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

Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc

[Brian G. <ell...@gm...>]

Also, when I use seterr(all='ignore') the the tests fail:

======================================================================
FAIL: Ticket #112
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/common/homes/g/granger/usr/local/lib/python/numpy/core/tests/test_regression.py",
line 219, in check_longfloat_repr
    assert(str(a)[1:9] == str(a[0])[:8])
AssertionError

----------------------------------------------------------------------
Ran 516 tests in 0.823s

FAILED (failures=1)

Thanks for helping out on this.

On 10/20/06, Tim Hochberg <tim...@ie...> wrote:
> Brian Granger wrote:
> > Hi,
> >
> > i am running numpy on aix compiling with xlc.  Revision 1.0rc2 works
> > fine and passes all tests.  But 1.0rc3 and more recent give the
> > following on import:
> >
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in add
> > Warning: invalid value encountered in not_equal
> > Warning: invalid value encountered in absolute
> > Warning: invalid value encountered in less
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in equal
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in add
> > Warning: invalid value encountered in not_equal
> > Warning: invalid value encountered in absolute
> > Warning: invalid value encountered in less
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in equal
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > [lots more of this]
> >
> > The odd thing is that all tests pass.  I have looked, but can't find
> > where this Warning is coming from  in the code.  Any thoughts on where
> > this is coming from?  What can I do to help debug this?  I am not sure
> > what revision introduced this issue.
> >
> The reason that you are seeing this now is that the default error state
> has been tightened up. There were some issues with tests failing as a
> result of this, but I believe I fixed those already and you're seeing
> this on import, not when running the tests correct? The first thing to
> do is figure out where the invalids are occurring, and the natural way
> to do that is to set the error state to raise, but you can't set the
> error state till you import it, so that's not going to help here.
>
> I think the first thing that I would try is to throw in a
> seterr(all='raise', under='ignore') right after the call to _setdef in
> numeric.py. If you're lucky, this will point out where the invalids are
> popping up. As a sanity check, you could instead make this
> seterr(all='ignore'), which should make all the warnings go away, but
> won't tell you anything about why there are warnings to begin with.
>
> Regards,
>
> -tim
>
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
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> _______________________________________________
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> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc

[Brian G. <ell...@gm...>]

Here is the traceback that I got:

In [1]: import numpy
---------------------------------------------------------------------------
exceptions.FloatingPointError                        Traceback (most
recent call last)

/u2/granger/<ipython console>

/usr/common/homes/g/granger/usr/local/lib/python/numpy/__init__.py
     36     import core
     37     from core import *
---> 38     import lib
     39     from lib import *
     40     import linalg

/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/__init__.py
      3 from numpy.version import version as __version__
      4
----> 5 from type_check import *
      6 from index_tricks import *
      7 from function_base import *

/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/type_check.py
      8 import numpy.core.numeric as _nx
      9 from numpy.core.numeric import asarray, array, isnan, obj2sctype, zeros
---> 10 from ufunclike import isneginf, isposinf
     11
     12 _typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?'

/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py
     44     return y
     45
---> 46 _log2 = umath.log(2)
     47 def log2(x, y=None):
     48     """Returns the base 2 logarithm of x

FloatingPointError: invalid value encountered in log

Obviously because I am having the error raised, I only get the first one.  Hmmm.

Brian

On 10/20/06, Tim Hochberg <tim...@ie...> wrote:
> Brian Granger wrote:
> > Hi,
> >
> > i am running numpy on aix compiling with xlc.  Revision 1.0rc2 works
> > fine and passes all tests.  But 1.0rc3 and more recent give the
> > following on import:
> >
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in add
> > Warning: invalid value encountered in not_equal
> > Warning: invalid value encountered in absolute
> > Warning: invalid value encountered in less
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in equal
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in add
> > Warning: invalid value encountered in not_equal
> > Warning: invalid value encountered in absolute
> > Warning: invalid value encountered in less
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in equal
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > Warning: invalid value encountered in multiply
> > [lots more of this]
> >
> > The odd thing is that all tests pass.  I have looked, but can't find
> > where this Warning is coming from  in the code.  Any thoughts on where
> > this is coming from?  What can I do to help debug this?  I am not sure
> > what revision introduced this issue.
> >
> The reason that you are seeing this now is that the default error state
> has been tightened up. There were some issues with tests failing as a
> result of this, but I believe I fixed those already and you're seeing
> this on import, not when running the tests correct? The first thing to
> do is figure out where the invalids are occurring, and the natural way
> to do that is to set the error state to raise, but you can't set the
> error state till you import it, so that's not going to help here.
>
> I think the first thing that I would try is to throw in a
> seterr(all='raise', under='ignore') right after the call to _setdef in
> numeric.py. If you're lucky, this will point out where the invalids are
> popping up. As a sanity check, you could instead make this
> seterr(all='ignore'), which should make all the warnings go away, but
> won't tell you anything about why there are warnings to begin with.
>
> Regards,
>
> -tim
>
>
> -------------------------------------------------------------------------
> 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] Problem introduced after 1.0rc2 on AIX with xlc

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

Brian Granger wrote:
> Hi,
>
> i am running numpy on aix compiling with xlc.  Revision 1.0rc2 works
> fine and passes all tests.  But 1.0rc3 and more recent give the
> following on import:
>
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in add
> Warning: invalid value encountered in not_equal
> Warning: invalid value encountered in absolute
> Warning: invalid value encountered in less
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in equal
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in add
> Warning: invalid value encountered in not_equal
> Warning: invalid value encountered in absolute
> Warning: invalid value encountered in less
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in equal
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> [lots more of this]
>
> The odd thing is that all tests pass.  I have looked, but can't find
> where this Warning is coming from  in the code.  Any thoughts on where
> this is coming from?  What can I do to help debug this?  I am not sure
> what revision introduced this issue.
>   
The reason that you are seeing this now is that the default error state 
has been tightened up. There were some issues with tests failing as a 
result of this, but I believe I fixed those already and you're seeing 
this on import, not when running the tests correct? The first thing to 
do is figure out where the invalids are occurring, and the natural way 
to do that is to set the error state to raise, but you can't set the 
error state till you import it, so that's not going to help here.

I think the first thing that I would try is to throw in a 
seterr(all='raise', under='ignore') right after the call to _setdef in 
numeric.py. If you're lucky, this will point out where the invalids are 
popping up. As a sanity check, you could instead make this 
seterr(all='ignore'), which should make all the warnings go away, but 
won't tell you anything about why there are warnings to begin with.

Regards,

-tim

[Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc

[Brian G. <ell...@gm...>]

Hi,

i am running numpy on aix compiling with xlc.  Revision 1.0rc2 works
fine and passes all tests.  But 1.0rc3 and more recent give the
following on import:

Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in add
Warning: invalid value encountered in not_equal
Warning: invalid value encountered in absolute
Warning: invalid value encountered in less
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in equal
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in add
Warning: invalid value encountered in not_equal
Warning: invalid value encountered in absolute
Warning: invalid value encountered in less
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in equal
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
[lots more of this]

The odd thing is that all tests pass.  I have looked, but can't find
where this Warning is coming from  in the code.  Any thoughts on where
this is coming from?  What can I do to help debug this?  I am not sure
what revision introduced this issue.

Thanks

Brian

Re: [Numpy-discussion] Model and experiment fitting.

[A. M. A. <per...@gm...>]

T24gMjAvMTAvMDYsIFNlYmFzdGlhbiCvdXJlayA8c2VienVyQHBpbi5pZi51ei56Z29yYS5wbD4g
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CgpJZiB5b3Ugd2FudCB0byBhdm9pZCB1c2luZyBhbiBhIHByaW9yaSBtb2RlbCwgTnVtZXJpY2Fs
IFJlY2lwZXMKZGlzY3VzcyBzb21lIHBvc3NpYmxlIGFwcHJvYWNoZXMgKCJEbyB0d28tZGltZW5z
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cm9ibGVtIHlvdSBkZXNjcmliZSBpbnRvIGEgc29sdmFibGUgb25lIC0gc29tZQphc3N1bXB0aW9u
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LiBNLiBBcmNoaWJhbGQK

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

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

Sebastien Bardeau wrote:

>>One possible solution (there can be more) is using ndarray:
>>
>>In [47]: a=numpy.array([1,2,3], dtype="i4")
>>In [48]: n=1    # the position that you want to share
>>In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>>  
>>    
>>
>Ok thanks. Actually that was also the solution I found. But this is much 
>more complicated when arrays are N dimensional with N>1, and above all 
>if user asks for a slice in one or more dimension. Here is how I 
>redefine the __getitem__ method for my arrays. Remember that the goal is 
>to return a 0-d array rather than a numpy.scalar when I extract a single 
>element out of a N-dimensional (N>=1) array:
>  
>

How about this.  To get the i,j,k,l element

a[i:i+1,j:j+1,k:k+1,l:l+1].squeeze()

-Travis

Re: [Numpy-discussion] Model and experiment fitting.

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

Sebastian Żurek wrote:
> Hi!
> 
> This is probably a silly question but I'm getting confused with a 
> certain problem: a comparison between experimental data points (2D 
> points set) and a model (2D points set - no analytical form).
> 
> The physical model produces (by a sophisticated simulations done by an 
> external program) some 2D points data and  one of my task is to compare 
> those calculated data with an experimental one.
> 
> The experimental and modeled data have form of 2D curves, build of n 
> 2D-points, i.e.:
> 
> expDat=[[x1,x2,x3,..xn],[y1,y2,y3,...,yn]]
> simDat=[[X1,X2,X3,...,Xn],[Y1,Y2,Y3,...,Yn]]
> 
> The task of determining, let's say, a root mean squarred error (RMSe)
> is trivial if x1==X1, x2==X2, etc.
> 
> In general, which is a common situation xk differs from Xk (k=0..n) and 
> one may not simply compare succeeding Yk and yk (k=0..n) to determine 
> the goodness-of-fit. The distance h=Xk-X(k-1) is constant, but similar
> distance m(k)=xk-x(k-1) depends on k-th point and is not a constant 
> value, although the data array lengths for simulation and experiment are 
> the same.

Your description is a bit vague. Do you mean that you have some model function f 
that maps X values to Y values?

   f(x) -> y

If that is the case, is there some reason that you cannot run your simulation 
using the same X points as your experimental data?

OTOH, is there some other independent variable (say Z) that *is* common between 
your experimental and simulated data?

   f(z) -> (x, y)

-- 
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] Model and experiment fitting.

[<se...@pi...>]

Hi!

This is probably a silly question but I'm getting confused with a 
certain problem: a comparison between experimental data points (2D 
points set) and a model (2D points set - no analytical form).

The physical model produces (by a sophisticated simulations done by an 
external program) some 2D points data and  one of my task is to compare 
those calculated data with an experimental one.

The experimental and modeled data have form of 2D curves, build of n 
2D-points, i.e.:

expDat=[[x1,x2,x3,..xn],[y1,y2,y3,...,yn]]
simDat=[[X1,X2,X3,...,Xn],[Y1,Y2,Y3,...,Yn]]

The task of determining, let's say, a root mean squarred error (RMSe)
is trivial if x1==X1, x2==X2, etc.

In general, which is a common situation xk differs from Xk (k=0..n) and 
one may not simply compare succeeding Yk and yk (k=0..n) to determine 
the goodness-of-fit. The distance h=Xk-X(k-1) is constant, but similar
distance m(k)=xk-x(k-1) depends on k-th point and is not a constant 
value, although the data array lengths for simulation and experiment are 
the same.

My first idea was to do some interpolations to obtain the missing 
points, but I held it 'by a hand' (which, BTW gave quite rewarding 
results)  and I suppose, there's some i.g. numpy method to do it for me, 
isn't it?

I suppose to do something like:

gfit(expDat,simDat,'measure_type')

which I hope will return the number determining the goodness-of-fit
(mean squarred error, root mean squarred error,...) of two sets of 
discrete 2D data points.

Is there something like that in any numerical python modules (numpy, 
pylab) I could use?


I can imagine, I can fit the data with some polynomial or whatever,
and than compare the fitted data, but my goal is to operate on
as raw data as it's possible.

Thanks for your comments!

Sebastian

Re: [Numpy-discussion] slicing suggestion

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

On 10/20/06, JJ <jos...@ya...> wrote:
> My suggestion is to
> create a new attribute, such as .AR, so that the
> following could be used: M[K.AR,:]

It would be even better if M[K,:] worked. Would such a patch be
accepted? (Not that I know how to make it.)

[Numpy-discussion] slicing suggestion

[JJ <jos...@ya...>]

Hello.
I have a suggestion that might make slicing using
matrices more user-friendly.  I often have a matrix of
row or column numbers that I wish to use as a slice. 
If K was a matrix of row numbers (nx1) and M was a nxm
matrix, then I would use ans = M[K.A.ravel(),:] to
obtain the matrix I want.  It turns out that I use
.A.ravel() quite a lot in my code, as I usually work
with matrices rather than arrays.  My suggestion is to
create a new attribute, such as .AR, so that the
following could be used: M[K.AR,:].  I believe this
would be more concise, easier to read, and well used. 
If slices are made in both directions of the matrix,
then the .A.ravel() becomes even more unwieldy.  Does
anyone else like this idea?

John     

__________________________________________________
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Re: [Numpy-discussion] histogram complete makeover

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

Thanks for the comments,
Here is the code for the new histogram, tests included.
I'll wait for comments or suggestions before submitting a patch (numpy /
scipy) ?

Cheers

David





2006/10/18, Tim Hochberg <tim...@ie...>:
>
>
> My $0.02:
>
> If histogram is going to get a makeover, particularly one that makes it
> more complex than at present, it should probably be moved to SciPy.
> Failing that, it should be moved to a submodule of numpy with similar
> statistical tools. Preferably with consistent interfaces for all of the
> functions.
>
>
> -------------------------------------------------------------------------
> 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] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

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

Sebastien Bardeau wrote:
> Ooops sorry there was two mistakes with the 'hasslice' flag. This seems 
> now to work for me.
>
>   
[SNIP code]

That looks overly complicated. I believe that this (minimally tested in 
a slightly different setting) or some variation should work:

return self[...,newaxis][index].reshape(self[index].shape)


-tim

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Sebastien B. <Seb...@ob...>]

Ooops sorry there was two mistakes with the 'hasslice' flag. This seems 
now to work for me.

   def __getitem__(self,index): # Index may be either an int or a tuple
      # Index length:
      if type(index) == int: # A single element through first dimension
         ilen = 1
         index = (index,)    # A tuple
      else:
         ilen = len(index)
      # Array rank:
      arank = len(self.shape)
      # Check if there is a slice:
      hasslice = False
      for i in index:
         if type(i) == slice:
            hasslice = True
      # Array is already a 0-d array:
      if arank == 0 and index == (0,):
         return self
      elif arank == 0:
         raise IndexError, "0-d array has only one element at index 0."
      # This will return a single element as a 0-d array:
      elif arank == ilen and not hasslice:
         # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar!
         # (Numpy scalars do not share their data with the parent array)
         newindex = list(index)
         newindex[0] = slice(index[0],index[0]+1,None)
         newindex = tuple(newindex)
         return self[newindex].reshape(())
      # This will return a n-D subarray (n>=1):
      else:
         return self[index]


Sebastien Bardeau wrote:
>> One possible solution (there can be more) is using ndarray:
>>
>> In [47]: a=numpy.array([1,2,3], dtype="i4")
>> In [48]: n=1    # the position that you want to share
>> In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>>   
>>     
> Ok thanks. Actually that was also the solution I found. But this is much 
> more complicated when arrays are N dimensional with N>1, and above all 
> if user asks for a slice in one or more dimension. Here is how I 
> redefine the __getitem__ method for my arrays. Remember that the goal is 
> to return a 0-d array rather than a numpy.scalar when I extract a single 
> element out of a N-dimensional (N>=1) array:
>
>    def __getitem__(self,index): # Index may be either an int or a tuple
>       # Index length:
>       if type(index) == int: # A single element through first dimension
>          ilen = 1
>          index = (index,)    # A tuple
>       else:
>          ilen = len(index)
>       # Array rank:
>       arank = len(self.shape)
>       # Check if there is a slice:
>       for i in index:
>          if type(i) == slice:
>             hasslice = True
>          else:
>             hasslice = False
>       # Array is already a 0-d array:
>       if arank == 0 and index == (0,):
>          return self[()]
>       elif arank == 0:
>          raise IndexError, "0-d array has only one element at index 0."
>       # This will return a single element as a 0-d array:
>       elif arank == ilen and hasslice:
>          # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar!
>          # (Numpy scalars do not share their data with the parent array)
>          newindex = list(index)
>          newindex[0] = slice(index[0],index[0]+1,None)
>          newindex = tuple(newindex)
>          return self[newindex].reshape(())
>       # This will return a n-D subarray (n>=1):
>       else:
>          return self[index]
>
>  Well... I do not think this is very nice. Someone has another idea? My 
> question in my first post was: is there a way to get a single element of 
> an array into
> a 0-d array which shares memory with its parent array?
>
>  Sebastien
>
> -------------------------------------------------------------------------
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>   

-- 
-------------------------
   Sebastien Bardeau
  L3AB - CNRS UMR 5804
 2 rue de l'observatoire
         BP 89
    F - 33270 Floirac
Tel: (+33) 5 57 77 61 46
-------------------------

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

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

Francesc Altet wrote:
> A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
> [snip]
>   
>> I can understand that numpy.scalars do not provide inplace operations
>> (like Python standard scalars, they are immutable), so I'd like to use
>>
>> 0-d Numpy.ndarrays. But:
>>  >>> d = numpy.array(a[2],copy=False)
>>  >>> d += 1
>>  >>> d
>>
>> array(4)
>>
>>  >>> a
>>
>> array([2, 3, 3])
>>
>>  >>> type(d)
>>
>> <type 'numpy.ndarray'>
>>
>>  >>> d.shape
>>
>> ()
>>
>>  >>> id(d)
>>
>> 169621280
>>
>>  >>> d += 1
>>  >>> id(d)
>>
>> 169621280
>>
>> This is not a solution because d is a copy since construction time...
>> My question is: is there a way to get a single element of an array into
>> a 0-d array which shares memory with its parent array?
>>     
>
> One possible solution (there can be more) is using ndarray:
[SNIP]

Here's a slightly more concise version of the same idea:

b = a[n:n+1].reshape([])


-tim

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Sebastien B. <Seb...@ob...>]

> One possible solution (there can be more) is using ndarray:
>
> In [47]: a=numpy.array([1,2,3], dtype="i4")
> In [48]: n=1    # the position that you want to share
> In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>   
Ok thanks. Actually that was also the solution I found. But this is much 
more complicated when arrays are N dimensional with N>1, and above all 
if user asks for a slice in one or more dimension. Here is how I 
redefine the __getitem__ method for my arrays. Remember that the goal is 
to return a 0-d array rather than a numpy.scalar when I extract a single 
element out of a N-dimensional (N>=1) array:

   def __getitem__(self,index): # Index may be either an int or a tuple
      # Index length:
      if type(index) == int: # A single element through first dimension
         ilen = 1
         index = (index,)    # A tuple
      else:
         ilen = len(index)
      # Array rank:
      arank = len(self.shape)
      # Check if there is a slice:
      for i in index:
         if type(i) == slice:
            hasslice = True
         else:
            hasslice = False
      # Array is already a 0-d array:
      if arank == 0 and index == (0,):
         return self[()]
      elif arank == 0:
         raise IndexError, "0-d array has only one element at index 0."
      # This will return a single element as a 0-d array:
      elif arank == ilen and hasslice:
         # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar!
         # (Numpy scalars do not share their data with the parent array)
         newindex = list(index)
         newindex[0] = slice(index[0],index[0]+1,None)
         newindex = tuple(newindex)
         return self[newindex].reshape(())
      # This will return a n-D subarray (n>=1):
      else:
         return self[index]

 Well... I do not think this is very nice. Someone has another idea? My 
question in my first post was: is there a way to get a single element of 
an array into
a 0-d array which shares memory with its parent array?

 Sebastien

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

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

On Fri, Oct 20, 2006 at 11:42:26AM +0200, Sebastien Bardeau wrote:
>  >>> a =3D numpy.array((1,2,3))
>  >>> b =3D a[:2]

Here you index by a slice.

>  >>> c =3D a[2]

Whereas here you index by a scalar.

So you want to do

b =3D a[[2]]
b +=3D 1

or in the general case

b =3D a[slice(2,3)]
b +=3D 1

Regards
St=E9fan

[Numpy-discussion] Helper function to "unroll" a array

[Gael V. <gae...@no...>]

Hi,

There is an operation I do a lot, I would call it "unrolling" a array.
The best way to describe it is probably to give the code:

def unroll(M):
    """ Flattens the array M and returns a 2D array with the first column=
s=20
	being the indices of M, and the last column the flatten M.
    """
    return hstack((indices(M.shape).reshape(-1,M.ndim),M.reshape(-1,1)))

Example:

>>> M
array([[ 0.73530097,  0.3553424 ,  0.3719772 ],
       [ 0.83353373,  0.74622133,  0.14748905],
       [ 0.72023762,  0.32306969,  0.19142366]])

>>> unroll(M)
array([[ 0.        ,  0.        ,  0.73530097],
       [ 0.        ,  1.        ,  0.3553424 ],
       [ 1.        ,  1.        ,  0.3719772 ],
       [ 2.        ,  2.        ,  0.83353373],
       [ 2.        ,  0.        ,  0.74622133],
       [ 1.        ,  2.        ,  0.14748905],
       [ 0.        ,  1.        ,  0.72023762],
       [ 2.        ,  0.        ,  0.32306969],
       [ 1.        ,  2.        ,  0.19142366]])


The docstring sucks. The function is trivial (when you know numpy a bit).
Maybe this function already exists in numpy, if so I couldn't find it.
Elsewhere I propose it for inclusion.

Cheers,

Ga=EBl

Re: [Numpy-discussion] Can' compile numpy 1.02rc3 on OSX 10.3.9

[Markus R. <mar...@el...>]

Am 20.10.2006 um 02:53 schrieb Jay Parlar:

>> Hi!
>> I try to compile numpy rc3 on Panther and get following errors.
>> (I start build with "python2.3 setup.py build" to be sure to use the
>> python shipped with OS X. I din't manage to compile Python2.5 either
>> yet with similar errors)
>> Does anynbody has an Idea?
>> gcc-3.3
>> XCode 1.5
>> November gcc updater is installed
>>
>
> I couldn't get numpy building with Python 2.5 on 10.3.9 (although I
> had different compile errors). The solution that ended up working for
> me was Python 2.4. There's a bug in the released version of Python 2.5
> that's preventing it from working with numpy, should be fixed in the
> next release.
>
> You can find a .dmg for Python 2.4 here:
> http://pythonmac.org/packages/py24-fat/index.html
>
> Jay P.
>


I have that installed already but i get some bus errors with that.  
Furthermore it is built with gcc4
and i need to compile an extra module(pytables) and I fear that will  
not work, hence I try to compile myself. Python 2.5 dosent't compile  
either (libSystemStubs is only on Tiger).
The linking works when i remove the -lSystemStubs and it compiled clean.
Numpy rc3 wass also compiling now with python 2.5, but the tests failed:

Python 2.5 (r25:51908, Oct 20 2006, 11:40:08)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1671)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
 >>> import numpy
 >>> numpy.test(10)
   Found 5 tests for numpy.distutils.misc_util
   Found 4 tests for numpy.lib.getlimits
   Found 31 tests for numpy.core.numerictypes
   Found 32 tests for numpy.linalg
   Found 13 tests for numpy.core.umath
   Found 4 tests for numpy.core.scalarmath
   Found 9 tests for numpy.lib.arraysetops
   Found 42 tests for numpy.lib.type_check
   Found 183 tests for numpy.core.multiarray
   Found 3 tests for numpy.fft.helper
   Found 36 tests for numpy.core.ma
   Found 1 tests for numpy.lib.ufunclike
   Found 12 tests for numpy.lib.twodim_base
   Found 10 tests for numpy.core.defmatrix
   Found 4 tests for numpy.ctypeslib
   Found 41 tests for numpy.lib.function_base
   Found 2 tests for numpy.lib.polynomial
   Found 8 tests for numpy.core.records
   Found 28 tests for numpy.core.numeric
   Found 4 tests for numpy.lib.index_tricks
   Found 47 tests for numpy.lib.shape_base
   Found 0 tests for __main__
........................................................................ 
................................Warning: invalid value encountered in  
divide
..Warning: invalid value encountered in divide
..Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
..Warning: invalid value encountered in divide
.Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
..Warning: invalid value encountered in divide
..Warning: invalid value encountered in divide
..Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
.Warning: divide by zero encountered in divide
........Warning: invalid value encountered in divide
.Warning: invalid value encountered in divide
..Warning: divide by zero encountered in divide
........................................................................ 
..............................................Warning: overflow  
encountered in exp
F....................................................................... 
..........Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
Warning: divide by zero encountered in divide
.....................Warning: invalid value encountered in sqrt
Warning: invalid value encountered in log
Warning: invalid value encountered in log10
..Warning: invalid value encountered in sqrt
Warning: invalid value encountered in sqrt
Warning: divide by zero encountered in log
Warning: divide by zero encountered in log
Warning: divide by zero encountered in log10
Warning: divide by zero encountered in log10
Warning: invalid value encountered in arcsin
Warning: invalid value encountered in arcsin
Warning: invalid value encountered in arccos
Warning: invalid value encountered in arccos
Warning: invalid value encountered in arccosh
Warning: invalid value encountered in arccosh
Warning: divide by zero encountered in arctanh
Warning: divide by zero encountered in arctanh
Warning: invalid value encountered in divide
Warning: invalid value encountered in true_divide
Warning: invalid value encountered in floor_divide
Warning: invalid value encountered in remainder
Warning: invalid value encountered in fmod
........................................................................ 
........................................................................ 
.................
======================================================================
FAIL: Ticket #112
----------------------------------------------------------------------
Traceback (most recent call last):
   File  
"/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
packages/numpy/core/tests/test_regression.py", line 219, in  
check_longfloat_repr
     assert(str(a)[1:9] == str(a[0])[:8])
AssertionError

----------------------------------------------------------------------
Ran 519 tests in 3.760s

FAILED (failures=1)
<unittest.TextTestRunner object at 0x1545f70>
 >>>


regards
Markus

Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Francesc A. <fa...@ca...>]

A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
[snip]
> I can understand that numpy.scalars do not provide inplace operations
> (like Python standard scalars, they are immutable), so I'd like to use
>
> 0-d Numpy.ndarrays. But:
>  >>> d =3D numpy.array(a[2],copy=3DFalse)
>  >>> d +=3D 1
>  >>> d
>
> array(4)
>
>  >>> a
>
> array([2, 3, 3])
>
>  >>> type(d)
>
> <type 'numpy.ndarray'>
>
>  >>> d.shape
>
> ()
>
>  >>> id(d)
>
> 169621280
>
>  >>> d +=3D 1
>  >>> id(d)
>
> 169621280
>
> This is not a solution because d is a copy since construction time...
> My question is: is there a way to get a single element of an array into
> a 0-d array which shares memory with its parent array?

One possible solution (there can be more) is using ndarray:

In [47]: a=3Dnumpy.array([1,2,3], dtype=3D"i4")
In [48]: n=3D1    # the position that you want to share
In [49]: b=3Dnumpy.ndarray(buffer=3Da[n:n+1], shape=3D(), dtype=3D"i4")
In [50]: a
Out[50]: array([1, 2, 3])
In [51]: b
Out[51]: array(2)
In [52]: b +=3D 1
In [53]: b
Out[53]: array(3)
In [54]: a
Out[54]: array([1, 3, 3])


Cheers,

=2D-=20
>0,0<   Francesc Altet =A0 =A0 http://www.carabos.com/
V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
 "-"

[Numpy-discussion] =?iso-2022-jp?b?GyRCJCQkRCRHJGIkSSQzJEcbKEI=?= =?iso-2022-jp?b?GyRCJGIlJCUrJDk9UDJxJCRFNzlxGyhC?=

[<EA...@HO...>]

中だし体験記！携帯番号ゲットで中だし・・・最高です！
http://carmastorra.com/ika/

[Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Sebastien B. <Seb...@ob...>]

Hi!

I am confused with Numpy behavior with its scalar or 0-d arrays objects:

 >>> numpy.__version__
'1.0rc2'
 >>> a = numpy.array((1,2,3))
 >>> b = a[:2]
 >>> b += 1
 >>> b
array([2, 3])
 >>> a
array([2, 3, 3])
 >>> type(b)
<type 'numpy.ndarray'>

To this point all is ok for me: subarrays share (by default) memory with 
their parent array. But:

 >>> c = a[2]
 >>> c += 1
 >>> c
4
 >>> a
array([2, 3, 3])
 >>> type(c)
<type 'numpy.int32'>
 >>> id(c)
169457808
 >>> c += 1
 >>> id(c)
169737448

That's really confusing, because slices (from __getslice__ method) are 
not copies (they share memory), and items (single elements from 
__getitem__ ) are copies to one of the scalar objects provided by Numpy. 
I can understand that numpy.scalars do not provide inplace operations 
(like Python standard scalars, they are immutable), so I'd like to use 
0-d Numpy.ndarrays. But:

 >>> d = numpy.array(a[2],copy=False)
 >>> d += 1
 >>> d
array(4)
 >>> a
array([2, 3, 3])
 >>> type(d)
<type 'numpy.ndarray'>
 >>> d.shape
()
 >>> id(d)
169621280
 >>> d += 1
 >>> id(d)
169621280

This is not a solution because d is a copy since construction time...
My question is: is there a way to get a single element of an array into 
a 0-d array which shares memory with its parent array?

Thx for your help,

Sebastien

Re: [Numpy-discussion] adding an attribute to an nd-array

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

On Thu, Oct 19, 2006 at 09:03:57PM -0400, Pierre GM wrote:
> Indeed. That's basically why you have to edit your __array_finalize__ .
>=20
> class InfoArray(N.ndarray):
>     def __new__(info_arr_cls,arr,info=3D{}):
>         info_arr_cls._info =3D info
>         return N.array(arr).view(info_arr_cls)
>     def __array_finalize__(self, obj):
>         if hasattr(obj,'info'):
>             self.info =3D obj.info
>         else:
>             self.info =3D self._info
>         return
>=20
> OK, so you end up w/ two attributes 'info' and '_info', the latter havi=
ng the=20
> info you want, the latter playing a temporary placeholder. That looks a=
 bit=20
> overkill, but that works pretty nice.

Is there any reason why one can't simply do

class InfoArray(N.ndarray):
    def __new__(info_arr_cls,arr,info=3D{}):
        x =3D N.array(arr).view(info_arr_cls)
	x.info =3D info
        return x
    def __array_finalize__(self, obj):
        if hasattr(obj,'info'):
            self.info =3D obj.info
        return

Regards
St=E9fan

Re: [Numpy-discussion] The NumPy Fortran-ordering quiz

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

A. M. Archibald wrote:

>On 18/10/06, Travis Oliphant <oli...@ie...> wrote:
>
>  
>
>>If there are any cases satisfying these rules where a copy does not have
>>to occur then let me know.
>>    
>>
>
>For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied.
>
>I filed a bug in trac and supplied a patch to multiarray.c that avoids
>copies in PyArray_NewShape unless absolutely necessary.
>
>  
>
Very, very nice.  Thanks. 

Re: [Numpy-discussion] The NumPy Fortran-ordering quiz

[A. M. A. <per...@gm...>]

On 18/10/06, Travis Oliphant <oli...@ie...> wrote:

> If there are any cases satisfying these rules where a copy does not have
> to occur then let me know.

For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied.

I filed a bug in trac and supplied a patch to multiarray.c that avoids
copies in PyArray_NewShape unless absolutely necessary.

A. M. Archibald

Re: [Numpy-discussion] adding an attribute to an nd-array

[Pierre GM <pgm...@gm...>]

> > >class InfoArray(N.ndarray):
> > >    def __new__(info_arr_cls,arr,info={}):
> > >        info_arr_cls.info = info
> > >        return N.array(arr).view(info_arr_cls)
>
> One has to be careful of this approach.  It ads *the same* information
> to all arrays, i.e.

Indeed. That's basically why you have to edit your __array_finalize__ .

class InfoArray(N.ndarray):
    def __new__(info_arr_cls,arr,info={}):
        info_arr_cls._info = info
        return N.array(arr).view(info_arr_cls)
    def __array_finalize__(self, obj):
        if hasattr(obj,'info'):
            self.info = obj.info
        else:
            self.info = self._info
        return

OK, so you end up w/ two attributes 'info' and '_info', the latter having the 
info you want, the latter playing a temporary placeholder. That looks a bit 
overkill, but that works pretty nice.

a = InfoArray(N.array([1,2,3]),{1:1})
b = InfoArray(N.array([1,2,3]),{1:2})

assert a.info=={1:1}
assert b.info=={1:2}
assert (a+1).info==a.info 
assert (b-2).info==b.info