Why you don't create a mask to select only the points in array that
satisfies the condition on x and y coordinates. For example the code
below applies filter only to the values that have x coordinate bigger
than 0.7 and y coordinate smaller than 0.3:
mask = numpy.logical_and(points[:,0] 0.7,
Eric,
You question raised my attention due to a recent post of mine related to
the same kind of problem. I was solving it without using
apply_along_axis (due to ignorance).
However I tried to use apply_along_axis to solve my problem and it
proved to be very slow. Try the following:
---
Hello,
I have a function that receives a array of shape (2,) and returns a
number (a function from R^2 - R). It basically looks like this:
def weirdDistance2(x):
return dot(dot(weirdMatrix, x), x)
(weirdMatrix is a global (2,2) array)
I want to see its level sets in the box [0, 1] x
Chuck,
Thanks, your version is much faster. I would prefer a solution that
doesn't force me to re-implement weirdDistance (as my two solutions
were). But the function is so simple that it is easier just to re-write
it for speed as you did.
By the way, I came out with one more solution that looks
x = np.frombuffer(int_asbuffer(C.addressof(x.contents), n*8))
I'll go with your faster solution. Very good. Thank you very much.
Paulo
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
Hello,
I am writing some code interfacing C and Python using ctypes. In a
callback function (in Python) I get in a parameter x which is c_double
pointer and a parameter n which is c_int representing the length of the
array.
How can I transform this information into a numpy array?
Paulo
Em Qui, 2008-09-04 às 15:01 -0500, Travis E. Oliphant escreveu:
Paulo J. S. Silva wrote:
Something like this may work:
from numpy import ctypeslib
r = ctypeslib.as_array(x._type_ * n)
Unfortunately, it didn't work.
If that doesn't work, then you can create an array from
,
Paulo
Em Qui, 2008-09-04 às 17:31 -0400, Paulo J. S. Silva escreveu:
Em Qui, 2008-09-04 às 15:01 -0500, Travis E. Oliphant escreveu:
Paulo J. S. Silva wrote:
Something like this may work:
from numpy import ctypeslib
r = ctypeslib.as_array(x._type_ * n)
Unfortunately
Hello,
I am trying to write some unit tests to my new Automatic matrix code
and I think I bumped into a bug in scipy.linalg.lu_factor. If you give a
matrix to it, it doesn't honor the overwrite_a option:
In [1]:import numpy as num
In [2]:M = num.mat(num.random.rand(2,2))
In [3]:print M
[[
Em Qui, 2007-01-25 às 19:46 +0100, Nils Wagner escreveu:
It works if you use
M=num.random.rand(2,2)
Nils
Yes, it works for arrays but not for matrices. I thought that
scipy.linalg functions were supposed to work with matrices.
Paulo
___
10 matches
Mail list logo
