i.select(outBands).Not()) + return i.addBands(maskedBands, overwrite=True).select(initBands) + + ic = ic if drop is False else ic.map(maskOutliers) + + return ee.ImageCollection(ic) + + def to_xarray( + self, + drop_variables: tuple[str, ...] | None = None, + io_chunks: object = None, + n_images: int = -1, + mask_and_scale: bool = True, + decode_times: bool = True, + decode_timedelta: bool | None = None, + use_cftime: bool | None = None, + concat_characters: bool = True, + decode_coords: bool = True, + crs: str | None = None, + scale: float | int | None = None, + projection: ee.Projection | None = None, + geometry: ee.Geometry | None = None, + primary_dim_name: str | None = None, + primary_dim_property: str | None = None, + ee_mask_value: float | None = None, + request_byte_limit: int = REQUEST_BYTE_LIMIT, + ) -> Dataset: + """Open an Earth Engine ImageCollection as an ``xarray.Dataset``. + + Args: + drop_variables: Variables or bands to drop before opening. + io_chunks: Specifies the chunking strategy for loading data from EE. By default, this automatically calculates optional chunks based on the ``request_byte_limit``. + n_images: The max number of EE images in the collection to open. Useful when there are a large number of images in the collection since calculating collection size can be slow. -1 indicates that all images should be included. + mask_and_scale: Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). + decode_times: Decode cf times (e.g., integers since "hours since 2000-01-01") to np.datetime64. + decode_timedelta: If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. + use_cftime: Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. + concat_characters: Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello" + decode_coords: bool or {"coordinates", "all"}, Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the ``'coordinates'`` attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in ``'grid_mapping'``, ``'bounds'`` and other attributes as coordinate variables. + crs: The coordinate reference system (a CRS code or WKT string). This defines the frame of reference to coalesce all variables upon opening. By default, data is opened with 'EPSG:4326'. + scale: The scale in the ``crs`` or ``projection``'s units of measure -- either meters or degrees. This defines the scale that all data is represented in upon opening. By default, the scale is 1° when the CRS is in degrees or 10,000 when in meters. + projection: Specify an ``ee.Projection`` object to define the ``scale`` and ``crs`` (or other coordinate reference system) with which to coalesce all variables upon opening. By default, the scale and reference system is set by the the ``crs`` and ``scale`` arguments. + geometry: Specify an ``ee.Geometry`` to define the regional bounds when opening the data. When not set, the bounds are defined by the CRS's ``area_of_use`` boundaries. If those aren't present, the bounds are derived from the geometry of the first image of the collection. + primary_dim_name: Override the name of the primary dimension of the output Dataset. By default, the name is 'time'. + primary_dim_property: Override the ``ee.Image`` property for which to derive the values of the primary dimension. By default, this is 'system:time_start'. + ee_mask_value: Value to mask to EE nodata values. By default, this is 'np.iinfo(np.int32).max' i.e. 2147483647. + request_byte_limit: the max allowed bytes to request at a time from Earth Engine. By default, it is 48MBs. + + Returns: + An ``xarray.Dataset`` that streams in remote data from Earth Engine. + """ + return xarray.open_dataset( + self._obj, + engine="ee", + drop_variables=drop_variables, + io_chunks=io_chunks, + n_images=n_images, + mask_and_scale=mask_and_scale, + decode_times=decode_times, + decode_timedelta=decode_timedelta, + use_cftime=use_cftime, + concat_characters=concat_characters, + decode_coords=decode_coords, + crs=crs, + scale=scale, + projection=projection, + geometry=geometry, + primary_dim_name=primary_dim_name, + primary_dim_property=primary_dim_property, + ee_mask_value=ee_mask_value, + request_byte_limit=request_byte_limit, + ) + + def validPixel(self, band: str | ee.String = "") -> ee.Image: + """Compute the number of valid pixels in the specified band. + + Compute the number of valid pixels in the specified band. 2 bands will be created: + one with the number of valid pixels (``valid``) and another with the percentage of valid pixels (``pct_valid``). + + Args: + band: the band to evaluate for valid pixels. If empty, use the first band + Returns: + an Image with the number of valid pixels or the percentage of valid pixels. + + Examples: + .. code-block:: python + import ee, LDCGEETools + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + valid = collection.geetools.validPixels("B1") + print(valid.getInfo()). + """ + # compute the mask for the specified band + band = self._obj.first().bandNames().get(0) if band == "" else ee.String(band) + masks = self._obj.select([band]).map(lambda i: i.mask().eq(1)) + validPixel = masks.sum().rename("valid").clip(self._obj.geometry()) + validPct = validPixel.divide(self._obj.size()).multiply(100).rename("pct_valid") + return validPixel.addBands(validPct) + + def containsBandNames(self, bandNames: list | ee.List, filter: str) -> ee.ImageCollection: + """Filter the ImageCollection by band names using the provided filter. + + Args: + bandNames: list of band names to filter + filter: type of filter to apply. To keep images that contains all the specified bands use "ALL". To get the images including at least one of the specified band use "ANY". + + Returns: + A filtered ImageCollection + + Examples: + .. code-block:: python + + import ee, LDCGEETools + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + filtered = collection.geetools.containsBandNames(["B1", "B2"], "ALL") + print(filtered.getInfo()) + """ + # cast parameters + filter = {"ALL": "Filter.and", "ANY": "Filter.or"}[filter] + bandNames = ee.List(bandNames) + + # add bands as metadata in a temporary property + band_name = uuid.uuid4().hex + ic = self._obj.map(lambda i: i.set(band_name, i.bandNames())) + + # create a filter by combining a listContain filter over all the band names from the + # user list. Combine them with a "Or" to get a "any" filter and "And" to get a "all". + # We use a workaround until this is solved: https://issuetracker.google.com/issues/322838709 + filterList = bandNames.map(lambda b: ee.Filter.listContains(band_name, b)) + filterCombination = apifunction.ApiFunction.call_(filter, ee.List(filterList)) + + # apply this filter and remove the temporary property. Exclude parameter is additive so + # we do a blank multiplication to remove all the properties beforhand + ic = ee.ImageCollection(ic.filter(filterCombination)) + ic = ic.map(lambda i: ee.Image(i.multiply(1).copyProperties(i, exclude=[band_name]))) + + return ee.ImageCollection(ic) + + def containsAllBands(self, bandNames: list | ee.List) -> ee.ImageCollection: + """Filter the ImageCollection keeping only the images with all the provided bands. + + Args: + bandNames: list of band names to filter + + Returns: + A filtered ImageCollection + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + filtered = collection.geetools.containsAllBands(["B1", "B2"]) + print(filtered.getInfo()) + """ + return self.containsBandNames(bandNames, "ALL") + + def containsAnyBands(self, bandNames: list | ee.List) -> ee.ImageCollection: + """Filter the ImageCollection keeping only the images with any of the provided bands. + + Args: + bandNames: list of band names to filter + + Returns: + A filtered ImageCollection + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + filtered = collection.geetools.containsAnyBands(["B1", "B2"]) + print(filtered.getInfo()) + """ + return self.containsBandNames(bandNames, "ANY") + + def aggregateArray(self, properties: list | ee.List | None = None) -> ee.Dict: + """Aggregate the ImageCollection selected properties into a dictionary. + + Args: + properties: list of properties to aggregate. If None, all properties are aggregated. + + Returns: + A dictionary with the properties as keys and the aggregated values as values. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + aggregated = collection.geetools.aggregateArray(["CLOUD_COVER", "system:time_start"]) + print(aggregated.getInfo()) + """ + keys = ee.List(properties) if properties is not None else self._obj.first().propertyNames() + values = keys.map(lambda p: self._obj.aggregate_array(p)) + return ee.Dictionary.fromLists(keys, values) + + def groupInterval(self, unit: str = "month", duration: int = 1) -> ee.List: + """Transform the ImageCollection into a list of smaller collection of the specified duration. + + For example using unit as "month" and duration as 1, the ImageCollection will be transformed + into a list of ImageCollection with each ImageCollection containing images for each month. + Make sure the collection is filtered beforeend to reduce the number of images that needs to be + processed. + + Args: + unit: The unit of time to split the collection. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. + duration: The duration of each split. + + Returns: + A list of imagecollection grouped by interval + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + split = collection.geetools.groupInterval("month", 1) + print(split.getInfo()) + """ + # as everything is relyin on the "system:time_start" property + # we sort the image collection in the first place. In most collection it will change nothing + # so free of charge unless for plumbing + ic = self._obj.sort("system:time_start") + + # transform the interval into a duration in milliseconds + # I can use the DateRangeAccessor as it's imported earlier in the __init__.py file + # I don't know if it should be properly imported here, let's see with user feedback + timeList = ic.aggregate_array("system:time_start") + start, end = timeList.get(0), timeList.get(-1) + DateRangeList = ee.DateRange(start, end).geetools.split(duration, unit) + imageCollectionList = DateRangeList.map( + lambda dr: ic.filterDate(ee.DateRange(dr).start(), ee.DateRange(dr).end()) + ) + + return ee.List(imageCollectionList) + + def reduceInterval( + self, + reducer: str | ee.Reducer = "mean", + unit: str = "month", + duration: int = 1, + ) -> ee.ImageCollection: + """Reduce the images included in the same duration interval using the provided reducer. + + For example using unit as "month" and duration as 1, the ImageCollection will be reduced + into a new ImageCollection with each image containing the reduced values for each month. + Make sure the collection is filtered beforehand to reduce the number of images that needs to be + processed. + + Args: + reducer: The name of the reducer to use or a Reducer object. Default is "mean". + unit: The unit of time to split the collection. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'. + duration: The duration of each split. + + Returns: + A new ImageCollection with the reduced images. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + reduced = collection.geetools.reduceInterval("mean", "month", 1) + print(reduced.getInfo()) + """ + # create a list of image collections to be reduced + # Every subcollection is sorted in case one use the "first" reducer + imageCollectionList = self.groupInterval(unit, duration) + + # create a reducer from user parameters + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + + def reduce(ic): + timeList = ee.ImageCollection(ic).aggregate_array("system:time_start") + start, end = timeList.get(0), timeList.get(-1) + bandNames = ee.ImageCollection(ic).first().bandNames() + image = ee.ImageCollection(ic).reduce(red).rename(bandNames) + return image.set("system:time_start", start, "system:time_end", end) + + reducedImagesList = imageCollectionList.map(reduce) + + # set back the original properties + ic = ee.ImageCollection(reducedImagesList).copyProperties(self._obj) + + return ee.ImageCollection(ic) + + def closestDate(self) -> ee.ImageCollection: + """Fill masked pixels with the first valid pixel in the stack of images. + + The method will for every image, fill all the pixels with the latest nono masked pixel in the stack of images. + I requires the image to have a valid "system:time_start" property. + As the imageCollection will need to be sorted limit the analysis to a reasonable number of image by filtering your data beforehand. + + Returns: + An ImageCollection with all pixels unmasked in every image. + + Examples: + .. code:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + filled = collection.geetools.fillWithFirst() + print(filled.getInfo()) + """ + # retrieve all the time starts as an ordered list to iterate + timeList = self._obj.aggregate_array("system:time_start").sort() + + # for each time start find all the images thata are before and use the mosaic reducer + # to only keep the first one with a non masked pixel + def fill(date): + return self._obj.filter(ee.Filter.lte("system:time_start", date)).mosaic() + + imageList = timeList.map(fill) + + return ee.ImageCollection(imageList) + + def medoid(self) -> ee.image: + """Compute the medoid of the ImageCollection. + + The medoid is the image that has the smallest sum of distances to all other images in the collection. + The distance is computed using the Euclidean distance between the pixels of the images. + + Returns: + An Image that is the medoid of the ImageCollection. + + Examples: + .. code:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + medoid = collection.geetools.medoid() + print(medoid.getInfo()) + """ + # create a random name for the sum of distances band to avoid conflicts + sumOfDistancesName = uuid.uuid4().hex + + # discover bandname from the first image of the collection + bandNames = self._obj.first().bandNames() + + # normalize the band used to compute the distance + # first extract the min and max value of each band pixelwizse along the stac and then + # normalize the pixel values. + minMax = self._obj.reduce(ee.Reducer.minMax()) + + def normalizeBands(image): + def normalizeBand(bandName): + band = image.select([bandName]) + bandMin = minMax.select(ee.String(bandName).cat("_min")) + bandMax = minMax.select(ee.String(bandName).cat("_max")) + return band.subtract(bandMin).divide(bandMax.subtract(bandMin)) + + return ee.ImageCollection(bandNames.map(normalizeBand)).toBands().rename(bandNames) + + normalized = self._obj.map(normalizeBands) + + # compute the distance between each image and all the others + def computeSumDistance(image): + def computeDistance(other): + return image.subtract(other).pow(2).reduce(ee.Reducer.sum()).sqrt() + + sumDistances = normalized.map(computeDistance).reduce(ee.Reducer.sum()) + return image.addBands(sumDistances.rename(sumOfDistancesName)) + + sumDistance = normalized.map(computeSumDistance) + + # use the computed sum of distances as a sorting band for a quality mossaic + # to get the image with the smallest sum of distances + medoid = sumDistance.qualityMosaic(sumOfDistancesName) + + return ee.Image(medoid).select(bandNames) + + def datesByBands( + self, + region: ee.Geometry, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + bands: list = [], + labels: list = [], + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> ee.Dictionary: + """Reduce the data for each image in the collection by bands on a specific region. + + This method is returning a dictionary with all the bands as keys and their reduced value for each date over the specified region as value. + + .. code-block:: + + { + "band1": {"date1": value1, "date2": value2, ...}, + "band2": {"date1": value1, "date2": value2, ...}, + ... + } + + Parameters: + region: The region to reduce the data on. + reducer: The name of the reducer or a reducer object use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + bands: The bands to reduce. If empty, all bands are reduced. + labels: The labels to use for the bands. If empty, the bands names are used. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each band and each date. + + See Also: + - :docstring:`ee.ImageCollection.geetools.datesByRegions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) + reduced = collection.geetools.datesByBands(region, "mean", 10000, "system:time_start") + print(reduced.getInfo()) + """ + # cast parameters + eeBands = ee.List(bands) if len(bands) else self._obj.first().bandNames() + eeLabels = ee.List(labels) if len(labels) else eeBands + + # recast band names as labels in the source collection + ic = self._obj.select(eeBands).map(lambda i: i.rename(eeLabels)) + + # aggregate all the dates contained in the collection + dateList = ic.aggregate_array(dateProperty).map(lambda d: ee.Date(d).format(EE_DATE_FORMAT)) + + # create a reducer from the specified parameters + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + + # create a list of dictionaries with the reduced values for each band + def reduce(lbl: ee.String) -> ee.Dictionary: + image = ic.select([lbl]).toBands().rename(dateList) + return image.reduceRegion( + reducer=red, + geometry=region, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + + return ee.Dictionary.fromLists(eeLabels, eeLabels.map(reduce)) + + def datesByRegions( + self, + band: str, + regions: ee.FeatureCollection, + label: str = "system:index", + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> ee.Dictionary: + """Reduce the data for each image in the collection by regions for a single band. + + This method is returning a dictionary with all the regions as keys and their reduced value for each date over the specified region for a specific band as value. + + .. code-block:: + + { + "region1": {"date1": value1, "date2": value2, ...}, + "region2": {"date1": value1, "date2": value2, ...}, + ... + } + + Parameters: + band: The band to reduce. + regions: The regions to reduce the data on. + label: The property to use as label for each region. Default is "system:index". + reducer: The name of the reducer or a reducer object use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each region and each date. + + See Also: + - :docstring:`ee.ImageCollection.geetools.datesByBands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + regions = ee.FeatureCollection([ + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) + ]) + + reduced = collection.geetools.datesByRegions("B1", regions, "name", "mean", 10000, "system:time_start") + print(reduced.getInfo()) + """ + + # aggregate all the dates of the image collection into bands of a single image + def to_string(date: ee.Date) -> ee.String: + return ee.Date(date).format(EE_DATE_FORMAT) + + dateList = self._obj.aggregate_array(dateProperty).map(to_string) + + # reduce the data for each region + image = self._obj.select([band]).toBands().rename(dateList) + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + reduced = image.reduceRegions( + collection=regions, + reducer=red, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ) + + # create a list of dictionaries for each region and aggregate them into a dictionary + values = reduced.toList(regions.size()).map(lambda f: ee.Feature(f).toDictionary(dateList)) + keys = ee.List(regions.aggregate_array(label)) + + return ee.Dictionary.fromLists(keys, values) + + def doyByBands( + self, + region: ee.Geometry, + spatialReducer: str | ee.Reducer = "mean", + timeReducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + bands: list = [], + labels: list = [], + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> ee.Dictionary: + """Aggregate the images that occurs on the same day and then reduce each band on a single region. + + This method is returning a dictionary with all the bands as keys and their reduced value for each day over the specified region as value. + + .. code-block:: + + { + "band1": {"doy1": value1, "doy2": value2, ...}, + "band2": {"doy1": value1, "doy2": value2, ...}, + ... + } + + Parameters: + region: The region to reduce the data on. + spatialReducer: The name of the reducer or a reducer object to use for spatial reduction. Default is "mean". + timeReducer: The name of the reducer or a reducer object to use for time reduction. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + bands: The bands to reduce. If empty, all bands are reduced. + labels: The labels to use for the bands. If empty, the bands names are used. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each band and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + """ + # cast parameters + bands = ee.List(bands) if len(bands) else self._obj.first().bandNames() + labels = ee.List(labels) if len(labels) else bands + + # recast band names as labels in the source collection + ic = self._obj.select(bands).map(lambda i: i.rename(labels)) + + # create 2 metadata name as random string to avoid any risk of conflicts + doy_metadata, size_metadata = uuid.uuid4().hex, uuid.uuid4().hex + + # add the day of year as metadata to each image + def doy_tag(i: ee.Image) -> ee.Image: + doy = ee.Date(i.get(dateProperty)).getRelative("day", "year") + return i.set(doy_metadata, doy) + + ic = self._obj.map(doy_tag) + + # create a list of ImageCollection where every images of the same day are grouped together + dayList = ee.List.sequence(0, 366) + + def filter_doy(d: ee.Number) -> ee.ImageCollection: + c = ic.filter(ee.Filter.eq(doy_metadata, d)) + c = c.set(size_metadata, c.size()) + return c.set(doy_metadata, d) + + icList = dayList.map(filter_doy) + + # reduce every sub ImageCollection in the list into images (it's the temporal reduction) + # and aggregate the result as a single ImageCollection + timeRed = ( + getattr(ee.Reducer, timeReducer)() if isinstance(timeReducer, str) else timeReducer + ) + + def timeReduce(c: ee.imageCollection) -> ee.image: + c = ee.ImageCollection(c) + i = c.reduce(timeRed).rename(labels) + i = i.set(size_metadata, c.get(size_metadata)) + return i.set(doy_metadata, c.get(doy_metadata)) + + ic = ee.ImageCollection(icList.map(timeReduce)).filter(ee.Filter.gt(size_metadata, 0)) + + # spatially reduce the generated imagecollection over the region for each band + doyList = ic.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) + spatialRed = ( + getattr(ee.Reducer, spatialReducer)() + if isinstance(spatialReducer, str) + else spatialReducer + ) + + def spatialReduce(label: ee.String) -> ee.Dictionary: + image = ic.select([label]).toBands().rename(doyList) + return image.reduceRegion( + reducer=spatialRed, + geometry=region, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + + return ee.Dictionary.fromLists(labels, ee.List(labels).map(spatialReduce)) + + def doyByRegions( + self, + band: str, + regions: ee.FeatureCollection, + label: str = "system:index", + spatialReducer: str | ee.Reducer = "mean", + timeReducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> ee.Dictionary: + """Aggregate the images that occurs on the same day and then reduce a single band on multiple regions. + + This method is returning a dictionary with all the regions as keys and their reduced value for each day over the specified region for a specific band as value. + + .. code-block:: + + { + "region1": {"doy1": value1, "doy2": value2, ...}, + "region2": {"doy1": value1, "doy2": value2, ...}, + ... + } + + Parameters: + band: The band to reduce. + regions: The regions to reduce the data on. + label: The property to use as label for each region. Default is "system:index". + spatialReducer: The name of the reducer or a reducer object to use for spatial reduction. Default is "mean". + timeReducer: The name of the reducer or a reducer object to use for time reduction. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each region and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + """ + # create 2 metadata name as random string to avoid any risk of conflicts + doy_metadata, size_metadata = uuid.uuid4().hex, uuid.uuid4().hex + + # add the day of year as metadata to each image + def doy_tag(i: ee.Image) -> ee.Image: + doy = ee.Date(i.get(dateProperty)).getRelative("day", "year") + return i.set(doy_metadata, doy) + + ic = self._obj.select([band]).map(doy_tag) + + # create a list of ImageCollection where every images of the same day are grouped together + dayList = ee.List.sequence(0, 366) + + def filter_doy(d: ee.Number) -> ee.ImageCollection: + c = ic.filter(ee.Filter.eq(doy_metadata, d)) + c = c.set(size_metadata, c.size()) + return c.set(doy_metadata, d) + + icList = dayList.map(filter_doy) + + # reduce every sub ImageCollection in the list into images (it's the temporal reduction) + # and aggregate the result as a single ImageCollection + timeRed = ( + getattr(ee.Reducer, timeReducer)() if isinstance(timeReducer, str) else timeReducer + ) + + def timeReduce(c: ee.imageCollection) -> ee.image: + c = ee.ImageCollection(c) + i = c.reduce(timeRed).rename([band]) + i = i.set(size_metadata, c.get(size_metadata)) + return i.set(doy_metadata, c.get(doy_metadata)) + + ic = ee.ImageCollection(icList.map(timeReduce)).filter(ee.Filter.gt(size_metadata, 0)) + + # reduce the data for each region + doyList = ic.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) + spatialRed = ( + getattr(ee.Reducer, spatialReducer)() + if isinstance(spatialReducer, str) + else spatialReducer + ) + image = ic.toBands().rename(doyList) + reduced = image.reduceRegions( + collection=regions, + reducer=spatialRed, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ) + + # create a list of dictionaries for each region and aggregate them into a dictionary + values = reduced.toList(regions.size()).map(lambda f: ee.Feature(f).toDictionary(doyList)) + keys = ee.List(regions.aggregate_array(label)) + + return ee.Dictionary.fromLists(keys, values) + + def doyBySeasons( + self, + band: str, + region: ee.Geometry, + seasonStart: int | ee.Number, + seasonEnd: int | ee.Number, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> ee.Dictionary: + """Aggregate for each year on a single region a single band. + + This method is returning a dictionary with all the years as keys and their reduced value for each day of the season over the specified region for a specific band as value. + To set the start and end of the season, use the :py:method:`ee.Date.getRelative` or :py:class:`time.struct_time` method to get the day of the year. + + .. code-block:: + + { + "year1": {"doy1": value1, "doy2": value2, ...}, + "year2": {"doy1": value1, "doy2": value2, ...}, + ... + } + + Parameters: + band: The band to reduce. + region: The region to reduce the data on. + seasonStart: The day of the year that marks the start of the season. + seasonEnd: The day of the year that marks the end of the season. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each year and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filter(ee.Filter.Or( + ee.Filter.date("2022-01-01", "2022-12-31"), + ee.Filter.date("2016-01-01", "2016-12-31"), + )) + .map(lambda i: ee.Image(i).addBands( + ee.Image(i) + .normalizedDifference(["B5", "B4"]) + .rename("NDVI") + )) + ) + + reduced = collection.geetools.doyBySeasons( + band = "NDVI", + region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), + seasonStart = ee.Date("2016-05-01").getRelative("day", "year"), + seasonEnd = ee.Date("2016-10-31").getRelative("day", "year"), + reducer = "mean", + dateProperty = "system:time_start", + scale = 10000 + ) + reduced.getInfo() + """ + # force cast the start and end of season as ee.Number + seasonStart, seasonEnd = ee.Number(seasonStart), ee.Number(seasonEnd) + + # add a doy metadata to the images + doy_metadata, year_metadata = uuid.uuid4().hex, uuid.uuid4().hex + + def date_tag(i: ee.Image) -> ee.Image: + date = ee.Date(i.get(dateProperty)) + doy = date.getRelative("day", "year") + year = date.get("year") + return i.set(doy_metadata, doy).set(year_metadata, year) + + ic = self._obj.select([band]).map(date_tag) + + # create a List of image collection where every images from the same year are grouped together + yearList = ic.aggregate_array(year_metadata).distinct().sort() + yearKeys = yearList.map(lambda y: ee.Number(y).int().format()) + red = getattr(ee.Reducer, reducer)() if isinstance(reducer, str) else reducer + + def reduce(year: ee.Number) -> ee.Dictionary: + c = ic.filter(ee.Filter.eq(year_metadata, year)) + c = c.filter(ee.Filter.rangeContains(doy_metadata, seasonStart, seasonEnd)) + doyList = c.aggregate_array(doy_metadata).map(lambda d: ee.Number(d).int().format()) + return ( + c.toBands() + .rename(doyList) + .reduceRegion( + reducer=red, + geometry=region, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + ) + + return ee.Dictionary.fromLists(yearKeys, yearList.map(reduce)) + + def doyByYears( + self, + band: str, + region: ee.Geometry, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> ee.Dictionary: + """Aggregate for each year on a single region a single band. + + This method is returning a dictionary with all the years as keys and their reduced value for each day over the specified region for a specific band as value. + + .. code-block:: + + { + "year1": {"doy1": value1, "doy2": value2, ...}, + "year2": {"doy1": value1, "doy2": value2, ...}, + ... + } + + Parameters: + band: The band to reduce. + region: The region to reduce the data on. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each year and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filter(ee.Filter.Or( + ee.Filter.date("2022-01-01", "2022-12-31"), + ee.Filter.date("2016-01-01", "2016-12-31"), + )) + .map(lambda i: ee.Image(i).addBands( + ee.Image(i) + .normalizedDifference(["B5", "B4"]) + .rename("NDVI") + )) + ) + + reduced = collection.geetools.doyByYears( + band = "NDVI", + region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), + reducer = "mean", + dateProperty = "system:time_start", + scale = 10000 + ) + reduced.getInfo() + """ + return self.doyBySeasons( + band=band, + region=region, + seasonStart=ee.Number(0), + seasonEnd=ee.Number(366), + reducer=reducer, + dateProperty=dateProperty, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + + def plot_dates_by_bands( + self, + region: ee.Geometry, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + bands: list = [], + labels: list = [], + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by bands on a specific region. + + This method is plotting the reduced data for each image in the collection by bands on a specific region. + + Parameters: + region: The region to reduce the data on. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + bands: The bands to reduce. If empty, all bands are reduced. + labels: The labels to use for the bands. If empty, the bands names are used. + colors: The colors to use for the bands. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each band and each date. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) + collection.geetools.plot_dates_by_bands(region, "mean", 10000, "system:time_start") + """ + # get the reduced data + raw_data = self.datesByBands( + region=region, + reducer=reducer, + dateProperty=dateProperty, + bands=bands, + labels=labels, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ).getInfo() + + # transform all the dates int datetime objects + def to_date(dict): + return {dt.strptime(d, PY_DATE_FORMAT): v for d, v in dict.items()} + + data = {e: to_date(dict) for e, dict in raw_data.items()} + + # create the plot + ax = plot_data("date", data, "Date", colors, ax) + + return ax + + def plot_dates_by_regions( + self, + band: str, + regions: ee.FeatureCollection, + label: str = "system:index", + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by regions for a single band. + + This method is plotting the reduced data for each image in the collection by regions for a single band. + + Parameters: + band: The band to reduce. + regions: The regions to reduce the data on. + label: The property to use as label for each region. Default is "system:index". + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + colors: The colors to use for the regions. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each region and each date. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + regions = ee.FeatureCollection([ + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) + ]) + + collection.geetools.plot_dates_by_regions("B1", regions, "name", "mean", 10000, "system:time_start") + """ + # get the reduced data + raw_data = self.datesByRegions( + band=band, + regions=regions, + label=label, + reducer=reducer, + dateProperty=dateProperty, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ).getInfo() + + # transform all the dates int datetime objects + def to_date(dict): + return {dt.strptime(d, PY_DATE_FORMAT): v for d, v in dict.items()} + + data = {e: to_date(dict) for e, dict in raw_data.items()} + + # create the plot + ax = plot_data("date", data, "Date", colors, ax) + + return ax + + def plot_doy_by_bands( + self, + region: ee.Geometry, + spatialReducer: str | ee.Reducer = "mean", + timeReducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + bands: list = [], + labels: list = [], + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by bands on a specific region. + + This method is plotting the reduced data for each image in the collection by bands on a specific region. + + Parameters: + region: The region to reduce the data on. + spatialReducer: The name of the reducer or a reducer object to use. Default is "mean". + timeReducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + bands: The bands to reduce. If empty, all bands are reduced. + labels: The labels to use for the bands. If empty, the bands names are used. + colors: The colors to use for the bands. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each band and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + region = ee.Geometry.Point(-122.262, 37.8719).buffer(10000) + collection.geetools.plot_doy_by_bands(region, "mean", "mean", 10000, "system:time_start") + """ + # get the reduced data + raw_data = self.doyByBands( + region=region, + spatialReducer=spatialReducer, + timeReducer=timeReducer, + dateProperty=dateProperty, + bands=bands, + labels=labels, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ).getInfo() + + # transform all the dates strings into int object and reorder the dictionary + def to_int(d): + return {int(k): v for k, v in d.items()} + + data = {e: dict(sorted(to_int(raw_data[e]).items())) for e in raw_data} + + # create the plot + ax = plot_data("doy", data, "Day of Year", colors, ax) + + return ax + + def plot_doy_by_regions( + self, + band: str, + regions: ee.FeatureCollection, + label: str = "system:index", + spatialReducer: str | ee.Reducer = "mean", + timeReducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by regions for a single band. + + This method is plotting the reduced data for each image in the collection by regions for a single band. + + Parameters: + band: The band to reduce. + regions: The regions to reduce the data on. + label: The property to use as label for each region. Default is "system:index". + spatialReducer: The name of the reducer or a reducer object to use. Default is "mean". + timeReducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + colors: The colors to use for the regions. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each region and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + + regions = ee.FeatureCollection([ + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}), + ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"}) + ]) + + collection.geetools.plot_doy_by_regions("B1", regions, "name", "mean", "mean", 10000, "system:time_start") + """ + # get the reduced data + raw_data = self.doyByRegions( + band=band, + regions=regions, + label=label, + spatialReducer=spatialReducer, + timeReducer=timeReducer, + dateProperty=dateProperty, + scale=scale, + crs=crs, + crsTransform=crsTransform, + tileScale=tileScale, + ).getInfo() + + # transform all the dates strings into int object and reorder the dictionary + def to_int(d): + return {int(k): v for k, v in d.items()} + + data = {e: dict(sorted(to_int(raw_data[e]).items())) for e in raw_data} + + # create the plot + ax = plot_data("doy", data, "Day of Year", colors, ax) + + return ax + + def plot_doy_by_seasons( + self, + band: str, + region: ee.Geometry, + seasonStart: int | ee.Number, + seasonEnd: int | ee.Number, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by years for a single band. + + This method is plotting the reduced data for each image in the collection by years for a single band. + To set the start and end of the season, use the :py:method:`ee.Date.getRelative` or :py:class:`time.struct_time` method to get the day of the year. + + Parameters: + band: The band to reduce. + region: The region to reduce the data on. + seasonStart: The day of the year that marks the start of the season. + seasonEnd: The day of the year that marks the end of the season. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + colors: The colors to use for the regions. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each year and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_years` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filter(ee.Filter.Or( + ee.Filter.date("2022-01-01", "2022-12-31"), + ee.Filter.date("2016-01-01", "2016-12-31"), + )) + .map(lambda i: ee.Image(i).addBands( + ee.Image(i) + .normalizedDifference(["B5", "B4"]) + .rename("NDVI") + )) + ) + + collection.geetools.plot_doy_by_seasons( + band = "NDVI", + region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), + seasonStart = ee.Date("2016-05-01").getRelative("day", "year"), + seasonEnd = ee.Date("2016-10-31").getRelative("day", "year"), + reducer = "mean", + dateProperty = "system:time_start", + scale = 10000 + ) + """ + # get the reduced data + raw_data = self.doyBySeasons( + band=band, + region=region, + seasonStart=seasonStart, + seasonEnd=seasonEnd, + reducer=reducer, + dateProperty=dateProperty, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ).getInfo() + + # transform all the dates strings into int object and reorder the dictionary + def to_int(d): + return {int(k): v for k, v in d.items()} + + data = {i: dict(sorted(to_int(raw_data[i]).items())) for i in raw_data} + + # create the plot + ax = plot_data("doy", data, "Day of Year", colors, ax) + + return ax + + def plot_doy_by_years( + self, + band: str, + region: ee.Geometry, + reducer: str | ee.Reducer = "mean", + dateProperty: str = "system:time_start", + colors: list = [], + ax: Axes | None = None, + scale: int = 10000, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = 10**7, + tileScale: float = 1, + ) -> Axes: + """Plot the reduced data for each image in the collection by years for a single band. + + This method is plotting the reduced data for each image in the collection by years for a single band. + + Parameters: + band: The band to reduce. + region: The region to reduce the data on. + reducer: The name of the reducer or a reducer object to use. Default is "mean". + dateProperty: The property to use as date for each image. Default is "system:time_start". + colors: The colors to use for the regions. If empty, the default colors are used. + ax: The matplotlib axes to plot the data on. If None, a new figure is created. + scale: The scale in meters to use for the reduction. default is 10000m + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. Defaults to 1e7. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A matplotlib axes with the reduced values for each year and each day. + + See Also: + - :docstring:`ee.ImageCollection.geetools.doyByBands` + - :docstring:`ee.ImageCollection.geetools.doyByRegions` + - :docstring:`ee.ImageCollection.geetools.doyBySeasons` + - :docstring:`ee.ImageCollection.geetools.doyByYears` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_bands` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_regions` + - :docstring:`ee.ImageCollection.geetools.plot_doy_by_seasons` + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filter(ee.Filter.Or( + ee.Filter.date("2022-01-01", "2022-12-31"), + ee.Filter.date("2016-01-01", "2016-12-31"), + )) + .map(lambda i: ee.Image(i).addBands( + ee.Image(i) + .normalizedDifference(["B5", "B4"]) + .rename("NDVI") + )) + ) + + collection.geetools.plot_doy_by_years( + band = "NDVI", + region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000), + reducer = "mean", + dateProperty = "system:time_start", + scale = 10000 + ) + """ + return self.plot_doy_by_seasons( + band=band, + region=region, + seasonStart=ee.Number(0), + seasonEnd=ee.Number(366), + reducer=reducer, + dateProperty=dateProperty, + colors=colors, + ax=ax, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + + def reduceRegion( + self, + reducer: str, + geometry: ee.Geometry, + idProperty: str = "system:index", + idType: type = ee.Number, + idReducer: str | ee.Reducer = "first", + idFormat: str | ee.String | None = None, + scale: int | float | None = None, + crs: str | None = None, + crsTransform: list | None = None, + bestEffort: bool = False, + maxPixels: int | None = None, + tileScale: float = 1, + ) -> ee.Dictionary: + """Apply a reducer to all the pixels in a specific region on each image of the collection. + + The result will be shaped as a dictionary with the idProperty as key and for each f them the reduced band values. + + .. code-block:: + + { + "image1": {"band1": value1, "band2": value2, ...}, + "image2": {"band1": value1, "band2": value2, ...}, + } + + Warning: + The method makes a call to the pure Python ``uuid`` package so it cannot be used in a server-side ``map`` function. + + Parameters: + idProperty: The property to use as the key of the resulting dictionary. If not specified, the key of the dictionary is the index of the image in the collection. One should use a meaningful property to avoid conflicts. in case of conflicts, the images with the same property will be mosaicked together (e.g. all raw satellite imagery with the same date) to make sure the final reducer have 1 single entry per idProperty. + reducer: THe reducer to apply. + idType: The type of the idProperty. Default is ee.Number. As Dates are stored as numbers in metadata, we need to know what parsing to apply to the property in advance. + idReducer: If the multiple images have the same idProperty, they will be aggregated beforehand using the provided reducer. default to a mosaic behaviour to match most of the satellite imagery collection where the world is split for each date between multiple images. + idFormat: If a date format is used for the IdProperty, the values will be formatted as "YYYY-MM-ddThh-mm-ss". If a number format is used for the IdProperty, the values will be formatted as a string ("%s"). You can specify any other format compatible with band names. + geometry: The region over which to reduce the data. + scale: A nominal scale in meters to work in. + crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. + crstransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection. + bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. + maxPixels: The maximum number of pixels to reduce. + tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. + + Returns: + A dictionary with the reduced values for each image. + + Examples: + .. code-block:: python + + import ee, geetools + + ee.Initialize() + + collection = ( + ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA") + .filterBounds(ee.Geometry.Point(-122.262, 37.8719)) + .filterDate("2014-01-01", "2014-12-31") + ) + data = collection.geetools.reduceRegion("mean", geometry=ee.Geometry.Point(-122.262, 37.8719), scale=30) + print(data.getInfo()) + """ + # filter the imageCollection with the region parameter to reduce the number of manipulated images and speed up the computation + ic = self._obj.filterBounds(geometry) + + # raise an error if the idType is not supported + if idType not in [ee.String, ee.Number, ee.Date]: + msg = f"idPropertyType format {idType} not supported (yet)!" + raise ValueError(msg) + + # create a unique property name to avoid conflict with any + # existing property in the image collection + pname = uuid.uuid4().hex + + # add to each image the idProperty as metadata converted to string according + # to the idPropertyType parameter + def addIdProperty(i: ee.Image) -> ee.Image: + p = i.get(idProperty) + if idType == ee.String: + p = ee.String(p) + elif idType == ee.Number: + p = ee.Number(p).format(idFormat or "%s") + elif idType == ee.Date: + p = ee.Date(p).format(idFormat or EE_DATE_FORMAT) + return i.set(pname, p) + + ic = ic.map(addIdProperty) + + # reduce the images collection to an collection of image with unique idproperty + # in case of duplication the images arereduced together using the idReducer + idRed = idReducer # renaming of the variable to save space + red = getattr(ee.Reducer, idRed)() if isinstance(idRed, str) else idRed + pList = ic.aggregate_array(pname).distinct() + bands = ic.first().bandNames() + iList = pList.map(lambda p: ic.filter(ee.Filter.eq(pname, p)).reduce(red).rename(bands)) + ic = ee.ImageCollection(iList) + + # The tobands method will produce an image with the following band names: _ + # What we want is: _ so we can make more advance filtering downstream. + bandNames = pList.map(lambda p: bands.map(lambda b: ee.String(p).cat("_").cat(b))) + bandNames = bandNames.flatten() + + # reduce the collection to a single image and run the reducer on it + image = ic.toBands().rename(bandNames) + reduced = image.reduceRegion( + reducer=reducer, + geometry=geometry, + scale=scale, + crs=crs, + crsTransform=crsTransform, + bestEffort=bestEffort, + maxPixels=maxPixels, + tileScale=tileScale, + ) + + # reshape the result dictionary into the desired structure + def getProp(p: ee.String) -> ee.Dictionary: + p = ee.String(p) + keys = reduced.keys().filter(ee.Filter.stringStartsWith("item", p)) + values = reduced.select(keys).values() + keys = keys.map(lambda k: ee.String(k).replace(p, "").slice(1)) + return ee.Dictionary.fromLists(keys, values) + + values = pList.map(lambda p: getProp(p)) + + return ee.Dictionary.fromLists(pList, values) diff --git a/geetools/ee_initialize.py b/geetools/ee_initialize.py new file mode 100644 index 00000000..d3b7ebc9 --- /dev/null +++ b/geetools/ee_initialize.py @@ -0,0 +1,124 @@ +"""Tools for the ``ee.Initialize`` function.""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path + +import ee +import httplib2 +from google.oauth2.credentials import Credentials + +from .accessors import register_function_accessor + +_project_id: str | None = None +"The project Id used by the current user." + + +@register_function_accessor(ee.Initialize, "geetools") +class InitializeAccessor: + """Toolbox for the ``ee.Initialize`` function.""" + + @staticmethod + def from_user(name: str = "", credential_pathname: str = "", project: str = "") -> None: + """Initialize Earthengine API using a specific user. + + Equivalent to the ``ee.initialize`` function but with a specific credential file stored in + the machine by the :py:meth:`ee.Authenticate.geetools.new_user ` + function. + + Args: + name: The name of the user as saved when created. use default if not set + credential_pathname: The path to the folder where the credentials are stored. If not set, it uses the default path + project: The project_id to use. If not set, it uses the default project_id of the saved credentials. + + Example: + .. code-block:: python + + import ee + import geetools + + ee.Initialize.from_user("") + """ + # gather global variable to be modified + global _project_id + + # set the user profile information + name = f"credentials{name}" + credential_pathname = credential_pathname or ee.oauth.get_credentials_path() + credential_folder = Path(credential_pathname).parent + credential_path = credential_folder / name + + # check if the user exists + if not credential_path.exists(): + msg = "Please register this user first by using geetools.User.create first" + raise ee.EEException(msg) + + # Set the credential object and Init GEE API + tokens = json.loads((credential_path / name).read_text()) + credentials = Credentials( + None, + refresh_token=tokens["refresh_token"], + token_uri=ee.oauth.TOKEN_URI, + client_id=tokens["client_id"], + client_secret=tokens["client_secret"], + scopes=ee.oauth.SCOPES, + ) + ee.Initialize(credentials) + + # save the project_id in a dedicated global variable as it's not saved + # from GEE side + _project_id = project or tokens["project_id"] + + @staticmethod + def from_service_account(private_key: str) -> None: + """Initialize Earthengine API using a service account. + + Equivalent to the ``ee.initialize`` function but with a specific service account json key. + + Args: + private_key: The private key of the service account in json format. + + Example: + .. code-block:: python + + import ee + import geetools + + private_key = "your_private_key" + + ee.Initialize.from_service_account(private_key) + """ + # gather global variable to be modified + global _project_id + + # connect to GEE using a temp file to avoid writing the key to disk + with tempfile.TemporaryDirectory() as temp_dir: + file = Path(temp_dir) / "private_key.json" + file.write_text(private_key) + ee_user = json.loads(private_key)["client_email"] + _project_id = json.loads(private_key)["project_id"] + credentials = ee.ServiceAccountCredentials(ee_user, str(file)) + ee.Initialize(credentials=credentials, http_transport=httplib2.Http()) + + @staticmethod + def project_id() -> str: + """Get the project_id of the current account. + + Returns: + The project_id of the connected profile + + Raises: + RuntimeError: If the account is not initialized. + + Examples: + .. code-block:: + + import ee, geetools + + ee.Initialize.geetools.project_id() + """ + if _project_id is None: + raise RuntimeError("The GEE account is not initialized") + return _project_id diff --git a/geetools/ee_join.py b/geetools/ee_join.py new file mode 100644 index 00000000..8647fe3b --- /dev/null +++ b/geetools/ee_join.py @@ -0,0 +1,69 @@ +"""Extra methods for the ``ee.Join`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Join, "geetools") +class JoinAccessor: + """Toolbox for the ``ee.Join`` class.""" + + def __init__(self, obj: ee.join): + """Initialize the Join class.""" + self._obj = obj + + @staticmethod + def byProperty( + primary: ee.Collection, + secondary: ee.Collection, + field: str | ee.String, + outer: bool = False, + ) -> ee.Collection: + """Join 2 collections by a given property field. + + It assumes ids are unique so uses ee.Join.saveFirst. + + Args: + primary: the first collection + secondary: the second collection + field: the field to join by + outer: whether to keep non matching features + + Returns: + the joined collection + + + Example: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + # build fake featureCollections on the same point + point = ee.Geometry.Point([0,0]) + prop1 = {'id': 1, 'prop_from_fc1': 'I am from fc1'} + prop2 = {'id': 1, 'prop_from_fc2': 'I am from fc2'} + fc1 = ee.FeatureCollection([ee.Feature(point, prop1)]) + fc2 = ee.FeatureCollection([ee.Feature(point, prop2)]) + + # join them together in the same featureCollection + joined = ee.Join.geetools.byProperty(fc1, fc2, 'id') + joined.getInfo() + """ + field = ee.String(field) + primary, secondary = ee.FeatureCollection(primary), ee.FeatureCollection(secondary) + Filter = ee.Filter.equals(leftField=field, rightField=field) + join = ee.Join.saveFirst(matchKey="match", outer=outer) + joined = join.apply(primary, secondary, Filter) + + def cleanJoin(feat): + primaryProp = feat.propertyNames().remove("match") + secondaryProp = ee.Feature(feat.get("match")).toDictionary() + return feat.select(primaryProp).setMulti(secondaryProp) + + return ee.FeatureCollection(joined.map(cleanJoin)) diff --git a/geetools/ee_list.py b/geetools/ee_list.py new file mode 100644 index 00000000..9e34f798 --- /dev/null +++ b/geetools/ee_list.py @@ -0,0 +1,290 @@ +"""Extra methods for the ``ee.List`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.List, "geetools") +class ListAccessor: + """Toolbox for the ``ee.List`` class.""" + + def __init__(self, obj: ee.List): + """Initialize the List class.""" + self._obj = obj + + def product(self, other: list | ee.List) -> ee.List: + """Compute the cartesian product of 2 list. + + Values will all be considered as string and will be joined with **no spaces**. + + Parameters: + other: The list to compute the cartesian product with. + + Returns: + A list of strings corresponding to the cartesian product. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l1 = ee.List(["1", "2", "3"]) + l2 = ee.List(["a", "b", "c"]) + + l1.geetools.product(l2).getInfo() + """ + l1 = ee.List(self._obj).map(lambda e: ee.String(e)) + l2 = ee.List(other).map(lambda e: ee.String(e)) + product = l1.map( + lambda e: l2.map(lambda f: ee.Algorithms.String(e).cat(ee.Algorithms.String(f))) + ) + return product.flatten() + + def complement(self, other: list | ee.List) -> ee.List: + """Compute the complement of the current list and the ``other`` list. + + The mathematical complement is the list of elements that are in the current list but not in the ``other`` list and vice-versa. + + Parameters: + other: The list to compute the complement with. + + Returns: + A list of strings corresponding to the complement of the current list and the ``other`` list. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l1 = ee.List(["1", "2", "3"]) + l2 = ee.List(["2", "3", "4"]) + + l1.geetools.complement(l2).getInfo() + """ + l1, l2 = ee.List(self._obj), ee.List(other) + return l1.removeAll(l2).cat(l2.removeAll(l1)) + + def intersection(self, other: list | ee.List) -> ee.List: + """Compute the intersection of the current list and the ``other`` list. + + The intersection is the list of elements that are in both lists. + + Parameters: + other: The list to compute the intersection with. + + Returns: + A list of strings corresponding to the intersection of the current list and the ``other`` list. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l1 = ee.List(["1", "2", "3"]) + l2 = ee.List(["2", "3", "4"]) + + l1.geetools.intersection(l2).getInfo() + """ + l1, l2 = ee.List(self._obj), ee.List(other) + return l1.removeAll(l1.removeAll(l2)) + + def union(self, other: list | ee.List) -> ee.List: + """Compute the union of the current list and the ``other`` list. + + This list will drop duplicated items. + + Parameters: + other: The list to compute the union with. + + Returns: + A list of strings corresponding to the union of the current list and the ``other`` list. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l1 = ee.List(["1", "2", "3"]) + l2 = ee.List(["2", "3", "4"]) + + l1.geetools.union(l2).getInfo() + """ + l1, l2 = ee.List(self._obj), ee.List(other) + return l1.cat(l2).distinct() + + # this method is simply a del but the name is protected in the GEE context + def delete(self, index: int | ee.Number) -> ee.List: + """Delete an element from a list. + + Parameters: + index: The index of the element to delete. + + Returns: + The list without the element at the given index. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List(["a", "b", "c"]) + l.geetools.delete(1).getInfo() + """ + index = ee.Number(index).toInt() + return self._obj.slice(0, index).cat(self._obj.slice(index.add(1))) + + @classmethod + def sequence( + cls, + ini: int | ee.Number, + end: int | ee.Number, + step: int | ee.Number = 1, + ) -> ee.List: + """Create a sequence from ini to end by step. + + Similar to ``ee.List.sequence``, but if end != last item then adds the end to the end of the resuting list. + + Parameters: + ini: The initial value of the sequence. + end: The final value of the sequence. + step: The step of the sequence. + + Returns: + A list of numbers corresponding to the sequence. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List.geetools.sequence(0, 11, 2) + l.getInfo() + """ + ini, end = ee.Number(ini), ee.Number(end) + step = ee.Number(step).toInt().max(1) + return ee.List.sequence(ini, end, step).add(end.toFloat()).distinct() + + def replaceMany(self, replace: dict | ee.Dictionary) -> ee.List: + """Replace many values in a list. + + Parameters: + replace: the dictionary with the values to replace. the keys are the values to replace and the values are the new values. + + Returns: + A list with the values replaced + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List(["a", "b", "c"]) + replace = ee.Dictionary({"a": "foo", "c": "bar"}) + l = l.geetools.replaceMany(replace) + l.getInfo() + """ + replace = ee.Dictionary(replace) + keys = replace.keys() + list = keys.iterate(lambda k, p: ee.List(p).replace(k, replace.get(k)), self._obj) + return ee.List(list) # to avoid returning a ComputedObject + + def join(self, separator: str | ee.String = ", ") -> ee.string: + """Format a list to a string. + + Same as the join method but elements that cannot be stringified will be returned as the object type. + + Parameters: + separator: The separator to use. + + Returns: + A string with the list elements separated by commas. + + Examples: + .. juptyer-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List(['a', 1, ee.Image(0)]) + l = l.geetools.join() + l.getInfo() + """ + return self.toStrings().join(separator) + + def toStrings(self) -> ee.List: + """Convert elements of a list into Strings. + + If the list contains other elements that are not strings or numbers, it will return the object type. For example, ['a', 1, ee.Image(0)] -> ['a', '1', 'Image']. + + Returns: + A list of strings corresponding to the elements of the list. + + Examples: + .. juptyer-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List(["a", 1, ee.Image(0)]) + l = l.geetools.toStrings() + l.getInfo() + """ + klasses = ee.List(["Float", "Integer", "String"]) + + def getString(el): + otype = ee.Algorithms.ObjectType(el) + stringReady = klasses.contains(otype) + return ee.Algorithms.If(stringReady, ee.Algorithms.String(el), otype) + + return self._obj.map(getString) + + def zip(self) -> ee.List: + """Zip a list of lists. + + The nested lists need to all have the same size. The size of the first element will be taken as reference. + + Returns: + A list of lists with the zipped elements + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + l = ee.List([[1,2,3], [4,5,6], [7,8,9]]) + l = l.geetools.zip() + l.getInfo() + """ + indices = ee.List.sequence(0, ee.List(self._obj.get(0)).size().subtract(1)) + return indices.map(lambda i: self._obj.map(lambda j: ee.List(j).get(i))) diff --git a/geetools/ee_number.py b/geetools/ee_number.py new file mode 100644 index 00000000..bff2efd1 --- /dev/null +++ b/geetools/ee_number.py @@ -0,0 +1,40 @@ +"""Extra methods for the ``ee.Number`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.Number, "geetools") +class NumberAccessor: + """toolbox for the ``ee.Number`` class.""" + + def __init__(self, obj: ee.Number): + """Initialize the Number class.""" + self._obj = obj + + def truncate(self, nbDecimals: int | ee.Number = 2) -> ee.Number: + """Truncate a number to a given number of decimals. + + Parameters: + nbDecimals: The number of decimals to truncate to. + + Returns: + The truncated number. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + n = ee.Number(1.23456).geetools.truncate(3) + n.getInfo() + """ + nbDecimals = ee.Number(nbDecimals).toInt() + factor = ee.Number(10).pow(nbDecimals) + return self._obj.multiply(factor).toInt().divide(factor) diff --git a/geetools/ee_profiler.py b/geetools/ee_profiler.py new file mode 100644 index 00000000..6d92e66b --- /dev/null +++ b/geetools/ee_profiler.py @@ -0,0 +1,105 @@ +"""A profiler context manager for Earth Engine Python API.""" + +from __future__ import annotations + +import io +import re + +import ee +from anyascii import anyascii + +from .accessors import _register_extention + + +@_register_extention(ee.geetools) +class Profiler: + """A profiler context manager for Earth Engine Python API. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + with ee.geetools.Profiler() as p: + ee.Number(3.14).add(0.00159).getInfo() + res = p.profile + res + """ + + _output_capture: io.StringIO | None = None + "The output of the profiler." + + _profile_context: ee.profilePrinting | None = None + "The raw profile context." + + profile: dict | None = None + "The profile data as a dictionary." + + def __enter__(self): + """Enter the context manager.""" + self._output_capture = io.StringIO() + self._profile_context = ee.profilePrinting(destination=self._output_capture) + self._profile_context.__enter__() + return self + + def __exit__(self, *args): + """Exit the context manager.""" + self._profile_context.__exit__(*args) + + # Check if there's anything captured + profile_output = self._output_capture.getvalue() + if profile_output: + self.profile = self._to_dict(profile_output) + else: + self.profile = None # Handle the case where no output is captured + print("Warning: No profile output was captured.") + + self._output_capture.close() + + def _memory(self, mem_str: str) -> int: + """Transform a memory string to an integer.""" + mapping = {"": 1, "k": 3, "M": 6, "G": 9, "T": 12} + + # Match numbers with optional multipliers (k, M, etc.) + # and apply the multiplier to the number + match = re.match(r"([\d.]+)([kMGT]?)", mem_str) + if match is None: + raise ValueError(f"Invalid memory string: {mem_str}") + + number, multiplier = float(match.group(1)), match.group(2) + + return int(number * 10 ** mapping[multiplier]) + + def _to_dict(self, input: str) -> dict: + """Transform the output of a Earthengine profiler into a dictionary compatible with pandas DataFrame.""" + # Split the string into lines + lines = input.strip().splitlines() + + # First line contains column headers + # Initialize a dictionary to hold lists for each column + headers = [anyascii(h.strip()) for h in lines[0].split()] + result: dict = {header: [] for header in headers} + # functions to process/format each header + process = { + "EECU-s": lambda eecus: float(eecus) if eecus != "-" else None, + "CurrMem": lambda mem: self._memory(mem), # Mem is a string to convert + "PeakMem": lambda mem: self._memory(mem), # Mem is a string to convert + "Count": lambda count: int(count), # Count is an integer + "Description": lambda desc: " ".join(desc) + if isinstance(desc, list) + else desc, # Description can have multiple words + } + + # Process each line of data after the header + for line in lines[1:]: + # Split the line by spaces, considering multiple spaces as a separator + # Handle missing values denoted by "-" + parts = line.split() + part_result = dict(zip(headers, parts)) + # Populate the dictionary with values for each column + for head in headers: + result[head].append(process[head](part_result[head])) + return result diff --git a/geetools/ee_string.py b/geetools/ee_string.py new file mode 100644 index 00000000..7d967007 --- /dev/null +++ b/geetools/ee_string.py @@ -0,0 +1,72 @@ +"""Extra methods for the ``ee.String`` class.""" + +from __future__ import annotations + +import ee + +from .accessors import register_class_accessor + + +@register_class_accessor(ee.String, "geetools") +class StringAccessor: + """Toolbox for the ``ee.String`` class.""" + + def __init__(self, obj: ee.String): + """Initialize the String class.""" + self._obj = obj + + def eq(self, other: str | ee.String) -> ee.Number: + """Compare two strings and return a ``ee.Number``. + + Parameters: + other: The string to compare with. + + Returns: + ``1`` if the strings are equal, ``0`` otherwise. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + isEqual = ee.String("foo").geetools.eq("foo") + isEqual.getInfo() + """ + return self._obj.compareTo(ee.String(other)).Not() + + def format(self, template: dict | ee.Dictionary) -> ee.String: + """Format a string with a dictionary. + + Replace the keys in the string using the values provided in the dictionary. Follow the same pattern: value format as Python string.format method. + + Parameters: + template: A dictionary with the values to replace. + + Returns: + The formatted string. + + Examples: + .. jupyter-execute:: + + import ee, geetools + from geetools.utils import initialize_documentation + + initialize_documentation() + + s = ee.String("{greeting} {name} !") + s = s.geetools.format({"greeting": "Hello", "name": "bob"}) + s.getInfo() + """ + template = ee.Dictionary(template) + templateList = template.keys().zip(template.values()) + + def replace_format(kv, s): + kv = ee.List(kv) + key, value = ee.String(kv.get(0)), ee.String(kv.get(1)) + pattern = ee.String("{").cat(key).cat(ee.String("}")) + return ee.String(s).replace(pattern, value) + + return ee.String(templateList.iterate(replace_format, self._obj)) diff --git a/geetools/py.typed b/geetools/py.typed deleted file mode 100644 index 2c85c451..00000000 --- a/geetools/py.typed +++ /dev/null @@ -1 +0,0 @@ -# Marker file for PEP 561. The mypy package uses inline types. \ No newline at end of file diff --git a/geetools/tools/__init__.py b/geetools/tools/__init__.py new file mode 100644 index 00000000..ea8f5c98 --- /dev/null +++ b/geetools/tools/__init__.py @@ -0,0 +1,3 @@ +"""Legacy import package for tools.""" + +from . import _deprecated_imagecollection as imagecollection # noqa: F401 diff --git a/geetools/tools/_deprecated_imagecollection.py b/geetools/tools/_deprecated_imagecollection.py new file mode 100644 index 00000000..31788e89 --- /dev/null +++ b/geetools/tools/_deprecated_imagecollection.py @@ -0,0 +1,118 @@ +# coding=utf-8 +"""legacy Module holding tools for ee.ImageCollections.""" + +import ee +from deprecated.sphinx import deprecated + +import geetools # noqa: F401 + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.closestDate instead.") +def fillWithLast(collection, reverse=False, proxy=-999): + """Fill each masked pixels with the last available not masked pixel.""" + return ee.ImageCollection(collection).geetools.closestDate() + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") +def mosaicSameDay(collection, qualityBand=""): + """Return a collection where images from the same day are mosaicked.""" + reducer = "mosaic" if qualityBand == "" else "qualityMosaic" + return ee.ImageCollection(collection).geetools.reduceInterval(reducer, "day", 1, qualityBand) + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") +def reduceEqualInterval( + collection, interval=30, unit="day", reducer=None, start_date=None, end_date=None +): + """Reduce an ImageCollection into a new one that has one image per reduced interval.""" + return ee.ImageCollection(collection).geetools.reduceInterval(reducer, unit, interval) + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.groupInterval instead.") +def makeEqualInterval(collection, interval=1, unit="month"): + """Make a list of image collections filtered by the given interval.""" + return ee.ImageCollection(collection).geetools.groupInterval(unit, interval) + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.groupInterval instead..") +def makeDayIntervals(collection, interval=30, reverse=False, buffer="second"): + """Make day intervals.""" + return ee.ImageCollection(collection).geetools.groupInterval("day", 1) + + +@deprecated(version="1.5.0", reason="Use ee.ImageCollection.geetools.reduceInterval instead.") +def reduceDayIntervals(collection, reducer, interval=30, reverse=False, buffer="second"): + """Reduce Day Intervals.""" + return ee.ImageCollection(collection).geetools.reduceInterval(reducer, "day", 1) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested") +def linearFunctionBand(*args, **kwargs): + """Apply a linear function over the bands across every image of the ImageCollection.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested") +def linearFunctionProperty(*args, **kwargs): + """Apply a linear function over the properties across every image of the ImageCollection.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested.") +def linearInterpolation(collection, date_property="system:time_start"): + """TODO missing docstring.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested.") +def gaussFunctionBand(*args, **kwargs): + """Compute a Gauss function using a specified band over an ImageCollection, See: https://en.wikipedia.org/wiki/Gaussian_function.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested.") +def gaussFunctionProperty(*args, **kwargs): + """Compute a Gauss function using a specified property over an ImageCollection.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested.") +def normalDistributionProperty(*args, **kwargs): + """Compute a normal distribution using a specified property, over an ImageCollection.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.4.0", reason="Removed from the lib as untested.") +def normalDistributionBand(collection, band, mean=None, std=None, name="normal_distribution"): + """Compute a normal distribution using a specified band, over an ImageCollection.""" + raise NotImplementedError( + "As it was vastly untested this method has been removed from the lib." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) + + +@deprecated(version="1.5.0", reason="The output format is unclear.") +def moving_average(collection, back=5, reducer=None, use_original=True): + """Compute the moving average over a time series.""" + raise NotImplementedError( + "This method has been deprecated as the output format is unclear." + "If a real use case for this method can be provided, please open an issue and we'll reimplement it." + ) diff --git a/geetools/utils.py b/geetools/utils.py new file mode 100644 index 00000000..cc6cb78b --- /dev/null +++ b/geetools/utils.py @@ -0,0 +1,270 @@ +"""Utils methods for file and asset manipulation in the context of batch processing.""" + +from __future__ import annotations + +import os +import re +from datetime import datetime as dt + +import ee +import httplib2 +import numpy as np +from anyascii import anyascii +from matplotlib import pyplot as plt +from matplotlib.axes import Axes +from matplotlib.colors import to_rgba + + +def format_description(description: str) -> str: + """Format a name to be accepted as a Task description. + + The rule is: + The description must contain only the following characters: a..z, A..Z,0..9, ".", ",", ":", ";", + "_" or "-". The description must be at most 100 characters long. + + Args: + description: The description to format. + + Returns: + The formatted description. + """ + replacements = [ + [[" "], "_"], + [["/"], "-"], + [["?", "!", "¿", "*"], "."], + [["(", ")", "[", "]", "{", "}"], ":"], + ] + + desc = anyascii(description) + for chars, rep in replacements: + pattern = "|".join(re.escape(c) for c in chars) + desc = re.sub(pattern, rep, desc) # type: ignore + + return desc[:100] + + +def format_asset_id(description: str) -> str: + """Format a name to be accepted as an asset Id. + + The rule is: + Each segment must contain only the following characters: a..z, A..Z, 0..9, "_" or "-". + Each segment must be at least 1 character long and at most 100 characters long. + + Args: + description: The description to format. + + Returns: + The formatted description. + """ + replacements = [ + [[" "], "_"], + [["/"], "-"], + [["?", "!", "¿", "*"], "."], + [["(", ")", "[", "]", "{", "}", ";", ":", ",", "."], "_"], + ] + + desc = anyascii(description) + for chars, rep in replacements: + pattern = "|".join(re.escape(c) for c in chars) + desc = re.sub(pattern, rep, desc) # type: ignore + + return desc + + +def plot_data( + type: str, + data: dict, + label_name: str, + colors: list = [], + ax: Axes | None = None, + **kwargs, +) -> Axes: + """Plotting mechanism used in all the plotting functions. + + It binds the matplotlib capabilities with the data aggregated by different xes. + the shape of the data should as follows: + + .. code-block:: + + { + "label1": {"properties1": value1, "properties2": value2, ...} + "label2": {"properties1": value1, "properties2": value2, ...}, + ... + } + + Args: + type: The type of plot to use. can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue! + data: the data to use as inputs of the graph. please follow the fomrmat specified in the documentation. + label_name: The name of the property that was used to generate the labels + property_names: The list of names that was used to name the values. They will be used to order the keys of the data dictionary. + colors: A list of colors to use for the plot. If not provided, the default colors from the matplotlib library will be used. + ax: The matplotlib axes to use. If not provided, the plot will be send to a new figure. + kwargs: Additional arguments from the ``pyplot`` chat type selected. + """ + # define the ax if not provided by the user + if ax is None: + _, ax = plt.subplots() + + # gather the data from parameters + labels = list(data.keys()) + props = list(data[labels[0]].keys()) + colors = colors if colors else plt.get_cmap("tab10").colors + + # draw the chart based on the type + if type == "plot": + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + name = props[0] if len(props) == 1 else "Properties values" + values = list(data[label].values()) + ax.plot(props, values, label=label, **kwargs) + ax.set_ylabel(name) + ax.set_xlabel(f"Features (labeled by {label_name})") + grid_axis = "y" + + elif type == "scatter": + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + name = props[0] if len(props) == 1 else "Properties values" + values = list(data[label].values()) + ax.scatter(props, values, label=label, **kwargs) + ax.set_ylabel(name) + ax.set_xlabel(f"Features (labeled by {label_name})") + grid_axis = "y" + + elif type == "fill_between": + for i, label in enumerate(labels): + kwargs["facecolor"] = to_rgba(colors[i], 0.2) + kwargs["edgecolor"] = to_rgba(colors[i], 1) + name = props[0] if len(props) == 1 else "Properties values" + values = list(data[label].values()) + ax.fill_between(props, values, label=label, **kwargs) + ax.set_ylabel(name) + ax.set_xlabel(f"Features (labeled by {label_name})") + grid_axis = "y" + + elif type == "bar": + x = np.arange(len(props)) + width = 1 / (len(labels) + 0.8) + margin = width / 10 + kwargs["width"] = width - margin + ax.set_xticks(x + width * len(labels) / 2, props) + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + values = list(data[label].values()) + ax.bar(x + width * i, values, label=label, **kwargs) + grid_axis = "y" + + elif type == "barh": + y = np.arange(len(props)) + height = 1 / (len(labels) + 0.8) + margin = height / 10 + kwargs["height"] = height - margin + ax.set_yticks(y + height * len(labels) / 2, props) + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + values = list(data[label].values()) + ax.barh(y + height * i, values, label=label, **kwargs) + grid_axis = "x" + + elif type == "stacked": + x = np.arange(len(props)) + bottom = np.zeros(len(props)) + ax.set_xticks(x, props) + for i, label in enumerate(labels): + kwargs.update(color=colors[i], bottom=bottom) + values = list(data[label].values()) + ax.bar(x, values, label=label, **kwargs) + bottom += values + grid_axis = "y" + + elif type == "pie": + if len(labels) != 1: + raise ValueError("Pie chart can only be used with one property") + kwargs["autopct"] = kwargs.get("autopct", "%1.1f%%") + kwargs["normalize"] = kwargs.get("normalize", True) + kwargs["labeldistance"] = kwargs.get("labeldistance", None) + kwargs["wedgeprops"] = kwargs.get("wedgeprops", {"edgecolor": "w"}) + kwargs["textprops"] = kwargs.get("textprops", {"color": "w"}) + kwargs.update(autopct="%1.1f%%", colors=colors) + values = [data[labels[0]][p] for p in props] + ax.pie(values, labels=props, **kwargs) + grid_axis = "y" + + elif type == "donut": + if len(labels) != 1: + raise ValueError("Pie chart can only be used with one property") + kwargs["autopct"] = kwargs.get("autopct", "%1.1f%%") + kwargs["normalize"] = kwargs.get("normalize", True) + kwargs["labeldistance"] = kwargs.get("labeldistance", None) + kwargs["wedgeprops"] = kwargs.get("wedgeprops", {"width": 0.6, "edgecolor": "w"}) + kwargs["textprops"] = kwargs.get("textprops", {"color": "w"}) + kwargs["pctdistance"] = kwargs.get("pctdistance", 0.7) + kwargs.update(autopct="%1.1f%%", colors=colors) + values = [data[labels[0]][p] for p in props] + ax.pie(values, labels=props, **kwargs) + grid_axis = "y" + + elif type == "date": + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + x, y = list(data[label].keys()), list(data[label].values()) + ax.plot(x, y, label=label, **kwargs) + ax.set_xlabel("Date") + grid_axis = "both" + + elif type == "doy": + xmin, xmax = 366, 0 # inverted initialization to get the first iteration values + for i, label in enumerate(labels): + kwargs["color"] = colors[i] + x, y = list(data[label].keys()), list(data[label].values()) + ax.plot(x, y, label=label, **kwargs) + ax.set_xlabel("Day of year") + grid_axis = "both" + dates = [dt(2023, i + 1, 1) for i in range(12)] + idates = [int(d.strftime("%j")) - 1 for d in dates] + ndates = [d.strftime("%B")[:3] for d in dates] + ax.set_xticks(idates, ndates) + xmin, xmax = min(xmin, min(x)), max(xmax, max(x)) + ax.set_xlim(xmin - 5, xmax + 5) + + else: + raise ValueError(f"Type {type} is not (yet?) supported") + + # customize the layout of the axis + ax.grid(axis=grid_axis) + ax.set_axisbelow(True) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.legend(bbox_to_anchor=(1.02, 1), loc="upper left") + + # make sure the canvas is only rendered once. + ax.figure.canvas.draw_idle() + + return ax + + +def initialize_documentation(): + """Initialize Earthe Engine Python API in the context of the Documentation build. + + Warning: + This method is only used in the documentation build and should not be used in a production environment. + ``geetools`` need to be imported prior to import this function. + """ + # use a saved service account key if available + if "EARTHENGINE_SERVICE_ACCOUNT" in os.environ: + private_key = os.environ["EARTHENGINE_SERVICE_ACCOUNT"] + # small massage of the key to remove the quotes coming from RDT + private_key = ( + private_key[1:-1] if re.compile(r"^'[^']*'$").match(private_key) else private_key + ) + ee.Initialize.geetools.from_service_account(private_key) + + elif "EARTHENGINE_PROJECT" in os.environ: + ee.Initialize(project=os.environ["EARTHENGINE_PROJECT"], http_transport=httplib2.Http()) + + else: + raise ValueError( + "EARTHENGINE_SERVICE_ACCOUNT or EARTHENGINE_PROJECT environment variable is missing" + ) + + pass diff --git a/pyproject.toml b/pyproject.toml index e547cda5..f4464410 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,6 +95,9 @@ docstring-quotes = "double" [tool.ruff.lint.pydocstyle] convention = "google" +[tool.ruff.lint.per-file-ignores] +"geetools/__init__.py" = ["F401"] + [tool.coverage.run] source = ["geetools"] From 3cb4f000c740764c41f56513678212bdd452faa8 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 14:19:22 +0000 Subject: [PATCH 3/9] fix: rollback prerelease --- .github/workflows/prerelease.yaml | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 .github/workflows/prerelease.yaml diff --git a/.github/workflows/prerelease.yaml b/.github/workflows/prerelease.yaml new file mode 100644 index 00000000..6926d965 --- /dev/null +++ b/.github/workflows/prerelease.yaml @@ -0,0 +1,27 @@ +name: Schedule + +on: + schedule: + # Runs every Sunday at 18:00 (UTC time) + - cron: "0 18 * * 0" + workflow_dispatch: + +env: + EARTHENGINE_SERVICE_ACCOUNT: ${{ secrets.EARTHENGINE_SERVICE_ACCOUNT }} + EARTHENGINE_PROJECT: ${{ secrets.EARTHENGINE_PROJECT }} + +jobs: + prerelease: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install dependencies + run: pip install .[test] + - name: Install EE pre-release + run: pip install --pre --upgrade --force-reinstall earthengine-api + - name: test with pytest + run: pytest --color=yes tests From ba2a6541536ee5108a54baba9b9fa50f0b9d5712 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 14:50:18 +0000 Subject: [PATCH 4/9] fix: rollback tests --- pyproject.toml | 6 +- tests/check_warnings.py | 59 - tests/conftest.py | 267 + tests/data/warning_list.txt | 0 tests/test_Array.py | 46 + tests/test_Asset.py | 357 + tests/test_Asset/test_glob.yml | 1 + tests/test_Asset/test_iterdir.yml | 2 + tests/test_Asset/test_iterdir_recursive.yml | 3 + tests/test_Asset/test_parents.yml | 2 + tests/test_Asset/test_rglob.yml | 2 + .../test_rmdir_recursive_dry_run.yml | 4 + tests/test_ComputedObect.py | 52 + tests/test_Date.py | 82 + tests/test_DateRange.py | 32 + tests/test_Dictionary.py | 31 + tests/test_Export.py | 33 + tests/test_Feature.py | 17 + tests/test_Feature/test_remove_properties.yml | 10 + .../test_to_feature_collection.yml | 26 + tests/test_FeatureCollection.py | 295 + .../test_by_features.yml | 264 + .../test_by_features_with_id.yml | 264 + .../test_by_features_with_properties.yml | 9 + .../test_by_properties.yml | 348 + .../test_by_properties_with_id.yml | 348 + .../test_by_properties_with_properties.yml | 8 + .../test_deprecated_merge.yml | 34238 ++++++++++++++++ .../test_merge_geometries.yml | 34238 ++++++++++++++++ tests/test_FeatureCollection/test_plot.png | Bin 0 -> 49217 bytes .../test_plot_by_features_bar.png | Bin 0 -> 11338 bytes .../test_plot_by_features_donut.png | Bin 0 -> 21949 bytes .../test_plot_by_features_pie.png | Bin 0 -> 19842 bytes .../test_plot_by_features_scatter.png | Bin 0 -> 17139 bytes .../test_plot_by_features_stacked.png | Bin 0 -> 12276 bytes .../test_plot_by_properties_area.png | Bin 0 -> 37706 bytes .../test_plot_by_properties_bar.png | Bin 0 -> 12194 bytes .../test_plot_by_properties_plot.png | Bin 0 -> 38333 bytes .../test_FeatureCollection/test_plot_hist.png | Bin 0 -> 13014 bytes .../test_plot_with_boundaries.png | Bin 0 -> 45469 bytes .../test_plot_with_cmap.png | Bin 0 -> 49725 bytes .../test_plot_with_property.png | Bin 0 -> 49217 bytes .../test_to_dictionary.yml | 12 + tests/test_Filter.py | 12 + tests/test_Float.py | 15 + tests/test_Geometry.py | 11 + .../test_Geometry/test_deprecated_polygon.npz | Bin 0 -> 247 bytes tests/test_Image.py | 906 + tests/test_Image/test_add_date.csv | 2 + tests/test_Image/test_add_date_format.csv | 2 + tests/test_Image/test_add_prefix_to_all.yml | 13 + .../test_add_prefix_to_selected.yml | 13 + tests/test_Image/test_add_suffix_to_all.yml | 13 + .../test_add_suffix_to_selected.yml | 13 + .../test_clip_on_collection_bands.yml | 13 + .../test_clip_on_collection_property.yml | 86 + ...on_collection_without_properties_bands.yml | 13 + ...collection_without_properties_property.yml | 85 + .../test_default_spectral_indices.csv | 2 + .../test_deprecated_distance_to_mask.csv | 2 + .../test_deprecated_euclidian_distance.csv | 2 + tests/test_Image/test_distance.csv | 2 + tests/test_Image/test_distance_to_mask.csv | 2 + tests/test_Image/test_doy_to_date.csv | 2 + .../test_Image/test_doy_to_date_with_band.csv | 2 + .../test_doy_to_date_with_format.csv | 2 + tests/test_Image/test_full.csv | 2 + tests/test_Image/test_full_like.csv | 2 + tests/test_Image/test_full_like_with_mask.csv | 2 + tests/test_Image/test_full_with_lists.csv | 2 + tests/test_Image/test_full_with_name.csv | 2 + tests/test_Image/test_full_with_value.csv | 2 + tests/test_Image/test_gauss.csv | 2 + tests/test_Image/test_gauss_with_band.csv | 2 + tests/test_Image/test_get_scale_params.yml | 23 + tests/test_Image/test_get_values.csv | 2 + .../test_Image/test_get_values_with_scale.csv | 2 + tests/test_Image/test_histogram_match.csv | 2 + tests/test_Image/test_interpolate_bands.csv | 2 + tests/test_Image/test_islet_mask.png | Bin 0 -> 119 bytes tests/test_Image/test_mask_S2_clouds.csv | 2 + tests/test_Image/test_merge.yml | 39 + tests/test_Image/test_negative_clip.csv | 2 + tests/test_Image/test_pan_sharpen.csv | 2 + tests/test_Image/test_plot.png | Bin 0 -> 40513 bytes tests/test_Image/test_plot_by_bands_area.png | Bin 0 -> 39040 bytes tests/test_Image/test_plot_by_bands_bar.png | Bin 0 -> 12250 bytes tests/test_Image/test_plot_by_bands_donut.png | Bin 0 -> 41938 bytes tests/test_Image/test_plot_by_bands_pie.png | Bin 0 -> 45512 bytes tests/test_Image/test_plot_by_bands_plot.png | Bin 0 -> 40436 bytes tests/test_Image/test_plot_by_regions_bar.png | Bin 0 -> 11344 bytes .../test_Image/test_plot_by_regions_barh.png | Bin 0 -> 10928 bytes .../test_plot_by_regions_stacked.png | Bin 0 -> 12312 bytes tests/test_Image/test_plot_hist.png | Bin 0 -> 21260 bytes tests/test_Image/test_plot_one_band.png | Bin 0 -> 39816 bytes tests/test_Image/test_plot_one_band_cmap.png | Bin 0 -> 41736 bytes tests/test_Image/test_plot_with_crs.png | Bin 0 -> 48242 bytes tests/test_Image/test_plot_with_fc.png | Bin 0 -> 53492 bytes tests/test_Image/test_prefix.yml | 13 + tests/test_Image/test_preprocess.csv | 2 + tests/test_Image/test_reduce_bands.csv | 2 + .../test_reduce_bands_with_bands.csv | 2 + .../test_reduce_bands_with_name.csv | 2 + tests/test_Image/test_remove.yml | 11 + tests/test_Image/test_remove_properties.yml | 79 + tests/test_Image/test_rename.yml | 13 + tests/test_Image/test_scale_and_offset.csv | 2 + tests/test_Image/test_suffix.yml | 13 + tests/test_Image/test_tasseled_cap.csv | 2 + tests/test_Image/test_to_grid.npz | Bin 0 -> 74199 bytes tests/test_ImageCollection.py | 714 + .../test_aggregate_array.yml | 845 + .../test_aggregate_array_with_properties.yml | 22 + tests/test_ImageCollection/test_append.yml | 2 + .../test_closest_date.csv | 2 + .../test_closest_s2_sr.yml | 2 + .../test_collection_mask.csv | 2 + .../test_deprecated_closest_date.csv | 2 + .../test_deprecated_composite_by_month.csv | 2 + ...deprecated_composite_regular_intervals.csv | 2 + .../test_deprecated_fill_with_last.csv | 2 + .../test_deprecated_medoid.csv | 2 + .../test_deprecated_mosaic_same_day.csv | 2 + .../test_deprecated_reduce_day_intervals.csv | 2 + .../test_deprecated_reduce_equal_interval.csv | 2 + .../test_get_citation.yml | 2 + tests/test_ImageCollection/test_get_doi.yml | 2 + .../test_get_offset_params.yml | 23 + .../test_get_scale_params.yml | 23 + tests/test_ImageCollection/test_iloc.csv | 2 + tests/test_ImageCollection/test_integral.csv | 2 + tests/test_ImageCollection/test_mask_s2.yml | 27 + .../test_ImageCollection/test_mask_s2_sr.csv | 2 + tests/test_ImageCollection/test_medoid.csv | 2 + tests/test_ImageCollection/test_outliers.csv | 2 + .../test_outliers_with_bands.csv | 2 + .../test_outliers_with_drop.csv | 2 + .../test_outliers_with_sigma.csv | 2 + .../test_ImageCollection/test_pan_sharpen.csv | 2 + .../test_plot_dates_by_bands.png | Bin 0 -> 65575 bytes .../test_plot_dates_by_regions.png | Bin 0 -> 77256 bytes .../test_plot_doy_by_bands.png | Bin 0 -> 37163 bytes .../test_plot_doy_by_regions.png | Bin 0 -> 40217 bytes .../test_plot_doy_by_seasons.png | Bin 0 -> 30384 bytes .../test_plot_doy_by_years.png | Bin 0 -> 38221 bytes .../test_ImageCollection/test_preprocess.csv | 2 + .../test_reduce_interval.csv | 2 + .../test_reduce_interval_with_reducer.csv | 2 + .../test_reduce_region_by_date_property.yml | 12 + .../test_reduce_region_by_dates.yml | 12 + .../test_reduce_region_by_doy.yml | 12 + .../test_scale_and_offset.csv | 2 + .../test_spectral_indices.csv | 2 + .../test_tasseled_cap.csv | 2 + tests/test_ImageCollection/test_to_xarray.yml | 552 + .../test_ImageCollection/test_validPixel.csv | 2 + .../test_valid_pixels.csv | 2 + tests/test_Integer.py | 15 + tests/test_Join.py | 28 + tests/test_Join/test_by_property.yml | 14 + tests/test_Join/test_by_property_outer.yml | 14 + tests/test_Join/test_deprecated_join.yml | 14 + tests/test_List.py | 115 + .../test_complement_with_different_type.yml | 5 + .../test_complement_with_same_type.yml | 1 + tests/test_List/test_delete.yml | 2 + .../test_intersection_with_different_type.yml | 1 + .../test_intersection_with_same_type.yml | 3 + .../test_product_with_different_type.yml | 6 + .../test_List/test_product_with_same_type.yml | 9 + tests/test_List/test_replace_many.yml | 3 + tests/test_List/test_to_strings.yml | 3 + tests/test_List/test_union_with_duplicate.yml | 3 + .../test_union_without_dupplicates.yml | 5 + tests/test_List/test_zip.yml | 6 + tests/test_Number.py | 21 + tests/test_Profiler.py | 16 + tests/test_String.py | 29 + tests/test_batch/test_utils.py | 55 + tests/test_deprecated.py | 65 + .../test_deprecated_cast_image.yml | 1 + tests/test_deprecated/test_merge_geometry.yml | 394 + tests/test_deprecated/test_tobands.yml | 39 + tests/test_geetools.py | 8 - 184 files changed, 75613 insertions(+), 68 deletions(-) delete mode 100644 tests/check_warnings.py delete mode 100644 tests/data/warning_list.txt create mode 100644 tests/test_Array.py create mode 100644 tests/test_Asset.py create mode 100644 tests/test_Asset/test_glob.yml create mode 100644 tests/test_Asset/test_iterdir.yml create mode 100644 tests/test_Asset/test_iterdir_recursive.yml create mode 100644 tests/test_Asset/test_parents.yml create mode 100644 tests/test_Asset/test_rglob.yml create mode 100644 tests/test_Asset/test_rmdir_recursive_dry_run.yml create mode 100644 tests/test_ComputedObect.py create mode 100644 tests/test_Date.py create mode 100644 tests/test_DateRange.py create mode 100644 tests/test_Dictionary.py create mode 100644 tests/test_Export.py create mode 100644 tests/test_Feature.py create mode 100644 tests/test_Feature/test_remove_properties.yml create mode 100644 tests/test_Feature/test_to_feature_collection.yml create mode 100644 tests/test_FeatureCollection.py create mode 100644 tests/test_FeatureCollection/test_by_features.yml create mode 100644 tests/test_FeatureCollection/test_by_features_with_id.yml create mode 100644 tests/test_FeatureCollection/test_by_features_with_properties.yml create mode 100644 tests/test_FeatureCollection/test_by_properties.yml create mode 100644 tests/test_FeatureCollection/test_by_properties_with_id.yml create mode 100644 tests/test_FeatureCollection/test_by_properties_with_properties.yml create mode 100644 tests/test_FeatureCollection/test_deprecated_merge.yml create mode 100644 tests/test_FeatureCollection/test_merge_geometries.yml create mode 100644 tests/test_FeatureCollection/test_plot.png create mode 100644 tests/test_FeatureCollection/test_plot_by_features_bar.png create mode 100644 tests/test_FeatureCollection/test_plot_by_features_donut.png create mode 100644 tests/test_FeatureCollection/test_plot_by_features_pie.png create mode 100644 tests/test_FeatureCollection/test_plot_by_features_scatter.png create mode 100644 tests/test_FeatureCollection/test_plot_by_features_stacked.png create mode 100644 tests/test_FeatureCollection/test_plot_by_properties_area.png create mode 100644 tests/test_FeatureCollection/test_plot_by_properties_bar.png create mode 100644 tests/test_FeatureCollection/test_plot_by_properties_plot.png create mode 100644 tests/test_FeatureCollection/test_plot_hist.png create mode 100644 tests/test_FeatureCollection/test_plot_with_boundaries.png create mode 100644 tests/test_FeatureCollection/test_plot_with_cmap.png create mode 100644 tests/test_FeatureCollection/test_plot_with_property.png create mode 100644 tests/test_FeatureCollection/test_to_dictionary.yml create mode 100644 tests/test_Filter.py create mode 100644 tests/test_Float.py create mode 100644 tests/test_Geometry.py create mode 100644 tests/test_Geometry/test_deprecated_polygon.npz create mode 100644 tests/test_Image.py create mode 100644 tests/test_Image/test_add_date.csv create mode 100644 tests/test_Image/test_add_date_format.csv create mode 100644 tests/test_Image/test_add_prefix_to_all.yml create mode 100644 tests/test_Image/test_add_prefix_to_selected.yml create mode 100644 tests/test_Image/test_add_suffix_to_all.yml create mode 100644 tests/test_Image/test_add_suffix_to_selected.yml create mode 100644 tests/test_Image/test_clip_on_collection_bands.yml create mode 100644 tests/test_Image/test_clip_on_collection_property.yml create mode 100644 tests/test_Image/test_clip_on_collection_without_properties_bands.yml create mode 100644 tests/test_Image/test_clip_on_collection_without_properties_property.yml create mode 100644 tests/test_Image/test_default_spectral_indices.csv create mode 100644 tests/test_Image/test_deprecated_distance_to_mask.csv create mode 100644 tests/test_Image/test_deprecated_euclidian_distance.csv create mode 100644 tests/test_Image/test_distance.csv create mode 100644 tests/test_Image/test_distance_to_mask.csv create mode 100644 tests/test_Image/test_doy_to_date.csv create mode 100644 tests/test_Image/test_doy_to_date_with_band.csv create mode 100644 tests/test_Image/test_doy_to_date_with_format.csv create mode 100644 tests/test_Image/test_full.csv create mode 100644 tests/test_Image/test_full_like.csv create mode 100644 tests/test_Image/test_full_like_with_mask.csv create mode 100644 tests/test_Image/test_full_with_lists.csv create mode 100644 tests/test_Image/test_full_with_name.csv create mode 100644 tests/test_Image/test_full_with_value.csv create mode 100644 tests/test_Image/test_gauss.csv create mode 100644 tests/test_Image/test_gauss_with_band.csv create mode 100644 tests/test_Image/test_get_scale_params.yml create mode 100644 tests/test_Image/test_get_values.csv create mode 100644 tests/test_Image/test_get_values_with_scale.csv create mode 100644 tests/test_Image/test_histogram_match.csv create mode 100644 tests/test_Image/test_interpolate_bands.csv create mode 100644 tests/test_Image/test_islet_mask.png create mode 100644 tests/test_Image/test_mask_S2_clouds.csv create mode 100644 tests/test_Image/test_merge.yml create mode 100644 tests/test_Image/test_negative_clip.csv create mode 100644 tests/test_Image/test_pan_sharpen.csv create mode 100644 tests/test_Image/test_plot.png create mode 100644 tests/test_Image/test_plot_by_bands_area.png create mode 100644 tests/test_Image/test_plot_by_bands_bar.png create mode 100644 tests/test_Image/test_plot_by_bands_donut.png create mode 100644 tests/test_Image/test_plot_by_bands_pie.png create mode 100644 tests/test_Image/test_plot_by_bands_plot.png create mode 100644 tests/test_Image/test_plot_by_regions_bar.png create mode 100644 tests/test_Image/test_plot_by_regions_barh.png create mode 100644 tests/test_Image/test_plot_by_regions_stacked.png create mode 100644 tests/test_Image/test_plot_hist.png create mode 100644 tests/test_Image/test_plot_one_band.png create mode 100644 tests/test_Image/test_plot_one_band_cmap.png create mode 100644 tests/test_Image/test_plot_with_crs.png create mode 100644 tests/test_Image/test_plot_with_fc.png create mode 100644 tests/test_Image/test_prefix.yml create mode 100644 tests/test_Image/test_preprocess.csv create mode 100644 tests/test_Image/test_reduce_bands.csv create mode 100644 tests/test_Image/test_reduce_bands_with_bands.csv create mode 100644 tests/test_Image/test_reduce_bands_with_name.csv create mode 100644 tests/test_Image/test_remove.yml create mode 100644 tests/test_Image/test_remove_properties.yml create mode 100644 tests/test_Image/test_rename.yml create mode 100644 tests/test_Image/test_scale_and_offset.csv create mode 100644 tests/test_Image/test_suffix.yml create mode 100644 tests/test_Image/test_tasseled_cap.csv create mode 100644 tests/test_Image/test_to_grid.npz create mode 100644 tests/test_ImageCollection.py create mode 100644 tests/test_ImageCollection/test_aggregate_array.yml create mode 100644 tests/test_ImageCollection/test_aggregate_array_with_properties.yml create mode 100644 tests/test_ImageCollection/test_append.yml create mode 100644 tests/test_ImageCollection/test_closest_date.csv create mode 100644 tests/test_ImageCollection/test_closest_s2_sr.yml create mode 100644 tests/test_ImageCollection/test_collection_mask.csv create mode 100644 tests/test_ImageCollection/test_deprecated_closest_date.csv create mode 100644 tests/test_ImageCollection/test_deprecated_composite_by_month.csv create mode 100644 tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv create mode 100644 tests/test_ImageCollection/test_deprecated_fill_with_last.csv create mode 100644 tests/test_ImageCollection/test_deprecated_medoid.csv create mode 100644 tests/test_ImageCollection/test_deprecated_mosaic_same_day.csv create mode 100644 tests/test_ImageCollection/test_deprecated_reduce_day_intervals.csv create mode 100644 tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv create mode 100644 tests/test_ImageCollection/test_get_citation.yml create mode 100644 tests/test_ImageCollection/test_get_doi.yml create mode 100644 tests/test_ImageCollection/test_get_offset_params.yml create mode 100644 tests/test_ImageCollection/test_get_scale_params.yml create mode 100644 tests/test_ImageCollection/test_iloc.csv create mode 100644 tests/test_ImageCollection/test_integral.csv create mode 100644 tests/test_ImageCollection/test_mask_s2.yml create mode 100644 tests/test_ImageCollection/test_mask_s2_sr.csv create mode 100644 tests/test_ImageCollection/test_medoid.csv create mode 100644 tests/test_ImageCollection/test_outliers.csv create mode 100644 tests/test_ImageCollection/test_outliers_with_bands.csv create mode 100644 tests/test_ImageCollection/test_outliers_with_drop.csv create mode 100644 tests/test_ImageCollection/test_outliers_with_sigma.csv create mode 100644 tests/test_ImageCollection/test_pan_sharpen.csv create mode 100644 tests/test_ImageCollection/test_plot_dates_by_bands.png create mode 100644 tests/test_ImageCollection/test_plot_dates_by_regions.png create mode 100644 tests/test_ImageCollection/test_plot_doy_by_bands.png create mode 100644 tests/test_ImageCollection/test_plot_doy_by_regions.png create mode 100644 tests/test_ImageCollection/test_plot_doy_by_seasons.png create mode 100644 tests/test_ImageCollection/test_plot_doy_by_years.png create mode 100644 tests/test_ImageCollection/test_preprocess.csv create mode 100644 tests/test_ImageCollection/test_reduce_interval.csv create mode 100644 tests/test_ImageCollection/test_reduce_interval_with_reducer.csv create mode 100644 tests/test_ImageCollection/test_reduce_region_by_date_property.yml create mode 100644 tests/test_ImageCollection/test_reduce_region_by_dates.yml create mode 100644 tests/test_ImageCollection/test_reduce_region_by_doy.yml create mode 100644 tests/test_ImageCollection/test_scale_and_offset.csv create mode 100644 tests/test_ImageCollection/test_spectral_indices.csv create mode 100644 tests/test_ImageCollection/test_tasseled_cap.csv create mode 100644 tests/test_ImageCollection/test_to_xarray.yml create mode 100644 tests/test_ImageCollection/test_validPixel.csv create mode 100644 tests/test_ImageCollection/test_valid_pixels.csv create mode 100644 tests/test_Integer.py create mode 100644 tests/test_Join.py create mode 100644 tests/test_Join/test_by_property.yml create mode 100644 tests/test_Join/test_by_property_outer.yml create mode 100644 tests/test_Join/test_deprecated_join.yml create mode 100644 tests/test_List.py create mode 100644 tests/test_List/test_complement_with_different_type.yml create mode 100644 tests/test_List/test_complement_with_same_type.yml create mode 100644 tests/test_List/test_delete.yml create mode 100644 tests/test_List/test_intersection_with_different_type.yml create mode 100644 tests/test_List/test_intersection_with_same_type.yml create mode 100644 tests/test_List/test_product_with_different_type.yml create mode 100644 tests/test_List/test_product_with_same_type.yml create mode 100644 tests/test_List/test_replace_many.yml create mode 100644 tests/test_List/test_to_strings.yml create mode 100644 tests/test_List/test_union_with_duplicate.yml create mode 100644 tests/test_List/test_union_without_dupplicates.yml create mode 100644 tests/test_List/test_zip.yml create mode 100644 tests/test_Number.py create mode 100644 tests/test_Profiler.py create mode 100644 tests/test_String.py create mode 100644 tests/test_batch/test_utils.py create mode 100644 tests/test_deprecated.py create mode 100644 tests/test_deprecated/test_deprecated_cast_image.yml create mode 100644 tests/test_deprecated/test_merge_geometry.yml create mode 100644 tests/test_deprecated/test_tobands.yml delete mode 100644 tests/test_geetools.py diff --git a/pyproject.toml b/pyproject.toml index f4464410..d4270d69 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -96,7 +96,8 @@ docstring-quotes = "double" convention = "google" [tool.ruff.lint.per-file-ignores] -"geetools/__init__.py" = ["F401"] +"geetools/__init__.py" = ["F401"] # import but unused | that used to work with ignore_init_import +"tests/*" = ["D102"] # Missing docstring in public method | this is test who cares [tool.coverage.run] source = ["geetools"] @@ -107,3 +108,6 @@ ignore_missing_imports = true install_types = true non_interactive = true warn_redundant_casts = true + +[tool.codespell] +skip = "geetools/tools/*,geetools/classification.py,**/*.ipynb,**/*.yml" diff --git a/tests/check_warnings.py b/tests/check_warnings.py deleted file mode 100644 index 04db76e0..00000000 --- a/tests/check_warnings.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Check the warnings from doc builds.""" - -import sys -from pathlib import Path - - -def check_warnings(file: Path) -> int: - """Check the list of warnings produced by the CI tests. - - Raises errors if there are unexpected ones and/or if some are missing. - - Args: - file: the path to the generated warning.txt file from - the CI build - - Returns: - 0 if the warnings are all there - 1 if some warning are not registered or unexpected - """ - # print some log - print("\n=== Sphinx Warnings test ===\n") - - # find the file where all the known warnings are stored - warning_file = Path(__file__).parent / "data" / "warning_list.txt" - - test_warnings = file.read_text().strip().split("\n") - ref_warnings = warning_file.read_text().strip().split("\n") - - print( - f'Checking build warnings in file: "{file}" and comparing to expected ' - f'warnings defined in "{warning_file}"\n\n' - ) - - # find all the missing warnings - missing_warnings = [] - for wa in ref_warnings: - index = [i for i, twa in enumerate(test_warnings) if wa in twa] - if len(index) == 0: - missing_warnings += [wa] - print(f"Warning was not raised: {wa}") - else: - test_warnings.pop(index[0]) - - # the remaining one are unexpected - for twa in test_warnings: - print(f"Unexpected warning: {twa}") - - # delete the tmp warnings file - file.unlink() - - return len(missing_warnings) != 0 or len(test_warnings) != 0 - - -if __name__ == "__main__": - # cast the file to path and resolve to an absolute one - file = Path.cwd() / "warnings.txt" - - # execute the test - sys.exit(check_warnings(file)) diff --git a/tests/conftest.py b/tests/conftest.py index 23c19ef8..c71f89d4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1 +1,268 @@ """Pytest session configuration.""" + +import string + +import ee +import pytest +import pytest_gee +import requests + +S2_BAND_COMBO = ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12", "SCL"] +"""Sentinel-2 band combination.""" + +L8_BAND_COMBO = ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"] +"""Landsat-8 band combination.""" + + +def pytest_configure() -> None: + """Initialize earth engine according to the environment.""" + pytest_gee.init_ee_from_service_account() + + +@pytest.fixture(scope="session") +def gee_folder_structure(): + """Override the default test folder structure.""" + point = ee.Geometry.Point([0, 0]) + return { + "folder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + "subfolder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + }, + }, + "rmdir_folder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + "subfolder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + }, + }, + "move_folder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + "subfolder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + }, + }, + "copy_folder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + "subfolder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + }, + }, + "unlink_folder::Folder": { + "image": ee.Image(1).clipToBoundsAndScale(point.buffer(100), scale=30), + }, + } + + +# -- fixtures that will be used throughout the tests --------------------------- +@pytest.fixture +def amazonas() -> ee.FeatureCollection: + """Return the Amazonas state from colombia.""" + level2 = ee.FeatureCollection("FAO/GAUL/2015/level2") + colombia = level2.filter(ee.Filter.eq("ADM0_NAME", "Colombia")) + return colombia.filter(ee.Filter.eq("ADM1_NAME", "Amazonas")) + + +@pytest.fixture +def s2_sr(amazonas) -> ee.ImageCollection: + """Return a copernicus based collection. + + the 100 first images of the Sentinel-2 Surface Reflectance ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. + """ + return ( + ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") + .select(S2_BAND_COMBO) + .filterBounds(amazonas) + .filterDate("2021-01-01", "2021-12-01") + ) + + +@pytest.fixture +def vatican(): + """Return the vatican city.""" + level0 = ee.FeatureCollection("FAO/GAUL/2015/level0") + return level0.filter(ee.Filter.eq("ADM0_NAME", "Holy See")) + + +@pytest.fixture +def vatican_buffer(): + """A 100 buffer around vatican city.""" + return ee.Geometry.Point([12.4534, 41.9033]).buffer(100) + + +@pytest.fixture +def s2_sr_vatican_2020(): + """A single image from 2020 on top of vatican city from S2 SR collection.""" + src = "COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM" + return ee.Image(src).select(S2_BAND_COMBO) + + +@pytest.fixture +def l8_sr_vatican_2020(): + """A single image from 2020 on top of vatican city from L8 SR collection.""" + src = "LANDSAT/LC08/C02/T1/LC08_191031_20130711" + return ee.Image(src).select(L8_BAND_COMBO) + + +@pytest.fixture +def l8_toa(amazonas) -> ee.ImageCollection: + """Return a landsat based collection. + + the 100 first images of the landast 8 TOA ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. + """ + return ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_RT_TOA") + .select(L8_BAND_COMBO) + .filterBounds(amazonas) + .filterDate("2021-01-01", "2021-12-01") + ) + + +@pytest.fixture +def l8_sr(amazonas): + """Return a landsat based collection. + + the 100 first images of the landast 8 SR ImageCollection centered on the amazonas state of colombia and from 2021-01-01 to 2021-12-01. + """ + return ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") + .filterBounds(amazonas) + .filterDate("2021-01-01", "2021-12-01") + ) + + +@pytest.fixture +def l8_sr_raw(): + """Return a defined image collection.""" + return ee.ImageCollection("LANDSAT/LC08/C02/T1").select(L8_BAND_COMBO) + + +@pytest.fixture +def date_instance(): + """Return a defined date instance.""" + return ee.Date("2020-01-01") + + +@pytest.fixture +def daterange_instance(): + """Return a DateRange instance.""" + return ee.DateRange("2020-01-01", "2020-01-31") + + +@pytest.fixture +def letter_list(): + """Return a defined list instance.""" + return ee.List([*string.ascii_lowercase[:3]]) + + +@pytest.fixture +def int_list(): + """Return a defined list instance.""" + return ee.List([*range(1, 3)]) + + +@pytest.fixture +def mix_list(): + """Return a defined list instance.""" + return ee.List(["a", 1, ee.Image(1)]) + + +@pytest.fixture +def number_instance(): + """Return a defined number instance.""" + return ee.Number(1234.56785678) + + +@pytest.fixture +def string_instance(): + """Return a defined string instance.""" + return ee.String("foo") + + +@pytest.fixture +def format_string_instance(): + """Return a defined string instance.""" + return ee.String("{greeting} {name} !") + + +@pytest.fixture +def geom_instance(): + """Set a geometryCollection instance.""" + point0 = ee.Geometry.Point([0, 0], proj="EPSG:4326") + point1 = ee.Geometry.Point([0, 1], proj="EPSG:4326") + poly0 = point0.buffer(1, proj="EPSG:4326") + poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") + line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") + multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") + return ee.Algorithms.GeometryConstructors.MultiGeometry( + [multiPoly, poly0, poly1, point0, line], + crs="EPSG:4326", + geodesic=True, + maxError=1, + ) + + +@pytest.fixture +def fc_instance(): + """Return a fc collection containing 1 single geometryCollection.""" + point0 = ee.Geometry.Point([0, 0], proj="EPSG:4326") + point1 = ee.Geometry.Point([0, 1], proj="EPSG:4326") + poly0 = point0.buffer(1, proj="EPSG:4326") + poly1 = point1.buffer(1, proj="EPSG:4326").bounds(proj="EPSG:4326") + line = ee.Geometry.LineString([point1, point0], proj="EPSG:4326") + multiPoly = ee.Geometry.MultiPolygon([poly0, poly1], proj="EPSG:4326") + geometryCollection = ee.Algorithms.GeometryConstructors.MultiGeometry( + [multiPoly, poly0, poly1, point0, line], + crs="EPSG:4326", + geodesic=True, + maxError=1, + ) + return ee.FeatureCollection([geometryCollection]) + + +@pytest.fixture +def gaul_3_countries(): + """Return Italy switzerland and France.""" + fc = ee.FeatureCollection("FAO/GAUL/2015/level0") + return fc.filter(ee.Filter.inList("ADM0_CODE", [122, 237, 85])) + + +@pytest.fixture +def doy_image(): + """Return an Image instance with 2 random doy bands.""" + doy = ee.Image.random(seed=0).multiply(365).toInt().rename("doy1") + return doy.rename("doy1").addBands(doy.rename("doy2")) + + +@pytest.fixture +def multipoint_feature(): + """Return a ``Feature`` instance.""" + geoms = ee.Geometry.MultiPoint([[0, 0], [0, 1]]) + return ee.Feature(geoms).set({"foo": "bar", "bar": "foo"}) + + +@pytest.fixture +def ecoregions(): + """Return the ecoregion collection.""" + return ee.FeatureCollection("projects/google/charts_feature_example") + + +@pytest.fixture +def climSamp(): + """Return the climate sample collection.""" + normClim = ee.ImageCollection("OREGONSTATE/PRISM/Norm81m").toBands() + region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14) + return normClim.sample(region, 5000) + + +@pytest.fixture(scope="session") +def stac_schema(): + """Return the STAC collection schema.""" + url = "https://raw.githubusercontent.com/radiantearth/stac-spec/v1.0.0/collection-spec/json-schema/collection.json" + return requests.get(url).json() + + +@pytest.fixture(scope="session") +def jaxa_rainfall(): + """Return the JAXA rain collection.""" + return ee.ImageCollection("JAXA/GPM_L3/GSMaP/v6/operational") diff --git a/tests/data/warning_list.txt b/tests/data/warning_list.txt deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/test_Array.py b/tests/test_Array.py new file mode 100644 index 00000000..14a5c8fb --- /dev/null +++ b/tests/test_Array.py @@ -0,0 +1,46 @@ +"""Test the Array class methods.""" + +import ee + + +class TestFull: + """Test the full method.""" + + def test_full_with_integers(self): + full_array = ee.Array.geetools.full(3, 3, 1) + assert full_array.getInfo() == [[1, 1, 1], [1, 1, 1], [1, 1, 1]] + + def test_full_with_floats(self): + full_array = ee.Array.geetools.full(3.1, 3.1, 1.0) + assert full_array.getInfo() == [ + [1.0, 1.0, 1.0], + [1.0, 1.0, 1.0], + [1.0, 1.0, 1.0], + ] + + def test_full_with_ee_numbers(self): + full_array = ee.Array.geetools.full(ee.Number(3), ee.Number(3), ee.Number(1)) + assert full_array.getInfo() == [[1, 1, 1], [1, 1, 1], [1, 1, 1]] + + +class TestSet: + """Test the set method.""" + + def test_set_with_integers(self): + array = ee.Array.geetools.full(3, 3, 1) + set_array = array.geetools.set(1, 1, 0) + assert set_array.getInfo() == [[1, 1, 1], [1, 0, 1], [1, 1, 1]] + + def test_set_with_floats(self): + array = ee.Array.geetools.full(3.1, 3.1, 1.0) + set_array = array.geetools.set(1, 1, 0.0) + assert set_array.getInfo() == [ + [1.0, 1.0, 1.0], + [1.0, 0.0, 1.0], + [1.0, 1.0, 1.0], + ] + + def test_set_with_ee_numbers(self): + array = ee.Array.geetools.full(3, 3, ee.Number(1)) + set_array = array.geetools.set(1, 1, ee.Number(0)) + assert set_array.getInfo() == [[1, 1, 1], [1, 0, 1], [1, 1, 1]] diff --git a/tests/test_Asset.py b/tests/test_Asset.py new file mode 100644 index 00000000..e88523e6 --- /dev/null +++ b/tests/test_Asset.py @@ -0,0 +1,357 @@ +"""Test cases for the Asset class.""" + +import os +from unittest.mock import patch + +import ee +import pytest + +import geetools # noqa F401 + +EARTHENGINE_PROJECT = os.environ.get("EARTHENGINE_PROJECT") + + +class TestConstructors: + """Test the constructors of the Asset class.""" + + @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) + def test_home(self): + asset = ee.Asset.home() + assert asset == f"projects/{EARTHENGINE_PROJECT}/assets" + + def test_not_absolute(self): + asset = ee.Asset("/projects/foo/bar") + assert asset == "projects/foo/bar" + + +class TestStr: + """Test the to_string method.""" + + def test_str(self): + asset = ee.Asset("projects/bar") + assert str(asset) == "projects/bar" + + def test_repr(self): + asset = ee.Asset("projects/bar") + assert repr(asset) == "ee.Asset('projects/bar')" + + def test_as_posix(self): + asset = ee.Asset("projects/bar") + assert asset.as_posix() == "projects/bar" + + def test_as_uri(self): + asset = ee.Asset("projects/bar") + assert asset.as_uri() == "https://code.earthengine.google.com/?asset=projects/bar" + + def test_as_description(self): + asset = ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets/a weird name") + assert asset.as_description() == "a_weird_name" + + +class TestOperations: + """Test the operations that can be run on the asset.""" + + def test_truediv(self): + asset = ee.Asset("projects/bar") + assert asset / "foo" == "projects/bar/foo" + + def test_lt(self): + asset = ee.Asset("projects/bar/foo") + assert (asset < "projects/bar/foo/bar") is True + assert (asset < "projects/bar/foo") is False + assert (asset < "projects/bar") is False + + def test_gt(self): + asset = ee.Asset("projects/bar/foo") + assert (asset > "projects/bar/foo/bar") is False + assert (asset > "projects/bar/foo") is False + assert (asset > "projects/bar") is True + + def test_le(self): + asset = ee.Asset("projects/bar/foo") + assert (asset <= "projects/bar/foo/bar") is True + assert (asset <= "projects/bar/foo") is True + assert (asset <= "projects/bar") is False + + def test_ge(self): + asset = ee.Asset("projects/bar/foo") + assert (asset >= "projects/bar/foo/bar") is False + assert (asset >= "projects/bar/foo") is True + assert (asset >= "projects/bar") is True + + def test_eq(self): + asset = ee.Asset("projects/bar") + assert (asset == "projects/bar") is True + assert (asset == "projects/bar/foo") is False + + def test_ne(self): + asset = ee.Asset("projects/bar") + assert (asset != "projects/bar") is False + assert (asset != "projects/bar/foo") is True + + def test_idiv(self): + asset = ee.Asset("projects/bar") + asset /= "foo" + assert asset == "projects/bar/foo" + + def test_is_absolute(self): + assert ee.Asset("projects/bar/assets/foo").is_absolute() is True + assert ee.Asset("projects/bar/foo").is_absolute() is False + assert ee.Asset("projects/assets/foo").is_absolute() is False + assert ee.Asset("bar/assets/foo").is_absolute() is False + with pytest.raises(ValueError): + ee.Asset("projects/bar").is_absolute(raised=True) + + @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) + def test_is_user_project(self): + assert ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets/foo").is_user_project() is True + assert ee.Asset("projects/foo").is_user_project() is False + with pytest.raises(ValueError): + ee.Asset("projects/foo").is_user_project(raised=True) + + @patch("ee.data._cloud_api_user_project", EARTHENGINE_PROJECT) + def test_expanduser(self): + asset = ee.Asset("~/foo").expanduser() + assert asset == f"projects/{EARTHENGINE_PROJECT}/assets/foo" + + def test_is_relative_to(self): + asset1 = ee.Asset("projects/bar") + asset2 = ee.Asset("projects/bar/foo") + assert asset2.is_relative_to(asset1) is True + assert asset1.is_relative_to(asset2) is False + + def test_joinpath(self): + asset = ee.Asset("projects/bar") + assert asset.joinpath("foo") == "projects/bar/foo" + + def test_match(self): + asset = ee.Asset("projects/bar/foo") + assert asset.match("projects/bar/*") is True + assert asset.match("projects/bar") is False + assert asset.match("**/foo") is True + + def test_with_name(self): + asset = ee.Asset("projects/bar") + assert asset.with_name("foo") == "projects/foo" + + +class TestProperties: + """Test the properties of the Asset class.""" + + def test_parts(self): + asset = ee.Asset("projects/bar/foo") + assert asset.parts == ("projects", "bar", "foo") + + def test_parent(self): + asset = ee.Asset("projects/test/assets/foo/bar/baz") + assert asset.parent == "projects/test/assets/foo/bar" + + def test_parents(self, data_regression): + asset = ee.Asset("projects/test/assets/foo/bar/baz") + parents = [str(p) for p in asset.parents] + data_regression.check(parents) + + def test_name(self): + asset = ee.Asset("projects/bar/foo") + assert asset.name == "foo" + + def test_st_size(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + assert (gee_test_folder / "folder/image").st_size == 31 + with pytest.raises(ValueError): + gee_test_folder.st_size + + def test_owner(self): + assert ee.Asset("projects/bar/assets/foo").owner == "bar" + with pytest.raises(ValueError): + ee.Asset("projects/foo").owner + + +class TestServerMethods: + """Test methods that are run on the server.""" + + def test_exists(self, gee_test_folder): + # cast to asset as it's just a regular path in pytest-gee + gee_test_folder = ee.Asset(gee_test_folder) + + assert gee_test_folder.exists() is True + assert (gee_test_folder / "folder").exists() is True + assert (gee_test_folder / "folder" / "image").exists() is True + assert (gee_test_folder / "folder" / "fake").exists() is False + + def test_is_project(self): + assert ee.Asset(f"projects/{EARTHENGINE_PROJECT}/assets").is_project() is True + assert ee.Asset("projects/bar").is_project() is False + with pytest.raises(ValueError): + ee.Asset("projects/bar").is_project(raised=True) + + def test_is_folder(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + assert (gee_test_folder / "folder").is_folder() is True + assert (gee_test_folder / "folder" / "image").is_folder() is False + + def test_is_image(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + assert (gee_test_folder / "folder" / "image").is_image() is True + assert (gee_test_folder / "folder").is_feature_collection() is False + + def test_is_type(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + assert gee_test_folder.is_type("FOLDER") is True + with pytest.raises(ValueError): + gee_test_folder.is_type("IMAGE", raised=True) + + def test_iterdir(self, gee_hash, gee_test_folder, data_regression): + folder = ee.Asset(gee_test_folder) / "folder" + assets = [ + str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") + for a in folder.iterdir() + ] + data_regression.check(assets) + + def test_iterdir_recursive(self, gee_hash, gee_test_folder, data_regression): + folder = ee.Asset(gee_test_folder) / "folder" + assets = [ + str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") + for a in folder.iterdir(recursive=True) + ] + data_regression.check(assets) + + def test_iterdir_nodir(self, gee_test_folder): + with pytest.raises(ValueError): + (ee.Asset(gee_test_folder) / "folder" / "image").iterdir() + + def test_glob(self, gee_test_folder, gee_hash, data_regression): + folder = ee.Asset(gee_test_folder) / "folder" + assets = [ + str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") + for a in folder.glob("*/image") + ] + data_regression.check(assets) + + def test_rglob(self, gee_test_folder, gee_hash, data_regression): + folder = ee.Asset(gee_test_folder) / "folder" + assets = [ + str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") + for a in folder.rglob("*/image") + ] + data_regression.check(assets) + + def test_mkdir(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = (gee_test_folder / "new_mkdir_folder").mkdir() + assert asset.is_folder() is True + ee.data.deleteAsset(str(asset)) + + def test_mkdir_exists(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + with pytest.raises(ValueError): + (gee_test_folder / "folder").mkdir() + asset = (gee_test_folder / "folder").mkdir(exist_ok=True) + assert asset.is_folder() is True + + def test_mkdir_parents(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + with pytest.raises(ValueError): + (gee_test_folder / "new_mkdir_parent" / "subfolder").mkdir() + asset = (gee_test_folder / "new_mkdir_parent" / "subfolder").mkdir(parents=True) + assert asset.parent.is_folder() is True + assert asset.is_folder() is True + ee.data.deleteAsset(str(asset)) + ee.data.deleteAsset(str(asset.parent)) + + def test_unlink(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "unlink_folder" / "image" + assert asset.is_image() is True + asset.unlink() + assert asset.exists() is False + + def test_unlink_not_exists(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + with pytest.raises(ValueError): + (gee_test_folder / "new_unlink_folder").unlink() + + def test_delete(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = (gee_test_folder / "new_delete_folder").mkdir() + assert asset.is_folder() is True + asset.delete() + assert asset.exists() is False + + def test_rmdir_dry_run(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = (gee_test_folder / "test_rmdir_folder").mkdir() + assert asset.is_folder() is True + assets = asset.rmdir(dry_run=True) + assert assets == [str(asset)] + assert asset.exists() is True + ee.data.deleteAsset(str(asset)) + + def test_rmdir(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = (gee_test_folder / "test_rmdir_folder").mkdir() + assert asset.is_folder() is True + asset.rmdir() + assert asset.exists() is False + + def test_rmdir_not_folder(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + with pytest.raises(ValueError): + (gee_test_folder / "folder" / "image").rmdir() + + def test_rmdir_recursive_dry_run(self, gee_hash, gee_test_folder, data_regression): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "rmdir_folder" + assets = [ + str(a).replace(EARTHENGINE_PROJECT, "ee-project").replace(gee_hash, "hash") + for a in asset.rmdir(recursive=True) + ] + data_regression.check(assets) + + def test_rmdir_recursive(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "rmdir_folder" + asset.rmdir(recursive=True, dry_run=False) + assert asset.exists() is False + + def test_copy(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "copy_folder" / "image" + new_asset = gee_test_folder / "copy_folder" / "new_image" + asset.copy(new_asset) + assert asset.exists() is True + assert new_asset.exists() is True + + def test_copy_folder(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "copy_folder" + new_asset = gee_test_folder / "new_copy_folder" + asset.copy(new_asset) + assert asset.exists() is True + assert new_asset.exists() is True + + def test_move(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "move_folder" / "image" + new_asset = gee_test_folder / "move_folder" / "new_image" + asset.move(new_asset) + assert asset.exists() is False + assert new_asset.exists() is True + + def test_move_folder(self, gee_test_folder): + gee_test_folder = ee.Asset(gee_test_folder) + asset = gee_test_folder / "move_folder" + new_asset = gee_test_folder / "new_move_folder" + asset.move(new_asset) + assert asset.exists() is False + assert new_asset.exists() is True + + +class TestSetProperties: + """Test the ``set_properties`` method.""" + + def test_set_properties(self, gee_test_folder): + asset = ee.Asset(gee_test_folder) / "folder" / "image" + asset.setProperties(foo="bar") + assert ee.Image(asset.as_posix()).get("foo").getInfo() == "bar" diff --git a/tests/test_Asset/test_glob.yml b/tests/test_Asset/test_glob.yml new file mode 100644 index 00000000..5f21f759 --- /dev/null +++ b/tests/test_Asset/test_glob.yml @@ -0,0 +1 @@ +- projects/ee-project/assets/hash/folder/image diff --git a/tests/test_Asset/test_iterdir.yml b/tests/test_Asset/test_iterdir.yml new file mode 100644 index 00000000..08bc8524 --- /dev/null +++ b/tests/test_Asset/test_iterdir.yml @@ -0,0 +1,2 @@ +- projects/ee-project/assets/hash/folder/image +- projects/ee-project/assets/hash/folder/subfolder diff --git a/tests/test_Asset/test_iterdir_recursive.yml b/tests/test_Asset/test_iterdir_recursive.yml new file mode 100644 index 00000000..059ac2d3 --- /dev/null +++ b/tests/test_Asset/test_iterdir_recursive.yml @@ -0,0 +1,3 @@ +- projects/ee-project/assets/hash/folder/image +- projects/ee-project/assets/hash/folder/subfolder +- projects/ee-project/assets/hash/folder/subfolder/image diff --git a/tests/test_Asset/test_parents.yml b/tests/test_Asset/test_parents.yml new file mode 100644 index 00000000..2852d6bb --- /dev/null +++ b/tests/test_Asset/test_parents.yml @@ -0,0 +1,2 @@ +- projects/test/assets/foo/bar +- projects/test/assets/foo diff --git a/tests/test_Asset/test_rglob.yml b/tests/test_Asset/test_rglob.yml new file mode 100644 index 00000000..8f013e97 --- /dev/null +++ b/tests/test_Asset/test_rglob.yml @@ -0,0 +1,2 @@ +- projects/ee-project/assets/hash/folder/image +- projects/ee-project/assets/hash/folder/subfolder/image diff --git a/tests/test_Asset/test_rmdir_recursive_dry_run.yml b/tests/test_Asset/test_rmdir_recursive_dry_run.yml new file mode 100644 index 00000000..29fa7a73 --- /dev/null +++ b/tests/test_Asset/test_rmdir_recursive_dry_run.yml @@ -0,0 +1,4 @@ +- projects/ee-project/assets/hash/rmdir_folder/subfolder/image +- projects/ee-project/assets/hash/rmdir_folder/image +- projects/ee-project/assets/hash/rmdir_folder/subfolder +- projects/ee-project/assets/hash/rmdir_folder diff --git a/tests/test_ComputedObect.py b/tests/test_ComputedObect.py new file mode 100644 index 00000000..32cc4854 --- /dev/null +++ b/tests/test_ComputedObect.py @@ -0,0 +1,52 @@ +"""Test the ComputedObject class methods.""" + +import ee +import pytest + + +class TestIsinstance: + """Test the isInstance method.""" + + def test_isinstance_with_string(self): + assert ee.String("").isInstance(ee.String).getInfo() == 1 + + def test_isinstance_with_integer(self): + assert ee.Number(1).isInstance(ee.Integer).getInfo() == 1 + + def test_isinstance_with_float(self): + assert ee.Number(1.1).isInstance(ee.Float).getInfo() == 1 + + def test_isinstance_with_image(self): + assert ee.Image().isInstance(ee.Image).getInfo() == 1 + + def test_isinstance_with_imagecollection(self): + ic = ee.ImageCollection([ee.Image()]) + assert ic.isInstance(ee.ImageCollection).getInfo() == 1 + + def test_isinstance_with_feature(self): + assert ee.Feature(None).isInstance(ee.Feature).getInfo() == 1 + + def test_isinstance_with_geometry(self): + assert ee.Geometry.Point([0, 0]).isInstance(ee.Geometry).getInfo() == 1 + + +class TestSave: + """Test the ``save`` method.""" + + def test_save(self, tmp_path): + file = tmp_path / "test.gee" + ee.Number(1.1).save(file) + assert file.exists() + + +class TestOpen: + """Test the ``open`` method.""" + + def test_open(self, tmp_path): + (object := ee.Number(1.1)).save((file := tmp_path / "test.gee")) + opened = ee.Number.open(file) + assert object.eq(opened).getInfo() + + def test_open_not_correct_suffix(self): + with pytest.raises(ValueError): + ee.Number.open("file.toto") diff --git a/tests/test_Date.py b/tests/test_Date.py new file mode 100644 index 00000000..02050b1b --- /dev/null +++ b/tests/test_Date.py @@ -0,0 +1,82 @@ +"""Test the Date class methods.""" + +import ee +import pytest + +import geetools # noqa: F401 + + +class TestToDatetime: + """Test the toDatetime method.""" + + def test_to_datetime(self, date_instance): + datetime = date_instance.geetools.to_datetime() + assert datetime.year == 2020 + assert datetime.month == 1 + assert datetime.day == 1 + + +class TestGetUnitSinceEpoch: + """Test the getUnitSinceEpoch method.""" + + def test_unit_since_epoch(self, date_instance): + unit = date_instance.geetools.getUnitSinceEpoch("year") + assert unit.getInfo() >= 49 # 2020 - 1970 + + def test_wrong_unit(self, date_instance): + with pytest.raises(ValueError): + date_instance.geetools.getUnitSinceEpoch("foo") + + +class TestFromEpoch: + """Test the fromEpoch method.""" + + def test_from_epoch(self): + date = ee.Date.geetools.fromEpoch(49, "year") + assert date.format("YYYY-MM-DD").getInfo() == "2019-01-01" + + def test_wrong_unit(self): + with pytest.raises(ValueError): + ee.Date.geetools.fromEpoch(49, "foo") + + +class TestFromDOY: + """Test the fromDOY method.""" + + def test_from_doy(self): + date = ee.Date.geetools.fromDOY(1, 2020) + assert date.format("YYYY-MM-DD").getInfo() == "2020-01-01" + + def test_wrong_year(self): + # check GEE can use year < EPOCH + date = ee.Date.geetools.fromDOY(1, 3) + assert date.format("YYYY-MM-DD").getInfo() == "0003-01-01" + + def test_wrong_doy(self): + # check that GEE can use > 365 doy + date = ee.Date.geetools.fromDOY(367, 2020) + assert date.format("YYYY-MM-DD").getInfo() == "2021-01-01" + + +class TestIsLeap: + """Test the isLeap method.""" + + def test_is_leap_1992(self): + leap = ee.Date("1992-01-01").geetools.isLeap() + assert leap.getInfo() == 1 + + def test_is_leap_2000(self): + leap = ee.Date("2000-01-01").geetools.isLeap() + assert leap.getInfo() == 1 + + def test_is_leap_1900(self): + leap = ee.Date("1900-01-01").geetools.isLeap() + assert leap.getInfo() == 0 + + +class TestNow: + """Test the now method.""" + + def test_now(self): + date = ee.Date.geetools.now() + assert date.getInfo() is not None diff --git a/tests/test_DateRange.py b/tests/test_DateRange.py new file mode 100644 index 00000000..81d3b0f2 --- /dev/null +++ b/tests/test_DateRange.py @@ -0,0 +1,32 @@ +"""Test the ``DateRange`` class.""" + +import ee +import pytest + + +class TestSplit: + """Test the ``split`` method.""" + + def test_split(self, daterange_instance): + list = daterange_instance.geetools.split(1, "day") + first = ee.DateRange(list.get(0)).start() + last = ee.DateRange(list.get(-1)).end() + assert list.size().getInfo() == 30 + assert first.format("YYYY-MM-dd").getInfo() == "2020-01-01" + assert last.format("YYYY-MM-dd").getInfo() == "2020-01-31" + + def split_with_end_outside(self, daterange_instance): + list = daterange_instance.geetools.split(2, "month") + first = ee.DateRange(list.get(0)).start() + last = ee.DateRange(list.get(-1)).end() + assert list.size().getInfo() == 1 + assert first.format("YYYY-MM-dd").getInfo() == "2020-01-01" + assert last.format("YYYY-MM-dd").getInfo() == "2020-01-31" + + +class TestCheckUnit: + """Test the ``check_unit`` method exception.""" + + def test_check_unit(self): + with pytest.raises(ValueError): + ee.DateRange.geetools.check_unit("toto") diff --git a/tests/test_Dictionary.py b/tests/test_Dictionary.py new file mode 100644 index 00000000..bf23e27e --- /dev/null +++ b/tests/test_Dictionary.py @@ -0,0 +1,31 @@ +"""Test the Dictionary class methods.""" + +import ee + + +class TestFromPairs: + """Test the fromPairs method.""" + + def test_from_pairs_with_list(self): + d = ee.Dictionary.geetools.fromPairs([["foo", 1], ["bar", 2]]) + assert d.getInfo() == {"foo": 1, "bar": 2} + + def test_from_pairs_with_ee_list(self): + d = ee.Dictionary.geetools.fromPairs(ee.List([["foo", 1], ["bar", 2]])) + assert d.getInfo() == {"foo": 1, "bar": 2} + + +class TestSort: + """Test the sort method.""" + + def test_sort(self): + d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.sort() + assert d.getInfo() == {"bar": 2, "foo": 1} + + +class TestGetMany: + """Test the getMany method.""" + + def test_getMany(self): + d = ee.Dictionary({"foo": 1, "bar": 2}).geetools.getMany(["foo"]) + assert d.getInfo() == [1] diff --git a/tests/test_Export.py b/tests/test_Export.py new file mode 100644 index 00000000..13344b15 --- /dev/null +++ b/tests/test_Export.py @@ -0,0 +1,33 @@ +"""Test the ``Export`` class.""" + +import ee +import pytest +from ee.cli.utils import wait_for_task + +import geetools # noqa F401 + + +class TestImageCollection: + """Test the ``imagecollection`` namespace.""" + + @pytest.mark.skip(reason="The export task timeout when to many tests are run at the same time") + def test_toAsset(self, gee_test_folder): + task_list = ee.batch.Export.geetools.imagecollection.toAsset( + imagecollection=self.ic, + description="ic_to_asset", + index_property="index", + assetId=(gee_test_folder / "ic_to_asset").as_posix(), + region=ee.Geometry.Point([0, 0]).buffer(100).bounds(), + scale=50, + ) + [task.start() for task in task_list] + [wait_for_task(task.id, timeout=50) for task in task_list] + + ic = ee.ImageCollection((gee_test_folder / "ic_to_asset").as_posix()) + assert ic.size().getInfo() == 2 + + @property + def ic(self): + """Return a test image collection.""" + image_list = [ee.Image(i).set("index", f"image_{i}") for i in range(2)] + return ee.ImageCollection(image_list) diff --git a/tests/test_Feature.py b/tests/test_Feature.py new file mode 100644 index 00000000..c0515e7c --- /dev/null +++ b/tests/test_Feature.py @@ -0,0 +1,17 @@ +"""Test the ``Feature`` class.""" + + +class TestToFeatureCollection: + """Test the ``toFeatureCollection`` method.""" + + def test_to_feature_collection(self, multipoint_feature, data_regression): + fc = multipoint_feature.geetools.toFeatureCollection() + data_regression.check(fc.getInfo()) + + +class TestRemoveProperties: + """Test the ``removeProperties`` method.""" + + def test_remove_properties(self, multipoint_feature, data_regression): + feature = multipoint_feature.geetools.removeProperties(["foo"]) + data_regression.check(feature.getInfo()) diff --git a/tests/test_Feature/test_remove_properties.yml b/tests/test_Feature/test_remove_properties.yml new file mode 100644 index 00000000..b7943b95 --- /dev/null +++ b/tests/test_Feature/test_remove_properties.yml @@ -0,0 +1,10 @@ +geometry: + coordinates: + - - 0 + - 0 + - - 0 + - 1 + type: MultiPoint +properties: + bar: foo +type: Feature diff --git a/tests/test_Feature/test_to_feature_collection.yml b/tests/test_Feature/test_to_feature_collection.yml new file mode 100644 index 00000000..49ad297e --- /dev/null +++ b/tests/test_Feature/test_to_feature_collection.yml @@ -0,0 +1,26 @@ +columns: + bar: String + foo: String + system:index: String +features: +- geometry: + coordinates: + - 0 + - 0 + type: Point + id: '0' + properties: + bar: foo + foo: bar + type: Feature +- geometry: + coordinates: + - 0 + - 1 + type: Point + id: '1' + properties: + bar: foo + foo: bar + type: Feature +type: FeatureCollection diff --git a/tests/test_FeatureCollection.py b/tests/test_FeatureCollection.py new file mode 100644 index 00000000..0e594c67 --- /dev/null +++ b/tests/test_FeatureCollection.py @@ -0,0 +1,295 @@ +"""Test the ``FeatureCollection`` class.""" + +import io + +import ee +import geopandas as gpd +import pytest +from matplotlib import pyplot as plt + +import geetools # noqa: F401 + + +class TestToImage: + """Test the ``toImage`` method.""" + + def test_to_image(self, fc_instance, vatican): + image = fc_instance.geetools.toImage() + values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) + assert values.getInfo() == {"constant": 0} + + def test_to_image_with_color(self, fc_instance, vatican): + image = fc_instance.geetools.toImage(color="ADM0_CODE") + values = image.reduceRegion(ee.Reducer.mean(), vatican, 1) + assert values.getInfo() == {"constant": 110} + + @pytest.fixture + def fc_instance(self): + gaul = ee.FeatureCollection("FAO/GAUL/2015/level0") + return gaul.filter(ee.Filter.eq("ADM0_CODE", 110)) + + @pytest.fixture + def vatican(self): + """Return a buffer around the Vatican City.""" + return ee.Geometry.Point([12.453386, 41.903282]).buffer(1) + + +class TestToDictionary: + """Test the ``toDictionary`` method.""" + + def test_to_dictionary(self, data_regression): + output = self.table.geetools.toDictionary("ADM0_NAME", ["ADM0_CODE", "Shape_Area"]) + data_regression.check(output.getInfo()) + + @property + def table(self): + return ee.FeatureCollection("FAO/GAUL/2015/level0").filter( + ee.Filter.stringStartsWith(leftField="ADM0_NAME", rightValue="Ar") + ) + + +class TestAddId: + """Test the ``addId`` method.""" + + def test_add_id(self, fc_instance): + fc = fc_instance.geetools.addId() + assert fc.first().get("id").getInfo() == 1 + + @pytest.fixture + def fc_instance(self): + return ee.FeatureCollection("FAO/GAUL/2015/level0").limit(10) + + +class TestMergeGeometries: + """Test the ``mergeGeometries`` method.""" + + def test_merge_geometries(self, gaul_3_countries, data_regression): + geom = gaul_3_countries.geetools.mergeGeometries() + data_regression.check(geom.getInfo()) + + +class TestToPolygons: + """Test the ``toPolygons`` method.""" + + def test_to_polygons(self, fc_instance, dataframe_regression): + fc = fc_instance.geetools.toPolygons() + gdf = gpd.GeoDataFrame.from_features(fc.getInfo()) + vertex = gdf.geometry.apply(lambda g: sum(len(p.exterior.coords) for p in g.geoms)) + assert vertex.sum() == 66 + + +class TestByProperties: + """Test the ``byProperties`` method.""" + + def test_by_properties(self, ecoregions, data_regression): + fc = ecoregions.geetools.byProperties() + data_regression.check(fc.getInfo()) + + def test_by_properties_with_id(self, ecoregions, data_regression): + fc = ecoregions.geetools.byProperties("label") + data_regression.check(fc.getInfo()) + + def test_by_properties_with_properties(self, ecoregions, data_regression): + fc = ecoregions.geetools.byProperties("label", properties=["01_tmean", "02_tmean"]) + data_regression.check(fc.getInfo()) + + +class TestByFeatures: + """Test the ``byFeatures`` method.""" + + def test_by_features(self, ecoregions, data_regression): + fc = ecoregions.geetools.byFeatures() + data_regression.check(fc.getInfo()) + + def test_by_features_with_id(self, ecoregions, data_regression): + fc = ecoregions.geetools.byFeatures("label") + data_regression.check(fc.getInfo()) + + def test_by_features_with_properties(self, ecoregions, data_regression): + fc = ecoregions.geetools.byFeatures("label", properties=["01_tmean", "02_tmean"]) + data_regression.check(fc.getInfo()) + + +class TestPlotByFeatures: + """Test the ``plot_by_features`` method.""" + + def test_plot_by_features_bar(self, ecoregions, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregions.geetools.plot_by_features( + type="bar", + featureId="label", + properties=["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean","11_tmean", "12_tmean"], + labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors=["#604791", "#1d6b99", "#39a8a7", "#0f8755", "#76b349", "#f0af07", "#e37d05", "#cf513e", "#96356f", "#724173", "#9c4f97", "#696969"], + ax=ax, + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_features_stacked(self, ecoregions, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregions.geetools.plot_by_features( + type="stacked", + featureId="label", + properties=["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean","11_tmean", "12_tmean"], + labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors=["#604791", "#1d6b99", "#39a8a7", "#0f8755", "#76b349", "#f0af07", "#e37d05", "#cf513e", "#96356f", "#724173", "#9c4f97", "#696969"], + ax=ax, + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_features_scatter(self, ecoregions, image_regression): + fig, ax = plt.subplots() + ecoregions.geetools.plot_by_features( + type="scatter", + featureId="label", + properties=["01_ppt", "06_ppt", "09_ppt"], + labels=["jan", "jun", "sep"], + ax=ax, + ) + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_features_pie(self, ecoregions, image_regression): + fig, ax = plt.subplots() + ecoregions.geetools.plot_by_features( + type="pie", + featureId="label", + properties=["06_ppt"], + colors=["#f0af07", "#0f8755", "#76b349"], + ax=ax, + ) + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_features_donut(self, ecoregions, image_regression): + fig, ax = plt.subplots() + ecoregions.geetools.plot_by_features( + type="donut", + featureId="label", + properties=["06_ppt"], + colors=["#f0af07", "#0f8755", "#76b349"], + ax=ax, + ) + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + +class TestPlotByPropperties: + """Test the ``plot_by_properties`` method.""" + + def test_plot_by_properties_bar(self, ecoregions, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregions.geetools.plot_by_properties( + type="bar", + properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], + labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + featureId="label", + colors=["#f0af07", "#0f8755", "#76b349"], + ax=ax, + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_properties_plot(self, ecoregions, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregions.geetools.plot_by_properties( + type="plot", + properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], + labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + featureId="label", + colors=["#f0af07", "#0f8755", "#76b349"], + ax=ax, + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_properties_area(self, ecoregions, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregions.geetools.plot_by_properties( + type="fill_between", + properties=["01_ppt", "02_ppt", "03_ppt", "04_ppt", "05_ppt", "06_ppt", "07_ppt", "08_ppt", "09_ppt", "10_ppt", "11_ppt", "12_ppt"], + labels=["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + featureId="label", + colors=["#f0af07", "#0f8755", "#76b349"], + ax=ax, + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + +class TestPlotHist: + """Test the ``plot_hist`` method.""" + + def test_plot_hist(self, climSamp, image_regression): + fig, ax = plt.subplots() + climSamp.geetools.plot_hist( + property="07_ppt", label="July Precipitation (mm)", color="#1d6b99", ax=ax, bins=30 + ) + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + +class TestPlot: + """Test the ``plot`` method.""" + + def test_plot(self, image_regression): + fig, ax = plt.subplots() + self.hydroshed.select(["UP_AREA"]).geetools.plot(ax=ax) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_with_property(self, image_regression): + fig, ax = plt.subplots() + self.hydroshed.geetools.plot(ax=ax, property="UP_AREA") + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_with_cmap(self, image_regression): + fig, ax = plt.subplots() + self.hydroshed.geetools.plot(ax=ax, property="UP_AREA", cmap="magma") + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_with_boundaries(self, image_regression): + fig, ax = plt.subplots() + self.hydroshed.geetools.plot( + ax=ax, property="UP_AREA", cmap="magma", boundaries=True, color="g" + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def hydroshed(self): + """The level 4 hydroshed of South america.""" + dataset = "WWF/HydroATLAS/v1/Basins/level04" + region = ee.Geometry.BBox(-80, -60, -20, 20) + return ee.FeatureCollection(dataset).filterBounds(region) diff --git a/tests/test_FeatureCollection/test_by_features.yml b/tests/test_FeatureCollection/test_by_features.yml new file mode 100644 index 00000000..de724356 --- /dev/null +++ b/tests/test_FeatureCollection/test_by_features.yml @@ -0,0 +1,264 @@ +'00000000000000000000': + 01_ppt: 27.95434081213815 + 01_tdmean: -5.938981422015599 + 01_tmax: 14.05185354641506 + 01_tmean: 5.791035977772305 + 01_tmin: -2.469781775474549 + 01_vpdmax: 12.487591886520388 + 01_vpdmin: 1.3548382043838505 + 02_ppt: 21.858468787329542 + 02_tdmean: -5.516507593563626 + 02_tmax: 16.323491311754502 + 02_tmean: 7.6450114270619 + 02_tmin: -1.0334683659672739 + 02_vpdmax: 15.010737727028985 + 02_vpdmin: 1.6896190329960419 + 03_ppt: 17.579124287196574 + 03_tdmean: -5.821656778880528 + 03_tmax: 19.68566257476807 + 03_tmean: 10.454658155441285 + 03_tmin: 1.2236530038288664 + 03_vpdmax: 19.560661269596647 + 03_vpdmin: 2.66291476556233 + 04_ppt: 8.252543340410508 + 04_tdmean: -5.8467324072974085 + 04_tmax: 24.170578130994524 + 04_tmean: 14.251640262603761 + 04_tmin: 4.332702394894191 + 04_vpdmax: 26.725532964978907 + 04_vpdmin: 4.364877526419504 + 05_ppt: 8.372215850693841 + 05_tdmean: -3.4355346179008484 + 05_tmax: 29.04781422478813 + 05_tmean: 19.0328149005345 + 05_tmin: 9.017815884181434 + 05_vpdmax: 35.71408178601947 + 05_vpdmin: 6.715028816631862 + 06_ppt: 14.802122592926029 + 06_tdmean: 0.9473899156706674 + 06_tmax: 33.579262025015694 + 06_tmean: 23.790505815233505 + 06_tmin: 14.001748946053644 + 06_vpdmax: 45.70917290278844 + 06_vpdmin: 9.571135237557549 + 07_ppt: 80.38940920148579 + 07_tdmean: 10.336808161054341 + 07_tmax: 33.16552563803537 + 07_tmean: 25.06657654081073 + 07_tmin: 16.967626991271974 + 07_vpdmax: 39.50814271654403 + 07_vpdmin: 5.347937705857414 + 08_ppt: 79.32688581194199 + 08_tdmean: 13.058130756105697 + 08_tmax: 31.466057499476847 + 08_tmean: 23.845259066990447 + 08_tmin: 16.224460500989643 + 08_vpdmax: 33.170324205671044 + 08_vpdmin: 3.090083679471698 + 09_ppt: 37.412246641431544 + 09_tdmean: 8.827760635103498 + 09_tmax: 29.893881457192563 + 09_tmean: 21.454794812883655 + 09_tmin: 13.015708386557444 + 09_vpdmax: 31.64241425105504 + 09_vpdmin: 3.494040838650295 + 10_ppt: 32.728693926674985 + 10_tdmean: 1.6853334009647374 + 10_tmax: 25.111735046931678 + 10_tmean: 15.997987973349439 + 10_tmin: 6.884241169520789 + 10_vpdmax: 26.030083217620856 + 10_vpdmin: 3.16713012967791 + 11_ppt: 22.80909897395543 + 11_tdmean: -3.7638271621295383 + 11_tmax: 18.830497095925473 + 11_tmean: 9.84932084628514 + 11_tmin: 0.868143749833107 + 11_vpdmax: 18.034088944026404 + 11_vpdmin: 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2.08159842036693 + 04_tmax: 12.972551097358323 + 04_tmean: 7.120137078551952 + 04_tmin: 1.2677232892577217 + 04_vpdmax: 8.23875221895532 + 04_vpdmin: 0.3165283716958144 + 05_ppt: 112.39213898172781 + 05_tdmean: 4.564188383776566 + 05_tmax: 16.913518057929146 + 05_tmean: 10.395376066595201 + 05_tmin: 3.8772325109247956 + 05_vpdmax: 11.690270211961533 + 05_vpdmin: 0.5732159946841755 + 06_ppt: 72.70987686800318 + 06_tdmean: 7.317597124311658 + 06_tmax: 20.782044538592924 + 06_tmean: 13.776134290000945 + 06_tmin: 6.770223435770962 + 06_vpdmax: 15.288111427277894 + 06_vpdmin: 0.7901813986200937 + 07_ppt: 23.409216248669384 + 07_tdmean: 9.576722269314002 + 07_tmax: 25.87413356678696 + 07_tmean: 17.850177143725396 + 07_tmin: 9.826222514740806 + 07_vpdmax: 22.29618453248707 + 07_vpdmin: 1.6784769748819284 + 08_ppt: 25.395566238754096 + 08_tdmean: 9.128160319565813 + 08_tmax: 26.156366136338974 + 08_tmean: 17.919982716498247 + 08_tmin: 9.683599841092281 + 08_vpdmax: 24.004596637126582 + 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+from matplotlib import pyplot as plt + +import geetools + + +class TestAddDate: + """Test the ``addDate`` method.""" + + def test_add_date(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.addDate() + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + def test_add_date_format(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.addDate("yyyyMMdd") + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + +class TestAddSuffix: + """Test the ``addSuffix`` method.""" + + def test_add_suffix_to_all(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addSuffix("_suffix") + data_regression.check(image.bandNames().getInfo()) + + def test_add_suffix_to_selected(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addSuffix("_suffix", bands=["B1", "B2"]) + data_regression.check(image.bandNames().getInfo()) + + +class TestAddPrefix: + """Test the ``addPrefix`` method.""" + + def test_add_prefix_to_all(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addPrefix("prefix_") + data_regression.check(image.bandNames().getInfo()) + + def test_add_prefix_to_selected(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addPrefix("prefix_", bands=["B1", "B2"]) + data_regression.check(image.bandNames().getInfo()) + + +class TestGetValues: + """Test the ``getValues`` method.""" + + def test_get_values(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + values = s2_sr_vatican_2020.geetools.getValues(vatican_buffer.centroid()) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + def test_get_values_with_scale(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + values = s2_sr_vatican_2020.geetools.getValues(vatican_buffer.centroid(), scale=100) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + +class TestMinScale: + """Test the ``minScale`` method.""" + + def test_min_scale(self, s2_sr_vatican_2020): + scale = s2_sr_vatican_2020.geetools.minScale() + assert scale.getInfo() == 10 + + +class TestMerge: + """Test the ``merge`` method.""" + + def test_merge(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.merge([s2_sr_vatican_2020, s2_sr_vatican_2020]) + data_regression.check(image.bandNames().getInfo()) + + +class TestRename: + """Test the ``rename`` method.""" + + def test_rename(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.rename({"B1": "newB1", "B2": "newB2"}) + data_regression.check(image.bandNames().getInfo()) + + +class TestRemove: + """Test the ``remove`` method.""" + + def test_remove(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.remove(["B1", "B2"]) + data_regression.check(image.bandNames().getInfo()) + + +class TestToGrid: + """Test the ``toGrid`` method.""" + + def test_to_grid(self, s2_sr_vatican_2020, vatican_buffer, ndarrays_regression): + grid = s2_sr_vatican_2020.geetools.toGrid(1, "B2", vatican_buffer) + grid = [f["geometry"]["coordinates"] for f in grid.getInfo()["features"]] + grid = {f"geometry_{i}": np.array(c) for i, c in enumerate(grid)} + ndarrays_regression.check(grid) + + +class TestClipOnCollection: + """Test the ``clipOnCollection`` method.""" + + def test_clip_on_collection(self, s2_sr_vatican_2020, fc_instance, data_regression): + clipped = s2_sr_vatican_2020.geetools.clipOnCollection(fc_instance) + name = "test_clip_on_collection" + data_regression.check(clipped.first().bandNames().getInfo(), f"{name}_bands") + data_regression.check(clipped.first().propertyNames().getInfo(), f"{name}_property") + + def test_clip_on_collection_without_properties( + self, s2_sr_vatican_2020, fc_instance, data_regression + ): + clipped = s2_sr_vatican_2020.geetools.clipOnCollection(fc_instance, 0) + name = "test_clip_on_collection_without_properties" + data_regression.check(clipped.first().bandNames().getInfo(), f"{name}_bands") + data_regression.check(clipped.first().propertyNames().getInfo(), f"{name}_property") + + @pytest.fixture + def fc_instance(self): + """Return 2 little buffers in vaticanc city as a featurecollection.""" + return ee.FeatureCollection( + [ + ee.Feature(ee.Geometry.Point([12.4534, 41.9029]).buffer(50), {"Id": 1}), + ee.Feature(ee.Geometry.Point([12.4534, 41.9029]).buffer(100), {"Id": 2}), + ] + ) + + +class TestBufferMask: + """Test the ``bufferMask`` method.""" + + @pytest.mark.xfail + def test_buffer_mask(self): + """I don't know what to test here.""" + assert False + + +class TestFull: + """Test the ``full`` method.""" + + def test_full(self, vatican_buffer, num_regression): + image = ee.Image.geetools.full() + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_full_with_value(self, vatican_buffer, num_regression): + image = ee.Image.geetools.full([1]) + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_full_with_name(self, vatican_buffer, num_regression): + image = ee.Image.geetools.full([1], ["toto"]) + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_full_with_lists(self, vatican_buffer, num_regression): + image = ee.Image.geetools.full([1, 2, 3], ["toto", "titi", "tata"]) + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestFullLike: + """Test the ``fullLike`` method.""" + + def test_full_like(self, vatican_buffer, s2_sr_vatican_2020, num_regression): + image = s2_sr_vatican_2020.set({"props": "toto"}) + image = image.geetools.fullLike(0) + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + assert "props" not in image.propertyNames().getInfo() + num_regression.check(values.getInfo()) + + def test_full_like_with_properties(self, s2_sr_vatican_2020): + image = s2_sr_vatican_2020.set({"props": "toto"}) + image = image.geetools.fullLike(0, copyProperties=1) + assert "props" in image.propertyNames().getInfo() + + def test_full_like_with_mask(self, s2_sr_vatican_2020, num_regression): + image = s2_sr_vatican_2020.geetools.fullLike(0, keepMask=1) + values = image.geetools.getValues(ee.Geometry.Point(0, 0)) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + +class TestReduceBands: + """Test the ``reduceBands`` method.""" + + def test_reduce_bands(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.reduceBands("sum") + values = image.select("sum").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_reduce_bands_with_bands(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.reduceBands("sum", ["B1", "B2"]) + values = image.select("sum").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_reduce_bands_with_name(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.reduceBands("sum", name="toto") + values = image.select("toto").reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestNegativeClip: + """Test the ``negativeClip`` method.""" + + def test_negative_clip(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.negativeClip(vatican_buffer) + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + +class testFormat: + """Test the ``toString`` method.""" + + def test_to_string(self, s2_sr_vatican_2020): + string = s2_sr_vatican_2020.geetools.toString("date: {system_date}") + assert string.getInfo() == "date: 2020-01-01" + + +class TestPrefixSuffix: + """Test the ``prefix`` and ``suffix`` methods.""" + + def test_prefix(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addPrefix("prefix_") + data_regression.check(image.bandNames().getInfo()) + + def test_suffix(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.addSuffix("_suffix") + data_regression.check(image.bandNames().getInfo()) + + +class TestGauss: + """Test the ``gauss`` method.""" + + def test_gauss(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.gauss() + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_gauss_with_band(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.gauss("B2") + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestDoyToDate: + """Test the ``doyToDate`` method.""" + + def test_doy_to_date(self, doy_image, vatican_buffer, num_regression): + image = doy_image.geetools.doyToDate(2023) + values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_doy_to_date_with_format(self, doy_image, vatican_buffer, num_regression): + image = doy_image.geetools.doyToDate(2023, dateFormat="yyyy.DDD") + values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_doy_to_date_with_band(self, doy_image, vatican_buffer, num_regression): + image = doy_image.geetools.doyToDate(2023, band="doy2") + values = image.reduceRegion(ee.Reducer.min(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestRepeat: + """Test the ``repeat`` method.""" + + def test_repeat(self, image_instance): + image = image_instance.geetools.repeat("B1", 2) + assert image.bandNames().getInfo() == ["B1", "B2", "B3", "B1_1", "B1_2"] + + @pytest.fixture + def image_instance(self): + """Return an Image instance.""" + src = "COPERNICUS/S2_SR_HARMONIZED/20200101T100319_20200101T100321_T32TQM" + return ee.Image(src).select(["B1", "B2", "B3"]) + + +class TestmatchHistogram: + """Test the ``histogramMatch`` method.""" + + def test_histogram_match(self, image_source, image_target, vatican_buffer, num_regression): + bands = {"R": "R", "G": "G", "B": "B"} + image = image_source.geetools.matchHistogram(image_target, bands) + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + @pytest.fixture + def dates(self): + """The dates of my imagery.""" + return "2023-06-01", "2023-06-30" + + @pytest.fixture + def image_source(self, vatican_buffer, dates): + """Image from the S2 copernicus program over vatican city.""" + return ( + ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") + .filterBounds(vatican_buffer) + .filterDate(*dates) + .first() + .select("B4", "B3", "B2") + .rename("R", "G", "B") + ) + + @pytest.fixture + def image_target(self, vatican_buffer, dates): + """Image from the L8 Landsat program over vatican city.""" + return ( + ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") + .filterBounds(vatican_buffer) + .filterDate(*dates) + .first() + .select("SR_B4", "SR_B3", "SR_B2") + .rename("R", "G", "B") + ) + + +class TestRemoveZeros: + """Test the ``removeZeros`` method.""" + + def test_remove_zeros(self, image_instance, vatican_buffer): + image = image_instance.geetools.removeZeros() + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + assert values.getInfo() == {"array": [1, 2]} + + @pytest.fixture + def image_instance(self): + """A random image instance with array data containing zeros.""" + return ee.Image([0, 1, 2]).toArray() + + +class TestInterpolateBands: + """Test the ``interpolateBands`` method.""" + + def test_interpolate_bands(self, image_instance, vatican_buffer, num_regression): + image = image_instance.geetools.interpolateBands([0, 3000], [0, 30]) + values = image.reduceRegion(ee.Reducer.first(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + @pytest.fixture + def image_instance(self, vatican_buffer): + """A sentinel 2 single image centered on the vatican.""" + src = "COPERNICUS/S2_SR_HARMONIZED" + return ee.ImageCollection(src).filterBounds(vatican_buffer).first().select(["B4", "B2"]) + + +class TestIsletMask: + """Test the ``isletMask`` method.""" + + def test_islet_mask(self, image_instance, tmp_path, image_regression): + image = image_instance.geetools.isletMask(20) + file = self.get_image(image, tmp_path / "test.tif") + image_regression.check(file.read_bytes()) + + def get_image(self, image, dst): + link = image.getDownloadURL( + { + "name": "test", + "region": ee.Geometry.Point([12.4534, 41.9033]).buffer(1000), + "filePerBand": False, + "scale": 10, + } + ) + + with TemporaryDirectory() as dir: + tmp = Path(dir) / "tmp.zip" + urlretrieve(link, tmp) + with zipfile.ZipFile(tmp, "r") as zip_: + dst.write_bytes(zip_.read(zip_.namelist()[0])) + + return dst + + @pytest.fixture + def image_instance(self): + """An image on top of the buffer.""" + buffer = ee.Geometry.Point([12.4534, 41.9033]).buffer(1000) + return ( + ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") + .filterBounds(buffer) + .filterDate("2023-01-01", "2023-01-31") + .first() + .select("B4", "B3", "B2") + ) + + +class TestIndicexList: + """Test the ``index_list`` method.""" + + def test_indices(self): + indices = ee.Image.geetools.index_list() + assert "NDVI" in indices.keys() + assert len(indices) == 228 + + +class TestSpectralIndices: + """Test the ``spectralIndices`` method.""" + + def test_default_spectral_indices(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.spectralIndices("all") + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check({k: np.nan if v is None else v for k, v in values.getInfo().items()}) + + +class TestMaskClouds: + """Test the ``maskClouds`` method.""" + + def test_mask_S2_clouds(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + image = s2_sr_vatican_2020.geetools.maskClouds() + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + values = {k: np.nan if v is None else v for k, v in values.getInfo().items()} + num_regression.check(values) + + +class TestGetscaleParams: + """Test the ``getScaleParams`` method.""" + + def test_get_scale_params(self, s2_sr_vatican_2020, data_regression): + params = s2_sr_vatican_2020.geetools.getScaleParams() + data_regression.check(params) + + +class TestGetOffsetParams: + """Test the ``getOffsetParams`` method.""" + + def get_offset_params(self, s2_sr_vatican_2020, data_regression): + params = s2_sr_vatican_2020.geetools.getOffsetParams() + data_regression.check(params) + + +class TestScaleAndOffset: + """Test the ``scaleAndOffset`` method.""" + + def test_scale_and_offset(self, vatican_buffer, s2_sr_vatican_2020, num_regression): + image = s2_sr_vatican_2020.geetools.scaleAndOffset() + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestPreprocess: + """Test the ``preprocess`` method.""" + + def test_preprocess(self, vatican_buffer, s2_sr_vatican_2020, num_regression): + image = s2_sr_vatican_2020.geetools.preprocess() + values = image.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestGetSTAC: + """Test the ``getSTAC`` method.""" + + def test_get_stac_schema(self, s2_sr_vatican_2020, stac_schema): + stac = s2_sr_vatican_2020.geetools.getSTAC() + validate(stac, stac_schema) + + def test_get_stac(self, s2_sr_vatican_2020): + stac = s2_sr_vatican_2020.geetools.getSTAC() + assert stac["id"] == "COPERNICUS/S2_SR_HARMONIZED" + + +class TestGetDOI: + """Test the ``getDOI`` method.""" + + def get_doi(self, s2_sr_vatican_2020, data_regression): + doi = s2_sr_vatican_2020.geetools.getDOI() + data_regression.check(doi) + + +class TestGetCitation: + """Test the ``getCitation`` method.""" + + def get_citation(self, s2_sr_vatican_2020, data_regression): + citation = s2_sr_vatican_2020.first().geetools.getCitation() + data_regression.check(citation) + + +class TestPanSharpen: + """Test the panSharpen method.""" + + @pytest.mark.xfail( + reason="This test is failing because the panSharpen method is not implemented for this platform." + ) + def test_pan_sharpen(self, l8_sr_vatican_2020, num_regression): + sharp = l8_sr_vatican_2020.geetools.panSharpen( + method="HPFA", qa=["MSE", "RMSE"], maxPixels=1e13 + ) + centroid = sharp.geometry().centroid().buffer(100) + values = sharp.reduceRegion(ee.Reducer.mean(), centroid, 1) + num_regression.check(values.getInfo()) + + +class TestTasseledCap: + """Test the tasseledCap method.""" + + @pytest.mark.xfail( + reason="This test is failing because the tasseledCap method is not implemented for this platform." + ) + def test_tasseled_cap(self, l8_sr_vatican_2020, num_regression): + img = l8_sr_vatican_2020.geetools.tasseledCap() + centroid = img.geometry().centroid().buffer(100) + values = img.reduceRegion(ee.Reducer.mean(), centroid, 1) + num_regression.check(values.getInfo()) + + +class TestRemoveProperties: + """Test the removeProperties method.""" + + def test_remove_properties(self, s2_sr_vatican_2020, data_regression): + image = s2_sr_vatican_2020.geetools.removeProperties(["system:time_start"]) + data_regression.check(image.propertyNames().getInfo()) + + +class TestDistanceToMask: + """Test the distanceToMask method.""" + + def test_distance_to_mask(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + centerBuffer = vatican_buffer.centroid().buffer(100) + BufferMask = ee.Image.constant(1).clip(centerBuffer) + mask = ee.Image.constant(0).where(BufferMask, 1) + distance = s2_sr_vatican_2020.geetools.distanceToMask(mask) + values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_deprecated_distance_to_mask(self, s2_sr_vatican_2020, vatican_buffer, num_regression): + centerBuffer = vatican_buffer.centroid().buffer(100) + BufferMask = ee.Image.constant(1).clip(centerBuffer) + mask = ee.Image.constant(0).where(BufferMask, 1) + with pytest.deprecated_call(): + distance = geetools.algorithms.distanceToMask(s2_sr_vatican_2020, mask) + values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + +class TestDistance: + """Test the ``distance`` method.""" + + def test_distance(self, vatican_buffer, num_regression): + # 2 images from june in vatican + distance = self.image.geetools.distance(self.other) + values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + def test_deprecated_euclidian_distance(self, vatican_buffer, num_regression): + # 2 images from june in vatican + with pytest.deprecated_call(): + distance = geetools.algorithms.euclideanDistance(self.image, self.other) + values = distance.reduceRegion(ee.Reducer.mean(), vatican_buffer, 10) + num_regression.check(values.getInfo()) + + @property + def image(self): + """Return an image from june in vatican.""" + image_id = "COPERNICUS/S2_SR_HARMONIZED/20210604T100029_20210604T100027_T32TQM" + return ee.Image(image_id).select(["B4", "B3", "B2"]) + + @property + def other(self): + """Return another image from june in vatican.""" + other_id = "COPERNICUS/S2_SR_HARMONIZED/20210604T100029_20210604T100027_T33TTG" + return ee.Image(other_id).select(["B4", "B3", "B2"]) + + +class TestMaskCover: + """Test the ``maskCoverRegion`` method.""" + + def test_mask_cover_region(self): + aoi = ee.Geometry.Point([12.210900891755129, 41.928551351175386]).buffer(2200) + ratio = self.image.geetools.maskCoverRegion(aoi, scale=10) + assert isclose(ratio.getInfo(), 9.99, abs_tol=0.01) + + def test_mask_cover_region_zero(self): + aoi = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(1000) + ratio = self.image.geetools.maskCoverRegion(aoi, scale=10) + assert isclose(ratio.getInfo(), 0) + + def test_mask_cover_regions(self): + geom = ee.Geometry.Point([12.210900891755129, 41.928551351175386]).buffer(2200) + aoi = ee.FeatureCollection([ee.Feature(geom, {"test_property": 1})]) + result = self.image.geetools.maskCoverRegions(aoi, scale=10) + feat = ee.Feature(result.first()) + ratio = feat.getInfo()["properties"]["mask_cover"] + # ratio = ee.Number(feat.get('mask_cover')) + # the last line should work, but it doesn't, I don't know why + assert isclose(ratio, 9.99, abs_tol=0.01) + + def test_mask_cover_regions_zero(self): + geom = ee.Geometry.Point([11.880190936531116, 42.0159494554553]).buffer(1000) + aoi = ee.FeatureCollection([ee.Feature(geom, {"test_property": 1})]) + result = self.image.geetools.maskCoverRegions(aoi, scale=10) + feat = ee.Feature(result.first()) + ratio = feat.getInfo()["properties"]["mask_cover"] + # ratio = ee.Number(feat.get('mask_cover')) + # the last line should work, but it doesn't, I don't know why + assert isclose(ratio, 0) + + def test_deprecated_mask_cover(self): + with pytest.deprecated_call(): + image = geetools.algorithms.maskCover(self.image) + assert isclose(image.get("mask_cover").getInfo(), 18.04, rel_tol=0.01) + + @property + def image(self): + image_id = "COPERNICUS/S2_SR_HARMONIZED/20180401T100019_20180401T100022_T32TQM" + image = ee.Image(image_id) + qa = image.select("QA60") + cloudBitMask, cirrusBitMask = 1 << 10, 1 << 11 + mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0)) + image = image.updateMask(mask) + return image.select(["B4", "B3", "B2"]) + + +class TestPlot: + """Test the ``plot`` method.""" + + def test_plot(self, s2_sr_vatican_2020, vatican, image_regression): + fig, ax = plt.subplots() + s2_sr_vatican_2020.geetools.plot(["B4", "B3", "B2"], vatican.geometry(), ax) + + with BytesIO() as image_byte: + fig.savefig(image_byte, format="png") + image_byte.seek(0) + image_regression.check(image_byte.getvalue()) + + def test_plot_one_band(self, s2_sr_vatican_2020, vatican, image_regression): + fig, ax = plt.subplots() + ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") + ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax) + + with BytesIO() as image_byte: + fig.savefig(image_byte, format="png") + image_byte.seek(0) + image_regression.check(image_byte.getvalue()) + + def test_plot_one_band_cmap(self, s2_sr_vatican_2020, vatican, image_regression): + fig, ax = plt.subplots() + ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") + ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax, cmap="RdYlGn") + + with BytesIO() as image_byte: + fig.savefig(image_byte, format="png") + image_byte.seek(0) + image_regression.check(image_byte.getvalue()) + + def test_plot_with_fc(self, s2_sr_vatican_2020, vatican, image_regression): + fig, ax = plt.subplots() + s2_sr_vatican_2020.geetools.plot(["B4", "B3", "B2"], vatican.geometry(), ax, fc=vatican) + + with BytesIO() as image_byte: + fig.savefig(image_byte, format="png") + image_byte.seek(0) + image_regression.check(image_byte.getvalue()) + + def test_plot_with_crs(self, s2_sr_vatican_2020, vatican, image_regression): + fig, ax = plt.subplots() + ndvi = s2_sr_vatican_2020.geetools.spectralIndices("NDVI") + ndvi.geetools.plot(["NDVI"], vatican.geometry(), ax, crs="EPSG:3857", scale=10) + + with BytesIO() as image_byte: + fig.savefig(image_byte, format="png") + image_byte.seek(0) + image_regression.check(image_byte.getvalue()) + + +class TestFromList: + """Test ``fromList`` method.""" + + def test_from_list_unique(self): + """Test using a list of unique band names.""" + sequence = ee.List([1, 2, 3]) + images = sequence.map(lambda i: ee.Image(ee.Number(i)).rename(ee.Number(i).int().format())) + image = ee.Image.geetools.fromList(images) + assert image.bandNames().getInfo() == ["1", "2", "3"] + + def test_from_list_multiband(self): + """Test using a list of multiband images.""" + images = ee.List( + [ + ee.Image([1, 2, 3]).rename(["1", "2", "3"]), + ee.Image([4, 5]).rename(["4", "5"]), + ] + ) + image = ee.Image.geetools.fromList(images) + assert image.bandNames().getInfo() == ["1", "2", "3", "4", "5"] + + +class TestPlotByRegions: + """Test the ``plot_by_regions`` method.""" + + def test_plot_by_regions_bar(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_regions( + type = "bar", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_regions_barh(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_regions( + type = "barh", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_regions_stacked(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_regions( + type = "stacked", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ["01_tmean", "02_tmean", "03_tmean", "04_tmean", "05_tmean", "06_tmean", "07_tmean", "08_tmean", "09_tmean", "10_tmean", "11_tmean", "12_tmean"], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def ecoregions(self): + return ee.FeatureCollection("projects/google/charts_feature_example").select( + ["label", "value", "warm"] + ) + + @property + def image(self): + return ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands() + + +class TestPlotByBands: + """Test the ``plot_by_bands`` method.""" + + def test_plot_by_bands_bar(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_bands( + type = "bar", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ["#f0af07", "#0f8755", "#76b349"], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_bands_plot(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_bands( + type = "plot", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ["#f0af07", "#0f8755", "#76b349"], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_bands_area(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + self.image.geetools.plot_by_bands( + type = "fill_between", + regions = self.ecoregions, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ["#f0af07", "#0f8755", "#76b349"], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_bands_pie(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregion = self.ecoregions.filter(ee.Filter.eq("label", "Forest")) + self.image.geetools.plot_by_bands( + type = "pie", + regions = ecoregion, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + def test_plot_by_bands_donut(self, image_regression): + fig, ax = plt.subplots() + # fmt: off + ecoregion = self.ecoregions.filter(ee.Filter.eq("label", "Forest")) + self.image.geetools.plot_by_bands( + type = "donut", + regions = ecoregion, + reducer = "mean", + scale = 500, + regionId = "label", + bands = ['01_ppt', '02_ppt', '03_ppt', '04_ppt', '05_ppt', '06_ppt', '07_ppt', '08_ppt', '09_ppt', '10_ppt', '11_ppt', '12_ppt'], + labels = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"], + colors = ['#604791', '#1d6b99', '#39a8a7', '#0f8755', '#76b349', '#f0af07', '#e37d05', '#cf513e', '#96356f', '#724173', '#9c4f97', '#696969'], + ax = ax + ) + # fmt: on + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def ecoregions(self): + return ee.FeatureCollection("projects/google/charts_feature_example").select( + ["label", "value", "warm"] + ) + + @property + def image(self): + return ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands() + + +class TestPlotHist: + """Test the ``plot_hist`` method.""" + + def test_plot_hist(self, image_regression): + fig, ax = plt.subplots() + self.image.geetools.plot_hist( + bands=["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"], + labels=[["Red", "NIR", "SWIR"]], + colors=["#cf513e", "#1d6b99", "#f0af07"], + ax=ax, + bins=100, + scale=500, + region=self.region, + ) + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue(), diff_threshold=0.2) + + @property + def image(self): + return ( + ee.ImageCollection("MODIS/061/MOD09A1") + .filter(ee.Filter.date("2018-06-01", "2018-09-01")) + .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"]) + .mean() + ) + + @property + def region(self): + return ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22]) diff --git a/tests/test_Image/test_add_date.csv b/tests/test_Image/test_add_date.csv new file mode 100644 index 00000000..c29eacba --- /dev/null +++ b/tests/test_Image/test_add_date.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP,date +0,72,214,1175,958,188,366,285,910,1232,1328,1216,1431,2104,,,,2,0,0,0,0,5,21,38,29,588,1577873347042 diff --git a/tests/test_Image/test_add_date_format.csv b/tests/test_Image/test_add_date_format.csv new file mode 100644 index 00000000..81f8da1c --- /dev/null +++ b/tests/test_Image/test_add_date_format.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP,date +0,72,214,1175,958,188,366,285,910,1232,1328,1216,1431,2104,2,0,0,0,0,5,21,38,29,588,20200101 diff --git a/tests/test_Image/test_add_prefix_to_all.yml b/tests/test_Image/test_add_prefix_to_all.yml new file mode 100644 index 00000000..1319709a --- /dev/null +++ b/tests/test_Image/test_add_prefix_to_all.yml @@ -0,0 +1,13 @@ +- prefix_B1 +- prefix_B2 +- prefix_B3 +- prefix_B4 +- prefix_B5 +- prefix_B6 +- prefix_B7 +- prefix_B8 +- prefix_B8A +- prefix_B9 +- prefix_B11 +- prefix_B12 +- prefix_SCL diff --git a/tests/test_Image/test_add_prefix_to_selected.yml b/tests/test_Image/test_add_prefix_to_selected.yml new file mode 100644 index 00000000..b2245db3 --- /dev/null +++ b/tests/test_Image/test_add_prefix_to_selected.yml @@ -0,0 +1,13 @@ +- prefix_B1 +- prefix_B2 +- B3 +- B4 +- B5 +- B6 +- B7 +- B8 +- B8A +- B9 +- B11 +- B12 +- SCL diff --git a/tests/test_Image/test_add_suffix_to_all.yml b/tests/test_Image/test_add_suffix_to_all.yml new file mode 100644 index 00000000..4130bb9e --- /dev/null +++ b/tests/test_Image/test_add_suffix_to_all.yml @@ -0,0 +1,13 @@ +- B1_suffix +- B2_suffix +- B3_suffix +- B4_suffix +- B5_suffix +- B6_suffix +- B7_suffix +- B8_suffix +- B8A_suffix +- B9_suffix +- B11_suffix +- B12_suffix +- SCL_suffix diff --git a/tests/test_Image/test_add_suffix_to_selected.yml b/tests/test_Image/test_add_suffix_to_selected.yml new file mode 100644 index 00000000..73df0092 --- /dev/null +++ b/tests/test_Image/test_add_suffix_to_selected.yml @@ -0,0 +1,13 @@ +- B1_suffix +- B2_suffix +- B3 +- B4 +- B5 +- B6 +- B7 +- B8 +- B8A +- B9 +- B11 +- B12 +- SCL diff --git a/tests/test_Image/test_clip_on_collection_bands.yml b/tests/test_Image/test_clip_on_collection_bands.yml new file mode 100644 index 00000000..2a05ac63 --- /dev/null +++ b/tests/test_Image/test_clip_on_collection_bands.yml @@ -0,0 +1,13 @@ +- B1 +- B2 +- B3 +- B4 +- B5 +- B6 +- B7 +- B8 +- B8A +- B9 +- B11 +- B12 +- SCL diff --git a/tests/test_Image/test_clip_on_collection_property.yml b/tests/test_Image/test_clip_on_collection_property.yml new file mode 100644 index 00000000..6db17f8d --- /dev/null +++ b/tests/test_Image/test_clip_on_collection_property.yml @@ -0,0 +1,86 @@ +- system:footprint +- system:version +- system:id +- Id +- system:index +- DATATAKE_IDENTIFIER +- AOT_RETRIEVAL_ACCURACY +- SPACECRAFT_NAME +- SATURATED_DEFECTIVE_PIXEL_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8A +- CLOUD_SHADOW_PERCENTAGE +- MEAN_SOLAR_AZIMUTH_ANGLE +- VEGETATION_PERCENTAGE +- SOLAR_IRRADIANCE_B12 +- SOLAR_IRRADIANCE_B10 +- SENSOR_QUALITY +- SOLAR_IRRADIANCE_B11 +- GENERATION_TIME +- SOLAR_IRRADIANCE_B8A +- FORMAT_CORRECTNESS +- CLOUD_COVERAGE_ASSESSMENT +- THIN_CIRRUS_PERCENTAGE +- system:time_end +- WATER_VAPOUR_RETRIEVAL_ACCURACY +- system:time_start +- DATASTRIP_ID +- PROCESSING_BASELINE +- SENSING_ORBIT_NUMBER +- NODATA_PIXEL_PERCENTAGE +- SENSING_ORBIT_DIRECTION +- GENERAL_QUALITY +- GRANULE_ID +- REFLECTANCE_CONVERSION_CORRECTION +- MEDIUM_PROBA_CLOUDS_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8 +- DATATAKE_TYPE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B9 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B6 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B7 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B4 +- MEAN_INCIDENCE_ZENITH_ANGLE_B1 +- NOT_VEGETATED_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B5 +- RADIOMETRIC_QUALITY +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B2 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B3 +- MEAN_INCIDENCE_ZENITH_ANGLE_B5 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B1 +- MEAN_INCIDENCE_ZENITH_ANGLE_B4 +- MEAN_INCIDENCE_ZENITH_ANGLE_B3 +- MEAN_INCIDENCE_ZENITH_ANGLE_B2 +- MEAN_INCIDENCE_ZENITH_ANGLE_B9 +- MEAN_INCIDENCE_ZENITH_ANGLE_B8 +- MEAN_INCIDENCE_ZENITH_ANGLE_B7 +- DARK_FEATURES_PERCENTAGE +- HIGH_PROBA_CLOUDS_PERCENTAGE +- MEAN_INCIDENCE_ZENITH_ANGLE_B6 +- UNCLASSIFIED_PERCENTAGE +- MEAN_SOLAR_ZENITH_ANGLE +- MEAN_INCIDENCE_ZENITH_ANGLE_B8A +- RADIATIVE_TRANSFER_ACCURACY +- MGRS_TILE +- CLOUDY_PIXEL_PERCENTAGE +- PRODUCT_ID +- MEAN_INCIDENCE_ZENITH_ANGLE_B10 +- SOLAR_IRRADIANCE_B9 +- SNOW_ICE_PERCENTAGE +- DEGRADED_MSI_DATA_PERCENTAGE +- MEAN_INCIDENCE_ZENITH_ANGLE_B11 +- MEAN_INCIDENCE_ZENITH_ANGLE_B12 +- SOLAR_IRRADIANCE_B6 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B10 +- SOLAR_IRRADIANCE_B5 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B11 +- SOLAR_IRRADIANCE_B8 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B12 +- SOLAR_IRRADIANCE_B7 +- SOLAR_IRRADIANCE_B2 +- SOLAR_IRRADIANCE_B1 +- SOLAR_IRRADIANCE_B4 +- GEOMETRIC_QUALITY +- SOLAR_IRRADIANCE_B3 +- system:asset_size +- WATER_PERCENTAGE +- system:bands +- system:band_names diff --git a/tests/test_Image/test_clip_on_collection_without_properties_bands.yml b/tests/test_Image/test_clip_on_collection_without_properties_bands.yml new file mode 100644 index 00000000..2a05ac63 --- /dev/null +++ b/tests/test_Image/test_clip_on_collection_without_properties_bands.yml @@ -0,0 +1,13 @@ +- B1 +- B2 +- B3 +- B4 +- B5 +- B6 +- B7 +- B8 +- B8A +- B9 +- B11 +- B12 +- SCL diff --git a/tests/test_Image/test_clip_on_collection_without_properties_property.yml b/tests/test_Image/test_clip_on_collection_without_properties_property.yml new file mode 100644 index 00000000..b6dadcb9 --- /dev/null +++ b/tests/test_Image/test_clip_on_collection_without_properties_property.yml @@ -0,0 +1,85 @@ +- system:footprint +- system:version +- system:id +- system:index +- DATATAKE_IDENTIFIER +- AOT_RETRIEVAL_ACCURACY +- SPACECRAFT_NAME +- SATURATED_DEFECTIVE_PIXEL_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8A +- CLOUD_SHADOW_PERCENTAGE +- MEAN_SOLAR_AZIMUTH_ANGLE +- VEGETATION_PERCENTAGE +- SOLAR_IRRADIANCE_B12 +- SOLAR_IRRADIANCE_B10 +- SENSOR_QUALITY +- SOLAR_IRRADIANCE_B11 +- GENERATION_TIME +- SOLAR_IRRADIANCE_B8A +- FORMAT_CORRECTNESS +- CLOUD_COVERAGE_ASSESSMENT +- THIN_CIRRUS_PERCENTAGE +- system:time_end +- WATER_VAPOUR_RETRIEVAL_ACCURACY +- system:time_start +- DATASTRIP_ID +- PROCESSING_BASELINE +- SENSING_ORBIT_NUMBER +- NODATA_PIXEL_PERCENTAGE +- SENSING_ORBIT_DIRECTION +- GENERAL_QUALITY +- GRANULE_ID +- REFLECTANCE_CONVERSION_CORRECTION +- MEDIUM_PROBA_CLOUDS_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B8 +- DATATAKE_TYPE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B9 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B6 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B7 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B4 +- MEAN_INCIDENCE_ZENITH_ANGLE_B1 +- NOT_VEGETATED_PERCENTAGE +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B5 +- RADIOMETRIC_QUALITY +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B2 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B3 +- MEAN_INCIDENCE_ZENITH_ANGLE_B5 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B1 +- MEAN_INCIDENCE_ZENITH_ANGLE_B4 +- MEAN_INCIDENCE_ZENITH_ANGLE_B3 +- MEAN_INCIDENCE_ZENITH_ANGLE_B2 +- MEAN_INCIDENCE_ZENITH_ANGLE_B9 +- MEAN_INCIDENCE_ZENITH_ANGLE_B8 +- MEAN_INCIDENCE_ZENITH_ANGLE_B7 +- DARK_FEATURES_PERCENTAGE +- HIGH_PROBA_CLOUDS_PERCENTAGE +- MEAN_INCIDENCE_ZENITH_ANGLE_B6 +- UNCLASSIFIED_PERCENTAGE +- MEAN_SOLAR_ZENITH_ANGLE +- MEAN_INCIDENCE_ZENITH_ANGLE_B8A +- RADIATIVE_TRANSFER_ACCURACY +- MGRS_TILE +- CLOUDY_PIXEL_PERCENTAGE +- PRODUCT_ID +- MEAN_INCIDENCE_ZENITH_ANGLE_B10 +- SOLAR_IRRADIANCE_B9 +- SNOW_ICE_PERCENTAGE +- DEGRADED_MSI_DATA_PERCENTAGE +- MEAN_INCIDENCE_ZENITH_ANGLE_B11 +- MEAN_INCIDENCE_ZENITH_ANGLE_B12 +- SOLAR_IRRADIANCE_B6 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B10 +- SOLAR_IRRADIANCE_B5 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B11 +- SOLAR_IRRADIANCE_B8 +- MEAN_INCIDENCE_AZIMUTH_ANGLE_B12 +- SOLAR_IRRADIANCE_B7 +- SOLAR_IRRADIANCE_B2 +- SOLAR_IRRADIANCE_B1 +- SOLAR_IRRADIANCE_B4 +- GEOMETRIC_QUALITY +- SOLAR_IRRADIANCE_B3 +- system:asset_size +- WATER_PERCENTAGE +- system:bands +- system:band_names diff --git a/tests/test_Image/test_default_spectral_indices.csv b/tests/test_Image/test_default_spectral_indices.csv new file mode 100644 index 00000000..cc877775 --- /dev/null +++ b/tests/test_Image/test_default_spectral_indices.csv @@ -0,0 +1,2 @@ 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diff --git a/tests/test_Image/test_deprecated_distance_to_mask.csv b/tests/test_Image/test_deprecated_distance_to_mask.csv new file mode 100644 index 00000000..a230510b --- /dev/null +++ b/tests/test_Image/test_deprecated_distance_to_mask.csv @@ -0,0 +1,2 @@ +,distance_to_mask +0,1000 diff --git a/tests/test_Image/test_deprecated_euclidian_distance.csv b/tests/test_Image/test_deprecated_euclidian_distance.csv new file mode 100644 index 00000000..a4033ae1 --- /dev/null +++ b/tests/test_Image/test_deprecated_euclidian_distance.csv @@ -0,0 +1,2 @@ +,sum_distance +0,157.25661609998866 diff --git a/tests/test_Image/test_distance.csv b/tests/test_Image/test_distance.csv new file mode 100644 index 00000000..a4033ae1 --- /dev/null +++ b/tests/test_Image/test_distance.csv @@ -0,0 +1,2 @@ +,sum_distance +0,157.25661609998866 diff --git a/tests/test_Image/test_distance_to_mask.csv b/tests/test_Image/test_distance_to_mask.csv new file mode 100644 index 00000000..a7c4d6cb --- /dev/null +++ b/tests/test_Image/test_distance_to_mask.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL,distance_to_mask +0,238.7171666246534,948.66361230148675,750.75646584320634,320.95785228132081,415.14734055961685,426.16661204940726,604.81486009578975,1007.6004411393997,1140.2466599445429,1217.7626543987888,1200.1272498109399,1389.4672800604985,3.3245021426770851,1000 diff --git a/tests/test_Image/test_doy_to_date.csv b/tests/test_Image/test_doy_to_date.csv new file mode 100644 index 00000000..a14db8df --- /dev/null +++ b/tests/test_Image/test_doy_to_date.csv @@ -0,0 +1,2 @@ +,doy1 +0,20230101 diff --git a/tests/test_Image/test_doy_to_date_with_band.csv b/tests/test_Image/test_doy_to_date_with_band.csv new file mode 100644 index 00000000..bd3ec55f --- /dev/null +++ b/tests/test_Image/test_doy_to_date_with_band.csv @@ -0,0 +1,2 @@ +,doy2 +0,20230101 diff --git a/tests/test_Image/test_doy_to_date_with_format.csv b/tests/test_Image/test_doy_to_date_with_format.csv new file mode 100644 index 00000000..150d4f03 --- /dev/null +++ b/tests/test_Image/test_doy_to_date_with_format.csv @@ -0,0 +1,2 @@ +,doy1 +0,2023.001 diff --git a/tests/test_Image/test_full.csv b/tests/test_Image/test_full.csv new file mode 100644 index 00000000..69e59a7f --- /dev/null +++ b/tests/test_Image/test_full.csv @@ -0,0 +1,2 @@ +,constant +0,0 diff --git a/tests/test_Image/test_full_like.csv b/tests/test_Image/test_full_like.csv new file mode 100644 index 00000000..698b9a23 --- /dev/null +++ b/tests/test_Image/test_full_like.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/tests/test_Image/test_full_like_with_mask.csv b/tests/test_Image/test_full_like_with_mask.csv new file mode 100644 index 00000000..e9999946 --- /dev/null +++ b/tests/test_Image/test_full_like_with_mask.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_Image/test_full_with_lists.csv b/tests/test_Image/test_full_with_lists.csv new file mode 100644 index 00000000..db6d556e --- /dev/null +++ b/tests/test_Image/test_full_with_lists.csv @@ -0,0 +1,2 @@ +,tata,titi,toto +0,3,2,1 diff --git a/tests/test_Image/test_full_with_name.csv b/tests/test_Image/test_full_with_name.csv new file mode 100644 index 00000000..f39e2049 --- /dev/null +++ b/tests/test_Image/test_full_with_name.csv @@ -0,0 +1,2 @@ +,toto +0,1 diff --git a/tests/test_Image/test_full_with_value.csv b/tests/test_Image/test_full_with_value.csv new file mode 100644 index 00000000..03fb3c1f --- /dev/null +++ b/tests/test_Image/test_full_with_value.csv @@ -0,0 +1,2 @@ +,constant +0,1 diff --git a/tests/test_Image/test_gauss.csv b/tests/test_Image/test_gauss.csv new file mode 100644 index 00000000..2597d3a7 --- /dev/null +++ b/tests/test_Image/test_gauss.csv @@ -0,0 +1,2 @@ +,B1_gauss +0,0.66461378242572766 diff --git a/tests/test_Image/test_gauss_with_band.csv b/tests/test_Image/test_gauss_with_band.csv new file mode 100644 index 00000000..41577224 --- /dev/null +++ b/tests/test_Image/test_gauss_with_band.csv @@ -0,0 +1,2 @@ +,B2_gauss +0,0.50929481299884138 diff --git a/tests/test_Image/test_get_scale_params.yml b/tests/test_Image/test_get_scale_params.yml new file mode 100644 index 00000000..46506551 --- /dev/null +++ b/tests/test_Image/test_get_scale_params.yml @@ -0,0 +1,23 @@ +AOT: 0.001 +B1: 0.0001 +B11: 0.0001 +B12: 0.0001 +B2: 0.0001 +B3: 0.0001 +B4: 0.0001 +B5: 0.0001 +B6: 0.0001 +B7: 0.0001 +B8: 0.0001 +B8A: 0.0001 +B9: 0.0001 +MSK_CLDPRB: 1.0 +MSK_SNWPRB: 1.0 +QA10: 1.0 +QA20: 1.0 +QA60: 1.0 +SCL: 1.0 +TCI_B: 1.0 +TCI_G: 1.0 +TCI_R: 1.0 +WVP: 0.001 diff --git a/tests/test_Image/test_get_values.csv b/tests/test_Image/test_get_values.csv new file mode 100644 index 00000000..1c5c1dc8 --- /dev/null +++ b/tests/test_Image/test_get_values.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,72,159,1341,1105,487,641,714,861,1356,1538,1622,1617,1298,,,,0,0,0,0,0,4,50,66,73,789 diff --git a/tests/test_Image/test_get_values_with_scale.csv b/tests/test_Image/test_get_values_with_scale.csv new file mode 100644 index 00000000..17592770 --- /dev/null +++ b/tests/test_Image/test_get_values_with_scale.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLASSI_CIRRUS,MSK_CLASSI_OPAQUE,MSK_CLASSI_SNOW_ICE,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,72,117,553,332,161,247,175,384,952,1071,1168,1145,1523,,,,0,0,0,0,0,4,18,26,19,693 diff --git a/tests/test_Image/test_histogram_match.csv b/tests/test_Image/test_histogram_match.csv new file mode 100644 index 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= image.geometry() if geometry is None else geometry.geometry() + geometry = geometry.centroid(1).buffer(100) + return image.reduceRegion(ee.Reducer.mean(), geometry, 1) + + +def round_dict(d: dict | None = None, decimals: int = 2) -> dict: + """Round all the values of a dictionary.""" + d = d or {} + for k, v in d.items(): + if isinstance(v, dict): + round_dict(v, decimals) + else: + d[k] = round(v, decimals) + return d + + +class TestMaskClouds: + """Test the ``maskClouds`` method.""" + + @pytest.mark.xfail( + reason="ee_extra is joining ImgeCollection which is not compatible with ee v1.x." + ) + def test_mask_s2_sr(self, s2_sr, num_regression): + masked = s2_sr.geetools.maskClouds(prob=75, buffer=300, cdi=-0.5) + num_regression.check(reduce(masked).getInfo()) + + +class TestClosest: + """Test the ``closest`` method.""" + + def test_closest_s2_sr(self, s2_sr, data_regression): + closest = s2_sr.geetools.closest("2021-10-01") + data_regression.check(closest.size().getInfo()) + + +class TestSpectralIndices: + """Test the ``spectralIndices`` method.""" + + def test_spectral_indices(self, s2_sr, num_regression): + indices = s2_sr.geetools.spectralIndices(["NDVI", "NDWI"]) + num_regression.check(reduce(indices).getInfo()) + + +class TestGetScaleParams: + """Test the ``getScaleParams`` method.""" + + def test_get_scale_params(self, s2_sr, data_regression): + scale_params = s2_sr.geetools.getScaleParams() + data_regression.check(scale_params) + + +class TestGetOffsetParams: + """Test the ``getOffsetParams`` method.""" + + def test_get_offset_params(self, s2_sr, data_regression): + offset_params = s2_sr.geetools.getOffsetParams() + data_regression.check(offset_params) + + +class TestScaleAndOffset: + """Test the ``scaleAndOffset`` method.""" + + def test_scale_and_offset(self, s2_sr, num_regression): + scaled = s2_sr.geetools.scaleAndOffset() + num_regression.check(reduce(scaled).getInfo()) + + +class TestPreprocess: + """Test the ``preprocess`` method.""" + + @pytest.mark.xfail( + reason="ee_extra is joining ImgeCollection which is not compatible with ee v1.x." + ) + def test_preprocess(self, s2_sr, num_regression): + preprocessed = s2_sr.geetools.preprocess() + values = {k: np.nan if v is None else v for k, v in reduce(preprocessed).getInfo().items()} + num_regression.check(values) + + +class TestGetSTAC: + """Test the ``getSTAC`` method.""" + + def test_get_stac(self, s2_sr): + stac = s2_sr.geetools.getSTAC() + assert stac["id"] == "COPERNICUS/S2_SR_HARMONIZED" + + def test_get_stac_schema(self, s2_sr, stac_schema): + stac = s2_sr.geetools.getSTAC() + validate(stac, stac_schema) + + +class TestGetDOI: + """Test the ``getDOI`` method.""" + + def test_get_doi(self, s2_sr, data_regression): + doi = s2_sr.geetools.getDOI() + data_regression.check(doi) + + +class TestGetCitation: + """Test the ``getCitation`` method.""" + + def test_get_citation(self, s2_sr, data_regression): + citation = s2_sr.geetools.getCitation() + data_regression.check(citation) + + +class TestPanSharpen: + """Test the ``panSharpen`` method.""" + + @pytest.mark.xfail(reason="ee_extra does not accept C02 L08 collection yet.") + def test_pan_sharpen(self, l8_toa, num_regression): + sharpened = l8_toa.geetools.panSharpen() + num_regression.check(reduce(sharpened).getInfo()) + + +class TestTasseledCap: + """Test the ``tasseledCap`` method.""" + + def test_tasseled_cap(self, l8_sr, num_regression): + tc = l8_sr.geetools.tasseledCap() + num_regression.check(reduce(tc).getInfo()) + + +class TestAppend: + """Test the ``append`` method.""" + + def test_append(self, s2_sr, data_regression): + appended = s2_sr.geetools.append(s2_sr.first()) + data_regression.check(appended.size().getInfo()) + + +class TestcollectionMask: + """Test the ``collectionMask`` method.""" + + def test_collection_mask(self, s2_sr, amazonas, num_regression): + masked = s2_sr.geetools.collectionMask() + num_regression.check(reduce(ee.ImageCollection([masked]), amazonas).getInfo()) + + +class TestIloc: + """Test the iloc class.""" + + def test_iloc(self, s2_sr, num_regression): + ic = ee.ImageCollection([s2_sr.geetools.iloc(0).subtract(s2_sr.first())]) + values = {k: np.nan if v is None else v for k, v in reduce(ic).getInfo().items()} + num_regression.check(values) + + +class TestIntegral: + """Test the ``integral`` method.""" + + def test_integral(self, s2_sr, amazonas, num_regression): + integral = s2_sr.limit(10).geetools.integral("B4").select("integral") + ic = ee.ImageCollection([integral]) + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + +class TestOutliers: + """Test the ``outliers`` method.""" + + def test_outliers(self, s2_sr, amazonas, num_regression): + ic = s2_sr.limit(10).geetools.outliers() + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + def test_outliers_with_bands(self, s2_sr, amazonas, num_regression): + ic = s2_sr.limit(10).geetools.outliers(bands=["B4", "B2"]) + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + def test_outliers_with_sigma(self, s2_sr, amazonas, num_regression): + ic = s2_sr.limit(10).geetools.outliers(sigma=3) + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + def test_outliers_with_drop(self, s2_sr, amazonas, num_regression): + ic = s2_sr.limit(10).geetools.outliers(drop=True) + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + +class TestToXarray: + """Test the ``toXarray`` method.""" + + def test_to_xarray(self, s2_sr, data_regression): + ds = s2_sr.geetools.to_xarray() + + # drop all the dtype as they are not consistently setup depending on the xarray version + def drop_dtype(d=ds): + for k, v in ds.items(): + if isinstance(v, dict): + drop_dtype(v) + elif k == "dtype": + del ds[k] + + drop_dtype() + + # ds = ds.astype(np.float64) + data_regression.check(ds.to_dict(data=False)) + + +class TestValidPixel: + """Test the ``validPixel`` method.""" + + def test_validPixel(self, s2_sr, amazonas, num_regression): + s2_sr = s2_sr.filterDate("2021-01-01", "2021-01-31") + ic = ee.ImageCollection([s2_sr.geetools.validPixel("B1")]) + values = {k: np.nan if v is None else v for k, v in reduce(ic, amazonas).getInfo().items()} + num_regression.check(values) + + +class TestContainsBandNames: + """Test the ``containsBandNames`` method and derivated.""" + + def test_contains_all(self, s2_sr): + ic = s2_sr.select(["B2", "B3", "B4"]) + ic = ic.geetools.containsAllBands(["B2", "B3"]) + assert ic.size().getInfo() == 2449 + + def test_contains_all_mismatch(self, s2_sr): + ic = s2_sr.select(["B2", "B3", "B4"]) + ic = ic.geetools.containsAllBands(["B2", "B3", "B5"]) + assert ic.size().getInfo() == 0 + + def test_contains_any(self, s2_sr): + ic = s2_sr.select(["B2", "B3", "B4"]) + ic = ic.geetools.containsAnyBands(["B2", "B3", "B5"]) + assert ic.size().getInfo() == 2449 + + def test_contains_any_mismatch(self, s2_sr): + ic = s2_sr.select(["B2", "B3", "B4"]) + ic = ic.geetools.containsAnyBands(["B5", "B6"]) + assert ic.size().getInfo() == 0 + + +class TestAggregateArray: + """Test the ``aggregateArray`` method.""" + + def test_aggregate_array(self, s2_sr, data_regression): + # reduce the number of properties beforehand to avoid the test to fail + keys = s2_sr.first().propertyNames() + keys = keys.filter(ee.Filter.stringStartsWith("item", "system:")).remove("system:version") + s2_sr_filtered = s2_sr.limit(3).map( + lambda i: ee.Image().addBands(i).copyProperties(i, keys) + ) + aggregated = s2_sr_filtered.geetools.aggregateArray() + data_regression.check(aggregated.getInfo()) + + def test_aggregate_array_with_properties(self, s2_sr, data_regression): + aggregated = s2_sr.limit(10).geetools.aggregateArray(["system:time_start", "system:index"]) + data_regression.check(aggregated.getInfo()) + + +class TestGroupInterval: + """Test the ``groupInterval`` method.""" + + def test_group_interval(self, jaxa_rainfall): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + grouped = ic.geetools.groupInterval() + assert grouped.size().getInfo() == 3 + assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 720 + + def test_group_interval_with_interval(self, jaxa_rainfall): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + grouped = ic.geetools.groupInterval(duration=2) + assert grouped.size().getInfo() == 2 + assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 1440 + assert ee.ImageCollection(grouped.get(1)).size().getInfo() == 719 + + def test_group_interval_with_interval_and_unit(self, jaxa_rainfall): + # get 3 days worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") + grouped = ic.geetools.groupInterval(duration=1, unit="day") + assert grouped.size().getInfo() == 3 + assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 24 + + def test_deprecated_make_equal_interval(self, jaxa_rainfall): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + with pytest.deprecated_call(): + grouped = geetools.imagecollection.makeEqualInterval(ic) + assert grouped.size().getInfo() == 3 + assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 720 + + def test_deprecated_make_day_intervals(self, jaxa_rainfall): + # get 3 days worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") + with pytest.deprecated_call(): + grouped = geetools.imagecollection.makeDayIntervals(ic) + assert grouped.size().getInfo() == 3 + assert ee.ImageCollection(grouped.get(0)).size().getInfo() == 24 + + +class TestReduceInterval: + """Test the ``reduceInterval`` method.""" + + def test_reduce_interval(self, jaxa_rainfall, amazonas, num_regression): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + reduced = ic.geetools.reduceInterval() + values = { + k: np.nan if v is None else v for k, v in reduce(reduced, amazonas).getInfo().items() + } + num_regression.check(values) + + def test_reduce_interval_with_reducer(self, jaxa_rainfall, amazonas, num_regression): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + reduced = ic.geetools.reduceInterval("max") + values = reduce(reduced, amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_reduce_interval_with_non_existing_reducer_and_properties(self, jaxa_rainfall): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + with pytest.raises(AttributeError): + ic.geetools.reduceInterval("toto") + + def test_deprecated_reduce_equal_interval(self, jaxa_rainfall, amazonas, num_regression): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-31") + with pytest.deprecated_call(): + reduced = geetools.imagecollection.reduceEqualInterval(ic, reducer="mean") + values = reduce(reduced, amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_reduce_day_intervals(self, jaxa_rainfall, amazonas, num_regression): + # get 3 days worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") + with pytest.deprecated_call(): + reduced = geetools.imagecollection.reduceDayIntervals(ic, reducer="mean") + values = reduce(reduced, amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_composite_regular_intervals(self, jaxa_rainfall, amazonas, num_regression): + # get 3 days worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-01-04") + with pytest.deprecated_call(): + reduced = geetools.composite.compositeRegularIntervals(ic, unit="day") + values = reduce(reduced, amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_composite_by_month(self, jaxa_rainfall, amazonas, num_regression): + # get 3 month worth of data and group it with default parameters + ic = jaxa_rainfall.filterDate("2020-01-01", "2020-03-01") + with pytest.deprecated_call(): + reduced = geetools.composite.compositeByMonth(ic) + values = reduce(reduced, amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + +class TestClosestDate: + """Test the ``closestDate`` method.""" + + def test_closest_date(self, s2_sr, amazonas, num_regression): + # we need less images as the test will fail otherwise + filled = s2_sr.filterDate("2021-01-01", "2021-01-15").geetools.closestDate() + values = reduce(filled, amazonas, "mean").getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_fill_with_last(self, s2_sr, amazonas, num_regression): + with pytest.deprecated_call(): + filled = geetools.imagecollection.fillWithLast( + s2_sr.filterDate("2021-01-01", "2021-01-15") + ) + values = reduce(filled, amazonas, "mean").getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_closest_date(self, s2_sr, amazonas, num_regression): + with pytest.deprecated_call(): + filled = geetools.composite.closestDate(s2_sr.filterDate("2021-01-01", "2021-01-15")) + values = reduce(filled, amazonas, "mean").getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + +class TestMedoid: + """Test the ``medoid`` method.""" + + def test_medoid(self, s2_sr, amazonas, num_regression): + # we need less images as the test will fail otherwise + medoid = s2_sr.filterDate("2021-01-01", "2021-01-05").geetools.medoid() + values = reduce(ee.ImageCollection(medoid), amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + def test_deprecated_medoid(self, s2_sr, amazonas, num_regression): + with pytest.deprecated_call(): + # we need less images as the test will fail otherwise + medoid = geetools.composite.medoid(s2_sr.filterDate("2021-01-01", "2021-01-05")) + values = reduce(ee.ImageCollection(medoid), amazonas).getInfo() + values = {k: np.nan if v is None else v for k, v in values.items()} + num_regression.check(values) + + +class TestPlotDatesByBands: + """Test the ``plot_dates_by_bands`` method.""" + + def test_plot_dates_by_bands(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_dates_by_bands( + region=self.region.geometry(), + reducer="mean", + scale=500, + bands=["NDVI", "EVI"], + ax=ax, + dateProperty="system:time_start", + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def region(self): + return ( + ee.FeatureCollection("projects/google/charts_feature_example") + .select(["label", "value", "warm"]) + .filter(ee.Filter.eq("label", "Forest")) + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .filter(ee.Filter.date("2010-01-01", "2020-01-01")) + .select(["NDVI", "EVI"]) + ) + + +class TestPlotDatesByRegions: + """Test the ``plot_dates_by_regions`` method.""" + + def test_plot_dates_by_regions(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_dates_by_regions( + regions=self.regions, + label="label", + band="NDVI", + reducer="mean", + scale=500, + ax=ax, + dateProperty="system:time_start", + colors=["#f0af07", "#0f8755", "#76b349"], + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def regions(self): + return ee.FeatureCollection("projects/google/charts_feature_example").select( + ["label", "value", "warm"] + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .filter(ee.Filter.date("2010-01-01", "2020-01-01")) + .select(["NDVI", "EVI"]) + ) + + +class TestPlotDoyByBands: + """Test the ``plot_doy_by_bands`` method.""" + + def test_plot_doy_by_bands(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_doy_by_bands( + region=self.region.geometry(), + spatialReducer="mean", + timeReducer="mean", + scale=500, + bands=["NDVI", "EVI"], + ax=ax, + dateProperty="system:time_start", + colors=["#e37d05", "#1d6b99"], + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def region(self): + return ( + ee.FeatureCollection("projects/google/charts_feature_example") + .select(["label", "value", "warm"]) + .filter(ee.Filter.eq("label", "Grassland")) + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .filter(ee.Filter.date("2010-01-01", "2020-01-01")) + .select(["NDVI", "EVI"]) + ) + + +class TestPlotDoyByRegions: + """Test the ``plot_doy_by_regions`` method.""" + + def test_plot_doy_by_regions(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_doy_by_regions( + regions=self.regions, + label="label", + band="NDVI", + spatialReducer="mean", + timeReducer="mean", + scale=500, + ax=ax, + dateProperty="system:time_start", + colors=["#f0af07", "#0f8755", "#76b349"], + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def regions(self): + return ee.FeatureCollection("projects/google/charts_feature_example").select( + ["label", "value", "warm"] + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .filter(ee.Filter.date("2010-01-01", "2020-01-01")) + .select(["NDVI", "EVI"]) + ) + + +class TestPlotDoyByYears: + """Test the ``plot_doy_by_years`` method.""" + + def test_plot_doy_by_years(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_doy_by_years( + region=self.region.geometry(), + band="NDVI", + reducer="mean", + scale=500, + ax=ax, + colors=["#39a8a7", "#9c4f97"], + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def region(self): + return ( + ee.FeatureCollection("projects/google/charts_feature_example") + .select(["label", "value", "warm"]) + .filter(ee.Filter.eq("label", "Grassland")) + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .select(["NDVI", "EVI"]) + .filter( + ee.Filter.Or( + ee.Filter.date("2012-01-01", "2012-12-31"), + ee.Filter.date("2019-01-01", "2019-12-31"), + ) + ) + ) + + +class TestPlotDoyBySeasons: + """Test the ``plot_doy_by_seasons`` method.""" + + def test_plot_doy_by_seasons(self, image_regression): + fig, ax = plt.subplots() + self.collection.geetools.plot_doy_by_seasons( + region=self.region.geometry(), + seasonStart=ee.Date("2019-04-15").getRelative("day", "year"), + seasonEnd=ee.Date("2019-09-15").getRelative("day", "year"), + band="NDVI", + reducer="mean", + scale=500, + ax=ax, + colors=["#39a8a7", "#9c4f97"], + ) + + with io.BytesIO() as buffer: + fig.savefig(buffer) + image_regression.check(buffer.getvalue()) + + @property + def region(self): + return ( + ee.FeatureCollection("projects/google/charts_feature_example") + .select(["label", "value", "warm"]) + .filter(ee.Filter.eq("label", "Grassland")) + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .select(["NDVI", "EVI"]) + .filter( + ee.Filter.Or( + ee.Filter.date("2012-01-01", "2012-12-31"), + ee.Filter.date("2019-01-01", "2019-12-31"), + ) + ) + ) + + +class TestReduceRegion: + """Test the reduceRegion method.""" + + def test_reduce_region_by_dates(self, data_regression): + values = self.collection.geetools.reduceRegion( + reducer=ee.Reducer.mean(), + idProperty="system:time_start", + idType=ee.Date, + geometry=self.region.geometry(), + scale=500, + ).getInfo() + data_regression.check(round_dict(values, 4)) + + def test_reduce_region_by_date_property(self, data_regression): + values = self.collection.geetools.reduceRegion( + reducer=ee.Reducer.mean(), + idProperty="system:time_start", + idType=ee.Date, + idReducer="mean", + geometry=self.region.geometry(), + scale=500, + ).getInfo() + data_regression.check(round_dict(values, 4)) + + def test_reduce_region_by_doy(self, data_regression): + values = self.year_collection.geetools.reduceRegion( + reducer=ee.Reducer.mean(), + idProperty="system:time_start", + idType=ee.Date, + idFormat="DDD", + geometry=self.region.geometry(), + scale=500, + ).getInfo() + data_regression.check(round_dict(values, 4)) + + @property + def region(self): + return ( + ee.FeatureCollection("projects/google/charts_feature_example") + .select(["label", "value", "warm"]) + .filter(ee.Filter.eq("label", "Forest")) + ) + + @property + def collection(self): + return ( + ee.ImageCollection("MODIS/061/MOD13A1") + .filter(ee.Filter.date("2010-01-01", "2010-02-28")) + .select(["NDVI", "EVI"]) + ) + + @property + def year_collection(self): + return ( + ee.ImageCollection("MODIS/006/MOD13Q1") + .filter( + ee.Filter.Or( + ee.Filter.date("2010-01-01", "2010-02-28"), + ee.Filter.date("2011-01-01", "2011-02-28"), + ) + ) + .select(["NDVI", "EVI"]) + ) diff --git a/tests/test_ImageCollection/test_aggregate_array.yml b/tests/test_ImageCollection/test_aggregate_array.yml new file mode 100644 index 00000000..0c1a0660 --- /dev/null +++ b/tests/test_ImageCollection/test_aggregate_array.yml @@ -0,0 +1,845 @@ +system:asset_size: +- 361149596 +- 207607455 +- 1145104866 +system:band_names: +- - B1 + - B2 + - B3 + - B4 + - B5 + - B6 + - B7 + - B8 + - B8A + - B9 + - B11 + - B12 + - SCL +- - B1 + - B2 + - B3 + - B4 + - B5 + - B6 + - B7 + - B8 + - B8A + - B9 + - B11 + - B12 + - SCL +- - B1 + - B2 + - B3 + - B4 + - B5 + - B6 + - B7 + - B8 + - B8A + - B9 + - B11 + - B12 + - SCL +system:bands: +- B1: + crs: EPSG:32718 + crs_transform: + - 60 + - 0 + - 699960 + - 0 + - -60 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 1830 + - 1830 + B11: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 5490 + - 5490 + B12: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 5490 + - 5490 + B2: + crs: EPSG:32718 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 10980 + - 10980 + B3: + crs: EPSG:32718 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 10980 + - 10980 + B4: + crs: EPSG:32718 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 10980 + - 10980 + B5: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 5490 + - 5490 + B6: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 5490 + - 5490 + B7: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 5490 + - 5490 + B8: + crs: EPSG:32718 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 10980 + - 10980 + B8A: + crs: EPSG:32718 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: + max: 65535 + min: 0 + precision: int + type: PixelType + dimensions: + - 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b/tests/test_ImageCollection/test_aggregate_array_with_properties.yml new file mode 100644 index 00000000..b77c0635 --- /dev/null +++ b/tests/test_ImageCollection/test_aggregate_array_with_properties.yml @@ -0,0 +1,22 @@ +system:index: +- 20210102T150719_20210102T150942_T18MYC +- 20210102T150719_20210102T150942_T18MYD +- 20210102T150719_20210102T150942_T18MZC +- 20210102T150719_20210102T150942_T18MZD +- 20210102T150719_20210102T150942_T18MZE +- 20210102T150719_20210102T150942_T18NZF +- 20210102T150719_20210102T150942_T19MBT +- 20210102T150719_20210102T150942_T19MBU +- 20210102T150719_20210102T150942_T19MBV +- 20210102T150719_20210102T150942_T19MCR +system:time_start: +- 1609600422628 +- 1609600409366 +- 1609600419630 +- 1609600405212 +- 1609600390766 +- 1609600376122 +- 1609600417688 +- 1609600403048 +- 1609600388626 +- 1609600443330 diff --git a/tests/test_ImageCollection/test_append.yml b/tests/test_ImageCollection/test_append.yml new file mode 100644 index 00000000..1644dd3f --- /dev/null +++ b/tests/test_ImageCollection/test_append.yml @@ -0,0 +1,2 @@ +2450 +... diff --git a/tests/test_ImageCollection/test_closest_date.csv b/tests/test_ImageCollection/test_closest_date.csv new file mode 100644 index 00000000..118aae1b --- /dev/null +++ b/tests/test_ImageCollection/test_closest_date.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL +0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_closest_s2_sr.yml b/tests/test_ImageCollection/test_closest_s2_sr.yml new file mode 100644 index 00000000..ece6e09e --- /dev/null +++ b/tests/test_ImageCollection/test_closest_s2_sr.yml @@ -0,0 +1,2 @@ +8 +... diff --git a/tests/test_ImageCollection/test_collection_mask.csv b/tests/test_ImageCollection/test_collection_mask.csv new file mode 100644 index 00000000..398ec81d --- /dev/null +++ b/tests/test_ImageCollection/test_collection_mask.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 diff --git a/tests/test_ImageCollection/test_deprecated_closest_date.csv b/tests/test_ImageCollection/test_deprecated_closest_date.csv new file mode 100644 index 00000000..118aae1b --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_closest_date.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL +0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_deprecated_composite_by_month.csv b/tests/test_ImageCollection/test_deprecated_composite_by_month.csv new file mode 100644 index 00000000..8fe785e6 --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_composite_by_month.csv @@ -0,0 +1,2 @@ +,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag +0,0,0.18768584728240967,0.1922166645526886,-0.0021944569889456034,9149832.9472222216 diff --git a/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv b/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv new file mode 100644 index 00000000..1a9c76e2 --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_composite_regular_intervals.csv @@ -0,0 +1,2 @@ +,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag +0,0,0,0.16104844212532043,-0.16916666924953461,6294879.5 diff --git a/tests/test_ImageCollection/test_deprecated_fill_with_last.csv b/tests/test_ImageCollection/test_deprecated_fill_with_last.csv new file mode 100644 index 00000000..118aae1b --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_fill_with_last.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL +0,7013.1499356644645,4707.9961241683077,3919.893125538782,6578.679351288074,6183.2784174693725,5784.3242832570231,6164.3671240171716,6327.8494090005433,6550.4332522288923,6188.5737541288727,6620.4358590515458,11144.482102417895,8.8304504414322942 diff --git a/tests/test_ImageCollection/test_deprecated_medoid.csv b/tests/test_ImageCollection/test_deprecated_medoid.csv new file mode 100644 index 00000000..cf49bd0b --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_medoid.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL 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+,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag +0,0,0,0.16104844212532043,-0.16916666924953461,6294879.5 diff --git a/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv b/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv new file mode 100644 index 00000000..8fe785e6 --- /dev/null +++ b/tests/test_ImageCollection/test_deprecated_reduce_equal_interval.csv @@ -0,0 +1,2 @@ +,gaugeQualityInfo,hourlyPrecipRate,hourlyPrecipRateGC,observationTimeFlag,satelliteInfoFlag +0,0,0.18768584728240967,0.1922166645526886,-0.0021944569889456034,9149832.9472222216 diff --git a/tests/test_ImageCollection/test_get_citation.yml b/tests/test_ImageCollection/test_get_citation.yml new file mode 100644 index 00000000..866e45cf --- /dev/null +++ b/tests/test_ImageCollection/test_get_citation.yml @@ -0,0 +1,2 @@ +Citation not available +... diff --git a/tests/test_ImageCollection/test_get_doi.yml b/tests/test_ImageCollection/test_get_doi.yml new file mode 100644 index 00000000..57e63a82 --- /dev/null +++ b/tests/test_ImageCollection/test_get_doi.yml @@ -0,0 +1,2 @@ +DOI not available +... diff --git a/tests/test_ImageCollection/test_get_offset_params.yml b/tests/test_ImageCollection/test_get_offset_params.yml new file mode 100644 index 00000000..a3314864 --- /dev/null +++ b/tests/test_ImageCollection/test_get_offset_params.yml @@ -0,0 +1,23 @@ +AOT: 0.0 +B1: 0.0 +B11: 0.0 +B12: 0.0 +B2: 0.0 +B3: 0.0 +B4: 0.0 +B5: 0.0 +B6: 0.0 +B7: 0.0 +B8: 0.0 +B8A: 0.0 +B9: 0.0 +MSK_CLDPRB: 0.0 +MSK_SNWPRB: 0.0 +QA10: 0.0 +QA20: 0.0 +QA60: 0.0 +SCL: 0.0 +TCI_B: 0.0 +TCI_G: 0.0 +TCI_R: 0.0 +WVP: 0.0 diff --git a/tests/test_ImageCollection/test_get_scale_params.yml b/tests/test_ImageCollection/test_get_scale_params.yml new file mode 100644 index 00000000..46506551 --- /dev/null +++ b/tests/test_ImageCollection/test_get_scale_params.yml @@ -0,0 +1,23 @@ +AOT: 0.001 +B1: 0.0001 +B11: 0.0001 +B12: 0.0001 +B2: 0.0001 +B3: 0.0001 +B4: 0.0001 +B5: 0.0001 +B6: 0.0001 +B7: 0.0001 +B8: 0.0001 +B8A: 0.0001 +B9: 0.0001 +MSK_CLDPRB: 1.0 +MSK_SNWPRB: 1.0 +QA10: 1.0 +QA20: 1.0 +QA60: 1.0 +SCL: 1.0 +TCI_B: 1.0 +TCI_G: 1.0 +TCI_R: 1.0 +WVP: 0.001 diff --git a/tests/test_ImageCollection/test_iloc.csv b/tests/test_ImageCollection/test_iloc.csv new file mode 100644 index 00000000..52a10b2b --- /dev/null +++ b/tests/test_ImageCollection/test_iloc.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,,,,,,,,,,,,,,0,0,0,0,0,,,,, diff --git a/tests/test_ImageCollection/test_integral.csv b/tests/test_ImageCollection/test_integral.csv new file mode 100644 index 00000000..5268a191 --- /dev/null +++ b/tests/test_ImageCollection/test_integral.csv @@ -0,0 +1,2 @@ +,integral +0, diff --git a/tests/test_ImageCollection/test_mask_s2.yml b/tests/test_ImageCollection/test_mask_s2.yml new file mode 100644 index 00000000..eb745b9d --- /dev/null +++ b/tests/test_ImageCollection/test_mask_s2.yml @@ -0,0 +1,27 @@ +AOT: 354 +B1: 590.1282626148882 +B11: 1986.408679038172 +B12: 1522.041930986203 +B2: 564.3067665633657 +B3: 784.1374447316587 +B4: 821.3239234989915 +B5: 1217.444006928795 +B6: 2003.3571632505652 +B7: 2236.471328753445 +B8: 2390.463396773381 +B8A: 2407.3933334274057 +B9: 2422.9298342189186 +CLOUD_MASK: 0 +CLOUD_MASK_CDI: 0 +CLOUD_SHADOW_MASK: 0 +MSK_CLDPRB: null +MSK_SNWPRB: null +QA10: 0 +QA20: 0 +QA60: 0 +SCL: 4.598658742666373 +SHADOW_MASK: 0 +TCI_B: 57.67908547074245 +TCI_G: 80.82178790722993 +TCI_R: 83.79020123070153 +WVP: 602.9321499075425 diff --git a/tests/test_ImageCollection/test_mask_s2_sr.csv b/tests/test_ImageCollection/test_mask_s2_sr.csv new file mode 100644 index 00000000..72e1d438 --- /dev/null +++ b/tests/test_ImageCollection/test_mask_s2_sr.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,CLOUD_MASK,CLOUD_MASK_CDI,CLOUD_SHADOW_MASK,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,SHADOW_MASK,TCI_B,TCI_G,TCI_R,WVP +0,199,16640.094805201559,1568.290431797825,1968.3295548610204,15419.765396984198,14101.613293902175,12931.892357709678,14237.406449381348,12795.376786673241,11806.409740942607,12098.870170407052,11006.397712738837,15899,1,0,0,0,0,0,0,1024,8,0,255,255,255,3645 diff --git a/tests/test_ImageCollection/test_medoid.csv b/tests/test_ImageCollection/test_medoid.csv new file mode 100644 index 00000000..cf49bd0b --- /dev/null +++ b/tests/test_ImageCollection/test_medoid.csv @@ -0,0 +1,2 @@ +,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,SCL +0,0.6177746823789384,0.79167387556605839,0.75502788442188418,0.60222084925854291,0.63257736183390867,0.60337927161154525,0.63801273546323556,0.79096266749901301,0.85579134939633583,0.79265361382284005,0.87208650729675008,1,0 diff --git a/tests/test_ImageCollection/test_outliers.csv b/tests/test_ImageCollection/test_outliers.csv new file mode 100644 index 00000000..7abbfc1a --- /dev/null +++ b/tests/test_ImageCollection/test_outliers.csv @@ -0,0 +1,2 @@ +,AOT,AOT_outlier,B1,B11,B11_outlier,B12,B12_outlier,B1_outlier,B2,B2_outlier,B3,B3_outlier,B4,B4_outlier,B5,B5_outlier,B6,B6_outlier,B7,B7_outlier,B8,B8A,B8A_outlier,B8_outlier,B9,B9_outlier,MSK_CLDPRB,MSK_CLDPRB_outlier,MSK_SNWPRB,MSK_SNWPRB_outlier,QA10,QA10_outlier,QA20,QA20_outlier,QA60,QA60_outlier,SCL,SCL_outlier,TCI_B,TCI_B_outlier,TCI_G,TCI_G_outlier,TCI_R,TCI_R_outlier,WVP,WVP_outlier +0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_ImageCollection/test_outliers_with_bands.csv b/tests/test_ImageCollection/test_outliers_with_bands.csv new file mode 100644 index 00000000..ae77511b --- /dev/null +++ b/tests/test_ImageCollection/test_outliers_with_bands.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B2_outlier,B3,B4,B4_outlier,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_ImageCollection/test_outliers_with_drop.csv b/tests/test_ImageCollection/test_outliers_with_drop.csv new file mode 100644 index 00000000..27b115a9 --- /dev/null +++ b/tests/test_ImageCollection/test_outliers_with_drop.csv @@ -0,0 +1,2 @@ +,AOT,B1,B11,B12,B2,B3,B4,B5,B6,B7,B8,B8A,B9,MSK_CLDPRB,MSK_SNWPRB,QA10,QA20,QA60,SCL,TCI_B,TCI_G,TCI_R,WVP +0,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_ImageCollection/test_outliers_with_sigma.csv b/tests/test_ImageCollection/test_outliers_with_sigma.csv new file mode 100644 index 00000000..7abbfc1a --- /dev/null +++ b/tests/test_ImageCollection/test_outliers_with_sigma.csv @@ -0,0 +1,2 @@ +,AOT,AOT_outlier,B1,B11,B11_outlier,B12,B12_outlier,B1_outlier,B2,B2_outlier,B3,B3_outlier,B4,B4_outlier,B5,B5_outlier,B6,B6_outlier,B7,B7_outlier,B8,B8A,B8A_outlier,B8_outlier,B9,B9_outlier,MSK_CLDPRB,MSK_CLDPRB_outlier,MSK_SNWPRB,MSK_SNWPRB_outlier,QA10,QA10_outlier,QA20,QA20_outlier,QA60,QA60_outlier,SCL,SCL_outlier,TCI_B,TCI_B_outlier,TCI_G,TCI_G_outlier,TCI_R,TCI_R_outlier,WVP,WVP_outlier +0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/tests/test_ImageCollection/test_pan_sharpen.csv b/tests/test_ImageCollection/test_pan_sharpen.csv new file mode 100644 index 00000000..8ad930bc --- /dev/null +++ b/tests/test_ImageCollection/test_pan_sharpen.csv @@ -0,0 +1,2 @@ +,B2,B3,B4,B5,B6,B7 +0,0.42341190228971326,0.39605810178360285,0.39928781304364891,0.56361231196164929,0.42966321799378288,0.36453387368774781 diff --git a/tests/test_ImageCollection/test_plot_dates_by_bands.png b/tests/test_ImageCollection/test_plot_dates_by_bands.png new file mode 100644 index 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zz(jrjfD!Dq+0K}A@CW4`8F|^#%4%cyc^$*eo7uxFUWW_JaQFj2`9ZYv+Gve#b3G+m z^j{18Kb_*(sPgjiB-lxj#%fUAs|-6VxTubUQ86({32NY{PdC8gy$o9j7rQ!Bgh6kL z%)JK>8i9&EK0p5&+Bx#_^CRfynp5h6f&v?7XK-59=S?AVLIgf0Hy@u2n9?6(V{H#M zjeqs^89?}oQtX8QViS6uc_Q5NF^@f(FlxacOO%x8&vJ)Vq}R}vHo<{`45;(o+uPgU ze*Adn>+8!REF3#DJlyviS^}~mha4C;{(gQvu!-QI`uh5^a-DaVpJGZt{OX1A!NZ3S z$>`{m;9x?G4GatnhlL-EC5~>lai_EJNSS&-*Ho$V0V)+>Q@!!_9vG@2Op0;$0xr^# ze_&>0d<#Z22y>KSB3ccXhQeaIDmH{tWIPhU3DF9>p1(Z;Gd^|pZn9x9- zm$jM3}k|-YhU|4ie*VpYKV&_wb<%v^v;Cgx!Lp x7ORsIP9OenHU#wloISentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging\ + \ mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring\ + \ of vegetation, soil and water cover,\nas well as observation of inland waterways\ + \ and coastal areas.

The Sentinel-2 L2 data are downloaded from scihub.\ + \ They were\ncomputed by running sen2cor. WARNING: ESA did not produce L2 data\n\ + for all L1 assets, and earlier L2 coverage is not global.

The assets contain\n\ + 12 UINT16 spectral bands representing SR scaled by 10000 (unlike in L1 data,\n\ + there is no B10). There are also several more L2-specific bands (see band\nlist\ + \ for details). See the\nSentinel-2 User Handbook\nfor details. In addition, three QA bands are present\ + \ where one\n(QA60) is a bitmask band with cloud mask information. For more\n\ + details, see the full explanation of how cloud masks are computed.

EE asset\ + \ ids for Sentinel-2 L2 assets have the following format:\nCOPERNICUS/S2_SR/20151128T002653_20151128T102149_T56MNN.\ + \ Here the\nfirst numeric part represents the sensing date and time, the\nsecond\ + \ numeric part represents the product generation date and\ntime, and the final\ + \ 6-character string is a unique granule identifier\nindicating its UTM grid reference\ + \ (see MGRS).

Clouds can be removed by using\nCOPERNICUS/S2_CLOUD_PROBABILITY.\nSee\nthis tutorial\nexplaining how to apply the cloud mask.

For more details\ + \ on Sentinel-2 radiometric resolution, see this page.

Provider: European Union/ESA/Copernicus

Revisit Interval
\n 5\ + \ days\n

BandsDescription
Name
B1

Aerosols

B2

Blue

B3

Green

B4

Red

B5

Red\ + \ Edge 1

B6

Red Edge 2

B7

Red\ + \ Edge 3

B8

NIR

B8A

Red\ + \ Edge 4

B9

Water vapor

B11

SWIR\ + \ 1

B12

SWIR 2

AOT

Aerosol\ + \ Optical Thickness

WVP

Water Vapor Pressure.\ + \ The height the water would occupy if the vapor were condensed into\nliquid and\ + \ spread evenly across the column.

SCL

Scene Classification\ + \ Map (The "No Data" value of 0 is masked out)

TCI_R

True\ + \ Color Image, Red channel

TCI_G

True Color Image,\ + \ Green channel

TCI_B

True Color Image, Blue channel

MSK_CLDPRB

Cloud\ + \ Probability Map (missing in some products)

MSK_SNWPRB

Snow\ + \ Probability Map (missing in some products)

QA10

Always\ + \ empty

QA20

Always empty

QA60

Cloud\ + \ mask

\n Bitmask for QA60\n
  • \n \ + \ Bit 10: Opaque clouds\n
    • 0: No opaque clouds
    • 1: Opaque\ + \ clouds present
  • \n Bit 11: Cirrus clouds\n
    • 0:\ + \ No cirrus clouds
    • 1: Cirrus clouds present

Image\ + \ Properties
NameTypeDescription
AOT_RETRIEVAL_ACCURACYDOUBLE

Accuracy\ + \ of Aerosol Optical thickness model

CLOUDY_PIXEL_PERCENTAGEDOUBLE

Granule-specific\ + \ cloudy pixel percentage taken from the original metadata

CLOUD_COVERAGE_ASSESSMENTDOUBLE

Cloudy\ + \ pixel percentage for the whole archive that\ncontains this granule. Taken from\ + \ the original metadata

CLOUDY_SHADOW_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as cloud shadow

DARK_FEATURES_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as dark features or shadows

DATASTRIP_IDSTRING

Unique\ + \ identifier of the datastrip Product Data Item (PDI)

DATATAKE_IDENTIFIERSTRING

Uniquely\ + \ identifies a given Datatake. The ID contains\nthe Sentinel-2 satellite, start\ + \ date and time, absolute orbit\nnumber, and processing baseline.

DATATAKE_TYPESTRING

MSI\ + \ operation mode

DEGRADED_MSI_DATA_PERCENTAGEDOUBLE

Percentage\ + \ of degraded MSI and ancillary data

FORMAT_CORRECTNESSSTRING

Synthesis\ + \ of the On-Line Quality Control (OLQC) checks\nperformed at granule (Product_Syntax)\ + \ and datastrip (Product\nSyntax and DS_Consistency) levels

GENERAL_QUALITYSTRING

Synthesis\ + \ of the OLQC checks performed at the datastrip level (Relative_Orbit_Number)

GENERATION_TIMEDOUBLE

Product\ + \ generation time

GEOMETRIC_QUALITYSTRING

Synthesis\ + \ of the OLQC checks performed at the datastrip level (Attitude_Quality_Indicator)

GRANULE_IDSTRING

Unique\ + \ identifier of the granule PDI (PDI_ID)

HIGH_PROBA_CLOUDS_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as high probability clouds

MEAN_INCIDENCE_AZIMUTH_ANGLE_B1DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B1 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B2DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B2 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B3DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B3 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B4DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B4 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B5DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B5 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B6DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B6 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B7DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B7 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B8DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B8 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B8ADOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B8a and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B9DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B9 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B10DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B10 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B11DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B11 and for\ + \ all detectors

MEAN_INCIDENCE_AZIMUTH_ANGLE_B12DOUBLE

Mean\ + \ value containing viewing incidence azimuth angle average for band B12 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B1DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B1 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B2DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B2 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B3DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B3 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B4DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B4 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B5DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B5 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B6DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B6 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B7DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B7 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B8DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B8 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B8ADOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B8a and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B9DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B9 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B10DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B10 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B11DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B11 and for\ + \ all detectors

MEAN_INCIDENCE_ZENITH_ANGLE_B12DOUBLE

Mean\ + \ value containing viewing incidence zenith angle average for band B12 and for\ + \ all detectors

MEAN_SOLAR_AZIMUTH_ANGLEDOUBLE

Mean\ + \ value containing sun azimuth angle average for all bands and detectors

MEAN_SOLAR_ZENITH_ANGLEDOUBLE

Mean\ + \ value containing sun zenith angle average for all bands and detectors

MEDIUM_PROBA_CLOUDS_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as medium probability clouds

MGRS_TILESTRING

US-Military\ + \ Grid Reference System (MGRS) tile

NODATA_PIXEL_PERCENTAGEDOUBLE

Percentage\ + \ of No Data pixels

NOT_VEGETATED_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as non-vegetated

PROCESSING_BASELINESTRING

Configuration\ + \ baseline used at the time of the product\ngeneration in terms of processor software\ + \ version and major Ground\nImage Processing Parameters (GIPP) version

PRODUCT_IDSTRING

The\ + \ full id of the original Sentinel-2 product

RADIATIVE_TRANSFER_ACCURACYDOUBLE

Accuracy\ + \ of radiative transfer model

RADIOMETRIC_QUALITYSTRING

Based\ + \ on the OLQC reports contained in the Datastrips/QI_DATA with RADIOMETRIC_QUALITY\ + \ checklist name

REFLECTANCE_CONVERSION_CORRECTIONDOUBLE

Earth-Sun\ + \ distance correction factor

SATURATED_DEFECTIVE_PIXEL_PERCENTAGEDOUBLE

Percentage\ + \ of saturated or defective pixels

SENSING_ORBIT_DIRECTIONSTRING

Imaging\ + \ orbit direction

SENSING_ORBIT_NUMBERDOUBLE

Imaging\ + \ orbit number

SENSOR_QUALITYSTRING

Synthesis\ + \ of the OLQC checks performed at granule\n(Missing_Lines, Corrupted_ISP, and\ + \ Sensing_Time) and datastrip\n(Degraded_SAD and Datation_Model) levels

SOLAR_IRRADIANCE_B1DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B1

SOLAR_IRRADIANCE_B2DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B2

SOLAR_IRRADIANCE_B3DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B3

SOLAR_IRRADIANCE_B4DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B4

SOLAR_IRRADIANCE_B5DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B5

SOLAR_IRRADIANCE_B6DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B6

SOLAR_IRRADIANCE_B7DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B7

SOLAR_IRRADIANCE_B8DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B8

SOLAR_IRRADIANCE_B8ADOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B8a

SOLAR_IRRADIANCE_B9DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B9

SOLAR_IRRADIANCE_B10DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B10

SOLAR_IRRADIANCE_B11DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B11

SOLAR_IRRADIANCE_B12DOUBLE

Mean\ + \ solar exoatmospheric irradiance for band B12

SNOW_ICE_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as snow or ice

SPACECRAFT_NAMESTRING

Sentinel-2\ + \ spacecraft name: Sentinel-2A, Sentinel-2B

THIN_CIRRUS_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as thin cirrus clouds

UNCLASSIFIED_PERCENTAGEDOUBLE

Percentage\ + \ of unclassified pixels

VEGETATION_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as vegetation

WATER_PERCENTAGEDOUBLE

Percentage\ + \ of pixels classified as water

WATER_VAPOUR_RETRIEVAL_ACCURACYDOUBLE

Declared\ + \ accuracy of the Water Vapor model

Terms of Use

The\ + \ use of Sentinel data is governed by the Copernicus\nSentinel Data Terms and Conditions.

" + keywords: + - copernicus + - esa + - eu + - msi + - reflectance + - sentinel + - sr + period: 0 + product_tags: + - msi + - sr + - reflectance + provider: European Union/ESA/Copernicus + provider_url: https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a + sample: https://mw1.google.com/ges/dd/images/COPERNICUS_S2_SR_sample.png + source_tags: + - eu + - esa + - copernicus + - sentinel + tags: + - copernicus + - esa + - eu + - msi + - reflectance + - sentinel + - sr + thumb: https://mw1.google.com/ges/dd/images/COPERNICUS_S2_SR_thumb.png + title: 'Sentinel-2 MSI: MultiSpectral Instrument, Level-2A' + type_name: ImageCollection + visualization_0_bands: B4,B3,B2 + visualization_0_max: '3000.0' + visualization_0_min: '0.0' + visualization_0_name: RGB +coords: + lat: + attrs: {} + dims: + - lat + dtype: float64 + shape: + - 180 + lon: + attrs: {} + dims: + - lon + dtype: float64 + shape: + - 360 + time: + attrs: {} + dims: + - time + dtype: datetime64[ns] + shape: + - 2449 +data_vars: + B1: + attrs: + crs: EPSG:4326 + crs_transform: + - 60 + - 0 + - 699960 + - 0 + - -60 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 1830 + - 1830 + id: B1 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B11: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B11 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B12: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B12 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B2: + attrs: + crs: EPSG:4326 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 10980 + - 10980 + id: B2 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B3: + attrs: + crs: EPSG:4326 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 10980 + - 10980 + id: B3 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B4: + attrs: + crs: EPSG:4326 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 10980 + - 10980 + id: B4 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B5: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B5 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B6: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B6 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B7: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B7 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B8: + attrs: + crs: EPSG:4326 + crs_transform: + - 10 + - 0 + - 699960 + - 0 + - -10 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 10980 + - 10980 + id: B8 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B8A: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 5490 + - 5490 + id: B8A + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + B9: + attrs: + crs: EPSG:4326 + crs_transform: + - 60 + - 0 + - 699960 + - 0 + - -60 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 65535}' + dimensions: + - 1830 + - 1830 + id: B9 + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 + SCL: + attrs: + crs: EPSG:4326 + crs_transform: + - 20 + - 0 + - 699960 + - 0 + - -20 + - 9800020 + data_type: '{''type'': ''PixelType'', ''precision'': ''int'', ''min'': 0, ''max'': + 255}' + dimensions: + - 5490 + - 5490 + id: SCL + dims: + - time + - lon + - lat + dtype: float32 + shape: + - 2449 + - 360 + - 180 +dims: + lat: 180 + lon: 360 + time: 2449 diff --git a/tests/test_ImageCollection/test_validPixel.csv b/tests/test_ImageCollection/test_validPixel.csv new file mode 100644 index 00000000..1c1f49db --- /dev/null +++ b/tests/test_ImageCollection/test_validPixel.csv @@ -0,0 +1,2 @@ +,pct_valid,valid +0,5.4298642533936654,12 diff --git a/tests/test_ImageCollection/test_valid_pixels.csv b/tests/test_ImageCollection/test_valid_pixels.csv new file mode 100644 index 00000000..2a50ff96 --- /dev/null +++ b/tests/test_ImageCollection/test_valid_pixels.csv @@ -0,0 +1,2 @@ +,pct_valid,valid +0,100,12 diff --git a/tests/test_Integer.py b/tests/test_Integer.py new file mode 100644 index 00000000..8e7f4933 --- /dev/null +++ b/tests/test_Integer.py @@ -0,0 +1,15 @@ +"""Test the Integer placeholder object.""" + +import ee +import pytest + + +class TestInteger: + """Test the Integer placeholder class.""" + + def test_init(self): + with pytest.raises(NotImplementedError): + ee.Integer() + + def test_name(self): + assert ee.Integer.__name__ == "Integer" diff --git a/tests/test_Join.py b/tests/test_Join.py new file mode 100644 index 00000000..d4944e0c --- /dev/null +++ b/tests/test_Join.py @@ -0,0 +1,28 @@ +"""Test the ``Join`` class.""" + +import ee +import pytest + + +class TestByProperty: + """Test the ``byProperty`` method.""" + + def test_by_property(self, fc1, fc2, data_regression): + joined = ee.Join.geetools.byProperty(fc1, fc2, "id") + data_regression.check(joined.getInfo()) + + def test_by_property_outer(self, fc1, fc2, data_regression): + joined = ee.Join.geetools.byProperty(fc1, fc2, "id", outer=True) + data_regression.check(joined.getInfo()) + + @pytest.fixture + def fc1(self): + point = ee.Geometry.Point([0, 0]) + prop1 = {"id": 1, "prop_from_fc1": "I am from fc1"} + return ee.FeatureCollection([ee.Feature(point, prop1)]) + + @pytest.fixture + def fc2(self): + point = ee.Geometry.Point([0, 0]) + prop2 = {"id": 1, "prop_from_fc2": "I am from fc2"} + return ee.FeatureCollection([ee.Feature(point, prop2)]) diff --git a/tests/test_Join/test_by_property.yml b/tests/test_Join/test_by_property.yml new file mode 100644 index 00000000..dc663ee8 --- /dev/null +++ b/tests/test_Join/test_by_property.yml @@ -0,0 +1,14 @@ +columns: {} +features: +- geometry: + coordinates: + - 0 + - 0 + type: Point + id: '0' + properties: + id: 1 + prop_from_fc1: I am from fc1 + prop_from_fc2: I am from fc2 + type: Feature +type: FeatureCollection diff --git a/tests/test_Join/test_by_property_outer.yml b/tests/test_Join/test_by_property_outer.yml new file mode 100644 index 00000000..dc663ee8 --- /dev/null +++ b/tests/test_Join/test_by_property_outer.yml @@ -0,0 +1,14 @@ +columns: {} +features: +- geometry: + coordinates: + - 0 + - 0 + type: Point + id: '0' + properties: + id: 1 + prop_from_fc1: I am from fc1 + prop_from_fc2: I am from fc2 + type: Feature +type: FeatureCollection diff --git a/tests/test_Join/test_deprecated_join.yml b/tests/test_Join/test_deprecated_join.yml new file mode 100644 index 00000000..dc663ee8 --- /dev/null +++ b/tests/test_Join/test_deprecated_join.yml @@ -0,0 +1,14 @@ +columns: {} +features: +- geometry: + coordinates: + - 0 + - 0 + type: Point + id: '0' + properties: + id: 1 + prop_from_fc1: I am from fc1 + prop_from_fc2: I am from fc2 + type: Feature +type: FeatureCollection diff --git a/tests/test_List.py b/tests/test_List.py new file mode 100644 index 00000000..415a2446 --- /dev/null +++ b/tests/test_List.py @@ -0,0 +1,115 @@ +"""Test the List class methods.""" + +import ee + + +class TestProduct: + """Test the product method.""" + + def test_product_with_same_type(self, letter_list, data_regression): + product_list = letter_list.geetools.product(letter_list) + data_regression.check(product_list.getInfo()) + + def test_product_with_different_type(self, letter_list, int_list, data_regression): + product_list = letter_list.geetools.product(int_list) + data_regression.check(product_list.getInfo()) + + +class TestComplement: + """Test the complement method.""" + + def test_complement_with_same_type(self, letter_list, data_regression): + complement_list = letter_list.geetools.complement(letter_list) + data_regression.check(complement_list.getInfo()) + + def test_complement_with_different_type(self, letter_list, int_list, data_regression): + complement_list = letter_list.geetools.complement(int_list) + data_regression.check(complement_list.getInfo()) + + +class TestIntersection: + """Test the intersection method.""" + + def test_intersection_with_same_type(self, letter_list, data_regression): + intersection_list = letter_list.geetools.intersection(letter_list) + data_regression.check(intersection_list.getInfo()) + + def test_intersection_with_different_type(self, letter_list, int_list, data_regression): + intersection_list = letter_list.geetools.intersection(int_list) + data_regression.check(intersection_list.getInfo()) + + +class TestUnion: + """Test the union method.""" + + def test_union_with_duplicate(self, letter_list, data_regression): + union_list = letter_list.geetools.union(letter_list) + data_regression.check(union_list.getInfo()) + + def test_union_without_dupplicates(self, letter_list, int_list, data_regression): + union_list = letter_list.geetools.union(int_list) + data_regression.check(union_list.getInfo()) + + +class TestDelete: + """Test the delete method.""" + + def test_delete(self, letter_list, data_regression): + deleted_list = letter_list.geetools.delete(1) + data_regression.check(deleted_list.getInfo()) + + +class TestSequence: + """Test the sequence method.""" + + def test_sequence(self): + seq = ee.List.geetools.sequence(1, 10) + assert seq.getInfo() == list(range(1, 11)) + + def test_sequence_with_step(self): + seq = ee.List.geetools.sequence(1, 10, 2) + assert seq.getInfo() == [*list(range(1, 11, 2)), 10] + + def test_sequence_with_uneven_step(self): + seq = ee.List.geetools.sequence(1, 10, 3) + assert seq.getInfo() == [*list(range(1, 10, 3)), 10] + + def test_sequence_with_0_step(self): + seq = ee.List.geetools.sequence(1, 10, 0) + assert seq.getInfo() == list(range(1, 11)) + + +class TestReplaceMany: + """Test the replaceMany method.""" + + def test_replace_many(self, letter_list, data_regression): + replaced_list = letter_list.geetools.replaceMany({"a": "foo", "c": "bar"}) + data_regression.check(replaced_list.getInfo()) + + +class TestZip: + """Test the zip method.""" + + def test_zip(self, letter_list, data_regression): + zipped_list = ee.List([letter_list, letter_list]).geetools.zip() + data_regression.check(zipped_list.getInfo()) + + +class TestToStrings: + """Test the toStrings method.""" + + def test_to_strings(self, mix_list, data_regression): + strings = mix_list.geetools.toStrings() + data_regression.check(strings.getInfo()) + + +class TestJoin: + """Test the join method.""" + + def test_join(self, mix_list): + formatted = mix_list.geetools.join() + assert formatted.getInfo() == "a, 1, Image" + + def test_join_with_separator(self, mix_list): + formatted = mix_list.geetools.join(separator="; ") + assert formatted.getInfo() == "a; 1; Image" diff --git a/tests/test_List/test_complement_with_different_type.yml b/tests/test_List/test_complement_with_different_type.yml new file mode 100644 index 00000000..1606269f --- /dev/null +++ b/tests/test_List/test_complement_with_different_type.yml @@ -0,0 +1,5 @@ +- a +- b +- c +- 1 +- 2 diff --git a/tests/test_List/test_complement_with_same_type.yml b/tests/test_List/test_complement_with_same_type.yml new file mode 100644 index 00000000..fe51488c --- /dev/null +++ b/tests/test_List/test_complement_with_same_type.yml @@ -0,0 +1 @@ +[] diff --git a/tests/test_List/test_delete.yml b/tests/test_List/test_delete.yml new file mode 100644 index 00000000..a7cc122f --- /dev/null +++ b/tests/test_List/test_delete.yml @@ -0,0 +1,2 @@ +- a +- c diff --git a/tests/test_List/test_intersection_with_different_type.yml b/tests/test_List/test_intersection_with_different_type.yml new file mode 100644 index 00000000..fe51488c --- /dev/null +++ b/tests/test_List/test_intersection_with_different_type.yml @@ -0,0 +1 @@ +[] diff --git a/tests/test_List/test_intersection_with_same_type.yml b/tests/test_List/test_intersection_with_same_type.yml new file mode 100644 index 00000000..d729eb69 --- /dev/null +++ b/tests/test_List/test_intersection_with_same_type.yml @@ -0,0 +1,3 @@ +- a +- b +- c diff --git a/tests/test_List/test_product_with_different_type.yml b/tests/test_List/test_product_with_different_type.yml new file mode 100644 index 00000000..b41eee5c --- /dev/null +++ b/tests/test_List/test_product_with_different_type.yml @@ -0,0 +1,6 @@ +- a1 +- a2 +- b1 +- b2 +- c1 +- c2 diff --git a/tests/test_List/test_product_with_same_type.yml b/tests/test_List/test_product_with_same_type.yml new file mode 100644 index 00000000..c4c0d3f6 --- /dev/null +++ b/tests/test_List/test_product_with_same_type.yml @@ -0,0 +1,9 @@ +- aa +- ab +- ac +- ba +- bb +- bc +- ca +- cb +- cc diff --git a/tests/test_List/test_replace_many.yml b/tests/test_List/test_replace_many.yml new file mode 100644 index 00000000..de49b690 --- /dev/null +++ b/tests/test_List/test_replace_many.yml @@ -0,0 +1,3 @@ +- foo +- b +- bar diff --git a/tests/test_List/test_to_strings.yml b/tests/test_List/test_to_strings.yml new file mode 100644 index 00000000..ead42ea7 --- /dev/null +++ b/tests/test_List/test_to_strings.yml @@ -0,0 +1,3 @@ +- a +- '1' +- Image diff --git a/tests/test_List/test_union_with_duplicate.yml b/tests/test_List/test_union_with_duplicate.yml new file mode 100644 index 00000000..d729eb69 --- /dev/null +++ b/tests/test_List/test_union_with_duplicate.yml @@ -0,0 +1,3 @@ +- a +- b +- c diff --git a/tests/test_List/test_union_without_dupplicates.yml b/tests/test_List/test_union_without_dupplicates.yml new file mode 100644 index 00000000..1606269f --- /dev/null +++ b/tests/test_List/test_union_without_dupplicates.yml @@ -0,0 +1,5 @@ +- a +- b +- c +- 1 +- 2 diff --git a/tests/test_List/test_zip.yml b/tests/test_List/test_zip.yml new file mode 100644 index 00000000..ff5bbe0c --- /dev/null +++ b/tests/test_List/test_zip.yml @@ -0,0 +1,6 @@ +- - a + - a +- - b + - b +- - c + - c diff --git a/tests/test_Number.py b/tests/test_Number.py new file mode 100644 index 00000000..0d4bd9cf --- /dev/null +++ b/tests/test_Number.py @@ -0,0 +1,21 @@ +"""test the Number class methods.""" + + +class TestTruncate: + """Test the truncate method.""" + + def test_truncate_with_default_decimals(self, number_instance): + truncated_number = number_instance.geetools.truncate() + assert truncated_number.getInfo() == 1234.56 + + def test_truncate_with_custom_decimals(self, number_instance): + truncated_number = number_instance.geetools.truncate(1) + assert truncated_number.getInfo() == 1234.5 + + def test_truncate_with_zero_decimals(self, number_instance): + truncated_number = number_instance.geetools.truncate(0) + assert truncated_number.getInfo() == 1234.0 + + def test_truncate_with_large_decimals(self, number_instance): + truncated_number = number_instance.geetools.truncate(5) + assert truncated_number.getInfo() == 1234.56785 diff --git a/tests/test_Profiler.py b/tests/test_Profiler.py new file mode 100644 index 00000000..c2d3298d --- /dev/null +++ b/tests/test_Profiler.py @@ -0,0 +1,16 @@ +"""Test the ee_profiler module.""" + +import ee + +import geetools # noqa: F401 + + +class TestProfiler: + """Test the Profiler class.""" + + def test_profiler(self): + """Test the Profiler class.""" + + with ee.geetools.Profiler() as p: + ee.Number(3.14).add(0.00159).getInfo() + assert [k for k in p.profile] == ["EECU-s", "PeakMem", "Count", "Description"] diff --git a/tests/test_String.py b/tests/test_String.py new file mode 100644 index 00000000..1f6e9741 --- /dev/null +++ b/tests/test_String.py @@ -0,0 +1,29 @@ +"""Test the String class methods.""" + +import ee + + +class TestEq: + """Test the eq method.""" + + def test_eq_with_same_string(self, string_instance): + eq_number = string_instance.geetools.eq("foo") + assert eq_number.getInfo() == 1 + + def test_eq_with_different_string(self, string_instance): + eq_number = string_instance.geetools.eq("bar") + assert eq_number.getInfo() == 0 + + +class TestFormat: + """test the format method.""" + + def test_format_with_dictionary(self, format_string_instance): + params = {"greeting": "Hello", "name": "bob"} + formatted_string = format_string_instance.geetools.format(params) + assert formatted_string.getInfo() == "Hello bob !" + + def test_with_number(self, format_string_instance): + params = {"greeting": "Hello", "name": ee.Number(1)} + formatted_string = format_string_instance.geetools.format(params) + assert formatted_string.getInfo() == "Hello 1 !" diff --git a/tests/test_batch/test_utils.py b/tests/test_batch/test_utils.py new file mode 100644 index 00000000..09b2ba57 --- /dev/null +++ b/tests/test_batch/test_utils.py @@ -0,0 +1,55 @@ +"""Test the utils module.""" + +from geetools import utils + + +class TestFormatDescription: + """Test the utils.format_description function.""" + + def test_valid_description(self): + """Test a valid description.""" + description = "This is a valid description 123.,:;_-" + result = utils.format_description(description) + assert result == "This_is_a_valid_description_123.,:;_-" + + def test_replacements(self): + """Test replacements.""" + description = "Testing / replacements ?!{}()" + result = utils.format_description(description) + assert result == "Testing_-_replacements_..::::" + + def test_long_description(self): + description = "A" * 150 + result = utils.format_description(description) + assert len(result) == 100 + + def test_unicode_characters(self): + description = "Unicode characters like é, ä, and ñ should be changed" + result = utils.format_description(description) + assert result == "Unicode_characters_like_e,_a,_and_n_should_be_changed" + + +class TestFormatAssetID: + """Test the utils.format_asset_id function.""" + + def test_valid_description(self): + """Test a valid description.""" + description = "This is a valid description 123.,:;_-" + result = utils.format_asset_id(description) + assert result == "This_is_a_valid_description_123_____-" + + def test_replacements(self): + """Test replacements.""" + description = "Testing / replacements ?!{}()" + result = utils.format_asset_id(description) + assert result == "Testing_-_replacements_______" + + def test_long_description(self): + description = "A" * 150 + result = utils.format_asset_id(description) + assert len(result) == 150 + + def test_unicode_characters(self): + description = "Unicode characters like é, ä, and ñ should be changed" + result = utils.format_asset_id(description) + assert result == "Unicode_characters_like_e__a__and_n_should_be_changed" diff --git a/tests/test_deprecated.py b/tests/test_deprecated.py new file mode 100644 index 00000000..9a7fae7a --- /dev/null +++ b/tests/test_deprecated.py @@ -0,0 +1,65 @@ +"""Test all the deprecated methods that have not been kept in the new implementation.""" + +import pytest + +import geetools + + +class TestImageCollection: + """Test the deprecated_imagecollection module.""" + + def test_linear_function_band(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.linearFunctionBand(None, None, None, None) + + def test_linear_function_property(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.linearFunctionProperty(None, None, None, None) + + def linear_interpolation_property(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.linearInterpolationProperty(None, None, None, None) + + def test_gauss_function_band(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.gaussFunctionBand(None, None, None, None, None) + + def test_gauss_function_property(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.gaussFunctionProperty(None, None, None, None, None) + + def testnormal_distribution_property(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.normalDistributionProperty(None, None, None, None, None) + + def test_normal_distribution_band(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.normalDistributionBand(None, None, None, None, None) + + def test_moving_average(self): + with pytest.raises(NotImplementedError): + geetools.imagecollection.moving_average(None) + + +class TestAlgorithm: + """Test the deprecated_algorithms module.""" + + def test_pansharpenkernel(self): + with pytest.raises(NotImplementedError): + geetools.algorithms.pansharpenKernel(None, None) + + def test_pansharpenihsFusion(self): + with pytest.raises(NotImplementedError): + geetools.algorithms.pansharpenIhsFusion(None) + + +class TestComposite: + """Test the deprecated_composite module.""" + + def test_max(self, s2_sr): + with pytest.deprecated_call(): + geetools.composite.max(s2_sr) + + def test_medoidScore(self, s2_sr): + with pytest.raises(NotImplementedError): + geetools.composite.medoidScore(s2_sr) diff --git a/tests/test_deprecated/test_deprecated_cast_image.yml b/tests/test_deprecated/test_deprecated_cast_image.yml new file mode 100644 index 00000000..461ce28f --- /dev/null +++ b/tests/test_deprecated/test_deprecated_cast_image.yml @@ -0,0 +1 @@ +constant: 1 diff --git a/tests/test_deprecated/test_merge_geometry.yml b/tests/test_deprecated/test_merge_geometry.yml new file mode 100644 index 00000000..cda39444 --- /dev/null +++ b/tests/test_deprecated/test_merge_geometry.yml @@ -0,0 +1,394 @@ +coordinates: +- - - - -72.69953001836296 + - -2.800112889120924 + - 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20210102T150719_20210102T150942_T18MYC_B4 +- 20210102T150719_20210102T150942_T18MYC_B5 +- 20210102T150719_20210102T150942_T18MYC_B6 +- 20210102T150719_20210102T150942_T18MYC_B7 +- 20210102T150719_20210102T150942_T18MYC_B8 +- 20210102T150719_20210102T150942_T18MYC_B8A +- 20210102T150719_20210102T150942_T18MYC_B9 +- 20210102T150719_20210102T150942_T18MYC_B11 +- 20210102T150719_20210102T150942_T18MYC_B12 +- 20210102T150719_20210102T150942_T18MYC_SCL +- 20210102T150719_20210102T150942_T18MYD_B1 +- 20210102T150719_20210102T150942_T18MYD_B2 +- 20210102T150719_20210102T150942_T18MYD_B3 +- 20210102T150719_20210102T150942_T18MYD_B4 +- 20210102T150719_20210102T150942_T18MYD_B5 +- 20210102T150719_20210102T150942_T18MYD_B6 +- 20210102T150719_20210102T150942_T18MYD_B7 +- 20210102T150719_20210102T150942_T18MYD_B8 +- 20210102T150719_20210102T150942_T18MYD_B8A +- 20210102T150719_20210102T150942_T18MYD_B9 +- 20210102T150719_20210102T150942_T18MYD_B11 +- 20210102T150719_20210102T150942_T18MYD_B12 +- 20210102T150719_20210102T150942_T18MYD_SCL +- 20210102T150719_20210102T150942_T18MZC_B1 +- 20210102T150719_20210102T150942_T18MZC_B2 +- 20210102T150719_20210102T150942_T18MZC_B3 +- 20210102T150719_20210102T150942_T18MZC_B4 +- 20210102T150719_20210102T150942_T18MZC_B5 +- 20210102T150719_20210102T150942_T18MZC_B6 +- 20210102T150719_20210102T150942_T18MZC_B7 +- 20210102T150719_20210102T150942_T18MZC_B8 +- 20210102T150719_20210102T150942_T18MZC_B8A +- 20210102T150719_20210102T150942_T18MZC_B9 +- 20210102T150719_20210102T150942_T18MZC_B11 +- 20210102T150719_20210102T150942_T18MZC_B12 +- 20210102T150719_20210102T150942_T18MZC_SCL diff --git a/tests/test_geetools.py b/tests/test_geetools.py deleted file mode 100644 index bb90f44f..00000000 --- a/tests/test_geetools.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Test the geetools package.""" - -import geetools - - -def test_hello_world(): - """Hello world test.""" - assert geetools.Hello().hello_world() == "hello world !" From 394910c536730854f108f2c93a7654f4daecd564 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 15:07:29 +0000 Subject: [PATCH 5/9] fix: rollback the documentation --- docs/_extension/api_admonition.py | 79 ++ docs/_extension/docstring.py | 60 ++ docs/_static/banner.html | 7 + docs/_static/custom.css | 44 +- docs/_static/logo.png | Bin 0 -> 467521 bytes docs/_static/logo.svg | 141 ++++ docs/_static/switcher.json | 32 + docs/_static/usage/plot/index/histogram.png | Bin 0 -> 34330 bytes docs/_static/usage/plot/index/hydroshed.png | Bin 0 -> 122068 bytes docs/_static/we-need-you.jpg | Bin 0 -> 24616 bytes ...age-credit.html => pypackage-credits.html} | 0 docs/_template/python/class.rst | 106 +++ docs/_template/python/function.rst | 23 + docs/_template/python/method.rst | 23 + docs/_template/python/module.rst | 180 +++++ docs/conf.py | 80 +- docs/contribute.rst | 5 - docs/index.rst | 77 +- docs/setup/author.rst | 4 + docs/setup/contribute.rst | 5 + docs/setup/index.rst | 46 ++ docs/setup/inspiration.rst | 20 + docs/setup/install.rst | 42 + docs/setup/layout.rst | 383 +++++++++ docs/setup/license.rst | 4 + docs/setup/migration.rst | 51 ++ docs/setup/pattern.rst | 70 ++ docs/setup/quickstart.rst | 75 ++ docs/usage.rst | 4 - docs/usage/asset.ipynb | 617 ++++++++++++++ docs/usage/export.ipynb | 186 +++++ docs/usage/index.rst | 47 ++ docs/usage/plot/index.rst | 99 +++ docs/usage/plot/map-featurecollection.ipynb | 195 +++++ docs/usage/plot/map-image.ipynb | 211 +++++ docs/usage/plot/plot-featurecollection.ipynb | 665 +++++++++++++++ docs/usage/plot/plot-image.ipynb | 760 ++++++++++++++++++ docs/usage/plot/plot-imagecollection.ipynb | 707 ++++++++++++++++ docs/usage/profile.ipynb | 215 +++++ docs/usage/reduce.ipynb | 164 ++++ docs/usage/template.ipynb | 121 +++ geetools/__init__.py | 28 +- pyproject.toml | 2 +- 43 files changed, 5531 insertions(+), 47 deletions(-) create mode 100644 docs/_extension/api_admonition.py create mode 100644 docs/_extension/docstring.py create mode 100644 docs/_static/banner.html create mode 100644 docs/_static/logo.png create mode 100644 docs/_static/logo.svg create mode 100644 docs/_static/switcher.json create mode 100644 docs/_static/usage/plot/index/histogram.png create mode 100644 docs/_static/usage/plot/index/hydroshed.png create mode 100644 docs/_static/we-need-you.jpg rename docs/_template/{pypackage-credit.html => pypackage-credits.html} (100%) create mode 100644 docs/_template/python/class.rst create mode 100644 docs/_template/python/function.rst create mode 100644 docs/_template/python/method.rst create mode 100644 docs/_template/python/module.rst delete mode 100644 docs/contribute.rst create mode 100644 docs/setup/author.rst create mode 100644 docs/setup/contribute.rst create mode 100644 docs/setup/index.rst create mode 100644 docs/setup/inspiration.rst create mode 100644 docs/setup/install.rst create mode 100644 docs/setup/layout.rst create mode 100644 docs/setup/license.rst create mode 100644 docs/setup/migration.rst create mode 100644 docs/setup/pattern.rst create mode 100644 docs/setup/quickstart.rst delete mode 100644 docs/usage.rst create mode 100644 docs/usage/asset.ipynb create mode 100644 docs/usage/export.ipynb create mode 100644 docs/usage/index.rst create mode 100644 docs/usage/plot/index.rst create mode 100644 docs/usage/plot/map-featurecollection.ipynb create mode 100644 docs/usage/plot/map-image.ipynb create mode 100644 docs/usage/plot/plot-featurecollection.ipynb create mode 100644 docs/usage/plot/plot-image.ipynb create mode 100644 docs/usage/plot/plot-imagecollection.ipynb create mode 100644 docs/usage/profile.ipynb create mode 100644 docs/usage/reduce.ipynb create mode 100644 docs/usage/template.ipynb diff --git a/docs/_extension/api_admonition.py b/docs/_extension/api_admonition.py new file mode 100644 index 00000000..88800204 --- /dev/null +++ b/docs/_extension/api_admonition.py @@ -0,0 +1,79 @@ +"""A directive to generate an API admonition.""" + +from __future__ import annotations + +from typing import ClassVar + +from docutils import nodes +from docutils.parsers.rst import directives +from docutils.parsers.rst.directives.admonitions import BaseAdmonition +from sphinx.application import Sphinx +from sphinx.util import logging +from sphinx.util.docutils import SphinxDirective +from sphinx.writers.html5 import HTML5Translator + +logger = logging.getLogger(__name__) + + +class api_node(nodes.Admonition, nodes.Element): + pass + + +def visit_api_node(self: HTML5Translator, node: api_node) -> None: + """Visit the api node.""" + self.visit_admonition(node) + + +def depart_api_node(self: HTML5Translator, node: api_node) -> None: + """Depart from the api node.""" + self.depart_admonition(node) + + +class APIAdmonitionDirective(BaseAdmonition, SphinxDirective): + """An API entry, displayed (if configured) in the form of an admonition.""" + + node_class = api_node + has_content = True + required_arguments = 0 + optional_arguments = 0 + final_argument_whitespace = False + option_spec: ClassVar = { + "class": directives.class_option, + "name": directives.unchanged, + } + + def run(self) -> list[nodes.Node]: + """Run the directive.""" + if not self.options.get("class"): + self.options["class"] = ["admonition-api"] + + (api,) = super().run() + if isinstance(api, nodes.system_message): + return [api] + elif isinstance(api, api_node): + api.insert(0, nodes.title(text="See API")) + api["docname"] = self.env.docname + self.add_name(api) + self.set_source_info(api) + self.state.document.note_explicit_target(api) + return [api] + else: + raise RuntimeError # never reached here + + +def setup(app: Sphinx) -> dict[str, object]: + """Add custom configuration to sphinx app. + + Args: + app: the Sphinx application + + Returns: + the 2 parallel parameters set to ``True``. + """ + app.add_directive("api", APIAdmonitionDirective) + app.add_node(api_node, html=(visit_api_node, depart_api_node)) + + return { + "parallel_read_safe": True, + "parallel_write_safe": True, + } diff --git a/docs/_extension/docstring.py b/docs/_extension/docstring.py new file mode 100644 index 00000000..f6de3127 --- /dev/null +++ b/docs/_extension/docstring.py @@ -0,0 +1,60 @@ +"""A docstring role to read the docstring from a Python method.""" + +from __future__ import annotations + +import inspect +from functools import reduce + +import ee +from docutils import nodes +from sphinx.application import Sphinx +from sphinx.util import logging +from sphinx.util.docutils import SphinxRole + +import geetools # noqa: F401 + +logger = logging.getLogger(__name__) + + +class DocstringRole(SphinxRole): + """The docstring role interpreter.""" + + def run(self) -> tuple[list[nodes.Node], list[str]]: + """Setup the role in the builder context.""" + # retrieve the environment from the node members + env = self.inliner.document.settings.env + builder = env.app.builder + current_doc = self.env.docname + + # extract the members we try to reach from the ee lib + members = self.text.split(".")[1:] + + # reach the final object using getattr. It will allow us to access the complete information + # of the object (docstring, qualname, name, source module) + try: + o = reduce(getattr, members, ee) + modules = inspect.getmodule(o).__name__.split(".") + except Exception as e: + logger.warning(f"Failed to retrieve {members}: {e}") + return [nodes.Text(f"{self.text} not found")], [] + + # create the docstring node + docstring = nodes.Text(f": {o.__doc__.splitlines()[0]}") + + # create a complete link to the object using the url and the name of the object + target_doc = f"autoapi/{'/'.join(modules)}/{o.__qualname__}" + refuri = builder.get_relative_uri(current_doc, target_doc) + inline_node = nodes.literal(members[-1], members[-1], classes=["py", "py-meth"]) + link = nodes.reference("", "", inline_node, internal=True, refuri=refuri) + + return [link, docstring], [] + + +def setup(app: Sphinx) -> dict[str, object]: + """Add custom configuration to sphinx application.""" + app.add_role("docstring", DocstringRole()) + + return { + "parallel_read_safe": True, + "parallel_write_safe": True, + } diff --git a/docs/_static/banner.html b/docs/_static/banner.html new file mode 100644 index 00000000..55183f3a --- /dev/null +++ b/docs/_static/banner.html @@ -0,0 +1,7 @@ +
+ This is a community-supported extension. If you'd like to contribute, + check out our GitHub repository. + Your contributions are welcome! +
diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 0af9db66..77c3b154 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -1,6 +1,48 @@ -/* add dollar sign in console code-block */ +/******************************************************************************* +* add dollar sign in console code-block +*/ div.highlight-console pre span.go::before { content: "$"; margin-right: 10px; margin-left: 5px; } + +/******************************************************************************* +* Create a custom api admonition +*/ +div.admonition.admonition-api > .admonition-title::after { + content: "\f121"; /* the fa-code icon */ +} + +/******************************************************************************* +* custom sizing +*/ +.bd-main .bd-content .bd-article-container { + max-width: 100%; /* default is 60em */ +} + +.bd-page-width { + max-width: 110rem; /* default is 88rem */ +} + +/******************************************************************************* +* custom article footer rendering +*/ +footer.bd-footer-article { + background-color: transparent; + border-top: 1px solid var(--pst-color-border); + margin-top: 2em; +} + +.bd-footer-article .footer-article-items { + flex-direction: row; +} + +.bd-footer-article .footer-article-items .footer-article-item { + flex-grow: 1; +} + +.bd-footer-article .last-updated { + color: var(--pst-color-text-muted); + text-align: right; +} diff --git a/docs/_static/logo.png b/docs/_static/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..8fcf87fc8fa8210910d5f4442b59acfc6907125d GIT binary patch literal 467521 zcmeFZ`9IX(8$UcEOERQFme8iMjFNpTiXwziwjoLOeP6~>sf5a&Y)P_{JjSo#Qm>M>mItc@trH*I2(IyDUHp7gXHoC-MV;ucnp+~X81!QGUc?i+ zbGHxi(aRCshuLunV$>`gk6OLGR$C9~;B)@(um8IP|91!e|JVUslraXJtpz{CuuvN>afwNk)@6*)P 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zWkCNIR)W+yV=t-le1$vV^1VN%xT}3!I*pcujZLzRfdui*V=-}cWj&sv(9)ME%mTY)ed69+HX!^wtG)}+>V%; zDz*!isFZG3f^xsb6&Bmb(`afG+in)Pj~*xE_Vy3~)Q(mUW0M=bq7q92mD?<`vSa8B zO_d}K0DxfSi)6AL=S{A@Litv%KFcYONkIm`rVst1N*}O(3`I~zFPIc>O>1UvTwd4h z6|HyFe{56k6!v#1+Ryo9JS(z&TE53I0k~IlgX~F{d<+PrgF$@Ie1F>!EZUrOC|l#B yh7ob=Vr2|Sfq;^3p&?^O-`|7zXk#Qy-JinR0q literal 0 HcmV?d00001 diff --git a/docs/_template/pypackage-credit.html b/docs/_template/pypackage-credits.html similarity index 100% rename from docs/_template/pypackage-credit.html rename to docs/_template/pypackage-credits.html diff --git a/docs/_template/python/class.rst b/docs/_template/python/class.rst new file mode 100644 index 00000000..057e8232 --- /dev/null +++ b/docs/_template/python/class.rst @@ -0,0 +1,106 @@ +{% if obj.display %} + {% if is_own_page %} +:html_theme.sidebar_secondary.remove: + +{{ obj.id.split(".")[-1] }} +{{ "=" * obj.id.split(".")[-1] | length }} + + {% endif %} + {% set visible_children = obj.children|selectattr("display")|list %} + {% set own_page_children = visible_children|selectattr("type", "in", own_page_types)|list %} + {% if is_own_page and own_page_children %} +.. toctree:: + :hidden: + + {% for child in own_page_children %} + {{ child.include_path }} + {% endfor %} + + {% endif %} +.. py:{{ obj.type }}:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}{% if obj.args %}({{ obj.args }}){% endif %} + + {% for (args, return_annotation) in obj.overloads %} + {{ " " * (obj.type | length) }} {{ obj.short_name }}{% if args %}({{ args }}){% endif %} + + {% endfor %} + {% if obj.bases %} + {% if "show-inheritance" in autoapi_options %} + + Bases: {% for base in obj.bases %}{{ base|link_objs }}{% if not loop.last %}, {% endif %}{% endfor %} + {% endif %} + + + {% if "show-inheritance-diagram" in autoapi_options and obj.bases != ["object"] %} + .. autoapi-inheritance-diagram:: {{ obj.obj["full_name"] }} + :parts: 1 + {% if "private-members" in autoapi_options %} + :private-bases: + {% endif %} + + {% endif %} + {% endif %} + {% if obj.docstring %} + + {{ obj.docstring|indent(3) }} + {% endif %} + {% for obj_item in visible_children %} + {% if obj_item.type not in own_page_types %} + + {{ obj_item.render()|indent(3) }} + {% endif %} + {% endfor %} + {% if is_own_page and own_page_children %} + {% set visible_attributes = own_page_children|selectattr("type", "in", ("attribute", "property"))|list %} + {% if visible_attributes %} +Attributes +---------- + +.. autoapisummary:: + + {% for attribute in visible_attributes %} + {{ attribute.id }} + {% endfor %} + + + {% endif %} + {% set visible_exceptions = own_page_children|selectattr("type", "equalto", "exception")|list %} + {% if visible_exceptions %} +Exceptions +---------- + +.. autoapisummary:: + + {% for exception in visible_exceptions %} + {{ exception.id }} + {% endfor %} + + + {% endif %} + {% set visible_classes = own_page_children|selectattr("type", "equalto", "class")|list %} + {% if visible_classes %} +Classes +------- + +.. autoapisummary:: + + {% for klass in visible_classes %} + {{ klass.id }} + {% endfor %} + + + {% endif %} + {% set visible_methods = own_page_children|selectattr("type", "equalto", "method")|list %} + {% if visible_methods %} +Methods +------- + +.. autoapisummary:: + + {% for method in visible_methods %} + {{ method.id }} + {% endfor %} + + + {% endif %} + {% endif %} +{% endif %} diff --git a/docs/_template/python/function.rst b/docs/_template/python/function.rst new file mode 100644 index 00000000..2895d1da --- /dev/null +++ b/docs/_template/python/function.rst @@ -0,0 +1,23 @@ +{% if obj.display %} + {% if is_own_page %} +:html_theme.sidebar_secondary.remove: + +{{ obj.id.split(".")[-1] }} +{{ "=" * obj.id.split(".")[-1] | length }} + + {% endif %} +.. py:function:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %} + {% for (args, return_annotation) in obj.overloads %} + + {%+ if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %} + {% endfor %} + {% for property in obj.properties %} + + :{{ property }}: + {% endfor %} + + {% if obj.docstring %} + + {{ obj.docstring|indent(3) }} + {% endif %} +{% endif %} diff --git a/docs/_template/python/method.rst b/docs/_template/python/method.rst new file mode 100644 index 00000000..0b31a378 --- /dev/null +++ b/docs/_template/python/method.rst @@ -0,0 +1,23 @@ +{% if obj.display %} + {% if is_own_page %} +:html_theme.sidebar_secondary.remove: + +{{ obj.id.split(".")[-1] }} +{{ "=" * obj.id.split(".")[-1] | length }} + + {% endif %} +.. py:method:: {% if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ obj.args }}){% if obj.return_annotation is not none %} -> {{ obj.return_annotation }}{% endif %} + {% for (args, return_annotation) in obj.overloads %} + + {%+ if is_own_page %}{{ obj.id }}{% else %}{{ obj.short_name }}{% endif %}({{ args }}){% if return_annotation is not none %} -> {{ return_annotation }}{% endif %} + {% endfor %} + {% for property in obj.properties %} + + :{{ property }}: + {% endfor %} + + {% if obj.docstring %} + + {{ obj.docstring|indent(3) }} + {% endif %} +{% endif %} diff --git a/docs/_template/python/module.rst b/docs/_template/python/module.rst new file mode 100644 index 00000000..73d6194f --- /dev/null +++ b/docs/_template/python/module.rst @@ -0,0 +1,180 @@ +{% if obj.display %} + {% if is_own_page %} +:html_theme.sidebar_secondary.remove: + +{{ obj.id.split(".")[-1] }} +{{ "=" * obj.id.split(".")[-1] | length }} + +.. py:module:: {{obj.name}} + + {% if obj.docstring %} +.. autoapi-nested-parse:: + + {{obj.docstring|indent(3)}} + + {% endif %} + + {% block subpackages %} + {% set visible_subpackages = obj.subpackages|selectattr("display")|list %} + {% if visible_subpackages %} + +Subpackages +----------- + +.. toctree:: + :maxdepth: 1 + + {% for subpackage in visible_subpackages %} + {{subpackage.include_path}} + {% endfor %} + + {% endif %} + {% endblock %} + {% block submodules %} + {% set visible_submodules = obj.submodules|selectattr("display")|list %} + {% if visible_submodules %} + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + {% for submodule in visible_submodules %} + {{submodule.include_path}} + {% endfor %} + + + {% endif %} + {% endblock %} + {% block content %} + {% if obj.all is not none %} + {% set visible_children = obj.children|selectattr("short_name", "in", obj.all)|list %} + {% elif obj.type is equalto("package") %} + {% set visible_children = obj.children|selectattr("display")|list %} + {% else %} + {% set visible_children = obj.children|selectattr("display")|rejectattr("imported")|list %} + {% endif %} + {% if visible_children %} + {% set visible_attributes = visible_children|selectattr("type", "equalto", "data")|list %} + {% if visible_attributes %} + {% if "attribute" in own_page_types or "show-module-summary" in autoapi_options %} + +Attributes +---------- + + {% if "attribute" in own_page_types %} +.. toctree:: + :hidden: + + {% for attribute in visible_attributes %} + {{attribute.include_path}} + {% endfor %} + + {% endif %} +.. autoapisummary:: + + {% for attribute in visible_attributes %} + {{attribute.id}} + {% endfor %} + {% endif %} + + + {% endif %} + {% set visible_exceptions = visible_children|selectattr("type", "equalto", "exception")|list %} + {% if visible_exceptions %} + {% if "exception" in own_page_types or "show-module-summary" in autoapi_options %} +Exceptions +---------- + + {% if "exception" in own_page_types %} +.. toctree:: + :hidden: + + {% for exception in visible_exceptions %} + {{exception.include_path}} + {% endfor %} + + {% endif %} +.. autoapisummary:: + + {% for exception in visible_exceptions %} + {{exception.id}} + {% endfor %} + {% endif %} + + + {% endif %} + {% set visible_classes = visible_children|selectattr("type", "equalto", "class")|list %} + {% if visible_classes %} + {% if "class" in own_page_types or "show-module-summary" in autoapi_options %} +Classes +------- + + {% if "class" in own_page_types %} +.. toctree:: + :hidden: + + {% for klass in visible_classes %} + {{klass.include_path}} + {% endfor %} + + {% endif %} +.. autoapisummary:: + + {% for klass in visible_classes %} + {{klass.id}} + {% endfor %} + {% endif %} + + + {% endif %} + {% set visible_functions = visible_children|selectattr("type", "equalto", "function")|list %} + {% if visible_functions %} + {% if "function" in own_page_types or "show-module-summary" in autoapi_options %} +Functions +--------- + + {% if "function" in own_page_types %} +.. toctree:: + :hidden: + + {% for function in visible_functions %} + {{function.include_path}} + {% endfor %} + + {% endif %} +.. autoapisummary:: + + {% for function in visible_functions %} + {{function.id}} + {% endfor %} + {% endif %} + + + {% endif %} + {% set this_page_children = visible_children|rejectattr("type", "in", own_page_types)|list %} + {% if this_page_children %} +{{obj.type|title}} Contents +{{"-" * obj.type|length}}--------- + + {% for obj_item in this_page_children %} +{{obj_item.render()|indent(0)}} + {% endfor %} + {% endif %} + {% endif %} + {% endblock %} + {% else %} +.. py:module:: {{obj.name}} + + {% if obj.docstring %} + .. autoapi-nested-parse:: + + {{obj.docstring|indent(6)}} + + {% endif %} + {% for obj_item in visible_children %} + {{obj_item.render()|indent(3)}} + {% endfor %} + {% endif %} +{% endif %} diff --git a/docs/conf.py b/docs/conf.py index 911498b0..e4095c39 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,45 +6,77 @@ """ # -- Path setup ---------------------------------------------------------------- +import os +import sys from datetime import datetime +from pathlib import Path + +import geetools as geetools + +# add . to sys to import local extensions +sys.path.append(str(Path(".").resolve())) # -- Project information ------------------------------------------------------- project = "geetools" -author = "Pierrick Rambaud" +author = "Rodrigo E. Principe" copyright = f"2017-{datetime.now().year}, {author}" -release = "0.0.0" +release = "1.9.1" # -- General configuration ----------------------------------------------------- extensions = [ - "sphinx_copybutton", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.intersphinx", + "sphinx.ext.autosectionlabel", + "sphinxcontrib.icon", "sphinx_design", + "sphinx_last_updated_by_git", + "sphinx_copybutton", "autoapi.extension", + "jupyter_sphinx", + "myst_nb", + "_extension.docstring", + "_extension.api_admonition", ] exclude_patterns = ["**.ipynb_checkpoints"] -templates_path = ["_template"] # -- Options for HTML output --------------------------------------------------- +# Define the json_url for our version switcher. +json_url = "https://geetools.readthedocs.io/en/latest/_static/switcher.json" + +# Define the version we use for matching in the version switcher. +version_match = os.environ.get("READTHEDOCS_VERSION") + +# If READTHEDOCS_VERSION doesn't exist, we're not on RTD +# for local development and the latest dev build use the local file instead of the distant one. +if not version_match or version_match.isdigit() or version_match == "latest": + version_match = "dev" + json_url = "_static/switcher.json" +elif version_match == "stable": + version_match = f"v{release}" + html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] +templates_path = ["_template"] +html_logo = "_static/logo.png" +html_favicon = "_static/logo.png" html_theme_options = { "logo": { "text": project, }, "use_edit_page_button": True, - "footer_end": ["theme-version", "pypackage-credit"], "icon_links": [ { "name": "GitHub", "url": "https://github.com/gee-community/geetools", "icon": "fa-brands fa-github", + "type": "fontawesome", }, { "name": "Pypi", "url": "https://pypi.org/project/geetools/", "icon": "fa-brands fa-python", + "type": "fontawesome", }, { "name": "Conda", @@ -53,20 +85,52 @@ "type": "fontawesome", }, ], + "announcement": "https://raw.githubusercontent.com/gee-community/geetools/main/docs/_static/banner.html", + "secondary_sidebar_items": [ + "page-toc.html", + "edit-this-page.html", + ], + "article_footer_items": ["last-updated"], + "footer_end": ["theme-version", "pypackage-credit"], + "show_toc_level": 2, } html_context = { "github_user": "gee-community", "github_repo": "geetools", - "github_version": "", + "github_version": "main", "doc_path": "docs", } html_css_files = ["custom.css"] +html_js_files = ["custom-icon.js"] # -- Options for autosummary/autodoc output ------------------------------------ autodoc_typehints = "description" autoapi_dirs = ["../geetools"] -autoapi_python_class_content = "init" +autoapi_python_class_content = "both" autoapi_member_order = "groupwise" +autoapi_template_dir = "_template" +autoapi_options = [ + "members", + "undoc-members", + "show-inheritance", + "show-module-summary", + "special-members", +] +autoapi_own_page_level = "method" +autoapi_keep_files = False # -- Options for intersphinx output -------------------------------------------- -intersphinx_mapping = {} +# fmt: off +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "matplotlib": ("https://matplotlib.org/stable/", None), + "ee": ("https://developers.google.com/earth-engine/apidocs", "https://raw.githubusercontent.com/gee-community/sphinx-inventory/refs/heads/main/inventory/earthengine-api.inv"), +} +# fmt: on + +# -- options for the autolabel extension --------------------------------------- +autosectionlabel_prefix_document = True + +# -- options for myst-nb ------------------------------------------------------ +nb_execution_mode = "force" +nb_execution_timeout = 120 diff --git a/docs/contribute.rst b/docs/contribute.rst deleted file mode 100644 index 5b77c9c9..00000000 --- a/docs/contribute.rst +++ /dev/null @@ -1,5 +0,0 @@ -Contribute -========== - -.. include:: ../CONTRIBUTING.rst - :start-line: 3 diff --git a/docs/index.rst b/docs/index.rst index a6c81f2a..f04a0ef0 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,33 +7,82 @@ geetools .. toctree:: :hidden: - usage - contribute + setup/index + usage/index + Reference + Changelogs + earth-engine API -Documentation contents ----------------------- +Overview +-------- -The documentation contains 3 main sections: +.. image:: _static/logo.png + :width: 20% + :align: right + :class: dark-light + +`Google Earth Engine `__ is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a `JavaScript and a Python API `__ with different methods to process geospatial objects. + +The **geetools** package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different Earth Engine Objects that are friendly with the Python method chaining using the ``geetools`` namespace. + +content +------- .. grid:: 1 2 3 3 .. grid-item:: - .. card:: Usage - :link: usage.html + .. card:: :icon:`fa-solid fa-chart-simple` plot + :link: usage/plot/index.html - Usage and installation + Display EE objects directly as static plots. .. grid-item:: - .. card:: Contribute - :link: contribute.html + .. card:: :icon:`fa-solid fa-folder` asset + :link: usage/asset.html - Help us improve the lib. + Manage your assets as a object-oriented file system. + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-images` export ImageCollections + :link: usage/export.html + + Useful wrapper to export ImageCollections as simply as Images. .. grid-item:: - .. card:: API - :link: autoapi/index.html + .. card:: :icon:`fa-solid fa-handshake-angle` Contribute + :link: setup/contribute.html + + Help us improve the lib. + +Why using it ? +-------------- + +New utility methods and constructors are added to most of the GEE classes. They can be simple wrapper for repetitive tasks, complex algorithm or mandatory preprocessing steps. The goal is to make the code more fluid and easy to read for researchers, students and analysts. + +The package design is mostly performing server-side computation making it also very friendly with commercial users of Earth Engine. + +This small example wrapping of the excellent ``ee_extra`` package functionalities shows how to preprocess sentinel 2 data in 5 lines of code: + +.. code-block:: python + + import ee + import geetools #noqa: F401 + import pygaul # another gee-community package to access FAO GAUl 2015 dataset + + # we assume you are already authenticated to GEE + ee.Initialize.geetools.from_account("toto") # yes we also support multi users + + amazonas = pygaul.Items(name="Amazonas").centroid() + + S2 = ( + ee.ImageCollection('COPERNICUS/S2_SR') + .filterBounds(point) + .geetools.closest('2020-10-15') # Extended (pre-processing) + .geetools.maskClouds(prob = 70) # Extended (pre-processing) + .geetools.scaleAndOffset() # Extended (pre-processing) + .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) - Discover the lib API. diff --git a/docs/setup/author.rst b/docs/setup/author.rst new file mode 100644 index 00000000..c3b0c9f9 --- /dev/null +++ b/docs/setup/author.rst @@ -0,0 +1,4 @@ +Authors +======== + +.. include:: ../../AUTHORS.rst diff --git a/docs/setup/contribute.rst b/docs/setup/contribute.rst new file mode 100644 index 00000000..3e1236cd --- /dev/null +++ b/docs/setup/contribute.rst @@ -0,0 +1,5 @@ +Contributing workflow +===================== + +.. include:: ../../CONTRIBUTING.rst + :start-line: 3 diff --git a/docs/setup/index.rst b/docs/setup/index.rst new file mode 100644 index 00000000..9e54a1c1 --- /dev/null +++ b/docs/setup/index.rst @@ -0,0 +1,46 @@ +Setup +===== + +Overview +-------- + +The User Guide covers all of **geetools** by topic area. The :doc:`quickstart` page is a good place to start if you are new to the package or just want to refresh your memory. The :doc:`layout` page provides a high-level overview of the package's layout, and the :doc:`pattern` page provides a high-level overview of the package's design decsisions. + +The use of the package requires a basic understanding of the **Python** programming language and the **GEE Python API**. Users brand-new to Earth Engine should refer to the `Google documentation `__ first. + +Further hands-on example of specific tasks can be found in the :doc:`../usage/index` section. and for the most advance user please refe to the :doc:`../autoapi/index` section for a complete description of each individual functionality. + +Refactoring +----------- + +Since version v1.0.0, the package has been drastically modified to adopt the extension pattern (see :doc:`pattern` for more information). Many functions have also bee dropped or fully refactored to improve overall performances, and to make the package more consistent and easy to use. For more information about the miregation process please refer to the :doc:`migration` page. + +.. important:: + + The refactoring process is not finished yet, we will progressively reintegrate all the methods in the new pattern and add many cool functionalities. If any of your previous is not working anymore and the :doc:`migration` page did not provided any solution, please open an issue in the `GitHub repository `__. + +.. toctree:: + :hidden: + :caption: Get started + + install + quickstart + layout + +.. toctree:: + :hidden: + :caption: Extension Layout + + pattern + migration + inspiration + +.. toctree:: + :hidden: + :caption: Contributor guide + + contribute + author + license + + diff --git a/docs/setup/inspiration.rst b/docs/setup/inspiration.rst new file mode 100644 index 00000000..353fa4f8 --- /dev/null +++ b/docs/setup/inspiration.rst @@ -0,0 +1,20 @@ +Aknowledgment +============= + +This project was inspired by other very cool initiatives and we would like to acknowledge them here. + +xarray +------ + +the `xarray `__ lib is a great lib to handle n-dimensional data and has very well documented the use of the extension pattern. Without their guidances, `documentation `__ and `sphinx-extention `__, this project would have not been possible. + +eemont +------ + +the `eemont `__ lib has already implemented the extension pattern and allow the users to perform many different operations from preprocessing to extra construction methods. We loved some of them so much that we rewired within **geetools** every method available in `ee_extra `__. + +Our implementations diverge on 2 main points: + +- We decided to be more careful with the extensions and avoid adding them directly after the ``earthengine-api`` objects. We preferred to follow ``xarray`` recommendation and create systematically a ``geetools`` intermediate member to notify to the user that no, these methods are not from the vanilla ``earthengine-api`` but from the ``geetools`` lib. + +- We decided not to reimplement the Python magic methods. Now that EarthEngine is a commercial product, the users need to always be in control of what is performed server-side and what is performed client-side. We believe that spercharging magic method (although they allow cool things as ``ee.Image(1) + ee.image(2)``) make it even more confusing for new users to understand what is performed server-side and what is performed client-side. That's why all our method are using camel case naming convention and only return ``ee.ComputedObject``. Few exceptions are made for converters and plotting method that are forced to run an interactive ``getInfo()`` but they use snake case convention to notify their user and have a disclaimer in the docstring. diff --git a/docs/setup/install.rst b/docs/setup/install.rst new file mode 100644 index 00000000..3a08ea96 --- /dev/null +++ b/docs/setup/install.rst @@ -0,0 +1,42 @@ +Installation instructions +========================= + +The package is a pure python package, installation is thus very straight forward. + +pypi +---- + +the python package is available on `pypi `__ and can be installed it using ``pip``: + +.. code-block:: console + + pip install geetools + +conda +----- + +The python package is available on `conda `__ and can be installed using ``conda`` and the ``conda-forge`` channel: + +.. code-block:: console + + conda install -c conda-forge geetools + +from source +----------- + +You can also install the package from source. First clone the repository: + +.. code-block:: console + + git clone https://github.com/gee-community/geetools.git + +Then, navigate to the folder and install the package using pip: + +.. code-block:: console + + cd geetools + pip install . + +Multiple version of the package requirements are available and are specifically important for local development. +See the :doc:`contribute` page for more information. + diff --git a/docs/setup/layout.rst b/docs/setup/layout.rst new file mode 100644 index 00000000..b4209bf7 --- /dev/null +++ b/docs/setup/layout.rst @@ -0,0 +1,383 @@ +Layout of the extensions +======================== + +Overview +-------- + +This section is a glossary of all the methods that are added to ``ee`` objects by the extension. +They are gathered by sections and link to the API reference for detailed examples but give a good overview of what can be done. + +If you search for a method in the package, note that we decided to use names that are meaningful for python users so if you know what you are looking for in ``rasterio``, ``pandas`` or ``numpy`` you should be able to find it here under the same name. + +.. warning:: + + Method that were replaced during refactoring or that are still not reimplemented are not listed here. + They remain fully accecible in the geetools package. If they raise a deprecation warning, it means that they are still available but will be removed in the future by their extension equivalent. + +Earth Engine classes +-------------------- + +ee.Array +^^^^^^^^ + +constructor +########### +- :docstring:`ee.Array.geetools.full` + + +data manipulation +################# + +- :docstring:`ee.Array.geetools.set` + +ee.Authenticate +^^^^^^^^^^^^^^^ + +- :docstring:`ee.Authenticate.geetools.new_user` +- :docstring:`ee.Authenticate.geetools.delete_user` +- :docstring:`ee.Authenticate.geetools.list_user` +- :docstring:`ee.Authenticate.geetools.rename_user` + + +ee.ComputedObject +^^^^^^^^^^^^^^^^^ + +The ``ee.ComputedObject`` is the base object of all API object. The methods added here can thus be used in every object of the API. +That's also the only Object where the methods are directly added as members without the need to call `geetools` before. + +Types management +################ + +- :docstring:`ee.ComputedObject.isInstance` + +save json representations +######################### + +- :docstring:`ee.ComputedObject.save` +- :docstring:`ee.ComputedObject.open` + +ee.Date +^^^^^^^ + +Constructors +############ + +- :docstring:`ee.Date.geetools.fromEpoch` +- :docstring:`ee.Date.geetools.fromDOY` + +Extra operations +################ + +- :docstring:`ee.Date.geetools.getUnitSinceEpoch` +- :docstring:`ee.Date.geetools.isLeap` + +Exportation +########### + +.. warning:: + + As the snake case suggests, this method is client side. + +- :docstring:`ee.Date.geetools.to_datetime` + +helper +###### + +- :docstring:`ee.Date.geetools.check_unit` + +ee.DateRange +^^^^^^^^^^^^ + +Extra operations +################ + +- :docstring:`ee.DateRange.geetools.split` + +Helper +###### + +- :docstring:`ee.DateRange.geetools.check_unit` +- :docstring:`ee.DateRange.geetools.unitMillis` + +ee.Dictionary +^^^^^^^^^^^^^ + +Constructors +############ + +- :docstring:`ee.Dictionary.geetools.fromPairs` + +Extra operations +################ + +- :docstring:`ee.Dictionary.geetools.sort` +- :docstring:`ee.Dictionary.geetools.getMany` + +ee.Feature +^^^^^^^^^^ + +- :docstring:`ee.Feature.geetools.toFeatureCollection` +- :docstring:`ee.Feature.geetools.removeProperties` + +ee.FeatureCollection +^^^^^^^^^^^^^^^^^^^^ + +Properties management +##################### + +- :docstring:`ee.FeatureCollection.geetools.addId` + +Geometry management +################### + +- :docstring:`ee.FeatureCollection.geetools.mergeGeometries` +- :docstring:`ee.FeatureCollection.geetools.toPolygons` + +Converter +######### + +- :docstring:`ee.FeatureCollection.geetools.toImage` +- :docstring:`ee.FeatureCollection.geetools.byFeatures` +- :docstring:`ee.FeatureCollection.geetools.byProperties` + +Plotting +######## + +- :docstring:`ee.FeatureCollection.geetools.plot_by_features` +- :docstring:`ee.FeatureCollection.geetools.plot_by_properties` +- :docstring:`ee.FeatureCollection.geetools.plot_hist` + +ee.Filter +^^^^^^^^^ + +- :docstring:`ee.Filter.geetools.dateRange` + + +ee.Geometry +^^^^^^^^^^^ + +- :docstring:`ee.Geometry.geetools.keepType` + +ee.Image +^^^^^^^^ + +Constructor +########### + +- :docstring:`ee.Image.geetools.full` +- :docstring:`ee.Image.geetools.fullLike` + +Band manipulation +################# + +- :docstring:`ee.Image.geetools.addDate` +- :docstring:`ee.Image.geetools.addSuffix` +- :docstring:`ee.Image.geetools.addPrefix` +- :docstring:`ee.Image.geetools.rename` +- :docstring:`ee.Image.geetools.remove` +- :docstring:`ee.Image.geetools.doyToDate` +- :docstring:`ee.Image.geetools.negativeClip` +- :docstring:`ee.Image.geetools.gauss` +- :docstring:`ee.Image.geetools.repeat` + +Data extraction +############### + +- :docstring:`ee.Image.geetools.getValues` +- :docstring:`ee.Image.geetools.minScale` +- :docstring:`ee.Image.geetools.reduceBands` +- :docstring:`ee.Image.geetools.format` +- :docstring:`ee.Image.geetools.index_list` +- :docstring:`ee.Image.geetools.spectralIndices` +- :docstring:`ee.Image.geetools.getScaleParams` +- :docstring:`ee.Image.geetools.getOffsetParams` +- :docstring:`ee.Image.geetools.getSTAC` +- :docstring:`ee.Image.geetools.getDOI` +- :docstring:`ee.Image.geetools.getCitation` + + +Data manipulation +################# + +- :docstring:`ee.Image.geetools.doyToDate` +- :docstring:`ee.Image.geetools.clipOnCollection` +- :docstring:`ee.Image.geetools.bufferMask` +- :docstring:`ee.Image.geetools.removeZeros` +- :docstring:`ee.Image.geetools.interpolateBands` +- :docstring:`ee.Image.geetools.isletMask` +- :docstring:`ee.Image.geetools.scaleAndOffset` +- :docstring:`ee.Image.geetools.preprocess` +- :docstring:`ee.Image.geetools.panSharpen` +- :docstring:`ee.Image.geetools.tasseledCap` +- :docstring:`ee.Image.geetools.matchHistogram` +- :docstring:`ee.Image.geetools.maskClouds` + +Converter +######### + +- :docstring:`ee.Image.geetools.toGrid` + +Properties +########## + +- :docstring:`ee.Image.geetools.removeProperties` + +ee.ImageCollection +^^^^^^^^^^^^^^^^^^ + +Data manipulation +################# + +- :docstring:`ee.ImageCollection.geetools.maskClouds` +- :docstring:`ee.ImageCollection.geetools.closest` +- :docstring:`ee.ImageCollection.geetools.scaleAndOffset` +- :docstring:`ee.ImageCollection.geetools.preprocess` +- :docstring:`ee.ImageCollection.geetools.panSharpen` +- :docstring:`ee.ImageCollection.geetools.tasseledCap` +- :docstring:`ee.ImageCollection.geetools.append` +- :docstring:`ee.ImageCollection.geetools.outliers` + +Data extraction +############### + +- :docstring:`ee.ImageCollection.geetools.spectralIndices` +- :docstring:`ee.ImageCollection.geetools.getScaleParams` +- :docstring:`ee.ImageCollection.geetools.getOffsetParams` +- :docstring:`ee.ImageCollection.geetools.getDOI` +- :docstring:`ee.ImageCollection.geetools.getCitation` +- :docstring:`ee.ImageCollection.geetools.getSTAC` +- :docstring:`ee.ImageCollection.geetools.collectionMask` +- :docstring:`ee.ImageCollection.geetools.iloc` +- :docstring:`ee.ImageCollection.geetools.integral` +- :docstring:`ee.ImageCollection.geetools.aggregateArray` +- :docstring:`ee.ImageCollection.geetools.validPixel` + +Filter +###### + +- :docstring:`ee.ImageCollection.geetools.containsBandNames` +- :docstring:`ee.ImageCollection.geetools.containsAllBands` +- :docstring:`ee.ImageCollection.geetools.containsAnyBands` + +Converter +######### + +- :docstring:`ee.ImageCollection.geetools.to_xarray` + +ee.Initialize +^^^^^^^^^^^^^ + +- :docstring:`ee.Initialize.geetools.from_user` +- :docstring:`ee.Initialize.geetools.project_id` + +ee.Join +^^^^^^^ + +- :docstring:`ee.Join.geetools.byProperty` + +ee.List +^^^^^^^ + +Constructor +########### + +- :docstring:`ee.List.geetools.sequence` +- :docstring:`ee.List.geetools.zip` + +operations +########## + +- :docstring:`ee.List.geetools.product` +- :docstring:`ee.List.geetools.complement` +- :docstring:`ee.List.geetools.intersection` +- :docstring:`ee.List.geetools.union` +- :docstring:`ee.List.geetools.delete` +- :docstring:`ee.List.geetools.replaceMany` + +Converter +######### + +- :docstring:`ee.List.geetools.join` +- :docstring:`ee.List.geetools.toStrings` + +ee.Number +^^^^^^^^^ + +- :docstring:`ee.Number.geetools.truncate` + +ee.String +^^^^^^^^^ + +- :docstring:`ee.String.geetools.eq` +- :docstring:`ee.String.geetools.format` + +Added classes +------------- + +ee.Float +^^^^^^^^ + +.. note:: + + This object does not exist in the original API. It is a custom Placeholder Float class to be used in the :py:meth:`ee.ComputedObject.isInstance` method. + +ee.Integer +^^^^^^^^^^ + +.. note:: + + This object does not exist in the original API. It is a custom Placeholder Float class to be used in the :py:meth:`ee.ComputedObject.isInstance` method. + +ee.Asset +^^^^^^^^ + +.. note:: + + This object is overriding most of the pathlib methods. We are simply gathering them here for convenience. + +Constructor +########### + +- :docstring:`ee.Asset.home` + +Operation +######### + +- :docstring:`ee.Asset.as_posix` +- :docstring:`ee.Asset.as_uri` +- :docstring:`ee.Asset.is_absolute` +- :docstring:`ee.Asset.is_user_project` +- :docstring:`ee.Asset.expanduser` +- :docstring:`ee.Asset.exists` +- :docstring:`ee.Asset.is_relative_to` +- :docstring:`ee.Asset.joinpath` +- :docstring:`ee.Asset.match` +- :docstring:`ee.Asset.with_name` +- :docstring:`ee.Asset.is_image` +- :docstring:`ee.Asset.is_image_collection` +- :docstring:`ee.Asset.is_feature_collection` +- :docstring:`ee.Asset.is_folder` +- :docstring:`ee.Asset.is_project` +- :docstring:`ee.Asset.is_type` +- :docstring:`ee.Asset.iterdir` +- :docstring:`ee.Asset.mkdir` +- :docstring:`ee.Asset.move` +- :docstring:`ee.Asset.rmdir` +- :docstring:`ee.Asset.unlink` +- :docstring:`ee.Asset.delete` +- :docstring:`ee.Asset.copy` +- :docstring:`ee.Asset.glob` +- :docstring:`ee.Asset.rglob` +- :docstring:`ee.Asset.setProperties` + +Property +######## + +- :docstring:`ee.Asset.parts` +- :docstring:`ee.Asset.parent` +- :docstring:`ee.Asset.parents` +- :docstring:`ee.Asset.name` +- :docstring:`ee.Asset.st_size` +- :docstring:`ee.Asset.type` +- :docstring:`ee.Asset.owner` + diff --git a/docs/setup/license.rst b/docs/setup/license.rst new file mode 100644 index 00000000..f4954d17 --- /dev/null +++ b/docs/setup/license.rst @@ -0,0 +1,4 @@ +License +======== + +.. include:: ../../LICENSE diff --git a/docs/setup/migration.rst b/docs/setup/migration.rst new file mode 100644 index 00000000..30dcbe11 --- /dev/null +++ b/docs/setup/migration.rst @@ -0,0 +1,51 @@ +Upgrade from v0 to v1 +===================== + +in v1 **geetoold** has fully embraced the extension pattern and has revamped most of it's functionalities. +This page will gather the changes that you need to make to your code to upgrade from v0 to v1. + +The lib is following a deprecation cycle of several month so no function has been directly removed. +They will simply raised a deprecation warning and provide some suggestion on the potential replacement. + +None the less, as previous implementation was leading to internal issues such as circular imports, it is recommended to update your code to the new pattern as soon as possible to avoid any future breakage. + +.. warning:: + + This documentation is gathering problems faced by the community and the solutions that were found. + If you have a problem that is not listed here, please open an item in our `issue tracker `__. + + +Import the modules +------------------ + +If in the previous implementation, most of the modules were brought back to the main ``__init.py`` which was causing circular import issues. +In v1 the internal structure was revisited and some modules are no longer accecible from ``geetools``. + +A v0 implementation would look like this: + +.. code-block:: python + + import ee + from geetools.tools import geometry + + image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first()) + tools.geometry.getRegion(image) + +It will raise an error as the "tools" file has been replaced by a "_deprecated_tools" one. + +Now to run the same code you should do: + +.. code-block:: python + + import ee + from geetools import tools + + image = ee.Image(ee.ImageCollection('COPERNICUS/S2').first()) + tools.geometry.getRegion(image) + +This will simply raise a deprecation warning and will work as expected. + + + + + diff --git a/docs/setup/pattern.rst b/docs/setup/pattern.rst new file mode 100644 index 00000000..28df4393 --- /dev/null +++ b/docs/setup/pattern.rst @@ -0,0 +1,70 @@ +The extension pattern +===================== + +.. note:: + + This page is vastly inspired from the ``xarray`` `documentation `__ that is the inspiration for the ``geetools`` implementation. + +**Google Earth Engine** is designed as a general purpose library and hence tries to avoid including overly user specific functionality. But inevitably, the need for open-source community to contribute arises. This is where the extension pattern comes in. + +Composition over Inheritance +---------------------------- + +One potential solution to this problem is to subclass every ``ee.ComputedObject`` to add user specific functionality. However, inheritance is not very robust. It’s easy to inadvertently use internal APIs when subclassing, which means that your code may break when ``earthengine-api`` upgrades. Furthermore, many builtin methods will only return native ``ee.ComputedObject`` objects. + +The standard advice is to use composition over inheritance, but reimplementing an API as large as ``earthengine-api`` on your own objects can be an onerous task, even if most methods are only forwarding to ``earthengine-api`` implementations (That was the technical choice made prior in v0 of ``geetools``). + + +Writing Custom Accessors +------------------------ + +To resolve this issue for more complex cases, ``geetools`` has implemented 3 decorators: + +- A class decorator: :py:meth:`register_class_accessor ` +- A function decorator: :py:meth:`register_function_accessor ` + +They are used to add custom “accessors” on objects/functions/modules thereby “extending” the functionality of your ``ee`` object. + +Here’s how we use these decorators to write a custom “geetools” accessor implementing a extra method to ``ee.Number`` object: + +.. code-block:: python + + import ee + from geetools.accessor import register_class_accessor + + @register_class_accessor(ee.Number, "geetools") + class NumberAccessor: + + def __init__(self, obj: ee.Number): + self._obj = obj + + def truncate(self, nbDecimals = 2): + """Truncate a number to a given number of decimals.""" + nbDecimals = ee.Number(nbDecimals).toInt() + factor = ee.Number(10).pow(nbDecimals) + return self._obj.multiply(factor).toInt().divide(factor) + +In general, the only restriction on the accessor class is that the ``__init__`` method must have a single parameter: the object it is supposed to work on. + +This achieves the same result as if the Dataset class had a cached property defined that returns an instance of your class: + +.. code-block:: python + + class Number: + ... + + @property + def geetools(self): + return NumberAccessor(self) + +However, using the register accessor decorators is preferable to simply adding your own ad-hoc property (i.e., ``ee.number.geetools = property(...)``), for several reasons: + +- It ensures that the name of your property does not accidentally conflict with any other attributes or methods (including other accessors). +- Instances of accessor object will be cached on the object that creates them. This means you can save state on them (e.g., to cache computed properties). +- Using an accessor provides an implicit namespace for your custom functionality that clearly identifies it as separate from built-in ``earthengine-api`` methods. + +.. note:: + + Accessors are created once per object instance. New instances, like those created from mapping operations or when accessing a ``ee.Feature`` from a ``ee.FeatureCollection`` (ex. ``fc.first()``), will have new accessors created. + +The intent here is that libraries that extend ``earthengine-api`` could add such an accessor to implement subclass specific functionality rather than using actual subclasses or patching in a large number of domain specific methods. For further reading on ways to write new accessors and the philosophy behind the approach, see https://github.com/pydata/xarray/issues/1080. diff --git a/docs/setup/quickstart.rst b/docs/setup/quickstart.rst new file mode 100644 index 00000000..d9cfc446 --- /dev/null +++ b/docs/setup/quickstart.rst @@ -0,0 +1,75 @@ +Quickstart +========== + + + +An extension ? +-------------- + +**geetools** is an extension package. It means that it cannot be used without the **Google Earth Engine Python API** but also that you don't need to call it explicitly to use all the methods and functions available. + +To summarize, these functions are added to GEE objects inside a member called ``geetools``. + +The first step is as always to authenticate to GEE and import the **geetools** package. + +.. code-block:: python + + import ee + import geetools #noqa: F401 + + ee.Initialize() + +At this stage all the methods of the package have been added to the ``ee`` objects. You can use them as if they were part of the original API in the ``geetools`` member. + +The following example will use the ``truncate`` method to truncate a number to a given number of decimals. + +.. code-block:: python + :emphasize-lines: 2, 5 + + import ee + import geetools #noqa: F401 + + number = ee.Number(3.14159265359) + truncated = number.geetools.truncate(2) + truncated.getInfo() + +Real life example +----------------- + +This small example shows how **geetools** is wrapping the excellent ``ee_extra`` package functionalities to preprocess sentinel 2 data in 5 lines of code: + +.. code-block:: python + + import ee + import geetools #noqa: F401 + import pygaul # another gee-community package to access FAO GAUl 2015 dataset + + # we assume you are already authenticated to GEE + ee.Initialize.geetools.from_account("toto") # yes we also support multi users + + amazonas = pygaul.Items(name="Amazonas").centroid() + + S2 = ( + ee.ImageCollection('COPERNICUS/S2_SR') + .filterBounds(point) + .geetools.closest('2020-10-15') # Extended (pre-processing) + .geetools.maskClouds(prob = 70) # Extended (pre-processing) + .geetools.scaleAndOffset() # Extended (pre-processing) + .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) + +More examples of more complex and meaningful analysis can be found in the :doc:`../usage/index` gallery. + +F401 ? +------ + +In Python it's recommended to clean you code using automatic tools like ``flake8``, ``ruff``, ``isort``...etc + +These tool will raise an error if you import a package and don't use it, it's the ``F401`` error. In some tools the erroring lines can be deleted from the file. This will break your code as even if the **geetools** package is never called it's required to import it to extend the ``ee`` package. + +The ``#noqa: F401`` comment is used to avoid the linter to raise an error when the package is imported but not used. It is not mandatory if you don't use linters but it is a good practice to keep your code clean. + +.. note:: + + as per flake8 `documentation `__: + + F401: A module has been imported but is not used anywhere in the file. The module should either be used or the import should be removed. diff --git a/docs/usage.rst b/docs/usage.rst deleted file mode 100644 index 81cf3d6d..00000000 --- a/docs/usage.rst +++ /dev/null @@ -1,4 +0,0 @@ -Usage -===== - -**geetools** usage documentation. diff --git a/docs/usage/asset.ipynb b/docs/usage/asset.ipynb new file mode 100644 index 00000000..469bf8e0 --- /dev/null +++ b/docs/usage/asset.ipynb @@ -0,0 +1,617 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Object-oriented asset file system" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/asset.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/asset.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the required libs if necessary and perform the import statements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if initialization is required\n", + "# ee.Initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `Asset` object\n", + "\n", + "In Google Earth Engine API, users are working with Assets. An asset is a filelike object that englobes a wide variety of types: IMAGE, IMAGE_COLLECTION, FOLDER, TABLE, FEATURE_COLLECTION, etc.\n", + "\n", + "They are identified by a unique ID, which is a string that looks like: `projects/username/assets/foo`. Using the vanila Earthengine API, They can be modified using the `ee.data` module. This module has been proven complicated when dealing with basic file manipulation operation such as listing, moving, copying, etc.\n", + "\n", + "`geetools` provides a simple way to manage assets as an object-oriented filesystem paths using the `Asset` object. This object is a subclass of the `pathlib.Path` object, which is a powerful way to manage file paths in Python. Most of the methods and properties are overwritten to work with the Google Earth Engine context.\n", + "\n", + "`ee.Asset` objects implement the os.PathLike interface, allowing them to be used anywhere the interface is accepted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic use\n", + "\n", + "Importing the main class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create asset objects \n", + "\n", + "The Asset objects etend the pathlib.Path object and thus behave exactly the same when dealing with constructor. THe only differnece is that asset path only supports posix-like file separator: `/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each element of pathsegments can be either a string representing a path segment, or an object implementing the os.PathLike interface where the __fspath__() method returns a string, such as another path object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Asset(\"projects\", \"ee-geetools\", \"assets\", \"documentation\", \"image1\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Asset(\"projects/ee-geetools/assets/documentation\") / \"image1\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Asset(\"projects/ee-geetools/assets/documentation\").joinpath(\"image1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Listing subdirectories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# a public folder created for this docuemntation\n", + "folder = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", + "\n", + "# list all its direct subdirectories\n", + "[a for a in folder.iterdir() if a.is_folder()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.Asset.iterdir`\n", + "- {{docstring}`ee.Asset.is_folder`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Listing Image in this folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[a for a in folder.iterdir() if a.is_image()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[a for a in folder.glob(\"**/image*\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.Asset.iterdir`\n", + "- {docstring}`ee.Asset.glob`\n", + "- {docstring}`ee.Asset.is_image`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Querying asset properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder.exists()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fakeImage = folder / \"image6\"\n", + "fakeImage.exists()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.Asset.exists`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General properties\n", + "\n", + "Paths are immutable and hashable. Paths of a same flavour are comparable and orderable. These properties respect the flavour’s case-folding semantics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder = ee.Asset(\"projects/ee-geetools/assets/documentation\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder == ee.Asset(\"projects/ee-geetools/assets/DOCUMENTATION\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder in {ee.Asset(\"projects/ee-geetools/assets/documentation\")}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The slash operator helps create child asset, like `os.path.join()`. If the argument is an absolute asset, the previous path is ignored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Asset(\"projects/ee-geetools/assets/documentation\") / \"image1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An asset object can be used anywhere an object implementing `os.PathLike` is accepted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "a = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", + "os.fspath(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The string representation of an asset is the asset id itself, which you can pass to any function taking an asset id as a string:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")\n", + "str(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing individual parts\n", + "\n", + "To access the individual “parts” (components) of a path, use the following property:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/image1\")\n", + "a.parts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.Asset.parts`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### access parent container\n", + "\n", + "Asset parent containers can be access either by the `parent` property or the `parents` property. Note This is a purely lexical operation and the parent is not checked to exist.\n", + "\n", + "```{api}\n", + "- {docstring}`ee.Asset.parent`\n", + "- {docstring}`ee.Asset.parents`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.parent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.parents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Name of the asset\n", + "\n", + "A string representing the final path component can be used to get the name of the asset.add\n", + "\n", + "```{api}\n", + "- {docstring}`ee.Asset.name`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General Methods\n", + "\n", + "Pure paths provide the following methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### evaluate relation between assets\n", + "\n", + "It's possible to check if files are related between one another using the following methods:\n", + "\n", + "```{api}\n", + "- {docstring}`ee.Asset.is_relative_to`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "b = ee.Asset(\"projects/ee-geetools/assets/documentation\")\n", + "a.is_relative_to(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### create a siblings\n", + "\n", + "One can create a siblings asset in the same container by using the `with_name()` method:\n", + "\n", + "```{api} \n", + "- {docstring}`ee.Asset.with_name`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.with_name(\"image2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### resolve unix like symbols\n", + "\n", + "One can use some unix-like descriptors in it's Asset constructor parameters. If so before using the Asset object, it is necessary to resolve these symbols. The method `expanduser` does that.\n", + "\n", + "```{api}\n", + "- {py:meth}`expanduser `: {docstring}`ee.Asset.expanduser`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"~/documentation/subfolder1/image1\")\n", + "a.expanduser()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### check existence\n", + "\n", + "One can check if an asset exists using the `exists` method:\n", + "\n", + "```{api}\n", + "- {py:meth}`exists `: {docstring}`ee.Asset.exists`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.exists()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image10\")\n", + "a.exists()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate asset type\n", + "\n", + "As Earth Engine is not using any file extention to differentiate the asset type, one can use the `is_type` method with any of the following types: `IMAGE`, `IMAGE_COLLECTION`, `FOLDER`, `TABLE`, `FEATURE_COLLECTION`, `UNKNOWN`.\n", + "\n", + "```{api}\n", + "- {py:meth}`is_type `: {docstring}`ee.Asset.is_type`\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = ee.Asset(\"projects/ee-geetools/assets/documentation/subfolder1/image1\")\n", + "a.is_type(\"IMAGE\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All type checks are available in dedicated wrapped methods like `is_image`, `is_folder`, `is_table` ...etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a.is_image()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many other useful methods are available and are described in the {py:class}`API documentation `." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/export.ipynb b/docs/usage/export.ipynb new file mode 100644 index 00000000..2399848d --- /dev/null +++ b/docs/usage/export.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exporting ImageCollections\n", + "\n", + "Earth Engine provides numbers of ways to export `ee.Image` as explained in their [documentation](https://developers.google.com/earth-engine/guides/image_export). `geetools`provides an extention to the `ee.Export` class to export `ee.ImageCollection` as well. This is useful when you have a collection of images and you want to export them all at once.\n", + "\n", + "As the vanilla Earth Engine methods were returning Task objects, these method will return lists of Task objects. This ensures that Once the task are launched they can be fully monitored outside from your initial script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/export.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/export.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Set up \n", + "\n", + "Start by defining the image data that will be exported." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load a landsat image and select three bands over the whole mont of january 2023\n", + "landsat = (\n", + " ee.ImageCollection(\"LANDSAT/LC08/C02/T1_TOA\")\n", + " .select([\"B4\", \"B3\", \"B2\"])\n", + " .filterDate(\"2023-01-01\", \"2023-01-31\")\n", + ")\n", + "\n", + "# Create a geometry representing an export region.\n", + "geometry = ee.Geometry.Rectangle([116.2621, 39.8412, 116.4849, 40.01236])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## to Drive\n", + "\n", + "To export an imageCollection to your Drive account, use `ee.batch.Export.geetools.imagecollection.toDrive()`. For example, to export portions of a Landsat collection, define a region to export, then call Export:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python \n", + "# Export the image to Cloud Storage.\n", + "ee.batch.Export.geetools.imagecollection.toDrive(\n", + " imagecollection = landsat,\n", + " index_property = \"system:id\",\n", + " description = 'imageCollectionToDriveExample',\n", + " scale = 30,\n", + " region = geometry,\n", + " folder = 'geetools_example',\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## to Cloud Storage\n", + "\n", + "To export an ImageCollection to a Google Cloud Storage bucket, use `ee.batch.Export.geetools.imagecollection.toCloudStorage()`. To export the Landsat image in the previous example to Cloud Storage instead of Drive, use:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python \n", + "# Export the image to Cloud Storage.\n", + "ee.batch.Export.image.toCloudStorage(\n", + " imagecollection = landsat,\n", + " index_property = \"system:id\",\n", + " description = 'imageToCloudExample',\n", + " bucket = 'your-bucket-name',\n", + " scale = 30,\n", + " region = geometry\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To Asset\n", + "\n", + "To export an ImageCollection to an Earth Engine asset, use `ee.batch.Export.geetools.imagecollection.toAsset()`. To export the Landsat image in the previous example to an asset, use:\n", + "\n", + "When this code is run, a list of export task will be created you will need to `start` them to start the export computation in the server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Start the export process.\n", + "ee.batch.Export.geetools.imagecollection.toAsset(\n", + " imagecollection = landsat,\n", + " index_property = \"system:id\",\n", + " assetId = 'projects/username/ladnsat_collection',\n", + " scale = 30,\n", + " region = geometry,\n", + " maxPixels = 1e13,\n", + " pyramidingPolicy = {\n", + " 'b4': 'mean',\n", + " 'b3': 'mean',\n", + " 'b2': 'mean'\n", + " }\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For all function please refer to offcial documentation for complete list of parameters of the `ee.batch.Export.image` methods." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/index.rst b/docs/usage/index.rst new file mode 100644 index 00000000..f12729be --- /dev/null +++ b/docs/usage/index.rst @@ -0,0 +1,47 @@ +Guides +====== + +Overview +-------- + +This section gathered many real life examples of the Lib usage gathered by the community. +If you think your workflow should be shared please open a PR and follow the contribution guildelines shared in the next section. + +.. warning:: + + The example gallery is a work in progress as the library was recently refactored. + All contributions are welcolmed! + +Add a new example +----------------- + +.. image:: /_static/we-need-you.jpg + :alt: We need you! + :align: center + +Currently most of the examples built by `@Rodrigo `__ are still using the old implementation of the library. +They should be transformed into modern example and moved from the old `notebook `__ folder to the new `example `__ one to be displayed in our doc. + +The examples are regular notebook files that are interpreted by the ``myst-nb`` lib and displayed in the doc, clicking on the :guilabel:`open in colab` button will open a colab notebook with the code ready to be executed and the :guilabel:`view source` will bring you back to github. + +To add a new example, you can use the `example template `__ and replace things with your code. + +Adapt the code of the 2 first buttons to your file so users can lunch it in collab and view the source in github. + +.. code-block:: md + + [![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/template.ipynb) + [![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/template.ipynb) + + +Then you can open a PR with the new file and it will be reviewed and merged. + +.. toctree:: + :hidden: + + template + export + plot/index + asset + profile + reduce diff --git a/docs/usage/plot/index.rst b/docs/usage/plot/index.rst new file mode 100644 index 00000000..bdd057d5 --- /dev/null +++ b/docs/usage/plot/index.rst @@ -0,0 +1,99 @@ +Plotting +======== + +We embed some plotting capabilities in the library to help you visualize your data. For simplicity we decided to map all the plotting function to the :doc:`matplotlib ` library as it's the most used static plotting library in the Python ecosystem. + +.. toctree:: + :hidden: + :maxdepth: 1 + + plot-featurecollection + plot-image + plot-imagecollection + map-image + map-featurecollection + +.. grid:: 1 2 3 3 + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-chart-simple` FeatureCollection + :link: plot-featurecollection.html + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-chart-simple` Image + :link: plot-image.html + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-chart-simple` ImageCollection + :link: plot-imagecollection.html + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-image` Image + :link: map-image.html + + .. grid-item:: + + .. card:: :icon:`fa-solid fa-map` FeatureCollection + :link: map-featurecollection.html + + + +In all these examples we will use the object interface of matplotlib creating the :py:class:`Figure ` and :py:class:`Axes ` object before plotting the data. This is the recommended way to use matplotlib as it gives you more control over the plot and the figure. + +.. code-block:: python + + # custom image for this specific chart + modisSr = ( + ee.ImageCollection("MODIS/061/MOD09A1") + .filter(ee.Filter.date("2018-06-01", "2018-09-01")) + .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"]) + .mean() + ) + histRegion = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22]) + + #create a matplotlib figure + fig, ax = plt.subplots(figsize=(10, 4)) + + # plot the histogram of the reds + modisSr.geetools.plot_hist( + bands = ["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"], + labels = [['Red', 'NIR', 'SWIR']], + colors = ["#cf513e", "#1d6b99", "#f0af07"], + ax = ax, + bins = 100, + scale = 500, + region = histRegion, + ) + + # once created the axes can be modified as needed using pure matplotlib functions + ax.set_title("Modis SR Reflectance Histogram") + ax.set_xlabel("Reflectance (x1e4)") + +.. image:: ../../_static/usage/plot/index/histogram.png + :alt: Modis SR Reflectance Histogram + :align: center + +If you are used to the :py:mod:`pyplot ` interface of matplotlib you can still use it with the state-base module of matplotlib. Just be aware that the module is a stateful interface and you will have less control over the figure and the plot. + +.. code-block:: python + + # get all hydroshed from the the south amercias within the WWF/HydroATLAS dataset. + region = ee.Geometry.BBox(-80, -60, -20, 20); + fc = ee.FeatureCollection('WWF/HydroATLAS/v1/Basins/level04').filterBounds(region) + + # create the plot + fc.geetools.plot(property="UP_AREA", cmap="viridis") + + # Customized display + plt.colorbar(ax.collections[0], label="Upstream area (km²)") + plt.title("HydroATLAS basins of level4") + plt.xlabel("Longitude (°)") + plt.ylabel("Latitude (°)") + +.. image:: ../../_static/usage/plot/index/hydroshed.png + :alt: HydroATLAS basins of level4 + :align: center diff --git a/docs/usage/plot/map-featurecollection.ipynb b/docs/usage/plot/map-featurecollection.ipynb new file mode 100644 index 00000000..7f0b35ea --- /dev/null +++ b/docs/usage/plot/map-featurecollection.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Map FeatureCollection\n", + "\n", + "The `geetools` extension contains a set of functions for rendering maps from `ee.FeatureCollection` objects. Use the following function descriptions and examples to determine the best function and chart type for your purpose." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/map-featurecollection.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/map-featurecollection.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the required packages and perform the import statement upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data \n", + "\n", + "The following examples rely on a `ee.FeatureCollection` composed of all the hydroshed bassins from south america." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region = ee.Geometry.BBox(-80, -60, -20, 20)\n", + "fc = ee.FeatureCollection(\"WWF/HydroATLAS/v1/Basins/level04\").filterBounds(region)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map Vector\n", + "\n", + "```{api}\n", + "{docstring}`ee.FeatureCollection.geetools.plot`\n", + "```\n", + "\n", + "An `ee.FeatureCollection` is a vector representation of geographical properties. A user can be interested by either the property evolution across the landscape or the geometries associated with it. The {py:meth}`plot ` is coverinig both use cases. \n", + "\n", + "### Map a property\n", + "\n", + "A single property can be ploted on a map using matplotlib. The following example is showing the bassin area in km².\n", + "\n", + "First create a matplotlib figure and axis, then you can add the bassins to the map using the `plot` method. By default it will display the first property of the features. In our case we will opt to display the area of the bassins in km² i.e. the \"UP_AREA\" property. Finally that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "# generate the graph\n", + "fc.geetools.plot(ax=ax, property=\"UP_AREA\", cmap=\"viridis\")\n", + "\n", + "# you can then customize the figure as you would for any other matplotlib object\n", + "fig.colorbar(ax.collections[0], label=\"Upstream area (km²)\")\n", + "ax.set_title(\"HydroATLAS basins of level4\")\n", + "ax.set_xlabel(\"Longitude (°)\")\n", + "ax.set_ylabel(\"Latitude (°)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map geometries\n", + "\n", + "Alternatively if you only want to plot the geometries of the featurecollection on a map, you can use the `plot` method with the `boundares` parameter set to `True`.\n", + "\n", + "Similarly to the previous example we start by creating a pyplot figure and axis, then you can start plotting the geometries and finally customize the plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.ioff() # remove interactive for the sake of the example\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "# create the graph\n", + "fc.geetools.plot(ax=ax, boundaries=True)\n", + "\n", + "# you can then customize the figure as you would for any other matplotlib object\n", + "ax.set_title(\"Borders of the HydroATLAS basins of level4\")\n", + "ax.set_xlabel(\"Longitude (°)\")\n", + "ax.set_ylabel(\"Latitude (°)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/plot/map-image.ipynb b/docs/usage/plot/map-image.ipynb new file mode 100644 index 00000000..bb5fb5e7 --- /dev/null +++ b/docs/usage/plot/map-image.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Map Image\n", + "\n", + "The `geetools` extension contains a set of functions for rendering maps from `ee.Image` objects. Use the following function descriptions and examples to determine the best function and chart type for your purpose." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/map-image.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/map-image.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install the required packages and authenticate your Earth Engine account." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data \n", + "\n", + "The following examples rely on the \"COPERNICUS/S2_HARMONIZED\" `ee.ImageCollection` filtered between 2022-06-01 and 2022-06-30. We then build the NDVI spectral indice and use mosaic to get an `ee.Image` object. This object is clipped over the Vatican city as it's one of the smallest country in the world." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the vatican\n", + "level0 = ee.FeatureCollection(\"FAO/GAUL/2015/level0\")\n", + "vatican = level0.filter(ee.Filter.eq(\"ADM0_NAME\", \"Holy See\"))\n", + "\n", + "# pre-process the imagecollection and mosaic the month of June 2022\n", + "image = (\n", + " ee.ImageCollection(\"COPERNICUS/S2_HARMONIZED\")\n", + " .filterDate(\"2022-06-01\", \"2022-06-30\")\n", + " .filterBounds(vatican)\n", + " .geetools.maskClouds()\n", + " .geetools.spectralIndices(\"NDVI\")\n", + " .mosaic()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map Raster\n", + "\n", + "```{api}\n", + "{py:meth}`plot `: \n", + " {docstring}`geetools.ImageAccessor.plot`\n", + "```\n", + "\n", + "An `ee.image` is a raster representation of the Earth's surface. The `plot` function allows you to visualize the raster data on a map. The function provides options to customize the visualization, such as the color palette, opacity, and the visualization range.\n", + "\n", + "### Map pseudo color\n", + "\n", + "A pseudo-color image is a single-band raster image that uses a color palette to represent the data. The following example demonstrates how to plot the NDVI pseudo-color image using the `plot` function.\n", + "\n", + "First create a matplotlib figure and axis. Then you can add the map to the axis. Provide a single element list in the bands parameter to plot the NDVI image. \n", + "As per interactive representation an image needs to be reduced to a region, here \"Vatican City\". In this example we also select a pseudo-mercator projection and we displayed the `ee.FeatureCollection` on top of it. Now that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar. Now that we have the plot, we can customize it with matplotlib. For example, we can add a title and a colorbar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "image.geetools.plot(\n", + " bands=[\"NDVI\"],\n", + " ax=ax,\n", + " region=vatican.geometry(),\n", + " crs=\"EPSG:3857\",\n", + " scale=10,\n", + " fc=vatican,\n", + " cmap=\"viridis\",\n", + " color=\"k\",\n", + ")\n", + "\n", + "# as it's a figure you can then edit the information as you see fit\n", + "ax.set_title(\"NDVI in Vatican City\")\n", + "ax.set_xlabel(\"x coordinates (m)\")\n", + "ax.set_ylabel(\"y coordinates (m)\")\n", + "fig.colorbar(ax.images[0], label=\"NDVI\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map RGB combo\n", + "\n", + "An RGB image is a three-band raster image that uses the red, green, and blue bands to represent the data. The following example demonstrates how to plot the RGB image using the `plot` function.\n", + "\n", + "First create a matplotlib figure and axis. Then you can add the map to the axis. Provide a 3 elements list in the bands parameter to plot the NDVI image. \n", + "As per interactive representation an image needs to be reduced to a region, here \"Vatican City\". In this example we displayed the `ee.FeatureCollection` on top of it. Finally customize the plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the plot figure\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Create the graph\n", + "image.geetools.plot(\n", + " bands=[\"B4\", \"B3\", \"B2\"], ax=ax, region=vatican.geometry(), fc=vatican, color=\"k\"\n", + ")\n", + "\n", + "# as it's a figure you can then edit the information as you see fit\n", + "ax.set_title(\"Sentinel 2 composite in Vatican City\")\n", + "ax.set_xlabel(\"longitude (°)\")\n", + "ax.set_ylabel(\"latitude (°)\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/plot/plot-featurecollection.ipynb b/docs/usage/plot/plot-featurecollection.ipynb new file mode 100644 index 00000000..ff7bc8e5 --- /dev/null +++ b/docs/usage/plot/plot-featurecollection.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot FeatureCollection\n", + "\n", + "The `geetools` extension contains a set of functions for rendering charts from `ee.FeatureCollection` objects. The choice of function determines the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function and chart type for your purpose." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-featurecollection.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-featurecollection.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the required libs if necessary and perform the import statements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "The following examples rely on a FeatureCollection composed of three ecoregion features with properties that describe climate normals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the example feature collection.\n", + "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot by features\n", + "\n", + "Features are plotted along the x-axis by values of a selected property. Series are defined by a list of property names whose values are plotted along the y-axis. The type of produced chart can be controlled by the `type` parameter as shown in the following examples.\n", + "\n", + "If you want to use another plotting library you can get the raw data using the `byFeatures` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Data for the chart\n", + "features = [\"f1\", \"f2\", \"f3\"]\n", + "p1_values = [0.5, 2.5, 4.5]\n", + "p2_values = [1.5, 3.5, 5.5]\n", + "p3_values = [2.5, 4.0, 6.5]\n", + "\n", + "# Set the width of the bars\n", + "bar_width = 0.25\n", + "index = np.arange(len(features))\n", + "offset = 0.005\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Plotting the bars\n", + "rects1 = ax.bar(index, p1_values, bar_width, label=\"p1\", color=\"#1d6b99\")\n", + "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label=\"p2\", color=\"#cf513e\")\n", + "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label=\"p3\", color=\"#f0af07\")\n", + "\n", + "# Add labels, title, and custom x-axis tick labels\n", + "ax.set_xlabel(\"Features by property value\")\n", + "ax.set_ylabel(\"Series property value\")\n", + "ax.set_xticks(index + bar_width)\n", + "ax.set_xticklabels(features)\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Property names\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`geetools.FeatureCollectionAccessor.plot_by_features`\n", + "- {docstring}`ee.FeatureCollection.geetools.byFeatures`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Column chart\n", + "\n", + "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by adjacent columns defined by a list of property names whose values are plotted along the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize the plot with the ecoregions data\n", + "ecoregions.geetools.plot_by_features(\n", + " type=\"bar\",\n", + " featureId=\"label\",\n", + " properties=[\n", + " \"01_tmean\",\n", + " \"02_tmean\",\n", + " \"03_tmean\",\n", + " \"04_tmean\",\n", + " \"05_tmean\",\n", + " \"06_tmean\",\n", + " \"07_tmean\",\n", + " \"08_tmean\",\n", + " \"09_tmean\",\n", + " \"10_tmean\",\n", + " \"11_tmean\",\n", + " \"12_tmean\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", + "ax.set_xlabel(\"Ecoregion\")\n", + "ax.set_ylabel(\"Temperature (°C)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stacked column chart\n", + "\n", + "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by stacked columns defined by a list of property names whose values are plotted along the y-axis as the cumulative series sum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ecoregions.geetools.plot_by_features(\n", + " type=\"stacked\",\n", + " featureId=\"label\",\n", + " properties=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Ecoregion\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scatter chart\n", + "\n", + "Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by points defined by a list of property names whose values are plotted along the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ecoregions.geetools.plot_by_features(\n", + " type=\"scatter\",\n", + " featureId=\"label\",\n", + " properties=[\"01_ppt\", \"06_ppt\", \"09_ppt\"],\n", + " labels=[\"jan\", \"jun\", \"sep\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Ecoregion\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pie chart\n", + "\n", + "The pie is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the pie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ecoregions.geetools.plot_by_features(\n", + " type=\"pie\",\n", + " featureId=\"label\",\n", + " properties=[\"06_ppt\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Share of precipitation in June by Ecoregion\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Donut chart\n", + "\n", + "The donut is a property, each slice is the share from each feature whose value is cast as a percentage of the sum of all values of features composing the donut." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ecoregions.geetools.plot_by_features(\n", + " type=\"donut\",\n", + " featureId=\"label\",\n", + " properties=[\"07_ppt\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Share of precipitation in July by Ecoregion\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot by properties\n", + "\n", + "Feature properties are plotted along the x-axis by name; values of the given properties are plotted along the y-axis. Series are features labeled by values of a selected property. The type of produced chart can be controlled by the `type` parameter as shown in the following examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Data for the chart\n", + "features = [\"p1\", \"p2\", \"p3\"]\n", + "p1_values = [0.5, 2.5, 4.5]\n", + "p2_values = [1.5, 3.5, 5.5]\n", + "p3_values = [2.5, 4.0, 6.5]\n", + "\n", + "# Set the width of the bars\n", + "bar_width = 0.25\n", + "index = np.arange(len(features))\n", + "offset = 0.005\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Plotting the bars\n", + "rects1 = ax.bar(index, p1_values, bar_width, label=\"f1\", color=\"#1d6b99\")\n", + "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label=\"f2\", color=\"#cf513e\")\n", + "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label=\"f3\", color=\"#f0af07\")\n", + "\n", + "# Add labels, title, and custom x-axis tick labels\n", + "ax.set_xlabel(\"Property names\")\n", + "ax.set_ylabel(\"Series property value\")\n", + "ax.set_xticks(index + bar_width)\n", + "ax.set_xticklabels(features)\n", + "\n", + "# Add a legend\n", + "ax.legend(\n", + " loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Features by property value\"\n", + ")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.FeatureCollection.geetools.plot_by_properties`\n", + "- {docstring}`ee.FeatureCollection.geetools.byProperties`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Column chart\n", + "\n", + "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ax = ecoregions.geetools.plot_by_properties(\n", + " type=\"bar\",\n", + " properties=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " featureId=\"label\",\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Line chart\n", + "\n", + "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by columns, labeled by values of a selected property." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize theplot with the ecoregions data\n", + "ax = ecoregions.geetools.plot_by_properties(\n", + " type=\"plot\",\n", + " properties=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " featureId=\"label\",\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Area chart \n", + "\n", + "Feature properties are plotted along the x-axis, labeled and sorted by a dictionary input; the values of the given properties are plotted along the y-axis. Series are features, represented by lines and shaded areas, labeled by values of a selected property." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize the plot with the ecoregions data\n", + "ax = ecoregions.geetools.plot_by_properties(\n", + " type=\"fill_between\",\n", + " properties=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " featureId=\"label\",\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot hist\n", + "\n", + "```{api}\n", + "{docstring}`ee.FeatureCollection.geetools.plot_hist`\n", + "```\n", + "\n", + "The x-axis is defined by value bins for the range of values of a selected property; the y-axis is the number of elements in the given bin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# load some data\n", + "normClim = ee.ImageCollection(\"OREGONSTATE/PRISM/Norm91m\").toBands()\n", + "\n", + "# Make a point sample of climate variables for a region in western USA.\n", + "region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14)\n", + "climSamp = normClim.sample(region, 5000)\n", + "\n", + "\n", + "# initialize the plot with the ecoregions data\n", + "ax = climSamp.geetools.plot_hist(\n", + " property=\"07_ppt\", label=\"July Precipitation (mm)\", color=\"#1d6b99\", ax=ax, bins=30\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"July Precipitation Distribution for NW USA\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/plot/plot-image.ipynb b/docs/usage/plot/plot-image.ipynb new file mode 100644 index 00000000..1b025f5f --- /dev/null +++ b/docs/usage/plot/plot-image.ipynb @@ -0,0 +1,760 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Image\n", + "\n", + "The `geetools` extention contains a set of functions for reducing `ee.Image` objects by region(s) and rendering charts from the results. The choice of function dictates the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function and chart type for your purpose.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-image.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-image.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the requireed libs if necessary. and perform the import satements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The Image data are PRISM climate normals, where bands describe climate variables per month; e.g., July precipitation or January mean temperature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\").select(\n", + " [\"label\", \"value\", \"warm\"]\n", + ")\n", + "normClim = ee.ImageCollection(\"OREGONSTATE/PRISM/Norm91m\").toBands()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot by regions\n", + "\n", + "Reduction regions are plotted along the x-axis, labeled by values of a selected feature property. Series are defined by band names whose region reduction results are plotted along the y-axis.\n", + "\n", + "If you want to use another plotting library, you can use the `byRegions` function to get the data and plot it with your favorite library.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Data for the chart\n", + "features = [\"r1\", \"r2\", \"r3\"]\n", + "p1_values = [0.5, 2.5, 4.5]\n", + "p2_values = [1.5, 3.5, 5.5]\n", + "p3_values = [2.5, 4.0, 6.5]\n", + "\n", + "# Set the width of the bars\n", + "bar_width = 0.25\n", + "index = np.arange(len(features))\n", + "offset = 0.005\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Plotting the bars\n", + "rects1 = ax.bar(index, p1_values, bar_width, label=\"b1\", color=\"#1d6b99\")\n", + "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label=\"b2\", color=\"#cf513e\")\n", + "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label=\"b3\", color=\"#f0af07\")\n", + "\n", + "# Add labels, title, and custom x-axis tick labels\n", + "ax.set_xlabel(\"Regions by feature property value\")\n", + "ax.set_ylabel(\"Series reduction value\")\n", + "ax.set_xticks(index + bar_width)\n", + "ax.set_xticklabels(features)\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Band names\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.Image.geetools.plot_by_regions`\n", + "- {docstring}`ee.Image.geetools.byRegions`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Column chart\n", + "\n", + "In this example, image bands representing average monthly temperature are reduced to the mean among pixels intersecting each of three ecoregions. The results are plotted as columns per month by ecoregion, where column height indicates the respective mean monthly temperature." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "normClim.geetools.plot_by_regions(\n", + " type=\"bar\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_tmean\",\n", + " \"02_tmean\",\n", + " \"03_tmean\",\n", + " \"04_tmean\",\n", + " \"05_tmean\",\n", + " \"06_tmean\",\n", + " \"07_tmean\",\n", + " \"08_tmean\",\n", + " \"09_tmean\",\n", + " \"10_tmean\",\n", + " \"11_tmean\",\n", + " \"12_tmean\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", + "ax.set_xlabel(\"Ecoregion\")\n", + "ax.set_ylabel(\"Temperature (°C)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bar chart \n", + "\n", + "The previous column chart can be swiped from vertical column to horizontal bars a bar chart by changing the `type` input from 'bar' to 'barh'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_regions(\n", + " type=\"barh\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_tmean\",\n", + " \"02_tmean\",\n", + " \"03_tmean\",\n", + " \"04_tmean\",\n", + " \"05_tmean\",\n", + " \"06_tmean\",\n", + " \"07_tmean\",\n", + " \"08_tmean\",\n", + " \"09_tmean\",\n", + " \"10_tmean\",\n", + " \"11_tmean\",\n", + " \"12_tmean\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", + "ax.set_ylabel(\"Ecoregion\")\n", + "ax.set_xlabel(\"Temperature (°C)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stacked bar chart \n", + "\n", + "An absolute stacked bar chart relates the total of a numeric variable by increments of a contributing categorical variable series. For instance, in this example, total precipitation is plotted as the accumulation of monthly precipitation over a year, by ecoregion. Monthly precipitation totals are derived from image bands, where each band represents a grid of average total precipitation for a given month, reduced to the mean of the pixels intersecting each of three ecoregions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_regions(\n", + " type=\"stacked\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Temperature by Ecoregion\")\n", + "ax.set_xlabel(\"Ecoregion\")\n", + "ax.set_ylabel(\"Temperature (°C)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot by bands\n", + "\n", + "Bands are plotted along the x-axis. Series are labeled by values of a feature property. Reduction of the region defined by the geometry of respective series features are plotted along the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Data for the chart\n", + "features = [\"b1\", \"b2\", \"b3\"]\n", + "p1_values = [0.5, 2.5, 4.5]\n", + "p2_values = [1.5, 3.5, 5.5]\n", + "p3_values = [2.5, 4.0, 6.5]\n", + "\n", + "# Set the width of the bars\n", + "bar_width = 0.25\n", + "index = np.arange(len(features))\n", + "offset = 0.005\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Plotting the bars\n", + "rects1 = ax.bar(index, p1_values, bar_width, label=\"r1\", color=\"#1d6b99\")\n", + "rects2 = ax.bar(index + (bar_width + offset), p2_values, bar_width, label=\"r2\", color=\"#cf513e\")\n", + "rects3 = ax.bar(index + 2 * (bar_width + offset), p3_values, bar_width, label=\"r3\", color=\"#f0af07\")\n", + "\n", + "# Add labels, title, and custom x-axis tick labels\n", + "ax.set_xlabel(\"band names\")\n", + "ax.set_ylabel(\"Series reduction value\")\n", + "ax.set_xticks(index + bar_width)\n", + "ax.set_xticklabels(features)\n", + "\n", + "# Add a legend\n", + "ax.legend(\n", + " loc=\"upper center\",\n", + " bbox_to_anchor=(0.85, 1.15),\n", + " ncol=3,\n", + " title=\"Regions by feature property value\",\n", + ")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colmun chart \n", + "\n", + "This chart shows total average precipitation per month for three ecoregions. The results are derived from the region reduction of an image where each band is a grid of average total precipitation for a given month. Bands are plotted along the x-axis and regions define the series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_bands(\n", + " type=\"bar\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Line chart \n", + "\n", + "The previous column chart can be rendered as a line chart by changing the `type` input from \"bar\" to \"plot\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_bands(\n", + " type=\"plot\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Area chart \n", + "\n", + "The previous column chart can be rendered as a line chart by changing the `type` input from \"plot\" to \"fill_between\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_bands(\n", + " type=\"fill_between\",\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation by Ecoregion\")\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Precipitation (mm)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pie chart \n", + "\n", + "Average monthly precipitation is displayed as a proportion of the average total annual precipitation for a forest ecoregion. Image bands representing monthly precipitation are subset from a climate normals dataset and reduced to the mean of pixels intersecting the ecoregion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "normClim.geetools.plot_by_bands(\n", + " type=\"pie\",\n", + " regions=ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation in Forest\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Donuts chart \n", + "\n", + "The previous chart can be represented as a donut by replacing the `type` parameter with `donut`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "fc = normClim.geetools.plot_by_bands(\n", + " type=\"donut\",\n", + " regions=ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[\n", + " \"01_ppt\",\n", + " \"02_ppt\",\n", + " \"03_ppt\",\n", + " \"04_ppt\",\n", + " \"05_ppt\",\n", + " \"06_ppt\",\n", + " \"07_ppt\",\n", + " \"08_ppt\",\n", + " \"09_ppt\",\n", + " \"10_ppt\",\n", + " \"11_ppt\",\n", + " \"12_ppt\",\n", + " ],\n", + " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " colors=[\n", + " \"#604791\",\n", + " \"#1d6b99\",\n", + " \"#39a8a7\",\n", + " \"#0f8755\",\n", + " \"#76b349\",\n", + " \"#f0af07\",\n", + " \"#e37d05\",\n", + " \"#cf513e\",\n", + " \"#96356f\",\n", + " \"#724173\",\n", + " \"#9c4f97\",\n", + " \"#696969\",\n", + " ],\n", + " ax=ax,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Average Monthly Precipitation in Forest\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## histogram plot \n", + "\n", + "A histogram of pixel values within a region surrounding Salt Lake City, Utah, USA are displayed for three MODIS surface reflectance bands. The histogram is plotted as a line chart, where x-axis values are pixel values and y-axis values are the frequency of pixels with the respective value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# custom image for this specific chart\n", + "modisSr = (\n", + " ee.ImageCollection(\"MODIS/061/MOD09A1\")\n", + " .filter(ee.Filter.date(\"2018-06-01\", \"2018-09-01\"))\n", + " .select([\"sur_refl_b01\", \"sur_refl_b02\", \"sur_refl_b06\"])\n", + " .mean()\n", + ")\n", + "histRegion = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# initialize the plot with the ecoregions data\n", + "modisSr.geetools.plot_hist(\n", + " bands=[\"sur_refl_b01\", \"sur_refl_b02\", \"sur_refl_b06\"],\n", + " labels=[[\"Red\", \"NIR\", \"SWIR\"]],\n", + " colors=[\"#cf513e\", \"#1d6b99\", \"#f0af07\"],\n", + " ax=ax,\n", + " bins=100,\n", + " scale=500,\n", + " region=histRegion,\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_title(\"Modis SR Reflectance Histogram\")\n", + "ax.set_xlabel(\"Reflectance (x1e4)\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/plot/plot-imagecollection.ipynb b/docs/usage/plot/plot-imagecollection.ipynb new file mode 100644 index 00000000..5baa1eec --- /dev/null +++ b/docs/usage/plot/plot-imagecollection.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot ImageCollection\n", + "\n", + "The `geetools` extention contains a set of functions for rendering charts from the results of spatiotemporal reduction of images within an `ee.ImageCollection`. The choice of function dictates the arrangement of data in the chart, i.e., what defines x- and y-axis values and what defines the series. Use the following function descriptions and examples to determine the best function for your purpose." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/plot/plot-imagecollection.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/plot/plot-imagecollection.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the required libs if necessary and perform the import satements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime as dt\n", + "\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data \n", + "\n", + "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The ImageCollection data loads the modis vegetation indicies and subset the 2010 2020 decade of images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Import the example feature collection and drop the data property.\n", + "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\").select(\n", + " [\"label\", \"value\", \"warm\"]\n", + ")\n", + "\n", + "\n", + "## Load MODIS vegetation indices data and subset a decade of images.\n", + "vegIndices = (\n", + " ee.ImageCollection(\"MODIS/061/MOD13A1\")\n", + " .filter(ee.Filter.date(\"2010-01-01\", \"2020-01-01\"))\n", + " .select([\"NDVI\", \"EVI\"])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot dates\n", + "\n", + "The `plot_dates*` methods will plot the values of the image collection using their dates as x-axis values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### series by bands \n", + "\n", + "Image date is plotted along the x-axis according to the `dateProperty` property. Series are defined by image bands. Y-axis values are the reduction of images, by date, for a single region." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Sample data (replace these with your actual data)\n", + "dates = [\"date1\", \"date2\", \"date3\"]\n", + "b1 = [1, 2, 1]\n", + "b2 = [2, 3, 2]\n", + "b3 = [3, 4, 3]\n", + "\n", + "# Create the plot\n", + "ax.plot(dates, b1, label=\"b1\", color=\"#1d6b99\")\n", + "ax.plot(dates, b2, label=\"b2\", color=\"#cf513e\")\n", + "ax.plot(dates, b3, label=\"b3\", color=\"#f0af07\")\n", + "\n", + "# Add titles and labels\n", + "ax.set_title(\"Single-region spatial reduction\")\n", + "ax.set_xlabel(\"Image date\")\n", + "ax.set_ylabel(\"Spatial reduction\")\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Band names\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_ylim(0, 5)\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `plot_series_by_bands` to display an image time series for a given region; each image band is presented as a unique series. It is useful for comparing the time series of individual image bands. Here, a MODIS image collection with bands representing NDVI and EVI vegetation indices are plotted. The date of every image observation is included along the x-axis, while the mean reduction of pixels intersecting a forest ecoregion defines the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "region = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\"))\n", + "vegIndices.geetools.plot_dates_by_bands(\n", + " region=region.geometry(),\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " bands=[\"NDVI\", \"EVI\"],\n", + " ax=ax,\n", + " dateProperty=\"system:time_start\",\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", + "ax.set_title(\"Average Vegetation index Values by date in the Forest ecoregion\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_dates_by_bands`\n", + "- {docstring}`ee.ImageCollection.geetools.datesByBands`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot series by region\n", + "\n", + "Image date is plotted along the x-axis according to the `dateProperty` property. Series are defined by regions. Y-axis values are the reduction of images, by date, for a single image band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Sample data (replace these with your actual data)\n", + "dates = [\"date1\", \"date2\", \"date3\"]\n", + "b1 = [1, 2, 1]\n", + "b2 = [2, 3, 2]\n", + "b3 = [3, 4, 3]\n", + "\n", + "# Create the plot\n", + "ax.plot(dates, b1, label=\"r1\", color=\"#1d6b99\")\n", + "ax.plot(dates, b2, label=\"r2\", color=\"#cf513e\")\n", + "ax.plot(dates, b3, label=\"r3\", color=\"#f0af07\")\n", + "\n", + "# Add titles and labels\n", + "ax.set_title(\"Single-band spatial reduction\")\n", + "ax.set_xlabel(\"Image date\")\n", + "ax.set_ylabel(\"Spatial reduction\")\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Regions\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_ylim(0, 5)\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `plot_dates_by_regions` to display a single image band time series for multiple regions; each region is presented as a unique series. It is useful for comparing the time series of a single band among several regions. Here, a MODIS image collection representing an NDVI time series is plotted for three ecoregions. The date of every image observation is included along the x-axis, while mean reduction of pixels intersecting forest, desert, and grasslands ecoregions define y-axis series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "region = ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\"))\n", + "vegIndices.geetools.plot_dates_by_regions(\n", + " band=\"NDVI\",\n", + " regions=ecoregions,\n", + " label=\"label\",\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " ax=ax,\n", + " dateProperty=\"system:time_start\",\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", + "ax.set_title(\"Average Vegetation index Values by date in the Forest ecoregion\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_dates_by_regions`\n", + "- {docstring}`ee.ImageCollection.geetools.datesByRegions`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PLot DOY\n", + "\n", + "DOY stands for day of year. The `plot_doyseries*` methods will plot the values of the image collection using the day of year as x-axis values.\n", + "\n", + "Note that `.plot_doyseries*` functions take two reducers: one for region reduction (`regionReducer`) and another for intra-annual coincident day-of-year reduction (`yearReducer`). Examples in the following sections use `ee.Reducer.mean()` as the argument for both of these parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot DOY by bands \n", + "\n", + "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by image bands. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Sample data (replace these with your actual data)\n", + "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", + "b1 = [1, 2, 1]\n", + "b2 = [2, 3, 2]\n", + "b3 = [3, 4, 3]\n", + "\n", + "# Create the plot\n", + "ax.plot(dates, b1, label=\"b1\", color=\"#1d6b99\")\n", + "ax.plot(dates, b2, label=\"b2\", color=\"#cf513e\")\n", + "ax.plot(dates, b3, label=\"b3\", color=\"#f0af07\")\n", + "\n", + "# Add titles and labels\n", + "ax.set_title(\"Single-region spatiotemporal reduction\")\n", + "ax.set_xlabel(\"Image date\")\n", + "ax.set_ylabel(\"Reduced values\")\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"Band names\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_ylim(0, 5)\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `plot_doy_by_bands` to display a day-of-year time series for a given region; each image band is presented as a unique series. It is useful for reducing observations occurring on the same day-of-year, across multiple years, to compare e.g. average annual NDVI and EVI profiles from MODIS, as in this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "vegIndices.geetools.plot_doy_by_bands(\n", + " region=ecoregions.filter(ee.Filter.eq(\"label\", \"Grassland\")).geometry(),\n", + " spatialReducer=\"mean\",\n", + " timeReducer=\"mean\",\n", + " scale=500,\n", + " bands=[\"NDVI\", \"EVI\"],\n", + " ax=ax,\n", + " dateProperty=\"system:time_start\",\n", + " colors=[\"#e37d05\", \"#1d6b99\"],\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"Vegetation indices (x1e4)\")\n", + "ax.set_title(\"Average Vegetation index Values by doy in the Grassland ecoregion\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_bands`\n", + "- {docstring}`ee.ImageCollection.geetools.doyByBands`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot doy by regions \n", + "\n", + "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by regions. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year, for a selected image band.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Sample data (replace these with your actual data)\n", + "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", + "b1 = [1, 2, 1]\n", + "b2 = [2, 3, 2]\n", + "b3 = [3, 4, 3]\n", + "\n", + "# Create the plot\n", + "ax.plot(dates, b1, label=\"r1\", color=\"#1d6b99\")\n", + "ax.plot(dates, b2, label=\"r2\", color=\"#cf513e\")\n", + "ax.plot(dates, b3, label=\"r3\", color=\"#f0af07\")\n", + "\n", + "# Add titles and labels\n", + "ax.set_title(\"Single-region spatiotemporal reduction\")\n", + "ax.set_xlabel(\"Image date\")\n", + "ax.set_ylabel(\"Reduced values\")\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"regions\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_ylim(0, 5)\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `plot_doy_by_regions` to display a single image band day-of-year time series for multiple regions, where each distinct region is presented as a unique series. It is useful for comparing annual single-band time series among regions. For instance, in this example, annual MODIS-derived NDVI profiles for forest, desert, and grassland ecoregions are plotted, providing a convenient comparison of NDVI response by region. Note that intra-annual observations occurring on the same day-of-year are reduced by their mean." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "vegIndices.geetools.plot_doy_by_regions(\n", + " regions=ecoregions,\n", + " label=\"label\",\n", + " spatialReducer=\"mean\",\n", + " timeReducer=\"mean\",\n", + " scale=500,\n", + " band=\"NDVI\",\n", + " ax=ax,\n", + " dateProperty=\"system:time_start\",\n", + " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"NDVI (x1e4)\")\n", + "ax.set_title(\"Average NDVI Values by doy in each ecoregion\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_regions`\n", + "- {docstring}`ee.ImageCollection.geetools.doyByRegions`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plot doy by year \n", + "\n", + "Image day-of-year is plotted along the x-axis according to the `dateProperty` property. Series are defined by years present in the ImageCollection. Y-axis values are the reduction of image pixels in a given region, grouped by day-of-year, for a selected image band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "# Sample data (replace these with your actual data)\n", + "dates = [\"doy1\", \"doy2\", \"doy3\"]\n", + "b1 = [1, 2, 1]\n", + "b2 = [2, 3, 2]\n", + "b3 = [3, 4, 3]\n", + "\n", + "# Create the plot\n", + "ax.plot(dates, b1, label=\"year1\", color=\"#1d6b99\")\n", + "ax.plot(dates, b2, label=\"year2\", color=\"#cf513e\")\n", + "ax.plot(dates, b3, label=\"year3\", color=\"#f0af07\")\n", + "\n", + "# Add titles and labels\n", + "ax.set_title(\"Single-region/band spatiotemporal reduction\")\n", + "ax.set_xlabel(\"Image date\")\n", + "ax.set_ylabel(\"Reduced values\")\n", + "\n", + "# Add a legend\n", + "ax.legend(loc=\"upper center\", bbox_to_anchor=(0.85, 1.15), ncol=3, title=\"iage years\")\n", + "\n", + "# set the grid display\n", + "ax.grid(axis=\"y\")\n", + "ax.set_ylim(0, 5)\n", + "ax.set_axisbelow(True)\n", + "ax.spines[\"top\"].set_visible(False)\n", + "ax.spines[\"right\"].set_visible(False)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `plot_doy_by_years` to display a day-of-year time series for a given region and image band, where each distinct year in the image collection is presented as a unique series. It is useful for comparing annual time series among years. For instance, in this example, annual MODIS-derived NDVI profiles for a grassland ecoregion are plotted for years 2012 and 2019, providing convenient year-over-year interpretation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# reduce the regions to grassland\n", + "grassland = ecoregions.filter(ee.Filter.eq(\"label\", \"Grassland\"))\n", + "\n", + "# for plot speed and lisibility only keep 2 years (2010 and 2020) for the example\n", + "indices = vegIndices.filter(\n", + " ee.Filter.Or(\n", + " ee.Filter.date(\"2012-01-01\", \"2012-12-31\"),\n", + " ee.Filter.date(\"2019-01-01\", \"2019-12-31\"),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "indices.geetools.plot_doy_by_years(\n", + " band=\"NDVI\",\n", + " region=grassland.geometry(),\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " ax=ax,\n", + " colors=[\"#39a8a7\", \"#9c4f97\"],\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"NDVI (x1e4)\")\n", + "ax.set_title(\"Average NDVI Values by day of year for Grassland\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_years`\n", + "- {docstring}`ee.ImageCollection.geetools.doyByYears`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plot doy by seasons \n", + "\n", + "In case the observation you want to analyse are only meaningful on a subset of the year a variant of the previous method allows you to plot the data by season. The season is defined by the `seasonStart` and `seasonEnd` parameters, which are 2 numbers between 1 and 366 representing the start and end of the season. To set them, the user can use the {py:method}`ee.Date.getRelative` or {py:class}`time.struct_time` method to get the day of the year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ee.Date(\"2022-06-01\").getRelative(\"day\", \"year\").getInfo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt(2022, 6, 1).timetuple().tm_yday" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "\n", + "indices.geetools.plot_doy_by_seasons(\n", + " band=\"NDVI\",\n", + " region=grassland.geometry(),\n", + " seasonStart=ee.Date(\"2022-04-15\").getRelative(\"day\", \"year\"),\n", + " seasonEnd=ee.Date(\"2022-09-15\").getRelative(\"day\", \"year\"),\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " ax=ax,\n", + " colors=[\"#39a8a7\", \"#9c4f97\"],\n", + ")\n", + "\n", + "# once created the axes can be modified as needed using pure matplotlib functions\n", + "ax.set_ylabel(\"NDVI (x1e4)\")\n", + "ax.set_title(\"Average NDVI Values during growing season in Grassland\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{api}\n", + "- {docstring}`ee.ImageCollection.geetools.plot_doy_by_seasons`\n", + "- {docstring}`ee.ImageCollection.geetools.doyBySeasons`\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/profile.ipynb b/docs/usage/profile.ipynb new file mode 100644 index 00000000..4ccbefa3 --- /dev/null +++ b/docs/usage/profile.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Profile Earth Engine computation\n", + "\n", + "The Earth Engine API provides tools for profiling the performance of your computations but they are not always the easiest to use to get the number you are looking for. The `geetools` library supercharge the original profiler to make any computation evaluation the easiest possible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/profile.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/profile.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the requireed libs if necessary. and perform the import satements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()\n", + "# ee.Intialize(project=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The Image data are PRISM climate normals, where bands describe climate variables per month; e.g., July precipitation or January mean temperature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\").select(\n", + " [\"label\", \"value\", \"warm\"]\n", + ")\n", + "\n", + "normClim = ee.ImageCollection(\"OREGONSTATE/PRISM/Norm91m\").toBands()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## default profiler \n", + "\n", + "The default profiler from Earth Engine can be called as a context manager, it will print at the end of the cell the extensive description of your computation. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ee.profilePrinting():\n", + " normClim.geetools.byBands(\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[f\"{i:02d}_tmean\" for i in range(1, 13)],\n", + " ).getInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This result is extremely useful but cannot be further explored in the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## geetools profiler\n", + "\n", + "The `geetools` profiler is a context manager object that fill a dictionary member (`profile`) with the content of the string profile. This dictionary can be transformed into a table easily." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example with a simple function\n", + "with ee.geetools.Profiler() as p:\n", + " ee.Number(3.14).add(0.00159).getInfo()\n", + "p.profile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a bigger method we can valorized the results as a pandas dataframe and extract key informations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with ee.geetools.Profiler() as p:\n", + " normClim.geetools.byBands(\n", + " regions=ecoregions,\n", + " reducer=\"mean\",\n", + " scale=500,\n", + " regionId=\"label\",\n", + " bands=[f\"{i:02d}_tmean\" for i in range(1, 13)],\n", + " ).getInfo()\n", + "df = pd.DataFrame(p.profile)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# total EECU cost of the computation\n", + "float(df[\"EECU-s\"].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/reduce.ipynb b/docs/usage/reduce.ipynb new file mode 100644 index 00000000..f30cab3e --- /dev/null +++ b/docs/usage/reduce.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reduce ImageCollection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/reduce.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/reduce.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "THe Earth Engine API provides 2 ways to reduce images: `reduceRegion` and `reduceRegion`. `geetools` is making these methods also available for `ee.ImageCollection` objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environment\n", + "\n", + "Install all the required libs if necessary and perform the import statements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if initialization is required\n", + "# ee.Initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example data \n", + "\n", + "The following examples rely on a `ee.FeatureCollection` composed of three ecoregion features that define regions by which to reduce image data. The ImageCollection data loads the modis vegetation indicies and subset the 2010 2020 decade of images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Import the example feature collection and drop the data property.\n", + "ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\").select(\n", + " [\"label\", \"value\", \"warm\"]\n", + ")\n", + "\n", + "\n", + "## Load MODIS vegetation indices data and subset of 4 images.\n", + "vegIndices = (\n", + " ee.ImageCollection(\"MODIS/061/MOD13A1\")\n", + " .filter(ee.Filter.date(\"2010-01-01\", \"2010-02-28\"))\n", + " .select([\"NDVI\", \"EVI\"])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reduce over single region\n", + "\n", + "Using `reduceRegion` you can reduce an `ee.ImageCollection` over a single region. The function will return a `ee.Dictionary` with the reduced values of each band grouped under each image Id as key.\n", + "\n", + "It will return a `ee.Dictionary` with the following shape: \n", + "\n", + "```\n", + "{\n", + " \"image1\": {\"band1\": value1, \"band2\": value2, ...},\n", + " \"image2\": {\"band1\": value1, \"band2\": value2, ...},\n", + "}\n", + "```\n", + "\n", + "where `image*`is the id of the image as per specified property (casted to string) and `band*` is the name of the band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vegIndices.geetools.reduceRegion(\n", + " reducer=ee.Reducer.mean(),\n", + " idProperty=\"system:time_start\",\n", + " idType=ee.Date,\n", + " geometry=ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")).geometry(),\n", + " scale=500,\n", + ").getInfo()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geetools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/usage/template.ipynb b/docs/usage/template.ipynb new file mode 100644 index 00000000..06562b2b --- /dev/null +++ b/docs/usage/template.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# example Template" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import ee\n", + "\n", + "from geetools.utils import initialize_documentation\n", + "\n", + "initialize_documentation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![github](https://img.shields.io/badge/-see%20sources-white?logo=github&labelColor=555)](https://github.com/gee-community/geetools/blob/main/docs/usage/template.ipynb)\n", + "[![colab](https://img.shields.io/badge/-open%20in%20colab-blue?logo=googlecolab&labelColor=555)](https://colab.research.google.com/github/gee-community/geetools/blob/main/docs/usage/template.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up environement\n", + "\n", + "Install all the required libs if necessary and perform the import statements upstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if installation of libs is necessary\n", + "# !pip install earthengine-api geetools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if authetication to GEE is needed\n", + "# ee.Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment if initialization is required\n", + "# ee.Initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Truncate a number\n", + "\n", + "This template is demonstrating how to truncate a EE number to a given number of decimal places." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize your GEE object\n", + "pi = 3.14159265359\n", + "ee_pi = ee.Number(pi)\n", + "ee_pi.getInfo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# truncate the number to 3 decimals directly in GEE\n", + "ee_pi_truncated = ee_pi.geetools.truncate(3)\n", + "ee_pi_truncated.getInfo()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/geetools/__init__.py b/geetools/__init__.py index a9143982..8640ac08 100644 --- a/geetools/__init__.py +++ b/geetools/__init__.py @@ -7,38 +7,38 @@ import ee +# import the accessor namespace +from .accessors import geetools + +# it needs to be imported first as it's the mother class +from .ee_computed_object import * + # reproduce older structure of the lib (deprecated) # will be removed along the deprecation cycle from . import _deprecated_algorithms as algorithms from . import _deprecated_composite as composite - -# import the accessor namespace -from .accessors import geetools -from .ee_array import ArrayAccessor +from .tools import imagecollection # then we extend all the other classes from .ee_asset import Asset -from .ee_authenticate import AuthenticateAccessor - -# it needs to be imported first as it's the mother class -from .ee_computed_object import * # noqa: F403 from .ee_date import DateAccessor -from .ee_date_range import DateRangeAccessor from .ee_dictionary import DictionaryAccessor -from .ee_export import ExportAccessor from .ee_feature import FeatureAccessor from .ee_feature_collection import FeatureCollectionAccessor from .ee_filter import FilterAccessor from .ee_geometry import GeometryAccessor from .ee_image import ImageAccessor -from .ee_image_collection import ImageCollectionAccessor -from .ee_initialize import InitializeAccessor from .ee_join import JoinAccessor from .ee_list import ListAccessor from .ee_number import NumberAccessor -from .ee_profiler import Profiler from .ee_string import StringAccessor -from .tools import imagecollection +from .ee_image_collection import ImageCollectionAccessor +from .ee_initialize import InitializeAccessor +from .ee_authenticate import AuthenticateAccessor +from .ee_array import ArrayAccessor +from .ee_date_range import DateRangeAccessor +from .ee_export import ExportAccessor +from .ee_profiler import Profiler __version__ = "1.9.1" __author__ = "Rodrigo E. Principe" diff --git a/pyproject.toml b/pyproject.toml index d4270d69..d209c1e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,6 +80,7 @@ testpaths = "tests" [tool.ruff] line-length = 100 fix = true +extend-exclude = ["geetools/__init__.py"] [tool.ruff.lint] select = ["E", "F", "W", "I", "D", "RUF"] @@ -96,7 +97,6 @@ docstring-quotes = "double" convention = "google" [tool.ruff.lint.per-file-ignores] -"geetools/__init__.py" = ["F401"] # import but unused | that used to work with ignore_init_import "tests/*" = ["D102"] # Missing docstring in public method | this is test who cares [tool.coverage.run] From 6254b7f5c379a31277d40cc4da19156882e7b596 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 15:53:07 +0000 Subject: [PATCH 6/9] fix: refactor las tfiles --- .github/workflows/unit.yaml | 7 +++-- AUTHORS.rst | 45 +++++++++++++++++++++++++-- CITATION.cff | 6 ++-- CONTRIBUTING.rst | 51 ++++++++++++++++++++++++++---- LICENSE | 2 +- README.rst | 62 ++++++++++++++++++++++++++++++++----- RELEASE.rst | 35 +++++++++++++++++++++ noxfile.py | 5 ++- pyproject.toml | 53 ++++++++++++++++++++++--------- 9 files changed, 228 insertions(+), 38 deletions(-) create mode 100644 RELEASE.rst diff --git a/.github/workflows/unit.yaml b/.github/workflows/unit.yaml index 5bc1935d..7e12e6ae 100644 --- a/.github/workflows/unit.yaml +++ b/.github/workflows/unit.yaml @@ -8,6 +8,8 @@ on: pull_request: env: + EARTHENGINE_SERVICE_ACCOUNT: ${{ secrets.EARTHENGINE_SERVICE_ACCOUNT }} + EARTHENGINE_PROJECT: ${{ secrets.EARTHENGINE_PROJECT }} FORCE_COLOR: 1 PIP_ROOT_USER_ACTION: ignore @@ -49,10 +51,10 @@ jobs: build: needs: [lint, mypy] strategy: - fail-fast: true + fail-fast: false matrix: os: [ubuntu-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ["3.9", "3.10", "3.11"] include: - os: macos-latest # macos test python-version: "3.11" @@ -93,3 +95,4 @@ jobs: file: ./coverage.xml token: ${{ secrets.CODECOV_TOKEN }} verbose: true + fail_ci_if_error: true diff --git a/AUTHORS.rst b/AUTHORS.rst index d655e1ff..30bba2ab 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -6,11 +6,50 @@ Thanks goes to these wonderful people (`emoji key - - gee-community
+ + 12rambau
+ Rodrigo E. Principe + + + + + 12rambau
Pierrick Rambaud - 💻 + + + + 12rambau
+ Marc Rußwurm + + + + + + + 12rambau
+ hubert-crea + + + + + 12rambau
+ Sam Murphy + + + + + 12rambau
+ lumbric + + + + + + + 12rambau
+ emmanuel-ferdman + diff --git a/CITATION.cff b/CITATION.cff index 4daaf511..07722944 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -1,11 +1,13 @@ cff-version: "1.2.0" message: "If you use this software, please cite it as below." authors: + - family-names: "Esteban Principe" + given-names: "Rodrigo" - family-names: "Rambaud" given-names: "Pierrick" orcid: "https://orcid.org/0000-0001-8764-5749" title: "geetools" -version: "0.0.0" +version: "1.9.1" doi: "" -date-released: "2024-12-09" +date-released: "2017-11-15" url: "https://github.com/gee-community/geetools" diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index a10bc443..d56adef7 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -66,6 +66,47 @@ Lastly, activate the pre-commit hooks by running: This will install the necessary dependencies to run pre-commit every time you make a commit with Git. +Initialize GEE available +^^^^^^^^^^^^^^^^^^^^^^^^ + +All the ``geetools`` package is build around the Google Earth Engine API. It is thus impossible to work wihin it if you are not registered. Please follow `Google instructions `__ to use the tool. + +Once you get access to GEE, you have 2 authentication options to work within `geetools`: + +#. Specify your project +#. use a service account + +Specify your project +#################### + +Here we assume your machine is already authenticated to GEE and some credentials are saved in your computer in the confi folder: ``~/.config/earthengine/credentials``. + +In this case ``geetools`` ``docs`` and ``test`` session will only need to get the name of the project to use. Specify it in a environment variable: + +.. code-block:: console + + export EARTHENGINE_PROJECT= + + +Service account +############### + +.. note:: + + This is the method used by all the CI/CD project pipeline from documentation to deployment. + +If your machine is not authenticated you can use a service account from the GCP console and save its json API key in an environment variable: + +.. code-block:: console + + export EARTHENGINE_SERVICE_ACCOUNT= + +The key should have the following format and is generated from the `GCP console `__: + +.. code-block:: json + + {"client_id": "value", "client_secret": "value", "refresh_token": "value", "project": "value"} + Contribute to the codebase ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -97,12 +138,10 @@ For each pull request, the documentation is built and deployed to make it easier Release new version ^^^^^^^^^^^^^^^^^^^ -To release a new version, start by pushing a new bump from the local directory: - -.. code-block:: +.. danger:: - cz bump + Only maintainers can release new versions of **geetools**. -The commitizen-tool will detect the semantic version name based on the existing commits messages. +To release a new version, open an issue with the new version number e.g. ``RLS: 1.0.0``. copy/paste the instructions from the `release instructions `__ and follow the presented workflow. -Then push to Github. In Github design a new release using the same tag name nad the ``release.yaml`` job will send it to pipy. +Once you are done you can close the issue and celebrate! diff --git a/LICENSE b/LICENSE index a1ef2a38..8127d6ff 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2017 Pierrick Rambaud +Copyright (c) 2017 Rodrigo E. Principe Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.rst b/README.rst index ccb5842c..f9c9e379 100644 --- a/README.rst +++ b/README.rst @@ -18,14 +18,18 @@ geetools :target: https://github.com/prettier/prettier :alt: prettier badge -.. |pre-commmit| image:: https://img.shields.io/badge/pre--commit-active-yellow?logo=pre-commit&logoColor=white +.. |pre-commit| image:: https://img.shields.io/badge/pre--commit-active-yellow?logo=pre-commit&logoColor=white :target: https://pre-commit.com/ :alt: pre-commit -.. |pypi| image:: https://img.shields.io/pypi/v/geetools?color=blue&logo=pypi&logoColor=white +.. |pypi| image:: https://img.shields.io/pypi/v/geetools?color=blue&logo=python&logoColor=white :target: https://pypi.org/project/geetools/ :alt: PyPI version +.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/geetools?color=blue&logo=anaconda&logoColor=white + :target: https://github.com/conda-forge/geetools-feedstock + :alt: Conda-forge version + .. |build| image:: https://img.shields.io/github/actions/workflow/status/gee-community/geetools/unit.yaml?logo=github&logoColor=white :target: https://github.com/gee-community/geetools/actions/workflows/unit.yaml :alt: build @@ -35,15 +39,59 @@ geetools :alt: Test Coverage .. |docs| image:: https://img.shields.io/readthedocs/geetools?logo=readthedocs&logoColor=white - :target: https://geetools.readthedocs.io/en/latest/ + :target: https://app.readthedocs.org/projects/geetools/ :alt: Documentation Status -|license| |commit| |ruff| |prettier| |pre-commmit| |pypi| |build| |coverage| |docs| +|license| |commit| |ruff| |prettier| |pre-commmit| |pypi| |conda| |build| |coverage| |docs| + +Google Earth Engine tools +------------------------- + +.. image:: https://raw.githubusercontent.com/gee-community/geetools/main/docs/_static/logo.svg + :width: 20% + :align: right + +`Google Earth Engine `__ is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a `JavaScript and a Python API `__ with different methods to process geospatial objects. + +The **geetools** package extends the Google Earth Engine Python API with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different Earth Engine Objects that are friendly with the Python method chaining using the ``geetools`` namespace. + +There is JavaScript module that you can import from the code editor that has +similar functions (not exactly the same) and it's available `here `__. + +Installation +------------ + +.. code-block:: python + + pip install geetools + +Why using it ? +-------------- + +New utility methods and constructors are added to most of the GEE classes. They can be simple wrapper for repetitive tasks, complex algorithm or mandatory preprocessing steps. The goal is to make the code more fluid and easy to read for researchers, students and analysts. + +The package design is mostly performing server-side computation making it also very friendly with commercial users of Earth Engine. + +This small example wrapping of the excellent ``ee_extra`` package functionalities shows how to preprocess sentinel 2 data in 5 lines of code: + +.. code-block:: python + + import ee + import geetools #noqa: F401 + import pygaul # another gee-community package to access FAO GAUl 2015 dataset + + # we assume you are already authenticated to GEE + ee.Initialize.geetools.from_account("toto") # yes we also support multi users -Overview --------- + amazonas = pygaul.Items(name="Amazonas").centroid() -A collection of tools to work with Google Earth Engine Python API + S2 = ( + ee.ImageCollection('COPERNICUS/S2_SR') + .filterBounds(point) + .geetools.closest('2020-10-15') # Extended (pre-processing) + .geetools.maskClouds(prob = 70) # Extended (pre-processing) + .geetools.scaleAndOffset() # Extended (pre-processing) + .geetools.spectralIndices(['NDVI','NDWI','BAIS2'])) # Extended (processing) Credits ------- diff --git a/RELEASE.rst b/RELEASE.rst new file mode 100644 index 00000000..28565e44 --- /dev/null +++ b/RELEASE.rst @@ -0,0 +1,35 @@ +Release instructions +==================== + +This page contains the steps to make a release and some helpful resources to get you started. + +Create an issue and copy/paste the steps below to release a new version. Close the issue when it is done. + +These steps should be taken in order to create a new release! + +.. code-block:: md + + **Double check for quality-control** + + - [ ] There are no [open issues with a `impact: block-release` label](https://github.com/gee-community/geetools/labels/impact%3A%20block-release) + + **Prepare the codebase for a new version** + + - [ ] Bump a new version using `commitizen`. use the `--pre-release` flag if it's a **release candidate**. + - [ ] Push the RLS commit `git push upstream main` + - [ ] If a **release candidate** is needed, tick this box when we're now ready for a full release. + + **Make the release** + + - [ ] [Start a new GitHub release](https://github.com/gee-community/geetools/releases/new) + - Call the release the current version, e.g. `v0.2.0` + - In the **`Choose a Tag:`** dropdown, type in the release name (e.g., `v0.2.0`) and click "Create new tag" + - In the **`Target:`** dropdown, pin it to the release commit that you've just pushed. + - Generate the automatic release notes, eventually manually specify the previous version (useful when several release candidate have been made) + - [ ] Confirm that the release completed + - [The `publish` github action job](https://github.com/gee-community/geetools/actions/workflows/release.yaml) has completed successfully in the [actions tab](https://github.com/gee-community/geetools/actions). + - [The PyPI version is updated](https://pypi.org/project/geetools/) + - [ ] Hide the previous patch version build in the RDT interface if needed. + - [ ] Celebrate, you're done! + + Taken from [the release checklist](https://github.com/gee-community/geetools/blob/main/RELEASE.rst). diff --git a/noxfile.py b/noxfile.py index 4fd34326..b21043bd 100644 --- a/noxfile.py +++ b/noxfile.py @@ -45,9 +45,8 @@ def docs(session): """Build the documentation.""" build = session.posargs.pop() if session.posargs else "html" session.install(".[doc]") - dst, warn = f"docs/_build/{build}", "warnings.txt" - session.run("sphinx-build", "-v", "-b", build, "docs", dst, "-w", warn) - session.run("python", "tests/check_warnings.py") + dst = f"docs/_build/{build}" + session.run("sphinx-build", "-v", "-b", build, "docs", dst) @nox.session(name="mypy", reuse_venv=True, venv_backend="uv") diff --git a/pyproject.toml b/pyproject.toml index d209c1e6..5845ba86 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,29 +4,41 @@ build-backend = "hatchling.build" [project] name = "geetools" -version = "0.0.0" +version = "1.9.1" description = "A collection of tools to work with Google Earth Engine Python API" keywords = [ - "skeleton", - "Python" + "python", + "geospatial", + "remote-sensing", + "google-earth-engine", + "earthengine", ] classifiers = [ - "Development Status :: 3 - Alpha", + "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", + "Topic :: Software Development :: Build Tools", "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ] -requires-python = ">=3.8" +requires-python = ">=3.9" dependencies = [ - "deprecated>=1.2.14" + "earthengine-api>=1", # fully static implementation + "requests", + "pandas", + "geopandas", + "deprecated", + "ee-extra", + "xee>=0.0.11", # xee change dtype management + "yamlable", + "matplotlib", + "anyascii", ] [[project.authors]] -name = "Pierrick Rambaud" -email = "pierrick.rambaud49@gmail.com" +name = "Rodrigo E. Principe" +email = "fitoprincipe82@gmail.com" [project.license] text = "MIT" @@ -40,16 +52,28 @@ Homepage = "https://github.com/gee-community/geetools" [project.optional-dependencies] test = [ - "pytest", - "pytest-cov", - "pytest-deadfixtures" + "pytest", + "pytest-cov", + "pytest-deadfixtures", + "httplib2", + "pytest-regressions", + "Pillow", + "pytest-gee>=0.3.5", # to make IC creation possible in the test suit + "jsonschema", ] doc = [ "sphinx>=6.2.1", "pydata-sphinx-theme", "sphinx-copybutton", "sphinx-design", - "sphinx-autoapi" + "sphinx-autoapi>=3.1", # single page implementation + "ipykernel", + "httplib2", + "jupyter-sphinx", + "myst-nb", + "pytest-gee>=0.3.7", # avoid issue with rdt env variables + "sphinx-icon", + "sphinx-last-updated-by-git", ] [tool.hatch.build.targets.wheel] @@ -66,7 +90,7 @@ post-install-commands = ["pre-commit install"] [tool.commitizen] tag_format = "v$major.$minor.$patch$prerelease" update_changelog_on_bump = false -version = "0.0.0" +version = "1.9.1" version_files = [ "pyproject.toml:version", "geetools/__init__.py:__version__", @@ -101,6 +125,7 @@ convention = "google" [tool.coverage.run] source = ["geetools"] +branch = true [tool.mypy] scripts_are_modules = true From b6361dba96eeda11397632f5a05a142c62bdce62 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 16:20:37 +0000 Subject: [PATCH 7/9] fix: rollback the docs examples --- docs/usage/asset.ipynb | 4 +- docs/usage/export.ipynb | 4 +- docs/usage/plot/map-featurecollection.ipynb | 4 +- docs/usage/plot/map-image.ipynb | 4 +- docs/usage/plot/plot-featurecollection.ipynb | 145 +++------- docs/usage/plot/plot-image.ipynb | 263 +++++-------------- docs/usage/plot/plot-imagecollection.ipynb | 4 +- docs/usage/profile.ipynb | 4 +- docs/usage/reduce.ipynb | 4 +- docs/usage/template.ipynb | 4 +- 10 files changed, 129 insertions(+), 311 deletions(-) diff --git a/docs/usage/asset.ipynb b/docs/usage/asset.ipynb index 469bf8e0..25c157da 100644 --- a/docs/usage/asset.ipynb +++ b/docs/usage/asset.ipynb @@ -56,7 +56,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import geetools # noqa: F401" + ] }, { "cell_type": "code", diff --git a/docs/usage/export.ipynb b/docs/usage/export.ipynb index 2399848d..e4cea9ae 100644 --- a/docs/usage/export.ipynb +++ b/docs/usage/export.ipynb @@ -50,7 +50,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import geetools # noqa: F401" + ] }, { "cell_type": "code", diff --git a/docs/usage/plot/map-featurecollection.ipynb b/docs/usage/plot/map-featurecollection.ipynb index 7f0b35ea..3f5b7817 100644 --- a/docs/usage/plot/map-featurecollection.ipynb +++ b/docs/usage/plot/map-featurecollection.ipynb @@ -59,7 +59,9 @@ "metadata": {}, "outputs": [], "source": [ - "from matplotlib import pyplot as plt" + "from matplotlib import pyplot as plt\n", + "\n", + "import geetools # noqa: F401" ] }, { diff --git a/docs/usage/plot/map-image.ipynb b/docs/usage/plot/map-image.ipynb index bb5fb5e7..122e5c55 100644 --- a/docs/usage/plot/map-image.ipynb +++ b/docs/usage/plot/map-image.ipynb @@ -59,7 +59,9 @@ "metadata": {}, "outputs": [], "source": [ - "from matplotlib import pyplot as plt" + "from matplotlib import pyplot as plt\n", + "\n", + "import geetools # noqa: F401" ] }, { diff --git a/docs/usage/plot/plot-featurecollection.ipynb b/docs/usage/plot/plot-featurecollection.ipynb index ff7bc8e5..3760a0cc 100644 --- a/docs/usage/plot/plot-featurecollection.ipynb +++ b/docs/usage/plot/plot-featurecollection.ipynb @@ -59,7 +59,11 @@ "metadata": {}, "outputs": [], "source": [ - "from matplotlib import pyplot as plt" + "from datetime import datetime as dt\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import geetools # noqa: F401" ] }, { @@ -181,39 +185,21 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors for to use in the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "\n", "# initialize the plot with the ecoregions data\n", "ecoregions.geetools.plot_by_features(\n", " type=\"bar\",\n", " featureId=\"label\",\n", - " properties=[\n", - " \"01_tmean\",\n", - " \"02_tmean\",\n", - " \"03_tmean\",\n", - " \"04_tmean\",\n", - " \"05_tmean\",\n", - " \"06_tmean\",\n", - " \"07_tmean\",\n", - " \"08_tmean\",\n", - " \"09_tmean\",\n", - " \"10_tmean\",\n", - " \"11_tmean\",\n", - " \"12_tmean\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " properties=[f\"0{i}_tmean\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\").lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -241,39 +227,22 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors for to use in the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "# fmt: on\n", + "\n", "# initialize theplot with the ecoregions data\n", "ecoregions.geetools.plot_by_features(\n", " type=\"stacked\",\n", " featureId=\"label\",\n", - " properties=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " properties=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\").lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -473,21 +442,8 @@ "# initialize theplot with the ecoregions data\n", "ax = ecoregions.geetools.plot_by_properties(\n", " type=\"bar\",\n", - " properties=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " properties=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\").lower() for m in range(1, 13)],\n", " featureId=\"label\",\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", @@ -520,22 +476,9 @@ "# initialize theplot with the ecoregions data\n", "ax = ecoregions.geetools.plot_by_properties(\n", " type=\"plot\",\n", - " properties=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", + " properties=[f\"0{i}_ppt\" for i in range(1, 13)],\n", " featureId=\"label\",\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\").lower() for m in range(1, 13)],\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", ")\n", @@ -567,21 +510,8 @@ "# initialize the plot with the ecoregions data\n", "ax = ecoregions.geetools.plot_by_properties(\n", " type=\"fill_between\",\n", - " properties=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " properties=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\").lower() for m in range(1, 13)],\n", " featureId=\"label\",\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", @@ -632,13 +562,6 @@ "ax.set_title(\"July Precipitation Distribution for NW USA\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/usage/plot/plot-image.ipynb b/docs/usage/plot/plot-image.ipynb index 1b025f5f..9fec375e 100644 --- a/docs/usage/plot/plot-image.ipynb +++ b/docs/usage/plot/plot-image.ipynb @@ -59,7 +59,11 @@ "metadata": {}, "outputs": [], "source": [ - "from matplotlib import pyplot as plt" + "from datetime import datetime as dt\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import geetools # noqa: F401" ] }, { @@ -183,41 +187,24 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors to use for the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "# fmt: on\n", + "\n", "normClim.geetools.plot_by_regions(\n", " type=\"bar\",\n", " regions=ecoregions,\n", " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_tmean\",\n", - " \"02_tmean\",\n", - " \"03_tmean\",\n", - " \"04_tmean\",\n", - " \"05_tmean\",\n", - " \"06_tmean\",\n", - " \"07_tmean\",\n", - " \"08_tmean\",\n", - " \"09_tmean\",\n", - " \"10_tmean\",\n", - " \"11_tmean\",\n", - " \"12_tmean\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " bands=[f\"0{i}_tmean\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -245,41 +232,23 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors to use for the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "\n", "fc = normClim.geetools.plot_by_regions(\n", " type=\"barh\",\n", " regions=ecoregions,\n", " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_tmean\",\n", - " \"02_tmean\",\n", - " \"03_tmean\",\n", - " \"04_tmean\",\n", - " \"05_tmean\",\n", - " \"06_tmean\",\n", - " \"07_tmean\",\n", - " \"08_tmean\",\n", - " \"09_tmean\",\n", - " \"10_tmean\",\n", - " \"11_tmean\",\n", - " \"12_tmean\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " bands=[f\"0{i}_tmean\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime('%b')[:3].lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -307,41 +276,24 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors to use for the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "# fmt: on\n", + "\n", "fc = normClim.geetools.plot_by_regions(\n", " type=\"stacked\",\n", " regions=ecoregions,\n", " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -441,21 +393,8 @@ " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", ")\n", @@ -490,21 +429,8 @@ " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", ")\n", @@ -539,21 +465,8 @@ " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", " colors=[\"#f0af07\", \"#0f8755\", \"#76b349\"],\n", " ax=ax,\n", ")\n", @@ -582,41 +495,24 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors to use for the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "# fmt: on\n", + "\n", "normClim.geetools.plot_by_bands(\n", " type=\"pie\",\n", " regions=ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -642,41 +538,24 @@ "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", + "# define the colors to use for the display\n", + "# fmt: off\n", + "colors = [\n", + " \"#604791\", \"#1d6b99\", \"#39a8a7\", \"#0f8755\",\n", + " \"#76b349\", \"#f0af07\", \"#e37d05\", \"#cf513e\",\n", + " \"#96356f\", \"#724173\", \"#9c4f97\", \"#696969\",\n", + "]\n", + "# fmt: on\n", + "\n", "fc = normClim.geetools.plot_by_bands(\n", " type=\"donut\",\n", " regions=ecoregions.filter(ee.Filter.eq(\"label\", \"Forest\")),\n", " reducer=\"mean\",\n", " scale=500,\n", " regionId=\"label\",\n", - " bands=[\n", - " \"01_ppt\",\n", - " \"02_ppt\",\n", - " \"03_ppt\",\n", - " \"04_ppt\",\n", - " \"05_ppt\",\n", - " \"06_ppt\",\n", - " \"07_ppt\",\n", - " \"08_ppt\",\n", - " \"09_ppt\",\n", - " \"10_ppt\",\n", - " \"11_ppt\",\n", - " \"12_ppt\",\n", - " ],\n", - " labels=[\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"],\n", - " colors=[\n", - " \"#604791\",\n", - " \"#1d6b99\",\n", - " \"#39a8a7\",\n", - " \"#0f8755\",\n", - " \"#76b349\",\n", - " \"#f0af07\",\n", - " \"#e37d05\",\n", - " \"#cf513e\",\n", - " \"#96356f\",\n", - " \"#724173\",\n", - " \"#9c4f97\",\n", - " \"#696969\",\n", - " ],\n", + " bands=[f\"0{i}_ppt\" for i in range(1, 13)],\n", + " labels=[dt(2000, m, 1).strftime(\"%b\")[:3].lower() for m in range(1, 13)],\n", + " colors=colors,\n", " ax=ax,\n", ")\n", "\n", @@ -752,7 +631,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/docs/usage/plot/plot-imagecollection.ipynb b/docs/usage/plot/plot-imagecollection.ipynb index 5baa1eec..1ac3a48b 100644 --- a/docs/usage/plot/plot-imagecollection.ipynb +++ b/docs/usage/plot/plot-imagecollection.ipynb @@ -61,7 +61,9 @@ "source": [ "from datetime import datetime as dt\n", "\n", - "from matplotlib import pyplot as plt" + "from matplotlib import pyplot as plt\n", + "\n", + "import geetools # noqa: F401" ] }, { diff --git a/docs/usage/profile.ipynb b/docs/usage/profile.ipynb index 4ccbefa3..e9c5477f 100644 --- a/docs/usage/profile.ipynb +++ b/docs/usage/profile.ipynb @@ -59,7 +59,9 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd" + "import pandas as pd\n", + "\n", + "import geetools # noqa: F401" ] }, { diff --git a/docs/usage/reduce.ipynb b/docs/usage/reduce.ipynb index f30cab3e..ba0b2889 100644 --- a/docs/usage/reduce.ipynb +++ b/docs/usage/reduce.ipynb @@ -63,7 +63,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import geetools # noqa: F401" + ] }, { "cell_type": "code", diff --git a/docs/usage/template.ipynb b/docs/usage/template.ipynb index 06562b2b..f71364d8 100644 --- a/docs/usage/template.ipynb +++ b/docs/usage/template.ipynb @@ -56,7 +56,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import geetools # noqa: F401" + ] }, { "cell_type": "code", From 7e952934181816091c1585ac63ef014befa00cb8 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 16:24:04 +0000 Subject: [PATCH 8/9] fix: correct image link --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index f9c9e379..e86823ac 100644 --- a/README.rst +++ b/README.rst @@ -42,7 +42,7 @@ geetools :target: https://app.readthedocs.org/projects/geetools/ :alt: Documentation Status -|license| |commit| |ruff| |prettier| |pre-commmit| |pypi| |conda| |build| |coverage| |docs| +|license| |commit| |ruff| |prettier| |pre-commit| |pypi| |conda| |build| |coverage| |docs| Google Earth Engine tools ------------------------- From a769c5a0ad11eef200f219c59a8d9b929a98ffc9 Mon Sep 17 00:00:00 2001 From: Pierrick Rambaud Date: Mon, 9 Dec 2024 16:41:48 +0000 Subject: [PATCH 9/9] fix: mypy cannot be run on uv for old libs --- noxfile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index b21043bd..a9b7051f 100644 --- a/noxfile.py +++ b/noxfile.py @@ -49,7 +49,7 @@ def docs(session): session.run("sphinx-build", "-v", "-b", build, "docs", dst) -@nox.session(name="mypy", reuse_venv=True, venv_backend="uv") +@nox.session(name="mypy", reuse_venv=True) def mypy(session): """Run a mypy check of the lib.""" session.install("mypy")

+ From + @12rambau/pypackage + 0.1.16 Copier project. +