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        "description": "# Elastic search wrapper for Python\n\nThis library allows access to a Elasticsearch from a Python program.\n\nhttps://pypi.org/project/elasticsearchlib/\n\n\n## Installation\n\n### Command line\nThis library can be installed with the following command:\n\n```\npip3 install elasticsearchlib\n```\n### Dockerfile\n\nYou can add these lines in your Dockerfile to include this library in your image:\n\n```\nRUN pip3 install elasticsearchlib\n```\n\n## Uploading to pip\n\nThese steps are needed to distribute the library on Pip repository manager:\n\n### Prerequisites\n\nFirst, these packages must be installed on your Python environment:\n\n* Setuptools\n* Wheel\n* Twine\n* Tqdm\n\n```\nsudo python -m pip install --upgrade pip setuptools wheel\nsudo python -m pip install tqdm\nsudo python -m pip install --user --upgrade twine\n```\n\n### Customization\n\nOn the _setup.py_ file, these fields can be customized:\n\n* **Version**: The current version of the build.\n\n### Execution\n\n```\npython3 setup.py bdist_wheel\n```\nThis command will generate a _.whl_ file inside the _dist_ folder of the root of the project. Then, execute the following command to upload this file to PyPi repository:\n\n```\npython3 -m twine upload dist/*\n```\n\n\n## Usage\nThis section will explain the usage of this library.\n\n### Constructor\n```\nElasticsearchlib()\n``` \n\n### start_connection\nThis function creates the connection to the elasticsearch database, checking if the server is up. Returns True if the database answered correctly.\n```\ndef start_connection(self, host, port, request_retries=3, total_retries=9):\n```\n- host: Base IP address for the elasticsearch database.\n- port: Port where the elasticsearch database is published.\n- request_retries: number of times a request will be retried before being dropped (defaults to 3).\n- total_retries: number of consecutive retries before dropping the connection and throwing an Exception (defaults to 9).\n\n### create_index\nThis function checks if an index is already created and, if not, creates it, according to the provided mapping.\n```\ndef create_index(self, index, mapping=None): \n```\n- index: Name of the index to create.\n- mapping: Mapping provided as the template for this index.\n\n### add_to index \nThis function adds a document to the provided index. If the index does not exist, it will be created first.\n```\ndef add_to_index(self, index, body, id=''):\n```\n- index: Index where the document will be added.\n- body: Body for the document.\n- id: Optional argument for the document id on the database. If not provided, a random one will be created.\n\n### search_last_n_measures\nThis functions allows for the retrieval of the last n measures of one dataset item.\n```\ndef search_last_n_measures(self, index, id_dataset, n):\n```\n- index: Index to search in.\n- id_dataset: Dataset id to retrieve the measures.\n- n: Number of measures desired.\n\n### scrolled_query\nThis function returns all measures stored in an index, following a query.\n```\ndef scrolled_query(self, query, index, filter_path=None):\n```\n- query: Query to use on the request.\n- index: Index on which to use the query.\n- filter_path: Filter that can be applied to the request.\n\n### get_entities_ids\nThis function returns all the entity ids stored under an index.\n```\ndef get_entities_ids(self, index):\n```\n- index: Index on which to request the ids.\n\n### get_last_document\nThis function returns the last document stored under an index, for a specific device_id.\n```\ndef get_last_document(self, index, device_id):\n```\n- index: Index on which to request the document.\n- device_id: Id of the device to query.\n\n### get_data_history\nThis function returns the data history for a specific device in a period of time.\n```\ndef get_data_history(self, index, device_id, gte, lte='now'):\n```\n- index: Index on which to request the data.\n- device_id: Id of the device to query.\n- gte: Lower bound for the time period.\n- lte: Upper bound for the time period (defaults to now).\n\n",
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