├── .gitattributes ├── .github └── workflows │ ├── ci.yaml │ └── publish.yml ├── .gitignore ├── .gitmodules ├── .pre-commit-config.yaml ├── CITATION.cff ├── LICENSE ├── MANIFEST.in ├── README.md ├── ci └── environment.yml ├── setup.cfg ├── setup.py ├── source ├── aerobulk │ ├── __init__.py │ └── flux.py └── fortran │ ├── .f2py_f2cmap │ ├── mod_aerobulk_wrap_noskin.f90 │ ├── mod_aerobulk_wrap_noskin.pyf │ ├── mod_aerobulk_wrap_skin.f90 │ └── mod_aerobulk_wrap_skin.pyf └── tests ├── create_test_data.py ├── test_flux_np.py ├── test_flux_xr.py └── test_fortran.py /.gitattributes: -------------------------------------------------------------------------------- 1 | source/aerobulk/_version.py export-subst 2 | -------------------------------------------------------------------------------- /.github/workflows/ci.yaml: -------------------------------------------------------------------------------- 1 | name: CI 2 | on: 3 | push: 4 | branches: 5 | - "main" 6 | pull_request: 7 | branches: 8 | - "*" 9 | schedule: 10 | - cron: "0 13 * * 1" 11 | workflow_dispatch: 12 | 13 | concurrency: 14 | group: ${{ github.workflow }}-${{ github.ref }} 15 | cancel-in-progress: true 16 | 17 | jobs: 18 | build: 19 | name: Build (${{ matrix.python-version }} | ${{ matrix.os }}) 20 | if: github.repository == 'xgcm/aerobulk-python' 21 | runs-on: ${{ matrix.os }} 22 | timeout-minutes: 30 23 | defaults: 24 | run: 25 | shell: bash -l {0} 26 | strategy: 27 | fail-fast: false 28 | matrix: 29 | os: ["ubuntu-latest"] 30 | python-version: ["3.8", "3.9", "3.10", "3.11"] 31 | steps: 32 | - uses: actions/checkout@v3 33 | with: 34 | submodules: recursive 35 | - name: Create conda environment 36 | uses: mamba-org/provision-with-micromamba@main 37 | with: 38 | cache-downloads: true 39 | micromamba-version: 'latest' 40 | environment-file: ci/environment.yml 41 | extra-specs: | 42 | python=${{ matrix.python-version }} 43 | - name: Install aerobulk-python 44 | run: | 45 | python -m pip install -e . --no-deps 46 | conda list 47 | - name: Run Tests 48 | run: | 49 | pytest -vv --cov=./ --cov-report=xml 50 | - name: Upload code coverage to Codecov 51 | uses: codecov/codecov-action@v3.1.1 52 | with: 53 | file: ./coverage.xml 54 | flags: unittests 55 | env_vars: OS,PYTHON 56 | name: codecov-umbrella 57 | fail_ci_if_error: false 58 | -------------------------------------------------------------------------------- /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Upload Python Package 2 | 3 | on: 4 | release: 5 | types: [created] 6 | 7 | jobs: 8 | deploy: 9 | if: github.repository == 'xgcm/aerobulk-python' 10 | runs-on: ubuntu-latest 11 | steps: 12 | - uses: actions/checkout@v3 13 | with: 14 | submodules: recursive 15 | - name: Set up Python 16 | uses: actions/setup-python@v3 17 | with: 18 | python-version: '3.11' 19 | - name: Install dependencies 20 | run: | 21 | python -m pip install --upgrade pip 22 | pip install numpy setuptools setuptools-scm wheel twine 23 | - name: Build tarball 24 | run: python setup.py sdist 25 | - name: Check dist 26 | run: ls -la dist 27 | - name: Check version 28 | run: python setup.py --version 29 | - name: Check built artifacts 30 | run: | 31 | python -m twine check dist/* 32 | pwd 33 | if [ -f dist/aerobulk-python-0.0.0.tar.gz ]; then 34 | echo "❌ INVALID VERSION NUMBER" 35 | exit 1 36 | else 37 | echo "✅ Looks good" 38 | fi 39 | - name: Publish a Package to PyPI 40 | uses: pypa/gh-action-pypi-publish@v1.5.0 41 | with: 42 | user: __token__ 43 | password: ${{ secrets.PYPI_API_TOKEN }} 44 | verbose: true 45 | verify_metadata: true 46 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | # might have to remove this later, if we want to add nbs 3 | *.ipynb 4 | 5 | _version.py 6 | *.egg-info 7 | .eggs/ 8 | build/ 9 | __pycache__ 10 | dist/ 11 | 12 | # files built by f2py 13 | source/fortran/*f2pywrappers* 14 | source/fortran/*.c 15 | source/aerobulk/aerobulk/ 16 | mod_*.so 17 | 18 | # should we ignore submodules 19 | .DS_Store 20 | .vscode/ 21 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "source/fortran/aerobulk"] 2 | path = source/fortran/aerobulk 3 | url = git@github.com:jbusecke/aerobulk.git 4 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | autoupdate_schedule: quarterly 2 | default_language_version: 3 | python: python3 4 | repos: 5 | - repo: https://github.com/pre-commit/pre-commit-hooks 6 | rev: v5.0.0 7 | hooks: 8 | - id: trailing-whitespace 9 | - id: end-of-file-fixer 10 | - id: check-yaml 11 | - repo: https://github.com/PyCQA/isort 12 | rev: 5.13.2 13 | hooks: 14 | - id: isort 15 | - repo: https://github.com/psf/black 16 | rev: 24.10.0 17 | hooks: 18 | - id: black 19 | - repo: https://github.com/PyCQA/flake8 20 | rev: 7.1.1 21 | hooks: 22 | - id: flake8 23 | -------------------------------------------------------------------------------- /CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | message: "If you use this software, please cite it as below." 3 | authors: 4 | - family-names: "Busecke" 5 | given-names: "Julius J. M." 6 | orcid: "https://orcid.org/0000-0001-8571-865X" 7 | - family-names: "Martin" 8 | given-names: "Paige E." 9 | - family-names: "Abernathey" 10 | given-names: "Ryan P." 11 | orcid: "https://orcid.org/0000-0001-5999-4917" 12 | title: "aerobulk-python" 13 | url: "https://github.com/xgcm/aerobulk-python" 14 | # version and doi are completed by Zenodo automatically, do not provide here. 15 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.md 2 | include LICENSE 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/xgcm/aerobulk-python/main.svg)](https://results.pre-commit.ci/latest/github/xgcm/aerobulk-python/main) 2 | [![DOI](https://zenodo.org/badge/481218255.svg)](https://zenodo.org/doi/10.5281/zenodo.11205090) 3 | 4 | 5 | # aerobulk-python 6 | A python wrapper for aerobulk (https://github.com/brodeau/aerobulk) 7 | 8 | ## Installation 9 | Due to the heavy dependencies in this package we recommend installing the package from conda-forge with mamba/conda 10 | 11 | ``` 12 | mamba install -c conda-forge aerobulk-python 13 | ``` 14 | or 15 | ``` 16 | conda install -c conda-forge aerobulk-python 17 | ``` 18 | 19 | ## aerobulk-python developer guide 20 | 21 | These are the steps to install and develop the package locally 22 | 23 | 1. Set up a development environment. 24 | ``` 25 | mamba create -n aerobulk-python-dev python=3.9 dask numpy gfortran xarray pytest ipython 26 | conda activate aerobulk-python-dev 27 | ``` 28 | 29 | 2. Clone the repository 30 | 31 | Make sure to clone the repo with `--recursive` to also pull the git submodule! 32 | 33 | ``` 34 | git clone --recursive git@github.com:xgcm/aerobulk-python.git 35 | ``` 36 | 37 | 3. Compile/Install aerobulk-python (this has to be rerun after every change to the fortran code/wrapper/signature file) 38 | Run these commands from within the `aerobulk-python` folder. 39 | 40 | a. Use pip to install aerobulk locally (this will install the python package and compile). 41 | ``` 42 | pip install -e . 43 | ``` 44 | b. Manual version (you will have to install the python module separately) 45 | ``` 46 | python -m numpy.f2py --verbose -c --f90flags="-fdefault-real-8 -ffree-line-length-200 --std=gnu" ./source/fortran/aerobulk/src/mod_const.f90 ./source/fortran/aerobulk/src/mod_phymbl.f90 ./source/fortran/aerobulk/src/mod_skin_coare.f90 ./source/fortran/aerobulk/src/mod_skin_ecmwf.f90 ./source/fortran/aerobulk/src/mod_blk_andreas.f90 ./source/fortran/aerobulk/src/mod_common_coare.f90 ./source/fortran/aerobulk/src/mod_blk_coare3p0.f90 ./source/fortran/aerobulk/src/mod_blk_coare3p6.f90 ./source/fortran/aerobulk/src/mod_blk_ecmwf.f90 ./source/fortran/aerobulk/src/mod_blk_ncar.f90 ./source/fortran/aerobulk/src/mod_blk_neutral_10m.f90 ./source/fortran/aerobulk/src/mod_aerobulk_compute.f90 ./source/fortran/aerobulk/src/mod_aerobulk.f90 ./source/fortran/mod_aerobulk_wrap_skin.f90 ./source/fortran/mod_aerobulk_wrap_skin.pyf 47 | ``` 48 | and 49 | 50 | ``` 51 | python -m numpy.f2py --verbose -c --f90flags="-fdefault-real-8 -ffree-line-length-200 --std=gnu" ./source/fortran/aerobulk/src/mod_const.f90 ./source/fortran/aerobulk/src/mod_phymbl.f90 ./source/fortran/aerobulk/src/mod_skin_coare.f90 ./source/fortran/aerobulk/src/mod_skin_ecmwf.f90 ./source/fortran/aerobulk/src/mod_blk_andreas.f90 ./source/fortran/aerobulk/src/mod_common_coare.f90 ./source/fortran/aerobulk/src/mod_blk_coare3p0.f90 ./source/fortran/aerobulk/src/mod_blk_coare3p6.f90 ./source/fortran/aerobulk/src/mod_blk_ecmwf.f90 ./source/fortran/aerobulk/src/mod_blk_ncar.f90 ./source/fortran/aerobulk/src/mod_blk_neutral_10m.f90 ./source/fortran/aerobulk/src/mod_aerobulk_compute.f90 ./source/fortran/aerobulk/src/mod_aerobulk.f90 ./source/fortran/mod_aerobulk_wrap_noskin.f90 ./source/fortran/mod_aerobulk_wrap_noskin.pyf 52 | ``` 53 | 4. Make sure things work 54 | ``` 55 | pytest tests/test_fortran.py 56 | ``` 57 | 🎉 58 | 59 | ## Local Aerobulk compilation guide 60 | Assumes that you have an environment with a gfortran compiler set up, have aerobulk cloned and that you are in the root aerobulk (not aerobulk-python) directory. 61 | 62 | 1. Build the aerobulk source 63 | 64 | First copy the appropriate macro file to the source directory: 65 | ``` 66 | cp arch/make.macro_GnuLinux make.macro 67 | ``` 68 | 69 | those of us on mac needed to edit `make.macro` make this change 70 | ```diff 71 | # These are needed for the C/C++ interface 72 | - FF += -std=gnu -lstdc++ 73 | + FF += -std=gnu 74 | ``` 75 | 76 | Then you should be able to run `make` 77 | 78 | This will build: 79 | - Object files `src/*.o` 80 | - Mode files `mod/*.mod` 81 | - Library file `lib/libaerobulk.a` 82 | - Binary executables `bin/*x` 83 | 84 | I was able to run one of the binaries with `bin/example_call_aerobulk.x` 85 | 86 | 87 |
Output 88 | ``` 89 | *********** COARE 3.0 ***************** 90 | 91 | =================================================================== 92 | ----- AeroBulk_init ----- 93 | 94 | *** Bulk parameterization to be used => "coare3p0" 95 | ==> will use the Cool-skin & Warm-ayer scheme of `coare3p0` ! 96 | *** Computational domain shape: Ni x Nj = 00002 x 00001 97 | *** Number of time records that will be treated: 1 98 | *** Number of iterations in bulk algos: nb_iter = 10 99 | *** Filling the `mask` array... 100 | ==> no points need to be masked! :) 101 | *** Type of prescribed air humidity `specific humidity [kg/kg]` 102 | =================================================================== 103 | =================================================================== 104 | ----- AeroBulk_bye ----- 105 | =================================================================== 106 | 107 | 108 | --------------------------------------------------------------------- 109 | Parameter | Unstable ASL | Stable ASL | units 110 | --------------------------------------------------------------------- 111 | Wind speed at zu = 5.00000000 5.00000000 m/s 112 | SST = 22.0000000 22.0000000 deg.C 113 | Abs. temperature at zt = 20.0000000 25.0000000 deg.C 114 | Pot. temperature at zt = 20.0134144 25.0150242 deg.C 115 | 116 | Sensible heat flux: QH = -15.1545086 17.8423691 W/m**2 117 | Latent heat flux: QL = -81.3846741 -50.8408585 W/m**2 118 | Evaporation: Evap = -2.87061906 -1.79333246 mm/day 119 | Skin temperature: SSST = 21.7219677 21.7576351 deg.C 120 | Tau_x = 3.57834995E-02 1.73626728E-02 N/m**2 121 | Tau_y = 0.00000000 0.00000000 N/m**2 122 | Tau = 3.57834995E-02 1.73626728E-02 N/m**2 123 | 124 | 125 | 126 | *********** COARE 3.6 ***************** 127 | 128 | =================================================================== 129 | ----- AeroBulk_init ----- 130 | 131 | *** Bulk parameterization to be used => "coare3p6" 132 | ==> will use the Cool-skin & Warm-ayer scheme of `coare3p6` ! 133 | *** Computational domain shape: Ni x Nj = 00002 x 00001 134 | *** Number of time records that will be treated: 1 135 | *** Number of iterations in bulk algos: nb_iter = 10 136 | *** Filling the `mask` array... 137 | ==> no points need to be masked! :) 138 | *** Type of prescribed air humidity `specific humidity [kg/kg]` 139 | =================================================================== 140 | =================================================================== 141 | ----- AeroBulk_bye ----- 142 | =================================================================== 143 | 144 | 145 | --------------------------------------------------------------------- 146 | Parameter | Unstable ASL | Stable ASL | units 147 | --------------------------------------------------------------------- 148 | Wind speed at zu = 5.00000000 5.00000000 m/s 149 | SST = 22.0000000 22.0000000 deg.C 150 | Abs. temperature at zt = 20.0000000 25.0000000 deg.C 151 | Pot. temperature at zt = 20.0134144 25.0150242 deg.C 152 | 153 | Sensible heat flux: QH = -15.3865499 17.0818920 W/m**2 154 | Latent heat flux: QL = -83.0788422 -48.4459152 W/m**2 155 | Evaporation: Evap = -2.93033028 -1.70883954 mm/day 156 | Skin temperature: SSST = 21.7057972 21.7485600 deg.C 157 | Tau_x = 3.21817845E-02 1.51577257E-02 N/m**2 158 | Tau_y = 0.00000000 0.00000000 N/m**2 159 | Tau = 3.21817845E-02 1.51577257E-02 N/m**2 160 | 161 | 162 | 163 | *********** ECMWF ***************** 164 | 165 | =================================================================== 166 | ----- AeroBulk_init ----- 167 | 168 | *** Bulk parameterization to be used => "ecmwf" 169 | ==> will use the Cool-skin & Warm-ayer scheme of `ecmwf` ! 170 | *** Computational domain shape: Ni x Nj = 00002 x 00001 171 | *** Number of time records that will be treated: 1 172 | *** Number of iterations in bulk algos: nb_iter = 10 173 | *** Filling the `mask` array... 174 | ==> no points need to be masked! :) 175 | *** Type of prescribed air humidity `specific humidity [kg/kg]` 176 | =================================================================== 177 | =================================================================== 178 | ----- AeroBulk_bye ----- 179 | =================================================================== 180 | 181 | 182 | --------------------------------------------------------------------- 183 | Parameter | Unstable ASL | Stable ASL | units 184 | --------------------------------------------------------------------- 185 | Wind speed at zu = 5.00000000 5.00000000 m/s 186 | SST = 22.0000000 22.0000000 deg.C 187 | Abs. temperature at zt = 20.0000000 25.0000000 deg.C 188 | Pot. temperature at zt = 20.0134144 25.0150242 deg.C 189 | 190 | Sensible heat flux: QH = -14.3822346 17.6531811 W/m**2 191 | Latent heat flux: QL = -80.2958984 -52.4623947 W/m**2 192 | Evaporation: Evap = -2.83224440 -1.85053933 mm/day 193 | Skin temperature: SSST = 21.7325401 21.7630310 deg.C 194 | Tau_x = 3.84389125E-02 1.93256922E-02 N/m**2 195 | Tau_y = 0.00000000 0.00000000 N/m**2 196 | Tau = 3.84389125E-02 1.93256922E-02 N/m**2 197 | 198 | 199 | 200 | *********** NCAR ***************** 201 | 202 | =================================================================== 203 | ----- AeroBulk_init ----- 204 | 205 | *** Bulk parameterization to be used => "ncar" 206 | *** Cool-skin & Warm-layer schemes will NOT be used! 207 | *** Computational domain shape: Ni x Nj = 00002 x 00001 208 | *** Number of time records that will be treated: 1 209 | *** Number of iterations in bulk algos: nb_iter = 10 210 | *** Filling the `mask` array... 211 | ==> no points need to be masked! :) 212 | *** Type of prescribed air humidity `specific humidity [kg/kg]` 213 | =================================================================== 214 | =================================================================== 215 | ----- AeroBulk_bye ----- 216 | =================================================================== 217 | 218 | 219 | --------------------------------------------------------------------- 220 | Parameter | Unstable ASL | Stable ASL | units 221 | --------------------------------------------------------------------- 222 | Wind speed at zu = 5.00000000 5.00000000 m/s 223 | SST = 22.0000000 22.0000000 deg.C 224 | Abs. temperature at zt = 20.0000000 25.0000000 deg.C 225 | Pot. temperature at zt = 20.0134144 25.0150242 deg.C 226 | 227 | Sensible heat flux: QH = -16.6969528 10.7261686 W/m**2 228 | Latent heat flux: QL = -88.4781876 -71.9012222 W/m**2 229 | Evaporation: Evap = -3.12166286 -2.53679895 mm/day 230 | Tau_x = 3.58519591E-02 2.77329944E-02 N/m**2 231 | Tau_y = 0.00000000 0.00000000 N/m**2 232 | Tau = 3.58519591E-02 2.77329944E-02 N/m**2 233 | 234 | 235 | 236 | *********** ANDREAS ***************** 237 | 238 | =================================================================== 239 | ----- AeroBulk_init ----- 240 | 241 | *** Bulk parameterization to be used => "andreas" 242 | *** Cool-skin & Warm-layer schemes will NOT be used! 243 | *** Computational domain shape: Ni x Nj = 00002 x 00001 244 | *** Number of time records that will be treated: 1 245 | *** Number of iterations in bulk algos: nb_iter = 10 246 | *** Filling the `mask` array... 247 | ==> no points need to be masked! :) 248 | *** Type of prescribed air humidity `specific humidity [kg/kg]` 249 | =================================================================== 250 | =================================================================== 251 | ----- AeroBulk_bye ----- 252 | =================================================================== 253 | 254 | 255 | --------------------------------------------------------------------- 256 | Parameter | Unstable ASL | Stable ASL | units 257 | --------------------------------------------------------------------- 258 | Wind speed at zu = 5.00000000 5.00000000 m/s 259 | SST = 22.0000000 22.0000000 deg.C 260 | Abs. temperature at zt = 20.0000000 25.0000000 deg.C 261 | Pot. temperature at zt = 20.0134144 25.0150242 deg.C 262 | 263 | Sensible heat flux: QH = -14.4129944 15.1970539 W/m**2 264 | Latent heat flux: QL = -74.4637756 -51.7018318 W/m**2 265 | Evaporation: Evap = -2.62721038 -1.82412970 mm/day 266 | Tau_x = 3.02770734E-02 1.79436151E-02 N/m**2 267 | Tau_y = 0.00000000 0.00000000 N/m**2 268 | Tau = 3.02770734E-02 1.79436151E-02 N/m**2 269 | ``` 270 |
271 | -------------------------------------------------------------------------------- /ci/environment.yml: -------------------------------------------------------------------------------- 1 | name: test_env_aerobulk_python 2 | channels: 3 | - conda-forge 4 | dependencies: 5 | - codecov 6 | - dask 7 | - gfortran 8 | - numpy 9 | - pytest 10 | - pytest-cov 11 | - python 12 | - xarray 13 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [flake8] 2 | exclude = __init__.py,.eggs,doc 3 | ignore = 4 | # whitespace before ':' - doesn't work well with black 5 | E203 6 | E402 7 | # line too long - let black worry about that 8 | E501 9 | # do not assign a lambda expression, use a def 10 | E731 11 | # line break before binary operator 12 | W503 13 | E265 14 | F811 15 | [tool:pytest] 16 | testpaths = tests 17 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # We need to import setuptools here in order for it to persist in sys.modules. 4 | # Its presence/absence is used in subclassing setup in numpy/distutils/core.py. 5 | # However, we need to run the distutils version of sdist, so import that first 6 | # so that it is in sys.modules 7 | import numpy.distutils.command.sdist # noqa 8 | import setuptools # noqa 9 | from numpy.distutils.core import Extension, setup 10 | from numpy.distutils.fcompiler import get_default_fcompiler 11 | 12 | # Trying this from the numpy setup.py 13 | 14 | 15 | here = os.path.dirname(__file__) 16 | with open(os.path.join(here, "README.md"), encoding="utf-8") as f: 17 | long_description = f.read() 18 | 19 | install_requires = [ 20 | "numpy", 21 | ] 22 | 23 | # figure out which compiler we're going to use 24 | compiler = get_default_fcompiler() 25 | # set some fortran compiler-dependent flags 26 | f90flags = [] 27 | if compiler == "gnu95": 28 | # f90flags.append('-fno-range-check') 29 | # f90flags.append('-ffree-form') 30 | # These are the flags I used in the manual version (might have some more here) 31 | f90flags.append("-fdefault-real-8") 32 | f90flags.append("-ffree-line-length-200") 33 | elif compiler == "intel" or compiler == "intelem": 34 | f90flags.append("-132") 35 | # Set aggressive optimization level 36 | f90flags.append("-O3") 37 | # Suppress all compiler warnings (avoid huge CI log files) 38 | f90flags.append("-w") 39 | 40 | # for this API we will only expose a single extension? 41 | ext_modules = [ 42 | Extension( 43 | name="mod_aerobulk_wrap_noskin", 44 | sources=[ 45 | "./source/fortran/aerobulk/src/mod_const.f90", 46 | "./source/fortran/aerobulk/src/mod_phymbl.f90", 47 | "./source/fortran/aerobulk/src/mod_skin_coare.f90", 48 | "./source/fortran/aerobulk/src/mod_skin_ecmwf.f90", 49 | "./source/fortran/aerobulk/src/mod_blk_andreas.f90", 50 | "./source/fortran/aerobulk/src/mod_common_coare.f90", 51 | "./source/fortran/aerobulk/src/mod_blk_coare3p0.f90", 52 | "./source/fortran/aerobulk/src/mod_blk_coare3p6.f90", 53 | "./source/fortran/aerobulk/src/mod_blk_ecmwf.f90", 54 | "./source/fortran/aerobulk/src/mod_blk_ncar.f90", 55 | "./source/fortran/aerobulk/src/mod_blk_neutral_10m.f90", 56 | "./source/fortran/aerobulk/src/mod_aerobulk_compute.f90", 57 | "./source/fortran/aerobulk/src/mod_aerobulk.f90", 58 | "./source/fortran/mod_aerobulk_wrap_noskin.f90", 59 | "./source/fortran/mod_aerobulk_wrap_noskin.pyf", 60 | ], 61 | extra_f90_compile_args=f90flags, 62 | # f2py_options=['--quiet'], 63 | ), 64 | Extension( 65 | name="mod_aerobulk_wrap_skin", 66 | sources=[ 67 | "./source/fortran/aerobulk/src/mod_const.f90", 68 | "./source/fortran/aerobulk/src/mod_phymbl.f90", 69 | "./source/fortran/aerobulk/src/mod_skin_coare.f90", 70 | "./source/fortran/aerobulk/src/mod_skin_ecmwf.f90", 71 | "./source/fortran/aerobulk/src/mod_blk_andreas.f90", 72 | "./source/fortran/aerobulk/src/mod_common_coare.f90", 73 | "./source/fortran/aerobulk/src/mod_blk_coare3p0.f90", 74 | "./source/fortran/aerobulk/src/mod_blk_coare3p6.f90", 75 | "./source/fortran/aerobulk/src/mod_blk_ecmwf.f90", 76 | "./source/fortran/aerobulk/src/mod_blk_ncar.f90", 77 | "./source/fortran/aerobulk/src/mod_blk_neutral_10m.f90", 78 | "./source/fortran/aerobulk/src/mod_aerobulk_compute.f90", 79 | "./source/fortran/aerobulk/src/mod_aerobulk.f90", 80 | "./source/fortran/mod_aerobulk_wrap_skin.f90", 81 | "./source/fortran/mod_aerobulk_wrap_skin.pyf", 82 | ], 83 | extra_f90_compile_args=f90flags, 84 | f2py_options=["--quiet"], 85 | ), 86 | ] 87 | 88 | setup( 89 | name="aerobulk-python", 90 | description="General Circulation Model Postprocessing with xarray", 91 | url="https://github.com/xgcm/aerobulk-python", 92 | author="aerobulk-python Developers", 93 | author_email="julius@ldeo.columbia.edu", 94 | license="GPLv3", 95 | classifiers=[ 96 | "Development Status :: 2 - Pre-Alpha", 97 | "Intended Audience :: Science/Research", 98 | "Topic :: Scientific/Engineering", 99 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 100 | "Operating System :: OS Independent", 101 | "Programming Language :: Python :: 3.8", 102 | "Programming Language :: Python :: 3.9", 103 | "Programming Language :: Python :: 3.10", 104 | "Programming Language :: Python :: 3.11", 105 | ], 106 | install_requires=install_requires, 107 | python_requires=">=3.8, <3.12", 108 | # long_description=long_description, 109 | # long_description_content_type="text/x-rst", 110 | setup_requires="setuptools_scm", 111 | use_scm_version={ 112 | "write_to": "source/aerobulk/_version.py", 113 | "write_to_template": '__version__ = "{version}"', 114 | "tag_regex": r"^(?Pv)?(?P[^\+]+)(?P.*)?$", 115 | }, 116 | package_dir={"": "source"}, 117 | packages=["aerobulk"], 118 | ext_package="aerobulk.aerobulk", 119 | ext_modules=ext_modules, 120 | ) 121 | -------------------------------------------------------------------------------- /source/aerobulk/__init__.py: -------------------------------------------------------------------------------- 1 | from importlib.metadata import PackageNotFoundError, version 2 | 3 | try: 4 | __version__ = version("aerobulk-python") 5 | except PackageNotFoundError: 6 | __version__ = "unknown" 7 | pass 8 | 9 | from .flux import noskin, skin 10 | -------------------------------------------------------------------------------- /source/aerobulk/flux.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import aerobulk.aerobulk.mod_aerobulk_wrap_noskin as aeronoskin 4 | import aerobulk.aerobulk.mod_aerobulk_wrap_skin as aeroskin 5 | import numpy as np 6 | import xarray as xr 7 | 8 | VALID_ALGOS = ["coare3p0", "coare3p6", "ecmwf", "ncar", "andreas"] 9 | VALID_ALGOS_SKIN = ["coare3p0", "coare3p6", "ecmwf"] 10 | VALID_VALUE_RANGES = { 11 | "sst": [270, 320], 12 | "t_zt": [180, 330], 13 | "hum_zt": [0, 0.08], 14 | "u_zu": [-50, 50], 15 | "v_zu": [-50, 50], 16 | "slp": [80000, 110000], 17 | "rad_sw": [0, 1500], 18 | "rad_lw": [0, 750], 19 | } 20 | 21 | 22 | def _check_algo(algo, valids): 23 | if algo not in valids: 24 | raise ValueError(f"Algorithm {algo} not valid. Choose from {valids}.") 25 | 26 | 27 | def _check_value_range(*args): 28 | """Checks the input ranges for input fields""" 29 | # parse inputs to names 30 | args_dict = { 31 | k: v 32 | for k, v in zip( 33 | ["sst", "t_zt", "hum_zt", "u_zu", "v_zu", "slp", "rad_sw", "rad_lw"], args 34 | ) 35 | } 36 | for var, data in args_dict.items(): 37 | # check for misaligned nans 38 | if var != "sst": 39 | if np.isnan(data).any(): 40 | raise ValueError( 41 | f"Found nans in {var} that do not align with nans in `sst`. Check that nans in all fields are matched." 42 | ) 43 | 44 | # check for valid range 45 | range = VALID_VALUE_RANGES[var] 46 | 47 | # check that values are in range 48 | out_of_range = ~np.logical_and(data >= range[0], data <= range[1]) 49 | if out_of_range.any(): 50 | raise ValueError( 51 | f"Found values in {var} that are out of the valid range ({range[0]}-{range[1]})." 52 | ) 53 | 54 | 55 | # Unshrink the data (i.e. put land NaN values back in their correct locations) 56 | def unshrink_arr(shrunk_array, shape, ocean_index): 57 | unshrunk_array = np.full(shape, np.nan) 58 | unshrunk_array[ocean_index] = np.squeeze(shrunk_array) 59 | return unshrunk_array 60 | 61 | 62 | def noskin_np( 63 | sst, t_zt, hum_zt, u_zu, v_zu, slp, algo, zt, zu, niter, input_range_check 64 | ): 65 | """Python wrapper for aerobulk without skin correction. 66 | !ATTENTION If input not provided in correct units, will crash. 67 | !ATTENTION Missing values taken from NaN values in sst field. No input variables may have NaN values that are not reflected in the sst. 68 | 69 | Parameters 70 | ---------- 71 | sst : numpy.array 72 | Bulk sea surface temperature [K] 73 | t_zt : numpy.array 74 | Absolute air temperature at height zt [K] 75 | hum_zt : numpy.array 76 | air humidity at zt, given as specific humidity [kg/kg] 77 | u_zu : numpy.array 78 | zonal wind speed at zu [m/s] 79 | v_zu : numpy.array 80 | meridional wind speed at zu [m/s] 81 | slp : numpy.array, optional 82 | mean sea-level pressure [Pa] ~101000 Pa, 83 | by default 101000.0 84 | algo : str 85 | Algorithm, can be one of: "coare3p0", "coare3p6", "ecmwf", "ncar", "andreas", 86 | zt : int 87 | height for temperature and spec. hum. of air [m], 88 | zu : int 89 | height for wind (10m = traditional anemometric height [m], 90 | niter : int 91 | Number of iteration steps used in the algorithm, 92 | 93 | Returns 94 | ------- 95 | ql : numpy.array 96 | Latent heat flux [W/m^2] 97 | qh : numpy.array 98 | Sensible heat flux [W/m^2] 99 | taux : numpy.array 100 | zonal wind stress [N/m^2] 101 | tauy : numpy.array 102 | meridional wind stress [N/m^2] 103 | evap : numpy.array 104 | evaporation [mm/s] aka [kg/m^2/s] (usually <0, as ocean loses water!) 105 | """ 106 | 107 | # Define the land mask from the native SST land mask 108 | ocean_index = np.where(~np.isnan(sst)) 109 | 110 | # Shrink the input data (i.e. remove all land points) 111 | args_shrunk = tuple( 112 | np.atleast_3d(a[ocean_index]) for a in (sst, t_zt, hum_zt, u_zu, v_zu, slp) 113 | ) 114 | 115 | if input_range_check: 116 | _check_value_range(*args_shrunk) 117 | 118 | out_data = aeronoskin.mod_aerobulk_wrapper_noskin.aerobulk_model_noskin( 119 | algo, zt, zu, *args_shrunk, niter 120 | ) 121 | 122 | return tuple(unshrink_arr(o, sst.shape, ocean_index) for o in out_data) 123 | 124 | 125 | def skin_np( 126 | sst, 127 | t_zt, 128 | hum_zt, 129 | u_zu, 130 | v_zu, 131 | slp, 132 | rad_sw, 133 | rad_lw, 134 | algo, 135 | zt, 136 | zu, 137 | niter, 138 | input_range_check, 139 | ): 140 | """Python wrapper for aerobulk with skin correction. 141 | !ATTENTION If input not provided in correct units, will crash. 142 | !ATTENTION Missing values taken from NaN values in sst field. No input variables may have NaN values that are not reflected in the sst. 143 | 144 | Parameters 145 | ---------- 146 | sst : numpy.array 147 | Bulk sea surface temperature [K] 148 | t_zt : numpy.array 149 | Absolute air temperature at height zt [K] 150 | hum_zt : numpy.array 151 | air humidity at zt, given as specific humidity [kg/kg] 152 | u_zu : numpy.array 153 | zonal wind speed at zu [m/s] 154 | v_zu : numpy.array 155 | meridional wind speed at zu [m/s] 156 | rad_sw : numpy.array 157 | downwelling shortwave radiation at the surface (>0) [W/m^2] 158 | rad_lw : numpy.array 159 | rad_lw : downwelling longwave radiation at the surface (>0) [W/m^2] 160 | slp : numpy.array, optional 161 | mean sea-level pressure [Pa] ~101000 Pa, 162 | by default 101000.0 163 | algo : str 164 | Algorithm, can be one of: "coare3p0", "coare3p6", "ecmwf", 165 | zt : int 166 | height for temperature and spec. hum. of air [m], 167 | zu : int 168 | height for wind (10m = traditional anemometric height [m], 169 | niter : int 170 | Number of iteration steps used in the algorithm, 171 | 172 | Returns 173 | ------- 174 | ql : numpy.array 175 | Latent heat flux [W/m^2] 176 | qh : numpy.array 177 | Sensible heat flux [W/m^2] 178 | taux : numpy.array 179 | zonal wind stress [N/m^2] 180 | tauy : numpy.array 181 | meridional wind stress [N/m^2] 182 | t_s : numpy.array 183 | skin temperature [K] (only when l_use_skin_schemes=TRUE) 184 | evap : numpy.array 185 | evaporation [mm/s] aka [kg/m^2/s] (usually <0, as ocean loses water!) 186 | """ 187 | # Define the land mask from the native SST land mask 188 | ocean_index = np.where(~np.isnan(sst)) 189 | 190 | # Shrink the input data (i.e. remove all land points) 191 | args_shrunk = tuple( 192 | np.atleast_3d(a[ocean_index]) 193 | for a in (sst, t_zt, hum_zt, u_zu, v_zu, slp, rad_sw, rad_lw) 194 | ) 195 | 196 | if input_range_check: 197 | _check_value_range(*args_shrunk) 198 | 199 | out_data = aeroskin.mod_aerobulk_wrapper_skin.aerobulk_model_skin( 200 | algo, zt, zu, *args_shrunk, niter 201 | ) 202 | 203 | return tuple(unshrink_arr(o, sst.shape, ocean_index) for o in out_data) 204 | 205 | 206 | def noskin( 207 | sst, 208 | t_zt, 209 | hum_zt, 210 | u_zu, 211 | v_zu, 212 | slp=101000.0, 213 | algo="coare3p0", 214 | zt=2, 215 | zu=10, 216 | niter=6, 217 | input_range_check=True, 218 | ): 219 | """xarray wrapper for aerobulk without skin correction. 220 | 221 | Warnings 222 | -------- 223 | !ATTENTION If input not provided in the units shown in [] below the code will crash. 224 | !ATTENTION Missing values taken from NaN values in sst field. No input variables may have NaN values that are not reflected in the sst. 225 | 226 | Parameters 227 | ---------- 228 | sst : xarray.DataArray 229 | Bulk sea surface temperature [K] 230 | t_zt : xarray.DataArray 231 | Absolute air temperature at height zt [K] 232 | hum_zt : xarray.DataArray 233 | air humidity at zt, given as specific humidity [kg/kg] 234 | u_zu : xarray.DataArray 235 | zonal wind speed at zu [m/s] 236 | v_zu : xarray.DataArray 237 | meridional wind speed at zu [m/s] 238 | slp : xarray.DataArray, optional 239 | mean sea-level pressure [Pa] ~101000 Pa, 240 | by default 101000.0 241 | algo : str, optional 242 | Algorithm, can be one of: "coare3p0", "coare3p6", "ecmwf", "ncar", "andreas", 243 | by default "coare3p0" 244 | zt : int, optional 245 | height for temperature and spec. hum. of air [m], 246 | by default 2 247 | zu : int, optional 248 | height for wind (10m = traditional anemometric height [m], 249 | by default 10 250 | niter : int, optional 251 | Number of iteration steps used in the algorithm, 252 | by default 6 253 | input_range_check: bool, optional 254 | Turn on/off explicit checking of input variables for valid ranges. 255 | On by default, but for best performance should be turned off 256 | 257 | Returns 258 | ------- 259 | ql : xarray.DataArray 260 | Latent heat flux [W/m^2] 261 | qh : xarray.DataArray 262 | Sensible heat flux [W/m^2] 263 | taux : xarray.DataArray 264 | zonal wind stress [N/m^2] 265 | tauy : xarray.DataArray 266 | meridional wind stress [N/m^2] 267 | evap : xarray.DataArray 268 | evaporation [mm/s] aka [kg/m^2/s] (usually <0, as ocean loses water!) 269 | """ 270 | _check_algo(algo, VALID_ALGOS) 271 | 272 | sst, t_zt, hum_zt, u_zu, v_zu, slp = xr.broadcast( 273 | sst, t_zt, hum_zt, u_zu, v_zu, slp 274 | ) 275 | if input_range_check: 276 | performance_msg = ( 277 | "Checking for misaligned nans and values outside of the valid range is performed by default, but reduces performance. \n" 278 | "If you are sure your data is valid you can deactivate these checks by setting `input_range_check=False`" 279 | ) 280 | warnings.warn(performance_msg) 281 | 282 | out_vars = xr.apply_ufunc( 283 | noskin_np, 284 | sst, 285 | t_zt, 286 | hum_zt, 287 | u_zu, 288 | v_zu, 289 | slp, 290 | input_core_dims=[()] * 6, 291 | output_core_dims=[()] * 5, 292 | dask="parallelized", 293 | kwargs=dict( 294 | algo=algo, zt=zt, zu=zu, niter=niter, input_range_check=input_range_check 295 | ), 296 | output_dtypes=[sst.dtype] 297 | * 5, # deactivates the 1 element check which aerobulk does not like 298 | ) 299 | 300 | if not isinstance(out_vars, tuple) or len(out_vars) != 5: 301 | raise TypeError("F2Py returned unexpected types") 302 | 303 | return out_vars 304 | 305 | 306 | def skin( 307 | sst, 308 | t_zt, 309 | hum_zt, 310 | u_zu, 311 | v_zu, 312 | rad_sw, 313 | rad_lw, 314 | slp=101000.0, 315 | algo="coare3p0", 316 | zt=2, 317 | zu=10, 318 | niter=6, 319 | input_range_check=True, 320 | ): 321 | """xarray wrapper for aerobulk with skin correction. 322 | 323 | Warnings 324 | -------- 325 | !ATTENTION If input not provided in the units shown in [] below the code will crash. 326 | !ATTENTION Missing values taken from NaN values in sst field. No input variables may have NaN values that are not reflected in the sst. 327 | 328 | Parameters 329 | ---------- 330 | sst : xr.DataArray 331 | Bulk sea surface temperature [K] 332 | t_zt : xr.DataArray 333 | Absolute air temperature at height zt [K] 334 | hum_zt : xr.DataArray 335 | air humidity at zt, given as specific humidity [kg/kg] 336 | u_zu : xr.DataArray 337 | zonal wind speed at zu [m/s] 338 | v_zu : xr.DataArray 339 | meridional wind speed at zu [m/s] 340 | rad_sw : xr.DataArray 341 | downwelling shortwave radiation at the surface (>0) [W/m^2] 342 | rad_lw : xr.DataArray 343 | rad_lw : downwelling longwave radiation at the surface (>0) [W/m^2] 344 | slp : xr.DataArray, optional 345 | mean sea-level pressure [Pa] ~101000 Pa, 346 | by default 101000.0 347 | algo : str, optional 348 | Algorithm, can be one of: "coare3p0", "coare3p6", "ecmwf", 349 | by default "coare3p0" 350 | zt : int, optional 351 | height for temperature and spec. hum. of air [m], 352 | by default 2 353 | zu : int, optional 354 | height for wind (10m = traditional anemometric height [m], 355 | by default 10 356 | niter : int, optional 357 | Number of iteration steps used in the algorithm, 358 | by default 6 359 | input_range_check: bool, optional 360 | Turn on/off explicit checking of input variables for valid ranges. 361 | On by default, but for best performance should be turned off 362 | 363 | Returns 364 | ------- 365 | ql : xr.DataArray 366 | Latent heat flux [W/m^2] 367 | qh : xr.DataArray 368 | Sensible heat flux [W/m^2] 369 | taux : xr.DataArray 370 | zonal wind stress [N/m^2] 371 | tauy : xr.DataArray 372 | meridional wind stress [N/m^2] 373 | t_s : xr.DataArray 374 | skin temperature [K] (only when l_use_skin_schemes=TRUE) 375 | evap : xr.DataArray 376 | evaporation [mm/s] aka [kg/m^2/s] (usually <0, as ocean loses water!) 377 | """ 378 | 379 | _check_algo(algo, VALID_ALGOS_SKIN) 380 | 381 | sst, t_zt, hum_zt, u_zu, v_zu, rad_sw, rad_lw, slp = xr.broadcast( 382 | sst, t_zt, hum_zt, u_zu, v_zu, rad_sw, rad_lw, slp 383 | ) 384 | 385 | if input_range_check: 386 | performance_msg = ( 387 | "Checking for misaligned nans and values outside of the valid range is performed by default, but reduces performance. \n" 388 | "If you are sure your data is valid you can deactivate these checks by setting `input_range_check=False`" 389 | ) 390 | warnings.warn(performance_msg) 391 | 392 | out_vars = xr.apply_ufunc( 393 | skin_np, 394 | sst, 395 | t_zt, 396 | hum_zt, 397 | u_zu, 398 | v_zu, 399 | rad_sw, 400 | rad_lw, 401 | slp, 402 | input_core_dims=[()] * 8, 403 | output_core_dims=[()] * 6, 404 | dask="parallelized", 405 | kwargs=dict( 406 | algo=algo, zt=zt, zu=zu, niter=niter, input_range_check=input_range_check 407 | ), 408 | output_dtypes=[sst.dtype] 409 | * 6, # deactivates the 1 element check which aerobulk does not like 410 | ) 411 | 412 | if not isinstance(out_vars, tuple) or len(out_vars) != 6: 413 | raise TypeError("F2Py returned unexpected types") 414 | 415 | return out_vars 416 | -------------------------------------------------------------------------------- /source/fortran/.f2py_f2cmap: -------------------------------------------------------------------------------- 1 | {"real": {"wp": "double", "dp": "double", "sp": "float"}} 2 | -------------------------------------------------------------------------------- /source/fortran/mod_aerobulk_wrap_noskin.f90: -------------------------------------------------------------------------------- 1 | MODULE mod_aerobulk_wrapper_noskin 2 | 3 | USE mod_const 4 | USE mod_aerobulk, ONLY: AEROBULK_INIT, AEROBULK_BYE 5 | USE mod_aerobulk_compute 6 | 7 | IMPLICIT NONE 8 | 9 | PRIVATE 10 | 11 | PUBLIC :: AEROBULK_MODEL_NOSKIN 12 | 13 | CONTAINS 14 | 15 | SUBROUTINE AEROBULK_MODEL_NOSKIN( Ni, Nj, Nt, & 16 | & calgo, zt, zu, sst, t_zt, & 17 | & hum_zt, U_zu, V_zu, slp, & 18 | & Niter, & 19 | & QL, QH, Tau_x, Tau_y, Evap) 20 | 21 | INTEGER, INTENT(in) :: Ni, Nj, Nt 22 | CHARACTER(len=*), INTENT(in) :: calgo 23 | REAL(8), INTENT(in) :: zt, zu 24 | REAL(8), DIMENSION(Ni,Nj,Nt), INTENT(in) :: sst, t_zt, hum_zt, U_zu, V_zu, slp 25 | INTEGER, INTENT(in) :: Niter 26 | REAL(8), DIMENSION(Ni,Nj,Nt), INTENT(out) :: QL, QH, Tau_x, Tau_y, Evap 27 | 28 | nb_iter = Niter 29 | !! All the variables that are set in INIT 30 | ctype_humidity = 'sh' 31 | l_use_skin_schemes = .FALSE. 32 | 33 | CALL AEROBULK_COMPUTE(1, calgo, zt, zu, sst(:, :, 1), t_zt(:, :, 1), & 34 | & hum_zt(:, :, 1), U_zu(:, :, 1), V_zu(:, :, 1), slp(:, :, 1), & 35 | & QL(:, :, 1), QH(:, :, 1), Tau_x(:, :, 1), Tau_y(:, :, 1), & 36 | & Evp=Evap(:, :, 1)) 37 | 38 | END SUBROUTINE AEROBULK_MODEL_NOSKIN 39 | 40 | END MODULE mod_aerobulk_wrapper_noskin 41 | -------------------------------------------------------------------------------- /source/fortran/mod_aerobulk_wrap_noskin.pyf: -------------------------------------------------------------------------------- 1 | ! -*- f90 -*- 2 | ! Note: the context of this file is case sensitive. 3 | 4 | python module mod_aerobulk_wrap_noskin ! in 5 | interface ! in :mod_aerobulk_wrap_noskin 6 | module mod_aerobulk_wrapper_noskin ! in :mod_aerobulk_wrap_noskin:mod_aerobulk_wrap_noskin.f90 7 | use mod_const 8 | use mod_aerobulk, only: aerobulk_init,aerobulk_bye 9 | use mod_aerobulk_compute 10 | subroutine aerobulk_model_noskin(ni,nj,nt,calgo,zt,zu,sst,t_zt,hum_zt,u_zu,v_zu,slp,niter,ql,qh,tau_x,tau_y,evap) ! in :mod_aerobulk_wrap_noskin:mod_aerobulk_wrap_noskin.f90:mod_aerobulk_wrapper_noskin 11 | threadsafe 12 | integer, optional,intent(in),check(shape(sst, 0) == ni),depend(sst) :: ni=shape(sst, 0) 13 | integer, optional,intent(in),check(shape(sst, 1) == nj),depend(sst) :: nj=shape(sst, 1) 14 | integer, optional,intent(in),check(shape(sst, 2) == nt),depend(sst) :: nt=shape(sst, 2) 15 | character*(*) intent(in) :: calgo 16 | real(kind=8) intent(in) :: zt 17 | real(kind=8) intent(in) :: zu 18 | real(kind=8) dimension(ni,nj,nt),intent(in) :: sst 19 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: t_zt 20 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: hum_zt 21 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: u_zu 22 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: v_zu 23 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: slp 24 | integer intent(in) :: niter 25 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: ql 26 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: qh 27 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: tau_x 28 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: tau_y 29 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: evap 30 | end subroutine aerobulk_model_noskin 31 | end module mod_aerobulk_wrapper_noskin 32 | end interface 33 | end python module mod_aerobulk_wrap_noskin 34 | 35 | ! This file was auto-generated with f2py (version:1.22.3). 36 | ! See: 37 | ! https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e 38 | -------------------------------------------------------------------------------- /source/fortran/mod_aerobulk_wrap_skin.f90: -------------------------------------------------------------------------------- 1 | MODULE mod_aerobulk_wrapper_skin 2 | 3 | USE mod_const 4 | USE mod_aerobulk, ONLY: AEROBULK_INIT, AEROBULK_BYE 5 | USE mod_aerobulk_compute 6 | 7 | IMPLICIT NONE 8 | 9 | PRIVATE 10 | 11 | PUBLIC :: AEROBULK_MODEL_SKIN 12 | 13 | CONTAINS 14 | SUBROUTINE AEROBULK_MODEL_SKIN( Ni, Nj, Nt, & 15 | & calgo, zt, zu, sst, t_zt, & 16 | & hum_zt, U_zu, V_zu, slp, & 17 | & rad_sw, rad_lw, & 18 | & Niter, & 19 | & QL, QH, Tau_x, Tau_y, T_s, Evap) 20 | 21 | INTEGER, INTENT(in) :: Ni, Nj, Nt 22 | CHARACTER(len=*), INTENT(in) :: calgo 23 | REAL(8), INTENT(in) :: zt, zu 24 | REAL(8), DIMENSION(Ni,Nj,Nt), INTENT(in) :: sst, t_zt, hum_zt, U_zu, V_zu, slp, rad_sw, rad_lw 25 | INTEGER, INTENT(in) :: Niter 26 | REAL(8), DIMENSION(Ni,Nj,Nt), INTENT(out) :: QL, QH, Tau_x, Tau_y, T_s, Evap 27 | 28 | nb_iter = Niter 29 | !! All the variables that are set in INIT 30 | ctype_humidity = 'sh' 31 | l_use_skin_schemes = .TRUE. 32 | 33 | CALL AEROBULK_COMPUTE(1, calgo, zt, zu, sst(:, :, 1), t_zt(:, :, 1), & 34 | & hum_zt(:, :, 1), U_zu(:, :, 1), V_zu(:, :, 1), slp(:, :, 1), & 35 | & QL(:, :, 1), QH(:, :, 1), Tau_x(:, :, 1), Tau_y(:, :, 1), & 36 | & rad_sw=rad_sw(:, :, 1), rad_lw=rad_lw(:, :, 1), T_s=T_s(:, :, 1), Evp=Evap(:, :, 1)) 37 | 38 | END SUBROUTINE AEROBULK_MODEL_SKIN 39 | 40 | END MODULE mod_aerobulk_wrapper_skin 41 | -------------------------------------------------------------------------------- /source/fortran/mod_aerobulk_wrap_skin.pyf: -------------------------------------------------------------------------------- 1 | ! -*- f90 -*- 2 | ! Note: the context of this file is case sensitive. 3 | 4 | python module mod_aerobulk_wrap_skin ! in 5 | interface ! in :mod_aerobulk_wrap_skin 6 | module mod_aerobulk_wrapper_skin ! in :mod_aerobulk_wrap_skin:mod_aerobulk_wrap_skin.f90 7 | use mod_const 8 | use mod_aerobulk, only: aerobulk_init,aerobulk_bye 9 | use mod_aerobulk_compute 10 | subroutine aerobulk_model_skin(ni,nj,nt,calgo,zt,zu,sst,t_zt,hum_zt,u_zu,v_zu,slp,rad_sw,rad_lw,niter,ql,qh,tau_x,tau_y,t_s,evap) ! in :mod_aerobulk_wrap_skin:mod_aerobulk_wrap_skin.f90:mod_aerobulk_wrapper_skin 11 | 12 | integer, optional,intent(in),check(shape(sst, 0) == ni),depend(sst) :: ni=shape(sst, 0) 13 | integer, optional,intent(in),check(shape(sst, 1) == nj),depend(sst) :: nj=shape(sst, 1) 14 | integer, optional,intent(in),check(shape(sst, 2) == nt),depend(sst) :: nt=shape(sst, 2) 15 | character*(*) intent(in) :: calgo 16 | real(kind=8) intent(in) :: zt 17 | real(kind=8) intent(in) :: zu 18 | real(kind=8) dimension(ni,nj,nt),intent(in) :: sst 19 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: t_zt 20 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: hum_zt 21 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: u_zu 22 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: v_zu 23 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: slp 24 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: rad_sw 25 | real(kind=8) dimension(ni,nj,nt),intent(in),depend(nj,ni,nt) :: rad_lw 26 | integer intent(in) :: niter 27 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: ql 28 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: qh 29 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: tau_x 30 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: tau_y 31 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: t_s 32 | real(kind=8) dimension(ni,nj,nt),intent(out),depend(nj,ni,nt) :: evap 33 | end subroutine aerobulk_model_skin 34 | end module mod_aerobulk_wrapper_skin 35 | end interface 36 | end python module mod_aerobulk_wrap_skin 37 | 38 | ! This file was auto-generated with f2py (version:1.22.3). 39 | ! See: 40 | ! https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e 41 | -------------------------------------------------------------------------------- /tests/create_test_data.py: -------------------------------------------------------------------------------- 1 | from typing import Dict, Tuple 2 | 3 | import numpy as np 4 | import xarray as xr 5 | from numpy.random import default_rng 6 | 7 | 8 | def create_data( 9 | shape: Tuple[int, ...], 10 | chunks: Dict[str, int] = {}, 11 | skin_correction: bool = False, 12 | order: str = "F", 13 | use_xr=True, 14 | land_mask=False, 15 | sst=290.0, 16 | t_zt=280.0, 17 | hum_zt=0.001, 18 | u_zu=1.0, 19 | v_zu=-1.0, 20 | slp=101000.0, 21 | rad_sw=0.000001, 22 | rad_lw=350.0, 23 | ): 24 | size = shape[0] * shape[1] 25 | shape2d = (shape[0], shape[1]) 26 | rng = default_rng() 27 | indices = rng.choice(size, size=int(size * 0.3), replace=False) 28 | multi_indices = np.unravel_index(indices, shape2d) 29 | 30 | def _arr(value, chunks, order): 31 | arr = np.full(shape, value, order=order) 32 | # adds random noise scaled by a percentage of the value 33 | randomize_factor = 0.001 34 | randomize_range = value * randomize_factor 35 | noise = np.random.rand(*shape) * randomize_range 36 | arr = arr + noise 37 | 38 | if land_mask: 39 | arr[multi_indices[0], multi_indices[1], :] = ( 40 | np.nan 41 | ) # add NaNs to mimic land mask 42 | if use_xr: 43 | arr = xr.DataArray(arr) 44 | if chunks: 45 | arr = arr.chunk(chunks) 46 | return arr 47 | 48 | if skin_correction: 49 | return tuple( 50 | _arr(a, chunks, order) 51 | for a in ( 52 | sst, 53 | t_zt, 54 | hum_zt, 55 | u_zu, 56 | v_zu, 57 | slp, 58 | rad_sw, 59 | rad_lw, 60 | ) 61 | ) 62 | else: 63 | return tuple( 64 | _arr(a, chunks, order) for a in (sst, t_zt, hum_zt, u_zu, v_zu, slp) 65 | ) 66 | -------------------------------------------------------------------------------- /tests/test_flux_np.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pytest 3 | from aerobulk.flux import noskin_np, skin_np 4 | from create_test_data import create_data 5 | 6 | """Tests for the numpy land_mask wrapper""" 7 | 8 | 9 | @pytest.mark.parametrize( 10 | "algo, skin_correction", 11 | [ 12 | ("ecmwf", True), 13 | ("ecmwf", False), 14 | ("coare3p0", True), 15 | ("coare3p0", False), 16 | ("coare3p6", True), 17 | ("coare3p6", False), 18 | ("andreas", False), 19 | ("ncar", False), 20 | ], 21 | ) 22 | def test_land_mask(skin_correction, algo): 23 | shape = (2, 3, 4) 24 | size = shape[0] * shape[1] * shape[2] 25 | 26 | if skin_correction: 27 | func = skin_np 28 | else: 29 | func = noskin_np 30 | 31 | args = create_data( 32 | shape, 33 | chunks=False, 34 | skin_correction=skin_correction, 35 | use_xr=False, 36 | land_mask=True, 37 | ) 38 | out_data = func(*args, algo, 2, 10, 6, input_range_check=True) 39 | 40 | # Check the location of all NaNs is correct 41 | for o in out_data: 42 | np.testing.assert_allclose(np.isnan(args[0]), np.isnan(o)) 43 | 44 | # Check that values of the unshrunk array are correct 45 | for i in range(size): 46 | index = np.unravel_index(i, shape) 47 | if not np.isnan(out_data[0][index]): 48 | single_inputs = tuple(np.atleast_3d(i[index]) for i in args) 49 | 50 | single_outputs = func( 51 | *single_inputs, algo, 2, 10, 6, input_range_check=True 52 | ) 53 | for so, o in zip(single_outputs, out_data): 54 | assert so == o[index] 55 | 56 | 57 | @pytest.mark.parametrize( 58 | "varname", 59 | [ 60 | "t_zt", 61 | "hum_zt", 62 | "u_zu", 63 | "v_zu", 64 | "slp", 65 | "rad_sw", 66 | "rad_lw", 67 | ], 68 | ) 69 | class Test_Range_Check: 70 | def test_range_check_nan(self, varname): 71 | shape = (1, 1) 72 | args_noskin = create_data(shape, skin_correction=False, **{varname: np.nan}) 73 | args_skin = create_data(shape, skin_correction=True, **{varname: np.nan}) 74 | msg = f"Found nans in {varname} that do not align with nans in `sst`. Check that nans in all fields are matched." 75 | 76 | if varname not in ["rad_sw", "rad_lw"]: # Test these only for skin 77 | with pytest.raises(ValueError, match=msg): 78 | noskin_np(*args_noskin, "ecmwf", 2, 10, 6, input_range_check=True) 79 | 80 | with pytest.raises(ValueError, match=msg): 81 | skin_np(*args_skin, "ecmwf", 2, 10, 6, input_range_check=True) 82 | 83 | def test_range_check_invalid(self, varname): 84 | invalid_values = { 85 | "sst": 200.0, 86 | "t_zt": 100, 87 | "hum_zt": 0.1, 88 | "u_zu": 60, 89 | "v_zu": 60, 90 | "slp": 2000, 91 | "rad_sw": -20, 92 | "rad_lw": 4000, 93 | } 94 | shape = (1, 1) 95 | args_noskin = create_data( 96 | shape, skin_correction=False, **{varname: invalid_values[varname]} 97 | ) 98 | args_skin = create_data( 99 | shape, skin_correction=True, **{varname: invalid_values[varname]} 100 | ) 101 | # I hate regex sooo much. If someone has a nice way to just test that the varname is in here and the error message contains 'range' 102 | # that would be amazing. This is the best I could do... 103 | msg = r"\b(?:out of the valid range)\b" 104 | if varname not in ["rad_sw", "rad_lw"]: 105 | with pytest.raises(ValueError, match=str(msg)): 106 | noskin_np(*args_noskin, "ecmwf", 2, 10, 6, input_range_check=True) 107 | 108 | with pytest.raises(ValueError, match=str(msg)): 109 | skin_np(*args_skin, "ecmwf", 2, 10, 6, input_range_check=True) 110 | -------------------------------------------------------------------------------- /tests/test_flux_xr.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import xarray as xr 3 | from aerobulk import noskin, skin 4 | from create_test_data import create_data 5 | 6 | """Tests for the xarray wrapper""" 7 | 8 | 9 | @pytest.mark.parametrize("algo", ["wrong"]) 10 | def test_algo_error_noskin(algo): 11 | shape = (1, 1, 1) 12 | args = create_data(shape, chunks=False, skin_correction=False) 13 | with pytest.raises(ValueError): 14 | noskin(*args, algo=algo) 15 | 16 | 17 | @pytest.mark.parametrize("algo", ["wrong", "ncar"]) 18 | def test_algo_error_skin(algo): 19 | shape = (1, 1, 1) 20 | args = create_data(shape, chunks=False, skin_correction=True) 21 | with pytest.raises(ValueError): 22 | skin(*args, algo=algo) 23 | 24 | 25 | @pytest.mark.parametrize( 26 | "chunks", [{"dim_2": -1}, {"dim_0": 10}, {"dim_0": 1}, {"dim_2": 8}, {"dim_2": 1}] 27 | ) 28 | @pytest.mark.parametrize("skin_correction", [False, True]) 29 | class Test_xarray: 30 | def test_chunked(self, chunks, skin_correction): 31 | shape = (10, 13, 12) 32 | args = create_data(shape, chunks=chunks, skin_correction=skin_correction) 33 | if skin_correction: 34 | func = skin 35 | else: 36 | func = noskin 37 | out_vars_chunked = func(*args) 38 | out_vars_nochunks = func(*(a.load() for a in args)) 39 | 40 | for out_chunk, out_nochunk in zip(out_vars_chunked, out_vars_nochunks): 41 | assert out_chunk.shape == shape 42 | xr.testing.assert_allclose(out_chunk, out_nochunk) 43 | 44 | @pytest.mark.parametrize("order", ["F", "C"]) 45 | def test_transpose_invariance_noskin(self, chunks, skin_correction, order): 46 | shape = (10, 13, 12) 47 | chunks = {"dim_0": 10} 48 | args = create_data( 49 | shape, chunks=chunks, skin_correction=skin_correction, order=order 50 | ) 51 | if skin_correction: 52 | func = skin 53 | else: 54 | func = noskin 55 | 56 | out_vars_012 = func(*(a.transpose("dim_0", "dim_1", "dim_2") for a in args)) 57 | out_vars_210 = func(*(a.transpose("dim_2", "dim_1", "dim_0") for a in args)) 58 | out_vars_120 = func(*(a.transpose("dim_1", "dim_2", "dim_0") for a in args)) 59 | 60 | for i, ii, iii in zip(out_vars_012, out_vars_120, out_vars_210): 61 | xr.testing.assert_allclose( 62 | i.transpose("dim_0", "dim_1", "dim_2"), 63 | ii.transpose("dim_0", "dim_1", "dim_2"), 64 | ) 65 | xr.testing.assert_allclose( 66 | ii.transpose("dim_0", "dim_1", "dim_2"), 67 | iii.transpose("dim_0", "dim_1", "dim_2"), 68 | ) 69 | 70 | 71 | @pytest.mark.parametrize("skin_correction", [True, False]) 72 | def test_all_input_array_sizes_valid(skin_correction): 73 | shapes = ( 74 | (3, 4), 75 | (2, 3, 4), 76 | (2, 3, 4, 5), 77 | ) # create_data() only allows for inputs of 2 or more dimensions 78 | data = (create_data(s, skin_correction=skin_correction) for s in shapes) 79 | if skin_correction: 80 | func = skin 81 | else: 82 | func = noskin 83 | tuple(func(*d, "coare3p0", 2, 10, 6) for d in data) 84 | assert ( 85 | 1 == 1 86 | ) # This line is always true, but verifies that the above line doesn't crash the Fortran code 87 | -------------------------------------------------------------------------------- /tests/test_fortran.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pytest 3 | from aerobulk.flux import noskin_np, skin_np 4 | 5 | 6 | @pytest.mark.parametrize( 7 | "algo, expected", 8 | [ 9 | ( 10 | "andreas", 11 | { 12 | "ql": -74.4637756, 13 | "qh": -14.4129944, 14 | "evap": -2.62721038, 15 | "taux": 3.02770734e-02, 16 | "tauy": 0.0, 17 | }, 18 | ), 19 | ( 20 | "ncar", 21 | { 22 | "ql": -88.4781876, 23 | "qh": -16.6969528, 24 | "evap": -3.12166286, 25 | "taux": 3.58519591e-02, 26 | "tauy": 0.0, 27 | }, 28 | ), 29 | ], 30 | ) 31 | def test_fortran_noskin(algo, expected): 32 | # Compare python wrapper output with fortran results from (...) 33 | 34 | # inputs are the same for all test cases in the fortran example 35 | rt0 = np.atleast_3d(273.15) # conversion to Kelvin 36 | sst = np.atleast_3d(rt0 + 22) # in Kelvin 37 | t_zt = np.atleast_3d(rt0 + 20) 38 | hum_zt = np.atleast_3d(0.012) # kg/kg 39 | u_zu = np.atleast_3d(5) # m/s 40 | v_zu = np.atleast_3d(0) 41 | slp = np.atleast_3d(101000.0) # Pa 42 | ql, qh, taux, tauy, evap = noskin_np( 43 | sst, 44 | t_zt, 45 | hum_zt, 46 | u_zu, 47 | v_zu, 48 | slp, 49 | algo=algo, 50 | zt=2, 51 | zu=10, 52 | niter=10, 53 | input_range_check=True, 54 | ) 55 | evap = evap * 3600 * 24 # convert to mm/day 56 | 57 | np.testing.assert_allclose(ql, expected["ql"]) 58 | np.testing.assert_allclose(qh, expected["qh"]) 59 | np.testing.assert_allclose(evap, expected["evap"]) 60 | np.testing.assert_allclose(taux, expected["taux"]) 61 | np.testing.assert_allclose(tauy, expected["tauy"]) 62 | 63 | 64 | @pytest.mark.parametrize( 65 | "algo, expected", 66 | [ 67 | ( 68 | "coare3p0", 69 | { 70 | "ql": -81.3846741, 71 | "qh": -15.1545086, 72 | "evap": -2.87061906, 73 | "taux": 3.57834995e-02, 74 | "tauy": 0.0, 75 | "t_s": 21.7219677, 76 | }, 77 | ), 78 | ( 79 | "coare3p6", 80 | { 81 | "ql": -83.0788422, 82 | "qh": -15.3865499, 83 | "evap": -2.93033028, 84 | "taux": 3.21817845e-02, 85 | "tauy": 0.0, 86 | "t_s": 21.7057972, 87 | }, 88 | ), 89 | ( 90 | "ecmwf", 91 | { 92 | "ql": -80.2958984, 93 | "qh": -14.3822346, 94 | "evap": -2.83224440, 95 | "taux": 3.84389125e-02, 96 | "tauy": 0.0, 97 | "t_s": 21.7325401, 98 | }, 99 | ), 100 | ], 101 | ) 102 | def test_fortran_skin(algo, expected): 103 | # Compare python wrapper output with fortran results from (...) 104 | 105 | # inputs are the same for all test cases in the fortran example 106 | rt0 = np.atleast_3d(273.15) # conversion to Kelvin 107 | sst = np.atleast_3d(rt0 + 22) # in Kelvin 108 | t_zt = np.atleast_3d(rt0 + 20) 109 | hum_zt = np.atleast_3d(0.012) # kg/kg 110 | u_zu = np.atleast_3d(5) # m/s 111 | v_zu = np.atleast_3d(0) 112 | slp = np.atleast_3d(101000.0) # Pa 113 | rad_sw = np.atleast_3d(0) 114 | rad_lw = np.atleast_3d(350) 115 | ql, qh, taux, tauy, t_s, evap = skin_np( 116 | sst, 117 | t_zt, 118 | hum_zt, 119 | u_zu, 120 | v_zu, 121 | slp, 122 | rad_sw, 123 | rad_lw, 124 | algo=algo, 125 | zt=2, 126 | zu=10, 127 | niter=10, 128 | input_range_check=True, 129 | ) 130 | evap = evap * 3600 * 24 # convert to mm/day 131 | t_s = t_s - rt0 132 | 133 | np.testing.assert_allclose(ql, expected["ql"]) 134 | np.testing.assert_allclose(qh, expected["qh"]) 135 | np.testing.assert_allclose(evap, expected["evap"]) 136 | np.testing.assert_allclose(taux, expected["taux"]) 137 | np.testing.assert_allclose(tauy, expected["tauy"]) 138 | np.testing.assert_allclose(t_s, expected["t_s"]) 139 | --------------------------------------------------------------------------------