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    "info": {
        "author": "Datos Argentina",
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            "Natural Language :: English",
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        "description": "\n# Series de Tiempo de la Rep\u00fablica Argentina\n\n[![Coverage Status](https://coveralls.io/repos/github/datosgobar/series-tiempo-ar/badge.svg?branch=master)](https://coveralls.io/github/datosgobar/series-tiempo-ar?branch=master)\n[![Build Status](https://travis-ci.org/datosgobar/series-tiempo-ar.svg?branch=master)](https://travis-ci.org/datosgobar/series-tiempo-ar)\n[![PyPI](https://badge.fury.io/py/series-tiempo-ar.svg)](http://badge.fury.io/py/series-tiempo-ar)\n[![Stories in Ready](https://badge.waffle.io/datosgobar/series-tiempo-ar.png?label=ready&title=Ready)](https://waffle.io/datosgobar/series-tiempo-ar)\n[![Documentation Status](http://readthedocs.org/projects/series-tiempo-ar/badge/?version=latest)](http://series-tiempo-ar.readthedocs.org/en/latest/?badge=latest)\n\n* Versi\u00f3n python: 3.6+ (0.3)\n* Licencia: MIT license\n* Documentaci\u00f3n: https://series-tiempo-ar.readthedocs.io\n\nEl proyecto Series de Tiempo se basa en una extensi\u00f3n del perfil de metadatos de la pol\u00edtica de apertura. Esta fue dise\u00f1ada para facilitar la publicaci\u00f3n de series de tiempo de organismos gubernamentales que son parte de la Red de Nodos de Datos Abiertos de la Rep\u00fablica Argentina.\n\nA partir de esta especificaci\u00f3n, se desarrollaron aplicaciones para extraer y compilar las series en una base de datos unificada que permitiera el desarrollo de una API.\n\n<!-- START doctoc generated TOC please keep comment here to allow auto update -->\n<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->\n\n## Indice\n\n- [Repositorios oficiales](#repositorios-oficiales)\n- [Repositorios de terceros](#repositorios-de-terceros)\n- [Instalaci\u00f3n](#instalaci%C3%B3n)\n- [Uso](#uso)\n- [Tests](#tests)\n- [Contacto](#contacto)\n\n<!-- END doctoc generated TOC please keep comment here to allow auto update -->\n\n## Repositorios oficiales\n\n* [`series-tiempo-ar`](https://github.com/datosgobar/series-tiempo-ar): Paquete de m\u00f3dulos con funcionalidades para extraer, transformar y analizar series de tiempo basados en la versi\u00f3n 1.1 del [`Perfil de Metadatos`](http://paquete-apertura-datos.readthedocs.io/es/stable/guia_metadatos.html) del [`Paquete de Apertura de Datos de la Rep\u00fablica Argentina`](http://paquete-apertura-datos.readthedocs.io). Es una extensi\u00f3n de [`pydatajson`](https://github.com/datosgobar/pydatajson) y la dependencia principal de los otros repositorios del proyecto.\n\n* [`series-tiempo-ar-etl`](https://github.com/datosgobar/series-tiempo-ar-etl): Rutinas de ETL usadas para compilar diariamente la [`Base de Series de Tiempo de la Administraci\u00f3n P\u00fablica Nacional`](http://datos.gob.ar/dataset/base-series-tiempo-administracion-publica-nacional). **Este proyecto est\u00e1 agendado para su discontinuaci\u00f3n**\n\n* [`series-tiempo-ar-api`](https://github.com/datosgobar/series-tiempo-ar-api): Aplicaci\u00f3n basada en Django que extrae series de tiempo de los cat\u00e1logos de datos abiertos de la Red de Nodos y las indexa en un motor Elastic Search para su consumo como servicio web.\n\n* [`series-tiempo-ar-landing`](https://github.com/datosgobar/series-tiempo-ar-landing): Landing web configurable para una sencilla publicaci\u00f3n y visualizaci\u00f3n de series de tiempo.\n\n## Repositorios de terceros\n\n* \u00a1Todav\u00eda no hay ninguno! \u00bfNo deber\u00edas ser el primero? Si escribiste una librer\u00eda para usar la API o la base completa en alg\u00fan lenguaje, [nos gustar\u00eda saberlo](https://github.com/datosgobar/series-tiempo-ar/issues/new?title=Nueva librer\u00eda en {lenguaje} para usar la API).\n\n## Instalaci\u00f3n\n\n* **Producci\u00f3n:** Desde cualquier parte\n\n```bash\n$ pip install series-tiempo-ar\n```\n\n* **Desarrollo:** Clonar este repositorio, y desde su ra\u00edz, ejecutar:\n```bash\n$ pip install -e .\n```\n\n## Uso\n\nSe provee una clase `TimeSeriesDataJson` como abstracci\u00f3n sobre un cat\u00e1logo que cumple el Perfil de Metadatos. \u00c9ste cat\u00e1logo extiende las funcionalidades de `pydatajson`, y provee m\u00e9todos adicionales para trabajar con datos de series de tiempo. Ver [referencia](./reference.md)\n\n## Tests\n\nLos tests se corren con `nose`. Desde la ra\u00edz del repositorio:\n\n**Configuraci\u00f3n inicial:**\n\n```bash\n$ pip install -r requirements_dev.txt\n$ mkdir tests/temp\n```\n\n**Correr la suite de tests:**\n\nCorrer todos los pasos de testeo (pruebas, estilos de c\u00f3digo)\n`$ scripts/run_all.sh`\n\nLa rutina de pruebas utiliza un servidor web en el puerto 3456. Asegurarse de que ese puerto est\u00e9 disponible antes de correr el script.\n\n## Contacto\n\nTe invitamos a [crearnos un issue](https://github.com/datosgobar/series-tiempo-ar/issues/new?title=Encontre un bug en series-tiempo-ar) en caso de que encuentres alg\u00fan bug o tengas feedback de alguna parte de `series-tiempo-ar`.\n\nPara todo lo dem\u00e1s, pod\u00e9s mandarnos tu comentario o consulta a [datos@modernizacion.gob.ar](mailto:datos@modernizacion.gob.ar).\n\n\nVersiones\n===\n\n0.3.5 (2019-09-09)\n------------------\n\n* Corrige identificaci\u00f3n de par\u00e1metros de scraping para que sea m\u00e1s robusto a problemas de texto / n\u00famero en Excels.\n\n0.3.4 (2019-09-06)\n------------------\n\n* Cambia \"-\" por \"_\" para el id compuesto de series que se leen de un archivo de texto plano que contiene una base de series de tiempo.\n\n0.3.3 (2019-08-08)\n------------------\n\n* Modifica forma de convertir paneles en distribuciones de series, m\u00e1s eficiente pero con menos control de errores de la fuente original. Ahora es m\u00e1s eficiente.\n\n0.3.2 (2019-07-24)\n------------------\n\n* Agrego par\u00e1metro opcional en TimeSeriesDataJson.load_ts_distribution() para poder cargar una distribuci\u00f3n de TXT que no re-descargue la fuente original, sino que la busque en un path local.\n\n0.3.1 (2019-07-23)\n------------------\n\n* Par\u00e1metro para fozar el formato de un cat\u00e1logo\n* Fix construyendo los errores de validaci\u00f3n  \n\n0.3.0 (2019-06-25)\n------------------\n\n* Primer release estable de la librer\u00eda. Agrega m\u00e9todos adicionales de validaciones, y centraliza la lectura de distribuciones en el m\u00e9todo `TimeSeriesDataJson.load_ts_distribution`\n\n0.2.3 (2019-03-01)\n------------------\n\n* Actualizado de validaciones para distribuciones CSV\n\n0.1.4 (2018-05-06)\n------------------\n\n* Flexibiliza la restricci\u00f3n de series demasiado cortas: ahora se admite hasta un 2% del total de las series de una distribuci\u00f3n con una cantidad demasiado baja de valores para ser una serie.\n\n0.1.3 (2018-05-04)\n------------------\n\n* Actualiza las validaciones al schema de la versi\u00f3n 0.4.12 de pydatajson\n\n0.1.2 (2018-04-30)\n------------------\n\n* El campo `temporal` en dataset pasa a ser de uso opcional.\n\n0.1.0 (2018-04-17)\n------------------\n\n* Soporte para Python 3\n\n0.0.3 (2017-10-18)\n------------------\n\n* Restauro la validaci\u00f3n `validate_df_shape` que est\u00e1 testeada y corregida.\n* Flexibilizo al l\u00edmite el m\u00e1ximo de missings admitidos en una serie (999 missings por cada 1000 valores).\n\n0.0.2 (2017-10-10)\n------------------\n\n* Bug fix: elimino temporalmente validaci\u00f3n `validate_df_shape` que tiene alg\u00fan error en el tratamiento de los tipos.\n\n0.0.1 (2017-10-08)\n------------------\n\n* Primer release a PyPI.\n* Release todav\u00eda no documentado.\n\n\n",
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