On Sat, Dec 3, 2011 at 7:18 PM, Travis Oliphant wrote:
>
> Hi everyone,
>
> There have been some wonderfully vigorous discussions over the past few
> months that have made it clear that we need some clarity about how
> decisions will be made in the NumPy community.
>
> When we were a smaller bunch
In numpy 1.6.1, what's the most straightforward way to convert a datetime64
to a python datetime.datetime? E.g. I have
In [1]: d = datetime64("2011-12-03 12:34:56.75")
In [2]: d
Out[2]: 2011-12-03 12:34:56.75
I want the same time as a datetime.datetime instance. My best hack so far
is to p
I like the idea of trying to reach consensus first. The only point of
having a board is to have someway to resolve issues should consensus not be
reachable. Believe me, I'm not that excited about a separate mailing list.
It would be great if we could resolve everything on a single list.
Hi Travis,
On Sat, Dec 3, 2011 at 6:18 PM, Travis Oliphant wrote:
>
> Hi everyone,
>
> There have been some wonderfully vigorous discussions over the past few
> months that have made it clear that we need some clarity about how decisions
> will be made in the NumPy community.
>
> When we were a
Hello,
I'm trying to add a fixed precision rational number dtype to numpy,
and am running into an issue trying to register ufunc loops. The code
in question looks like
int npy_rational = PyArray_RegisterDataType(&rational_descr);
PyObject* equal = ... // extract equal object from the imp
Hi everyone,
There have been some wonderfully vigorous discussions over the past few months
that have made it clear that we need some clarity about how decisions will be
made in the NumPy community.
When we were a smaller bunch of people it seemed easier to come to an agreement
and things
When attempting to cast to a user defined type, PyArray_GetCast looks
up the cast function in the dictionary but doesn't check if the entry
exists. This causes segfaults. Here's a patch.
Geoffrey
diff --git a/numpy/core/src/multiarray/convert_datatype.c
b/numpy/core/src/multiarray/convert_datat
Ha! I knew it had to be possible! Thanks Derek. So for and N = 2 (now on my
laptop):
In [70]: M = 1200
In [69]: N = 2
In [71]: a = np.random.randint(0, 255, (M**2)).reshape(M,-1)
In [76]: timeit np.rollaxis(np.tile(a, N**2).reshape(M,N,-1), 2,
1).reshape(M*N,-1)
10 loops, best of 3: 99.1 ms per
On 03.12.2011, at 6:47PM, Olivier Delalleau wrote:
> Ah sorry, I hadn't read carefully enough what you were trying to achieve. I
> think the double repeat solution looks like your best option then.
Considering that it is a lot shorter than fixing the tile() result, you
are probably right (I've
On 03.12.2011, at 6:22PM, Robin Kraft wrote:
> That does repeat the elements, but doesn't get them into the desired order.
>
> In [4]: print a
> [[1 2]
> [3 4]]
>
> In [7]: np.tile(a, 4)
> Out[7]:
> array([[1, 2, 1, 2, 1, 2, 1, 2],
>[3, 4, 3, 4, 3, 4, 3, 4]])
>
> In [8]: np.tile(a, 4)
Ah sorry, I hadn't read carefully enough what you were trying to achieve. I
think the double repeat solution looks like your best option then.
-=- Olivier
2011/12/3 Robin Kraft
> That does repeat the elements, but doesn't get them into the desired order.
>
> In [4]: print a
> [[1 2]
> [3 4]]
>
That does repeat the elements, but doesn't get them into the desired order.
In [4]: print a
[[1 2]
[3 4]]
In [7]: np.tile(a, 4)
Out[7]:
array([[1, 2, 1, 2, 1, 2, 1, 2],
[3, 4, 3, 4, 3, 4, 3, 4]])
In [8]: np.tile(a, 4).reshape(4,4)
Out[8]:
array([[1, 2, 1, 2],
[1, 2, 1, 2],
You can also use numpy.tile
-=- Olivier
2011/12/3 Robin Kraft
> Thanks Warren, this is great, and even handles giant arrays just fine if
> you've got enough RAM.
>
> I also just found this StackOverflow post with another solution.
>
> a.repeat(2, axis=0).repeat(2, axis=1).
> http://stackoverflo
Thanks Warren, this is great, and even handles giant arrays just fine if you've
got enough RAM.
I also just found this StackOverflow post with another solution.
a.repeat(2, axis=0).repeat(2, axis=1).
http://stackoverflow.com/questions/7525214/how-to-scale-a-numpy-array
np.kron lets you do more
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