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

Re: [Numpy-discussion] Release of 1.0b5 this weekend

[Fernando P. <fpe...@gm...>]

On 8/29/06, Charles R Harris <cha...@gm...> wrote:

> Speaking of features, I wonder if more of the methods should return
> references. For instance, it might be nice to write something like:
>
>  a.sort().searchsorted([...])
>
> instead of making two statements out of it.

+1 for more 'return self' at the end of methods which currently don't
return anything (well, we get the default None), as long as it's
sensible.  I really like this 'message chaining' style of programming,
and it annoys me that much of the python stdlib gratuitously prevents
it by NOT returning self in places where it would be a perfectly
sensible thing to do.

I find it much cleaner to write

x = foo.bar().baz(param).frob()

than

foo.bar()
foo.baz(param)
x = foo.frob()

but perhaps others disagree.

Cheers,

f

Re: [Numpy-discussion] Release of 1.0b5 this weekend

[Charles R H. <cha...@gm...>]

Hi Travis,

On 8/29/06, Travis Oliphant <oli...@ie...> wrote:
>
>
> Hi all,
>
> Classes start for me next Tuesday, and I'm teaching a class for which I
> will be using NumPy / SciPy extensively.  I need to have a release of
> these two (and hopefully matplotlib) that work with each other.
>
> Therefore, I'm going to make a 1.0b5 release of NumPy over the weekend
> (probably Monday), and also get a release of SciPy out as well.  At that
> point, I'll only be available for bug-fixes to 1.0.  Therefore, the next
> release after 1.0b5 I would like to be 1.0rc1 (release-candidate 1).
>
> To facilitate that, after 1.0b5 there will be a feature-freeze (except
> for in the compatibility modules and the alter_code scripts which can
> still be modified to ease the transition burden).


Speaking of features, I wonder if more of the methods should return
references. For instance, it might be nice to write something like:

a.sort().searchsorted([...])

instead of making two statements out of it.

The 1.0rc1 release of NumPy will be mid September I suspect.
>
> Also, I recognize that the default-axis switch is a burden for those who
> have already transitioned code to use NumPy (for those just starting out
> it's not a big deal because of the compatibility layer).


I am curious as to why you made this switch. Not complaining, mind.

Chuck

Re: [Numpy-discussion] For loop tips

[Alan G I. <ai...@am...>]

You can get some speed up for numeric data:
def list2index2(L):
  aL =3D asarray(L)
  eL =3D empty_like(L)
  for v,k in enumerate(set(L)):
  =09eL[aL =3D=3D k] =3D v
  return numpy.asmatrix(eL).T

fwiw,
Alan Isaac

Re: [Numpy-discussion] For loop tips

[Torgil S. <tor...@gm...>]

def list2index(L):
    idx=dict((y,x) for x,y in enumerate(set(L)))
    return asmatrix(fromiter((idx[x] for x in L),dtype=int))

# old
$ python test.py
Numbers: 29.4062280655 seconds
Characters: 84.6239070892 seconds
Dates: 117.560418844 seconds

# new
$ python test.py
Numbers: 1.79700994492 seconds
Characters: 1.6025249958 seconds
Dates: 1.7974088192 seconds

16, 52 and 100 times faster

//Torgil

On 8/29/06, Keith Goodman <kwg...@gm...> wrote:
> I have a very long list that contains many repeated elements. The
> elements of the list can be either all numbers, or all strings, or all
> dates [datetime.date].
>
> I want to convert the list into a matrix where each unique element of
> the list is assigned a consecutive integer starting from zero.
>
> I've done it by brute force below. Any tips for making it faster? (5x
> would make it useful; 10x would be a dream.)
>
> >> list2index.test()
> Numbers: 5.84955787659 seconds
> Characters: 24.3192870617 seconds
> Dates: 39.288228035 seconds
>
>
> import datetime, time
> from numpy import nan, asmatrix, ones
>
> def list2index(L):
>
>   # Find unique elements in list
>   uL = dict.fromkeys(L).keys()
>
>   # Convert list to matrix
>   L = asmatrix(L).T
>
>   # Initialize return matrix
>   idx = nan * ones((L.size, 1))
>
>   # Assign numbers to unique L values
>   for i, uLi in enumerate(uL):
>     idx[L == uLi,:] = i
>
> def test():
>
>     L = 5000*range(255)
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Numbers:', t2-t1, 'seconds'
>
>     L = 5000*[chr(z) for z in range(255)]
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Characters:', t2-t1, 'seconds'
>
>     d = datetime.date
>     step = datetime.timedelta
>     L = 5000*[d(2006,1,1)+step(z) for z in range(255)]
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Dates:', t2-t1, 'seconds'
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

[Numpy-discussion] Release of 1.0b5 this weekend

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

Hi all,

Classes start for me next Tuesday, and I'm teaching a class for which I 
will be using NumPy / SciPy extensively.  I need to have a release of 
these two (and hopefully matplotlib) that work with each other.

Therefore, I'm going to make a 1.0b5 release of NumPy over the weekend 
(probably Monday), and also get a release of SciPy out as well.  At that 
point, I'll only be available for bug-fixes to 1.0.  Therefore, the next 
release after 1.0b5 I would like to be 1.0rc1 (release-candidate 1). 

To facilitate that, after 1.0b5 there will be a feature-freeze (except 
for in the compatibility modules and the alter_code scripts which can 
still be modified to ease the transition burden).

The 1.0rc1 release of NumPy will be mid September I suspect.

Also, I recognize that the default-axis switch is a burden for those who 
have already transitioned code to use NumPy (for those just starting out 
it's not a big deal because of the compatibility layer).  As a result, 
I've added a module called fix_default_axis whose converttree method 
will walk a hierarchy and change all .py files to fix the default axis 
problem in those files.  This can be done in one of two ways (depending 
on the boolean argument import_change).

If import_change is False

   a) Add and axis=<olddefault> keyword argument to any function whose 
default changed in 1.0b2 or 1.0b3,
      which does not already have the axis argument --- this method does 
not distinguish where the
      function came from and so can do the wrong thing with similarly 
named functions from other modules
      (.e.g. builtin sum and itertools.repeat).

If import_change is True

   b) Change the location where the function is imported from numpy to 
numpy.oldnumeric where the default axis
      is the same as before.  This approach looks for several flavors of 
the import statement and alters the
      import location for any function whose default axis argument 
changed --- this can get confused if you
      use from numpy import sum as mysum --- it will not replace that 
usage of sum.

I used this script on the scipy tree in mode a) as a test (followed by a 
manual replacement of all? incorrect substitutions).  I hope it helps.  
I know it's annoying to have such things change.  But, it does make 
NumPy much more consistent with respect to the default axis argument.

With a few exceptions (concatenate, diff, trapz, split, array_split), 
the rule is that you need to specify the axis if there is more than 1 
dimension or it will ravel the input.

-Travis

Re: [Numpy-discussion] For loop tips

[Tim H. <tim...@ie...>]

Tim Hochberg wrote:
> Keith Goodman wrote:
>   
>> I have a very long list that contains many repeated elements. The
>> elements of the list can be either all numbers, or all strings, or all
>> dates [datetime.date].
>>
>> I want to convert the list into a matrix where each unique element of
>> the list is assigned a consecutive integer starting from zero.
>>   
>>     
> If what you want is that the first unique element get's zero, the second 
> one, I don't think the code below will work in general since the dict 
> does not preserve order. You might want to look at the results for the 
> character case to see what I mean. If you're looking for something else, 
> you'll need to elaborate a bit. Since list2index doesn't return 
> anything, it's not entirely clear what the answer consists of. Just idx? 
> Idx plus uL?
>
>   
>> I've done it by brute force below. Any tips for making it faster? (5x
>> would make it useful; 10x would be a dream.)
>>   
>>     
> Assuming I understand what you're trying to do, this might help:
>
>     def list2index2(L):
>         idx = ones([len(L)])
>         map = {}
>         for i, x in enumerate(L):
>             index = map.get(x)
>             if index is None:
>                 map[x] = index = len(map)
>             idx[i] = index
>         return idx
>
>
> It's almost 10x faster for numbers and about 40x faster for characters 
> and dates. However it produces different results from list2index in the 
> second two cases. That may or may not be a good thing depending on what 
> you're really trying to do.
>   
Ugh! I fell victim to premature optimization disease. The following is 
both clearer and faster: Sigh.

    def list2index3(L):
        idx = ones([len(L)])
        map = {}
        for i, x in enumerate(L):
            if x not in map:
                map[x] = len(map)
            idx[i] = map[x]
        return idx



> -tim
>
>   
>>   
>>     
>>>> list2index.test()
>>>>       
>>>>         
>> Numbers: 5.84955787659 seconds
>> Characters: 24.3192870617 seconds
>> Dates: 39.288228035 seconds
>>
>>
>> import datetime, time
>> from numpy import nan, asmatrix, ones
>>
>> def list2index(L):
>>
>>   # Find unique elements in list
>>   uL = dict.fromkeys(L).keys()
>>
>>   # Convert list to matrix
>>   L = asmatrix(L).T
>>
>>   # Initialize return matrix
>>   idx = nan * ones((L.size, 1))
>>
>>   # Assign numbers to unique L values
>>   for i, uLi in enumerate(uL):
>>     idx[L == uLi,:] = i
>>
>> def test():
>>
>>     L = 5000*range(255)
>>     t1 = time.time()
>>     idx = list2index(L)
>>     t2 = time.time()
>>     print 'Numbers:', t2-t1, 'seconds'
>>
>>     L = 5000*[chr(z) for z in range(255)]
>>     t1 = time.time()
>>     idx = list2index(L)
>>     t2 = time.time()
>>     print 'Characters:', t2-t1, 'seconds'
>>
>>     d = datetime.date
>>     step = datetime.timedelta
>>     L = 5000*[d(2006,1,1)+step(z) for z in range(255)]
>>     t1 = time.time()
>>     idx = list2index(L)
>>     t2 = time.time()
>>     print 'Dates:', t2-t1, 'seconds'
>>
>> -------------------------------------------------------------------------
>> Using Tomcat but need to do more? Need to support web services, security?
>> Get stuff done quickly with pre-integrated technology to make your job easier
>> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
>> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
>> _______________________________________________
>> Numpy-discussion mailing list
>> Num...@li...
>> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>>
>>
>>   
>>     
>
>
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
>   

Re: [Numpy-discussion] For loop tips

[Tim H. <tim...@ie...>]

Keith Goodman wrote:
> I have a very long list that contains many repeated elements. The
> elements of the list can be either all numbers, or all strings, or all
> dates [datetime.date].
>
> I want to convert the list into a matrix where each unique element of
> the list is assigned a consecutive integer starting from zero.
>   
If what you want is that the first unique element get's zero, the second 
one, I don't think the code below will work in general since the dict 
does not preserve order. You might want to look at the results for the 
character case to see what I mean. If you're looking for something else, 
you'll need to elaborate a bit. Since list2index doesn't return 
anything, it's not entirely clear what the answer consists of. Just idx? 
Idx plus uL?

> I've done it by brute force below. Any tips for making it faster? (5x
> would make it useful; 10x would be a dream.)
>   
Assuming I understand what you're trying to do, this might help:

    def list2index2(L):
        idx = ones([len(L)])
        map = {}
        for i, x in enumerate(L):
            index = map.get(x)
            if index is None:
                map[x] = index = len(map)
            idx[i] = index
        return idx


It's almost 10x faster for numbers and about 40x faster for characters 
and dates. However it produces different results from list2index in the 
second two cases. That may or may not be a good thing depending on what 
you're really trying to do.

-tim

>   
>>> list2index.test()
>>>       
> Numbers: 5.84955787659 seconds
> Characters: 24.3192870617 seconds
> Dates: 39.288228035 seconds
>
>
> import datetime, time
> from numpy import nan, asmatrix, ones
>
> def list2index(L):
>
>   # Find unique elements in list
>   uL = dict.fromkeys(L).keys()
>
>   # Convert list to matrix
>   L = asmatrix(L).T
>
>   # Initialize return matrix
>   idx = nan * ones((L.size, 1))
>
>   # Assign numbers to unique L values
>   for i, uLi in enumerate(uL):
>     idx[L == uLi,:] = i
>
> def test():
>
>     L = 5000*range(255)
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Numbers:', t2-t1, 'seconds'
>
>     L = 5000*[chr(z) for z in range(255)]
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Characters:', t2-t1, 'seconds'
>
>     d = datetime.date
>     step = datetime.timedelta
>     L = 5000*[d(2006,1,1)+step(z) for z in range(255)]
>     t1 = time.time()
>     idx = list2index(L)
>     t2 = time.time()
>     print 'Dates:', t2-t1, 'seconds'
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
>   

[Numpy-discussion] For loop tips

[Keith G. <kwg...@gm...>]

I have a very long list that contains many repeated elements. The
elements of the list can be either all numbers, or all strings, or all
dates [datetime.date].

I want to convert the list into a matrix where each unique element of
the list is assigned a consecutive integer starting from zero.

I've done it by brute force below. Any tips for making it faster? (5x
would make it useful; 10x would be a dream.)

>> list2index.test()
Numbers: 5.84955787659 seconds
Characters: 24.3192870617 seconds
Dates: 39.288228035 seconds


import datetime, time
from numpy import nan, asmatrix, ones

def list2index(L):

  # Find unique elements in list
  uL = dict.fromkeys(L).keys()

  # Convert list to matrix
  L = asmatrix(L).T

  # Initialize return matrix
  idx = nan * ones((L.size, 1))

  # Assign numbers to unique L values
  for i, uLi in enumerate(uL):
    idx[L == uLi,:] = i

def test():

    L = 5000*range(255)
    t1 = time.time()
    idx = list2index(L)
    t2 = time.time()
    print 'Numbers:', t2-t1, 'seconds'

    L = 5000*[chr(z) for z in range(255)]
    t1 = time.time()
    idx = list2index(L)
    t2 = time.time()
    print 'Characters:', t2-t1, 'seconds'

    d = datetime.date
    step = datetime.timedelta
    L = 5000*[d(2006,1,1)+step(z) for z in range(255)]
    t1 = time.time()
    idx = list2index(L)
    t2 = time.time()
    print 'Dates:', t2-t1, 'seconds'

[Numpy-discussion] Documentation

[Charles R H. <cha...@gm...>]

Hi All,

I've finished moving all the docstrings in arraymethods to add_newdocs. Much
of the documentation is still incomplete and needs nicer formatting, so if
you are so inclined, or even annoyed with some of the help messages, feel
free to fix things up.

Chuck

[Numpy-discussion] py2exe error

[<kor...@id...>]

>Hi, Travis

>I can pack my scripts into an executable with py2exe, but errors occur
>once it runs:

>No scipy-style subpackage 'random' found in D:\test\dist\numpy.
>Ignoring: No module named info
>import core -> failed: No module named _internal
>import lib -> failed: 'module' object has no attribute '_ARRAY_API'
>import linalg -> failed: 'module' object has no attribute '_ARRAY_API'
>import dft -> failed: 'module' object has no attribute '_ARRAY_API'
>Traceback (most recent call last):
>  File "main.py", line 9, in ?
>  File "numpy\__init__.pyc", line 49, in ?
>    

>  File "numpy\add_newdocs.pyc", line 2, in ?
>    gkDc
>  File "numpy\lib\__init__.pyc", line 5, in ?
>
>  File "numpy\lib\type_check.pyc", line 8, in ?
>
>  File "numpy\core\__init__.pyc", line 6, in ?
>
>  File "numpy\core\umath.pyc", line 12, in ?
>
>  File "numpy\core\umath.pyc", line 10, in __load

I am cross referencing this from the py2exe mailing list.  There seems to
have been a fix for this problem
#---------------------------begining of setup.py--------------------#
from distutils.core import setup
import py2exe

from distutils.filelist import findall
import os
import matplotlib
matplotlibdatadir = matplotlib.get_data_path()
matplotlibdata = findall(matplotlibdatadir)
matplotlibdata_files = []
for f in matplotlibdata:
    dirname = os.path.join('matplotlibdata', f[len(matplotlibdatadir)+1:])
    matplotlibdata_files.append((os.path.split(dirname)[0], [f]))

packages = ['matplotlib', 'pytz']
includes = []
excludes = []
dll_excludes = ['libgdk_pixbuf-2.0-0.dll',
                'libgobject-2.0-0.dll',
                'libgdk-win32-2.0-0.dll',
                'wxmsw26uh_vc.dll']


opts = { 'py2exe': { 'packages' : packages,
                     'includes' : includes,
                     'excludes' : excludes,
                     'dll_excludes' : dll_excludes
                    }
         }

setup ( console=['test.py'],
        options = opts,
        data_files = matplotlibdata_files
        )

#--------------------------End of setup.py--------------#



>>1) First Problem: numpy\core\_internal.pyc not included in Library.zip

>>No scipy-style subpackage 'core' found in C:\WinCE\Traces\py2exe
>>test\dist\library.zip\numpy. Ignoring: No module named _internal
>>Traceback (most recent call last):
>>  File "profiler_ftt.py", line 15, in ?
>>    from matplotlib.backends.backend_wx import Toolbar, FigureCanvasWx,\
>>  File "matplotlib\backends\backend_wx.pyc", line 152, in ?
>>  File "matplotlib\backend_bases.pyc", line 10, in ?
>>  File "matplotlib\colors.pyc", line 33, in ?
>>  File "matplotlib\numerix\__init__.pyc", line 67, in ?
>>  File "numpy\__init__.pyc", line 35, in ?
>>  File "numpy\_import_tools.pyc", line 173, in __call__
>> File "numpy\_import_tools.pyc", line 68, in _init_info_modules
>>  File "<string>", line 1, in ?
>>  File "numpy\lib\__init__.pyc", line 5, in ?
>>  File "numpy\lib\type_check.pyc", line 8, in ?
>>  File "numpy\core\__init__.pyc", line 6, in ?
>>  File "numpy\core\umath.pyc", line 12, in ?
>>  File "numpy\core\umath.pyc", line 10, in __load
>>AttributeError: 'module' object has no attribute '_ARRAY_API'

>>I resolved that issue by adding the file
>>...\Python24\Lib\site-packages\numpy\core\_internal.pyc in
>>...\test\dist\library.zip\numpy\core.
>>Each time I compile that executable, I add the file by hand.
>>Does anybody know how to automatically add that file?

the setup.py was from the person who wrote the instructions for this fix.

also here is my setup.py just for reference although mine is probably
incorrect due to me being new with py2exe

#------------------------setup.py------------------------#
from distutils.core import setup
import py2exe

from distutils.filelist import findall
import os
import matplotlib
matplotlibdatadir = matplotlib.get_data_path()
matplotlibdata = findall(matplotlibdatadir)
matplotlibdata_files = []
for f in matplotlibdata:
    dirname = os.path.join('matplotlibdata', f[len(matplotlibdatadir)+1:])
    matplotlibdata_files.append((os.path.split(dirname)[0], [f]))


setup(
    console=['templatewindow.py'],
    options={
             "py2exe": {
                        "compressed": 1,
                        "optimize": 2,
                        "packages": ["encodings",
                                    "kinterbasdb",
                                    "pytz.zoneinfo.UTC",
                                    "matplotlib.numerix",
                                    ],
                        "dll_excludes": ["tcl84.dll", "tk84.dll"]
            }
mpldata = glob.glob(r'C:\Python24\share\matplotlib\*')
mpldata.append(r'C:\Python24\share\matplotlib\.matplotlibrc')

       data_files = [("prog\\locale\\fr\\LC_MESSAGES",
                        mylocaleFR),
                     ("prog\\locale\\de\\LC_MESSAGES",
                        mylocaleDE),
                     ("prog\\locale\\en\\LC_MESSAGES",
                        mylocaleEN),
...
                     ("matplotlibdata", mpldata),
                     ("prog\\amaradata", amaradata),
                     ("prog\\amaradata\\Schemata", amaraschemata),
                     ]


    )
#-----------------------EOF-----------------#


I was receiving this same "AttributeError: 'module' object has no
attribute '_ARRAY_API'" error, and i did the same thing this person did,
unzipped the folder, put the _internal.pyc file in the numpy/core folder
and then rezipped the folder and I am receiving a wx error, but the numpy
array_api error is gone.  You may want to check this out and let us know
if it works for you also.

-Kenny


p.s. i tried sending this 4 times prior but believe it did not send
because it was alot longer so i shortened it, sorry if it posted 4 times

Re: [Numpy-discussion] Is numpy supposed to support the buffer protocol?

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

Robin Dunn wrote:
> BTW Chris, try using buffer(RGB) and buffer(Alpha) in your sample, I 
> expect that will work with the current code.

yup. that does work. I was concerned that it would make a copy, but it 
looks like it makes a new buffer object, but using the same data buffer, 
so that should be fine.

Thanks for all this,

-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] possible bug with numpy.object_

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

Matt Knox wrote:
> is the following behaviour expected? or is this a bug with 
> numpy.object_  ?  I'm using numpy 1.0b1
>  
> >>> print numpy.array([],numpy.float64).size
> 0
>
> >>> print numpy.array([],numpy.object_).size
> 1
>
> Should the size of an array initialized from an empty list not always 
> be 1 ? or am I just crazy?
>  
Not in this case.  Explictly creating an object array from any object 
(even the empty-list object) gives you a 0-d array containing that 
object.   When you explicitly create an object array a different section 
of code handles it and gives this result.  This is a recent change, and 
I don't think this use-case was considered as a backward incompatibility 
(which I believe it is).   Perhaps we should make it so array([],....) 
always returns an empty array.   I'm not sure.   Comments?

-Travis

[Numpy-discussion] Problem with randn

[Keith G. <kwg...@gm...>]

randn incorrectly returns random numbers only between 0 and 1 in numpy
1.0b1. random.randn works.

>> from numpy.matlib import *

>> randn(3,4)

matrix([[ 0.60856413,  0.35500732,  0.48089868,  0.7044022 ],
        [ 0.71098538,  0.8506885 ,  0.56154652,  0.4243273 ],
        [ 0.89655777,  0.92339559,  0.62247685,  0.70340003]])

>> randn(3,4)

matrix([[ 0.84349201,  0.55638171,  0.19052097,  0.0927636 ],
        [ 0.60144183,  0.3788309 ,  0.41451568,  0.61766302],
        [ 0.98992704,  0.94276652,  0.18569066,  0.69976656]])

>> randn(3,4)

matrix([[ 0.69003273,  0.07171546,  0.34549767,  0.20901683],
        [ 0.1333439 ,  0.4086678 ,  0.80960253,  0.86864547],
        [ 0.75329427,  0.6760677 ,  0.32496542,  0.99402779]])

>> random.randn(3,4)

array([[ 1.00107604,  0.41418557, -0.07923699,  0.19203247],
       [-0.29386593,  0.02343702, -0.42366834, -1.27978993],
       [ 0.25722357, -0.53765827,  0.50569238, -2.44592854]])

[Numpy-discussion] error in ctypes example from the numpy book?

[W. B. S. <cs...@gm...>]

hi,

i posted this to the forum, but it looks like the email list gets much more
traffic, so here goes.

i am attempting to reproduce a portion of the example on using ctypes from
the current version of the numpy book (the example can be found on pps
313-16).

here is what i am trying to do:

import numpy
import interface

x = numpy.array(range(1,1))
y = numpy.ones_like(x)
z = interface.add(a,b)

prints the following error:
BEGIN ERROR>>
26 b = N.require(b, dtype, requires)
27 c = N.empty_like(a)
---> 28 func(a,b,c,a.size)
29 return c
30

ArgumentError: argument 1: exceptions.TypeError: Don't know how to convert
parameter 1
<<END ERROR

In case it matters, here are the relevant bits of code I am using (since I
have not just copied the entire text from the example):

BEGIN testAddInt.c >>
/* Add arrays of contiguous data */
typedef struct {double real;} cdouble;
typedef struct {float real;} cfloat;

void dadd(double *a, double *b, double *c, long n)
{
while (n--) {
*c++ = *a++ + *b++;
}
}
void sadd(float *a, float *b, float *c, long n)
{
while (n--) {
*c++ = *a++ + *b++;
}
}

<<END testAddInt.c

and the interface code:
BEGIN interface.py>>

__all__ = ['add']

import numpy as N
from ctypes import *
import os

_path = os.path.dirname('__file__')

lib = N.ctypeslib.ctypes_load_library('testAddInt', _path)

for name in ['sadd','dadd']:
getattr(lib,name).restype=None

def select(dtype):
if dtype.char in['?bBhHf']:
return lib.sadd, single
else:
return lib.dadd, float
return func, ntype

def add(a,b):
requires = ['CONTIGUOUS','ALIGNED']
a = N.asanyarray(a)
func, dtype = select(a.dtype)
a = N.require(a, dtype, requires)
b = N.require(b, dtype, requires)
c = N.empty_like(a)
func(a,b,c,a.size)
return c
<<END interface.py

any hints/clues/suggestions as to what i am doing wrong here? since i am so
new at this, i am assuming there is a simple mistake in what i am doing, but
i cannot find it! thanks so much,

bryan

Re: [Numpy-discussion] possible bug with numpy.object_

[Matt K. <mat...@ho...>]

# is the following behaviour expected? or is this a bug with numpy.object_ =
 ?  I'm using numpy 1.0b1#  # >>> print numpy.array([],numpy.float64).size#=
 0## >>> print numpy.array([],numpy.object_).size# 1## Should the size of a=
n array initialized from an empty list not always be 1 ? or am I just crazy=
?## Thanks,# # - Matt Knox
=20
Correction... I mean shouldn't it always be 0, not 1
_________________________________________________________________
Be one of the first to try Windows Live Mail.
http://ideas.live.com/programpage.aspx?versionId=3D5d21c51a-b161-4314-9b0e-=
4911fb2b2e6d=

[Numpy-discussion] possible bug with numpy.object_

[Matt K. <mat...@ho...>]

is the following behaviour expected? or is this a bug with numpy.object_  ?=
  I'm using numpy 1.0b1
=20
>>> print numpy.array([],numpy.float64).size0
>>> print numpy.array([],numpy.object_).size1
Should the size of an array initialized from an empty list not always be 1 =
? or am I just crazy?
=20
Thanks,
=20
- Matt Knox
_________________________________________________________________
Be one of the first to try Windows Live Mail.
http://ideas.live.com/programpage.aspx?versionId=3D5d21c51a-b161-4314-9b0e-=
4911fb2b2e6d=

[Numpy-discussion] Py2exe / numpy troubles

[tristan C. <tco...@ya...>]

Hello,
 
I am having troubles with py2exe and numpy/matplotlib...

Configuration : 
    Windows XP pro
    ActivePython 2.4.2.10
    Scipy 0.4.9
    Numpy 0.9.8
    MatplotLib 0.87.1
    Py2exe 0.6.5
    WxPython 2.6
 
I am using the following setup.py file:
 
 #---------------------------------------------------------
from distutils.core import setup
import py2exe
 
from distutils.filelist import findall
import os
import matplotlib
matplotlibdatadir = matplotlib.get_data_path()
matplotlibdata = findall(matplotlibdatadir)
matplotlibdata_files = []
for f in matplotlibdata:
    dirname = os.path.join('matplotlibdata', f[len(matplotlibdatadir)+1:])
    matplotlibdata_files.append((os.path.split(dirname)[0], [f]))
 

packages = ['matplotlib', 'pytz']
includes = []
excludes = []
dll_excludes = ['libgdk_pixbuf-2.0-0.dll',
                'libgobject-2.0-0.dll',
                'libgdk-win32-2.0-0.dll',
                'wxmsw26uh_vc.dll']
 

opts = { 'py2exe': { 'packages' : packages,
                     'includes' : includes,
                     'excludes' : excludes,
                     'dll_excludes' : dll_excludes
                    }
         }
 
setup ( console=['test.py'],
        options = opts,
        data_files = matplotlibdata_files
        )
 
#----------------------------- EOF ---------------------------
 
I compile the application by running ">setup.py py2exe" 
At the end of compilation phase, it is written :
 
The following modules appear to be missing
['AppKit', 'FFT', 'Foundation', 'Image', 'LinearAlgebra', 'MA', 'MLab', 'Matrix', 'Numeric', 'PyObjCTools', 'P
yQt4', 'Pyrex', 'Pyrex.Compiler', 'RandomArray', '_curses', '_ssl', 'backends.draw_if_interactive', 'backends.
new_figure_manager', 'backends.pylab_setup', 'backends.show', 'cairo', 'cairo.gtk', 'fcompiler.FCompiler', 'fc
ompiler.show_fcompilers', 'fltk', 'gd', 'gobject', 'gtk', 'lib.add_newdoc', 'matplotlib.enthought.pyface.actio
n', 'mlab.amax', 'mlab.amin', 'numarray', 'numarray.convolve', 'numarray.fft', 'numarray.ieeespecial', 'numarr
ay.linear_algebra', 'numarray.linear_algebra.mlab', 'numarray.ma', 'numarray.numeric', 'numarray.random_array'
, 'numerix.ArrayType', 'numerix.Complex', 'numerix.Complex32', 'numerix.Complex64', 'numerix.Float', 'numerix.
Float32', 'numerix.Float64', 'numerix.Int', 'numerix.Int16', 'numerix.Int32', 'numerix.Int8', 'numerix.NewAxis
', 'numerix.UInt16', 'numerix.UInt32', 'numerix.UInt8', 'numerix.absolute', 'numerix.add', 'numerix.all', 'num
erix.allclose', 'numerix.alltrue', 'numerix.arange', 'numerix.arccos', 'numerix.arccosh', 'numerix.arcsin', 'n
umerix.arcsinh', 'numerix.arctan', 'numerix.arctan2', 'numerix.arctanh', 'numerix.argmax', 'numerix.argmin', '
numerix.argsort', 'numerix.around', 'numerix.array', 'numerix.arrayrange', 'numerix.asarray', 'numerix.asum',
'numerix.bitwise_and', 'numerix.bitwise_or', 'numerix.bitwise_xor', 'numerix.ceil', 'numerix.choose', 'numerix
.clip', 'numerix.compress', 'numerix.concatenate', 'numerix.conjugate', 'numerix.convolve', 'numerix.cos', 'nu
merix.cosh', 'numerix.cross_correlate', 'numerix.cumproduct', 'numerix.cumsum', 'numerix.diagonal', 'numerix.d
ivide', 'numerix.dot', 'numerix.equal', 'numerix.exp', 'numerix.fabs', 'numerix.fft.fft', 'numerix.fft.inverse
_fft', 'numerix.floor', 'numerix.fmod', 'numerix.fromfunction', 'numerix.fromstring', 'numerix.greater', 'nume
rix.greater_equal', 'numerix.hypot', 'numerix.identity', 'numerix.indices', 'numerix.innerproduct', 'numerix.i
scontiguous', 'numerix.less', 'numerix.less_equal', 'numerix.log', 'numerix.log10', 'numerix.logical_and', 'nu
merix.logical_not', 'numerix.logical_or', 'numerix.logical_xor', 'numerix.matrixmultiply', 'numerix.maximum',
'numerix.minimum', 'numerix.mlab.amax', 'numerix.mlab.amin', 'numerix.mlab.cov', 'numerix.mlab.diff', 'numerix
.mlab.hanning', 'numerix.mlab.rand', 'numerix.mlab.std', 'numerix.mlab.svd', 'numerix.multiply', 'numerix.nega
tive', 'numerix.newaxis', 'numerix.nonzero', 'numerix.not_equal', 'numerix.nx', 'numerix.ones', 'numerix.outer
product', 'numerix.pi', 'numerix.power', 'numerix.product', 'numerix.put', 'numerix.putmask', 'numerix.rank',
'numerix.ravel', 'numerix.repeat', 'numerix.reshape', 'numerix.resize', 'numerix.searchsorted', 'numerix.shape
', 'numerix.sin', 'numerix.sinh', 'numerix.size', 'numerix.sometrue', 'numerix.sort', 'numerix.sqrt', 'numerix
.subtract', 'numerix.swapaxes', 'numerix.take', 'numerix.tan', 'numerix.tanh', 'numerix.trace', 'numerix.trans
pose', 'numerix.typecode', 'numerix.typecodes', 'numerix.where', 'numerix.which', 'numerix.zeros', 'numpy.Comp
lex', 'numpy.Complex32', 'numpy.Complex64', 'numpy.Float', 'numpy.Float32', 'numpy.Float64', 'numpy.Infinity',
 'numpy.Int', 'numpy.Int16', 'numpy.Int32', 'numpy.Int8', 'numpy.UInt16', 'numpy.UInt32', 'numpy.UInt8', 'nump
y.inf', 'numpy.infty', 'numpy.oldnumeric', 'objc', 'paint', 'pango', 'pre', 'pyemf', 'qt', 'setuptools', 'setu
ptools.command', 'setuptools.command.egg_info', 'trait_sheet', 'matplotlib.numerix.Float', 'matplotlib.numerix
.Float32', 'matplotlib.numerix.absolute', 'matplotlib.numerix.alltrue', 'matplotlib.numerix.asarray', 'matplot
lib.numerix.ceil', 'matplotlib.numerix.equal', 'matplotlib.numerix.fromstring', 'matplotlib.numerix.indices',
'matplotlib.numerix.put', 'matplotlib.numerix.ravel', 'matplotlib.numerix.sqrt', 'matplotlib.numerix.take', 'm
atplotlib.numerix.transpose', 'matplotlib.numerix.where', 'numpy.core.conjugate', 'numpy.core.equal', 'numpy.c
ore.less', 'numpy.core.less_equal', 'numpy.dft.old', 'numpy.random.rand', 'numpy.random.randn']
 
 
1) First Problem: numpy\core\_internal.pyc not included in Library.zip
   
No scipy-style subpackage 'core' found in C:\WinCE\Traces\py2exe test\dist\library.zip\numpy. Ignoring: No module named _internal
Traceback (most recent call last):
  File "profiler_ftt.py", line 15, in ?
    from matplotlib.backends.backend_wx import Toolbar, FigureCanvasWx,\
  File "matplotlib\backends\backend_wx.pyc", line 152, in ?
  File "matplotlib\backend_bases.pyc", line 10, in ?
  File "matplotlib\colors.pyc", line 33, in ?
  File "matplotlib\numerix\__init__.pyc", line 67, in ?
  File "numpy\__init__.pyc", line 35, in ?
  File "numpy\_import_tools.pyc", line 173, in __call__
  File "numpy\_import_tools.pyc", line 68, in _init_info_modules
  File "<string>", line 1, in ?
  File "numpy\lib\__init__.pyc", line 5, in ?
  File "numpy\lib\type_check.pyc", line 8, in ?
  File "numpy\core\__init__.pyc", line 6, in ?
  File "numpy\core\umath.pyc", line 12, in ?
  File "numpy\core\umath.pyc", line 10, in __load
AttributeError: 'module' object has no attribute '_ARRAY_API'
 
I resolved that issue by adding the file ...\Python24\Lib\site-packages\numpy\core\_internal.pyc in ...\test\dist\library.zip\numpy\core.
Each time I compile that executable, I add the file by hand.
Does anybody know how to automatically add that file?
 
 
2) Second problem: I don't know how to resolve that issue:
 
Traceback (most recent call last):
  File "profiler_ftt.py", line 15, in ?
    from matplotlib.backends.backend_wx import Toolbar, FigureCanvasWx,\
  File "matplotlib\backends\backend_wx.pyc", line 152, in ?
  File "matplotlib\backend_bases.pyc", line 10, in ?
  File "matplotlib\colors.pyc", line 33, in ?
  File "matplotlib\numerix\__init__.pyc", line 67, in ?
  File "numpy\__init__.pyc", line 35, in ?
  File "numpy\_import_tools.pyc", line 173, in __call__
  File "numpy\_import_tools.pyc", line 68, in _init_info_modules
  File "<string>", line 1, in ?
  File "numpy\random\__init__.pyc", line 3, in ?
  File "numpy\random\mtrand.pyc", line 12, in ?
  File "numpy\random\mtrand.pyc", line 10, in __load
  File "numpy.pxi", line 32, in mtrand
AttributeError: 'module' object has no attribute 'dtype'
 
I don't find the file numpy.pxi in my file tree nor in \test\dist\library.zip.
I browsed the web in the hope to find a solution but nothing.
It seems that this issue is well known but no solution provided in mailing lists.
 
What is that file  "numpix.pxi"? Where to find it or how is it generated?
How to resolve that execution issue?
 
Thanks,
Regards,
Tristan

Re: [Numpy-discussion] [ANN] NumPy 1.0b4 now available

[Bruce W. <bru...@gm...>]

Hi, Travis

I can pack my scripts into an executable with py2exe, but errors occur
once it runs:

No scipy-style subpackage 'random' found in D:\test\dist\numpy.
Ignoring: No module named info
import core -> failed: No module named _internal
import lib -> failed: 'module' object has no attribute '_ARRAY_API'
import linalg -> failed: 'module' object has no attribute '_ARRAY_API'
import dft -> failed: 'module' object has no attribute '_ARRAY_API'
Traceback (most recent call last):
  File "main.py", line 9, in ?
  File "numpy\__init__.pyc", line 49, in ?
    =01=07=02
  File "numpy\add_newdocs.pyc", line 2, in ?
    =10gkDc
  File "numpy\lib\__init__.pyc", line 5, in ?

  File "numpy\lib\type_check.pyc", line 8, in ?

  File "numpy\core\__init__.pyc", line 6, in ?

  File "numpy\core\umath.pyc", line 12, in ?

  File "numpy\core\umath.pyc", line 10, in __load

AttributeError: 'module' object has no attribute '_ARRAY_API'


This is the main.py file:
#=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
# filename:main.py
import wx
import numpy

class myFrame(wx.Frame):
    def __init__(self, *args, **kwds):
        wx.Frame.__init__(self, *args, **kwds)
        ##------ your widgets
        ##------ put stuff into sizer
        self.sizer_ =3D wx.BoxSizer(wx.VERTICAL)
        ## self.sizer_.Add(your_ctrl, proportion =3D 1, flag =3D wx.EXPAND)

        ## apply sizer
        self.SetSizer(self.sizer_)
        self.SetAutoLayout(True)

def main():    ## {{{
    app =3D wx.PySimpleApp(0)
    frame =3D myFrame(None, -1, title =3D '')
    frame.Show(True)
    app.SetTopWindow(frame)
    app.MainLoop()
##   }}}

if __name__ =3D=3D "__main__":main()
#=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D
# filename:setup.py
import glob
import sys

from distutils.core import setup
import py2exe

includes =3D ["encodings",
            "encodings.*",
           ]

excludes =3D ["javax.comm"]

options =3D {
    "py2exe":
    {
        #"compressed": 1,
        #"optimize": 0,
        #"bundle_files":2,
        "skip_archive":1,
        "includes": includes,
        'excludes': excludes
    }
          }

setup(
    version =3D "0.1",
    description =3D "",
    name =3D "test",
    options =3D options,
    windows =3D [
        {
            "script":"main.py",
        }
        ],
    #zipfile =3D None,
    )


and I run this command to compile the scripts:
python setup.py py2exe

and all packages I use are:
python2.4.3
numpy-0.98
py2exe-0.6.5
wxpython-2.6.3.2

I unistalled Numeric before I compiled scripts.

If you google "numpy py2exe", you can find others guys stumbled by the
same issue with ease:

http://aspn.activestate.com/ASPN/Mail/Message/py2exe-users/3249182
http://www.nabble.com/matplotlib,-numpy-and-py2exe-t1901429.html

I just hope this can be fixed in the next table release of numpy.

On 8/29/06, Travis Oliphant <oli...@ie...> wrote:
> bruce.who.hk wrote:
> > Hi, Travis
> >
> > I just wonder if NumPy 1.0b4 can get along with py2exe? Just a few week=
s ago I made a application in Python. At first I used Numpy, it works OK, b=
ut I cannot pack it into a workable executable with py2exe and the XXX.log =
saied that numpy cannot find some module. I found some hints in py2exe wiki=
, but it still doesn't work. At Last I tried Numeric instead and it got OK.=
 I just hope that you donnot stop the maintenance of Numeric before you are=
 sure that Numpy can work with py2exe.
> >
> We've already stopped maintenance of Numeric nearly 1 year ago.    If
> NumPy doesn't work with py2exe then we need help figuring out why.  The
> beta-release period is the perfect time to fix that.  I've never used
> py2exe myself, but I seem to recall that some have been able to make it
> work.
>
> The problem may just be listing the right set of modules to carry along
> because you may not be able to get that with just the Python-side
> imports.  Post any errors you receive to
> num...@li...
>
> Thanks,
>
>
> -Travis
>
>


Bruce Who

Re: [Numpy-discussion] Is numpy supposed to support the buffer protocol?

[Robin D. <ro...@al...>]

Travis Oliphant wrote:
> Christopher Barker wrote:
>> HI all,
>>
>> File 
>> "/usr/lib/python2.4/site-packages/wx-2.6-gtk2-unicode/wx/_core.py", 
>> line 2814, in SetDataBuffer
>>     return _core_.Image_SetDataBuffer(*args, **kwargs)
>> TypeError: non-character array cannot be interpreted as character buffer
>>
>> If I try to pass in a numpy array, while it works great with a 
>> numarray array.
> 
> This error sounds like wx is using the *wrong* buffer protocol.  Don't 
> use bf_getcharbuffer as it is of uncertain utility.  It is slated for 
> removal from Python 3000.  It was meant to be used as a way to determine 
> buffers that were supposed to contain characters (not arbitrary data).
> 
> Just use bf_getreadbuffer and bf_getwritebuffer  from tp_as_buffer.

I'm using PyArg_Parse($input, "t#", ...) to get the buffer pointer and 
size.  Is there another format specifier to use for the buffer pointer 
using the other slots or do I need to drop down to a lower level API to 
get it?  I didn't realize there was a distinction between buffer and 
character buffer.  Another read of the PyArg_Parse docs with that new 
fact makes things a little more clear.

Looking at the code I guess "s#" will do it, I guess I thought it would 
try to coerce the object to a PyString like some other APIs do, which I 
was trying to avoid, but it doesn't appear to do that, (only encoding a 
unicode object if that is passed.)    I think I'll take a shot at using 
tp_as_buffer directly to avoid any confusion in the future and avoid the 
arg parse overhead...  Any other suggestions?

BTW Chris, try using buffer(RGB) and buffer(Alpha) in your sample, I 
expect that will work with the current code.

-- 
Robin Dunn
Software Craftsman
http://wxPython.org  Java give you jitters?  Relax with wxPython!

[Numpy-discussion] A minor annoyance with MA

[PGM <pgm...@gm...>]

Folks, 
I keep running into the following problem since some recent update (I'm 
currently running 1.0b3, but the problem occurred roughly around 0.9.8):

>>> import numpy.core.ma as MA
>>> x=MA.array([[1],[2]],mask=False)
>>> x.sum(None)
/usr/lib64/python2.4/site-packages/numpy/core/ma.py in reduce(self, target, 
axis, dtype)
    393                 m.shape = (1,)
    394         if m is nomask:
--> 395             return masked_array (self.f.reduce (t, axis))
    396         else:
    397             t = masked_array (t, m)

TypeError: an integer is required
#................................

Note that x.sum(0) and x.sum(1)  work fine. I know some consensus seems to be 
lacking with MA, but still, I can't see why axis=None is not recognized.

Corollary: with masked array, the default axis for sum is 0, when it's None 
for regular arrays. Is there a reason for this inconsistency ?

Thanks a lot

Re: [Numpy-discussion] tensor dot ?

[Bill B. <wb...@gm...>]

On 8/29/06, Travis Oliphant <oli...@ie...> wrote:
> Example:
>
> If a.shape is (3,4,5)
> and b.shape is (4,3,2)
>
> Then
>
> tensordot(a, b, axes=([1,0],[0,1]))
>
> returns a (5,2) array which is equivalent to the code:
>
> c = zeros((5,2))
> for i in range(5):
>     for j in range(2):
>         for k in range(3):
>             for l in range(4):
>                 c[i,j] += a[k,l,i]*b[l,k,j]

That's pretty cool.
>From there it shouldn't be too hard to make a wrapper that would allow
you to write c_ji = a_kli * b_lkj  (w/sum over k and l) like:

   tensordot_ez(a,'kli', b,'lkj', out='ji')

or maybe with numexpr-like syntax:

   tensor_expr('_ji = a_kli * b_lkj')  [pulling a and b out of the
globals()/locals()]

Might be neat to be able to build a callable function for repeated reuse:

   tprod = tensor_func('_ji = [0]_kli * [1]_lkj')   # [0] and [1]
become parameters 0 and 1
   c = tprod(a, b)

or to pass the output through a (potentially reused) array argument:

   tprod1 = tensor_func('[0]_ji = [1]_kli * [2]_lkj')
   tprod1(c, a, b)

--bb

Re: [Numpy-discussion] tensor dot ?

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

Simon Burton wrote:
>>>> numpy.dot.__doc__
>>>>         
> matrixproduct(a,b)
> Returns the dot product of a and b for arrays of floating point types.
> Like the generic numpy equivalent the product sum is over
> the last dimension of a and the second-to-last dimension of b.
> NB: The first argument is not conjugated.
>
> Does numpy support summing over arbitrary dimensions,
> as in tensor calculus ?
>
> I could cook up something that uses transpose and dot, but it's
> reasonably tricky i think :)
>   

I've just added tensordot to NumPy (adapted and enhanced from 
numarray).   It allows you to sum over an arbitrary number of axes.   It 
uses a 2-d dot-product internally as that is optimized if you have a 
fast blas installed.

Example:

If a.shape is (3,4,5)
and b.shape is (4,3,2)

Then

tensordot(a, b, axes=([1,0],[0,1]))

returns a (5,2) array which is equivalent to the code:

c = zeros((5,2))
for i in range(5):
    for j in range(2):
        for k in range(3):
            for l in range(4):
                c[i,j] += a[k,l,i]*b[l,k,j]


-Travis

Re: [Numpy-discussion] std(axis=1) memory footprint issues + moving avg / stddev

[Torgil S. <tor...@gm...>]

> The C-code is basically a directy "translation" of the original Python
> code.
...
> If I had to do it over again, I would place the std implementation there where
> it could be appropriately optimized.

Isn't C-code a good place for optimizations?

//Torgil


On 8/27/06, Travis Oliphant <oli...@ie...> wrote:
> Torgil Svensson wrote:
> > Hi
> >
> > ndarray.std(axis=1) seems to have memory issues on large 2D-arrays. I
> > first thought I had a performance issue but discovered that std() used
> > lots of memory and therefore caused lots of swapping.
> >
> There are certainly lots of intermediate arrays created as the
> calculation proceeds.  The calculation is not particularly "smart."  It
> just does the basic averaging and multiplication needed.
>
> > I want to get an array where element i is the stadard deviation of row
> > i in the 2D array. Using valgrind on the std() function...
> >
> > $ valgrind --tool=massif python -c "from numpy import *;
> > a=reshape(arange(100000*100),(100000,100)).std(axis=1)"
> >
> > ... showed me a peak of 200Mb memory while iterating line by line...
> >
> >
> The C-code is basically a directy "translation" of the original Python
> code.  There are lots of temporaries created (apparently 5 at one point
> :-).  I did this before I had the _internal.py code in place where I
> place Python functions that need to be accessed from C.  If I had to do
> it over again, I would place the std implementation there where it could
> be appropriately optimized.
>
>
>
> -Travis
>
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Re: [Numpy-discussion] 1.0b4 problem continuted from 1.0b3

[Torgil S. <tor...@gm...>]

This is really a matplotlib problem.

>From matplotlib users mailing-list archives:

> From: Charlie Moad <cwmoad@gm...>
> Snapshot build for use with numpy-1.0b3
> 2006-08-23 06:11
>
> Here is a snapshot of svn this morning for those wanting to work with
 the numpy beta.  Both builds are for python2.4 and windows.
>
> exe: http://tinyurl.com/gf299
> egg: http://tinyurl.com/fbjmg
>
> -Charlie

That exe-file worked for me.

//Torgil



On 8/28/06, Travis Oliphant <oli...@ie...> wrote:
> kor...@id... wrote:
> > On 8/25/06, Travis Oliphant <oli...@ie...> wrote:
> >
> >> kor...@id... wrote:
> >>
> >>> Message: 4
> >>> Date: Thu, 24 Aug 2006 14:17:44 -0600
> >>> From: Travis Oliphant <oli...@ee...>
> >>> Subject: Re: [Numpy-discussion] (no subject)
> >>> To: Discussion of Numerical Python
> >>>         <num...@li...>
> >>> Message-ID: <44E...@ee...>
> >>> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
> >>>
> >>> kor...@id... wrote:
> >>>
> >>>
> >>>
> >>> You have a module built against an older version of NumPy.  What modules
> >>> are being loaded?  Perhaps it is matplotlib or SciPy
> >>>
> >>>
> >> You need to re-build matplotlib.  They should be producing a binary that
> >> is compatible with 1.0b2 (I'm being careful to make sure future releases
> >> are binary compatible with 1.0b2).
> >>
> >> Also, make sure that you remove the build directory under numpy if you
> >> have previously built a version of numpy prior to 1.0b2.
> >>
>
> You have to download the SVN version of matplotlib.  The released
> version does not support 1.0b2 and above yet.
>
> -Travis
>
>
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>

Re: [Numpy-discussion] Is numpy supposed to support the buffer protocol?

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

Christopher Barker wrote:

> While I'm a great advocate of the new array protocol, it seems 
> supporting the buffer protocol also would be a good idea. I've enclosed 
> some simple test code. It works with numarray, but not numpy 1.0b4

Instead of

   I.SetDataBuffer(some_array)

you can use

   I.SetDataBuffer(buffer(some_array))

and it seems to work on OS X with Python 2.4, numpy 1.0b2 and wxMac 2.6.3.3 .

-- 
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