Enhydris-autoprocess is an app that automatically processes time series to produce new time series. For example, it performs range checking, saving a new time series that is range checked.
- Install Enhydris 3 or later
- Make sure
enhydris_autoprocessis in the PYTHONPATH, or link to it from the top-level directory of Enhydris. - In the Enhydris
enhydris_project/settings/local.pyfile, addenhydris_autoprocesstoINSTALLED_APPS. - In the Enhydris configuration directory, execute
python manage.py migrate. - Run
celery. - Go to the admin, visit a station, and see the "auto-process" section at the bottom.
You have a meteorological station called "Hobbiton". It measures temperature. Because of sensor, transmission, or other errors, sometimes the temperature is wrong—for example, 280 °C. What you want to do (and what this app does, among other things) is delete these measurements automatically as they come in. In this case, assuming that the low and high all-time temperature records in Hobbiton are -18 and +38 °C, you might decide that anything below -25 or above +50 °C (the "hard" limits) is an error, whereas anything below -20 or above +40 °C (the "soft" limits) is a suspect value. In that case, you configure enhydris-autoprocess with the soft and hard limits. Each time data is uploaded, an event is triggered, resulting in an asynchronous process processing the initial uploaded data, deleting the values outside the hard limits, flagging as suspect the values outside the soft limits, and saving the result to the "checked" time series of the time series group.
(More specifically, enhydris-autoprocess uses the post_save Django
signal for enhydris.Timeseries to trigger a Celery task that does
the auto processing—see apps.py and tasks.py.)
Range checking is only one of the ways in which a time series can be
auto-processed—there's also aggregation (e.g. deriving hourly from
ten-minute time series) and curve interpolation (e.g. deriving
discharge from stage, or estimating the air speed at a height of 2 m
above ground when the wind sensor is at a different height). The name
we use for all these together (i.e. checking, aggregation,
interpolation) is "auto process". Technically, AutoProcess is the
super class and it has some subclasses such as Checks,
Aggregation and CurveInterpolation. These are implemented
using Django's multi-table inheritance. (The checking subclass is
called Checks because there can be many checks—range checking,
time consistency checking, etc; these are performed one after the
other and they result in the "checked" time series.)
AutoProcess objects have these attributes and methods:
timeseries_group. The time series group to which this auto-process applies.execute(). Performs the auto-processing. It retrieves the new part of the source time series (i.e. the part that starts after the last date of the target time series) and calls theprocess_timeseries()method.source_timeseries(property). The source time series of the time series group for this auto-process. It depends on the kind of auto-process: forChecksit is the initial time series; forAggregationandCurveInterpolationit is the checked time series if it exists, or the initial if it does not exist. If no suitable time series exists, it is created.target_timeseries(property). The target time series of the time series group for this auto-process. It depends on the kind of auto-process: forChecksit is the checked time series; forAggregationit is the aggregated time series with the target time step; forCurveInterpolationit is the initial time series of the target time series group (CurveInterpolationhas an additionaltarget_timeseries_groupattribute). The target time series is created if it does not exist.process_timeseries(). Performs the actual processing.
© 2019-2020 National Technical University of Athens © 2019-2020 Institute of Communication and Computer Systems
Enhydris-autoprocess was funded by NTUA and ICCS as part of the OpenHi project.
Enhydris-autoprocess is free software, available under the GNU Affero General Public License.