# [Numpy-discussion] Speedup a code using apply_along_axis

Xavier Gnata xavier.gnata@gmail....
Sun Feb 28 13:43:19 CST 2010

```On 02/28/2010 08:17 PM, josef.pktd@gmail.com wrote:
> On Sun, Feb 28, 2010 at 1:51 PM, Xavier Gnata <xavier.gnata@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
>
>
Correct! It should be M[where(M<1000)]
>>    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
>
>
>
Ok but the problem is that I also want to apply a threshold.
In some cases, I end up less than 2 values below the threshold: There is
nothing to fit and it should return 0.

Hum....sounds like masked arrays could help...but I'm not familiar with