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and Hydrology Data Access","monthly_downloads":1404,"total_dependent_repos":4,"total_dependent_packages":2,"readme":".. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/pydaymet_logo.png\n    :target: https://github.com/hyriver/HyRiver\n\n|\n\n.. image:: https://joss.theoj.org/papers/b0df2f6192f0a18b9e622a3edff52e77/status.svg\n    :target: https://joss.theoj.org/papers/b0df2f6192f0a18b9e622a3edff52e77\n    :alt: JOSS\n\n|\n\n.. |pygeohydro| image:: https://github.com/hyriver/pygeohydro/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pygeohydro/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pygeoogc| image:: https://github.com/hyriver/pygeoogc/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pygeoogc/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pygeoutils| image:: https://github.com/hyriver/pygeoutils/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pygeoutils/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pynhd| image:: https://github.com/hyriver/pynhd/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pynhd/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |py3dep| image:: https://github.com/hyriver/py3dep/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/py3dep/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pydaymet| image:: https://github.com/hyriver/pydaymet/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pydaymet/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pygridmet| image:: https://github.com/hyriver/pygridmet/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pygridmet/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |pynldas2| image:: https://github.com/hyriver/pynldas2/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/pynldas2/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |async| image:: https://github.com/hyriver/async-retriever/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/async-retriever/actions/workflows/test.yml\n    :alt: Github Actions\n\n.. |signatures| image:: https://github.com/hyriver/hydrosignatures/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/hyriver/hydrosignatures/actions/workflows/test.yml\n    :alt: Github Actions\n\n================ ====================================================================\nPackage          Description\n================ ====================================================================\nPyNHD_           Navigate and subset NHDPlus (MR and HR) using web services\nPy3DEP_          Access topographic data through National Map's 3DEP web service\nPyGeoHydro_      Access NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databases\nPyDaymet_        Access daily, monthly, and annual climate data via Daymet\nPyGridMET_       Access daily climate data via GridMET\nPyNLDAS2_        Access hourly NLDAS-2 data via web services\nHydroSignatures_ A collection of tools for computing hydrological signatures\nAsyncRetriever_  High-level API for asynchronous requests with persistent caching\nPyGeoOGC_        Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services\nPyGeoUtils_      Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data\n================ ====================================================================\n\n.. _PyGeoHydro: https://github.com/hyriver/pygeohydro\n.. _AsyncRetriever: https://github.com/hyriver/async-retriever\n.. _PyGeoOGC: https://github.com/hyriver/pygeoogc\n.. _PyGeoUtils: https://github.com/hyriver/pygeoutils\n.. _PyNHD: https://github.com/hyriver/pynhd\n.. _Py3DEP: https://github.com/hyriver/py3dep\n.. _PyDaymet: https://github.com/hyriver/pydaymet\n.. _PyGridMET: https://github.com/hyriver/pygridmet\n.. _PyNLDAS2: https://github.com/hyriver/pynldas2\n.. _HydroSignatures: https://github.com/hyriver/hydrosignatures\n\nPyDaymet: Daily climate data through Daymet\n-------------------------------------------\n\n.. image:: https://img.shields.io/pypi/v/pydaymet.svg\n    :target: https://pypi.python.org/pypi/pydaymet\n    :alt: PyPi\n\n.. image:: https://img.shields.io/conda/vn/conda-forge/pydaymet.svg\n    :target: https://anaconda.org/conda-forge/pydaymet\n    :alt: Conda Version\n\n.. image:: https://codecov.io/gh/hyriver/pydaymet/branch/main/graph/badge.svg\n    :target: https://codecov.io/gh/hyriver/pydaymet\n    :alt: CodeCov\n\n.. image:: https://img.shields.io/pypi/pyversions/pydaymet.svg\n    :target: https://pypi.python.org/pypi/pydaymet\n    :alt: Python Versions\n\n.. image:: https://static.pepy.tech/badge/pydaymet\n    :target: https://pepy.tech/project/pydaymet\n    :alt: Downloads\n\n|\n\n.. image:: https://www.codefactor.io/repository/github/hyriver/pydaymet/badge\n   :target: https://www.codefactor.io/repository/github/hyriver/pydaymet\n   :alt: CodeFactor\n\n.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\n    :target: https://github.com/astral-sh/ruff\n    :alt: Ruff\n\n.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026logoColor=white\n    :target: https://github.com/pre-commit/pre-commit\n    :alt: pre-commit\n\n.. image:: https://mybinder.org/badge_logo.svg\n    :target: https://mybinder.org/v2/gh/hyriver/HyRiver-examples/main?urlpath=lab/tree/notebooks\n    :alt: Binder\n\n|\n\n.. warning::\n\n    Since the release of Daymet v4 R1 on November 2022, the URL of\n    Daymet's server has been changed. Therefore, only PyDaymet v0.13.7+\n    is going to work, and previous versions will not work anymore.\n\nFeatures\n--------\n\nPyDaymet is a part of `HyRiver \u003chttps://github.com/hyriver/HyRiver\u003e`__ software stack that\nis designed to aid in hydroclimate analysis through web services. This package provides\naccess to climate data from\n`Daymet V4 R1 \u003chttps://daymet.ornl.gov/overview\u003e`__ database using NetCDF\nSubset Service (NCSS). Both single pixel (using ``get_bycoords`` function) and gridded data (using\n``get_bygeom``) are supported which are returned as\n``pandas.DataFrame`` and ``xarray.Dataset``, respectively. Climate data is available for North\nAmerica, Hawaii from 1980, and Puerto Rico from 1950 at three time scales: daily, monthly,\nand annual. Additionally, PyDaymet can compute Potential EvapoTranspiration (PET)\nusing three methods: ``penman_monteith``, ``priestley_taylor``, and ``hargreaves_samani`` for\nboth single pixel and gridded data.\n\nFor PET computations, PyDaymet accepts four additional user-defined parameters:\n\n* ``penman_monteith``: ``soil_heat_flux``, ``albedo``, ``alpha``,\n    and ``arid_correction``.\n* ``priestley_taylor``: ``soil_heat_flux``, ``albedo``, and ``arid_correction``.\n* ``hargreaves_samani``: None.\n\nDefault values for the parameters are: ``soil_heat_flux`` = 0, ``albedo`` = 0.23,\n``alpha`` = 1.26, and ``arid_correction`` = False.\nAn important parameter for ``priestley_taylor`` and ``penman_monteith`` methods\nis ``arid_correction`` which is used to correct the actual vapor pressure\nfor arid regions. Since relative humidity is not provided by Daymet, the actual\nvapor pressure is computed assuming that the dew point temperature is equal to\nthe minimum temperature. However, for arid regions, FAO 56 suggests subtracting\nminimum temperature by 2-3 °C to account for the fact that in arid regions,\nthe air might not be saturated when its temperature is at its minimum. For such\nareas, you can pass ``{\"arid_correction\": True, ...}`` to subtract 2 °C from the\nminimum temperature for computing the actual vapor pressure.\n\nBoth ``get_bygeom`` and ``get_bycoords`` functions save the intermediate files\nreturned by the web service in a local cache folder (``./cache`` in the current\ndirectory). The cache folder is created automatically when the functions are\ncalled for the first time. The cache folder is used to store the intermediate\nfiles to avoid re-downloading them. These two functions allow modifying the\nweb service calls via two options:\n\n- ``conn_timeout``: Sets the connection timeout in seconds. The default value\n  is 5 minutes. This can be increaseed for larger requests. If running these\n  functions fails with a connection timeout error, try increasing this value.\n- ``validate_filesize``: If ``True``, the functions compares the file size\n  of the previously cached files in the ``./cache`` folder, if they exist, with\n  their size on the remote server. If the sizes do not match, the cached files are\n  removed and they will be re-download. By default this is set to ``False`` since\n  the files on the server rarely change. So, if a request has already been cached\n  there shouldn't be a need for re-donwloading them from scratch. However, if you\n  suspect that the files on the server have changed or the functions fails to process\n  the cached files, you can set this to ``True`` or manually delete the cached\n  files in the ``./cache`` folder.\n\nYou can find some example notebooks\n`here \u003chttps://github.com/hyriver/HyRiver-examples\u003e`__.\nYou can also try using PyDaymet without installing\nit on your system by clicking on the binder badge. A Jupyter Lab\ninstance with the HyRiver stack pre-installed will be launched in your web browser,\nand you can start coding!\n\nMoreover, requests for additional functionalities can be submitted via\n`issue tracker \u003chttps://github.com/hyriver/pydaymet/issues\u003e`__.\n\nCitation\n--------\nIf you use any of HyRiver packages in your research, we appreciate citations:\n\n.. code-block:: bibtex\n\n    @article{Chegini_2021,\n        author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},\n        doi = {10.21105/joss.03175},\n        journal = {Journal of Open Source Software},\n        month = {10},\n        number = {66},\n        pages = {1--3},\n        title = {{HyRiver: Hydroclimate Data Retriever}},\n        volume = {6},\n        year = {2021}\n    }\n\nInstallation\n------------\n\nYou can install PyDaymet using ``pip`` after installing ``libgdal`` on your system\n(for example, in Ubuntu run ``sudo apt install libgdal-dev``):\n\n.. code-block:: console\n\n    $ pip install pydaymet\n\nAlternatively, PyDaymet can be installed from the ``conda-forge`` repository\nusing `Conda \u003chttps://docs.conda.io/en/latest/\u003e`__:\n\n.. code-block:: console\n\n    $ conda install -c conda-forge pydaymet\n\nQuick start\n-----------\n\nYou can use PyDaymet using command-line or as a Python library. The commanda-line\nprovides access to two functionality:\n\n- Getting gridded climate data: You must create a ``geopandas.GeoDataFrame`` that contains\n  the geometries of the target locations. This dataframe must have four columns:\n  ``id``, ``start``, ``end``, ``geometry``. The ``id`` column is used as\n  filenames for saving the obtained climate data to a NetCDF (``.nc``) file. The ``start``\n  and ``end`` columns are starting and ending dates of the target period. Then,\n  you must save the dataframe as a shapefile (``.shp``) or geopackage (``.gpkg``) with\n  CRS attribute.\n- Getting single pixel climate data: You must create a CSV file that\n  contains coordinates of the target locations. This file must have at four columns:\n  ``id``, ``start``, ``end``, ``lon``, and ``lat``. The ``id`` column is used as filenames\n  for saving the obtained climate data to a CSV (``.csv``) file. The ``start`` and ``end``\n  columns are the same as the ``geometry`` command. The ``lon`` and ``lat`` columns are\n  the longitude and latitude coordinates of the target locations.\n\n.. code-block:: console\n\n    $ pydaymet -h\n    Usage: pydaymet [OPTIONS] COMMAND [ARGS]...\n\n    Command-line interface for PyDaymet.\n\n    Options:\n    -h, --help  Show this message and exit.\n\n    Commands:\n    coords    Retrieve climate data for a list of coordinates.\n    geometry  Retrieve climate data for a dataframe of geometries.\n\nThe ``coords`` sub-command is as follows:\n\n.. code-block:: console\n\n    $ pydaymet coords -h\n    Usage: pydaymet coords [OPTIONS] FPATH\n\n    Retrieve climate data for a list of coordinates.\n\n    FPATH: Path to a csv file with four columns:\n        - ``id``: Feature identifiers that daymet uses as the output netcdf filenames.\n        - ``start``: Start time.\n        - ``end``: End time.\n        - ``lon``: Longitude of the points of interest.\n        - ``lat``: Latitude of the points of interest.\n        - ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.\n        - ``pet``: (optional) Method to compute PET. Supported methods are:\n                    ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).\n        - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.\n\n    Examples:\n        $ cat coords.csv\n        id,lon,lat,start,end,pet\n        california,-122.2493328,37.8122894,2012-01-01,2014-12-31,hargreaves_samani\n        $ pydaymet coords coords.csv -v prcp -v tmin\n\n    Options:\n    -v, --variables TEXT  Target variables. You can pass this flag multiple\n                            times for multiple variables.\n    -s, --save_dir PATH   Path to a directory to save the requested files.\n                            Extension for the outputs is .nc for geometry and .csv\n                            for coords.\n    --disable_ssl         Pass to disable SSL certification verification.\n    -h, --help            Show this message and exit.\n\nAnd, the ``geometry`` sub-command is as follows:\n\n.. code-block:: console\n\n    $ pydaymet geometry -h\n    Usage: pydaymet geometry [OPTIONS] FPATH\n\n    Retrieve climate data for a dataframe of geometries.\n\n    FPATH: Path to a shapefile (.shp) or geopackage (.gpkg) file.\n    This file must have four columns and contain a ``crs`` attribute:\n        - ``id``: Feature identifiers that daymet uses as the output netcdf filenames.\n        - ``start``: Start time.\n        - ``end``: End time.\n        - ``geometry``: Target geometries.\n        - ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.\n        - ``pet``: (optional) Method to compute PET. Supported methods are:\n                    ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).\n        - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.\n\n    Examples:\n        $ pydaymet geometry geo.gpkg -v prcp -v tmin\n\n    Options:\n    -v, --variables TEXT  Target variables. You can pass this flag multiple\n                            times for multiple variables.\n    -s, --save_dir PATH   Path to a directory to save the requested files.\n                            Extension for the outputs is .nc for geometry and .csv\n                            for coords.\n    --disable_ssl         Pass to disable SSL certification verification.\n    -h, --help            Show this message and exit.\n\nNow, let's see how we can use PyDaymet as a library.\n\nPyDaymet offers two functions for getting climate data; ``get_bycoords`` and ``get_bygeom``.\nThe arguments of these functions are identical except the first argument where the latter\nshould be polygon and the former should be a coordinate (a tuple of length two as in (x, y)).\nThe input geometry or coordinate can be in any valid CRS (defaults to ``EPSG:4326``). The\n``dates`` argument can be either a tuple of length two like ``(start_str, end_str)`` or a list of\nyears like ``[2000, 2005]``. It is noted that both functions have a ``pet`` flag for computing PET\nand a ``snow`` flag for separating snow from precipitation using\n`Martinez and Gupta (2010) \u003chttps://doi.org/10.1029/2009WR008294\u003e`__ method.\nAdditionally, we can pass ``time_scale`` to get daily, monthly or annual summaries. This flag\nby default is set to daily.\n\n.. code-block:: python\n\n    from pynhd import NLDI\n    import pydaymet as daymet\n\n    geometry = NLDI().get_basins(\"01031500\").geometry[0]\n\n    var = [\"prcp\", \"tmin\"]\n    dates = (\"2000-01-01\", \"2000-06-30\")\n\n    daily = daymet.get_bygeom(geometry, dates, variables=var, pet=\"priestley_taylor\", snow=True)\n    monthly = daymet.get_bygeom(geometry, dates, variables=var, time_scale=\"monthly\")\n\n.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/daymet_grid.png\n    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/daymet.ipynb\n\nIf the input geometry (or coordinate) is in a CRS other than ``EPSG:4326``, we should pass\nit to the functions.\n\n.. code-block:: python\n\n    coords = (-1431147.7928, 318483.4618)\n    crs = 3542\n    dates = (\"2000-01-01\", \"2006-12-31\")\n    annual = daymet.get_bycoords(coords, dates, variables=var, loc_crs=crs, time_scale=\"annual\")\n\n.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/daymet_loc.png\n    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/daymet.ipynb\n\nAdditionally, the ``get_bycoords`` function accepts a list of coordinates and by setting the\n``to_xarray`` flag to ``True`` it can return the results as a ``xarray.Dataset`` instead of\na ``pandas.DataFrame``:\n\n.. code-block:: python\n\n    coords = [(-94.986, 29.973), (-95.478, 30.134)]\n    idx = [\"P1\", \"P2\"]\n    clm_ds = daymet.get_bycoords(coords, range(2000, 2021), coords_id=idx, to_xarray=True)\n\nAlso, we can use the ``potential_et`` function to compute PET by passing the daily climate data.\nWe can either pass a ``pandas.DataFrame`` or a ``xarray.Dataset``. Note that, ``penman_monteith``\nand ``priestley_taylor`` methods have parameters that can be passed via the ``params`` argument,\nif any value other than the default values are needed. For example, default value of ``alpha``\nfor ``priestley_taylor`` method is 1.26 (humid regions), we can set it to 1.74 (arid regions)\nas follows:\n\n.. code-block:: python\n\n    pet_hs = daymet.potential_et(daily, methods=\"priestley_taylor\", params={\"alpha\": 1.74})\n\nNext, let's get annual total precipitation for Hawaii and Puerto Rico for 2010.\n\n.. code-block:: python\n\n    hi_ext = (-160.3055, 17.9539, -154.7715, 23.5186)\n    pr_ext = (-67.9927, 16.8443, -64.1195, 19.9381)\n    hi = daymet.get_bygeom(hi_ext, 2010, variables=\"prcp\", region=\"hi\", time_scale=\"annual\")\n    pr = daymet.get_bygeom(pr_ext, 2010, variables=\"prcp\", region=\"pr\", time_scale=\"annual\")\n\nSome example plots are shown below:\n\n.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/hi.png\n    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/daymet.ipynb\n\n.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/pr.png\n    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/daymet.ipynb\n\nContributing\n------------\n\nContributions are very welcomed. Please read\n`CONTRIBUTING.rst \u003chttps://github.com/hyriver/pygeoogc/blob/main/CONTRIBUTING.rst\u003e`__\nfile for instructions.\n\nCredits\n-------\nCredits to `Koen Hufkens \u003chttps://github.com/khufkens\u003e`__ for his implementation of\naccessing the Daymet RESTful service, `daymetpy \u003chttps://github.com/bluegreen-labs/daymetpy\u003e`__.\n","funding_links":["https://github.com/sponsors/cheginit"],"readme_doi_urls":["https://doi.org/10.1029/2009WR008294"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["archiving","measur","optimize","transforms","meshing","observation","conversion","projection","compose","profiles"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/3740","html_url":"https://ost.ecosyste.ms/projects/3740"}