On 26 Aug 2014, at 09:05 pm, Adrian Altenhoff adrian.altenh...@inf.ethz.ch
wrote:
But you are right that the problem with using the first_values, which should
of course be valid,
somehow stems from the use of usecols, it seems that in that loop
for (i, conv) in user_converters.items():
Hi everyone, how can I convert (1L, 480L, 1440L) shaped numpy array into
(480L, 1440L)?
Thanks in the advance.
phinn
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Hi,
Our short example-data:
np.arange(10).reshape(1,5,2)
array([[[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]]])
Shape is (1,5,2)
Two possibilies:
data.reshape(5,2)
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
Or just:
data[0]
There is also np.squeeze(), which will eliminate any singleton dimensions
(but I personally hate using it because it can accidentally squeeze out
dimensions that you didn't intend to squeeze when you have arbitrary input
data).
Ben Root
On Wed, Aug 27, 2014 at 11:12 AM, Wagner Sebastian
On 27.08.2014 17:08, phinn stuart wrote:
Hi everyone, how can I convert (1L, 480L, 1440L) shaped numpy array into
(480L, 1440L)?
Thanks in the advance.
np.squeeze removes empty dimensions:
In [2]: np.squeeze(np.ones((1,23,232))).shape
Out[2]: (23, 232)
After reading this stackoverflow question:
http://stackoverflow.com/questions/25530223/append-a-list-at-the-end-of-each-row-of-2d-array
I was reminded that the `np.concatenate` family of functions do not
broadcast the shapes of their inputs:
import numpy as np
a = np.arange(6).reshape(3, 2)
On Wed, Aug 27, 2014 at 5:44 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
After reading this stackoverflow question:
http://stackoverflow.com/questions/25530223/append-a-list-at-the-end-of-each-row-of-2d-array
I was reminded that the `np.concatenate` family of functions do not
A request was open in github to add a `merge` function to numpy that would
merge two sorted 1d arrays into a single sorted 1d array. I have been
playing around with that idea for a while, and have a branch in my numpy
fork that adds a `mergesorted` function to `numpy.lib`:
Hello,
Almost punctually for EuroScipy we have finally managed to release the
first release candidate of NumPy 1.9.
We intend to only fix bugs until the final release which we plan to do
in the next 1-2 weeks.
In this release numerous performance improvements have been added, most
significantly
On Wed, Aug 27, 2014 at 10:01 AM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Aug 27, 2014 at 5:44 PM, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
After reading this stackoverflow question:
It wouldn't hurt to have this function, but my intuition is that its use
will be minimal. If you are already working with sorted arrays, you already
have a flop cost on that order of magnitude, and the optimized merge saves
you a factor two at the very most. Using numpy means you are sacrificing
Hi Eelco,
I took a deeper look into your code a couple of weeks back. I don't think I
have fully grasped what it allows completely, but I agree that some form of
what you have there is highly desirable. Along the same lines, for sometime
I have been thinking that the right place for a `groupby`
If I understand you correctly, the current implementation supports these
operations. All reductions over groups (except for median) are performed
through the corresponding ufunc (see GroupBy.reduce). This works on
multidimensional arrays as well, although this broadcasting over the
non-grouping
i.e, if the grouped axis is small but the other axes are not, you could
write this, which avoids the python loop over the long axis that
np.vectorize would otherwise perform.
import numpy as np
from grouping import group_by
keys = np.random.randint(0,4,10)
values = np.random.rand(10,2000)
for k,g
Yes, I was aware of that. But the point would be to provide true
vectorization on those operations.
The way I see it, numpy may not have to have a GroupBy implementation, but
it should at least enable implementing one that is fast and efficient over
any axis.
On Wed, Aug 27, 2014 at 12:38 PM,
On 08/27/2014 11:07 AM, Julian Taylor wrote:
Hello,
Almost punctually for EuroScipy we have finally managed to release the
first release candidate of NumPy 1.9.
We intend to only fix bugs until the final release which we plan to do
in the next 1-2 weeks.
I'm seeing the following errors
On 27 August 2014 19:02, Jaime Fernández del Río jaime.f...@gmail.com
wrote:
Since there is at least one other person out there that likes it, is there
any more interest in such a function? If yes, any comments on what the
proper interface for extra output should be? Although perhaps the best
I just checked the docs on ufuncs, and it appears that's a solved problem
now, since ufunc.reduceat now comes with an axis argument. Or maybe it
already did when I wrote that, but I simply wasn't paying attention. Either
way, the code is fully vectorized now, in both grouped and non-grouped
axes.
On Wed, Aug 27, 2014 at 3:52 PM, Orion Poplawski or...@cora.nwra.com
wrote:
On 08/27/2014 11:07 AM, Julian Taylor wrote:
Hello,
Almost punctually for EuroScipy we have finally managed to release the
first release candidate of NumPy 1.9.
We intend to only fix bugs until the final
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