Christopher Barker wrote:
> static int
> @fn...@_fromstr(char *str, @type@ *ip, char **endptr, PyArray_Descr
> *NPY_UNUSED(ignore))
> {
> double result;
> result = NumPyOS_ascii_strtod(str, endptr);
> *ip = (@type@) result;
> return 0;
> }
OK, I've done the diagnostics, but no
On Tue, Jan 12, 2010 at 3:33 PM, Marc Schwarzschild
wrote:
>
>
> I have a csv file like this:
>
> Account, Symbol, Quantity, Price
> One,SPY,5,119.00
> One,SPY,3,120.00
> One,SPY,-2,125.00
> One,GE,...
> One,GE,...
> Two,SPY, ...
> Three,GE, ...
> ...
>
> The data is mu
I have a csv file like this:
Account, Symbol, Quantity, Price
One,SPY,5,119.00
One,SPY,3,120.00
One,SPY,-2,125.00
One,GE,...
One,GE,...
Two,SPY, ...
Three,GE, ...
...
The data is much larger, could be 10,000 records. I can load it
into a numpy array using m
On Jan 12, 2010, at 1:52 PM, Charles R Harris wrote:
>
>
>
> On Tue, Jan 12, 2010 at 11:32 AM, Pauli Virtanen wrote:
> ti, 2010-01-12 kello 12:51 -0500, Pierre GM kirjoitti:
> [clip]
> > a = numpy.ma.MaskedArray([[1,2,3],[4,5,6]])
> > numpy.ma.sum(a, 1)
> > > Traceback (most recent ca
Sebastian Walter wrote:
>>> However, this particular problem occurs when you try to automatically
>>> differentiate an algorithm by using an Algorithmic Differentiation
>>> (AD) tool.
>>> E.g. given a function
>>>
>>> x = numpy.ones(2)
>>> def f(x):
>>> a = numpy.ones(2)
>>> a *= x
>>> return
On Tue, Jan 12, 2010 at 7:38 PM, Robert Kern wrote:
> On Tue, Jan 12, 2010 at 12:31, Sebastian Walter
> wrote:
>> On Tue, Jan 12, 2010 at 7:09 PM, Robert Kern wrote:
>>> On Tue, Jan 12, 2010 at 12:05, Sebastian Walter
>>> wrote:
Hello,
I have a question about the augmented assignment
On Tue, Jan 12, 2010 at 11:32 AM, Pauli Virtanen wrote:
> ti, 2010-01-12 kello 12:51 -0500, Pierre GM kirjoitti:
> [clip]
> > a = numpy.ma.MaskedArray([[1,2,3],[4,5,6]])
> > numpy.ma.sum(a, 1)
> > > Traceback (most recent call last):
> > > File "", line 1, in
> > > File
> > >
> "/usr
On Tue, Jan 12, 2010 at 12:31, Sebastian Walter
wrote:
> On Tue, Jan 12, 2010 at 7:09 PM, Robert Kern wrote:
>> On Tue, Jan 12, 2010 at 12:05, Sebastian Walter
>> wrote:
>>> Hello,
>>> I have a question about the augmented assignment statements *=, +=, etc.
>>> Apparently, the casting of types i
ti, 2010-01-12 kello 12:51 -0500, Pierre GM kirjoitti:
[clip]
> a = numpy.ma.MaskedArray([[1,2,3],[4,5,6]])
> numpy.ma.sum(a, 1)
> > Traceback (most recent call last):
> > File "", line 1, in
> > File
> > "/usr/lib64/python2.5/site-packages/numpy-1.4.0-py2.5-linux-x86_64.egg/n
> > ump
On Tue, Jan 12, 2010 at 7:09 PM, Robert Kern wrote:
> On Tue, Jan 12, 2010 at 12:05, Sebastian Walter
> wrote:
>> Hello,
>> I have a question about the augmented assignment statements *=, +=, etc.
>> Apparently, the casting of types is not working correctly. Is this
>> known resp. intended behavi
Pauli Virtanen wrote:
> ma, 2010-01-11 kello 16:11 -0800, Christopher Barker kirjoitti:
> [clip]
>> "If no conversion is performed, zero is returned and the value of nptr
>> is stored in the location referenced by endptr."
>>
>> off do do some more testing, but I guess that means that those pointe
On Tue, Jan 12, 2010 at 1:05 PM, Sebastian Walter
wrote:
> Hello,
> I have a question about the augmented assignment statements *=, +=, etc.
> Apparently, the casting of types is not working correctly. Is this
> known resp. intended behavior of numpy?
> (I'm using numpy.__version__ = '1.4.0.dev703
I'm trying to use sphinx to build documentation for our project (CDAT)
that uses numpy. I'm running into an exception due to
numpy.numarray.numerictypes.SignedType not having an __init__ attribute,
which causes problems with numpydoc. I'm sure there must be a
workaround or I'm doing something
On Tue, Jan 12, 2010 at 12:05, Sebastian Walter
wrote:
> Hello,
> I have a question about the augmented assignment statements *=, +=, etc.
> Apparently, the casting of types is not working correctly. Is this
> known resp. intended behavior of numpy?
Augmented assignment modifies numpy arrays in-p
Hello,
I have a question about the augmented assignment statements *=, +=, etc.
Apparently, the casting of types is not working correctly. Is this
known resp. intended behavior of numpy?
(I'm using numpy.__version__ = '1.4.0.dev7039' on this machine but I
remember a recent checkout of numpy yielded
On Jan 12, 2010, at 10:52 AM,
wrote:
> We have noticed the MaskedArray implementation in numpy-1.4.0 breaks
> some of our code. For instance we see the following:
My, that's embarrassing. Sorry for the inconvenience.
>
> in 1.3.0:
>
a = numpy.ma.MaskedArray([[1,2,3],[4,5,6]])
nu
On Tue, Jan 12, 2010 at 11:33 AM, denis wrote:
> On 11/01/2010 18:10, josef.p...@gmail.com wrote:
>
>> For this problem, it's supposed to be only those packages that have or
>> import cython generated code.
>
> Right; is this a known bug, is there a known fix for mac dmgs ?
> (Whisper, how'd it g
On Tue, Jan 12, 2010 at 10:33, denis wrote:
> On 11/01/2010 18:10, josef.p...@gmail.com wrote:
>
>> For this problem, it's supposed to be only those packages that have or
>> import cython generated code.
>
> Right; is this a known bug, is there a known fix for mac dmgs ?
> (Whisper, how'd it get
On 11/01/2010 18:10, josef.p...@gmail.com wrote:
> For this problem, it's supposed to be only those packages that have or
> import cython generated code.
Right; is this a known bug, is there a known fix for mac dmgs ?
(Whisper, how'd it get past testing ?)
scipy/stats/__init__.py has an apparen
Thanks so so much.
Finally, it works.
>>> import scipy.sparse.linalg.eigen.arpack as arpack
>>> dir(arpack)
['__builtins__', '__doc__', '__file__', '__name__', '__package__',
'__path__', '
_arpack', 'arpack', 'aslinearoperator', 'eigen', 'eigen_symmetric',
'np', 'speig
s', 'warnings']
>>>
Bu
We have noticed the MaskedArray implementation in numpy-1.4.0 breaks
some of our code. For instance we see the following:
in 1.3.0:
>>> a = numpy.ma.MaskedArray([[1,2,3],[4,5,6]])
>>> numpy.ma.sum(a, 1)
masked_array(data = [ 6 15],
mask = False,
fill_value = 99)
in 1.4.0
>>> a = numpy.ma.
On Tue, Jan 12, 2010 at 4:11 PM, Jankins wrote:
Hi
On my Ubuntu, I would reach the arpack wrapper as follows:
from scipy.sparse.linalg.eigen.arpack import eigen
However, I'd guess that you deal with a symmetric matrix (Laplacian or
adjacency matrix), so the symmetric solver might be the best c
>>> import scipy.sparse.linalg as linalg
>>> dir(linalg)
['LinearOperator', 'Tester', '__all__', '__builtins__', '__doc__',
'__file__', '
__name__', '__package__', '__path__', 'aslinearoperator', 'bench',
'bicg', 'bicg
stab', 'cg', 'cgs', 'dsolve', 'eigen', 'factorized', 'gmres',
'interface',
On 1/12/2010 1:35 AM, Jankins wrote:
from scipy.sparse.linalg.eigen import eigen
> Traceback (most recent call last):
> File "", line 1, in
> ImportError: cannot import name eigen
Look at David's example:
from scipy.sparse.linalg import eigen
hth,
Alan Isaac
___
>>> filter(lambda x: x.startswith('eig'),dir(np.linalg))
['eig', 'eigh', 'eigvals', 'eigvalsh']
>>> import scipy.linalg as spla
>>> filter(lambda x: x.startswith('eig'),dir(spla))
['eig', 'eig_banded', 'eigh', 'eigvals', 'eigvals_banded', 'eigvalsh']
hth,
Alan Isaac
__
Hi,
The problem is that f2py does not support callbacks that
return arrays. There is easy workaround to that: provide
returnable arrays as arguments to callback functions.
Using your example:
SUBROUTINE CallbackTest(dv,v0,Vout,N)
IMPLICIT NONE
!F2PY intent( hide ):: N
INTEGER:: N, ic
Hi,
I'm trying to build a differential equation integrator and later a stochastic
differential equation integrator.
I'm having trouble getting f2py to work where the callback itself receives an
array from the Fortran routine does some work on it and then passes an array
back.
For the stoach
ma, 2010-01-11 kello 16:11 -0800, Christopher Barker kirjoitti:
[clip]
> "If no conversion is performed, zero is returned and the value of nptr
> is stored in the location referenced by endptr."
>
> off do do some more testing, but I guess that means that those pointers
> need to be checked afte
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