[Numpy-discussion] help creating a reversed cumulative histogram

[Tim Michelsen]

> My first stop is usually wikipedia:
[...]
Thanks.
So I I'known that I have to call the beast a 
"empirical inverse survival function", Robert would 
also have foundit easier to help.
Anyway, step by step...

> In the case of the weight of pigs, it would be to cumulative weight of
> all pigs with a weight less than the given bin boundary weight.
> If values were income, then it would be the aggregated income of all
> individual with an income below the bin bin boundary.
> So it makes sense, given this is what you want (below).
Exactly! 

Or for precipitation:
a) count: number of precipitation events that 
    ocurred up to a certain limit
b) sum: precipitation total registered up to that limit

> there might be a mistake in the treatment of a cell when
> reversing, when I run your example the highest value is
> not equal to values.sum()
This has made me think again. Small point.

See here:
ecdf_sums = np.hstack([0.0, sums[0].cumsum() ])
ecdf_sums = np.hstack([sums[0].cumsum() ])

I had to adjust the classes in the spreadsheet by 
replacing the first class limit by 0.0.
I had modifed this yesterday to a different value 
(0.265152) as I was testing the code.

from:
0.265152, 0.487273, 0.709394, 0.931515, 
1.153636, 1.375758, 1.597879, 1.820000, 
2.042121, 2.264242, 2.486364

to:
0.0, 0.487273, 0.709394, 0.931515, 
1.153636, 1.375758, 1.597879, 1.820000, 
2.042121, 2.264242, 2.486364

Now everything is fine. Results and curves match.

> But I'm not sure yet, what's going on.
1) first I didn't know how to develop the code for a 
    "empirical inverse survival function" in numpy
2) I screwed my spreadsheet classes up while 
    testing and verifying my numpy code.

Again, would a function for the
"empirical inverse survival function" qualify for the 
inclusion into numpy or scipy?

Thanks for the help.

Best regards,
Timmie