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 [0, 1], hence I have to
create a meshgrid and then compute it at each point of the mesh:
x = linspace(0, 1, 200)
y = x.copy()
X, Y = meshgrid(x, y)
My problem is how to actually compute the function at each point of the
mesh. I have come out with two solutions. One very short and clear, but
slow, and another longer and more convoluted (it has a loop, I hate
loops in numpy code), but faster. Does anyone know a "no explicit-loops"
and fast solution?
Solution1:
def myDistance(a, b):
return weirdDistance(np.array((a, b)))
vecDistance = np.vectorize(myDistance)
return vecDistance(X, Y)
Solution 2:
nPoints = X.size
result = np.zeros(nPoints)
points = np.array( [X.ravel(), Y.ravel()] ).T
for i in xrange(nPoints):
result[i] = weirdDistance(points[i])
result = result.reshape(X.shape)
Of course, the first one is slow because the myDistance function creates
an array at each call. The second one, even with a loop, avoids the
array creations.
Best,
Paulo
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
