{ "info": { "author": "Stijn Van Hoey", "author_email": "stijnvanhoey@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 1 - Planning", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Utilities" ], "description": "# Hydropy\n\n[![Pypi](https://img.shields.io/pypi/v/hydropy.svg)](https://pypi.python.org/pypi/hydropy) [![Build Status](https://img.shields.io/travis/stijnvanhoey/hydropy.svg)](https://travis-ci.org/stijnvanhoey/hydropy) [![License](https://img.shields.io/badge/License-BSD%202--Clause-blue.svg)](https://opensource.org/licenses/BSD-2-Clause)\n\nAnalysis of hydrological oriented time series.\n\nThis package is designed to simplify the collection and analysis of\nhydrology data. Use HydroPy in a Jupyter\nnotebook and save your analysis so that you can recreate your procedures and\nshare them with others. \n\nHydropy uses the power of Numpy and Pandas to quickly process large datasets. \nMatplotlib and Seaborn are built-in to Hydropy, allowing you to create\npublication-ready diagrams quickly and easily.\n\nTry Hydropy in a notebook: [hydropy_tutorial.ipynb](https://github.com/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb)\n\n## Example:\n\n # Recession periods in June 2011:\n myflowserie.get_year('2011').get_month(\"Jun\").get_recess()\n\n![Recession periods](./data/recession.png)\n\n # Peak values above 90th percentile for station LS06_347 in july 2010:\n myflowserie['LS06_347'].get_year('2010').get_month(\"Jul\").get_highpeaks(150, above_percentile=0.9)\n\n![Selected peaks](./data/peaks.png)\n\n # Select 3 storms out of the series\n storms = myflowserie.derive_storms(raindata['P06_014'], 'LS06_347', number_of_storms=3, drywindow=96, makeplot=True)\n\n![Selected storms](./data/storms.png)\n\nA more extended tutorial/introduction is provided in a ipython notebook: [hydropy_tutorial.ipynb](https://github.com/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb)\n\nWe acknowledge the Flemish Environmental Agency (VMM) for the data used in the tutorial. It can be downloaded from http://www.waterinfo.be/.\n\nTo install this, git clone the repo and then install it by:\n\n python setup.py install\n\nTo test the functionalities yourself without installing it, use following test environment provided by Binder:\n[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/stijnvanhoey/hydropy)\n\nInspiration or possible useful extensions:\n* Basically this is a restart of hydropy https://code.google.com/p/hydropy/\n* Hydroclimpy http://hydroclimpy.sourceforge.net/\n* Georgakakos2004, ROC\n* http://cran.r-project.org/web/packages/hydroTSM/vignettes/hydroTSM_Vignette.pdf\n\nThe slides version of the notebook was made with nbconvert (using reveal.js), by following command:\n\n ipython nbconvert hydropy_tutorial.ipynb --to=slides --post=serve --reveal-prefix=reveal.js --config slides_config.py\n\nhttp://nbviewer.ipython.org/format/slides/github/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb#/\n\n\nCopyright (c) 2015-2017 Stijn Van Hoey, Martin Roberge, and Contributors\n\n\n=======\nCredits\n=======\n\nDevelopment Lead\n----------------\n\n* Stijn Van Hoey \n\nContributors\n------------\n\nMartin Roberge ", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/stijnvanhoey/hydropy/", "keywords": "hydrology time series hydroTSM", "license": "BSD", "maintainer": "", "maintainer_email": "", "name": "hydropy", "package_url": "https://pypi.org/project/hydropy/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/hydropy/", "project_urls": { "Homepage": "https://github.com/stijnvanhoey/hydropy/" }, "release_url": "https://pypi.org/project/hydropy/0.1.2/", "requires_dist": [ "IPython", "matplotlib", "numpy", "pandas", "requests", "scipy", "seaborn" ], "requires_python": "", "summary": "Analysis of hydrological oriented time series", "version": "0.1.2" }, "last_serial": 2615930, 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