{
    "info": {
        "author": "Teofilo Sibileau",
        "author_email": "teo.sibileau@gmail.com",
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        "description": "# Grafane\n\nA very opinionated influxdb client that uses the [official python client](https://github.com/influxdata/influxdb-python) and is very much inspired in grafana's query builder.\n\n## Setup\n\n```\npip install grafane\n```\n\nIn order to query influxdb this library expects the following environment variables to be set:\n\n\n+ `INFLUXDB_HOST`: Defaults to **0.0.0.0**\n+ `INFLUXDB_PORT`: Defaults to **8086**\n+ `INFLUXDB_DB`: Defaults to **metrics**\n+ `INFLUXDB_USER`: Defaults to **admin**\n+ `INFLUXDB_USER_PASSWORD`: Defaults to **admin123**\n\n## Drop measurement\n\n```\nc = Grafane(metric='test')\nc.drop_measurement() # Drops test from influxdb\n```\n## Write\n\nWith:\n\n```python\npoints = [\n    {\n        'fields': {\n            'value': 1.2,\n            'value2': 1.3,\n        },\n        'tags': {\n            'tag1': 'value1',\n                    'tag2': 'value2'\n        }\n    },\n    {\n        'fields': {\n            'value': 1.86,\n            'value2': 2.3,\n        },\n        'tags': {\n            'tag1': 'value2',\n                    'tag2': 'value1'\n        }\n    },\n    {\n        'fields': {\n            'value': 1.4,\n            'value2': 1.1,\n        },\n        'tags': {\n            'tag1': 'value3',\n                    'tag2': 'value2'\n        }\n    },\n    {\n        'fields': {\n            'value': 1.8,\n            'value2': 1.95,\n        },\n        'tags': {\n            'tag1': 'value1',\n                    'tag2': 'value2'\n        }\n    },\n]\n```\n\nYou can do either do multiple single queries:\n\n```python\nfrom grafane import Grafane\nc = Grafane(metric='generic') # Metric defaults to generic\nfor p in points:\n\tc.report(p['fields'], p['tags'])\n```\n\nOr a single query with multiple points:\n\n```python\nc.report_points(points)\n```\n\nif you don't provide `time` for a point it defaults to:\n\n```python\n>>> datetime.utcnow().replace(tzinfo=pytz.utc)\ndatetime.datetime(2019, 2, 8, 19, 32, 38, 788003, tzinfo=<UTC>)\n```\n\n## Read\n\n### Select\n\n![](docs/select.png)\n\n```python\nIn [6]: c.select(fields='value')                                                                                                                                                                            \n\nIn [7]: c.execute_query()                                                                                                                                                                                   \nOut[7]: \n[{'time': '2019-02-10T20:37:13.786477056Z', 'value': 1.2},\n {'time': '2019-02-10T20:37:13.786508032Z', 'value': 1.86},\n {'time': '2019-02-10T20:37:13.786518016Z', 'value': 1.4},\n {'time': '2019-02-10T20:37:13.786535936Z', 'value': 1.8}]\n```\n\n### Select multiple fields\n\n![](docs/select_multiple.png)\n\n```python\nIn [16]: c.select(fields=['value', 'value2'])                                                                                                                                                               \n\nIn [17]: c.execute_query()                                                                                                                                                                                  \nOut[17]: \n[{'time': '2019-02-10T20:42:37.22864512Z', 'value': 1.2, 'value2': 1.3},\n {'time': '2019-02-10T20:42:37.228871936Z', 'value': 1.86, 'value2': 2.3},\n {'time': '2019-02-10T20:42:37.228883968Z', 'value': 1.4, 'value2': 1.1},\n {'time': '2019-02-10T20:42:37.22889216Z', 'value': 1.8, 'value2': 1.95}]\n```\n\n### Select w/ aggregation\n\n![](docs/select_w_aggregation.png)\n\n```python\nIn [18]: c.select(fields='value', aggregation='sum')                                                                                                                                                        \n\nIn [19]: c.execute_query()                                                                                                                                                                                  \nOut[19]: [{'time': '1970-01-01T00:00:00Z', 'sum': 6.26}]\n\n```\n\n### Select multiple fields w/ aggregation\n\n![](docs/select_multiple_w_aggregation.png)\n\n```python\nIn [20]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  \n\nIn [21]: c.execute_query()                                                                                                                                                                                  \nOut[21]: [{'time': '1970-01-01T00:00:00Z', 'sum': 6.26, 'mean': 1.6625}]\n```\n\n### Group aggregated results in time blocks\n\n![](docs/select_group_by_timeblock.png)\n\n```python\nIn [22]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  \n\nIn [23]: c.time_block('1m')                                                                                                                                                                                 \n\nIn [24]: c.execute_query()                                                                                                                                                                                  \nOut[24]: \n[{'time': '2019-02-10T20:42:00Z', 'sum': 6.26, 'mean': 1.6625},\n {'time': '2019-02-10T20:43:00Z', 'sum': None, 'mean': None},\n {'time': '2019-02-10T20:44:00Z', 'sum': None, 'mean': None},\n {'time': '2019-02-10T20:45:00Z', 'sum': None, 'mean': None},\n {'time': '2019-02-10T20:46:00Z', 'sum': None, 'mean': None},\n {'time': '2019-02-10T20:47:00Z', 'sum': None, 'mean': None}]\n```\n\nWhen grouping time blocks, in order to avoid empty rows you need to fill results with **None**\n\n![](docs/select_group_by_timeblock_filled_w_none.png)\n\n```python\nIn [29]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  \n\nIn [30]: c.time_block('1m')                                                                                                                                                                                 \n\nIn [31]: c.fill_with('none')                                                                                                                                                                                \n\nIn [32]: c.execute_query()                                                                                                                                                                                  \nOut[32]: [{'time': '2019-02-10T20:42:00Z', 'sum': 6.26, 'mean': 1.6625}]\n```\n\n### Group aggregated results by tag values\n\n![](docs/group_by.png)\n\n```python\nIn [34]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  \n\nIn [35]: c.group_by('tag1')                                                                                                                                                                                 \n\nIn [36]: c.execute_query()                                                                                                                                                                                  \nOut[36]: \n[{'tags': {'tag1': 'value1'},\n  'time': '1970-01-01T00:00:00Z',\n  'sum': 3,\n  'mean': 1.625},\n {'tags': {'tag1': 'value2'},\n  'time': '1970-01-01T00:00:00Z',\n  'sum': 1.86,\n  'mean': 2.3},\n {'tags': {'tag1': 'value3'},\n  'time': '1970-01-01T00:00:00Z',\n  'sum': 1.4,\n  'mean': 1.1}]  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