On 17/04/2008, Santanu Chatterjee [EMAIL PROTECTED] wrote:
Hi Numpy users,
I used MATLAB to do numerical calculations for a long time. Recently I
am digging into python and numpy. I am wondering about the following
question :
1) What is the difference between ndarray and matrix in numpy?
On 17/04/2008, Robert Kern [EMAIL PROTECTED] wrote:
On Thu, Apr 17, 2008 at 1:21 PM, Eric Firing [EMAIL PROTECTED] wrote:
Arg! Cancel that! I didn't look carefully enough. How embarrassing!
Sorry for the noise.
Don't apologize. That is very odd code. Stefan, is there a reason to
On 16/04/2008, Stéfan van der Walt [EMAIL PROTECTED] wrote:
On 16/04/2008, Alan G Isaac [EMAIL PROTECTED] wrote:
The whole issue occurs because a Matrix is not a proper
container.
Right. And *that* is the case because of the attempt to
treat matrices as containers of
On 15/04/2008, Jon Wright [EMAIL PROTECTED] wrote:
On 15/04/2008, Alan G Isaac [EMAIL PROTECTED] wrote:
...
The proposal on the table is to remove an unneeded (and
unwanted) deviation of the matrix API from the ndarray API.
...
How about writing up the changes needed PEP style on
On 10/04/2008, Charles R Harris [EMAIL PROTECTED] wrote:
I think you want the isreal function, but it will also return true for
complex with 0 imaginary part. Hmm... the various iswhatever functions seem
to be lacking in coverage. Maybe we should fix that.
icomplexobj is designed to solve
On 06/04/2008, Alan G Isaac [EMAIL PROTECTED] wrote:
Just checking:
it's important to me that this won't change
the behavior of boolean matrices, but I don't
see a test for this. E.g., ::
import numpy as N
A = N.mat('1 0;1 1',dtype='bool')
A**2
matrix([[ True,
and for negative powers some sort of floating-point
inverse.
That deserves discussion.
Not all invertible boolean matrices have an inverse in the algebra.
Just the orthogonal ones do.
I guess I would special case inverses for Boolean matrices.
Just test if the matrix B is
Hi,
I was just going through tidying up the documentation for all the many
functions in numpy that compute standard deviations or variances (the
functions, the methods, the methods on matrices, the methods on
maskedarrays, all needed their docstrings updated in approximately the
same way). I
On 05/04/2008, Bruce Southey [EMAIL PROTECTED] wrote:
1) Should the first bin contain all values less than or equal to the
value of the first limit and the last bin contain all values greater
than the value of the last limit?
This produced the counts as: array([3, 3, 9]) (I termed this
On 05/04/2008, James Philbin [EMAIL PROTECTED] wrote:
I've posted patches for:
#630: If float('123.45') works, so should numpy.float32('123.45')
#581: random.set_state does not reset state of random.standard_normal
Patches for #601, #622, #692, #696, #717 now in trac; I'd like to do
On 05/04/2008, Charles R Harris [EMAIL PROTECTED] wrote:
On Sat, Apr 5, 2008 at 4:10 PM, Anne Archibald [EMAIL PROTECTED]
wrote:
More generally, my local working copy is now rater divergent from the
upstream. What's the recommended way to deal with this? Make sure I
have all the patches
On 05/04/2008, Stéfan van der Walt [EMAIL PROTECTED] wrote:
Some discussion recently took place around raising a square matrices
to integer powers. See ticket #601:
http://scipy.org/scipy/numpy/ticket/601
Anne Archibald wrote a patch which factored 'matrix_multiply' out of
defmatrix
On 04/04/2008, Jarrod Millman [EMAIL PROTECTED] wrote:
Since I sent my email last night another 5+ tickets have been closed.
If we keep going at this rate, we should be able to release 1.0.5 next
Friday (4/11) with every ticket closed. Specifically, thanks to
Travis Oliphant, David Huard,
On 04/04/2008, Travis E. Oliphant [EMAIL PROTECTED] wrote:
Hey Anne,
Do you currently have SVN access? Would you like it?
I think the SciPy/NumPy sprint would be a good time to clean-up the
committers list and add new people interested in helping.
I don't have SVN access. I'd be happy
On 04/04/2008, Alan G Isaac [EMAIL PROTECTED] wrote:
On Fri, 4 Apr 2008, Gael Varoquaux apparently wrote:
I really thing numpy should be as thin as possible, so
that you can really say that it is only an array
manipulation package. This will also make it easier to
sell as a core
On 03/04/2008, Travis E. Oliphant [EMAIL PROTECTED] wrote:
fred wrote:
Hi,
I use a lot of ConVeX OPTimsation and fortran (via f2py) routines in my
Traits app.
As I want to compute the data and want to display them, I use threads.
The issue I get is that data displayed
On 23/03/2008, David Cournapeau [EMAIL PROTECTED] wrote:
Gnata Xavier wrote:
Hi,
I have a very limited knowledge of openmp but please consider this
testcase :
Honestly, if it was that simple, it would already have been done for a
long time. The problem is that your
On 22/03/2008, Thomas Grill [EMAIL PROTECTED] wrote:
I've experimented with branching the ufuncs into different constant
strides and aligned/unaligned cases to be able to use SSE using
compiler intrinsics.
I expected a considerable gain as i was using float32 with stride 1
most of the
On 22/03/2008, Travis E. Oliphant [EMAIL PROTECTED] wrote:
James Philbin wrote:
Personally, I think that the time would be better spent optimizing
routines for single-threaded code and relying on BLAS and LAPACK
libraries to use multiple cores for more complex calculations. In
On 21/03/2008, Sebastian Haase [EMAIL PROTECTED] wrote:
Comment: I have read the module- or directory-name core many times
on this list, however: Who really knows where a given functions
belongs ? Isn't that mostly only the numpy svn commiters ?
In other words, using only the python side
On 21/03/2008, Stéfan van der Walt [EMAIL PROTECTED] wrote:
On Fri, Mar 21, 2008 at 2:47 PM, Anne Archibald
[EMAIL PROTECTED] wrote:
Is it perhaps possible to make all numpy functions accessible in
submodules (in addition to in numpy, for backwards compatibility) and
then promote
On 20/03/2008, Gael Varoquaux [EMAIL PROTECTED] wrote:
On Thu, Mar 20, 2008 at 06:17:44PM +, James Philbin wrote:
Hi,
This cannot work, because the inplace operation does not
take place as a for loop.
Well, this would be fine if I was assigning the values to tempories as
On 15/03/2008, Damian Eads [EMAIL PROTECTED] wrote:
Robert Kern wrote:
Eric Jones tried to use multithreading to split the computation of
ufuncs across CPUs. Ultimately, the overhead of locking and unlocking
made it prohibitive for medium-sized arrays and only somewhat
disappointing
On 14/03/2008, Dinesh B Vadhia [EMAIL PROTECTED] wrote:
For the following code:
I = 18000
J = 33000
filename = 'ij.txt'
A = scipy.asmatrix(numpy.empty((I,J), dtype=numpy.int))
for line in open(filename, 'r'):
etc.
The following message appears:
Traceback (most recent call
On 11/03/2008, Dinesh B Vadhia [EMAIL PROTECTED] wrote:
Hello! I'm reading a text file with two numbers in str format on each line.
The numbers are converted into integers. Each integer is then assigned to
a 2-dimensional array ij (see code below). The problem is that neither of
the array
On 08/03/2008, Vince Fulco [EMAIL PROTECTED] wrote:
I have an ND array with shape (10,15) and want to slice or subset(?) the data
into a new 2D array with the following criteria:
1) Separate each 5 observations along axis=0 (row) and transpose them to
the new array with shape (50,3)
On 04/03/2008, Pierre GM [EMAIL PROTECTED] wrote:
All,
Let a b be two ndarrays of the same shape. I'm trying to find the elements
of b that correspond to the minima of a along an arbitrary axis.
The problem is trivial when axis=None or when a.ndim=2, but I'm getting
confused with higher
On 04/03/2008, Pierre GM [EMAIL PROTECTED] wrote:
Anne,
Thanks a lot for your suggestion. Something like
if axis is None:
return b.flat[a.argmin()]
else:
return numpy.choose(a.argmin(axis),numpy.rollaxis(b,axis,0))
seems to do the trick fairly nicely indeed. The other
On 03/03/2008, Ray Schumacher [EMAIL PROTECTED] wrote:
I'm trying to figure out what numpy.correlate does, and, what are people
using to calculate the phase shift of 1D signals?
I use a hand-rolled Fourier-domain cross-correlation, but then, I'm
using a Fourier-domain representation of my
On 03/03/2008, Ray Schumacher [EMAIL PROTECTED] wrote:
Xie's 2D algorithm reduced to 1D works nicely for computing the
relative phase, but is it the fastest way? It might be, since some
correlation algorithms use FFTs as well. What does _correlateND use, in
scipy?
Which way will be the
On 03/03/2008, Dinesh B Vadhia [EMAIL PROTECTED] wrote:
When you pickle a numpy/scipy matrix does it have to be initialized by
another program? For example:
Most python objects do not need to be initialized. You just call a
function that makes the one you want:
l = range(10)
This makes a
On 01/03/2008, Charles R Harris [EMAIL PROTECTED] wrote:
On Fri, Feb 29, 2008 at 10:53 AM, John Hunter [EMAIL PROTECTED] wrote:
I have a boolean array and would like to find the lowest index ind
where N contiguous elements are all True. Eg, if x is
[...]
Oops, ind = arange(len(x)). I
On 25/02/2008, Trond Kristiansen [EMAIL PROTECTED] wrote:
I have attached the function that the FOR loop is part of as a python file.
What I am trying to do is to create a set of functions that will read the
output files (NetCDF) from running the ROMS model (ocean model). The output
file
On 22/02/2008, Travis E. Oliphant [EMAIL PROTECTED] wrote:
Is there a ticket on the NumPy trac for this? We won't see it if there
isn't. Thanks for pointing us to the bug.
It appears to be fixed in SVN (that was quick!). But the Debian bug
report also points out a peculiar unnecessary use
On 21/02/2008, Stefan van der Walt [EMAIL PROTECTED] wrote:
Could I ask that we also consider implementing len() for 0-d arrays?
numpy.asarray returns those as-is, and I would like to be able to
handle them just as I do any other 1-dimensional array. I don't know
if a length of 1 would
On 13/02/2008, Dan Goodman
[EMAIL PROTECTED] wrote:
Background: I'm writing a package to run simulations which make extensive use
of
linear algebra, for which I'm using numpy. However - it is important to my
package that quantities can have dimesions, so I've written a class Quantity
On 12/02/2008, Matthew Brett [EMAIL PROTECTED] wrote:
Suggestion 1:
def median(a, axis=0, out=None)
[...]
Suggestion 2:
def median(a, axis=0, scratch_input=False)
No reason not to combine the two. It's a pretty straightforward
modification to do the sorting in place, and it could make a lot
On 12/02/2008, Matthew Brett [EMAIL PROTECTED] wrote:
Is it possible, in fact, to do an inplace sort on an array with
axis=None (ie flat sort)?
It is, sometimes; just make an array object to point to the flattened
version and sort that:
In [16]: b = a[:]
In [17]: b.shape = (16,)
In [18]:
On 12/02/2008, Anne Archibald [EMAIL PROTECTED] wrote:
An efficient way to handle in-place (or out-of-place, come to think of
it) median along multiple axes is actually to take medians along all
axes in succession. That saves you some sorting effort, and some
programming effort, and doesn't
On 11/02/2008, Matthew Brett [EMAIL PROTECTED] wrote:
I can also see that this could possibly be improved by using a for
loop to iterate over the output elements, so that there was no need to
duplicate the large input array, or perhaps a blocked iteration that
duplicated arrays of modest
On 06/02/2008, Robert Kern [EMAIL PROTECTED] wrote:
I guess the all function doesn't know about generators?
Yup. It works on arrays and things it can turn into arrays by calling the C
API
equivalent of numpy.asarray(). There's a ton of magic and special cases in
asarray() in order to
On 05/02/2008, Chris Finley [EMAIL PROTECTED] wrote:
After searching the archives, I was unable to find a good method for
changing the stride of the correlate or convolve routines. I am doing a
Daubechies analysis of some sample data, say data = arange(0:80). The
coefficient array or four
On 30/01/2008, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 30 January 2008, Nadav Horesh escrigué:
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
R[ax,:][:,ax] =
On 08/01/2008, Charles R Harris [EMAIL PROTECTED] wrote:
I'm starting to get interested in implementing float16 support ;) My
tentative program goes something like this:
1) Add the operators to the scalar type. This will give sorting, basic
printing, addition, etc.
2) Add conversions to
On 08/01/2008, Charles R Harris [EMAIL PROTECTED] wrote:
Well, at a minimum people will want to read, write, print, and promote them.
That would at least let people work with the numbers, and since my
understanding is that the main virtue of the format is compactness for
storage and
On 07/01/2008, Charles R Harris [EMAIL PROTECTED] wrote:
One place where Numpy differs from MatLab is the way memory is handled.
MatLab is always generating new arrays, so for efficiency it is worth
preallocating arrays and then filling in the parts. This is not the case in
Numpy where lists
On 07/01/2008, Timothy Hochberg [EMAIL PROTECTED] wrote:
I'm fairly dubious about assigning float to ints as is. First off it looks
like a bug magnet to me due to accidentally assigning a floating point value
to a target that one believes to be float but is in fact integer. Second,
C-style
On 01/01/2008, Neal Becker [EMAIL PROTECTED] wrote:
This is a c-api question.
I'm trying to get iterators that are both fast and reasonably general. I
did confirm that iterating using just the general PyArrayIterObject
protocol is not as fast as using c-style pointers for contiguous arrays.
On 28/12/2007, Christopher Barker [EMAIL PROTECTED] wrote:
I like the array methods a lot -- is there any particular reason there
is no ndarray.abs(), or has it just not been added?
Here I have to disagree with you.
Numpy provides ufuncs as general powerful tools for operating on
matrices.
On 28/12/2007, Christopher Barker [EMAIL PROTECTED] wrote:
Anne Archibald wrote:
Numpy provides ufuncs as general powerful tools for operating on
matrices. More can be added relatively easily, they provide not just
the basic apply operation but also outer and others. Adding
another way
On 27/12/2007, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
in my code i am trying to normalise a matrix as below
mymatrix=matrix(..# items are of double type..can be negative
values)
numrows,numcols=mymatrix.shape
for i in range(numrows):
temp=mymatrix[i].max()
for j in
On 16/12/2007, Hans Meine [EMAIL PROTECTED] wrote:
(*: It's similar with math.hypot, which I have got to know and appreciate
nowadays.)
I'd like to point out that math.hypot is a nontrivial function which
is easy to get wrong:
In [6]: x=1e200; y=1e200;
In [7]: math.hypot(x,y)
Out[7]:
On 20/11/2007, Geoffrey Zhu [EMAIL PROTECTED] wrote:
I have N tabulated data points { (x_i, y_i, z_i) } that describes a 3D
surface. The surface is pretty smooth. However, the number of data
points is too large to be stored and manipulated efficiently. To make
it easier to deal with, I am
On 16/11/2007, Rahul Garg [EMAIL PROTECTED] wrote:
It would be awesome if you guys could respond to some of the following
questions :
a) Can you guys tell me briefly about the kind of problems you are
tackling with numpy and scipy?
b) Have you ever felt that numpy/scipy was slow and had to
On 08/11/2007, David Cournapeau [EMAIL PROTECTED] wrote:
For copy and array creation, I understand this, but for element-wise
operations (mean, min, and max), this is not enough to explain the
difference, no ? For example, I can understand a 50 % or 100 % time
increase for simple operations
On 31/10/2007, Ray S [EMAIL PROTECTED] wrote:
I am using
fftRes = abs(fft.rfft(data_array[end-2**15:end]))
to do running analysis on streaming data. The N never changes.
It sucks memory up at ~1MB/sec with 70kHz data rate and 290 ffts/sec.
(Interestingly, Numeric FFT accumulates much
On 26/10/2007, Travis E. Oliphant [EMAIL PROTECTED] wrote:
There is an optimization where-in the inner-loops are done over the
dimension with the smallest stride.
What other cache-coherent optimizations do you recommend?
That sounds like a very good first step. I'm far from an expert on
this
On 11/10/2007, Robert Kern [EMAIL PROTECTED] wrote:
Appending to a list then converting the list to an array is the most
straightforward way to do it. If the performance of this isn't a problem, I
recommend leaving it alone.
Just a speculation:
Python strings have a similar problem - they're
On 05/10/2007, Matthieu Brucher [EMAIL PROTECTED] wrote:
I'd like to have the '2.', because if the number is negative, only '-' is
returned, not the real value.
For string arrays you need to specify the length of the string as part
of the data type (and it defaults to length 1):
In [11]:
On 05/10/2007, Christopher Barker [EMAIL PROTECTED] wrote:
I don't know how to generalize this to n-d though -- maybe numpy.vectorize?
Oops! Looks like there's a big somewhere:
In [1]: from numpy import *
In [2]: vectorize(lambda x: %5.3g % x)(ones((2,2,2)))
Out[2]:
array([[[' ', '\xc1'],
On 19/09/2007, Travis E. Oliphant [EMAIL PROTECTED] wrote:
Anne Archibald wrote:
vectorize, of course, is a good example of my point above: it really
just loops, in python IIRC, but conceptually it's extremely handy for
doing exactly what the OP wanted. Unfortunately vectorize() does
On 15/09/2007, Christopher Barker [EMAIL PROTECTED] wrote:
Oh, and could someone post an actual example of a use for which FP
arange is required (with fudges to try to accommodate decimal to binary
conversion errors), and linspace won't do?
Well, here's one: evaluating a function we know to
On 12/09/2007, Robert Kern [EMAIL PROTECTED] wrote:
That sentence applies to the 3-argument form, which has nothing to do with
nonzero() and does not yield a tuple. But in general, yes, the docstring
leaves
much to be desired.
Well, here's what I hope is a step in the right direction.
Anne
On 30/08/2007, Brian Donovan [EMAIL PROTECTED] wrote:
Hello all,
I'm wondering if there is a way to use a numpy array that uses disk as a
memory store rather than ram. I'm looking for something like mmap but which
can be used like a numpy array. The general idea is this. I'm simulating a
Hi,
numpy's Fourier transforms have the handy feature of being able to
upsample and downsample signals; for example the documentation cites
irfft(rfft(A),16*len(A)) as a way to get a Fourier interpolation of A.
However, there is a peculiarity with the way numpy handles the
highest-frequency
On 29/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
What is going on is that the coefficient at the Nyquist frequency appears
once in the unextended array, but twice when the array is extended with
zeros because of the Hermitean symmetry. That should probably be fixed in
the upsampling
On 29/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
Is this also appropriate for the other FFTs? (inverse real, complex,
hermitian, what have you) I have written a quick hack (attached) that
should do just that rescaling, but I don't know that it's a good idea,
as implemented.
On 21/08/07, Timothy Hochberg [EMAIL PROTECTED] wrote:
This is just a general comment on recent threads of this type and not
directed specifically at Chuck or anyone else.
IMO, the emphasis on avoiding FOR loops at all costs is misplaced. It is
often more memory friendly and thus faster to
On 16/08/07, Glen W. Mabey [EMAIL PROTECTED] wrote:
On Wed, Aug 15, 2007 at 08:50:28PM -0400, Anne Archibald wrote:
But to be pythonic, or numpythonic, when the original A is
garbage-collected, the garbage collection should certainly close the
mmap.
Humm, this would be less than ideal
On 15/08/07, Glen W. Mabey [EMAIL PROTECTED] wrote:
On Tue, Aug 14, 2007 at 12:23:26AM -0400, Anne Archibald wrote:
On 13/08/07, Glen W. Mabey [EMAIL PROTECTED] wrote:
As I have tried to think through what should be the appropriate
behavior for the returned value of __getitem__, I have
On 13/08/07, Glen W. Mabey [EMAIL PROTECTED] wrote:
As I have tried to think through what should be the appropriate
behavior for the returned value of __getitem__, I have not been able to
see an appropriate solution (let alone know how to implement it) to this
issue.
Is the problem one of
On 08/08/2007, Stefan van der Walt [EMAIL PROTECTED] wrote:
On Tue, Aug 07, 2007 at 01:33:24AM -0400, Anne Archibald wrote:
Well, it can be done in Python: just allocate a too-big ndarray and
take a slice that's the right shape and has the right alignment. But
this sucks.
Could you
On 08/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
On 8/8/07, Anne Archibald [EMAIL PROTECTED] wrote:
Oh. Well, it's not *terrible*; it gets you an aligned array. But you
have to allocate the original array as a 1D byte array (to allow for
arbitrary realignments) and then align
On 08/08/2007, mark [EMAIL PROTECTED] wrote:
Thanks for the ideas to circumvent vectorization.
But the real function I need to vectorize is quite a bit more
complicated.
So I would really like to use vectorize.
Are there any reasons against vectorization? Is it slow?
The way Tim suggests I
On 06/08/07, David Cournapeau [EMAIL PROTECTED] wrote:
Well, when I proposed the SIMD extension, I was willing to implement the
proposal, and this was for a simple goal: enabling better integration
with many numeric libraries which need SIMD alignment.
As nice as a custom allocator might be,
On 07/08/07, David Cournapeau [EMAIL PROTECTED] wrote:
Anne, you said previously that it was easy to allocate buffers for a
given alignment at runtime. Could you point me to a document which
explains how ? For platforms without posix_memalign, I don't see how to
implement a memory allocator
On 04/08/07, David Cournapeau [EMAIL PROTECTED] wrote:
Here's a hack that google turned up:
I'd avoid hacks in favour of posix_memalign (which allows arbitrary
degrees of alignment. For one thing, freeing becomes a headache (you
can't free a pointer you've jiggered!).
- Check whether a
On 20/07/07, Nils Wagner [EMAIL PROTECTED] wrote:
lorenzo bolla wrote:
hi all.
is there a function in numpy to compute the exp of a matrix, similar
to expm in matlab?
for example:
expm([[0,0],[0,0]]) = eye(2)
Numpy doesn't provide expm but scipy does.
from scipy.linalg import expm,
On 07/07/07, Mark.Miller [EMAIL PROTECTED] wrote:
A quick question for the group. I'm working with some code to generate
some arrays of random numbers. The random numbers, however, need to
meet certain criteria. So for the moment, I have things that look like
this (code is just an
On 14/06/07, Will Woods [EMAIL PROTECTED] wrote:
I want to choose a subset of all possible permutations of a sequence of
length N, with each element of the subset unique. This is then going to
be scattered across multiple machines using mpi. Since there is a
one-to-one mapping between the
On 05/06/07, Charles R Harris [EMAIL PROTECTED] wrote:
On 6/5/07, dmitrey [EMAIL PROTECTED] wrote:
Thank you, but all your examples deal with 3-dimensional arrays. and I
still misunderstood, is it possible somehow for 2-dimensional arrays or
no?
D.
There is nothing special about the
On 01/06/07, dmitrey [EMAIL PROTECTED] wrote:
y = x.flatten(1)
turn array into vector (note that this forces a copy)
Is there any way to do the trick wthout copying?
What are the problems here? Just other way of array elements indexing...
It is sometimes possible to flatten an array
On 31/05/07, Travis Oliphant [EMAIL PROTECTED] wrote:
2) I think it's scope should be limited to papers that describe
algorithms and code that are in NumPy / SciPy / SciKits. Perhaps we
could also accept papers that describe code that depends on NumPy /
SciPy that is also easily available.
On 30/05/07, Matthew Brett [EMAIL PROTECTED] wrote:
I think the point is that you can have several different situations
with byte ordering:
1) Your data and dtype endianess match, but you want the data swapped
and the dtype to reflect this
2) Your data and dtype endianess don't match, and
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Consider the following example:
First a comment: almost nobody needs to care how the data is stored
internally. Try to avoid looking at the flags unless you're
interfacing with a C library. The nice feature of numpy is that it
hides all
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
If you are correct that this is in fact a fresh new array, I really
don't understand where the values of these flags. To recap:
In [19]: x = N.zeros((3,2))
In [20]: x.flags
Out[20]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
On 18/05/07, David M. Cooke [EMAIL PROTECTED] wrote:
It'll act like appending to a list, where it will grow the array (by
doubling, I think) when it needs to, so appending each value is
amortized to O(1) time. A list though would use more memory
per element as each element is a full Python
Hi,
Numpy has a max() function. It takes an array, and possibly some extra
arguments (axis and default). Unfortunately, this means that
numpy.max(-1.3,2,7)
-1.3
This can lead to surprising bugs in code that either explicitly
expects it to behave like python's max() or implicitly expects that
On 16/05/07, Alan G Isaac [EMAIL PROTECTED] wrote:
On Wed, 16 May 2007, Anne Archibald apparently wrote:
numpy.max(-1.3,2,7)
-1.3
Is that new behavior?
I get a TypeError on the last argument.
(As expected.)
For which version of numpy?
In [2]: numpy.max(-1.3,2.7)
Out[2]: -1.3
In [3
On 12/05/07, Dave P. Novakovic [EMAIL PROTECTED] wrote:
core 2 duo with 4gb RAM.
I've heard about iterative svd functions. I actually need a complete
svd, with all eigenvalues (not LSI). I'm actually more interested in
the individual eigenvectors.
As an example, a single row could probably
On 10/05/07, Perry Greenfield [EMAIL PROTECTED] wrote:
I have updated the Using Python for Interactive Data Analysis
tutorial to use numpy instead of numarray (finally!). There are
further improvements I would like to make in its organization and
formatting (in the process including
On 08/05/07, Gael Varoquaux [EMAIL PROTECTED] wrote:
On Tue, May 08, 2007 at 12:18:56PM +0200, Giorgio Luciano wrote:
A good workspace (with an interactive button) just to not get figures
freezed
I am not sure what you mean by figures freezed but I would like to
check that you are aware of
On 29/04/07, David Goldsmith [EMAIL PROTECTED] wrote:
Far be it from me to challenge the mighty Wolfram, but I'm not sure that
using the *formula* for calculating the arctan of a *single* complex
argument from its real and imaginary parts makes any sense if x and/or y
are themselves complex
On 23/04/07, Pierre GM [EMAIL PROTECTED] wrote:
Note that in addition of the bitwise operators, you can use the logical_
functions. OK, you'll still end up w/ temporaries, but I wonder whether there
couldn't be some tricks to bypass that...
If you're really determined not to make many temps,
On 17/04/07, Francesc Altet [EMAIL PROTECTED] wrote:
Finally, don't let benchmarks fool you. If you can, it is always better
to run your own benchmarks made of your own problems. A tool that can be
killer for one application can be just mediocre for another (that's
somewhat extreme, but I
On 17/04/07, Lou Pecora [EMAIL PROTECTED] wrote:
Now, I didn't know that. That's cool because I have a
new dual core Intel Mac Pro. I see I have some
learning to do with multithreading. Thanks.
No problem. I had completely forgotten about the global interpreter
lock, wrote a little
On 17/04/07, Lou Pecora [EMAIL PROTECTED] wrote:
I get what you are saying, but I'm not even at the
Stupidly Easy Parallel level, yet. Eventually.
Well, it's hardly wonderful, but I wrote a little package to make idioms like:
d = {}
def work(f):
d[f] = sum(exp(2.j*pi*f*times))
On 17/04/07, James Turner [EMAIL PROTECTED] wrote:
Hi Anne,
Your reply to Lou raises a naive follow-up question of my own...
Normally, python's multithreading is effectively cooperative, because
the interpreter's data structures are all stored under the same lock,
so only one thread can
On 18/04/07, Robert Kern [EMAIL PROTECTED] wrote:
Sebastian Haase wrote:
Hi,
I don't know much about ATLAS -- would there be other numpy functions
that *could* or *should* be implemented using ATLAS !?
Any ?
Not really, no.
ATLAS is a library designed to implement linear algebra
On 18/04/07, Sebastian Haase [EMAIL PROTECTED] wrote:
Hi Anne,
I'm just starting to look into your code (sound very interesting -
should probably be put onto the wiki)
-- quick note:
you are mixing tabs and spaces :-(
what editor are you using !?
Agh. vim is misbehaving. Sorry about that.
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