The following fails after fixing datetime_data
assert_equal(datetime_data(a.dtype), ('us', 1, 1, 1))
The problem is that 'us' is unicode and the function call yields bytes. The
question is: should datetime units use unicode when compiled on python =
3k?
Chuck
On Tue, May 4, 2010 at 7:05 AM, David Cournapeau courn...@gmail.com wrote:
On Mon, May 3, 2010 at 7:23 PM, Austin Bingham austin.bing...@gmail.com
wrote:
Hi everyone,
I've recently been developing a python module and C++ library in
parallel, with core functionality in python and C++ largely
On 03/05/2010 16:02, Neal Becker wrote:
I have coded in c++ a histogram object that can be used as:
h += my_sample
or
h += my_vector
This is very useful in simulations which are looping and developing results
incrementally. It would me great to have such a feature in numpy.
Neal,
I
denis wrote:
On 03/05/2010 16:02, Neal Becker wrote:
I have coded in c++ a histogram object that can be used as:
h += my_sample
or
h += my_vector
This is very useful in simulations which are looping and developing
results
incrementally. It would me great to have such a feature in
On 04/05/2010 14:09, Neal Becker wrote:
denis wrote:
Neal,
I like the idea of a faster np.histogram / histogramdd;
but it would have to be compatible with numpy and pylab
or at least a clear, documented subset (doc first).
The point is not to be faster, it's to be incremental.
OK,
On Thu, 2009-03-12 at 19:59 +0100, Dag Sverre Seljebotn wrote:
(First off, is it OK to continue polling the NumPy list now and then on
Cython language decisions? Or should I expect that any interested Cython
users follow the Cython list?)
In Python, if I write -1 % 5, I get 4. However, in
On Tue, May 4, 2010 at 12:20 PM, S. Chris Colbert sccolb...@gmail.comwrote:
On Thu, 2009-03-12 at 19:59 +0100, Dag Sverre Seljebotn wrote:
(First off, is it OK to continue polling the NumPy list now and then on
Cython language decisions? Or should I expect that any interested Cython
users
On Thu, Apr 29, 2010 at 12:30 PM, Pauli Virtanen p...@iki.fi wrote:
Wed, 28 Apr 2010 14:12:07 -0400, Alan G Isaac wrote:
[clip]
Here is a related ticket that proposes a more explicit alternative:
adding a ``dot`` method to ndarray.
http://projects.scipy.org/numpy/ticket/1456
I kind of
Hello, I have written a very simple code that computes the gradient by finite
differences of any general function. Keeping the same idea, I would like
modify the code using numpy to make it faster.
Any ideas?
Thanks.
def grad_finite_dif(self,x,user_data = None):
If your x data are equispaced I would do something like this
def derive( func, x):
Approximate the first derivative of function func at points x.
# compute the values of y = func(x)
y = func(x)
# compute the step
dx = x[1] - x[0]
# kernel array for second order accuracy centered
playing devil's advocate I'd say use Algorithmic Differentiation
instead of finite differences ;)
that would probably speed things up quite a lot.
On Tue, May 4, 2010 at 11:36 PM, Davide Lasagna lasagnadav...@gmail.com wrote:
If your x data are equispaced I would do something like this
def
On Tue, May 4, 2010 at 2:57 PM, Sebastian Walter
sebastian.wal...@gmail.com wrote:
playing devil's advocate I'd say use Algorithmic Differentiation
instead of finite differences ;)
that would probably speed things up quite a lot.
I would suggest that too, but aside from FuncDesigner[0]
I forgot to mention one thing: if you are doing optimization, a good
solution is a modeling package like AMPL (or GAMS or AIMMS, but I only
know AMPL, so I will restrict my attention to it). AMPL has a natural
modeling language and provides you with automatic differentiation.
It's not free, but
Hello,
I have the following arrays read as masked array.
I[10]: basic.data['Air_Temp'].mask
O[10]: array([ True, False, False, ..., False, False, False], dtype=bool)
[12]: basic.data['Press_Alt'].mask
O[12]: False
I[13]: len basic.data['Air_Temp']
- len(basic.data['Air_Temp'])
O[13]: 1758
On Tue, May 4, 2010 at 8:23 PM, Guilherme P. de Freitas
guilhe...@gpfreitas.com wrote:
On Tue, May 4, 2010 at 2:57 PM, Sebastian Walter
sebastian.wal...@gmail.com wrote:
playing devil's advocate I'd say use Algorithmic Differentiation
instead of finite differences ;)
that would probably speed
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