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

Re: [Numpy-discussion] Re: numpy and mayavi

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

On Sun, 21 May 2006, Bryan Cole apparently wrote: 
> another way to get going is to convert your numpy array 
> directly to a VTK data file using a pure python script. 

For which
http://cens.ioc.ee/projects/pyvtk/
may be useful.

Cheers,
Alan Isaac

[Numpy-discussion] Re: numpy and mayavi

[Bryan C. <br...@co...>]

On Sat, 2006-05-20 at 22:57 +0200, Emanuele Olivetti wrote:
> I need to use a 3D visualization toolkit together with the
> amazing numpy. On the scipy website mayavi/VTK are suggested but
> as far as I tried, mayavi uses only Numeric and doesn't like
> numpy arrays (and mayavi, last release, dates 13 September 2005
> so a bit earlier for numpy).

You can safely install Numeric along side numpy; you'll need to do this
in order to run mayavi. Alternatively, you could try Paraview
(http://www.paraview.org). Like mayavi, Paraview is based on VTK, but
it's written in Tcl/Tk rather than python. It's more feature complete
than mayavi and easier to use, in my view.

I quick test shows that VTK-5 is happy to accept numpy arrays as "Void
Pointers" for its vtkDataArrays. Using this method, you can construct
any vtkDataObject from numpy arrays. If you just want to turn your 3D
array into vtkImageData, you can use the vtkImageImport filter. 

Once you've got a vtkDataSet (vtkImageData or some other form), you can
save this as a .vtk file, which either mayavi or paraview can then open.

If you're not already familiar with VTK programming, then another way to
get going is to convert your numpy array directly to a VTK data file
using a pure python script. The file formats are documented at
http://www.vtk.org/pdf/file-formats.pdf and the can be written in either
binary or ascii form. The 'legacy' .vtk formats and are quite simple to
construct. 

If you want to use mayavi from a script, then you need to convert your
numpy arrays to Numeric arrays (using the Numeric.asarray function) for
transfer to mayavi as needed.

HTH
Bryan

>  Do you know something about it or
> could suggest alternative 3D visualization packages?
> Basically my needs are related to this task: I've 3D matrices
> and I'like to see them not only as 2D slices.
> 
> Thanks in advance,
> 
> Emanuele
> 
> 
> -------------------------------------------------------
> 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
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Re: [Numpy-discussion] implicit coersion int8 -> int16 ?

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

Simon Burton wrote:
>>>> a=numpy.array([1,2,3],numpy.Int8)
>>>> a
>>>>         
> array([1, 2, 3], dtype=int8)
>   
>>>> a*2
>>>>         
> array([2, 4, 6], dtype=int16)
>   
>
> My little 2 lives happily as an int8, so why is the int8 array
> cast to an int16 ?
>   
Thanks for catching this bug in the logic of scalar upcasting.  It 
should only affect this one case.

It's now been fixed.

-Travis

Re: [Numpy-discussion] implicit coersion int8 -> int16 ?

[Simon B. <si...@ar...>]

On Sun, 21 May 2006 12:28:10 +0100
Simon Burton <si...@ar...> wrote:

> 
> >>> a=numpy.array([1,2,3],numpy.Int8)
> >>> a
> array([1, 2, 3], dtype=int8)
> >>> a*2
> array([2, 4, 6], dtype=int16)
> >>> 
> 
> My little 2 lives happily as an int8, so why is the int8 array
> cast to an int16 ?

As a workaround, i found this:
>>> a*=2
>>> a
array([2, 4, 6], dtype=int8)

Simon.

-- 
Simon Burton, B.Sc.
Licensed PO Box 8066
ANU Canberra 2601
Australia
Ph. 61 02 6249 6940
http://arrowtheory.com 

[Numpy-discussion] implicit coersion int8 -> int16 ?

[Simon B. <si...@ar...>]

>>> a=numpy.array([1,2,3],numpy.Int8)
>>> a
array([1, 2, 3], dtype=int8)
>>> a*2
array([2, 4, 6], dtype=int16)
>>> 

My little 2 lives happily as an int8, so why is the int8 array
cast to an int16 ?
I spent quite a while finding this bug.

Furthermore, this works:
>>> a*a
array([1, 4, 9], dtype=int8)

but not this:
>>> a*numpy.array(2,dtype=numpy.Int8)
array([2, 4, 6], dtype=int16)

>>> print numpy.__version__
0.9.9.2533

Simon.

-- 
Simon Burton, B.Sc.
Licensed PO Box 8066
ANU Canberra 2601
Australia
Ph. 61 02 6249 6940
http://arrowtheory.com 

Re: [Numpy-discussion] ascii input/output cookbook doc

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

jo...@st... wrote:
> For your information: I just created a small cookbook document
> http://scipy.org/Cookbook/InputOutput
> where it is explained how one can read and write Numpy arrays in human
> readable (ascii) format. 
>
>
> The document describes how one can use read_array/write_array if SciPy
> is installed, or how one can use load/save if Matplotlib is installed.
> When neither of these two packages is installed, one basically has no
> other choice then to improvise, so I also give here a few examples how
> one could do this.
>
> Imho, there is something unsatisfactorily about this need to improvise. 
> Ascii input/output of numpy arrays seems to me a very basic need. Even
> when one defines Numpy crudely as the N-dimensional array object, and 
> Scipy as the science you can do with these array objects, then I would
> intuitively still expect that ascii input/output would belong to Numpy
> rather than to Scipy. There are Numpy support functions for binary format,
> and for pickled format, but strangely enough not really for ascii format.
> tofile() and fromfile() do not preserve the shape of a 2d array, and are
> in practice therefore hardly usable. 
>
> There may be a signficant fraction of Numpy users that do not need SciPy 
> for their work, and have only Numpy installed. My guess is that the 
> read_array and write_array functions have already been re-invented many 
> many times by these users. Imho, I therefore think that Numpy deserves
> its own read_array/write_array method. Does anyone else have this feeling,
> or am I the only one? :o)
>   
I think you are correct.  I'd like to see better ascii input-output.  
That's why it's supported on a fundamental level in tofile and 
fromfile.   SciPy's support for ascii reading and writing is rather slow 
as it has a lot of features.  Something a little-less grandiose, but 
still able to read and write simple ascii tables would be a good thing 
to bring into NumPy.   

General-purpose parsing can be very difficult, but a simple parser for 
2-d arrays would probably be very useful.

On the other hand, I've found that even though it understands only one 
separator at this point, fromfile is still pretty useful for table 
processing as long as you know the shape of what you want.


-Travis


> Joris
>
>
> Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
>
>
>
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>   

[Numpy-discussion] ascii input/output cookbook doc

[<jo...@st...>]

For your information: I just created a small cookbook document
http://scipy.org/Cookbook/InputOutput
where it is explained how one can read and write Numpy arrays in human
readable (ascii) format. 


The document describes how one can use read_array/write_array if SciPy
is installed, or how one can use load/save if Matplotlib is installed.
When neither of these two packages is installed, one basically has no
other choice then to improvise, so I also give here a few examples how
one could do this.

Imho, there is something unsatisfactorily about this need to improvise. 
Ascii input/output of numpy arrays seems to me a very basic need. Even
when one defines Numpy crudely as the N-dimensional array object, and 
Scipy as the science you can do with these array objects, then I would
intuitively still expect that ascii input/output would belong to Numpy
rather than to Scipy. There are Numpy support functions for binary format,
and for pickled format, but strangely enough not really for ascii format.
tofile() and fromfile() do not preserve the shape of a 2d array, and are
in practice therefore hardly usable. 

There may be a signficant fraction of Numpy users that do not need SciPy 
for their work, and have only Numpy installed. My guess is that the 
read_array and write_array functions have already been re-invented many 
many times by these users. Imho, I therefore think that Numpy deserves
its own read_array/write_array method. Does anyone else have this feeling,
or am I the only one? :o)

Joris


Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

[Numpy-discussion] numpy and mayavi

[Emanuele O. <oli...@it...>]

I need to use a 3D visualization toolkit together with the
amazing numpy. On the scipy website mayavi/VTK are suggested but
as far as I tried, mayavi uses only Numeric and doesn't like
numpy arrays (and mayavi, last release, dates 13 September 2005
so a bit earlier for numpy). Do you know something about it or
could suggest alternative 3D visualization packages?
Basically my needs are related to this task: I've 3D matrices
and I'like to see them not only as 2D slices.

Thanks in advance,

Emanuele

[Numpy-discussion] Updates to "Guide to NumPy"

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

Owners of "Guide to NumPy":

I'm a little late with updates.   I really apologize for that.  It's due 
to not having a good system in place to allow people to get the updates 
in a timely manner.  The latest version is from March.  It includes a 
bit more information about writing C-extensions and some corrections. 

I've been sending them to people who urgently need them and request it.  
I'm working on an update to go along with 0.9.8 release.  When it is 
ready I will send a location and password for people to download it.  I 
expect 2-3 weeks for its release. 

If you need the March copy earlier than that, please let me know and 
I'll send you a personal update. 

Thanks for your patience.  I hope to send updates to the manual at major 
releases of NumPy.

Best regards,

-Travis Oliphant

Re[2]: [Numpy-discussion] Guide to NumPy latest?

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

On Sat, 20 May 2006, Sven Schreiber apparently wrote: 
> I would also kindly like to ask why nobody (?) gets the 
> updates of the numpy book. I am totally in favor of the 
> idea to raise funds by selling good documentation, but the 
> deal was to get the updates when they come out. 

While you are phrasing this fairly politely, I think you 
should not *assume* you have not gotten your most recent 
"update".  Rather you should ask: what is the definition of 
an "update"?  You seem to be defining it by the date on the 
title page, but that need not be Travis's definition.  (I 
would agree that he could reduce some traffic on this list 
by clarifying that, perhaps by versioning the book.) 

Personally, I do not care to receive a copy each time a few 
typos are corrected or some grammar is changed.  I'd rather 
receive a copy each time there are important additions or 
corrections.  I assume that this is Travis's practice.

Cheers, 
Alan Isaac 

Re: [Numpy-discussion] Guide to NumPy latest?

[Sven S. <sve...@gm...>]

Christian Marquardt schrieb:
> Mine says February 28, 2006 on the title page.
> 
> As this has come up quite often - is there any kind of mechanism to
> obtain the most recent version?
> 

I would also kindly like to ask why nobody (?) gets the updates of the
numpy book. I am totally in favor of the idea to raise funds by selling
good documentation, but the deal was to get the updates when they come out.

Thanks,
Sven

[Numpy-discussion] a.squeeze() for 1d and 0d arrays

[Ed S. <sch...@ft...>]

Hi all,

I've discovered a bug in my SciPy code for sparse matrix slicing that
was caused by the following behaviour of squeeze():

>>> a = array([3])    # array of shape (1,)
>>> type(a.squeeze())
<type 'int32scalar'>

That is, squeezing a 1-dim array returns an array scalar.  Could we
change this to return a 0-dim array instead?

Another related question is this:

>>> b = array(3)    # 0-dim array
>>> type(a.squeeze())
<type 'int32scalar'>

I find this behaviour surprising too; the docstring claims that squeeze
eliminates any length-1 dimensions, but a 0-dimensional array has shape
(), without any length-1 dimensions.  So shouldn't squeeze() leave
0-dimensional arrays alone?

-- Ed

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[Numpy-discussion] matrix direct sum

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

It seems to me that the core question is whether you really 
need the direct sum to be constructed (seem unlikely),
or whether you just need the information in the constituent
matrices.  How about a direct sum class, which takes a list 
of matrices as an argument, and appropriately defines the
operations you need.

Cheers,
Alan Isaac

PS I have argued that this is a better way
to handle the Kronecker product as well,
but as here I did not offer code.  ;-)
If you code it, please share it.

[Numpy-discussion] isdtype

[Fred C. <fr...@uc...>]

None of the functions in section 4.5 of the manual seem to
be implemented:

 >>> import numpy
 >>> numpy.__version__
'0.9.6'
 >>> a = numpy.array([0.])
 >>> numpy.isdtype(a)
Traceback (most recent call last):
   File "<stdin>", line 1, in ?
AttributeError: 'module' object has no attribute 'isdtype'

?

Fred Clare

Re: [Numpy-discussion] Numpy Example List

[Pau G. <pau...@gm...>]

On 5/19/06, jo...@st... <jo...@st...> wrote:
>
> For your information: http://scipy.org/Numpy_Example_List
> - all examples are now syntax highlighted (thanks Pau Gargallo!)
> - most examples have now a clickable "See also:" footnote
> - is extended to 118 examples
>
> Joris
>

Thanks to you Joris !!
118 examples in a week, you did a huge work!

The color syntax have some problems.
We are using now the moinmoin command:
 {{{#!python numbers=3Ddisable
so moinmoin is coloring the interactive sessions as if they were
python files. This gives error when the output of the interactive
session don't look like python code. See
for example http://www.scipy.org/Numpy_Example_List#histogram where
the print commands print opening brackets "[" and not the closing
ones.

Does someone know an easy way to solve this?
Is there some way to deactivate the #!python color highlighting for
some lines of the code?

All around the scipy.org site there are examples of interactive python
sessions, it will be nice to have a special syntax highlighter for
them. Like:
 {{{#!python_interactive

may be to much efforts just for colors?

pau

[Numpy-discussion] Numpy Example List

[<jo...@st...>]

For your information: http://scipy.org/Numpy_Example_List
- all examples are now syntax highlighted (thanks Pau Gargallo!)
- most examples have now a clickable "See also:" footnote
- is extended to 118 examples

Joris
 


Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

[Numpy-discussion] Matrix query

[Satya U. <sat...@ya...>]

Dear Python Users and Gurus,
    My problem is that i have a distance matrix obtained from the software package PHYLIP. I have placed it in a text file, and using ordinary python i am able to save the matrix elements as a multidimensional array. My problem is that i wish to now convert this multidimensional array into a matrix and subsequently i wish to find the eigenvectors and eigenvalues of this matrix. I tried using scipy and also earlier  the Numeric and LinearAlgebra modules but i am facing problems. I can generate a new matrix using scipy (and also with Numeric/LinearAlgebra) and can obtain the corresponding eigenvectors. But i am facing a real problem in making scipy or Numeric/LinearAlgebra accept my multidimensional array as a matrix it can recognize. Please help/give a pointer to a similar query.
   
  Thanking you,
  Satya

				
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Re: [Numpy-discussion] Guide to NumPy latest?

[Christian M. <chr...@ma...>]

Mine says February 28, 2006 on the title page.

As this has come up quite often - is there any kind of mechanism to
obtain the most recent version?

Regards,

  Christian.


On Thu, May 18, 2006 23:07, Mark Heslep wrote:
> So is the January '06 version of the Guide to Numpy still the most
> recent version?
>
> Mark
>
>
> -------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, securit=
y?
> 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 Geron=
imo
> http://sel.as-us.falkag.net/sel?cmd=3Dlnk&kid=3D120709&bid=3D263057&dat=
=3D121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Indexing arrays with arrays

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

I read the chapter in your book and sort of vaguely understand what it does
now.

Take an array A and index array ind:
>>> A
array([[ 0,  5, 10, 15],
       [ 1,  6, 11, 16],
       [ 2,  7, 12, 17],
       [ 3,  8, 13, 18],
       [ 4,  9, 14, 19]])
>>> ind
array([[1, 3, 4],
       [0, 2, 1]])

And you get
>>> A[ind]
array([[[ 1,  6, 11, 16],
        [ 3,  8, 13, 18],
        [ 4,  9, 14, 19]],

       [[ 0,  5, 10, 15],
        [ 2,  7, 12, 17],
        [ 1,  6, 11, 16]]])

In this case it's roughly equivalent to [ A[row] for row in ind ].

>>> num.asarray( [ A[r] for r in ind ] )
array([[[ 1,  6, 11, 16],
        [ 3,  8, 13, 18],
        [ 4,  9, 14, 19]],

       [[ 0,  5, 10, 15],
        [ 2,  7, 12, 17],
        [ 1,  6, 11, 16]]])
>>>

So I guess it could be useful if you want to take a bunch of different
random samples of your data and stack them all up.  E.g. you have a
(1000,50) shaped grid of data, and you want to take N random samplings, eac=
h
consisting of 100 rows from the original grid, and then put them all
together into an (N,100,50) array.   Or say you want to make a stack of
sliding windows on the data like rows 0-5, then rows 1-6, then rows 2-7, et=
c
to make a big (1000-5,5,50) array. Might be useful for that kind of thing.

But thinking about applying an index obj of shape (2,3,4) to a
(10,20,30,40,50) shaped array just makes my head hurt. :-)

Does anyone actually use it, though?  I also found it unexpected that A[
(ind[0], ind[1] ) ]  doesn't do the same thing as A[ind]  when ind.shape=3D=
(
A.ndim, N).
List of array --  as in A[ [ind[0], ind[1]] ] -- seems to act just like
tuple of array also.

--bill

[Numpy-discussion] Guide to NumPy latest?

[Mark H. <ma...@mi...>]

So is the January '06 version of the Guide to Numpy still the most 
recent version?

Mark

Re: [Numpy-discussion] NumPy 0.9.8 to be released today

[Jonathan T. <jon...@ut...>]

When 0.9.8 comes out does that mean the API is stable, or will there
be API changes before 1.0?

Jon.

On 5/17/06, Travis Oliphant <oli...@ie...> wrote:
>
> If there are now further difficulties, I'm going to release 0.9.8
> today.   Then work on 1.0 can begin.  The 1.0 release will consist of a
> series of release candidates.
>
> Thank you to David Cooke for his recent flurry of fixes..  Thanks to all
> the other developers who have contributed as well.
>
> -Travis
>
>
>
> -------------------------------------------------------
> 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 ea=
sier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronim=
o
> http://sel.as-us.falkag.net/sel?cmd=3Dlnk&kid=3D120709&bid=3D263057&dat=
=3D121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re[2]: [Numpy-discussion] nonzero() behaviour has changed

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

On Thu, 18 May 2006, Ed Schofield apparently wrote: 
> I've never thought of zip as a transpose operation 
> before, but I guess it is 

http://mail.python.org/pipermail/python-list/2004-December/257416.html
But I like it.

Cheers,
Alan Isaac

Re: [Numpy-discussion] Re: Dot product threading?

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

On 5/18/06, Bill Baxter <wb...@gm...> wrote:
> In Einstein summation notation, what numpy.dot() does now is:
> c_riqk =3D a_rij * b_qjk
>
> And you want:
> c_[r]ik =3D a_[r]ij * b_[r]jk
>
> where the brackets indicate a 'no summation' index.
> Writing the ESN makes it clearer to me anyway.  :-)

I recently needed something similar to this, and being too lazy to
think up the proper numpy ax-swapping kung-fu, I just opened up weave
and was done with it in a hurry.   Here it is, in case anyone finds
the basic idea of any use.

Cheers,

f

###

class mt3_dot_factory(object):
    """Generate the mt3t contract function, holding necessary state.

    This class only needs to be instantiated once, though it doesn't try to
    enforce this via singleton/borg approaches at all."""

    def __init__(self):
        # The actual expression to contract indices, as a list of strings t=
o be
        # interpolated into the C++ source
        mat_ten =3D ['mat(i,m)*ten(m,j,k)', # first index
                   'mat(j,m)*ten(i,m,k)', # second
                   'mat(k,m)*ten(i,j,m)', # third
                   ]
        # Source template
        code_tpl =3D """
        for (int i=3D0;i<order;i++) {
            for (int j=3D0;j<order;j++) {
                for (int k=3D0;k<order;k++) {
                    double sum=3D0;
                    for (int m=3D0;m<order;m++) {
                        sum +=3D %s;
                    }
                    out(i,j,k) =3D sum;
                }
            }
        }
        """
        self.code =3D [code_tpl % mat_ten[idx] for idx in (0,1,2)]

    def __call__(self,mat,ten,idx):
        """mt3s_contract(mat,ten,idx) -> tensor.

        A special type of matrix-tensor contraction over a single index.  T=
he
        returned array has the following structure:

              out(i,j,k) =3D sum_m(mat(i,m)*ten(m,j,k)) if idx=3D=3D0
              out(i,j,k) =3D sum_m(mat(j,m)*ten(i,m,k)) if idx=3D=3D1
              out(i,j,k) =3D sum_m(mat(k,m)*ten(i,j,m)) if idx=3D=3D2

        Inputs:

         - mat: an NxN array.
         - ten: an NxNxN array.
         - idx: the position of the index to contract over, 0 1 or 2."""

        # Minimal input validation - we use asserts so they don't kick in
        # under a -O run of python.
        assert len(mat.shape)=3D=3D2,\
               "mat must be a rank 2 array, shape=3D%s" % mat.shape
        assert mat.shape[0]=3D=3Dmat.shape[1],\
               "Only square matrices are supported: mat shape=3D%s" % mat.s=
hape
        assert len(ten.shape)=3D=3D3,\
               "mat must be a rank 3 array, shape=3D%s" % ten.shape
        assert ten.shape[0]=3D=3Dten.shape[1]=3D=3Dten.shape[2],\
               "Only equal-dim tensors are supported: ten shape=3D%s" % ten=
.shape

        order =3D mat.shape[0]
        out =3D zeros_like(ten)
        inline(self.code[idx],('mat','ten','out','order'),
               type_converters =3D converters.blitz)
        return out

# Make actual instance
mt3_dot =3D mt3_dot_factory()

Re: [Numpy-discussion] nonzero() behaviour has changed

[Ed S. <sch...@ft...>]

George Nurser wrote:
> This is a very useful thread. Made me think about what the present
> arrangement is supposed to do.

Great!

> Alternatively, instead of transpose, we can simply use zip.
>
> E.g. (3D)
> In [42]: for i,j,k in zip(*xx):
>   ....:     print i,j,k

That's interesting.  I've never thought of zip as a transpose operation
before, but I guess it is ...

-- Ed