diff --git a/.coveragerc b/.coveragerc index def87278..2a51894d 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,8 +1,7 @@ [run] source = - engine/ - visualize/ - probtest.py - util/ + engine + util + visualize omit = - tests/* + tests/* diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 45300dc7..354cd0cf 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -20,18 +20,13 @@ jobs: uses: actions/setup-python@v2 with: python-version: 3.10.8 - - uses: conda-incubator/setup-miniconda@v2 - with: - miniconda-version: "latest" - channels: conda-forge - channel-priority: flexible - show-channel-urls: true - - name: Create env from pinned environment + - name: Install dependencies with pip run: | - conda env create --name dev_env --file requirements/environment.yml + python -m pip install --upgrade pip + pip install -r requirements.txt - name: Install pre-commit hooks run: | - conda run --name dev_env pre-commit install-hooks + pre-commit install-hooks - name: Run pre-commit hooks run: | - conda run --name dev_env pre-commit run --all-files + pre-commit run --all-files diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index bf63a01f..b3e595f7 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -14,23 +14,28 @@ jobs: defaults: run: shell: bash -l {0} + steps: - uses: actions/checkout@v2 + - name: Set up Python uses: actions/setup-python@v2 with: python-version: 3.10.8 - - uses: conda-incubator/setup-miniconda@v2 - with: - miniconda-version: "latest" - channels: conda-forge - channel-priority: flexible - show-channel-urls: true - - name: Create env from pinned environment + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + - name: Configure Poetry to use the right Python version + run: | + poetry env use python3.10 + - name: Install dependencies with Poetry + run: | + poetry install + - name: Run setup_env for eccodes definition run: | - ./setup_env.sh -n probtest - - name: Run Pytest + poetry run python setup_env.py + - name: Run Pytest with Poetry env: TZ: Europe/Zurich run: | - conda run --name probtest pytest -v -s --cov --cov-report=term tests/ + poetry run pytest -v -s --cov --cov-report=term tests/ diff --git a/.gitignore b/.gitignore index 21dbc3d8..b2b74639 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +.coverage +.env .idea* .vscode *pycache* @@ -6,12 +8,9 @@ *.png *.log *.nc -.coverage coverage.xml -miniconda +resource -# output from unittest -test_stats.csv -test_stats_csv.csv -test_stats_csv.dat +# output from pytest random_tolerance.csv +random_tolerance_None.csv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ae63ad84..67f2d840 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -83,7 +83,7 @@ repos: description: Check for spelling errors language: system entry: codespell - exclude: tests/data/ + exclude: ^(poetry.lock|tests/data/.*)$ args: ['-L astroid,fof'] - repo: local hooks: diff --git a/AUTHORS.md b/AUTHORS.md index 37d6c2f5..f28d533c 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -6,6 +6,7 @@ | Marek Jacob | DWD | | Jonas Jucker | C2SM | | Annika Lauber | C2SM | +| Andrea Leuthard | MeteoSwiss | | Giacomo Serafini | MeteoSwiss | | Mikael Stellio | C2SM | | Ben Weber | MeteoSwiss | diff --git a/README.md b/README.md index 7b3a07f9..3d00e70c 100644 --- a/README.md +++ b/README.md @@ -80,31 +80,45 @@ Visualize the performance database generated with `performance`. Even though probtest is used exclusively with ICON at the moment, it does not contain any information about the model or its directory structure. This makes it very flexible and applicable to any circumstance (e.g. usable by Buildbot, Jenkins and human users alike). However, it also requires a lot of information about the model and the data to be processed upon invocation. Since a typical probtest usage involves multiple commands (e.g. run-ensemble -> stats -> tolerance -> check) this leads to a lot of redundancy in the invocation. Therefore, probtest can read commonly used input variables (e.g. the model output directory, the experiment name, the name of the submit script, ...) from a configuration file in json format. To further ease the process, these configuration files can be created from templates using the `init` command. A template for ICON is contained in this repository in the `templates` subdirectory. -### Setup conda +### Setup probtest -All requirements for using probtest can be easily installed with conda using the -setup scripts. +All requirements for using probtest can be easily installed with pip or poetry. -For setting up conda you use +#### Setup with pip +The setup with pip is recommended for users. + +For setting up the venv you use ```console -./setup_miniconda.sh -u +python3.10 -m venv venv +source venv/bin/activate +pip install --upgrade pip +pip install -r requirements.txt ``` -which will modify your `.bashrc`, or you can use +To check that everything is setup correctly (note this requires +[eccodes definition](#Setup-the-eccodes-definition)), you can do ```console -./setup_miniconda.sh -source miniconda/bin/activate +pytest -v -s --cov --cov-report=term tests/ ``` -which requires to source minconda. -The pinned requirements can be installed by +#### Setup with poetry +The setup with poetry is recommended for developers and needed for changing or +adding requirements. +It is assumed you are having a poetry installation. ```console -./setup_env.sh +poetry env use python3.10 +poetry install ``` -The unpinned requirements and updating the environment can be done by +To check that everything is setup correctly (note this requires +[eccodes definition](#Setup-the-eccodes-definition)), you can do ```console -./setup_env.sh -u -e +poetry run pytest -v -s --cov --cov-report=term tests/ ``` +#### Setup the eccodes definition +For setting up the eccode definition, you can use +```console +python setup_env.py +``` ### The init command @@ -138,7 +152,17 @@ here should refer to your experiment script: ```console cd icon-base-dir/reference-build -python ../externals/probtest/probtest.py init --codebase-install $PWD --experiment-name exp_name --reference $PWD --file-id NetCDF "*atm_3d_ml*.nc" --file-id NetCDF "*atm_3d_il*.nc" --file-id NetCDF "*atm_3d_hl*.nc" --file-id NetCDF "*atm_3d_pl*.nc" --file-id latlon "*atm_2d_ll*.nc" --file-id meteogram "Meteogram*.nc" +python ../externals/probtest/probtest.py init \ + --codebase-install $PWD \ + --experiment-name exp_name \ + --reference $PWD \ + --file-id NetCDF "*atm_3d_ml*.nc" \ + --file-id NetCDF "*atm_3d_il*.nc" \ + --file-id NetCDF "*atm_3d_hl*.nc" \ + --file-id NetCDF "*atm_3d_pl*.nc" \ + --file-id latlon "*atm_2d_ll*.nc" \ + --file-id meteogram "Meteogram*.nc" \ + --file-id GRIB "lfff0*z" ``` You might need to update the used account in the json file. The perturbation amplitude may also need to be changed in the json file diff --git a/engine/cdo_table.py b/engine/cdo_table.py index 95d0d113..70ee82ba 100644 --- a/engine/cdo_table.py +++ b/engine/cdo_table.py @@ -17,7 +17,7 @@ from util.click_util import CommaSeperatedInts, cli_help from util.constants import cdo_bins from util.dataframe_ops import df_from_file_ids -from util.file_system import file_names_from_pattern +from util.file_system import get_file_names_from_pattern from util.log_handler import logger @@ -139,21 +139,21 @@ def cdo_table( assert isinstance(file_specification, dict), "must be dict" # save original method and restore at the end of this module - dataframe_from_ncfile_orig = model_output_parser.dataframe_from_ncfile + dataframe_from_ncfile_orig = model_output_parser.create_statistics_dataframe # modify netcdf parse method: - model_output_parser.dataframe_from_ncfile = rel_diff_stats + model_output_parser.create_statistics_dataframe = rel_diff_stats # step 1: compute rel-diff netcdf files with tempfile.TemporaryDirectory() as tmpdir: for _, file_pattern in file_id: - ref_files, err = file_names_from_pattern(model_output_dir, file_pattern) + ref_files, err = get_file_names_from_pattern(model_output_dir, file_pattern) if err > 0: logger.info( "did not find any files for pattern %s. Continue.", file_pattern ) continue ref_files.sort() - perturb_files, err = file_names_from_pattern( + perturb_files, err = get_file_names_from_pattern( perturbed_model_output_dir.format(member_id=member_id), file_pattern ) if err > 0: @@ -202,4 +202,4 @@ def cdo_table( Path(cdo_table_file).parent.mkdir(parents=True, exist_ok=True) df.to_csv(cdo_table_file) - model_output_parser.dataframe_from_ncfile = dataframe_from_ncfile_orig + model_output_parser.create_statistics_dataframe = dataframe_from_ncfile_orig diff --git a/engine/performance.py b/engine/performance.py index 6edf2bce..82814e9d 100644 --- a/engine/performance.py +++ b/engine/performance.py @@ -15,7 +15,7 @@ import click from util.click_util import cli_help -from util.file_system import file_names_from_pattern +from util.file_system import get_file_names_from_pattern from util.icon.extract_timings import read_logfile from util.log_handler import logger from util.tree import TREEFILE_TEMPLATE, TimingTree @@ -36,7 +36,9 @@ def 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--hash=sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff \ + --hash=sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec \ + --hash=sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2 \ + --hash=sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222 \ + --hash=sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106 \ + --hash=sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272 \ + --hash=sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a \ + --hash=sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7 +tomlkit==0.13.2 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde \ + --hash=sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79 +toolz==1.0.0 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:292c8f1c4e7516bf9086f8850935c799a874039c8bcf959d47b600e4c44a6236 \ + --hash=sha256:2c86e3d9a04798ac556793bced838816296a2f085017664e4995cb40a1047a02 +tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \ + --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2 +traitlets==5.14.3 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \ + --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f +typer==0.15.1 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:7994fb7b8155b64d3402518560648446072864beefd44aa2dc36972a5972e847 \ + --hash=sha256:a0588c0a7fa68a1978a069818657778f86abe6ff5ea6abf472f940a08bfe4f0a +typing-extensions==4.12.2 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d \ + --hash=sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8 +tzdata==2024.2 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc \ + --hash=sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd +urllib3==2.2.3 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac \ + --hash=sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9 +virtualenv==20.28.0 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:23eae1b4516ecd610481eda647f3a7c09aea295055337331bb4e6892ecce47b0 \ + --hash=sha256:2c9c3262bb8e7b87ea801d715fae4495e6032450c71d2309be9550e7364049aa +wcwidth==0.2.13 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ + --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 +xarray==2024.11.0 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:1ccace44573ddb862e210ad3ec204210654d2c750bec11bbe7d842dfc298591f \ + --hash=sha256:6ee94f63ddcbdd0cf3909d1177f78cdac756640279c0e32ae36819a89cdaba37 +zipp==3.21.0 ; python_version >= "3.10" and python_version < "3.12" \ + --hash=sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4 \ + --hash=sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931 diff --git a/requirements/environment.yml b/requirements/environment.yml deleted file mode 100644 index c4070ab1..00000000 --- a/requirements/environment.yml +++ /dev/null @@ -1,248 +0,0 @@ -name: probtest -channels: - - conda-forge - - defaults -dependencies: - - _libgcc_mutex=0.1 - - _openmp_mutex=4.5 - - alsa-lib=1.2.12 - - annotated-types=0.7.0 - - astroid=3.3.5 - - asttokens=2.4.1 - - black=24.8.0 - - blosc=1.21.6 - - brotli=1.1.0 - - brotli-bin=1.1.0 - - bzip2=1.0.8 - - c-ares=1.33.1 - - ca-certificates=2024.8.30 - - cairo=1.18.0 - - certifi=2024.8.30 - - cffi=1.17.1 - - cfgv=3.3.1 - - cftime=1.6.4 - - click=8.1.7 - - codespell=2.3.0 - - colorama=0.4.6 - - contourpy=1.3.0 - - cycler=0.12.1 - - cyrus-sasl=2.1.27 - - dbus=1.13.6 - - decorator=5.1.1 - - dill=0.3.9 - - distlib=0.3.8 - - docutils=0.21.2 - - double-conversion=3.3.0 - - exceptiongroup=1.2.2 - - executing=2.1.0 - - expat=2.6.3 - - filelock=3.16.1 - - flake8=7.1.1 - - flake8-black=0.3.6 - - font-ttf-dejavu-sans-mono=2.37 - - font-ttf-inconsolata=3.000 - - font-ttf-source-code-pro=2.038 - - font-ttf-ubuntu=0.83 - - fontconfig=2.14.2 - - fonts-conda-ecosystem=1 - - fonts-conda-forge=1 - - fonttools=4.54.1 - - freetype=2.12.1 - - graphite2=1.3.13 - - harfbuzz=9.0.0 - - hdf4=4.2.15 - - hdf5=1.14.4 - - icu=75.1 - - identify=2.6.1 - - importlib-metadata=8.5.0 - - iniconfig=2.0.0 - - ipdb=0.13.13 - - ipython=8.28.0 - - isort=5.13.2 - - jedi=0.19.1 - - jinja2=3.1.4 - - keyutils=1.6.1 - - kiwisolver=1.4.7 - - krb5=1.21.3 - - lcms2=2.16 - - ld_impl_linux-64=2.43 - - lerc=4.0.0 - - libaec=1.1.3 - - libblas=3.9.0 - - libbrotlicommon=1.1.0 - - libbrotlidec=1.1.0 - - libbrotlienc=1.1.0 - - libcblas=3.9.0 - - libclang-cpp19.1=19.1.0 - - libclang13=19.1.0 - - libcups=2.3.3 - - libcurl=8.10.1 - - libdeflate=1.22 - - libdrm=2.4.123 - - libedit=3.1.20191231 - - libegl=1.7.0 - - libev=4.33 - - libexpat=2.6.3 - - libffi=3.4.2 - - libgcc=14.1.0 - - libgcc-ng=14.1.0 - - libgfortran=14.1.0 - - libgfortran-ng=14.1.0 - - libgfortran5=14.1.0 - - libgl=1.7.0 - - libglib=2.82.1 - - libglvnd=1.7.0 - - libglx=1.7.0 - - libgomp=14.1.0 - - libiconv=1.17 - - libjpeg-turbo=3.0.0 - - liblapack=3.9.0 - - libllvm19=19.1.1 - - libnetcdf=4.9.2 - - libnghttp2=1.58.0 - - libnsl=2.0.1 - - libntlm=1.4 - - libopenblas=0.3.27 - - libopengl=1.7.0 - - libpciaccess=0.18 - - libpng=1.6.44 - - libpq=17.0 - - libsqlite=3.46.1 - - libssh2=1.11.0 - - libstdcxx=14.1.0 - - libstdcxx-ng=14.1.0 - - libtiff=4.7.0 - - libuuid=2.38.1 - - libwebp-base=1.4.0 - - libxcb=1.17.0 - - libxkbcommon=1.7.0 - - libxml2=2.12.7 - - libxslt=1.1.39 - - libzip=1.11.1 - - libzlib=1.3.1 - - lz4-c=1.9.4 - - markdown-it-py=3.0.0 - - markupsafe=3.0.0 - - matplotlib=3.9.2 - - matplotlib-base=3.9.2 - - matplotlib-inline=0.1.7 - - mccabe=0.7.0 - - mdurl=0.1.2 - - munkres=1.1.4 - - mypy=1.11.2 - - mypy_extensions=1.0.0 - - mysql-common=9.0.1 - - mysql-libs=9.0.1 - - ncurses=6.5 - - netcdf4=1.7.1 - - nodeenv=1.9.1 - - numpy=2.1.2 - - openjpeg=2.5.2 - - openldap=2.6.8 - - openssl=3.3.2 - - packaging=24.1 - - pandas=2.2.3 - - parso=0.8.4 - - pathlib=1.0.1 - - pathspec=0.12.1 - - pcre2=10.44 - - pexpect=4.9.0 - - pickleshare=0.7.5 - - pillow=10.4.0 - - pip=24.2 - - pixman=0.43.2 - - platformdirs=4.3.6 - - pluggy=1.5.0 - - pre-commit=4.0.0 - - pre-commit-hooks=5.0.0 - - prompt-toolkit=3.0.48 - - psutil=6.0.0 - - pthread-stubs=0.4 - - ptyprocess=0.7.0 - - pure_eval=0.2.3 - - pycodestyle=2.12.1 - - pycparser=2.22 - - pydantic=2.9.2 - - pydantic-core=2.23.4 - - pydocstyle=6.3.0 - - pyflakes=3.2.0 - - pygments=2.18.0 - - pylint=3.3.1 - - pyparsing=3.1.4 - - pyside6=6.7.3 - - pytest=8.3.3 - - python=3.10.8 - - python-dateutil=2.9.0 - - python-tzdata=2024.2 - - python_abi=3.10 - - pytoolconfig=1.2.5 - - pytz=2024.1 - - pyyaml=6.0.2 - - qhull=2020.2 - - qt6-main=6.7.3 - - readline=8.2 - - regex=2024.9.11 - - rich=13.9.2 - - rope=1.13.0 - - rstcheck=6.2.4 - - rstcheck-core=1.2.1 - - ruamel.yaml=0.18.6 - - ruamel.yaml.clib=0.2.8 - - scipy=1.14.1 - - setuptools=75.1.0 - - shellingham=1.5.4 - - six=1.16.0 - - snappy=1.2.1 - - snowballstemmer=2.2.0 - - stack_data=0.6.2 - - tk=8.6.13 - - toml=0.10.2 - - tomli=2.0.2 - - tomlkit=0.13.2 - - tornado=6.4.1 - - traitlets=5.14.3 - - typer=0.12.5 - - typer-slim=0.12.5 - - typer-slim-standard=0.12.5 - - typing-extensions=4.12.2 - - typing_extensions=4.12.2 - - tzdata=2024b - - ukkonen=1.0.1 - - unicodedata2=15.1.0 - - virtualenv=20.26.6 - - wayland=1.23.1 - - wcwidth=0.2.13 - - wheel=0.44.0 - - xarray=2024.9.0 - - xcb-util=0.4.1 - - xcb-util-cursor=0.1.5 - - xcb-util-image=0.4.0 - - xcb-util-keysyms=0.4.1 - - xcb-util-renderutil=0.3.10 - - xcb-util-wm=0.4.2 - - xkeyboard-config=2.43 - - xorg-libice=1.1.1 - - xorg-libsm=1.2.4 - - xorg-libx11=1.8.10 - - xorg-libxau=1.0.11 - - xorg-libxcomposite=0.4.6 - - xorg-libxcursor=1.2.2 - - xorg-libxdamage=1.1.6 - - xorg-libxdmcp=1.1.5 - - xorg-libxext=1.3.6 - - xorg-libxfixes=6.0.1 - - xorg-libxi=1.8.2 - - xorg-libxrandr=1.5.4 - - xorg-libxrender=0.9.11 - - xorg-libxtst=1.2.5 - - xorg-libxxf86vm=1.1.5 - - xorg-xorgproto=2024.1 - - xz=5.2.6 - - yaml=0.2.5 - - zipp=3.20.2 - - zlib=1.3.1 - - zstd=1.5.6 - - pip: - - coverage==7.6.1 - - flake8-pyproject==1.2.3 - - pytest-cov==5.0.0 diff --git a/requirements/requirements.yml b/requirements/requirements.yml deleted file mode 100644 index a982d2e7..00000000 --- a/requirements/requirements.yml +++ /dev/null @@ -1,37 +0,0 @@ -channels: - - conda-forge - - defaults -dependencies: - - python==3.10.8 - - pip>=22.3 - # runtime - - click>=7.1.2 - - Jinja2>=3.0.1 - - matplotlib>=3.2.1 - - netCDF4>=1.5.3 - - numpy>=1.18.2 - - pathlib>=1.0.1 - - pandas>=1.0.3 - - pytest>=6.2.1 - - regex>=2020.11.13 - - scipy>=1.4.1 - - xarray>=0.16.1 - # development - - black>=22.8.0 - - codespell>=2.2.2 - - flake8>=5.0.4 - - flake8-black - - ipdb - - ipython - - isort>=5 - - mypy - - pre-commit>=2.19.0 - - pre-commit-hooks>=4.3.0 - - pydocstyle>=6.1.1 - - pylint>=2.6.0 - - rope - - rstcheck>=6.1.1 - - pip: - # development - - flake8-pyproject - - pytest-cov diff --git a/setup_env.py b/setup_env.py new file mode 100755 index 00000000..3cb28121 --- /dev/null +++ b/setup_env.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python +""" Setup .env with local eccodes definition """ + +import os +import shutil +import subprocess + + +def main(): + base_path = os.getcwd() + + resource_path = os.path.join(base_path, "resource") + + eccodes_definition_path = os.path.join(resource_path, "eccodes") + cosmo_definition_path = os.path.join(resource_path, "eccodes-cosmo-resources") + + # remove existing resource + if os.path.exists(resource_path): + shutil.rmtree(resource_path) + + # Clone repositories + subprocess.run( + [ + "git", + "clone", + "--depth", + "1", + "-b", + "2.39.0", + "https://github.com/ecmwf/eccodes.git", + eccodes_definition_path, + ], + check=True, + ) + subprocess.run( + [ + "git", + "clone", + "--depth", + "1", + "-b", + "v2.36.0.3", + "https://github.com/COSMO-ORG/eccodes-cosmo-resources.git", + cosmo_definition_path, + ], + check=True, + ) + + # Remove deprecated directory + deprecated_path = os.path.join(eccodes_definition_path, "deprecated") + if os.path.exists(deprecated_path): + for root, dirs, files in os.walk(deprecated_path, topdown=False): + for name in files: + os.remove(os.path.join(root, name)) + for name in dirs: + os.rmdir(os.path.join(root, name)) + os.rmdir(deprecated_path) + + # Remove existing .env file + env_file = ".env" + if os.path.exists(env_file): + os.remove(env_file) + + # Write to .env file + env_content = ( + f"ECCODES_DEFINITION_PATH={eccodes_definition_path}/definitions:" + f"{cosmo_definition_path}/definitions\n" + ) + with open(env_file, "w", encoding="utf-8") as file: + file.write(env_content) + + +if __name__ == "__main__": + main() diff --git a/setup_env.sh b/setup_env.sh deleted file mode 100755 index a2020bfb..00000000 --- a/setup_env.sh +++ /dev/null @@ -1,70 +0,0 @@ -#!/bin/bash -# -# Create conda environment with pinned or unpinned requirements - - -if [[ "${BASH_SOURCE[0]}" != "${0}" ]]; then - echo "Please simply call the script instead of sourcing it!" - return -fi - -# Default env names -DEFAULT_ENV_NAME="probtest" - -# Default options -ENV_NAME="${DEFAULT_ENV_NAME}" -PYVERSION=3.10.8 -PINNED=true -EXPORT=false -CONDA=conda -HELP=false - -help_msg="Usage: $(basename "${0}") [-n NAME] [-p VER] [-u] [-e] [-m] [-h] - -Options: - -n NAME Env name [default: ${DEFAULT_ENV_NAME} - -p VER Python version [default: ${PYVERSION}] - -u Use unpinned requirements (minimal version restrictions) - -e Export environment files (requires -u) - -m Use mamba instead of conda - -h Print this help message and exit -" - -# Eval command line options -while getopts n:p:defhimu flag; do - case ${flag} in - n) ENV_NAME=${OPTARG};; - p) PYVERSION=${OPTARG};; - e) EXPORT=true;; - h) HELP=true;; - m) CONDA=mamba;; - u) PINNED=false;; - ?) echo -e "\n${help_msg}" >&2; exit 1;; - esac -done - -if ${HELP}; then - echo "${help_msg}" - exit 0 -fi - -echo "Setting up environment for installation" -eval "$(conda shell.bash hook)" || exit # NOT ${CONDA} (doesn't work with mamba) -conda activate || exit # NOT ${CONDA} (doesn't work with mamba) - -# Create new env; pass -f to overwriting any existing one -echo "Creating ${CONDA} environment" -${CONDA} create -n ${ENV_NAME} python=${PYVERSION} --yes || exit - -# Install requirements in new env -if ${PINNED}; then - echo "Pinned installation" - ${CONDA} env update --name ${ENV_NAME} --file requirements/environment.yml || exit -else - echo "Unpinned installation" - ${CONDA} env update --name ${ENV_NAME} --file requirements/requirements.yml || exit - if ${EXPORT}; then - echo "Export pinned prod environment" - ${CONDA} env export --name ${ENV_NAME} --no-builds | \grep -v '^prefix:' > requirements/environment.yml || exit - fi -fi diff --git a/setup_miniconda.sh b/setup_miniconda.sh deleted file mode 100755 index 5b5c3cba..00000000 --- a/setup_miniconda.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash -# -# Install miniconda if necessary - - -if [[ "${BASH_SOURCE[0]}" != "${0}" ]]; then - echo "Please simply call the script instead of sourcing it!" - return -fi - -# Default options -INSTALL_PREFIX=${PWD} -USER_INSTALL=false - -# here the conda version is fixed, the sha256 hash has to be set accordingly -ARCH="$(arch)" -MINICONDA_URL=https://repo.anaconda.com/miniconda/Miniconda3-py310_23.11.0-1-Linux-${ARCH}.sh -if [[ ${ARCH} == "x86_64" ]]; then - SHA256=6581658486be8e700d77e24eccafba586a0fbafafadcf73d35ab13eaee4b80b1 -elif [[ ${ARCH} == "aarch64" ]]; then - SHA256=30b3f26fee441c5d70bd50ec06ea1acaa0e373ad30771165eada3f6bdf27766a -fi - - -# Eval command line options -while getopts p:u flag; do - case ${flag} in - p) INSTALL_PREFIX=${OPTARG};; - u) USER_INSTALL=true;; - esac -done - -# Install conda executable if not yet available -if [[ -f $CONDA_EXE ]]; then - echo "Found a conda executable at: ${CONDA_EXE}" -else - echo "No conda executable available, fetching Miniconda install script" - wget -O ${INSTALL_PREFIX}/miniconda.sh ${MINICONDA_URL} - echo "${SHA256} ${INSTALL_PREFIX}/miniconda.sh" | sha256sum --check || exit 1 - bash ${INSTALL_PREFIX}/miniconda.sh -b -p ${INSTALL_PREFIX}/miniconda - source ${INSTALL_PREFIX}/miniconda/etc/profile.d/conda.sh - conda config --set always_yes yes --set changeps1 no - conda config --add channels conda-forge - if ${USER_INSTALL}; then - conda init bash - else - # this is a workaround as plain --no-user is not working as it should - conda init bash --no-user --install --system - fi - conda activate - rm ${INSTALL_PREFIX}/miniconda.sh -fi diff --git a/templates/ICON.jinja b/templates/ICON.jinja index b97ade1e..2ff2cec7 100644 --- a/templates/ICON.jinja +++ b/templates/ICON.jinja @@ -11,6 +11,7 @@ "member_type": "{{member_type}}", "factor": 5, "file_specification": [{ + "GRIB": {"format": "GRIB", "time_dim": "step", "horizontal_dims": ["values"], "var_excl": ["tlon", "tlat", "vlon", "vlat", "ulon", "ulat", "h", "slor", "anor", "isor", "sdor"], "fill_value_key": "_FillValue"}, "latlon": { "format": "netcdf", "time_dim": "time", "horizontal_dims": ["lat:lon"] }, "meteogram": { "format": "netcdf", "time_dim": "time", "horizontal_dims": ["max_nlevs:nstations", "nstations"] }, "dace":{ "format": "netcdf", "time_dim": null, "horizontal_dims": ["d_body"]}, diff --git a/tests/conftest.py b/tests/conftest.py index b18435e7..507f5d90 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -16,11 +16,18 @@ import pandas as pd import pytest import xarray as xr +from dotenv import load_dotenv from tests.helpers import create_random_stats_file, generate_ensemble, load_pandas from util.tree import TimingTree +@pytest.fixture(scope="session", autouse=True) +def load_env(): + """Automatically load .env file for all tests.""" + load_dotenv() # Optionally specify the path to your .env file + + @pytest.fixture(autouse=True, scope="session") def new_ref() -> str: ref = os.path.join(tempfile.mkdtemp()) diff --git a/tests/engine/test_perturb.py b/tests/engine/test_perturb.py index f78b5c69..a420bbe6 100644 --- a/tests/engine/test_perturb.py +++ b/tests/engine/test_perturb.py @@ -12,8 +12,8 @@ from engine.perturb import perturb_array -atype = np.float32 -AMPLITUDE = atype(1e-14) +ATYPE = np.float32 +AMPLITUDE = ATYPE(1e-14) ARRAY_DIM = 100 @@ -33,8 +33,8 @@ def fixture_create_nc_files(tmp_dir): def test_perturb_array(): # create two arrays, perturb one. - x1 = np.ones((ARRAY_DIM, ARRAY_DIM), dtype=atype) - x2 = np.ones((ARRAY_DIM, ARRAY_DIM), dtype=atype) + x1 = np.ones((ARRAY_DIM, ARRAY_DIM), dtype=ATYPE) + x2 = np.ones((ARRAY_DIM, ARRAY_DIM), dtype=ATYPE) x_perturbed = perturb_array(x2, 10, AMPLITUDE) # compute some stats and do assertions diff --git a/tests/engine/test_stats.py b/tests/engine/test_stats.py index a4701695..0893d73c 100644 --- a/tests/engine/test_stats.py +++ b/tests/engine/test_stats.py @@ -4,10 +4,9 @@ dataframes from both NetCDF and CSV files. """ -import os -import unittest - +import eccodes import numpy as np +import pytest from netCDF4 import Dataset # pylint: disable=no-name-in-module from engine.stats import create_stats_dataframe @@ -16,6 +15,16 @@ HOR_DIM_SIZE = 100 HEIGHT_DIM_SIZE = 5 +TIME_DIM_GRIB_SIZE = 1 +STEP_DIM_SIZE = 1 +HEIGHT_DIM_GRIB_SIZE = 1 +HORIZONTAL_DIM_GRIB_SIZE = 6114 + +GRIB_FILE_NAME = "test_stats_grib.grib" +STATS_FILE_NAME = "test_stats.csv" +NC_FILE_NAME = "test_stats.nc" +NC_FILE_GLOB = "test_s*.nc" + def initialize_dummy_netcdf_file(name): data = Dataset(name, "w") @@ -35,147 +44,212 @@ def initialize_dummy_netcdf_file(name): return data -class TestStatsNetcdf(unittest.TestCase): - """ - Unit test class for validating statistical calculations from NetCDF files. - - This class tests the accuracy of statistical calculations (mean, max, min) - performed on data extracted from NetCDF files. - It ensures that the statistics DataFrame produced from the NetCDF data - matches expected values. - """ - - nc_file_name = "test_stats.nc" - nc_file_glob = "test_s*.nc" - stats_file_names = "test_stats.csv" - - def setUp(self): - data = initialize_dummy_netcdf_file(self.nc_file_name) - - data.createVariable("v1", np.float64, dimensions=("t", "z", "x")) - data.variables["v1"][:] = np.ones( - (TIME_DIM_SIZE, HEIGHT_DIM_SIZE, HOR_DIM_SIZE) - ) - data.variables["v1"][:, :, 0] = 0 - data.variables["v1"][:, :, -1] = 2 - - data.createVariable("v2", np.float64, dimensions=("t", "x"), fill_value=42) - data.variables["v2"][:] = np.ones((TIME_DIM_SIZE, HOR_DIM_SIZE)) * 2 - data.variables["v2"][:, 0] = 1 - data.variables["v2"][:, 1] = 42 # shall be ignored in max-statistic - data.variables["v2"][:, -1] = 3 - - data.createVariable("v3", np.float64, dimensions=("t", "x")) - data.variables["v3"][:] = np.ones((TIME_DIM_SIZE, HOR_DIM_SIZE)) * 3 - data.variables["v3"][:, 0] = 2 - data.variables["v3"][:, -1] = 4 - - data.close() - - def tear_down(self): - os.remove(self.nc_file_name) - os.remove(self.stats_file_names) - - def test_stats(self): - file_specification = { - "Test data": { - "format": "netcdf", - "time_dim": "t", - "horizontal_dims": ["x"], - "fill_value_key": "_FillValue", # should be the name for fill_value - }, - } - - df = create_stats_dataframe( - input_dir=".", - file_id=[["Test data", self.nc_file_glob]], - stats_file_name=self.stats_file_names, - file_specification=file_specification, +def add_variable_to_grib(filename, dict_data): + with open(filename, "wb") as f_out: + for short_name in list(dict_data.keys()): + gid = eccodes.codes_grib_new_from_samples("reduced_rotated_gg_sfc_grib2") + eccodes.codes_set(gid, "edition", 2) + eccodes.codes_set(gid, "centre", "lssw") + eccodes.codes_set(gid, "dataDate", 20230913) + eccodes.codes_set(gid, "dataTime", 0) + eccodes.codes_set(gid, "stepRange", 0) + eccodes.codes_set(gid, "typeOfLevel", "surface") + eccodes.codes_set(gid, "level", 0) + eccodes.codes_set(gid, "shortName", short_name) + eccodes.codes_set_values(gid, dict_data[short_name]) + eccodes.codes_write(gid, f_out) + eccodes.codes_release(gid) + + +@pytest.fixture +def setup_grib_file(tmp_path): + array_v = np.ones( + ( + TIME_DIM_GRIB_SIZE, + STEP_DIM_SIZE, + HEIGHT_DIM_GRIB_SIZE, + HORIZONTAL_DIM_GRIB_SIZE, ) - - # check that the mean/max/min are correct - expected = np.array( - [ - [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], - [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], - [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], - [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], - [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], - [2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0], - [3.0, 4.0, 2.0, 3.0, 4.0, 2.0, 3.0, 4.0, 2.0], - ] + ) + array_v[:, :, :, 0] = 0 + array_v[:, :, :, -1] = 2 + + array_t = ( + np.ones( + ( + TIME_DIM_GRIB_SIZE, + STEP_DIM_SIZE, + HEIGHT_DIM_GRIB_SIZE, + HORIZONTAL_DIM_GRIB_SIZE, + ) ) + * 3 + ) + array_t[:, :, :, 0] = 2 + array_t[:, :, :, -1] = 4 + + dict_data = {"t": array_t, "v": array_v} + + # This would be where your grib file is created + add_variable_to_grib(tmp_path / GRIB_FILE_NAME, dict_data) + + +@pytest.mark.usefixtures("setup_grib_file") +def test_stats_grib(tmp_path): + file_specification = { + "Test data": { + "format": "grib", + "time_dim": "step", + "horizontal_dims": ["values"], + "var_excl": [], + "fill_value_key": "_FillValue", # This should be the name for fill_value. + }, + } + + df = create_stats_dataframe( + input_dir=str(tmp_path), + file_id=[["Test data", GRIB_FILE_NAME]], + stats_file_name=tmp_path / STATS_FILE_NAME, + file_specification=file_specification, + ) + + # check that the mean/max/min are correct + expected = np.array( + [ + [1.0, 2.0, 0.0], + [3.0, 4.0, 2.0], + ] + ) + + assert np.array_equal( + df.values, expected + ), f"Stats dataframe incorrect. Difference:\n{df.values == expected}" + + +@pytest.fixture(name="setup_netcdf_file") +def fixture_setup_netcdf_file(tmp_path): + """Fixture to create and initialize a dummy NetCDF file for testing.""" + + data = initialize_dummy_netcdf_file(tmp_path / NC_FILE_NAME) + + # Creating variable "v1" with specified dimensions and setting its values + data.createVariable("v1", np.float64, dimensions=("t", "z", "x")) + data.variables["v1"][:] = np.ones((TIME_DIM_SIZE, HEIGHT_DIM_SIZE, HOR_DIM_SIZE)) + data.variables["v1"][:, :, 0] = 0 + data.variables["v1"][:, :, -1] = 2 + + # Creating variable "v2" with fill_value, and setting its values + data.createVariable("v2", np.float64, dimensions=("t", "x"), fill_value=42) + data.variables["v2"][:] = np.ones((TIME_DIM_SIZE, HOR_DIM_SIZE)) * 2 + data.variables["v2"][:, 0] = 1 + data.variables["v2"][:, 1] = 42 # should be ignored in max-statistic + data.variables["v2"][:, -1] = 3 + + # Creating variable "v3" and setting its values + data.createVariable("v3", np.float64, dimensions=("t", "x")) + data.variables["v3"][:] = np.ones((TIME_DIM_SIZE, HOR_DIM_SIZE)) * 3 + data.variables["v3"][:, 0] = 2 + data.variables["v3"][:, -1] = 4 + + data.close() + + yield + + +def test_stats_netcdf(setup_netcdf_file, tmp_path): # pylint: disable=unused-argument + """Test that the statistics generated from the NetCDF file match the + expected values.""" + + file_specification = { + "Test data": { + "format": "netcdf", + "time_dim": "t", + "horizontal_dims": ["x"], + "fill_value_key": "_FillValue", # should be the name for fill_value + }, + } + + # Call the function to generate the statistics dataframe + df = create_stats_dataframe( + input_dir=str(tmp_path), + file_id=[["Test data", NC_FILE_GLOB]], + stats_file_name=tmp_path / STATS_FILE_NAME, + file_specification=file_specification, + ) + + # Define the expected values for comparison + expected = np.array( + [ + [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], + [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], + [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], + [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], + [1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0], + [2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0], + [3.0, 4.0, 2.0, 3.0, 4.0, 2.0, 3.0, 4.0, 2.0], + ] + ) + + # Check that the dataframe matches the expected values + assert np.array_equal( + df.values, expected + ), f"Stats dataframe incorrect. Difference:\n{df.values == expected}" + + +@pytest.fixture(name="setup_csv_file") +def fixture_setup_csv_file(tmp_path): + """ + Fixture to set up a temporary CSV file. + """ + dat_file_name = tmp_path / "test_stats_csv.dat" - self.assertTrue( - np.array_equal(df.values, expected), - f"stats dataframe incorrect. Difference:\n{df.values == expected}", - ) + # Create the CSV file with the necessary content + lines = ( + "time v1 v2 v3 v4 v5", + "10 1.4 15 16 17 18", + "20 2.4 25 26 27 28", + "30 3.4 35 36 37 38", + ) + with open(dat_file_name, "w", encoding="utf-8") as f: + f.write("\n".join(lines)) -class TestStatsCsv(unittest.TestCase): +def test_stats_csv(setup_csv_file, tmp_path): # pylint: disable=unused-argument """ - Test suite for validating statistical calculations and CSV file handling. - - This class contains unit tests for creating and validating statistics from a - CSV file. - The primary focus is on ensuring that the statistics calculated from the - input data match the expected values. - The CSV file used for testing is created and cleaned up during the test - lifecycle. + Test that the statistics generated from the CSV file match the expected values. """ - dat_file_name = "test_stats_csv.dat" - stats_file_name = "test_stats_csv.csv" - - def setUp(self): - lines = ( - "time v1 v2 v3 v4 v5", - "10 1.4 15 16 17 18", - "20 2.4 25 26 27 28", - "30 3.4 35 36 37 38", - ) - with open(self.dat_file_name, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - def tear_down(self): - os.remove(self.dat_file_name) - os.remove(self.stats_file_name) - - def test_stats(self): - file_specification = { - "Test data": { - "format": "csv", - "parser_args": { - "delimiter": "\\s+", - "header": 0, - "index_col": 0, - }, + file_specification = { + "Test data": { + "format": "csv", + "parser_args": { + "delimiter": "\\s+", + "header": 0, + "index_col": 0, }, - } - - df = create_stats_dataframe( - input_dir=".", - file_id=[["Test data", self.dat_file_name]], - stats_file_name=self.stats_file_name, - file_specification=file_specification, - ) - - # check that the mean/max/min are correct (i.e. the same as in CSV) - expected = np.array( - [ - [1.4, 1.4, 1.4, 2.4, 2.4, 2.4, 3.4, 3.4, 3.4], - [15, 15, 15, 25, 25, 25, 35, 35, 35], - [16, 16, 16, 26, 26, 26, 36, 36, 36], - [17, 17, 17, 27, 27, 27, 37, 37, 37], - [18, 18, 18, 28, 28, 28, 38, 38, 38], - ], - ) - - self.assertTrue( - np.array_equal(df.values, expected), - f"stats dataframe incorrect. Difference:\n{df.values == expected}", - ) - - -if __name__ == "__main__": - unittest.main() + }, + } + + # Call the function that creates the stats DataFrame + df = create_stats_dataframe( + input_dir=str(tmp_path), + file_id=[["Test data", "test_stats_csv.dat"]], + stats_file_name=tmp_path / "test_stats_csv.csv", + file_specification=file_specification, + ) + + # Expected result + expected = np.array( + [ + [1.4, 1.4, 1.4, 2.4, 2.4, 2.4, 3.4, 3.4, 3.4], + [15, 15, 15, 25, 25, 25, 35, 35, 35], + [16, 16, 16, 26, 26, 26, 36, 36, 36], + [17, 17, 17, 27, 27, 27, 37, 37, 37], + [18, 18, 18, 28, 28, 28, 38, 38, 38], + ] + ) + + # Assert the DataFrame matches the expected values + assert np.array_equal( + df.values, expected + ), f"Stats DataFrame incorrect. Difference:\n{df.values != expected}" diff --git a/tests/util/test_dataframe_ops.py b/tests/util/test_dataframe_ops.py index ec58928c..306e2b24 100644 --- a/tests/util/test_dataframe_ops.py +++ b/tests/util/test_dataframe_ops.py @@ -4,9 +4,40 @@ from unittest.mock import patch +import numpy as np import pandas as pd -from util.dataframe_ops import parse_check +from util.dataframe_ops import adjust_time_index, parse_check + + +def test_adjust_time_index(): + # Create a sample DataFrame with MultiIndex for 'time' and 'statistic' + index = pd.MultiIndex.from_product( + [["0 days 00:00:00", "0 days 00:01:00"], ["mean", "max", "min"]], + names=["time", "statistic"], + ) + data = np.random.random((5, 6)) # Sample data + df = pd.DataFrame(data, columns=index) + + # Apply the function to a list of DataFrames (in this case, just one DataFrame) + result = adjust_time_index([df])[0] + + # Create the expected new time index based on unique statistics + expected_time_values = np.repeat( + range(2), 3 + ) # Two unique time points, three statistics per time + expected_index = pd.MultiIndex.from_arrays( + [expected_time_values, ["mean", "max", "min"] * 2], + names=["time", "statistic"], + ) + + # Verify that the new MultiIndex matches the expected index + assert result.columns.equals( + expected_index + ), "The time index was not adjusted correctly." + + # Verify that the data remains unchanged + pd.testing.assert_frame_equal(df, result, check_like=True) @patch("util.dataframe_ops.parse_probtest_csv") diff --git a/tests/util/test_model_output_parser.py b/tests/util/test_model_output_parser.py new file mode 100644 index 00000000..fbda4b65 --- /dev/null +++ b/tests/util/test_model_output_parser.py @@ -0,0 +1,114 @@ +""" +This module contains unit tests for the `model_output_parser` module. +""" + +from unittest.mock import MagicMock + +import pytest + +from util.model_output_parser import get_dataset + + +@pytest.fixture(name="mock_ds_grib") +def fixture_mock_ds_grib(): + """ + Fixture that creates a mock GRIB object to simulate different cases of data + selection and exceptions that `get_ds` needs to handle. + """ + ds_grib = MagicMock() + + # Simulating the successful selection and conversion to xarray + ds_grib.sel.return_value.to_xarray.return_value = "valid_xarray" + + # Simulating the metadata retrieval (unique values) + ds_grib.sel.return_value.metadata.side_effect = [ + ["forecast"], # stepType + [100], # numberOfPoints + ["hours"], # stepUnits + ["analysis"], # dataType + ["regular_ll"], # gridType + ] + + return ds_grib + + +def test_get_ds_success(mock_ds_grib): + """ + Test case where get_ds successfully retrieves the dataset on the first attempt. + """ + pid = 1 + lev = "surface" + + result = get_dataset(mock_ds_grib, pid, lev) + + # Ensure the dataset is selected once + mock_ds_grib.sel.assert_called_once_with(paramId=pid, typeOfLevel=lev) + + # The result should contain the mocked xarray dataset + assert result == ["valid_xarray"] + + +@pytest.mark.parametrize( + "to_xarray_return_value, expected_result", + [ + ((KeyError(), "valid_stepType_xarray"), ["valid_stepType_xarray"]), + ( + (KeyError(), KeyError(), "valid_numberOfPoints_xarray"), + ["valid_numberOfPoints_xarray"], + ), + ( + (KeyError(), KeyError(), KeyError(), "valid_stepUnits_xarray"), + ["valid_stepUnits_xarray"], + ), + ( + (KeyError(), KeyError(), KeyError(), KeyError(), "valid_dataType_xarray"), + ["valid_dataType_xarray"], + ), + ( + ( + KeyError(), + KeyError(), + KeyError(), + KeyError(), + KeyError(), + "valid_gridType_xarray", + ), + ["valid_gridType_xarray"], + ), + ], +) +def test_get_ds_recursive_selection( + mock_ds_grib, to_xarray_return_value, expected_result +): + """ + Test case where get_ds recursively selects the dataset by metadata fields. + """ + pid = 1 + lev = "surface" + + mock_ds_grib.sel.return_value.to_xarray.side_effect = to_xarray_return_value + + result = get_dataset(mock_ds_grib, pid, lev) + + # Ensure the recursive logic is triggered by calling sel multiple times + assert mock_ds_grib.sel.call_count >= len(to_xarray_return_value) + + # The result should contain the mocked xarray dataset + assert result == expected_result + + +def test_get_ds_keyerror_handling(caplog, mock_ds_grib): + """ + Test case where get_ds fails to retrieve data and handles multiple KeyErrors. + """ + pid = 1 + lev = "surface" + + # Simulate KeyErrors for all attempts to select datasets + mock_ds_grib.sel.return_value.to_xarray.side_effect = KeyError() + + result = get_dataset(mock_ds_grib, pid, lev) + + # Assert that the warning was logged + assert "GRIB file of level surface and paramId 1 cannot be read." in caplog.text + assert not result diff --git a/util/dataframe_ops.py b/util/dataframe_ops.py index 711ac0e0..3a573f0f 100644 --- a/util/dataframe_ops.py +++ b/util/dataframe_ops.py @@ -12,7 +12,7 @@ import pandas as pd from util.constants import CHECK_THRESHOLD, compute_statistics -from util.file_system import file_names_from_pattern +from util.file_system import get_file_names_from_pattern from util.log_handler import logger from util.model_output_parser import model_output_parser @@ -88,6 +88,13 @@ def read_input_file(label, file_name, specification): def df_from_file_ids(file_id, input_dir, file_specification): """ + Collect data frames for each combination of file id (fid) and specification + (spec). + Frames for the same fid and spec represent different timestamps and have to + be concatenated along time-axis (axis=1). + Time-concatenated frames from different ids and/or specifications will be + concatenated along variable-axis (axis=0). + file_id: [[file_type, file_pattern], [file_type, file_pattern], ...] List of 2-tuples. The 2-tuple combines two strings. The first sets the file_type and must be a key in file_specification. The second string @@ -111,14 +118,9 @@ def df_from_file_ids(file_id, input_dir, file_specification): separated by ":". """ - # Collect data frames for each combination of file id (fid) and - # specification (spec). Frames for the same fid and spec represent - # different timestamps and have to be concatenated along time-axis (axis=1). - # Time-concatenated frames from different ids and/or specifications will be - # concatenated along variable-axis (axis=0). fid_dfs = [] for file_type, file_pattern in file_id: - input_files, err = file_names_from_pattern(input_dir, file_pattern) + input_files, err = get_file_names_from_pattern(input_dir, file_pattern) if err > 0: logger.info( "Can not find any files for file_pattern %s. Continue.", file_pattern @@ -152,7 +154,12 @@ def df_from_file_ids(file_id, input_dir, file_specification): logger.error("Could not find any file.") sys.exit(2) - fid_dfs = unify_time_index(fid_dfs) + # workaround for not properly set time column + try: + fid_dfs = unify_time_index(fid_dfs) + except ValueError: + fid_dfs = adjust_time_index(fid_dfs) + # different file IDs will have different variables but with same timestamps: # concatenate along variable axis df = pd.concat(fid_dfs, axis=0) @@ -185,6 +192,58 @@ def unify_time_index(fid_dfs): return fid_dfs_out +def adjust_time_index(fid_dfs): + """ + Adjust the 'time' level of the MultiIndex in DataFrame columns by replacing + it with a sequential range based on the number of unique 'statistic' values. + + Parameters: + ----------- + fid_dfs : list of pandas.DataFrame + A list of DataFrames with MultiIndex columns containing 'time' and + 'statistic' levels. + + Returns: + -------- + fid_dfs_out : list of pandas.DataFrame + DataFrames with corrected 'time' values in their MultiIndex columns. + """ + fid_dfs_out = [] + for df in fid_dfs: + # Get the existing MultiIndex + current_multiindex = df.columns + + # Find the number of unique values in the 'statistic' level + unique_statistic_count = current_multiindex.get_level_values( + "statistic" + ).nunique() + + # Create a sequential integer range for 'time' values (based on the + # number of unique statistics) + new_time_values = list( + range( + len(current_multiindex.get_level_values("time")) + // unique_statistic_count + ) + ) + + # Repeat these integer values to match the length of your columns + new_time_repeated = np.repeat(new_time_values, unique_statistic_count) + + # Construct a new MultiIndex with updated 'time' values + new_multiindex = pd.MultiIndex.from_arrays( + [new_time_repeated, current_multiindex.get_level_values("statistic")], + names=["time", "statistic"], + ) + + # Assign the new MultiIndex to the DataFrame + df.columns = new_multiindex + + fid_dfs_out.append(df) + + return fid_dfs_out + + def check_intersection(df_ref, df_cur): # Check if variable names in reference and test case have any intersection # Check if numbers of time steps agree diff --git a/util/file_system.py b/util/file_system.py index 99ad3baf..8dda9e73 100644 --- a/util/file_system.py +++ b/util/file_system.py @@ -7,7 +7,7 @@ from util.log_handler import logger -def file_names_from_pattern(dir_name, file_pattern): +def get_file_names_from_pattern(dir_name, file_pattern): """ Search for all file patching file_pattern in directory dir_name @@ -38,5 +38,6 @@ def file_names_from_pattern(dir_name, file_pattern): for f in Path(dir_name).glob("*"): logger.debug(f.name) err = 1 + file_names = sorted(file_names) return file_names, err diff --git a/util/model_output_parser.py b/util/model_output_parser.py index 882fb1af..4a2d8ea4 100644 --- a/util/model_output_parser.py +++ b/util/model_output_parser.py @@ -26,6 +26,7 @@ import sys from collections.abc import Iterable +import earthkit.data import numpy as np import pandas as pd import xarray @@ -48,7 +49,7 @@ def parse_netcdf(file_id, filename, specification): var_dfs = [] for v in var_tmp: - sub_df = dataframe_from_ncfile( + sub_df = create_statistics_dataframe( file_id=file_id, filename=filename, varname=v, @@ -63,6 +64,108 @@ def parse_netcdf(file_id, filename, specification): return var_dfs +def parse_grib(file_id, filename, specification): + logger.debug("parse GRIB file %s", filename) + time_dim = specification["time_dim"] + horizontal_dims = specification["horizontal_dims"] + fill_value_key = specification.get("fill_value_key", None) + + ds_grib = earthkit.data.from_source("file", filename) + short_name_excl = specification["var_excl"] + + short_names = np.unique(ds_grib.metadata("shortName")) + short_names = short_names[ + np.isin(short_names, short_name_excl, invert=True, assume_unique=True) + ].tolist() + + level_types = np.unique(ds_grib.metadata("typeOfLevel")).tolist() + + var_dfs = [] + for lev in level_types: + param_ids = np.unique( + ds_grib.sel(typeOfLevel=lev, shortName=short_names).metadata("paramId") + ).tolist() + for pid in param_ids: + ds_temp_list = get_dataset(ds_grib, pid, lev) + for ds_temp in ds_temp_list: + v = list(ds_temp.keys())[0] + + dim_to_squeeze = [ + dim + for dim, size in zip(ds_temp[v].dims, ds_temp[v].shape) + if size == 1 and dim != time_dim + ] + ds = ds_temp.squeeze(dim=dim_to_squeeze) + + sub_df = create_statistics_dataframe( + file_id=file_id, + filename=filename, + varname=v, + time_dim=time_dim, + horizontal_dims=horizontal_dims, + xarray_ds=ds, + fill_value_key=fill_value_key, + ) + var_dfs.append(sub_df) + + return var_dfs + + +def get_dataset(ds_grib, pid, lev): + """ + Retrieve datasets from a GRIB file based on specified parameters and + hierarchical metadata. + + This function attempts to extract data from the GRIB file by selecting + fields that match the given `paramId` and `typeOfLevel`. If the initial + selection fails due to missing or mismatched metadata, the function + will explore other metadata fields such as `stepType`, `numberOfPoints`, + `stepUnits`, `dataType`, and `gridType` to find matching datasets. + + Parameters: + ----------- + ds_grib : GRIB object + The GRIB file object to extract data from. + pid : int + The parameter ID (`paramId`) to select in the GRIB file. + lev : str + The level type (`typeOfLevel`) to select in the GRIB file. + + Returns: + -------- + ds_list : list + A list of xarray datasets that match the specified parameter and level, + with additional filtering based on hierarchical metadata fields. + """ + ds_list = [] + selectors = {"paramId": pid, "typeOfLevel": lev} + metadata_keys = ["stepType", "numberOfPoints", "stepUnits", "dataType", "gridType"] + + def recursive_select(selects, depth=0): + try: + ds = ds_grib.sel(**selects).to_xarray() + ds_list.append(ds) + except KeyError: + if depth == len(metadata_keys): # No more metadata keys to try + return + key = metadata_keys[depth] + try: + values = np.unique(ds_grib.sel(**selects).metadata(key)).tolist() + for value in values: + selects[key] = value + recursive_select(selects, depth + 1) # Recurse to next level + except KeyError: + pass + + # Try initial selection + recursive_select(selectors) + + if not ds_list: + logger.warning("GRIB file of level %s and paramId %s cannot be read.", lev, pid) + + return ds_list + + def __get_variables(data, time_dim, horizontal_dims): # return a list of variable names from the dataset data that have a time dimension # and horizontal dimension or in case there is no time dimension just the variables @@ -105,9 +208,41 @@ def __get_variables(data, time_dim, horizontal_dims): return variables -def dataframe_from_ncfile( +def create_statistics_dataframe( file_id, filename, varname, time_dim, horizontal_dims, xarray_ds, fill_value_key ): # pylint: disable=too-many-positional-arguments + """ + Create a DataFrame of statistical values for a given variable from an xarray + dataset. + + This function computes statistics (mean, max, min, etc.) over horizontal + dimensions and organizes them into a pandas DataFrame, indexed by file ID, + variable name, and height (if applicable). + The columns represent time and the computed statistics. + + Parameters: + ----------- + file_id : str + Identifier for the file. + filename : str + Name of the input file. + varname : str + Name of the variable to process. + time_dim : str + Name of the time dimension. + horizontal_dims : list + List of dimensions to compute statistics over. + xarray_ds : xarray.Dataset + The xarray dataset containing the data. + fill_value_key : str + Key for the fill value in the dataset. + + Returns: + -------- + pd.DataFrame + DataFrame with the computed statistics indexed by file ID, variable, and + height. + """ statistics = statistics_over_horizontal_dim( xarray_ds[varname], horizontal_dims, @@ -232,4 +367,5 @@ def parse_csv(file_id, filename, specification): model_output_parser = { # global lookup dict "netcdf": parse_netcdf, "csv": parse_csv, + "grib": parse_grib, }