From 016e1901f3b1c7fcd01ba46c2154091b28465da5 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Mon, 24 Mar 2025 16:24:11 +0100 Subject: [PATCH 01/54] ci: add gitlab-ci.yml --- .gitlab-ci.yml | 102 + conda-lock.yml | 2691 +++++++++++++++++ .../scripts/check_mccabe_complexity.sh | 36 + environment.yml | 215 ++ pyproject.toml | 71 + 5 files changed, 3115 insertions(+) create mode 100644 .gitlab-ci.yml create mode 100644 conda-lock.yml create mode 100755 continuous_integration/scripts/check_mccabe_complexity.sh create mode 100644 environment.yml diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000..9be42f8 --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,102 @@ +workflow: + rules: + - if: $CI_PIPELINE_SOURCE == "merge_request_event" + - if: $CI_PIPELINE_SOURCE == "web" + - if: $CI_PIPELINE_SOURCE == "schedule" + - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS + when: never + - if: $CI_COMMIT_BRANCH + +stages: + - init + - quality + +# Default configuration for all jobs +default: + tags: + - Usine_Logicielle + timeout: 30 minutes + +variables: + DEBUG: + value: 'false' + description: "Afficher des logs supplémentaires" + + TAG_IMAGE_CONDA: "publicremotes-docker/continuumio/miniconda3:23.5.2-0" + TAG_IMAGE_SONAR: "publicremotes-docker/sonarsource/sonar-scanner-cli:4.5" + CI: "true" + JFROG_CLI_HOME_DIR: ".jfrog/" + JFROG_CLI_TEMP_DIR: ".jfrog_tmp" + JFROG_VERSION: "v2/2.14.0" + JFROG_OS: "jfrog-cli-linux-amd64" + JFROG_CLI_BUILD_NAME: "${CI_PROJECT_PATH}_${CI_COMMIT_REF_SLUG}_gitlab-ci" + JFROG_CLI_BUILD_NUMBER: "${CI_PIPELINE_ID}" + + ARTIFACTORY_BUILD_URL: "https://${artifactory_host}/artifactory/webapp/#/builds/${JFROG_CLI_BUILD_NAME}/${JFROG_CLI_BUILD_NUMBER}" + CI_TEMPLATE_REGISTRY_HOST: "${ARTIFACTORY_HOST}/publicremotes-docker" + PIP_INDEX_URL: "https://${ARTIFACTORY_USER}:${ARTIFACTORY_TOKEN}@${ARTIFACTORY_HOST}/artifactory/api/pypi/pypi/simple" + PIP_CERT: "${CNES_CERTIFICATE}" + PIP_CACHE_DIR: ".pip-cache/" + CONDA_ENVS_DIRS: ".conda/envs" + CONDA_SSL_VERIFY: "${CNES_CERTIFICATE}" + PIP_SSL_VERIFY: "${CNES_CERTIFICATE}" + REQUESTS_CA_BUNDLE: "${CNES_CERTIFICATE}" + +build-env: + stage: init + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + before_script: + - export no_proxy=$NO_PROXY + - export http_proxy=$HTTP_PROXY + - export https_proxy=$HTTP_PROXY + - mkdir -p ${CONDA_ENVS_DIRS} + - pip install conda-lock + script: + - conda-lock install --name lenapy_env conda-lock.yml + timeout: 15 minutes + artifacts: + untracked: true + expire_in: 1 day + +formater: + stage: quality + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + before_script: + - source activate lenapy_env + - pip install .[formatter] + script: + - python -m black --check --diff lenapy + - python -m isort lenapy --check --diff + +pytest: + stage: quality + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + script: + - source activate lenapy_env + - pip install -e .[test] + - pytest -s tests -m ci --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report=term --junitxml=.ci-reports/junit-report.xml + artifacts: + paths: + - ./.ci-reports/ + expire_in: 1 day + +pylint_analysis: + stage: quality + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + script: + - source activate lenapy_env + - pip install .[quality] + - pylint --recursive=y --disable=all --fail-under=10 --enable=too-many-statements,too-many-nested-blocks lenapy | tee pylint_report.txt + artifacts: + when: always + paths: + - pylint_report.txt + expire_in: 1 day + +mccabe_analysis: + stage: quality + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + script: + - source activate lenapy_env + - pip install .[quality] + - ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy diff --git a/conda-lock.yml b/conda-lock.yml new file mode 100644 index 0000000..3ba0cec --- /dev/null +++ b/conda-lock.yml @@ -0,0 +1,2691 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV conda-lock.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile conda-lock.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f environment.yml --lockfile conda-lock.yml +version: 1 +metadata: + content_hash: + linux-64: b1b5f96a490d24f162d7dabba6cab614c93578c60569d26310a812b1ec77c8dc + channels: + - url: pytorch + used_env_vars: [] + - url: conda-forge + used_env_vars: [] + - url: defaults + used_env_vars: [] + - url: https://repo.anaconda.com/pkgs/main + used_env_vars: [] + - url: https://repo.anaconda.com/pkgs/r + used_env_vars: [] + platforms: + - linux-64 + sources: 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zlib=1.3.1=hb9d3cd8_2 + - zstd=1.5.7=hb8e6e7a_2 + - pip: + - anyio==4.9.0 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - asttokens==3.0.0 + - async-lru==2.0.5 + - attrs==25.3.0 + - babel==2.17.0 + - beautifulsoup4==4.13.3 + - black==24.8.0 + - bleach==6.2.0 + - certifi==2025.1.31 + - cf-xarray==0.10.4 + - cffi==1.17.1 + - cfgv==3.4.0 + - cftime==1.6.4.post1 + - charset-normalizer==3.4.1 + - click==8.1.8 + - cloudpickle==3.1.1 + - comm==0.2.2 + - contourpy==1.3.1 + - cycler==0.12.1 + - dask==2025.2.0 + - debugpy==1.8.13 + - decorator==5.2.1 + - defusedxml==0.7.1 + - distlib==0.3.9 + - executing==2.2.0 + - fastjsonschema==2.21.1 + - filelock==3.18.0 + - fonttools==4.56.0 + - fqdn==1.5.1 + - fsspec==2025.3.0 + - gsw==3.6.19 + - h11==0.14.0 + - httpcore==1.0.7 + - httpx==0.28.1 + - identify==2.6.9 + - idna==3.10 + - importlib-metadata==8.6.1 + - ipykernel==6.29.5 + - ipython==9.0.2 + - ipython-pygments-lexers==1.1.1 + - isoduration==20.11.0 + - isort==5.13.2 + - jinja2==3.1.6 + - json5==0.10.0 + - jsonpointer==3.0.0 + - jsonschema==4.23.0 + - jsonschema-specifications==2024.10.1 + - jupyter-client==8.6.3 + - jupyter-core==5.7.2 + - jupyter-events==0.12.0 + - jupyter-lsp==2.2.5 + - jupyter-server==2.15.0 + - jupyter-server-terminals==0.5.3 + - jupyterlab==4.3.6 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.3 + - kiwisolver==1.4.8 + - llvmlite==0.44.0 + - locket==1.0.0 + - markupsafe==3.0.2 + - matplotlib==3.10.1 + - matplotlib-inline==0.1.7 + - mistune==3.1.3 + - mypy-extensions==1.0.0 + - nbclient==0.10.2 + - nbconvert==7.16.6 + - nbformat==5.10.4 + - nest-asyncio==1.6.0 + - netcdf4==1.7.2 + - nodeenv==1.9.1 + - notebook==7.3.3 + - notebook-shim==0.2.4 + - numba==0.61.0 + - numpy==2.1.3 + - overrides==7.7.0 + - packaging==24.2 + - pandas==2.2.3 + - pandocfilters==1.5.1 + - partd==1.4.2 + - pathspec==0.12.1 + - pexpect==4.9.0 + - pillow==11.1.0 + - platformdirs==4.3.7 + - pre-commit==4.2.0 + - prometheus-client==0.21.1 + - prompt-toolkit==3.0.50 + - psutil==7.0.0 + - ptyprocess==0.7.0 + - pure-eval==0.2.3 + - pycparser==2.22 + - pygments==2.19.1 + - pyparsing==3.2.1 + - python-dateutil==2.9.0.post0 + - python-json-logger==3.3.0 + - pytz==2025.1 + - pyyaml==6.0.2 + - pyzmq==26.3.0 + - referencing==0.36.2 + - requests==2.32.3 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rpds-py==0.23.1 + - scipy==1.15.2 + - send2trash==1.8.3 + - shapely==2.0.7 + - six==1.17.0 + - sniffio==1.3.1 + - soupsieve==2.6 + - sparse==0.15.5 + - stack-data==0.6.3 + - terminado==0.18.1 + - tinycss2==1.4.0 + - toolz==1.0.0 + - tornado==6.4.2 + - traitlets==5.14.3 + - types-python-dateutil==2.9.0.20241206 + - typing-extensions==4.12.2 + - tzdata==2025.1 + - uri-template==1.3.0 + - urllib3==2.3.0 + - virtualenv==20.29.3 + - wcwidth==0.2.13 + - webcolors==24.11.1 + - webencodings==0.5.1 + - websocket-client==1.8.0 + - xarray==2025.3.0 + - xesmf==0.8.8 + - zipp==3.21.0 diff --git a/pyproject.toml b/pyproject.toml index e59d28b..1dd3f7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,49 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "lenapy" +version = "0.9" +description = "Second beta release of lenapy on github" +readme = "README.md" +authors = [ + { name = "Sebastien Fourest & EMC2 team", email = "sebastien.fourest@cnes.fr" } +] +license = { text = "GPL-3.0" } +requires-python = ">=3.7" +dependencies = [ + "matplotlib>=3.6", + "esmpy>=8.4.0", + "xesmf>=0.8.2", + "xarray>=2024.2", + "gsw>=3.6.16", + "netCDF4>=1.6.5", + "pyyaml>=6.0", + "dask>=2023.6", +] + +[tool.setuptools] +packages = [ + "lenapy", + "lenapy.utils", + "lenapy.readers", + "lenapy.writers", + "lenapy.plots", + "lenapy.resources", +] + +[tool.setuptools.package-data] +"lenapy.resources" = ["*"] + +[project.entry-points."xarray.backends"] +lenapyNetcdf = "lenapy.readers.geo_reader:lenapyNetcdf" +lenapyMask = "lenapy.readers.geo_reader:lenapyMask" +lenapyOceanProducts = "lenapy.readers.ocean:lenapyOceanProducts" +lenapyGfc = "lenapy.readers.gravi_reader:ReadGFC" +lenapyGraceL2 = "lenapy.readers.gravi_reader:ReadGRACEL2" +lenapyShLoading = "lenapy.readers.gravi_reader:ReadShLoading" + [tool.black] line-length = 88 target-version = ['py312'] @@ -5,3 +51,28 @@ target-version = ['py312'] [tool.isort] profile = "black" line_length = 88 + +[project.optional-dependencies] +formatter = [ + "black ==24.8.0", + "isort ==5.13.2", +] +test = [ + "pytest", + "pytest-cov", +] +doc = [ + "sphinx", +] +notebook = [ + "notebook ==7.3.3", + "myst-nb ==1.2.0" +] +quality = [ + "pylint", + "mccabe", +] +dev = [ + "pre-commit", + "lenapy[formatter, test, doc, notebook, quality]", +] From f91326fb4e35af80ce22271a59764c6688f3631b Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 25 Mar 2025 11:40:50 +0100 Subject: [PATCH 02/54] test: add pytest fixture lenapy_paths --- tests/conftest.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 tests/conftest.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..76163ee --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,17 @@ +from dataclasses import dataclass +from pathlib import Path + +import pytest + + +@dataclass +class LenapyTestsPath: + project_dir: Path + + def __post_init__(self): + self.data = self.project_dir / "data" + + +@pytest.fixture +def lenapy_paths(request) -> LenapyTestsPath: + return LenapyTestsPath(Path(__file__).parent.parent) From 3901ae2f815e0c2fd9d2f8bb9e9e47149cbc1e66 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 25 Mar 2025 11:44:32 +0100 Subject: [PATCH 03/54] ci: check notebooks --- .gitlab-ci.yml | 9 + doc/tutorials/Tutorial_lnharmo.ipynb | 346 ++++++++++----------------- lenapy/plots/plotting.py | 10 +- pyproject.toml | 4 +- 4 files changed, 149 insertions(+), 220 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 9be42f8..1c8eff6 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -80,6 +80,15 @@ pytest: - ./.ci-reports/ expire_in: 1 day +test_notebooks: + stage: quality + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + script: + - source activate lenapy_env + - pip install -e .[test] + - pip install -e .[notebook] + - pytest --nbmake --nbmake-kernel=python3 doc/tutorials/ + pylint_analysis: stage: quality image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} diff --git a/doc/tutorials/Tutorial_lnharmo.ipynb b/doc/tutorials/Tutorial_lnharmo.ipynb index 7d02df3..d224421 100755 --- a/doc/tutorials/Tutorial_lnharmo.ipynb +++ b/doc/tutorials/Tutorial_lnharmo.ipynb @@ -3,7 +3,13 @@ { "cell_type": "markdown", "id": "4ec25494-49d2-45d8-b3c3-aa6a5a47632c", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Lenapy for spherical harmonics and gravity data\n", "Lenapy includes functions to read and process spherical harmonics and gravity data.\n", @@ -20,16 +26,17 @@ "cell_type": "code", "execution_count": 1, "id": "bf0ea491-b215-42d8-9583-90f5935ce724", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:32:43.508449Z", - "iopub.status.busy": "2024-11-28T13:32:43.508192Z", - "iopub.status.idle": "2024-11-28T13:32:44.456444Z", - "shell.execute_reply": "2024-11-28T13:32:44.456079Z", - "shell.execute_reply.started": "2024-11-28T13:32:43.508427Z" + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/avincent/miniconda3/envs/pluto_lenapy/lib/python3.11/site-packages/esmpy/interface/loadESMF.py:94: VersionWarning: ESMF installation version 8.8.0, ESMPy version 8.8.0b0\n", + " warnings.warn(\"ESMF installation version {}, ESMPy version {}\".format(\n" + ] } - }, - "outputs": [], + ], "source": [ "import xarray as xr\n", "import lenapy\n", @@ -68,15 +75,7 @@ "cell_type": "code", "execution_count": 2, "id": "d287106d-dcfe-4552-a9c9-8ec5a7ab2bd1", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:32:47.012054Z", - "iopub.status.busy": "2024-11-28T13:32:47.011771Z", - "iopub.status.idle": "2024-11-28T13:32:47.049106Z", - "shell.execute_reply": "2024-11-28T13:32:47.048792Z", - "shell.execute_reply.started": "2024-11-28T13:32:47.012040Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -111,14 +110,14 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", - "html[theme=dark],\n", - "html[data-theme=dark],\n", - "body[data-theme=dark],\n", + "html[theme=\"dark\"],\n", + "html[data-theme=\"dark\"],\n", + "body[data-theme=\"dark\"],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", - " --xr-border-color: #1F1F1F;\n", + " --xr-border-color: #1f1f1f;\n", " --xr-disabled-color: #515151;\n", " --xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", @@ -173,6 +172,7 @@ ".xr-section-item input {\n", " display: inline-block;\n", " opacity: 0;\n", + " height: 0;\n", "}\n", "\n", ".xr-section-item input + label {\n", @@ -209,7 +209,7 @@ "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", - " content: '►';\n", + " content: \"►\";\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", @@ -220,7 +220,7 @@ "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", - " content: '▼';\n", + " content: \"▼\";\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", @@ -292,15 +292,15 @@ "}\n", "\n", ".xr-dim-list:before {\n", - " content: '(';\n", + " content: \"(\";\n", "}\n", "\n", ".xr-dim-list:after {\n", - " content: ')';\n", + " content: \")\";\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", + " content: \",\";\n", " padding-right: 5px;\n", "}\n", "\n", @@ -472,29 +472,29 @@ " tide_system: tide_free\n", " max_degree: 90\n", " errors: empirical\n", - " modelname: GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100
  • product_name :
    GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100
    earth_gravity_constant :
    398600441500000.0
    radius :
    6378136.3
    norm :
    4pi
    tide_system :
    tide_free
    max_degree :
    90
    errors :
    empirical
    modelname :
    GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100
    • " ], "text/plain": [ " Size: 266kB\n", @@ -589,17 +589,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "87763e92-1798-4d55-9abf-4bc41c06a3a1", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:38:49.921173Z", - "iopub.status.busy": "2024-11-28T13:38:49.920354Z", - "iopub.status.idle": "2024-11-28T13:38:49.946142Z", - "shell.execute_reply": "2024-11-28T13:38:49.945199Z", - "shell.execute_reply.started": "2024-11-28T13:38:49.921131Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Dataset with COST-G months of GRACE and GRACE-FO\n", @@ -625,17 +617,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "44161857-e83d-491c-8dc3-552dc39e2aff", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:38:40.527548Z", - "iopub.status.busy": "2024-11-28T13:38:40.527049Z", - "iopub.status.idle": "2024-11-28T13:38:40.544976Z", - "shell.execute_reply": "2024-11-28T13:38:40.544311Z", - "shell.execute_reply.started": "2024-11-28T13:38:40.527516Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from lenapy.readers.gravi_reader import read_tn14, read_tn13\n", @@ -657,31 +641,23 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "f5935aef-442b-4ce0-adca-374de3df3737", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:39:06.725213Z", - "iopub.status.busy": "2024-11-28T13:39:06.724865Z", - "iopub.status.idle": "2024-11-28T13:39:06.980400Z", - "shell.execute_reply": "2024-11-28T13:39:06.979703Z", - "shell.execute_reply.started": "2024-11-28T13:39:06.725185Z" - } - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
    " ] @@ -803,17 +779,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "869ba935-328d-42e7-b8bd-15cd37ea5226", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:18.926687Z", - "iopub.status.busy": "2024-11-28T13:40:18.926218Z", - "iopub.status.idle": "2024-11-28T13:40:19.675685Z", - "shell.execute_reply": "2024-11-28T13:40:19.675301Z", - "shell.execute_reply.started": "2024-11-28T13:40:18.926659Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -848,14 +816,14 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", - "html[theme=dark],\n", - "html[data-theme=dark],\n", - "body[data-theme=dark],\n", + "html[theme=\"dark\"],\n", + "html[data-theme=\"dark\"],\n", + "body[data-theme=\"dark\"],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", - " --xr-border-color: #1F1F1F;\n", + " --xr-border-color: #1f1f1f;\n", " --xr-disabled-color: #515151;\n", " --xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", @@ -910,6 +878,7 @@ ".xr-section-item input {\n", " display: inline-block;\n", " opacity: 0;\n", + " height: 0;\n", "}\n", "\n", ".xr-section-item input + label {\n", @@ -946,7 +915,7 @@ "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", - " content: '►';\n", + " content: \"►\";\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", @@ -957,7 +926,7 @@ "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", - " content: '▼';\n", + " content: \"▼\";\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", @@ -1029,15 +998,15 @@ "}\n", "\n", ".xr-dim-list:before {\n", - " content: '(';\n", + " content: \"(\";\n", "}\n", "\n", ".xr-dim-list:after {\n", - " content: ')';\n", + " content: \")\";\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", + " content: \",\";\n", " padding-right: 5px;\n", "}\n", "\n", @@ -1237,7 +1206,7 @@ " units: mewh\n", " max_degree: 12\n", " radius: 6378136.3\n", - " earth_gravity_constant: 398600441500000.0
  • units :
    mewh
    max_degree :
    12
    radius :
    6378136.3
    earth_gravity_constant :
    398600441500000.0
    • " ], "text/plain": [ " Size: 112MB\n", @@ -1372,7 +1341,7 @@ " earth_gravity_constant: 398600441500000.0" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1403,31 +1372,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "afc5b250-fa4f-4b11-8cbb-bf310e7e700e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:22.960176Z", - "iopub.status.busy": "2024-11-28T13:40:22.959645Z", - "iopub.status.idle": "2024-11-28T13:40:23.130075Z", - "shell.execute_reply": "2024-11-28T13:40:23.129522Z", - "shell.execute_reply.started": "2024-11-28T13:40:22.960144Z" - } - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1457,39 +1418,46 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "6f6f678f-68c5-4f4d-84b2-602287462da0", + "execution_count": 8, + "id": "40538476-ce0b-4033-8f4d-3f4832ac4c56", + "metadata": {}, + "outputs": [], + "source": [ + "# create grid with residual values\n", + "grid_residual = (ds - ds.mean(dim='time')).isel(time=0).lnharmo.to_grid()" + ] + }, + { + "cell_type": "markdown", + "id": "b77c6a53-8108-478d-aedf-76b906b3b7de", + "metadata": {}, + "source": [ + "You can then visualise the result" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "330f9cd5-ff20-437c-ad19-2510698843a0", "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:25.538534Z", - "iopub.status.busy": "2024-11-28T13:40:25.538141Z", - "iopub.status.idle": "2024-11-28T13:40:27.012996Z", - "shell.execute_reply": "2024-11-28T13:40:27.012700Z", - "shell.execute_reply.started": "2024-11-28T13:40:25.538505Z" - } + "tags": [ + "skip-execution" + ] }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/hugolecomte/anaconda3/envs/legos/lib/python3.11/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip\n", - " warnings.warn(f'Downloading: {url}', DownloadWarning)\n" - ] - }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -1499,9 +1467,6 @@ } ], "source": [ - "# create grid with residual values\n", - "grid_residual = (ds - ds.mean(dim='time')).isel(time=0).lnharmo.to_grid()\n", - "\n", "fig, axis = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.PlateCarree()))\n", "grid_residual.plot(transform=ccrs.PlateCarree(), cbar_kwargs={'shrink': 0.7})\n", "axis.coastlines()" @@ -1522,17 +1487,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "id": "10591840-eb52-46f4-b890-15de7eda5da4", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:38.758803Z", - "iopub.status.busy": "2024-11-28T13:40:38.758445Z", - "iopub.status.idle": "2024-11-28T13:40:38.854156Z", - "shell.execute_reply": "2024-11-28T13:40:38.853712Z", - "shell.execute_reply.started": "2024-11-28T13:40:38.758775Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -1567,14 +1524,14 @@ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", - "html[theme=dark],\n", - "html[data-theme=dark],\n", - "body[data-theme=dark],\n", + "html[theme=\"dark\"],\n", + "html[data-theme=\"dark\"],\n", + "body[data-theme=\"dark\"],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", - " --xr-border-color: #1F1F1F;\n", + " --xr-border-color: #1f1f1f;\n", " --xr-disabled-color: #515151;\n", " --xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", @@ -1629,6 +1586,7 @@ ".xr-section-item input {\n", " display: inline-block;\n", " opacity: 0;\n", + " height: 0;\n", "}\n", "\n", ".xr-section-item input + label {\n", @@ -1665,7 +1623,7 @@ "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", - " content: '►';\n", + " content: \"►\";\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", @@ -1676,7 +1634,7 @@ "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", - " content: '▼';\n", + " content: \"▼\";\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", @@ -1748,15 +1706,15 @@ "}\n", "\n", ".xr-dim-list:before {\n", - " content: '(';\n", + " content: \"(\";\n", "}\n", "\n", ".xr-dim-list:after {\n", - " content: ')';\n", + " content: \")\";\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", - " content: ',';\n", + " content: \",\";\n", " padding-right: 5px;\n", "}\n", "\n", @@ -1919,13 +1877,13 @@ " gm_earth: 398600441500000.0\n", " a_earth: 6378137.0\n", " max_degree: 60\n", - " norm: 4pi
  • gm_earth :
    398600441500000.0
    a_earth :
    6378137.0
    max_degree :
    60
    norm :
    4pi
    • " ], "text/plain": [ " Size: 61kB\n", @@ -1976,7 +1934,7 @@ " norm: 4pi" ] }, - "execution_count": 16, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2003,17 +1961,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "3017d7e3-4b33-49c9-9c82-59bb22051e0e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:45.375986Z", - "iopub.status.busy": "2024-11-28T13:40:45.375668Z", - "iopub.status.idle": "2024-11-28T13:40:45.412088Z", - "shell.execute_reply": "2024-11-28T13:40:45.411760Z", - "shell.execute_reply.started": "2024-11-28T13:40:45.375960Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "ds_sum = ds.isel(time=slice(0)).lnharmo + ds.isel(time=slice(1))\n", @@ -2037,17 +1987,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "id": "2ae20baf-44ba-4c4c-9e24-93eeffb932b5", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:40:47.236170Z", - "iopub.status.busy": "2024-11-28T13:40:47.235746Z", - "iopub.status.idle": "2024-11-28T13:40:47.336649Z", - "shell.execute_reply": "2024-11-28T13:40:47.336270Z", - "shell.execute_reply.started": "2024-11-28T13:40:47.236136Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -2055,13 +1997,13 @@ "Text(0.5, 1.0, '$C_{2,0}$')" ] }, - "execution_count": 18, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -2089,21 +2031,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "id": "eb19637e-24cb-4f62-aedb-801b9876ca44", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:41:00.192851Z", - "iopub.status.busy": "2024-11-28T13:41:00.191986Z", - "iopub.status.idle": "2024-11-28T13:41:00.351660Z", - "shell.execute_reply": "2024-11-28T13:41:00.350984Z", - "shell.execute_reply.started": "2024-11-28T13:41:00.192814Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -2135,17 +2069,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "id": "f11d32a7-d9a7-4110-9317-3621a70570d0", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-28T13:42:47.452392Z", - "iopub.status.busy": "2024-11-28T13:42:47.452003Z", - "iopub.status.idle": "2024-11-28T13:42:47.583567Z", - "shell.execute_reply": "2024-11-28T13:42:47.583314Z", - "shell.execute_reply.started": "2024-11-28T13:42:47.452363Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -2153,13 +2079,13 @@ "Text(0.5, 1.0, 'Degree power spectrum')" ] }, - "execution_count": 26, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -2193,20 +2119,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "3ada73e7-ff39-427f-8a6c-fe74ef9437e7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-02T14:59:36.368294Z", - "iopub.status.busy": "2024-05-02T14:59:36.367898Z", - "iopub.status.idle": "2024-05-02T14:59:55.095764Z", - "shell.execute_reply": "2024-05-02T14:59:55.095395Z", - "shell.execute_reply.started": "2024-05-02T14:59:36.368262Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "example_path = '/tmp/test.gfc'\n", + "example_path = 'tmp/test.gfc'\n", "\n", "# Write the spherical harmonics data of the first time slice to a .gfc file\n", "ds.isel(time=0).lnharmo.to_gfc(example_path)" @@ -2229,7 +2147,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.11" }, "scenes_data": { "active_scene": "Default Scene", diff --git a/lenapy/plots/plotting.py b/lenapy/plots/plotting.py index 6a4aafa..cdb4cbb 100644 --- a/lenapy/plots/plotting.py +++ b/lenapy/plots/plotting.py @@ -506,18 +506,18 @@ def plot_hs( cbar_kwargs.setdefault("cax", cbar_ax) # -- Creation of the array for matshow with clm and slm - mat = np.zeros((lmax + 1, 2 * lmax + 1)) * np.NaN + mat = np.zeros((lmax + 1, 2 * lmax + 1)) * np.nan i, j = np.tril_indices(lmax + 1) # set slm before clm to plot order 0 coefficient of clm mat[i, lmax - j] = ( - ds.slm.where(ds.l >= lmin, np.NaN) - .where(np.logical_and(ds.m >= mmin, ds.m <= mmax), np.NaN) + ds.slm.where(ds.l >= lmin, np.nan) + .where(np.logical_and(ds.m >= mmin, ds.m <= mmax), np.nan) .isel(l=xr.DataArray(i, dims="tril"), m=xr.DataArray(j, dims="tril")) ).values mat[i, lmax + j] = ( - ds.clm.where(ds.l >= lmin, np.NaN) - .where(np.logical_and(ds.m >= mmin, ds.m <= mmax), np.NaN) + ds.clm.where(ds.l >= lmin, np.nan) + .where(np.logical_and(ds.m >= mmin, ds.m <= mmax), np.nan) .isel(l=xr.DataArray(i, dims="tril"), m=xr.DataArray(j, dims="tril")) .values ) diff --git a/pyproject.toml b/pyproject.toml index 1dd3f7d..6df2fac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,13 +60,15 @@ formatter = [ test = [ "pytest", "pytest-cov", + "nbmake", ] doc = [ "sphinx", ] notebook = [ "notebook ==7.3.3", - "myst-nb ==1.2.0" + "myst-nb ==1.2.0", + "cartopy" ] quality = [ "pylint", From 625b976e8538946e8eacfc8a534a05ec343e9d64 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 26 Mar 2025 13:21:49 +0100 Subject: [PATCH 04/54] test: start adding tests --- tests/readers/test_gravi_reader.py | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 tests/readers/test_gravi_reader.py diff --git a/tests/readers/test_gravi_reader.py b/tests/readers/test_gravi_reader.py new file mode 100644 index 0000000..a196bf7 --- /dev/null +++ b/tests/readers/test_gravi_reader.py @@ -0,0 +1,11 @@ +from lenapy.readers.gravi_reader import read_tn14 + + +def test_read_tn14(lenapy_paths): + # TODO : asserts + ds_C20_C30 = read_tn14( + lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=False + ) + ds_C20_C30 = read_tn14( + lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=True + ) From 783a397579be229314394ed3f61a671122f1f46e Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 26 Mar 2025 15:41:33 +0100 Subject: [PATCH 05/54] test: add dummy tests --- tests/readers/test_gravi_reader.py | 11 ------- tests/readers/test_readers.py | 49 ++++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 11 deletions(-) delete mode 100644 tests/readers/test_gravi_reader.py create mode 100644 tests/readers/test_readers.py diff --git a/tests/readers/test_gravi_reader.py b/tests/readers/test_gravi_reader.py deleted file mode 100644 index a196bf7..0000000 --- a/tests/readers/test_gravi_reader.py +++ /dev/null @@ -1,11 +0,0 @@ -from lenapy.readers.gravi_reader import read_tn14 - - -def test_read_tn14(lenapy_paths): - # TODO : asserts - ds_C20_C30 = read_tn14( - lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=False - ) - ds_C20_C30 = read_tn14( - lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=True - ) diff --git a/tests/readers/test_readers.py b/tests/readers/test_readers.py new file mode 100644 index 0000000..49c9904 --- /dev/null +++ b/tests/readers/test_readers.py @@ -0,0 +1,49 @@ +import xarray as xr + +from lenapy.readers.gravi_reader import read_tn14 + + +def test_read_tn13(lenapy_paths): + pass + + +def test_read_tn14(lenapy_paths): + # TODO : asserts + ds_C20_C30 = read_tn14( + lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=False + ) + ds_C20_C30 = read_tn14( + lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=True + ) + + +def test_lenapy_netcdf(lenapy_paths): + # TODO : asserts + gmsl = xr.open_dataset( + lenapy_paths.data / "MSL_wo_seasonal_signal.nc", engine="lenapyNetcdf" + ) + + +def test_lenapy_mask(lenapy_paths): + # TODO test class lenapy.readers.geo_reader.lenapyMask + pass + + +def test_read_gfc(lenapy_paths): + # TODO test class lenapy.readers.gravi_reader.ReadGFC + pass + + +def test_read_gracel2(lenapy_paths): + # TODO test class lenapy.readers.gravi_reader.ReadGRACEL2 + pass + + +def test_read_sh_loading(lenapy_paths): + # TODO test class lenapy.readers.gravi_reader.ReadShLoading + pass + + +def test_ocean_products(lenapy_paths): + # TODO test class lenapy.readers.ocean.lenapyOceanProducts + pass From 6c1a143f15b22c9f61eddf636ac5d6911dc4731b Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 26 Mar 2025 15:54:32 +0100 Subject: [PATCH 06/54] ci: update pytest --- .gitlab-ci.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 1c8eff6..66cd762 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -74,10 +74,12 @@ pytest: script: - source activate lenapy_env - pip install -e .[test] - - pytest -s tests -m ci --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report=term --junitxml=.ci-reports/junit-report.xml + - pytest -s tests --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report html:cov_html --cov-report=term --junitxml=.ci-reports/junit-report.xml artifacts: + when: always paths: - ./.ci-reports/ + - ./cov_html/ expire_in: 1 day test_notebooks: From 5b806be6d020960b04f17666233757f0ddb57f8f Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 26 Mar 2025 16:23:43 +0100 Subject: [PATCH 07/54] test: add writers test --- tests/writers/test_writers.py | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 tests/writers/test_writers.py diff --git a/tests/writers/test_writers.py b/tests/writers/test_writers.py new file mode 100644 index 0000000..16e9a3f --- /dev/null +++ b/tests/writers/test_writers.py @@ -0,0 +1,9 @@ +import xarray as xr + + +def test_gravi_writers(lenapy_paths): + # TODO : asserts + # TODO : test to_gfc parameters + ds_path = lenapy_paths.data / "COSTG_n12_2002_2022.nc" + ds = xr.open_dataset(ds_path) + ds.isel(time=0).lnharmo.to_gfc("tmp/test.gfc") From 358d0dfe04d68ed4745c27f49fdc77fcdbe18eaf Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 26 Mar 2025 17:01:38 +0100 Subject: [PATCH 08/54] test: add climato test --- tests/utils/test_climato.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 tests/utils/test_climato.py diff --git a/tests/utils/test_climato.py b/tests/utils/test_climato.py new file mode 100644 index 0000000..3f09564 --- /dev/null +++ b/tests/utils/test_climato.py @@ -0,0 +1,17 @@ +import xarray as xr + + +def test_climato_coeffs(lenapy_paths): + moheacan = xr.open_dataset(lenapy_paths.data / "ohc.nc") + clim = moheacan.gohc.lntime.Coeffs_climato() + ref_coeffs = [ + "cosAnnual", + "cosSemiAnnual", + "order_0", + "order_1", + "sinAnnual", + "sinSemiAnnual", + ] + assert ( + sorted(clim.coeff_names) == ref_coeffs + ), "Coeffs_climato outputs do not correspond to the ref." From 19a8a0a7544af63aaa1a59e5717a9cbb41d5ce8e Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 09:56:49 +0200 Subject: [PATCH 09/54] test: add test dep --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6df2fac..5099387 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,7 @@ test = [ "pytest", "pytest-cov", "nbmake", + "pooch" ] doc = [ "sphinx", From 443e625f6630b792dd9664b33f4e0ad2c0888587 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 09:57:39 +0200 Subject: [PATCH 10/54] test: add lngeo tests --- tests/conftest.py | 17 +++++++++ tests/test_lngeo.py | 84 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 101 insertions(+) create mode 100644 tests/test_lngeo.py diff --git a/tests/conftest.py b/tests/conftest.py index 76163ee..fc33aa4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,6 +2,9 @@ from pathlib import Path import pytest +import xarray as xr + +import lenapy @dataclass @@ -15,3 +18,17 @@ def __post_init__(self): @pytest.fixture def lenapy_paths(request) -> LenapyTestsPath: return LenapyTestsPath(Path(__file__).parent.parent) + + +@pytest.fixture(scope="session") +def air_temperature_data(): + """Fixture session to load et rename data from xarray tutorials.""" + dataset = xr.tutorial.open_dataset("air_temperature") + return lenapy.utils.geo.rename_data(dataset) + + +@pytest.fixture(scope="session") +def ersstv5_data(): + """Fixture session to load et rename data from xarray tutorials.""" + dataset = xr.tutorial.open_dataset("ersstv5") + return lenapy.utils.geo.rename_data(dataset) diff --git a/tests/test_lngeo.py b/tests/test_lngeo.py new file mode 100644 index 0000000..88e0d9e --- /dev/null +++ b/tests/test_lngeo.py @@ -0,0 +1,84 @@ +import numpy as np +import pytest +import xarray as xr + +# TODO add tests about id() + + +@pytest.mark.parametrize("operation", ["mean", "sum"]) +def test_mean_or_sum(air_temperature_data, operation): + raw_values = air_temperature_data.air.values + ref_result = getattr(np, operation)(raw_values, axis=(1, 2)) + + # TODO : Possible refactoring: Extract the weight computations in mean as a utility function and use it as an input. + # Currently, both mean and sum share the same weight computations ? + # Then it will be possible to test it + weighted_new_dataset = getattr(air_temperature_data.lngeo, operation)( + ["latitude", "longitude"], weights=["latitude"] + ) + + dataset_result = getattr(air_temperature_data.lngeo, operation)( + ["latitude", "longitude"] + ) + dataarray_result = getattr(air_temperature_data.air.lngeo, operation)( + ["latitude", "longitude"] + ) + + assert np.allclose(ref_result, dataset_result.air.values) + assert np.allclose(ref_result, dataarray_result.values) + + +def test_regridder(air_temperature_data, ersstv5_data): + # TODO : asserts + air_temperature_data.lngeo.regridder(ersstv5_data, method="bilinear") + + +def test_regrid(air_temperature_data): + # TODO : asserts + ds_out = xr.Dataset( + { + "latitude": (["latitude"], np.arange(-89.5, 90, 1.0)), + "longitude": (["longitude"], np.arange(-179.5, 180, 1.0)), + } + ) + regridder = air_temperature_data.lngeo.regridder( + ds_out, "conservative_normed", periodic=True + ) + out = air_temperature_data.lngeo.regrid(regridder) + + +def test_surface_cell(lenapy_paths): + # TODO : asserts + data = xr.open_dataset(lenapy_paths.data / "ecco.nc", engine="lenapyNetcdf") + surface = data.lngeo.surface_cell() + + +def test_distance(air_temperature_data): + # TODO : asserts + lat_coords = [45] + lon_coords = [45] + lat = xr.DataArray( + lat_coords, + dims=["N_PROF"], + name="lat", + attrs={"standard_name": "lat", "units": "degree_north", "axis": "Y"}, + ) + lon = xr.DataArray( + lon_coords, + dims=["N_PROF"], + name="lon", + attrs={"standard_name": "lon", "units": "degree_north", "axis": "Y"}, + ) + pt_coords = xr.Dataset( + {"latitude": lat, "longitude": lon}, + coords={"N_PROF": np.arange(len(lon_coords))}, + ) + air_temperature_data.lngeo.distance(pt_coords) + + +@pytest.mark.skip(reason="data isas.nc is missing") +def test_isosurface(lenapy_paths): + # TODO : asserts + input_data = lenapy_paths.data / "isas.nc" + data = xr.open_dataset(input_data, engine="lenapyNetcdf").temp + data.isosurface(3, "depth") From 72bbfc4e300106d7271c87c495426e63cbc2dd17 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 10:09:31 +0200 Subject: [PATCH 11/54] ci: export proxy for tests --- .gitlab-ci.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 66cd762..f45f1b6 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -71,6 +71,10 @@ formater: pytest: stage: quality image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + before_script: + - export no_proxy=$NO_PROXY + - export http_proxy=$HTTP_PROXY + - export https_proxy=$HTTP_PROXY script: - source activate lenapy_env - pip install -e .[test] From f6f882016bb9e0704a0d6eafc9f0df1d8f24bb59 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 15:49:54 +0200 Subject: [PATCH 12/54] test: add test_lngeo.test_isosurface --- tests/test_lngeo.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/tests/test_lngeo.py b/tests/test_lngeo.py index 88e0d9e..b02b757 100644 --- a/tests/test_lngeo.py +++ b/tests/test_lngeo.py @@ -76,9 +76,6 @@ def test_distance(air_temperature_data): air_temperature_data.lngeo.distance(pt_coords) -@pytest.mark.skip(reason="data isas.nc is missing") -def test_isosurface(lenapy_paths): +def test_isosurface(ohc_data): # TODO : asserts - input_data = lenapy_paths.data / "isas.nc" - data = xr.open_dataset(input_data, engine="lenapyNetcdf").temp - data.isosurface(3, "depth") + depth = ohc_data.lnocean.sigma0.lngeo.isosurface(27, "depth") From 77af278c255314780bee0e48a572aae9f254be49 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 15:50:12 +0200 Subject: [PATCH 13/54] test: add test_lnocean --- tests/conftest.py | 8 ++++++++ tests/test_lnocean.py | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 42 insertions(+) create mode 100644 tests/test_lnocean.py diff --git a/tests/conftest.py b/tests/conftest.py index fc33aa4..4fcd491 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -27,6 +27,14 @@ def air_temperature_data(): return lenapy.utils.geo.rename_data(dataset) +@pytest.fixture(scope="session") +def ohc_data(): + return xr.open_dataset( + LenapyTestsPath(Path(__file__).parent.parent).data / "ecco.nc", + engine="lenapyNetcdf", + ) + + @pytest.fixture(scope="session") def ersstv5_data(): """Fixture session to load et rename data from xarray tutorials.""" diff --git a/tests/test_lnocean.py b/tests/test_lnocean.py new file mode 100644 index 0000000..0239e12 --- /dev/null +++ b/tests/test_lnocean.py @@ -0,0 +1,34 @@ +def test_attributes(ohc_data): + temp = ohc_data.lnocean.temp + pt = ohc_data.lnocean.PT + ct = ohc_data.lnocean.CT + psal = ohc_data.lnocean.psal + sr = ohc_data.lnocean.SR + sa = ohc_data.lnocean.SA + pressure = ohc_data.lnocean.P + rho = ohc_data.lnocean.rho + sigma0 = ohc_data.lnocean.sigma0 + cp = ohc_data.lnocean.Cp + heat = ohc_data.lnocean.heat + slh = ohc_data.lnocean.slh + ohc = ohc_data.lnocean.ohc + ssl = ohc_data.lnocean.ssl + tssl = ohc_data.lnocean.tssl + hssl = ohc_data.lnocean.hssl + ssl_above = ohc_data.lnocean.ssl_above + eeh = ohc_data.lnocean.eeh + ieeh = ohc_data.lnocean.ieeh + gohc = ohc_data.lnocean.gohc + msl = ohc_data.lnocean.msl + tmsl = ohc_data.lnocean.tmsl + hmsl = ohc_data.lnocean.hmsl + gohc_toa = ohc_data.lnocean.gohc_TOA + ohc_above = ohc_data.lnocean.ohc_above + ocean_depth = ohc_data.lnocean.ocean_depth + mld_theta0 = ohc_data.lnocean.mld_theta0 + mld_theta0minus_only = ohc_data.lnocean.mld_theta0minus_only + mld_sigma0 = ohc_data.lnocean.mld_sigma0 + mld_sigma0var = ohc_data.lnocean.mld_sigma0var + msl = ohc_data.lnocean.msl + msl = ohc_data.lnocean.msl + msl = ohc_data.lnocean.msl From 08eab9b06045b814a2b9151ed2cc1792866d9fbb Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 2 Apr 2025 15:56:50 +0200 Subject: [PATCH 14/54] ci: add github ci --- .github/workflows/ci.yml | 155 +++++++++++++++++++++++++++++++++++++++ pyproject.toml | 1 - 2 files changed, 155 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/ci.yml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..c73ce59 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,155 @@ +name: CI Pipeline + +on: + push: + branches: + - main + - develop + pull_request: + branches: + - '**' + +jobs: + build-env: + name: Build Conda Environment + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + activate-environment: lenapy_env + environment-file: environment.yml + auto-activate-base: false + + - name: Cache Conda environment + uses: actions/cache@v4 + with: + path: /usr/share/miniconda/envs/lenapy_env + key: conda-${{ runner.os }}-${{ hashFiles('environment.yml') }} + restore-keys: | + conda-${{ runner.os }}- + + formater: + name: Code Formatting Check + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install Dependencies + shell: bash -l {0} + run: pip install -e .[formatter] + + - name: Run Black & Isort + shell: bash -l {0} + run: | + black --check --diff lenapy + isort lenapy --check --diff + + pytest: + name: Run Pytest + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: false + activate-environment: lenapy_env + + - name: Restore Conda environment + uses: actions/cache@v4 + with: + path: /usr/share/miniconda/envs/lenapy_env + key: conda-${{ runner.os }}-${{ hashFiles('environment.yml') }} + + - name: Run Pytest with Coverage + shell: bash -l {0} + run: | + pip install -e .[test] + pytest -s tests --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report html:cov_html --cov-report=term + + - name: Upload Coverage Report + uses: actions/upload-artifact@v4 + with: + name: coverage-report + path: | + .ci-reports/ + cov_html/ + + test_notebooks: + name: Test Jupyter Notebooks + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: false + activate-environment: lenapy_env + + - name: Restore Conda environment + uses: actions/cache@v4 + with: + path: /usr/share/miniconda/envs/lenapy_env + key: conda-${{ runner.os }}-${{ hashFiles('environment.yml') }} + + - name: Install Notebook Dependencies + shell: bash -l {0} + run: | + pip install -e .[test] + pip install -e .[notebook] + + - name: Run Pytest on Notebooks + shell: bash -l {0} + run: | + pytest --nbmake --nbmake-kernel=python3 doc/tutorials/ + + pylint_analysis: + name: Pylint Code Analysis + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install Dependencies + shell: bash -l {0} + run: pip install -e .[quality] + + - name: Run Pylint + shell: bash -l {0} + run: | + pylint --recursive=y --disable=all --fail-under=10 --enable=too-many-statements,too-many-nested-blocks lenapy + + mccabe_analysis: + name: McCabe Complexity Analysis + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Conda + uses: conda-incubator/setup-miniconda@v2 + with: + activate-environment: lenapy + auto-activate-base: false + + - name: Install Quality Tools + shell: bash -l {0} + run: pip install .[quality] + + - name: Run McCabe Complexity Check + shell: bash -l {0} + run: ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy diff --git a/pyproject.toml b/pyproject.toml index 5099387..8540b2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ license = { text = "GPL-3.0" } requires-python = ">=3.7" dependencies = [ "matplotlib>=3.6", - "esmpy>=8.4.0", "xesmf>=0.8.2", "xarray>=2024.2", "gsw>=3.6.16", From a3dae9f99a305e884deb686c2f8ee80380201e63 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 3 Apr 2025 13:42:46 +0200 Subject: [PATCH 15/54] ci: add action 'publish-unit-test-result-action' --- .github/workflows/ci.yml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c73ce59..c14cacf 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -74,7 +74,7 @@ jobs: shell: bash -l {0} run: | pip install -e .[test] - pytest -s tests --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report html:cov_html --cov-report=term + pytest -s tests --cov=lenapy --cov-report=xml:.ci-reports/coverage.xml --cov-report html:cov_html --cov-report=term --junitxml=pytest-results.xml - name: Upload Coverage Report uses: actions/upload-artifact@v4 @@ -83,6 +83,11 @@ jobs: path: | .ci-reports/ cov_html/ + pytest-results.xml + - name: Publish test results + uses: EnricoMi/publish-unit-test-result-action@v2 + with: + files: pytest-results.xml test_notebooks: name: Test Jupyter Notebooks From 66e088485153432bb04b97c645f129adc9a9a195 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 3 Apr 2025 14:37:30 +0200 Subject: [PATCH 16/54] ci: add doc build job --- .github/workflows/ci.yml | 36 ++++++++++++++++++++++++++++++++++++ pyproject.toml | 3 +++ 2 files changed, 39 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c14cacf..35e7b41 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -158,3 +158,39 @@ jobs: - name: Run McCabe Complexity Check shell: bash -l {0} run: ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy + + build_doc: + name: Build sphinx documentation + runs-on: ubuntu-latest + needs: build-env + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: false + activate-environment: lenapy_env + + - name: Restore Conda environment + uses: actions/cache@v4 + with: + path: /usr/share/miniconda/envs/lenapy_env + key: conda-${{ runner.os }}-${{ hashFiles('environment.yml') }} + + - name: Install documentation dependencies + shell: bash -l {0} + run: pip install .[doc] + - name: Build doc + shell: bash -l {0} + run: | + conda install pandoc + sphinx-build -b html doc doc/build + + - name: Upload doc as artefact + uses: actions/upload-artifact@v4 + with: + name: doc-build + path: | + doc/build diff --git a/pyproject.toml b/pyproject.toml index 8540b2c..7fa06a1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,9 @@ test = [ ] doc = [ "sphinx", + "sphinx-rtd-theme", + "sphinx-mdinclude", + "nbsphinx", ] notebook = [ "notebook ==7.3.3", From 2e51ba42a4fcb26b7c00d5719bf011678e35c79d Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 3 Apr 2025 15:00:58 +0200 Subject: [PATCH 17/54] ci: add retention-days to doc artifact --- .github/workflows/ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 35e7b41..4f625a2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -194,3 +194,4 @@ jobs: name: doc-build path: | doc/build + retention-days: 7 From 0c5d184b6aa9b013b956fc1ef6471cc7a09bb10a Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 3 Apr 2025 16:47:04 +0200 Subject: [PATCH 18/54] refact: refactor gravi_writer --- lenapy/writers/gravi_writer.py | 82 ++++++++++++++++++---------------- 1 file changed, 43 insertions(+), 39 deletions(-) diff --git a/lenapy/writers/gravi_writer.py b/lenapy/writers/gravi_writer.py index 57d598a..551beac 100644 --- a/lenapy/writers/gravi_writer.py +++ b/lenapy/writers/gravi_writer.py @@ -7,6 +7,43 @@ from lenapy.utils.harmo import assert_sh +def check_dimensions(var, var_name): + """Check if a variable has only 'l' and 'm' dimensions or extra dims of size 1.""" + if not (set(var.dims) == {"l", "m"}) and not ( + len(var.dims) > 2 and max(var.shape[2:]) <= 1 + ): + raise ValueError( + f"Variable '{var_name}' has extra dimension with a size that exceeds 1." + "\n You can reduce this extra dimension by using .isel(dim=0) on your dataset." + ) + + +def prepare_attributes(ds, include_errors: bool, extra_kwargs: dict): + """Prepare dataset attributes for .gfc file header.""" + attrs = ds.attrs.copy() + mandatory_attrs_defaults = { + "product_type": "gravity_field", + "modelname": "unnamed_model", + "earth_gravity_constant": "not_set", + "radius": "not_set", + "max_degree": str(ds.l.max().values), + } + if include_errors and "eclm" in ds and "eslm" in ds: + mandatory_attrs_defaults["errors"] = "formal" + else: + mandatory_attrs_defaults["errors"] = "no" + for attr, default_value in mandatory_attrs_defaults.items(): + attrs.setdefault(attr, default_value) + # Keep normalization coherent with .gfc standard + if "norm" in attrs and attrs["norm"] == "4pi": + attrs["norm"] = "fully_normalized" + elif "norm" in attrs and attrs["norm"] == "unnorm": + attrs["norm"] = "unnormalized" + # Update dataset attributes with any additional attributes specified by the user + attrs.update(extra_kwargs) + return attrs + + def dataset_to_gfc( ds, filename, @@ -61,21 +98,11 @@ def dataset_to_gfc( assert_sh(ds) # Verify dimensions of 'clm', 'slm' and errors array - if include_errors: - list_var = ["clm", "slm", "eclm", "eslm"] - else: - list_var = ["clm", "slm"] - for var_name in list_var: - if var_name not in ds: - raise ValueError(f"Variable '{var_name}' not found in dataset.") - var_dims = ds[var_name].dims - if not (set(var_dims) == {"l", "m"}) and not ( - len(var_dims) > 2 and max(ds[var_name].shape[2:]) <= 1 - ): - raise ValueError( - f"Variable '{var_name}' has extra dimension with a size that exceeds 1." - f"\n You can reduce this extra dimension by using .isel(dim=0) on your dataset." - ) + list_var = ["clm", "slm"] + (["eclm", "eslm"] if include_errors else []) + for var in list_var: + if var not in ds: + raise ValueError(f"Variable '{var}' not found in dataset.") + check_dimensions(ds[var], var) # reduce the dataset to 'l' and 'm' dimensions extra_dims = [dim for dim in ds.dims if dim not in ["l", "m"]] @@ -83,30 +110,7 @@ def dataset_to_gfc( ds = ds.isel(**{dim: 0 for dim in extra_dims}) # Set default values for missing mandatory attributes - attrs = ds.attrs.copy() - mandatory_attrs_defaults = { - "product_type": "gravity_field", - "modelname": "unnamed_model", - "earth_gravity_constant": "not_set", - "radius": "not_set", - "max_degree": str(ds.l.max().values), - } - if include_errors and "eclm" in ds and "eslm" in ds: - mandatory_attrs_defaults["errors"] = "formal" - else: - mandatory_attrs_defaults["errors"] = "no" - - for attr, default_value in mandatory_attrs_defaults.items(): - attrs.setdefault(attr, default_value) - - # Keep normalization coherent with .gfc standard - if "norm" in attrs and attrs["norm"] == "4pi": - attrs["norm"] = "fully_normalized" - elif "norm" in attrs and attrs["norm"] == "unnorm": - attrs["norm"] = "unnormalized" - - # Update dataset attributes with any additional attributes specified by the user - attrs.update(kwargs) + attrs = prepare_attributes(ds, include_errors, kwargs) # ensure the directory exists os.makedirs(os.path.dirname(filename), exist_ok=True) From f3ede30eeab982801b415b4f1658df30817b4593 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Mon, 7 Apr 2025 12:49:42 +0200 Subject: [PATCH 19/54] test: add test_harmo --- tests/__init__.py | 0 tests/conftest.py | 1 + tests/ref_data/utils/costg_grid.nc | Bin 0 -> 67648 bytes .../utils/l_factor_conv_ext_radial_mag.npy | Bin 0 -> 144 bytes .../utils/l_factor_conv_int_radial_mag.npy | Bin 0 -> 144 bytes tests/ref_data/utils/l_factor_conv_mecu.npy | Bin 0 -> 144 bytes tests/ref_data/utils/l_factor_conv_mewh.npy | Bin 0 -> 152 bytes .../ref_data/utils/l_factor_conv_microGal.npy | Bin 0 -> 152 bytes .../ref_data/utils/l_factor_conv_mmgeoid.npy | Bin 0 -> 144 bytes tests/ref_data/utils/l_factor_conv_mvcu.npy | Bin 0 -> 144 bytes tests/ref_data/utils/l_factor_conv_norm.npy | Bin 0 -> 144 bytes tests/ref_data/utils/l_factor_conv_pascal.npy | Bin 0 -> 144 bytes .../utils/l_factor_conv_potential.npy | Bin 0 -> 144 bytes tests/ref_data/utils/plm_4pi.npy | Bin 0 -> 3008 bytes tests/ref_data/utils/plm_ortho.npy | Bin 0 -> 3008 bytes tests/ref_data/utils/plm_schmidt.npy | Bin 0 -> 3008 bytes tests/utilities.py | 29 +++++ tests/utils/__init__.py | 0 tests/utils/test_harmo.py | 122 ++++++++++++++++++ 19 files changed, 152 insertions(+) create mode 100644 tests/__init__.py create mode 100644 tests/ref_data/utils/costg_grid.nc create mode 100644 tests/ref_data/utils/l_factor_conv_ext_radial_mag.npy create mode 100644 tests/ref_data/utils/l_factor_conv_int_radial_mag.npy create mode 100644 tests/ref_data/utils/l_factor_conv_mecu.npy create mode 100644 tests/ref_data/utils/l_factor_conv_mewh.npy create mode 100644 tests/ref_data/utils/l_factor_conv_microGal.npy create mode 100644 tests/ref_data/utils/l_factor_conv_mmgeoid.npy create mode 100644 tests/ref_data/utils/l_factor_conv_mvcu.npy create mode 100644 tests/ref_data/utils/l_factor_conv_norm.npy create mode 100644 tests/ref_data/utils/l_factor_conv_pascal.npy create mode 100644 tests/ref_data/utils/l_factor_conv_potential.npy create mode 100644 tests/ref_data/utils/plm_4pi.npy create mode 100644 tests/ref_data/utils/plm_ortho.npy create mode 100644 tests/ref_data/utils/plm_schmidt.npy create mode 100644 tests/utilities.py create mode 100644 tests/utils/__init__.py create mode 100644 tests/utils/test_harmo.py diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py index 4fcd491..4cba0a6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,6 +13,7 @@ class LenapyTestsPath: def __post_init__(self): self.data = self.project_dir / "data" + self.ref_data = self.project_dir / "tests" / "ref_data" @pytest.fixture diff --git a/tests/ref_data/utils/costg_grid.nc b/tests/ref_data/utils/costg_grid.nc new file mode 100644 index 0000000000000000000000000000000000000000..55d19ddd94d8af00e3d1628a171496a4e9262c4a GIT binary patch literal 67648 zcmeFZ1yr2TvM$&_aQEPD!6m`n-66QUI|PCgB)Ge4aCdi?5Zv9}bwWt)J@?JI^Uj@_ zch{R)*=u!wU0dp}{q5TSU$vViP((lo777yz7y<(Dr#$~IJP<&?s_1?N{a}{ z0mD=Q6aK0L0sGzbXUPAEP{4p+I>4}BIs(1<-5mgFZe?j?V(*}DaQwRp0paBXA?T}U z{U2&`L0(0Q-?gaNfEP_5K!E@8dVgd9+)LMI0QfI>trecKZ~A3EY#I;{%1Z+<5D*aY z%ZH!k4}ze)Sd56z4R|Gl2fzdkV0!*(0Vv!{3&^{FC+f!I)oN(uR~?bB0Qh4c*nest 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For example, factor=10 will keep 1 value out of every 10 + along each dimension. + + Returns + ------- + Union[xr.DataArray, xr.Dataset] + A new xarray object subsampled along all its dimensions. + + Examples + -------- + >>> subsample_xr(da, 5) # for a DataArray + >>> subsample_xr(ds, 2) # for a Dataset + """ + indexers = {dim: slice(None, None, factor) for dim in obj.dims} + return obj.isel(**indexers) diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/utils/test_harmo.py b/tests/utils/test_harmo.py new file mode 100644 index 0000000..9f13a8f --- /dev/null +++ b/tests/utils/test_harmo.py @@ -0,0 +1,122 @@ +from dataclasses import dataclass + +import numpy as np +import pytest +import xarray as xr + +from lenapy.utils.harmo import compute_plm, l_factor_conv +from tests.utilities import subsample_xr + + +def test_sh_to_grid(lenapy_paths): + """ + Test for converting and subsampling a dataset's grid and comparing it to a reference grid. + + Parameters + ---------- + lenapy_paths : object + An object that provides paths to reference data and datasets. + + Raises + ------ + AssertionError + If the subsampled grid does not match the reference grid exactly. + """ + ref_grid_file = lenapy_paths.ref_data / "utils" / "costg_grid.nc" + grid_ref = xr.open_dataarray(ref_grid_file) + + costg_ds = xr.open_dataset(lenapy_paths.data / "COSTG_n12_2002_2022.nc") + grid = costg_ds.lnharmo.to_grid() + grid = subsample_xr(grid, 10) + xr.testing.assert_equal(grid_ref, grid) + + +@pytest.mark.parametrize( + "lmax, z, normalization, ref_filename", + [ + (5, np.linspace(-1, 1, 10), "4pi", "plm_4pi.npy"), + (5, np.linspace(-1, 1, 10), "ortho", "plm_ortho.npy"), + (5, np.linspace(-1, 1, 10), "schmidt", "plm_schmidt.npy"), + ], +) +def test_plm_normalization(lenapy_paths, lmax, z, normalization, ref_filename): + """Test compute_plm with different normalization methods""" + ref_file = lenapy_paths.ref_data / "utils" / ref_filename + ref_plm = np.load(ref_file) + + plm = compute_plm(lmax, z, normalization=normalization) + assert np.allclose(ref_plm, plm), f"Failed for normalization {normalization}" + + +def test_plm_invalid_normalization(): + """Test compute_plm with an invalid normalization type""" + lmax = 5 + z = np.linspace(-1, 1, 10) + with pytest.raises(AssertionError): + compute_plm(lmax, z, normalization="invalid") + + +@pytest.mark.parametrize( + "l, unit", + [ + (np.array([2, 3, 4]), "mewh"), + (np.array([1, 2]), "mmgeoid"), + (np.array([0, 1, 2]), "microGal"), + (np.array([3, 4]), "norm"), + (np.array([1, 2]), "pascal"), + (np.array([1, 2]), "potential"), + (np.array([1, 2]), "mvcu"), + (np.array([1, 2]), "mecu"), + (np.array([1, 2]), "int_radial_mag"), + (np.array([1, 2]), "ext_radial_mag"), + ], +) +def test_l_factor_conv(lenapy_paths, l, unit): + """ + Test l_factor_conv function for different units and options + """ + ref_file = lenapy_paths.ref_data / "utils" / f"l_factor_conv_{unit}.npy" + ref_l_factor = np.load(ref_file) + + ds_love = None + if unit == "mecu": + + @dataclass + class Love: + hl: int + kl: int + + ds_love = Love(1, 1) + l_factor, cst = l_factor_conv( + l=l, + unit=unit, + include_elastic=True, + ds_love=ds_love, + ellipsoidal_earth=True, + geocentric_colat=[1] * len(l), + ) + + assert np.allclose(ref_l_factor, l_factor), f"Failed for scale factor {unit}" + assert "gm_earth" in cst, "Missing 'gm_earth' in constants" + assert "a_earth" in cst, "Missing 'a_earth' in constants" + + +def test_l_factor_conv_invalid_unit(): + """ + Test l_factor_conv function for invalid unit input + """ + with pytest.raises(ValueError): + l_factor_conv(l=(np.array([1, 2])), unit="invalid_unit") + + +def test_ellipsoidal_earth_missing_colatitude(): + with pytest.raises( + ValueError, + match="For ellipsoidal Earth, you need to set the parameter 'geocentric_colat'", + ): + l_factor_conv( + l=np.array([1, 2]), + unit="mewh", + ellipsoidal_earth=True, + geocentric_colat=None, + ) From b9a273f4c6db60da76b782c45f4c559b3dcf2c65 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Mon, 7 Apr 2025 13:49:17 +0200 Subject: [PATCH 20/54] test: remove some file from coverage measurements --- pyproject.toml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 7fa06a1..b9ae6a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,12 @@ target-version = ['py312'] profile = "black" line_length = 88 +[tool.coverage.run] +omit = [ + "lenapy/readers/ocean.py", + "lenapy/plots/*.py" +] + [project.optional-dependencies] formatter = [ "black ==24.8.0", From 295480bc8575e67ff381c5f0bee2a091337d8cf5 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Mon, 7 Apr 2025 16:07:14 +0200 Subject: [PATCH 21/54] test: add test_lenapy_grace --- ...-2_2022001-2022031_GRFO_UTCSR_BA01_0601.gz | Bin 0 -> 50967 bytes ...2_2023001-2023031_GRFO_CNESG_TSVD_0500.txt | 4192 +++++++++++++++++ tests/readers/test_readers.py | 28 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0.000000000000E+00 1.7957E-13 0.0000E+00 20230101.0000 20230201.0000 ynyn +GRCOF2 4 1 -.536340491273E-06 -.473607387974E-06 1.9323E-13 2.0208E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 4 2 0.350456236701E-06 0.662446473915E-06 2.7931E-13 2.7487E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 4 3 0.990865757720E-06 -.200880098265E-06 1.3446E-13 1.3114E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 4 4 -.188520922751E-06 0.308855177261E-06 7.5320E-14 7.3479E-14 20230101.0000 20230201.0000 yyyy +GRCOF2 5 0 0.685560298395E-07 0.000000000000E+00 1.4252E-13 0.0000E+00 20230101.0000 20230201.0000 ynyn +GRCOF2 5 1 -.628798384366E-07 -.941483615944E-07 1.5279E-13 1.5244E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 5 2 0.651936361257E-06 -.323324262888E-06 2.0527E-13 2.0491E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 5 3 -.451819611751E-06 -.214904057654E-06 1.4676E-13 1.4692E-13 20230101.0000 20230201.0000 yyyy +GRCOF2 5 4 -.295293154000E-06 0.498720191718E-07 1.1564E-13 1.1511E-13 20230101.0000 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-.249101369091E-09 -.134434287724E-09 2.0431E-16 2.1608E-16 20230101.0000 20230201.0000 yyyy +GRCOF2 90 89 0.270714266692E-08 -.121763811920E-08 5.0342E-16 4.9445E-16 20230101.0000 20230201.0000 yyyy +GRCOF2 90 90 0.881653729390E-09 0.216646743664E-08 6.3891E-16 5.9899E-16 20230101.0000 20230201.0000 yyyy diff --git a/tests/readers/test_readers.py b/tests/readers/test_readers.py index 49c9904..668ec60 100644 --- a/tests/readers/test_readers.py +++ b/tests/readers/test_readers.py @@ -1,3 +1,4 @@ +import pytest import xarray as xr from lenapy.readers.gravi_reader import read_tn14 @@ -17,6 +18,33 @@ def test_read_tn14(lenapy_paths): ) +def test_lenapy_gfc(lenapy_paths): + + gsm = xr.open_dataset( + lenapy_paths.data / "GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100.gfc", + engine="lenapyGfc", + no_date=False, + ) + + +@pytest.mark.parametrize( + "input_file, ref_file", + [ + ("GSM-2_2023001-2023031_GRFO_CNESG_TSVD_0500.txt", "txt_grace_l2.nc"), + ("GSM-2_2022001-2022031_GRFO_UTCSR_BA01_0601.gz", "gz_grace_l2.nc"), + ], +) +def test_lenapy_grace(lenapy_paths, input_file, ref_file): + """ + Test loading GRACE Level-2 files (TXT and GZ) with the lenapyGraceL2 engine. + + Compares parsed datasets to reference NetCDF datasets to ensure consistent structure and values. + """ + gsm_ref = xr.open_dataset(lenapy_paths.ref_data / ref_file) + gsm = xr.open_dataset(lenapy_paths.data / input_file, engine="lenapyGraceL2") + xr.testing.assert_equal(gsm_ref, gsm) + + def test_lenapy_netcdf(lenapy_paths): # TODO : asserts gmsl = xr.open_dataset( diff --git a/tests/ref_data/gz_grace_l2.nc b/tests/ref_data/gz_grace_l2.nc new file mode 100644 index 0000000000000000000000000000000000000000..ff68eeab749b42f0d2a8c840f1b95fdcd5c0f719 GIT binary patch literal 134883 zcmeFa2V7K5vNt^BoO8}O4LMGca}EjulA}aPiUCnj5kXN@P*ISeqL>j(Qz}M8QA7|$ zQ31t3Mnys3n-SUF`@Flq_wM`My?1x7twT>&^{G=`|4ygQIa6C4Z0tB#MOkT?nF$MN 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Subject: [PATCH 22/54] docs: remove functions from pylint checks --- lenapy/plots/plotting.py | 1 + lenapy/readers/ocean.py | 1 + 2 files changed, 2 insertions(+) diff --git a/lenapy/plots/plotting.py b/lenapy/plots/plotting.py index cdb4cbb..fc63bbb 100644 --- a/lenapy/plots/plotting.py +++ b/lenapy/plots/plotting.py @@ -7,6 +7,7 @@ from lenapy.utils.harmo import l_factor_conv +# pylint: disable=too-many-statements def plot_timeseries_uncertainty( xgeo_data, thick_line="median", diff --git a/lenapy/readers/ocean.py b/lenapy/readers/ocean.py index 9936c71..348f0b2 100644 --- a/lenapy/readers/ocean.py +++ b/lenapy/readers/ocean.py @@ -104,6 +104,7 @@ class lenapyOceanProducts(BackendEntrypoint): """ + # pylint: disable=too-many-statements def open_dataset( self, directory, From 92634fc6ee7a26088ac678f0aad286cf8a916273 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 16 Apr 2025 09:42:07 +0200 Subject: [PATCH 23/54] ci: remove bash -l {0} from formater job --- .github/workflows/ci.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4f625a2..1d17cad 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -45,7 +45,6 @@ jobs: run: pip install -e .[formatter] - name: Run Black & Isort - shell: bash -l {0} run: | black --check --diff lenapy isort lenapy --check --diff From 5963136a397d5cbd4dd5c68f55404bdb3b9cb85b Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 17 Apr 2025 13:52:42 +0200 Subject: [PATCH 24/54] test: add pytest parameter '--overwrite_references' Set to True to overwrite existing reference data with new values during this test run. --- tests/conftest.py | 15 +++++++++++++++ tests/utils/test_harmo.py | 9 ++++++--- 2 files changed, 21 insertions(+), 3 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 4cba0a6..aad58cd 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -7,6 +7,21 @@ import lenapy +def pytest_addoption(parser): + parser.addoption( + "--overwrite_references", + action="store_true", + default=False, + help="Set to True to overwrite existing reference data with new values during this test run. " + "Useful when updating expected outputs", + ) + + +@pytest.fixture +def overwrite_references(request): + return request.config.getoption("--overwrite_references") + + @dataclass class LenapyTestsPath: project_dir: Path diff --git a/tests/utils/test_harmo.py b/tests/utils/test_harmo.py index 9f13a8f..b8f448d 100644 --- a/tests/utils/test_harmo.py +++ b/tests/utils/test_harmo.py @@ -39,12 +39,15 @@ def test_sh_to_grid(lenapy_paths): (5, np.linspace(-1, 1, 10), "schmidt", "plm_schmidt.npy"), ], ) -def test_plm_normalization(lenapy_paths, lmax, z, normalization, ref_filename): +def test_plm_normalization( + overwrite_references, lenapy_paths, lmax, z, normalization, ref_filename +): """Test compute_plm with different normalization methods""" ref_file = lenapy_paths.ref_data / "utils" / ref_filename - ref_plm = np.load(ref_file) - plm = compute_plm(lmax, z, normalization=normalization) + if overwrite_references: + np.save(ref_file, plm) + ref_plm = np.load(ref_file) assert np.allclose(ref_plm, plm), f"Failed for normalization {normalization}" From 5e8ff3afb2a31d07d1808944859504c8adf68fab Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 12:12:25 +0200 Subject: [PATCH 25/54] style: add type hints --- lenapy/utils/filters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lenapy/utils/filters.py b/lenapy/utils/filters.py index 0f98457..76cecc3 100644 --- a/lenapy/utils/filters.py +++ b/lenapy/utils/filters.py @@ -2,7 +2,7 @@ import xarray as xr -def lanczos(cutoff, order): +def lanczos(cutoff: int, order: int): """ Lanczos Filter Implementation of a filter whose spectral response is a door with a temporal width specified by "cutoff", From f3e6bb004fb1780ad8842dc499105343ea4dbc0b Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 12:12:56 +0200 Subject: [PATCH 26/54] refact: remove unused argument --- lenapy/lenapy_time.py | 4 ++-- lenapy/utils/time.py | 6 ++---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/lenapy/lenapy_time.py b/lenapy/lenapy_time.py index a6764d2..2a41ec9 100644 --- a/lenapy/lenapy_time.py +++ b/lenapy/lenapy_time.py @@ -451,12 +451,12 @@ def corr(self, other, remove_trend=False, **kwargs): return xr.corr(r1, r2, **kwargs) - def fillna_climato(self, time_period=slice(None, None)): + def fillna_climato(self): """ Returns a DataArray with all NaN values replaced by climatology and trend Climatology is computed over the optional time_period slice """ - return fillna_climato(self._obj, time_period=time_period) + return fillna_climato(self._obj) def EOF(self, dim, k): """ diff --git a/lenapy/utils/time.py b/lenapy/utils/time.py index f2cd39d..1299095 100644 --- a/lenapy/utils/time.py +++ b/lenapy/utils/time.py @@ -379,9 +379,7 @@ def JJ_to_date(jj): return t0 + dt -def fillna_climato(data, time_period=slice(None, None)): +def fillna_climato(data): # - val = climato( - data, signal=False, mean=True, trend=True, cycle=True, time_period=time_period - ) + val = climato(data, signal=False, mean=True, trend=True, cycle=True) return xr.where(data.isnull(), val, data) From f365981998d16c5c85b55054b1a0613466848f50 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 12:16:03 +0200 Subject: [PATCH 27/54] test: add test_lntime --- tests/ref_data/lenapy_time/lntime_climato.nc | Bin 0 -> 10715 bytes .../lntime_covariance_analysis_time.nc | Bin 0 -> 10749 bytes tests/ref_data/lenapy_time/lntime_detrend.nc | Bin 0 -> 10715 bytes .../ref_data/lenapy_time/lntime_diff_2pts.nc | Bin 0 -> 10706 bytes .../ref_data/lenapy_time/lntime_diff_3pts.nc | Bin 0 -> 10715 bytes .../lenapy_time/lntime_fillna_climato.nc | Bin 0 -> 10715 bytes tests/ref_data/lenapy_time/lntime_filltime.nc | Bin 0 -> 10715 bytes tests/ref_data/lenapy_time/lntime_filter.nc | Bin 0 -> 10715 bytes .../ref_data/lenapy_time/lntime_interptime.nc | Bin 0 -> 10715 bytes tests/ref_data/lenapy_time/lntime_plot.png | Bin 0 -> 79000 bytes tests/ref_data/lenapy_time/lntime_trend.nc | Bin 0 -> 2906 bytes tests/test_lntime.py | 131 ++++++++++++++++++ tests/utilities.py | 44 ++++++ 13 files changed, 175 insertions(+) create mode 100644 tests/ref_data/lenapy_time/lntime_climato.nc create mode 100644 tests/ref_data/lenapy_time/lntime_covariance_analysis_time.nc create mode 100644 tests/ref_data/lenapy_time/lntime_detrend.nc create mode 100644 tests/ref_data/lenapy_time/lntime_diff_2pts.nc create mode 100644 tests/ref_data/lenapy_time/lntime_diff_3pts.nc create mode 100644 tests/ref_data/lenapy_time/lntime_fillna_climato.nc create mode 100644 tests/ref_data/lenapy_time/lntime_filltime.nc create mode 100644 tests/ref_data/lenapy_time/lntime_filter.nc create mode 100644 tests/ref_data/lenapy_time/lntime_interptime.nc create mode 100644 tests/ref_data/lenapy_time/lntime_plot.png create mode 100644 tests/ref_data/lenapy_time/lntime_trend.nc create mode 100644 tests/test_lntime.py diff --git 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HcmV?d00001 diff --git a/tests/test_lntime.py b/tests/test_lntime.py new file mode 100644 index 0000000..dba44dd --- /dev/null +++ b/tests/test_lntime.py @@ -0,0 +1,131 @@ +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr + +from tests.utilities import compare_pngs + + +def test_lntime_climato(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_climato.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.climato() + if overwrite_references: + result.to_netcdf(ref_file) + ref_climato = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_climato, result) + + +def test_lntime_filter(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_filter.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.filter(cutoff=1, order=1) + if overwrite_references: + result.to_netcdf(ref_file) + ref_filter = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_filter, result) + + +def test_lntime_interp_time(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_interptime.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.interp_time(other=ohc.gohc) + if overwrite_references: + result.to_netcdf(ref_file) + ref_interptime = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_interptime, result) + + +def test_lntime_plot(overwrite_references, lenapy_paths, tmp_path): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_plot.png" + test_file = tmp_path / "lntime_plot.png" + + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + fig, ax = plt.subplots(1, 1, figsize=(12, 8), dpi=100) + ohc.gohc.lntime.plot() + fig.savefig(test_file) + if overwrite_references: + fig.savefig(ref_file) + same_pngs, message = compare_pngs(ref_file, test_file) + assert same_pngs + + +def test_lntime_to_datetime(overwrite_references, lenapy_paths): + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + ref = ohc.gohc.time.values + test = ohc.gohc.lntime.to_datetime(time_type="360_day").time.values + assert np.array_equal(ref, test) + + +def test_lntime_diff_3pts(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_diff_3pts.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.diff_3pts(dim="time") + + if overwrite_references: + result.to_netcdf(ref_file) + ref_diff3pts = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_diff3pts, result) + + +def test_lntime_diff_2pts(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_diff_2pts.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.diff_2pts(dim="time") + + if overwrite_references: + result.to_netcdf(ref_file) + ref_diff2pts = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_diff2pts, result) + + +def test_lntime_trend(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_trend.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.trend() + + if overwrite_references: + result.to_netcdf(ref_file) + ref_trend = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_trend, result) + + +def test_lntime_detrend(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_detrend.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.detrend() + if overwrite_references: + result.to_netcdf(ref_file) + ref_detrend = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_detrend, result) + + +def test_lntime_fill_time(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_filltime.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.fill_time() + if overwrite_references: + result.to_netcdf(ref_file) + ref_filltime = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_filltime, result) + + +def test_lntime_covariance_analysis(overwrite_references, lenapy_paths): + ref_file = ( + lenapy_paths.ref_data / "lenapy_time" / "lntime_covariance_analysis_time.nc" + ) + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.covariance_analysis().time + if overwrite_references: + result.to_netcdf(ref_file) + ref_covtime = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_covtime, result) + + +def test_lntime_fillna_climato(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_fillna_climato.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.fillna_climato() + if overwrite_references: + result.to_netcdf(ref_file) + ref_fillna_climato = xr.open_dataarray(ref_file) + xr.testing.assert_equal(ref_fillna_climato, result) diff --git a/tests/utilities.py b/tests/utilities.py index 2b853fb..2af2d5e 100644 --- a/tests/utilities.py +++ b/tests/utilities.py @@ -1,4 +1,48 @@ +from pathlib import Path + +import numpy as np import xarray as xr +from PIL import Image + + +def compare_pngs( + path1: str | Path, path2: str | Path, tol: int = 0 +) -> tuple[bool, str]: + """ + Compare two PNG images pixel by pixel, with an optional tolerance. + + Parameters + ---------- + path1: str or Path + Path to the first PNG image. + path2: str or Path + Path to the second PNG image. + tol: int, optional + Tolerance level (0–255) for pixel differences. Default is 0, which means exact match. + + Returns + ------- + bool + True if the images match within the given tolerance, False otherwise. + str + An explanation message if the comparison fails; empty string if it succeeds. + + Examples + -------- + >>> compare_pngs("plot1.png", "plot2.png", tol=5) + (True, '') + """ + img1 = np.asarray(Image.open(path1).convert("RGB")) + img2 = np.asarray(Image.open(path2).convert("RGB")) + + if img1.shape != img2.shape: + return False, "Shape mismatch" + + diff = np.abs(img1 - img2) + if np.max(diff) > tol: + return False, f"Images differ by more than {tol} levels" + + return True, "" def subsample_xr( From fef592acdc1f21dae291eca3c0cfbff0ea182e34 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 15:33:48 +0200 Subject: [PATCH 28/54] test: add test_gravity --- tests/utils/test_gravity.py | 171 ++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 tests/utils/test_gravity.py diff --git a/tests/utils/test_gravity.py b/tests/utils/test_gravity.py new file mode 100644 index 0000000..c5b9a37 --- /dev/null +++ b/tests/utils/test_gravity.py @@ -0,0 +1,171 @@ +import numpy as np +import pytest +import xarray as xr + +from lenapy.utils.gravity import ( + change_love_reference_frame, + change_reference, + change_tide_system, + gauss_weights, +) + +OLD_RADIUS = 6371000.0 +OLD_GM = 3.986004418e14 +NEW_RADIUS = 6378137.0 +NEW_GM = 3.986004418e14 * 1.0001 +A0 = 4.4228e-8 +H0 = -0.31460 +K20_DEFAULT = 0.30190 + + +@pytest.fixture +def dataset_love(): + # Données synthétiques simples pour l=1 uniquement + kl = xr.DataArray([0.1, 0.2, 0.3], dims="l", coords={"l": [0, 1, 2]}) + hl = xr.DataArray([0.4, 0.5, 0.6], dims="l", coords={"l": [0, 1, 2]}) + ll = xr.DataArray([0.7, 0.8, 0.9], dims="l", coords={"l": [0, 1, 2]}) + return xr.Dataset({"kl": kl, "hl": hl, "ll": ll}) + + +@pytest.mark.parametrize( + "old_frame,new_frame,expected", + [ + # From CE to CM → subtract 1 + ("CE", "CM", (-0.8, -0.5, -0.2)), + # From CM to CE → add 1 + ("CM", "CE", (1.2, 1.5, 1.8)), + # From CE to CL + ("CE", "CL", (-0.8, -0.3, 0.0)), + # From CE to CH + ("CE", "CH", (-0.5, 0.0, 0.3)), + ( + "CE", + "CF", + ( + -0.5 / 3 - 2 * 0.8 / 3, # kl + 2 * (0.5 - 0.8) / 3, # hl + (0.8 - 0.5) / 3, # ll + ), + ), + ], +) +def test_reference_conversion(dataset_love, old_frame, new_frame, expected): + ds_out = change_love_reference_frame( + dataset_love.copy(deep=True), new_frame=new_frame, old_frame=old_frame + ) + kl1, hl1, ll1 = ( + ds_out.kl.sel(l=1).item(), + ds_out.hl.sel(l=1).item(), + ds_out.ll.sel(l=1).item(), + ) + assert np.isclose(kl1, expected[0]) + assert np.isclose(hl1, expected[1]) + assert np.isclose(hl1, expected[1]) + + +@pytest.fixture +def base_dataset(): + clm = xr.DataArray( + np.zeros((3, 3)), dims=["l", "m"], coords={"l": [0, 1, 2], "m": [0, 1, 2]} + ) + slm = xr.DataArray( + np.zeros((3, 3)), dims=["l", "m"], coords={"l": [0, 1, 2], "m": [0, 1, 2]} + ) + ds = xr.Dataset({"clm": clm, "slm": slm}) + return ds + + +@pytest.mark.parametrize( + "old_tide, new_tide, expected_delta", + [ + ("mean_tide", "zero_tide", -1 * A0 * H0), + ("zero_tide", "mean_tide", A0 * H0), + ("mean_tide", "tide_free", -(1 + K20_DEFAULT) * A0 * H0), + ("tide_free", "mean_tide", (1 + K20_DEFAULT) * A0 * H0), + ("zero_tide", "tide_free", -K20_DEFAULT * A0 * H0), + ("tide_free", "zero_tide", K20_DEFAULT * A0 * H0), + ], +) +def test_tide_conversion_correct(base_dataset, old_tide, new_tide, expected_delta): + ds = base_dataset.copy(deep=True) + ds.attrs["tide_system"] = old_tide + ds_out = change_tide_system(ds, new_tide) + result = ds_out.clm.sel(l=2, m=0).item() + assert np.isclose( + result, expected_delta + ), f"Conversion {old_tide} → {new_tide} incorrect" + assert ds_out.attrs["tide_system"] == new_tide + + +@pytest.fixture +def dummy_dataset(): + l_values = np.arange(0, 4) + clm = xr.DataArray(np.ones(4), dims=["l"], coords={"l": l_values}) + slm = xr.DataArray(np.ones(4), dims=["l"], coords={"l": l_values}) + ds = xr.Dataset({"clm": clm, "slm": slm}) + ds.attrs["radius"] = OLD_RADIUS + ds.attrs["earth_gravity_constant"] = OLD_GM + return ds + + +def test_scaling_applied_correctly(dummy_dataset): + ds_out = change_reference( + dummy_dataset, new_radius=NEW_RADIUS, new_earth_gravity_constant=NEW_GM + ) + scale = (OLD_GM / NEW_GM) * (OLD_RADIUS / NEW_RADIUS) ** dummy_dataset.l + expected = 1.0 * scale + np.testing.assert_allclose(ds_out.clm.values, expected) + np.testing.assert_allclose(ds_out.slm.values, expected) + + +def test_deep_copy_behavior(dummy_dataset): + ds_copy = change_reference(dummy_dataset, NEW_RADIUS, NEW_GM, apply=False) + assert not ds_copy.clm.identical( + dummy_dataset.clm + ), "Deep copy should return a modified clone" + assert ds_copy is not dummy_dataset + + +def test_apply_in_place(dummy_dataset): + ds_out = change_reference(dummy_dataset, NEW_RADIUS, NEW_GM, apply=True) + assert ds_out is dummy_dataset, "Should modify in place when apply=True" + assert not np.allclose(ds_out.clm.values, 1.0), "Values should be updated" + + +def test_reads_attrs_if_old_constants_not_provided(dummy_dataset): + ds_out = change_reference(dummy_dataset, NEW_RADIUS, NEW_GM) + assert "radius" in ds_out.attrs + assert ds_out.attrs["radius"] == NEW_RADIUS + + +def test_raises_if_no_attrs_provided(): + ds = xr.Dataset( + { + "clm": xr.DataArray([1.0], dims=["l"], coords={"l": [0]}), + "slm": xr.DataArray([1.0], dims=["l"], coords={"l": [0]}), + } + ) + with pytest.raises(KeyError): + change_reference(ds, NEW_RADIUS, NEW_GM) + + +def test_returns_dataarray(): + weights = gauss_weights(radius=100_000, lmax=60) + assert isinstance(weights, xr.DataArray), "Output is not a xarray.DataArray" + + +def test_length_matches_lmax(): + lmax = 20 + weights = gauss_weights(radius=100_000, lmax=lmax) + assert len(weights) == lmax + 1, "Length of weights array does not match lmax+1" + + +def test_first_weight_is_one(): + weights = gauss_weights(radius=100_000, lmax=5) + assert weights[0].item() == pytest.approx(1.0), "First weight should be exactly 1" + + +def test_weights_are_non_increasing(): + weights = gauss_weights(radius=300_000, lmax=30) + diffs = np.diff(weights) + assert np.all(diffs <= 1e-8), "Weights should be non-increasing" From d385d8aa9495fe57ed7c4b761bc7be5bb1e46f42 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 16:39:14 +0200 Subject: [PATCH 29/54] test: add test_lnharmo.py --- tests/test_lnharmo.py | 34 ++++++++++++++++++++++++++++++++++ tests/utilities.py | 13 +++++++++++++ 2 files changed, 47 insertions(+) create mode 100644 tests/test_lnharmo.py diff --git a/tests/test_lnharmo.py b/tests/test_lnharmo.py new file mode 100644 index 0000000..4b0dbc3 --- /dev/null +++ b/tests/test_lnharmo.py @@ -0,0 +1,34 @@ +import numpy as np +import pytest +import xarray as xr + +from tests.utilities import lambda_source + +cases = [ + (lambda h: h + 10), + (lambda h: 10 + h), + (lambda h: h - 10), + (lambda h: 10 - h), + (lambda h: h * 2), + (lambda h: 2 * h), + (lambda h: -h), + (lambda h: h**2), +] + + +@pytest.mark.parametrize("op", cases) +def test_lenapy_gfc(lenapy_paths, op): + """ + Parametrized regression–test for all arithmetic operators overloaded by + the **`lnharmo`** object produced with the *lenapyGfc* engine. + """ + gsm = xr.open_dataset( + lenapy_paths.data / "GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100.gfc", + engine="lenapyGfc", + no_date=False, + ) + gsm_harmo_1 = gsm.copy(deep=True).lnharmo + out = op(gsm_harmo_1) + assert np.allclose( + op(gsm.clm.values), out.clm.values + 1 + ), f"operator {lambda_source(op)} failed" diff --git a/tests/utilities.py b/tests/utilities.py index 2af2d5e..b5b304d 100644 --- a/tests/utilities.py +++ b/tests/utilities.py @@ -1,3 +1,5 @@ +import inspect +import textwrap from pathlib import Path import numpy as np @@ -5,6 +7,17 @@ from PIL import Image +def lambda_source(func): + """Return the one‑line source of a lambda (trimmed).""" + try: + src = inspect.getsource(func) + src = textwrap.dedent(src).strip() + return src.replace("\n", " ") + except OSError: + # Fallback if source not found (e.g. in interactive) + return "" + + def compare_pngs( path1: str | Path, path2: str | Path, tol: int = 0 ) -> tuple[bool, str]: From d83c396a594afdb408bf20df9ecdf05405bb8eff Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 23 Apr 2025 16:46:27 +0200 Subject: [PATCH 30/54] test: add test_lnharmo.py --- tests/test_lnharmo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_lnharmo.py b/tests/test_lnharmo.py index 4b0dbc3..def9383 100644 --- a/tests/test_lnharmo.py +++ b/tests/test_lnharmo.py @@ -30,5 +30,5 @@ def test_lenapy_gfc(lenapy_paths, op): gsm_harmo_1 = gsm.copy(deep=True).lnharmo out = op(gsm_harmo_1) assert np.allclose( - op(gsm.clm.values), out.clm.values + 1 + op(gsm.clm.values), out.clm.values ), f"operator {lambda_source(op)} failed" From 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HcmV?d00001 diff --git a/tests/test_lntime.py b/tests/test_lntime.py index dba44dd..37c137c 100644 --- a/tests/test_lntime.py +++ b/tests/test_lntime.py @@ -2,7 +2,7 @@ import numpy as np import xarray as xr -from tests.utilities import compare_pngs +from tests.utilities import compare_pngs, result_to_dataset def test_lntime_climato(overwrite_references, lenapy_paths): @@ -129,3 +129,14 @@ def test_lntime_fillna_climato(overwrite_references, lenapy_paths): result.to_netcdf(ref_file) ref_fillna_climato = xr.open_dataarray(ref_file) xr.testing.assert_equal(ref_fillna_climato, result) + + +def test_lntime_gls(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "lenapy_time" / "lntime_gls.nc" + ohc = xr.open_dataset(lenapy_paths.data / "ohc.nc") + result = ohc.gohc.lntime.GLS(degree=1) + data = result_to_dataset(result) + if overwrite_references: + data.to_netcdf(ref_file) + ref_gls = xr.open_dataset(ref_file) + xr.testing.assert_equal(ref_gls, data) diff --git a/tests/utilities.py b/tests/utilities.py index b5b304d..2fea420 100644 --- a/tests/utilities.py +++ b/tests/utilities.py @@ -7,6 +7,53 @@ from PIL import Image +def result_to_dataset(res) -> xr.Dataset: + """ + Build a single xr.Dataset from every xarray / numpy attribute in *res*, + making sure dimensions do not clash. + + Any unnamed dimension like 'dim_0' is renamed to '_dim'. + """ + data_vars = [] + + for attr in dir(res): + if attr.startswith("_"): + continue + + value = getattr(res, attr) + + # xarray.DataArray + if isinstance(value, xr.DataArray): + # give the array a name if none + da = value if value.name else value.rename(attr) + + # rename generic dims ('dim_0', ...) to unique names + new_dims = {d: f"{attr}_{d}" for d in da.dims if d.startswith("dim_")} + da = da.rename(new_dims) + + data_vars.append(da) + + # xarray.Dataset + elif isinstance(value, xr.Dataset): + # recursively tidy each var + tidied = {} + for vname, v in value.data_vars.items(): + new = v.rename(attr + "_" + vname) + new_dims = {d: f"{attr}_{d}" for d in new.dims if d.startswith("dim_")} + tidied[attr + "_" + vname] = new.rename(new_dims) + data_vars.extend(tidied.values()) + + # numpy array + elif isinstance(value, np.ndarray): + name = attr + da = xr.DataArray(value, name=name) + new_dims = {d: f"{attr}_{d}" for d in da.dims if d.startswith("dim_")} + da = da.rename(new_dims) + data_vars.append(da) + + return xr.merge(data_vars).assign_attrs(source="GLS unit test") + + def lambda_source(func): """Return the one‑line source of a lambda (trimmed).""" try: From 7a3136276ade52f7fda8d5dd338a32caf159cdb3 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 15 Apr 2025 15:25:05 +0200 Subject: [PATCH 32/54] refact: refactor sh_to_grid move some code into dedicated functions. --- lenapy/utils/harmo.py | 243 ++++++++++++++++++++++++++++++++---------- 1 file changed, 184 insertions(+), 59 deletions(-) diff --git a/lenapy/utils/harmo.py b/lenapy/utils/harmo.py index 5e1832a..d8ba5dd 100644 --- a/lenapy/utils/harmo.py +++ b/lenapy/utils/harmo.py @@ -25,6 +25,178 @@ from lenapy.constants import * +def _generate_grid( + bounds: list[float], + dlon: float, + dlat: float, + longitude: np.ndarray | None, + latitude: np.ndarray | None, + radians_in: bool, +) -> tuple[np.ndarray, np.ndarray]: + """ + Generate longitude and latitude arrays for the grid. + + load longitude and latitude if given + if not : compute longitude and latitude in degrees between given or defaults bounds + + Parameters + ---------- + bounds : list of float + [lonmin, lonmax, latmin, latmax] + dlon, dlat : float + Grid spacing in degrees. + longitude, latitude : np.ndarray or None + Optionally provided coordinate arrays. + radians_in : bool + Whether the input arrays are in radians. + + Returns + ------- + longitude : np.ndarray + Array of longitudes in degrees. + latitude : np.ndarray + Array of latitudes in degrees. + """ + if longitude is None: + longitude = np.arange(bounds[0] + dlon / 2.0, bounds[1] + dlon / 2.0, dlon) + elif radians_in: + longitude = np.rad2deg(longitude) + + if latitude is None: + latitude = np.arange(bounds[2] + dlat / 2.0, bounds[3] + dlat / 2.0, dlat) + elif radians_in: + latitude = np.rad2deg(latitude) + + return longitude, latitude + + +def _init_bounds_and_grid( + bounds: list[float] | None, + lonmin: float, + lonmax: float, + latmin: float, + latmax: float, + dlon: float, + dlat: float, + radians_in: bool, +) -> tuple[list[float], float, float]: + """ + Initialize bounds and grid resolution. + + Parameters + ---------- + bounds: list of float or None + [lonmin, lonmax, latmin, latmax] + lonmin, lonmax, latmin, latmax: float + Default bounds if `bounds` is None. + dlon, dlat: float + Grid resolution in degrees or radians. + radians_in: bool + Whether the inputs are in radians. + + Returns + ------- + bounds: list of float + Bounds in degrees. + dlon: float + Longitude resolution in degrees. + dlat: float + Latitude resolution in degrees. + """ + if bounds is None: + bounds = [lonmin, lonmax, latmin, latmax] + if not isinstance(bounds, list) or len(bounds) != 4: + raise TypeError('"bounds" must be a list of 4 elements') + + if radians_in: + bounds = [np.rad2deg(b) for b in bounds] + if dlon != 1: + dlon = np.rad2deg(dlon) + if dlat != 1: + dlat = np.rad2deg(dlat) + return bounds, dlon, dlat + + +def _handle_mass_conservation( + used_l: np.ndarray, + force_mass_conservation: bool, +) -> tuple[np.ndarray, bool, bool]: + """ + Test if mass conservation has to be forced to remove mass induced by the projection of C2n,0 coefficients + + Parameters + ---------- + used_l: np.ndarray + Degrees used in the computation. + force_mass_conservation: bool + Whether to enforce mass conservation. + + Returns + ------- + used_l: np.ndarray + Possibly modified degrees list. + use_czero_coef: bool + Whether to re-add degree 0 later. + force_mass_conservation: bool + Updated flag (can be False if nothing to conserve). + """ + if force_mass_conservation and 0 in used_l: + if len(used_l) > 1: + return used_l[1:], True, force_mass_conservation + elif len(used_l) == 1: + return used_l, False, False + return used_l, False, force_mass_conservation + + +def _init_degrees( + data: xr.Dataset, + lmin: int = None, + lmax: int = None, + mmin: int = None, + mmax: int = None, + used_l: np.ndarray = None, + used_m: np.ndarray = None, +) -> tuple[np.ndarray, np.ndarray, int, int, int, int]: + """ + Initialize spherical harmonic degrees and orders to be used. + + It prioritizes used_l and used_m if given over lmin, lmax, mmin and mmax + + Parameters + ---------- + data : xr.Dataset + Spherical harmonics dataset with 'l' and 'm' dimensions. + lmin, lmax : int or None + Minimum and maximum degrees. + mmin, mmax : int or None + Minimum and maximum orders. + used_l, used_m : np.ndarray or None + Explicit list of degrees and orders to use. + + Returns + ------- + used_l: np.ndarray + Degrees to use. + used_m: np.ndarray + Orders to use. + lmin: int + Minimum degree + lmax: int + Maximum degree. + mmin: + Minimum order. + mmax: + Maximum order. + """ + lmax = int(data.l.max()) if lmax is None else lmax + mmax = int(min(lmax, data.m.max())) if mmax is None else mmax + lmin = int(data.l.min()) if lmin is None else lmin + mmin = int(data.m.min()) if mmin is None else mmin + used_l = np.arange(lmin, lmax + 1) if used_l is None else used_l + used_m = np.arange(mmin, mmax + 1) if used_m is None else used_m + return used_l, used_m, lmin, lmax, mmin, mmax + + def sh_to_grid( data, unit="mewh", @@ -131,65 +303,18 @@ def sh_to_grid( """ # addition error propagation, add mask in output variable - # -- set degree and order default parameters - # it prioritizes used_l and used_m if given over lmin, lmax, mmin and mmax - lmax = int(data.l.max()) if lmax is None else lmax - mmax = int(min(lmax, data.m.max())) if mmax is None else mmax - lmin = int(data.l.min()) if lmin is None else lmin - mmin = int(data.m.min()) if mmin is None else mmin - used_l = np.arange(lmin, lmax + 1) if used_l is None else used_l - used_m = np.arange(mmin, mmax + 1) if used_m is None else used_m - - # test if mass conservation has to be forced to remove mass induced by the projection of C2n,0 coefficients - if force_mass_conservation and 0 in used_l and len(used_l) > 1: - use_czero_coef = True - used_l.sort() - used_l = used_l[1:] - elif force_mass_conservation and 0 in used_l and len(used_l) == 1: - force_mass_conservation = ( - False # no need of mass conservation with only coefficient C0,0 - ) - else: - use_czero_coef = False - - # -- set grid output latitude and longitude - # Verify input variable to create bounds of grid information - bounds = [lonmin, lonmax, latmin, latmax] if bounds is None else bounds - - # verify integrity of the argument "bounds" if given - try: - test_iter = iter(bounds) - if len(bounds) != 4: - raise TypeError - except TypeError: - raise TypeError( - 'Given argument "bounds" has to be a list with 4 elements [lonmin, lonmax, latmin, latmax]' - ) - - # Convert bounds in radians if necessary - # work only if bounds or all lonmin, lonmax, latmin, latmax are given in the announced unit - if radians_in: - bounds = [np.rad2deg(i) for i in bounds] - - # convert dlon, dlat from radians to degree if necessary - if radians_in and dlon != 1: - dlon = np.rad2deg(dlon) - if radians_in and dlat != 1: - dlat = np.rad2deg(dlat) - - # load longitude and latitude if given - # if not : compute longitude and latitude in degrees between given or defaults bounds - if longitude is None: - longitude = np.arange(bounds[0] + dlon / 2.0, bounds[1] + dlon / 2.0, dlon) - else: - if radians_in: - longitude = np.rad2deg(longitude) - - if latitude is None: - latitude = np.arange(bounds[2] + dlat / 2.0, bounds[3] + dlat / 2.0, dlat) - else: - if radians_in: - latitude = np.rad2deg(latitude) + used_l, used_m, lmin, lmax, mmin, mmax = _init_degrees( + data, lmin, lmax, mmin, mmax, used_l, used_m + ) + used_l, use_czero_coef, force_mass_conservation = _handle_mass_conservation( + used_l, force_mass_conservation + ) + bounds, dlon, dlat = _init_bounds_and_grid( + bounds, lonmin, lonmax, latmin, latmax, dlon, dlat, radians_in + ) + longitude, latitude = _generate_grid( + bounds, dlon, dlat, longitude, latitude, radians_in + ) cos_latitude = np.cos(np.deg2rad(latitude)) sin_latitude = np.sin(np.deg2rad(latitude)) From de18a613d947ee56892061a415331ec8e013d264 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 15 Apr 2025 16:02:29 +0200 Subject: [PATCH 33/54] refact: refactor compute_plm move some code into dedicated functions. --- lenapy/utils/harmo.py | 140 ++++++++++++++++++++++---------------- tests/utils/test_harmo.py | 2 +- 2 files changed, 82 insertions(+), 60 deletions(-) diff --git a/lenapy/utils/harmo.py b/lenapy/utils/harmo.py index d8ba5dd..ee17675 100644 --- a/lenapy/utils/harmo.py +++ b/lenapy/utils/harmo.py @@ -15,6 +15,7 @@ import datetime import inspect import pathlib +from typing import Literal import cf_xarray as cfxr import numpy as np @@ -25,6 +26,83 @@ from lenapy.constants import * +def _compute_factors( + lmax: int, normalization: Literal["4pi", "ortho", "schmidt"] +) -> tuple[np.ndarray, np.ndarray, float, float]: + """Compute recurrence coefficients f1 and f2 for the Legendre recursion. + + Parameters + ---------- + lmax : int + Maximum degree. + normalization : {'4pi', 'ortho', 'schmidt'} + Normalization scheme. + + Returns + ------- + f1 : np.ndarray + Recurrence factor f1. + f2 : np.ndarray + Recurrence factor f2. + norm_p10 : float + Normalization for P(1,0). + norm_4pi : float + Overall normalization factor. + """ + size = (lmax + 1) * (lmax + 2) // 2 + f1 = np.zeros(size) + f2 = np.zeros(size) + + k = 2 + if normalization in ("4pi", "ortho"): + norm_p10 = np.sqrt(3) + norm_4pi = 1 if normalization == "4pi" else 4 * np.pi + for l in range(2, lmax + 1): + k += 1 + f1[k] = np.sqrt(2 * l - 1) * np.sqrt(2 * l + 1) / l + f2[k] = (l - 1) * np.sqrt(2 * l + 1) / (np.sqrt(2 * l - 3) * l) + for m in range(1, l - 1): + k += 1 + f1[k] = ( + np.sqrt(2 * l + 1) + * np.sqrt(2 * l - 1) + / (np.sqrt(l + m) * np.sqrt(l - m)) + ) + f2[k] = ( + np.sqrt(2 * l + 1) + * np.sqrt(l - m - 1) + * np.sqrt(l + m - 1) + / (np.sqrt(2 * l - 3) * np.sqrt(l + m) * np.sqrt(l - m)) + ) + k += 2 + elif normalization == "schmidt": + norm_p10 = 1 + norm_4pi = 1 + for l in range(2, lmax + 1): + k += 1 + f1[k] = (2 * l - 1) / l + f2[k] = (l - 1) / l + for m in range(1, l - 1): + k += 1 + f1[k] = (2 * l - 1) / (np.sqrt(l + m) * np.sqrt(l - m)) + f2[k] = ( + np.sqrt(l - m - 1) + * np.sqrt(l + m - 1) + / (np.sqrt(l + m) * np.sqrt(l - m)) + ) + k += 2 + else: + raise ValueError( + ( + f"Unknown normalization '{normalization}'. " + "It should be either " + "'4pi', 'ortho' or 'schmidt'" + ) + ) + + return f1, f2, norm_p10, norm_4pi + + def _generate_grid( bounds: list[float], dlon: float, @@ -685,74 +763,18 @@ def compute_plm(lmax, z, mmax=None, normalization="4pi"): `doi: 10.1029/2018GC007529 `_ """ # removing singleton dimensions of x - z = np.atleast_1d(z).flatten() # update type to provide more memory for computation (np.float32 create some problems) - z = z.astype(np.float128) + z = np.atleast_1d(z).flatten().astype(np.float128) # if default mmax, set mmax to be maximal degree mmax = lmax if mmax is None else mmax + f1, f2, norm_p10, norm_4pi = _compute_factors(lmax, normalization) + # scale factor based on Holmes2002 scalef = 1e-280 - # create multiplicative factors and p - f1 = np.zeros(((lmax + 1) * (lmax + 2) // 2)) - f2 = np.zeros(((lmax + 1) * (lmax + 2) // 2)) p = np.zeros(((lmax + 1) * (lmax + 2) // 2, len(z))) - - k = 2 - if normalization in ("4pi", "ortho"): - norm_p10 = np.sqrt(3) - - for l in range(2, lmax + 1): - k += 1 - f1[k] = np.sqrt(2 * l - 1) * np.sqrt(2 * l + 1) / l - f2[k] = (l - 1) * np.sqrt(2 * l + 1) / (np.sqrt(2 * l - 3) * l) - for m in range(1, l - 1): - k += 1 - f1[k] = ( - np.sqrt(2 * l + 1) - * np.sqrt(2 * l - 1) - / (np.sqrt(l + m) * np.sqrt(l - m)) - ) - f2[k] = ( - np.sqrt(2 * l + 1) - * np.sqrt(l - m - 1) - * np.sqrt(l + m - 1) - / (np.sqrt(2 * l - 3) * np.sqrt(l + m) * np.sqrt(l - m)) - ) - k += 2 - - if normalization == "4pi": - norm_4pi = 1 - else: - norm_4pi = 4 * np.pi - - elif normalization == "schmidt": - norm_p10 = 1 - norm_4pi = 1 - - for l in range(2, lmax + 1): - k += 1 - f1[k] = (2 * l - 1) / l - f2[k] = (l - 1) / l - for m in range(1, l - 1): - k += 1 - f1[k] = (2 * l - 1) / (np.sqrt(l + m) * np.sqrt(l - m)) - f2[k] = ( - np.sqrt(l - m - 1) - * np.sqrt(l + m - 1) - / (np.sqrt(l + m) * np.sqrt(l - m)) - ) - k += 2 - - else: - raise AssertionError( - "Unknown normalization given: ", - normalization, - ". It should be either " "'4pi', 'ortho' or 'schmidt'", - ) - # u is sine of colatitude (cosine of latitude), for z=cos(th): u=sin(th) u = np.sqrt(1 - z**2) # update where u==0 to minimal numerical precision different from 0 to prevent invalid divisions diff --git a/tests/utils/test_harmo.py b/tests/utils/test_harmo.py index b8f448d..5d4b58f 100644 --- a/tests/utils/test_harmo.py +++ b/tests/utils/test_harmo.py @@ -55,7 +55,7 @@ def test_plm_invalid_normalization(): """Test compute_plm with an invalid normalization type""" lmax = 5 z = np.linspace(-1, 1, 10) - with pytest.raises(AssertionError): + with pytest.raises(ValueError): compute_plm(lmax, z, normalization="invalid") From 79be79270c170c74ea51f825da0985119d88ddbd Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 16 Apr 2025 10:49:28 +0200 Subject: [PATCH 34/54] refact: refactor l_factor_conv move some code into dedicated functions. --- lenapy/utils/harmo.py | 163 +++++++++++++++++++++++++++++++++--------- 1 file changed, 131 insertions(+), 32 deletions(-) diff --git a/lenapy/utils/harmo.py b/lenapy/utils/harmo.py index ee17675..5517ca1 100644 --- a/lenapy/utils/harmo.py +++ b/lenapy/utils/harmo.py @@ -982,6 +982,75 @@ def change_normalization( return ds_out +def get_earth_parameters( + attrs: dict | None, a_earth: float | None, gm_earth: float | None +) -> tuple[float, float]: + """ + Retrieve Earth parameters from inputs or fallback constants. + + Parameters + ---------- + attrs : dict or None + Attributes potentially containing Earth parameters. + a_earth : float or None + Semi-major axis. + gm_earth : float or None + Gravitational constant. + + Returns + ------- + a_earth : float + Resolved semi-major axis. + gm_earth : float + Resolved gravitational constant. + """ + if attrs is None: + attrs = {} + + resolved_a = a_earth or float(attrs.get("radius", LNPY_A_EARTH_GRS80)) + resolved_gm = gm_earth or float(attrs.get("earth_gravity_constant", LNPY_GM_EARTH)) + return resolved_a, resolved_gm + + +def load_default_love_numbers() -> xr.Dataset: + """ + Load default Love numbers dataset. + + Returns + ------- + ds_love : xr.Dataset + Love numbers dataset. + """ + current_file = inspect.getframeinfo(inspect.currentframe()).filename + folderpath = pathlib.Path(current_file).absolute().parent.parent + default_love_file = folderpath / "resources" / "LoveNumbers_Gegout97.txt" + + df = pd.read_csv(default_love_file, names=["kl"]) + ds = xr.Dataset.from_dataframe(df).rename({"index": "l"}) + return ds + + +def compute_a_div_r_lat(geocentric_colat: np.ndarray, f_earth: float) -> np.ndarray: + """ + Compute a/r(θ) for ellipsoidal Earth correction. + + Parameters + ---------- + geocentric_colat : np.ndarray + Geocentric colatitudes in radians. + f_earth : float + Earth flattening. + + Returns + ------- + a_div_r_lat : np.ndarray + """ + # e = sqrt(2f - f**2) + e_earth = np.sqrt(2 * f_earth - f_earth**2) + # a_div_r_lat = a / r(theta) with r(theta) = a(1-f)/sqrt(1 - e**2*sin(theta)**2) + return np.sqrt(1 - e_earth**2 * np.sin(geocentric_colat) ** 2) / (1 - f_earth) + + def l_factor_conv( l, unit="mewh", @@ -1046,17 +1115,8 @@ def l_factor_conv( *Journal of Geodesy*, 92, 1401--1412, (2018). `doi: 10.1007/s00190-018-1128-0 `_ """ - # -- define constants - if attrs is None: - attrs = {} - if a_earth is None: - a_earth = float(attrs["radius"]) if "radius" in attrs else LNPY_A_EARTH_GRS80 - if gm_earth is None: - gm_earth = ( - float(attrs["earth_gravity_constant"]) - if "earth_gravity_constant" in attrs - else LNPY_GM_EARTH - ) + + a_earth, gm_earth = get_earth_parameters(attrs, a_earth, gm_earth) l = xr.DataArray(l, dims=["l"], coords={"l": l}) fraction = xr.ones_like(l) @@ -1064,19 +1124,10 @@ def l_factor_conv( # include elastic redistribution with kl Love numbers if include_elastic: if ds_love is None: - current_file = inspect.getframeinfo(inspect.currentframe()).filename - folderpath = pathlib.Path(current_file).absolute().parent.parent - default_love_file = folderpath.joinpath( - "resources", "LoveNumbers_Gegout97.txt" - ) - ds_love = xr.Dataset.from_dataframe( - pd.read_csv(default_love_file, names=["kl"]) - ) - ds_love = ds_love.rename({"index": "l"}) - + ds_love = load_default_love_numbers() fraction = fraction + ds_love.kl - # test for ellipsoidal_earth + a_div_r_lat = None if ellipsoidal_earth: # test if geocentric_colat is set if geocentric_colat is None: @@ -1085,14 +1136,64 @@ def l_factor_conv( "the parameter 'geocentric_colat' in l_factor_conv function" ) - # compute variable for ellipsoidal_earth - # e = sqrt(2f - f**2) - e_earth = np.sqrt(2 * f_earth - f_earth**2) - # a_div_r_lat = a / r(theta) with r(theta) = a(1-f)/sqrt(1 - e**2*sin(theta)**2) - a_div_r_lat = np.sqrt(1 - e_earth**2 * np.sin(geocentric_colat) ** 2) / ( - 1 - f_earth - ) + a_div_r_lat = compute_a_div_r_lat(geocentric_colat, f_earth) + + l_factor = compute_l_factor( + a_div_r_lat, + a_earth, + ds_love, + ellipsoidal_earth, + fraction, + gm_earth, + l, + rho_earth, + unit, + ) + + cst = {"gm_earth": gm_earth, "a_earth": a_earth} + return l_factor, cst + + +def compute_l_factor( + a_div_r_lat: np.ndarray | None, + a_earth: float, + ds_love: xr.Dataset | None, + ellipsoidal_earth: bool, + fraction: xr.DataArray, + gm_earth: float, + l: np.ndarray | xr.DataArray, + rho_earth, + unit, +): + """ + Compute the degree-dependent scale factor. + + Parameters + ---------- + a_div_r_lat: np.ndarray or None + Ellipsoidal correction factor + a_earth: float + Earth radius + ds_love: xr.Dataset, optional + Love numbers + ellipsoidal_earth: bool + Whether to apply ellipsoidal correction + fraction: xr.DataArray + Redistribution factor + gm_earth: float + Earth gravitational constant + l: np.ndarray or xr.DataArray + Degrees + rho_earth: float + Earth density + unit: str + Unit type for conversion + Returns + ------- + l_factor : xr.DataArray + Degree-dependent conversion factor. + """ # l_factor is degree dependant if unit == "norm": # norm, fully normalized spherical harmonics @@ -1163,9 +1264,7 @@ def l_factor_conv( "Invalid 'unit' parameter value in l_factor_conv function, valid values are: " "(norm, mewh, mmgeoid, microGal, bar, mvcu)" ) - - cst = {"gm_earth": gm_earth, "a_earth": a_earth} - return l_factor, cst + return l_factor def assert_sh(ds): From 70a49ab9ce4aaddc15059c9ae758183103472f58 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 16 Apr 2025 12:00:50 +0200 Subject: [PATCH 35/54] refact: refactor ReadGFC.open_dataset move some code into dedicated functions. --- lenapy/readers/gravi_reader.py | 241 ++++++++++++++++++--------------- 1 file changed, 134 insertions(+), 107 deletions(-) diff --git a/lenapy/readers/gravi_reader.py b/lenapy/readers/gravi_reader.py index 7e3626b..e2dd495 100644 --- a/lenapy/readers/gravi_reader.py +++ b/lenapy/readers/gravi_reader.py @@ -34,6 +34,7 @@ import re import tarfile import zipfile +from typing import IO import numpy as np import xarray as xr @@ -255,6 +256,136 @@ class ReadGFC(BackendEntrypoint): "date_format", ] + @staticmethod + def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: + """ + Parse the header of a .gfc file and extract relevant metadata. + + Parameters + ---------- + fileIO : IO + File object. + ext : str + File extension. + + Returns + ------- + header : dict + Parsed header metadata. + legend_line : str + Last header line before 'end_of_head'. + + Raises + ------ + ValueError + If required header keys are missing or malformed. + """ + header_parameters = [ + "modelname", + "product_name", + "earth_gravity_constant", + "radius", + "max_degree", + "errors", + "norm", + "tide_system", + ] + regex = "(" + "|".join(header_parameters) + ")" + header = {} + legend_before_end_header = "" + while True: + line = fileIO.readline() + if ext.lower() in [".gz", ".gzip", ".zip", ".tar", ".ZIP"]: + line = line.decode() + if re.match(regex, line): + header[line.split()[0]] = line.split()[1] + if "end_of_head" in line: + break + elif "0 0" in line: + raise ValueError(f"Missing 'end_of_head' in header of file {fileIO}") + legend_before_end_header = line + + # case for COSTG header where 'modelname' is created as 'product_name' + header["modelname"] = header.get("product_name", header.get("modelname")) + # default norm is fully_normalized, change it to 4pi if needed to be coherent with lenapy functions + header["norm"] = "4pi" if "norm" not in header else header["norm"] + header["norm"] = ( + "4pi" if header["norm"] == "fully_normalized" else header["norm"] + ) + header["norm"] = ( + "unnorm" if header["norm"] == "unnormalized" else header["norm"] + ) + header["tide_system"] = header.get("tide_system", "missing") + + # test for mandatory keywords (https://icgem.gfz-potsdam.de/docs/ICGEM-Format-2023.pdf) + required = [ + "modelname", + "earth_gravity_constant", + "radius", + "max_degree", + "errors", + ] + if not all(k in header for k in required): + raise ValueError( + ( + "File header does not contains mandatory keywords" + " (https://icgem.gfz-potsdam.de/docs/ICGEM-Format-2023.pdf)" + ) + ) + + header["max_degree"] = int(header["max_degree"]) + header["earth_gravity_constant"] = float(header["earth_gravity_constant"]) + header["radius"] = float(header["radius"]) + return header, legend_before_end_header + + def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str, bool]: + """ + Open a .gfc file or a compressed archive containing a .gfc file. + + Parameters + ---------- + filename : str or os.PathLike + Path to the .gfc file or archive. + + Returns + ------- + file : IO + Opened file object. + ext : str + Original file extension. + is_binary : bool + Whether the file should be read as binary. + + Raises + ------ + ValueError + If the file extension is unsupported or no .gfc is found in archive. + """ + ext = os.path.splitext(filename)[-1] + compress_extensions = [".gz", ".zip", ".tar", ".gzip", ".ZIP"] + + if ext.lower() == ".gfc": + return open(filename, "r"), ext, False + + if ext in (".gz", ".gzip"): + return gzip.open(filename, "rb"), ext, True + + if ext in (".zip", ".ZIP"): + zip_file = zipfile.ZipFile(filename, "r") + gfc_files = [f for f in zip_file.namelist() if f.lower().endswith(".gfc")] + if not gfc_files: + raise ValueError("No .gfc file found in ZIP archive.") + return zip_file.open(gfc_files[0], "r"), ext, True + + if ext == ".tar": + tar_file = tarfile.open(filename, "r") + gfc_files = [f for f in tar_file.getnames() if f.lower().endswith(".gfc")] + if not gfc_files: + raise ValueError("No .gfc file found in TAR archive.") + return tar_file.extractfile(gfc_files[0]), ext, True + + raise ValueError("Unsupported file extension.") + def open_dataset( self, filename, @@ -308,113 +439,13 @@ def open_dataset( No date information in the file: >>> ds = xr.open_mfdataset('path/to/file.gfc', engine='lenapyGfc', no_date=True) """ - # -- Create a pointer to the '.gfc' file - ext = os.path.splitext(filename)[-1] - compress_extensions = [".gz", ".zip", ".tar", ".gzip", ".ZIP"] - - if ext in (".gfc", ".GFC"): - file = open(filename, "r") - - elif ext in compress_extensions: - if ext in (".gz", ".gzip"): - file = gzip.open(filename, "rb") - elif ext in (".zip", ".ZIP"): - zip_file = zipfile.ZipFile(filename, "r") - filenamezip = [ - file - for file in zip_file.namelist() - if file.endswith(".gfc") or file.endswith(".GFC") - ][0] - file = zip_file.open(filenamezip, "r") - elif ext == ".tar": - tar_file = tarfile.open(filename, "r") - filenametar = [ - file - for file in tar_file.getnames() - if file.endswith(".gfc") or file.endswith(".GFC") - ][0] - file = tar_file.extractfile(filenametar) - - else: - raise ValueError( - "File does not have the good extension. " - "Should be .gfc or a compress format with a .gfc file in it." - ) - - # -- Extract parameters from '.gfc' header - header_parameters = [ - "modelname", - "product_name", - "earth_gravity_constant", - "radius", - "max_degree", - "errors", - "norm", - "tide_system", - ] - parameters_regex = "(" + "|".join(header_parameters) + ")" - header = {} - line = True - - # goes while up to end of header or end of file - while line: - line = file.readline() - if ext in compress_extensions: - line = line.decode() - if re.match(parameters_regex, line): - header[line.split()[0]] = line.split()[1] - - # test to break when end of header - if "end_of_head" in line: - break - # try to intercept case where no end_of_head to raise Error (if file is starting with degree 0 and order 0) - elif "0 0" in line: - raise ValueError("No 'end_of_head' line in file ", filename) - # keep keys information from the line before end_of_head (to know if there is a time key or not) - legend_before_end_header = line + file, ext, is_binary = self._open_file(filename) + header, legend = self._parse_header(file, ext) - # case for COSTG header where 'modelname' is created as 'product_name' - header["modelname"] = ( - header["product_name"] if "product_name" in header else header["modelname"] - ) - - # default norm is fully_normalized, change it to 4pi if needed to be coherent with lenapy functions - header["norm"] = "4pi" if "norm" not in header else header["norm"] - header["norm"] = ( - "4pi" if header["norm"] == "fully_normalized" else header["norm"] - ) - header["norm"] = ( - "unnorm" if header["norm"] == "unnormalized" else header["norm"] - ) - - header["tide_system"] = ( - "missing" if "tide_system" not in header else header["tide_system"] - ) - - # test for mandatory keywords (https://icgem.gfz-potsdam.de/docs/ICGEM-Format-2023.pdf) - if not all( - key in header - for key in [ - "modelname", - "earth_gravity_constant", - "radius", - "max_degree", - "errors", - ] - ): - raise ValueError( - "File header does not contains mandatory keywords" - " (https://icgem.gfz-potsdam.de/docs/ICGEM-Format-2023.pdf)" - ) - - # convert str to numbers for adapted header info - header["max_degree"] = int(header["max_degree"]) - header["earth_gravity_constant"] = float(header["earth_gravity_constant"]) - header["radius"] = float(header["radius"]) lmax = header["max_degree"] # test if gfct key then have to deal with time - if "t" not in legend_before_end_header: + if "t" not in legend: # -- Compute time if not no_date: if ( @@ -538,10 +569,6 @@ def open_dataset( ds["exact_time"] = xr.DataArray([exact_time], dims=["time"]) # -- Close all file pointers - if ext in (".zip", ".ZIP"): - zip_file.close() - elif ext == ".tar": - tar_file.close() file.close() return ds From 115241f1289878639550f547cf70e769ae263ac4 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 16 Apr 2025 13:02:32 +0200 Subject: [PATCH 36/54] refact: refactor ReadGRACEL2.open_dataset move some code into dedicated functions. --- lenapy/readers/gravi_reader.py | 185 +++++++++++++++++++-------------- 1 file changed, 107 insertions(+), 78 deletions(-) diff --git a/lenapy/readers/gravi_reader.py b/lenapy/readers/gravi_reader.py index e2dd495..6644bd7 100644 --- a/lenapy/readers/gravi_reader.py +++ b/lenapy/readers/gravi_reader.py @@ -623,6 +623,107 @@ def guess_can_open(self, filename): class ReadGRACEL2(BackendEntrypoint): open_dataset_parameters = ["filename_or_obj", "drop_variables"] + @staticmethod + def _open_file(filename: str | os.PathLike) -> any: + """ + Open a file, supporting gzip-compressed files. + + Parameters + ---------- + filename: str or os.PathLike + Path to the file to open + + Returns + ------- + file: file-like + Opened file object + """ + ext = os.path.splitext(filename)[-1] + if ext in (".gz", ".gzip"): + return gzip.open(filename, "rb") + return open(filename, "r") + + @staticmethod + def _read_cnes_header(file: any, ext: str) -> dict: + """ + Read header from CNES, GRGS, or TUGRZ formatted files. + + Parameters + ---------- + file: file-like + Opened file object + ext: str + File extension + + Returns + ------- + header: dict + Parsed header information + """ + header = {} + while True: + line = file.readline() + if ext in (".gz", ".gzip"): + line = line.decode() + infos = line.split() + if line[:5] == "EARTH": + header["earth_gravity_constant"] = float(infos[1]) + header["radius"] = float(infos[2]) + elif line[:3] == "SHM": + header["max_degree"] = int(infos[1]) + header["norm"] = " ".join(infos[4:6]) + header["tide_system"] = " ".join(infos[6:]) + elif "GRCOF2 " in line: + break + return header + + @staticmethod + def _read_yaml_header(file: any, ext: str, filename: str) -> dict: + """ + Read header from COST-G, UTCSR, JPL, or GFZ formatted files using YAML. + + Parameters + ---------- + file: file-like + Opened file object + ext: str + File extension + filename: str + File name for error reporting + + Returns + ------- + header: dict + Parsed header information + """ + yaml_header_text = [] + while True: + line = file.readline() + if ext in (".gz", ".gzip"): + line = line.decode() + if "End of YAML header" in line: + break + elif "GRCOF2 " in line: + raise ValueError(f"No 'End of YAML header' line in file {filename}") + elif "date_issued" in line or "acknowledgement" in line: + continue + yaml_header_text.append(line) + yaml_header = yaml.safe_load("".join(yaml_header_text))["header"] + header = { + "earth_gravity_constant": float( + yaml_header["non-standard_attributes"]["earth_gravity_param"]["value"] + ), + "radius": float( + yaml_header["non-standard_attributes"]["mean_equator_radius"]["value"] + ), + "max_degree": int(yaml_header["dimensions"]["degree"]), + "norm": yaml_header["non-standard_attributes"]["normalization"], + "tide_system": yaml_header["non-standard_attributes"].get( + "permanent_tide_flag", "missing" + ), + } + return header + def open_dataset(self, filename, drop_variables=None): """ Read a GRACE Level-2 gravity field product ASCII file (or compressed) from processing centers and @@ -645,88 +746,16 @@ def open_dataset(self, filename, drop_variables=None): """ # -- Create a pointer to the file ext = os.path.splitext(filename)[-1] + file = self._open_file(filename) - if ext in (".gz", ".gzip"): - file = gzip.open(filename, "rb") - else: - file = open(filename, "r") - - line = True # read CNES level 2 products (or GRAZ reprocessed by CNES) - if ( - "CNES" in os.path.basename(filename) - or "GRGS" in os.path.basename(filename) - or "TUGRZ" in os.path.basename(filename) - ): - header = {} - while line: - line = file.readline() - if ext in (".gz", ".gzip"): - line = line.decode() - infos = line.split() - - if line[:5] == "EARTH": # line with GM and a - header["earth_gravity_constant"] = float(infos[1]) - header["radius"] = float(infos[2]) - elif line[:3] == "SHM": # line with lmax, norm and tide - header["max_degree"] = int(infos[1]) - header["norm"] = " ".join(infos[4:6]) - header["tide_system"] = " ".join(infos[6:]) - - # first line with C00 = 1 (because no end of header information to deal with) - elif "GRCOF2 " in line: - break - - # Read other L2 products (COST-G, CSR, JPL or GFZ) where header follows the yaml format + if any(key in os.path.basename(filename) for key in ["CNES", "GRGS", "TUGRZ"]): + header = self._read_cnes_header(file, ext) elif any( - [ - name in os.path.basename(filename) - for name in ("COSTG", "UTCSR", "JPLEM", "GFZOP") - ] + key in os.path.basename(filename) + for key in ["COSTG", "UTCSR", "JPLEM", "GFZOP"] ): - yaml_header_text = [] - while line: - line = file.readline() - if ext in (".gz", ".gzip"): - line = line.decode() - # test to break when end of header - if "End of YAML header" in line: - break - - # try to intercept case where no end_of_head (starting with degree and order 0) - elif "GRCOF2 " in line: - raise ValueError(f"No 'End of YAML header' line in file {filename}") - - # deal with case where file is weirdly filled with 'date_issued:0000-00-00T00:00:00' to avoid yaml crash - # deal also with acknowledgement line from GFZ on GRACE-FO periods that crash the yaml parser - elif "date_issued" in line or "acknowledgement" in line: - continue - yaml_header_text.append(line) - - # read yaml header to create a dict - yaml_header = yaml.safe_load("".join(yaml_header_text))["header"] - - header = { - "earth_gravity_constant": float( - yaml_header["non-standard_attributes"]["earth_gravity_param"][ - "value" - ] - ), - "radius": float( - yaml_header["non-standard_attributes"]["mean_equator_radius"][ - "value" - ] - ), - "max_degree": int(yaml_header["dimensions"]["degree"]), - "norm": yaml_header["non-standard_attributes"]["normalization"], - } - try: - header["tide_system"] = yaml_header["non-standard_attributes"][ - "permanent_tide_flag" - ] - except KeyError: - header["tide_system"] = "missing" - + header = self._read_yaml_header(file, ext, filename) else: raise ValueError( "Name of the file does not corresponds to GRACE L2 products (https://archive.podaac." From 021c715791799c8609b1f662f90b1b48e9854f3d Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 17 Apr 2025 09:19:56 +0200 Subject: [PATCH 37/54] feat: args to ignore some files in cc. --- .github/workflows/ci.yml | 2 +- .gitlab-ci.yml | 2 +- .../scripts/check_mccabe_complexity.sh | 20 ++++++++++++++++--- 3 files changed, 19 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1d17cad..34b4290 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -156,7 +156,7 @@ jobs: - name: Run McCabe Complexity Check shell: bash -l {0} - run: ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy + run: ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy lenapy/readers/ocean.py lenapy/plots/plotting.py build_doc: name: Build sphinx documentation diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index f45f1b6..39dd3db 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -114,4 +114,4 @@ mccabe_analysis: script: - source activate lenapy_env - pip install .[quality] - - ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy + - ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy lenapy/readers/ocean.py lenapy/plots/plotting.py diff --git a/continuous_integration/scripts/check_mccabe_complexity.sh b/continuous_integration/scripts/check_mccabe_complexity.sh index c7e6766..6f3d553 100755 --- a/continuous_integration/scripts/check_mccabe_complexity.sh +++ b/continuous_integration/scripts/check_mccabe_complexity.sh @@ -2,12 +2,18 @@ if [ -z "$1" ] || [ -z "$2" ]; then echo "Error: You must specify a McCabe threshold and a directory to analyze." - echo "Usage: $0 " + echo "Usage: $0 [file1.py file2.py ...]" exit 1 fi threshold=$1 directory=$2 +shift 2 + +declare -A ignore_map +for ignore_file in "$@"; do + ignore_map["$ignore_file"]=1 +done if [ ! -d "$directory" ]; then echo "Error: The directory '$directory' does not exist." @@ -15,7 +21,15 @@ if [ ! -d "$directory" ]; then fi all_files_ok=true -for file in $(find "$directory" -name "*.py"); do +while IFS= read -r file; do + rel_path="${file#$directory/}" # chemin relatif + + # Vérifie si le fichier est dans la liste à ignorer + if [[ ${ignore_map["$rel_path"]} ]] || [[ ${ignore_map["$file"]} ]]; then + echo "Skipping $file (ignored)" + continue + fi + echo "Analyzing $file ..." output=$(python -m mccabe --min "$threshold" "$file") @@ -24,7 +38,7 @@ for file in $(find "$directory" -name "*.py"); do echo "$output" all_files_ok=false fi -done +done < <(find "$directory" -name "*.py") if $all_files_ok; then echo "✅ All files have McCabe scores less than or equal to $threshold. ✅" From 1bbc171744609404228812e0e7ed7ae1bd593a14 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Thu, 24 Apr 2025 12:06:53 +0200 Subject: [PATCH 38/54] test: overwrite references if required --- tests/readers/test_readers.py | 47 +++++++++++++++----- tests/ref_data/lngeo_distance.nc | Bin 0 -> 20520 bytes tests/ref_data/lngeo_isosurface.nc | Bin 0 -> 141557 bytes tests/ref_data/lngeo_regrid.nc | Bin 0 -> 198604 bytes tests/ref_data/lngeo_regridder.nc | Bin 0 -> 133042 bytes tests/ref_data/lngeo_regrider.nc | Bin 0 -> 133042 bytes tests/ref_data/lngeo_surface.nc | Bin 0 -> 141556 bytes tests/ref_data/lnocean_attributes.nc | Bin 0 -> 150809 bytes tests/ref_data/readers/lenapyGfc.nc | Bin 0 -> 283213 bytes tests/ref_data/readers/lenapyNetcdf.nc | Bin 0 -> 38160 bytes tests/ref_data/readers/tn14_rmmeanF.nc | Bin 0 -> 35465 bytes tests/ref_data/readers/tn14_rmmeanT.nc | Bin 0 -> 35465 bytes tests/test_lngeo.py | 57 +++++++++++++++++++------ tests/test_lnocean.py | 50 +++++++--------------- tests/utilities.py | 2 +- 15 files changed, 97 insertions(+), 59 deletions(-) create mode 100644 tests/ref_data/lngeo_distance.nc create mode 100644 tests/ref_data/lngeo_isosurface.nc create mode 100644 tests/ref_data/lngeo_regrid.nc create mode 100644 tests/ref_data/lngeo_regridder.nc create mode 100644 tests/ref_data/lngeo_regrider.nc create mode 100644 tests/ref_data/lngeo_surface.nc create mode 100644 tests/ref_data/lnocean_attributes.nc create mode 100644 tests/ref_data/readers/lenapyGfc.nc create mode 100644 tests/ref_data/readers/lenapyNetcdf.nc create mode 100644 tests/ref_data/readers/tn14_rmmeanF.nc create mode 100644 tests/ref_data/readers/tn14_rmmeanT.nc diff --git a/tests/readers/test_readers.py b/tests/readers/test_readers.py index 668ec60..f665868 100644 --- a/tests/readers/test_readers.py +++ b/tests/readers/test_readers.py @@ -8,23 +8,46 @@ def test_read_tn13(lenapy_paths): pass -def test_read_tn14(lenapy_paths): - # TODO : asserts - ds_C20_C30 = read_tn14( - lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=False - ) - ds_C20_C30 = read_tn14( - lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", rmmean=True +@pytest.mark.parametrize( + "rmmean, ref_name", + [ + (False, "tn14_rmmeanF.nc"), # F = False + (True, "tn14_rmmeanT.nc"), # T = True + ], +) +def test_read_tn14(overwrite_references, lenapy_paths, rmmean, ref_name): + """ + Regression test for ``read_tn14`` with and without mean‑removal. + + The test is executed twice: + * (rmmean=False) -> reference *tn14_rmmeanF.nc* + * (rmmean=True) -> reference *tn14_rmmeanT.nc* + """ + ref_file = lenapy_paths.ref_data / "readers" / ref_name + + ds = read_tn14( + lenapy_paths.data / "TN-14_C30_C20_GSFC_SLR.txt", + rmmean=rmmean, ) + if overwrite_references: + ds.to_netcdf(ref_file) + + ref_ds = xr.open_dataset(ref_file) + xr.testing.assert_equal(ref_ds, ds) -def test_lenapy_gfc(lenapy_paths): +def test_lenapy_gfc(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "readers" / "lenapyGfc.nc" gsm = xr.open_dataset( lenapy_paths.data / "GSM-2_2002213-2002243_GRAC_COSTG_BF01_0100.gfc", engine="lenapyGfc", no_date=False, ) + if overwrite_references: + gsm.to_netcdf(ref_file) + ref_gsm = xr.open_dataset(ref_file) + xr.testing.assert_equal(ref_gsm, gsm) @pytest.mark.parametrize( @@ -45,11 +68,15 @@ def test_lenapy_grace(lenapy_paths, input_file, ref_file): xr.testing.assert_equal(gsm_ref, gsm) -def test_lenapy_netcdf(lenapy_paths): - # TODO : asserts +def test_lenapy_netcdf(overwrite_references, lenapy_paths): + ref_file = lenapy_paths.ref_data / "readers" / "lenapyNetcdf.nc" gmsl = xr.open_dataset( lenapy_paths.data / "MSL_wo_seasonal_signal.nc", engine="lenapyNetcdf" ) + if overwrite_references: + gmsl.to_netcdf(ref_file) + ref_gsm = xr.open_dataset(ref_file) + xr.testing.assert_equal(ref_gsm, gmsl) def test_lenapy_mask(lenapy_paths): diff --git a/tests/ref_data/lngeo_distance.nc b/tests/ref_data/lngeo_distance.nc new file mode 100644 index 0000000000000000000000000000000000000000..fd8501f1e68b5d7c688251e44ff27deaaa3dfdfa GIT binary patch literal 20520 zcmeHtc{G+?+xLZ3rcy~FLX+l*M&xG*31zzG`MMA)NomlasE|sUs8CT!gG!1JMf0Fp zlcae-qeR2Iuh_TydEWc|<5}zb-fw+twb$Bq$GMN;cO82l`#g?)7VF|TQA=H4UAbdN 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z8f}AlVlC7hU4(8%qtJ`!BUGjm_7{*u?Br@K#SR+-dym@d@#!aJnRhmxq?=1pq3}t(Hng03l=W} zV^)Act3a zo&|FSV0b(jlmt4afQIRyS|-?#4OZrYf;@0dA!vIU)GG#6Zh$Xuf@Ni3!d)=90(7ea z&8k7I8u0BCFu4xgTMv4@2F>4r8V#WGXRz)on2*J6kyzZe28-KVu((YVi`!ndf&;B0 zEN+X!1GEiT+%_MJ+sv@Itv438F<9JI-Ur?vqyZ*lahnDfx5?;0_exzbAB)@8U~!u* z7Psk{;QLKMM{}?Yi`$~GxXllX+YGItzY-R=)y{?XVk~ZR#NxJaELxmes5j>T;{Slnia#cgU>+}02c z!`ESPn?@qE+hTE>Iu^HmNr&z-8K55)T=?ZfyA~F=1(!g3F&4M^V{w}o7Pt9hahuE| z7%smCG<*hny#*VvxNRF2w<(JYUKqxr8SeP7xGfhC#C@^2Eny(^x8Z)8k8O&uxGf5c z+k&yU%>WBRI^u!*TN4<*7>nCtu(&MZ*f+qql zSlnie#ck?X-1fyChA+e7wk#}eJB-C`epuXQg~e@JSlreTi`&XK!283oxXmAn+ibA7 zO$UqHl(D$2VIK@%g2inDEN xr.Dataset: da = da.rename(new_dims) data_vars.append(da) - return xr.merge(data_vars).assign_attrs(source="GLS unit test") + return xr.merge(data_vars, compat="override").assign_attrs(source="GLS unit test") def lambda_source(func): From 4d1be13e22705c47f1eae680201aac1844344c8d Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Mon, 28 Apr 2025 11:38:08 +0200 Subject: [PATCH 39/54] style: docstring to english --- lenapy/utils/time.py | 55 +++++++++++++++++++++++++++----------------- 1 file changed, 34 insertions(+), 21 deletions(-) diff --git a/lenapy/utils/time.py b/lenapy/utils/time.py index 1299095..96528e6 100644 --- a/lenapy/utils/time.py +++ b/lenapy/utils/time.py @@ -107,30 +107,43 @@ def climato( t_max=None, ): """ - Analyse du cycle annuel, bi-annuel et de la tendance - Decompose les données en entrée en : - * Un cycle annuel - * Un cycle semi-annuel - * Une tendance - * Une moyenne - * Un signal résiduel - Retourne la combinaison voulue de ces éléments en fonction des arguments choisis (signal, mean, trend, cycle) - Si return_coeffs=True, retourne les coefficients des cycles et tendances + Annual, Semi-Annual Cycle and Trend Analysis. + + Decomposes the input data into: + - An annual cycle + - A semi-annual cycle + - A trend + - A mean + - A residual signal + + Returns the desired combination of these components based on the selected arguments + (`signal`, `mean`, `trend`, `cycle`). + + If `return_coeffs=True`, also returns the coefficients of the cycles and the trend. Parameters ---------- - signal : Bool (default=True) - Renvoie le signal résiduel après retrait de la climato, de la tendance, et de la moyenne - mean : Bool (default=True) - renvoie la valeur moyenne des données d'entrée - trend : Bool (default=True) - renvoie la tendance (en jour-1) - cycle : Bool (default=False) - renvoie le cycle annuel et semi-annuel - return_coeffs : Bool (default=False) - retourne en plus les coefficients des cycles et de la tendance linéaire - time_period : slice (default=slice(None,None== - Periode de reference sur laquelle est calculee la climato + signal : bool, optional + If True (default), returns the residual signal after removing the climatology + (annual and semi-annual cycles), the trend, and the mean. + mean : bool, optional + If True (default), returns the mean value of the input data. + trend : bool, optional + If True (default), returns the trend (in day⁻¹). + cycle : bool, optional + If True (default is False), returns the annual and semi-annual cycles. + return_coeffs : bool, optional + If True (default is False), also returns the coefficients of the cycles and the + linear trend. + time_period : slice, optional + Reference period over which the climatology is computed. + Default is `slice(None, None)` (i.e., the entire time range). + + Returns + ------- + xarray.Dataset or tuple + The requested components as an xarray.Dataset. If `return_coeffs=True`, + returns a tuple `(dataset, coeffs)` where `coeffs` contains the cycle and trend coefficients. """ a = Coeffs_climato(xr.Dataset(dict(measure=data)), Nmin=Nmin) From 1b0573c57bc3a4f5f52f37b3b1b98132af722e77 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 6 May 2025 11:17:14 +0200 Subject: [PATCH 40/54] refact: remove useless variable --- lenapy/readers/gravi_reader.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/lenapy/readers/gravi_reader.py b/lenapy/readers/gravi_reader.py index 6644bd7..4b2ab02 100644 --- a/lenapy/readers/gravi_reader.py +++ b/lenapy/readers/gravi_reader.py @@ -338,7 +338,7 @@ def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: header["radius"] = float(header["radius"]) return header, legend_before_end_header - def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str, bool]: + def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str]: """ Open a .gfc file or a compressed archive containing a .gfc file. @@ -353,8 +353,6 @@ def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str, bool]: Opened file object. ext : str Original file extension. - is_binary : bool - Whether the file should be read as binary. Raises ------ @@ -365,24 +363,24 @@ def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str, bool]: compress_extensions = [".gz", ".zip", ".tar", ".gzip", ".ZIP"] if ext.lower() == ".gfc": - return open(filename, "r"), ext, False + return open(filename, "r"), ext if ext in (".gz", ".gzip"): - return gzip.open(filename, "rb"), ext, True + return gzip.open(filename, "rb"), ext if ext in (".zip", ".ZIP"): zip_file = zipfile.ZipFile(filename, "r") gfc_files = [f for f in zip_file.namelist() if f.lower().endswith(".gfc")] if not gfc_files: raise ValueError("No .gfc file found in ZIP archive.") - return zip_file.open(gfc_files[0], "r"), ext, True + return zip_file.open(gfc_files[0], "r"), ext if ext == ".tar": tar_file = tarfile.open(filename, "r") gfc_files = [f for f in tar_file.getnames() if f.lower().endswith(".gfc")] if not gfc_files: raise ValueError("No .gfc file found in TAR archive.") - return tar_file.extractfile(gfc_files[0]), ext, True + return tar_file.extractfile(gfc_files[0]), ext raise ValueError("Unsupported file extension.") @@ -439,7 +437,7 @@ def open_dataset( No date information in the file: >>> ds = xr.open_mfdataset('path/to/file.gfc', engine='lenapyGfc', no_date=True) """ - file, ext, is_binary = self._open_file(filename) + file, ext = self._open_file(filename) header, legend = self._parse_header(file, ext) lmax = header["max_degree"] From 080fa4ab6e0a763525dce8b8807b95941278134d Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 6 May 2025 13:41:40 +0200 Subject: [PATCH 41/54] ci: add doc generation --- .gitlab-ci.yml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 39dd3db..3ec79e9 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -115,3 +115,18 @@ mccabe_analysis: - source activate lenapy_env - pip install .[quality] - ./continuous_integration/scripts/check_mccabe_complexity.sh 25 lenapy lenapy/readers/ocean.py lenapy/plots/plotting.py + +build_doc: + stage: documentation + image: ${ARTIFACTORY_HOST}/${TAG_IMAGE_CONDA} + before_script: + - source activate lenapy_env + - pip install .[doc] + script: + - conda install pandoc + - sphinx-build -b html doc doc/build + artifacts: + when: always + paths: + - doc/build + expire_in: 7 day From a673658c83a54c1578c25f5c647f247a3f39fd9c Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 6 May 2025 13:43:50 +0200 Subject: [PATCH 42/54] test: assert_allclose instead of assert_equal --- tests/readers/test_readers.py | 8 ++++---- tests/test_lngeo.py | 10 +++++----- tests/test_lnocean.py | 2 +- tests/test_lntime.py | 22 +++++++++++----------- tests/utils/test_harmo.py | 2 +- 5 files changed, 22 insertions(+), 22 deletions(-) diff --git a/tests/readers/test_readers.py b/tests/readers/test_readers.py index f665868..184487f 100644 --- a/tests/readers/test_readers.py +++ b/tests/readers/test_readers.py @@ -34,7 +34,7 @@ def test_read_tn14(overwrite_references, lenapy_paths, rmmean, ref_name): ds.to_netcdf(ref_file) ref_ds = xr.open_dataset(ref_file) - xr.testing.assert_equal(ref_ds, ds) + xr.testing.assert_allclose(ref_ds, ds) def test_lenapy_gfc(overwrite_references, lenapy_paths): @@ -47,7 +47,7 @@ def test_lenapy_gfc(overwrite_references, lenapy_paths): if overwrite_references: gsm.to_netcdf(ref_file) ref_gsm = xr.open_dataset(ref_file) - xr.testing.assert_equal(ref_gsm, gsm) + xr.testing.assert_allclose(ref_gsm, gsm) @pytest.mark.parametrize( @@ -65,7 +65,7 @@ def test_lenapy_grace(lenapy_paths, input_file, ref_file): """ gsm_ref = xr.open_dataset(lenapy_paths.ref_data / ref_file) gsm = xr.open_dataset(lenapy_paths.data / input_file, engine="lenapyGraceL2") - xr.testing.assert_equal(gsm_ref, gsm) + xr.testing.assert_allclose(gsm_ref, gsm) def test_lenapy_netcdf(overwrite_references, lenapy_paths): @@ -76,7 +76,7 @@ def test_lenapy_netcdf(overwrite_references, lenapy_paths): if overwrite_references: gmsl.to_netcdf(ref_file) ref_gsm = xr.open_dataset(ref_file) - xr.testing.assert_equal(ref_gsm, gmsl) + xr.testing.assert_allclose(ref_gsm, gmsl) def test_lenapy_mask(lenapy_paths): diff --git a/tests/test_lngeo.py b/tests/test_lngeo.py index 44e24e2..ff13cc9 100644 --- a/tests/test_lngeo.py +++ b/tests/test_lngeo.py @@ -40,7 +40,7 @@ def test_regridder( result.to_netcdf(ref_file) ref_nc = xr.open_dataset(ref_file) result_nc = xr.open_dataset(test_file) - xr.testing.assert_equal(ref_nc, result_nc) + xr.testing.assert_allclose(ref_nc, result_nc) def test_regrid(overwrite_references, lenapy_paths, air_temperature_data): @@ -61,7 +61,7 @@ def test_regrid(overwrite_references, lenapy_paths, air_temperature_data): if overwrite_references: result.to_netcdf(ref_file) ref_regrid = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_regrid, result.air) + xr.testing.assert_allclose(ref_regrid, result.air) def test_surface_cell(overwrite_references, lenapy_paths): @@ -71,7 +71,7 @@ def test_surface_cell(overwrite_references, lenapy_paths): if overwrite_references: surface.to_netcdf(ref_file) ref_surface = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_surface, surface) + xr.testing.assert_allclose(ref_surface, surface) def test_distance(overwrite_references, lenapy_paths, air_temperature_data): @@ -98,7 +98,7 @@ def test_distance(overwrite_references, lenapy_paths, air_temperature_data): if overwrite_references: results.to_netcdf(ref_file) ref_distance = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_distance, results) + xr.testing.assert_allclose(ref_distance, results) def test_isosurface(overwrite_references, lenapy_paths, ohc_data): @@ -107,4 +107,4 @@ def test_isosurface(overwrite_references, lenapy_paths, ohc_data): if overwrite_references: depth.to_netcdf(ref_file) ref_depth = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_depth, depth) + xr.testing.assert_allclose(ref_depth, depth) diff --git a/tests/test_lnocean.py b/tests/test_lnocean.py index a59f246..2782da4 100644 --- a/tests/test_lnocean.py +++ b/tests/test_lnocean.py @@ -13,4 +13,4 @@ def test_attributes(overwrite_references, lenapy_paths, ohc_data): encoding={var: {"zlib": True, "complevel": 8} for var in dataset.data_vars}, ) ref_ocean = xr.open_dataset(ref_file) - xr.testing.assert_equal(ref_ocean, dataset) + xr.testing.assert_allclose(ref_ocean, dataset) diff --git a/tests/test_lntime.py b/tests/test_lntime.py index 37c137c..1e549ed 100644 --- a/tests/test_lntime.py +++ b/tests/test_lntime.py @@ -12,7 +12,7 @@ def test_lntime_climato(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_climato = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_climato, result) + xr.testing.assert_allclose(ref_climato, result) def test_lntime_filter(overwrite_references, lenapy_paths): @@ -22,7 +22,7 @@ def test_lntime_filter(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_filter = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_filter, result) + xr.testing.assert_allclose(ref_filter, result) def test_lntime_interp_time(overwrite_references, lenapy_paths): @@ -32,7 +32,7 @@ def test_lntime_interp_time(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_interptime = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_interptime, result) + xr.testing.assert_allclose(ref_interptime, result) def test_lntime_plot(overwrite_references, lenapy_paths, tmp_path): @@ -64,7 +64,7 @@ def test_lntime_diff_3pts(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_diff3pts = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_diff3pts, result) + xr.testing.assert_allclose(ref_diff3pts, result) def test_lntime_diff_2pts(overwrite_references, lenapy_paths): @@ -75,7 +75,7 @@ def test_lntime_diff_2pts(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_diff2pts = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_diff2pts, result) + xr.testing.assert_allclose(ref_diff2pts, result) def test_lntime_trend(overwrite_references, lenapy_paths): @@ -86,7 +86,7 @@ def test_lntime_trend(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_trend = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_trend, result) + xr.testing.assert_allclose(ref_trend, result) def test_lntime_detrend(overwrite_references, lenapy_paths): @@ -96,7 +96,7 @@ def test_lntime_detrend(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_detrend = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_detrend, result) + xr.testing.assert_allclose(ref_detrend, result) def test_lntime_fill_time(overwrite_references, lenapy_paths): @@ -106,7 +106,7 @@ def test_lntime_fill_time(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_filltime = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_filltime, result) + xr.testing.assert_allclose(ref_filltime, result) def test_lntime_covariance_analysis(overwrite_references, lenapy_paths): @@ -118,7 +118,7 @@ def test_lntime_covariance_analysis(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_covtime = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_covtime, result) + xr.testing.assert_allclose(ref_covtime, result) def test_lntime_fillna_climato(overwrite_references, lenapy_paths): @@ -128,7 +128,7 @@ def test_lntime_fillna_climato(overwrite_references, lenapy_paths): if overwrite_references: result.to_netcdf(ref_file) ref_fillna_climato = xr.open_dataarray(ref_file) - xr.testing.assert_equal(ref_fillna_climato, result) + xr.testing.assert_allclose(ref_fillna_climato, result) def test_lntime_gls(overwrite_references, lenapy_paths): @@ -139,4 +139,4 @@ def test_lntime_gls(overwrite_references, lenapy_paths): if overwrite_references: data.to_netcdf(ref_file) ref_gls = xr.open_dataset(ref_file) - xr.testing.assert_equal(ref_gls, data) + xr.testing.assert_allclose(ref_gls, data) diff --git a/tests/utils/test_harmo.py b/tests/utils/test_harmo.py index 5d4b58f..24aae32 100644 --- a/tests/utils/test_harmo.py +++ b/tests/utils/test_harmo.py @@ -28,7 +28,7 @@ def test_sh_to_grid(lenapy_paths): costg_ds = xr.open_dataset(lenapy_paths.data / "COSTG_n12_2002_2022.nc") grid = costg_ds.lnharmo.to_grid() grid = subsample_xr(grid, 10) - xr.testing.assert_equal(grid_ref, grid) + xr.testing.assert_allclose(grid_ref, grid) @pytest.mark.parametrize( From 3a87ab9a28351171fb3eb89bb6c0600de95f51c1 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 6 May 2025 13:50:54 +0200 Subject: [PATCH 43/54] ci: missing stage --- .gitlab-ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 3ec79e9..b420ed8 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -10,6 +10,7 @@ workflow: stages: - init - quality + - documentation # Default configuration for all jobs default: From 9ef18e170ce3d2509265266661bcf7ea12f0c820 Mon Sep 17 00:00:00 2001 From: lecomth Date: Fri, 23 May 2025 16:12:51 +0200 Subject: [PATCH 44/54] Improve ICGEM gfc reader --- lenapy/readers/gravi_reader.py | 391 ++++++++++++++++++++++++--------- lenapy/utils/geo.py | 4 +- lenapy/utils/gravity.py | 109 +++++++++ 3 files changed, 400 insertions(+), 104 deletions(-) diff --git a/lenapy/readers/gravi_reader.py b/lenapy/readers/gravi_reader.py index 4b2ab02..e855531 100644 --- a/lenapy/readers/gravi_reader.py +++ b/lenapy/readers/gravi_reader.py @@ -37,6 +37,7 @@ from typing import IO import numpy as np +import pandas as pd import xarray as xr import yaml from xarray.backends import BackendEntrypoint @@ -257,13 +258,13 @@ class ReadGFC(BackendEntrypoint): ] @staticmethod - def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: + def _parse_header(file_io: IO, ext: str) -> tuple[dict, str]: """ Parse the header of a .gfc file and extract relevant metadata. Parameters ---------- - fileIO : IO + file_io : IO File object. ext : str File extension. @@ -294,7 +295,7 @@ def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: header = {} legend_before_end_header = "" while True: - line = fileIO.readline() + line = file_io.readline() if ext.lower() in [".gz", ".gzip", ".zip", ".tar", ".ZIP"]: line = line.decode() if re.match(regex, line): @@ -302,7 +303,7 @@ def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: if "end_of_head" in line: break elif "0 0" in line: - raise ValueError(f"Missing 'end_of_head' in header of file {fileIO}") + raise ValueError(f"Missing 'end_of_head' in header of file {file_io}") legend_before_end_header = line # case for COSTG header where 'modelname' is created as 'product_name' @@ -338,7 +339,8 @@ def _parse_header(fileIO: IO, ext: str) -> tuple[dict, str]: header["radius"] = float(header["radius"]) return header, legend_before_end_header - def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str]: + @staticmethod + def _open_file(filename: str | os.PathLike) -> tuple[IO, str]: """ Open a .gfc file or a compressed archive containing a .gfc file. @@ -360,22 +362,21 @@ def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str]: If the file extension is unsupported or no .gfc is found in archive. """ ext = os.path.splitext(filename)[-1] - compress_extensions = [".gz", ".zip", ".tar", ".gzip", ".ZIP"] if ext.lower() == ".gfc": return open(filename, "r"), ext - if ext in (".gz", ".gzip"): + elif ext in (".gz", ".gzip"): return gzip.open(filename, "rb"), ext - if ext in (".zip", ".ZIP"): + elif ext in (".zip", ".ZIP"): zip_file = zipfile.ZipFile(filename, "r") gfc_files = [f for f in zip_file.namelist() if f.lower().endswith(".gfc")] if not gfc_files: raise ValueError("No .gfc file found in ZIP archive.") return zip_file.open(gfc_files[0], "r"), ext - if ext == ".tar": + elif ext == ".tar": tar_file = tarfile.open(filename, "r") gfc_files = [f for f in tar_file.getnames() if f.lower().endswith(".gfc")] if not gfc_files: @@ -384,6 +385,241 @@ def _open_file(self, filename: str | os.PathLike) -> tuple[IO, str]: raise ValueError("Unsupported file extension.") + @staticmethod + def _get_date(date_regex, date_format, filename, header): + """ + Extract date from header file information and format dates in datetime objects. + + Parameters + ---------- + date_regex : str | None, optional + A regular expression pattern used to search for the date in the modelname header information. It should + contain at least one capturing group for the begin_time, and optionally a second group for the `end_time`. + date_format : str | None, optional + A format string compatible with `datetime.strptime` to parse the extracted date strings. + Must be provided if `date_regex` is specified. + filename : str | os.PathLike[Any] + Name/path of the file to open. + header : dict + Parsed header metadata. + + Returns + ------- + mid_month : datatime.Datetime + Middle of the month. + exact_time : datatime.Datetime + Exact time of the data in the month. + begin_time : datatime.Datetime + Begin time of the data in the month. + end_time : datatime.Datetime + End time of the data in the month. + """ + if ( + date_regex + and date_format + and bool(re.search(date_regex, header["modelname"])) + ): + # Get dates from the given date_regex and date_format + dates = re.search(date_regex, header["modelname"]) + + elif bool(re.search(r"_(\d{7})-(\d{7})", header["modelname"])): + # For some products, time is stored as YYYYDOY-YYYYDOY in modelname + # For GRACE products, DOY can not coincide with 1st and last day of month + dates = re.search(r"_(\d{7})-(\d{7})", header["modelname"]) + date_format = "%Y%j" + + elif bool(re.search(r"(\d{4}-\d{2})", header["modelname"])): + # For other products, time is stored as YYYY-MM in modelname + dates = re.search(r"(\d{4}-\d{2})", header["modelname"]) + date_format = "%Y-%m" + + else: + raise ValueError( + f"Could not extract date information from modelname in the header " + f"of {filename}\n Try with the parameter no_date=True or " + f"check the use of arguments date_regex and date_format." + ) + + # Read begin date and end date if it exists + begin_time = datetime.datetime.strptime(dates.group(1), date_format) + end_time_str = dates.group(2) if dates.lastindex >= 2 else None + + if end_time_str: # Case with a date for the end + end_time = datetime.datetime.strptime( + end_time_str, date_format + ) + datetime.timedelta(days=1) + exact_time = begin_time + (end_time - begin_time) / 2 + + # Compute middle of the month for GRACE products + mid_month = mid_month_grace_estimate(begin_time, end_time) + else: # Case without a date for the end + if ( + begin_time.day == 1 + ): # Case where the date contains no day in the month information + end_time = (begin_time + datetime.timedelta(days=32)).replace(day=1) + mid_month = begin_time + (end_time - begin_time) / 2 + exact_time = mid_month + else: # Case where the date contains day in the month information, we keep the date as reference + end_time = begin_time + mid_month = begin_time + exact_time = begin_time + + return mid_month, exact_time, begin_time, end_time + + @staticmethod + def _format_icgem1(data, lmax, header, clm, slm, eclm=None, eslm=None): + """ + Subfunction of the gfc reader to read the gfct icgem1.0 format. + + Parameters + ---------- + data : pd.Dataframe + pandas Dataframe containing the coefficients information. + lmax : int + Maximal degree for the spherical harmonics coefficients. + header : dict + Dictionary of header information. + clm : np.array + Pre-created clm coefficients array. + slm : np.array + Pre-created slm coefficients array. + eclm : np.array | None + Pre-created eclm coefficients array. + eslm : np.array | None + Pre-created eslm coefficients array. + + Returns + ------- + ds : xr.Dataset + Information from the file stored in `xr.Dataset` format. + """ + periods_acos = data[(data["tag"] == "acos")]["ref_time"].unique() + periods_asin = data[(data["tag"] == "acos")]["ref_time"].unique() + + trnd_clm, trnd_slm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros( + (lmax + 1, lmax + 1, 1) + ) + acos_clm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_acos))) + acos_slm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_acos))) + asin_clm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_asin))) + asin_slm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_asin))) + + ref_time = np.zeros((lmax + 1, lmax + 1, 1), dtype="datetime64[D]") + + if ( + header["errors"] != "no" + ): # (does not deal with calibrated_and_formal error case) + trnd_eclm, trnd_eslm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros( + (lmax + 1, lmax + 1, 1) + ) + acos_eclm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_acos))) + acos_eslm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_acos))) + asin_eclm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_asin))) + asin_eslm = np.zeros((lmax + 1, lmax + 1, 1, len(periods_asin))) + + index_gfc_trnd = [ + (data["tag"] == "gfc") | (data["tag"] == "gfct"), + data["tag"] == "trnd", + ] + for ind, c, s in zip(index_gfc_trnd, [clm, trnd_clm], [slm, trnd_slm]): + c[data[ind]["degree"].values, data[ind]["order"].values] = data[ind][ + "clm" + ].values[:, np.newaxis] + s[data[ind]["degree"].values, data[ind]["order"].values] = data[ind][ + "slm" + ].values[:, np.newaxis] + + index_acos_asin = [data["tag"] == "acos", data["tag"] == "asin"] + for ind, c, s, periods in zip( + index_acos_asin, + [acos_clm, asin_slm], + [acos_slm, asin_slm], + [periods_acos, periods_asin], + ): + for i, period in enumerate(periods): + k = ind & (data["ref_time"] == period) + c[data[k]["degree"].values, data[k]["order"].values, 0, i] = data[k][ + "clm" + ].values + s[data[k]["degree"].values, data[k]["order"].values, 0, i] = data[k][ + "slm" + ].values + + ind = data["tag"] == "gfct" + ref_time[data[ind]["degree"].values, data[ind]["order"].values] = ( + pd.to_datetime( + data[ind]["ref_time"].values.astype(int).astype(str), format="%Y%m%d" + ).to_numpy()[:, np.newaxis] + ) + + ds = xr.Dataset( + { + "clm": (["l", "m", "name"], clm), + "slm": (["l", "m", "name"], slm), + "trnd_clm": (["l", "m", "name"], trnd_clm), + "trnd_slm": (["l", "m", "name"], trnd_slm), + "acos_clm": (["l", "m", "name", "periods_acos"], acos_clm), + "acos_slm": (["l", "m", "name", "periods_acos"], acos_slm), + "asin_clm": (["l", "m", "name", "periods_asin"], asin_clm), + "asin_slm": (["l", "m", "name", "periods_asin"], asin_slm), + "ref_time": (["l", "m", "name"], ref_time), + }, + coords={ + "l": np.arange(lmax + 1), + "m": np.arange(lmax + 1), + "name": [header["modelname"]], + "periods_acos": periods_acos, + "periods_asin": periods_asin, + }, + attrs=header, + ) + + # case with error information (does not deal with calibrated_and_formal error case) + if header["errors"] != "no": + for ind, ec, es in zip( + index_gfc_trnd, [eclm, trnd_eclm], [eslm, trnd_eslm] + ): + ec[data[ind]["degree"].values, data[ind]["order"].values] = data[ind][ + "eclm" + ].values[:, np.newaxis] + es[data[ind]["degree"].values, data[ind]["order"].values] = data[ind][ + "eslm" + ].values[:, np.newaxis] + + for ind, ec, es, periods in zip( + index_acos_asin, + [acos_eclm, asin_eslm], + [acos_eslm, asin_eslm], + [periods_acos, periods_asin], + ): + for i, period in enumerate(periods): + k = ind & (data["ref_time"] == period) + ec[data[k]["degree"].values, data[k]["order"].values, 0, i] = data[ + k + ]["eclm"].values + es[data[k]["degree"].values, data[k]["order"].values, 0, i] = data[ + k + ]["eslm"].values + + ds["eclm"] = xr.DataArray(eclm, dims=["l", "m", "name"]) + ds["eslm"] = xr.DataArray(eslm, dims=["l", "m", "name"]) + ds["trnd_eclm"] = xr.DataArray(trnd_eclm, dims=["l", "m", "name"]) + ds["trnd_eslm"] = xr.DataArray(trnd_eslm, dims=["l", "m", "name"]) + ds["acos_eclm"] = xr.DataArray( + acos_eclm, dims=["l", "m", "name", "periods_acos"] + ) + ds["acos_eslm"] = xr.DataArray( + acos_eslm, dims=["l", "m", "name", "periods_acos"] + ) + ds["asin_eclm"] = xr.DataArray( + asin_eclm, dims=["l", "m", "name", "periods_asin"] + ) + ds["asin_eslm"] = xr.DataArray( + asin_eslm, dims=["l", "m", "name", "periods_asin"] + ) + + return ds + def open_dataset( self, filename, @@ -442,105 +678,47 @@ def open_dataset( lmax = header["max_degree"] + # -- Load clm and slm data + clm, slm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros((lmax + 1, lmax + 1, 1)) + + col_names = ["tag", "degree", "order", "clm", "slm"] + if ( + header["errors"] != "no" + ): # (does not deal with calibrated_and_formal error case) + col_names.append("eclm") + col_names.append("eslm") + + eclm, eslm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros( + (lmax + 1, lmax + 1, 1) + ) + + if "t" in legend: + col_names.append("ref_time") + + # Read file with pandas, delim_whitespace for variable space delimiters + data = pd.read_csv( + file, delim_whitespace=True, header=None, names=col_names, engine="python" + ) + # test if gfct key then have to deal with time if "t" not in legend: # -- Compute time if not no_date: - if ( - date_regex - and date_format - and bool(re.search(date_regex, header["modelname"])) - ): - # Get dates from the given date_regex and date_format - dates = re.search(date_regex, header["modelname"]) - - elif bool(re.search(r"_(\d{7})-(\d{7})", header["modelname"])): - # For some products, time is stored as YYYYDOY-YYYYDOY in modelname - # For GRACE products, DOY can not coincide with 1st and last day of month - dates = re.search(r"_(\d{7})-(\d{7})", header["modelname"]) - date_format = "%Y%j" - - elif bool(re.search(r"(\d{4}-\d{2})", header["modelname"])): - # For other products, time is stored as YYYY-MM in modelname - dates = re.search(r"(\d{4}-\d{2})", header["modelname"]) - date_format = "%Y-%m" - - else: - raise ValueError( - f"Could not extract date information from modelname in the header " - f"of {filename}\n Try with the parameter no_date=True or " - f"check the use of arguments date_regex and date_format." - ) - - # Read begin date and end date if it exists - begin_time = datetime.datetime.strptime(dates.group(1), date_format) - end_time_str = dates.group(2) if dates.lastindex >= 2 else None - - if end_time_str: # Case with a date for the end - end_time = datetime.datetime.strptime( - end_time_str, date_format - ) + datetime.timedelta(days=1) - exact_time = begin_time + (end_time - begin_time) / 2 - - # Compute middle of the month for GRACE products - mid_month = mid_month_grace_estimate(begin_time, end_time) - else: # Case without a date for the end - if ( - begin_time.day == 1 - ): # Case where the date contains no day in the month information - end_time = (begin_time + datetime.timedelta(days=32)).replace( - day=1 - ) - mid_month = begin_time + (end_time - begin_time) / 2 - exact_time = mid_month - else: # Case where the date contains day in the month information, we keep the date as reference - end_time = begin_time - mid_month = begin_time - exact_time = begin_time + mid_month, exact_time, begin_time, end_time = self._get_date( + date_regex, date_format, filename, header + ) # If no time, time info will be a string with modelname else: mid_month = header["modelname"] - # -- Load clm and slm data - clm, slm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros( - (lmax + 1, lmax + 1, 1) - ) - # case with error information (does not deal with calibrated_and_formal error case) - if header["errors"] != "no": - eclm, eslm = np.zeros((lmax + 1, lmax + 1, 1)), np.zeros( - (lmax + 1, lmax + 1, 1) - ) - data = np.genfromtxt( - file, - dtype=[ - ("tag", "U4"), - ("degree", int), - ("order", int), - ("clm", float), - ("slm", float), - ("eclm", float), - ("eslm", float), - ], - ) - eclm[data["degree"], data["order"]] = data["eclm"][:, np.newaxis] - eslm[data["degree"], data["order"]] = data["eslm"][:, np.newaxis] + clm[data["degree"].values, data["order"].values] = data["clm"].values[ + :, np.newaxis + ] + slm[data["degree"].values, data["order"].values] = data["slm"].values[ + :, np.newaxis + ] - # case for no error in file - else: - data = np.genfromtxt( - file, - dtype=[ - ("tag", "U4"), - ("degree", int), - ("order", int), - ("clm", float), - ("slm", float), - ], - ) - - clm[data["degree"], data["order"]] = data["clm"][:, np.newaxis] - slm[data["degree"], data["order"]] = data["slm"][:, np.newaxis] ds = xr.Dataset( {"clm": (["l", "m", "time"], clm), "slm": (["l", "m", "time"], slm)}, coords={ @@ -551,17 +729,26 @@ def open_dataset( attrs=header, ) + # case with error information (does not deal with calibrated_and_formal error case) if header["errors"] != "no": + eclm[data["degree"].values, data["order"].values] = data["eclm"].values[ + :, np.newaxis + ] + eslm[data["degree"].values, data["order"].values] = data["eslm"].values[ + :, np.newaxis + ] + ds["eclm"] = xr.DataArray(eclm, dims=["l", "m", "time"]) ds["eslm"] = xr.DataArray(eslm, dims=["l", "m", "time"]) else: - raise AssertionError( - "Reading of .gfc file with time is not implemented yet" - ) + if header["errors"] == "no": + ds = self._format_icgem1(data, lmax, header, clm, slm) + else: + ds = self._format_icgem1(data, lmax, header, clm, slm, eclm, eslm) # -- Add various time information in dataset - if not no_date: + if not no_date and "t" not in legend: ds["begin_time"] = xr.DataArray([begin_time], dims=["time"]) ds["end_time"] = xr.DataArray([end_time], dims=["time"]) ds["exact_time"] = xr.DataArray([exact_time], dims=["time"]) diff --git a/lenapy/utils/geo.py b/lenapy/utils/geo.py index a07171f..9e900bd 100644 --- a/lenapy/utils/geo.py +++ b/lenapy/utils/geo.py @@ -416,8 +416,8 @@ def assert_grid(ds): Parameters ---------- - ds : xr.Dataset - Dataset to verify. + ds : xr.DataArray + Spatial grid to verify. Returns ------- diff --git a/lenapy/utils/gravity.py b/lenapy/utils/gravity.py index c2034e1..50c841d 100644 --- a/lenapy/utils/gravity.py +++ b/lenapy/utils/gravity.py @@ -25,6 +25,8 @@ >>> ds['slm'] *= ds_gauss_weights """ +import warnings + import numpy as np import xarray as xr @@ -348,3 +350,110 @@ def gauss_weights(radius, lmax, a_earth=LNPY_A_EARTH_GRS80, cutoff=1e-10): break return xr.DataArray(gaussian_weights, dims=["l"], coords={"l": np.arange(lmax + 1)}) + + +def gfct_field_estimation(ds, time): + """ + Compute time-variable gravity field from variation coefficients contain in '.gfc' file with icgem1.0 format. + + Parameters + ---------- + ds : xr.Dataset + Output of the reader from the '.gfc' file with gfct information. + time : np.datetime64 | list, tuple, np.ndarray of np.datetime64 | xr.DataArray of np.datetime64 + Time information to compute the coefficients variations on. + + Returns + ------- + ds_out : xr.Dataset + Estimated coefficients time variations. + """ + if isinstance(time, xr.DataArray): + if not np.issubdtype(time.dtype, np.datetime64): + raise TypeError("The time xr.DataArray does not contain np.datetime64.") + + elif isinstance(time, (list, tuple, np.ndarray)): + if not np.issubdtype(time.dtype, np.datetime64): + raise TypeError("The time xr.DataArray does not contain np.datetime64.") + else: + time = xr.DataArray(time, dims=["time"]) + + elif isinstance(time, np.datetime64): + time = xr.DataArray(np.array([time]), dims=["time"]) + + else: + raise TypeError( + "The 'time' parameter has to be a xr.DataArray with np.datetime64, " + "or a list/array of datetime64, or a datetime64 object." + ) + + if "name" in ds.dims: + if ds.dims["name"] > 1: + warnings.warn( + "Multiple object on the dimension 'name', only the first one is converted to a time dataset." + ) + ds = ds.isel(name=0) + + delta_year = (time - ds.ref_time).dt.days / 365.25 + + clm = ( + ds.clm + + ds.trnd_clm * delta_year + + (ds.acos_clm * np.cos(2 * np.pi * delta_year / ds.periods_acos)).sum( + "periods_acos" + ) + + (ds.asin_slm * np.sin(2 * np.pi * delta_year / ds.periods_asin)).sum( + "periods_asin" + ) + ) + slm = ( + ds.slm + + ds.trnd_slm * delta_year + + (ds.acos_slm * np.cos(2 * np.pi * delta_year / ds.periods_acos)).sum( + "periods_acos" + ) + + (ds.asin_slm * np.sin(2 * np.pi * delta_year / ds.periods_asin)).sum( + "periods_asin" + ) + ) + + print(clm.values.shape) + ds_out = xr.Dataset( + { + "clm": (["l", "m", "time"], clm.values), + "slm": (["l", "m", "time"], slm.values), + }, + coords={ + "l": ds.l, + "m": ds.m, + "time": time, + }, + attrs=ds.attrs, + ) + + if ds.attrs["errors"] != "no": + eclm = np.sqrt( + ds.eclm**2 + + (ds.trnd_eclm * delta_year) ** 2 + + ( + (ds.acos_eclm * np.cos(2 * np.pi * delta_year / ds.periods_acos)) ** 2 + ).sum("periods_acos") + + ( + (ds.asin_eslm * np.sin(2 * np.pi * delta_year / ds.periods_asin)) ** 2 + ).sum("periods_asin") + ) + eslm = np.sqrt( + ds.eslm**2 + + (ds.trnd_eslm * delta_year) ** 2 + + ( + (ds.acos_eslm * np.cos(2 * np.pi * delta_year / ds.periods_acos)) ** 2 + ).sum("periods_acos") + + ( + (ds.asin_eslm * np.sin(2 * np.pi * delta_year / ds.periods_asin)) ** 2 + ).sum("periods_asin") + ) + + ds["eclm"] = xr.DataArray(eclm, dims=["l", "m", "time"]) + ds["eslm"] = xr.DataArray(eslm, dims=["l", "m", "time"]) + + return ds_out From b927e30fa697306f83b867c529560bb1bfc04b80 Mon Sep 17 00:00:00 2001 From: Fourest Date: Mon, 26 May 2025 10:18:53 +0000 Subject: [PATCH 45/54] Gestion des cycles annuels dans lntime.filter --- lenapy/lenapy_time.py | 6 ++ lenapy/utils/time.py | 185 ++++++++++++++++++++---------------------- 2 files changed, 96 insertions(+), 95 deletions(-) diff --git a/lenapy/lenapy_time.py b/lenapy/lenapy_time.py index a6764d2..e641e7e 100644 --- a/lenapy/lenapy_time.py +++ b/lenapy/lenapy_time.py @@ -463,3 +463,9 @@ def EOF(self, dim, k): Return an instance of the *eof* class based on the data array and the dimension names of the eof """ return EOF(self._obj, dim, k) + + def SavitzkyGolay(self, dim="time", window=5, order=1, step=1, sigma=None): + """ + Perform a Savitzky-Golay filter on a dataArray and return filtered derivatives up to maximal order + """ + return SavitzkyGolay(self._obj, dim, window, order, step, sigma) diff --git a/lenapy/utils/time.py b/lenapy/utils/time.py index f2cd39d..ca77ec1 100644 --- a/lenapy/utils/time.py +++ b/lenapy/utils/time.py @@ -4,6 +4,7 @@ import numpy as np import pandas as pd import xarray as xr +from scipy.special import factorial as factorial from lenapy.constants import * from lenapy.utils import filters @@ -54,7 +55,8 @@ def filter( if annual_cycle is True: # remove the climato - data0, coeffs = climato(data, mean=False, trend=False, return_coeffs=True) + cc = Coeffs_climato(data, cycle=True, order=1).solve() + data0 = cc.signal(coefficients=[]) else: data0 = data @@ -63,15 +65,17 @@ def filter( v0 = xr.polyval(data0[time_coord], pf).polyfit_coefficients # Remove the polynomial to the initial data untreated v1 = data0 - v0 - v1[time_coord] = v1[time_coord].astype("float") + v1[time_coord] = v1[time_coord].astype(np.int64) # Fullfil the data with the mirror effect at the beginning and at the end. v2 = v1.pad({time_coord: (k, k)}, mode="reflect", reflect_type="even") - v2[time_coord] = v1[time_coord].pad( - {time_coord: (k, k)}, mode="reflect", reflect_type="odd" + v2[time_coord] = ( + v1[time_coord] + .pad({time_coord: (k, k)}, mode="reflect", reflect_type="odd") + .astype(" Date: Tue, 27 May 2025 08:28:54 +0000 Subject: [PATCH 46/54] Filtrage Stavitzky Golay --- lenapy/utils/time.py | 58 ++++++++++++++++++++++++++++---------------- 1 file changed, 37 insertions(+), 21 deletions(-) diff --git a/lenapy/utils/time.py b/lenapy/utils/time.py index ca77ec1..d197991 100644 --- a/lenapy/utils/time.py +++ b/lenapy/utils/time.py @@ -301,57 +301,74 @@ def fillna_climato(data, time_period=slice(None, None)): return xr.where(data.isnull(), val, data) -def SavitzkyGolay(da, dim="time", window=5, order=1, step=1): +def SavitzkyGolay(da, dim="time", window=12, order=1, step=1, sigma=None): """ Perform a Savitzky-Golay filter on a dataArray and return filtered derivatives up to maximal order """ + def convolution_matrix(M, C): + return np.dot(np.linalg.inv(np.dot(X.T, np.dot(C, X))), np.dot(X.T, C)) + + def weights(xx): + if sigma is None: + return np.diag(xx * 0.0 + 1) + else: + return np.diag(np.exp(-(xx**2) / sigma**2)) + + if np.mod(window, 2) != 1: + print("Warning, window is even, set to window-1") + half_window = int((window - 1) / 2.0) + # [0,1,...,order] o = xr.DataArray(np.int32(np.arange(order + 1)), dims="order") # Normalize polynomial coefficients to obtain the corresponding derivative norm = xr.DataArray(factorial(o) / step**o) - # Filter impementation at the edges of the signal (between 0 and window, and between n-window and n) + # Filter impementation at the edges of the signal (between 0 and half_window, and between n-half_window and n) lateral_signal = [] - for i in range(window): + for i in range(half_window): # Signal start # X-axis indices - xm = xr.DataArray(np.arange(-i, window + 1, 1), dims=dim) + xm = xr.DataArray(np.arange(-i, half_window + 1, 1), dims=dim) # Least-squares obervables matrix X = xm**o + # Weight matrix + W = weights(xm) # SG filter implementation filtrem = ( xr.DataArray( - np.dot(np.linalg.inv(np.dot(X.T, X)), X.T), + convolution_matrix(X, W), coords={ "order": o, - dim: da[dim].isel({dim: slice(None, window + i + 1)}), + dim: da[dim].isel({dim: slice(None, half_window + i + 1)}), }, ) * norm ) # Signal end # X-axis indices - xp = xr.DataArray(np.arange(-window, window - i, 1), dims=dim) + xp = xr.DataArray(np.arange(-half_window, half_window - i, 1), dims=dim) # Least-squares obervables matrix X = xp**o + # Weight matrix + W = weights(xp) # SG filter implementation filtrep = ( xr.DataArray( - np.dot(np.linalg.inv(np.dot(X.T, X)), X.T), + convolution_matrix(X, W), coords={ "order": o, - dim: da[dim].isel({dim: slice(-2 * window + i, None)}), + dim: da[dim].isel({dim: slice(-2 * half_window + i, None)}), }, ) * norm ) - # Concolution du sign - signalm = da.isel({dim: slice(None, window + i + 1)}) + # Convolution of the signal by the filter + signalm = da.isel({dim: slice(None, half_window + i + 1)}) xvm = da[dim].isel({dim: [i]}).values - signalp = da.isel({dim: slice(-2 * window + i, None)}) - xvp = da[dim].isel({dim: [-window + i]}).values + signalp = da.isel({dim: slice(-2 * half_window + i, None)}) + xvp = da[dim].isel({dim: [-half_window + i]}).values lateral_signal.append( (signalm * filtrem).sum(dim).expand_dims({dim: xvm}).rename("ok") ) @@ -359,23 +376,22 @@ def SavitzkyGolay(da, dim="time", window=5, order=1, step=1): (signalp * filtrep).sum(dim).expand_dims({dim: xvp}).rename("ok") ) - # Filter impementation for the central part of the signal (between window and n-window) - x = xr.DataArray(np.arange(-window, window + 1, 1), dims="x_win") + # Filter impementation for the central part of the signal (between half_window and n-half_window) + x = xr.DataArray(np.arange(-half_window, half_window + 1, 1), dims="x_win") X = x**o + # Weight matrix + W = weights(x) filtre = ( - xr.DataArray( - np.dot(np.linalg.inv(np.dot(X.T, X)), X.T), coords=dict(order=o, x_win=x) - ) - * norm + xr.DataArray(convolution_matrix(X, W), coords=dict(order=o, x_win=x)) * norm ) # Fenetres de largeur 2*l+1, convoluées par le filtre (multiplication par le filtre et somme des éléments central_signal = ( - da.rolling({dim: 2 * l + 1}, center=True) + da.rolling({dim: 2 * half_window + 1}, center=True) .construct("x_win") .weighted(filtre) .sum("x_win") - .isel({dim: slice(window, -window)}) + .isel({dim: slice(half_window, -half_window)}) .rename("ok") ) From b8515483bf00ec4de9444f5aea426b74e53cd675 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 29 Apr 2025 16:03:17 +0200 Subject: [PATCH 47/54] doc: refac doc --- doc/CHANGELOG.md => CHANGELOG.md | 0 doc/.templates/autosummary/module.rst | 8 ++ doc/api.rst | 25 ------ doc/api/index.rst | 50 ++++++++++++ doc/changelog.rst | 3 +- doc/conf.py | 5 +- doc/contributing.rst | 112 ++++++++++++++++++++++++++ doc/index.rst | 20 ++--- doc/release_procedure.rst | 29 +++++++ lenapy/__init__.py | 15 ---- lenapy/lenapy_ocean.py | 4 +- lenapy/readers/__init__.py | 13 --- lenapy/utils/__init__.py | 12 --- lenapy/writers/__init__.py | 11 --- pyproject.toml | 1 + 15 files changed, 214 insertions(+), 94 deletions(-) rename doc/CHANGELOG.md => CHANGELOG.md (100%) create mode 100644 doc/.templates/autosummary/module.rst delete mode 100644 doc/api.rst create mode 100644 doc/api/index.rst create mode 100644 doc/contributing.rst create mode 100644 doc/release_procedure.rst diff --git a/doc/CHANGELOG.md b/CHANGELOG.md similarity index 100% rename from doc/CHANGELOG.md rename to CHANGELOG.md diff --git a/doc/.templates/autosummary/module.rst b/doc/.templates/autosummary/module.rst new file mode 100644 index 0000000..244d08c --- /dev/null +++ b/doc/.templates/autosummary/module.rst @@ -0,0 +1,8 @@ +{{ fullname }} +{{ underline }} + + +.. automodule:: {{ fullname }} + :members: + :undoc-members: + :show-inheritance: diff --git a/doc/api.rst b/doc/api.rst deleted file mode 100644 index 3c07436..0000000 --- a/doc/api.rst +++ /dev/null @@ -1,25 +0,0 @@ -========== -LENAPY API -========== - -.. automodule:: lenapy - :show-inheritance: - -.. automodule:: lenapy.readers - :show-inheritance: - -.. automodule:: lenapy.utils - :show-inheritance: - -.. automodule:: lenapy.writers - :show-inheritance: - -Lenapy constants -^^^^^^^^^^^^^^^^ - -.. automodule:: lenapy.constants - :noindex: - -.. raw:: latex - - \clearpage diff --git a/doc/api/index.rst b/doc/api/index.rst new file mode 100644 index 0000000..1e64488 --- /dev/null +++ b/doc/api/index.rst @@ -0,0 +1,50 @@ +lenapy API Reference +==================== + +Module contents +~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: _autosummary + + lenapy.lenapy_geo + lenapy.lenapy_time + lenapy.lenapy_ocean + lenapy.lenapy_harmo + lenapy.constants + +Submodules +~~~~~~~~~~ + +Readers +******* + +Reader modules : define specific engines for the xarray.open_dataset() method + +.. autosummary:: + :toctree: _autosummary + + lenapy.readers.geo_reader + lenapy.readers.gravi_reader + lenapy.readers.ocean + +Utils +***** + +Utils modules + +.. autosummary:: + :toctree: _autosummary + + lenapy.utils.harmo + lenapy.utils.gravity + +Writers +******* + +Writer modules : define writers + +.. autosummary:: + :toctree: _autosummary + + lenapy.writers.gravi_writer diff --git a/doc/changelog.rst b/doc/changelog.rst index 536db33..daef996 100644 --- a/doc/changelog.rst +++ b/doc/changelog.rst @@ -1 +1,2 @@ -.. mdinclude:: CHANGELOG.md +.. _changes: +.. mdinclude:: ../CHANGELOG.md diff --git a/doc/conf.py b/doc/conf.py index 4a7aecb..172c06d 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -46,6 +46,7 @@ autoclass_content = "both" # sort class members by their order in the source autodoc_member_order = "bysource" +autodoc_typehints = "both" # show all members of a class in the Methods and Attributes sections automatically numpydoc_show_class_members = True @@ -66,7 +67,6 @@ html_theme = "sphinx_rtd_theme" html_theme_options = { "logo_only": False, - "display_version": True, "prev_next_buttons_location": "both", "collapse_navigation": False, "sticky_navigation": True, @@ -76,7 +76,7 @@ "style_nav_header_background": "#2980B9", # header color en haut à gauche # github_url for open access version } -html_static_path = ["_static"] +# html_static_path = ["_static"] # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { @@ -94,3 +94,4 @@ # See also: # https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#confval-intersphinx_disabled_reftypes intersphinx_disabled_reftypes = ["*"] +templates_path = [".templates"] diff --git a/doc/contributing.rst b/doc/contributing.rst new file mode 100644 index 0000000..10c3ed6 --- /dev/null +++ b/doc/contributing.rst @@ -0,0 +1,112 @@ +Contributing +============ + +Thanks for helping to build lenapy! + +Retrieve the code: forking and cloning the Repository +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Make a fork of the `lenapy repo `__ and clone +the fork. + +A documentation is available on GitHub to help platform users create a fork: `https://docs.github.com/fork-a-repo `__ + +.. code-block:: none + + git clone https://github.com//lenapy.git + cd lenapy + +You may want to add ``https://github.com/CNES/lenapy`` as an upstream remote +repository. + +.. code-block:: none + + git remote add upstream https://github.com/CNES/lenapy + +Creating a Python environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We have conda environment YAML file with all the necessary dependencies. + +.. code-block:: none + + conda env create -f environment.yml --name=lenapy-dev + +to create a conda environment and install all the dependencies. + +Building lenapy +~~~~~~~~~~~~~~~ + +Lenapy is a pure-python repository. Development installation should be as simple as +cloning the repository and running the following in the cloned directory: + +.. code-block:: none + + conda activate lenapy-dev + python -m pip install --no-deps -e . + +If you have any trouble, please open an issue on the +`lenapy issue tracker `_. + +Style +~~~~~ + +Lenapy uses `black `_ and `isort `_ +for formatting. If you installed lenapy with ``python -m pip install -e ".[dev]"`` these tools will already be +installed. + +Running tests +~~~~~~~~~~~~~ + +Lenapy uses `pytest `_ for testing. You +can run tests from the main lenapy directory as follows: + +.. code-block:: none + + pytest tests + +Alternatively you may choose to run only a subset of the full test suite. For +example to test only the `writers` submodule we would run tests as follows: + +.. code-block:: none + + pytest tests/writers + +Coverage +~~~~~~~~ + +It is possible to check code coverage + +.. code-block:: none + + pytest --cov=lenapy --cov-report=html + +You can still use all the usual pytest command-line options in addition to those. + +Pre-Commit Hooks +~~~~~~~~~~~~~~~~ + +Install and build the `pre commit `_ tool as: + +.. code-block:: none + + python -m pip install -e ".[dev]" + pre-commit install + +to install a few plugins like black, isort, and pylint. These tools will automatically +be run on each commit. You can skip the checks with ``git commit --no-verify``. + +Documentation +~~~~~~~~~~~~~ + +We use `numpydoc `_ for our docstrings. + +Building the docs is possible with + +.. code-block:: none + + $ conda env create -f environment.yml --name=lenapy-dev + $ conda activate lenapy-dev + $ python -m pip install -e ".[dev]" + $ cd doc + $ sphinx-build -b html doc doc/build diff --git a/doc/index.rst b/doc/index.rst index 6cea6b7..c305cc9 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -18,19 +18,13 @@ This library is based on the principle of class extension, i.e. functions can be Reading interfaces are implemented, enabling netcdf files to be opened with a compatible formalism from the **lenapy** library. .. toctree:: - :maxdepth: 4 + :maxdepth: 1 :caption: Contents: - - changelog + :hidden: + gettingStarted - api + Changelog tutorials - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` + lenapy API Reference + How to contribute + Release procedure diff --git a/doc/release_procedure.rst b/doc/release_procedure.rst new file mode 100644 index 0000000..9550a5a --- /dev/null +++ b/doc/release_procedure.rst @@ -0,0 +1,29 @@ +Release procedure +================= + +Release for lenapy, from within your fork: + +* Submit a PR that updates the release notes in `doc/changelog.rst`. + +We submit a PR to inform other developers of the pending release, and possibly +discuss its content. + +* Once the PR is merged, checkout the main branch: + +.. code-block:: none + + git checkout upstream/main + + +* Create a tag and push to github: + +.. code-block:: none + + git tag -a x.x.x -m 'Version x.x.x' + git push --tags upstream + +* The Conda Forge bots should pick up the change automatically within an hour or two. Then follow the instructions from the automatic e-mail that you will receive from Conda Forge, basically: + + - Check that dependencies have not changed. + + - Merge the PR on conda-forge/lenapy-feedstock once tests have passed. diff --git a/lenapy/__init__.py b/lenapy/__init__.py index 1fa987f..bdb4cfe 100644 --- a/lenapy/__init__.py +++ b/lenapy/__init__.py @@ -1,18 +1,3 @@ -""" - -Modules -^^^^^^^ -Accessor modules - -.. autosummary:: - :toctree: - - lenapy_geo - lenapy_time - lenapy_ocean - lenapy_harmo -""" - import warnings from lenapy import constants, lenapy_geo, lenapy_harmo, lenapy_time diff --git a/lenapy/lenapy_ocean.py b/lenapy/lenapy_ocean.py index 5e880ca..6652c95 100644 --- a/lenapy/lenapy_ocean.py +++ b/lenapy/lenapy_ocean.py @@ -94,7 +94,7 @@ class OceanSet: * longitude (longitude) float32 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0 Attributes: long_name: Ocean heat content - units: J/m² + units: J/m^2 print(data.lnocean.gohc) dask.array @@ -103,7 +103,7 @@ class OceanSet: depth float32 1.0 Attributes: long_name: Global ocean heat content wrt to ocean surface area - units: J/m² + units: J/m^2 """ diff --git a/lenapy/readers/__init__.py b/lenapy/readers/__init__.py index 4e64982..e69de29 100644 --- a/lenapy/readers/__init__.py +++ b/lenapy/readers/__init__.py @@ -1,13 +0,0 @@ -""" -Readers -^^^^^^^ -Reader modules : define specific engines for the xarray.open_dataset() method - -.. autosummary:: - :toctree: - - geo_reader - ocean - gravi_reader - -""" diff --git a/lenapy/utils/__init__.py b/lenapy/utils/__init__.py index c4a5218..e69de29 100644 --- a/lenapy/utils/__init__.py +++ b/lenapy/utils/__init__.py @@ -1,12 +0,0 @@ -""" -Utils -^^^^^ -Utils modules - -.. autosummary:: - :toctree: - - harmo - gravity - -""" diff --git a/lenapy/writers/__init__.py b/lenapy/writers/__init__.py index 1c3a9d6..e69de29 100644 --- a/lenapy/writers/__init__.py +++ b/lenapy/writers/__init__.py @@ -1,11 +0,0 @@ -""" -Writers -^^^^^^^ -Writer modules : define writers - -.. autosummary:: - :toctree: - - gravi_writer - -""" diff --git a/pyproject.toml b/pyproject.toml index b9ae6a7..4ff4496 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,6 +72,7 @@ doc = [ "sphinx", "sphinx-rtd-theme", "sphinx-mdinclude", + "sphinx-autodoc-typehints", "nbsphinx", ] notebook = [ From b63c634d0f29efe813323a0d21d3e35ab4bff9ac Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 20 May 2025 13:16:46 +0200 Subject: [PATCH 48/54] doc: sphinx complain about docstrings --- doc/tutorials/Climato.ipynb | 8 ++++---- lenapy/readers/geo_reader.py | 6 ++++-- lenapy/readers/ocean.py | 6 ++++-- lenapy/utils/gravity.py | 2 +- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/doc/tutorials/Climato.ipynb b/doc/tutorials/Climato.ipynb index cd83522..c632024 100644 --- a/doc/tutorials/Climato.ipynb +++ b/doc/tutorials/Climato.ipynb @@ -44,10 +44,10 @@ "_clim.add_coeffs(coefficients, func, *var, ref=None, scale=pd.to_timedelta(\"1D\").asm8,**kwargs) :_\n", "- coefficients : nom des coefficients correspondant aà la sortie de la fonction func\n", "- func : fonction prenant en entrée une timeserie, et retournant un dataarray\n", - "- *var : variables passées comme paramètres à la fonction *func*\n", + "- var : variables passées comme paramètres à la fonction *func*\n", "- ref : origine temporelle pour le calcul de la fonction func\n", "- scale : mise à l'échelle du temps pour le calcul de la fonction func\n", - "- **kwargs : paramètres complémentaires pour la fonction *func* \n", + "- kwargs : paramètres complémentaires pour la fonction *func* \n", "**La fonction calculée est en fait func((x-ref)/scale)**\n", "\n", "### Calcul des coefficients :\n", @@ -2885,9 +2885,9 @@ ], "metadata": { "kernelspec": { - "display_name": "lenapy2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "lenapy2" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/lenapy/readers/geo_reader.py b/lenapy/readers/geo_reader.py index 4b90758..5a6608f 100644 --- a/lenapy/readers/geo_reader.py +++ b/lenapy/readers/geo_reader.py @@ -109,8 +109,10 @@ class lenapyMask(BackendEntrypoint): The returned mask contains an extra dimension named 'zone', corresponding to the different values of the mask contained in the opened dataset. Required format for the dataset to be opened : - * contains several dataarrays, each one being a mask (identified as 'field') - * each mask is defined by values, whose signification is given in the attributes of the dataarray : {'value1' : 'label1',...} + + - contains several dataarrays, each one being a mask (identified as 'field') + - each mask is defined by values, whose signification is given in the attributes of the dataarray : {'value1' : 'label1',...} + If there is no valid attribute, the mask is returned with False where NaN, False or 0 are found, True for any other value. Parameters diff --git a/lenapy/readers/ocean.py b/lenapy/readers/ocean.py index 348f0b2..b0e0b40 100644 --- a/lenapy/readers/ocean.py +++ b/lenapy/readers/ocean.py @@ -60,8 +60,10 @@ class lenapyOceanProducts(BackendEntrypoint): """Open netcdf ocean product This module allows to load temperature and salinity data from different products and format the data with unified definition for variables and coordinates, compatible with the use of lenapy_ocean : - * standardized coordinates names : latitude, longitude, depth, time - * standardized variables names : temp or PT or CT for temperature, psal, SA, SR for salinity + + - standardized coordinates names : latitude, longitude, depth, time + - standardized variables names : temp or PT or CT for temperature, psal, SA, SR for salinity + When loading a product, all the files present in the product directory are parsed. To gain computing time, a first filter on the years to load can be applied, as well as a text filter. A second date filter can be apply afterwards with the .sel(time=slice('begin_date','end_date') method. All keyword arguments associated with xr.open_dataset can be passed. diff --git a/lenapy/utils/gravity.py b/lenapy/utils/gravity.py index 50c841d..2590dbc 100644 --- a/lenapy/utils/gravity.py +++ b/lenapy/utils/gravity.py @@ -114,7 +114,7 @@ def change_reference( def change_tide_system(ds, new_tide, old_tide=None, k20=None, apply=False): """ Apply a C20 offset to the dataset to change the tide system. - Follows IERS2010 convention [IERS2010]_ to convert between tide system ('tide_free', 'zero_tide', 'mean_tide'). + Follows [IERS2010]_ convention to convert between tide system ('tide_free', 'zero_tide', 'mean_tide'). Warning : It should be noted that each center use his one tide offset, it may differ in terms of offset formula that can come from [IERS2010]_ and [Smith1998]_. The value of k20 can also change between centers. From 3646cb48cddbddfa8fc9f85cb61c3f10ad9eeece Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 20 May 2025 14:46:55 +0200 Subject: [PATCH 49/54] doc: translate Climato notebook to english --- doc/tutorials/Climato.ipynb | 349 +++++++++++++++++------------------- 1 file changed, 165 insertions(+), 184 deletions(-) diff --git a/doc/tutorials/Climato.ipynb b/doc/tutorials/Climato.ipynb index c632024..f4d6891 100644 --- a/doc/tutorials/Climato.ipynb +++ b/doc/tutorials/Climato.ipynb @@ -13,85 +13,98 @@ "id": "49705b35-8af5-4de8-8848-845158fbf1ba", "metadata": {}, "source": [ - "## Présentation\n", - "La méthode climato de lntime permet à partir d'un dataset d'effectuer une régression d'un signal :\n", - " - sur des cycles annuels ou semi-annuels\n", - " - sur des polynomes d'ordre quelconque\n", - " - sur n'importe quelle fonction paramétrée par l'utilisateur \n", + "## Overview\n", + "The climato method from the lntime module enables regression of a signal from a dataset:\n", + " - on annual or semi-annual cycles\n", + " - on polynomials of any order\n", + " - on any user-defined function\n", "\n", - "Elle retourne une instance de la classe *Coeffs_climato*. \n", + "It returns an instance of the *Coeffs_climato* class.\n", "\n", "\n", - "L'échantillonnage temporel peut être quelconque.\n", + "The time sampling can be arbitrary.\n", "\n", - "Basiquement, la climato résoud un moindre carrés Y = A.X, où Y est un signal dépendant du temps, et X une matrice de fonctions temporelles définies (cycle annuel, moyenne, tendance, accélération, fonction quelconque, etc...). A contient donc les coefficients à appliquer à chacune de ces fonctions temporelles pour minimiser les résidus Y - A.X\n", + "Basically, the climato method solves a least squares problem **Y = A.X**, where **Y** is a time-dependent signal and **X** is a matrix of defined temporal functions (annual cycle, mean, trend, acceleration, arbitrary functions, etc.). **A** thus contains the coefficients to apply to each of these temporal functions to minimize the residuals **Y - A.X**.\n", "\n", - "Format du dataset en entrée :\n", - " - une dimension doit correspondre à la temporalité. La variable temporelle n'est pas nécessairement en coordonnée, elle peut être une variable du dataset\n", - " - Il peut y avoir un nombre quelconque de dimensions indépendantes du temps (latitude, longitude, profondeur, modèles, etc...)\n", + "Input dataset format\n", + " - One dimension must correspond to time. The time variable does not necessarily need to be a coordinate; it can be a variable in the dataset.\n", + " - There can be any number of dimensions independent of time (latitude, longitude, depth, models, etc.).\n", "\n", - "## Classe Coeffs_climato\n", - "### Initialisation\n", - "*clim = Coeffs_climato(ds, dim='time', var=None, Nmin=6, cycle=True, order=1) :*\n", - "- ds : dataset ou dataarray contenant les données\n", - "- dim :nom de la dimension sur laquelle faire les régressions\n", - "- var : nom de la variable ou coordonnée contenant les informations temporelles. Si None, var=dim\n", - "- Nmin : nombre minimum de données valides pour calculer les coefficients. Ce nombre est à ajouter au nombre des fonctions de régression. Si Nmin=0 et qu'on calcule les cycles annuels, semi-annuels, la tendance et la moyenne, il faut au moins 6 points valides pour avoir un résultat, sinon les coefficients sont tous renseignés avec NaN.\n", - "- cycle : utilisation par défaut des fonctions de cycles annuels et semi-annuels\n", - "- order : ordre du polynome sur lequel faire la régression. Si order=-1, pas de régression polynomiale\n", "\n", - "### Ajout de fonctions personnalisées :\n", - "_clim.add_coeffs(coefficients, func, *var, ref=None, scale=pd.to_timedelta(\"1D\").asm8,**kwargs) :_\n", - "- coefficients : nom des coefficients correspondant aà la sortie de la fonction func\n", - "- func : fonction prenant en entrée une timeserie, et retournant un dataarray\n", - "- var : variables passées comme paramètres à la fonction *func*\n", - "- ref : origine temporelle pour le calcul de la fonction func\n", - "- scale : mise à l'échelle du temps pour le calcul de la fonction func\n", - "- kwargs : paramètres complémentaires pour la fonction *func* \n", - "**La fonction calculée est en fait func((x-ref)/scale)**\n", + "## Coeffs_climato Class\n", + "### Initialisation\n", + "```python\n", + "clim = Coeffs_climato(ds, dim='time', var=None, Nmin=6, cycle=True, order=1)\n", + "```\n", + "- **ds**: dataset or dataarray containing the data \n", + "- **dim**: name of the dimension over which to perform the regressions \n", + "- **var**: name of the variable or coordinate containing time information. If None, `var = dim` \n", + "- **Nmin**: minimum number of valid data points required to compute the coefficients. This number is added to the number of regression functions. For example, if `Nmin=0` and you're computing annual and semi-annual cycles, trend, and mean, you need at least 6 valid points; otherwise, the coefficients will be set to NaN. \n", + "- **cycle**: use annual and semi-annual cycle functions by default \n", + "- **order**: polynomial order for regression. If `order = -1`, no polynomial regression is applied.\n", "\n", - "### Calcul des coefficients :\n", - "_coeffs = clim.solve(measure=None, chunk=None, weight=None, t_min=None, t_max=None) :_\n", - "- measure : nom de la variable du dataset ds en entrée de la classe dont on veut calculer la climatologie. Si on a un dataArray en entrée, ne rien mettre.\n", - "- chunk : si les données d'entrée sont volumineuses avec plusieurs dimensions, permet de paralléliser les calculs\n", - "- weight : matrice de poids à appliquer aux données d'entrée pour la moindre carré. Si weight=None, toutes les mesures ont le même poids\n", - "- t_min,t_max : début et fin de la période sur laquelle calculer la climato. Si None, pas de début ou pas de fin. \n", - "**La méthode retourne une instance de la classe Signal_climato**\n", + "### Adding custom functions:\n", + "```python\n", + "clim.add_coeffs(coefficients, func, *var, ref=None, scale=pd.to_timedelta(\"1D\").asm8, **kwargs)\n", + "```\n", + "- **coefficients**: names of the coefficients corresponding to the output of the `func` function \n", + "- **func**: function that takes a time series as input and returns a dataarray \n", + "- **var**: variables passed as parameters to the `func` \n", + "- **ref**: temporal origin used in computing the function \n", + "- **scale**: time scaling used in computing the function \n", + "- **kwargs**: additional parameters passed to the function \n", + "**The computed function is actually evaluated as**: `func((x - ref) / scale)`\n", "\n", + "### Coefficient computation:\n", + "```python\n", + "coeffs = clim.solve(measure=None, chunk=None, weight=None, t_min=None, t_max=None)\n", + "```\n", + "- **measure**: name of the variable in the input dataset `ds` for which to compute the climatology. If the input is a dataArray, leave this blank. \n", + "- **chunk**: enables parallel processing for large datasets with multiple dimensions \n", + "- **weight**: weighting matrix for the least squares computation. If `None`, all measurements are equally weighted \n", + "- **t_min, t_max**: start and end times for the climatology calculation. If `None`, no time limit is applied. \n", + "**This method returns an instance of the *Signal_climato* class.**\n", + " \n", + "## Signal_climato Class\n", + "This class is used to work with climatological coefficients. It is returned by the *solve* method from the *Coeffs_climato* class, but can also be constructed from previously saved coefficients. In that case, it must be properly initialized with all the parameters of the functions used to perform the regression.\n", "\n", - "## Classe Signal_climato\n", - "Cette classe permet d'exploiter les coefficients climatologique. On l'a en sortie de la méthode *solve* de la classe *Coeffs_climato*, mais elle peut aussi être créée à partir de coefficients sauvegardés par ailleurs. Dans ce cas, il faut la construire correctement en lui indiquant tous les paramètres des fonctions ayant servi à faire la régression du signal étudié.\n", + "### Initialization\n", + "```python\n", + "signal = Signal_climato(coeffs, dim='time', var=None, cycle=True, order=1, ref=None, ds=None, measure=None)\n", + "```\n", + "- **coeffs**: coefficients computed by the *Coeffs_climato* class \n", + "- **dim**: name of the dimension over which to perform the regressions \n", + "- **var**: name of the variable or coordinate containing time information. If None, `var = dim` \n", + "- **cycle**: use of annual and semi-annual cycle functions by default \n", + "- **order**: polynomial order for regression. If `order = -1`, no polynomial regression \n", + "- **ref**: default reference for built-in functions (annual cycle and polynomial) \n", + "- **ds**: dataset or dataarray used for coefficient computation (optional; only used for residual or interpolation calculations) \n", + "- **measure**: if `ds` is defined and is a dataset, name of the variable containing the measurements used to compute the coefficients \n", "\n", - "### Initialisation\n", - "_signal = Signal_climato(coeffs, dim='time', var=None, cycle=True, order=1, ref=None, ds=None, measure=None):_\n", - "- coeffs : coefficients calculés par la classe Coeffs_climato\n", - "- dim :nom de la dimension sur laquelle faire les régressions\n", - "- var : nom de la variable ou coordonnée contenant les informations temporelles. Si None, var=dim\n", - "- cycle : utilisation par défaut des fonctions de cycles annuels et semi-annuels\n", - "- order : ordre du polynome sur lequel faire la régression. Si order=-1, pas de régression polynomiale\n", - "- ref : référence par défaut pour les fonctions par défaut de cycle annuel et de polynome\n", - "- ds : dataSet ou dataArray utilisé pour le calcul des coefficients (optionnel, ne sert que pour les calculs de résidus ou d'interpolation)\n", - "- measure : is ds est défini et est un dataSet, nom de la variable contenant les mesures utilisées pour le calcul des coefficients\n", "\n", - "### Ajout de fonctions personnalisées :\n", - "Exactement la même fonction pour pour le calcul des coefficients. Ces fonctions doivent être définies de manière cohérente avec celles utilisées pour le calcul des coefficients. \n", - "Inutile quand on récupère l'instance en sortie de la méthode *solve* de la classe *Coeffs_climato*\n", + "### Adding custom functions:\n", + "Same method as for coefficient calculation. These functions must be defined in a way that is consistent with those used during coefficient computation. \n", + "Unnecessary when the instance is obtained from the *solve* method of the *Coeffs_climato* class.\n", "\n", - "### Sorties\n", + "### Outputs\n", "*signal.climatology(coefficients=None, x=None) :*\n", - "Fonction climatique régressée\n", - "- coefficients : noms des coefficients à inclure dans la timeserie en sortie. Si coefficients=None, on les prend tous en compte\n", - "- x : instants de calcul. Si x=None, on utilise les temps du dataset en entrée : x=ds.var\n", + "Regressed climatological function\n", + "- **coefficients**: names of coefficients to include in the output time series. If `None`, all coefficients are used \n", + "- **x**: time points for computation. If `None`, time values from the input dataset are used (`x = ds.var`)\n", "\n", - "*signal.residuals(coefficients=None) :*\n", - "Résidus entre les mesures et la fonction climatique régressée\n", - "- coefficients : noms des coefficients à considérer pour le calcul des résidus. Si coefficients=None, on les prend tous en compte\n", + "```python\n", + "signal.residuals(coefficients=None)\n", + "```\n", + "Residuals between measurements and the regressed climatological function \n", + "- **coefficients**: names of coefficients to consider for residuals calculation. If `None`, all coefficients are used \n", "\n", - "*signal.signal(x=None, coefficients=None, method='linear') :*\n", - "Signal incuant les résidus interpolés et la fonction climatique régressée. Si les résidus contiennent des NaN, ils sont interpolés avec la méthode choisie\n", - "- x :instants de calculs. Si x=None, on utilise les temps du dataset en entrée : x=ds.var\n", - "- coefficients : noms des coefficients à prendre en compte. Si coefficients=None, on les prend tous en compte\n", - "- method : filtre d'interpolation aux instants de calculs et pour les valeurs NaN. Parmi ceux de la méthode scipy.interp1d\n" + "```python\n", + "signal.signal(x=None, coefficients=None, method='linear')\n", + "```\n", + "Signal combining interpolated residuals and the regressed climatological function. If residuals contain NaNs, they are interpolated using the chosen method \n", + "- **x**: time points for computation. If `None`, time values from the input dataset are used (`x = ds.var`) \n", + "- **coefficients**: names of coefficients to consider. If `None`, all are used \n", + "- **method**: interpolation method for calculation times and NaN values, from `scipy.interp1d` options\n" ] }, { @@ -117,7 +130,7 @@ "id": "d61ab661-5d6a-457c-a96e-8613d2338bd7", "metadata": {}, "source": [ - "## Exemple sur une série temporelle simple" + "## Example on a Simple Time Series" ] }, { @@ -572,8 +585,8 @@ "id": "e4bbe2ad-05b7-4965-8486-aa132656299c", "metadata": {}, "source": [ - "## Appel à la classe Climato\n", - "Par défaut, les fonctions de régression sont le cycle annuel, le cycle semi-annuel, la moyenne et la tendance" + "## Calling the Climato Class\n", + "By default, the regression functions are the annual cycle, the semi-annual cycle, the mean, and the trend.\n" ] }, { @@ -610,15 +623,8 @@ "id": "22ea515e-e99e-4eef-90ba-a6b14c19fae3", "metadata": {}, "source": [ - "## Calcul des coefficients de la climatologie sur une variable du dataset" - ] - }, - { - "cell_type": "markdown", - "id": "dd9a5898-0f4d-46f2-969b-4926afda2197", - "metadata": {}, - "source": [ - "Dans un premier temps, il faut calculer la climatologie en précisant la variable du dataset d'entrée contenant les mesures" + "## Calculation of Climatology Coefficients on a Dataset Variable\n", + "First, the climatology must be calculated by specifying the variable in the input dataset that contains the measurements.\n" ] }, { @@ -638,16 +644,10 @@ "id": "5eb7a8d1-5ba3-4eec-bd93-de6c4f175461", "metadata": {}, "source": [ - "## Calcul du signal climatique correspondant" - ] - }, - { - "cell_type": "markdown", - "id": "5dba638d-806a-4e3a-a4b3-c767bce8ce05", - "metadata": {}, - "source": [ - "La méthode \"climatology\" retourne les différentes composantes du signal climatique. Si rien n'est précisé, la somme de toutes les composantes est calculées. Sinon, le paramètre \"coefficients\" contient une liste des noms des coefficients à retourner.\n", - "On peut calculer le signal climatique à des instants différents de ceux du signal d'origine, pour cela il faut passer dans l'argument x un dataset ou dataarray contenant toutes les variables nécessaires au calcul des fonctions à fitter. Généralement, 'time' suffit." + "## Calculation of the Corresponding Climate Signal\n", + "The `climatology` method returns the different components of the climate signal. If nothing is specified, the sum of all components is computed. Otherwise, the `coefficients` parameter should contain a list of the names of the coefficients to return.\n", + "\n", + "The climate signal can be computed at time steps different from those of the original signal. To do this, pass a dataset or data array to the `x` argument that includes all the variables required to compute the fitted functions. Typically, `'time'` is sufficient.\n" ] }, { @@ -694,7 +694,7 @@ "id": "a12c4e18-be5a-40cb-90c4-788be1b9a2e0", "metadata": {}, "source": [ - "La climatologie peut être calculée sur une période restreinte en utilisant les paramètres t_min et t_max" + "Climatology can be computed over a restricted period by using the `t_min` and `t_max` parameters." ] }, { @@ -739,25 +739,29 @@ "id": "4eeabc6d-aceb-46d4-9f74-7f3077726971", "metadata": {}, "source": [ - "Les fonctions à fitter sont appelées an appliquant une référence et une mise à l'échelle aux données d'entrées : *func((t-tref)/scale)*\n", + "The functions to be fitted are called by applying a reference and scaling to the input data: *func((t - tref) / scale)*\n", + "\n", + "The default functions for annual and semi-annual cycles, mean, and trend used when instantiating the `Climato` class apply the first date of the time series as the reference, and use a scaling so that the time unit is one day.\n", + "\n", + "To use different references, scalings, or entirely different functions to fit, you must specify which default functions to exclude at instantiation (*cycle* or *order*), then manually add the functions to fit. \n", + "To add a function, there are two options:\n", "\n", - "Les fonctions de cycles annuels et semi-annuels, de moyenne et de tendance utilisées par défaut lors de l'instanciation de la classe Climato utilisent comme référence la première date de la série temporelle, et une mise à l'échelle pour avoir un jour comme unité de temps.\n", + "- Using one of the two functions instantiated in the `Climato` class:\n", + " - *poly(order, ref, scale)*: creates a polynomial function of the chosen order, with optional reference and scaling.\n", + " - *cycle(ref)*: generates annual and semi-annual cycles, with optional reference.\n", "\n", - "Pour utiliser d'autres références ou mises à l'échelle, ou d'autres fonctions à fitter, il faut spécifier à l'instanciation de la classe les fonctions par défaut qu'on ne veut pas (*cycle* ou *order*), puis ajouter manuellement les fonctions à fitter. \n", - "Pour ajouter une fonction, deux possibilités :\n", - "- Avec une des deux fonctions instanciées dans la classe Climato :\n", - " - *poly(oder,ref,scale)* : instancie une fonction polynomiale d'ordre au choix, avec possibilité de choisir la référence et la mise à l'échelle\n", - " - *cycle(ref)* : cycles annuels et semi-annuels, avec possibilité de choisir la référence \n", - "- Avec la méthode \"add_coeffs\" en définissant entièrement une fonction :\n", - " - Il faut d'abord définir une fonction de une ou plusieurs variables, retournant un dataArray ayant deux dimensions :\n", - " - une dimension égale à celle du dataset sur laquelle est calculée la climato (le plus souvent : 'time'), \n", - " - une autre appelée 'coeffs' permettant de retourner plusieurs fonctions à fitter en un seul appel \n", - " Cette fonction est de la forme *func(p1,p2,p3,...)*, et sera appelée ainsi *func((p1-ref)/scale, p2,p3,...)\n", - " - Il faut attribuer cette fonction à l'instance de Climato avec la méthode *add_coeffs(coefficients,func,*args,ref,scale) :\n", - " - *coefficients* contient les noms des composants de la fonction à fitter (*sinAnnual* ,*trend*, *acc*, ... au choix)\n", - " - *func* est la fonction définie précédemment\n", - " - **args* : le ou les noms des variables du dataset qui sont les paramètres de la fonctions (le plus souvent, *time*, mais ça peut être n'importe quelle autre variable)\n", - " - *ref* et *scale* : référence et mise à l'échelle, **qui ne s'applique que sur le premier argument passé à la fonction**. Par défaut, ref est la valeur minimale de la variable, et scale=pd.to_timedelta(\"1D\").asm8 (1 jour)" + "- Using the `add_coeffs` method to define a custom function:\n", + " - First, define a function of one or more variables that returns a DataArray with two dimensions:\n", + " - One dimension matching that of the dataset used for climatology computation (most commonly `'time'`),\n", + " - Another called `'coeffs'`, allowing multiple functions to be returned in a single call.\n", + "\n", + " The function should have the form *func(p1, p2, p3, ...)*, and will be called as *func((p1 - ref) / scale, p2, p3, ...)*\n", + "\n", + " - Assign this function to the `Climato` instance using the *add_coeffs(coefficients, func, *args, ref, scale)* method:\n", + " - *coefficients*: list of component names for the function to be fitted (*sinAnnual*, *trend*, *acc*, ... of your choice).\n", + " - *func*: the previously defined function.\n", + " - *args*: one or more names of the dataset variables that are function parameters (usually *time*, but can be any variable).\n", + " - *ref* and *scale*: reference and scaling, **applied only to the first argument of the function**. By default, *ref* is the minimum value of the variable, and *scale* is `pd.to_timedelta(\"1D\").asm8` (i.e., 1 day).\n" ] }, { @@ -836,7 +840,7 @@ } ], "source": [ - "# Exemple des fonctions implémentées par défaut :\n", + "# Example of the Default Implemented Functions:\n", "omega=2*np.pi/LNPY_DAYS_YEAR\n", "\n", "def annual(x):\n", @@ -848,22 +852,22 @@ "def pol(x,order=1):\n", " return x**xr.DataArray(np.arange(order+1),dims='coeffs')\n", "\n", - "# Exemple de fonction permettant de rajouter une deuxième tendance et un biais à partir d'une date:\n", + "# Example of a Function to Add a Second Trend and a Bias Starting from a Given Date:\n", "def trend(x):\n", " return xr.where(x<0,0,x)\n", "\n", "def bias(x):\n", " return xr.where(x<0,0,1.)\n", " \n", - "# Ajout des fonctions à fitter à l'instance Climato\n", + "# Adding Functions to Fit to the Climato Instance\n", "clim=moheacan.lntime.Coeffs_climato(cycle=False,order=1)\n", - "# Cycle annuel\n", + "# Annual cycle \n", "clim.add_coeffs(['cosAnnual','sinAnnual'],annual,'time')\n", - "# Deuxième biais à partir de 2012\n", + "# Second biais since 2012\n", "clim.add_coeffs('bias2',bias,'time',ref=pd.to_datetime('2012').asm8)\n", - "# Deuxième tendance à partir de 2010\n", + "# Second trend since 2010\n", "clim.add_coeffs('trend2',trend,'time',ref=pd.to_datetime('2010').asm8)\n", - "# Troisième tendance à partir de 2014 (utilisation de la même fonction)\n", + "# Third Trend Starting from 2014 (using the same function)\n", "clim.add_coeffs('trend3',trend,'time',ref=pd.to_datetime('2014').asm8)\n", "\n", "clim_gohc = clim.solve('gohc')\n", @@ -879,15 +883,8 @@ "id": "08764a9b-bdba-4079-a12c-dbb4233efcce", "metadata": {}, "source": [ - "## Calcul des residus" - ] - }, - { - "cell_type": "markdown", - "id": "b9aad417-f61c-4907-97c4-b660f17ad8b2", - "metadata": {}, - "source": [ - "La méthode *residuals* retourne les résidus du signal en entrée par rapport au signal climatique. On peut choisir quels coefficients on intègre dans le signal climatique" + "## Calculation of Residuals\n", + "The *residuals* method returns the residuals of the input signal relative to the climate signal. You can choose which coefficients to include in the climate signal." ] }, { @@ -932,19 +929,13 @@ "id": "9da386c7-664e-4888-9fc2-7720a44c3be3", "metadata": {}, "source": [ - "## Generation d'un signal et interpolation" - ] - }, - { - "cell_type": "markdown", - "id": "ba68dd9e-75e1-4098-beeb-ff23e3d715ee", - "metadata": {}, - "source": [ - "La méthode *signal* permet de générer le signal d'origine :\n", - "- intégrant les coefficients du signal climatique que l'on souhaite\n", - "- interpolant les valeurs NaN du résidu du signal d'origine avec une méthode d'interpolation existant dans scipy.interp1d\n", - "- interpolé à d'autres dates que celle du signal d'origine (pas d'extrapolation possible) \n", - "(Cette dernière fonctionnalité d'interpolation ne fonctionne pas si les fonctions climatiques prennent un autre argument que le temps en entrée, car cela supposerait une interpolation multi-dimensionnelle qui sort du cadre de cette classe)" + "## Generation of a Signal and Interpolation\n", + "The *signal* method allows generating the original signal by:\n", + "- including the coefficients of the desired climate signal components,\n", + "- interpolating the NaN values in the residuals of the original signal using an interpolation method available in `scipy.interp1d`,\n", + "- interpolating at dates different from those of the original signal (no extrapolation possible).\n", + "\n", + "(Note: This last interpolation feature does not work if the climate functions take arguments other than time as input, since that would require multi-dimensional interpolation, which is beyond the scope of this class.)\n" ] }, { @@ -992,7 +983,7 @@ "id": "a87146d8-88c1-418c-9216-82173b1e252a", "metadata": {}, "source": [ - "Exemple d'interpolation en mettant à NaN deux années de mesure. " + "Example of Interpolation by Setting Two Years of Measurements to NaN." ] }, { @@ -1029,7 +1020,7 @@ "clim=trous.lntime.Coeffs_climato(order=2)\n", "clim_gohc = clim.solve('gohc')\n", "\n", - "# Signal reconstitué avec toutes les composantes de la climato, interpolation des résidus avec plusieurs méthodes\n", + "# Reconstructed Signal with All Climatology Components, Residuals Interpolated Using Various Methods\n", "clim_gohc.signal(method='previous').plot(label='Residuals interpolated with \"previous\" method')\n", "clim_gohc.signal(method='linear').plot(label='Residuals interpolated with \"linear\" method')\n", "clim_gohc.signal(method='quadratic').plot(label='Residuals interpolated with \"quadratic\" method')\n", @@ -1069,7 +1060,7 @@ } ], "source": [ - "# Résidus uniquement, interpolés avec plusieurs méthodes\n", + "# Residuals Only, Interpolated Using Various Methods\n", "\n", "clim_gohc.signal(coefficients=[],method='previous').plot(label='Residuals interpolated with \"previous\" method')\n", "clim_gohc.signal(coefficients=[],method='linear').plot(label='Residuals interpolated with \"linear\" method')\n", @@ -1084,16 +1075,9 @@ "id": "21740e15-435d-4927-b48b-d576c7602182", "metadata": {}, "source": [ - "## Climatologie sur une grille" - ] - }, - { - "cell_type": "markdown", - "id": "4b2b8575-e093-441a-8fdb-8511d20c58fb", - "metadata": {}, - "source": [ - "Avec plusieurs dimensions, le principe est exactement le même. Le calcul est distribué pour chaque point de la grille, et on obtient une grille de coefficients.\n", - "L'exemple suivant montre comment tracer la tendance d'ohc" + "## Climatology on a Grid\n", + "With multiple dimensions, the principle is exactly the same. The calculation is distributed for each point on the grid, resulting in a grid of coefficients. \n", + "The following example shows how to plot the trend of OHC.\n" ] }, { @@ -1136,17 +1120,10 @@ "id": "fcf08dd4-74bf-4195-b3b1-5912921082d7", "metadata": {}, "source": [ - "## Climatologie sur des points irréguliers" - ] - }, - { - "cell_type": "markdown", - "id": "0d6bae8b-1a20-4948-92e5-ad34732f82da", - "metadata": {}, - "source": [ - "Le dataset exemple est le contenu en chaleur de l'océan entre 0 et 680m de profondeur, dans l'Atlantique Nord, entre -20 et -10 degrés de longitude et 40 et 50 degrés de latitude \n", - "Les mesures sont faites entre 2004 et 2024, chaque mesure étant caractérisée par sa date, sa position, et le contenu en chaleur. \n", - "On voit clairement un gradiant en fonction de la latitude" + "## Climatology on Irregular Points\n", + "The example dataset is the ocean heat content between 0 and 680m depth, in the North Atlantic, between -20 and -10 degrees longitude and 40 and 50 degrees latitude. \n", + "Measurements are taken between 2004 and 2024, each characterized by its date, position, and heat content. \n", + "A clear gradient is visible depending on latitude." ] }, { @@ -1576,7 +1553,7 @@ "id": "168f1705-0b4a-4edb-84ff-fdc357289c2f", "metadata": {}, "source": [ - "Pour faire une climatologie de la chaleur dans cette zone, il faut spécifier la dimension selon laquelle faire les calculs (*N_PROF*) et le nom de la variable contenant la date (*time*)" + "To compute a heat climatology in this area, you need to specify the dimension along which to perform the calculations (*N_PROF*) and the name of the variable containing the date (*time*)." ] }, { @@ -1631,7 +1608,7 @@ "id": "e3b31f2b-7198-4198-828d-a8574ab2fda0", "metadata": {}, "source": [ - "Cette climatologie ne tient pas compte du gradiant en latitude, on a des mesures très dispersées et de grands résidus" + "This climatology does not account for the latitude gradient; the measurements are very scattered and the residuals are large." ] }, { @@ -1672,7 +1649,7 @@ "id": "d56af769-3a9a-41b0-8095-f8898f7db5df", "metadata": {}, "source": [ - "On peut effectuer une régression sur l'écart en latitude et en longitude par rapport au centre de la zone" + "A regression can be performed on the deviation in latitude and longitude relative to the center of the area." ] }, { @@ -1724,7 +1701,7 @@ "tags": [] }, "source": [ - "On voit que la climato intégrant le gradiant en position reflète plus la réalité, et les résidus sont plus petits" + "We can see that the climatology including the positional gradient better reflects reality, and the residuals are smaller." ] }, { @@ -1769,7 +1746,7 @@ "tags": [] }, "source": [ - "Les résidus aont beaucoup plus proches d'un bruit blanc" + "The residuals are much closer to white noise." ] }, { @@ -1810,7 +1787,7 @@ "id": "628b4794-e41f-489e-982d-2e21fdc30a8c", "metadata": {}, "source": [ - "On peut aussi, pour calculer la climato en un point, ajouter du poids en fonction de la distance de la mesure à ce point" + "It is also possible, when calculating the climatology at a point, to add weights based on the distance from the measurement to that point." ] }, { @@ -1843,7 +1820,7 @@ } ], "source": [ - "# Calcul de la fonction de poids et tracé du poids pour tous les points de mesure\n", + "# Calculation of the Weighting Function and Plotting the Weights for All Measurement Points\n", "sig_dist=100000.\n", "centre=xr.DataArray(data=0,dims=['latitude','longitude'],coords=dict(latitude=[45],longitude=[-15]))\n", "dist=centre.lngeo.distance(argo).squeeze().drop_vars(['latitude','longitude'])\n", @@ -1860,7 +1837,8 @@ "tags": [] }, "source": [ - "On résoud la climatologie an appliquant la fonction de poids à chaque mesure, et on calcule le signal climatologique sur une série temporelle régulière. A noter qu'il faut passer à la méthode *climatology* un dataset contenant toutes les coordonnées utiles pour le calcul des fonctions à fitter, c'est à dire *time*, *latitude*, et *longitude*" + "The climatology is solved by applying the weighting function to each measurement, and the climatological signal is calculated on a regular time series. \n", + "Note that the *climatology* method must be passed a dataset containing all coordinates needed for calculating the functions to fit, namely *time*, *latitude*, and *longitude*." ] }, { @@ -2353,17 +2331,20 @@ "id": "b2c36ca4-1331-4496-8db1-b1c257b90c8a", "metadata": {}, "source": [ - "## Utilisation des coefficients de la climatologie indépendamment du signal d'origine\n", - "Si par exemple on a sauvegardé l'attribut \"result\" de l'instance Climato, on peut vouloir recréer un signal après avoir rechargé ces coefficients. Pour cela, il faut connaitre tous les paramètres ayant servi à générer cette climato :\n", - "- le nom des coefficients\n", - "- le nom des fonction associées\n", - "- le nom des variables pour la fonction\n", - "- la référence et la mise à l'échelle\n", - "- les paramètres supplémentaires éventuels à passer à la fonction (par exemple, l'ordre pour le polynome à régresser) \n", + "## Using Climatology Coefficients Independently of the Original Signal\n", + "For example, if the \"result\" attribute of the `Climato` instance has been saved, you might want to recreate a signal after reloading these coefficients. To do this, you need to know all the parameters used to generate this climatology:\n", + "- the names of the coefficients\n", + "- the names of the associated functions\n", + "- the names of the variables for the functions\n", + "- the reference and scaling\n", + "- any additional parameters to pass to the function (e.g., the order for the polynomial to regress)\n", + "\n", + "You need to import the *Generate_climato* class from *lenapy.utils.climato*, initialize it with the coefficients (the saved *result* field), and add the regression functions just like with the `Climato` class. \n", + "By default, as with the `Climato` class, the annual and semi-annual cycles, as well as the mean and trend, are added with a default reference which will be the minimum date of the time series on which the signal is calculated. If you do not want these default functions, specify `cycle=False` and/or `order=-1` when calling the class.\n", + "\n", + "Then call *climatology* exactly as with the `Climato` instance, except that it is mandatory to specify the vector on which to calculate the signal. This vector is a dataset or data array containing the variables necessary for executing the climatology functions (usually *time*).\n", "\n", - "Il faut importer la classe *Generate_climato* depuis *lenapy.utils.climato*, l'initialiser avec les coefficients (le champ *result* sauvegardé), et ajouter les fonctions de régression comme pour la classe climatology. Par défaut, comme pour la classe *Climato*, les cycles anuels et semi-annuels ainsi que la moyenne et la tendance sont ajoutés avec une référence par défaut qui sera la date min de la série temporelle sur laquelle calculer le signal. Si on ne veut pas ces fonctions par défaut, il faut spécifier cycle=False et/ou order=-1 à l'appel de la classe. \n", - "On appel ensuite *climatology* exactement comme avec l'instance de *Climato*, à ceci près qu'il est obligatoire de spécifier le vecteur sur lequel calculer le signal. Ce vecteur est un dataset ou un dataarray contenant les variables nécessaires à l'exécution des fonctions de climato (généralement, *time*). \n", - "On n'est pas obligé d'attribuer une fonction à tous les coefficients présents, dans ce cas, seuls les coefficicents pour lesquels on a défini une fonction seront calculés." + "It is not mandatory to assign a function to all present coefficients; in that case, only the coefficients for which a function has been defined will be calculated." ] }, { @@ -2781,18 +2762,18 @@ "outputs": [], "source": [ "from lenapy.utils.climato import Signal_climato\n", - "# On crée une instance avec les fonctions de cycles annuels et sans régression polynomiale. (var et cycle sont inutiles avec ce sont les valeurs par défaut)\n", + "# An instance is created with the annual cycle functions and without polynomial regression. (var and cycle are unnecessary as they are the default values)\n", "generate = Signal_climato(clim_saved, var='time', cycle=True, order=-1)\n", - "# A titre pédagogique, pour ajouter une fonction polynomiale, il faut utiliser la méthode add_coeffs en précisant :\n", - "# - les noms des coefficients concernés dans \"clim_saved\"\n", - "# - le nom de la fonctions à appliquer (pol)\n", - "# - le nom de la variable sur laquelle appliquer la fonction\n", - "# - la référence (None : ce sera la valeur min de la variable sur laquelle calculer le signal) (inutile ici car c'est la valeur par défaut si le paramètre est absent)\n", - "# - la mise à l'échelle (inutile ici car c'est la valeur par défaut si le paramètre est absent)\n", - "# * les paramètres complémentaires pour la fonction (l'ordre du polynome)\n", + "# For educational purposes, to add a polynomial function, use the add_coeffs method specifying:\n", + "# - the names of the coefficients concerned in \"clim_saved\"\n", + "# - the name of the function to apply (pol)\n", + "# - the name of the variable on which to apply the function\n", + "# - the reference (None: this will be the minimum value of the variable on which to calculate the signal) (not needed here as it is the default if absent)\n", + "# - the scaling (not needed here as it is the default if absent)\n", + "# * additional parameters for the function (the polynomial order)\n", "generate.add_coeffs(['order_%i'%i for i in np.arange(4)],pol,'time', ref=None, scale=pd.to_timedelta(\"1D\").asm8, order=3)\n", "\n", - "# On n'a pas défini de fonction pour gradLat et gradLon, ces coefficients ne seront pas utilisés dans le signal de sortie. " + "# No function has been defined for gradLat and gradLon; these coefficients will not be used in the output signal." ] }, { @@ -2835,7 +2816,7 @@ "id": "49ffbdd0-fec4-4f62-ab55-b446e6677cb9", "metadata": {}, "source": [ - "## Compatibilité ascendante" + "## Ascending Compatibility" ] }, { From a3ea8f46bced44b3f6319988d0e107eb649e321c Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Tue, 20 May 2025 15:09:53 +0200 Subject: [PATCH 50/54] doc: add rtd specific --- .readthedocs.yaml | 8 ++++---- doc/conf.py | 6 ++++++ doc/environment.yml | 23 ++++++++++++----------- doc/requirements.txt | 12 ------------ 4 files changed, 22 insertions(+), 27 deletions(-) delete mode 100644 doc/requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml index bc8ed17..0cc6c9d 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -30,9 +30,9 @@ sphinx: # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -python: - install: - - requirements: doc/requirements.txt - conda: environment: doc/environment.yml +python: + install: + - method: pip + path: . diff --git a/doc/conf.py b/doc/conf.py index 172c06d..0b49e72 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -4,11 +4,17 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html import datetime +import inspect import os import sys +__location__ = os.path.join( + os.getcwd(), os.path.dirname(inspect.getfile(inspect.currentframe())) +) + sys.path.insert(0, os.path.abspath("..")) sys.path.insert(0, os.path.abspath("../lenapy")) +sys.path.insert(0, os.path.join(__location__, "../lenapy")) # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information diff --git a/doc/environment.yml b/doc/environment.yml index f7d7d15..7340615 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -6,16 +6,17 @@ channels: dependencies: - python=3.11 - esmpy>=8.4.0 + - gsw>=3.6.16 - pip>=20.1 # pip is needed as dependency - - pip: - - sphinx_rtd_theme - - sphinx-mdinclude - - nbsphinx - - matplotlib>=3.6 - - xarray>=2024.2 - - gsw>=3.6.16 - - netCDF4>=1.6.5 - - pyyaml>=6.0 - - dask>=2023.6 - - xesmf>=0.8.2 + - sphinx_rtd_theme + - sphinx-mdinclude + - nbsphinx + - matplotlib>=3.6 + - xarray>=2024.2 + - netCDF4>=1.6.5 + - pyyaml>=6.0 + - dask>=2023.6 + - xesmf>=0.8.2 + - cf_xarray + diff --git a/doc/requirements.txt b/doc/requirements.txt deleted file mode 100644 index c081d39..0000000 --- a/doc/requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -# Defining the exact version will make sure things don't break -sphinx_rtd_theme -sphinx-mdinclude -nbsphinx -matplotlib>=3.6 -xarray>=2024.2 -gsw>=3.6.16 -netCDF4>=1.6.5 -pyyaml>=6.0 -dask>=2023.6 -esmpy -xesmf>=0.8.2 From be9a60478340e49abc52cdf6cba2d05846af5b31 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 28 May 2025 08:46:25 +0200 Subject: [PATCH 51/54] doc: small edits --- doc/tutorials.rst | 2 +- doc/tutorials/Climato.ipynb | 12 ++++++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/doc/tutorials.rst b/doc/tutorials.rst index 1c3f7cc..d85e03c 100644 --- a/doc/tutorials.rst +++ b/doc/tutorials.rst @@ -7,7 +7,7 @@ LENAPY Tutorials In the following you will find several Tutorials on how to use LENAPY. .. toctree:: - :maxdepth: 0 + :maxdepth: 2 tutorials/Timeseries.ipynb tutorials/GriddedData.ipynb diff --git a/doc/tutorials/Climato.ipynb b/doc/tutorials/Climato.ipynb index f4d6891..9c0d5a7 100644 --- a/doc/tutorials/Climato.ipynb +++ b/doc/tutorials/Climato.ipynb @@ -52,7 +52,8 @@ "- **var**: variables passed as parameters to the `func` \n", "- **ref**: temporal origin used in computing the function \n", "- **scale**: time scaling used in computing the function \n", - "- **kwargs**: additional parameters passed to the function \n", + "- **kwargs**: additional parameters passed to the function\n", + "\n", "**The computed function is actually evaluated as**: `func((x - ref) / scale)`\n", "\n", "### Coefficient computation:\n", @@ -63,6 +64,7 @@ "- **chunk**: enables parallel processing for large datasets with multiple dimensions \n", "- **weight**: weighting matrix for the least squares computation. If `None`, all measurements are equally weighted \n", "- **t_min, t_max**: start and end times for the climatology calculation. If `None`, no time limit is applied. \n", + "\n", "**This method returns an instance of the *Signal_climato* class.**\n", " \n", "## Signal_climato Class\n", @@ -87,7 +89,9 @@ "Unnecessary when the instance is obtained from the *solve* method of the *Coeffs_climato* class.\n", "\n", "### Outputs\n", - "*signal.climatology(coefficients=None, x=None) :*\n", + "```python\n", + "signal.climatology(coefficients=None, x=None) :\n", + "```\n", "Regressed climatological function\n", "- **coefficients**: names of coefficients to include in the output time series. If `None`, all coefficients are used \n", "- **x**: time points for computation. If `None`, time values from the input dataset are used (`x = ds.var`)\n", @@ -739,6 +743,8 @@ "id": "4eeabc6d-aceb-46d4-9f74-7f3077726971", "metadata": {}, "source": [ + "## Using custom fit functions\n", + "\n", "The functions to be fitted are called by applying a reference and scaling to the input data: *func((t - tref) / scale)*\n", "\n", "The default functions for annual and semi-annual cycles, mean, and trend used when instantiating the `Climato` class apply the first date of the time series as the reference, and use a scaling so that the time unit is one day.\n", @@ -797,12 +803,10 @@ "moheacan.gohc.plot(label='Measurements')\n", "\n", "clim=moheacan.lntime.Coeffs_climato(cycle=True,order=2,ref=pd.to_datetime('2016').asm8)\n", - "#clim.poly(ref=pd.to_datetime('2016').asm8)\n", "clim_gohc = clim.solve('gohc')\n", "clim_gohc.climatology(coefficients=['order_0','cosAnnual','sinAnnual']).plot(label='Reference in 2016 with annual cycle')\n", "\n", "clim=moheacan.lntime.Coeffs_climato(cycle=False,order=2)\n", - "#clim.poly()\n", "clim_gohc=clim.solve('gohc')\n", "clim_gohc.climatology(coefficients=['order_0']).plot(label='Reference at period start without annual cycle')\n", "clim_gohc.climatology(coefficients=['order_0','order_1','order_2']).plot(label='Reference at period start with order 2 and without annual cycle')\n", From 0b29d79d15204d43ebb2aecf22a6d9bfbe33dcde Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 28 May 2025 10:51:27 +0200 Subject: [PATCH 52/54] doc: missing dep --- doc/environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/environment.yml b/doc/environment.yml index 7340615..873854b 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -18,5 +18,7 @@ dependencies: - dask>=2023.6 - xesmf>=0.8.2 - cf_xarray + - pandas + - scipy From 9ce15f07f8dedd6acb89f286c3c4044d10795721 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 28 May 2025 15:02:46 +0200 Subject: [PATCH 53/54] doc: update maxdepth --- doc/tutorials.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/tutorials.rst b/doc/tutorials.rst index d85e03c..ccae777 100644 --- a/doc/tutorials.rst +++ b/doc/tutorials.rst @@ -7,7 +7,7 @@ LENAPY Tutorials In the following you will find several Tutorials on how to use LENAPY. .. toctree:: - :maxdepth: 2 + :maxdepth: 1 tutorials/Timeseries.ipynb tutorials/GriddedData.ipynb From e5ad467be7851ef3729067f556587d9940e562d6 Mon Sep 17 00:00:00 2001 From: Arthur VINCENT Date: Wed, 28 May 2025 15:57:26 +0200 Subject: [PATCH 54/54] doc: force navigation_depth to 3 --- doc/conf.py | 2 +- doc/tutorials.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 0b49e72..43a35ee 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -76,7 +76,7 @@ "prev_next_buttons_location": "both", "collapse_navigation": False, "sticky_navigation": True, - "navigation_depth": 4, + "navigation_depth": 3, "includehidden": True, "titles_only": False, "style_nav_header_background": "#2980B9", # header color en haut à gauche diff --git a/doc/tutorials.rst b/doc/tutorials.rst index ccae777..d85e03c 100644 --- a/doc/tutorials.rst +++ b/doc/tutorials.rst @@ -7,7 +7,7 @@ LENAPY Tutorials In the following you will find several Tutorials on how to use LENAPY. .. toctree:: - :maxdepth: 1 + :maxdepth: 2 tutorials/Timeseries.ipynb tutorials/GriddedData.ipynb