On Tue, Sep 14, 2010 at 9:25 AM, kee chen keekychen.sha...@gmail.com wrote:
Dear All,
Suppose I have a list group some kind like DNA sequence:
1 ATGCATGCAATTGGCC
2 ATGCATGCAATTGGCCATCD
3 CATGCAATTGGC
..
10 CATGCAAATTGGC
the string length of each item is not
On Fri, Sep 10, 2010 at 10:36 AM, Hagen Fürstenau ha...@zhuliguan.net wrote:
I'm multiplying two 1000x1000 arrays with numpy.dot() and seeing
significant performance differences depending on the data. It seems to
take much longer on matrices with many zeros than on random ones. I
don't know
On Thu, Sep 9, 2010 at 7:22 PM, cpblpublic cpblpublic+nu...@gmail.com wrote:
I am looking for some reaally basic statistical tools. I have some
sample data, some sample weights for those measurements, and I want to
calculate a mean and a standard error of the mean.
How about using a bootstrap?
On Thu, Sep 9, 2010 at 8:07 PM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Sep 9, 2010 at 7:22 PM, cpblpublic cpblpublic+nu...@gmail.com wrote:
I am looking for some reaally basic statistical tools. I have some
sample data, some sample weights for those measurements, and I want
On Thu, Sep 9, 2010 at 8:44 PM, josef.p...@gmail.com wrote:
On Thu, Sep 9, 2010 at 11:32 PM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Sep 9, 2010 at 8:07 PM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Sep 9, 2010 at 7:22 PM, cpblpublic cpblpublic+nu...@gmail.com
wrote:
I am
On Fri, Sep 3, 2010 at 9:39 AM, Rick Muller rpmul...@gmail.com wrote:
There just *has* to be a better way of doing this. I want to cut off small
values of a vector, and I'm currently doing something like:
for i in xrange(n):
if abs(A[i]) tol: A[i] = 0
Which is slow, since A can be
2010/8/31 Ernest Adrogué eadro...@gmx.net:
Hi,
I find this a bit odd:
In [18]: np.array(['a','b','c','d']) 'a'
Out[18]: array([False, True, True, True], dtype=bool)
In [19]: np.array(['a','b','c','d']) 4
Out[19]: True
In [20]: np.array(['a','b','c','d']) 4.5
Out[20]: True
Is
On Mon, Aug 23, 2010 at 2:17 PM, Skipper Seabold jsseab...@gmail.com wrote:
Is Fernando's github still the most up to date location for datarray?
Yes.
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On Mon, Aug 23, 2010 at 3:28 PM, Skipper Seabold jsseab...@gmail.com wrote:
hhold_ax = 'households', np.unique(ddd[:,0]).tolist()
snip
As for the bug report. If I don't tolist() the ticks above there is
an error. I can file a bug report if it's warranted.
If you add it to the tracker
On Thu, Aug 19, 2010 at 10:00 AM, John Salvatier
jsalv...@u.washington.edu wrote:
Hello,
I am trying to load some time series data into numpy arrays from a MySQL
database using pyodbc, but I am not sure what the standard way to do this
is. I found the following:
On Fri, Aug 6, 2010 at 3:01 AM, Martin Spacek nu...@mspacek.mm.st wrote:
Keith Goodman wrote:
Here's one way:
a.flat[i + a.shape[1] * np.arange(a.shape[0])]
array([0, 3, 5, 6, 9])
I'm afraid I made my example a little too simple. In retrospect, what I really
want is to be able
On Thu, Aug 5, 2010 at 10:12 AM, Martin Spacek nu...@mspacek.mm.st wrote:
I want to take an n x m array a and index into it using an integer index
array
i of length n that will pull out the value at the designated column from
each
corresponding row of a.
a = np.arange(10)
a.shape = 5, 2
On Thu, Aug 5, 2010 at 10:26 AM, josef.p...@gmail.com wrote:
On Thu, Aug 5, 2010 at 1:12 PM, Martin Spacek nu...@mspacek.mm.st wrote:
I want to take an n x m array a and index into it using an integer index
array
i of length n that will pull out the value at the designated column from
each
On Thu, Aug 5, 2010 at 1:32 PM, josef.p...@gmail.com wrote:
On Thu, Aug 5, 2010 at 4:07 PM, Martin Spacek nu...@mspacek.mm.st wrote:
josef.pkt wrote:
a = np.array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
i =
On Tue, Jul 27, 2010 at 11:13 AM, Keith Goodman kwgood...@gmail.com wrote:
Join us for a datarray sprint on July 28. Several of us will meet at
UC Berkeley from 2pm (Pacific Time) until our fingers bleed from
typing or until 6 or 7pm, whichever comes first.
If you can't be there in person
On Wed, Jul 28, 2010 at 6:42 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
Please forgive me if this is obvious, but this surprised me:
In [15]: x = np.array(['a', 'b'])
In [16]: x == 'a' # this was what I expected
Out[16]: array([ True, False], dtype=bool)
In [17]: x == 1 # this
On Tue, Jul 27, 2010 at 9:29 AM, Robert Faryabi
robert.fary...@gmail.com wrote:
I just looked at my system more carefully.
There are two executable files
/usr/local/bin/python
and
/usr/bin/python
this is a link to python2.6
I believe that the first one is source compiled version. So,
Join us for a datarray sprint on July 28. Several of us will meet at
UC Berkeley from 2pm (Pacific Time) until our fingers bleed from
typing or until 6 or 7pm, whichever comes first.
If you can't be there in person, grab an item from the issue tracker
or create your own. I'm told some of us will
On Sat, Jul 24, 2010 at 3:12 PM, Jonathan Tu j...@princeton.edu wrote:
On Jul 24, 2010, at 6:09 PM, David Cournapeau wrote:
On Sun, Jul 25, 2010 at 7:00 AM, Jonathan Tu j...@princeton.edu wrote:
If you install shared libraries into a directory which is not looked
in by default by ld, you
On Sat, Jul 24, 2010 at 3:39 PM, Jonathan Tu j...@princeton.edu wrote:
On Jul 24, 2010, at 6:21 PM, David Cournapeau wrote:
On Sun, Jul 25, 2010 at 7:12 AM, Jonathan Tu j...@princeton.edu wrote:
What does that part do? It turns out that by fixing my library path, numpy
now imports. I
Report datarray bugs here: http://github.com/fperez/datarray/issues
A datarray is a subclass of a Numpy array that adds the ability to
label the axes and to label the elements along each axis.
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On Thu, Jul 22, 2010 at 7:48 AM, Warren Weckesser
warren.weckes...@enthought.com wrote:
Actually, because of the use of reshape(3,3,4), your second
example does make a copy.
When does reshape return a view and when does it return a copy?
Here's a simple example that returns a view:
x =
On Thu, Jul 22, 2010 at 10:35 AM, Warren Weckesser
warren.weckes...@enthought.com wrote:
Keith Goodman wrote:
On Thu, Jul 22, 2010 at 7:48 AM, Warren Weckesser
warren.weckes...@enthought.com wrote:
Actually, because of the use of reshape(3,3,4), your second
example does make a copy.
When
About a dozen people attended what was billed as a continuation of the
SciPy 2010 datarray BoF. We met at UC Berkeley on July 19 as part of
the py4science series.
A datarray is a subclass of a Numpy array that adds the ability to
label the axes and to label the elements along each axis.
We spent
On Wed, Jul 21, 2010 at 10:58 AM, M Trumpis mtrum...@berkeley.edu wrote:
Separately, regarding the permissible axis labels, I think we must not
allow any enumerated axis labels (ie, ints and floats). I don't
remember if there was a consensus about that yesterday. We don't have
the flexibility
On Wed, Jul 21, 2010 at 11:41 AM, Vincent Davis
vinc...@vincentdavis.net wrote:
On Wed, Jul 21, 2010 at 11:08 AM, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Jul 21, 2010 at 9:56 AM, John Salvatier
jsalv...@u.washington.edu wrote:
I don't really know much about this topic, but what about
On Wed, Jul 21, 2010 at 2:32 PM, Rob Speer rsp...@mit.edu wrote:
I agree with the idea that axis labels must be strings.
Yes, this is the opposite of my position on tick labels (names), but
there's a reason: ticks are often defined by whatever data you happen
to be working with, but axis
On Wed, Jul 21, 2010 at 8:24 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Jul 21, 2010 at 5:44 PM, Keith Goodman kwgood...@gmail.com wrote:
Can someone confirm that the copy in np.linalg.lstsq
bstar[:b.shape[0],:n_rhs] = b.copy()
is not needed? I'm assuming that ndarray
On Tue, Jul 20, 2010 at 7:24 AM, Skipper Seabold jsseab...@gmail.com wrote:
On Tue, Jul 20, 2010 at 5:11 AM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
Is there in numpy a function that does:
np.concatenate([a_[np.newaxis] for a_ in a])
?
ie: add a dimension in front and stack
On Mon, Jul 19, 2010 at 10:08 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Jul 19, 2010 at 9:40 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Jul 19, 2010 at 8:27 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Jul 19, 2010 at 9:02 PM, Keith
On Tue, Jul 20, 2010 at 6:35 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Jul 19, 2010 at 10:08 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Jul 19, 2010 at 9:40 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Jul 19, 2010 at 8:27 PM, Charles R Harris
On Mon, Jul 19, 2010 at 6:31 PM, Ondrej Certik ond...@certik.cz wrote:
Hi,
I was always using something like
abs(x-y) eps
or
(abs(x-y) eps).all()
but today I needed to also make sure this works for larger numbers,
where I need to compare relative errors, so I found this:
On Mon, Jul 19, 2010 at 6:53 PM, Joshua Holbrook
josh.holbr...@gmail.com wrote:
On Mon, Jul 19, 2010 at 5:50 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
I'm thinking about adding some functionality to lstsq because I find myself
doing the same fixes over and over. List
On Mon, Jul 19, 2010 at 8:27 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Jul 19, 2010 at 9:02 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Jul 19, 2010 at 6:53 PM, Joshua Holbrook
josh.holbr...@gmail.com wrote:
On Mon, Jul 19, 2010 at 5:50 PM, Charles R Harris
On Thu, Jul 15, 2010 at 9:38 AM, Emmanuel Bengio beng...@gmail.com wrote:
Hello,
I have a list of 4x4 transformation matrices, that I want to dot with
another list of the same size (elementwise).
Making a for loop that calculates the dot product of each is extremely slow,
I thought that
On Thu, Jul 15, 2010 at 9:45 AM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Jul 15, 2010 at 9:38 AM, Emmanuel Bengio beng...@gmail.com wrote:
Hello,
I have a list of 4x4 transformation matrices, that I want to dot with
another list of the same size (elementwise).
Making a for loop
On Tue, Jul 13, 2010 at 9:54 AM, John Reid j.r...@mail.cryst.bbk.ac.uk wrote:
Hi,
I have some arrays of various shapes in which I need to set any NaNs to
0. I have been doing the following:
a[numpy.where(numpy.isnan(a)] = 0.
as you can see here:
In [20]: a=numpy.ones(2)
In [21]:
On Tue, Jul 13, 2010 at 10:45 AM, Kurt Smith kwmsm...@gmail.com wrote:
You could make use of np.atleast_1d, and then everything would be
canonicalized:
In [33]: a = np.array(np.nan)
In [34]: a
Out[34]: array(nan)
In [35]: a1d = np.atleast_1d(a)
In [36]: a1d
Out[36]: array([ NaN])
On Tue, Jul 13, 2010 at 10:36 AM, Pauli Virtanen p...@iki.fi wrote:
ti, 2010-07-13 kello 10:06 -0700, Keith Goodman kirjoitti:
No need to use where. You can just do a[np.isnan(a)] = 0. But you do
have to watch out for 0d arrays, can't index into those.
You can, but the index must
. That is, you should be able to use .named
directly on the top-level datarray without referring to any axis
labels, to say something like arr.named['Netherlands', 2010], but you
can't yet.
-- Rob
On Thu, Jul 8, 2010 at 11:44 PM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Jul 8, 2010 at 1:20 PM
On Fri, Jul 9, 2010 at 1:53 PM, Rob Speer rsp...@mit.edu wrote:
Keith Goodman wrote:
I ran into a few more questions while playing with datarrays, so I started a
list:
http://github.com/kwgoodman/datarrayQ
I have quick answers to some of the questions.
Thank you! Comments below.
Can I
On Fri, Jul 9, 2010 at 4:05 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
So what would you get if you wanted:
MyDataArray['jones':'wilson']
or
MyDataArray.names[slice('jones','wilson')]
or whatever the syntax would be?
If it was in alphabetical order, you'd be all set, but what
On Fri, Jul 9, 2010 at 5:00 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Keith Goodman wrote:
On Fri, Jul 9, 2010 at 4:05 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
So what would you get if you wanted:
MyDataArray['jones':'wilson']
or
MyDataArray.names[slice('jones
On Fri, Jul 9, 2010 at 5:52 PM, Joshua Holbrook josh.holbr...@gmail.com wrote:
On Fri, Jul 9, 2010 at 4:22 PM, Keith Goodman kwgood...@gmail.com wrote:
On Fri, Jul 9, 2010 at 5:00 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Keith Goodman wrote:
On Fri, Jul 9, 2010 at 4:05 PM
On Thu, Jul 8, 2010 at 12:27 PM, Sebastian Haase seb.ha...@gmail.com wrote:
isn't this related to
http://projects.scipy.org/numpy/ticket/626
percentile() and clamp()
which was set to invalid
-Sebastian
The new percentile function has an axis input. I like that.
What do you think of adding a ticks parameter to DataArray? Would that
make sense?
Current behavior:
x = DataArray([[1, 2], [3, 4]], (('row', ['A','B']), ('col', ['C', 'D'])))
x.axes
(Axis(label='row', index=0, ticks=['A', 'B']),
Axis(label='col', index=1, ticks=['C', 'D']))
Proposed ticks
The main class of the la package is a labeled array, larry. A larry
consists of data and labels. The data is stored as a NumPy array and
the labels as a list of lists (one list per dimension).
Alignment by label is automatic when you add (or subtract, multiply,
divide) two larrys.
The focus of
On Tue, Jul 6, 2010 at 7:47 AM, Joshua Holbrook josh.holbr...@gmail.com wrote:
I really really really want to work on this. I already forked datarray
on github and did some research on What Other People Have Done (
http://jesusabdullah.github.com/2010/07/02/datarray.html ). With any
luck I'll
, Jul 6, 2010 at 8:23 AM, Keith Goodman kwgood...@gmail.com wrote:
On Tue, Jul 6, 2010 at 9:13 AM, Skipper Seabold jsseab...@gmail.com
wrote:
On Tue, Jul 6, 2010 at 11:55 AM, Keith Goodman kwgood...@gmail.com
wrote:
On Tue, Jul 6, 2010 at 7:47 AM, Joshua Holbrook
josh.holbr...@gmail.com wrote
On Fri, Jul 2, 2010 at 11:33 AM, Benjamin Root ben.r...@ou.edu wrote:
I am moving this over to numpy-discussion maillist...
I don't have a firm answer for you, but I did notice one issue in your
code. You call arange(len(dx) - 1) for your loops, but you probably really
need arange(1, len(dx)
On Fri, Jul 2, 2010 at 11:45 AM, Keith Goodman kwgood...@gmail.com wrote:
On Fri, Jul 2, 2010 at 11:33 AM, Benjamin Root ben.r...@ou.edu wrote:
I am moving this over to numpy-discussion maillist...
I don't have a firm answer for you, but I did notice one issue in your
code. You call arange
On Fri, Jul 2, 2010 at 12:53 PM, Geoffrey Ely g...@usc.edu wrote:
Hi All,
Sorry if this has been documented or discussed already, but my searches have
come up short. Can someone please recommend a way to setup both Cython and
Fortran extensions in a single package with numpy.distutils (or
On Wed, Jun 30, 2010 at 10:56 AM, Neal Becker ndbeck...@gmail.com wrote:
What are ways to construct object arrays? I want an array of objects, each
element default constructed of a particular object type.
Say my object is class A. I want a multi-dimensional array, each element
constructed
On Wed, Jun 23, 2010 at 3:46 AM, Ruben Salvador rsalvador...@gmail.com wrote:
Hi there,
I have a .npy file built by succesively adding results from different test
runs of an algorithm. Each time it's run, I save a numpy.array using
numpy.save as follows:
fn = 'file.npy'
f = open(fn, 'a+b')
On Fri, Jun 11, 2010 at 11:51 AM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Fri, Jun 11, 2010 at 12:57:37PM -0500, Bruce Southey wrote:
2. Do we really need to build custom data structures (larry, pandas,
tabular, etc.) or are structured ndarrays enough? (My conclusion is
that
Some of the numpy.testing assert functions call the input x and y,
others call it actual and desired:
actual = np.array([1+1j])
desired = np.array([2+2j])
assert_almost_equal(actual, desired)
snip
AssertionError: Items are not equal:
ACTUAL: [ 1.+1.j]
DESIRED: [ 2.+2.j]
On Thu, Jun 3, 2010 at 10:07 AM, Keith Goodman kwgood...@gmail.com wrote:
Some of the numpy.testing assert functions call the input x and y,
others call it actual and desired:
actual = np.array([1+1j])
desired = np.array([2+2j])
assert_almost_equal(actual, desired)
snip
AssertionError
On Tue, Jun 1, 2010 at 6:47 AM, Neal Becker ndbeck...@gmail.com wrote:
Not sure what to call this.
Any suggestion on computing the vector:
sum(u[i*M:i*M+N]) for i in range (len(u)/M)
How about a cumsum and then a loop to take the differences of the
desired indices of the cumsum? Might be
On Tue, Jun 1, 2010 at 6:56 AM, Keith Goodman kwgood...@gmail.com wrote:
On Tue, Jun 1, 2010 at 6:47 AM, Neal Becker ndbeck...@gmail.com wrote:
Not sure what to call this.
Any suggestion on computing the vector:
sum(u[i*M:i*M+N]) for i in range (len(u)/M)
How about a cumsum and then a loop
On Tue, Jun 1, 2010 at 1:07 PM, Mathew Yeates mat.yea...@gmail.com wrote:
Hi
Can anyone think of a clever (non-lopping) solution to the following?
A have a list of latitudes, a list of longitudes, and list of data values.
All lists are the same length.
I want to compute an average of data
On Sat, May 29, 2010 at 2:49 PM, Anne Archibald
aarch...@physics.mcgill.ca wrote:
On 29 May 2010 15:09, Robert Kern robert.k...@gmail.com wrote:
On Sat, May 29, 2010 at 12:27, Keith Goodman kwgood...@gmail.com wrote:
Will making changes to arr2 never change arr1 if
arr2 = arr1[np.ix_(*lists
np.random.shuffle: Modify a sequence in-place by shuffling its contents.
Matches doc string:
a = np.arange(10)
np.random.shuffle(a[:-1])
a
array([0, 7, 8, 4, 3, 6, 2, 1, 5, 9])
Doesn't match doc string:
l = range(10)
np.random.shuffle(l[:-1])
l
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Is
On Sat, May 29, 2010 at 11:45 AM, Robert Kern robert.k...@gmail.com wrote:
On Sat, May 29, 2010 at 13:35, Keith Goodman kwgood...@gmail.com wrote:
np.random.shuffle: Modify a sequence in-place by shuffling its contents.
Matches doc string:
a = np.arange(10)
np.random.shuffle(a[:-1
On Thu, May 27, 2010 at 6:02 AM, Vincent Davis vinc...@vincentdavis.net wrote:
On Thu, May 27, 2010 at 1:27 AM, Francesc Alted fal...@pytables.org wrote:
A Thursday 27 May 2010 05:52:22 Vincent Davis escrigué:
How do I determine if an array's (or column in a structured array) dtype is
a
, Keith Goodman wrote:
a1 = np.array(['a', 'b'], dtype=object)
a2 = np.array(['a', 'b'])
a1 == a2
array([ True, True], dtype=bool) # Looks good
a2 == a1
False # Should I have expected this?
Could you open a ticket for this and mark it for review?
Here's the ticket
I don't understand this:
a1 = np.array(['a', 'b'], dtype=object)
a2 = np.array(['a', 'b'])
a1 == a2
array([ True, True], dtype=bool) # Looks good
a2 == a1
False # Should I have expected this?
This works like I expected:
a1 = np.array([1, 2], dtype=object)
a2 = np.array([1, 2])
For those not familiar with the la package and its labeled array,
larry, here's a table that gives you a quick overview:
http://bazaar.launchpad.net/~kwgoodman/larry/trunk/annotate/head:/doc/source/intro.rst#L120
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On Thu, May 20, 2010 at 1:19 PM, josef.p...@gmail.com wrote:
On Thu, May 20, 2010 at 4:04 PM, Keith Goodman kwgood...@gmail.com wrote:
Why do the follow expressions give different dtype?
np.array([1, 2, 3], dtype=str)
array(['1', '2', '3'],
dtype='|S1')
np.array(np.array([1, 2, 3
While automating some unit tests for my labeled array class, larry, I
assumed that
np.array([1, 2], dtype=dtype)
would give the same result as
np.array([1, 2]).astype(dtype)
But it doesn't for dtype=None:
np.array([1, 2, 3], dtype=None)
array([1, 2, 3])
np.array([1, 2, 3]).astype(None)
I'd like to include modified numpy doc strings in my package. Do I
just put a note in my license file that says my package contains numpy
doc strings and then paste in the numpy license? My package is
distributed under a Simplifed BSD license, if that matters.
On Thu, May 20, 2010 at 7:36 PM, josef.p...@gmail.com wrote:
On Thu, May 20, 2010 at 9:00 PM, Keith Goodman kwgood...@gmail.com wrote:
While automating some unit tests for my labeled array class, larry, I
assumed that
np.array([1, 2], dtype=dtype)
would give the same result as
np.array
On Thu, May 20, 2010 at 7:38 PM, Robert Kern robert.k...@gmail.com wrote:
On Thu, May 20, 2010 at 21:21, Keith Goodman kwgood...@gmail.com wrote:
I'd like to include modified numpy doc strings in my package. Do I
just put a note in my license file that says my package contains numpy
doc
On Mon, May 17, 2010 at 11:06 AM, Francesc Alted fal...@pytables.org wrote:
A Sunday 16 May 2010 21:14:34 Davide Lasagna escrigué:
Hi all,
What is the fastest and lowest memory consumption way to compute this?
y = np.arange(2**24)
bases = y[1:] + y[:-1]
Actually it is already quite fast,
On Sun, May 16, 2010 at 12:14 PM, Davide Lasagna
lasagnadav...@gmail.com wrote:
Hi all,
What is the fastest and lowest memory consumption way to compute this?
y = np.arange(2**24)
bases = y[1:] + y[:-1]
Actually it is already quite fast, but i'm not sure whether it is occupying
some
On Sun, May 16, 2010 at 1:18 PM, Eric Firing efir...@hawaii.edu wrote:
On 05/16/2010 09:24 AM, Keith Goodman wrote:
On Sun, May 16, 2010 at 12:14 PM, Davide Lasagna
lasagnadav...@gmail.com wrote:
Hi all,
What is the fastest and lowest memory consumption way to compute this?
y = np.arange(2
On Thu, May 6, 2010 at 10:25 AM, T J tjhn...@gmail.com wrote:
Hi,
Is there a way to sort the columns in an array? I need to sort it so
that I can easily go through and keep only the unique columns.
ndarray.sort(axis=1) doesn't do what I want as it destroys the
relative ordering between the
On Sat, May 1, 2010 at 1:36 PM, Gökhan Sever gokhanse...@gmail.com wrote:
Hello,
Is b an expected value? I am suspecting another floating point arithmetic
issue.
I[1]: a = np.arange(1.6, 1.8, 0.1, dtype='float32')
I[2]: a
O[2]: array([ 1.6002, 1.7005], dtype=float32)
I[3]: b =
On Thu, Apr 29, 2010 at 9:56 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
It looks like the consensus is that zero should be returned. This is a
change from current behaviour and that bothers me a bit. Here are some other
oddities
In [6]: nanmax([nan])
Out[6]: nan
In [7]:
I am pleased to announce the second release of the la package, version 0.2.
The main class of the la package is a labeled array, larry. A larry
consists of a data array and a label list. The data array is stored as
a NumPy array and the label list as a list of lists.
larry has built-in methods
On Mon, Apr 26, 2010 at 9:55 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hi All,
We need to make a decision for ticket #1123 regarding what nansum should
return when all values are nan. At some earlier point it was zero, but
currently it is nan, in fact it is nan whatever the
On Sun, Apr 25, 2010 at 6:16 AM, josef.p...@gmail.com wrote:
(some) numpy functions take floats as valid axis argument. Is this a feature?
np.ones((2,3)).sum(1.2)
array([ 3., 3.])
np.ones((2,3)).sum(1.99)
array([ 3., 3.])
np.mean((1.5,0.5))
1.0
np.mean(1.5,0.5)
1.5
Keith pointed
On Tue, Apr 20, 2010 at 6:03 AM, Andreas Hilboll li...@hilboll.de wrote:
Hi there,
is there an easy way to do something like trim_zeros() does, but for a
n-dimensional array? I have a 2d array with only zeros in the first and
last rows and columns, and would like to trim this array to only
On Thu, Apr 15, 2010 at 12:41 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 8:49 AM, Keith Goodman kwgood
On Thu, Apr 15, 2010 at 1:48 PM, Keith Goodman kwgood...@gmail.com wrote:
On Thu, Apr 15, 2010 at 12:41 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood
On Wed, Apr 14, 2010 at 8:16 AM, Nikolaus Rath nikol...@rath.org wrote:
Hello,
How do I best find out the indices of the largest x elements in an
array?
Example:
a = [ [1,8,2], [2,1,3] ]
magic_function(a, 2) == [ (0,1), (1,2) ]
Since the largest 2 elements are at positions (0,1) and
On Wed, Apr 14, 2010 at 8:49 AM, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Apr 14, 2010 at 8:16 AM, Nikolaus Rath nikol...@rath.org wrote:
Hello,
How do I best find out the indices of the largest x elements in an
array?
Example:
a = [ [1,8,2], [2,1,3] ]
magic_function(a, 2
On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 8:49 AM, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Apr 14, 2010 at 8:16 AM, Nikolaus Rath nikol...@rath.org wrote:
Hello,
How do I best find out
On Wed, Apr 14, 2010 at 1:56 PM, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 8:49 AM, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Apr 14, 2010 at 8:16 AM
On Wed, Apr 14, 2010 at 3:12 PM, Anne Archibald
peridot.face...@gmail.com wrote:
On 14 April 2010 16:56, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath nikol...@rath.org wrote:
Keith Goodman kwgood...@gmail.com writes:
On Wed, Apr 14, 2010 at 8:49 AM
On Sat, Apr 10, 2010 at 5:17 PM, Gökhan Sever gokhanse...@gmail.com wrote:
Hello,
Is there a simpler way to get c from a
I[1]: a = np.arange(10)
I[2]: b = a[3:]
I[3]: b
O[3]: array([3, 4, 5, 6, 7, 8, 9])
I[4]: c = np.insert(b, [7]*3, 0)
O[5]: array([3, 4, 5, 6, 7, 8, 9, 0, 0, 0])
a
On Mon, Apr 5, 2010 at 8:44 AM, Ken Basye kbas...@jhu.edu wrote:
Hi Folks,
I have two arrays, A and B, with the same shape. I want to find the
highest values in A along some axis, then extract the corresponding
values from B. I can get the highest values in A with A.max(axis=0) and
the
On Fri, Mar 19, 2010 at 7:53 AM, gerardob gberbeg...@gmail.com wrote:
Hello, i would like to produce lists of lists 1's and 0's.
For example, to produce the list composed of:
L = [[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]
I just need to do the following:
n=4
On Fri, Mar 19, 2010 at 8:17 AM, Joe Kington jking...@wisc.edu wrote:
See itertools.permutations (python standard library)
e.g.
In [3]: list(itertools.permutations([1,1,0,0]))
Out[3]:
[(1, 1, 0, 0),
(1, 1, 0, 0),
(1, 0, 1, 0),
(1, 0, 0, 1),
(1, 0, 1, 0),
(1, 0, 0, 1),
(1, 1, 0,
On Wed, Mar 17, 2010 at 8:51 AM, gerardob gberbeg...@gmail.com wrote:
How can i modified all the values of a numpy array whose value is smaller
than a given epsilon to zero?
Example
epsilon=0.01
a = [[0.003,2][23,0.0001]]
output:
[[0,2][23,0]]
Here's one way:
a =
On Wed, Mar 17, 2010 at 11:47 AM, gerardob gberbeg...@gmail.com wrote:
Let A and B be two n x n matrices.
I would like to have another n x n matrix C such that
C_ij = min {A_ij, B_ij}
Example:
A = numpy.array([[2,3],[10,12]])
B = numpy.array([[1,4],[9,13]])
Output
C = [[1,3],[9,12]]
On Wed, Mar 17, 2010 at 12:04 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Friedrich Romstedt wrote:
Code:
import numpy
import time
a = numpy.random.random((2000, 2000))
start = time.time()
a[abs(a) 10] = 0
stop = time.time()
I highly recommend ipython and its timeit function
On Wed, Mar 17, 2010 at 12:08 PM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Mar 17, 2010 at 14:03, Keith Goodman kwgood...@gmail.com wrote:
On Wed, Mar 17, 2010 at 12:04 PM, Christopher Barker
chris.bar...@noaa.gov wrote:
Friedrich Romstedt wrote:
Code:
import numpy
import time
On Tue, Mar 16, 2010 at 5:56 AM, josef.p...@gmail.com wrote:
On Tue, Mar 16, 2010 at 9:43 AM, gerardo.berbeglia gberbeg...@gmail.com
wrote:
How can i take out the diagonal values of a matrix and fix them to zero?
Example:
input: [[2,3,4],[3,4,5],[4,5,6]]
output:
On Tue, Mar 16, 2010 at 7:43 AM, Keith Goodman kwgood...@gmail.com wrote:
On Tue, Mar 16, 2010 at 5:56 AM, josef.p...@gmail.com wrote:
On Tue, Mar 16, 2010 at 9:43 AM, gerardo.berbeglia gberbeg...@gmail.com
wrote:
How can i take out the diagonal values of a matrix and fix them to zero
On Sun, Feb 21, 2010 at 2:30 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
I would be much obliged if some folks would run the attached script and
report the output, numpy version, and python version.
import isinf
Warning: invalid value encountered in isinf
True
Python 2.6.4
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