LagCorrectedQuery Class
The LagCorrectedQuery class uses a samplestream to search backwards in time to produce a single data-frame that includes lag-corrected values.
This allows you to easily create analytics that compare quality or deal with lag caused by the distance between different sensors along a line.
Note that this function only works with simple forms of lag.
Functions
The class has the following functions…
Setup
Adding Subqueries
Running
Variables
The following member variables are available
| Name | Type | Usage |
|---|---|---|
| expected | int | Normal length of the lag when running (seconds) |
| maxtime | int | Maximum length of the search (seconds) |
| multiplier | float | Multiplier to be applied to the value in data-frame index |
| shavems | bool | When True, times are rounded to the nearest second |
Usage
You build up your query out of three main parts…
Source of Lag
First, you need a number that can be used to compensate for lag. This might be a distance, a flow-rate, a speed or some other counter or rate that can be used to see how much of a thing (other than time) has passed. This is called the source of lag.
End Asset
Next, you'll choose an end asset. This is usually the last part of your system. This is because the class only searches backwards through time rather than forwards.
Assets and Distances
Next, identify all of the individual pieces you'll want to add to the query, and how much distance there is between them.
For example, if a conveyor moves through three different temperature sensors, you'd identify them and measure how far away they are from one-another.
Additional Parameters
The multiplier is usually used to convert the time-base of a rate. The class expects your rate to be per second, so if you wanted to use a per minute input time, you can set the multiplier to 0.016666.
The shavems option is useful if you want to simplify the data you're getting by eliminating sub-second results. This will effectively 'round up' your results so you have no more than one point per second.