[Numpy-discussion] subclassing matrix
Colin J. Williams
cjw@sympatico...
Sat Jan 12 18:43:56 CST 2008
Basilisk96 wrote:
> On Jan 12, 1:36 am, "Timothy Hochberg" <tim.hochb...@ieee.org> wrote:
>> I believe that you need to look at __array_finalize__ and __array_priority__
>> (and there may be one other thing as well, I can't remember; it's late).
>> Search for __array_finalize__ and that will probably help get you started.
>>
>
> Well sonovagun!
> I removed the hack.
> Then just by setting __array_priority__ = 20.0 in the class body,
> things are magically working almost as I expect. I say "almost"
> because of this custom method:
>
> def cross(self, other):
> """Cross product of this vector and another vector"""
> return _N.cross(self, other, axis=0)
>
> That call to numpy.cross returns a numpy.ndarray. Unless I do return
> Vector(_N.cross(self, other, axis=0)), I get problems downstream.
>
> When is __array_finalize__ called? By adding some print traces, I can
> see it's called every time an array is modified in any way i.e.,
> reshaped, transposed, etc., and also during operations like u+v, u-v,
> A*u. But it's not called during the call to numpy.cross. Why?
>
> Cheers,
> -Basilisk96
This may help. It is based on your
initial script.
The Vectors are considered as columns
but presented as rows.
This adds a complication which is not
resolved.
Colin W.
#---------- vector.py
import numpy as _N
import math as _M
#default tolerance for equality tests
TOL_EQ = 1e-6
#default format for pretty-printing
Vector instances
FMT_VECTOR_DEFAULT = "%+.5f"
class Vector(_N.matrix):
"""
2D/3D vector class that supports
numpy matrix operations and more.
Examples:
u = Vector([1,2,3])
v = Vector('3 4 5')
w = Vector([1, 2])
"""
def __new__(cls, data="0. 0. 0.",
dtype=_N.float64):
"""
Subclass instance constructor.
If data is not specified, a
zero Vector is constructed.
The constructor always
returns a Vector instance.
The instance gets a
customizable Format attribute, which
controls the printing
precision.
"""
data= [1, 2, 3]
ret= _N.matrix(data, dtype)
## ret = super(Vector,
cls).__new__(cls, data, dtype=dtype)
## #promote the instance to cls type.
## ret.__class__ = cls
assert ret.size in (2, 3),
'Vector must have either two or three
components'
if ret.shape[0] == 1:
ret = ret.T
assert ret.shape ==
(ret.shape[0], 1), 'could not express
Vector as a Mx1 matrix'
if ret.shape[0] == 2:
ret = _N.vstack((ret, 0.))
ret.Format = FMT_VECTOR_DEFAULT
ret= _N.ndarray.__new__(cls,
ret.shape, dtype,
buffer=ret.data)
return ret
def __str__(self):
fmt = getattr(self, "Format",
FMT_VECTOR_DEFAULT)
fmt = ', '.join([fmt]*3)
return ''.join(["(", fmt, ")"])
% tuple(self.T.tolist()[0])
def __repr__(self):
fmt = ', '.join(['%s']*3)
return ''.join(["%s([", fmt,
"])"]) % tuple([self.__class__.__name__]
+ self.T.tolist()[0])
def __mul__(self, mult):
''' self * multiplicand '''
if isinstance(mult, _N.matrix):
return _N.dot(self, mult)
else:
raise DataError, 'multiplicand
must be a Vector or a matrix'
def __rmul__(self, mult):
''' multiplier * self.__mul__ '''
if isinstance(mult, _N.matrix):
return Vector(_N.dot(mult, self))
else:
raise DataError, 'multiplier
must be a Vector or a matrix'
#### the remaining methods are
Vector-specific math operations,
including the X,Y,Z properties...
if __name__ == '__main__':
u = Vector('1 2 3')
print str(u)
print repr(u)
A = _N.matrix('2 0 0; 0 2 0; 0 0 2')
print A
p = A * u
print p
print p.__class__
q= u.T * A
try:
print q
except:
print "we don't allow for the
display of row vectors"
print q.A, q.T
print q.__class__
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