Hello everyone,
I save data to a file with the following statement:
np.savetxt(fileName, transpose((average_dist, std_deviation, maximum_dist,
sum_of_dist)), delimiter = ';', fmt='%6.10f')
is there a possibility to change the decimal seperator from a point to
comma ?
And another question I
Hi - I've never written a Python extension before, so I apologise in advance
for my lack of knowledge. I'm trying to interpret a variable length tuple of
variable length numpy arrays, convert then to C double arrays and pass them
to a C++ function. I'm using SIP (as I also need to deal with
On Thu, Sep 24, 2009 at 2:07 AM, markus.proel...@ifm.com wrote:
Hello everyone,
I save data to a file with the following statement:
np.savetxt(fileName, transpose((average_dist, std_deviation, maximum_dist,
sum_of_dist)), delimiter = ';', fmt='%6.10f')
is there a possibility to change
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of fixed dimension.
Therefore, is not clear to me why in numpy the size of an
Alice Invernizzi wrote:
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of fixed dimension.
Therefore, is not clear to
V. Armando Solé wrote:
Sorry, there was a bug in the sent code. It should be:
import numpy
a=numpy.arange(100.)
a.shape = 10, 10
b = a * 1 # just to get a copy
b.shape = 5, 2, 5, 2
b = (b.sum(axis=3)).sum(axis=1)
In that way, on b I have a binned image of a.
On Sep 24, 2009, at 3:07 AM, markus.proel...@ifm.com wrote:
Hello everyone,
I save data to a file with the following statement:
np.savetxt(fileName, transpose((average_dist, std_deviation,
maximum_dist, sum_of_dist)), delimiter = ';', fmt='%6.10f')
is there a possibility to change the
Michael Droettboom wrote:
As I'm looking into fixing a number of bugs in chararray, I'm running
into some surprising behavior.
In [14]: x = np.array(['abcdefgh', 'ijklmnop'], 'O')
# Without specifying the length, it seems to default to sizeof(int)... ???
In [15]: np.array(x, 'S')
Out[15]:
On 09/24/2009 01:02 PM, Christopher Barker wrote:
Michael Droettboom wrote:
As I'm looking into fixing a number of bugs in chararray, I'm running
into some surprising behavior.
In [14]: x = np.array(['abcdefgh', 'ijklmnop'], 'O')
# Without specifying the length, it seems to default to
Alice Invernizzi wrote:
Therefore, is not clear to me why in numpy the size of an array can be
changed (either with the 'returning-value' resize() function either with
the 'in-place' array method resize()).
Would you please be so kind to give some explanation for the existence
of resize
Hi All,
Would it be appropriate to add a class similar to poly but instead using
chebyshev polynomials? That is, where we currently have
'poly',
'poly1d',
'polyadd',
'polyder',
'polydiv',
'polyfit',
'polyint',
'polymul',
'polysub',
'polyval',
change poly to cheb. The rational here is
I have filed a bug against this, along with a patch that fixes casting
to fixed-size string arrays:
http://projects.scipy.org/numpy/ticket/1235
Undefined-sized string arrays is a harder problem, which I'm deferring
for later.
Mike
On 09/24/2009 01:19 PM, Michael Droettboom wrote:
On
to, 2009-09-24 kello 11:51 -0600, Charles R Harris kirjoitti:
Would it be appropriate to add a class similar to poly but instead
using chebyshev polynomials? That is, where we currently have
[clip]
Yes, I think. scipy.special.orthogonal would be the best place for this,
I think. Numpy would
On Thu, Sep 24, 2009 at 09:58, Alice Invernizzi inverni...@cilea.it wrote:
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen p...@iki.fi wrote:
As a side note, should the cheby* versions of `polyval`, `polymul` etc.
just be dropped to reduce namespace clutter? You can do the same things
already within just class methods and arithmetic.
Just to clarify, you mean having
to, 2009-09-24 kello 14:31 -0500, Robert Kern kirjoitti:
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen p...@iki.fi wrote:
As a side note, should the cheby* versions of `polyval`, `polymul` etc.
just be dropped to reduce namespace clutter? You can do the same things
already within just class
On Thu, Sep 24, 2009 at 1:18 PM, Pauli Virtanen p...@iki.fi wrote:
to, 2009-09-24 kello 11:51 -0600, Charles R Harris kirjoitti:
Would it be appropriate to add a class similar to poly but instead
using chebyshev polynomials? That is, where we currently have
[clip]
Yes, I think.
On Thu, Sep 24, 2009 at 1:31 PM, Robert Kern robert.k...@gmail.com wrote:
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen p...@iki.fi wrote:
As a side note, should the cheby* versions of `polyval`, `polymul` etc.
just be dropped to reduce namespace clutter? You can do the same things
to, 2009-09-24 kello 13:53 -0600, Charles R Harris kirjoitti:
[clip]
I was thinking of storing the chebyshev internally as the values at
the chebyschev points. This makes multiplication, differentiation and
such quite easy (resample and multiply/divide appropriatately). Its
equivalent to
And another question I import this file to excel, is there also a
possiblity to create a headline for each column, that the file looks
like the following example:
average; standard deviation; maximum distance; sum of distances
0,26565; 0,65565; 2,353535; 25, 5656
I was fiddeling with
On Thu, Sep 24, 2009 at 2:34 PM, Pauli Virtanen p...@iki.fi wrote:
to, 2009-09-24 kello 13:53 -0600, Charles R Harris kirjoitti:
[clip]
I was thinking of storing the chebyshev internally as the values at
the chebyschev points. This makes multiplication, differentiation and
such quite
Hello
I noticed that python's any can be faster than numpy's any (and the
similarly for all).
Then I wondered why.
I realized that numpy implements any as logical_or.reduce (and all as
logical_and.reduce).
This means that numpy cannot take advantage of short-circuiting.
Looking at the timings
On Thu, Sep 24, 2009 at 3:43 PM, Citi, Luca lc...@essex.ac.uk wrote:
Hello
I noticed that python's any can be faster than numpy's any (and the
similarly for all).
Then I wondered why.
I realized that numpy implements any as logical_or.reduce (and all as
logical_and.reduce).
This means that
On Thu, Sep 24, 2009 at 16:43, Citi, Luca lc...@essex.ac.uk wrote:
What is the correct way of running the tests (without installing the
development version in the system)?
Build inplace:
$ python setup.py build_src --inplace build_ext --inplace
--
Robert Kern
I have come to believe that
Thank you for your instantaneuos reply!
This is what I usually do:
from the numpy folder I run (emptying the build folder if I just fetched svn
updates)
$ python setup build.py
$ cd build/lib-...
$ ipython
In [1]: import numpy as np
In [2]: np.__version__
Out[2]: '1.4.0.dev7417'
Everything
Thank you both for your help!
$ python setup.py build_src --inplace build_ext --inplace
I'll give it a try.
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Robert Kern skrev:
While this description is basically true of numpy arrays, I would
caution you that every language has a different lexicon, and the same
word can mean very different things in each. For example, Python lists
are *not* linked lists; they are like C++'s std::vectors with a
On Thu, Sep 24, 2009 at 17:32, Sturla Molden stu...@molden.no wrote:
Robert Kern skrev:
While this description is basically true of numpy arrays, I would
caution you that every language has a different lexicon, and the same
word can mean very different things in each. For example, Python lists
Robert Kern skrev:
collections.deque() is a linked list of 64-item chunks.
Thanks for that useful information. :-) But it would not help much for a
binary tree...
Since we are on the NumPy list... One could image making linked lists
using NumPy arrays with dtype=object. They are storage
On 23-Sep-09, at 7:55 PM, David Warde-Farley wrote:
It seems that either dtype(str) should do something more sensible than
zero-length string, or it should be possible to create it with
dtype('|
S0'). Which should it be?
Since there wasn't any response I went ahead and fixed it by making
I am sorry.
I followed your suggestion.
I re-checked out the svn folder and then compiled with
$ python setup.py build_src --inplace build_ext --inplace
but I get the same behaviour.
If I am inside I get the NameError, if I am outside and use path.insert, it
successfully performs zero tests.
I
Hi
I'm trying to understand the following code:
import numpy as np
dt=np.dtype([(c,np.int32,(2))])
data=np.ndarray([2],dtype=dt)
x=np.array([0,10])
#the following line doesn't set data[0][c] = x
#only data[0][c][0] changes
On Fri, Sep 25, 2009 at 9:50 AM, Citi, Luca lc...@essex.ac.uk wrote:
I am sorry.
I followed your suggestion.
I re-checked out the svn folder and then compiled with
$ python setup.py build_src --inplace build_ext --inplace
but I get the same behaviour.
If I am inside I get the NameError, if I
On Thu, Sep 24, 2009 at 1:31 PM, Robert Kern robert.k...@gmail.com wrote:
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen p...@iki.fi wrote:
As a side note, should the cheby* versions of `polyval`, `polymul` etc.
just be dropped to reduce namespace clutter? You can do the same things
On Thu, Sep 24, 2009 at 21:00, Charles R Harris
charlesr.har...@gmail.com wrote:
On Thu, Sep 24, 2009 at 1:31 PM, Robert Kern robert.k...@gmail.com wrote:
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen p...@iki.fi wrote:
As a side note, should the cheby* versions of `polyval`, `polymul`
On Thu, Sep 24, 2009 at 8:14 PM, Robert Kern robert.k...@gmail.com wrote:
On Thu, Sep 24, 2009 at 21:00, Charles R Harris
charlesr.har...@gmail.com wrote:
On Thu, Sep 24, 2009 at 1:31 PM, Robert Kern robert.k...@gmail.com
wrote:
On Thu, Sep 24, 2009 at 14:18, Pauli Virtanen
I was fiddeling with the same problem here:
http://thread.gmane.org/gmane.comp.python.numeric.general/23418
So far, one can only open the file and prepend the header line.
I just files an enhancement request for this:
proposal: add a header and footer function to numpy.savetxt
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