{
    "info": {
        "author": "Datos Argentina",
        "author_email": "datos@modernizacion.gob.ar",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "pydatajson\n==========\n\n[![Coverage Status](https://coveralls.io/repos/github/datosgobar/pydatajson/badge.svg?branch=master)](https://coveralls.io/github/datosgobar/pydatajson?branch=master)\n[![Build Status](https://travis-ci.org/datosgobar/pydatajson.svg?branch=master)](https://travis-ci.org/datosgobar/pydatajson)\n[![PyPI](https://badge.fury.io/py/pydatajson.svg)](http://badge.fury.io/py/pydatajson)\n[![Documentation Status](http://readthedocs.org/projects/pydatajson/badge/?version=stable)](http://pydatajson.readthedocs.io/es/stable/?badge=stable)\n\nPaquete en python con herramientas para manipular y validar metadatos de cat\u00e1logos de datos.\n\n* Versi\u00f3n python: 2 y 3\n* Licencia: MIT license\n* Documentaci\u00f3n: [https://pydatajson.readthedocs.io/es/stable](https://pydatajson.readthedocs.io/es/stable)\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\n- [Instalaci\u00f3n](#instalacion)\n- [Usos](#usos)\n  - [Setup](#setup)\n  - [Validaci\u00f3n de metadatos de cat\u00e1logos](#validacion-de-metadatos-de-catalogos)\n    - [Archivo data.json local](#archivo-datajson-local)\n    - [Otros formatos](#otros-formatos)\n  - [Generaci\u00f3n de reportes y configuraciones del Harvester](#generacion-de-reportes-y-configuraciones-del-harvester)\n    - [Crear un archivo de configuraci\u00f3n eligiendo manualmente los datasets a federar](#crear-un-archivo-de-configuracion-eligiendo-manualmente-los-datasets-a-federar)\n    - [Crear un archivo de configuraci\u00f3n que incluya \u00fanicamente los datasets con metadata v\u00e1lida](#crear-un-archivo-de-configuracion-que-incluya-unicamente-los-datasets-con-metadata-valida)\n  - [Transformaci\u00f3n de un archivo de metados XLSX al est\u00e1ndar JSON](#transformacion-de-un-archivo-de-metados-xlsx-al-estandar-json)\n  - [Generaci\u00f3n de indicadores de monitoreo de cat\u00e1logos](#generacion-de-indicadores-de-monitoreo-de-catalogos)\n- [Tests](#tests)\n- [Recursos de inter\u00e9s](#recursos-de-interes)\n- [Cr\u00e9ditos](#creditos)\n\n<!-- END doctoc generated TOC please keep comment here to allow auto update -->\n\nEste README cubre los casos de uso m\u00e1s comunes para la librer\u00eda, junto con ejemplos de c\u00f3digo, pero sin mayores explicaciones. Para una versi\u00f3n m\u00e1s detallada de los comportamientos, revise la [documentaci\u00f3n oficial](http://pydatajson.readthedocs.io) o el [Manual de Uso](docs/MANUAL.md) de la librer\u00eda.\n\n## Instalaci\u00f3n\n\n* **Producci\u00f3n:** Desde cualquier parte\n\n```bash\n$ pip install pydatajson\n```\n\n* **Desarrollo:** Clonar este repositorio, y desde su ra\u00edz, ejecutar:\n```bash\n$ pip install -e .\n```\n\nA partir de la versi\u00f3n 0.2.x (Febrero 2017), la funcionalidad del paquete se mantendr\u00e1 fundamentalmente estable hasta futuro aviso. De todas maneras, si piensa utilizar esta librer\u00eda en producci\u00f3n, le sugerimos fijar la versi\u00f3n que emplea en un archivo `requirements.txt`.\n\n## Usos\n\nLa librer\u00eda cuenta con funciones para cuatro objetivos principales:\n- **validaci\u00f3n de metadatos de cat\u00e1logos** y los _datasets_,\n- **generaci\u00f3n de reportes** sobre el contenido y la validez de los metadatos de cat\u00e1logos y _datasets_,\n- **transformaci\u00f3n de archivos de metadatos** al formato est\u00e1ndar (JSON), y\n- **generaci\u00f3n de indicadores de monitoreo de cat\u00e1logos** y sus _datasets_.\n\nA continuaci\u00f3n se proveen ejemplos de cada uno de estas acciones. Si desea analizar un flujo de trabajo m\u00e1s completo, refi\u00e9rase a los Jupyter Notebook de [`samples/`](samples/)\n\n### Setup\n\n`DataJson` utiliza un esquema default que cumple con el perfil de metadatos recomendado en la [Gu\u00eda para el uso y la publicaci\u00f3n de metadatos (v0.1)](https://github.com/datosgobar/paquete-apertura-datos/raw/master/docs/Guia%20para%20el%20uso%20y%20la%20publicacion%20de%20metadatos%20(v0.1).pdf) del [Paquete de Apertura de Datos](https://github.com/datosgobar/paquete-apertura-datos).\n\n```python\nfrom pydatajson import DataJson\n\ndj = DataJson()\n```\n\nSi se desea utilizar un esquema alternativo, por favor, consulte la secci\u00f3n \"Uso > Setup\" del [manual oficial](docs/MANUAL.md), o la documentaci\u00f3n oficial.\n\n### Validaci\u00f3n de metadatos de cat\u00e1logos\n\n- Si se desea un **resultado sencillo (V o F)** sobre la validez de la estructura del cat\u00e1logo, se utilizar\u00e1 **`is_valid_catalog(catalog)`**.\n- Si se desea un **mensaje de error detallado**, se utilizar\u00e1 **`validate_catalog(catalog)`**.\n\nPor conveniencia, la carpeta [`tests/samples/`](tests/samples/) contiene varios ejemplos de `data.json` bien y mal formados con distintos tipos de errores.\n\n#### Archivo data.json local\n\n```python\nfrom pydatajson import DataJson\n\ndj = DataJson()\ncatalog = \"tests/samples/full_data.json\"\nvalidation_result = dj.is_valid_catalog(catalog)\nvalidation_report = dj.validate_catalog(catalog)\n\nprint validation_result\nTrue\n\nprint validation_report\n{\n    \"status\": \"OK\",\n    \"error\": {\n        \"catalog\": {\n            \"status\": \"OK\",\n            \"errors\": [],\n            \"title\": \"Datos Argentina\"\n        },\n        \"dataset\": [\n            {\n                \"status\": \"OK\",\n                \"errors\": [],\n                \"title\": \"Sistema de contrataciones electr\u00f3nicas\"\n            }\n        ]\n    }\n}\n```\n\n#### Otros formatos\n\n`pydatajson` puede interpretar cat\u00e1logos en formatos:\n\n* JSON\n* XLSX (ver [plantilla de cat\u00e1logo en XLSX](docs/assets/catalog.xlsx))\n\nLos cat\u00e1logos pueden estar almacenados localmente o remotamente a trav\u00e9s de URLs de descarga directa. Tambi\u00e9n es capaz de interpretar diccionarios de Python con metadatos de cat\u00e1logos.\n\n\n```python\nfrom pydatajson import DataJson\n\ndj = DataJson()\ncatalogs = [\n    \"tests/samples/full_data.json\", # archivo JSON local\n    \"http://181.209.63.71/data.json\", # archivo JSON remoto\n    \"tests/samples/catalogo_justicia.xlsx\", # archivo XLSX local\n    \"https://raw.githubusercontent.com/datosgobar/pydatajson/master/tests/samples/catalogo_justicia.xlsx\", # archivo XLSX remoto\n    {\n        \"title\": \"Cat\u00e1logo del Portal Nacional\",\n\t\"description\" \"Datasets abiertos para el ciudadano.\"\n        \"dataset\": [...],\n\t(...)\n    } # diccionario de Python\n]\n\nfor catalog in catalogs:\n    validation_result = dj.is_valid_catalog(catalog)\n    validation_report = dj.validate_catalog(catalog)\n```\n\n### Generaci\u00f3n de reportes y configuraciones del Harvester\n\nSi ya se sabe que se desean cosechar todos los _datasets_ [v\u00e1lidos] de uno o varios cat\u00e1logos, se pueden utilizar directamente el m\u00e9todo `generate_harvester_config()`, proveyendo `harvest='all'` o `harvest='valid'` respectivamente. Si se desea revisar manualmente la lista de _datasets_ contenidos, se puede invocar primero `generate_datasets_report()`, editar el reporte generado y luego prove\u00e9rselo a `generate_harvester_config()`, junto con la opci\u00f3n `harvest='report'`.\n\n#### Crear un archivo de configuraci\u00f3n eligiendo manualmente los datasets a federar\n\n```python\ncatalogs = [\"tests/samples/full_data.json\", \"http://181.209.63.71/data.json\"]\nreport_path = \"path/to/report.xlsx\"\ndj.generate_datasets_report(\n    catalogs=catalogs,\n    harvest='none', # El reporte tendr\u00e1 `harvest==0` para todos los datasets\n    export_path=report_path\n)\n\n# A continuaci\u00f3n, se debe editar el archivo de Excel 'path/to/report.xlsx',\n# cambiando a '1' el campo 'harvest' en los datasets que se quieran cosechar.\n\nconfig_path = 'path/to/config.csv'\ndj.generate_harvester_config(\n    harvest='report',\n    report=report_path,\n    export_path=config_path\n)\n```\nEl archivo `config_path` puede ser provisto a Harvester para federar los _datasets_ elegidos al editar el reporte intermedio `report_path`.\n\nPor omisi\u00f3n, en la salida de `generate_harvester_config` la frecuencia de actualizaci\u00f3n deseada para cada _dataset_ ser\u00e1 \"R/P1D\", para intentar cosecharlos diariamente. De preferir otra frecuencia (siempre y cuando sea v\u00e1lida seg\u00fan ISO 8601), se la puede especificar a trav\u00e9s del par\u00e1metro opcional `frequency`. Si especifica exp\u013aicitamente `frequency=None`, se conservar\u00e1n las frecuencias de actualizaci\u00f3n indicadas en el campo `accrualPeriodicity` de cada _dataset_.\n\n#### Crear un archivo de configuraci\u00f3n que incluya \u00fanicamente los datasets con metadata v\u00e1lida\n\nConservando las variables anteriores:\n\n```python\ndj.generate_harvester_config(\n    catalogs=catalogs,\n    harvest='valid'\n    export_path='path/to/config.csv'\n)\n```\n\n### Transformaci\u00f3n de un archivo de metados XLSX al est\u00e1ndar JSON\n\n```python\nfrom pydatajson.readers import read_catalog\nfrom pydatajson.writers import write_json\nfrom pydatajson import DataJson\n\ndj = DataJson()\ncatalogo_xlsx = \"tests/samples/catalogo_justicia.xlsx\"\n\ncatalogo = read_catalog(catalogo_xlsx)\nwrite_json(obj=catalogo, path=\"tests/temp/catalogo_justicia.json\")\n```\n\n### Generaci\u00f3n de indicadores de monitoreo de cat\u00e1logos\n\n`pydatajson` puede calcular indicadores sobre uno o m\u00e1s cat\u00e1logos. Estos indicadores recopilan informaci\u00f3n de inter\u00e9s sobre los _datasets_ de cada uno, tales como:\n- el estado de validez de los cat\u00e1logos;\n- el n\u00famero de d\u00edas desde su \u00faltima actualizaci\u00f3n;\n- el formato de sus distribuciones;\n- frecuencia de actualizaci\u00f3n de los _datasets_;\n- estado de federaci\u00f3n de los _datasets_, compar\u00e1ndolo con el cat\u00e1logo central\n\nLa funci\u00f3n usada es `generate_catalogs_indicators`, que acepta los cat\u00e1logos como par\u00e1metros. Devuelve dos valores:\n- una lista con tantos valores como cat\u00e1logos, con cada elemento siendo un diccionario con los indicadores del cat\u00e1logo respectivo;\n- un diccionario con los indicadores de la red entera (una suma de los individuales)\n\n```python\ncatalogs = [\"tests/samples/full_data.json\", \"http://181.209.63.71/data.json\"]\nindicators, network_indicators = dj.generate_catalogs_indicators(catalogs)\n\n# Opcionalmente podemos pasar como segundo argumento un cat\u00e1logo central,\n# para poder calcular indicadores sobre la federaci\u00f3n de los datasets en 'catalogs'\n\ncentral_catalog = \"http://datos.gob.ar/data.json\"\nindicators, network_indicators = dj.generate_catalogs_indicators(catalogs, central_catalog)\n```\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\n```bash\n$ nosetests\n```\n\n## Recursos de inter\u00e9s\n\n* [Est\u00e1ndar ISO 8601 - Wikipedia](https://es.wikipedia.org/wiki/ISO_8601)\n* [JSON SChema - Sitio oficial del est\u00e1ndar](http://json-schema.org/)\n* [Documentaci\u00f3n completa de `pydatajson` - Read the Docs](http://pydatajson.readthedocs.io)\n* [Gu\u00eda para el uso y la publicaci\u00f3n de metafatos](https://docs.google.com/document/d/1Z7XhpzOinvITN_9wqUbOYpceDzic3KTOHLtHcGCPAwo/edit)\n\n## Cr\u00e9ditos\n\nEl validador de archivos `data.json` desarrollado es mayormente un envoltorio (*wrapper*) alrededor de la librer\u00eda [`jsonschema`](https://github.com/Julian/jsonschema), que implementa el vocabulario definido por [JSONSchema.org](http://json-schema.org/) para anotar y validar archivos JSON.\n\n\nVersiones\n=========\n0.4.67 (2019-12-17)\n-------------------\n* Extensi\u00f3n de las capacidades de `resources>_update()`  \n\n0.4.61 (2019-11-28)\n-------------------\n* Fix de la parametrizaci\u00f3n de threads.\n\n0.4.60 (2019-11-28)\n-------------------\n* Parametrizaci\u00f3n de cantidad de threads a usar en validaci\u00f3n de urls.\n\n0.4.59 (2019-11-21)\n-------------------\n* Validaci\u00f3n de formatos y extensiones es menos restrictiva.\n* Mail para cat\u00e1logo ya no es obligatorio.\n* Temporal se puede usar con s\u00f3lo una fecha de comienzo (sin final).\n* Se admiten espacios iniciales o finales en los mails.\n\n0.4.58 (2019-11-20)\n-------------------\n* Fix de estilo de codigo\n\n0.4.57 (2019-11-20)\n-------------------\n* Validaci\u00f3n de fileName de distribuciones es menos estricto\n\n0.4.53 (2019-10-29)\n-------------------\n* Parametrizaci\u00f3n del timeout en los heads que chequean urls\n\n0.4.52 (2019-10-22)\n-------------------\n* Validaci\u00f3n electiva de urls con certificados SSL, enviando verify_ssl=False desactiva la validacion de certificados v\u00e1lidos\n* Se modifica el criterio de urls validas, ahora solo las urls 4xx y 5xx son invalidas (a excepcion de 429)\n\n0.4.51 (2019-10-15)\n-------------------\n* Los m\u00e9todos de validaciones e indicadores ahora aceptan un flag que determina si validar o no campos con URLs.\n\n0.4.50 (2019-10-10)\n-------------------\n* Bugfix de c\u00e1lculo de indicadores de `downloadURL` en indicadores de red.\n* Fields con may\u00fasculas y min\u00fasculas mixtasse muestran en may\u00fasculas en indicadores. \n\n0.4.49 (2019-09-24)\n-------------------\n* Optimizaci\u00f3n de tiempo de ejecuci\u00f3n de validaciones, usando threads.\n\n0.4.48 (2019-09-24)\n-------------------\n* Se calcula 3 nuevos indicadores en base al `downloadURL` de las distribuciones.\n\n\n0.4.47 (2019-09-17)\n-------------------\n* Implementaci\u00f3n de timezones para los nodos de origen y destino en una federaci\u00f3n\n\n\n0.4.46 (2019-08-16)\n-------------------\n* Fix a lecturas de cat\u00e1logos XLSX en contextos concurrentes\n\n\n0.4.45 (2019-08-01)\n-------------------\n* Subo versi\u00f3n de python-dateutil\n\n0.4.44 (2019-07-30)\n-------------------\n* Bugfix en validaci\u00f3n de campos de mail\n* subo versi\u00f3n de openpyxl\n\n\n0.4.43 (2019-06-26)\n-------------------\n* Validaci\u00f3n para ids num\u00e9ricos en la resturaci\u00f3n de cat\u00e1logos.\n\n\n0.4.42 (2019-06-04)\n-------------------\n* Agrega par\u00e1metros `verify_ssl` y `requests_timeout` a los m\u00e9todos de federaci\u00f3n.\n\n\n0.4.41 (2019-05-28)\n-------------------\n* Agrega par\u00e1metros `verify_ssl` y `requests_timeout` a `DataJson` que controla el comportamiento de descarga de cat\u00e1logos remotos.\n\n\n0.4.40 (2019-05-28)\n-------------------\n* Refactor de validaciones. Ahora `DataJson` acepta un par\u00e1metro `validator_class` que corre validaciones sobre el cat\u00e1logo.\n\n\n0.4.39 (2019-05-07)\n-------------------\n* Cambia el nivel del logging para los warnings que se logueaban bajo error\n\n\n0.4.38 (2019-04-15)\n-------------------\n* Bugfix en la escritura de cat\u00e1logos sin themes\n* Actualiza planilla xlsx de cat\u00e1logo\n* La validaci\u00f3n de datasets devuelve una lista en vez de un generador\n\n\n0.4.37 (2019-04-03)\n-------------------\n* Permite el c\u00e1lculo de indicadores de federaci\u00f3n usando ids\n\n\n0.4.36 (2019-03-01)\n-------------------\n* Cambia el c\u00e1lculo de indicadores porcentuales para que calculen de 0 a 1\n* Bugfix para ciertos cat\u00e1logos con sufijos no reconocidos\n\n\n0.4.35 (2019-02-19)\n-------------------\n* Actualiza la validaci\u00f3n para aceptar el string vac\u00edo como valor v\u00e1lido\n* Marca los identificadores de distribuciones y datasets como campos requeridos\n\n\n0.4.34 (2019-02-01)\n-------------------\n* Implementa m\u00e9todo para tomar frecuencia de una serie de tiempo\n\n\n0.4.33 (2019-01-10)\n-------------------\n* Cambia el kwarg dj_format por catalog_format\n* Peque\u00f1o fix para los catalogos remotos json incorrectamente le\u00eddos como xlsx\n\n\n0.4.32 (2019-01-08)\n-------------------\n* Fix al dj_format para las lecturas\n\n\n0.4.31 (2019-01-08)\n-------------------\n* Se aceptan cat\u00e1logos sin formato para la lectura del DataJson\n* Nuevo par\u00e1metro para forzar la lectura de un cat\u00e1logo en cierto formato\n* Actualizaci\u00f3n de pyyaml  \n\n\n0.4.30 (2018-12-28)\n-------------------\n* No se validan URLs repetidas para datasets, hay casos v\u00e1lidos donde ocurren \n\n\n0.4.29 (2018-12-21)\n-------------------\n* M\u00e9todo `remove_organizations_from_ckan()`.\n* Cambia la estrategia de lectura para json sin extensi\u00f3n. \n\n\n0.4.28 (2018-12-11)\n-------------------\n* Parametro opcional a `push_dataset_to_ckan()` para regenerar `accessURL` de recursos.\n* Permite el c\u00e1lculo de indicadores con cat\u00e1logo central opcional. \n\n\n0.4.27 (2018-11-23)\n-------------------\n* Las funcionalidades que estaban en `restore_catalog_to_ckan()` pasan a ser de `restore_organization`. `restore_catalog` se compone de varias llamadas a `restore_organization`.\n* Documentaci\u00f3n de `restore_catalog_to_ckan`.\n\n0.4.26 (2018-11-05)\n-------------------\n* Agrega m\u00e9todos de manejo de organizaciones para bajar la informaci\u00f3n o subir a un portal CKAN.\n* Fix en indicador 'datasets_con_datos_pct' al calcular el porcentaje.\n* Cambio en los tests para que usen archivos temporales en lugar de crearlos en la carpeta results.\n\n\n0.4.25 (2018-10-22)\n-------------------\n* Agrega indicador 'datasets_con_datos_cant' para identificar la cantidad de datasets que tienen alguna distribuci\u00f3n potencialmente con datos y los que no.\n* Expande la funci\u00f3n `backup.make_catalogs_backup()` con argumentos opcionales para facilitar la generaci\u00f3n de backups descargando las distribuciones.\n\n\n0.4.24 (2018-10-16)\n-------------------\n* Cambia el valor default en el indicador `datasets_frecuencias_cant`.\n\n\n0.4.23 (2018-10-2)\n-------------------\n* Se agregan HTML, PHP y RAR como formatos de datos posibles.\n* Bugfix relacionado a los valores default en el c\u00e1lculo de indicadores.\n\n\n0.4.22 (2018-09-05)\n-------------------\n* Agrega espacios a los caracteres permitidos en keyword.\n\n\n0.4.21 (2018-08-21)\n-------------------\n* Tests y peque\u00f1os bugfixes a ckan_reader.\n* Adecua el codigo a pycodestyle.\n* Fija piso de 80% de coverage para CI.\n\n\n0.4.20 (2018-08-09)\n-------------------\n\n* Agrega tildes y \u00f1 a los caracteres permitidos en keyword.\n* Cuenta los campos faltantes como `None` en los indicadores.\n\n\n0.4.19 (2018-08-07)\n-------------------\n\n* Validaci\u00f3n de caracteres permitidos en los keywords.\n* Bugfix a la lectura de listas en xlsx con comas extras.\n* Bugfix en el cual se repet\u00edan los errores de validaci\u00f3n si se ped\u00eda formato lista.\n\n\n0.4.18 (2018-07-30)\n-------------------\n\n* Agrega interfaz por l\u00ednea de comandos para validar r\u00e1pidamente un cat\u00e1logo: `pydatajson validation http://datos.gob.ar/data.json`.\n* Validaci\u00f3n de keywords, themes, y lenguajes vac\u00edos.\n* Bugfix en `distribution_has_time_index` para capturar excepciones en field inv\u00e1lidos.\n\n\n0.4.17 (2018-07-10)\n-------------------\n\n* Agregados 3 indicadores `distribuciones_federadas`, `datasets_licencias_cant` y `distribuciones_tipos_cant`.\n* `harvest_catalog_to_ckan` devuelve el mensaje en lugar de las representaciones de las excepciones.\n\n\n0.4.16 (2018-06-19)\n-------------------\n\n* Bugfix en la escritura y lectura de cat\u00e1logos xlsx.\n* Federar campo `type` en distribuciones.\n* Refactor del logging del m\u00f3dulo. Todos los eventos se escriben en el logger `pydatajson`.\n* Reestructuraci\u00f3n de la respuesta de `harvest_catalog_to_ckan()`, devuelve adicionalmente los datasets con errores de federaci\u00f3n.\n\n\n0.4.15 (2018-05-15)\n-------------------\n\n* Cambios en los requerimientos y `setup.py` para definir los environment markers de manera que soporte setuptools.\n\n\n0.4.14 (2018-05-11)\n-------------------\n\n* `harvest_catalog_to_ckan()` atrapa todas las excepciones de un dataset y no detiene la ejecuci\u00f3n.\n\n\n0.4.13 (2018-05-06)\n-------------------\n\n* Agrega una primer interfaz sencilla por l\u00ednea de comandos. Cualquier m\u00f3dulo puede ser usado como `pydatajson module_name arg1 arg2 arg3` siempre que defina un m\u00e9todo `main()` a nivel del m\u00f3dulo que procese los par\u00e1metros.\n\n0.4.12 (2018-05-04)\n-------------------\n\n* Agrega funci\u00f3n `get_distribution_time_index()` que devuelve el `title` del `field` marcado como time_index en una distribuci\u00f3n de series de tiempo, si este lo tiene.\n\n0.4.11 (2018-04-25)\n-------------------\n\n* Corrige bug de `harvest_catalog_ot_ckan` para manejar excepciones de validaci\u00f3n de los datasets\n\n\n0.4.10 (2018-04-24)\n-------------------\n\n* Mejora manejo de errores de las funciones para federar cat\u00e1logos completos.\n\n0.4.9 (2018-04-24)\n-------------------\n\n* Agrego funci\u00f3n para generar ids de distribuciones en cat\u00e1logos que nos los tienen (compatibilidad con perfil 1.0)\n* Agrega funci\u00f3n para eliminar todos los datastets federados de un cat\u00e1logo que se encuentren en un CKAN\n* Implemento fallback que busca un theme por identifier primero o por label despu\u00e9s (si falla)\n* Agrego excepciones a los chequeos de formato vs. extensi\u00f3n\n* Agrego param\u00e9tros a la funci\u00f3n title_to_name() para establecer una longitud m\u00e1xima del resultado de la transformaci\u00f3n en caracteres\n\n0.4.8 (2018-04-18)\n-------------------\n\n* Mejoro manejo de errores de los m\u00e9todos optimizados de b\u00fasqueda\n\n0.4.7 (2018-04-17)\n-------------------\n\n* Flexibiliza m\u00e9todos de b\u00fasqueda optimizados para aceptar data.json's versi\u00f3n 1.0\n* Mejora la performance de los m\u00e9todos de b\u00fasqueda optimizados\n\n0.4.6 (2018-04-17)\n-------------------\n\n* Re-estructura el archivo de configuraci\u00f3n para federaci\u00f3n (nueva versi\u00f3n simplificada)\n* Agrega m\u00f3dulo para hacer backups de datos y metadatos de un cat\u00e1logo\n* Mejora la performance de guardar cat\u00e1logos en Excel\n\n0.4.4 (2018-04-09)\n-------------------\n\n* Agrega wrappers para `push_dataset_to_ckan()`\n\n0.4.3 (2018-03-20)\n-------------------\n\n* Mejora el manejo de themes para recrear un cat\u00e1logo\n\n0.4.2 (2018-03-13)\n-------------------\n\n* Agrega funciones auxiliares para la administraci\u00f3n de un CKAN v\u00eda API para facilitar la administraci\u00f3n de la federaci\u00f3n de datasets\n    - `remove_dataset_to_ckan()`\n* Incorpora nuevas validaciones (formatos y fileNames)\n* Agrega flags opcionales para que `push_dataset_to_ckan()` sea un m\u00e9todo que transforma opcionalmente la metadata de un dataset\n\n0.4.1 (2018-02-16)\n-------------------\n\n* `datasets_equal()` permite especificar los campos a tener en cuenta para la comparaci\u00f3n, como un par\u00e1metro.\n\n0.4.0 (2018-02-08)\n-------------------\n\n* Incorpora m\u00e9todos para federar un dataset de un cat\u00e1logo a un CKAN o un Andino: `push_dataset_to_ckan()`.\n* Actualiza validaciones y esquema de metadatos al Perfil Nacional de Metadatos versi\u00f3n 1.1.\n\n0.3.21 (2017-12-22)\n-------------------\n\n* Agrega soporte para Python 3.6\n\n0.3.20 (2017-11-16)\n-------------------\n\n* Agrego m\u00e9todo `get_theme()` para devolver un tema de la taxonom\u00eda espec\u00edfica del cat\u00e1logo seg\u00fan su `id` o `label`.\n\n0.3.19 (2017-10-31)\n-------------------\n\n* Agrego m\u00e9todos de b\u00fasqueda de series de tiempo en un cat\u00e1logo (`get_time_series()`) y un par\u00e1metro `only_time_series=True or False` para filtrar datasets y distribuciones en sus m\u00e9todos de b\u00fasqueda (`get_datasets(only_time_series=True)` devuelve s\u00f3lo aquellos datasets que tengan alguna serie de tiempo).\n\n0.3.18 (2017-10-19)\n-------------------\n\n* Agrego posibilidad de pasar un logger desde afuera a la funci\u00f3n de lectura de cat\u00e1logos en Excel.\n\n0.3.15 (2017-10-09)\n-------------------\n\n* Agrega filtro por series de tiempo en `get_datasets()` y `get_distributions()`. Tienen un par\u00e1metro `only_time_series` que devuelve s\u00f3lo aquellos que tengan o sean distribuciones con series de tiempo.\n\n0.3.12 (2017-09-21)\n-------------------\n\n* Agrega funci\u00f3n para escribir un cat\u00e1logo en Excel.\n* Agrega funciones para remover datasets o distribuciones de un cat\u00e1logo.\n\n0.3.11 (2017-09-13)\n-------------------\n\n* Incorpora par\u00e1metro para excluir campos de metadatos en la devoluci\u00f3n de la b\u00fasqueda de datasets y distribuciones.\n\n0.3.10 (2017-09-11)\n-------------------\n\n* Agregar referencia interna a los ids de las entidades padre de otras (distribuciones y fields.)\n\n0.3.9 (2017-09-05)\n-------------------\n\n* Flexibiliza lectura de extras en ckan to datajson.\n* Flexibiliza longitud m\u00ednima de campos para recomendar su federaci\u00f3n o no.\n* Agrega m\u00e9todo para devolver los metadatos a nivel de cat\u00e1logo.\n* Resuelve la escritura de objetos python como texto en excel.\n\n0.3.8 (2017-08-25)\n-------------------\n\n* Agrega stop words a `helpers.title_to_name()`\n\n0.3.4 (2017-08-21)\n-------------------\n\n* Agrega m\u00e9todo para buscar la localizaci\u00f3n de un `field` en un cat\u00e1logo.\n\n0.3.3 (2017-08-20)\n-------------------\n\n* Agrega m\u00e9todo para convertir el t\u00edtulo de un dataset o distribuci\u00f3n en un nombre normalizado para la creaci\u00f3n de URLs.\n\n0.3.2 (2017-08-16)\n-------------------\n\n* Ampl\u00eda reporte de federaci\u00f3n en markdown.\n\n0.3.0 (2017-08-14)\n-------------------\n\n* Agrega m\u00e9todos para navegar un cat\u00e1logo desde el objeto DataJson.\n\n0.2.27 (2017-08-11)\n-------------------\n\n* Agrega validacion de que el campo `superTheme` s\u00f3lo contenga ids en may\u00fasculas o min\u00fasculas de alguno de los 13 temas de la taxonom\u00eda tem\u00e1tica de datos.gob.ar.\n* Agrega validaci\u00f3n limitando a 60 caracteres los nombres de los campos `field_title`.\n* Mejoras al reporte de asistencia a la federaci\u00f3n.\n\n0.2.26 (2017-08-04)\n-------------------\n\n* Agrega validaci\u00f3n de que no haya ids repetidos en la lista de temas de `themeTaxonomy`.\n* Agrega traducci\u00f3n de ckan del campo extra `Cobertura temporal` a `temporal`.\n\n0.2.24 (2017-08-03)\n-------------------\n* Mejoras en los reportes de errores y an\u00e1lisis de datasets para federaci\u00f3n\n* M\u00e9todos `DataJson.validate_catalog()` y `DataJson.generate_datasets_report()` tienen nuevas opciones para mejorar los reportes, especialmente en excel.\n\n0.2.23 (2017-08-02)\n-------------------\n\n* Bug fixes\n\n0.2.22 (2017-08-02)\n-------------------\n\n* Agrega estilo y formato al reporte de datasets\n* Agrega nuevos campos al reporte de datasets\n* Agrega un campo identificador del cat\u00e1logo en el archivo de configuraci\u00f3n de federaci\u00f3n\n\n0.2.21 (2017-08-02)\n-------------------\n\n* Tolera el caso de intentar escribir un reporte de datasets sobre un cat\u00e1logo que no tiene datasets. Loggea un warning en lugar de levantar una excepci\u00f3n.\n\n0.2.20 (2017-08-01)\n-------------------\n\n* Elimina la verificaci\u00f3n de SSL en las requests de ckan_reader.\n\n0.2.19 (2017-08-01)\n-------------------\n\n* Elimina la verificaci\u00f3n de SSL en las requests.\n\n0.2.18 (2017-07-25)\n-------------------\n\n* Mejora la validaci\u00f3n del campo `temporal`\n* Agrega formas de reporte de errores para el m\u00e9todo `DataJson.validate_catalog()`:\n    - Devuelve s\u00f3lo errores con  `only_errors=True`\n    - Devuelve una lista de errores lista para ser convertida en tabla con `fmt=\"list\"`\n\n0.2.17 (2017-07-18)\n-------------------\n\n* Agrega un m\u00e9todo para convertir un intervalo repetido (Ej.: R/P1Y) en su representaci\u00f3n en prosa (\"Anualmente\").\n* Agrego m\u00e9todo que estima los datasets federados que fueron borrados de un cat\u00e1logo espec\u00edfico. Se consideran datasets federados y borrados de un cat\u00e1logo espec\u00edfico aquellos cuyo publisher.name existe dentro de alg\u00fan otro dataset todav\u00eda presente en el cat\u00e1logo espec\u00edfico.\n\n0.2.16 (2017-07-13)\n-------------------\n\n* Bug fix: convierte a unicode antes de escribir un objeto a JSON.\n\n0.2.15 (2017-07-11)\n-------------------\n\n* Modifica la definici\u00f3n de dataset actualizado usando el campo \"modified\" del perfil de metadatos. Si este campo no est\u00e1 presente en la metadata de un dataset, se lo considera desactualizado.\n\n0.2.14 (2017-07-10)\n-------------------\n\n* Modifica la definici\u00f3n de dataset usada para comparar limit\u00e1ndola a la comparaci\u00f3n por \"title\" y \"publisher_name\".\n\n0.2.13 (2017-06-22)\n-------------------\n\n* Agrega m\u00e9todo para verificar si un dataset individual est\u00e1 actualizado\n\n0.2.12 (2017-06-22)\n-------------------\n\n* Se modifica el template de CATALOG README\n* Se agrega el indicador \"datasets_no_federados\" a generate_catalogs_indicators\n\n0.2.11 (2017-05-23)\n-------------------\n\n* Se agrega en `core` el m\u00e9todo `DataJson.generate_catalogs_indicators`, que genera indicadores de monitoreo de cat\u00e1logos, recopilando informaci\u00f3n sobre, entre otras cosas, su validez, actualidad y formato de sus contenidos.\n\n0.2.10 (2017-05-11)\n-------------------\n\n* Correci\u00f3n ortogr\u00e1fica del listado de frecuencias de actualizaci\u00f3n admisibles (`pydatajson/schemas/accrualPeriodicity.json`).\n\n0.2.9 (2017-05-04)\n------------------\n\n* Hotfixes para que `pydatajson` sea deployable en nuevos entornos donde el `setup.py` estaba fallando.\n\n0.2.5 (2017-02-16)\n------------------\n\n* Se agrega una nueva funci\u00f3n a `readers`, `read_ckan_catalog`, que traduce los metadatos que disponibiliza la Action API v3 de CKAN al est\u00e1ndar `data.json`. Esta funci\u00f3n _no_ est\u00e1 integrada a `read_catalog`.\n\n* Se modifican todos los esquemas de validaci\u00f3n, de modo que los campos opcionales de cualquier tipo y nivel acepten strings vac\u00edas.\n\n0.2.0 (2017-01-31)\n------------------\n\n* Se reestructura la librer\u00eda en 4 m\u00f3dulos: `core`, `readers`, `writers` y `helpers`. Toda la funcionalidad se mantiene intacta, pero algunas funciones muy utilizadas cambian de m\u00f3dulo. En particular, `pydatajson.pydatajson.read_catalog` es ahora `pydatajson.readers.read_catalog`, y `pydatajson.xlsx_to_json.write_json_catalog` es ahora `pydatajson.writers.write_json_catalog` (o `pydatajson.writers.write_json`).\n\n* Se agrega el par\u00e1metro `frequency` a `pydatajson.DataJson.generate_harvester_config`, que controla la frecuencia de cosecha que se pretende de los datasets a incluir en el archivo de configuraci\u00f3n. Por omisi\u00f3n, se usa `'R/P1D'` (diariamente) para todos los datasets.\n\n* Se agrega la carpeta `samples/`, con dos rutinas de transformaci\u00f3n y reporte sobre cat\u00e1logos de metadatos en formato XLSX.\n\n0.1.7 (2017-01-10)\n------------------\n\n* Se agrega el m\u00f3dulo `xlsx_to_json`, con dos m\u00e9todos para lectura de archivos locales o remotos, sean JSON gen\u00e9ricos (`xlsx_to_json.read_json()`) o metadatos de cat\u00e1logos en formato XLSX (`read_local_xlsx_catalog()`).\n* Se agrega el m\u00e9todo `pydatajson.read_catalog()` que interpreta todos las representaciones externas o internas de cat\u00e1logos conocidas, y devuelve un diccionario con sus metadatos.\n\n0.1.6 (2017-01-04)\n------------------\n\n* Se incorpora el m\u00e9todo `DataJson.generate_harvestable_catalogs()`, que filtra los datasets no deseados de un conjunto de cat\u00e1logos.\n* Se agrega el par\u00e1metro `harvest` a los m\u00e9todos `DataJson.generate_harvestable_catalogs()`, `DataJson.generate_datasets_report()` y `DataJson.generate_harvester_config()`, para controlar el criterio de elecci\u00f3n de los datasets a cosechar.\n* Se agrega el par\u00e1metro `export_path` a los m\u00e9todos `DataJson.generate_harvestable_catalogs()`, `DataJson.generate_datasets_report()` y `DataJson.generate_harvester_config()`, para controlar la exportaci\u00f3n de sus resultados.\n\n0.1.4 (2016-12-23)\n------------------\n\n* Se incorpora el m\u00e9todo `DataJson.generate_datasets_report()`, que reporta sobre los datasets y la calidad de calidad de metadatos de un conjunto de cat\u00e1logos.\n* Se incorpora el m\u00e9todo `DataJson.generate_harvester_config()`, que crea archivos de configuraci\u00f3n para el Harvester a partir de los reportes de `generate_datasets_report()`.\n\n0.1.3 (2016-12-19)\n------------------\n\n* Al resultado de `DataJson.validate_catalog()` se le incorpora una lista (`\"errors\"`) con informaci\u00f3n de los errores encontrados durante la validaci\u00f3n en cada nivel de jerarqu\u00eda (\"catalog\" y cada elemento de \"dataset\")\n\n0.1.2 (2016-12-14)\n------------------\n\n* Se incorpora validaci\u00f3n de tipo y formato de campo\n* Los m\u00e9todos `DataJson.is_valid_catalog()` y `DataJson.validate_catalog()` ahora aceptan un `dict` adem\u00e1s de un `path/to/data.json` o una url a un data.json.\n\n0.1.0 (2016-12-01)\n------------------\n\nPrimera versi\u00f3n para uso productivo del paquete.\n\n* La instalaci\u00f3n via `pip install` deber\u00eda reconocer correctamente la ubicaci\u00f3n de los validadores por default.\n* El manejo de data.json's ubicados remotamente se hace en funci\u00f3n del resultado de `urlparse.urlparse`\n* El formato de respuesta de `validate_catalog` se adec\u00faa a la \u00faltima especificaci\u00f3n (ver [`samples/validate_catalog_returns.json`](samples/validate_catalog_returns.json).\n\n0.0.13 (2016-11-25)\n-------------------\n\n* Intentar que la instalaci\u00f3n del paquete sepa donde est\u00e1n instalados los schemas por default\n\n0.0.12 (2016-11-25)\n-------------------\n\n* Primera versi\u00f3n propuesta para v0.1.0\n\n\n",
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