Hi,
I am trying to create an ndarry subclass for vector calculations. The
result is not what I intent:
import numpy as np
class Vector(np.ndarray):
def __abs__(self):
return(np.sqrt(sum(self**2)))
V = Vector([1,2,3])
print np.sqrt(sum(self**2))
print abs(V)
I do not understand the docs at http://www.scipy.org/Subclasses but it
is late now.
Martijn
P.S. I know this is not strictly matplotlib related, but since MP uses
numpy so heavily, I felt free to ask.
Hi,
I found that ndarray is the wrong class to use for this purpose. The
vector I created in the example below was just en uninitialised 3D
vector. Arrays cannot be subclassed:
class Vector(np.array):
pass
returns an error. But using a matrix as base class works:
class Vector(np.matrix):
def __abs__(self):
l = np.sqrt(self*self.transpose())
return(l[0,0])
V1 = Vector([1,2,2])
V2 = Vector([5,0,4])
print abs(V2-2*V1)
prints 5.0, as it should.
It is very crude (no check on dimensions!), but works.
Martijn
On Thu, 2011-09-22 at 23:54 +0200, Martijn wrote:
Hi,
I am trying to create an ndarry subclass for vector calculations. The
result is not what I intent:import numpy as np
class Vector(np.ndarray):
def __abs__(self):
return(np.sqrt(sum(self**2)))V = Vector([1,2,3])
print np.sqrt(sum(self**2))
print abs(V)I do not understand the docs at http://www.scipy.org/Subclasses but it
is late now.Martijn
P.S. I know this is not strictly matplotlib related, but since MP uses
numpy so heavily, I felt free to ask.------------------------------------------------------------------------------
All the data continuously generated in your IT infrastructure contains a
definitive record of customers, application performance, security
threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2dcopy1
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
matplotlib-users List Signup and Options