On Sun, Jul 7, 2013 at 9:28 AM, Alan G Isaac alan.is...@gmail.com
mailto:alan.is...@gmail.com wrote:
I miss being able to spell a.conj().T as a.H, as one can
with numpy matrices.
On 7/7/2013 4:49 PM, Charles R Harris wrote:
There was a long thread about this back around 1.1 or so,
long
On 7/13/2013 1:46 PM, Nathaniel Smith wrote:
Why not just write
def H(a):
return a.conj().T
in your local namespace?
First of all, I am sympathetic to being conservative
about the addition of attributes!
But the question about adding a.H about the possibility of improving
- speed
The docs for numpy.sign at
http://docs.scipy.org/doc/numpy/reference/generated/numpy.sign.html
do not indicate how complex numbers are handled. Currently, np.sign
appears to return the sign of the real part as a complex value.
Was this an explicit choice? Was x/abs(x) considered (for non-zero
With numpy arrays, I miss being able to spell a.conj().T as a.H,
as one can with numpy matrices.
Is adding this attribute to arrays ever under consideration?
Thanks,
Alan Isaac
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On 7/5/2013 2:50 PM, Alan G Isaac wrote:
I see that CLARTG is here:
https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstqrb.f
But is there a Python interface in SciPy?
(Or any other SciPy access to Givens rotation?)
Sorry, that was SLARTG, whereas
On 6/29/2013 3:00 PM, Nathaniel wrote:
any objections to np.full?
Still curious:
why isn't ``tile`` the right name?
(It already exists.)
import numpy as np
np.tile(3.0, (2,3))
array([[ 3., 3., 3.],
[ 3., 3., 3.]])
If someone explained this, sorry to
have missed it.
Alan
On 6/29/2013 3:00 PM, Nathaniel wrote:
any objections to np.full?
On Sat, Jun 29, 2013 at 9:55 PM, Alan G Isaac wrote:
Still curious:
why isn't ``tile`` the right name?
(It already exists.)
import numpy as np
np.tile(3.0, (2,3))
array([[ 3., 3., 3.],
[ 3., 3., 3
and I don't see the problem with ``tile_like``.
On 6/29/2013 6:15 PM, Robert Kern wrote:
It makes no sense except in the scalar case.
I would think it makes sense in every case that
can be normally broadcast to the shape of the
paradigm array.
Anyway, I drop the suggestion.
Cheers,
Alan
On 6/14/2013 9:27 AM, Aldcroft, Thomas wrote:
If I just saw np.values(..) in some code I would never guess what it is doing
from the name
That suggests np.fromvalues.
But more important than the name I think
is allowing broadcasting of the values,
based on NumPy's broadcasting rules.
On 2013/06/14 5:15 AM, Alan G Isaac wrote:
But more important than the name I think
is allowing broadcasting of the values,
based on NumPy's broadcasting rules.
Broadcasting a scalar is then a special case,
even if it is the case that has dominated this thread.
On 6/14/2013 1:18 PM, Eric
On 6/13/2013 4:36 PM, Benjamin Root wrote:
np.values() might be a decent alternative.
This could then reasonably support broadcasting
from the shape of the input to the shape of
the array.
Alan Isaac
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Le 12/06/2013 16:18, Nathaniel Smith a écrit :
Now imagine a new version of this page, if we add 'filled'. There will
be a list at the top with functions named:
empty
filled
ones
zeros
It's immediately obvious what all of these things do, and how they
differ from each other,
On 6/7/2013 12:30 PM, Will Lee wrote:
Can somebody tell me why these operations are not the same in numpy?
http://docs.python.org/2/reference/datamodel.html#object.__rmul__
hth,
Alan Isaac
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On 4/10/2013 3:31 AM, Robert Kern wrote:
You cannot use objects that do not have a valid __eq__() (as in,
returns boolean True if and only if they are to be considered
equivalent for the purpose of dictionary lookup, otherwise returns
False) as dictionary keys. Your oofun object still violates
On 4/3/2013 2:44 PM, huangkan...@gmail.com wrote:
I suggest add function dot to matrix
import numpy as np; x = np.arange(5); I = np.asmatrix(np.identity(5));
I.dot(x)
matrix([[ 0., 1., 2., 3., 4.]])
Alan Isaac
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On 3/15/2013 9:21 AM, Dmitrey wrote:
Temporary walkaround for a serious bug in FuncDesigner automatic
differentiation kernel due to a bug in some versions of Python or NumPy,
Are the suspected bugs documented somewhere?
Alan
PS The word 'banausic' is very rare in English.
Perhaps you meant
On 3/15/2013 3:34 PM, Dmitrey wrote:
the suspected bugs are not documented yet
I'm going to guess that the state of the F_i changes
when you use them as keys (i.e., when you call __le__.
It is very hard to imagine that this is a Python or NumPy bug.
Cheers,
Alan
Under what conditions should I expect fminbound
to call the supplied function with argument values
substantially outside the user-provided bounds?
Thanks,
Alan Isaac
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On 3/1/2013 9:32 AM, Henry Gomersall wrote:
there should be an equivalent for floats that
unambiguously returns a range for the half open interval
If I've understood you:
start + stepsize*np.arange(nsteps)
fwiw,
Alan Isaac
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One motivation of this thread was that
adding a step parameter to linspace might make
things easier for beginners.
I claim this thread has put the lie to that,
starting with the initial post. So what is the
persuasive case for the change?
Imo, the current situation is good:
use arange if you
On 2/26/2013 1:11 PM, josef.p...@gmail.com wrote:
Alan was in favor of the dot method
I still really like this, and it probably violates
any simple rule for drawing the line.
Nevertheless it would be nice to have some
principle(s) other than the squeaky wheel principle
for thinking about such
I'm hoping this discussion will return to the drawing the line question.
http://stackoverflow.com/questions/8108688/in-python-when-should-i-use-a-function-instead-of-a-method
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x=[1,2,3,4,5,6]
ind=[1,3,9,3,4,1]
f=np.zeros(10)
np.bincount(ind,x)
hth,
Alan Isaac
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Is it really better to have `permute` and `permuted`
than to add a keyword? (Note that these are actually
still ambiguous, except by convention.)
Btw, two separate issues seem to be running side by side.
i. should in-place operations return their result?
ii. how can we signal that an operation
Just changing the subject line so a good suggestion
does not get lost ...
Alan
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Thanks Pierre for noting that np.tile already
provides a chunk of this functionality:
a = np.tile(5,(1,2,3))
a
array([[[5, 5, 5],
[5, 5, 5]]])
np.tile(1,a.shape)
array([[[1, 1, 1],
[1, 1, 1]]])
I had not realized a scalar first argument was possible.
Alan Isaac
On Sun, Jan 13, 2013 at 11:24 PM, Robert Kern robert.k...@gmail.com wrote:
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)
On 1/13/2013 6:39 PM, Nathaniel Smith wrote:
This violates the
I'm just a Python+NumPy user and not a CS type.
May I ask a naive question on this thread?
Given the work that has (as I understand it) gone into
making NumPy usable as a C library, why is the discussion not
going in a direction like the following:
What changes to the NumPy code base would be
On 1/9/2013 9:58 AM, Nathaniel Smith wrote:
I don't think most happy current numpy users are wishing they
could switch to writing Lisp on the JVM or vice-versa, so I don't
think it's surprising that no-one's jumped up to do this work.
Sure. I'm trying to look at this more from the Clojure
On 1/8/2013 1:48 PM, Olivier Delalleau wrote:
As I mentioned in another post, I also agree that it would make things
simpler and safer to just yield the same result as if we were using a
one-element array.
Yes!
Anything else is going to drive people insane,
especially new users.
Alan Isaac
On 11/9/2012 12:21 PM, Nathaniel Smith wrote:
you might want to double-check that the
np.random.choice in 1.7 actually*does* give an error if the input
array is not 1-d
Any idea where I can look at the code?
I browsed github after failing to find
a productive search string, but failed
to
On 11/12/2012 8:59 AM, Sebastian Berg wrote:
https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/mtrand.pyx#L919
Sounds like it should be pretty simple to add axis=None which would
change the current behavior very little, it would stop give an error
anymore for none 1-d arrays
On 11/12/2012 10:00 AM, Nathaniel Smith wrote:
I don't really have an opinion on whether those
things should be supported, or what the right API should be; I haven't
really thought about it. Maybe others on the list have opinions. I was
just saying that we have plenty of time to decide about
In a comment on the issue https://github.com/numpy/numpy/issues/2724 Sebastian
notes:
it could also be reasonable to have size=None as default and have it return a
scalar/the given axes removed in that case. That would be a real change
in functionality unfortunately, but it would make sense for
On 11/12/2012 12:16 PM, Sebastian Berg wrote:
So instead of taking a sequence of length 1, take an element as default.
Sebastien has proposed that np.random.choice return
a single *element* by default, not a 1d array of length 1.
He proposes to associate this with a default value of
On 11/12/2012 5:46 PM, Nathaniel Smith wrote:
Want to make a pull request?
Well, I'd be happy to help Sebastien to change the
code, but I'm not a git user.
And I'd have some questions. E.g., with `size=None`,
couldn't we just call Python's random.choice? And for
sampling without replacement,
On 11/12/2012 8:18 PM, Sebastian Berg wrote:
I have created a pull request
This is still a bit different than I thought you intended.
With `size=None` we don't get an element,
but rather a 0d array.
I thought the idea was to return an element in this case?
Alan
I just noticed that 1.7 is scheduled to add a random.choice function.
I wonder if the best structure has been chosen. Specifically, it does
not provide for array flattening, and it does not provide for subarray
choice.
Back in 2006 (?) Robert Kern suggested something like the below
(forgive any
On 7/12/2012 1:45 PM, Nathaniel Smith wrote:
I'd actually like to see out= as a kw-only arg.
That would be great.
Numpy 2.0?
Alan Isaac
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On 6/8/2012 9:14 AM, Neal Becker wrote:
The fact that this proposed numpy behavior would not match python list
behavior
holds little weight for me.
It is not just Python behavior for lists.
It is the semantics for all sequence types.
Breaking this would be appalling.
Alan Isaac
On 4/25/2012 4:51 PM, Andreas H. wrote:
I would assume that most users see numpy
as infrastructure, they write their own code on top of it. As a normal
user of numpy, I wouldn't know where it would need improvement to suit
my needs because it already does all I need. (Okay, masked arrays are
http://docs.scipy.org/doc/numpy/reference/routines.matlib.html#module-numpy.matlib
promises a list of functions that does not appear (at the moment, anyway).
Alan Isaac
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On 2/27/2012 10:10 AM, Paulo Jabardo wrote:
I have a few features that I believe would make text file easier for many
people. In some countries (most?) the decimal separator in real numbers is
not a point but a comma.
I think it would be very useful that the decimal separator be specified
On 2/27/2012 1:00 PM, Paulo Jabardo wrote:
First of all '.' isn't international notation
That is in fact a standard designation.
http://en.wikipedia.org/wiki/Decimal_mark#Influence_of_calculators_and_computers
Alan Isaac
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On 2/27/2012 2:28 PM, Pauli Virtanen wrote:
ISO specifies comma to be used in international standards
(ISO/IEC Directives, part 2 / 6.6.8.1):
http://isotc.iso.org/livelink/livelink?func=llobjId=10562502objAction=download
I do not think you are right.
I think that is a presentational
On 2/27/2012 2:47 PM, Matthew Brett wrote:
Maybe we can just agree it is an important option to have rather than
an unimportant one,
It depends on what you mean by option.
If you mean there should be conversion tools
from other formats to a specified supported
format, then I agree.
If you
On 2/25/2012 4:44 PM, James Bergstra wrote:
bincount([]) makes no sense,
I disagree:
http://permalink.gmane.org/gmane.comp.python.numeric.general/42041
but if a minlength argument is provided,
then the routine should succeed.
Definitely!
Alan Isaac
On 2/23/2012 7:20 PM, Travis Oliphant wrote:
https://www.surveymonkey.com/s/numpy_list_survey
After you complete the survey, I would really appreciate any feedback on
questions that could be improved, removed, or added.
I felt the survey was targeting business users rather than academic
On 2/18/2012 10:20 AM, josef.p...@gmail.com wrote:
we need to streamline the code so the bunch of amateurs doesn't
understand what's going on and cannot effectively threaten a fork
anymore.
I don't mean to take today's peculiar post too seriously,
and your opening line undermines that. But
On 2/14/2012 10:07 PM, Bruce Southey wrote:
The one thing that gets over looked here is that there is a huge
diversity of users with very different skill levels. But very few
people have an understanding of the core code. (In fact the other
thread about type-casting suggests that it is
On 2/15/2012 1:50 PM, Matthew Brett wrote:
I believe that leaving the governance informal and underspecified at
this stage would be a grave mistake, for everyone concerned.
To justify that concern, can you point to an
analogous case, where things went awry by not
formalizing the governance
On 2/15/2012 2:46 PM, Benjamin Root wrote:
I think it is only fair that the group occasionally pings this mailing-list
for important progress reports.
No offense intended, but that sounds like an unfunded mandate.
More useful would be an offer to liaison between the two.
Cheers,
Alan
On 2/15/2012 2:46 PM, Benjamin Root wrote:
The NA discussion is the perfect example where a governance structure would
help resolve disputes.
How? I'm not seeing it.
Who would have behaved differently and why?
Alan
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My analysis is fundamentally different than Matthew
and Benjamin's for a few reasons.
1. The problem has been miscast.
The economic interests of the developers *always*
has had an apparent conflict with the economic
interests of the users: users want developers to work more
on the
On 2/3/2012 5:16 AM, santhu kumar wrote:
x = nX3 vector.
mass = nX1 vector
inert = zeros((3,3))
for i in range(n):
ri = x[i,:].reshape(1,3)
inert = inert + mass[i,]*(sum(ri*ri)*eye(3) - dot(ri.T,ri))
This should buy you a bit.
xdot = (x*x).sum(axis=1)
for (massi,xi,xdoti)
On 2/3/2012 3:37 PM, josef.p...@gmail.com wrote:
res = - np.dot(x.T, mass*x)
res[np.arange(3), np.arange(3)] -= np.trace(res)
Nice!
Get some speed gain with slicing:
res = - np.dot(x.T, mass*x)
res.flat[slice(0,None,4)] -= np.trace(res)
Alan
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On 1/31/2012 8:26 AM, Neal Becker wrote:
I was just bitten by this unexpected behavior:
In [24]: all ([i 0 for i in xrange (10)])
Out[24]: False
In [25]: all (i 0 for i in xrange (10))
Out[25]: True
Turns out:
In [31]: all is numpy.all
Out[31]: True
np.array([i 0 for i in xrange
On 1/31/2012 10:35 AM, Benjamin Root wrote:
A generator is an input that could be converted into an array.
def mygen():
i = 0
while True:
yield i
i += 1
Alan Isaac
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If I seed NumPy's random number generator, I get the
expected sequence. If I use the same seed for Python's
random number generator, I get a different sequence.
1. Why does the Python sequence differ from others?
2. Can I somehow put both MT's in a common state?
Thank you,
Alan Isaac
On Fri, Dec 30, 2011 at 15:13, Alan wrote:
If I seed NumPy's random number generator, I get the
expected sequence.
On 12/30/2011 10:36 AM, Robert Kern wrote:
What do you mean by expected? Where are these expectations coming
from? Other implementations of the Mersenne Twister?
Right.
On 12/4/2011 1:43 AM, Charles R Harris wrote:
I don't think there are 5 active developers, let alone 11.
With hard work you might scrape together two or three.
Having 5 or 11 people making decisions for the two or
three actually doing the work isn't going to go over well.
Very true! But you
On 11/30/2011 6:09 AM, Giovanni Plantageneto wrote:
I find ConfigParser a bit low level, is there any function that
automatically reads everything from a file?
You could just use a dictionary for your params,
and import it from your configuration file.
If you insist on an ini format,
On 11/3/2011 5:36 AM, Pierre Raybaut wrote:
* PySide support
Congratulations! I hope this means that Spyder
will be included in the Enthought Python Distribution
(so that I can have my students use it).
Alan Isaac
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As a simple example, if I have y0 and a white noise series e,
what is the best way to produces a series y such that y[t] = 0.9*y[t-1] + e[t]
for t=1,2,...?
1. How can I best simulate an autoregressive process using NumPy?
2. With SciPy, it looks like I could do this as
e[0] = y0
Assuming stationarity ...
On 10/14/2011 1:22 PM, josef.p...@gmail.com wrote:
maybe ?
I just meant that the MA approximation is
not reliable for a non-stationary AR.
E.g., http://www.jstor.org/stable/2348631
Cheers,
Alan
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On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
If I remember correctly, signal.lfilter doesn't require stationarity,
but handling of the starting values is a bit difficult.
Hmm. Yes.
AR(1) is trivial, but how do you handle higher orders?
Thanks,
Alan
On 10/3/2011 6:59 PM, Pengkui Luo wrote:
Most functions in numpy return ndarray by default.
Use numpy.asmatrix() if you want a matrix.
Please note that the example is using matlib.reshape,
not numpy.reshape.
Alan Isaac
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Is this the intended behavior?
from numpy import matlib
m = matlib.reshape([1,2],(2,1))
type(m)
type 'numpy.ndarray'
For any 2d shape, I expected a matrix.
(And probably an exception if the shape is not 2d.)
Thanks,
Alan Isaac
On 9/12/2011 7:18 AM, Jonas Wallin wrote:
Why does
MuY += MuY.transpose()
and
MuY = MuY + MuY.transpose()
give different answers?
Because the first one is done in-place,
so you are changing MuY (and thus MuY.transpose)
as the operation proceeds.
MuY.transpose() is generally a
On 8/10/2011 8:50 PM, jp d wrote:
i am trying to invert matrices like this:
[[ 0.01643777 -0.13539939 0.11946689]
[ 0.12479926 0.01210898 -0.09217618]
[-0.13050087 0.07575163 0.01144993]]
in perl using Math::MatrixReal;
and in various online calculators i get
[ 2.472715991745
On 7/17/2011 1:57 PM, Sturla Molden wrote:
I suggest inverting a NumPy matrix could result in an unsolved linear
system type, instead of actually computing the matrix inverse and
returning a new matrix.
1. Too implicit.
2. Too confusing for new users.
2a. Too confusing for students.
On 6/25/2011 2:06 PM, Benjamin Root wrote:
Note that np.sum([]) also returns 0.0. I think the
reason why it has been returning zero instead of NaN was
because there wasn't a NaN-equivalent for integers.
http://en.wikipedia.org/wiki/Empty_sum
fwiw,
Alan Isaac
On Thu, Jun 2, 2011 at 1:49 AM, Mark Millermarkperrymil...@gmail.com wrote:
Not quite. Bincount is fine if you have a set of approximately
sequential numbers. But if you don't
On 6/1/2011 9:35 PM, David Cournapeau wrote:
Even worse, it fails miserably if you sequential numbers but with
On 5/28/2011 3:40 PM, Robert wrote:
(myarray in mylist) turns into mylist.__contains__(myarray).
Only the list object is ever checked for this method. There is no
paired method myarray.__rcontains__(mylist) so there is nothing that
numpy can override to make this operation do anything
On 5/28/2011 3:46 PM, Robert Kern wrote:
mylist.__contains__(x), it should treat all objects exactly
the same: check if it equals any item that it contains. There is no
way for it to say, Oh, I don't know how to deal with this type, so
I'll pass it over to x.__contains__().
Which makes my
On 5/19/2011 2:07 PM, Mathew Yeates wrote:
I have installed a new version of Python27 in a new directory. I want to get
this info into the registry so, when I install Numpy, it will use my new
Python
It probably will already.
Did you try?
(Assumption: you're using Windows installers.)
Alan
On 5/19/2011 2:15 PM, Mathew Yeates wrote:
I*am* using the windows installer.
And you find that it does not find your most recent
Python 2.7 install, for which you also used the
Windows installer?
Alan Isaac
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On 5/19/2011 2:24 PM, Mathew Yeates wrote:
The Registry keys point to the old Python27.
Odd. The default installation settings
should have reset this. Or so I believed.
Maybe this will help?
http://effbot.org/zone/python-register.htm
Alan Isaac
On 4/20/2011 9:55 PM, pratik wrote:
If the place where he is seeking tenure does not know his name (i.e
hasn't heard of ATLAS)
Letters are often more for administrators, who can be from any field,
than for the department faculty.
fwiw,
Alan Isaac
On 4/8/2011 6:54 AM, dileep kunjaai wrote:
I defined a function hit_rate( ) i want to use this into import
function(name of function).
http://docs.python.org/tutorial/modules.html
Alan Isaac
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On 4/6/2011 9:14 AM, dileep kunjaai wrote:
Is there any function for rounding the real number, for n (say) decimal
places:
http://www.google.com/search?q=numpy+round
produces
http://docs.scipy.org/doc/numpy/reference/generated/numpy.round_.html
Cheers,
Alan Isaac
On 4/5/2011 5:49 AM, François Steinmetz wrote:
a = eye(2, dtype='int')
a *= 1.0
a ; a.dtype
array([[1, 0],
[0, 1]])
dtype('int64')
This in-place (!) multiplication should not change
the dtype of a. I suspect you did not exactly cut
and paste...
Alan Isaac
On 4/5/2011 9:26 AM, François Steinmetz wrote:
It does not change the dtype, 'int' is just interpreted as 'int64' :
So the meaning of 'int' is system specific?
import numpy as np; a=np.eye(2,dtype='int'); a.dtype
dtype('int32')
Alan Isaac
On 4/5/2011 10:37 AM, Nathaniel Smith wrote:
Yes, this is a fact about Python 'int', not a fact about numpy -- a
Python 'int' is defined to hold a C 'long', which will be either 32 or
64 bits depending on platform.
So what is the rule for Python 3, where iiuc it can no longer be a fact about
On 4/5/2011 6:11 PM, Skipper Seabold wrote:
To my mind, skip_headers is a bool
I agree.
Alan Isaac
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On 4/4/2011 6:49 AM, Alex Ter-Sarkissov wrote:
divide(float(var1),float(var2))
http://docs.scipy.org/doc/numpy/reference/generated/numpy.true_divide.html
hth,
Alan Isaac
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On 3/20/2011 11:08 AM, Ben Smith wrote:
I'd like to do is limit myself to just the functions that
are implemented in python, package it with py2exe and hand
that to anyone that needs it. So, my question, if anyone
knows, what's implemented in python and what depends on
the c libraries?
On 2/22/2011 3:45 PM, Sturla Molden wrote:
I came accross some NumPy performance tests by NASA. Comparisons against
pure Python, Matlab, gfortran, Intel Fortran, Intel Fortran with MKL,
and Java. For those that are interested, it is here:
https://modelingguru.nasa.gov/docs/DOC-1762
I don't
On 2/9/2011 10:58 AM, Neal Becker wrote:
But where is numpy's 'find_first' function?
np.argmax(arrayT)
(Of course that constructs a boolean array...)
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On 2/9/2011 11:39 AM, Bruce Southey wrote:
np.argmax(x5) # doesn't appear to be correct
It was an answer to the particular question
of how to do find_first, which it does
(at the cost of a boolean array):
it finds the first element greater than 5.
x
array([5, 4, 3, 6, 7, 3, 2, 1])
On 12/22/2010 9:16 AM, Ian Stokes-Rees wrote:
a != 0
will be used, but I'm not sure then how to count the number of True
entries.
(a != 0).sum()
hth,
Alan Isaac
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On 12/20/2010 10:49 PM, josef.p...@gmail.com wrote:
What's the difference between a numpy Random and a python
random.Random instance of separate states of the random number
generators?
Sorry, I don't understand the question. The difference
for my use is that a np.RandomState instance
::
np.bincount([])
Traceback (most recent call last):
File stdin, line 1, in module
ValueError: The first argument cannot be empty.
Why not?
(I.e., why isn't an empty array the right answer?)
Thanks,
Alan Isaac
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bincount does not currently allow a generator as an argument.
I'm wondering if it is considered too costly to extend it to allow this.
(Motivation: I'm counting based on an attribute of a large number of objects,
and I don't need a list of the data.)
Thanks,
Alan Isaac
I want to sample *without* replacement from a vector
(as with Python's random.sample). I don't see a direct
replacement for this, and I don't want to carry two
PRNG's around. Is the best way something like this?
permutation(myvector)[:samplesize]
Thanks,
Alan Isaac
On 12/20/2010 9:41 PM, josef.p...@gmail.com wrote:
python has it in random
sample( population, k)
Yes, I mentioned this in my original post:
http://www.mail-archive.com/numpy-discussion@scipy.org/msg29324.html
But good simulation practice is perhaps to seed
a simulation specific random
On 12/10/2010 4:13 AM, Nils Becker wrote:
def f(dtype=None):
if not dtype:
I think you want:
if dtype is None:
fwiw,
Alan
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On 11/25/2010 5:55 AM, Jean-Luc Menut wrote:
it was just a test to compare the speed of
the cosine function in IDL and numpy
The point others are trying to make is that
you *instead* tested the speed of creation
of a certain object type. To test the *function*
speeds, feed both large arrays.
On 11/18/2010 9:48 PM, Sachin Kumar Sharma wrote:
Does graphic output like maps, histogram, crossplot, tornado charts is good
enough with basic installation or needs some additional packages?
For the graphics, you should probably first consider Matplotlib.
For your other questions, perhaps
On 10/27/2010 9:56 AM, Zachary Pincus wrote:
the structure of the python language prevents
meaningful short-circuiting in the case of
np.any(a!=b)
Maybe:
any((ai != bi) for ai,bi in izip(a.flat,b.flat))
?
fwiw,
Alan Isaac
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