visualisation for utility usage sought

(meant this to go to the list too)

Christiaan Putter wrote:

I'm having trouble understanding what it is you exactly want.

That's likely my fault :wink:

You said you want to indicate that 'the monthly usage between September 1st
and January 1st
was, on average, the same as that between January 1st and February 1st.'

Yes, but "monthly" is a red herring here, the time periods are however
long it's been since myself or the utility company checked the meter :wink:

The measurement your taking is not in fact the utility usage for one month,
but rather the sum of all usage over all prior months.

Not really, and this was definitely me being unclear...
We're talking about utility meters here, so you go and look at them and
they show how many units of whatever it is (water, electicity, etc) they
have delivered.

So, that's how you get the time series (with more variation, to show
reality, and avoid red herrings like "month"):

2007/09/13 - 5000
2008/01/02 - 8000
2008/02/08 - 9000
2008/02/12 - 9100

So, the differences tell us:

Between 2007-09-13 and 2008-01-02, 3000 units were used
Between 2007-01-02 and 2008-02-08, 1000 units were used
Between 2007-02-08 and 2008-02-12, 100 units were used

So I guess it's this data that I'm looking to visualise in such a way
that it's apprarent how much of the utility is being per unit time.

NB: The measurements aren't regular, since they often come from when a
person turns up and reads the meter, which isn't at all regular :wink:

Does that make more sense? Any ideas?

cheers,

Chris

···

--
Simplistix - Content Management, Zope & Python Consulting
            - http://www.simplistix.co.uk

:slight_smile: Hi there,

Just stick to the original plan. We know you’ve got some fixed measurement points, it really doesn’t matter how far apart or irregular they are. You use these points to interpolate all the values in between for the specific ‘time unit’ you want to represent. Look at http://www.scipy.org/Cookbook/Interpolation for an example of using b-splines.

Interpolation will give you a pretty good ‘guesstemate’ of what the usage would have been at that point had you gone and actually measured it.

Of course if you don’t want to interpolate, you can simply normalize your measurements to your time unit. Let’s assume time unit of 1 day…

Between 2007-09-13 and 2008-01-02, 3000 units were used - 3000/111 days = 27.02 units per day

Between 2008-01-02 and 2008-02-08, 1000 units were used - 1000/37 days = 27.02 units per day
Between 2008-02-08 and 2008-02-12, 100 units were used - 100/4 days = 25 units per day

This obviously has the same disadvantage as linear interpolation in that gradual changes over unmeasured periods will be shown as constant.

Hope we’re getting closer to something you can use…

Have a nice day.

cputter

···

On 14/03/2008, Chris Withers <chris@…1920…> wrote:

(meant this to go to the list too)

Christiaan Putter wrote:

I’m having trouble understanding what it is you exactly want.

That’s likely my fault :wink:

You said you want to indicate that 'the monthly usage between September 1st

and January 1st
was, on average, the same as that between January 1st and February 1st.’

Yes, but “monthly” is a red herring here, the time periods are however
long it’s been since myself or the utility company checked the meter :wink:

The measurement your taking is not in fact the utility usage for one month,
but rather the sum of all usage over all prior months.

Not really, and this was definitely me being unclear…

We’re talking about utility meters here, so you go and look at them and
they show how many units of whatever it is (water, electicity, etc) they
have delivered.

So, that’s how you get the time series (with more variation, to show

reality, and avoid red herrings like “month”):

2007/09/13 - 5000
2008/01/02 - 8000
2008/02/08 - 9000
2008/02/12 - 9100

So, the differences tell us:

Between 2007-09-13 and 2008-01-02, 3000 units were used

Between 2007-01-02 and 2008-02-08, 1000 units were used
Between 2007-02-08 and 2008-02-12, 100 units were used

So I guess it’s this data that I’m looking to visualise in such a way
that it’s apprarent how much of the utility is being per unit time.

NB: The measurements aren’t regular, since they often come from when a
person turns up and reads the meter, which isn’t at all regular :wink:

Does that make more sense? Any ideas?

cheers,

Chris


Simplistix - Content Management, Zope & Python Consulting
- http://www.simplistix.co.uk


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