apd.aggregation 1.1.0

A programme that queries apd.sensor endpoints and aggregates their results.

APD Sensor aggregator

A programme that queries apd.sensor endpoints and aggregates their results.

Generic single-database configuration.

Database setup

To generate the required database tables you must create an alembic.ini file, as follows:

[alembic]
script_location = apd.aggregation:alembic
sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd

and run alembic upgrade head. This should also be done after every upgrade of the software.

Defining endpoints

Endpoints to collect from are managed with the sensor_deployments CLI tool. After installation there will be no deployments defined

sensor_deployments add --db postgresql+psycopg2://apd@localhost/apd 
                       --api-key 97f6b3e5ceb64a6ba88968d7c3786b38
                       --colour xkcd:red
                       http://rpi4:8081
                       Loft

The optional colour argument is the colour to use when plotting charts with the built-in charting tools. This uses matplotlib's colour specification system, documented at https://matplotlib.org/tutorials/colors/colors.html

The sensors can then be listed with sensor_deployments list:

Loft
ID 53998a5160de48aeb71a5c37cd1455f2
URI http://rpi4:8081
API key 97f6b3e5ceb64a6ba88968d7c3786b38
Colour xkcd:red

The ID is the deployment ID, as set by the endpoint. It is only possible to add endpoints if they can be connected to at the time.

Collating data

Data can be collated from all defined endpoints with the collect_sensor_data command line tool. Although you can specify URLs and an API key to explicitly load data from a one-off endpoint, running without specifying these will use the configured endpoints from the database.

collect_sensor_data --db postgresql+psycopg2://apd@localhost/apd

Viewing data

You can write scripts to visualise the data from the database. I recommend using Jupyter for this, as it has good support for drawing charts and interactivity.

All configured charts can be displayed with:

from apd.aggregation.analysis import plot_multiple_charts
display(await plot_multiple_charts())

More complex charting can be achieved by passing configs= to this function, consisting of configuration objects as defined in apd.aggregation.analysis. Iteractivity can be achieved using the interactable_plot_multiple_charts function with Jupyter/IPyWidgets' existing interactivity support.

More control can be achieved using other functions from this module, such as getting all data points from a given sensor with:

from apd.aggregation.query import with_database, get_data

with with_database("postgresql+psycopg2://apd@localhost/apd") as session:
    points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name=="RelativeHumidity"]

These can be called from any Python code, not just Jupyter notebooks

Analysis and triggers

The aggregator allows for a long-running process that processes records as they are inserted to the database and apply rules to them.

This is configured with a Python-based configuration file, such as the following to log any time the Temperature fluctuates above or below 18c:

import operator

from apd.aggregation.actions.action import OnlyOnChangeActionWrapper, LoggingAction
from apd.aggregation.actions.runner import DataProcessor
from apd.aggregation.actions.trigger import ValueThresholdTrigger


handlers = [
    DataProcessor(
        name="TemperatureBelow18",
        action=OnlyOnChangeActionWrapper(LoggingAction()),
        trigger=ValueThresholdTrigger(
            name="TemperatureBelow18",
            threshold=18,
            comparator=operator.lt,
            sensor_name="Temperature",
        ),
    )
]

This is run with:

run_apd_actions --db postgresql+psycopg2://apd@localhost/apd sample_actions.py

The optional --historical option causes the actions to be triggered for all events in the database. If it's omitted then the default behaviour applies, which is to only analyse data that is added to the database after the actions process has started.

The possible actions are:

• apd.aggregation.actions.action.LoggingAction() - Log data points
• apd.aggregation.actions.action.SaveToDatabaseAction() - Save data points to the db

These can be wrapped with OnlyOnChangeActionWrapper(subaction) to only trigger an action when the underlying value changes and/or with OnlyAfterDateActionWrapper(subaction, min_date) to only trigger if the date on the discovered objects is strictly after min_date.

The possible triggers are:

• apd.aggregation.actions.trigger.ValueThresholdTrigger(...) - This compares the value of a sensor with threshold, using the specified comparator. Any records that don't match the sensor_name and deployment_id parameters are excluded.

Tips

The --db argument to all command-line tools can be omitted and the APD_DB_URI environment variable set instead.

Changes

1.1.0 (2020-05-21)

• Address technical review comments from APD book. Use this version over the 1.0.0 release.
• Improve compatibility with newer versions of Pint

1.0.0 (2020-01-27)

• Added management of known sensor endpoints
• Added CLI script to collate data
• Added analysis tools for Jupyter
• Added long-running data synthesis and actions system

Copyright (c) 2019, Matthew Wilkes

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