====Cupcake Example===

{{cupcakelag.png}}

In this case, we're going to ask for both the **oven temperature** and **final colour** on a cupcake-making machine. These two pieces of equipment are 50m apart from one-another. 

===Example Chart===

This is what a chart of our temperature and colour values looks like at the moment. Thanks to the distance between the two components, there's a significant amount of lag between the readings from the //oven// compared to the readings from the //inspection station//.

{{linechartbakerynormal.png}}

If we wanted to report on cupcake quality and get insight into //why// the quality was low, we can make this much clearer by compensating for the lag.

===Identify Key Elements===

First, we determine the [[source|source of our lag]]. The real source is //distance//. To compensate for the distance between our assets, we need either a //counter// or a //rate// that reflects the distance moved by our conveyor.

The **speed of the conveyor** is perfect for this - it's a rate of //distance// over //time//.

Next, we determine our [[end asset|end asset]]. In this case, the last asset in the system is the **Inspection Section**.

Finally, we want to measure our [[distance|distances]] between each asset and our end asset. From the diagram at the top of the page, we can see that the distance between the Oven and the Inspection Station is **50m**.

===The Code===

<code python>

srv = ardiapi.Server('cupcake.ardi')

lcq = samplestream.LagCorrectedQuery(srv)
lcq.RateLagQuery('Meters',"'Conveyor' ASSET 'Speed' PROPERTY VALUES")
lcq.AddQuery("'Inspection Station' ASSET 'Brownness' PROPERTY VALUES")
lcq.AddQuery("'Oven' ASSET 'Temperature' PROPERTY VALUES",lag=50)
lcq.multiplier = 0.0166
lcq.shavems = True

starttime = datetime.datetime.utcnow()-datetime.timedelta(seconds=60*60)
endtime = datetime.datetime.utcnow()

df = lcq.Execute(starttime,endtime,samples=3600)
</code>

===Key Functions===

   lcq.RateLagQuery('Meters',"'Conveyor' ASSET 'Speed' PROPERTY VALUES")

This line tells the class that we are going to correct for lag based on the //Speed// property from our //Conveyor//. This is a **rate of change**, so we'll use the //RateLagQuery// function.

   lcq.AddQuery("'Inspection Station' ASSET 'Brownness' PROPERTY VALUES")

This line adds a query for the 'brownness' of our cupcakes, measured at the **Inspection Station**. This station is our [[end asset|end asset]] (the machine at the end of our process), so we won't include a lag for it.

   lcq.AddQuery("'Oven' ASSET 'Temperature' PROPERTY VALUES",lag=50)

This line adds another query, this time for the **Oven**. In this case, we've added a lag of '50' - the distance in meters between the the inspection station and the ovens.

We then send a request for the last hour of data.

===The Results===

In our results, we get the following...

^Meters^Inspection Darkness^Oven Temp^
|0.0|60%|132.1|
|1.2|62%|132.0|
|2.4|61%|131.9|
|3.6|60%|131.8|
|4.8|55%|120.7|
|6.0|62%|131.6|

You'll notice that you don't have a //time// index anymore in the resulting dataframe. Instead, you've got a total amount of the [[source|source of lag]] seen (in this case, it's meters of conveyor-belt).

===What It Means===

Reading our original chart, people may //suspect// that there's a direct relationship between the two dips, but it requires technical knowledge and very clean data to be confident.

But after lag compensation, our chart looks like this...

{{linechartbakerysync.png}}

Each of the values are synchronised - so when **meters** is 0, the level of browning and the oven temperature are for **Cupcake #1**, even though those two points were measured minutes apart.

Looking at the data, you'll see a drop after 4.8m. **Both** the darkness and oven temperature decreased. This makes the correlation extremely obvious.

It also helps when dealing with noisy data. Variables are often influenced by a variety of different factors, which makes spotting root-causes difficult. But by correcting for lag, these relationships become **much** clearer, even for people unfamiliar with your system.

===Going Further===

You can extend this even more by using ARDIs relationships and storing the [[distance|distance]] between assets in ARDI itself.

See an [[adaptable_example|example of an adaptable solution]] for details.