josef.pktd | 2 Nov 04:13 2010
Picon

Re: scipy.stats.distributions: note on initial parameters for fitting the beta distribution

On Sun, Oct 31, 2010 at 9:34 AM, James Phillips <zunzun <at> zunzun.com> wrote:
> File attached.
>
> On Sun, Oct 31, 2010 at 8:33 AM, James Phillips <zunzun <at> zunzun.com> wrote:
>> Here is a more polished and quite smaller version of your example file
>> matchdist.py that uses either nnlf or residuals for ranking, and
>> includes checks for NaN, +inf and -inf.  I think this has all of the
>> logic and range checks that it needs.

I tried out both your scripts during the weekend but didn't get around
to replying. Most distributions work pretty fast but there are still a
few unsuccesful time wasters in there. For example, ksone is also
mainly a distribution for a statistical test, and in my run took a
long time without a successful fit.

I'm not sure how nnlf woill work as selection criterium for the
distributions, and similarly the residual sum of squares might not be
a good or robust criterium, for example with heavy tailed
distributions. But that's just a guess, the only (commercial) package
that I looked at, offered the choice between Kolmogorov-Smirnov,
Anderson-Darling and 2 chisquare tests (equal-spaced and equal
probability) as distance or goodness-of-fit measure. (Entropy would be
another criterium, but I haven't seen it yet for selecting the
distribution.)

Your version also will help to narrow down what might be good starting
values, eventually I would prefer to hardcode (optional) distribution
specific _start_values.

Your second script (after dropping the failures) has 10 distributions
(Continue reading)

Warren Weckesser | 2 Nov 06:24 2010

Sporadic failures of tests of signal.correlate with dtype complex64

On Mac OSX 10.5.8, I'm seeing occasional failures like the following:

$ python -c "import scipy.signal; scipy.signal.test()"
Running unit tests for scipy.signal
NumPy version 1.5.0.dev8716
NumPy is installed in /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
SciPy version 0.9.0.dev6856
SciPy is installed in /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010, 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
nose version 0.11.3
................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256: BadCoefficients: Badly conditioned filter coefficients (numerator): the results may be meaningless
  "results may be meaningless", BadCoefficients)
......................................................F...............................................................................................................................................................................................................................................
======================================================================
FAIL: test_rank1_same (test_signaltools.TestCorrelateComplex64)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py", line 606, in test_rank1_same
    assert_array_almost_equal(y, y_r)
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py", line 774, in assert_array_almost_equal
    header='Arrays are not almost equal')
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py", line 618, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal

(mismatch 10.0%)
 x: array([-6.76370811-8.55324841j,  0.68672836-4.2681613j ,
       -3.22760987-8.69287109j,  0.75051951-5.50820398j,
       -7.33016682-1.14685655j, -5.99573374+7.84123898j,...
 y: array([-6.76370859-8.55324745j,  0.68672895-4.2681613j ,
       -3.22761011-8.69286919j,  0.75051963-5.50820446j,
       -7.33016682-1.14685678j, -5.99573517+7.84123898j,...

----------------------------------------------------------------------
Ran 311 tests in 2.307s

FAILED (failures=1)

$ python -c "import scipy.signal; scipy.signal.test()"
Running unit tests for scipy.signal
NumPy version 1.5.0.dev8716
NumPy is installed in /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
SciPy version 0.9.0.dev6856
SciPy is installed in /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010, 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
nose version 0.11.3
................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256: BadCoefficients: Badly conditioned filter coefficients (numerator): the results may be meaningless
  "results may be meaningless", BadCoefficients)
.......................................................F..............................................................................................................................................................................................................................................
======================================================================
FAIL: test_rank1_same_old (test_signaltools.TestCorrelateComplex64)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/decorators.py", line 257, in _deprecated_imp
    f(*args, **kwargs)
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py", line 641, in test_rank1_same_old
    assert_array_almost_equal(y, y_r)
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py", line 774, in assert_array_almost_equal
    header='Arrays are not almost equal')
  File "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py", line 618, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal

(mismatch 10.0%)
 x: array([ 2.46665049+2.02072477j, -7.42591763-0.54789257j,
        3.41454220-0.15863085j, -0.14030695+5.01129198j,
       -2.11230707+2.68583822j,  7.78784609+7.19434834j,...
 y: array([ 2.46665049+2.02072501j, -7.42591763-0.54789257j,
        3.41454196-0.15863061j, -0.14030659+5.01129246j,
       -2.11230707+2.68583822j,  7.78784752+7.19434786j,...

----------------------------------------------------------------------
Ran 311 tests in 2.623s

FAILED (failures=1)


The above tests are part of a suite of tests that use random data, and usually the tests all pass.  It took several tries to get the above failures.

I suspect the problem is simply that the default tolerance of 'assert_array_almost_equal' is too small for the complex64 data type for these tests.

Could someone verify that they can reproduce those failures?  Does simply increasing the tolerance of the test look like a reasonable fix?


Warren

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Warren Weckesser | 2 Nov 07:21 2010

The function 'c0_P' in signal/bsplines.py

There is a function called c0_P in signal/bsplines.py that sets local variables c0 and P, but does not return anything.  Except for an update to the raise statement at the end of the function that I just checked in, this function hasn't been touched since r122.  Obviously no one has used it, since it doesn't return anything.  Any objection to removing it?  (Note: objections that don't include a patch containing a docstring that conforms to the scipy standard, an actual 'return' statement, and appropriate tests will be ignored. :)


Warren

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James Phillips | 2 Nov 11:07 2010

Re: scipy.stats.distributions: note on initial parameters for fitting the beta distribution

Thank you kindly for trying the scripts.

     James

On Mon, Nov 1, 2010 at 10:13 PM,  <josef.pktd <at> gmail.com> wrote:
>
> I tried out both your scripts during the weekend but didn't get around
> to replying. Most distributions work pretty fast but there are still a
> few unsuccesful time wasters in there. For example, ksone is also
> mainly a distribution for a statistical test, and in my run took a
> long time without a successful fit.
>
> I'm not sure how nnlf woill work as selection criterium for the
> distributions, and similarly the residual sum of squares might not be
> a good or robust criterium, for example with heavy tailed
> distributions. But that's just a guess, the only (commercial) package
> that I looked at, offered the choice between Kolmogorov-Smirnov,
> Anderson-Darling and 2 chisquare tests (equal-spaced and equal
> probability) as distance or goodness-of-fit measure. (Entropy would be
> another criterium, but I haven't seen it yet for selecting the
> distribution.)
>
> Your version also will help to narrow down what might be good starting
> values, eventually I would prefer to hardcode (optional) distribution
> specific _start_values.
>
> Your second script (after dropping the failures) has 10 distributions
> fewer than the first script (70 instead of 80), so there is still some
> distribution specific work left.
>
> Josef
>
>>>
>>>     James
>>>
>>> 2010/10/30 James Phillips <zunzun <at> zunzun.com>:
>>>> I'll parallelize this code and make a few more tweaks, and then add it
>>>> to my web site.
>>>
>>
>> _______________________________________________
>> SciPy-Dev mailing list
>> SciPy-Dev <at> scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev <at> scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
josef.pktd | 2 Nov 14:41 2010
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Re: Sporadic failures of tests of signal.correlate with dtype complex64

On Tue, Nov 2, 2010 at 1:24 AM, Warren Weckesser
<warren.weckesser <at> enthought.com> wrote:
> On Mac OSX 10.5.8, I'm seeing occasional failures like the following:
>
> $ python -c "import scipy.signal; scipy.signal.test()"
> Running unit tests for scipy.signal
> NumPy version 1.5.0.dev8716
> NumPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
> SciPy version 0.9.0.dev6856
> SciPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
> Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010,
> 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
> nose version 0.11.3
> ................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256:
> BadCoefficients: Badly conditioned filter coefficients (numerator): the
> results may be meaningless
>   "results may be meaningless", BadCoefficients)
> ......................................................F...............................................................................................................................................................................................................................................
> ======================================================================
> FAIL: test_rank1_same (test_signaltools.TestCorrelateComplex64)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py",
> line 606, in test_rank1_same
>     assert_array_almost_equal(y, y_r)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 774, in assert_array_almost_equal
>     header='Arrays are not almost equal')
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 618, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal
>
> (mismatch 10.0%)
>  x: array([-6.76370811-8.55324841j,  0.68672836-4.2681613j ,
>        -3.22760987-8.69287109j,  0.75051951-5.50820398j,
>        -7.33016682-1.14685655j, -5.99573374+7.84123898j,...
>  y: array([-6.76370859-8.55324745j,  0.68672895-4.2681613j ,
>        -3.22761011-8.69286919j,  0.75051963-5.50820446j,
>        -7.33016682-1.14685678j, -5.99573517+7.84123898j,...
>
> ----------------------------------------------------------------------
> Ran 311 tests in 2.307s
>
> FAILED (failures=1)
>
> $ python -c "import scipy.signal; scipy.signal.test()"
> Running unit tests for scipy.signal
> NumPy version 1.5.0.dev8716
> NumPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
> SciPy version 0.9.0.dev6856
> SciPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
> Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010,
> 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
> nose version 0.11.3
> ................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256:
> BadCoefficients: Badly conditioned filter coefficients (numerator): the
> results may be meaningless
>   "results may be meaningless", BadCoefficients)
> .......................................................F..............................................................................................................................................................................................................................................
> ======================================================================
> FAIL: test_rank1_same_old (test_signaltools.TestCorrelateComplex64)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/decorators.py",
> line 257, in _deprecated_imp
>     f(*args, **kwargs)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py",
> line 641, in test_rank1_same_old
>     assert_array_almost_equal(y, y_r)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 774, in assert_array_almost_equal
>     header='Arrays are not almost equal')
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 618, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal
>
> (mismatch 10.0%)
>  x: array([ 2.46665049+2.02072477j, -7.42591763-0.54789257j,
>         3.41454220-0.15863085j, -0.14030695+5.01129198j,
>        -2.11230707+2.68583822j,  7.78784609+7.19434834j,...
>  y: array([ 2.46665049+2.02072501j, -7.42591763-0.54789257j,
>         3.41454196-0.15863061j, -0.14030659+5.01129246j,
>        -2.11230707+2.68583822j,  7.78784752+7.19434786j,...
>
> ----------------------------------------------------------------------
> Ran 311 tests in 2.623s
>
> FAILED (failures=1)
>
>
> The above tests are part of a suite of tests that use random data, and
> usually the tests all pass.  It took several tries to get the above
> failures.

Is there a purpose behind the randomness in the tests?
If not, you could choose a seed that works. For example, after some
discussions on the mailing list, I set a seed for most of the
stats.distributions tests.

Josef

>
> I suspect the problem is simply that the default tolerance of
> 'assert_array_almost_equal' is too small for the complex64 data type for
> these tests.
>
> Could someone verify that they can reproduce those failures?  Does simply
> increasing the tolerance of the test look like a reasonable fix?
>
>
> Warren
>
>
> _______________________________________________
> SciPy-Dev mailing list
> SciPy-Dev <at> scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
Pauli Virtanen | 2 Nov 15:11 2010
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Picon

Re: Sporadic failures of tests of signal.correlate with dtype complex64

Tue, 02 Nov 2010 09:41:45 -0400, josef.pktd wrote:
[clip]
> Is there a purpose behind the randomness in the tests? If not, you could
> choose a seed that works. For example, after some discussions on the
> mailing list, I set a seed for most of the stats.distributions tests.

As a rule, all tests using random numbers should set the seed at the 
beginning of the test.

--

-- 
Pauli Virtanen
Vincent Davis | 2 Nov 15:18 2010
Picon

Re: Sporadic failures of tests of signal.correlate with dtype complex64

I remember running into a random number issue with edp
":Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010,
> 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]"
Becuase of the mkl library.

I wrote a whole set of numpy.random.test() just to make sure the results where consistent. but I am not sure they where ever used.

Not where I can get to this now but can look later.

Vincent



On Tue, Nov 2, 2010 at 8:11 AM, Pauli Virtanen <pav <at> iki.fi> wrote:
Tue, 02 Nov 2010 09:41:45 -0400, josef.pktd wrote:
[clip]
> Is there a purpose behind the randomness in the tests? If not, you could
> choose a seed that works. For example, after some discussions on the
> mailing list, I set a seed for most of the stats.distributions tests.

As a rule, all tests using random numbers should set the seed at the
beginning of the test.

--
Pauli Virtanen

_______________________________________________
SciPy-Dev mailing list
SciPy-Dev <at> scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-dev



--
Thanks
Vincent Davis
720-301-3003

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Ralf Gommers | 2 Nov 15:18 2010

Re: Sporadic failures of tests of signal.correlate with dtype complex64

On Tue, Nov 2, 2010 at 1:24 PM, Warren Weckesser
<warren.weckesser <at> enthought.com> wrote:
> On Mac OSX 10.5.8, I'm seeing occasional failures like the following:
>
> $ python -c "import scipy.signal; scipy.signal.test()"
> Running unit tests for scipy.signal
> NumPy version 1.5.0.dev8716
> NumPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
> SciPy version 0.9.0.dev6856
> SciPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
> Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010,
> 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
> nose version 0.11.3
> ................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256:
> BadCoefficients: Badly conditioned filter coefficients (numerator): the
> results may be meaningless
>   "results may be meaningless", BadCoefficients)
> ......................................................F...............................................................................................................................................................................................................................................
> ======================================================================
> FAIL: test_rank1_same (test_signaltools.TestCorrelateComplex64)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py",
> line 606, in test_rank1_same
>     assert_array_almost_equal(y, y_r)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 774, in assert_array_almost_equal
>     header='Arrays are not almost equal')
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 618, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal
>
> (mismatch 10.0%)
>  x: array([-6.76370811-8.55324841j,  0.68672836-4.2681613j ,
>        -3.22760987-8.69287109j,  0.75051951-5.50820398j,
>        -7.33016682-1.14685655j, -5.99573374+7.84123898j,...
>  y: array([-6.76370859-8.55324745j,  0.68672895-4.2681613j ,
>        -3.22761011-8.69286919j,  0.75051963-5.50820446j,
>        -7.33016682-1.14685678j, -5.99573517+7.84123898j,...
>
> ----------------------------------------------------------------------
> Ran 311 tests in 2.307s
>
> FAILED (failures=1)
>
> $ python -c "import scipy.signal; scipy.signal.test()"
> Running unit tests for scipy.signal
> NumPy version 1.5.0.dev8716
> NumPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy
> SciPy version 0.9.0.dev6856
> SciPy is installed in
> /Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy
> Python version 2.6.5 |EPD 6.2-2 (32-bit)| (r265:79063, May 28 2010,
> 15:13:03) [GCC 4.0.1 (Apple Inc. build 5488)]
> nose version 0.11.3
> ................./Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/filter_design.py:256:
> BadCoefficients: Badly conditioned filter coefficients (numerator): the
> results may be meaningless
>   "results may be meaningless", BadCoefficients)
> .......................................................F..............................................................................................................................................................................................................................................
> ======================================================================
> FAIL: test_rank1_same_old (test_signaltools.TestCorrelateComplex64)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/decorators.py",
> line 257, in _deprecated_imp
>     f(*args, **kwargs)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/scipy/signal/tests/test_signaltools.py",
> line 641, in test_rank1_same_old
>     assert_array_almost_equal(y, y_r)
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 774, in assert_array_almost_equal
>     header='Arrays are not almost equal')
>   File
> "/Users/warren/tmp_py_install/lib/python2.6/site-packages/numpy/testing/utils.py",
> line 618, in assert_array_compare
>     raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal
>
> (mismatch 10.0%)
>  x: array([ 2.46665049+2.02072477j, -7.42591763-0.54789257j,
>         3.41454220-0.15863085j, -0.14030695+5.01129198j,
>        -2.11230707+2.68583822j,  7.78784609+7.19434834j,...
>  y: array([ 2.46665049+2.02072501j, -7.42591763-0.54789257j,
>         3.41454196-0.15863061j, -0.14030659+5.01129246j,
>        -2.11230707+2.68583822j,  7.78784752+7.19434786j,...
>
> ----------------------------------------------------------------------
> Ran 311 tests in 2.623s
>
> FAILED (failures=1)
>
>
> The above tests are part of a suite of tests that use random data, and
> usually the tests all pass.  It took several tries to get the above
> failures.
>
> I suspect the problem is simply that the default tolerance of
> 'assert_array_almost_equal' is too small for the complex64 data type for
> these tests.
>
> Could someone verify that they can reproduce those failures?

Can't reproduce these.

> Does simply increasing the tolerance of the test look like a reasonable fix?

The default tolerance is decimal=6, which is not all that strict. I
notice that only the longdouble tests fail while the single/double
tests do not. This is very likely platform dependent, otherwise it
would have been noticed before.

Like Josef/Pauli say fixing the seed is one thing (preferably with a
value that's failing for you). But then I would split the tests and
only increase the tolerance of the longdouble version, perhaps even
only on certain platforms.

Cheers,
Ralf
Darren Dale | 2 Nov 21:56 2010
Picon

scipy.org unresponsive

I'm having trouble connecting to www.scipy.org, the server is not responding:

$ ping www.scipy.org
PING www.scipy.org (216.62.213.231): 56 data bytes
Request timeout for icmp_seq 0
Request timeout for icmp_seq 1
Request timeout for icmp_seq 2
Request timeout for icmp_seq 3
Dag Sverre Seljebotn | 3 Nov 11:26 2010
Picon
Picon

Work on fwrap and SciPy

This is just a quick note to inform people that I am currently working 
with Enthought to bring SciPy to the .NET platform. In particular, I 
will work on the Fortran parts of SciPy.

The primary strategy will be to improve fwrap enough to make it usable 
for SciPy, and then move SciPy over to fwrap instead of f2py. The point 
here is that Carl Witty is already working on a .NET backend for Cython, 
and since fwrap generates Cython code we get the .NET port that way.

All work is done in Enthought's "refactor" branches for now [1]. The 
intention is certainly to merge back to main eventually, but questions 
of how or when or whether will have to wait; getting things up and 
running on .NET has priority.

Some details:

  a) The most important missing feature in fwrap is callbacks. I'm sure 
there are other things I'll have to implement as well.

  b) The main strategy is to first move (our own branch of) SciPy over 
to fwrap and have that work on CPython, and then move to compiling 
things on .NET

  c) fwrap does not support g77, only Fortran 90 compilers like 
gfortran/ifort etc. For the purposes of the .NET port this is likely to 
be good enough. Before a merge with the main CPython branch one must 
perhaps look into implementing g77 support in fwrap. I know that David 
C. at least earlier stated that g77 support is not going away anytime 
soon. Feedback on this welcome.

Dag Sverre

[1]
http://github.com/teoliphant/numpy-refactor
http://github.com/jasonmccampbell/scipy-refactor

Gmane