Re: numpy migration (also posted to numpy-discussion)
Travis E. Oliphant wrote:
> Duncan Smith wrote:
>
>> Hello,
>> Since moving to numpy I've had a few problems with my existing
>> code. It basically revolves around the numpy scalar types. e.g.
>>
>
> You will probably get more help on the numpy discussion list:
>
> [EMAIL PROTECTED]
>
>
> You are encountering problems because numpy scalar types don't raise
> errors (unless you have set the appropriate hardware flag using
> numpy.seterr).
>
Unfortunately it seems to raise a FloatingPointError.
>>> import numpy as N
>>> N.__version__
'1.0.1'
>>> a = N.array([[0,1],[2,3]])
>>> a
array([[0, 1],
[2, 3]])
>>> i = a[0,0]
>>> 1/i
0
>>> N.seterr(divide='raise')
{'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'ignore'}
>>> 1/i
Traceback (most recent call last):
File "", line 1, in
1/i
FloatingPointError: divide by zero encountered in long_scalars
> You can get Python scalars out of NumPy arrays if you really want them
> using (for example...)
>
> a.item(0,0)
>
>
>>
>> An additional problem involves classes that have e.g. __rmul__ methods
>> defined and are sufficiently similar to numpy arrays that my classes'
>> __rmul__ methods are not invoked when using numpy scalars.
>>
>
> Could you please post an example showing the problem?
>
[snip]
-example.py
from __future__ import division
import numpy
class MyClass(object):
def __init__(self, arr, labels):
self.arr = arr
self.labels = labels
def __repr__(self):
return numpy.array2string(self.arr, separator=', ') +
repr(self.labels)
def __len__(self):
return len(self.labels)
def __getitem__(self, key):
return self.arr[key]
def __setitem__(self, key, item):
self.arr[key] = item
def __mul__(self, other):
return self.__class__(self.arr * other, self.labels)
__rmul__ = __mul__
>>> import example
>>> import numpy as N
>>> ex = example.MyClass(N.array([[6,7],[8,9]]), ['axis0', 'axis1'])
>>> i = ex.arr[0,0]
>>> ex
[[6, 7],
[8, 9]]['axis0', 'axis1']
>>> ex * i
[[36, 42],
[48, 54]]['axis0', 'axis1']
>>> i * ex
array([[36, 42],
[48, 54]])
>>>
It seems that it requires having __len__, __setitem__ and __getitem__
defined to get the undesired behaviour. Cheers.
Duncan
--
http://mail.python.org/mailman/listinfo/python-list
Re: numpy migration (also posted to numpy-discussion)
Travis E. Oliphant wrote: > Duncan Smith wrote: > >> Hello, >> Since moving to numpy I've had a few problems with my existing >> code. It basically revolves around the numpy scalar types. e.g. >> > > You will probably get more help on the numpy discussion list: > > [EMAIL PROTECTED] > > > You are encountering problems because numpy scalar types don't raise > errors (unless you have set the appropriate hardware flag using > numpy.seterr). > Aha! > You can get Python scalars out of NumPy arrays if you really want them > using (for example...) > > a.item(0,0) > > >> >> An additional problem involves classes that have e.g. __rmul__ methods >> defined and are sufficiently similar to numpy arrays that my classes' >> __rmul__ methods are not invoked when using numpy scalars. >> > > Could you please post an example showing the problem? > I'll try to post a minimal example tomorrow. But they are classes that have an ndarray as an attribute, and with __getitem__ and __setitem__ methods which simply call the corresponding array methods. Maybe that's enough to account for the behaviour? I'll check tomorrow. >> >> I might (I hope) be missing something obvious; but it seems like, to be >> safe, I'm going to have to do a lot of explicit conversions to Python >> types (or abandon catching zero division errors, and documenting some of >> my classes to highlight that whether scalar * a equals a * scalar >> depends on whether a.__rmul__ is called, which depends on the type of >> scalar). >> > > numpy scalars are try a lot more things before giving up on > multiplication and letting the other class have a stab at it. > > Post your problems to the numpy discussion list for better help and more > discussion. > Yes, I have done. But it's awaiting moderation; presumably because I posted using a different e-mail address than the one I registered with (I wasn't thinking). Thanks for the reply. Duncan -- http://mail.python.org/mailman/listinfo/python-list
Re: numpy migration (also posted to numpy-discussion)
Duncan Smith wrote: > Hello, > Since moving to numpy I've had a few problems with my existing > code. It basically revolves around the numpy scalar types. e.g. > You will probably get more help on the numpy discussion list: [EMAIL PROTECTED] You are encountering problems because numpy scalar types don't raise errors (unless you have set the appropriate hardware flag using numpy.seterr). You can get Python scalars out of NumPy arrays if you really want them using (for example...) a.item(0,0) > > An additional problem involves classes that have e.g. __rmul__ methods > defined and are sufficiently similar to numpy arrays that my classes' > __rmul__ methods are not invoked when using numpy scalars. > Could you please post an example showing the problem? > > I might (I hope) be missing something obvious; but it seems like, to be > safe, I'm going to have to do a lot of explicit conversions to Python > types (or abandon catching zero division errors, and documenting some of > my classes to highlight that whether scalar * a equals a * scalar > depends on whether a.__rmul__ is called, which depends on the type of > scalar). > numpy scalars are try a lot more things before giving up on multiplication and letting the other class have a stab at it. Post your problems to the numpy discussion list for better help and more discussion. -Travis -- http://mail.python.org/mailman/listinfo/python-list
numpy migration (also posted to numpy-discussion)
Hello, Since moving to numpy I've had a few problems with my existing code. It basically revolves around the numpy scalar types. e.g. >>> import Numeric as N >>> a = N.array([[0,1],[2,3]]) >>> a array([[0, 1], [2, 3]]) >>> i = a[0,0] >>> 1/i Traceback (most recent call last): File "", line 1, in -toplevel- 1/i ZeroDivisionError: integer division or modulo by zero >>> b = a * 1.5 >>> b array([[ 0. , 1.5], [ 3. , 4.5]]) >>> N.floor(b) array([[ 0., 1.], [ 3., 4.]]) >>> RESTART >>> import numpy as N >>> a = N.array([[0,1],[2,3]]) >>> a array([[0, 1], [2, 3]]) >>> i = a[0,0] >>> 1/i 0 >>> b = a * 1.5 >>> b array([[ 0. , 1.5], [ 3. , 4.5]]) >>> N.floor(b) array([[ 0., 1.], [ 3., 4.]]) >>> a = N.array([[0,1],[2,3]], dtype='O') >>> a array([[0, 1], [2, 3]], dtype=object) >>> i = a[0,0] >>> 1/i Traceback (most recent call last): File "", line 1, in -toplevel- 1/i ZeroDivisionError: integer division or modulo by zero >>> b = a * 1.5 >>> b array([[0.0, 1.5], [3.0, 4.5]], dtype=object) >>> N.floor(b) Traceback (most recent call last): File "", line 1, in -toplevel- N.floor(b) AttributeError: 'float' object has no attribute 'floor' >>> -- An additional problem involves classes that have e.g. __rmul__ methods defined and are sufficiently similar to numpy arrays that my classes' __rmul__ methods are not invoked when using numpy scalars. Using the 'O' dtype gives me Python types that raise zero division errors appropriately (for my code) and the desired calls to e.g. __rmul__ methods, but reduced functionality in other repects. I might (I hope) be missing something obvious; but it seems like, to be safe, I'm going to have to do a lot of explicit conversions to Python types (or abandon catching zero division errors, and documenting some of my classes to highlight that whether scalar * a equals a * scalar depends on whether a.__rmul__ is called, which depends on the type of scalar). I suppose I might get round both issues by subclassing existing numpy dtypes. Any ideas? Cheers. TIA. Duncan -- http://mail.python.org/mailman/listinfo/python-list
