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Observation and Forecast","monthly_downloads":1081,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"========\nmetloom\n========\n\n\n.. image:: https://img.shields.io/pypi/v/metloom.svg\n        :target: https://pypi.python.org/pypi/metloom\n.. image:: https://github.com/M3Works/metloom/actions/workflows/testing.yml/badge.svg\n        :target: https://github.com/M3Works/metloom/actions/workflows/testing.yml\n        :alt: Testing Status\n.. image:: https://readthedocs.org/projects/metloom/badge/?version=latest\n        :target: https://metloom.readthedocs.io/en/latest/?version=latest\n        :alt: Documentation Status\n.. image:: https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/micah-prime/04da387b53bdb4a3aa31253789550a9f/raw/metloom__heads_main.json\n        :target: https://github.com/M3Works/metloom\n        :alt: Code Coverage\n\n\nLocation Oriented Observed Meteorology\n\nmetloom is a python library created with the goal of consistent, simple sampling of\nmeteorology and snow related point measurments from a variety of datasources is developed by `M3 Works \u003chttps://m3works.io\u003e`_ as a tool for validating\ncomputational hydrology model results. Contributions welcome!\n\nWarning - This software is provided as is (see the license), so use at your own risk.\nThis is an opensource package with the goal of making data wrangling easier. We make\nno guarantees about the quality or accuracy of the data and any interpretation of the meaning\nof the data is up to you.\n\n* Free software: BSD license\n\n.. code-block:: python\n\n    # Find your data with ease\n    # !pip install folium mapclassify matplotlib\n    from metloom.pointdata import SnotelPointData, CDECPointData, USGSPointData\n    import geopandas as gpd\n    import pandas as pd\n\n    # Shapefile for the US states\n    shp = gpd.read_file('https://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json').to_crs(\"EPSG:4326\")\n    # Filter to states of interest\n    west_states = [\"Washington\", \"Oregon\", \"California\", \"Idaho\", \"Nevada\", \"Utah\", \"Wyoming\", \"Montana\", \"Colorado\" ]  # , \"Arizona\", \"New Mexico\"]\n    shp = shp.loc[shp[\"NAME\"].isin(west_states)].dissolve()\n\n    # Collect all points with SWE from CDEC and NRCS\n    dfs = []\n    for src in  [CDECPointData, SnotelPointData]:\n        dfs.append(src.points_from_geometry(shp, [src.ALLOWED_VARIABLES.SWE]).to_dataframe())\n    # Combine dataframes\n    gdf = pd.concat(dfs)\n    # plot the shapefile\n    m = shp.explore(\n        tooltip=False, color=\"grey\", highlight=False, style_kwds={\"opacity\": 0.2}, popup=[\"NAME\"]\n    )\n    # plot the points on top of the shapefile\n    gdf.explore(m=m, tooltip=[\"name\", \"id\", \"datasource\"], color=\"red\", marker_kwds={\"radius\":4})\n\n.. image:: docs/images/map_of_swe.png\n   :alt: Resulting plot of SWE trace at Banner summit\n\nFeatures\n--------\n.. code-block:: python\n\n    # !pip install plotly\n    from metloom.pointdata import SnotelPointData\n    import plotly.express as px\n    import pandas as pd\n\n    # Initialize your point\n    pt = SnotelPointData(\"312:ID:SNTL\", \"Banner Summit\")\n    swe_variable = pt.ALLOWED_VARIABLES.SWE\n    # Get the data\n    df = pt.get_daily_data(\n        pd.to_datetime(\"2024-10-01\"), pd.to_datetime(\"2025-03-11\"), [swe_variable]\n    ).reset_index()\n    # Create a time series plot using Plotly Express\n    px.line(df, x=\"datetime\", y=swe_variable.name, title=f\"{pt.name} SWE\")\n\n.. image:: docs/images/banner_swe.png\n   :alt: Resulting plot of SWE trace at Banner summit\n\n\n* Sampling of daily, hourly, and snow course data\n* Searching for stations from a datasource within a shapefile\n* Current data sources:\n    * `CDEC \u003chttps://cdec.water.ca.gov/\u003e`_\n    * `SNOTEL \u003chttps://www.nrcs.usda.gov/wps/portal/wcc/home/dataAccessHelp/webService/webServiceReference/\u003e`_\n    * `MESOWEST \u003chttps://developers.synopticdata.com/mesonet/\u003e`_\n    * `USGS \u003chttps://waterservices.usgs.gov/rest/\u003e`_\n    * `NWS FORECAST \u003chttps://api.weather.gov\u003e`_\n    * `GEOSPHERE AUSTRIA \u003chttps://data.hub.geosphere.at/dataset/\u003e`_\n    * `UCSB CUES \u003chttps://snow.ucsb.edu/#\u003e`_\n    * `MET NORWAY \u003chttps://frost.met.no/index.html\u003e`_\n    * `SNOWEX MET STATIONS \u003chttps://nsidc.org/data/snex_met/versions/1\u003e`_\n    * `CENTER FOR SNOW AND AVALANCHE STUDIES (CSAS) \u003chttps://snowstudies.org/csas-facilities/\u003e`_\n\nRequirements\n------------\npython \u003e= 3.7\n\nInstall\n-------\n.. code-block:: bash\n\n    python3 -m pip install metloom\n\n* Common install issues:\n    * Macbook M1 and M2 chips: some python packages run into issues with the new M chips\n        * ``error : from lxml import etree in utils.py ((mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64)``\n            The solution is the following\n\n            .. code-block:: bash\n\n                pip uninstall lxml\n                pip install --no-binary lxml lxml\n\n\n\nLocal install for dev\n---------------------\nThe recommendation is to use virtualenv, but other local python\nenvironment isolation tools will work (pipenv, conda)\n\n.. code-block:: bash\n\n    python3 -m pip install --upgrade pip\n    python3 -m pip install -r requirements_dev\n    python3 -m pip install .\n\nTesting\n-------\n\n.. code-block:: bash\n\n    pytest\n\nIf contributing to the codebase, code coverage should not decrease\nfrom the contributions. Make sure to check code coverage before\nopening a pull request.\n\n.. code-block:: bash\n\n    pytest --cov=metloom\n\nDocumentation\n-------------\nreadthedocs coming soon\n\nhttps://metloom.readthedocs.io.\n\nUsage\n-----\nSee usage documentation https://metloom.readthedocs.io/en/latest/usage.html\n\n**NOTES:**\nPointData methods that get point data return a GeoDataFrame indexed\non *both* datetime and station code. To reset the index simply run\n``df.reset_index(inplace=True)``\n\nSimple usage examples are provided in this readme and in the docs. See\nour `examples \u003chttps://github.com/M3Works/metloom/tree/main/docs/gallery\u003e`_\nfor code walkthroughs and more complicated use cases.\n\nUsage Examples\n==============\n\nUse metloom to find data for a station\n\n.. code-block:: python\n\n    from datetime import datetime\n    from metloom.pointdata import SnotelPointData\n\n    snotel_point = SnotelPointData(\"713:CO:SNTL\", \"MyStation\")\n    df = snotel_point.get_daily_data(\n        datetime(2020, 1, 2), datetime(2020, 1, 20),\n        [snotel_point.ALLOWED_VARIABLES.SWE]\n    )\n    print(df)\n\nUse metloom to find snow courses within a geometry\n\n.. code-block:: python\n\n    from metloom.pointdata import CDECPointData\n    from metloom.variables import CdecStationVariables\n\n    import geopandas as gpd\n\n    fp = \u003cpath to shape file\u003e\n    obj = gpd.read_file(fp)\n\n    vrs = [\n        CdecStationVariables.SWE,\n        CdecStationVariables.SNOWDEPTH\n    ]\n    points = CDECPointData.points_from_geometry(obj, vrs, snow_courses=True)\n    df = points.to_dataframe()\n    print(df)\n\nTutorials\n---------\nIn the ``Examples`` folder, there are multiple Jupyter notbook based\ntutorials. You can edit and run these notebooks by running Jupyter Lab\nfrom the command line\n\n.. code-block:: bash\n\n    pip install jupyterlab\n    jupyter lab\n\nThis will open a Jupyter Lab session in your default browser.\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["optimize","transforms","archiving","measur","compose","observation","conversion","water","projection","generic"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/194047","html_url":"https://ost.ecosyste.ms/projects/194047"}