[Numpy-discussion] Speedup a code using apply_along_axis

[josef.pktd at gmail.com]

On Sun, Feb 28, 2010 at 1:51 PM, Xavier Gnata <xavier.gnata at gmail.com> wrote:
> Hi,
>
> I'm sure I reinventing the wheel with the following code:
> from numpy import *
> from scipy import polyfit,stats
>
> def f(x,y,z):
>    return x+y+z
> M=fromfunction(f,(2000,2000,10))
>
> def foo(M):
>    ramp=where(M<1000)[0]

is this really what you want? I think this returns the indices not the values

>    l=len(ramp)
>    t=arange(l)
>    if(l>1):
>        return polyfit(t,ramp,1)[0]
>    else:
>        return 0
>
> print apply_along_axis(foo,2,M)
>
>
> In real life M is not the result of one fromfunction call but it does
> not matter.
> The basic idea is to compute the slope (and only the slope) along one
> axis of 3D array.
> Only the values below a given threshold should be taken into account.
>
> The current code is ugly and slow.
> How to remove the len and the if statement?
> How to rewrite the code in a numpy oriented way?

Getting the slope or the linear fit can be done completely vectorized
see numpy-discussion threads last April with titles
"polyfit on multiple data points"  "polyfit performance"

Josef


> Xavier
>
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