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        "author": "Mitch Negus",
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        "description": "# *pyleiades*\n[![Build Status](https://travis-ci.org/mitchnegus/pyleiades.svg?branch=master)](https://travis-ci.org/mitchnegus/pyleiades)\n\n## Python Library for EIA Data Examination & Exhibition\n\n###### A tool for creating visuals from historical energy data (e.g. the EIA monthly energy review).\n\nThis tool is designed to provide insightful, aesthetic and more flexible visualizations of the Energy Information Administration (EIA) monthly energy review datasets.\nThe datasets contain information about the sources of energy Americans have relied on for power since the middle of the 20th century. \nThe datasets begin in 1949 with annual energy production, consumption, import, and export values, and extend up until the present. \nMonthly energy datapoints are reported starting in 1973.\n\nThe basic energy sources are reported in the following groups:  \n\n###### Fossil Fuels\n* Coal\n* Natural Gas\n* Petroleum\n\n###### Renewables\n* Wind\n* Solar\n* Hydroelectric\n* Geothermal\n* Biomass\n\n###### Nuclear\n* Fission\n\nThe data is published monthly on the [EIA's website](https://www.eia.gov/totalenergy/data/monthly/), and as of March 31st, 2019 records were provided up through December 2018. This package also includes data up to date through the end of 2018, though more recent data can be downloaded using an included script. \n\nAll reported values are in units of quadrillion british thermal units (1.0E15 BTU). Be aware that the datasets may provide [more precision](https://www.eia.gov/totalenergy/data/monthly/dataunits.php) than is published in the PDF reports.\n\n## Installation\n\n_pyleiades_ is hosted through the Python Package Index (PyPI) and can be easily installed using pip.\nFrom the command line, run\n\n```\n$ pip install pyleiades\n```\n\nThe module requires a recent version of Python 3 (3.6 or greater), pandas, and matplotlib, among others.\nIf you run into trouble running the package, try using the Anaconda environment provided in this repo.\nInstall the environment using the command\n\n```\n$ conda env create -f environment.yml\n```\n\nand activate the environment by issuing the command\n\n```\n$ conda activate pyleiades\n```\n\n## Updating\n\nAn archive of EIA Monthly Energy Review datasets is kept in the `pyleiades` data repository. \nThis may not include the most up to date information, and so the package comes with a script to update the available data.\nOnce the package is installed, run \n\n```\n$ update_eia_data.py\n```\n\nfrom the command line to download the most recent data from the EIA website. \n\n## Using the API\n\nThe API is built around two main object types\u2014the `Energy` and `Visual` classes.\n\n### The `Energy` object\n\nTo access the EIA data directly for a certain energy type, use the `Energy` class.\nFor example, the energy consumption data for all renewable energy sources can be accessed with:\n\n```\n>>> from pyleiades import Energy\n>>> renewables = Energy('renewable')\n```\n\nThe resulting `renewables` object stores the complete consumption history within the `energy_data` dataframe attribute.\n\n```\n>>> renewables.energy_data\n     date_code      value\n6220    194913   2.973984\n6221    195013   2.977718\n6222    195113   2.958464\n6223    195213   2.940181\n...\n```\n\nThe `date_code` column gives the reporting date (in the format `YYYYMM`, where the month code 13 indicates a yearly total) and the `value` column gives the consumption amounts (in QBTU) for each date. \nIn the example above, the first four entries of the `energy_data` dataframe are the renewable energy yearly consumption totals for 1949 through 1952.\n\nEnergy consumption values are the default, however the `Energy` objects can also be used to access production, import and export statistics.\nThe type of statistic can be selected using the `stat_type` keyword argument.\n\n```\n>>> renewables = Energy('renewable', stat_type='production')\n>>> renewables.energy_data\n     date_code     value\n6220    194913  1.549262\n6221    195013  1.562307\n6222    195113  1.534669\n6223    195213  1.474369\n```\n\nPerhaps more interesting than the complete history, however, are more sophisticated features of the data, like interval specific totals and extremes.\n\nUsing the `totals` method of an `Energy` object allows the data to be totaled at a specified interval\u2014either monthly, yearly, or cumulatively.\n\n```\n>>> renewables.totals('monthly')\n           value\n  date\n197301  0.403981\n197302  0.360900\n197303  0.400161\n197304  0.380470\n```\n\nNotice that here the monthly data only goes back as far as 1973 (though the `energy_data` attribute showed yearly data for renewable energy dating back to 1949). \nBy default, the `totals` method selects the entire range of available data. \nThis  behavior can be overriden by providing start and end dates for some interval as keyword arguments.\nTo only get monthly renewable energy data from 2000 to 2010, this would be:\n\n```\n>>> renewables.totals(freq='monthly', start_date='200001', end_date='200912')\n           value\n  date\n200001  0.505523\n200002  0.498993\n200003  0.558474\n200004  0.567147\n```\n\nTo get extremes over a dataset interval, use the `maxima` or `minima` methods.\n\n### The `Visual` object\n\nA `Visual` allows the package to create plots of several energy types. \nThe initialization parameters for a `Visual` are similar to those for an `Energy` object. \nA `Visual` can accept a single energy type or a list of energy types, optionally followed by a type of statistic (consumption by default).\n\n```\nvisual = Visual(['coal', 'nuclear', 'renewable'])\n```\n\nThis visual object's methods can then be used to generate any of a variety of visuals. \nThe syntax is again similar to that of the `Energy` object, however it includes a subject argument corresponding to a method of the `Energy` object.\nHere's an example that generates a line graph of energy totals:\n\n```\nvisual.linegraph(subject='totals', freq='monthly', start_date='1970')\n```\n\n![visual comparing coal, nuclear, and renewable energy consumption since 1970](pyleiades/fig/demo-plot.png)\n\nRun the very simple installed script `pyleiades-demo.py` to see the package in action.\n\n",
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