├── .github └── workflows │ └── ci.yaml ├── .gitignore ├── .stickler.yml ├── .travis.yml ├── AUTHORS.md ├── LICENSE.txt ├── MANIFEST.in ├── README.rst ├── continuous_integration └── environment.yaml ├── doc ├── Makefile └── source │ ├── algorithms.rst │ ├── conf.py │ ├── filters.rst │ ├── fogcolbar.png │ ├── fogpy_docu_example_1.png │ ├── fogpy_docu_example_10.png │ ├── fogpy_docu_example_11.png │ ├── fogpy_docu_example_12.png │ ├── fogpy_docu_example_13.png │ ├── fogpy_docu_example_14.png │ ├── fogpy_docu_example_15.png │ ├── fogpy_docu_example_16.png │ ├── fogpy_docu_example_17.png │ ├── fogpy_docu_example_2.png │ ├── fogpy_docu_example_3.png │ ├── fogpy_docu_example_4.png │ ├── fogpy_docu_example_5.png │ ├── fogpy_docu_example_6.png │ ├── fogpy_docu_example_7.png │ ├── fogpy_docu_example_8.png │ ├── fogpy_docu_example_9.png │ ├── fogpy_docu_nexample_1.png │ ├── fogpy_docu_nexample_2.png │ ├── fogpy_docu_nexample_3.png │ ├── fogpy_docu_nexample_4.png │ ├── fogpy_fls_algo.png │ ├── fogpy_logo.png │ ├── index.rst │ ├── install.rst │ ├── lowcloud.rst │ └── quickstart.rst ├── fogpy ├── __init__.py ├── algorithms.py ├── composites.py ├── data │ └── DEM │ │ └── .gitignore ├── etc │ ├── composites │ │ ├── abi.yaml │ │ └── seviri.yaml │ ├── elevation_1km.npy │ ├── fog_testdata.npy │ ├── fog_testdata2.npy │ ├── fog_testdata_fogmask.npy │ ├── fog_testdata_hrv.npy │ ├── fog_testdata_night.npy │ ├── fog_testdata_night2.npy │ ├── fog_testdata_pre.npy │ ├── result_20131112.bufr │ ├── result_20131112_metar.bufr │ ├── result_20131112_swis.bufr │ ├── result_20140827.bufr │ └── testarea.txt ├── filters.py ├── lowwatercloud.py ├── test │ ├── __init__.py │ ├── conftest.py │ ├── test_algorithms.py │ ├── test_composites.py │ ├── test_filters.py │ ├── test_lowwatercloud.py │ └── test_utils.py └── utils │ ├── __init__.py │ ├── add_synop.py │ ├── export_synop.py │ ├── import_synop.py │ └── reproj_testdata.py ├── setup.cfg └── setup.py /.github/workflows/ci.yaml: -------------------------------------------------------------------------------- 1 | name: Python package 2 | 3 | on: 4 | - push 5 | - pull_request 6 | 7 | 8 | jobs: 9 | build: 10 | 11 | runs-on: ubuntu-latest 12 | strategy: 13 | matrix: 14 | python-version: [3.8, 3.9] 15 | steps: 16 | - uses: actions/checkout@v2 17 | - name: Setup Conda Environment 18 | uses: conda-incubator/setup-miniconda@v2 19 | with: 20 | miniconda-version: "latest" 21 | python-version: ${{ matrix.python-version }} 22 | mamba-version: "*" 23 | channels: conda-forge,defaults 24 | environment-file: continuous_integration/environment.yaml 25 | activate-environment: test-environment 26 | - name: Install fogpy 27 | shell: bash -l {0} 28 | run: | 29 | pip install --no-deps -e . 30 | - name: Install test dependencies 31 | shell: bash -l {0} 32 | run: | 33 | pip install pytest pytest-cov 34 | - name: Test with pytest 35 | shell: bash -l {0} 36 | run: | 37 | pytest --cov=fogpy fogpy/test --cov-report=xml 38 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | build 2 | doc/build 3 | *.so 4 | *.pyc 5 | *~ 6 | RELEASE-VERSION 7 | dist 8 | fogpy.egg-info 9 | fogpy/version.py 10 | -------------------------------------------------------------------------------- /.stickler.yml: -------------------------------------------------------------------------------- 1 | linters: 2 | flake8: 3 | python: 3 4 | config: setup.cfg 5 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | dist: xenial 2 | language: python 3 | python: 4 | - '3.7' 5 | env: 6 | global: 7 | - PYTHON_VERSION=$TRAVIS_PYTHON_VERSION 8 | - MAIN_CMD='pytest' 9 | - CONDA_DEPENDENCIES='numpy scipy matplotlib satpy pyresample opencv coverage appdirs requests' 10 | - PIP_DEPENDENCIES='trollimage pyorbital trollbufr opencv-contrib-python' 11 | - SETUP_XVFB=False 12 | - EVENT_TYPE='push pull_request' 13 | - SETUP_CMD='--cov=fogpy fogpy/test' 14 | - CONDA_CHANNELS='conda-forge' 15 | - CONDA_CHANNEL_PRIORITY='True' 16 | install: 17 | - git clone --depth 1 git://github.com/astropy/ci-helpers.git 18 | - source ci-helpers/travis/setup_conda.sh 19 | script: 20 | - travis_wait 45 $MAIN_CMD $SETUP_CMD 21 | after_success: 22 | - coveralls 23 | - codecov 24 | -------------------------------------------------------------------------------- /AUTHORS.md: -------------------------------------------------------------------------------- 1 | # Project Contributors 2 | 3 | The following people have made contributions to this project: 4 | 5 | 6 | 7 | 8 | 9 | 10 | - [Gerrit Holl (gerritholl)](https://github.com/gerritholl) 11 | - 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include LICENSE.txt 2 | include README.rst 3 | include MANIFEST.in 4 | include etc/* 5 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | Fogpy 2 | ===== 3 | 4 | .. image:: https://api.travis-ci.com/pytroll/fogpy.svg?branch=master 5 | :target: https://travis-ci.org/pytroll/fogpy 6 | 7 | .. image:: https://coveralls.io/repos/github/pytroll/fogpy/badge.svg?branch=master 8 | :target: https://coveralls.io/github/pytroll/fogpy?branch=master 9 | 10 | This is a package for satellite based detection and nowcasting of fog and low stratus clouds (FogPy). 11 | 12 | The documentation is available at http://fogpy.readthedocs.io/en/latest 13 | 14 | The implementation is based on the following publications: 15 | 16 | * Cermak, J., & Bendix, J. (2011). Detecting ground fog from space–a microphysics-based approach. International Journal of Remote Sensing, 32(12), 3345-3371. doi:10.1016/j.atmosres.2007.11.009 17 | * Cermak, J., & Bendix, J. (2007). Dynamical nighttime fog/low stratus detection based on Meteosat SEVIRI data: A feasibility study. Pure and applied Geophysics, 164(6-7), 1179-1192. doi:10.1007/s00024-007-0213-8 18 | * Cermak, J., & Bendix, J. (2008). A novel approach to fog/low stratus detection using Meteosat 8 data. Atmospheric Research, 87(3-4), 279-292. doi:10.1016/j.atmosres.2007.11.009 19 | * Cermak, J. (2006). SOFOS-a new satellite-based operational fog observation scheme. (PhD thesis), Philipps-Universität Marburg, Marburg, Germany. doi:doi.org/10.17192/z2006.0149 20 | -------------------------------------------------------------------------------- /continuous_integration/environment.yaml: -------------------------------------------------------------------------------- 1 | name: test-environment 2 | channels: 3 | - conda-forge 4 | dependencies: 5 | - numpy 6 | - scipy 7 | - matplotlib 8 | - satpy 9 | - pyresample 10 | - opencv 11 | - coverage 12 | - appdirs 13 | - requests 14 | - pip: 15 | - flake8 16 | - pytest 17 | - trollimage 18 | - pyorbital 19 | - trollbufr 20 | - opencv-contrib-python 21 | -------------------------------------------------------------------------------- /doc/Makefile: -------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | PAPER = 8 | BUILDDIR = build 9 | 10 | # Internal variables. 11 | PAPEROPT_a4 = -D latex_paper_size=a4 12 | PAPEROPT_letter = -D latex_paper_size=letter 13 | ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 14 | # the i18n builder cannot share the environment and doctrees with the others 15 | I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 16 | 17 | .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext 18 | 19 | help: 20 | @echo "Please use \`make ' where is one of" 21 | @echo " html to make standalone HTML files" 22 | @echo " dirhtml to make HTML files named index.html in directories" 23 | @echo " singlehtml to make a single large HTML file" 24 | @echo " pickle to make pickle files" 25 | @echo " json to make JSON files" 26 | @echo " htmlhelp to make HTML files and a HTML help project" 27 | @echo " qthelp to make HTML files and a qthelp project" 28 | @echo " devhelp to make HTML files and a Devhelp project" 29 | @echo " epub to make an epub" 30 | @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" 31 | @echo " latexpdf to make LaTeX files and run them through pdflatex" 32 | @echo " text to make text files" 33 | @echo " man to make manual pages" 34 | @echo " texinfo to make Texinfo files" 35 | @echo " info to make Texinfo files and run them through makeinfo" 36 | @echo " gettext to make PO message catalogs" 37 | @echo " changes to make an overview of all changed/added/deprecated items" 38 | @echo " linkcheck to check all external links for integrity" 39 | @echo " doctest to run all doctests embedded in the documentation (if enabled)" 40 | 41 | clean: 42 | -rm -rf $(BUILDDIR)/* 43 | 44 | html: 45 | $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html 46 | @echo 47 | @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." 48 | 49 | dirhtml: 50 | $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml 51 | @echo 52 | @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." 53 | 54 | singlehtml: 55 | $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml 56 | @echo 57 | @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." 58 | 59 | pickle: 60 | $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle 61 | @echo 62 | @echo "Build finished; now you can process the pickle files." 63 | 64 | json: 65 | $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json 66 | @echo 67 | @echo "Build finished; now you can process the JSON files." 68 | 69 | htmlhelp: 70 | $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp 71 | @echo 72 | @echo "Build finished; now you can run HTML Help Workshop with the" \ 73 | ".hhp project file in $(BUILDDIR)/htmlhelp." 74 | 75 | qthelp: 76 | $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp 77 | @echo 78 | @echo "Build finished; now you can run "qcollectiongenerator" with the" \ 79 | ".qhcp project file in $(BUILDDIR)/qthelp, like this:" 80 | @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/fogpy.qhcp" 81 | @echo "To view the help file:" 82 | @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/fogpy.qhc" 83 | 84 | devhelp: 85 | $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp 86 | @echo 87 | @echo "Build finished." 88 | @echo "To view the help file:" 89 | @echo "# mkdir -p $$HOME/.local/share/devhelp/fogpy" 90 | @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/fogpy" 91 | @echo "# devhelp" 92 | 93 | epub: 94 | $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub 95 | @echo 96 | @echo "Build finished. The epub file is in $(BUILDDIR)/epub." 97 | 98 | latex: 99 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 100 | @echo 101 | @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." 102 | @echo "Run \`make' in that directory to run these through (pdf)latex" \ 103 | "(use \`make latexpdf' here to do that automatically)." 104 | 105 | latexpdf: 106 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 107 | @echo "Running LaTeX files through pdflatex..." 108 | $(MAKE) -C $(BUILDDIR)/latex all-pdf 109 | @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." 110 | 111 | text: 112 | $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text 113 | @echo 114 | @echo "Build finished. The text files are in $(BUILDDIR)/text." 115 | 116 | man: 117 | $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man 118 | @echo 119 | @echo "Build finished. The manual pages are in $(BUILDDIR)/man." 120 | 121 | texinfo: 122 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 123 | @echo 124 | @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." 125 | @echo "Run \`make' in that directory to run these through makeinfo" \ 126 | "(use \`make info' here to do that automatically)." 127 | 128 | info: 129 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 130 | @echo "Running Texinfo files through makeinfo..." 131 | make -C $(BUILDDIR)/texinfo info 132 | @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." 133 | 134 | gettext: 135 | $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale 136 | @echo 137 | @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." 138 | 139 | changes: 140 | $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes 141 | @echo 142 | @echo "The overview file is in $(BUILDDIR)/changes." 143 | 144 | linkcheck: 145 | $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck 146 | @echo 147 | @echo "Link check complete; look for any errors in the above output " \ 148 | "or in $(BUILDDIR)/linkcheck/output.txt." 149 | 150 | doctest: 151 | $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest 152 | @echo "Testing of doctests in the sources finished, look at the " \ 153 | "results in $(BUILDDIR)/doctest/output.txt." 154 | -------------------------------------------------------------------------------- /doc/source/algorithms.rst: -------------------------------------------------------------------------------- 1 | .. _algorithms: 2 | 3 | =================== 4 | Algorithms in fogpy 5 | =================== 6 | 7 | The package provide different algorithms for fog and low stratus cloud 8 | detection and nowcasting. The implemented fog algorithms are inherited 9 | from a base algorithm class, which defines basic common functionalities for 10 | remote sensing procedures. 11 | 12 | The fog and low stratus detection algorithm consists of a sequence of different 13 | filter approaches that are successively applicated to the given satellite images. 14 | The sequence of filters and required inputs are shown in the scheme below: 15 | 16 | .. image:: ./fogpy_fls_algo.png 17 | :scale: 50 % 18 | 19 | The cloud microphysical products liquid water path (LWP), cloud optical depth (COD) 20 | and effective droplet radius (Reff) can be obtained from the software provided by the 21 | Nowcasting Satellite Application Facility (NWCSAF) for example. 22 | 23 | Fogpy algorithms 24 | ---------------- 25 | 26 | .. automodule:: fogpy.algorithms 27 | :members: 28 | :undoc-members: -------------------------------------------------------------------------------- /doc/source/conf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # 3 | # fogpy documentation build configuration file, created by 4 | # sphinx-quickstart on Thu Apr 20 12:31:41 2017. 5 | # 6 | # This file is execfile()d with the current directory set to its containing dir. 7 | # 8 | # Note that not all possible configuration values are present in this 9 | # autogenerated file. 10 | # 11 | # All configuration values have a default; values that are commented out 12 | # serve to show the default. 13 | 14 | import sys, os 15 | 16 | # If extensions (or modules to document with autodoc) are in another directory, 17 | # add these directories to sys.path here. If the directory is relative to the 18 | # documentation root, use os.path.abspath to make it absolute, like shown here. 19 | # sys.path.insert(0, os.path.abspath('.')) 20 | # sys.path.insert(0, os.path.abspath('../')) 21 | sys.path.append(os.path.abspath('../../')) 22 | 23 | 24 | class Mock(object): 25 | def __init__(self, *args, **kwargs): 26 | pass 27 | 28 | def __call__(self, *args, **kwargs): 29 | return Mock() 30 | 31 | @classmethod 32 | def __getattr__(cls, name): 33 | if name in ('__file__', '__path__'): 34 | return '/dev/null' 35 | elif name[0] == name[0].upper(): 36 | mockType = type(name, (), {}) 37 | mockType.__module__ = __name__ 38 | return mockType 39 | elif name == "inf": 40 | return 0 41 | else: 42 | return Mock() 43 | 44 | MOCK_MODULES = ['matplotlib', 'matplotlib.pyplot', 'matplotlib.cm', 45 | 'scipy', 'scipy.signal', 'scipy.optimize', 'scipy.ndimage', 46 | 'scipy.stats', 'pyresample', 'pyorbital', 'numpy', 'numpy.core', 47 | 'numpy.lib', 'numpy.lib.stride_tricks', 48 | 'pyresample.utils', 'pyresample.geometry', 49 | 'h5py', 'trollbufr', 'trollbufr.bufr'] 50 | 51 | for mod_name in MOCK_MODULES: 52 | sys.modules[mod_name] = Mock() 53 | 54 | # -- General configuration ----------------------------------------------------- 55 | 56 | # If your documentation needs a minimal Sphinx version, state it here. 57 | #needs_sphinx = '1.0' 58 | 59 | # Add any Sphinx extension module names here, as strings. They can be extensions 60 | # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. 61 | extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode'] 62 | 63 | # Add any paths that contain templates here, relative to this directory. 64 | templates_path = ['_templates'] 65 | 66 | # The suffix of source filenames. 67 | source_suffix = '.rst' 68 | 69 | # The encoding of source files. 70 | #source_encoding = 'utf-8-sig' 71 | 72 | # The master toctree document. 73 | master_doc = 'index' 74 | 75 | # General information about the project. 76 | project = u'fogpy' 77 | copyright = '2017-2020, Fogpy developers' 78 | 79 | # The version info for the project you're documenting, acts as replacement for 80 | # |version| and |release|, also used in various other places throughout the 81 | # built documents. 82 | # 83 | # The short X.Y version. 84 | 85 | # import fogpy.version as current_version 86 | 87 | # The full version, including alpha/beta/rc tags. 88 | # release = current_version.__version__ 89 | # The short X.Y version. 90 | # version = ".".join(release.split(".")[:2]) 91 | version = '1.2.0' 92 | # The full version, including alpha/beta/rc tags. 93 | release = '1.2.0' 94 | 95 | # The language for content autogenerated by Sphinx. Refer to documentation 96 | # for a list of supported languages. 97 | #language = None 98 | 99 | # There are two options for replacing |today|: either, you set today to some 100 | # non-false value, then it is used: 101 | #today = '' 102 | # Else, today_fmt is used as the format for a strftime call. 103 | #today_fmt = '%B %d, %Y' 104 | 105 | # List of patterns, relative to source directory, that match files and 106 | # directories to ignore when looking for source files. 107 | exclude_patterns = [] 108 | 109 | # The reST default role (used for this markup: `text`) to use for all documents. 110 | #default_role = None 111 | 112 | # If true, '()' will be appended to :func: etc. cross-reference text. 113 | #add_function_parentheses = True 114 | 115 | # If true, the current module name will be prepended to all description 116 | # unit titles (such as .. function::). 117 | #add_module_names = True 118 | 119 | # If true, sectionauthor and moduleauthor directives will be shown in the 120 | # output. They are ignored by default. 121 | #show_authors = False 122 | 123 | # The name of the Pygments (syntax highlighting) style to use. 124 | pygments_style = 'sphinx' 125 | 126 | # A list of ignored prefixes for module index sorting. 127 | #modindex_common_prefix = [] 128 | 129 | 130 | # -- Options for HTML output --------------------------------------------------- 131 | 132 | # The theme to use for HTML and HTML Help pages. See the documentation for 133 | # a list of builtin themes. 134 | html_theme = 'default' 135 | 136 | # Theme options are theme-specific and customize the look and feel of a theme 137 | # further. For a list of options available for each theme, see the 138 | # documentation. 139 | #html_theme_options = {} 140 | 141 | # Add any paths that contain custom themes here, relative to this directory. 142 | #html_theme_path = [] 143 | 144 | # The name for this set of Sphinx documents. If None, it defaults to 145 | # " v documentation". 146 | #html_title = None 147 | 148 | # A shorter title for the navigation bar. Default is the same as html_title. 149 | #html_short_title = None 150 | 151 | # The name of an image file (relative to this directory) to place at the top 152 | # of the sidebar. 153 | #html_logo = None 154 | 155 | # The name of an image file (within the static path) to use as favicon of the 156 | # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 157 | # pixels large. 158 | #html_favicon = None 159 | 160 | # Add any paths that contain custom static files (such as style sheets) here, 161 | # relative to this directory. They are copied after the builtin static files, 162 | # so a file named "default.css" will overwrite the builtin "default.css". 163 | html_static_path = ['_static'] 164 | 165 | # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, 166 | # using the given strftime format. 167 | #html_last_updated_fmt = '%b %d, %Y' 168 | 169 | # If true, SmartyPants will be used to convert quotes and dashes to 170 | # typographically correct entities. 171 | #html_use_smartypants = True 172 | 173 | # Custom sidebar templates, maps document names to template names. 174 | #html_sidebars = {} 175 | 176 | # Additional templates that should be rendered to pages, maps page names to 177 | # template names. 178 | #html_additional_pages = {} 179 | 180 | # If false, no module index is generated. 181 | #html_domain_indices = True 182 | 183 | # If false, no index is generated. 184 | #html_use_index = True 185 | 186 | # If true, the index is split into individual pages for each letter. 187 | #html_split_index = False 188 | 189 | # If true, links to the reST sources are added to the pages. 190 | #html_show_sourcelink = True 191 | 192 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 193 | #html_show_sphinx = True 194 | 195 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 196 | #html_show_copyright = True 197 | 198 | # If true, an OpenSearch description file will be output, and all pages will 199 | # contain a tag referring to it. The value of this option must be the 200 | # base URL from which the finished HTML is served. 201 | #html_use_opensearch = '' 202 | 203 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 204 | #html_file_suffix = None 205 | 206 | # Output file base name for HTML help builder. 207 | htmlhelp_basename = 'fogpydoc' 208 | 209 | 210 | # -- Options for LaTeX output -------------------------------------------------- 211 | 212 | latex_elements = { 213 | # The paper size ('letterpaper' or 'a4paper'). 214 | #'papersize': 'letterpaper', 215 | 216 | # The font size ('10pt', '11pt' or '12pt'). 217 | #'pointsize': '10pt', 218 | 219 | # Additional stuff for the LaTeX preamble. 220 | #'preamble': '', 221 | } 222 | 223 | # Grouping the document tree into LaTeX files. List of tuples 224 | # (source start file, target name, title, author, documentclass [howto/manual]). 225 | latex_documents = [ 226 | ('index', 'fogpy.tex', u'fogpy Documentation', 227 | 'Fogpy developers', 'manual'), 228 | ] 229 | 230 | # The name of an image file (relative to this directory) to place at the top of 231 | # the title page. 232 | #latex_logo = None 233 | 234 | # For "manual" documents, if this is true, then toplevel headings are parts, 235 | # not chapters. 236 | #latex_use_parts = False 237 | 238 | # If true, show page references after internal links. 239 | #latex_show_pagerefs = False 240 | 241 | # If true, show URL addresses after external links. 242 | #latex_show_urls = False 243 | 244 | # Documents to append as an appendix to all manuals. 245 | #latex_appendices = [] 246 | 247 | # If false, no module index is generated. 248 | #latex_domain_indices = True 249 | 250 | 251 | # -- Options for manual page output -------------------------------------------- 252 | 253 | # One entry per manual page. List of tuples 254 | # (source start file, name, description, authors, manual section). 255 | man_pages = [ 256 | ('index', 'fogpy', u'fogpy Documentation', 257 | [u'Fogpy developers'], 1) 258 | ] 259 | 260 | # If true, show URL addresses after external links. 261 | #man_show_urls = False 262 | 263 | 264 | # -- Options for Texinfo output ------------------------------------------------ 265 | 266 | # Grouping the document tree into Texinfo files. List of tuples 267 | # (source start file, target name, title, author, 268 | # dir menu entry, description, category) 269 | texinfo_documents = [ 270 | ('index', 'fogpy', u'fogpy Documentation', 271 | u'Fogpy developers', 'fogpy', 'One line description of project.', 272 | 'Miscellaneous'), 273 | ] 274 | 275 | # Documents to append as an appendix to all manuals. 276 | #texinfo_appendices = [] 277 | 278 | # If false, no module index is generated. 279 | #texinfo_domain_indices = True 280 | 281 | # How to display URL addresses: 'footnote', 'no', or 'inline'. 282 | #texinfo_show_urls = 'footnote' 283 | -------------------------------------------------------------------------------- /doc/source/filters.rst: -------------------------------------------------------------------------------- 1 | .. _filters: 2 | 3 | ================= 4 | Filters in fogpy 5 | ================= 6 | 7 | The FLS algorithms are based on filter methods for different meteorlogical 8 | phenomena or physical variables. All implemented filter methods are inherited 9 | from a base filter class, which defines basic common filter functionalities. 10 | 11 | Fogpy filters 12 | ---------------- 13 | 14 | .. automodule:: fogpy.filters 15 | :members: 16 | :undoc-members: 17 | 18 | 19 | -------------------------------------------------------------------------------- /doc/source/fogcolbar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytroll/fogpy/4883d2198da770d2e95911b792cc764b33d15568/doc/source/fogcolbar.png -------------------------------------------------------------------------------- /doc/source/fogpy_docu_example_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytroll/fogpy/4883d2198da770d2e95911b792cc764b33d15568/doc/source/fogpy_docu_example_1.png -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytroll/fogpy/4883d2198da770d2e95911b792cc764b33d15568/doc/source/fogpy_logo.png -------------------------------------------------------------------------------- /doc/source/index.rst: -------------------------------------------------------------------------------- 1 | .. fogpy documentation master file, created by 2 | sphinx-quickstart on Thu Apr 20 12:31:41 2017. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | ================================= 7 | Welcome to fogpy's documentation! 8 | ================================= 9 | 10 | .. image:: ./fogpy_logo.png 11 | 12 | This package provide algorithmns and methods for satellite based detection and 13 | nowcasting of fog and low stratus clouds (FLS). 14 | 15 | Related FogPy Version: 1.1.3 16 | 17 | It utilizes several functionalities from the pytroll_ project for weather 18 | satellite data processing in Python. The remote sensing algorithmns are 19 | currently implemented for the geostationary Meteosat Second Generation (MSG) 20 | satellites. But it is designed to be easly extendable to support other 21 | meteorological satellites in future. 22 | 23 | Contents: 24 | 25 | .. _pytroll: http://pytroll.org/ 26 | .. toctree:: 27 | :maxdepth: 2 28 | 29 | install 30 | quickstart 31 | algorithms 32 | filters 33 | lowcloud 34 | 35 | 36 | 37 | Indices and tables 38 | ================== 39 | 40 | * :ref:`genindex` 41 | * :ref:`modindex` 42 | * :ref:`search` 43 | 44 | -------------------------------------------------------------------------------- /doc/source/install.rst: -------------------------------------------------------------------------------- 1 | =========================== 2 | Installation instructions 3 | =========================== 4 | 5 | Getting the files and installing them 6 | ===================================== 7 | 8 | First you need to get the files from github:: 9 | 10 | cd /path/to/my/source/directory/ 11 | git clone https://github.com/m4sth0/fogpy 12 | 13 | You can also retreive a tarball from there if you prefer, then run:: 14 | 15 | tar zxvf tarball.tar.gz 16 | 17 | Then you need to install fogpy on you computer:: 18 | 19 | cd fogpy 20 | python setup.py install [--prefix=/my/custom/installation/directory] 21 | 22 | You can also install it in develop mode to make it easier to hack:: 23 | 24 | python setup.py develop [--prefix=/my/custom/installation/directory] 25 | 26 | -------------------------------------------------------------------------------- /doc/source/lowcloud.rst: -------------------------------------------------------------------------------- 1 | ================================= 2 | Low cloud model in fogpy 3 | ================================= 4 | A low water cloud model has been implemented to derive the cloud base height 5 | from satellite retrievable variables like liquid water path, cloud top height 6 | and temperature. 7 | 8 | Low water cloud model 9 | --------------------- 10 | 11 | .. automodule:: fogpy.lowwatercloud 12 | :members: 13 | :undoc-members: -------------------------------------------------------------------------------- /doc/source/quickstart.rst: -------------------------------------------------------------------------------- 1 | ============ 2 | Fogpy usage in a nutshell 3 | ============ 4 | 5 | The package uses OOP extensively, to allow higher level metaobject handling. 6 | 7 | For this tutorial, we will use a MSG scene for creating different 8 | fog products. 9 | 10 | Import satellite data first 11 | =========================== 12 | 13 | We start with the PyTroll package *satpy*. This package provide all functionalities 14 | to import and calibrate a MSG scene from HRIT files. Therefore you should make sure 15 | that mpop is properly configured and all environment variables like *PPP_CONFIG_DIR* 16 | are set and the HRIT files are in the given search path. For more guidance look up 17 | in the `satpy`_ documentation 18 | 19 | .. _satpy: http://satpy.readthedocs.io/en/latest/install.html#getting-the-files-and-installing-them/ 20 | 21 | .. note:: 22 | Make sure *satpy* is correctly configured! 23 | 24 | Ok, let's get it on:: 25 | 26 | >>> from satpy import Scene 27 | >>> from glob import glob 28 | >>> filenames = glob("/path/to/seviri/H-000*20131212000*") 29 | >>> msg_scene = Scene(reader="seviri_l1b_hrit", filenames=filenames) 30 | >>> msg_scene.load([10.8]) 31 | >>> msg_scene.load(["fog"]) 32 | 33 | We imported a MSG scene from 12. December 2013 and loaded the 10.8 µm channel 34 | and the built-in simple fog composite into the scene object. 35 | 36 | Now we want to look at the IR 10.8 channel:: 37 | 38 | >>> msg_scene.show(10.8) 39 | 40 | .. image:: ./fogpy_docu_example_1.png 41 | 42 | Everything seems correctly imported. We see a full disk image. So lets see if we can resample it to a central European region:: 43 | 44 | >>> eu_scene = msg_scene.resample("eurol") 45 | >>> eu_scene.show(10.8) 46 | 47 | .. image:: ./fogpy_docu_example_2.png 48 | 49 | A lot of clouds are present over central Europe. Let's test a fog RGB composite to find some low clouds:: 50 | 51 | >>> eu_scene.show("fog") 52 | 53 | .. image:: ./fogpy_docu_example_3.png 54 | 55 | The reddish and dark colored clouds represent cold and high altitude clouds, 56 | whereas the yellow-greenish color over central and eastern Europe is an indication for low clouds and fog. 57 | 58 | Continue with more metadata 59 | =========================== 60 | 61 | In the next step we want to create a fog and low stratus (FLS) composite 62 | for the imported scene. For this we need: 63 | 64 | * Seviri L1B data, read by Satpy with the ``seviri_l1b_hrit`` reader. 65 | * Cloud microphysical data, read by Satpy with the ``nwcsaf-geo`` reader. 66 | In principle, `CMSAF`_ data could also be used, but as of May 2019, there 67 | is no CM-SAF reader within Satpy. 68 | * A digital elevation model. This can derived from data available from 69 | the European Environmental Agency (`EEA`_). 70 | Although this can be read by Satpy using 71 | the ``generic_image`` reader, the Fogpy composite reads this as a static 72 | image. The path to this image needs to be defined in the Fogpy 73 | ``etc/composites/seviri.yaml`` file. 74 | 75 | We create a scene in which we load 76 | datasets using both the ``seviri_l1b_hrit`` and ``nwcsaf-geo`` readers. 77 | Here we choose to load all required channels and datasets explicitly:: 78 | 79 | >>> fn_nwcsaf = glob("/media/nas/x21308/scratch/NWCSAF/*100000Z.nc") 80 | >>> fn_sev = glob("/media/nas/x21308/scratch/SEVIRI/*201904151000*") 81 | >>> sc = Scene(filenames={"seviri_l1b_hrit": fn_sev, "nwcsaf-geo": fn_nwcsaf}) 82 | >>> sc.load(["cmic_reff", "IR_108", "IR_087", "cmic_cot", "IR_016", "VIS006", 83 | "IR_120", "VIS008", "cmic_lwp", "IR_039"]) 84 | 85 | 86 | .. _EEA: https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem 87 | .. _satpy: https://github.com/pytroll/satpy 88 | 89 | .. image:: ./fogpy_docu_example_5.png 90 | :scale: 74 % 91 | 92 | We can now visualise any of those datasets using the regular pytroll 93 | visualisation toolkit. Let's first resample the scene again:: 94 | 95 | >>> ls = sc.resample("eurol") 96 | 97 | And then inspect the cloud optical thickness product:: 98 | 99 | >>> from trollimage.xrimage import XRImage 100 | >>> from trollimage.colormap import set3 101 | >>> xrim = XRImage(ls["cmic_cot"]) 102 | >>> set3.set_range(0, 100) 103 | >>> xrim.palettize(set3) 104 | >>> xrim.show() 105 | 106 | .. _CMSAF: www.cmsaf.eu 107 | .. _pyresample: https://github.com/pytroll/pyresample 108 | .. _trollimage: http://trollimage.readthedocs.io/en/latest/ 109 | 110 | .. image:: ./fogpy_docu_example_6.png 111 | 112 | Get hands-on fogpy at daytime 113 | ================================= 114 | 115 | After we imported all required metadata we can continue with a fogpy composite. 116 | 117 | .. note:: 118 | Make sure that the ``PPP_CONFIG_DIR`` includes ``fogpy/etc/`` directory! 119 | 120 | Fogpy comes with its own ``etc/composites/seviri.yaml``. 121 | By setting ``PPP_CONFIG_DIR=/path/to/fogpy/etc``, Satpy will find the fogpy 122 | composites and all fogpy composites can be used directly in Satpy. 123 | 124 | Let's try it with the *fls_day* composite. This composite determines 125 | low clouds and ground fog cells from a satellite scene. It is limited 126 | to daytime because it requires channels in the visible spectrum to be 127 | successfully applicable. We create a fogpy composite for the resampled 128 | MSG scene:: 129 | 130 | >>> ls.load(["fls_day"]) 131 | 132 | This may take a while to complete. 133 | You see that we don't have to import the fogpy package manually. 134 | It's done automagically in the background after the satpy configuration. 135 | 136 | The *fls_day* composite function calculates a new dataset, that is now 137 | available like any other Satpy dataset, such as by ``ls["fls_day"]`` 138 | or ``ls.show("fls_day")``. 139 | The dataset has two bands: 140 | 141 | - Band ``L`` is an image of a selected channel (Default is the 10.8 IR channel) where only the detected ground fog cells are displayed 142 | - Band ``A`` is an image for the fog mask 143 | 144 | .. image:: ./fogpy_docu_example_10.png 145 | 146 | The result image shows the area with potential ground fog calculated 147 | by the algorithm, fine. But the remaining areas are missing... maybe 148 | a different visualization could be helpful. We can improve the image 149 | output by colorize the fog mask and blending it over an overview composite 150 | using trollimage: 151 | 152 | .. Wait for this composite to work correctly 153 | .. 154 | .. Fogpy comes with a Satpy enhancement file in 155 | .. ``etc/enhancements/generic.yaml``, which defines an enhanced visualisation 156 | .. for the Fogpy ``fls_day`` composite, which we will use:: 157 | 158 | :: 159 | 160 | >>> ov = satpy.writers.get_enhanced_image(ls["overview"]).convert("RGBA") 161 | >>> A = ls["fls_day"].sel(bands="A") 162 | >>> Ap = (1-A).where(1-A==0, 0.5) 163 | >>> im = XRImage(Ap) 164 | >>> im.stretch() 165 | >>> im.colorize(fogcol) 166 | >>> RGBA = xr.concat([im.data, Ap], dim="bands") 167 | >>> blend = ov.blend(XRImage(RGBA)) 168 | 169 | .. note:: 170 | Images not yet updated! 171 | 172 | .. image:: ./fogpy_docu_example_11.png 173 | 174 | Here are some example algorithm results for the given MSG scene. 175 | As described above, the different masks are blendes over the overview RGB composite in yellow, except the right image where the fog RGB is in the background: 176 | 177 | +----------------------------------------+----------------------------------------+----------------------------------------+ 178 | | .. image:: ./fogpy_docu_example_13.png | .. image:: ./fogpy_docu_example_12.png | .. image:: ./fogpy_docu_example_14.png | 179 | +----------------------------------------+----------------------------------------+----------------------------------------+ 180 | | Cloud mask | Low cloud mask | Low cloud mask + Fog RGB | 181 | +----------------------------------------+----------------------------------------+----------------------------------------+ 182 | 183 | It looks like the cloud mask works correctly, except of some missclassified snow pixels in the Alps. 184 | But this is not a problem due to the snow filter which successfully masked them out later in the algorithm. 185 | Interestingly low cloud areas that are found by the algorithm fit quite good to the fog RGB yellowish areas. 186 | 187 | On a foggy night ... 188 | ================================= 189 | 190 | We saw how daytime fog detection can be realized with the fogpy *fls_day* composite. 191 | But mostly fog occuring during nighttime. So let's continue with another composite 192 | for nighttime fog detection **fls_night**:. 193 | 194 | .. note:: 195 | Again make sure that the fogpy composites are made available in satpy! 196 | 197 | .. fixme:: 198 | This part of documentation needs updating! 199 | 200 | First we need the nighttime MSG scene:: 201 | 202 | >>> fn_nwcsaf = glob("/media/nas/x21308/scratch/NWCSAF/*100000Z.nc") # FIXME: UPDATE! 203 | >>> fn_sev = glob("/media/nas/x21308/scratch/SEVIRI/*201904151000*") # FIXME: UPDATE! 204 | >>> sc = Scene(filenames={"seviri_l1b_hrit": fn_sev, "nwcsaf-geo": fn_nwcsaf}) 205 | >>> sc.load(["IR_108, "IR_039", "night_fog"]) 206 | 207 | Reproject it to the central European section from above and have a look at the infrared channel:: 208 | 209 | >>> ls = sc.resample("eurol") 210 | >>> ls.show(10.8) 211 | 212 | .. image:: ./fogpy_docu_nexample_1.png 213 | 214 | We took the same day (12. December 2017) as above. Now we could check whether the low 215 | clouds, that are present at 10 am, already can be seen early in the the morning (4 am) before sun rise. 216 | 217 | So let's look at the nighttime fog RGB product:: 218 | 219 | >>> ls.show("night_fog") 220 | 221 | .. image:: ./fogpy_docu_nexample_2.png 222 | 223 | As we see, a lot of greenish-yellow colored pixels are present in the night scene. 224 | This is a clear indication for low clouds and fog. In addition these areas have a similar form and 225 | distribution as the low clouds in the daytime scene. 226 | We can conclude that these low clouds should have formed during the night. 227 | 228 | So let's create the fogpy nighttime composite. 229 | Fogpy will use the PyTroll package `pyorbital`_ for solar zenith angle 230 | calculations, so make sure this one is installed. 231 | The nightime composite for the resampled MSG scene 232 | is generated in the same way like the daytime composite with `satpy`_:: 233 | 234 | >>> ls.load(["fls_night"]) 235 | >>> ls.show("fls_night") 236 | 237 | .. image:: ./fogpy_docu_nexample_3.png 238 | 239 | .. _pyorbital: https://github.com/pytroll/pyorbital 240 | 241 | It seems, the detected low cloud cells in the composite overestimate the presence of low clouds, 242 | if we compare the RGB product to it. In general, the nighttime algorithm exhibit higher uncertainty for the detection of low 243 | clouds than the daytime approach. Therefore a comparison with weather station data could be useful. 244 | 245 | Gimme some ground truth! 246 | ======================== 247 | 248 | Fogpy features some additional utilities for validation and comparison attempts. 249 | This include methods to plot weather station data from Bufr files over the FLS image results. 250 | The Bufr data is thereby processed by the `trollbufr`_ PyTroll package and the images are generated with `trollimage`_. 251 | Here we load visibility data from German weather stations for the nighttime scene:: 252 | 253 | >>> import os 254 | >>> from fogpy.utils import add_synop 255 | # Define search path for bufr file 256 | >>> bufr_dir = '/path/to/bufr/file/' 257 | >>> nbufr_file = "result_{}_synop.bufr".format(ntime.strftime("%Y%m%d%H%M")) 258 | >>> inbufrn = os.path.join(bufr_dir, nbufr_file) 259 | # Create station image 260 | >>> station_nimg = add_synop.add_to_image(nfls_img, tiffarea, ntime, inbufrn, ptsize=4) 261 | >>> station_nimg.show() 262 | 263 | .. image:: ./fogpy_docu_nexample_4.png 264 | | 265 | .. image:: ./fogcolbar.png 266 | :scale: 60 % 267 | 268 | .. _trollbufr: https://github.com/alexmaul/trollbufr 269 | 270 | The red dots represent fog reports with visibilities below 1000 meters (compare with legend), 271 | whereas green dots show high visibility situations at ground level. 272 | We see that low clouds, classified by the nighttime algorithm not always correspond to ground fog. 273 | Here the station data is a useful addition to distinguish between ground fog and low stratus. 274 | 275 | At daytime we can make the same comparison with station data:: 276 | 277 | >>> bufr_file = "result_{}_synop.bufr".format(time.strftime("%Y%m%d%H%M")) 278 | >>> inbufr = os.path.join(bufr_dir, bufr_file) 279 | # Create station image 280 | >>> station_img = add_synop.add_to_image(fls_img, tiffarea, time, inbufr, ptsize=4) 281 | >>> station_img.show() 282 | 283 | .. image:: ./fogpy_docu_example_15.png 284 | 285 | We see that the low cloud area in Northern Germany has not been classified as ground fog by the algorithm, 286 | whereas the southern part fits quite good to the station data. 287 | Furthermore some mountain stations within the area of the ground fog mask exhibit high visibilities. 288 | This difference is induced by the averaged evelation from the DEM, the deviated lower cloud height and the 289 | real altitude of the station which could lie above the expected cloud top. 290 | In addition the low cloud top height assignment can exhibit uncertainty in cases where a elevation 291 | based height assignment is not possible and a fixed temperature gradient approach is applied. 292 | These missclassifications could be improved by using ground station visibility data 293 | as algorithm input. The usage of station data as additional filter could refine the ground fog mask. 294 | 295 | Luckily we can use the StationFusionFilter class from fogpy to combine the satellite mask with ground 296 | station visibility data. We use several dataset that had been calculated through out the tour as filter input 297 | and plot the filter result:: 298 | 299 | >>> from fogpy.filters import StationFusionFilter 300 | # Define filter input 301 | >>> flsoutmask = np.array(fogmask.channels[0], dtype=bool) 302 | >>> filterinput = {'ir108': dem_scene[10.8].data, 303 | >>> 'ir039': dem_scene[3.9].data, 304 | >>> 'lowcloudmask': flsoutask, 305 | >>> 'elev': elevation.image_data, 306 | >>> 'bufrfile': inbufr, 307 | >>> 'time': time, 308 | >>> 'area': tiffarea} 309 | # Create fusion filter 310 | >>> stationfilter = StationFusionFilter(dem_scene[10.8].data, **filterinput) 311 | >>> stationfilter.apply() 312 | >>> stationfilter.plot_filter() 313 | 314 | .. image:: ./fogpy_docu_example_16.png 315 | 316 | The data fusion revise the low cloud clusters in Northern Germany and East Europe as ground fog again. 317 | The filter uses ground station data to correct false classification and add missing ground fog cases 318 | by utilising a DEM based interpolation. Furthermore cases under high clouds are also extrapolated by 319 | elevation information. This cloud lead to low cloud confidence levels. For example the fog mask over 320 | France and England. The applicatin of this filter should be limited to a region for which station data 321 | is available to achieve a high qualitiy data fusion product. In this case the area should be cropped to 322 | Germany, which can be done by setting the *limit* attribute to *True*:: 323 | 324 | >>> filterinput['limit'] = True 325 | # Create fusion filter with limited region 326 | >>> stationfilter = StationFusionFilter(dem_scene[10.8].data, **filterinput) 327 | >>> stationfilter.apply() 328 | >>> stationfilter.plot_filter() 329 | 330 | .. image:: ./fogpy_docu_example_17.png 331 | :scale: 120 % 332 | 333 | The output is now limited automagically to the area for which station data is available. 334 | 335 | The above station fusion filter example can be used to code any other filter application in fogpy. 336 | The command sequence more or less looks like the same: 337 | 338 | - Prepare filter input 339 | - Instantiate filter class object 340 | - Run the filter 341 | - Enjoy the results 342 | 343 | All available filters are listed in the chapter :ref:`filters`. Whereas the algorithms that can be directly 344 | applied to PyTroll *Scene* objects can be found in the :ref:`algorithms` section. 345 | -------------------------------------------------------------------------------- /fogpy/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # fogpy is free software: you can redistribute it and/or modify it 8 | # under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # fogpy is distributed in the hope that it will be useful, but 13 | # WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 15 | # General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with fogpy. If not, see . 19 | 20 | """PP Package initializer. 21 | """ 22 | 23 | import os 24 | 25 | BASE_PATH = os.path.sep.join(os.path.dirname( 26 | os.path.realpath(__file__)).split(os.path.sep)[:-1]) 27 | -------------------------------------------------------------------------------- /fogpy/composites.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """Interface Fogpy functionality as Satpy composite. 21 | 22 | This module implements satellite image based fog and low stratus 23 | detection and forecasting algorithm as a Satpy custom composite object. 24 | """ 25 | 26 | import logging 27 | import numpy 28 | import xarray 29 | import pathlib 30 | 31 | import appdirs 32 | import satpy 33 | import satpy.composites 34 | import satpy.dataset 35 | import pyorbital.astronomy 36 | import pkg_resources 37 | 38 | from .algorithms import DayFogLowStratusAlgorithm 39 | from .algorithms import NightFogLowStratusAlgorithm 40 | from .utils import dl_dem 41 | 42 | 43 | logger = logging.getLogger(__name__) 44 | 45 | 46 | class FogCompositor(satpy.composites.GenericCompositor): 47 | """A compositor for fog. 48 | 49 | FIXME DOC 50 | """ 51 | 52 | def __init__(self, name, 53 | prerequisites=None, 54 | optional_prerequisites=None, 55 | **kwargs): 56 | return super().__init__( 57 | name, 58 | prerequisites=prerequisites, 59 | optional_prerequisites=optional_prerequisites, 60 | **kwargs) 61 | 62 | def _get_area_lat_lon(self, projectables): 63 | projectables = self.check_areas(projectables) 64 | 65 | # Get central lon/lat coordinates for the image 66 | area = projectables[0].area 67 | lon, lat = area.get_lonlats() 68 | 69 | return (area, lat, lon) 70 | 71 | @staticmethod 72 | def _convert_xr_to_ma(projectables): 73 | """Convert projectables to masked arrays 74 | 75 | fogpy is still working with masked arrays and does not yet support 76 | xarray / dask (see #6). For now, convert to masked arrays. This 77 | function takes a list (or other iterable) of 78 | ``:class:xarray.DataArray`` instances and converts this to a list 79 | of masked arrays. The mask corresponds to any non-finite data in 80 | each input data array. 81 | 82 | Args: 83 | projectables (iterable): Iterable with xarray.DataArray 84 | instances, such as `:func:satpy.Scene._generate_composite` 85 | passes on to the ``__call__`` method of each Compositor 86 | class. 87 | 88 | Returns: 89 | List of masked arrays, of the same length as ``projectables``, 90 | each projectable converted to a masked array. 91 | """ 92 | 93 | return [numpy.ma.masked_invalid(p.values, copy=False) 94 | for p in projectables] 95 | 96 | @staticmethod 97 | def _convert_ma_to_xr(projectables, *args): 98 | """Convert fogpy algorithm result to xarray images 99 | 100 | The fogpy algorithms return numpy masked arrays, but satpy 101 | compositors expect xarray DataArry objects. This method 102 | takes the output of the fogpy algorithm routine and converts 103 | it to an xarray DataArray, with the attributes corresponding 104 | to a Satpy composite. 105 | 106 | Args: 107 | projectables (iterable): Iterable with xarray.DataArray 108 | instances, such as `:func:satpy.Scene._generate_composite` 109 | passes on to the ``__call__`` method of each Compositor 110 | class. 111 | fls (masked_array): Masked array such as returned by 112 | ``fogpy.algorithms.BaseSatelliteAlgorithm.run`` or its 113 | subclasses 114 | mask (masked_array): Mask corresponding to fls. 115 | 116 | Returns: 117 | List[xarray.DataArray] list of xarray DataArrays, corresponding 118 | to the ``*args`` inputs passed. If an image and a mask, those 119 | can be passed to ``GenericCompositor.__call__`` to get a LA image 120 | ``xarray.DataArray``, or the latter can be constructed directly. 121 | """ 122 | 123 | fv = numpy.nan 124 | # convert to xarray images 125 | dims = projectables[0].dims 126 | coords = projectables[0].coords 127 | attrs = {k: projectables[0].attrs[k] 128 | for k in {"satellite_longitude", "satellite_latitude", 129 | "satellite_altitude", "sensor", "platform_name", 130 | "orbital_parameters", "georef_offset_corrected", 131 | "start_time", "end_time", "area", "resolution"} & 132 | projectables[0].attrs.keys()} 133 | 134 | das = [xarray.DataArray( 135 | ma.data if isinstance(ma, numpy.ma.MaskedArray) else ma, 136 | dims=dims, coords=coords, attrs=attrs) 137 | for ma in args] 138 | for (ma, da) in zip(args, das): 139 | try: 140 | da.values[ma.mask] = fv 141 | except AttributeError: # no mask 142 | pass 143 | da.encoding["_FillValue"] = fv 144 | 145 | return das 146 | 147 | 148 | class _IntermediateFogCompositorDay(FogCompositor): 149 | def __init__(self, path_dem, *args, **kwargs): 150 | dem = pathlib.Path(appdirs.user_data_dir("fogpy")) / path_dem 151 | if not dem.exists(): 152 | dl_dem(dem) 153 | filenames = [dem] 154 | self.elevation = satpy.Scene(reader="generic_image", 155 | filenames=filenames) 156 | self.elevation.load(["image"]) 157 | return super().__init__(*args, **kwargs) 158 | 159 | def _verify_requirements(self, optional_datasets): 160 | """Verify that required cloud microphysics present 161 | 162 | Can be either cmic_cot/cmic_lwp/cmic_reff or cot/lwp/reff. 163 | """ 164 | D = {} 165 | needs = {"cot": {"cot", "cmic_cot"}, 166 | "lwp": {"lwp", "cwp", "cmic_lwp"}, 167 | "reff": {"reff", "cmic_lwp"}} 168 | for x in optional_datasets: 169 | for (n, p) in needs.items(): 170 | if x.attrs["name"] in p: 171 | D[n] = x 172 | continue 173 | missing = needs.keys() - D.keys() 174 | if missing: 175 | raise ValueError("Missing fog inputs: " + ", ".join(missing)) 176 | return D 177 | 178 | def __call__(self, projectables, *args, optional_datasets, **kwargs): 179 | D = self._verify_requirements(optional_datasets) 180 | (area, lat, lon) = self._get_area_lat_lon(projectables) 181 | 182 | # fogpy is still working with masked arrays and does not yet support 183 | # xarray / dask (see #6). For now, convert to masked arrays. 184 | maskproj = self._convert_xr_to_ma(projectables) 185 | D = dict(zip(D.keys(), self._convert_xr_to_ma(D.values()))) 186 | 187 | elev = self.elevation.resample(area) 188 | flsinput = {'vis006': maskproj[0], 189 | 'vis008': maskproj[1], 190 | 'ir108': maskproj[5], 191 | 'nir016': maskproj[2], 192 | 'ir039': maskproj[3], 193 | 'ir120': maskproj[6], 194 | 'ir087': maskproj[4], 195 | 'lat': lat, 196 | 'lon': lon, 197 | 'time': projectables[0].start_time, 198 | 'elev': numpy.ma.masked_invalid( 199 | elev["image"].sel(bands="L").values, copy=False), 200 | 'cot': D["cot"], 201 | 'reff': D["reff"], 202 | 'lwp': D["lwp"], 203 | "cwp": D["lwp"]} 204 | # Compute fog mask 205 | flsalgo = DayFogLowStratusAlgorithm(**flsinput) 206 | fls, mask = flsalgo.run() 207 | 208 | (xrfls, xrmsk, xrvmask, xrcbh, xrfbh, xrlcth) = self._convert_ma_to_xr( 209 | projectables, fls, mask, flsalgo.vcloudmask, flsalgo.cbh, 210 | flsalgo.fbh, flsalgo.lcth) 211 | 212 | ds = xarray.Dataset({ 213 | "fls_day": xrfls, 214 | "fls_mask": xrmsk, 215 | "vmask": xrvmask, 216 | "cbh": xrcbh, 217 | "fbh": xrfbh, 218 | "lcthimg": xrlcth}) 219 | 220 | ds.attrs.update(satpy.dataset.combine_metadata( 221 | xrfls.attrs, xrmsk.attrs, xrvmask.attrs, 222 | xrcbh.attrs, xrfbh.attrs, xrlcth.attrs)) 223 | 224 | # NB: isn't this done somewhere more generically? 225 | for k in ("standard_name", "name", "resolution"): 226 | ds.attrs[k] = self.attrs.get(k) 227 | 228 | return ds 229 | 230 | 231 | class FogCompositorDay(satpy.composites.GenericCompositor): 232 | def __call__(self, projectables, *args, **kwargs): 233 | # in the yaml file, fls_day has as a single prerequisite 234 | # _intermediate_fls_day. Therefore, the first and only 235 | # projectable is actually a Dataset, and pass a DataArray 236 | # to the superclass.__call__ method. 237 | ds = projectables[0] 238 | # the fogpy algorithm has the mask as True where fog is absent and 239 | # False where fog is present, although that is OK for a masked array, 240 | # we want the opposite interpretation when visualising it as the A band 241 | # on a LA-type image, therefore invert the truthiness with a unary 242 | 243 | # normally we'd invert this with ~x, but due ta a bug this loses the 244 | # attributes: see https://github.com/pydata/xarray/issues/4065 245 | # True^x is equivalent to ~x for booleans 246 | with xarray.set_options(keep_attrs=True): 247 | return super().__call__((ds["fls_day"], True ^ ds["fls_mask"]), *args, **kwargs) 248 | 249 | 250 | class FogCompositorDayExtra(satpy.composites.GenericCompositor): 251 | def __call__(self, projectables, *args, **kwargs): 252 | ds = projectables[0] 253 | ds.attrs["standard_name"] = ds.attrs["name"] = "fls_day_extra" 254 | return ds 255 | 256 | 257 | class FogCompositorNight(FogCompositor): 258 | 259 | def __call__(self, projectables, *args, **kwargs): 260 | (area, lat, lon) = self._get_area_lat_lon(projectables) 261 | 262 | sza = pyorbital.astronomy.sun_zenith_angle( 263 | projectables[0].start_time, lon, lat) 264 | 265 | maskproj = self._convert_xr_to_ma(projectables) 266 | 267 | flsinput = {'ir108': maskproj[1], 268 | 'ir039': maskproj[0], 269 | 'sza': sza, 270 | 'lat': lat, 271 | 'lon': lon, 272 | 'time': projectables[0].start_time 273 | } 274 | 275 | # Compute fog mask 276 | flsalgo = NightFogLowStratusAlgorithm(**flsinput) 277 | fls, mask = flsalgo.run() 278 | 279 | (xrfls, xrmsk) = self._convert_ma_to_xr(projectables, fls, mask) 280 | 281 | return super().__call__((xrfls, xrmsk), *args, **kwargs) 282 | 283 | 284 | def save_extras(sc, fn): 285 | """Save the `fls_days_extra` dataset to NetCDF 286 | 287 | The ``fls_day_extra`` dataset as produced by the `FogCompositorDayExtra` and 288 | loaded using ``.load(["fls_day_extra"])`` is unique in the sense that it is 289 | an `xarray.Dataset` rather than an `xarray.DataArray`. This means it can't 290 | be stored with the usual satpy routines. Because some of its attributes 291 | contain special types, it can`t be stored with `Dataset.to_netcdf` either. 292 | 293 | This function transfers the data variables as direct members of a new 294 | `Scene` object and then use the `cf_writer` to write those to a NetCDF file. 295 | 296 | Args: 297 | sc : Scene 298 | Scene object with the already loaded ``fls_day_extra`` "composite" 299 | fn : str-like or path 300 | Path to which to write NetCDF 301 | """ 302 | s = satpy.Scene() 303 | ds = sc["fls_day_extra"] 304 | for k in ds.data_vars: 305 | s[k] = ds[k] 306 | s.save_datasets( 307 | writer="cf", 308 | datasets=ds.data_vars.keys(), 309 | filename=str(fn)) 310 | -------------------------------------------------------------------------------- /fogpy/data/DEM/.gitignore: -------------------------------------------------------------------------------- 1 | eu-1km.tif 2 | new-england-500m.tif 3 | -------------------------------------------------------------------------------- /fogpy/etc/composites/abi.yaml: -------------------------------------------------------------------------------- 1 | sensor_name: visir/abi 2 | 3 | composites: 4 | 5 | fls_night: 6 | compositor: !!python/name:fogpy.composites.FogCompositorNight 7 | prerequisites: 8 | - C07 9 | - C14 10 | standard_name: fls_night 11 | 12 | _intermediate_fls_day: 13 | compositor: !!python/name:fogpy.composites._IntermediateFogCompositorDay 14 | prerequisites: 15 | - C02 16 | - C03 17 | - C05 18 | - C07 19 | - C11 20 | - C14 21 | - C15 22 | optional_prerequisites: 23 | - cmic_cot 24 | - cmic_lwp 25 | - cmic_reff 26 | - cot 27 | - cwp 28 | - reff 29 | standard_name: _intermediate_fls_day 30 | path_dem: data/DEM/new-england-500m.tif 31 | 32 | fls_day: 33 | compositor: !!python/name:fogpy.composites.FogCompositorDay 34 | prerequisites: 35 | - _intermediate_fls_day 36 | standard_name: fls_day 37 | 38 | fls_day_extra: 39 | compositor: !!python/name:fogpy.composites.FogCompositorDayExtra 40 | prerequisites: 41 | - _intermediate_fls_day 42 | standard_name: fls_day_extra 43 | -------------------------------------------------------------------------------- /fogpy/etc/composites/seviri.yaml: -------------------------------------------------------------------------------- 1 | sensor_name: visir/seviri 2 | 3 | composites: 4 | 5 | fls_night: 6 | compositor: !!python/name:fogpy.composites.FogCompositorNight 7 | prerequisites: 8 | - IR_039 9 | - IR_108 10 | standard_name: fls_night 11 | 12 | _intermediate_fls_day: 13 | compositor: !!python/name:fogpy.composites._IntermediateFogCompositorDay 14 | prerequisites: 15 | - VIS006 16 | - VIS008 17 | - IR_016 18 | - IR_039 19 | - IR_087 20 | - IR_108 21 | - IR_120 22 | optional_prerequisites: 23 | - cmic_cot 24 | - cmic_lwp 25 | - cmic_reff 26 | - cot 27 | - cwp 28 | - reff 29 | standard_name: _intermediate_fls_day 30 | path_dem: data/DEM/eu-1km.tif 31 | 32 | fls_day: 33 | compositor: !!python/name:fogpy.composites.FogCompositorDay 34 | prerequisites: 35 | - _intermediate_fls_day 36 | standard_name: fls_day 37 | 38 | fls_day_extra: 39 | compositor: !!python/name:fogpy.composites.FogCompositorDayExtra 40 | prerequisites: 41 | - _intermediate_fls_day 42 | standard_name: fls_day_extra 43 | -------------------------------------------------------------------------------- /fogpy/etc/elevation_1km.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytroll/fogpy/4883d2198da770d2e95911b792cc764b33d15568/fogpy/etc/elevation_1km.npy -------------------------------------------------------------------------------- /fogpy/etc/fog_testdata.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytroll/fogpy/4883d2198da770d2e95911b792cc764b33d15568/fogpy/etc/fog_testdata.npy -------------------------------------------------------------------------------- /fogpy/etc/fog_testdata2.npy: 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-------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """ This module implements a class for a 1D low water cloud model. 21 | The approach can be used to determine fog cloud base heights by known 22 | cloud top height and temperature and cloud liquid water path, e.g. from 23 | satellite retrievals. 24 | The implemented approch is based on a publication: 25 | 26 | 27 | The implementation is based on the following publications: 28 | 29 | * Cermak, J., & Bendix, J. (2011). Detecting ground fog from space–a 30 | microphysics-based approach. International Journal of Remote Sensing, 31 | 32(12), 3345-3371. doi:10.1016/j.atmosres.2007.11.009 32 | * Cermak, J., & Bendix, J. (2008). A novel approach to fog/low 33 | stratus detection using Meteosat 8 data. Atmospheric Research, 34 | 87(3-4), 279-292. doi:10.1016/j.atmosres.2007.11.009 35 | * Cermak, J. (2006). SOFOS-a new satellite-based operational fog 36 | observation scheme. (PhD thesis), Philipps-Universität Marburg, 37 | Marburg, Germany. doi:doi.org/10.17192/z2006.0149 38 | 39 | """ 40 | 41 | import math 42 | import logging 43 | import time 44 | import matplotlib.pyplot as plt 45 | import numpy as np 46 | from scipy.optimize import basinhopping 47 | from scipy.optimize import brute 48 | 49 | # Configure logger. 50 | logger = logging.getLogger('lowwatercloud') 51 | 52 | 53 | class CloudLayer(object): 54 | """This class represent a cloud layer - 1D representation of a 55 | cloud section from its vertical profile with defined extent and homogenius 56 | cloud parameters. 57 | The layer is defined by the bottom and top height in the cloud profile""" 58 | def __init__(self, bottom, top, lowcloud, add=True): 59 | self.bottom = bottom # Bottom height of the cloud layer 60 | self.top = top # Top height of the cloud layer 61 | self.z = top - (top - bottom) / 2 # Central layer height 62 | self.debug = lowcloud.debug 63 | # Fix maximum z height to cloud top height from cloud object 64 | # Height optimasation can provoke layers above the cth 65 | if self.z > lowcloud.cth: 66 | self.z = lowcloud.cth 67 | 68 | # Get temperaure and air presure 69 | self.temp = lowcloud.get_moist_adiabatic_lapse_temp(self.z, 70 | lowcloud.cth, 71 | lowcloud.ctt, 72 | True) 73 | self.press = lowcloud.get_air_pressure(self.z) 74 | 75 | # Calculate Vapour pressure and mixing ratio 76 | self.psv = lowcloud.get_sat_vapour_pressure(self.temp, 77 | lowcloud.vapour_method) 78 | self.vmr = lowcloud.get_vapour_mixing_ratio(self.press, self.psv) 79 | 80 | # Get liquid water mixing ratio 81 | if lowcloud.cb_vmr is None: 82 | lowcloud.get_cloud_based_vapour_mixing_ratio() 83 | 84 | self.lmr = lowcloud.get_liquid_mixing_ratio(lowcloud.cb_vmr, self.vmr) 85 | 86 | # Get layer density 87 | self.rho = lowcloud.get_moist_air_density(self.press * 100, 88 | self.psv * 100, 89 | self.check_temp(self.temp, 90 | 'kelvin', 91 | self.debug)) 92 | 93 | # Get layer liquid water density 94 | self.lrho = lowcloud.get_liquid_density(self.press * 100, 95 | self.check_temp(self.temp, 96 | 'celsius')) 97 | self.lrho = 1000 # TODO Fix liquid water density method 98 | # Get in cloud mixing ratio beta 99 | self.beta = lowcloud.get_incloud_mixing_ratio(self.z, lowcloud.cth, 100 | lowcloud.cbh) 101 | 102 | # Get liquid water content 103 | self.lwc = lowcloud.get_liquid_water_content(self.z, lowcloud.cth, 104 | self.rho, self.lmr, 105 | self.beta, 106 | lowcloud.upthres, 107 | lowcloud.maxlwc) 108 | 109 | # Get layer effective radius 110 | self.reff = lowcloud.get_effective_radius(self.z) 111 | 112 | # Get layer extinction coefficient 113 | self.extinct = lowcloud.get_extinct(self.lwc, self.reff, 114 | (self.lrho * 1000.)) 115 | 116 | # Get visibility 117 | self.visibility = lowcloud.get_visibility(self.extinct) 118 | 119 | # Add cloud layer to low water cloud object 120 | if add: 121 | lowcloud.layers.append(self) 122 | 123 | if self.debug: 124 | logger.debug('New cloud layer: z: {} m | t: {} °C | p: {} hPa | ' 125 | 'psv: {} hPa | vmr: {} g kg-1 | lmr: {} g kg-1 | ' 126 | 'beta: {} | rho: {} kg m-3 | lwc: {} g m-3' 127 | .format(self.z, self.temp, round(self.press, 3), 128 | round(self.psv, 3), round(self.vmr, 3), 129 | round(self.lmr, 3), round(self.beta, 3), 130 | round(self.rho, 3), round(self.lwc, 3))) 131 | 132 | @classmethod 133 | def check_temp(self, temp, unit='celsius', debug=False): 134 | """Check for plausible range of temperature value for given unit. 135 | Convert if required""" 136 | if unit == 'celsius': 137 | if temp > 60: 138 | result = temp - 273.15 139 | if debug: 140 | logger.debug('Temperature {} is in Kelvin. Auto converting' 141 | ' to {} °C'.format(temp, result)) 142 | else: 143 | result = temp 144 | elif unit == 'kelvin': 145 | if temp <= 60 or temp < 0: 146 | result = temp + 273.15 147 | if debug: 148 | logger.debug('Temperature {} is in Celsius. ' 149 | 'Auto converting to {} K' 150 | .format(temp, result)) 151 | else: 152 | result = temp 153 | 154 | return(result) 155 | 156 | def get_layer_info(self): 157 | print('New cloud layer: z: {} m | t: {} °C | p: {} hPa | ' 158 | 'psv: {} hPa | vmr: {} g kg-1 | lmr: {} g kg-1 | ' 159 | 'beta: {} | rho: {} kg m-3 | lwc: {} g m-3' 160 | .format(self.z, self.temp, round(self.press, 3), 161 | round(self.psv, 3), round(self.vmr, 3), 162 | round(self.lmr, 3), round(self.beta, 3), 163 | round(self.rho, 3), round(self.lwc, 3))) 164 | 165 | 166 | class LowWaterCloud(object): 167 | """A class to simulate the water content of a low cloud and calculate its 168 | meteorological properties. 169 | 170 | Args: 171 | | cth (:obj:`float`): Cloud top height in m. 172 | | ctt (:obj:`float`): Cloud top temperature in K. 173 | | cwp (:obj:`float`): Cloud water path in kg / m^2. 174 | | cbh (:obj:`float`): Cloud base height in m. 175 | | reff (:obj:`float`): Droplet effective radius in m. 176 | | cbt (:obj:`float`): Cloud base temperature in K. 177 | | upthres (:obj:`float`): Top layer thickness with dry air 178 | entrainment in m. 179 | | lowthres (:obj:`float`): Bottem layer thickness with ground 180 | coupling in m. 181 | | thickness (:obj:`float`): Layer thickness in m. 182 | | debug (:obj:`bool`): Boolean to activate additional debug output. 183 | | nodata (:obj:`float`): Provide a specific Nodata value. 184 | Default is: -9999. 185 | 186 | Returns: 187 | Calibrated cloud base height in m. 188 | """ 189 | def __init__(self, cth=None, ctt=None, cwp=None, cbh=0, reff=None, 190 | cbt=None, upthres=50., lowthres=75., thickness=10., 191 | debug=False, nodata=-9999): 192 | self.upthres = upthres # Top layer thickness with dry air entrainment 193 | self.lowthres = lowthres # Bottom layer thickness with coupling 194 | self.cth = cth # Cloud top height 195 | self.ctt = ctt # Cloud top temperature 196 | self.cbh = cbh # Cloud base height 197 | self.cbt = cbt # Cloud base temperature 198 | self.cwp = cwp # Cloud water path 199 | self.lwp = None # Dummy for liquid water path calculation 200 | self.reff = reff # Droplet effective radius 201 | self.layers = [] # List of cloud layers 202 | self.vapour_method = "magnus" # Method for saturated vapour pressure 203 | self.cb_vmr = None # Water vapour mixing ratio 204 | self.thickness = thickness # Layer thickness in m 205 | self.debug = debug # Boolean to activate additional output 206 | self.nodata = nodata # Specific Nodata value 207 | 208 | # Get maximal liquid water content underneath cloud top 209 | self.maxlwc = None 210 | 211 | # Raise warnings when thresholds are in conflict with top and 212 | # base cloud height 213 | if self.cth - self.upthres <= self.cbh: 214 | logger.warning("Upper threshold starting level <{}> and cloud base" 215 | " <{}> are in conflict" 216 | .format(self.cth - self.upthres, self.cbh)) 217 | self.upthres = self.cth - self.cbh - 1 218 | logger.info("Reducing upper threshold <{}> to correct conflict" 219 | .format(self.upthres)) 220 | if self.lowthres >= self.cth: 221 | logger.warning("Lower threshold ending level <{}> and cloud top" 222 | " <{}> are in conflict" 223 | .format(self.lowthres, self.cth)) 224 | 225 | @property 226 | def cbh(self): 227 | return self.__cbh 228 | 229 | @cbh.setter 230 | def cbh(self, cbh): 231 | # Check conflicts for threshold and cloud base 232 | if cbh >= self.cth: 233 | self.__cbh = self.cth - 1 234 | self.upthres = self.cth - 1 235 | logger.info("Cloud base <{}> is higher than cloud top <{}>. " 236 | "Autmatically reduced to <{}>".format( 237 | cbh, self.cth, self.cth - 1)) 238 | elif self.cth - self.upthres <= cbh: 239 | self.upthres = self.cth - cbh - 1 240 | logger.info("Reducing upper threshold <{}> to correct conflict" 241 | .format(self.upthres)) 242 | self.__cbh = cbh 243 | else: 244 | self.__cbh = cbh 245 | 246 | def init_cloud_layers(self, init_cbh, thickness, overwrite=True): 247 | """Method to initialize cloud layers and corresponding parameters. 248 | the method needs a initial cloud base height and thickness in [m].""" 249 | self.cbh = init_cbh 250 | self.get_cloud_based_vapour_mixing_ratio() 251 | 252 | # Get maximal liquid water content underneath cloud top 253 | maxlwc_layer = CloudLayer(self.cth - self.upthres - thickness, 254 | self.cth - self.upthres + thickness, 255 | self, False) 256 | self.maxlwc = maxlwc_layer.lwc 257 | # Calculate layer properties 258 | # Contitional resetting cloud layers 259 | if overwrite: 260 | self.layers = [] 261 | # Cloud base layer 262 | CloudLayer(init_cbh - thickness, init_cbh + thickness, self) 263 | # Loop over layers 264 | layerrange = np.arange(init_cbh, self.cth, thickness) 265 | for b in layerrange: 266 | CloudLayer(b, b + thickness, self) 267 | # Cloud top layer 268 | CloudLayer(self.cth - thickness, self.cth + thickness, self) 269 | if self.debug: 270 | logger.info("Initialize {} cloud layers with {} m thickness" 271 | " and {} m cbh" 272 | .format(len(self.layers), thickness, init_cbh)) 273 | 274 | def get_cloud_base_height(self, start=0, method='basin'): 275 | """ Calculate cloud base height [m].""" 276 | # Calculate cloud base height 277 | self.cbh = self.optimize_cbh(start, method=method, debug=self.debug) 278 | 279 | return self.cbh 280 | 281 | def get_fog_base_height(self, substitude=False): 282 | """This method calculate the fog cloud base height for low clouds 283 | with visibilities below 1000 m. 284 | 285 | Args: 286 | | substitude (:obj:`bool`): Optional argument to substitude with 287 | cbh if no fbh could be found. 288 | 289 | Returns: 290 | Fog base height 291 | """ 292 | fog_z = [l.z for l in self.layers 293 | if l.visibility is not None and l.visibility <= 1000] 294 | if len(fog_z) > 0: 295 | # Get lowest heights with visibility treshold 296 | self.fbh = min(fog_z) 297 | else: 298 | if substitude: 299 | logger.warning("No fog base height found: Substitude with " 300 | "cloud base height: {}".format(self.cbh)) 301 | self.fbh = self.cbh 302 | else: 303 | logger.warning("No fog base height found: Set to NaN") 304 | self.fbh = np.nan 305 | 306 | return self.fbh 307 | 308 | def get_liquid_water_content(self, z, cth, hrho, lmr, beta, thres, 309 | maxlwc=None, debug=False): 310 | """Calculate liquid water content [g m-3] by air density and 311 | liquid water mixing ratio.""" 312 | if z > cth - thres: 313 | if maxlwc is None: 314 | maxlwc_layer = CloudLayer( 315 | self.cth - self.upthres - self.thickness, 316 | self.cth - self.upthres + self.thickness, 317 | self, False) 318 | maxlwc = maxlwc_layer.lwc 319 | self.maxlwc = maxlwc 320 | if self.maxlwc <= 0 and debug: 321 | logger.debug( 322 | "Maximum liquid water content is zero or negative") 323 | 324 | lwc = (cth - z) / (thres) * maxlwc 325 | # Test if liquid water content is negativ 326 | # This occures while optimizing with cbh=cth 327 | if lwc < 0: 328 | logger.debug("Liquid water content <{}> is negative for " 329 | "maximum of <{}> and cbh <{}>" 330 | .format(lwc, self.maxlwc, self.cbh)) 331 | lwc = 0 # Set lwc to zero 332 | else: 333 | lwc = (1 - beta) * hrho * lmr 334 | 335 | return lwc 336 | 337 | @classmethod 338 | def get_moist_air_density(self, pa, pv, temp, empiric=False, debug=False): 339 | """Calculate air density for humid air with known pressure and water 340 | vapour pressure and temperature.""" 341 | Rv = 461.495 # Specific gas constant for water vapour J kg-1 K-1 342 | Rd = 287.058 # Specific gas constant for dry air J kg-1 K-1 343 | d_sea = 1.2929 # Density of dry air at sea level 344 | if temp <= 60: 345 | newtemp = temp + 273.15 346 | if debug: 347 | logger.debug('Temperature {} is in Celsius. Auto converting to' 348 | ' {} K'.format(temp, newtemp)) 349 | temp = newtemp 350 | if empiric: 351 | hrho = d_sea * (273.15 / temp) * ((pa - 0.3783 * pv) / (1.013 * 352 | 10**5)) 353 | else: 354 | hrho = ((pa - pv) / (Rd * temp)) + (pv / (Rv * temp)) 355 | 356 | return hrho 357 | 358 | @classmethod 359 | def get_moist_adiabatic_lapse_temp(self, z, cth, ctt, convert=False): 360 | """Calculate air temperature for height z [K] following a moist 361 | adiabatic lapse rate. 362 | 363 | Requires values for cloud top height and temperature 364 | e.g. known from satellite retrievals.""" 365 | malr = 0.0065 # ## MALR: Moist adiabatic lapse rate [K m-1] 366 | temp = (cth - z) * malr + ctt 367 | 368 | # Optional convertion to Celsius degrees. 369 | if convert: 370 | temp = temp - 273.15 371 | 372 | return temp 373 | 374 | @classmethod 375 | def get_sat_vapour_pressure(self, temp, mode='buck', 376 | convert=False, debug=False): 377 | """Calculate satured water vapour pressure for temperature [hPa] 378 | using different empirical approaches. 379 | 380 | Options: Buck, Magnus 381 | 382 | Convert temperatures in K to °C 383 | """ 384 | if convert: 385 | newtemp = temp - 273.15 386 | if self.debug: 387 | logger.info("Converting temperature {} K to {} °C" 388 | .format(temp, newtemp)) 389 | temp = newtemp 390 | elif temp > 60: 391 | newtemp = temp - 273.15 392 | if debug: 393 | logger.debug('Temperature {} is in Kelvin. Auto converting to' 394 | ' {} °C'.format(temp, newtemp)) 395 | temp = newtemp 396 | 397 | if mode == 'buck': 398 | psv = 0.61121 * math.exp((18.678 - temp / 234.5) * 399 | (temp / (257.14 + temp))) 400 | elif mode == 'magnus': 401 | const1 = 6.1078 402 | if temp > 0: 403 | const2 = 17.08085 404 | const3 = 234.175 405 | else: 406 | const2 = 17.84362 407 | const3 = 245.425 408 | psv = const1 * math.exp(const2 * temp / (const3 + temp)) 409 | # Convert to hPa 410 | if mode == 'buck': 411 | result = psv * 10 412 | else: 413 | result = psv 414 | 415 | return result 416 | 417 | @classmethod 418 | def get_vapour_pressure(self, z, temp): 419 | """Calculate water vapour pressure for height z [hPa].""" 420 | # TODO Finish implementation 421 | wdensity = 0 # Density of water vapour 422 | gconst = 461.51 # Gas constante of water vapour in [J kg-1 K-1] 423 | # Calculate water vapur pressure 424 | vp = wdensity * gconst * temp 425 | 426 | return vp 427 | 428 | @classmethod 429 | def get_air_pressure(self, z, elevation=0): 430 | """Calculate ambient air pressure for height z [hPa].""" 431 | 432 | pa = 100 * ((44331.514 - z) / 11880.516) ** (1 / 0.1902632) 433 | 434 | return pa / 100 435 | 436 | @classmethod 437 | def get_vapour_mixing_ratio(self, pa, pv): 438 | """Calculate water vapour mixing ratio for given ambient pressure and 439 | water vapour pressure. Also usabale under saturated conditions.""" 440 | # Calculate water vapour mixing ratio 441 | vmr = 621.97 * pv / (pa - pv) 442 | 443 | return vmr 444 | 445 | @classmethod 446 | def get_liquid_mixing_ratio(self, cb_vmr, vmr, debug=False): 447 | """Calculate liquid water mixing ratio for given water vapour mixing 448 | ratio in a certain height and the maximum water vapour mixing ratio at 449 | cloud base condensation level [g/kg].""" 450 | lmr = cb_vmr - vmr 451 | if cb_vmr <= vmr and debug: 452 | logger.debug("Liquid water mixing ratio will be zero or negative" 453 | " for cbvmr: <{}> and vmr: <{}>".format(cb_vmr, vmr)) 454 | 455 | return lmr 456 | 457 | def get_cloud_based_vapour_mixing_ratio(self, debug=False): 458 | # Get temperature and air pressure 459 | temp = self.get_moist_adiabatic_lapse_temp(self.cbh, self.cth, 460 | self.ctt, True) 461 | press = self.get_air_pressure(self.cbh) 462 | 463 | # Calculate Vapour pressure and mixing ratio 464 | psv = self.get_sat_vapour_pressure(temp, self.vapour_method) 465 | cb_vmr = self.get_vapour_mixing_ratio(press, psv) 466 | self.cb_vmr = cb_vmr 467 | if debug: 468 | logger.debug("Cloud based vapour mixing ratio: " 469 | "<{}> at cloud base <{}>" 470 | .format(cb_vmr, self.cbh)) 471 | 472 | return cb_vmr 473 | 474 | @classmethod 475 | def get_incloud_mixing_ratio(self, z, cth, cbh, lowthres=75., upthres=50.): 476 | """Calculate in-cloud mixing ratio for given cloud height parameter.""" 477 | midbeta = 0.3 * cth / 1000 # Fixed in cloud mixing ratio 478 | # Separation in three major cloud layers 479 | # Apply fixed value for middle layer 480 | if z > cbh + lowthres and z < cth - upthres: 481 | beta = midbeta 482 | # Apply zero value for upper layer 483 | elif z >= cth - upthres: 484 | beta = midbeta 485 | # Apply linear increase from zero to fixed value in the lower layer 486 | elif z <= (cbh + lowthres): 487 | beta = (z - cbh) / (lowthres) * midbeta 488 | else: 489 | beta = midbeta # Default value 490 | 491 | return beta 492 | 493 | def get_liquid_water_path(self): 494 | """Calculate liquid water path for given cloud layers [g m-2].""" 495 | z = np.array([l.top - l.bottom for l in self.layers]) 496 | lwc = np.array([l.lwc for l in self.layers]) 497 | # Get sum of single layer water path 498 | lwp = np.sum(z * lwc) 499 | 500 | self.lwp = lwp 501 | 502 | return lwp 503 | 504 | def optimize_cbh(self, start, method='basin', debug=False): 505 | """Find best fitting cloud base height by comparing calculated 506 | liquid water path with given satellite retrieval. 507 | Minimization with basinhopping or brute force algorithm 508 | from python scipy package.""" 509 | cbhbounds = HeightBounds(self.cth - self.upthres, -1000) 510 | # Test input data 511 | if np.isnan(self.cwp) or np.isnan(self.cth): 512 | logger.warning("Input data for cwp: <{}> or cth: <{}> is not" 513 | " a number".format(self.cwp, self.cth)) 514 | result = np.nan 515 | return(result) 516 | elif self.cwp == self.nodata or self.cth == self.nodata: 517 | logger.warning("Input cwp: <{}> or cth: <{}> in NoData format" 518 | .format(self.cwp, self.cth)) 519 | result = np.nan 520 | return(result) 521 | # Choose method 522 | if method == 'basin': 523 | start_time = int(round(time.time() * 1000)) 524 | minimizer_kwargs = {"method": "BFGS", "bounds": (0, self.cth - 525 | self.upthres)} 526 | ret = basinhopping(self.minimize_cbh, 527 | start, 528 | T=5.0, 529 | minimizer_kwargs=minimizer_kwargs, 530 | niter=30, 531 | niter_success=5, 532 | stepsize=200, 533 | accept_test=cbhbounds, 534 | seed=42) 535 | time_diff = int(round(time.time() * 1000)) - start_time 536 | result = float(ret.x[0]) 537 | logger.info('Optimized lwp: start cbh: {:.2f}, cth: {:.2f}, ' 538 | 'ctt: {:.2f}, observed lwp {:.2f}' 539 | ' --> result lwp: {:.2f}, calibrated cbh: {:.2f},' 540 | ' elapsed time: {:.2f} ms for {} layers with {} ' 541 | 'm thickness' 542 | .format(start, float(self.cth), float(self.ctt), 543 | float(self.cwp), float(self.lwp), 544 | result, time_diff, 545 | len(self.layers), self.thickness)) 546 | elif method == 'brute': 547 | ranges = slice(0, self.cth - self.upthres, 1) 548 | ret = brute(self.minimize_cbh, (ranges,), finish=None) 549 | result = ret 550 | # the minimisation routine self.minimize_cbh calls 551 | # self.init_cloud_layers, so in every call the cloud layers get reset; 552 | # we don't know if the final call corresponded to the minimum (with 553 | # basin hopping it might, with brute force it won't), which was 554 | # triggering bug issue #29, therefore re-initialise layers with the 555 | # last value 556 | self.init_cloud_layers(result, self.thickness, True) 557 | self.get_liquid_water_path() # also has side-effect... 558 | # Add optional debug output 559 | if debug: 560 | for l in self.layers: 561 | l.get_layer_info() 562 | # Set class variable for cloud base height 563 | self.cbh = result 564 | 565 | return(result) 566 | 567 | def minimize_cbh(self, x): 568 | """Minimization function for liquid water path.""" 569 | x = np.reshape(x, (1,)) 570 | self.init_cloud_layers(x[0], self.thickness, True) 571 | lwp = self.get_liquid_water_path() 572 | diff = abs(lwp - self.cwp) 573 | 574 | return diff 575 | 576 | def get_liquid_density(self, temp, press): 577 | """Calculate the liquid water density in [kg m-3].""" 578 | t0 = 0. # Reference temperature in [°C] 579 | rho_0 = 999.8 # Density of water for 0°C: [kg/m3] 580 | p0 = 1e5 # Air pressure at 0°C in [Pa] 581 | beta = 0.000088 # Expansion coefficient of water at 10oC: [m3/m3°C] 582 | E = 2.15e9 # Bulk modulus of water: [N/m2] 583 | rho = rho_0 / (1 + beta * (temp - t0)) / (1 - (press - p0) / E) 584 | 585 | return rho 586 | 587 | def get_visibility(self, extinct, contrast=0.02): 588 | """Calculate visibility in [m] for given cloud layer. 589 | Extinction is directly related to visibility by 590 | Koschmieder’s law. 591 | """ 592 | if extinct is None: 593 | return None 594 | else: 595 | vis = (1 / extinct) * math.log(1 / contrast) 596 | # Remove negative visibilities 597 | if vis < 0: 598 | vis = 0 599 | return vis 600 | 601 | def get_extinct(self, lwc, reff, rho): 602 | """Calculate extingtion coeficient [m-1] 603 | 604 | The extinction therefore is a combination of radiation loss by 605 | (diffuse) scattering and molecular absorption. 606 | Required are the liquid water content, effective radius and 607 | liquid water density 608 | TODO: Recheck the unit of liquid water density g or kg? Should be in g 609 | """ 610 | if reff is None: 611 | return None 612 | elif lwc == 0: 613 | return None 614 | else: 615 | extinct = 3 * lwc / (2 * reff * rho) 616 | 617 | return extinct 618 | 619 | def get_effective_radius(self, z): 620 | """The droplet effective radius in [um] for each level is computed on 621 | the assumptions that reff retrieved at 3.9 μm is the cloud top value, 622 | Cloud base reff is at 1 μm and the intermediate values are scaled 623 | linearly in between. 624 | """ 625 | if self.reff is None: 626 | return None 627 | else: 628 | reff = 1e-6 + ((self.reff - 1e-6) / (self.cth - 629 | self.cbh)) * (z - self.cbh) 630 | 631 | return reff 632 | 633 | def plot_lowcloud(self, para, xlabel=None, save=None): 634 | """Plotting of selected low water cloud parameters.""" 635 | if self.layers == []: 636 | logger.info("No layer found. Nothing to plot") 637 | heights = [getattr(l, 'z') for l in self.layers] 638 | paralist = [getattr(l, para) for l in self.layers] 639 | plt.figure() 640 | plt.plot(paralist, heights, '-o') 641 | plt.ylim((0, max(heights) + 50)) 642 | plt.axvline(0, color='grey', ls='--') 643 | plt.axhline(self.cth, color='grey', ls='--') 644 | plt.axhline(self.cbh, color='grey', ls='--') 645 | plt.ylabel('Cloud height z in [m]') 646 | if xlabel is not None: 647 | plt.xlabel(xlabel) 648 | else: 649 | plt.xlabel(para) 650 | if save is not None: 651 | plt.savefig(save) 652 | else: 653 | plt.show() 654 | 655 | 656 | class HeightBounds(object): 657 | def __init__(self, xmax=2000, xmin=-1000): 658 | self.xmax = xmax 659 | self.xmin = xmin 660 | 661 | def __call__(self, **kwargs): 662 | x = kwargs["x_new"] 663 | tmax = bool(x <= self.xmax) 664 | tmin = bool(x >= self.xmin) 665 | return tmax and tmin 666 | -------------------------------------------------------------------------------- /fogpy/test/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """The fogpy test suite. 21 | """ 22 | 23 | from fogpy.test import (test_lowwatercloud, 24 | test_filters, 25 | test_algorithms 26 | ) 27 | 28 | import unittest 29 | 30 | 31 | def suite(): 32 | """The global test suite. 33 | """ 34 | 35 | mysuite = unittest.TestSuite() 36 | mysuite.addTests(test_lowwatercloud.suite()) 37 | mysuite.addTests(test_filters.suite()) 38 | mysuite.addTests(test_algorithms.suite()) 39 | 40 | return mysuite 41 | -------------------------------------------------------------------------------- /fogpy/test/conftest.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """conftest.py for fogpy.""" 3 | 4 | import os 5 | import pytest 6 | import pkg_resources 7 | 8 | 9 | @pytest.fixture(scope="session", autouse=True) 10 | def setUp(tmp_path_factory): 11 | for nm in {"XDG_CACHE_HOME", "XDG_DATA_HOME"}: 12 | os.environ[nm] = str(tmp_path_factory.mktemp(nm)) 13 | os.environ["SATPY_CONFIG_PATH"] = pkg_resources.resource_filename( 14 | "fogpy", "etc/") 15 | -------------------------------------------------------------------------------- /fogpy/test/test_composites.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | import pytest 21 | import numpy 22 | import datetime 23 | import tempfile 24 | import pathlib 25 | from numpy import array 26 | from xarray import Dataset, DataArray as xrda, open_dataset 27 | from unittest import mock 28 | 29 | import pkg_resources 30 | 31 | 32 | @pytest.fixture 33 | def fkattr(): 34 | import pyresample 35 | return { 36 | 'satellite_longitude': 0.0, 37 | 'satellite_latitude': 0.0, 38 | 'satellite_altitude': 35785831.0, 39 | 'orbital_parameters': { 40 | 'projection_longitude': 0.0, 41 | 'projection_latitude': 0.0, 42 | 'projection_altitude': 35785831.0, 43 | 'satellite_nominal_longitude': 0.0, 44 | 'satellite_nominal_latitude': 0.0, 45 | 'satellite_actual_longitude': 0.0688189107392428, 46 | 'satellite_actual_latitude': 0.1640766475684642, 47 | 'satellite_actual_altitude': 35782769.05113167}, 48 | 'sensor': 'seviri', 49 | 'platform_name': 'Meteosat-11', 50 | 'georef_offset_corrected': True, 51 | 'start_time': datetime.datetime(2020, 1, 6, 10, 0, 10, 604000), 52 | 'end_time': datetime.datetime(2020, 1, 6, 10, 12, 43, 608000), 53 | 'area': pyresample.AreaDefinition( 54 | "germ", 55 | "germ", 56 | None, 57 | { 58 | 'a': '6378144', 59 | 'b': '6356759', 60 | 'lat_0': '90', 61 | 'lat_ts': '50', 62 | 'lon_0': '5', 63 | 'no_defs': 'None', 64 | 'proj': 'stere', 65 | 'type': 'crs', 66 | 'units': 'm', 67 | 'x_0': '0', 68 | 'y_0': '0'}, 69 | 3, 70 | 3, 71 | (-155100.4363, -4441495.3795, 868899.5637, -3417495.3795)), 72 | 'resolution': 3000.403165817, 73 | 'calibration': 'reflectance', 74 | 'polarization': None, 75 | 'level': None, 76 | 'modifiers': (), 77 | 'ancillary_variables': []} 78 | 79 | 80 | @pytest.fixture 81 | def fogpy_inputs(fkattr): 82 | 83 | D = dict( 84 | ir108=array( 85 | [ 86 | [267.781, 265.75, 265.234], 87 | [266.771, 265.75, 266.432], 88 | [266.092, 266.771, 268.614], 89 | ] 90 | ), 91 | ir039=array( 92 | [ 93 | [274.07, 270.986, 269.281], 94 | [273.335, 275.477, 277.236], 95 | [277.023, 279.663, 279.663], 96 | ] 97 | ), 98 | vis008=array( 99 | [ 100 | [9.814, 10.652, 11.251], 101 | [11.49, 13.884, 15.799], 102 | [15.081, 16.158, 16.637], 103 | ] 104 | ), 105 | nir016=array( 106 | [ 107 | [9.439, 9.559, 10.156], 108 | [11.47, 13.86, 16.011], 109 | [15.055, 16.25, 16.967], 110 | ] 111 | ), 112 | vis006=array( 113 | [ 114 | [8.614, 9.215, 9.615], 115 | [9.916, 11.519, 12.921], 116 | [12.72, 13.422, 13.722], 117 | ] 118 | ), 119 | ir087=array( 120 | [ 121 | [265.139, 263.453, 262.67], 122 | [264.225, 263.141, 263.608], 123 | [263.453, 264.071, 266.338], 124 | ] 125 | ), 126 | ir120=array( 127 | [ 128 | [266.903, 265.208, 264.694], 129 | [265.55, 264.522, 265.379], 130 | [265.037, 265.037, 267.406], 131 | ] 132 | ), 133 | elev=array( 134 | [ 135 | [319.481, 221.918, 300.449], 136 | [388.51, 501.519, 431.15], 137 | [521.734, 520.214, 505.892], 138 | ] 139 | ), 140 | cot=array([[6.15, 10.98, 11.78], [13.92, 16.04, 7.93], [7.94, 10.01, 6.12]]), 141 | reff=array( 142 | [ 143 | [3.06e-06, 3.01e-06, 3.01e-06], 144 | [3.01e-06, 3.01e-06, 3.01e-06], 145 | [3.01e-06, 3.01e-06, 9.32e-06], 146 | ] 147 | ), 148 | lwp=array([[0.013, 0.022, 0.024], [0.028, 0.032, 0.016], [0.016, 0.02, 0.038]]), 149 | lat=array( 150 | [ 151 | [50.669, 50.669, 50.67], 152 | [50.614, 50.615, 50.616], 153 | [50.559, 50.56, 50.561], 154 | ] 155 | ), 156 | lon=array([[6.437, 6.482, 6.528], [6.428, 6.474, 6.52], [6.42, 6.466, 6.511]]), 157 | cth=array( 158 | [ 159 | [4400.0, 4200.0, 4000.0], 160 | [4200.0, 2800.0, 1200.0], 161 | [1600.0, 1000.0, 800.0], 162 | ] 163 | ), 164 | ) 165 | 166 | return {k: xrda(v, dims=("x", "y"), attrs={**fkattr, "name": k}) 167 | for (k, v) in D.items()} 168 | 169 | 170 | @pytest.fixture 171 | def fogpy_inputs_seviri_cmsaf(fogpy_inputs): 172 | fogpy_inputs = fogpy_inputs.copy() 173 | trans = {"ir108": "IR_108", 174 | "ir039": "IR_039", 175 | "vis008": "VIS008", 176 | "nir016": "IR_016", 177 | "vis006": "VIS006", 178 | "ir087": "IR_087", 179 | "ir120": "IR_120", 180 | "lwp": "cwp"} 181 | for (k, v) in trans.items(): 182 | fogpy_inputs[v] = fogpy_inputs.pop(k) 183 | return fogpy_inputs 184 | 185 | 186 | @pytest.fixture 187 | def fogpy_inputs_abi_nwcsaf(fogpy_inputs): 188 | fogpy_inputs = fogpy_inputs.copy() 189 | trans = {"ir108": "C14", 190 | "ir039": "C07", 191 | "vis008": "C03", 192 | "nir016": "C05", 193 | "vis006": "C02", 194 | "ir087": "C11", 195 | "ir120": "C15", 196 | "lwp": "cmic_lwp", 197 | "cot": "cmic_cot", 198 | "reff": "cmic_reff"} 199 | for (k, v) in trans.items(): 200 | fogpy_inputs[v] = fogpy_inputs.pop(k) 201 | return fogpy_inputs 202 | 203 | 204 | @pytest.fixture 205 | def fog_comp_base(): 206 | from fogpy.composites import FogCompositor 207 | return FogCompositor(name="fls_day") 208 | 209 | 210 | @pytest.fixture 211 | def fogpy_outputs(): 212 | fls = numpy.ma.masked_array( 213 | numpy.arange(9, dtype="f4").reshape((3, 3)), 214 | (numpy.arange(9) % 2).astype("?").reshape((3, 3))) 215 | mask = (numpy.arange(9, dtype="f4") % 2).astype("?").reshape((3, 3)) 216 | return (fls, mask) 217 | 218 | 219 | @pytest.fixture 220 | def comp_loader(): 221 | """Get a compositor loader for loading fogpy composites.""" 222 | from satpy.composites.config_loader import CompositorLoader 223 | cpl = CompositorLoader() 224 | with mock.patch("requests.get") as rg, mock.patch("satpy.Scene"): 225 | rg.return_value.content = b"12345" 226 | cpl.load_compositors(["seviri", "abi"]) 227 | return cpl 228 | 229 | 230 | @pytest.fixture 231 | def fog_extra(): 232 | return { 233 | "vcloudmask": numpy.ma.masked_array( 234 | data=[[True, True, True], [False, False, True], [True, False, False]], 235 | mask=numpy.zeros((3, 3), dtype="?"), 236 | fill_value=True), 237 | "cbh": numpy.zeros((3, 3)), 238 | "fbh": numpy.zeros((3, 3)), 239 | "lcth": numpy.full((3, 3), numpy.nan)} 240 | 241 | 242 | @pytest.fixture 243 | def fog_comp_day(): 244 | from fogpy.composites import FogCompositorDay 245 | return FogCompositorDay(name="fls_day") 246 | 247 | 248 | @pytest.fixture 249 | def fog_comp_day_extra(): 250 | from fogpy.composites import FogCompositorDayExtra 251 | return FogCompositorDayExtra(name="fls_day_extra") 252 | 253 | 254 | @pytest.fixture 255 | def fog_comp_night(): 256 | from fogpy.composites import FogCompositorNight 257 | return FogCompositorNight(name="fls_night") 258 | 259 | 260 | @pytest.fixture 261 | def fog_intermediate_dataset(fog_extra, fogpy_outputs, fkattr): 262 | ds = Dataset( 263 | {k: xrda(v, dims=("y", "x"), attrs=fkattr) for (k, v) in 264 | fog_extra.items()}, 265 | attrs=fkattr) 266 | (fls_day, fls_mask) = fogpy_outputs 267 | ds["fls_day"] = xrda(fls_day, dims=("y", "x"), attrs=fkattr) 268 | ds["fls_mask"] = xrda(fls_mask, dims=("y", "x"), attrs=fkattr) 269 | return ds 270 | 271 | 272 | def test_convert_xr_to_ma(fogpy_inputs, fog_comp_base): 273 | fi_ma = fog_comp_base._convert_xr_to_ma( 274 | [fogpy_inputs["ir108"], fogpy_inputs["vis008"]]) 275 | assert len(fi_ma) == 2 276 | assert all([isinstance(ma, numpy.ma.MaskedArray) for ma in fi_ma]) 277 | assert numpy.array_equal(fi_ma[0].data, fogpy_inputs["ir108"].values) 278 | # try with some attributes missing 279 | ir108 = fogpy_inputs["ir108"].copy() 280 | del ir108.attrs["satellite_longitude"] 281 | del ir108.attrs["end_time"] 282 | fi_ma = fog_comp_base._convert_xr_to_ma([ir108]) 283 | 284 | 285 | def test_convert_ma_to_xr(fogpy_inputs, fog_comp_base, fogpy_outputs): 286 | conv = fog_comp_base._convert_ma_to_xr( 287 | [fogpy_inputs["ir108"], fogpy_inputs["vis008"]], 288 | *fogpy_outputs) 289 | assert len(conv) == len(fogpy_outputs) 290 | assert all([isinstance(c, xrda) for c in conv]) 291 | numpy.testing.assert_array_equal(fogpy_outputs[0].data, conv[0].values) 292 | assert conv[0].attrs["sensor"] == fogpy_inputs["ir108"].attrs["sensor"] 293 | # check without mask 294 | conv = fog_comp_base._convert_ma_to_xr( 295 | [fogpy_inputs["ir108"], fogpy_inputs["vis008"]], 296 | *(fo.data for fo in fogpy_outputs)) 297 | # try with some attributes missing 298 | ir108 = fogpy_inputs["ir108"].copy() 299 | del ir108.attrs["satellite_longitude"] 300 | del ir108.attrs["end_time"] 301 | fog_comp_base._convert_ma_to_xr([ir108]) 302 | 303 | 304 | def test_get_area_lat_lon(fogpy_inputs, fog_comp_base): 305 | (area, lat, lon) = fog_comp_base._get_area_lat_lon( 306 | [fogpy_inputs["ir108"], fogpy_inputs["vis008"]]) 307 | assert area == fogpy_inputs["ir108"].area 308 | 309 | 310 | def test_interim(fogpy_inputs_seviri_cmsaf, fogpy_inputs_abi_nwcsaf, 311 | comp_loader, fogpy_outputs, fog_extra): 312 | fc_sev = comp_loader.get_compositor("_intermediate_fls_day", ["seviri"]) 313 | fc_abi = comp_loader.get_compositor("_intermediate_fls_day", ["abi"]) 314 | with mock.patch("satpy.Scene"), \ 315 | mock.patch("fogpy.composites.DayFogLowStratusAlgorithm") as fcD: 316 | fcD.return_value.run.return_value = fogpy_outputs 317 | fcD.return_value.vcloudmask = fog_extra["vcloudmask"] 318 | fcD.return_value.cbh = fog_extra["cbh"] 319 | fcD.return_value.fbh = fog_extra["fbh"] 320 | fcD.return_value.lcth = fog_extra["lcth"] 321 | for (fc, fpi) in ((fc_sev, fogpy_inputs_seviri_cmsaf), 322 | (fc_abi, fogpy_inputs_abi_nwcsaf)): 323 | ds = fc( 324 | [fpi[k] for k in fc.attrs["prerequisites"]], 325 | optional_datasets=[ 326 | fpi[k] for k in fc.attrs["optional_prerequisites"] 327 | if k in fpi]) 328 | assert isinstance(ds, Dataset) 329 | assert ds.data_vars.keys() == { 330 | 'vmask', 'fls_mask', 'fbh', 'cbh', 'fls_day', 'lcthimg'} 331 | numpy.testing.assert_equal(ds["cbh"].values, fcD.return_value.cbh) 332 | 333 | 334 | def test_fog_comp_day(fog_comp_day, fog_intermediate_dataset): 335 | composite = fog_comp_day([fog_intermediate_dataset]) 336 | assert isinstance(composite, xrda) 337 | 338 | 339 | def test_fog_comp_day_extra(fog_comp_day_extra, fog_intermediate_dataset): 340 | composite = fog_comp_day_extra([fog_intermediate_dataset]) 341 | assert isinstance(composite, Dataset) 342 | 343 | 344 | def test_fog_comp_night(fog_comp_night, fogpy_inputs, fogpy_outputs): 345 | with mock.patch("fogpy.composites.NightFogLowStratusAlgorithm") as fcN: 346 | fcN.return_value.run.return_value = fogpy_outputs 347 | composite = fog_comp_night([fogpy_inputs["ir039"], fogpy_inputs["ir108"]]) 348 | assert isinstance(composite, xrda) 349 | 350 | 351 | def test_save_extras(fog_intermediate_dataset): 352 | from satpy import Scene 353 | from fogpy.composites import save_extras 354 | sc = Scene() 355 | sc["fls_day_extra"] = fog_intermediate_dataset 356 | with tempfile.TemporaryDirectory() as td: 357 | fn = pathlib.Path(td) / "tofu.nc" 358 | save_extras(sc, fn) 359 | with open_dataset(fn) as ds: 360 | ds.load() 361 | assert set(ds.data_vars.keys()) >= { 362 | "vcloudmask", "fls_mask", "fbh", "cbh", "fls_day", "lcth"} 363 | numpy.testing.assert_array_equal(ds["fbh"].values, numpy.zeros((3, 3))) 364 | -------------------------------------------------------------------------------- /fogpy/test/test_lowwatercloud.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """ This module test the low cloud water class """ 21 | 22 | import unittest 23 | import numpy as np 24 | from fogpy.lowwatercloud import LowWaterCloud 25 | from fogpy.lowwatercloud import CloudLayer 26 | 27 | 28 | class Test_LowWaterCloud(unittest.TestCase): 29 | 30 | def setUp(self): 31 | self.lwc = LowWaterCloud(2000., 255., 400., 0, 10e-6) 32 | self.thinlwc = LowWaterCloud(2000., 255., 1., 0, 10e-6) 33 | self.nanlwc = LowWaterCloud(np.nan, 255., 400., 0, 10e-6) 34 | self.nodatalwc = LowWaterCloud(2000., 255., -9999, 0, 10e-6) 35 | 36 | def tearDown(self): 37 | pass 38 | 39 | def test_get_sat_vapour_pressure_buck(self): 40 | psv_m50 = self.lwc.get_sat_vapour_pressure(-50) 41 | psv_m20 = self.lwc.get_sat_vapour_pressure(-20) 42 | psv_0 = self.lwc.get_sat_vapour_pressure(0) 43 | psv_20 = self.lwc.get_sat_vapour_pressure(20) 44 | psv_50 = self.lwc.get_sat_vapour_pressure(50) 45 | self.assertAlmostEqual(psv_m50, 0.064, 3) 46 | self.assertAlmostEqual(psv_m20, 1.256, 3) 47 | self.assertAlmostEqual(psv_0, 6.112, 3) 48 | self.assertAlmostEqual(psv_20, 23.383, 3) 49 | self.assertAlmostEqual(psv_50, 123.494, 3) 50 | 51 | def test_get_sat_vapour_pressure_magnus(self): 52 | psv_m50 = self.lwc.get_sat_vapour_pressure(-50, 'magnus') 53 | psv_m20 = self.lwc.get_sat_vapour_pressure(-20, 'magnus') 54 | psv_0 = self.lwc.get_sat_vapour_pressure(0, 'magnus') 55 | psv_20 = self.lwc.get_sat_vapour_pressure(20, 'magnus') 56 | psv_50 = self.lwc.get_sat_vapour_pressure(50, 'magnus') 57 | self.assertAlmostEqual(psv_m50, 0.064, 3) 58 | self.assertAlmostEqual(psv_m20, 1.254, 3) 59 | self.assertAlmostEqual(psv_0, 6.108, 3) 60 | self.assertAlmostEqual(psv_20, 23.420, 3) 61 | self.assertAlmostEqual(psv_50, 123.335, 3) 62 | 63 | def test_get_air_pressure(self): 64 | pa_1 = self.lwc.get_air_pressure(610) 65 | pa_2 = self.lwc.get_air_pressure(0) 66 | self.assertAlmostEqual(pa_1, 942.08, 2) 67 | self.assertAlmostEqual(pa_2, 1013.25, 2) 68 | 69 | def test_get_moist_adiabatic_lapse_temp(self): 70 | temp_0 = self.lwc.get_moist_adiabatic_lapse_temp(500, 1500, 0) 71 | self.assertAlmostEqual(temp_0, 6.5, 1) 72 | 73 | def test_get_vapour_mixing_ratio(self): 74 | vmr = self.lwc.get_vapour_mixing_ratio(1007.26, 8.44) 75 | self.assertAlmostEqual(vmr, 5.26, 2) 76 | 77 | def test_get_cloud_based_vapour_mixing_ratio(self): 78 | self.lwc.cbh = 0 79 | cb_vmr = self.lwc.get_cloud_based_vapour_mixing_ratio() 80 | self.assertAlmostEqual(cb_vmr, 2.57, 2) 81 | 82 | def test_get_liquid_mixing_ratio(self): 83 | self.lwc.cbh = 0 84 | self.lwc.get_cloud_based_vapour_mixing_ratio() 85 | 86 | temp = self.lwc.get_moist_adiabatic_lapse_temp(0, self.lwc.cth, 87 | self.lwc.ctt, True) 88 | cb_press = self.lwc.get_air_pressure(self.lwc.cbh) 89 | psv = self.lwc.get_sat_vapour_pressure(temp, self.lwc.vapour_method) 90 | vmr = self.lwc.get_vapour_mixing_ratio(cb_press, psv) 91 | lmr = self.lwc.get_liquid_mixing_ratio(self.lwc.cb_vmr, vmr) 92 | self.assertAlmostEqual(self.lwc.cb_vmr, vmr) 93 | self.assertAlmostEqual(lmr, 0) 94 | 95 | def test_get_liquid_water_content(self): 96 | lwc = LowWaterCloud(2000., 255., 400., 0) 97 | lwc = self.lwc.get_liquid_water_content(1950, 2000, 1.091, 1.392, 0.0, 98 | 1.518, 50) 99 | self.assertAlmostEqual(lwc, 1.519, 3) 100 | 101 | def test_get_liquid_water_path(self): 102 | self.lwc.init_cloud_layers(421., 100) 103 | self.lwc.get_liquid_water_path() 104 | lwc = LowWaterCloud(2000., 255., 400., 0) 105 | CloudLayer(1900, 2000, lwc) 106 | lwc.get_liquid_water_path() 107 | self.assertAlmostEqual(len(lwc.layers), 1) 108 | self.assertAlmostEqual(lwc.lwp, 60.719, 3) 109 | self.assertAlmostEqual(self.lwc.lwp, 400., 1) 110 | 111 | def test_get_liquid_water_path2(self): 112 | self.lwc.init_cloud_layers(0, 50) 113 | lwc = LowWaterCloud(2000., 255., 400., 0) 114 | lwc.init_cloud_layers(0, 10) 115 | lwc.get_liquid_water_path() 116 | self.lwc.get_liquid_water_path() 117 | self.assertAlmostEqual(lwc.lwp, self.lwc.lwp, 1) 118 | 119 | def test_init_cloud_layers(self): 120 | self.lwc.init_cloud_layers(0, 100) 121 | self.lwc.plot_lowcloud('lwc', 'Liquid water content in [g m-3]', 122 | '/tmp/test_lowwatercloud_lwc.png') 123 | self.assertAlmostEqual(len(self.lwc.layers), 22) 124 | self.assertAlmostEqual(self.lwc.layers[0].z, 0) 125 | self.assertAlmostEqual(self.lwc.layers[1].z, 50) 126 | self.assertAlmostEqual(self.lwc.layers[20].press, 800, 0) 127 | self.assertAlmostEqual(self.lwc.layers[21].z, 2000) 128 | 129 | def test_cloud_layer(self): 130 | lwc = LowWaterCloud(2000., 255., 400., 0) 131 | cl = CloudLayer(0, 100, lwc) 132 | cl1 = CloudLayer(1945, 1955, lwc) 133 | cl2 = CloudLayer(1970, 1980, lwc) 134 | cl3 = CloudLayer(1950, 2050, lwc) 135 | self.assertAlmostEqual(cl.z, 50., 2) 136 | self.assertAlmostEqual(cl.temp, -5.47, 2) 137 | self.assertAlmostEqual(cl.press, 1007.26, 2) 138 | self.assertAlmostEqual(cl.psv, 4.07, 2) 139 | self.assertAlmostEqual(cl1.lwc, 0.607, 3) 140 | self.assertAlmostEqual(cl2.lwc, 0.304, 3) 141 | self.assertAlmostEqual(cl3.lwc, 0., 3) 142 | 143 | def test_cloud_layer_small(self): 144 | lwc = LowWaterCloud(1000., 235., 400., 950) 145 | cl = CloudLayer(950, 960, lwc, False) 146 | cl1 = CloudLayer(960, 970, lwc, False) 147 | cl2 = CloudLayer(970, 980, lwc, False) 148 | cl3 = CloudLayer(980, 990, lwc, False) 149 | cl4 = CloudLayer(990, 1000, lwc, False) 150 | self.assertAlmostEqual(cl.lwc, 8e-5, 5) 151 | self.assertAlmostEqual(cl1.lwc, 6e-5, 5) 152 | self.assertAlmostEqual(cl2.lwc, 4e-5, 5) 153 | self.assertAlmostEqual(cl3.lwc, 3e-5, 5) 154 | self.assertAlmostEqual(cl4.lwc, 1e-5, 5) 155 | self.assertAlmostEqual(lwc.upthres, 49) 156 | self.assertAlmostEqual(lwc.maxlwc, 8.2e-5, 5) 157 | 158 | def test_cloud_layer_small2(self): 159 | lwc = LowWaterCloud(1000., 235., 400., 950) 160 | cl1 = CloudLayer(970, 980, lwc, False) 161 | cl2 = CloudLayer(980, 990, lwc, False) 162 | cl3 = CloudLayer(990, 1000, lwc, False) 163 | self.assertAlmostEqual(cl1.lwc, 4e-5, 5) 164 | self.assertAlmostEqual(cl2.lwc, 3e-5, 5) 165 | self.assertAlmostEqual(cl3.lwc, 1e-5, 5) 166 | self.assertAlmostEqual(lwc.upthres, 49) 167 | self.assertAlmostEqual(lwc.maxlwc, 8.2e-5, 6) 168 | 169 | def test_get_moist_air_density(self): 170 | self.lwc.cbh = 0 171 | empiric_hrho_0 = self.lwc.get_moist_air_density(100000, 0, 273.15, 172 | True) 173 | empiric_hrho_20 = self.lwc.get_moist_air_density(101325, 0, 293.15, 174 | True) 175 | empiric_humid_hrho_20 = self.lwc.get_moist_air_density(101325, 2338, 176 | 293.15, True) 177 | empiric_humid_hrho_neg20 = self.lwc.get_moist_air_density(101325, 178 | 996.3, 179 | 253.15, True) 180 | 181 | ideal_hrho_15 = self.lwc.get_moist_air_density(101325, 0, 288.15) 182 | ideal_hrho_20 = self.lwc.get_moist_air_density(101325, 0, 293.15) 183 | humid_ideal_hrho_20 = self.lwc.get_moist_air_density(101325, 2338, 184 | 293.15) 185 | humid_ideal_hrho_neg20 = self.lwc.get_moist_air_density(101325, 996.3, 186 | 253.15) 187 | 188 | self.assertAlmostEqual(empiric_hrho_0, 1.276, 3) 189 | self.assertAlmostEqual(empiric_hrho_20, 1.205, 4) 190 | self.assertAlmostEqual(ideal_hrho_15, 1.2250, 4) 191 | self.assertAlmostEqual(ideal_hrho_20, 1.2041, 4) 192 | self.assertAlmostEqual(humid_ideal_hrho_20, 1.194, 3) 193 | self.assertAlmostEqual(empiric_humid_hrho_20, 1.1945, 4) 194 | self.assertAlmostEqual(humid_ideal_hrho_neg20, 1.3892, 4) 195 | self.assertAlmostEqual(empiric_humid_hrho_neg20, 1.3902, 4) 196 | 197 | def test_get_incloud_mixing_ratio(self): 198 | self.lwc.cbh = 100 199 | self.lwc.cth = 1000 200 | beta_0 = self.lwc.get_incloud_mixing_ratio(500, 1000, 100) 201 | beta_1 = self.lwc.get_incloud_mixing_ratio(100, 1000, 100) 202 | beta_2 = self.lwc.get_incloud_mixing_ratio(130, 1000, 100) 203 | beta_3 = self.lwc.get_incloud_mixing_ratio(175, 1000, 100) 204 | beta_4 = self.lwc.get_incloud_mixing_ratio(950, 1000, 100) 205 | self.assertAlmostEqual(beta_0, 0.3) 206 | self.assertAlmostEqual(beta_1, 0) 207 | self.assertAlmostEqual(beta_2, 0.12) 208 | self.assertAlmostEqual(beta_3, 0.3, 2) 209 | self.assertAlmostEqual(beta_4, 0.3) 210 | 211 | def test_get_incloud_mixing_ratio_limit(self): 212 | self.lwc.cbh = 950 213 | self.lwc.cth = 1000 214 | beta_0 = self.lwc.get_incloud_mixing_ratio(950, 1000, 950) 215 | beta_1 = self.lwc.get_incloud_mixing_ratio(960, 1000, 950) 216 | beta_2 = self.lwc.get_incloud_mixing_ratio(970, 1000, 950) 217 | beta_3 = self.lwc.get_incloud_mixing_ratio(980, 1000, 950) 218 | beta_4 = self.lwc.get_incloud_mixing_ratio(990, 1000, 950) 219 | beta_5 = self.lwc.get_incloud_mixing_ratio(1000, 1000, 950) 220 | self.assertAlmostEqual(beta_0, 0.3) 221 | self.assertAlmostEqual(beta_1, 0.3) 222 | self.assertAlmostEqual(beta_2, 0.3) 223 | self.assertAlmostEqual(beta_3, 0.3, 2) 224 | self.assertAlmostEqual(beta_4, 0.3) 225 | self.assertAlmostEqual(beta_5, 0.3) 226 | 227 | def test_optimize_cbh_brute(self): 228 | self.lwc.thickness = 100 229 | ret_brute = self.lwc.optimize_cbh(100., method='brute') 230 | self.assertAlmostEqual(ret_brute, 421., 1) 231 | 232 | def test_optimize_cbh_basin(self): 233 | self.lwc.thickness = 100 234 | np.random.seed(42) 235 | ret_basin = self.lwc.optimize_cbh(100., method='basin') 236 | self.assertIn(round(ret_basin, 0), [421, 479, 478, 477]) 237 | 238 | def test_optimize_cbh_start(self): 239 | self.lwc.thickness = 100. 240 | np.random.seed(42) 241 | listresult = [] 242 | listresult.append(self.lwc.optimize_cbh(1000., method='basin')) 243 | listresult.append(self.lwc.optimize_cbh(900., method='basin')) 244 | listresult.append(self.lwc.optimize_cbh(800., method='basin')) 245 | listresult.append(self.lwc.optimize_cbh(700., method='basin')) 246 | listresult.append(self.lwc.optimize_cbh(600., method='basin')) 247 | listresult.append(self.lwc.optimize_cbh(500., method='basin')) 248 | listresult.append(self.lwc.optimize_cbh(400., method='basin')) 249 | listresult.append(self.lwc.optimize_cbh(300., method='basin')) 250 | listresult.append(self.lwc.optimize_cbh(200., method='basin')) 251 | listresult.append(self.lwc.optimize_cbh(100., method='basin')) 252 | listresult.append(self.lwc.optimize_cbh(0., method='basin')) 253 | listresult.append(self.lwc.optimize_cbh(-100., method='basin')) 254 | test = [round(i, 0) == 421 for i in listresult] 255 | self.assertGreaterEqual(sum(test), 8) 256 | 257 | @unittest.expectedFailure 258 | def test_optimize_cbh_start_thin(self): 259 | self.thinlwc.thickness = 10. 260 | np.random.seed(42) 261 | listresult = [] 262 | listresult.append(self.thinlwc.optimize_cbh(1000., method='basin')) 263 | listresult.append(self.thinlwc.optimize_cbh(900., method='basin')) 264 | listresult.append(self.thinlwc.optimize_cbh(800., method='basin')) 265 | listresult.append(self.thinlwc.optimize_cbh(700., method='basin')) 266 | listresult.append(self.thinlwc.optimize_cbh(600., method='basin')) 267 | listresult.append(self.thinlwc.optimize_cbh(500., method='basin')) 268 | listresult.append(self.thinlwc.optimize_cbh(400., method='basin')) 269 | listresult.append(self.thinlwc.optimize_cbh(300., method='basin')) 270 | listresult.append(self.thinlwc.optimize_cbh(200., method='basin')) 271 | listresult.append(self.thinlwc.optimize_cbh(100., method='basin')) 272 | listresult.append(self.thinlwc.optimize_cbh(0., method='basin')) 273 | listresult.append(self.thinlwc.optimize_cbh(-100., method='basin')) 274 | test = [round(i, 0) == 421 for i in listresult] 275 | self.assertGreaterEqual(sum(test), 8) 276 | 277 | def test_optimize_cbh_basin_nan(self): 278 | self.nanlwc.thickness = 100 279 | np.random.seed(42) 280 | ret_basin = self.nanlwc.optimize_cbh(100., method='basin') 281 | self.assertTrue(np.isnan(ret_basin)) 282 | 283 | def test_optimize_cbh_basin_nodata(self): 284 | self.nodatalwc.thickness = 100 285 | np.random.seed(42) 286 | ret_basin = self.nodatalwc.optimize_cbh(100., method='basin') 287 | self.assertTrue(np.isnan(ret_basin)) 288 | 289 | def test_get_visibility(self): 290 | LowWaterCloud(2000., 255., 400., 0, 10e-6) 291 | vis = self.lwc.get_visibility(1) 292 | vis2 = self.lwc.get_visibility(1/1000.) 293 | self.assertAlmostEqual(vis, 3.912, 3) 294 | self.assertAlmostEqual(vis2, 3912, 0) 295 | 296 | def test_get_liquid_density(self): 297 | LowWaterCloud(2000., 285., 400., 0) 298 | rho1 = self.lwc.get_liquid_density(20, 100e5) 299 | rho2 = self.lwc.get_liquid_density(4, 1e5) 300 | rho3 = self.lwc.get_liquid_density(0, 1e5) 301 | self.assertAlmostEqual(rho1, 1002.66, 3) 302 | self.assertAlmostEqual(rho2, 999.448, 3) 303 | self.assertAlmostEqual(rho3, 999.80, 3) 304 | 305 | def test_get_effective_radius(self): 306 | lwc = LowWaterCloud(1000., 255., 400., reff=10e-6, cbh=0) 307 | reff_b = lwc.get_effective_radius(0) 308 | reff_m = lwc.get_effective_radius(500) 309 | reff_t = lwc.get_effective_radius(lwc.cth) 310 | self.assertAlmostEqual(reff_b, 1e-6) 311 | self.assertAlmostEqual(reff_m, 5.5e-6) 312 | self.assertAlmostEqual(reff_t, 10e-6) 313 | 314 | def test_get_effective_radius_with_cbh(self): 315 | lwc = LowWaterCloud(1000., 255., 400., reff=10e-6, cbh=100) 316 | reff_b = lwc.get_effective_radius(100) 317 | reff_m = lwc.get_effective_radius(550) 318 | reff_t = lwc.get_effective_radius(lwc.cth) 319 | self.assertAlmostEqual(reff_b, 1e-6) 320 | self.assertAlmostEqual(reff_m, 5.5e-6) 321 | self.assertAlmostEqual(reff_t, 10e-6) 322 | 323 | def test_get_fog_cloud_height(self): 324 | lwc = LowWaterCloud(2000., 275., 400., 100, 10e-6) 325 | lwc.init_cloud_layers(100, 50) 326 | fbh = lwc.get_fog_base_height() 327 | self.assertAlmostEqual(fbh, 125, 0) 328 | 329 | def test_get_fog_cloud_height2(self): 330 | lwc = LowWaterCloud(1000., 275., 100., 100., 10e-6) 331 | lwc.init_cloud_layers(100, 10) 332 | lwc.get_liquid_water_path() 333 | np.random.seed(42) 334 | lwc.optimize_cbh(lwc.cbh, method="basin") 335 | fbh = lwc.get_fog_base_height() 336 | self.assertAlmostEqual(lwc.lwp, 100, 3) 337 | self.assertAlmostEqual(lwc.maxlwc, 0.494, 3) 338 | self.assertAlmostEqual(fbh, 612, 0) 339 | 340 | def test_reset_layers_after_minimisation(self): 341 | """Test that layers are properly reset after minimisation 342 | 343 | Test for fix for #29 344 | """ 345 | lwc = LowWaterCloud(1000., 275., 100., 100., 10e-6) 346 | 347 | lwc.optimize_cbh(lwc.cbh, method="brute") 348 | brl = lwc.layers 349 | brfb = lwc.get_fog_base_height() 350 | 351 | np.random.seed(42) 352 | lwc.optimize_cbh(lwc.cbh, method="basin") 353 | bhl = lwc.layers 354 | bhfb = lwc.get_fog_base_height() 355 | 356 | np.testing.assert_almost_equal( 357 | [l.visibility for l in bhl if l.visibility is not None], 358 | [l.visibility for l in brl if l.visibility is not None], 359 | -1) 360 | self.assertAlmostEqual(brfb, bhfb, 0) 361 | 362 | 363 | def suite(): 364 | """The test suite for test_lowwatercloud. 365 | """ 366 | loader = unittest.TestLoader() 367 | mysuite = unittest.TestSuite() 368 | mysuite.addTest(loader.loadTestsFromTestCase(Test_LowWaterCloud)) 369 | 370 | return mysuite 371 | 372 | 373 | if __name__ == "__main__": 374 | unittest.main() 375 | -------------------------------------------------------------------------------- /fogpy/test/test_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2017-2020 Fogpy developers 2 | 3 | # This file is part of the fogpy package. 4 | 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | import logging 18 | 19 | import pytest 20 | import unittest.mock 21 | 22 | 23 | @unittest.mock.patch("requests.get") 24 | def test_dl_dem(rg, tmp_path, caplog): 25 | from fogpy.utils import dl_dem 26 | rg.return_value.content = b"12345" 27 | 28 | with caplog.at_level(logging.INFO): 29 | dl_dem(tmp_path / "foo") 30 | assert f"Downloading https://zenodo.org/record/3885398/files/foo to {tmp_path / 'foo'!s}" in caplog.text 31 | assert (tmp_path / "foo").exists() 32 | assert (tmp_path / "foo").open(mode="rb").read() == b"12345" 33 | with pytest.raises(FileExistsError): 34 | dl_dem(tmp_path / "foo") 35 | -------------------------------------------------------------------------------- /fogpy/utils/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # fogpy is free software: you can redistribute it and/or modify it 8 | # under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # fogpy is distributed in the hope that it will be useful, but 13 | # WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 15 | # General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with fogpy. If not, see . 19 | 20 | """Small utilities needed by Fogpy 21 | """ 22 | 23 | import logging 24 | 25 | import requests 26 | 27 | logger = logging.getLogger(__name__) 28 | 29 | 30 | def dl_dem(dem): 31 | """Download Digital Elevation Model 32 | 33 | Download a Digital Elevation Model (DEM) from Zenodo. 34 | 35 | The source URI is derived from the destination path. 36 | 37 | Args: 38 | dem (pathlib.Path): Destination 39 | """ 40 | src = "https://zenodo.org/record/3885398/files/" + dem.name 41 | 42 | if dem.exists(): 43 | raise FileExistsError("Already exists: {dem!s}") 44 | r = requests.get(src) 45 | logger.info(f"Downloading {src!s} to {dem!s}") 46 | dem.parent.mkdir(exist_ok=True, parents=True) 47 | with dem.open(mode="wb") as fp: 48 | fp.write(r.content) 49 | -------------------------------------------------------------------------------- /fogpy/utils/add_synop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2016-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """This script adds synope data for visibility to given geolocated image""" 21 | 22 | import fogpy 23 | import numpy as np 24 | import os 25 | from .import_synop import read_synop 26 | from datetime import datetime 27 | from trollimage.image import Image 28 | from trollimage.colormap import Colormap 29 | 30 | 31 | # Define custom fog colormap 32 | fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)), 33 | (1., (1.0, 1.0, 229 / 255.0))) 34 | 35 | maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0))) 36 | 37 | viscol = Colormap((0., (1.0, 0.0, 0.0)), 38 | (5000, (0.7, 0.7, 0.7))) 39 | # Red - Violet - Blue - Green 40 | vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)), 41 | (1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)), 42 | (5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)), 43 | (10000, (77 / 255.0, 175 / 255.0, 74 / 255.0))) 44 | 45 | 46 | def add_to_array(arr, area, time, bufr, savedir='/tmp', name=None, mode='L', 47 | resize=None, ptsize=None, save=False): 48 | """Add synoptical reports from stations to provided geolocated image array 49 | """ 50 | # Create array image 51 | arrshp = arr.shape[:2] 52 | print(np.nanmin(arr), np.nanmax(arr)) 53 | arr_img = Image(arr, mode=mode) 54 | # arr_img = Image(channels=[arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]], 55 | # mode='RGB') 56 | arr_img.stretch('crude') 57 | arr_img.invert() 58 | arr_img.colorize(maskcol) 59 | arr_img.invert() 60 | 61 | # Import bufr 62 | stations = read_synop(bufr, 'visibility') 63 | currentstations = stations[time.strftime("%Y%m%d%H0000")] 64 | lats = [i[2] for i in currentstations] 65 | lons = [i[3] for i in currentstations] 66 | vis = [i[4] for i in currentstations] 67 | 68 | # Create array for synop parameter 69 | visarr = np.empty(arrshp) 70 | visarr.fill(np.nan) 71 | 72 | x, y = (area.get_xy_from_lonlat(lons, lats)) 73 | vis_ma = np.ma.array(vis, mask=x.mask) 74 | if ptsize: 75 | xpt = np.array([]) 76 | ypt = np.array([]) 77 | for i, j in zip(x, y): 78 | xmesh, ymesh = np.meshgrid(np.linspace(i - ptsize, i + ptsize, 79 | ptsize * 2 + 1), 80 | np.linspace(j - ptsize, j + ptsize, 81 | ptsize * 2 + 1)) 82 | xpt = np.append(xpt, xmesh.ravel()) 83 | ypt = np.append(ypt, ymesh.ravel()) 84 | vispt = np.ma.array([np.full(((ptsize * 2 + 1, 85 | ptsize * 2 + 1)), p) for p in vis_ma]) 86 | visarr[ypt.astype(int), xpt.astype(int)] = vispt.ravel() 87 | else: 88 | visarr[y.compressed(), x.compressed()] = vis_ma.compressed() 89 | visarr_ma = np.ma.masked_invalid(visarr) 90 | station_img = Image(visarr_ma, mode='L') 91 | station_img.colorize(vis_colset) 92 | station_img.merge(arr_img) 93 | if resize is not None: 94 | station_img.resize((arrshp[0] * resize, arrshp[1] * resize)) 95 | if name is None: 96 | timestr = time.strftime("%Y%m%d%H%M") 97 | name = "fog_filter_example_stations_{}.png".format(timestr) 98 | if save: 99 | savepath = os.path.join(savedir, name) 100 | station_img.save(savepath) 101 | 102 | return(station_img) 103 | 104 | 105 | def add_to_image(image, area, time, bufr, savedir='/tmp', name=None, 106 | bgimg=None, resize=None, ptsize=None, save=False): 107 | """Add synoptical visibility reports from station data to provided 108 | geolocated image array 109 | """ 110 | arrshp = image.shape[:2] 111 | # Add optional background image 112 | if bgimg is not None: 113 | # Get background image 114 | bg_img = Image(bgimg.squeeze(), mode='L', fill_value=None) 115 | bg_img.stretch("crude") 116 | bg_img.convert("RGB") 117 | # bg_img.invert() 118 | image.merge(bg_img) 119 | # Import bufr 120 | stations = read_synop(bufr, 'visibility') 121 | currentstations = stations[time.strftime("%Y%m%d%H0000")] 122 | lats = [i[2] for i in currentstations] 123 | lons = [i[3] for i in currentstations] 124 | vis = [i[4] for i in currentstations] 125 | 126 | # Create array for synop parameter 127 | visarr = np.empty(arrshp) 128 | visarr.fill(np.nan) 129 | # Red - Violet - Blue - Green 130 | vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)), 131 | (1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)), 132 | (5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)), 133 | (10000, (77 / 255.0, 175 / 255.0, 74 / 255.0))) 134 | x, y = (area.get_xy_from_lonlat(lons, lats)) 135 | vis_ma = np.ma.array(vis, mask=x.mask) 136 | if ptsize: 137 | xpt = np.array([]) 138 | ypt = np.array([]) 139 | for i, j in zip(x, y): 140 | xmesh, ymesh = np.meshgrid(np.linspace(i - ptsize, i + ptsize, 141 | ptsize * 2 + 1), 142 | np.linspace(j - ptsize, j + ptsize, 143 | ptsize * 2 + 1)) 144 | xpt = np.append(xpt, xmesh.ravel()) 145 | ypt = np.append(ypt, ymesh.ravel()) 146 | vispt = np.ma.array([np.full(((ptsize * 2 + 1, 147 | ptsize * 2 + 1)), p) for p in vis_ma]) 148 | visarr[ypt.astype(int), xpt.astype(int)] = vispt.ravel() 149 | else: 150 | visarr[y.compressed(), x.compressed()] = vis_ma.compressed() 151 | visarr_ma = np.ma.masked_invalid(visarr) 152 | station_img = Image(visarr_ma, mode='L') 153 | station_img.colorize(vis_colset) 154 | image.convert("RGB") 155 | station_img.merge(image) 156 | if resize is not None: 157 | station_img.resize((arrshp[0] * resize, arrshp[1] * resize)) 158 | if name is None: 159 | timestr = time.strftime("%Y%m%d%H%M") 160 | name = "fog_filter_example_stations_{}.png".format(timestr) 161 | if save: 162 | savepath = os.path.join(savedir, name) 163 | station_img.save(savepath) 164 | 165 | return(station_img) 166 | 167 | 168 | if __name__ == '__main__': 169 | 170 | from pyresample import geometry 171 | from scipy import misc 172 | 173 | # Set time stamp 174 | time = datetime(2013, 11, 12, 8, 30) 175 | # Import image 176 | # imgfile = 'LowCloudFilter_201311120830.png' 177 | imgfile = 'LowCloudFilter_201311120830.png' 178 | imgdir = '/tmp/FLS' 179 | resize = 5 # Resize factor of FLS image 180 | imgpath = os.path.join(imgdir, imgfile) 181 | arr = misc.imread(imgpath) 182 | arr = np.ma.masked_where(arr == 0, arr) 183 | print(arr.shape) 184 | print(np.min(arr)) 185 | # Get bufr file 186 | base = os.path.split(fogpy.__file__) 187 | inbufr = os.path.join(base[0], '..', 'etc', 'result_{}.bufr' 188 | .format(time.strftime("%Y%m%d"))) 189 | # bufr_dir = '/data/tleppelt/skydata/' 190 | # bufr_file = "result_{}".format(time.strftime("%Y%m%d")) 191 | # inbufr = os.path.join(bufr_dir, bufr_file) 192 | 193 | area_id = "geos_germ" 194 | name = "geos_germ" 195 | proj_id = "geos" 196 | proj_dict = {'a': '6378169.00', 'lon_0': '0.00', 'h': '35785831.00', 197 | 'b': '6356583.80', 'proj': 'geos', 'lat_0': '0.00'} 198 | x_size = 298 * resize 199 | y_size = 141 * resize 200 | area_extent = (214528.82635591552, 4370087.2110124603, 1108648.9697693815, 201 | 4793144.0573926577) 202 | area_def = geometry.AreaDefinition(area_id, name, proj_id, proj_dict, 203 | x_size, y_size, area_extent) 204 | print(area_def) 205 | img = Image(arr[:, :, :3], mode='RGB') 206 | add_to_image(img, area_def, time, inbufr) 207 | -------------------------------------------------------------------------------- /fogpy/utils/export_synop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """ This module implements export routines for synoptical station data as 21 | shapefile file""" 22 | 23 | import fogpy 24 | import os 25 | import osgeo 26 | import osgeo.ogr 27 | import osgeo.osr 28 | 29 | from fogpy.utils import import_synop 30 | 31 | 32 | class DummyException(Exception): 33 | pass 34 | 35 | 36 | def create_shpfile(data, outfile, epsg=4326, para=['vis'], nodata=-9999): 37 | """ Function to export synoptical station data as ESRI shape file""" 38 | # Init spatial reference locally 39 | spatialReference = osgeo.osr.SpatialReference() 40 | # Define this reference to be the EPSG code 41 | spatialReference.ImportFromEPSG(int(epsg)) 42 | driver = osgeo.ogr.GetDriverByName('ESRI Shapefile') 43 | # Create export file 44 | shapeData = driver.CreateDataSource(outfile) 45 | # Create a corresponding layer for our data with given spatial information. 46 | layer = shapeData.CreateLayer( 47 | 'layer', spatialReference, osgeo.ogr.wkbPoint) 48 | # Gets parameters of the current shapefile 49 | layer_defn = layer.GetLayerDefn() 50 | index = 0 51 | fielddict = {'name': 0, 'altitude': 1, 'lat': 2, 'lon': 3} 52 | fields = ['name', 'altitude', 'lat', 'lon'] + para 53 | addindex = 4 54 | for ele in para: 55 | fielddict[ele] = addindex 56 | addindex += 1 57 | # Create new fields with the content of read synop data 58 | for field in fields: 59 | new_field = osgeo.ogr.FieldDefn(field, osgeo.ogr.OFTString) 60 | layer.CreateField(new_field) 61 | # Loop over stations and add them as vector points 62 | for row in data: 63 | point = osgeo.ogr.Geometry(osgeo.ogr.wkbPoint) 64 | point.AddPoint(row[3], row[2]) 65 | feature = osgeo.ogr.Feature(layer_defn) 66 | feature.SetGeometry(point) # Set the coordinates 67 | feature.SetFID(index) 68 | for field in fields: 69 | i = feature.GetFieldIndex(field) 70 | if row[fielddict[field]] is None: 71 | val = nodata 72 | else: 73 | val = row[fielddict[field]] 74 | try: 75 | feature.SetField(i, val) 76 | except DummyException: 77 | Warning("Index: {} - Value {} of type: {} can't be added" 78 | .format(i, val, type(val))) 79 | feature.SetField(i, None) 80 | layer.CreateFeature(feature) 81 | index += 1 82 | shapeData.Destroy() # Close the shapefile 83 | 84 | 85 | def main(): 86 | shpfile = '/tmp/FLS/stations_20131112080000.shp' 87 | base = os.path.split(fogpy.__file__) 88 | synopfile = os.path.join(base[0], '..', 'etc', 'result_20131112.bufr') 89 | metarfile = os.path.join( 90 | base[0], 91 | '..', 92 | 'etc', 93 | 'result_20131112_metar.bufr') 94 | swisfile = os.path.join(base[0], '..', 'etc', 'result_20131112_swis.bufr') 95 | synops = import_synop.read_synop(synopfile, 'visibility') 96 | metars = import_synop.read_metar(metarfile, 'visibility', latlim=(47, 56), 97 | lonlim=(4, 16)) 98 | swis = import_synop.read_swis(swisfile, 'visibility') 99 | input = synops['20131112080000'] + metars['20131112083000'] + \ 100 | swis['20131112083000'] 101 | create_shpfile(input, shpfile) 102 | 103 | 104 | if __name__ == '__main__': 105 | main() 106 | -------------------------------------------------------------------------------- /fogpy/utils/import_synop.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2016-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """ This module implements import routines for synoptical station data as 21 | bufr file""" 22 | 23 | import fogpy 24 | import logging 25 | import os 26 | import os.path 27 | 28 | from fogpy.lowwatercloud import CloudLayer as CL 29 | from trollbufr.bufr import Bufr 30 | from trollbufr import load_file 31 | from datetime import datetime 32 | 33 | trollbufr_logger = logging.getLogger('trollbufr') 34 | trollbufr_logger.setLevel(logging.CRITICAL) 35 | 36 | 37 | class DummyException(Exception): 38 | pass 39 | 40 | 41 | def read_synop(file, params, min=None, max=None): 42 | """ Reading bufr files for synoptical station data and provide dictionary 43 | with weather data for cloud base height and visibility. 44 | The results are subsequently filtered by cloud base height and visibility 45 | 46 | Arguments: 47 | file Bufr file with synop reports 48 | params List of parameter names that will be extracted 49 | min Threshold for minimum value of parameter 50 | max Threshold for maximum value of parameter 51 | 52 | Returns list of station dictionaries for given thresholds 53 | """ 54 | result = {} 55 | bfr = Bufr("libdwd", os.getenv("BUFR_TABLES")) 56 | for blob, size, header in load_file.next_bufr(file): 57 | bfr.decode(blob) 58 | try: 59 | for subset in bfr.next_subset(): 60 | stationdict = {} 61 | for (k, m, v, q) in subset.next_data(): 62 | if k == 1015: # Station name 63 | stationdict['name'] = v.strip() 64 | if k == 5001: # Latitude 65 | stationdict['lat'] = v 66 | if k == 6001: # Longitude 67 | stationdict['lon'] = v 68 | if k == 7030: # Altitude 69 | stationdict['altitude'] = v 70 | elif k == 4001: # Year 71 | stationdict['year'] = v 72 | elif k == 4002: # Month 73 | stationdict['month'] = v 74 | elif k == 4003: # Day 75 | stationdict['day'] = v 76 | elif k == 4004: # Hour 77 | stationdict['hour'] = v 78 | elif k == 4005: # Hour 79 | stationdict['minute'] = v 80 | elif k == 20003: # Present weather 81 | stationdict['present weather'] = v 82 | # Values from 40 to 49 are refering to fog and ice fog 83 | # Patchy fog or fog edges value 11 or 12 84 | elif k == 20004: # Past weather 85 | stationdict['past weather'] = v 86 | # Values from 40 to 49 are refering to fog and ice fog 87 | # Patchy fog or fog edges value 11 or 12 88 | elif k == 20013: # Cloud base height 89 | if v is not None: 90 | if ('cbh' in stationdict.keys() and 91 | stationdict["cbh"] is not None): 92 | if stationdict['cbh'] > v: 93 | stationdict['cbh'] = v 94 | else: 95 | stationdict['cbh'] = v 96 | else: 97 | stationdict['cbh'] = None 98 | elif k == 2001: # Auto/manual measurement 99 | # 1 - 3 : Manual human observations. Manned stations 100 | # 0, 4 - 7 : Only automatic observations 101 | stationdict['type'] = v 102 | elif k == 20001: # Visibility 103 | stationdict['visibility'] = v 104 | elif k == 12101: # Mean air temperature in K 105 | stationdict['air temperature'] = v 106 | elif k == 12103: # Dew point temperature in K 107 | stationdict['dew point'] = v 108 | elif k == 20010: # Cloud cover in % 109 | stationdict['cloudcover'] = v 110 | elif k == 13003: # Relative humidity in % 111 | stationdict['relative humidity'] = v 112 | elif k == 11001: # Wind direction in degree 113 | stationdict['wind direction'] = v 114 | elif k == 11002: # Wind speed in m s-1 115 | stationdict['wind speed'] = v 116 | elif k == 1002: # WMO station number 117 | stationdict['wmo'] = v 118 | # Apply thresholds 119 | stationtime = datetime(stationdict['year'], 120 | stationdict['month'], 121 | stationdict['day'], 122 | stationdict['hour'], 123 | stationdict['minute'], 124 | ).strftime("%Y%m%d%H%M%S") 125 | paralist = [] 126 | if not isinstance(params, list): 127 | params = [params] 128 | for param in params: 129 | if param not in stationdict: 130 | res = None 131 | elif min is not None and stationdict[param] < min: 132 | res = None 133 | elif max is not None and stationdict[param] >= max: 134 | res = None 135 | elif stationdict[param] is None: 136 | res = None 137 | else: 138 | res = stationdict[param] 139 | paralist.append(res) 140 | if all([i is None for i in paralist]): 141 | continue 142 | # Add station data to result list 143 | if stationtime in result.keys(): 144 | result[stationtime].append([stationdict['name'], 145 | stationdict['altitude'], 146 | stationdict['lat'], 147 | stationdict['lon']] + paralist) 148 | else: 149 | result[stationtime] = [[stationdict['name'], 150 | stationdict['altitude'], 151 | stationdict['lat'], 152 | stationdict['lon']] + paralist] 153 | except DummyException as e: 154 | "ERROR: Unresolved station request: {}".format(e) 155 | return(result) 156 | 157 | 158 | def read_metar(file, params, min=None, max=None, latlim=None, lonlim=None): 159 | """ Reading bufr files for METAR station data and provide dictionary 160 | with weather data for cloud base height and visibility. 161 | The results are subsequently filtered by cloud base height and visibility 162 | 163 | Arguments: 164 | file Bufr file with synop reports 165 | params List of parameter names that will be extracted 166 | min Threshold for minimum value of parameter 167 | max Threshold for maximum value of parameter 168 | latlim Tuple of minimum and maximum latitude values for valid result 169 | lonlim Tuple of minimum and maximum longitude values for valid result 170 | 171 | Returns list of station dictionaries for given thresholds 172 | """ 173 | result = {} 174 | bfr = Bufr("libdwd", os.getenv("BUFR_TABLES")) 175 | for blob, size, header in load_file.next_bufr(file): 176 | bfr.decode(blob) 177 | try: 178 | for subset in bfr.next_subset(): 179 | stationdict = {} 180 | for (k, m, v, q) in subset.next_data(): 181 | if k == 1063: # Station name 182 | stationdict['name'] = v.strip() 183 | if k == 5002: # Latitude 184 | stationdict['lat'] = v 185 | if k == 6002: # Longitude 186 | stationdict['lon'] = v 187 | if k == 7030: # Altitude 188 | stationdict['altitude'] = v 189 | elif k == 4001: # Year 190 | stationdict['year'] = v 191 | elif k == 4002: # Month 192 | stationdict['month'] = v 193 | elif k == 4003: # Day 194 | stationdict['day'] = v 195 | elif k == 4004: # Hour 196 | stationdict['hour'] = v 197 | elif k == 4005: # Hour 198 | stationdict['minute'] = v 199 | elif k == 20003: # Present weather 200 | stationdict['present weather'] = v 201 | # Values from 40 to 49 are refering to fog and ice fog 202 | # Patchy fog or fog edges value 11 or 12 203 | elif k == 20004: # Past weather 204 | stationdict['past weather'] = v 205 | # Values from 40 to 49 are refering to fog and ice fog 206 | # Patchy fog or fog edges value 11 or 12 207 | elif k == 20013: # Cloud base height 208 | if v is not None: 209 | if ('cbh' in stationdict.keys() and 210 | stationdict["cbh"] is not None): 211 | if stationdict['cbh'] > v: 212 | stationdict['cbh'] = v 213 | else: 214 | stationdict['cbh'] = v 215 | else: 216 | stationdict['cbh'] = None 217 | elif k == 2001: # Auto/manual measurement 218 | # 1 - 3 : Manual human observations. Manned stations 219 | # 0, 4 - 7 : Only automatic observations 220 | stationdict['type'] = v 221 | elif k == 20060: # Prevailing visibility 222 | stationdict['visibility'] = v 223 | elif k == 12023: # Mean air temperature in °C 224 | stationdict['air temperature'] = CL.check_temp( 225 | v, 'kelvin') 226 | elif k == 12024: # Dew point temperature in °C 227 | stationdict['dew point'] = CL.check_temp(v, 'kelvin') 228 | elif k == 20010: # Cloud cover in % 229 | stationdict['cloudcover'] = v 230 | elif k == 13003: # Relative humidity in % 231 | stationdict['relative humidity'] = v 232 | elif k == 11001: # Wind direction in degree 233 | stationdict['wind direction'] = v 234 | elif k == 11002: # Wind speed in m s-1 235 | stationdict['wind speed'] = v 236 | elif k == 1002: # WMO station number 237 | stationdict['wmo'] = v 238 | elif k == 1024: # WMO station number 239 | stationdict['coords'] = v 240 | # Apply thresholds 241 | stationtime = datetime(stationdict['year'], 242 | stationdict['month'], 243 | stationdict['day'], 244 | stationdict['hour'], 245 | stationdict['minute'], 246 | ).strftime("%Y%m%d%H%M%S") 247 | paralist = [] 248 | if not isinstance(params, list): 249 | params = [params] 250 | for param in params: 251 | if param not in stationdict: 252 | res = None 253 | elif min is not None and stationdict[param] < min: 254 | res = None 255 | elif max is not None and stationdict[param] >= max: 256 | res = None 257 | elif stationdict[param] is None: 258 | res = None 259 | else: 260 | res = stationdict[param] 261 | paralist.append(res) 262 | if all([i is None for i in paralist]): 263 | continue 264 | # Test for limited coordinates 265 | if latlim is not None: 266 | if stationdict['lat'] < latlim[0]: 267 | continue 268 | elif stationdict['lat'] > latlim[1]: 269 | continue 270 | if lonlim is not None: 271 | if stationdict['lon'] < lonlim[0]: 272 | continue 273 | elif stationdict['lon'] > lonlim[1]: 274 | continue 275 | # Add station data to result list 276 | if stationtime in result.keys(): 277 | result[stationtime].append([stationdict['name'], 278 | stationdict['altitude'], 279 | stationdict['lat'], 280 | stationdict['lon']] + paralist) 281 | else: 282 | result[stationtime] = [[stationdict['name'], 283 | stationdict['altitude'], 284 | stationdict['lat'], 285 | stationdict['lon']] + paralist] 286 | except DummyException as e: 287 | "ERROR: Unresolved station request: {}".format(e) 288 | return(result) 289 | 290 | 291 | def read_swis(file, params, min=None, max=None, latlim=None, lonlim=None): 292 | """ Reading bufr files for street weather information system data and 293 | provide dictionary with weather data for cloud base height and visibility. 294 | The results are subsequently filtered by cloud base height and visibility 295 | 296 | Arguments: 297 | file Bufr file with synop reports 298 | params List of parameter names that will be extracted 299 | min Threshold for minimum value of parameter 300 | max Threshold for maximum value of parameter 301 | latlim Tuple of minimum and maximum latitude values for valid result 302 | lonlim Tuple of minimum and maximum longitude values for valid result 303 | 304 | Returns list of station dictionaries for given thresholds 305 | """ 306 | result = {} 307 | bfr = Bufr("libdwd", os.getenv("BUFR_TABLES")) 308 | for blob, size, header in load_file.next_bufr(file): 309 | bfr.decode(blob) 310 | try: 311 | for subset in bfr.next_subset(): 312 | stationdict = {} 313 | for (k, m, v, q) in subset.next_data(): 314 | if k == 1015: # Station name 315 | stationdict['name'] = v.strip() 316 | if k == 5001: # Latitude 317 | stationdict['lat'] = v 318 | if k == 6001: # Longitude 319 | stationdict['lon'] = v 320 | if k == 7030: # Altitude 321 | stationdict['altitude'] = v 322 | elif k == 4001: # Year 323 | stationdict['year'] = v 324 | elif k == 4002: # Month 325 | stationdict['month'] = v 326 | elif k == 4003: # Day 327 | stationdict['day'] = v 328 | elif k == 4004: # Hour 329 | stationdict['hour'] = v 330 | elif k == 4005: # Hour 331 | stationdict['minute'] = v 332 | elif k == 20003: # Present weather 333 | stationdict['present weather'] = v 334 | # Values from 40 to 49 are refering to fog and ice fog 335 | # Patchy fog or fog edges value 11 or 12 336 | # 28 refers to fog or ice fog from manned station 337 | # Reference: FM 94, table: 4677 338 | elif k == 20004: # Past weather 339 | stationdict['past weather'] = v 340 | # Values from 40 to 49 are refering to fog and ice fog 341 | # Patchy fog or fog edges value 11 or 12 342 | elif k == 20013: # Cloud base height 343 | if v is not None: 344 | if ('cbh' in stationdict.keys() and 345 | stationdict["cbh"] is not None): 346 | if stationdict['cbh'] > v: 347 | stationdict['cbh'] = v 348 | else: 349 | stationdict['cbh'] = v 350 | else: 351 | stationdict['cbh'] = None 352 | elif k == 2001: # Auto/manual measurement 353 | # 1 - 3 : Manual human observations. Manned stations 354 | # 0, 4 - 7 : Only automatic observations 355 | stationdict['type'] = v 356 | elif k == 20001: # Prevailing visibility 357 | if v is not None: 358 | stationdict['visibility'] = v * 10 359 | else: 360 | stationdict['visibility'] = v 361 | elif k == 12101: # Mean air temperature in K 362 | stationdict['air temperature'] = v 363 | elif k == 12103: # Dew point temperature in K 364 | stationdict['dew point'] = v 365 | elif k == 20010: # Cloud cover in % 366 | stationdict['cloudcover'] = v 367 | elif k == 13003: # Relative humidity in % 368 | stationdict['relative humidity'] = v 369 | elif k == 11001: # Wind direction in degree 370 | stationdict['wind direction'] = v 371 | elif k == 11002: # Wind speed in m s-1 372 | stationdict['wind speed'] = v 373 | elif k == 1002: # WMO station number 374 | stationdict['wmo'] = v 375 | elif k == 1024: # WMO station number 376 | stationdict['coords'] = v 377 | elif k == 33005: # WMO station number 378 | stationdict['quality'] = v 379 | # Apply thresholds 380 | stationtime = datetime(stationdict['year'], 381 | stationdict['month'], 382 | stationdict['day'], 383 | stationdict['hour'], 384 | stationdict['minute'], 385 | ).strftime("%Y%m%d%H%M%S") 386 | paralist = [] 387 | if not isinstance(params, list): 388 | params = [params] 389 | for param in params: 390 | if param not in stationdict: 391 | res = None 392 | elif min is not None and stationdict[param] < min: 393 | res = None 394 | elif max is not None and stationdict[param] >= max: 395 | res = None 396 | elif stationdict[param] is None: 397 | res = None 398 | else: 399 | res = stationdict[param] 400 | paralist.append(res) 401 | if all([i is None for i in paralist]): 402 | continue 403 | # Test for limited coordinates 404 | if latlim is not None: 405 | if stationdict['lat'] < latlim[0]: 406 | continue 407 | elif stationdict['lat'] > latlim[1]: 408 | continue 409 | if lonlim is not None: 410 | if stationdict['lon'] < lonlim[0]: 411 | continue 412 | elif stationdict['lon'] > lonlim[1]: 413 | continue 414 | # Add station data to result list 415 | if stationtime in result.keys(): 416 | result[stationtime].append([stationdict['name'], 417 | stationdict['altitude'], 418 | stationdict['lat'], 419 | stationdict['lon']] + paralist) 420 | else: 421 | result[stationtime] = [[stationdict['name'], 422 | stationdict['altitude'], 423 | stationdict['lat'], 424 | stationdict['lon']] + paralist] 425 | except DummyException as e: 426 | "ERROR: Unresolved station request: {}".format(e) 427 | return(result) 428 | 429 | 430 | def main(): 431 | base = os.path.split(fogpy.__file__) 432 | synopfile = os.path.join(base[0], '..', 'etc', 'result_20131112.bufr') 433 | metarfile = os.path.join( 434 | base[0], 435 | '..', 436 | 'etc', 437 | 'result_20131112_metar.bufr') 438 | swisfile = os.path.join(base[0], '..', 'etc', 'result_20131112_swis.bufr') 439 | print(read_synop(synopfile, ['visibility'])) 440 | print(read_metar(metarfile, 'visibility', latlim=(45, 60), lonlim=(3, 18))) 441 | print(read_swis(swisfile, ['visibility'])) 442 | 443 | 444 | if __name__ == '__main__': 445 | main() 446 | -------------------------------------------------------------------------------- /fogpy/utils/reproj_testdata.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2020 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """ This module provide utilities to reproject the test data """ 21 | 22 | import os 23 | import numpy as np 24 | import fogpy 25 | from datetime import datetime 26 | from pyresample import image, geometry 27 | from pyresample import utils 28 | from satpy.scene import Scene 29 | from satpy.dataset import Dataset 30 | from trollimage.colormap import Colormap 31 | from satpy.utils import debug_on 32 | 33 | debug_on() 34 | # Define geos projection boundaries for Germany. 35 | # area_extent: (x_ll, y_ll, x_ur, y_ur) 36 | germ_areadef = utils.load_area('/home/mastho/git/satpy/satpy/etc/areas.def', 37 | 'germ') 38 | euro_areadef = utils.load_area('/home/mastho/git/satpy/satpy/etc/areas.def', 39 | 'euro4') 40 | germ_extent = germ_areadef.area_extent_ll 41 | print(dir(germ_extent)) 42 | geos_areadef = utils.load_area('/home/mastho/git/satpy/satpy/etc/areas.def', 43 | 'EuropeCanary') 44 | print(germ_extent[0::2], germ_extent[1::2]) 45 | print(list(germ_extent[1::2])) 46 | x, y = geos_areadef.get_xy_from_lonlat(list(germ_extent[0::2]), 47 | list(germ_extent[1::2])) 48 | print(x, y) 49 | print(y[0]) 50 | xproj, yproj = geos_areadef.get_proj_coords() 51 | xll, xur = xproj[y, x] 52 | yll, yur = yproj[y, x] 53 | new_extent = (xll, yll, xur, yur) 54 | print(new_extent) 55 | area_id = 'Ger_geos' 56 | name = 'Ger_geos' 57 | proj_id = 'Ger_geos' 58 | proj4_args = ("proj=geos, lat_0=0.0, lon_0=0, a=6378144.0, " 59 | "b=6356759.0, h=35785831.0, rf=295.49") 60 | x_size = 298 61 | y_size = 141 62 | proj_dict = {'a': '6378144.0', 'b': '6356759.0', 'units': 'm', 'lon_0': '0', 63 | 'h': '35785831.0', 'lat_0': '0', 'rf': '295.49', 64 | 'proj': 'geos'} 65 | area_def = geometry.AreaDefinition(area_id, name, proj_id, proj_dict, x_size, 66 | y_size, new_extent) 67 | 68 | print(area_def) 69 | 70 | # Import test data 71 | base = os.path.split(fogpy.__file__) 72 | testfile = os.path.join(base[0], '..', 'etc', 'fog_testdata.npy') 73 | testdata = np.load(testfile) 74 | 75 | # Load test data 76 | inputs = np.dsplit(testdata, 13) 77 | ir108 = inputs[0] 78 | ir039 = inputs[1] 79 | vis008 = inputs[2] 80 | nir016 = inputs[3] 81 | vis006 = inputs[4] 82 | ir087 = inputs[5] 83 | ir120 = inputs[6] 84 | elev = inputs[7] 85 | cot = inputs[8] 86 | reff = inputs[9] 87 | cwp = inputs[10] 88 | lat = inputs[11] 89 | lon = inputs[12] 90 | 91 | msg_con_quick = image.ImageContainerQuick(ir108.squeeze(), area_def) 92 | area_con_quick = msg_con_quick.resample(euro_areadef) 93 | result_data_quick = area_con_quick.image_data 94 | 95 | # Create satpy scene 96 | testscene = Scene(platform_name="msg", 97 | sensor="seviri", 98 | start_time=datetime(2013, 11, 12, 8, 30), 99 | end_time=datetime(2013, 11, 12, 8, 45), 100 | area=area_def) 101 | array_kwargs = {'area': area_def} 102 | 103 | testscene['ir108'] = Dataset(ir108.squeeze(), **array_kwargs) 104 | print(testscene['ir108']) 105 | testscene.show( 106 | 'ir108', 107 | overlay={'coast_dir': '/home/mastho/data/', 'color': 'gray'}) 108 | resampscene = testscene.resample('germ') 109 | print(resampscene.shape) 110 | 111 | # Define custom fog colormap 112 | fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)), 113 | (1., (1.0, 1.0, 229 / 255.0))) 114 | maskcol = (250 / 255.0, 200 / 255.0, 40 / 255.0) 115 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | name = fogpy 3 | description = Satellite based fog and low stratus detection and nowcasting 4 | license = GNU general public license version 3 5 | license_files = LICENSE.txt 6 | classifiers = 7 | Programming Language :: Python 8 | Development Status :: 4 - Beta 9 | Natural Language :: English 10 | Environment :: Web Environment 11 | Intended Audience :: Science/Research 12 | License :: OSI Approved :: GNU General Public License v3 (GPLv3) 13 | Operating System :: OS Independent 14 | Topic :: Scientific/Engineering :: Atmospheric Science 15 | Topic :: Scientific/Engineering :: Physics 16 | url = https://github.com/pytroll/fogpy 17 | author = Thomas Leppelt, Gerrit Holl 18 | maintainer = Gerrit Holl 19 | author_email = gerrit.holl@dwd.de 20 | platforms = any 21 | 22 | [options] 23 | packages = find: 24 | include_package_data = True 25 | install_requires = 26 | numpy 27 | scipy 28 | matplotlib 29 | pyorbital 30 | trollimage 31 | satpy 32 | pyresample 33 | opencv-python 34 | opencv-contrib-python 35 | trollbufr 36 | appdirs 37 | requests 38 | python_requires = >=3.7 39 | tests_require = pytest, pytest-cov 40 | 41 | [options.package_data] 42 | fogpy = 43 | etc 44 | etc/composites/*.yaml 45 | etc/enhancements/*.yaml 46 | 47 | [flake8] 48 | max-line-length = 120 49 | 50 | [coverage:run] 51 | omit = 52 | fogpy/version.py 53 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # Copyright (c) 2017-2021 Fogpy developers 4 | 5 | # This file is part of the fogpy package. 6 | 7 | # This program is free software: you can redistribute it and/or modify 8 | # it under the terms of the GNU General Public License as published by 9 | # the Free Software Foundation, either version 3 of the License, or 10 | # (at your option) any later version. 11 | 12 | # This program is distributed in the hope that it will be useful, 13 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | # GNU General Public License for more details. 16 | 17 | # You should have received a copy of the GNU General Public License 18 | # along with this program. If not, see . 19 | 20 | """Setup file for fogpy.""" 21 | 22 | import setuptools 23 | 24 | if __name__ == "__main__": 25 | setuptools.setup() 26 | --------------------------------------------------------------------------------