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[USER=235638]@betwixt[/USER] - yes: both systems use the same hall effect transistor and ESP32 MCU. Also your description to [USER=544298]@KlausST[/USER] is exactly right. The water meter is an Elster Volumetric type (https://www.mwatechnology.com/products/honeywell-elster-v100-cold-water-meters/).

I have attached a different plot of the data - the top red line is the raw data and the bottom blue data is the 'difference' you wanted to see. (I don't think it helps much TBH). The values are from midnight yesterday to now but are representative of the data.

[USER=134022]@D.A.(Tony)Stewart[/USER] - yes I know the noise is high and I should be trying to reduce that - perhaps a low pass filter etc.. As [USER=235638]@betwixt[/USER] mentions above, the wheel turns only when the water is flowing which is rather intermittent. Also using a hall effect transistor means that it can stop 'part way around'. However the key point is that I only need to count when it gets to the 'top' which indicates that the least significant wheel has turned to a given point - the next time it gets there means that the specified amount of water has flowed through the meter. Given that, I think the difference between the 'top' and the 'bottom' values (despite the current noise level) should be sufficient for me to find suitable trigger points for the Schmitt Trigger part of the program.

I have been thinking about this as well. Now that I have got my iMac reading the database from the home assistant system, I can start to perform some more sophisticated analyses (python, numpy, matplotlib - which I used for the attached plots rather than the previous ones which came directly from the home assistant package - and pandas etc.) That is the basis for my question: what sort of analysis might help.

Thanks for the assistance.

Susan

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(PS: Sorry about the delay in responding - its school holidays here and my daughter was sick today, so grandparent duties took precedence! Also I hope for more time shortly to try other analysis methods.)