numpy-discussion — Discussion list for all users of Numerical Python

Re: [Numpy-discussion] Moving docstrings from C to Python

[Sasha <nd...@ma...>]

On 7/28/06, Stefan van der Walt <st...@su...> wrote:

> Would anyone mind if the change was made?  If not, where should they
> go? (numpy/add_newdocs.py or numpy/core/something)

Another +1 for numpy/add_newdocs.py and a suggestion: check for
Py_OptimizeFlag > 1 in add_newdoc so that docstrings are not loaded if
python is invoked with -OO option.  This will  improve import numpy
time and reduce the memory footprint.  I'll make the change if no one
objects.

Re: [Numpy-discussion] C Extension intp problem

[Nick F. <nv...@MI...>]

On Jul 28, 2006, at 4:47 PM, David M. Cooke wrote:

> On Fri, 28 Jul 2006 16:25:00 -0400
> Nick Fotopoulos <nv...@MI...> wrote:
>
>> Dear all,
>>
>> I had a working C extension, but after upgrading to a recent numpy
>> from SVN, I can no longer compile it.  I've fixed the deprecation
>> warnings, but can't get past this:
>>
>> frgetvect.c:51: error: 'intp' undeclared (first use in this function)
>>
>> Now, I'm quite confused since I thought that intp should be a global
>> thing, not numpy related, and I believe I'm using the shipped Apple
>> gcc version.
>
> To avoid namespace clashes with other libraries, we've switched to  
> adding a
> unique prefix 'npy_' to types (and NPY_ to #defines). You can  
> either change
> your code to use 'npy_intp', or add
>
> #include "numpy/noprefix.h"
>
> to the top.
>

Aha!  I was under the impression that intp was a native C thing.   
Thanks for clarifying.

Take care,
Nick

Re: [Numpy-discussion] C Extension intp problem

[David M. C. <co...@ph...>]

On Fri, 28 Jul 2006 16:25:00 -0400
Nick Fotopoulos <nv...@MI...> wrote:

> Dear all,
> 
> I had a working C extension, but after upgrading to a recent numpy  
> from SVN, I can no longer compile it.  I've fixed the deprecation  
> warnings, but can't get past this:
> 
> frgetvect.c:51: error: 'intp' undeclared (first use in this function)
> 
> Now, I'm quite confused since I thought that intp should be a global  
> thing, not numpy related, and I believe I'm using the shipped Apple  
> gcc version.

To avoid namespace clashes with other libraries, we've switched to adding a
unique prefix 'npy_' to types (and NPY_ to #defines). You can either change
your code to use 'npy_intp', or add

#include "numpy/noprefix.h"

to the top.

-- 
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke                      http://arbutus.physics.mcmaster.ca/dmc/
|co...@ph...

Re: [Numpy-discussion] fixing diag() for matrices

[Robert K. <rob...@gm...>]

Christopher Barker wrote:
> Keith Goodman wrote:
>> diag(NxN matrix) should return a Nx1 matrix
>> diag(Nx1 or 1xN matrix) should return a NxN matrix
> 
> This is the key problem: extracting the diagonal of a matrix and 
> creating a matrix from a diagonal are two different operations: 
> overloading one function to do both was a bad idea to begin with.
> 
> Maybe we should just keep diag() as is is for backward compatibility 
> (deprecated), and make:
> 
> get_diag() and make_diag() instead.
> 
> Then it would be unambiguous what you wanted with:
> 
> make_diag(<Nx1array>)
> 
> You can call them something else, but you get the idea.

As .diagonal() is a method on both arrays and matrices, we don't need a separate 
get_diag().

In the abstract, I heartily approve. However, I'm not sure that deprecation in 
favor of a clumsier name is going to be effective in practice. diag([a, b, c]) 
is just too tempting and will remain an attractive nuisance. It matches 
mathematical notation ever so nicely.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco

[Numpy-discussion] C Extension intp problem

[Nick F. <nv...@MI...>]

Dear all,

I had a working C extension, but after upgrading to a recent numpy  
from SVN, I can no longer compile it.  I've fixed the deprecation  
warnings, but can't get past this:

frgetvect.c:51: error: 'intp' undeclared (first use in this function)

Now, I'm quite confused since I thought that intp should be a global  
thing, not numpy related, and I believe I'm using the shipped Apple  
gcc version.

Version info:
nvf@whitedwarf:~/temp/v6r20/pyfrgetvect$ python -V
Python 2.4.1
nvf@whitedwarf:~/temp/v6r20/pyfrgetvect$ python -c "import numpy;  
print numpy.__version__"
1.1.2881
nvf@whitedwarf:~/temp/v6r20/pyfrgetvect$ gcc --version
powerpc-apple-darwin8-gcc-4.0.0 (GCC) 4.0.0 20041026 (Apple Computer,  
Inc. build 4061)

Can anyone help me?

Thanks,
Nick

Full build output:
nvf@whitedwarf:~/temp/v6r20/pyfrgetvect$ python setup.py build
running build
running build_ext
building 'frgetvect' extension
C compiler: gcc -fno-strict-aliasing -Wno-long-double -no-cpp-precomp  
-mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3 -Wall -Wstrict- 
prototypes

creating build
creating build/temp.darwin-8.7.0-Power_Macintosh-2.4
compile options: '-DMAJOR_VERSION=0 -DMINOR_VERSION=2 -I../src -I/ 
Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site- 
packages/numpy/core/include -I/Library/Frameworks/Python.framework/ 
Versions/2.4/include/python2.4 -c'
extra options: '-w'
gcc: frgetvect.c
frgetvect.c: In function 'frgetvect':
frgetvect.c:51: error: 'intp' undeclared (first use in this function)
frgetvect.c:51: error: (Each undeclared identifier is reported only once
frgetvect.c:51: error: for each function it appears in.)
frgetvect.c:51: error: parse error before "shape"
frgetvect.c:138: error: 'shape' undeclared (first use in this function)
frgetvect.c:138: error: parse error before "nData"
frgetvect.c: In function 'frgetvect':
frgetvect.c:51: error: 'intp' undeclared (first use in this function)
frgetvect.c:51: error: (Each undeclared identifier is reported only once
frgetvect.c:51: error: for each function it appears in.)
frgetvect.c:51: error: parse error before "shape"
frgetvect.c:138: error: 'shape' undeclared (first use in this function)
frgetvect.c:138: error: parse error before "nData"
error: Command "gcc -fno-strict-aliasing -Wno-long-double -no-cpp- 
precomp -mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3 -Wall - 
Wstrict-prototypes -DMAJOR_VERSION=0 -DMINOR_VERSION=2 -I../src -I/ 
Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site- 
packages/numpy/core/include -I/Library/Frameworks/Python.framework/ 
Versions/2.4/include/python2.4 -c frgetvect.c -o build/ 
temp.darwin-8.7.0-Power_Macintosh-2.4/frgetvect.o -w" failed with  
exit status 1

Re: [Numpy-discussion] Moving docstrings from C to Python

[David H. <dav...@gm...>]

2006/7/28, Stefan van der Walt <st...@su...>:
>
> Hi,
>
> Travis recently advertised the add_docstring and add_newdoc functions,
> which are used to add documentation to functions written in C.  This
> brings the advantage that you don't have to recompile everytime you
> change a docstrings, and also that the docstrings are much simpler to
> format (no more "\n\" tokens all over the place).
>
> I took all the docstrings from numpy.core.src.multiarray and formatted
> them to be used with add_docstring.  I have two questions:
>
> Would anyone mind if the change was made?


I don't, I wanted to do the same thing.

numpy/add_newdocs.py
+1

David

[Numpy-discussion] numpy.arcsin strange behavior?

[Poitras, D. <Dan...@nr...>]

Hello,=20
=20
I tried different arcsin functions on a "complex" number (z=3D0.52+0j) =
and obtained=20
the following results:=20
=20
cmath.asin(z) gives (0.54685095069594414+0j)   #okay=20
=20
-1j*log(1j*z+sqrt(1-z**2)) gives (0.54685095069594414+0j)   #okay, by =
definition=20
=20
numarray.arcsin(z) gives (0.54685095069594414+0j)   #still okay=20
=20
but=20
=20
numpy.arcsin(z) gives (0.54685095069594414+0.54685095069594414j)   =
#bug??=20
=20
Is it a bug in numpy, or is there another explanation?=20
=20
Thanks=20
=20
Daniel Poitras=20
Ottawa

Re: [Numpy-discussion] fixing diag() for matrices

[Christopher B. <Chr...@no...>]

Keith Goodman wrote:
> diag(NxN matrix) should return a Nx1 matrix
> diag(Nx1 or 1xN matrix) should return a NxN matrix

This is the key problem: extracting the diagonal of a matrix and 
creating a matrix from a diagonal are two different operations: 
overloading one function to do both was a bad idea to begin with.

Maybe we should just keep diag() as is is for backward compatibility 
(deprecated), and make:

get_diag() and make_diag() instead.

Then it would be unambiguous what you wanted with:

make_diag(<Nx1array>)

You can call them something else, but you get the idea.

-Chris

-- 
Christopher Barker, Ph.D.
Oceanographer
                                     		
NOAA/OR&R/HAZMAT         (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

Re: [Numpy-discussion] object array binary pickling and nan

[David M. C. <co...@ph...>]

On Fri, 28 Jul 2006 12:17:39 -0500
"Tom Denniston" <tom...@al...> wrote:

> It seems that object arrays will not pickle if they have a nan when
> the pickle protocol is set to binary.  From what I can tell the object
> array simply delegates to python (which makes sense) which in turn
> cannot pickle nans in binary format.  It is unfortunate because it is
> very useful to have heterogenous arrays that include nans.  What do
> other people do in this situation?  Does anyone know why python has
> this limitation?  Is there an intelligent workaround other than search
> and replace?  Would it be worth it to put an intelligent workaround
> into numpy so it is transparent to the user?  I was wondering what
> people thought.
> 
> Code that reproduces the problem:
> 
> This is regular python:
> pickle.dumps(numpy.nan, 2)
> SystemError: frexp() result out of range
> 
> This is fine in numpy:
> pickle.dumps(numpy.array([numpy.nan]), 2)
> 
> This breaks:
> pickle.dumps(numpy.array([numpy.nan], numpy.PyObject), 2)
> SystemError: frexp() result out of range

I believe this will be fixed in Python 2.5 (see
http://www.python.org/sf/445484). I guess your best alternative would be to
use a numpy scalar object (like double) instead of a Python float for NaN's.

-- 
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke                      http://arbutus.physics.mcmaster.ca/dmc/
|co...@ph...

Re: [Numpy-discussion] object array binary pickling and nan

[Andrew S. <str...@as...>]

Tom Denniston wrote:

>Code that reproduces the problem:
>
>This is regular python:
>pickle.dumps(numpy.nan, 2)
>SystemError: frexp() result out of range
>
>This is fine in numpy:
>pickle.dumps(numpy.array([numpy.nan]), 2)
>
>This breaks:
>pickle.dumps(numpy.array([numpy.nan], numpy.PyObject), 2)
>SystemError: frexp() result out of range
>  
>
It seems to me that the example that shows what you want is the only
example that numpy has control over, anyway. In the other two cases,
you're really using a Python object.

So it's really a Python question as to why it can't pickle nans. My
understanding on this is weak, but here's one summary, albeit dated:
http://www.python.org/dev/summary/2000-10-1.html Here's something more
recent: http://mail.python.org/pipermail/python-list/2005-July/290272.html

NumPy sidesteps these issues by "forcing" IEEE-754 behavior with clever
C written AFAIK by the numarray people. These apparently work even if
the C compiler doesn't "officially" support IEEE-754, but it's not going
to work on every platform that Python targets.

You may also want to take a look at
http://scipy.org/FAQ#head-fff4d6fce7528974185715153cfbc1a191dcb915

Re: [Numpy-discussion] uniform() regression(?) in svn

[Darren D. <dd...@co...>]

On Wednesday 26 July 2006 13:22, Travis Oliphant wrote:
> Andrew Jaffe wrote:
> > Hi-
> >
> > On PPC Mac OSX universal build 2.4.3, gcc 4.0,
> >
> >
> > In [1]: import numpy as N
> >
> > In [2]: print N.__version__
> > 1.0.2897
> >
> > In [3]: N.random.uniform(0,1)
> > Segmentation fault
> >
> > (This originally showed up in the Ticket 83 regression test during
> > numpy.test()...)
>
> This should be O.K. now.

I dont know if this is related or not, but I just did an update from the trunk 
('1.0b2.dev2918') and discovered the following error:

======================================================================
FAIL: Ticket #99
----------------------------------------------------------------------
Traceback (most recent call last):
  
File "/usr/lib64/python2.4/site-packages/numpy/core/tests/test_regression.py", 
line 182, in check_intp
    self.failUnlessRaises(OverflowError,N.intp,'0xb72a7008',16)
AssertionError: OverflowError not raised

----------------------------------------------------------------------

I'm compiling with gcc-4.1.1, python-2.4.3.

Darren

[Numpy-discussion] numpy.arcsin of complex number... strange

[Daniel P. <dan...@nr...>]

Hello,

I tried different arcsin functions on a complex number (z=0.52+0j) and obtained
the following results:

cmath.asin(z) gives (0.54685095069594414+0j) #okay

-1j*log(1j*z+sqrt(1-z**2)) gives (0.54685095069594414+0j) #okay, by definition

numarray.arcsin(z) gives (0.54685095069594414+0j) #still okay

but

numpy.arcsin(z) gives (0.54685095069594414+0.54685095069594414j) #bug??


Is it really a bug in numpy, or is there another explanation?


Thanks

Daniel Poitras
Ottawa

Re: [Numpy-discussion] Release notes please; possible bugs

[Tom L. <lo...@as...>]

PS:  Sorry, I should've mentioned it's 1.0b2.dev2915 that I'm using,
not 1.0b1.

-Tom


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[Numpy-discussion] Release notes please; possible bugs

[Tom L. <lo...@as...>]

Hi folks,

It is turning out to be much more of a headache moving from
numpy-0.9.x to numpy-1.0b1 than it ever was moving from Numeric
to numpy.  Could someone please throw together some release
notes (the scipy & sourceforge RN pages are blank) or a Wiki
page warning what has to be changed?  Here are some stumbling
blocks I've come across:

* Type names are all now lower case.  I know this started
happening late in 0.9.x, but I only know it by accident, via
some email from Travis.  This is just one example of the kind
of change that really needs to be prominently noted somewhere.

* .nonzero() now returns a 1-tuple with an array of indices, 
for a 1-d array.  Before it returned an array of indices.

* In the C API, ContiguousFromObject is now ContiguousFromAny.
I am surprised that my libraries compile with no errors; I
only get a runtime error.  Shouldn't I be warned about this
at compile-time?

* mtrand still uses ContiguousFromObject in a few places; these
give exceptions when you call, e.g., setstate.

* rand, fft, ift are no longer in the numpy namespace.  Is
this a permanent change or a bug?

There are probably other obstacles I'll come across, but this
is what I encountered in code that ran perfectly with 0.9.6.
After several hours of work it's still not running.

-Tom Loredo


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Re: [Numpy-discussion] weave and Numeric vs. weave and numpy

[Travis O. <oli...@ie...>]

Rolf Wester wrote:
> David Grant wrote:
>   
>> A factor of 2! Is that short for a factor of **2?
>>
>>     
> That means Numeric/weave takes about 17 s (on my PC), and the 
> numby/weave version takes about 32 s.
>   

Please show your code.  There are several factors that have changed 
here.   For example,  Python 2.4 is different that Python 2.3. 

There are only a few usages when Numeric is faster than NumPy and in 
most of these using regular Python lists is faster than Numeric and 
NumPy anyway. 

Nonetheless, there are still improvements that can be made to NumPy.  
Which version of NumPy are you using?


-Travis

[Numpy-discussion] object array binary pickling and nan

[Tom D. <tom...@al...>]

It seems that object arrays will not pickle if they have a nan when
the pickle protocol is set to binary.  From what I can tell the object
array simply delegates to python (which makes sense) which in turn
cannot pickle nans in binary format.  It is unfortunate because it is
very useful to have heterogenous arrays that include nans.  What do
other people do in this situation?  Does anyone know why python has
this limitation?  Is there an intelligent workaround other than search
and replace?  Would it be worth it to put an intelligent workaround
into numpy so it is transparent to the user?  I was wondering what
people thought.

Code that reproduces the problem:

This is regular python:
pickle.dumps(numpy.nan, 2)
SystemError: frexp() result out of range

This is fine in numpy:
pickle.dumps(numpy.array([numpy.nan]), 2)

This breaks:
pickle.dumps(numpy.array([numpy.nan], numpy.PyObject), 2)
SystemError: frexp() result out of range


--Tom

Re: [Numpy-discussion] fixing diag() for matrices

[Robert K. <rob...@gm...>]

Sven Schreiber wrote:
> Robert Kern schrieb:
>> Sven Schreiber wrote:
>>> That would be fine with me. However, I'd like to point out that after
>>> some bug-squashing currently all numpy functions deal with
>>> numpy-matrices correctly, afaik. The current behavior of numpy.diag
>>> could be viewed as a violation of that principle. (Because if x has
>>> shape (n,1), diag(x) returns only the first entry, which is pretty
>>> stupid for a diag-function operating on a vector.)
>> I don't think so. It's operating exactly as it is documented to. It doesn't do 
>> what you want, but it's not supposed to read your mind and guess that you are 
>> treating (n,1) and (1,n) arrays as linear algebraic vectors and that you want to 
>> form a diagonal matrix from that vector. It handles matrix objects just fine; it 
>> does not obey a particular convention that you want to use, though. That's 
>> neither broken nor a violation of the principle that most numpy functions should 
>> accept matrix objects; it's just not what you want.
> 
> Ok, let me get some facts straight:
> 
> 1. If you're using numpy-matrices, algebraic vectors are *automatically*
> (n,1) or (1,n). I didn't make it up, and nobody has to read my mind to
> understand that; it's just a *fact* of numpy (and you knew that long
> before I was even aware of numpy's existence ;-).
> 
> 2. I never claimed that the documentation of diag is wrong. I'm just
> questioning whether its behavior with vectors represented as
> numpy-matrices (which have shape (n,1) or (1,n) and are therefore always
> 2d in the numpy sense) is really intended, because I currently doubt
> that it's useful for *anyone*. (Of course you can prove me wrong there...)
> 
> 3. The cited principle is not (only) about accepting matrices, it's
> about "honoring" them by either preserving their type for the output,
> and of course by doing the equivalent thing as for the equivalent
> numpy-array input. Currently, however, diag() is not providing the
> "vector-to-diagonal-matrix" functionality if you work with
> numpy-matrices (modulo some obvious workarounds). To me, that's a partly
> broken function, and it's *not* handling matrix objects "just fine".

Unfortunately, there is a many-to-one correspondence between numpy-array inputs 
and matrix-array inputs. Going the other way is ambiguous. There is still some 
guessing involved.

>> I don't want to introduce a backwards-compatibility-breaking special case to the 
>> function. "Special cases aren't special enough to break the rules." Different 
>> functionality should go into a different function.
> 
> I hope (but somehow doubt...) that I've convinced you that it's actually
> about applying the same logical rule to all input types, not about
> introducing a special case.

But you can't code it without special casing. None of the functions should be 
written like this:

   if type(input) is matrix:
     # do this one thing
   else:
     # do a different thing

They belong in two different functions. Or methods. You can't extend it to the n 
different subclasses of ndarray that people might want to use. This kind of 
special casing ought to give you screaming jibblies. The current behavior of 
diag() already gives me the screaming jibblies given that it both extracts a 
diagonal from an MxN array and creates a NxN array from an N array. <shudder>

If I wrote an API like that at work, my project manager would come over to my 
office and Have Words with me. And then tell me to rewrite it. It's simply bad 
design.

I think making a method on matrix objects that does what you want is probably 
your best bet.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco

Re: [Numpy-discussion] possible bug in seed argument

[Travis O. <oli...@ie...>]

Travis Oliphant wrote:
> I can verify that this now works with numpy 1.0b1 on Python 2.3.  The 
> change was using an updated Pyrex for generating mtrand.c (it no longer 
> calls PyLong_AsUnsignedLong on integer objects as it did before).
>
>   

Scratch that.  I meant it works with latest numpy (will be 1.0b2) 
because the mtrand.c was not re-generated until recently.

-Travis

Re: [Numpy-discussion] possible bug in seed argument

[Travis O. <oli...@ie...>]

Gary Ruben wrote:
> See below:
>
> Robert Kern wrote:
>   
>> Gary Ruben wrote:
>>     
>>> Should
>>>  >>> seed(1)
>>> act the same as
>>>  >>> seed(array([1]))
>>> in the random module?
>>>       
>> No. They use slightly different mechanisms to seed. The integer uses RandomKit's 
>> seeding routine. I borrowed Python's mechanism for seeding from an array of 
>> integers. Now that it comes up, though, it is probably a good idea to use the 
>> same mechanism for both cases.
>>
>>     
>>> It generates a traceback with the Windows 1.0b1 binary.
>>>       
>> Please always copy-and-paste tracebacks when reporting bugs. It works for me 
>> with r2881; I'll rebuild with a later version and try again.
>>     
>
> Thanks Robert,
> Here it is for reference. Not a very deep traceback:
>   

It looks like this is a Python 2.3 error.  In Python 2.4, the 
PyLong_AsUnsignedLong was changed to accept Python  integers as well as 
Python longs. 

The problem with fixing it is I'm not sure where PyLong_AsUnsignedLong 
is being called.  I can't find that call in the current source code.  
It's possible that Pyrex changed and this function is no longer being 
used in mtrand.c

-Travis

Re: [Numpy-discussion] possible bug in seed argument

[Travis O. <oli...@ie...>]

Gary Ruben wrote:
> See below:
>
> Robert Kern wrote:
>   
>> Gary Ruben wrote:
>>     
>>> Should
>>>  >>> seed(1)
>>> act the same as
>>>  >>> seed(array([1]))
>>> in the random module?
>>>       
>> No. They use slightly different mechanisms to seed. The integer uses RandomKit's 
>> seeding routine. I borrowed Python's mechanism for seeding from an array of 
>> integers. Now that it comes up, though, it is probably a good idea to use the 
>> same mechanism for both cases.
>>
>>     
>>> It generates a traceback with the Windows 1.0b1 binary.
>>>       
>> Please always copy-and-paste tracebacks when reporting bugs. It works for me 
>> with r2881; I'll rebuild with a later version and try again.
>>     

I can verify that this now works with numpy 1.0b1 on Python 2.3.  The 
change was using an updated Pyrex for generating mtrand.c (it no longer 
calls PyLong_AsUnsignedLong on integer objects as it did before).

-Travis

Re: [Numpy-discussion] ***[Possible UCE]*** Bug in memmap/python allocation code?

[Mike R. <mik...@al...>]

On 7/24/06, Travis Oliphant <oli...@ie...> wrote:
>
> Mike Ressler wrote:
> > I'm trying to work with memmaps on very large files, i.e. > 2 GB, up
> > to 10 GB.


Can't believe I'm really the first, but so be it.

I just discovered the problem.  All the places where
> PyObject_As<Read/Write>Buffer is used needs to have the final argument
> changed to Py_ssize_t (which in arrayobject.h is defined as int if you
> are using less than Python 2.5).
>
> This should be fixed in SVN shortly....


Yeess! My little script can handle everything I've thrown at it now. It can
read a 10 GB raw file, strip the top 16 bits, rearrange pixels, byte swap,
and write it all back to a 5 GB file in 16 minutes flat. Not bad at all. And
I've verified that the output is correct ...

If someone can explain the rules of engagement for Lightning Talks, I'm
thinking about presenting this at SciPy 2006. Then you'll see there is a
reason for my madness.

As an aside, the developer pages could use some polish on explaining the
different svn areas, and how to get what one wants. An svn checkout as
described on the page gets you the 1.1 branch that DOES NOT have the updated
memmap fix. After a minute or two of exploring, I found that  "svn co
http://svn.scipy.org/svn/numpy/branches/ver1.0/numpy numpy" got me what I
wanted.

Thanks for your help and the quick solution. FWIW, I got my copy of the book
a couple of weeks ago; very nice.

Mike

-- 
mik...@al...

[Numpy-discussion] Moving docstrings from C to Python

[Stefan v. d. W. <st...@su...>]

Hi,

Travis recently advertised the add_docstring and add_newdoc functions,
which are used to add documentation to functions written in C.  This
brings the advantage that you don't have to recompile everytime you
change a docstrings, and also that the docstrings are much simpler to
format (no more "\n\" tokens all over the place).

I took all the docstrings from numpy.core.src.multiarray and formatted
them to be used with add_docstring.  I have two questions:

Would anyone mind if the change was made?  If not, where should they
go? (numpy/add_newdocs.py or numpy/core/something)

Thanks for the feedback.
St=E9fan

Re: [Numpy-discussion] how to get an array with "varying" poisson distribution

[Travis O. <oli...@ee...>]

Sebastian Haase wrote:

>Hi,
>Essentially I'm looking  for the equivalent of what was in numarray:
>    from numarray import random_array
>    random_array.poisson(arr)
>
>
>  
>
I've just updated the mtrand library to allow this.  It will be in 1.0b2

So, if you have latest SVN you can do

from numpy import random
random.poisson(arr)

to do what you want.



-Travis

[Numpy-discussion] =?GB2312?B?cmU7s7W85LncwO3Iy9SxsMvP7tDewbY=?=

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Re: [Numpy-discussion] possible bug in seed argument

[Gary R. <gr...@bi...>]

See below:

Robert Kern wrote:
> Gary Ruben wrote:
>> Should
>>  >>> seed(1)
>> act the same as
>>  >>> seed(array([1]))
>> in the random module?
> 
> No. They use slightly different mechanisms to seed. The integer uses RandomKit's 
> seeding routine. I borrowed Python's mechanism for seeding from an array of 
> integers. Now that it comes up, though, it is probably a good idea to use the 
> same mechanism for both cases.
> 
>> It generates a traceback with the Windows 1.0b1 binary.
> 
> Please always copy-and-paste tracebacks when reporting bugs. It works for me 
> with r2881; I'll rebuild with a later version and try again.

Thanks Robert,
Here it is for reference. Not a very deep traceback:

 >>> seed(1)

Traceback (most recent call last):
   File "<pyshell#2>", line 1, in -toplevel-
     seed(1)
   File "mtrand.pyx", line 311, in mtrand.RandomState.seed
SystemError: C:\sf\python\dist23\src\Objects\longobject.c:240: bad 
argument to internal function