Hi all,
Thanks for the help. numarray doesn't provide what I look for either. e.g.
a = array( [[1,2,3],[4,5,6]] )
I sometimes what this: a[ [1,0], :], or even
a[ [1,0], [0,1] ] , which should give me
[[4, 5], [1,2]]
Because I don't really care about arrays with dimension >2, I did a
little hacking with Numeric.Matrix.Matrix. It is more compatible with
Matlab style slicing (except that the index starts from 0, not 1).
This class automatically does copy, which is what I needed.
Here is the code if somebody is interested (The wordy getAttr part is
due to a "feature" in Numeric.Matrix based on the assumption that
probably nobody is going to inherent it again :) ):
#==========begin code=========
from Numeric import *
from Matrix import Matrix, UserArray
import types
def _isSeq(t):
if isinstance(t, types.ListType):
return True
if (isinstance(t, ArrayType) or isinstance(t, UserArray)):
return len(t.shape)==1 or \
(len(t.shape)==2 and t.shape[0]==1)
return False
class Mat(Matrix):
def __getitem__(self, index):
"""Matlab style slicing:
e.g. a[ [3,2], [2,1] ], a[1]
"""
if isinstance(index, types.TupleType) and len(index)==1 \
or _isSeq(index):
if self.array.shape[0]!=1:
return self._rc(self.array.flat).__getitem__(index)
if not _isSeq(index):
return Matrix.__getitem__(self, index)
return self._rc(take(self, array(index).flat, 1))
elif isinstance(index, types.TupleType):
assert len(index)==2
if not (_isSeq(index[0]) or _isSeq(index[1])):
return Matrix.__getitem__(self, index)
r = self.array
tmp = slice(None, None, None)
if _isSeq(index[0]):
r = take(r, array(index[0]).flat,0)
else:
r = self._rc(r).__getitem__(self, (index[0], tmp))
if _isSeq(index[1]):
r = take(r, array(index[1]).flat,1)
else:
r = self._rc(r).__getitem__( (tmp,index[1]))
return self._rc(r)
elif isinstance(index, types.IntType):
return self.array.flat[index]
return Matrix.__getitem__(self, index)
def __getattr__(self, attr):
if attr == 'A':
return squeeze(self.array)
elif attr == 'T':
return self._rc(Numeric.transpose(self.array))
elif attr == 'H':
if len(self.array.shape) == 1:
self.array.shape = (1,self.array.shape[0])
return self._rc(Numeric.conjugate(Numeric.transpose(self.array)))
elif attr == 'I':
return self._rc(LinearAlgebra.inverse(self.array))
elif attr == 'real':
return self._rc(self.array.real)
elif attr == 'imag':
return self._rc(self.array.imag)
elif attr == 'flat':
return self._rc(self.array.flat)
elif attr == 'length':
return max(self.array.shape)
else:
raise AttributeError, attr + " not found."
#========end code========
> numarray supports matlab style indexing if you pass the ind as an
> array or list of indices (but not a tuple, I found to my surprise).
> As pointed out already, for Numeric you need to use the take function
>
> >>> from numarray import array
> >>> x = array([1,2,3,4,5,6,7,8,9])
> >>> ind = [3,5,7]
> >>> inda = array(ind)
> >>> indt = tuple(ind)
> >>> x[ind]
> array([4, 6, 8])
> >>> x[inda]
> array([4, 6, 8])
> >>> x[indt]
> Traceback (most recent call last):
> File "<stdin>", line 1, in ?
> IndexError: too many indices.
>
> I'm sure the tuple "surprise" is a documented feature.
>
> JDH
>
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