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raoulcollenteurraoulcollenteur
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Update master to pyet 1.3.1 (#72)
2 parents 990ae50 + a79066b commit 46660b9

11 files changed

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‎.github/workflows/ci.yml‎

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with:
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project-token: ${{ secrets.CODACY_PROJECT_TOKEN }}
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coverage-reports: coverage.xml
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‎.github/workflows/python-publish.yml‎

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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v2
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uses: actions/setup-python@v5
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with:
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python-version: '3.x'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install setuptools wheel twine
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- name: Build and publish
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env:
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TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
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TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: |
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python setup.py sdist bdist_wheel
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twine upload dist/*
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- name: Upload Python Package
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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user: __token__
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password: ${{ secrets.PYPI_TOKEN }}

‎README.md‎

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@@ -42,29 +42,29 @@ $^a$ $T_{max}$ and $T_{min}$ can also be provided. $^b$ $RH_{max}$ and $RH_{min}
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Examples of using *pyet* can be found in the example folder:
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* [Example 1](examples/01_example_zamg.ipynb): Estimating PET using pandas.Series
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* [Example 1](<https://pyet.readthedocs.io/en/dev/examples/01_example_zamg.html#>): Estimating PET using pandas.Series
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* [Example 2](examples/02_example_zamg_netcdf.ipynb): Estimating PET using xarray.DataArray
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* [Example 2](<https://pyet.readthedocs.io/en/dev/examples/02_example_zamg_netcdf.html>): Estimating PET using xarray.DataArray
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* [Example 3](examples/03_example_knmi.ipynb): Benchmarking Makkink
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* [Example 3](<https://pyet.readthedocs.io/en/dev/examples/03_example_knmi.html>): Benchmarking Makkink
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against [KNMI data](https://www.knmi.nl/over-het-knmi/about)
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* [Example 4](examples/04_example_coagmet.ipynb): Benchmarking FAO56
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* [Example 4](<https://pyet.readthedocs.io/en/dev/examples/04_example_coagmet.html>): Benchmarking FAO56
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against [CoAgMET data](https://coagmet.colostate.edu/)
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* [Example 5](examples/05_example_calibration.ipynb): Calibrating the Romanenko and Abtew method against the PM-FAO56
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* [Example 5](<https://pyet.readthedocs.io/en/dev/examples/05_example_calibration.html>): Calibrating the Romanenko and Abtew method against the PM-FAO56
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* [Example 6](examples/06_worked_examples_McMahon_etal_2013.ipynb): Worked examples for estimating meteorological
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* [Example 6](<https://pyet.readthedocs.io/en/dev/examples/06_worked_examples_McMahon_etal_2013.html>): Worked examples for estimating meteorological
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variables and potential evapotranspiration after McMahon et al., 2013
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* [Example 7](examples/07_example_climate_change.ipynb): Example for estimating potential evapotranspiration under
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* [Example 7](<https://pyet.readthedocs.io/en/dev/examples/07_example_climate_change.html>): Example for estimating potential evapotranspiration under
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warming and elevated $CO_2$ concentrations following Yang et al., (2019)
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* [Example 8](examples/08_crop_coefficient.ipynb): Determining the crop coefficient function with Python
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* [Example 8](<https://pyet.readthedocs.io/en/dev/examples/08_crop_coefficient.html>): Determining the crop coefficient function with Python
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* [Example 9](examples/09_CMIP6_data.ipynb): Estimating PET using CMIP data
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* [Example 9](<https://pyet.readthedocs.io/en/dev/examples/09_CMIP6_data.html>): Estimating PET using CMIP data
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* [Example 10](examples/10_example_paper.ipynb): Notebook supporting PyEt manuscript
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* [Example 10](<https://pyet.readthedocs.io/en/dev/examples/10_example_paper.html>): Notebook supporting PyEt manuscript
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Documentation is hosted on [ReadTheDocs](https://pyet.readthedocs.io).
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‎docs/conf.py‎

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year = date.today().strftime("%Y")
2020

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sys.path.insert(0, os.path.abspath('.'))
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sys.path.insert(0, os.path.abspath("."))
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# Get a Bibtex reference file from the Zotero group for referencing
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url = "https://api.zotero.org/groups/4846265/collections/M9ZRDX2U/items/"
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params = {"format": "bibtex",
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"style": "apa",
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"limit": 100}
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params = {"format": "bibtex", "style": "apa", "limit": 100}
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r = requests.get(url=url, params=params)
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with open("references.bib", mode="w") as file:
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file.write(r.text)
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# Get a Bibtex reference file from the Zotero group for publications list
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url = "https://api.zotero.org/groups/4846265/collections/UR7PHVDK/items/"
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params = {"format": "bibtex",
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"style": "apa",
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"limit": 100}
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params = {"format": "bibtex", "style": "apa", "limit": 100}
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r = requests.get(url=url, params=params)
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with open("publications.bib", mode="w") as file:
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file.write(r.text)
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# -- Project information -----------------------------------------------------
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project = 'pyet'
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copyright = '{}, M. Vremec, R.A. Collenteur'.format(year)
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author = 'M. Vremec, R.A. Collenteur'
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project = "pyet"
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copyright = "{}, M. Vremec, R.A. Collenteur".format(year)
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author = "M. Vremec, R.A. Collenteur"
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# The full version, including alpha/beta/rc tags
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release = '2020'
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release = "2020"
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# -- General configuration ---------------------------------------------------
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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'sphinx.ext.autodoc',
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'sphinx.ext.autosummary',
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'sphinx.ext.napoleon',
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'sphinx.ext.doctest',
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'sphinx.ext.intersphinx',
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'sphinx.ext.todo',
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'sphinx.ext.mathjax',
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'sphinx.ext.ifconfig',
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'sphinx.ext.viewcode',
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'IPython.sphinxext.ipython_console_highlighting', # lowercase didn't work
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'sphinx.ext.autosectionlabel',
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'sphinxcontrib.bibtex',
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'myst_nb',
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"sphinx.ext.autodoc",
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"sphinx.ext.autosummary",
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"sphinx.ext.napoleon",
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"sphinx.ext.doctest",
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"sphinx.ext.intersphinx",
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"sphinx.ext.todo",
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"sphinx.ext.mathjax",
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"sphinx.ext.ifconfig",
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"sphinx.ext.viewcode",
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"IPython.sphinxext.ipython_console_highlighting", # lowercase didn't work
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"sphinx.ext.autosectionlabel",
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"sphinxcontrib.bibtex",
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"myst_nb",
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"numpydoc",
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"sphinx_design",
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]
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# Create custom bracket style with round brackets
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import sphinxcontrib.bibtex.plugin
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from sphinxcontrib.bibtex.style.referencing import BracketStyle
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from sphinxcontrib.bibtex.style.referencing.author_year \
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import AuthorYearReferenceStyle
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from sphinxcontrib.bibtex.style.referencing.author_year import AuthorYearReferenceStyle
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def bracket_style() -> BracketStyle:
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return BracketStyle(
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left='(',
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right=')',
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left="(",
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right=")",
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)
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sphinxcontrib.bibtex.plugin.register_plugin(
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'sphinxcontrib.bibtex.style.referencing',
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'author_year_round', MyReferenceStyle)
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"sphinxcontrib.bibtex.style.referencing", "author_year_round", MyReferenceStyle
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)
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bibtex_bibfiles = ['references.bib', 'publications.bib']
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bibtex_bibfiles = ["references.bib", "publications.bib"]
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bibtex_reference_style = "author_year_round"
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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templates_path = ["_templates"]
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source_suffix = '.rst'
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source_suffix = ".rst"
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# The master toctree document.
113-
master_doc = 'index'
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master_doc = "index"
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
118-
exclude_patterns = ['_build', '**.ipynb_checkpoints']
115+
exclude_patterns = ["_build", "**.ipynb_checkpoints"]
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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html_theme = "pydata_sphinx_theme"
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html_theme_options = {
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"github_url": "https://github.com/phydrus/pyet",
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"use_edit_page_button": False
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}
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
133-
html_static_path = ['_static']
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html_static_path = ["_static"]
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html_logo = "_static/logo.png"
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html_use_smartypants = True
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html_show_sourcelink = True
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html_theme_options = {
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"github_url": "https://github.com/pyet-org/pyet",
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"use_edit_page_button": True,
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"header_links_before_dropdown": 6,
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"icon_links": [
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{
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"name": "GitHub", # Label for this link
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"url": "https://github.com/pyet-org/pyet", # required
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"icon": "fab fa-github-square",
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"type": "fontawesome", # Default is fontawesome
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}
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],
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}
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autosummary_generate = True
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numpydoc_show_class_members = False
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nb_execution_allow_errors = True # Allow errors in notebooks, to see the error online
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nb_execution_mode = "auto"
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# Enable specific MyST extensions, such as "dollarmath" for math rendering
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myst_enable_extensions = [
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"dollarmath",
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]
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# -- Numpydoc settings ----------------------------------------------------------------
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numpydoc_class_members_toctree = True
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numpydoc_show_class_members = False

‎docs/examples/10_example_paper.ipynb‎

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"import pylab\n",
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"import matplotlib.dates as mdates\n",
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"from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
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"import gc # Import the garbage collection module\n",
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"\n",
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"import pyet\n",
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"import spotpy\n",
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"source": [
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"#edit default values for plots\n",
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"params = {\n",
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" \"font.family\": \"Arial\",\n",
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" \"legend.fontsize\": \"15\",\n",
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" \"axes.labelsize\": \"16\",\n",
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" \"xtick.labelsize\": \"15\",\n",
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"fig.supxlabel(\"PET$_{benchmark}$ [mm/day]\", x=0.475, fontsize=16)\n",
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"fig.supylabel(\"PET$_{pyet}$ [mm/day]\", fontsize=16)\n",
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"fig.subplots_adjust(wspace=0.05, hspace=0.05, left=0.05)\n",
776-
"plt.tight_layout()\n",
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"plt.tight_layout();\n",
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"#fig.savefig(\"figure1.png\", dpi=600, bbox_inches=\"tight\")"
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]
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},
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"\n",
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"plt.tight_layout()\n",
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"\n",
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"axs[2].legend(loc=(1.05,0.1), ncol=2, bbox_transform=axs[0].transAxes, fontsize=8)\n",
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"axs[2].legend(loc=(1.05,0.1), ncol=2, bbox_transform=axs[0].transAxes, fontsize=8);\n",
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"#fig.savefig(\"figure2.png\", dpi=600, bbox_inches=\"tight\")"
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]
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},
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10071007
" cbar.set_label(\"PET [mm/day]\", labelpad=10)\n",
10081008
" plt.subplots_adjust(hspace=0.02, wspace=0.1)\n",
10091009
"\n",
1010-
"plt.subplots_adjust(hspace=0.02, wspace=0.01)\n",
1010+
"plt.subplots_adjust(hspace=0.02, wspace=0.01);\n",
10111011
"\n",
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"#fig.savefig(\"figure3.png\", dpi=300, bbox_inches=\"tight\")"
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]
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12871287
"axs[1,5].set_yticklabels([\"\",800,1000,\"\"])\n",
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"axs[0,5].legend(loc=(-3.65,1.05), ncol=7, bbox_transform=axs[1,5].transAxes)\n",
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"clb = fig.colorbar(density, orientation=\"horizontal\", ax=axs[0, 0], cax = axs[0, 0].inset_axes([0.04, 1.2, 1, 0.05]))\n",
1290-
"clb.ax.set_title(\"Number of points per pixel\")\n",
1290+
"clb.ax.set_title(\"Number of points per pixel\");\n",
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"#fig.savefig(\"figure4.png\", dpi=600, bbox_inches=\"tight\")"
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]
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},
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"source": [
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"# Load CO2 data for each RCP scenario\n",
13381338
"rcp_co2 = pd.DataFrame()\n",
1339-
"rcp_co2[\"rcp_26\"] = pd.read_csv(\"data/example_10/co2_conc/RCP3PD_MIDYR_CONC.dat\", skiprows=38, \n",
1339+
"rcp_co2[\"rcp_26\"] = pd.read_csv(\"data/example_10/co2_conc/RCP3PD_MIDYR_CONC.DAT\", skiprows=38, \n",
13401340
" delim_whitespace=True, index_col=\"YEARS\").loc[\"2020\":\"2100\", \"CO2\"]\n",
1341-
"rcp_co2[\"rcp_45\"] = pd.read_csv(\"data/example_10/co2_conc/RCP45_MIDYR_CONC.dat\", skiprows=38, \n",
1341+
"rcp_co2[\"rcp_45\"] = pd.read_csv(\"data/example_10/co2_conc/RCP45_MIDYR_CONC.DAT\", skiprows=38, \n",
13421342
" delim_whitespace=True, index_col=\"YEARS\").loc[\"2020\":\"2100\", \"CO2\"]\n",
1343-
"rcp_co2[\"rcp_60\"] = pd.read_csv(\"data/example_10/co2_conc/RCP6_MIDYR_CONC.dat\", skiprows=38, \n",
1343+
"rcp_co2[\"rcp_60\"] = pd.read_csv(\"data/example_10/co2_conc/RCP6_MIDYR_CONC.DAT\", skiprows=38, \n",
13441344
" delim_whitespace=True, index_col=\"YEARS\").loc[\"2020\":\"2100\", \"CO2\"]\n",
1345-
"rcp_co2[\"rcp_85\"] = pd.read_csv(\"data/example_10/co2_conc/RCP85_MIDYR_CONC.dat\", skiprows=38, \n",
1345+
"rcp_co2[\"rcp_85\"] = pd.read_csv(\"data/example_10/co2_conc/RCP85_MIDYR_CONC.DAT\", skiprows=38, \n",
13461346
" delim_whitespace=True, index_col=\"YEARS\").loc[\"2020\":\"2100\", \"CO2\"]"
13471347
]
13481348
},
@@ -1416,11 +1416,11 @@
14161416
"dpet_rcp_etco2 = pd.DataFrame(index=rcp_temp.index, columns=[\"rcp_26\", \"rcp_45\", \"rcp_60\", \"rcp_85\"])\n",
14171417
"dpet_rcp_etco2_5th = pd.DataFrame(index=rcp_temp.index, columns=[\"rcp_26\", \"rcp_45\", \"rcp_60\", \"rcp_85\"])\n",
14181418
"dpet_rcp_etco2_95th = pd.DataFrame(index=rcp_temp.index, columns=[\"rcp_26\", \"rcp_45\", \"rcp_60\", \"rcp_85\"])\n",
1419-
"for year in rcp_temp.index:\n",
1419+
"for year in rcp_temp.index: \n",
14201420
" for rcp in [\"rcp_26\", \"rcp_45\", \"rcp_60\", \"rcp_85\"]:\n",
14211421
" df_rcp_et = pd.DataFrame()\n",
14221422
" df_rcp_etco2 = pd.DataFrame()\n",
1423-
" for i in np.arange(0, len(methods)):\n",
1423+
" for i in np.arange(0, len(methods[:2])): # only for two methods to reduce processing for RTD to handle!!!!!!!!!!!!\n",
14241424
" input1 = input_rcp(rcp_temp.loc[year, rcp])\n",
14251425
" df_rcp_et[methods[i]] = simulate(sollutions2[i], methods[i], input1[i])\n",
14261426
" df_rcp_etco2[methods[i]] = simulate(sollutions2[i], methods[i], \n",
@@ -1430,7 +1430,9 @@
14301430
" dpet_rcp_et_95th.loc[year, rcp] = df_rcp_et.resample(\"y\").mean().mean().quantile(0.95)\n",
14311431
" dpet_rcp_etco2.loc[year, rcp] = df_rcp_etco2.resample(\"y\").mean().mean().mean()\n",
14321432
" dpet_rcp_etco2_5th.loc[year, rcp] = df_rcp_etco2.resample(\"y\").mean().mean().quantile(0.05)\n",
1433-
" dpet_rcp_etco2_95th.loc[year, rcp] = df_rcp_etco2.resample(\"y\").mean().mean().quantile(0.95)"
1433+
" dpet_rcp_etco2_95th.loc[year, rcp] = df_rcp_etco2.resample(\"y\").mean().mean().quantile(0.95)\n",
1434+
" # Call gc.collect() after processing each RCP scenario for a year\n",
1435+
" gc.collect()"
14341436
]
14351437
},
14361438
{
@@ -1487,7 +1489,7 @@
14871489
"\n",
14881490
"for i, letter in enumerate([\"a\", \"b\", \"c\", \"d\"]):\n",
14891491
" axs[i].text(0.85, 0.9, \"({})\".format(letter), transform=axs[i].transAxes, fontsize=12)\n",
1490-
" axs[i].tick_params(axis='both', which='major', labelsize=13)\n",
1492+
" axs[i].tick_params(axis='both', which='major', labelsize=13);\n",
14911493
"\n",
14921494
"#fig.savefig(\"figure5.png\", dpi=600, bbox_inches=\"tight\")"
14931495
]
@@ -1529,7 +1531,7 @@
15291531
"pet_df.plot() # daily PET [mm/day]\n",
15301532
"pet_df.boxplot() # boxplot PET[mm/day]\n",
15311533
"pet_df.cumsum().plot() # cummulative PET [mm]\n",
1532-
"plt.scatter(pyet_makkink, pet_knmi) # plot Makkink pyet vs KNMI"
1534+
"plt.scatter(pyet_makkink, pet_knmi) # plot Makkink pyet vs KNMI;"
15331535
]
15341536
},
15351537
{

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