> Well what about A*(B*C)?
Point taken...
Johannes
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>>> import numpy.core as _n
>>> _nt= _n.numerictypes
>>> value=
'[EMAIL PROTECTED]@[EMAIL PROTECTED]@\x00\x00\x00\x00\x00\x00\x18@'
>>> _n.array(value, dtype= _nt.complex128, copy=True)
Traceback (most recent call last):
File "", line 1, in
TypeError: a float is required
>>>
A bug?
Coli
Colin J. Williams wrote:
> >>> import numpy.core as _n
> >>> _nt= _n.numerictypes
> >>> value=
> '[EMAIL PROTECTED]@[EMAIL PROTECTED]@\x00\x00\x00\x00\x00\x00\x18@'
> >>> _n.array(value, dtype= _nt.complex128, copy=True)
> Traceback (most recent call last):
> File "", line 1, in
> TypeError
I understand why this happens, but I wonder if it should be in any way
'fixed' (if that is even feasible without introducing other problems):
In [28]: x = 99
In [29]: y = numpy.array([x])
In [30]: z = y[0]
In [31]: x==z
Out[31]: True
In [32]: x
Out[32]: 99
In [33]: z
Out[33]: 99
Robert Kern wrote:
> Colin J. Williams wrote:
>
>> >>> import numpy.core as _n
>> >>> _nt= _n.numerictypes
>> >>> value=
>> '[EMAIL PROTECTED]@[EMAIL PROTECTED]@\x00\x00\x00\x00\x00\x00\x18@'
>> >>> _n.array(value, dtype= _nt.complex128, copy=True)
>> Traceback (most recent call last):
>>
On Thu, Nov 09, 2006 at 03:18:57AM -0500, Colin J. Williams wrote:
>
> >>> import numpy.core as _n
> >>> _nt= _n.numerictypes
> >>> value=
> '[EMAIL PROTECTED]@[EMAIL PROTECTED]@\x00\x00\x00\x00\x00\x00\x18@'
> >>> _n.array(value, dtype= _nt.complex128, copy=True)
> Traceback (most recent cal
On Wed, 08 Nov 2006, koara apparently wrote:
> 'mat' is a numpy.array with shape=(22973, 1009),
> 'vec' is a numpy.array with shape=(22973,), both of type int:
> for i in xrange(1009):
> ...
> fr = vec[10001]
> mat[:, i] = vec # assign whole column
> if mat[10001, i] != fr:
Fernando Perez wrote:
> I understand why this happens, but I wonder if it should be in any way
> 'fixed' (if that is even feasible without introducing other problems):
>
> In [28]: x = 99
>
> In [29]: y = numpy.array([x])
>
> In [30]: z = y[0]
>
> In [31]: x==z
> Out[31]: True
>
> In [32]:
On 11/9/06, Robert Kern <[EMAIL PROTECTED]> wrote:
> Fernando Perez wrote:
> > I understand why this happens, but I wonder if it should be in any way
> > 'fixed' (if that is even feasible without introducing other problems):
[...]
> > I am sure it will be, to say the least, pretty surprising (and
ke, 2006-11-08 kello 16:08 -0800, David L Goldsmith kirjoitti:
> Hi! I tried to send this earlier: it made it into my sent mail folder,
> but does not appear to have made it to the list.
>
> I need to numerically solve:
> (1-t)x" + x' - x = f(t), x(0) = x0, x(1) = x1
> I've been trying to us
A. M. Archibald wrote:
> On 08/11/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
>
>
>> It has always been my experience (on various flavors or Pentium) that
>> operating on NANs is extremely slow. Does anyone know on what hardware
>> NANs are *not* slow? Of course it's always possible I just never
Stefan van der Walt wrote:
> On Thu, Nov 09, 2006 at 03:18:57AM -0500, Colin J. Williams wrote:
>
>> >>> import numpy.core as _n
>> >>> _nt= _n.numerictypes
>> >>> value=
>> '[EMAIL PROTECTED]@[EMAIL PROTECTED]@\x00\x00\x00\x00\x00\x00\x18@'
>> >>> _n.array(value, dtype= _nt.complex128, c
hi, i have a recarray of > 60K records and i'm wondering if there's a numpy/vectorized way to the following.get a new array where there will be unique column0 + column1 rows with the row that remains being chosen because it has the highest value in the last column. so in the paste below, there ar
Wow, thanks!
DG
Pauli Virtanen wrote:
> ke, 2006-11-08 kello 16:08 -0800, David L Goldsmith kirjoitti:
>
>> Hi! I tried to send this earlier: it made it into my sent mail folder,
>> but does not appear to have made it to the list.
>>
>> I need to numerically solve:
>> (1-t)x" + x' - x =
Robert Kern wrote:
> I think we decided long ago that an int32 really is an array of 32-bit
> integers
> and behaves like one.
That would apply to y*y:
>>> x = 99
>>> y = numpy.array([x])
>>> x*x
9801L
So Python ints automatically convert to Python longs on overflow.
>>> y*y
a
Robert Kern wrote:
> Fernando Perez wrote:
>
>> I understand why this happens, but I wonder if it should be in any way
>> 'fixed' (if that is even feasible without introducing other problems):
>>
>> In [28]: x = 99
>>
>> In [29]: y = numpy.array([x])
>>
>> In [30]: z = y[0]
>>
>> In [31]: x=
It looks like on my Pentium M multiplication with NaNs is slow, but
using a masked array ranges from slightly faster (with only one value
masked) to twice as slow (with all values masked):
In [15]:Timer("a.prod()", "import numpy as np; aa = np.ones(4096); a =
np.ma.masked_greater(aa,0)").timei
Tim Hochberg wrote:
> I've been told that operations on NANs are slow because they aren't
> always implemented in the FPU hardware. Instead they are trapped and
> implemented software or firmware or something or other.
which still doesn't make sense -- doesn't ANY operation with a NaN
return N
Christopher Barker wrote:
> Tim Hochberg wrote:
>
>> I've been told that operations on NANs are slow because they aren't
>> always implemented in the FPU hardware. Instead they are trapped and
>> implemented software or firmware or something or other.
>>
>
> which still doesn't make sens
Christopher Barker wrote:
> I'm a bit confused, because I thought that when you extracted a scalar
> from an array, you got regular python scalar for the datatypes that are
> supported. This made it clear that you always get a numpy Scalar, which,
> in a few situations, behaves differently than
On 11/9/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
> Let me add that I can't imagine that the bugs will be all that subtle
> given that numpy now spits out a warning on overflow.
> If you're really worried about this I suggest you crank up the error
> mode to make this an error - then you really
Travis Oliphant wrote:
> How about
>
> newdtype = N.dtype([('both','2i4'),('','b1')])
> res = data.view(newdtype)['both']
That works great! thanks,
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
NOAA/OR&R/HAZMAT (206) 526-6959 voic
Disappointed in NaN land?Since the function of old retired persons is to tell youngsters stories around the campfile:A supercomputer hardware designer told me that when forced to add IEEE arithmetic to his designs that it decreased performance substantially, maybe 25-30%; it wasn't that doing the o
Ok, so i installed the newest numpy and scipy over enthought defaults.
The original problem indeed disappeared! Given the highly suspicous
magic threshold number of 1, i suspect it was a bug in old numpy.
Makes me wonder if it's really been solved or just stopped manifesting
itself in my test d
[CJ]: I didn't find "frombuffer" in the Numpy Examples List.
I just added a frombuffer() example (the 197th example!) which
I extracted from your mail.
[CJ]: Incidentally, the List is a big help but it would be even better if
[CJ]: included the signatures of the functions.
Good id
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I'd love to be able to do automatic differentiation using numpy
ndarrays. There are some AD libraries in C++ that work through operator
overloading on custom numeric types (e.g. "adouble"). I have no
experience in in creating custom ndarrays, but would it be a huge
project (or even possible) to mak
Pau Gargallo wrote:
> c=N.dstack([a,b]) seems to do the trick.
indeed it does. Thanks.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
NOAA/OR&R/HAZMAT (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98
Please forgive the not-specifically-numpy post. I'll keep it short.
Some of us often, when trying to explain to newcomers the benefits of
Python for scientific work, use expressions like the famous 'it fits
your brain'. This is an attempt at conveying why it seems like such a
natural tool for exp
I think Ruby users say the same about Ruby, maybe even more
emphatically than Python users, and Ruby's chart looks like just about
the most complicated one there. C and Python look to be about on par.
Also I suspect a chart of Lisp's grammar would be even simpler than
any of those up there, but I
On 09/11/06, Paul Dubois <[EMAIL PROTECTED]> wrote:
> Since the function of old retired persons is to tell youngsters stories
> around the campfile:
I'll pull up a log. But since I'm uppity, and not especially young, I
hope you won't mind if I heckle.
> A supercomputer hardware designer told me
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