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        "author": "Sina Rezaei",
        "author_email": "sinarezaei1991@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "![alt text][pypi_version] ![alt text][licence_version]\n\n# Pinform: An InfluxDB ORM (OSTM) for Python\n\nPInfORM is an Object/TimeSeries Mapping layer for connecting to [InfluxDB](https://www.influxdata.com/) in python.\n\nTested with:\n* Python 3.6+\n* InfluxDB 1.7.3\n\nUse the following command to install using pip:\n```\npip install pinform\n```\n\n## Usage example\n### Create Measurement Models\nFirst, create your measurement model in \n```\nfrom pinform import Measurement\nfrom pinform.fields import FloatField\nfrom pinform.tags import Tag\n\nclass OHLC(Measurement):\n  class Meta:\n    measurement_name = 'ohlc'\n\n  symbol = Tag(null=False)\n  open = FloatField(null=False)\n  high = FloatField(null=False)\n  low = FloatField(null=False)\n  close = FloatField(null=False)\n```\n\n### Create InfluxClient\nThen you must create an instance of `InfluxClient` to connect to database:\n```\nfrom pinform.client import InfluxClient\n\ncli = InfluxClient(host=\"localhost\", port=8086, database_name=\"defaultdb\")\n```\n\nIf the database needs authentication, use:\n```\ncli = InfluxClient(host=\"localhost\", port=8086, database_name=\"defaultdb\", username='your db username', password='your db password')\n```\n\n\n### Save and Retrieve Points\nTo save data in database, use `save_points` or `save_dataframe` functions of InfluxClient:\n```\nohlc = OHLC(time_point=datetime.datetime.now(), symbol='AAPL', open=100.6, high=102.5, low=90.4, close=94.2)\ncli.save_points([ohlc])\n```\n\nTo retrieve data from database, use `load_points` or `load_points_as_dataframe` functions of InfluxClient:\n```\nohlc_points = cli.load_points(OHLC, {'symbol':'AAPL'})\n```\n\n### Get Distinct Tag Values\nTo get distinct tag values from all measurements, use `get_distinct_existing_tag_values` function from InfluxClient:\n```\ntag_values = cli.get_distinct_existing_tag_values('symbol')\n```\n\nTo get distinct tag values from an specific measurements,pass measurement to the previous function:\n```\ntag_values = cli.get_distinct_existing_tag_values('symbol', measurement=OHLC)\n```\n\n\n\n## Fields\nIt's possible to use `IntegerField`, `FloatField`, `BooleanField` and `StringField` to save field values in InfluxDB.\nThere are four other types of fields which help with storing fields with specific integer or string values. To create a field with multiple choice integer values, use `MultipleChoiceIntegerField` or `EnumIntegerField` classes. To create a field with multiple choice string values, use `MultipleChoiceStringField` or `EnumStringField` classes.\n\nExample for MultipleChoiceStringField:\n```\nfrom pinform.fields import MultipleChoiceStringField\n\nclass WeatherInfo(Measurement):\n  class Meta:\n    measurement_name = 'weather_info'\n\n  condition = MultipleChoiceStringField(options=['sunny','cloudy','rainy'], null=False)\n\n```\n\nExample for EnumStringField:\n```\nfrom enum import Enum\nfrom pinform.fields import EnumStringField\n\nclass WeatherCondition(Enum):\n  SUNNY = 'sunny'\n  CLOUDY = 'cloudy'\n  RAINY = 'rainy'\n\n\nclass WeatherInfo(Measurement):\n  class Meta:\n    measurement_name = 'weather_info'\n\n  condition = EnumStringField(enum=WeatherCondition, null=False)\n\n```\n\n\n\n## Advanced usage\n\n### Dynamic measurement names\nIt is possible to use tags in measurement name wrapped in parenthesis\n```\nclass OHLC(Measurement):\n  class Meta:\n    measurement_name = 'ohlc_(symbol)'\n\n  symbol = Tag(null=False)\n  ...\n```\n\n### Query Field and Pandas Series\nUse `get_fields_as_series` function from InfluxClient to get fields of specific measurement class as Pandas Series. It's also possible to aggregate data and group by time. This function returnes a `dict` with aggregated field names as keys and pandas series as values.\n```\nfrom pinform.client import AggregationMode\n\nseries_dict = cli.get_fields_as_series(OHLC, \n                field_aggregations={'close': [AggregationMode.MEAN, AggregationMode.STDDEV]},\n                tags={'symbol': 'AAPL'},\n                time_range=(start_datetime, end_datetime),\n                group_by_time_interval='10d')\nmean_close_series = series_dict['mean_close']\nstddev_close_series = series_dict['stddev_close']\n```\n\n\n\n[pypi_version]: https://img.shields.io/pypi/v/pinform.svg \"PYPI version\"\n[licence_version]: https://img.shields.io/badge/license-MIT%20v2-brightgreen.svg \"MIT Licence\"\n\n\n",
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