Sebastian Berg sebastian at sipsolutions.net writes:
On Mo, 2015-03-16 at 15:53 +, Dave Hirschfeld wrote:
I have a number of large arrays for which I want to compute the mean
and
standard deviation over a particular axis - e.g. I want to compute
the
statistics for axis=1
I have a number of large arrays for which I want to compute the mean and
standard deviation over a particular axis - e.g. I want to compute the
statistics for axis=1 as if the other axes were combined so that in the
example below I get two values back
In [1]: a = randn(30, 2, 1)
For the
Daniel Smith dgasmith at icloud.com writes:
Hello everyone,I originally brought an optimized einsum routine
forward a few weeks back that attempts to contract numpy arrays together
in an optimal way. This can greatly reduce the scaling and overall cost
of the einsum expression for the cost
Andrew Nelson writes:
Dear list,I have a 4D array, A, that has the shape (NX, NY, 2, 2). I
wish to perform matrix multiplication of the 'NY' 2x2 matrices, resulting
in the matrix B. B would have shape (NX, 2, 2). I believe that np.einsum
would be up to the task, but I'm not quite sure of
Julian Taylor jtaylor.debian at googlemail.com writes:
On 23.10.2014 19:21, Dave Hirschfeld wrote:
Hi,
I accidentally passed a pandas DatetimeIndex to `np.arange` which
caused
it to segfault. It's a pretty dumb thing to do but I don't think it
should cause a segfault!
thanks
Hi,
I accidentally passed a pandas DatetimeIndex to `np.arange` which caused
it to segfault. It's a pretty dumb thing to do but I don't think it
should cause a segfault!
Python 2.7.5 |Continuum Analytics, Inc.| (default, Jul 1 2013,
12:37:52)
[MSC v.1500 64 bit (AMD64)] on win32
Type help,
It seems that the docs website is down?
http://docs.scipy.org/doc/
-Dave
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Chao YUE chaoyuejoy at gmail.com writes:
Dear all,
I have a simple question. Is there a way to denote the unchanged dimension
in the reshape function? like suppose I have an array named arr having
three dims with the first dimension length as 48, I want to reshape the
first dim into
Julian Taylor jtaylor.debian at googlemail.com writes:
On 16.05.2014 10:59, Dave Hirschfeld wrote:
Julian Taylor jtaylor.debian at googlemail.com writes:
Yes, I'd heard about the improvements and am very excited to try them out
since indexing is one of the bottlenecks in our
Sebastian Berg sebastian at sipsolutions.net writes:
On Do, 2014-05-15 at 12:31 +, Dave Hirschfeld wrote:
As can be seen from the code below (or in the notebook linked beneath)
fancy
indexing of a structured array is twice as slow as indexing both fields
independently - making
Julian Taylor jtaylor.debian at googlemail.com writes:
if ~50% faster is fast enough a simple improvement would be to replace
the use of PyArg_ParseTuple with manual tuple unpacking.
The PyArg functions are incredibly slow and is not required in
VOID_copyswap which just extracts 'Oi.
As can be seen from the code below (or in the notebook linked beneath) fancy
indexing of a structured array is twice as slow as indexing both fields
independently - making it 4x slower?
I found that fancy indexing was a bottleneck in my application so I was
hoping to reduce the overhead by
Sankarshan Mudkavi smudkavi at uwaterloo.ca writes:
Hey all,
It's been a while since the last datetime and timezones discussion thread
was visited (linked below):
http://thread.gmane.org/gmane.comp.python.numeric.general/53805
It looks like the best approach to follow is the UTC only
Jeff Reback jeffreback at gmail.com writes:
Dave,
your example is not a problem with numpy per se, rather that the default
generation is in local timezone (same as what python datetime does).
If you localize to UTC you get the results that you expect.
The problem is that the default
alex argriffi at ncsu.edu writes:
Hello list,
Here's another idea resurrection from numpy github comments that I've
been advised could be posted here for re-discussion.
The proposal would be to make np.linalg.svd more like scipy.linalg.svd
with respect to input checking. The argument
Sturla Molden sturla.molden at gmail.com writes:
josef.pktd at gmail.com wrote:
I use official numpy release for development, Windows, 32bit python,
i.e. MingW 3.5 and whatever old ATLAS the release includes.
a constant 13% cpu usage is 1/8 th of my 8 virtual cores.
Based on this
Ralf Gommers ralf.gommers at gmail.com writes:
On Fri, Nov 8, 2013 at 8:22 PM, Charles R Harris charlesr.harris at
gmail.com wrote:
and think that the main thing missing at this point is fixing the datetime
problems.
Is anyone planning to work on this? If yes, you need a rough
josef.pktd at gmail.com writes:
I think a H is feature creep and too specialized
What's .H of a int a str a bool ?
It's just .T and a view, so you cannot rely that conj() makes a copy
if you don't work with complex.
.T is just a reshape function and has **nothing** to do with matrix
Nathaniel Smith njs at pobox.com writes:
As soon as you talk about attributes returning things you've already
broken Python's mental model... attributes are things that sit there,
not things that execute arbitrary code. Of course this is not how the
actual implementation works, attribute
Alan G Isaac alan.isaac at gmail.com writes:
On 7/22/2013 3:10 PM, Nathaniel Smith wrote:
Having .T but not .H is an example of this split.
Hate to do this but ...
Readability counts.
+10!
A.conjugate().transpose() is unspeakably horrible IMHO. Since there's no way
to avoid a copy
The example below demonstrates the fact that the datetime64 constructor
ignores the dtype argument if passed in. Is this conscious design decision or
a bug/oversight?
In [55]: from datetime import datetime
...: d = datetime.now()
...:
In [56]: d
Out[56]: datetime.datetime(2013, 6,
Charles R Harris charlesr.harris at gmail.com writes:
Hi All,I think it is time to start the runup to the 1.8 release. I don't
know of any outstanding blockers but if anyone has a PR/issue that they feel
needs to be in the next Numpy release now is the time to make it known.Chuck
It would
Andreas Hilboll lists at hilboll.de writes:
I think your point about using current timezone in interpreting user
input being dangerous is probably correct --- perhaps UTC all the way
would be a safer (and simpler) choice?
+1
+10 from me!
I've recently come across a bug due to
Nathaniel Smith njs at pobox.com writes:
On Wed, Apr 3, 2013 at 2:26 PM, Dave Hirschfeld
dave.hirschfeld at gmail.com wrote:
This isn't acceptable for my use case (in a multinational company) and I
found
no reasonable way around it other than bypassing the numpy conversion
entirely
Robert Kern robert.kern at gmail.com writes:
One alternative that does not expand the API with two-liners is to let
the ndarray.fill() method return self:
a = np.empty(...).fill(20.0)
This violates the convention that in-place operations never return
self, to avoid
Sebastian Berg sebastian at sipsolutions.net writes:
Hello,
looking at the code, when only adding/removing dimensions with size 1,
numpy takes a small shortcut, however it uses 0 stride lengths as value
for the new one element dimensions temporarily, then replacing it again
to ensure the
Mark Bakker markbak at gmail.com writes:
I think there is a problem with assigning a 1D complex array of length one
to a position in another complex array.
Example:
a = ones(1,'D')
b = ones(1,'D')
a[0] = b
---
Dave Hirschfeld dave.hirschfeld at gmail.com writes:
It seems that reshape doesn't work correctly on an array which has been
resized using the 0-stride trick e.g.
In [73]: x = array([5])
In [74]: y = as_strided(x, shape=(10,), strides=(0,))
In [75]: y
Out[75]: array([5, 5, 5, 5, 5
It seems that reshape doesn't work correctly on an array which has been
resized using the 0-stride trick e.g.
In [73]: x = array([5])
In [74]: y = as_strided(x, shape=(10,), strides=(0,))
In [75]: y
Out[75]: array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
In [76]: y.reshape([10,1])
Out[76]:
array([[
Pierre GM pgmdevlist at gmail.com writes:
Hello,
The idea behin having a lib.recfunctions and not a rec.recfunctions or
whatever was to illustrate that the
functions of this package are more generic than they appear. They work with
regular structured ndarrays
and don't need recarrays.
Wes McKinney wesmckinn at gmail.com writes:
- Fundamental need to be able to work with multiple time series,
especially performing operations involving cross-sectional data
- I think it's a bit hard for lay people to use (read: ex-MATLAB/R
users). This is just my opinion, but a few
Mark Wiebe mwwiebe at gmail.com writes:
It appears to me that a structured dtype with some further NumPy extensions
could entirely replace the 'events' metadata fairly cleanly. If the ufuncs
are extended to operate on structured arrays, and integers modulo n are
added as a new dtype, a
As a user of numpy/scipy in finance I thought I would put in my 2p worth as
it's something which is of great importance in this area.
I'm currently a heavy user of the scikits.timeseries package by Matt Pierre
and I'm also following the development of statsmodels and pandas should we
require
Robert Kern robert.kern at gmail.com writes:
On Tue, Jun 7, 2011 at 07:34, Dave Hirschfeld dave.hirschfeld at gmail.com
wrote:
I'm not convinced about the events concept - it seems to add complexity
for something which could be accomplished better in other ways. A [Y]//4
dtype
Christopher Barker Chris.Barker at noaa.gov writes:
Dave Hirschfeld wrote:
That would be one way of dealing with irregularly spaced data. I would argue
that the example is somewhat back-to-front though. If something happens
twice a month it's not occuring at a monthly frequency
Mark Wiebe mwwiebe at gmail.com writes:
a = np.datetime64('today')
a - a.astype('M8[Y]')
numpy.timedelta64(157,'D')
vs
a = np.datetime64('today')
a - a.astype('M8[Y]')
Traceback (most recent call last):
File stdin, line 1, in module
TypeError: ufunc subtract cannot use
Jean-Luc Menut jeanluc.menut at free.fr writes:
I have a little question about the speed of numpy vs IDL 7.0.
Here the IDL result:
% Compiled module: $MAIN$.
2.837
The python code:
from numpy import *
from time import time
time1 = time()
for j in range(1):
for
Venkat dvr002 at gmail.com writes:
Hi All,I am new to Numpy (also Scipy).I am trying to reshape my text data
which is in one single column (10,000 rows).I want the data to be in 100x100
array form.I have many files to convert like this. All of them are having file
names like 0, 1, 2, 500.
pv+numpy at math.duke.edu writes:
Hi, what is the best way to print (to a file or to stdout) formatted
numerical values? Analogously to C's printf(%d %g,x,y) etc?
For stdout you can simply do:
In [26]: w, x, y, z = np.randint(0,100,4)
In [27]: type(w)
Out[27]: type 'numpy.int32'
In
Charles R Harris charlesr.harris at gmail.com writes:
I was also thinking that someone might want to provide a better display at
some point, drawing on a canvas, for instance. And what happens when the
degree gets up over 100, which is quite reasonable with the Cheybshev
polynomials?
There
40 matches
Mail list logo
