{ "info": { "author": "Dewberry", "author_email": "abrazeau@dewberry.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7" ], "description": "\"fcast\"\n\n# [fcast](https://dewberry.github.io/fcast/)\n\n---\n\n## Description\n__fcast__ is a collection of python tools used for forecasting flood events and their impact on transportation infrastructure. The following datasets are used:\n- [The NHDPlus V2.1 dataset](http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php)\n- Staged Elevation from [The National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map). \n - [FTP LINK](ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/)\n- [USGS National Transportation Dataset (NTD)](https://catalog.data.gov/dataset/usgs-national-transportation-dataset-ntd-downloadable-data-collectionde7d2)\n- [The National Water Model (NWM)](https://console.cloud.google.com/marketplace/details/noaa-public/national-water-model?filter=category:climate&id=2b3b4e1c-20ad-455c-89c5-7c09b82c7f98)\n - [More information](https://water.noaa.gov/about/nwm)\n\n---\n\n## Installation\n\nSee https://dewberry.github.io/fcast/setup/ for installation details.\n\n---\n\n## Contents\n`fcast` - The collection of core .py files.\n - [__GageUSGS__](fcast/GageUSGS.py): An API for webscraping stream gage data from various USGS websites.\n - [__NHDPlusStreamSegment__](fcast/NHDPlusStreamSegment.py): An API for quick and easy access to the NHDPlus V2.1.\n - [__NWM__](fcast/NWM.py): An API for quick and easy access to the [NWM output files](https://console.cloud.google.com/marketplace/details/noaa-public/national-water-model?filter=category:climate&id=2b3b4e1c-20ad-455c-89c5-7c09b82c7f98).\n - [__plotting__](fcast/plotting.py): Functions for plotting using matplotlib and pandas.\n\n`notebooks` - The collection of [Jupyter Notebooks](https://jupyter.org/) running the code in the fcast subdirectory. Jupyter Notebooks are used because they produce easy, reproducible, and accesible outputs.\n - [__MediumRangeForecasts__](notebooks/MediumRangeForecasts.ipynb): Retrieve the medium range forecast of streamflow from the outputs of the National Water Model. More information can be found [here under Forecast Configurations.](https://water.noaa.gov/about/nwm)\n - [__NWM_classes-Dev__](notebooks/NWM_classes-Dev.ipynb): Development space for NWM class objects.\n - [__ShortRangeForecasts__](notebooks/ShortRangeForecasts.ipynb): Retrieve the short range forecast of streamflow from the outputs of the National Water Model. More information can be found [here under Forecast Configurations.](https://water.noaa.gov/about/nwm)\n - [__USGS_Gages__](notebooks/USGS_Gages.ipynb): Example usage of the GageUSGS class.\n - [__clean_GageLoc_shp__](notebooks/clean_GageLoc_shp.ipynb): The notebook used to clean and add attributes to the GageLoc shapefile. The output shapefile/json is used to easily match ComIDs with USGS gage IDs.\n - [__compare_rc_NWM_USGS-Dev__](notebooks/compare_rc_NWM_USGS-Dev.ipynb): Development space for comparing rating curves from the National Water Model to the rating curves from the USGS.\n - [__explore_NHDPlus-Dev__](notebooks/explore_NHDPlus-Dev.ipynb): Development space for the StreamSegmentNHD class.\n\n`notebooks/data_exploration` - A collection of Jupyter notebooks used for exploring 1D and 2D NWM rating curves and retrieving NWM HAND datasets.\n - [__rating_curves__](notebooks/data_exploration/rating_curves.ipynb): Exploration and comparison of the `hydroprop-fulltable-HUC6.nohand0.csv` and `hydroprop-fulltable-HUC6.csv` rating curve files.\n - [__rating_curves_1D_nc__](notebooks/data_exploration/rating_curves_1D_nc.ipynb): Exploration of rating curves from the NWM 1D netcdf file.\n - [__rating_curves_2D_nc__](notebooks/data_exploration/rating_curves_2D_nc.ipynb): Exploration of rating curves from the NWM 2D netcdf file.\n - [__retrieve_HAND_datasets__](notebooks/data_exploration/retrieve_HAND_datasets.ipynb): Download of HAND datasets for data exploration.\n\n`reanalysis` - An initial look at the NWM reanalysis netcdf files.\n - [__Clean_data_By_date__](reanalysis/Clean_data_By_date.py): Organizing downloaded data by dates.\n - [__Download_Reanalysis_Data__](reanalysis/Download_Reanalysis_Data.py): Downloading reanalysis netcdf files from s3.\n - [__Plot__](reanalysis/Plot.py): Plotting NWM reanalysis data.\n - [__Time_series__](reanalysis/Time_Series.py): Making a time series at a specific ComID.\n - [__explore_reanalysis-Dev__](reanalysis/explore_reanalysis-Dev.ipynb): A notebook for downloading a time series of reanalysis data from s3 and making a streamflow time series plot at one ComID.\n - [__reanalysis__](reanalysis/reanalysis.py): Functions used in the __explore_reanalysis-Dev__ notebook.\n\n## [View the Documentation](https://dewberry.github.io/fcast/)\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/Dewberry/fcast", "keywords": "", "license": "Apache License 2.0", "maintainer": "", "maintainer_email": "", "name": "fcast", "package_url": "https://pypi.org/project/fcast/", "platform": "", "project_url": "https://pypi.org/project/fcast/", "project_urls": { "Homepage": "https://github.com/Dewberry/fcast" }, "release_url": "https://pypi.org/project/fcast/0.1.0/", "requires_dist": [ "gcsfs", "xarray", "matplotlib", "numpy", "pandas", "scipy", "boto3", "requests", "beautifulsoup4", "fiona", "shapely" ], "requires_python": "", "summary": "A collection of python tools used for forecasting flood events and their impact on transportation infrastructure.", "version": "0.1.0" }, "last_serial": 5951535, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "d646e7c22f841113efd30e1d64c328c2", "sha256": "43d23b78e2bcf8adc1fa3be089867a00e6cea4256ef697dc5155e1937f7f5d45" }, "downloads": -1, "filename": "fcast-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "d646e7c22f841113efd30e1d64c328c2", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 18474, "upload_time": "2019-10-09T19:15:42", "url": "https://files.pythonhosted.org/packages/7d/65/45f2b19dbbdb8b9e3f1e6ea2872c7f2f0a01dc1bd491d36e9d7efa0bbdd5/fcast-0.1.0-py3-none-any.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "d646e7c22f841113efd30e1d64c328c2", "sha256": "43d23b78e2bcf8adc1fa3be089867a00e6cea4256ef697dc5155e1937f7f5d45" }, "downloads": -1, "filename": "fcast-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "d646e7c22f841113efd30e1d64c328c2", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 18474, "upload_time": "2019-10-09T19:15:42", "url": "https://files.pythonhosted.org/packages/7d/65/45f2b19dbbdb8b9e3f1e6ea2872c7f2f0a01dc1bd491d36e9d7efa0bbdd5/fcast-0.1.0-py3-none-any.whl" } ] }